Date: 2019-12-25 21:38:25 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 104 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 104
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 | ||
|---|---|---|---|---|---|
| ATC:kmeans | 2 | 1.000 | 0.998 | 0.999 | ** |
| ATC:skmeans | 2 | 1.000 | 0.994 | 0.997 | ** |
| SD:NMF | 2 | 0.918 | 0.920 | 0.967 | * |
| SD:mclust | 2 | 0.900 | 0.907 | 0.966 | * |
| CV:NMF | 2 | 0.900 | 0.931 | 0.971 | * |
| MAD:skmeans | 2 | 0.882 | 0.915 | 0.965 | |
| CV:skmeans | 2 | 0.864 | 0.901 | 0.962 | |
| MAD:NMF | 2 | 0.861 | 0.906 | 0.961 | |
| SD:skmeans | 2 | 0.845 | 0.934 | 0.969 | |
| SD:kmeans | 2 | 0.827 | 0.884 | 0.954 | |
| CV:kmeans | 2 | 0.824 | 0.902 | 0.958 | |
| MAD:kmeans | 2 | 0.824 | 0.911 | 0.961 | |
| ATC:pam | 2 | 0.812 | 0.920 | 0.960 | |
| SD:pam | 2 | 0.788 | 0.900 | 0.953 | |
| ATC:NMF | 2 | 0.756 | 0.875 | 0.947 | |
| MAD:pam | 2 | 0.733 | 0.876 | 0.945 | |
| CV:pam | 2 | 0.716 | 0.866 | 0.940 | |
| ATC:hclust | 2 | 0.641 | 0.901 | 0.941 | |
| ATC:mclust | 3 | 0.530 | 0.707 | 0.805 | |
| MAD:mclust | 3 | 0.275 | 0.535 | 0.745 | |
| CV:mclust | 3 | 0.262 | 0.393 | 0.733 | |
| CV:hclust | 4 | 0.199 | 0.355 | 0.620 | |
| SD:hclust | 3 | 0.155 | 0.497 | 0.652 | |
| MAD:hclust | 3 | 0.148 | 0.521 | 0.707 |
**: 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 0.918 0.920 0.967 0.477 0.522 0.522
#> CV:NMF 2 0.900 0.931 0.971 0.480 0.522 0.522
#> MAD:NMF 2 0.861 0.906 0.961 0.480 0.522 0.522
#> ATC:NMF 2 0.756 0.875 0.947 0.485 0.507 0.507
#> SD:skmeans 2 0.845 0.934 0.969 0.503 0.497 0.497
#> CV:skmeans 2 0.864 0.901 0.962 0.502 0.498 0.498
#> MAD:skmeans 2 0.882 0.915 0.965 0.503 0.498 0.498
#> ATC:skmeans 2 1.000 0.994 0.997 0.501 0.500 0.500
#> SD:mclust 2 0.900 0.907 0.966 0.285 0.724 0.724
#> CV:mclust 2 0.896 0.923 0.962 0.316 0.675 0.675
#> MAD:mclust 2 0.844 0.892 0.954 0.330 0.652 0.652
#> ATC:mclust 2 0.355 0.394 0.806 0.321 0.779 0.779
#> SD:kmeans 2 0.827 0.884 0.954 0.476 0.522 0.522
#> CV:kmeans 2 0.824 0.902 0.958 0.483 0.518 0.518
#> MAD:kmeans 2 0.824 0.911 0.961 0.486 0.510 0.510
#> ATC:kmeans 2 1.000 0.998 0.999 0.496 0.504 0.504
#> SD:pam 2 0.788 0.900 0.953 0.503 0.497 0.497
#> CV:pam 2 0.716 0.866 0.940 0.498 0.498 0.498
#> MAD:pam 2 0.733 0.876 0.945 0.499 0.498 0.498
#> ATC:pam 2 0.812 0.920 0.960 0.463 0.518 0.518
#> SD:hclust 2 0.402 0.772 0.879 0.304 0.751 0.751
#> CV:hclust 2 0.323 0.774 0.868 0.325 0.751 0.751
#> MAD:hclust 2 0.364 0.716 0.860 0.312 0.751 0.751
#> ATC:hclust 2 0.641 0.901 0.941 0.479 0.514 0.514
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.357 0.416 0.691 0.373 0.778 0.587
#> CV:NMF 3 0.377 0.567 0.772 0.346 0.809 0.647
#> MAD:NMF 3 0.369 0.519 0.729 0.361 0.713 0.496
#> ATC:NMF 3 0.476 0.532 0.790 0.352 0.662 0.423
#> SD:skmeans 3 0.471 0.607 0.779 0.326 0.743 0.529
#> CV:skmeans 3 0.424 0.335 0.626 0.328 0.730 0.508
#> MAD:skmeans 3 0.417 0.379 0.680 0.327 0.808 0.631
#> ATC:skmeans 3 0.735 0.856 0.873 0.290 0.814 0.638
#> SD:mclust 3 0.335 0.480 0.730 0.875 0.895 0.857
#> CV:mclust 3 0.262 0.393 0.733 0.719 0.795 0.708
#> MAD:mclust 3 0.275 0.535 0.745 0.763 0.647 0.480
#> ATC:mclust 3 0.530 0.707 0.805 0.761 0.586 0.496
#> SD:kmeans 3 0.372 0.578 0.774 0.345 0.765 0.579
#> CV:kmeans 3 0.382 0.568 0.759 0.331 0.769 0.584
#> MAD:kmeans 3 0.357 0.488 0.688 0.345 0.719 0.500
#> ATC:kmeans 3 0.631 0.740 0.856 0.292 0.751 0.550
#> SD:pam 3 0.712 0.807 0.908 0.291 0.782 0.590
#> CV:pam 3 0.750 0.833 0.927 0.307 0.780 0.587
#> MAD:pam 3 0.696 0.616 0.842 0.283 0.825 0.666
#> ATC:pam 3 0.883 0.893 0.956 0.372 0.728 0.529
#> SD:hclust 3 0.155 0.497 0.652 0.771 0.879 0.839
#> CV:hclust 3 0.109 0.528 0.617 0.656 0.827 0.774
#> MAD:hclust 3 0.148 0.521 0.707 0.778 0.673 0.580
#> ATC:hclust 3 0.502 0.491 0.782 0.290 0.911 0.827
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.462 0.586 0.759 0.1355 0.771 0.438
#> CV:NMF 4 0.500 0.647 0.776 0.1504 0.790 0.494
#> MAD:NMF 4 0.428 0.502 0.717 0.1327 0.788 0.467
#> ATC:NMF 4 0.446 0.491 0.722 0.1314 0.739 0.373
#> SD:skmeans 4 0.471 0.418 0.674 0.1239 0.851 0.597
#> CV:skmeans 4 0.479 0.519 0.709 0.1253 0.753 0.400
#> MAD:skmeans 4 0.485 0.455 0.674 0.1245 0.767 0.444
#> ATC:skmeans 4 0.817 0.839 0.899 0.1267 0.892 0.698
#> SD:mclust 4 0.295 0.425 0.621 0.2732 0.581 0.370
#> CV:mclust 4 0.316 0.334 0.650 0.2412 0.659 0.411
#> MAD:mclust 4 0.285 0.446 0.645 0.1771 0.742 0.407
#> ATC:mclust 4 0.642 0.807 0.891 0.0719 0.940 0.871
#> SD:kmeans 4 0.410 0.429 0.626 0.1450 0.814 0.545
#> CV:kmeans 4 0.424 0.463 0.651 0.1365 0.848 0.618
#> MAD:kmeans 4 0.406 0.372 0.650 0.1264 0.812 0.519
#> ATC:kmeans 4 0.595 0.641 0.753 0.1443 0.839 0.582
#> SD:pam 4 0.633 0.668 0.848 0.1367 0.852 0.608
#> CV:pam 4 0.604 0.616 0.818 0.1168 0.924 0.786
#> MAD:pam 4 0.675 0.713 0.862 0.1429 0.753 0.443
#> ATC:pam 4 0.785 0.738 0.871 0.1375 0.872 0.673
#> SD:hclust 4 0.194 0.423 0.623 0.1703 0.726 0.574
#> CV:hclust 4 0.199 0.355 0.620 0.1827 0.771 0.632
#> MAD:hclust 4 0.184 0.496 0.597 0.1957 0.841 0.680
#> ATC:hclust 4 0.522 0.556 0.639 0.1395 0.825 0.626
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.539 0.532 0.725 0.0703 0.869 0.547
#> CV:NMF 5 0.555 0.574 0.739 0.0715 0.867 0.542
#> MAD:NMF 5 0.523 0.520 0.731 0.0678 0.860 0.528
#> ATC:NMF 5 0.545 0.479 0.699 0.0668 0.868 0.544
#> SD:skmeans 5 0.539 0.397 0.642 0.0685 0.878 0.574
#> CV:skmeans 5 0.547 0.425 0.646 0.0675 0.832 0.450
#> MAD:skmeans 5 0.540 0.430 0.667 0.0671 0.900 0.637
#> ATC:skmeans 5 0.798 0.824 0.894 0.0583 0.952 0.825
#> SD:mclust 5 0.471 0.472 0.680 0.1113 0.802 0.420
#> CV:mclust 5 0.427 0.369 0.628 0.1089 0.851 0.554
#> MAD:mclust 5 0.442 0.430 0.681 0.1075 0.868 0.558
#> ATC:mclust 5 0.682 0.623 0.764 0.1411 0.827 0.607
#> SD:kmeans 5 0.517 0.459 0.685 0.0743 0.857 0.544
#> CV:kmeans 5 0.517 0.442 0.654 0.0776 0.843 0.511
#> MAD:kmeans 5 0.497 0.430 0.623 0.0727 0.845 0.502
#> ATC:kmeans 5 0.718 0.733 0.847 0.0742 0.856 0.529
#> SD:pam 5 0.651 0.626 0.830 0.0467 0.966 0.871
#> CV:pam 5 0.621 0.592 0.796 0.0511 0.906 0.696
#> MAD:pam 5 0.668 0.631 0.813 0.0580 0.961 0.855
#> ATC:pam 5 0.793 0.865 0.906 0.0747 0.893 0.654
#> SD:hclust 5 0.222 0.408 0.616 0.0696 0.971 0.926
#> CV:hclust 5 0.194 0.385 0.616 0.0796 0.886 0.737
#> MAD:hclust 5 0.263 0.407 0.589 0.0807 0.879 0.680
#> ATC:hclust 5 0.637 0.735 0.804 0.0854 0.815 0.492
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.554 0.416 0.650 0.0414 0.889 0.537
#> CV:NMF 6 0.577 0.477 0.669 0.0434 0.891 0.541
#> MAD:NMF 6 0.536 0.348 0.604 0.0472 0.864 0.474
#> ATC:NMF 6 0.570 0.373 0.632 0.0402 0.904 0.593
#> SD:skmeans 6 0.590 0.464 0.676 0.0421 0.904 0.578
#> CV:skmeans 6 0.592 0.479 0.666 0.0410 0.893 0.537
#> MAD:skmeans 6 0.592 0.428 0.659 0.0412 0.900 0.567
#> ATC:skmeans 6 0.776 0.636 0.835 0.0377 0.972 0.883
#> SD:mclust 6 0.564 0.497 0.699 0.0537 0.891 0.557
#> CV:mclust 6 0.519 0.445 0.659 0.0530 0.912 0.649
#> MAD:mclust 6 0.517 0.434 0.662 0.0399 0.924 0.665
#> ATC:mclust 6 0.636 0.423 0.713 0.1115 0.834 0.512
#> SD:kmeans 6 0.593 0.508 0.672 0.0454 0.909 0.608
#> CV:kmeans 6 0.572 0.502 0.668 0.0458 0.902 0.579
#> MAD:kmeans 6 0.580 0.453 0.652 0.0466 0.902 0.583
#> ATC:kmeans 6 0.742 0.694 0.813 0.0453 0.902 0.587
#> SD:pam 6 0.661 0.514 0.759 0.0466 0.916 0.667
#> CV:pam 6 0.684 0.663 0.824 0.0549 0.912 0.662
#> MAD:pam 6 0.676 0.507 0.744 0.0452 0.941 0.760
#> ATC:pam 6 0.886 0.862 0.939 0.0480 0.958 0.812
#> SD:hclust 6 0.280 0.363 0.560 0.0670 0.934 0.824
#> CV:hclust 6 0.271 0.419 0.602 0.0646 0.892 0.706
#> MAD:hclust 6 0.329 0.347 0.579 0.0533 0.970 0.897
#> ATC:hclust 6 0.680 0.722 0.815 0.0354 0.980 0.903
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_list, k = 2)
#> n disease.state(p) other(p) k
#> SD:NMF 100 0.22984 0.553 2
#> CV:NMF 101 0.30028 0.560 2
#> MAD:NMF 98 0.34772 0.263 2
#> ATC:NMF 97 0.27444 0.500 2
#> SD:skmeans 104 0.17952 0.527 2
#> CV:skmeans 96 0.20222 0.353 2
#> MAD:skmeans 101 0.23958 0.455 2
#> ATC:skmeans 104 0.32248 0.276 2
#> SD:mclust 97 0.84688 0.662 2
#> CV:mclust 100 0.75366 0.439 2
#> MAD:mclust 96 0.61459 0.667 2
#> ATC:mclust 56 NA NA 2
#> SD:kmeans 97 0.37906 0.589 2
#> CV:kmeans 101 0.30028 0.627 2
#> MAD:kmeans 101 0.36135 0.586 2
#> ATC:kmeans 104 0.24351 0.228 2
#> SD:pam 100 0.04819 0.386 2
#> CV:pam 100 0.03850 0.477 2
#> MAD:pam 99 0.00945 0.421 2
#> ATC:pam 102 0.08152 0.120 2
#> SD:hclust 90 0.82082 0.819 2
#> CV:hclust 93 0.85601 0.767 2
#> MAD:hclust 89 0.33023 0.766 2
#> ATC:hclust 103 0.34706 0.374 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) other(p) k
#> SD:NMF 43 0.155 0.233 3
#> CV:NMF 76 0.380 0.735 3
#> MAD:NMF 63 0.714 0.928 3
#> ATC:NMF 74 0.660 0.570 3
#> SD:skmeans 83 0.542 0.548 3
#> CV:skmeans 30 NA NA 3
#> MAD:skmeans 30 NA NA 3
#> ATC:skmeans 101 0.158 0.117 3
#> SD:mclust 68 0.611 0.841 3
#> CV:mclust 54 0.901 0.871 3
#> MAD:mclust 67 0.847 0.327 3
#> ATC:mclust 98 0.422 0.709 3
#> SD:kmeans 73 0.434 0.252 3
#> CV:kmeans 77 0.572 0.263 3
#> MAD:kmeans 56 0.263 0.047 3
#> ATC:kmeans 91 0.648 0.748 3
#> SD:pam 92 0.457 0.623 3
#> CV:pam 98 0.254 0.524 3
#> MAD:pam 79 0.402 0.922 3
#> ATC:pam 96 0.719 0.629 3
#> SD:hclust 73 0.392 0.881 3
#> CV:hclust 80 0.717 0.826 3
#> MAD:hclust 66 0.593 0.405 3
#> ATC:hclust 61 0.603 0.295 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) other(p) k
#> SD:NMF 75 0.3532 0.4984 4
#> CV:NMF 84 0.6901 0.6992 4
#> MAD:NMF 67 0.4507 0.6200 4
#> ATC:NMF 68 0.0170 0.0502 4
#> SD:skmeans 43 0.7404 0.3300 4
#> CV:skmeans 71 0.4913 0.0899 4
#> MAD:skmeans 56 0.0782 0.2920 4
#> ATC:skmeans 98 0.3051 0.1427 4
#> SD:mclust 53 0.9742 0.6602 4
#> CV:mclust 22 0.7831 0.6321 4
#> MAD:mclust 55 0.5474 0.7058 4
#> ATC:mclust 98 0.5127 0.8167 4
#> SD:kmeans 43 0.4172 0.3477 4
#> CV:kmeans 57 0.7676 0.8199 4
#> MAD:kmeans 37 0.0387 0.0577 4
#> ATC:kmeans 78 0.0956 0.7951 4
#> SD:pam 87 0.7596 0.8466 4
#> CV:pam 79 0.1188 0.5930 4
#> MAD:pam 88 0.4494 0.9093 4
#> ATC:pam 90 0.3490 0.7773 4
#> SD:hclust 31 0.1353 0.9739 4
#> CV:hclust 17 0.3742 0.8282 4
#> MAD:hclust 58 0.3618 0.9347 4
#> ATC:hclust 83 0.6378 0.5164 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) other(p) k
#> SD:NMF 67 0.4671 0.3882 5
#> CV:NMF 76 0.5020 0.4410 5
#> MAD:NMF 64 0.3727 0.6092 5
#> ATC:NMF 61 0.0257 0.0623 5
#> SD:skmeans 43 0.5107 0.2346 5
#> CV:skmeans 43 0.9920 0.0427 5
#> MAD:skmeans 48 0.3557 0.3589 5
#> ATC:skmeans 101 0.4461 0.1315 5
#> SD:mclust 63 0.7315 0.8363 5
#> CV:mclust 49 0.2198 0.4777 5
#> MAD:mclust 51 0.5896 0.7474 5
#> ATC:mclust 77 0.4734 0.5117 5
#> SD:kmeans 54 0.2734 0.2605 5
#> CV:kmeans 54 0.9086 0.7493 5
#> MAD:kmeans 50 0.5521 0.1921 5
#> ATC:kmeans 85 0.5267 0.5044 5
#> SD:pam 80 0.5766 0.6386 5
#> CV:pam 73 0.3134 0.2086 5
#> MAD:pam 83 0.6467 0.9218 5
#> ATC:pam 101 0.6185 0.7858 5
#> SD:hclust 30 0.2188 0.6712 5
#> CV:hclust 37 0.6299 0.4068 5
#> MAD:hclust 41 0.0896 0.6925 5
#> ATC:hclust 98 0.3182 0.8291 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) other(p) k
#> SD:NMF 42 0.64133 0.7302 6
#> CV:NMF 53 0.52021 0.4091 6
#> MAD:NMF 31 0.70736 0.7015 6
#> ATC:NMF 40 0.00311 0.0251 6
#> SD:skmeans 55 0.12424 0.1310 6
#> CV:skmeans 56 0.45148 0.2795 6
#> MAD:skmeans 44 0.52418 0.3711 6
#> ATC:skmeans 79 0.47249 0.0557 6
#> SD:mclust 60 0.20350 0.8550 6
#> CV:mclust 54 0.13245 0.5913 6
#> MAD:mclust 59 0.03929 0.5136 6
#> ATC:mclust 59 0.68492 0.7928 6
#> SD:kmeans 70 0.04354 0.3979 6
#> CV:kmeans 64 0.14249 0.7636 6
#> MAD:kmeans 49 0.17870 0.3921 6
#> ATC:kmeans 87 0.44104 0.5551 6
#> SD:pam 60 0.53419 0.1186 6
#> CV:pam 85 0.04701 0.1906 6
#> MAD:pam 59 0.20032 0.4665 6
#> ATC:pam 99 0.49690 0.6463 6
#> SD:hclust 23 0.55301 0.0129 6
#> CV:hclust 44 0.00963 0.0519 6
#> MAD:hclust 29 0.09400 0.3898 6
#> ATC:hclust 94 0.34652 0.8786 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 104 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 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 0.402 0.772 0.879 0.3037 0.751 0.751
#> 3 3 0.155 0.497 0.652 0.7713 0.879 0.839
#> 4 4 0.194 0.423 0.623 0.1703 0.726 0.574
#> 5 5 0.222 0.408 0.616 0.0696 0.971 0.926
#> 6 6 0.280 0.363 0.560 0.0670 0.934 0.824
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
#> GSM537341 2 0.9754 0.319 0.408 0.592
#> GSM537345 1 0.1414 0.771 0.980 0.020
#> GSM537355 2 0.2043 0.879 0.032 0.968
#> GSM537366 2 0.8327 0.658 0.264 0.736
#> GSM537370 2 0.9393 0.453 0.356 0.644
#> GSM537380 2 0.0938 0.874 0.012 0.988
#> GSM537392 2 0.0938 0.874 0.012 0.988
#> GSM537415 2 0.0938 0.874 0.012 0.988
#> GSM537417 2 0.4298 0.864 0.088 0.912
#> GSM537422 2 0.6148 0.816 0.152 0.848
#> GSM537423 2 0.0938 0.874 0.012 0.988
#> GSM537427 2 0.1414 0.878 0.020 0.980
#> GSM537430 2 0.3114 0.877 0.056 0.944
#> GSM537336 1 0.2603 0.780 0.956 0.044
#> GSM537337 2 0.2603 0.877 0.044 0.956
#> GSM537348 2 0.9754 0.319 0.408 0.592
#> GSM537349 2 0.0938 0.874 0.012 0.988
#> GSM537356 2 0.9552 0.423 0.376 0.624
#> GSM537361 2 0.7139 0.771 0.196 0.804
#> GSM537374 2 0.4298 0.860 0.088 0.912
#> GSM537377 1 0.1414 0.771 0.980 0.020
#> GSM537378 2 0.0938 0.874 0.012 0.988
#> GSM537379 2 0.4690 0.859 0.100 0.900
#> GSM537383 2 0.0938 0.874 0.012 0.988
#> GSM537388 2 0.1633 0.878 0.024 0.976
#> GSM537395 2 0.2603 0.877 0.044 0.956
#> GSM537400 2 0.6048 0.828 0.148 0.852
#> GSM537404 2 0.6343 0.815 0.160 0.840
#> GSM537409 2 0.1414 0.867 0.020 0.980
#> GSM537418 2 0.9427 0.456 0.360 0.640
#> GSM537425 2 0.7219 0.767 0.200 0.800
#> GSM537333 2 0.4161 0.864 0.084 0.916
#> GSM537342 2 0.1633 0.878 0.024 0.976
#> GSM537347 2 0.5519 0.841 0.128 0.872
#> GSM537350 2 0.6623 0.771 0.172 0.828
#> GSM537362 1 0.9661 0.452 0.608 0.392
#> GSM537363 2 0.4161 0.865 0.084 0.916
#> GSM537368 1 0.2423 0.780 0.960 0.040
#> GSM537376 2 0.3114 0.877 0.056 0.944
#> GSM537381 2 0.9129 0.535 0.328 0.672
#> GSM537386 2 0.0938 0.875 0.012 0.988
#> GSM537398 2 0.9795 0.288 0.416 0.584
#> GSM537402 2 0.1184 0.878 0.016 0.984
#> GSM537405 1 0.4431 0.769 0.908 0.092
#> GSM537371 1 0.2423 0.780 0.960 0.040
#> GSM537421 2 0.2778 0.872 0.048 0.952
#> GSM537424 2 0.5519 0.841 0.128 0.872
#> GSM537432 2 0.3733 0.874 0.072 0.928
#> GSM537331 2 0.2423 0.876 0.040 0.960
#> GSM537332 2 0.1184 0.878 0.016 0.984
#> GSM537334 2 0.5059 0.843 0.112 0.888
#> GSM537338 2 0.3584 0.871 0.068 0.932
#> GSM537353 2 0.3274 0.879 0.060 0.940
#> GSM537357 1 0.2603 0.780 0.956 0.044
#> GSM537358 2 0.0938 0.873 0.012 0.988
#> GSM537375 2 0.3584 0.869 0.068 0.932
#> GSM537389 2 0.0938 0.874 0.012 0.988
#> GSM537390 2 0.0938 0.873 0.012 0.988
#> GSM537393 2 0.4022 0.867 0.080 0.920
#> GSM537399 2 0.6247 0.791 0.156 0.844
#> GSM537407 2 0.6148 0.813 0.152 0.848
#> GSM537408 2 0.2423 0.879 0.040 0.960
#> GSM537428 2 0.3431 0.874 0.064 0.936
#> GSM537354 2 0.2603 0.877 0.044 0.956
#> GSM537410 2 0.1633 0.878 0.024 0.976
#> GSM537413 2 0.1414 0.867 0.020 0.980
#> GSM537396 2 0.1414 0.879 0.020 0.980
#> GSM537397 2 0.9661 0.360 0.392 0.608
#> GSM537330 2 0.2043 0.879 0.032 0.968
#> GSM537369 1 0.9087 0.604 0.676 0.324
#> GSM537373 2 0.1843 0.880 0.028 0.972
#> GSM537401 2 0.9754 0.319 0.408 0.592
#> GSM537343 2 0.5408 0.843 0.124 0.876
#> GSM537367 2 0.5519 0.835 0.128 0.872
#> GSM537382 2 0.3114 0.877 0.056 0.944
#> GSM537385 2 0.1843 0.877 0.028 0.972
#> GSM537391 1 0.9922 0.267 0.552 0.448
#> GSM537419 2 0.1184 0.878 0.016 0.984
#> GSM537420 1 0.9087 0.604 0.676 0.324
#> GSM537429 2 0.1843 0.879 0.028 0.972
#> GSM537431 2 0.3733 0.858 0.072 0.928
#> GSM537387 1 0.9922 0.267 0.552 0.448
#> GSM537414 2 0.6048 0.819 0.148 0.852
#> GSM537433 2 0.6531 0.797 0.168 0.832
#> GSM537335 2 0.5059 0.843 0.112 0.888
#> GSM537339 2 0.9754 0.319 0.408 0.592
#> GSM537340 2 0.4431 0.863 0.092 0.908
#> GSM537344 1 0.9087 0.604 0.676 0.324
#> GSM537346 2 0.3114 0.877 0.056 0.944
#> GSM537351 1 0.7883 0.697 0.764 0.236
#> GSM537352 2 0.3114 0.876 0.056 0.944
#> GSM537359 2 0.0672 0.871 0.008 0.992
#> GSM537360 2 0.1414 0.878 0.020 0.980
#> GSM537364 1 0.2778 0.780 0.952 0.048
#> GSM537365 2 0.4022 0.871 0.080 0.920
#> GSM537372 2 0.9686 0.349 0.396 0.604
#> GSM537384 2 0.9710 0.349 0.400 0.600
#> GSM537394 2 0.1414 0.877 0.020 0.980
#> GSM537403 2 0.2423 0.878 0.040 0.960
#> GSM537406 2 0.0938 0.877 0.012 0.988
#> GSM537411 2 0.3733 0.872 0.072 0.928
#> GSM537412 2 0.1414 0.867 0.020 0.980
#> GSM537416 2 0.1843 0.869 0.028 0.972
#> GSM537426 2 0.1414 0.867 0.020 0.980
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 3 0.9942 0.9381 0.288 0.332 0.380
#> GSM537345 1 0.1289 0.6882 0.968 0.000 0.032
#> GSM537355 2 0.6018 0.3063 0.008 0.684 0.308
#> GSM537366 2 0.9284 0.2713 0.192 0.512 0.296
#> GSM537370 2 0.9833 -0.8418 0.248 0.396 0.356
#> GSM537380 2 0.4399 0.5975 0.000 0.812 0.188
#> GSM537392 2 0.4399 0.5975 0.000 0.812 0.188
#> GSM537415 2 0.4002 0.6357 0.000 0.840 0.160
#> GSM537417 2 0.7564 0.5620 0.068 0.636 0.296
#> GSM537422 2 0.8548 0.4933 0.120 0.568 0.312
#> GSM537423 2 0.3816 0.6178 0.000 0.852 0.148
#> GSM537427 2 0.4465 0.5673 0.004 0.820 0.176
#> GSM537430 2 0.5681 0.4697 0.016 0.748 0.236
#> GSM537336 1 0.0892 0.6857 0.980 0.000 0.020
#> GSM537337 2 0.5036 0.5879 0.020 0.808 0.172
#> GSM537348 3 0.9942 0.9381 0.288 0.332 0.380
#> GSM537349 2 0.4346 0.5629 0.000 0.816 0.184
#> GSM537356 2 0.9884 -0.7805 0.260 0.376 0.364
#> GSM537361 2 0.8727 0.4575 0.148 0.572 0.280
#> GSM537374 2 0.6452 0.4400 0.036 0.712 0.252
#> GSM537377 1 0.1289 0.6882 0.968 0.000 0.032
#> GSM537378 2 0.4002 0.6357 0.000 0.840 0.160
#> GSM537379 2 0.6662 0.5102 0.052 0.716 0.232
#> GSM537383 2 0.4291 0.6003 0.000 0.820 0.180
#> GSM537388 2 0.5785 0.3113 0.004 0.696 0.300
#> GSM537395 2 0.5253 0.6003 0.020 0.792 0.188
#> GSM537400 2 0.7844 0.5323 0.108 0.652 0.240
#> GSM537404 2 0.7677 0.5149 0.096 0.660 0.244
#> GSM537409 2 0.6192 0.4986 0.000 0.580 0.420
#> GSM537418 2 0.9677 0.1152 0.312 0.452 0.236
#> GSM537425 2 0.8645 0.4436 0.148 0.584 0.268
#> GSM537333 2 0.7246 0.5709 0.052 0.648 0.300
#> GSM537342 2 0.4700 0.6339 0.008 0.812 0.180
#> GSM537347 2 0.6796 0.5001 0.056 0.708 0.236
#> GSM537350 2 0.7398 0.4708 0.120 0.700 0.180
#> GSM537362 1 0.8966 -0.0338 0.560 0.256 0.184
#> GSM537363 2 0.7597 0.5286 0.048 0.568 0.384
#> GSM537368 1 0.1315 0.6916 0.972 0.008 0.020
#> GSM537376 2 0.5455 0.6014 0.020 0.776 0.204
#> GSM537381 2 0.9601 0.1802 0.252 0.476 0.272
#> GSM537386 2 0.3941 0.6282 0.000 0.844 0.156
#> GSM537398 3 0.9951 0.9048 0.296 0.324 0.380
#> GSM537402 2 0.5216 0.5190 0.000 0.740 0.260
#> GSM537405 1 0.2903 0.6669 0.924 0.028 0.048
#> GSM537371 1 0.1315 0.6916 0.972 0.008 0.020
#> GSM537421 2 0.6910 0.5198 0.020 0.584 0.396
#> GSM537424 2 0.6796 0.5001 0.056 0.708 0.236
#> GSM537432 2 0.5986 0.5969 0.024 0.736 0.240
#> GSM537331 2 0.6008 0.2224 0.004 0.664 0.332
#> GSM537332 2 0.5216 0.6323 0.000 0.740 0.260
#> GSM537334 2 0.7424 0.0126 0.044 0.592 0.364
#> GSM537338 2 0.5756 0.5282 0.028 0.764 0.208
#> GSM537353 2 0.5585 0.6043 0.024 0.772 0.204
#> GSM537357 1 0.0892 0.6857 0.980 0.000 0.020
#> GSM537358 2 0.3482 0.6303 0.000 0.872 0.128
#> GSM537375 2 0.5849 0.5158 0.028 0.756 0.216
#> GSM537389 2 0.4346 0.5629 0.000 0.816 0.184
#> GSM537390 2 0.4178 0.6365 0.000 0.828 0.172
#> GSM537393 2 0.5921 0.5222 0.032 0.756 0.212
#> GSM537399 2 0.6936 0.5024 0.108 0.732 0.160
#> GSM537407 2 0.7596 0.5148 0.100 0.672 0.228
#> GSM537408 2 0.4589 0.6307 0.008 0.820 0.172
#> GSM537428 2 0.6105 0.4139 0.024 0.724 0.252
#> GSM537354 2 0.5253 0.6003 0.020 0.792 0.188
#> GSM537410 2 0.4700 0.6329 0.008 0.812 0.180
#> GSM537413 2 0.5968 0.4996 0.000 0.636 0.364
#> GSM537396 2 0.4473 0.6285 0.008 0.828 0.164
#> GSM537397 3 0.9930 0.9031 0.276 0.356 0.368
#> GSM537330 2 0.6129 0.2986 0.008 0.668 0.324
#> GSM537369 1 0.8346 0.2442 0.548 0.092 0.360
#> GSM537373 2 0.4782 0.6344 0.016 0.820 0.164
#> GSM537401 3 0.9942 0.9381 0.288 0.332 0.380
#> GSM537343 2 0.7607 0.5421 0.076 0.644 0.280
#> GSM537367 2 0.7622 0.5437 0.080 0.648 0.272
#> GSM537382 2 0.6067 0.5983 0.028 0.736 0.236
#> GSM537385 2 0.5845 0.2946 0.004 0.688 0.308
#> GSM537391 1 0.9589 -0.4506 0.424 0.200 0.376
#> GSM537419 2 0.3879 0.6174 0.000 0.848 0.152
#> GSM537420 1 0.8346 0.2442 0.548 0.092 0.360
#> GSM537429 2 0.6155 0.3020 0.008 0.664 0.328
#> GSM537431 2 0.7555 0.4333 0.040 0.520 0.440
#> GSM537387 1 0.9589 -0.4506 0.424 0.200 0.376
#> GSM537414 2 0.8079 0.5295 0.112 0.628 0.260
#> GSM537433 2 0.8350 0.4775 0.120 0.600 0.280
#> GSM537335 2 0.7424 0.0126 0.044 0.592 0.364
#> GSM537339 3 0.9942 0.9381 0.288 0.332 0.380
#> GSM537340 2 0.7705 0.5323 0.060 0.592 0.348
#> GSM537344 1 0.8346 0.2442 0.548 0.092 0.360
#> GSM537346 2 0.5053 0.6223 0.024 0.812 0.164
#> GSM537351 1 0.6234 0.5291 0.776 0.096 0.128
#> GSM537352 2 0.5849 0.6053 0.028 0.756 0.216
#> GSM537359 2 0.5529 0.5480 0.000 0.704 0.296
#> GSM537360 2 0.4346 0.6423 0.000 0.816 0.184
#> GSM537364 1 0.1129 0.6856 0.976 0.004 0.020
#> GSM537365 2 0.6302 0.6136 0.048 0.744 0.208
#> GSM537372 3 0.9936 0.9110 0.280 0.348 0.372
#> GSM537384 3 0.9962 0.7920 0.292 0.344 0.364
#> GSM537394 2 0.3752 0.6363 0.000 0.856 0.144
#> GSM537403 2 0.6016 0.6117 0.020 0.724 0.256
#> GSM537406 2 0.4110 0.6306 0.004 0.844 0.152
#> GSM537411 2 0.6295 0.5099 0.036 0.728 0.236
#> GSM537412 2 0.6154 0.5040 0.000 0.592 0.408
#> GSM537416 2 0.6451 0.4838 0.004 0.560 0.436
#> GSM537426 2 0.6126 0.5088 0.000 0.600 0.400
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.527 0.7063 0.724 0.228 0.004 0.044
#> GSM537345 4 0.363 0.8496 0.184 0.000 0.004 0.812
#> GSM537355 2 0.606 0.3849 0.336 0.604 0.060 0.000
#> GSM537366 3 0.908 0.3972 0.256 0.272 0.400 0.072
#> GSM537370 1 0.598 0.6108 0.652 0.296 0.024 0.028
#> GSM537380 2 0.385 0.4535 0.088 0.852 0.056 0.004
#> GSM537392 2 0.385 0.4535 0.088 0.852 0.056 0.004
#> GSM537415 2 0.435 0.3711 0.024 0.780 0.196 0.000
#> GSM537417 3 0.695 0.4875 0.064 0.340 0.568 0.028
#> GSM537422 3 0.768 0.5507 0.088 0.276 0.572 0.064
#> GSM537423 2 0.267 0.4828 0.044 0.908 0.048 0.000
#> GSM537427 2 0.429 0.5324 0.136 0.812 0.052 0.000
#> GSM537430 2 0.595 0.5125 0.288 0.644 0.068 0.000
#> GSM537336 4 0.238 0.8752 0.068 0.000 0.016 0.916
#> GSM537337 2 0.636 0.4808 0.180 0.656 0.164 0.000
#> GSM537348 1 0.527 0.7063 0.724 0.228 0.004 0.044
#> GSM537349 2 0.355 0.5231 0.128 0.848 0.024 0.000
#> GSM537356 1 0.732 0.5626 0.604 0.264 0.072 0.060
#> GSM537361 3 0.895 0.4778 0.132 0.324 0.432 0.112
#> GSM537374 2 0.623 0.4795 0.320 0.612 0.064 0.004
#> GSM537377 4 0.363 0.8496 0.184 0.000 0.004 0.812
#> GSM537378 2 0.435 0.3711 0.024 0.780 0.196 0.000
#> GSM537379 2 0.748 0.4051 0.272 0.548 0.168 0.012
#> GSM537383 2 0.331 0.4702 0.076 0.880 0.040 0.004
#> GSM537388 2 0.568 0.3960 0.332 0.628 0.040 0.000
#> GSM537395 2 0.648 0.4351 0.152 0.640 0.208 0.000
#> GSM537400 2 0.866 -0.3323 0.156 0.392 0.388 0.064
#> GSM537404 2 0.870 -0.2300 0.192 0.428 0.324 0.056
#> GSM537409 3 0.569 0.5219 0.024 0.280 0.676 0.020
#> GSM537418 3 0.994 0.3892 0.236 0.264 0.292 0.208
#> GSM537425 3 0.913 0.4460 0.184 0.340 0.384 0.092
#> GSM537333 3 0.755 0.4599 0.120 0.328 0.528 0.024
#> GSM537342 2 0.610 0.2261 0.068 0.616 0.316 0.000
#> GSM537347 2 0.791 0.2678 0.276 0.508 0.196 0.020
#> GSM537350 2 0.706 0.2211 0.228 0.628 0.116 0.028
#> GSM537362 1 0.883 0.0456 0.392 0.124 0.100 0.384
#> GSM537363 3 0.708 0.5409 0.088 0.288 0.596 0.028
#> GSM537368 4 0.343 0.8763 0.144 0.000 0.012 0.844
#> GSM537376 2 0.666 0.4011 0.160 0.620 0.220 0.000
#> GSM537381 3 0.967 0.4318 0.276 0.232 0.348 0.144
#> GSM537386 2 0.535 0.4657 0.108 0.756 0.132 0.004
#> GSM537398 1 0.571 0.6917 0.696 0.236 0.004 0.064
#> GSM537402 2 0.588 0.5139 0.248 0.672 0.080 0.000
#> GSM537405 4 0.400 0.8509 0.088 0.016 0.044 0.852
#> GSM537371 4 0.343 0.8763 0.144 0.000 0.012 0.844
#> GSM537421 3 0.602 0.5211 0.028 0.304 0.644 0.024
#> GSM537424 2 0.791 0.2678 0.276 0.508 0.196 0.020
#> GSM537432 2 0.731 0.3544 0.208 0.556 0.232 0.004
#> GSM537331 2 0.573 0.3418 0.364 0.600 0.036 0.000
#> GSM537332 2 0.668 -0.1539 0.076 0.516 0.404 0.004
#> GSM537334 2 0.644 0.1802 0.460 0.480 0.056 0.004
#> GSM537338 2 0.652 0.4898 0.256 0.620 0.124 0.000
#> GSM537353 2 0.676 0.4364 0.188 0.628 0.180 0.004
#> GSM537357 4 0.238 0.8752 0.068 0.000 0.016 0.916
#> GSM537358 2 0.307 0.4891 0.044 0.888 0.068 0.000
#> GSM537375 2 0.664 0.4766 0.268 0.604 0.128 0.000
#> GSM537389 2 0.355 0.5231 0.128 0.848 0.024 0.000
#> GSM537390 2 0.443 0.3113 0.016 0.756 0.228 0.000
#> GSM537393 2 0.657 0.4758 0.256 0.616 0.128 0.000
#> GSM537399 2 0.764 0.1912 0.240 0.556 0.184 0.020
#> GSM537407 2 0.862 -0.2470 0.180 0.444 0.320 0.056
#> GSM537408 2 0.500 0.3933 0.100 0.772 0.128 0.000
#> GSM537428 2 0.641 0.4765 0.320 0.592 0.088 0.000
#> GSM537354 2 0.648 0.4351 0.152 0.640 0.208 0.000
#> GSM537410 2 0.614 0.2307 0.072 0.616 0.312 0.000
#> GSM537413 2 0.730 -0.0315 0.100 0.532 0.348 0.020
#> GSM537396 2 0.514 0.3793 0.096 0.760 0.144 0.000
#> GSM537397 1 0.581 0.6738 0.684 0.260 0.016 0.040
#> GSM537330 2 0.631 0.3811 0.336 0.588 0.076 0.000
#> GSM537369 1 0.603 0.2689 0.604 0.032 0.012 0.352
#> GSM537373 2 0.619 0.2648 0.084 0.628 0.288 0.000
#> GSM537401 1 0.527 0.7063 0.724 0.228 0.004 0.044
#> GSM537343 2 0.841 -0.2162 0.156 0.464 0.328 0.052
#> GSM537367 3 0.804 0.4230 0.104 0.392 0.452 0.052
#> GSM537382 2 0.699 0.2153 0.136 0.540 0.324 0.000
#> GSM537385 2 0.560 0.4074 0.332 0.632 0.036 0.000
#> GSM537391 1 0.587 0.6328 0.712 0.116 0.004 0.168
#> GSM537419 2 0.324 0.4958 0.056 0.880 0.064 0.000
#> GSM537420 1 0.603 0.2689 0.604 0.032 0.012 0.352
#> GSM537429 2 0.641 0.3802 0.332 0.592 0.072 0.004
#> GSM537431 3 0.752 0.2706 0.100 0.296 0.564 0.040
#> GSM537387 1 0.587 0.6328 0.712 0.116 0.004 0.168
#> GSM537414 3 0.855 0.4550 0.120 0.332 0.464 0.084
#> GSM537433 3 0.860 0.4406 0.152 0.348 0.436 0.064
#> GSM537335 2 0.644 0.1802 0.460 0.480 0.056 0.004
#> GSM537339 1 0.527 0.7063 0.724 0.228 0.004 0.044
#> GSM537340 3 0.685 0.5257 0.040 0.360 0.560 0.040
#> GSM537344 1 0.603 0.2689 0.604 0.032 0.012 0.352
#> GSM537346 2 0.677 0.3548 0.148 0.644 0.196 0.012
#> GSM537351 4 0.615 0.6870 0.088 0.012 0.212 0.688
#> GSM537352 2 0.695 0.2857 0.144 0.560 0.296 0.000
#> GSM537359 2 0.671 0.1646 0.128 0.644 0.216 0.012
#> GSM537360 2 0.597 0.2454 0.064 0.632 0.304 0.000
#> GSM537364 4 0.284 0.8708 0.076 0.000 0.028 0.896
#> GSM537365 2 0.770 -0.1454 0.108 0.492 0.368 0.032
#> GSM537372 1 0.583 0.6772 0.688 0.252 0.016 0.044
#> GSM537384 1 0.730 0.5977 0.624 0.232 0.072 0.072
#> GSM537394 2 0.525 0.4306 0.088 0.748 0.164 0.000
#> GSM537403 3 0.593 0.2147 0.036 0.464 0.500 0.000
#> GSM537406 2 0.482 0.3969 0.076 0.780 0.144 0.000
#> GSM537411 2 0.672 0.4779 0.252 0.604 0.144 0.000
#> GSM537412 3 0.614 0.4916 0.036 0.312 0.632 0.020
#> GSM537416 3 0.626 0.4853 0.044 0.304 0.632 0.020
#> GSM537426 3 0.624 0.4949 0.040 0.316 0.624 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.382 0.6899 0.004 0.208 0.000 0.016 0.772
#> GSM537345 1 0.532 0.7635 0.716 0.000 0.068 0.040 0.176
#> GSM537355 2 0.525 0.3839 0.000 0.620 0.004 0.056 0.320
#> GSM537366 4 0.784 0.3715 0.036 0.248 0.024 0.444 0.248
#> GSM537370 5 0.463 0.5996 0.004 0.276 0.000 0.032 0.688
#> GSM537380 2 0.435 0.4205 0.000 0.800 0.108 0.036 0.056
#> GSM537392 2 0.427 0.4233 0.000 0.804 0.108 0.032 0.056
#> GSM537415 2 0.452 0.4104 0.000 0.740 0.032 0.212 0.016
#> GSM537417 4 0.579 0.4406 0.020 0.300 0.020 0.624 0.036
#> GSM537422 4 0.620 0.5193 0.040 0.228 0.028 0.652 0.052
#> GSM537423 2 0.306 0.4931 0.000 0.880 0.044 0.052 0.024
#> GSM537427 2 0.371 0.5437 0.000 0.820 0.004 0.052 0.124
#> GSM537430 2 0.541 0.5065 0.000 0.640 0.004 0.084 0.272
#> GSM537336 1 0.235 0.8022 0.912 0.000 0.016 0.016 0.056
#> GSM537337 2 0.574 0.4936 0.000 0.648 0.008 0.184 0.160
#> GSM537348 5 0.382 0.6899 0.004 0.208 0.000 0.016 0.772
#> GSM537349 2 0.338 0.5348 0.000 0.848 0.012 0.032 0.108
#> GSM537356 5 0.574 0.5609 0.016 0.240 0.004 0.088 0.652
#> GSM537361 4 0.789 0.4444 0.056 0.272 0.076 0.508 0.088
#> GSM537374 2 0.605 0.4594 0.000 0.592 0.024 0.088 0.296
#> GSM537377 1 0.532 0.7635 0.716 0.000 0.068 0.040 0.176
#> GSM537378 2 0.452 0.4104 0.000 0.740 0.032 0.212 0.016
#> GSM537379 2 0.688 0.4088 0.008 0.532 0.016 0.204 0.240
#> GSM537383 2 0.357 0.4781 0.000 0.852 0.072 0.036 0.040
#> GSM537388 2 0.482 0.3963 0.000 0.644 0.000 0.040 0.316
#> GSM537395 2 0.583 0.4476 0.000 0.624 0.008 0.240 0.128
#> GSM537400 4 0.839 0.3279 0.044 0.312 0.104 0.424 0.116
#> GSM537404 2 0.785 -0.1993 0.028 0.388 0.040 0.376 0.168
#> GSM537409 4 0.579 0.2367 0.000 0.164 0.172 0.652 0.012
#> GSM537418 4 0.937 0.3955 0.152 0.216 0.080 0.352 0.200
#> GSM537425 4 0.849 0.4144 0.060 0.308 0.084 0.420 0.128
#> GSM537333 4 0.739 0.4570 0.012 0.272 0.120 0.524 0.072
#> GSM537342 2 0.589 0.2508 0.000 0.564 0.040 0.356 0.040
#> GSM537347 2 0.744 0.2842 0.016 0.484 0.028 0.216 0.256
#> GSM537350 2 0.652 0.2379 0.000 0.584 0.032 0.152 0.232
#> GSM537362 5 0.914 -0.0637 0.288 0.088 0.108 0.156 0.360
#> GSM537363 4 0.713 0.3750 0.032 0.180 0.148 0.596 0.044
#> GSM537368 1 0.430 0.8056 0.800 0.004 0.028 0.040 0.128
#> GSM537376 2 0.620 0.4068 0.000 0.580 0.012 0.268 0.140
#> GSM537381 4 0.875 0.4132 0.072 0.196 0.064 0.388 0.280
#> GSM537386 2 0.561 0.4799 0.000 0.716 0.088 0.120 0.076
#> GSM537398 5 0.477 0.6593 0.044 0.216 0.000 0.016 0.724
#> GSM537402 2 0.549 0.5181 0.000 0.664 0.008 0.108 0.220
#> GSM537405 1 0.369 0.7753 0.856 0.016 0.024 0.040 0.064
#> GSM537371 1 0.430 0.8056 0.800 0.004 0.028 0.040 0.128
#> GSM537421 4 0.623 0.2357 0.008 0.180 0.192 0.612 0.008
#> GSM537424 2 0.744 0.2842 0.016 0.484 0.028 0.216 0.256
#> GSM537432 2 0.716 0.3569 0.004 0.512 0.040 0.268 0.176
#> GSM537331 2 0.488 0.3470 0.000 0.616 0.000 0.036 0.348
#> GSM537332 2 0.629 -0.0921 0.004 0.476 0.040 0.432 0.048
#> GSM537334 2 0.589 0.1864 0.000 0.484 0.016 0.060 0.440
#> GSM537338 2 0.574 0.5006 0.000 0.624 0.004 0.128 0.244
#> GSM537353 2 0.674 0.4403 0.004 0.572 0.032 0.228 0.164
#> GSM537357 1 0.235 0.8022 0.912 0.000 0.016 0.016 0.056
#> GSM537358 2 0.341 0.4963 0.000 0.856 0.040 0.084 0.020
#> GSM537375 2 0.594 0.4833 0.000 0.608 0.008 0.132 0.252
#> GSM537389 2 0.338 0.5348 0.000 0.848 0.012 0.032 0.108
#> GSM537390 2 0.460 0.3555 0.000 0.700 0.028 0.264 0.008
#> GSM537393 2 0.620 0.4822 0.000 0.596 0.016 0.144 0.244
#> GSM537399 2 0.702 0.1855 0.000 0.508 0.032 0.220 0.240
#> GSM537407 2 0.785 -0.1884 0.024 0.400 0.048 0.368 0.160
#> GSM537408 2 0.546 0.4041 0.000 0.720 0.064 0.148 0.068
#> GSM537428 2 0.565 0.4794 0.000 0.608 0.004 0.096 0.292
#> GSM537354 2 0.583 0.4476 0.000 0.624 0.008 0.240 0.128
#> GSM537410 2 0.594 0.2527 0.000 0.564 0.040 0.352 0.044
#> GSM537413 3 0.627 0.5942 0.000 0.380 0.492 0.120 0.008
#> GSM537396 2 0.525 0.3939 0.000 0.720 0.032 0.172 0.076
#> GSM537397 5 0.427 0.6613 0.004 0.232 0.000 0.028 0.736
#> GSM537330 2 0.546 0.3811 0.000 0.612 0.008 0.064 0.316
#> GSM537369 5 0.640 0.3384 0.176 0.032 0.140 0.012 0.640
#> GSM537373 2 0.619 0.2782 0.000 0.568 0.036 0.324 0.072
#> GSM537401 5 0.382 0.6899 0.004 0.208 0.000 0.016 0.772
#> GSM537343 2 0.815 -0.1771 0.020 0.396 0.092 0.348 0.144
#> GSM537367 4 0.701 0.3517 0.020 0.344 0.036 0.508 0.092
#> GSM537382 2 0.636 0.2054 0.000 0.496 0.012 0.372 0.120
#> GSM537385 2 0.483 0.3999 0.000 0.648 0.004 0.032 0.316
#> GSM537391 5 0.391 0.6300 0.044 0.096 0.032 0.000 0.828
#> GSM537419 2 0.342 0.4987 0.000 0.860 0.044 0.068 0.028
#> GSM537420 5 0.640 0.3384 0.176 0.032 0.140 0.012 0.640
#> GSM537429 2 0.546 0.3720 0.000 0.612 0.008 0.064 0.316
#> GSM537431 3 0.690 0.5528 0.016 0.164 0.568 0.228 0.024
#> GSM537387 5 0.391 0.6300 0.044 0.096 0.032 0.000 0.828
#> GSM537414 4 0.738 0.4406 0.040 0.276 0.068 0.544 0.072
#> GSM537433 4 0.762 0.3885 0.032 0.304 0.044 0.488 0.132
#> GSM537335 2 0.589 0.1864 0.000 0.484 0.016 0.060 0.440
#> GSM537339 5 0.382 0.6899 0.004 0.208 0.000 0.016 0.772
#> GSM537340 4 0.700 0.3928 0.036 0.224 0.164 0.564 0.012
#> GSM537344 5 0.640 0.3384 0.176 0.032 0.140 0.012 0.640
#> GSM537346 2 0.635 0.3605 0.004 0.608 0.036 0.252 0.100
#> GSM537351 1 0.584 0.6035 0.692 0.004 0.120 0.144 0.040
#> GSM537352 2 0.606 0.3055 0.000 0.540 0.004 0.336 0.120
#> GSM537359 2 0.584 -0.5162 0.000 0.488 0.444 0.036 0.032
#> GSM537360 2 0.587 0.2826 0.000 0.588 0.040 0.328 0.044
#> GSM537364 1 0.245 0.7963 0.912 0.000 0.024 0.032 0.032
#> GSM537365 2 0.722 -0.0822 0.020 0.452 0.060 0.396 0.072
#> GSM537372 5 0.433 0.6647 0.008 0.224 0.000 0.028 0.740
#> GSM537384 5 0.645 0.5723 0.028 0.208 0.028 0.096 0.640
#> GSM537394 2 0.550 0.4434 0.000 0.700 0.048 0.188 0.064
#> GSM537403 4 0.520 0.1767 0.000 0.408 0.016 0.556 0.020
#> GSM537406 2 0.485 0.4187 0.000 0.748 0.032 0.168 0.052
#> GSM537411 2 0.644 0.4742 0.004 0.584 0.016 0.160 0.236
#> GSM537412 4 0.629 0.1123 0.000 0.184 0.220 0.584 0.012
#> GSM537416 4 0.651 0.0577 0.000 0.188 0.256 0.544 0.012
#> GSM537426 4 0.627 0.1272 0.000 0.188 0.212 0.588 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.256 0.60594 0.000 0.156 0.000 0.004 0.840 0.000
#> GSM537345 1 0.580 0.49152 0.572 0.000 0.268 0.008 0.140 0.012
#> GSM537355 2 0.517 0.28693 0.000 0.564 0.004 0.088 0.344 0.000
#> GSM537366 4 0.755 0.34000 0.040 0.188 0.036 0.424 0.296 0.016
#> GSM537370 5 0.422 0.55020 0.004 0.232 0.008 0.036 0.720 0.000
#> GSM537380 2 0.429 0.40186 0.000 0.784 0.016 0.028 0.056 0.116
#> GSM537392 2 0.431 0.40564 0.000 0.784 0.020 0.024 0.060 0.112
#> GSM537415 2 0.462 0.44713 0.000 0.728 0.012 0.192 0.024 0.044
#> GSM537417 4 0.568 0.39658 0.032 0.204 0.028 0.672 0.044 0.020
#> GSM537422 4 0.597 0.43422 0.044 0.140 0.052 0.688 0.048 0.028
#> GSM537423 2 0.308 0.50821 0.000 0.868 0.008 0.032 0.036 0.056
#> GSM537427 2 0.382 0.54809 0.000 0.792 0.000 0.048 0.140 0.020
#> GSM537430 2 0.543 0.46216 0.000 0.584 0.008 0.128 0.280 0.000
#> GSM537336 1 0.232 0.69548 0.896 0.000 0.072 0.008 0.024 0.000
#> GSM537337 2 0.587 0.49903 0.000 0.576 0.016 0.216 0.188 0.004
#> GSM537348 5 0.256 0.60594 0.000 0.156 0.000 0.004 0.840 0.000
#> GSM537349 2 0.302 0.53861 0.000 0.840 0.000 0.016 0.128 0.016
#> GSM537356 5 0.488 0.49457 0.008 0.188 0.016 0.084 0.704 0.000
#> GSM537361 4 0.806 0.38412 0.072 0.172 0.092 0.504 0.116 0.044
#> GSM537374 2 0.597 0.39535 0.000 0.548 0.032 0.112 0.304 0.004
#> GSM537377 1 0.580 0.49152 0.572 0.000 0.268 0.008 0.140 0.012
#> GSM537378 2 0.462 0.44713 0.000 0.728 0.012 0.192 0.024 0.044
#> GSM537379 2 0.690 0.36747 0.008 0.444 0.036 0.260 0.248 0.004
#> GSM537383 2 0.342 0.46600 0.000 0.840 0.008 0.016 0.052 0.084
#> GSM537388 2 0.480 0.30020 0.000 0.592 0.000 0.068 0.340 0.000
#> GSM537395 2 0.587 0.45674 0.000 0.560 0.016 0.268 0.152 0.004
#> GSM537400 4 0.849 0.28916 0.040 0.220 0.080 0.432 0.120 0.108
#> GSM537404 4 0.792 0.21403 0.024 0.328 0.060 0.364 0.192 0.032
#> GSM537409 4 0.618 0.18664 0.000 0.100 0.068 0.568 0.004 0.260
#> GSM537418 4 0.901 0.11767 0.104 0.140 0.132 0.364 0.216 0.044
#> GSM537425 4 0.879 0.41798 0.060 0.224 0.072 0.388 0.168 0.088
#> GSM537333 4 0.752 0.32746 0.012 0.184 0.088 0.528 0.060 0.128
#> GSM537342 2 0.590 0.27668 0.000 0.544 0.036 0.348 0.040 0.032
#> GSM537347 2 0.723 0.24831 0.012 0.420 0.028 0.252 0.268 0.020
#> GSM537350 2 0.646 0.23073 0.000 0.556 0.052 0.156 0.224 0.012
#> GSM537362 3 0.860 0.00000 0.132 0.048 0.332 0.144 0.308 0.036
#> GSM537363 4 0.810 0.29176 0.028 0.144 0.124 0.476 0.052 0.176
#> GSM537368 1 0.482 0.62905 0.708 0.000 0.164 0.008 0.112 0.008
#> GSM537376 2 0.617 0.41968 0.000 0.560 0.020 0.248 0.156 0.016
#> GSM537381 4 0.848 0.29199 0.064 0.148 0.088 0.372 0.292 0.036
#> GSM537386 2 0.570 0.48159 0.000 0.684 0.020 0.112 0.080 0.104
#> GSM537398 5 0.412 0.55985 0.036 0.156 0.012 0.020 0.776 0.000
#> GSM537402 2 0.513 0.50009 0.000 0.660 0.004 0.092 0.228 0.016
#> GSM537405 1 0.251 0.65707 0.904 0.012 0.012 0.036 0.032 0.004
#> GSM537371 1 0.482 0.62905 0.708 0.000 0.164 0.008 0.112 0.008
#> GSM537421 4 0.694 0.12969 0.004 0.116 0.088 0.496 0.012 0.284
#> GSM537424 2 0.723 0.24831 0.012 0.420 0.028 0.252 0.268 0.020
#> GSM537432 2 0.724 0.35315 0.000 0.472 0.040 0.248 0.188 0.052
#> GSM537331 2 0.484 0.24051 0.000 0.564 0.000 0.064 0.372 0.000
#> GSM537332 2 0.653 0.00402 0.000 0.436 0.044 0.416 0.060 0.044
#> GSM537334 5 0.585 -0.10793 0.000 0.424 0.028 0.096 0.452 0.000
#> GSM537338 2 0.578 0.48025 0.000 0.552 0.012 0.172 0.264 0.000
#> GSM537353 2 0.675 0.42978 0.000 0.516 0.032 0.248 0.172 0.032
#> GSM537357 1 0.232 0.69548 0.896 0.000 0.072 0.008 0.024 0.000
#> GSM537358 2 0.363 0.51992 0.000 0.832 0.008 0.080 0.032 0.048
#> GSM537375 2 0.609 0.44330 0.000 0.528 0.008 0.180 0.272 0.012
#> GSM537389 2 0.302 0.53861 0.000 0.840 0.000 0.016 0.128 0.016
#> GSM537390 2 0.471 0.39891 0.000 0.696 0.012 0.232 0.012 0.048
#> GSM537393 2 0.638 0.46219 0.000 0.540 0.024 0.160 0.256 0.020
#> GSM537399 2 0.705 0.12783 0.000 0.464 0.044 0.228 0.240 0.024
#> GSM537407 4 0.807 0.20082 0.028 0.340 0.068 0.356 0.168 0.040
#> GSM537408 2 0.546 0.41934 0.000 0.700 0.060 0.152 0.052 0.036
#> GSM537428 2 0.539 0.39827 0.000 0.564 0.004 0.124 0.308 0.000
#> GSM537354 2 0.587 0.45674 0.000 0.560 0.016 0.268 0.152 0.004
#> GSM537410 2 0.595 0.27606 0.000 0.544 0.036 0.344 0.044 0.032
#> GSM537413 6 0.501 0.60230 0.000 0.280 0.020 0.064 0.000 0.636
#> GSM537396 2 0.511 0.42067 0.000 0.716 0.040 0.164 0.056 0.024
#> GSM537397 5 0.366 0.58728 0.004 0.184 0.008 0.024 0.780 0.000
#> GSM537330 2 0.558 0.28234 0.000 0.540 0.004 0.108 0.340 0.008
#> GSM537369 5 0.518 0.02708 0.052 0.016 0.424 0.000 0.508 0.000
#> GSM537373 2 0.613 0.30255 0.000 0.552 0.032 0.316 0.064 0.036
#> GSM537401 5 0.256 0.60594 0.000 0.156 0.000 0.004 0.840 0.000
#> GSM537343 2 0.834 -0.20833 0.028 0.352 0.060 0.328 0.144 0.088
#> GSM537367 4 0.736 0.34749 0.020 0.284 0.056 0.492 0.104 0.044
#> GSM537382 2 0.622 0.22977 0.000 0.444 0.016 0.396 0.132 0.012
#> GSM537385 2 0.500 0.31978 0.000 0.596 0.000 0.068 0.328 0.008
#> GSM537391 5 0.350 0.41635 0.016 0.068 0.072 0.008 0.836 0.000
#> GSM537419 2 0.342 0.51969 0.000 0.844 0.004 0.068 0.032 0.052
#> GSM537420 5 0.518 0.02708 0.052 0.016 0.424 0.000 0.508 0.000
#> GSM537429 2 0.567 0.27220 0.000 0.540 0.008 0.108 0.336 0.008
#> GSM537431 6 0.561 0.37840 0.024 0.072 0.084 0.128 0.000 0.692
#> GSM537387 5 0.350 0.41635 0.016 0.068 0.072 0.008 0.836 0.000
#> GSM537414 4 0.724 0.33506 0.048 0.188 0.092 0.572 0.060 0.040
#> GSM537433 4 0.761 0.38567 0.036 0.244 0.056 0.484 0.152 0.028
#> GSM537335 5 0.585 -0.10793 0.000 0.424 0.028 0.096 0.452 0.000
#> GSM537339 5 0.256 0.60594 0.000 0.156 0.000 0.004 0.840 0.000
#> GSM537340 4 0.731 0.25099 0.028 0.148 0.076 0.512 0.012 0.224
#> GSM537344 5 0.518 0.02708 0.052 0.016 0.424 0.000 0.508 0.000
#> GSM537346 2 0.686 0.28568 0.000 0.516 0.060 0.264 0.132 0.028
#> GSM537351 1 0.522 0.51130 0.716 0.000 0.104 0.084 0.008 0.088
#> GSM537352 2 0.594 0.32670 0.000 0.480 0.008 0.372 0.132 0.008
#> GSM537359 6 0.568 0.48144 0.000 0.400 0.036 0.024 0.028 0.512
#> GSM537360 2 0.599 0.32752 0.000 0.556 0.028 0.324 0.040 0.052
#> GSM537364 1 0.115 0.68972 0.960 0.000 0.016 0.020 0.000 0.004
#> GSM537365 2 0.753 -0.01762 0.028 0.412 0.056 0.372 0.084 0.048
#> GSM537372 5 0.359 0.58911 0.004 0.176 0.008 0.024 0.788 0.000
#> GSM537384 5 0.548 0.44120 0.012 0.168 0.036 0.088 0.688 0.008
#> GSM537394 2 0.571 0.43981 0.000 0.668 0.048 0.184 0.064 0.036
#> GSM537403 4 0.532 0.08659 0.000 0.352 0.024 0.576 0.024 0.024
#> GSM537406 2 0.465 0.44317 0.000 0.748 0.040 0.156 0.036 0.020
#> GSM537411 2 0.659 0.45800 0.000 0.512 0.028 0.184 0.256 0.020
#> GSM537412 4 0.656 0.06309 0.000 0.108 0.072 0.468 0.004 0.348
#> GSM537416 4 0.659 0.01388 0.000 0.108 0.072 0.440 0.004 0.376
#> GSM537426 4 0.667 0.07026 0.000 0.112 0.072 0.468 0.008 0.340
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> SD:hclust 90 0.821 0.8192 2
#> SD:hclust 73 0.392 0.8807 3
#> SD:hclust 31 0.135 0.9739 4
#> SD:hclust 30 0.219 0.6712 5
#> SD:hclust 23 0.553 0.0129 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 104 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 0.827 0.884 0.954 0.4757 0.522 0.522
#> 3 3 0.372 0.578 0.774 0.3449 0.765 0.579
#> 4 4 0.410 0.429 0.626 0.1450 0.814 0.545
#> 5 5 0.517 0.459 0.685 0.0743 0.857 0.544
#> 6 6 0.593 0.508 0.672 0.0454 0.909 0.608
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM537341 2 0.8713 0.5750 0.292 0.708
#> GSM537345 1 0.0000 0.9334 1.000 0.000
#> GSM537355 2 0.0000 0.9595 0.000 1.000
#> GSM537366 1 0.0000 0.9334 1.000 0.000
#> GSM537370 2 0.0672 0.9538 0.008 0.992
#> GSM537380 2 0.0000 0.9595 0.000 1.000
#> GSM537392 2 0.0000 0.9595 0.000 1.000
#> GSM537415 2 0.0000 0.9595 0.000 1.000
#> GSM537417 2 0.0376 0.9566 0.004 0.996
#> GSM537422 1 0.5629 0.8261 0.868 0.132
#> GSM537423 2 0.0000 0.9595 0.000 1.000
#> GSM537427 2 0.0000 0.9595 0.000 1.000
#> GSM537430 2 0.0000 0.9595 0.000 1.000
#> GSM537336 1 0.0000 0.9334 1.000 0.000
#> GSM537337 2 0.0000 0.9595 0.000 1.000
#> GSM537348 1 0.0000 0.9334 1.000 0.000
#> GSM537349 2 0.0000 0.9595 0.000 1.000
#> GSM537356 1 0.0000 0.9334 1.000 0.000
#> GSM537361 1 0.0000 0.9334 1.000 0.000
#> GSM537374 2 0.0000 0.9595 0.000 1.000
#> GSM537377 1 0.0000 0.9334 1.000 0.000
#> GSM537378 2 0.0000 0.9595 0.000 1.000
#> GSM537379 2 0.0000 0.9595 0.000 1.000
#> GSM537383 2 0.0000 0.9595 0.000 1.000
#> GSM537388 2 0.0000 0.9595 0.000 1.000
#> GSM537395 2 0.0000 0.9595 0.000 1.000
#> GSM537400 1 0.9732 0.3686 0.596 0.404
#> GSM537404 2 0.9850 0.1966 0.428 0.572
#> GSM537409 2 0.0000 0.9595 0.000 1.000
#> GSM537418 1 0.0000 0.9334 1.000 0.000
#> GSM537425 1 0.0000 0.9334 1.000 0.000
#> GSM537333 2 0.9954 0.0640 0.460 0.540
#> GSM537342 2 0.0000 0.9595 0.000 1.000
#> GSM537347 2 0.1843 0.9374 0.028 0.972
#> GSM537350 1 0.0000 0.9334 1.000 0.000
#> GSM537362 1 0.0938 0.9257 0.988 0.012
#> GSM537363 1 0.7674 0.7189 0.776 0.224
#> GSM537368 1 0.0000 0.9334 1.000 0.000
#> GSM537376 2 0.0000 0.9595 0.000 1.000
#> GSM537381 1 0.0000 0.9334 1.000 0.000
#> GSM537386 2 0.0000 0.9595 0.000 1.000
#> GSM537398 1 0.0000 0.9334 1.000 0.000
#> GSM537402 2 0.0000 0.9595 0.000 1.000
#> GSM537405 1 0.0000 0.9334 1.000 0.000
#> GSM537371 1 0.0000 0.9334 1.000 0.000
#> GSM537421 2 0.0938 0.9506 0.012 0.988
#> GSM537424 1 0.0000 0.9334 1.000 0.000
#> GSM537432 2 0.9850 0.1797 0.428 0.572
#> GSM537331 2 0.0000 0.9595 0.000 1.000
#> GSM537332 2 0.0000 0.9595 0.000 1.000
#> GSM537334 2 0.0000 0.9595 0.000 1.000
#> GSM537338 2 0.0000 0.9595 0.000 1.000
#> GSM537353 2 0.0000 0.9595 0.000 1.000
#> GSM537357 1 0.0000 0.9334 1.000 0.000
#> GSM537358 2 0.0000 0.9595 0.000 1.000
#> GSM537375 2 0.0000 0.9595 0.000 1.000
#> GSM537389 2 0.0000 0.9595 0.000 1.000
#> GSM537390 2 0.0000 0.9595 0.000 1.000
#> GSM537393 2 0.0000 0.9595 0.000 1.000
#> GSM537399 1 0.7056 0.7484 0.808 0.192
#> GSM537407 1 0.0000 0.9334 1.000 0.000
#> GSM537408 2 0.0000 0.9595 0.000 1.000
#> GSM537428 2 0.0000 0.9595 0.000 1.000
#> GSM537354 2 0.0000 0.9595 0.000 1.000
#> GSM537410 2 0.0000 0.9595 0.000 1.000
#> GSM537413 2 0.0000 0.9595 0.000 1.000
#> GSM537396 2 0.2603 0.9217 0.044 0.956
#> GSM537397 1 0.4690 0.8547 0.900 0.100
#> GSM537330 2 0.0000 0.9595 0.000 1.000
#> GSM537369 1 0.0000 0.9334 1.000 0.000
#> GSM537373 2 0.2043 0.9332 0.032 0.968
#> GSM537401 2 0.4298 0.8775 0.088 0.912
#> GSM537343 1 0.0000 0.9334 1.000 0.000
#> GSM537367 1 0.7745 0.7125 0.772 0.228
#> GSM537382 2 0.0000 0.9595 0.000 1.000
#> GSM537385 2 0.0000 0.9595 0.000 1.000
#> GSM537391 1 0.0000 0.9334 1.000 0.000
#> GSM537419 2 0.0000 0.9595 0.000 1.000
#> GSM537420 1 0.0000 0.9334 1.000 0.000
#> GSM537429 2 0.4690 0.8655 0.100 0.900
#> GSM537431 1 0.9933 0.2332 0.548 0.452
#> GSM537387 1 0.0000 0.9334 1.000 0.000
#> GSM537414 1 0.7219 0.7498 0.800 0.200
#> GSM537433 1 0.0000 0.9334 1.000 0.000
#> GSM537335 2 0.1843 0.9379 0.028 0.972
#> GSM537339 1 0.0376 0.9308 0.996 0.004
#> GSM537340 2 0.9491 0.3812 0.368 0.632
#> GSM537344 1 0.0000 0.9334 1.000 0.000
#> GSM537346 2 0.0000 0.9595 0.000 1.000
#> GSM537351 1 0.0000 0.9334 1.000 0.000
#> GSM537352 2 0.0000 0.9595 0.000 1.000
#> GSM537359 2 0.0000 0.9595 0.000 1.000
#> GSM537360 2 0.0000 0.9595 0.000 1.000
#> GSM537364 1 0.0000 0.9334 1.000 0.000
#> GSM537365 1 0.9998 0.0927 0.508 0.492
#> GSM537372 1 0.0000 0.9334 1.000 0.000
#> GSM537384 1 0.0000 0.9334 1.000 0.000
#> GSM537394 2 0.0000 0.9595 0.000 1.000
#> GSM537403 2 0.0000 0.9595 0.000 1.000
#> GSM537406 2 0.0000 0.9595 0.000 1.000
#> GSM537411 2 0.0000 0.9595 0.000 1.000
#> GSM537412 2 0.0000 0.9595 0.000 1.000
#> GSM537416 2 0.0938 0.9506 0.012 0.988
#> GSM537426 2 0.0000 0.9595 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.9876 0.00501 0.288 0.412 0.300
#> GSM537345 1 0.2165 0.77465 0.936 0.000 0.064
#> GSM537355 2 0.4605 0.70705 0.000 0.796 0.204
#> GSM537366 3 0.7188 -0.24114 0.488 0.024 0.488
#> GSM537370 2 0.7889 0.42497 0.088 0.624 0.288
#> GSM537380 2 0.2066 0.75466 0.000 0.940 0.060
#> GSM537392 2 0.1643 0.76065 0.000 0.956 0.044
#> GSM537415 2 0.4178 0.67828 0.000 0.828 0.172
#> GSM537417 3 0.6566 0.42197 0.016 0.348 0.636
#> GSM537422 3 0.5295 0.54693 0.156 0.036 0.808
#> GSM537423 2 0.0747 0.76221 0.000 0.984 0.016
#> GSM537427 2 0.2711 0.75841 0.000 0.912 0.088
#> GSM537430 2 0.0892 0.76545 0.000 0.980 0.020
#> GSM537336 1 0.2796 0.77080 0.908 0.000 0.092
#> GSM537337 2 0.3482 0.75205 0.000 0.872 0.128
#> GSM537348 1 0.6224 0.68000 0.688 0.016 0.296
#> GSM537349 2 0.0892 0.76250 0.000 0.980 0.020
#> GSM537356 1 0.6105 0.70350 0.724 0.024 0.252
#> GSM537361 3 0.5497 0.32085 0.292 0.000 0.708
#> GSM537374 2 0.4979 0.70432 0.020 0.812 0.168
#> GSM537377 1 0.2165 0.77465 0.936 0.000 0.064
#> GSM537378 2 0.0747 0.76221 0.000 0.984 0.016
#> GSM537379 3 0.6192 0.18930 0.000 0.420 0.580
#> GSM537383 2 0.0747 0.76441 0.000 0.984 0.016
#> GSM537388 2 0.2448 0.76224 0.000 0.924 0.076
#> GSM537395 2 0.2711 0.75801 0.000 0.912 0.088
#> GSM537400 3 0.4281 0.60184 0.056 0.072 0.872
#> GSM537404 3 0.6594 0.59347 0.116 0.128 0.756
#> GSM537409 3 0.6225 0.24332 0.000 0.432 0.568
#> GSM537418 1 0.5835 0.59465 0.660 0.000 0.340
#> GSM537425 3 0.6172 0.31292 0.308 0.012 0.680
#> GSM537333 3 0.4443 0.60758 0.052 0.084 0.864
#> GSM537342 2 0.6140 0.34211 0.000 0.596 0.404
#> GSM537347 3 0.6931 0.32388 0.032 0.328 0.640
#> GSM537350 1 0.4915 0.75462 0.804 0.012 0.184
#> GSM537362 1 0.6096 0.69643 0.704 0.016 0.280
#> GSM537363 3 0.7959 0.38621 0.288 0.092 0.620
#> GSM537368 1 0.2796 0.77080 0.908 0.000 0.092
#> GSM537376 2 0.4555 0.70358 0.000 0.800 0.200
#> GSM537381 1 0.4178 0.75913 0.828 0.000 0.172
#> GSM537386 2 0.3816 0.71464 0.000 0.852 0.148
#> GSM537398 1 0.6313 0.66497 0.676 0.016 0.308
#> GSM537402 2 0.3192 0.74929 0.000 0.888 0.112
#> GSM537405 1 0.2625 0.77858 0.916 0.000 0.084
#> GSM537371 1 0.2796 0.77080 0.908 0.000 0.092
#> GSM537421 3 0.6386 0.23184 0.004 0.412 0.584
#> GSM537424 1 0.4796 0.73377 0.780 0.000 0.220
#> GSM537432 3 0.5136 0.61301 0.044 0.132 0.824
#> GSM537331 2 0.5147 0.69087 0.020 0.800 0.180
#> GSM537332 3 0.6045 0.41167 0.000 0.380 0.620
#> GSM537334 2 0.5731 0.65687 0.020 0.752 0.228
#> GSM537338 2 0.5200 0.70577 0.020 0.796 0.184
#> GSM537353 2 0.3879 0.70462 0.000 0.848 0.152
#> GSM537357 1 0.2796 0.77080 0.908 0.000 0.092
#> GSM537358 2 0.0424 0.76368 0.000 0.992 0.008
#> GSM537375 2 0.5560 0.61818 0.000 0.700 0.300
#> GSM537389 2 0.0892 0.76250 0.000 0.980 0.020
#> GSM537390 2 0.3412 0.71671 0.000 0.876 0.124
#> GSM537393 2 0.5138 0.66000 0.000 0.748 0.252
#> GSM537399 3 0.9018 0.19957 0.276 0.176 0.548
#> GSM537407 3 0.6715 0.32740 0.312 0.028 0.660
#> GSM537408 2 0.1860 0.75544 0.000 0.948 0.052
#> GSM537428 2 0.4293 0.72100 0.004 0.832 0.164
#> GSM537354 2 0.3686 0.74806 0.000 0.860 0.140
#> GSM537410 2 0.6111 0.29520 0.000 0.604 0.396
#> GSM537413 2 0.3340 0.72998 0.000 0.880 0.120
#> GSM537396 2 0.5285 0.66710 0.064 0.824 0.112
#> GSM537397 1 0.9070 0.43894 0.536 0.172 0.292
#> GSM537330 3 0.6302 0.09573 0.000 0.480 0.520
#> GSM537369 1 0.1529 0.78829 0.960 0.000 0.040
#> GSM537373 2 0.7622 0.38623 0.060 0.608 0.332
#> GSM537401 2 0.8732 0.32128 0.132 0.552 0.316
#> GSM537343 3 0.6476 0.02219 0.448 0.004 0.548
#> GSM537367 3 0.6034 0.57142 0.152 0.068 0.780
#> GSM537382 2 0.5254 0.65615 0.000 0.736 0.264
#> GSM537385 2 0.0892 0.76488 0.000 0.980 0.020
#> GSM537391 1 0.3910 0.75138 0.876 0.020 0.104
#> GSM537419 2 0.0592 0.76221 0.000 0.988 0.012
#> GSM537420 1 0.1529 0.78829 0.960 0.000 0.040
#> GSM537429 2 0.7583 0.13540 0.040 0.492 0.468
#> GSM537431 3 0.4966 0.61430 0.060 0.100 0.840
#> GSM537387 1 0.2959 0.76702 0.900 0.000 0.100
#> GSM537414 3 0.5454 0.55511 0.152 0.044 0.804
#> GSM537433 3 0.6880 0.34130 0.304 0.036 0.660
#> GSM537335 2 0.8181 0.38189 0.092 0.584 0.324
#> GSM537339 1 0.7801 0.59030 0.616 0.076 0.308
#> GSM537340 3 0.8019 0.38685 0.076 0.348 0.576
#> GSM537344 1 0.1529 0.78829 0.960 0.000 0.040
#> GSM537346 2 0.6305 -0.05863 0.000 0.516 0.484
#> GSM537351 1 0.5926 0.38864 0.644 0.000 0.356
#> GSM537352 2 0.3340 0.75544 0.000 0.880 0.120
#> GSM537359 2 0.2356 0.75323 0.000 0.928 0.072
#> GSM537360 2 0.4605 0.64761 0.000 0.796 0.204
#> GSM537364 1 0.2796 0.77080 0.908 0.000 0.092
#> GSM537365 3 0.6511 0.57677 0.104 0.136 0.760
#> GSM537372 1 0.5992 0.69922 0.716 0.016 0.268
#> GSM537384 1 0.5070 0.72842 0.772 0.004 0.224
#> GSM537394 2 0.5706 0.38613 0.000 0.680 0.320
#> GSM537403 3 0.6225 0.23817 0.000 0.432 0.568
#> GSM537406 2 0.2711 0.74528 0.000 0.912 0.088
#> GSM537411 2 0.4002 0.74822 0.000 0.840 0.160
#> GSM537412 3 0.6267 0.21864 0.000 0.452 0.548
#> GSM537416 3 0.6126 0.38510 0.004 0.352 0.644
#> GSM537426 2 0.5621 0.51924 0.000 0.692 0.308
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.5744 0.43349 0.108 0.184 0.000 0.708
#> GSM537345 1 0.1452 0.75498 0.956 0.000 0.008 0.036
#> GSM537355 2 0.7188 0.49441 0.000 0.552 0.204 0.244
#> GSM537366 4 0.7709 0.27689 0.280 0.004 0.232 0.484
#> GSM537370 4 0.4936 0.17834 0.000 0.340 0.008 0.652
#> GSM537380 2 0.2179 0.65692 0.000 0.924 0.012 0.064
#> GSM537392 2 0.1938 0.66148 0.000 0.936 0.012 0.052
#> GSM537415 2 0.4737 0.47553 0.000 0.728 0.252 0.020
#> GSM537417 3 0.5940 0.51713 0.000 0.120 0.692 0.188
#> GSM537422 3 0.6246 0.48910 0.132 0.012 0.696 0.160
#> GSM537423 2 0.0469 0.66658 0.000 0.988 0.012 0.000
#> GSM537427 2 0.5327 0.61646 0.000 0.720 0.060 0.220
#> GSM537430 2 0.2450 0.67160 0.000 0.912 0.016 0.072
#> GSM537336 1 0.0000 0.76429 1.000 0.000 0.000 0.000
#> GSM537337 2 0.7120 0.52294 0.000 0.564 0.212 0.224
#> GSM537348 4 0.4382 0.36283 0.296 0.000 0.000 0.704
#> GSM537349 2 0.1936 0.65856 0.000 0.940 0.032 0.028
#> GSM537356 4 0.6461 0.26368 0.364 0.004 0.068 0.564
#> GSM537361 3 0.7452 0.17924 0.156 0.004 0.476 0.364
#> GSM537374 2 0.6263 0.51940 0.000 0.576 0.068 0.356
#> GSM537377 1 0.1452 0.75498 0.956 0.000 0.008 0.036
#> GSM537378 2 0.0592 0.66583 0.000 0.984 0.016 0.000
#> GSM537379 3 0.7058 0.43213 0.000 0.168 0.560 0.272
#> GSM537383 2 0.1151 0.66834 0.000 0.968 0.008 0.024
#> GSM537388 2 0.5628 0.60817 0.000 0.704 0.080 0.216
#> GSM537395 2 0.6033 0.57872 0.000 0.680 0.204 0.116
#> GSM537400 3 0.5449 0.49299 0.040 0.012 0.720 0.228
#> GSM537404 3 0.7147 0.34112 0.056 0.040 0.540 0.364
#> GSM537409 3 0.4635 0.43845 0.000 0.216 0.756 0.028
#> GSM537418 4 0.7486 0.19033 0.348 0.000 0.188 0.464
#> GSM537425 3 0.7872 -0.05280 0.196 0.008 0.404 0.392
#> GSM537333 3 0.5741 0.49090 0.036 0.020 0.696 0.248
#> GSM537342 3 0.6423 0.29066 0.004 0.252 0.640 0.104
#> GSM537347 3 0.7342 0.27010 0.000 0.156 0.432 0.412
#> GSM537350 1 0.6857 -0.03414 0.484 0.040 0.032 0.444
#> GSM537362 4 0.7588 0.10773 0.408 0.020 0.116 0.456
#> GSM537363 3 0.7122 0.31065 0.200 0.028 0.632 0.140
#> GSM537368 1 0.0524 0.76452 0.988 0.000 0.004 0.008
#> GSM537376 2 0.7040 0.26454 0.000 0.460 0.420 0.120
#> GSM537381 1 0.7076 -0.09404 0.460 0.000 0.124 0.416
#> GSM537386 2 0.4274 0.62026 0.000 0.820 0.072 0.108
#> GSM537398 4 0.5033 0.38533 0.268 0.004 0.020 0.708
#> GSM537402 2 0.6334 0.42912 0.000 0.592 0.328 0.080
#> GSM537405 1 0.1411 0.76250 0.960 0.000 0.020 0.020
#> GSM537371 1 0.0376 0.76370 0.992 0.000 0.004 0.004
#> GSM537421 3 0.5515 0.39855 0.016 0.204 0.732 0.048
#> GSM537424 4 0.6168 0.22438 0.388 0.000 0.056 0.556
#> GSM537432 3 0.5046 0.51280 0.004 0.032 0.732 0.232
#> GSM537331 2 0.6677 0.48660 0.000 0.540 0.096 0.364
#> GSM537332 3 0.7058 0.49875 0.000 0.228 0.572 0.200
#> GSM537334 2 0.7451 0.36061 0.000 0.416 0.172 0.412
#> GSM537338 2 0.7098 0.45528 0.000 0.492 0.132 0.376
#> GSM537353 2 0.4831 0.58107 0.000 0.752 0.208 0.040
#> GSM537357 1 0.0000 0.76429 1.000 0.000 0.000 0.000
#> GSM537358 2 0.1284 0.66203 0.000 0.964 0.024 0.012
#> GSM537375 2 0.7629 0.34972 0.000 0.452 0.328 0.220
#> GSM537389 2 0.1610 0.65503 0.000 0.952 0.032 0.016
#> GSM537390 2 0.3308 0.62068 0.000 0.872 0.092 0.036
#> GSM537393 2 0.7268 0.40979 0.000 0.516 0.312 0.172
#> GSM537399 4 0.6444 0.38409 0.080 0.036 0.192 0.692
#> GSM537407 4 0.7673 0.00778 0.136 0.016 0.400 0.448
#> GSM537408 2 0.2413 0.64626 0.000 0.916 0.020 0.064
#> GSM537428 2 0.6748 0.51403 0.000 0.560 0.112 0.328
#> GSM537354 2 0.7065 0.52332 0.000 0.572 0.216 0.212
#> GSM537410 3 0.6007 0.19512 0.004 0.372 0.584 0.040
#> GSM537413 2 0.4540 0.52602 0.000 0.772 0.196 0.032
#> GSM537396 2 0.6653 0.22185 0.000 0.548 0.096 0.356
#> GSM537397 4 0.5783 0.41624 0.220 0.088 0.000 0.692
#> GSM537330 3 0.7677 0.39442 0.000 0.272 0.460 0.268
#> GSM537369 1 0.3757 0.68917 0.828 0.000 0.020 0.152
#> GSM537373 3 0.8019 0.07903 0.004 0.352 0.372 0.272
#> GSM537401 4 0.5327 0.42880 0.056 0.208 0.004 0.732
#> GSM537343 4 0.7970 0.09778 0.184 0.016 0.352 0.448
#> GSM537367 3 0.4526 0.48485 0.076 0.004 0.812 0.108
#> GSM537382 3 0.7191 -0.12451 0.000 0.352 0.500 0.148
#> GSM537385 2 0.3427 0.66807 0.000 0.860 0.028 0.112
#> GSM537391 1 0.5774 0.07150 0.508 0.028 0.000 0.464
#> GSM537419 2 0.0895 0.66540 0.000 0.976 0.020 0.004
#> GSM537420 1 0.3757 0.68917 0.828 0.000 0.020 0.152
#> GSM537429 4 0.7534 -0.05544 0.000 0.240 0.268 0.492
#> GSM537431 3 0.4673 0.51101 0.024 0.012 0.780 0.184
#> GSM537387 1 0.3649 0.63663 0.796 0.000 0.000 0.204
#> GSM537414 3 0.6985 0.41923 0.108 0.024 0.624 0.244
#> GSM537433 4 0.7965 0.00364 0.176 0.016 0.404 0.404
#> GSM537335 4 0.7115 0.08244 0.012 0.248 0.144 0.596
#> GSM537339 4 0.5520 0.40848 0.244 0.060 0.000 0.696
#> GSM537340 3 0.6319 0.40998 0.068 0.232 0.676 0.024
#> GSM537344 1 0.3447 0.70508 0.852 0.000 0.020 0.128
#> GSM537346 3 0.7868 0.27568 0.000 0.352 0.372 0.276
#> GSM537351 1 0.4953 0.50009 0.776 0.000 0.120 0.104
#> GSM537352 2 0.6917 0.54444 0.000 0.592 0.208 0.200
#> GSM537359 2 0.2578 0.65305 0.000 0.912 0.036 0.052
#> GSM537360 2 0.5228 0.42465 0.000 0.664 0.312 0.024
#> GSM537364 1 0.0779 0.75923 0.980 0.000 0.016 0.004
#> GSM537365 3 0.7233 0.23710 0.036 0.060 0.492 0.412
#> GSM537372 4 0.5194 0.32242 0.332 0.004 0.012 0.652
#> GSM537384 4 0.5231 0.23715 0.384 0.000 0.012 0.604
#> GSM537394 2 0.6798 0.13110 0.000 0.604 0.224 0.172
#> GSM537403 3 0.4872 0.43983 0.004 0.212 0.752 0.032
#> GSM537406 2 0.4831 0.49711 0.000 0.752 0.208 0.040
#> GSM537411 2 0.7004 0.53925 0.000 0.580 0.200 0.220
#> GSM537412 3 0.5525 0.29251 0.004 0.336 0.636 0.024
#> GSM537416 3 0.2876 0.53918 0.008 0.092 0.892 0.008
#> GSM537426 3 0.5917 -0.04183 0.000 0.444 0.520 0.036
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.2749 0.6346 0.028 0.060 0.012 0.004 0.896
#> GSM537345 1 0.1282 0.8597 0.952 0.000 0.000 0.004 0.044
#> GSM537355 2 0.8501 0.2624 0.004 0.344 0.284 0.168 0.200
#> GSM537366 5 0.7384 -0.2042 0.112 0.004 0.396 0.072 0.416
#> GSM537370 5 0.4361 0.5059 0.000 0.204 0.032 0.012 0.752
#> GSM537380 2 0.1911 0.5950 0.000 0.932 0.028 0.004 0.036
#> GSM537392 2 0.1750 0.5963 0.000 0.936 0.028 0.000 0.036
#> GSM537415 2 0.4671 0.2242 0.004 0.640 0.008 0.340 0.008
#> GSM537417 3 0.5298 0.3800 0.004 0.052 0.696 0.224 0.024
#> GSM537422 3 0.5586 0.2788 0.064 0.004 0.536 0.396 0.000
#> GSM537423 2 0.0963 0.5945 0.000 0.964 0.000 0.036 0.000
#> GSM537427 2 0.6540 0.4888 0.000 0.584 0.160 0.032 0.224
#> GSM537430 2 0.3933 0.5838 0.000 0.824 0.100 0.024 0.052
#> GSM537336 1 0.1173 0.8652 0.964 0.000 0.004 0.020 0.012
#> GSM537337 2 0.8208 0.2972 0.000 0.408 0.216 0.216 0.160
#> GSM537348 5 0.3573 0.6477 0.124 0.012 0.032 0.000 0.832
#> GSM537349 2 0.1809 0.5819 0.000 0.928 0.000 0.060 0.012
#> GSM537356 5 0.5595 0.4674 0.140 0.004 0.184 0.004 0.668
#> GSM537361 3 0.4846 0.5921 0.060 0.000 0.772 0.064 0.104
#> GSM537374 2 0.7315 0.3210 0.000 0.436 0.152 0.056 0.356
#> GSM537377 1 0.1569 0.8593 0.944 0.000 0.008 0.004 0.044
#> GSM537378 2 0.1043 0.5952 0.000 0.960 0.000 0.040 0.000
#> GSM537379 3 0.6220 0.2350 0.004 0.072 0.640 0.224 0.060
#> GSM537383 2 0.1186 0.6030 0.000 0.964 0.020 0.008 0.008
#> GSM537388 2 0.7223 0.4616 0.000 0.536 0.200 0.072 0.192
#> GSM537395 2 0.7364 0.3874 0.000 0.524 0.188 0.204 0.084
#> GSM537400 3 0.5726 0.2366 0.020 0.004 0.532 0.408 0.036
#> GSM537404 3 0.5179 0.5919 0.016 0.020 0.752 0.096 0.116
#> GSM537409 4 0.4185 0.6055 0.004 0.080 0.104 0.804 0.008
#> GSM537418 3 0.6668 0.0443 0.132 0.000 0.424 0.020 0.424
#> GSM537425 3 0.6838 0.5100 0.092 0.004 0.596 0.092 0.216
#> GSM537333 3 0.5389 0.2983 0.012 0.004 0.584 0.368 0.032
#> GSM537342 4 0.4329 0.6214 0.000 0.076 0.048 0.808 0.068
#> GSM537347 3 0.4171 0.5190 0.004 0.060 0.816 0.024 0.096
#> GSM537350 5 0.7391 0.3822 0.196 0.056 0.144 0.032 0.572
#> GSM537362 5 0.7866 0.2904 0.264 0.008 0.208 0.072 0.448
#> GSM537363 4 0.6766 0.3070 0.100 0.012 0.204 0.616 0.068
#> GSM537368 1 0.0693 0.8652 0.980 0.000 0.008 0.000 0.012
#> GSM537376 4 0.6644 0.4090 0.000 0.224 0.064 0.596 0.116
#> GSM537381 3 0.7065 0.1126 0.212 0.000 0.428 0.020 0.340
#> GSM537386 2 0.3835 0.5587 0.000 0.836 0.076 0.032 0.056
#> GSM537398 5 0.3913 0.6481 0.116 0.008 0.040 0.012 0.824
#> GSM537402 4 0.6536 0.1261 0.000 0.404 0.028 0.468 0.100
#> GSM537405 1 0.1750 0.8615 0.936 0.000 0.036 0.000 0.028
#> GSM537371 1 0.0798 0.8651 0.976 0.000 0.008 0.000 0.016
#> GSM537421 4 0.3946 0.6111 0.020 0.056 0.068 0.840 0.016
#> GSM537424 5 0.4852 0.5818 0.184 0.000 0.100 0.000 0.716
#> GSM537432 4 0.6050 0.0227 0.008 0.016 0.404 0.516 0.056
#> GSM537331 2 0.7240 0.3375 0.000 0.420 0.224 0.028 0.328
#> GSM537332 3 0.5657 0.4721 0.000 0.124 0.676 0.180 0.020
#> GSM537334 5 0.7859 -0.2211 0.004 0.292 0.272 0.056 0.376
#> GSM537338 2 0.7732 0.2952 0.000 0.360 0.236 0.060 0.344
#> GSM537353 2 0.5696 0.2892 0.000 0.604 0.044 0.320 0.032
#> GSM537357 1 0.1278 0.8660 0.960 0.000 0.004 0.020 0.016
#> GSM537358 2 0.1299 0.5988 0.000 0.960 0.012 0.020 0.008
#> GSM537375 4 0.8391 -0.1648 0.000 0.292 0.244 0.316 0.148
#> GSM537389 2 0.1809 0.5829 0.000 0.928 0.000 0.060 0.012
#> GSM537390 2 0.2861 0.5604 0.000 0.884 0.024 0.076 0.016
#> GSM537393 2 0.8154 0.1705 0.000 0.356 0.248 0.288 0.108
#> GSM537399 3 0.5539 0.1606 0.032 0.012 0.492 0.004 0.460
#> GSM537407 3 0.5560 0.5063 0.036 0.008 0.668 0.036 0.252
#> GSM537408 2 0.3255 0.5659 0.000 0.868 0.056 0.020 0.056
#> GSM537428 2 0.7597 0.3682 0.000 0.412 0.244 0.052 0.292
#> GSM537354 2 0.8291 0.2565 0.000 0.380 0.216 0.248 0.156
#> GSM537410 4 0.4643 0.6244 0.000 0.132 0.044 0.776 0.048
#> GSM537413 2 0.4751 0.3552 0.004 0.712 0.020 0.244 0.020
#> GSM537396 5 0.6656 0.0493 0.000 0.388 0.008 0.172 0.432
#> GSM537397 5 0.2916 0.6497 0.072 0.032 0.008 0.004 0.884
#> GSM537330 3 0.5174 0.4661 0.000 0.128 0.744 0.076 0.052
#> GSM537369 1 0.5234 0.6903 0.708 0.000 0.064 0.028 0.200
#> GSM537373 4 0.7247 0.3358 0.000 0.188 0.044 0.472 0.296
#> GSM537401 5 0.2401 0.6139 0.008 0.076 0.008 0.004 0.904
#> GSM537343 3 0.5919 0.4689 0.048 0.008 0.628 0.036 0.280
#> GSM537367 4 0.5916 0.0749 0.044 0.004 0.364 0.560 0.028
#> GSM537382 4 0.6858 0.4650 0.000 0.184 0.096 0.596 0.124
#> GSM537385 2 0.4522 0.5754 0.000 0.792 0.040 0.072 0.096
#> GSM537391 5 0.4934 0.3719 0.304 0.012 0.008 0.016 0.660
#> GSM537419 2 0.1408 0.5901 0.000 0.948 0.000 0.044 0.008
#> GSM537420 1 0.5205 0.6916 0.708 0.000 0.060 0.028 0.204
#> GSM537429 3 0.7696 0.1415 0.004 0.080 0.420 0.148 0.348
#> GSM537431 3 0.5509 0.2188 0.016 0.012 0.524 0.432 0.016
#> GSM537387 1 0.4236 0.6790 0.728 0.000 0.008 0.016 0.248
#> GSM537414 3 0.3762 0.5593 0.036 0.004 0.828 0.120 0.012
#> GSM537433 3 0.6831 0.4852 0.064 0.008 0.584 0.096 0.248
#> GSM537335 5 0.6637 0.2821 0.004 0.128 0.252 0.036 0.580
#> GSM537339 5 0.3339 0.6515 0.084 0.024 0.032 0.000 0.860
#> GSM537340 4 0.4963 0.5981 0.040 0.080 0.084 0.780 0.016
#> GSM537344 1 0.5202 0.6964 0.712 0.000 0.064 0.028 0.196
#> GSM537346 3 0.4480 0.4551 0.000 0.220 0.732 0.004 0.044
#> GSM537351 1 0.2899 0.7584 0.872 0.000 0.096 0.028 0.004
#> GSM537352 2 0.8080 0.3086 0.000 0.428 0.164 0.244 0.164
#> GSM537359 2 0.2855 0.5819 0.000 0.892 0.040 0.028 0.040
#> GSM537360 4 0.6025 0.1530 0.004 0.432 0.060 0.488 0.016
#> GSM537364 1 0.0955 0.8464 0.968 0.000 0.028 0.004 0.000
#> GSM537365 3 0.5599 0.5665 0.012 0.028 0.704 0.072 0.184
#> GSM537372 5 0.3834 0.6346 0.140 0.012 0.036 0.000 0.812
#> GSM537384 5 0.4003 0.6058 0.180 0.004 0.036 0.000 0.780
#> GSM537394 2 0.5251 0.1748 0.000 0.584 0.372 0.012 0.032
#> GSM537403 4 0.4629 0.6100 0.000 0.076 0.112 0.780 0.032
#> GSM537406 2 0.5385 0.2017 0.000 0.616 0.004 0.312 0.068
#> GSM537411 2 0.7533 0.3175 0.000 0.488 0.080 0.236 0.196
#> GSM537412 4 0.4717 0.6045 0.008 0.140 0.072 0.768 0.012
#> GSM537416 4 0.3482 0.5537 0.016 0.008 0.132 0.836 0.008
#> GSM537426 4 0.4481 0.5982 0.004 0.188 0.032 0.760 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.1498 0.7152 0.000 0.028 0.000 0.000 0.940 0.032
#> GSM537345 1 0.1225 0.8248 0.952 0.000 0.000 0.000 0.036 0.012
#> GSM537355 6 0.6299 0.6015 0.000 0.244 0.016 0.068 0.092 0.580
#> GSM537366 5 0.6271 -0.0645 0.028 0.004 0.420 0.060 0.456 0.032
#> GSM537370 5 0.4602 0.5612 0.000 0.156 0.036 0.000 0.736 0.072
#> GSM537380 2 0.1938 0.7042 0.000 0.920 0.020 0.000 0.008 0.052
#> GSM537392 2 0.1760 0.7043 0.000 0.928 0.020 0.000 0.004 0.048
#> GSM537415 2 0.4841 0.2369 0.004 0.544 0.008 0.412 0.000 0.032
#> GSM537417 3 0.5622 0.4690 0.000 0.012 0.528 0.116 0.000 0.344
#> GSM537422 3 0.6220 0.4146 0.036 0.000 0.552 0.232 0.004 0.176
#> GSM537423 2 0.2052 0.7103 0.004 0.912 0.000 0.028 0.000 0.056
#> GSM537427 6 0.5323 0.4409 0.000 0.432 0.004 0.004 0.076 0.484
#> GSM537430 2 0.4141 0.0431 0.000 0.596 0.000 0.016 0.000 0.388
#> GSM537336 1 0.1546 0.8260 0.944 0.000 0.020 0.000 0.020 0.016
#> GSM537337 6 0.5493 0.6259 0.000 0.224 0.000 0.080 0.056 0.640
#> GSM537348 5 0.1570 0.7227 0.028 0.004 0.008 0.000 0.944 0.016
#> GSM537349 2 0.2400 0.7128 0.004 0.900 0.004 0.060 0.004 0.028
#> GSM537356 5 0.3688 0.6197 0.024 0.000 0.144 0.008 0.804 0.020
#> GSM537361 3 0.4862 0.6207 0.024 0.000 0.748 0.032 0.088 0.108
#> GSM537374 6 0.6561 0.5069 0.000 0.288 0.016 0.012 0.232 0.452
#> GSM537377 1 0.1679 0.8227 0.936 0.000 0.012 0.000 0.036 0.016
#> GSM537378 2 0.2322 0.7070 0.004 0.896 0.000 0.036 0.000 0.064
#> GSM537379 6 0.5606 0.1344 0.000 0.024 0.284 0.096 0.004 0.592
#> GSM537383 2 0.1701 0.7007 0.000 0.920 0.000 0.008 0.000 0.072
#> GSM537388 6 0.6373 0.5194 0.004 0.320 0.004 0.056 0.100 0.516
#> GSM537395 6 0.5004 0.4793 0.000 0.388 0.004 0.064 0.000 0.544
#> GSM537400 3 0.6718 0.1696 0.024 0.000 0.416 0.276 0.008 0.276
#> GSM537404 3 0.5617 0.6165 0.004 0.036 0.696 0.052 0.064 0.148
#> GSM537409 4 0.3870 0.6135 0.004 0.060 0.068 0.816 0.000 0.052
#> GSM537418 5 0.5650 0.0721 0.032 0.004 0.424 0.008 0.492 0.040
#> GSM537425 3 0.6112 0.5350 0.028 0.008 0.644 0.068 0.188 0.064
#> GSM537333 3 0.6526 0.2329 0.012 0.000 0.412 0.256 0.008 0.312
#> GSM537342 4 0.4924 0.6240 0.000 0.024 0.040 0.732 0.048 0.156
#> GSM537347 3 0.5106 0.5183 0.000 0.016 0.600 0.008 0.044 0.332
#> GSM537350 5 0.6795 0.4935 0.072 0.032 0.156 0.032 0.616 0.092
#> GSM537362 5 0.7927 0.0133 0.172 0.000 0.156 0.028 0.348 0.296
#> GSM537363 4 0.6242 0.4617 0.056 0.000 0.168 0.636 0.056 0.084
#> GSM537368 1 0.1180 0.8281 0.960 0.000 0.004 0.004 0.024 0.008
#> GSM537376 4 0.6622 0.2236 0.000 0.132 0.024 0.436 0.028 0.380
#> GSM537381 3 0.6010 0.0542 0.068 0.000 0.484 0.008 0.396 0.044
#> GSM537386 2 0.4340 0.6478 0.000 0.776 0.124 0.028 0.012 0.060
#> GSM537398 5 0.2164 0.7112 0.028 0.000 0.008 0.000 0.908 0.056
#> GSM537402 4 0.7031 0.1712 0.004 0.304 0.016 0.420 0.032 0.224
#> GSM537405 1 0.2439 0.8167 0.904 0.000 0.040 0.008 0.028 0.020
#> GSM537371 1 0.0951 0.8266 0.968 0.000 0.004 0.000 0.020 0.008
#> GSM537421 4 0.5195 0.5655 0.008 0.024 0.068 0.676 0.004 0.220
#> GSM537424 5 0.3611 0.6492 0.028 0.000 0.124 0.004 0.816 0.028
#> GSM537432 6 0.6781 -0.2860 0.012 0.012 0.268 0.332 0.004 0.372
#> GSM537331 6 0.5909 0.5905 0.000 0.264 0.004 0.004 0.204 0.524
#> GSM537332 3 0.5818 0.5065 0.000 0.064 0.636 0.184 0.004 0.112
#> GSM537334 6 0.5492 0.6107 0.000 0.140 0.012 0.000 0.252 0.596
#> GSM537338 6 0.5587 0.6299 0.000 0.172 0.004 0.008 0.216 0.600
#> GSM537353 2 0.6199 -0.0161 0.000 0.468 0.020 0.184 0.000 0.328
#> GSM537357 1 0.1546 0.8260 0.944 0.000 0.020 0.000 0.020 0.016
#> GSM537358 2 0.1820 0.7116 0.000 0.928 0.016 0.012 0.000 0.044
#> GSM537375 6 0.5537 0.5312 0.000 0.128 0.016 0.160 0.028 0.668
#> GSM537389 2 0.2679 0.7083 0.004 0.884 0.004 0.064 0.004 0.040
#> GSM537390 2 0.3022 0.6985 0.000 0.848 0.020 0.112 0.000 0.020
#> GSM537393 6 0.6233 0.5269 0.000 0.176 0.040 0.176 0.016 0.592
#> GSM537399 3 0.5184 0.1704 0.008 0.016 0.520 0.000 0.420 0.036
#> GSM537407 3 0.4381 0.5469 0.016 0.032 0.760 0.004 0.168 0.020
#> GSM537408 2 0.3012 0.6773 0.000 0.852 0.104 0.000 0.020 0.024
#> GSM537428 6 0.5385 0.6238 0.000 0.256 0.004 0.004 0.132 0.604
#> GSM537354 6 0.5788 0.6182 0.000 0.220 0.008 0.092 0.052 0.628
#> GSM537410 4 0.4882 0.6317 0.000 0.088 0.044 0.752 0.024 0.092
#> GSM537413 2 0.4511 0.5443 0.000 0.688 0.044 0.252 0.000 0.016
#> GSM537396 5 0.7447 0.0407 0.004 0.272 0.028 0.232 0.412 0.052
#> GSM537397 5 0.1592 0.7229 0.012 0.024 0.004 0.000 0.944 0.016
#> GSM537330 3 0.6099 0.4513 0.000 0.056 0.544 0.060 0.016 0.324
#> GSM537369 1 0.7006 0.5073 0.512 0.000 0.152 0.024 0.236 0.076
#> GSM537373 4 0.7386 0.3837 0.004 0.100 0.048 0.496 0.252 0.100
#> GSM537401 5 0.1720 0.7076 0.000 0.032 0.000 0.000 0.928 0.040
#> GSM537343 3 0.5210 0.5032 0.020 0.028 0.696 0.004 0.196 0.056
#> GSM537367 4 0.5489 0.0617 0.020 0.000 0.424 0.500 0.016 0.040
#> GSM537382 4 0.6721 0.3045 0.000 0.104 0.036 0.476 0.040 0.344
#> GSM537385 2 0.5892 0.3014 0.004 0.604 0.004 0.088 0.048 0.252
#> GSM537391 5 0.4645 0.5062 0.184 0.012 0.036 0.000 0.732 0.036
#> GSM537419 2 0.1629 0.7202 0.004 0.940 0.000 0.028 0.004 0.024
#> GSM537420 1 0.6995 0.5056 0.512 0.000 0.148 0.024 0.240 0.076
#> GSM537429 6 0.7596 0.2436 0.000 0.060 0.156 0.084 0.232 0.468
#> GSM537431 3 0.6621 0.1815 0.016 0.008 0.452 0.320 0.008 0.196
#> GSM537387 1 0.4800 0.5035 0.604 0.000 0.020 0.000 0.344 0.032
#> GSM537414 3 0.4542 0.6006 0.004 0.000 0.720 0.068 0.012 0.196
#> GSM537433 3 0.5788 0.5029 0.024 0.020 0.668 0.068 0.192 0.028
#> GSM537335 6 0.5000 0.3281 0.000 0.044 0.012 0.000 0.432 0.512
#> GSM537339 5 0.1515 0.7224 0.020 0.008 0.000 0.000 0.944 0.028
#> GSM537340 4 0.5790 0.5450 0.012 0.044 0.076 0.632 0.004 0.232
#> GSM537344 1 0.7006 0.5073 0.512 0.000 0.152 0.024 0.236 0.076
#> GSM537346 3 0.5561 0.4610 0.000 0.176 0.568 0.000 0.004 0.252
#> GSM537351 1 0.2214 0.7831 0.912 0.000 0.044 0.012 0.004 0.028
#> GSM537352 6 0.6425 0.5920 0.000 0.248 0.016 0.108 0.064 0.564
#> GSM537359 2 0.2601 0.6971 0.000 0.888 0.068 0.004 0.016 0.024
#> GSM537360 4 0.5800 0.1704 0.004 0.360 0.016 0.512 0.000 0.108
#> GSM537364 1 0.0893 0.8211 0.972 0.000 0.004 0.004 0.004 0.016
#> GSM537365 3 0.4741 0.5820 0.004 0.056 0.752 0.016 0.140 0.032
#> GSM537372 5 0.1457 0.7205 0.028 0.004 0.016 0.004 0.948 0.000
#> GSM537384 5 0.1706 0.7181 0.032 0.000 0.024 0.004 0.936 0.004
#> GSM537394 2 0.4786 0.3279 0.000 0.604 0.344 0.000 0.016 0.036
#> GSM537403 4 0.5240 0.6151 0.000 0.024 0.096 0.684 0.012 0.184
#> GSM537406 2 0.6042 0.1927 0.004 0.524 0.028 0.364 0.028 0.052
#> GSM537411 6 0.7690 0.4254 0.000 0.300 0.024 0.164 0.128 0.384
#> GSM537412 4 0.3964 0.6091 0.004 0.100 0.052 0.804 0.000 0.040
#> GSM537416 4 0.4269 0.5608 0.008 0.004 0.120 0.768 0.004 0.096
#> GSM537426 4 0.3921 0.6111 0.004 0.120 0.032 0.800 0.000 0.044
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> SD:kmeans 97 0.3791 0.589 2
#> SD:kmeans 73 0.4343 0.252 3
#> SD:kmeans 43 0.4172 0.348 4
#> SD:kmeans 54 0.2734 0.261 5
#> SD:kmeans 70 0.0435 0.398 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 104 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.845 0.934 0.969 0.5028 0.497 0.497
#> 3 3 0.471 0.607 0.779 0.3262 0.743 0.529
#> 4 4 0.471 0.418 0.674 0.1239 0.851 0.597
#> 5 5 0.539 0.397 0.642 0.0685 0.878 0.574
#> 6 6 0.590 0.464 0.676 0.0421 0.904 0.578
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
#> GSM537341 1 0.6438 0.806 0.836 0.164
#> GSM537345 1 0.0000 0.960 1.000 0.000
#> GSM537355 2 0.0000 0.974 0.000 1.000
#> GSM537366 1 0.0000 0.960 1.000 0.000
#> GSM537370 1 0.8267 0.678 0.740 0.260
#> GSM537380 2 0.0000 0.974 0.000 1.000
#> GSM537392 2 0.0000 0.974 0.000 1.000
#> GSM537415 2 0.0000 0.974 0.000 1.000
#> GSM537417 2 0.6247 0.809 0.156 0.844
#> GSM537422 1 0.0000 0.960 1.000 0.000
#> GSM537423 2 0.0000 0.974 0.000 1.000
#> GSM537427 2 0.0000 0.974 0.000 1.000
#> GSM537430 2 0.0000 0.974 0.000 1.000
#> GSM537336 1 0.0000 0.960 1.000 0.000
#> GSM537337 2 0.0000 0.974 0.000 1.000
#> GSM537348 1 0.0000 0.960 1.000 0.000
#> GSM537349 2 0.0000 0.974 0.000 1.000
#> GSM537356 1 0.0000 0.960 1.000 0.000
#> GSM537361 1 0.0000 0.960 1.000 0.000
#> GSM537374 2 0.0000 0.974 0.000 1.000
#> GSM537377 1 0.0000 0.960 1.000 0.000
#> GSM537378 2 0.0000 0.974 0.000 1.000
#> GSM537379 2 0.0000 0.974 0.000 1.000
#> GSM537383 2 0.0000 0.974 0.000 1.000
#> GSM537388 2 0.0000 0.974 0.000 1.000
#> GSM537395 2 0.0000 0.974 0.000 1.000
#> GSM537400 1 0.0000 0.960 1.000 0.000
#> GSM537404 1 0.6247 0.819 0.844 0.156
#> GSM537409 2 0.0000 0.974 0.000 1.000
#> GSM537418 1 0.0000 0.960 1.000 0.000
#> GSM537425 1 0.0000 0.960 1.000 0.000
#> GSM537333 1 0.5629 0.846 0.868 0.132
#> GSM537342 2 0.2423 0.939 0.040 0.960
#> GSM537347 1 0.6712 0.798 0.824 0.176
#> GSM537350 1 0.0000 0.960 1.000 0.000
#> GSM537362 1 0.0000 0.960 1.000 0.000
#> GSM537363 1 0.0938 0.952 0.988 0.012
#> GSM537368 1 0.0000 0.960 1.000 0.000
#> GSM537376 2 0.0000 0.974 0.000 1.000
#> GSM537381 1 0.0000 0.960 1.000 0.000
#> GSM537386 2 0.0000 0.974 0.000 1.000
#> GSM537398 1 0.0000 0.960 1.000 0.000
#> GSM537402 2 0.0000 0.974 0.000 1.000
#> GSM537405 1 0.0000 0.960 1.000 0.000
#> GSM537371 1 0.0000 0.960 1.000 0.000
#> GSM537421 2 0.6801 0.779 0.180 0.820
#> GSM537424 1 0.0000 0.960 1.000 0.000
#> GSM537432 1 0.2778 0.926 0.952 0.048
#> GSM537331 2 0.0000 0.974 0.000 1.000
#> GSM537332 2 0.0000 0.974 0.000 1.000
#> GSM537334 2 0.0000 0.974 0.000 1.000
#> GSM537338 2 0.0000 0.974 0.000 1.000
#> GSM537353 2 0.0000 0.974 0.000 1.000
#> GSM537357 1 0.0000 0.960 1.000 0.000
#> GSM537358 2 0.0000 0.974 0.000 1.000
#> GSM537375 2 0.0000 0.974 0.000 1.000
#> GSM537389 2 0.0000 0.974 0.000 1.000
#> GSM537390 2 0.0000 0.974 0.000 1.000
#> GSM537393 2 0.0000 0.974 0.000 1.000
#> GSM537399 1 0.0000 0.960 1.000 0.000
#> GSM537407 1 0.0000 0.960 1.000 0.000
#> GSM537408 2 0.0000 0.974 0.000 1.000
#> GSM537428 2 0.0000 0.974 0.000 1.000
#> GSM537354 2 0.0000 0.974 0.000 1.000
#> GSM537410 2 0.0000 0.974 0.000 1.000
#> GSM537413 2 0.0000 0.974 0.000 1.000
#> GSM537396 2 0.6247 0.808 0.156 0.844
#> GSM537397 1 0.0938 0.952 0.988 0.012
#> GSM537330 2 0.0000 0.974 0.000 1.000
#> GSM537369 1 0.0000 0.960 1.000 0.000
#> GSM537373 2 0.8608 0.607 0.284 0.716
#> GSM537401 1 0.6973 0.778 0.812 0.188
#> GSM537343 1 0.0000 0.960 1.000 0.000
#> GSM537367 1 0.0000 0.960 1.000 0.000
#> GSM537382 2 0.0376 0.971 0.004 0.996
#> GSM537385 2 0.0000 0.974 0.000 1.000
#> GSM537391 1 0.0000 0.960 1.000 0.000
#> GSM537419 2 0.0000 0.974 0.000 1.000
#> GSM537420 1 0.0000 0.960 1.000 0.000
#> GSM537429 1 0.9248 0.521 0.660 0.340
#> GSM537431 1 0.2778 0.926 0.952 0.048
#> GSM537387 1 0.0000 0.960 1.000 0.000
#> GSM537414 1 0.1843 0.941 0.972 0.028
#> GSM537433 1 0.0000 0.960 1.000 0.000
#> GSM537335 1 0.8909 0.602 0.692 0.308
#> GSM537339 1 0.0000 0.960 1.000 0.000
#> GSM537340 2 0.9087 0.528 0.324 0.676
#> GSM537344 1 0.0000 0.960 1.000 0.000
#> GSM537346 2 0.0000 0.974 0.000 1.000
#> GSM537351 1 0.0000 0.960 1.000 0.000
#> GSM537352 2 0.0000 0.974 0.000 1.000
#> GSM537359 2 0.0000 0.974 0.000 1.000
#> GSM537360 2 0.0000 0.974 0.000 1.000
#> GSM537364 1 0.0000 0.960 1.000 0.000
#> GSM537365 1 0.0000 0.960 1.000 0.000
#> GSM537372 1 0.0000 0.960 1.000 0.000
#> GSM537384 1 0.0000 0.960 1.000 0.000
#> GSM537394 2 0.0000 0.974 0.000 1.000
#> GSM537403 2 0.0000 0.974 0.000 1.000
#> GSM537406 2 0.0000 0.974 0.000 1.000
#> GSM537411 2 0.0000 0.974 0.000 1.000
#> GSM537412 2 0.0000 0.974 0.000 1.000
#> GSM537416 2 0.6623 0.791 0.172 0.828
#> GSM537426 2 0.0000 0.974 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.5263 0.7053 0.828 0.084 0.088
#> GSM537345 1 0.0424 0.8088 0.992 0.000 0.008
#> GSM537355 2 0.6313 0.5698 0.016 0.676 0.308
#> GSM537366 1 0.2261 0.7978 0.932 0.000 0.068
#> GSM537370 2 0.8246 0.3367 0.312 0.588 0.100
#> GSM537380 2 0.1031 0.7871 0.000 0.976 0.024
#> GSM537392 2 0.0592 0.7882 0.000 0.988 0.012
#> GSM537415 2 0.4555 0.6721 0.000 0.800 0.200
#> GSM537417 3 0.4095 0.6764 0.064 0.056 0.880
#> GSM537422 3 0.3619 0.6561 0.136 0.000 0.864
#> GSM537423 2 0.0892 0.7879 0.000 0.980 0.020
#> GSM537427 2 0.4921 0.7262 0.020 0.816 0.164
#> GSM537430 2 0.1031 0.7882 0.000 0.976 0.024
#> GSM537336 1 0.1529 0.8056 0.960 0.000 0.040
#> GSM537337 2 0.6200 0.6246 0.012 0.676 0.312
#> GSM537348 1 0.2537 0.7807 0.920 0.000 0.080
#> GSM537349 2 0.0892 0.7860 0.000 0.980 0.020
#> GSM537356 1 0.1163 0.8092 0.972 0.000 0.028
#> GSM537361 3 0.6307 -0.0534 0.488 0.000 0.512
#> GSM537374 2 0.4995 0.7268 0.032 0.824 0.144
#> GSM537377 1 0.0747 0.8079 0.984 0.000 0.016
#> GSM537378 2 0.1163 0.7874 0.000 0.972 0.028
#> GSM537379 3 0.3983 0.6164 0.004 0.144 0.852
#> GSM537383 2 0.0747 0.7880 0.000 0.984 0.016
#> GSM537388 2 0.4062 0.7258 0.000 0.836 0.164
#> GSM537395 2 0.5016 0.6868 0.000 0.760 0.240
#> GSM537400 3 0.3192 0.6591 0.112 0.000 0.888
#> GSM537404 3 0.6673 0.5743 0.224 0.056 0.720
#> GSM537409 3 0.4002 0.6282 0.000 0.160 0.840
#> GSM537418 1 0.1411 0.8074 0.964 0.000 0.036
#> GSM537425 1 0.6252 0.1989 0.556 0.000 0.444
#> GSM537333 3 0.2682 0.6799 0.076 0.004 0.920
#> GSM537342 3 0.8363 0.2178 0.084 0.412 0.504
#> GSM537347 3 0.7065 0.5148 0.072 0.228 0.700
#> GSM537350 1 0.1289 0.8088 0.968 0.000 0.032
#> GSM537362 1 0.2165 0.7969 0.936 0.000 0.064
#> GSM537363 1 0.9065 -0.1080 0.448 0.136 0.416
#> GSM537368 1 0.1411 0.8071 0.964 0.000 0.036
#> GSM537376 2 0.5431 0.6183 0.000 0.716 0.284
#> GSM537381 1 0.1411 0.8076 0.964 0.000 0.036
#> GSM537386 2 0.2356 0.7788 0.000 0.928 0.072
#> GSM537398 1 0.2796 0.7750 0.908 0.000 0.092
#> GSM537402 2 0.3879 0.7354 0.000 0.848 0.152
#> GSM537405 1 0.1411 0.8071 0.964 0.000 0.036
#> GSM537371 1 0.1411 0.8071 0.964 0.000 0.036
#> GSM537421 3 0.6820 0.5604 0.052 0.248 0.700
#> GSM537424 1 0.0892 0.8061 0.980 0.000 0.020
#> GSM537432 3 0.3587 0.6738 0.088 0.020 0.892
#> GSM537331 2 0.6143 0.6469 0.024 0.720 0.256
#> GSM537332 3 0.5621 0.5496 0.000 0.308 0.692
#> GSM537334 2 0.6589 0.6175 0.032 0.688 0.280
#> GSM537338 2 0.6420 0.6274 0.024 0.688 0.288
#> GSM537353 2 0.4796 0.6493 0.000 0.780 0.220
#> GSM537357 1 0.1411 0.8071 0.964 0.000 0.036
#> GSM537358 2 0.0592 0.7889 0.000 0.988 0.012
#> GSM537375 3 0.7063 -0.2693 0.020 0.464 0.516
#> GSM537389 2 0.0892 0.7860 0.000 0.980 0.020
#> GSM537390 2 0.3038 0.7566 0.000 0.896 0.104
#> GSM537393 2 0.6274 0.3884 0.000 0.544 0.456
#> GSM537399 1 0.7147 0.5999 0.696 0.076 0.228
#> GSM537407 1 0.6225 0.2359 0.568 0.000 0.432
#> GSM537408 2 0.0424 0.7882 0.000 0.992 0.008
#> GSM537428 2 0.6143 0.6469 0.024 0.720 0.256
#> GSM537354 2 0.6470 0.5707 0.012 0.632 0.356
#> GSM537410 3 0.6500 0.1815 0.004 0.464 0.532
#> GSM537413 2 0.2356 0.7729 0.000 0.928 0.072
#> GSM537396 2 0.4636 0.7043 0.104 0.852 0.044
#> GSM537397 1 0.4232 0.7474 0.872 0.044 0.084
#> GSM537330 3 0.5465 0.5198 0.000 0.288 0.712
#> GSM537369 1 0.1031 0.8090 0.976 0.000 0.024
#> GSM537373 1 0.9577 -0.1088 0.404 0.400 0.196
#> GSM537401 1 0.7107 0.5589 0.712 0.196 0.092
#> GSM537343 1 0.5678 0.4813 0.684 0.000 0.316
#> GSM537367 3 0.6079 0.5759 0.216 0.036 0.748
#> GSM537382 3 0.6664 -0.1877 0.008 0.464 0.528
#> GSM537385 2 0.1289 0.7905 0.000 0.968 0.032
#> GSM537391 1 0.2682 0.7796 0.920 0.004 0.076
#> GSM537419 2 0.0747 0.7867 0.000 0.984 0.016
#> GSM537420 1 0.1031 0.8090 0.976 0.000 0.024
#> GSM537429 3 0.9569 0.0641 0.384 0.196 0.420
#> GSM537431 3 0.4209 0.6675 0.120 0.020 0.860
#> GSM537387 1 0.2261 0.7850 0.932 0.000 0.068
#> GSM537414 3 0.5061 0.5903 0.208 0.008 0.784
#> GSM537433 1 0.6302 0.1193 0.520 0.000 0.480
#> GSM537335 1 0.9901 -0.0418 0.392 0.336 0.272
#> GSM537339 1 0.3359 0.7685 0.900 0.016 0.084
#> GSM537340 3 0.7533 0.5808 0.088 0.244 0.668
#> GSM537344 1 0.1031 0.8090 0.976 0.000 0.024
#> GSM537346 3 0.6204 0.3056 0.000 0.424 0.576
#> GSM537351 1 0.6204 0.2523 0.576 0.000 0.424
#> GSM537352 2 0.5578 0.7019 0.012 0.748 0.240
#> GSM537359 2 0.1529 0.7861 0.000 0.960 0.040
#> GSM537360 2 0.5138 0.6047 0.000 0.748 0.252
#> GSM537364 1 0.2066 0.7952 0.940 0.000 0.060
#> GSM537365 3 0.7710 0.2744 0.368 0.056 0.576
#> GSM537372 1 0.2066 0.7906 0.940 0.000 0.060
#> GSM537384 1 0.1031 0.8035 0.976 0.000 0.024
#> GSM537394 2 0.5497 0.4303 0.000 0.708 0.292
#> GSM537403 3 0.3879 0.6304 0.000 0.152 0.848
#> GSM537406 2 0.2959 0.7597 0.000 0.900 0.100
#> GSM537411 2 0.3983 0.7614 0.004 0.852 0.144
#> GSM537412 3 0.5835 0.4749 0.000 0.340 0.660
#> GSM537416 3 0.4007 0.6699 0.036 0.084 0.880
#> GSM537426 2 0.6008 0.4165 0.000 0.628 0.372
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.7763 0.5521 0.588 0.068 0.236 0.108
#> GSM537345 1 0.2124 0.7566 0.924 0.000 0.068 0.008
#> GSM537355 2 0.7905 0.0226 0.000 0.364 0.304 0.332
#> GSM537366 1 0.5257 0.6555 0.756 0.004 0.160 0.080
#> GSM537370 2 0.9060 0.2022 0.176 0.472 0.228 0.124
#> GSM537380 2 0.0524 0.6340 0.000 0.988 0.008 0.004
#> GSM537392 2 0.0376 0.6337 0.000 0.992 0.004 0.004
#> GSM537415 2 0.4454 0.4121 0.000 0.692 0.000 0.308
#> GSM537417 3 0.5723 0.2304 0.012 0.024 0.640 0.324
#> GSM537422 3 0.7429 0.3054 0.192 0.000 0.492 0.316
#> GSM537423 2 0.1557 0.6312 0.000 0.944 0.000 0.056
#> GSM537427 2 0.6798 0.3466 0.000 0.604 0.172 0.224
#> GSM537430 2 0.3764 0.5775 0.000 0.852 0.076 0.072
#> GSM537336 1 0.2021 0.7379 0.932 0.000 0.056 0.012
#> GSM537337 4 0.7597 0.1033 0.000 0.308 0.224 0.468
#> GSM537348 1 0.5564 0.6696 0.712 0.008 0.228 0.052
#> GSM537349 2 0.2011 0.6247 0.000 0.920 0.000 0.080
#> GSM537356 1 0.3389 0.7417 0.868 0.004 0.104 0.024
#> GSM537361 3 0.5984 0.3134 0.372 0.000 0.580 0.048
#> GSM537374 2 0.7811 0.2257 0.008 0.468 0.320 0.204
#> GSM537377 1 0.2271 0.7559 0.916 0.000 0.076 0.008
#> GSM537378 2 0.2053 0.6261 0.000 0.924 0.004 0.072
#> GSM537379 3 0.5746 0.1553 0.000 0.040 0.612 0.348
#> GSM537383 2 0.0804 0.6337 0.000 0.980 0.008 0.012
#> GSM537388 2 0.7348 0.3009 0.000 0.528 0.232 0.240
#> GSM537395 2 0.7293 0.1094 0.000 0.496 0.164 0.340
#> GSM537400 3 0.6607 0.2621 0.088 0.000 0.536 0.376
#> GSM537404 3 0.7388 0.4096 0.168 0.056 0.636 0.140
#> GSM537409 4 0.5763 0.4719 0.000 0.132 0.156 0.712
#> GSM537418 1 0.1824 0.7470 0.936 0.000 0.060 0.004
#> GSM537425 1 0.6655 -0.0734 0.476 0.000 0.440 0.084
#> GSM537333 3 0.5937 0.3042 0.052 0.000 0.608 0.340
#> GSM537342 4 0.4577 0.5270 0.016 0.148 0.032 0.804
#> GSM537347 3 0.5007 0.3633 0.024 0.076 0.800 0.100
#> GSM537350 1 0.2505 0.7503 0.920 0.008 0.052 0.020
#> GSM537362 1 0.5050 0.6931 0.764 0.000 0.152 0.084
#> GSM537363 1 0.7857 -0.0765 0.432 0.024 0.136 0.408
#> GSM537368 1 0.1584 0.7477 0.952 0.000 0.036 0.012
#> GSM537376 4 0.4635 0.4026 0.000 0.268 0.012 0.720
#> GSM537381 1 0.2530 0.7184 0.896 0.000 0.100 0.004
#> GSM537386 2 0.3732 0.5909 0.000 0.852 0.092 0.056
#> GSM537398 1 0.6064 0.6369 0.680 0.012 0.240 0.068
#> GSM537402 2 0.5602 0.0485 0.000 0.508 0.020 0.472
#> GSM537405 1 0.1677 0.7461 0.948 0.000 0.040 0.012
#> GSM537371 1 0.1488 0.7491 0.956 0.000 0.032 0.012
#> GSM537421 4 0.5042 0.4965 0.036 0.084 0.076 0.804
#> GSM537424 1 0.2647 0.7469 0.880 0.000 0.120 0.000
#> GSM537432 4 0.6798 -0.1784 0.072 0.008 0.456 0.464
#> GSM537331 2 0.7711 0.1709 0.000 0.428 0.340 0.232
#> GSM537332 3 0.7490 0.1513 0.000 0.284 0.496 0.220
#> GSM537334 3 0.7997 -0.0525 0.008 0.276 0.444 0.272
#> GSM537338 3 0.7923 -0.1638 0.000 0.332 0.344 0.324
#> GSM537353 2 0.4978 0.2801 0.000 0.612 0.004 0.384
#> GSM537357 1 0.1488 0.7491 0.956 0.000 0.032 0.012
#> GSM537358 2 0.1209 0.6346 0.000 0.964 0.004 0.032
#> GSM537375 4 0.6984 0.2777 0.000 0.184 0.236 0.580
#> GSM537389 2 0.2149 0.6206 0.000 0.912 0.000 0.088
#> GSM537390 2 0.4417 0.5544 0.000 0.796 0.044 0.160
#> GSM537393 4 0.7587 0.2524 0.000 0.276 0.244 0.480
#> GSM537399 3 0.6166 -0.0732 0.384 0.020 0.572 0.024
#> GSM537407 3 0.6102 0.1690 0.420 0.008 0.540 0.032
#> GSM537408 2 0.2002 0.6281 0.000 0.936 0.044 0.020
#> GSM537428 2 0.7677 0.1855 0.000 0.456 0.296 0.248
#> GSM537354 4 0.7463 0.1640 0.000 0.272 0.224 0.504
#> GSM537410 4 0.5512 0.4012 0.000 0.300 0.040 0.660
#> GSM537413 2 0.4008 0.4984 0.000 0.756 0.000 0.244
#> GSM537396 2 0.6207 0.4811 0.056 0.712 0.048 0.184
#> GSM537397 1 0.6747 0.6234 0.656 0.036 0.228 0.080
#> GSM537330 3 0.6506 0.2392 0.000 0.240 0.628 0.132
#> GSM537369 1 0.0779 0.7580 0.980 0.000 0.016 0.004
#> GSM537373 4 0.8096 0.1548 0.120 0.360 0.048 0.472
#> GSM537401 1 0.8875 0.4013 0.488 0.136 0.248 0.128
#> GSM537343 1 0.6069 0.2484 0.600 0.008 0.352 0.040
#> GSM537367 4 0.7569 -0.2084 0.196 0.000 0.368 0.436
#> GSM537382 4 0.4233 0.4853 0.008 0.140 0.032 0.820
#> GSM537385 2 0.4452 0.5832 0.000 0.796 0.048 0.156
#> GSM537391 1 0.5683 0.6663 0.728 0.012 0.188 0.072
#> GSM537419 2 0.1637 0.6302 0.000 0.940 0.000 0.060
#> GSM537420 1 0.0188 0.7578 0.996 0.000 0.000 0.004
#> GSM537429 3 0.9441 0.1448 0.176 0.148 0.404 0.272
#> GSM537431 3 0.6881 0.2912 0.120 0.000 0.540 0.340
#> GSM537387 1 0.3806 0.7214 0.824 0.000 0.156 0.020
#> GSM537414 3 0.6205 0.4343 0.196 0.000 0.668 0.136
#> GSM537433 3 0.7339 0.1244 0.420 0.020 0.468 0.092
#> GSM537335 3 0.9021 0.0713 0.120 0.176 0.476 0.228
#> GSM537339 1 0.6600 0.6255 0.656 0.024 0.236 0.084
#> GSM537340 4 0.6260 0.4616 0.060 0.100 0.108 0.732
#> GSM537344 1 0.0376 0.7572 0.992 0.000 0.004 0.004
#> GSM537346 3 0.6101 0.2728 0.004 0.284 0.644 0.068
#> GSM537351 1 0.6058 0.2079 0.604 0.000 0.336 0.060
#> GSM537352 4 0.7030 -0.0454 0.000 0.408 0.120 0.472
#> GSM537359 2 0.0927 0.6352 0.000 0.976 0.008 0.016
#> GSM537360 2 0.5883 0.1890 0.000 0.572 0.040 0.388
#> GSM537364 1 0.2563 0.7217 0.908 0.000 0.072 0.020
#> GSM537365 3 0.7821 0.4211 0.240 0.084 0.584 0.092
#> GSM537372 1 0.4973 0.6961 0.752 0.004 0.204 0.040
#> GSM537384 1 0.3972 0.7265 0.816 0.004 0.164 0.016
#> GSM537394 2 0.4868 0.3717 0.000 0.684 0.304 0.012
#> GSM537403 4 0.5159 0.4683 0.000 0.088 0.156 0.756
#> GSM537406 2 0.4134 0.4834 0.000 0.740 0.000 0.260
#> GSM537411 2 0.7129 0.1694 0.000 0.504 0.140 0.356
#> GSM537412 4 0.6113 0.4349 0.000 0.284 0.080 0.636
#> GSM537416 4 0.5185 0.3716 0.008 0.032 0.232 0.728
#> GSM537426 4 0.5619 0.3913 0.000 0.320 0.040 0.640
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.4460 0.23998 0.252 0.012 0.000 0.020 0.716
#> GSM537345 1 0.2130 0.72179 0.908 0.000 0.012 0.000 0.080
#> GSM537355 3 0.8497 -0.11754 0.000 0.224 0.316 0.188 0.272
#> GSM537366 1 0.6925 0.38608 0.492 0.000 0.172 0.028 0.308
#> GSM537370 5 0.6168 0.24826 0.064 0.292 0.040 0.004 0.600
#> GSM537380 2 0.1059 0.72400 0.000 0.968 0.020 0.004 0.008
#> GSM537392 2 0.0992 0.72399 0.000 0.968 0.024 0.000 0.008
#> GSM537415 2 0.4325 0.49062 0.000 0.684 0.012 0.300 0.004
#> GSM537417 3 0.4822 0.34537 0.012 0.008 0.748 0.176 0.056
#> GSM537422 3 0.7031 0.33390 0.328 0.000 0.372 0.292 0.008
#> GSM537423 2 0.1299 0.72468 0.000 0.960 0.012 0.020 0.008
#> GSM537427 2 0.6724 0.34543 0.000 0.576 0.124 0.056 0.244
#> GSM537430 2 0.4330 0.64173 0.000 0.800 0.068 0.028 0.104
#> GSM537336 1 0.0740 0.74263 0.980 0.000 0.008 0.008 0.004
#> GSM537337 5 0.8364 -0.15143 0.000 0.136 0.276 0.284 0.304
#> GSM537348 5 0.3932 0.14649 0.328 0.000 0.000 0.000 0.672
#> GSM537349 2 0.1484 0.71830 0.000 0.944 0.000 0.048 0.008
#> GSM537356 1 0.5092 0.29795 0.524 0.000 0.036 0.000 0.440
#> GSM537361 3 0.5078 0.43072 0.336 0.000 0.624 0.020 0.020
#> GSM537374 5 0.7119 0.07017 0.000 0.324 0.156 0.044 0.476
#> GSM537377 1 0.2228 0.72350 0.908 0.000 0.012 0.004 0.076
#> GSM537378 2 0.1682 0.72456 0.000 0.940 0.004 0.044 0.012
#> GSM537379 3 0.6199 0.14409 0.000 0.028 0.628 0.176 0.168
#> GSM537383 2 0.0798 0.72486 0.000 0.976 0.008 0.000 0.016
#> GSM537388 2 0.8105 0.16463 0.000 0.412 0.232 0.128 0.228
#> GSM537395 2 0.8200 0.07859 0.000 0.412 0.180 0.228 0.180
#> GSM537400 3 0.7270 0.20460 0.160 0.000 0.428 0.364 0.048
#> GSM537404 3 0.6352 0.51408 0.176 0.040 0.668 0.084 0.032
#> GSM537409 4 0.3967 0.53256 0.000 0.088 0.100 0.808 0.004
#> GSM537418 1 0.3392 0.72675 0.848 0.000 0.064 0.004 0.084
#> GSM537425 3 0.7109 0.15963 0.404 0.000 0.412 0.044 0.140
#> GSM537333 3 0.6522 0.25007 0.088 0.000 0.540 0.328 0.044
#> GSM537342 4 0.2718 0.53477 0.008 0.012 0.024 0.900 0.056
#> GSM537347 3 0.4385 0.45158 0.004 0.052 0.796 0.024 0.124
#> GSM537350 1 0.5477 0.45534 0.600 0.004 0.036 0.016 0.344
#> GSM537362 1 0.4719 0.57843 0.736 0.000 0.056 0.012 0.196
#> GSM537363 4 0.6164 0.09505 0.372 0.000 0.060 0.532 0.036
#> GSM537368 1 0.0613 0.74485 0.984 0.000 0.004 0.004 0.008
#> GSM537376 4 0.5578 0.52212 0.000 0.092 0.064 0.716 0.128
#> GSM537381 1 0.3648 0.70677 0.824 0.000 0.084 0.000 0.092
#> GSM537386 2 0.3452 0.68977 0.000 0.852 0.092 0.024 0.032
#> GSM537398 5 0.4497 0.11897 0.352 0.000 0.016 0.000 0.632
#> GSM537402 4 0.5966 0.19033 0.000 0.368 0.020 0.544 0.068
#> GSM537405 1 0.0932 0.74111 0.972 0.000 0.020 0.004 0.004
#> GSM537371 1 0.0613 0.74335 0.984 0.000 0.008 0.004 0.004
#> GSM537421 4 0.4456 0.52793 0.016 0.040 0.100 0.808 0.036
#> GSM537424 1 0.3961 0.62110 0.736 0.000 0.016 0.000 0.248
#> GSM537432 4 0.7901 0.01644 0.104 0.020 0.336 0.440 0.100
#> GSM537331 5 0.7459 0.14661 0.000 0.292 0.252 0.040 0.416
#> GSM537332 3 0.6057 0.36039 0.000 0.164 0.604 0.224 0.008
#> GSM537334 5 0.6999 0.18686 0.000 0.092 0.348 0.072 0.488
#> GSM537338 5 0.7712 0.12560 0.000 0.124 0.296 0.128 0.452
#> GSM537353 2 0.6605 0.30733 0.000 0.548 0.048 0.308 0.096
#> GSM537357 1 0.0854 0.74398 0.976 0.000 0.008 0.004 0.012
#> GSM537358 2 0.1356 0.72468 0.000 0.956 0.028 0.004 0.012
#> GSM537375 4 0.7883 0.21039 0.000 0.072 0.300 0.368 0.260
#> GSM537389 2 0.1628 0.71583 0.000 0.936 0.000 0.056 0.008
#> GSM537390 2 0.2321 0.71507 0.000 0.912 0.024 0.056 0.008
#> GSM537393 4 0.8426 0.21744 0.000 0.160 0.284 0.320 0.236
#> GSM537399 3 0.6001 0.28404 0.072 0.016 0.508 0.000 0.404
#> GSM537407 3 0.6797 0.35757 0.308 0.012 0.536 0.024 0.120
#> GSM537408 2 0.2452 0.70418 0.000 0.896 0.084 0.004 0.016
#> GSM537428 5 0.7633 0.04859 0.000 0.320 0.260 0.048 0.372
#> GSM537354 4 0.8316 0.10263 0.000 0.128 0.264 0.312 0.296
#> GSM537410 4 0.3751 0.53397 0.004 0.096 0.028 0.840 0.032
#> GSM537413 2 0.3197 0.65569 0.000 0.832 0.004 0.152 0.012
#> GSM537396 2 0.7223 0.14125 0.008 0.412 0.012 0.232 0.336
#> GSM537397 5 0.4474 0.13371 0.332 0.004 0.000 0.012 0.652
#> GSM537330 3 0.5958 0.37717 0.000 0.112 0.688 0.120 0.080
#> GSM537369 1 0.2536 0.71614 0.868 0.000 0.004 0.000 0.128
#> GSM537373 4 0.7602 0.30911 0.056 0.208 0.016 0.512 0.208
#> GSM537401 5 0.4160 0.31950 0.168 0.044 0.000 0.008 0.780
#> GSM537343 1 0.6312 0.00727 0.516 0.008 0.380 0.016 0.080
#> GSM537367 4 0.6793 -0.14161 0.200 0.000 0.316 0.472 0.012
#> GSM537382 4 0.4815 0.52548 0.004 0.044 0.060 0.776 0.116
#> GSM537385 2 0.5340 0.60442 0.000 0.732 0.048 0.104 0.116
#> GSM537391 1 0.4451 0.18147 0.504 0.000 0.000 0.004 0.492
#> GSM537419 2 0.1116 0.72478 0.000 0.964 0.004 0.028 0.004
#> GSM537420 1 0.2329 0.72222 0.876 0.000 0.000 0.000 0.124
#> GSM537429 5 0.8954 -0.11791 0.108 0.064 0.316 0.160 0.352
#> GSM537431 3 0.6713 0.31335 0.156 0.004 0.492 0.336 0.012
#> GSM537387 1 0.4108 0.48767 0.684 0.000 0.008 0.000 0.308
#> GSM537414 3 0.4587 0.52939 0.204 0.000 0.728 0.068 0.000
#> GSM537433 3 0.7535 0.29443 0.300 0.004 0.452 0.052 0.192
#> GSM537335 5 0.5871 0.27677 0.020 0.048 0.260 0.024 0.648
#> GSM537339 5 0.3816 0.18814 0.304 0.000 0.000 0.000 0.696
#> GSM537340 4 0.5560 0.49603 0.052 0.060 0.104 0.748 0.036
#> GSM537344 1 0.2074 0.72808 0.896 0.000 0.000 0.000 0.104
#> GSM537346 3 0.4187 0.41263 0.004 0.236 0.740 0.004 0.016
#> GSM537351 1 0.4495 0.33813 0.712 0.000 0.244 0.044 0.000
#> GSM537352 4 0.8394 0.14451 0.000 0.220 0.160 0.332 0.288
#> GSM537359 2 0.1364 0.72066 0.000 0.952 0.036 0.000 0.012
#> GSM537360 2 0.6501 0.03553 0.000 0.448 0.088 0.432 0.032
#> GSM537364 1 0.1557 0.71515 0.940 0.000 0.052 0.008 0.000
#> GSM537365 3 0.7561 0.51323 0.140 0.084 0.600 0.080 0.096
#> GSM537372 5 0.4321 -0.02078 0.396 0.000 0.004 0.000 0.600
#> GSM537384 5 0.4451 -0.26712 0.492 0.000 0.004 0.000 0.504
#> GSM537394 2 0.4521 0.43991 0.000 0.664 0.316 0.012 0.008
#> GSM537403 4 0.2866 0.52900 0.000 0.020 0.076 0.884 0.020
#> GSM537406 2 0.4871 0.44976 0.000 0.648 0.008 0.316 0.028
#> GSM537411 2 0.7967 0.05849 0.000 0.400 0.092 0.256 0.252
#> GSM537412 4 0.5043 0.48809 0.004 0.216 0.072 0.704 0.004
#> GSM537416 4 0.3451 0.50730 0.016 0.012 0.120 0.844 0.008
#> GSM537426 4 0.4837 0.51485 0.000 0.188 0.068 0.732 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.2582 0.6058 0.052 0.016 0.000 0.020 0.896 0.016
#> GSM537345 1 0.1007 0.7875 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM537355 6 0.7886 0.4679 0.000 0.136 0.084 0.188 0.128 0.464
#> GSM537366 5 0.6870 0.0558 0.368 0.000 0.196 0.044 0.384 0.008
#> GSM537370 5 0.5947 0.3387 0.008 0.212 0.084 0.008 0.632 0.056
#> GSM537380 2 0.2330 0.7191 0.000 0.908 0.040 0.004 0.024 0.024
#> GSM537392 2 0.1755 0.7193 0.000 0.932 0.032 0.000 0.008 0.028
#> GSM537415 2 0.5142 0.4147 0.000 0.624 0.012 0.292 0.008 0.064
#> GSM537417 6 0.5719 -0.1552 0.012 0.004 0.448 0.084 0.004 0.448
#> GSM537422 3 0.7355 0.3071 0.344 0.000 0.360 0.200 0.012 0.084
#> GSM537423 2 0.1565 0.7265 0.000 0.940 0.000 0.028 0.004 0.028
#> GSM537427 2 0.5815 -0.1289 0.000 0.480 0.008 0.008 0.112 0.392
#> GSM537430 2 0.4540 0.5234 0.000 0.712 0.020 0.008 0.036 0.224
#> GSM537336 1 0.0291 0.7930 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM537337 6 0.4654 0.5388 0.000 0.060 0.012 0.128 0.044 0.756
#> GSM537348 5 0.2980 0.6151 0.192 0.000 0.000 0.000 0.800 0.008
#> GSM537349 2 0.1285 0.7252 0.000 0.944 0.004 0.052 0.000 0.000
#> GSM537356 5 0.4445 0.4831 0.296 0.000 0.044 0.004 0.656 0.000
#> GSM537361 3 0.4472 0.5634 0.168 0.000 0.748 0.008 0.048 0.028
#> GSM537374 6 0.6426 0.4787 0.000 0.224 0.032 0.008 0.212 0.524
#> GSM537377 1 0.1219 0.7861 0.948 0.000 0.004 0.000 0.048 0.000
#> GSM537378 2 0.2830 0.7150 0.000 0.872 0.008 0.044 0.004 0.072
#> GSM537379 6 0.4865 0.2842 0.000 0.008 0.264 0.068 0.004 0.656
#> GSM537383 2 0.1003 0.7232 0.000 0.964 0.000 0.004 0.004 0.028
#> GSM537388 6 0.7739 0.4493 0.000 0.240 0.036 0.144 0.148 0.432
#> GSM537395 6 0.5795 0.3019 0.000 0.348 0.012 0.120 0.004 0.516
#> GSM537400 3 0.7847 0.1625 0.140 0.000 0.384 0.264 0.028 0.184
#> GSM537404 3 0.5803 0.4952 0.048 0.024 0.700 0.096 0.024 0.108
#> GSM537409 4 0.5259 0.5364 0.000 0.068 0.108 0.704 0.004 0.116
#> GSM537418 1 0.4160 0.7072 0.784 0.000 0.084 0.012 0.108 0.012
#> GSM537425 3 0.7158 0.2740 0.340 0.000 0.428 0.052 0.144 0.036
#> GSM537333 3 0.7297 0.2136 0.064 0.000 0.444 0.272 0.028 0.192
#> GSM537342 4 0.4079 0.5524 0.012 0.012 0.028 0.816 0.064 0.068
#> GSM537347 3 0.5865 0.2242 0.004 0.012 0.548 0.032 0.060 0.344
#> GSM537350 1 0.6605 -0.0727 0.440 0.036 0.104 0.020 0.396 0.004
#> GSM537362 1 0.5680 0.5117 0.668 0.000 0.048 0.016 0.152 0.116
#> GSM537363 4 0.6437 0.1891 0.316 0.000 0.112 0.508 0.056 0.008
#> GSM537368 1 0.0146 0.7953 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM537376 4 0.6002 0.3272 0.000 0.060 0.028 0.524 0.028 0.360
#> GSM537381 1 0.4912 0.5851 0.680 0.000 0.164 0.000 0.148 0.008
#> GSM537386 2 0.4752 0.6401 0.000 0.740 0.152 0.016 0.064 0.028
#> GSM537398 5 0.4132 0.5946 0.180 0.000 0.008 0.000 0.748 0.064
#> GSM537402 4 0.6034 0.3577 0.000 0.296 0.016 0.568 0.044 0.076
#> GSM537405 1 0.0717 0.7955 0.976 0.000 0.008 0.000 0.016 0.000
#> GSM537371 1 0.0260 0.7955 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM537421 4 0.5872 0.4874 0.032 0.020 0.076 0.616 0.004 0.252
#> GSM537424 1 0.4180 0.5329 0.692 0.000 0.020 0.004 0.276 0.008
#> GSM537432 4 0.8037 0.0608 0.076 0.008 0.284 0.316 0.040 0.276
#> GSM537331 6 0.6805 0.5350 0.000 0.196 0.036 0.024 0.244 0.500
#> GSM537332 3 0.5669 0.4094 0.000 0.096 0.664 0.180 0.028 0.032
#> GSM537334 6 0.5282 0.5602 0.000 0.036 0.060 0.004 0.260 0.640
#> GSM537338 6 0.3946 0.5923 0.000 0.032 0.004 0.020 0.168 0.776
#> GSM537353 2 0.6763 0.2552 0.000 0.480 0.036 0.196 0.016 0.272
#> GSM537357 1 0.0260 0.7955 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM537358 2 0.2171 0.7262 0.000 0.912 0.040 0.000 0.016 0.032
#> GSM537375 6 0.3924 0.4919 0.000 0.036 0.036 0.092 0.020 0.816
#> GSM537389 2 0.1728 0.7251 0.000 0.924 0.004 0.064 0.008 0.000
#> GSM537390 2 0.3822 0.6926 0.000 0.824 0.044 0.080 0.016 0.036
#> GSM537393 6 0.5478 0.3837 0.000 0.100 0.080 0.132 0.004 0.684
#> GSM537399 3 0.4844 0.2339 0.008 0.012 0.536 0.000 0.424 0.020
#> GSM537407 3 0.4930 0.5444 0.116 0.008 0.728 0.016 0.124 0.008
#> GSM537408 2 0.3633 0.6677 0.000 0.792 0.148 0.004 0.056 0.000
#> GSM537428 6 0.6203 0.5796 0.000 0.184 0.044 0.016 0.156 0.600
#> GSM537354 6 0.4451 0.5212 0.000 0.068 0.012 0.124 0.028 0.768
#> GSM537410 4 0.4328 0.5586 0.008 0.072 0.040 0.804 0.052 0.024
#> GSM537413 2 0.3929 0.6348 0.000 0.772 0.032 0.176 0.004 0.016
#> GSM537396 5 0.6719 -0.0663 0.004 0.304 0.020 0.268 0.400 0.004
#> GSM537397 5 0.3092 0.6286 0.168 0.004 0.004 0.004 0.816 0.004
#> GSM537330 3 0.7290 0.2132 0.000 0.064 0.480 0.136 0.052 0.268
#> GSM537369 1 0.2869 0.7179 0.832 0.000 0.020 0.000 0.148 0.000
#> GSM537373 4 0.6531 0.4320 0.028 0.120 0.052 0.600 0.192 0.008
#> GSM537401 5 0.2278 0.5954 0.044 0.012 0.004 0.004 0.912 0.024
#> GSM537343 3 0.6009 0.2741 0.356 0.008 0.504 0.012 0.116 0.004
#> GSM537367 4 0.6367 -0.0425 0.112 0.000 0.412 0.432 0.024 0.020
#> GSM537382 4 0.5940 0.4176 0.000 0.024 0.052 0.628 0.076 0.220
#> GSM537385 2 0.6685 0.3672 0.000 0.580 0.020 0.152 0.116 0.132
#> GSM537391 5 0.4255 0.0681 0.476 0.000 0.004 0.004 0.512 0.004
#> GSM537419 2 0.1647 0.7304 0.000 0.940 0.016 0.032 0.004 0.008
#> GSM537420 1 0.2581 0.7400 0.860 0.000 0.020 0.000 0.120 0.000
#> GSM537429 5 0.9126 -0.2102 0.060 0.052 0.176 0.192 0.312 0.208
#> GSM537431 3 0.6501 0.2701 0.072 0.000 0.524 0.308 0.020 0.076
#> GSM537387 1 0.3464 0.4433 0.688 0.000 0.000 0.000 0.312 0.000
#> GSM537414 3 0.5704 0.5244 0.140 0.000 0.648 0.052 0.004 0.156
#> GSM537433 3 0.6614 0.4754 0.200 0.008 0.576 0.048 0.148 0.020
#> GSM537335 6 0.5482 0.2955 0.004 0.028 0.048 0.000 0.456 0.464
#> GSM537339 5 0.2765 0.6347 0.132 0.004 0.000 0.000 0.848 0.016
#> GSM537340 4 0.7142 0.3985 0.112 0.024 0.080 0.496 0.008 0.280
#> GSM537344 1 0.2480 0.7495 0.872 0.000 0.024 0.000 0.104 0.000
#> GSM537346 3 0.5412 0.4024 0.000 0.148 0.648 0.000 0.028 0.176
#> GSM537351 1 0.3143 0.6617 0.840 0.000 0.124 0.016 0.012 0.008
#> GSM537352 6 0.6959 0.3685 0.000 0.144 0.024 0.260 0.068 0.504
#> GSM537359 2 0.2729 0.7116 0.000 0.876 0.080 0.004 0.032 0.008
#> GSM537360 4 0.7120 0.1317 0.000 0.348 0.056 0.352 0.008 0.236
#> GSM537364 1 0.0972 0.7838 0.964 0.000 0.028 0.000 0.008 0.000
#> GSM537365 3 0.4389 0.5315 0.024 0.052 0.792 0.024 0.100 0.008
#> GSM537372 5 0.3463 0.5722 0.240 0.000 0.008 0.000 0.748 0.004
#> GSM537384 5 0.3930 0.2724 0.420 0.000 0.004 0.000 0.576 0.000
#> GSM537394 2 0.4819 0.3956 0.000 0.592 0.360 0.004 0.032 0.012
#> GSM537403 4 0.4603 0.5511 0.000 0.024 0.100 0.760 0.016 0.100
#> GSM537406 2 0.5707 0.1205 0.000 0.488 0.028 0.416 0.060 0.008
#> GSM537411 2 0.8087 0.0261 0.000 0.392 0.072 0.152 0.120 0.264
#> GSM537412 4 0.5688 0.5327 0.000 0.160 0.076 0.664 0.008 0.092
#> GSM537416 4 0.4381 0.5255 0.004 0.004 0.116 0.752 0.004 0.120
#> GSM537426 4 0.5655 0.5049 0.000 0.184 0.044 0.644 0.004 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)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> SD:skmeans 104 0.180 0.527 2
#> SD:skmeans 83 0.542 0.548 3
#> SD:skmeans 43 0.740 0.330 4
#> SD:skmeans 43 0.511 0.235 5
#> SD:skmeans 55 0.124 0.131 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 104 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 0.788 0.900 0.953 0.5032 0.497 0.497
#> 3 3 0.712 0.807 0.908 0.2910 0.782 0.590
#> 4 4 0.633 0.668 0.848 0.1367 0.852 0.608
#> 5 5 0.651 0.626 0.830 0.0467 0.966 0.871
#> 6 6 0.661 0.514 0.759 0.0466 0.916 0.667
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
#> GSM537341 1 0.0938 0.926 0.988 0.012
#> GSM537345 1 0.0376 0.929 0.996 0.004
#> GSM537355 1 0.3431 0.897 0.936 0.064
#> GSM537366 1 0.0000 0.929 1.000 0.000
#> GSM537370 1 0.0938 0.926 0.988 0.012
#> GSM537380 2 0.0376 0.970 0.004 0.996
#> GSM537392 2 0.0000 0.972 0.000 1.000
#> GSM537415 2 0.0376 0.970 0.004 0.996
#> GSM537417 2 0.8144 0.643 0.252 0.748
#> GSM537422 1 0.0376 0.929 0.996 0.004
#> GSM537423 2 0.0000 0.972 0.000 1.000
#> GSM537427 2 0.0000 0.972 0.000 1.000
#> GSM537430 2 0.0000 0.972 0.000 1.000
#> GSM537336 1 0.0000 0.929 1.000 0.000
#> GSM537337 2 0.0000 0.972 0.000 1.000
#> GSM537348 1 0.0000 0.929 1.000 0.000
#> GSM537349 2 0.0376 0.970 0.004 0.996
#> GSM537356 1 0.0000 0.929 1.000 0.000
#> GSM537361 1 0.0376 0.929 0.996 0.004
#> GSM537374 2 0.9209 0.429 0.336 0.664
#> GSM537377 1 0.0376 0.929 0.996 0.004
#> GSM537378 2 0.0000 0.972 0.000 1.000
#> GSM537379 2 0.7674 0.693 0.224 0.776
#> GSM537383 2 0.0000 0.972 0.000 1.000
#> GSM537388 2 0.0000 0.972 0.000 1.000
#> GSM537395 2 0.0000 0.972 0.000 1.000
#> GSM537400 1 0.4562 0.875 0.904 0.096
#> GSM537404 1 0.8909 0.613 0.692 0.308
#> GSM537409 2 0.0000 0.972 0.000 1.000
#> GSM537418 1 0.0000 0.929 1.000 0.000
#> GSM537425 1 0.0376 0.929 0.996 0.004
#> GSM537333 1 0.0376 0.929 0.996 0.004
#> GSM537342 2 0.0000 0.972 0.000 1.000
#> GSM537347 1 0.0376 0.929 0.996 0.004
#> GSM537350 1 0.0000 0.929 1.000 0.000
#> GSM537362 1 0.0000 0.929 1.000 0.000
#> GSM537363 1 0.9323 0.523 0.652 0.348
#> GSM537368 1 0.0000 0.929 1.000 0.000
#> GSM537376 2 0.0000 0.972 0.000 1.000
#> GSM537381 1 0.0000 0.929 1.000 0.000
#> GSM537386 2 0.0376 0.970 0.004 0.996
#> GSM537398 1 0.0000 0.929 1.000 0.000
#> GSM537402 2 0.0376 0.970 0.004 0.996
#> GSM537405 1 0.0000 0.929 1.000 0.000
#> GSM537371 1 0.0000 0.929 1.000 0.000
#> GSM537421 2 0.0938 0.964 0.012 0.988
#> GSM537424 1 0.0376 0.929 0.996 0.004
#> GSM537432 1 0.1184 0.925 0.984 0.016
#> GSM537331 2 0.6148 0.811 0.152 0.848
#> GSM537332 2 0.0938 0.964 0.012 0.988
#> GSM537334 1 0.4815 0.869 0.896 0.104
#> GSM537338 2 0.0000 0.972 0.000 1.000
#> GSM537353 2 0.0000 0.972 0.000 1.000
#> GSM537357 1 0.0000 0.929 1.000 0.000
#> GSM537358 2 0.0000 0.972 0.000 1.000
#> GSM537375 2 0.1633 0.954 0.024 0.976
#> GSM537389 2 0.0376 0.970 0.004 0.996
#> GSM537390 2 0.0000 0.972 0.000 1.000
#> GSM537393 2 0.1414 0.958 0.020 0.980
#> GSM537399 1 0.0000 0.929 1.000 0.000
#> GSM537407 1 0.6801 0.787 0.820 0.180
#> GSM537408 2 0.0000 0.972 0.000 1.000
#> GSM537428 1 0.6148 0.827 0.848 0.152
#> GSM537354 2 0.0000 0.972 0.000 1.000
#> GSM537410 2 0.0376 0.970 0.004 0.996
#> GSM537413 2 0.0000 0.972 0.000 1.000
#> GSM537396 1 0.9795 0.366 0.584 0.416
#> GSM537397 1 0.8081 0.686 0.752 0.248
#> GSM537330 1 0.7376 0.744 0.792 0.208
#> GSM537369 1 0.0000 0.929 1.000 0.000
#> GSM537373 1 0.9661 0.431 0.608 0.392
#> GSM537401 1 0.5737 0.842 0.864 0.136
#> GSM537343 1 0.1843 0.917 0.972 0.028
#> GSM537367 1 0.9552 0.471 0.624 0.376
#> GSM537382 2 0.0000 0.972 0.000 1.000
#> GSM537385 2 0.0000 0.972 0.000 1.000
#> GSM537391 1 0.0000 0.929 1.000 0.000
#> GSM537419 2 0.0376 0.970 0.004 0.996
#> GSM537420 1 0.0000 0.929 1.000 0.000
#> GSM537429 1 0.0000 0.929 1.000 0.000
#> GSM537431 1 0.7950 0.720 0.760 0.240
#> GSM537387 1 0.0000 0.929 1.000 0.000
#> GSM537414 1 0.2603 0.909 0.956 0.044
#> GSM537433 1 0.1843 0.917 0.972 0.028
#> GSM537335 1 0.0376 0.929 0.996 0.004
#> GSM537339 1 0.0000 0.929 1.000 0.000
#> GSM537340 2 0.0376 0.970 0.004 0.996
#> GSM537344 1 0.0000 0.929 1.000 0.000
#> GSM537346 2 0.0000 0.972 0.000 1.000
#> GSM537351 1 0.0376 0.929 0.996 0.004
#> GSM537352 2 0.0000 0.972 0.000 1.000
#> GSM537359 2 0.0376 0.970 0.004 0.996
#> GSM537360 2 0.0000 0.972 0.000 1.000
#> GSM537364 1 0.0000 0.929 1.000 0.000
#> GSM537365 1 0.7453 0.735 0.788 0.212
#> GSM537372 1 0.0000 0.929 1.000 0.000
#> GSM537384 1 0.0000 0.929 1.000 0.000
#> GSM537394 2 0.3274 0.921 0.060 0.940
#> GSM537403 2 0.0000 0.972 0.000 1.000
#> GSM537406 2 0.0376 0.970 0.004 0.996
#> GSM537411 2 0.0000 0.972 0.000 1.000
#> GSM537412 2 0.0000 0.972 0.000 1.000
#> GSM537416 2 0.3584 0.909 0.068 0.932
#> GSM537426 2 0.0000 0.972 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.2998 0.896 0.916 0.016 0.068
#> GSM537345 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537355 3 0.5016 0.664 0.240 0.000 0.760
#> GSM537366 1 0.1170 0.935 0.976 0.008 0.016
#> GSM537370 1 0.3846 0.842 0.876 0.016 0.108
#> GSM537380 2 0.0237 0.893 0.000 0.996 0.004
#> GSM537392 2 0.1163 0.890 0.000 0.972 0.028
#> GSM537415 2 0.0592 0.892 0.000 0.988 0.012
#> GSM537417 3 0.8780 0.487 0.184 0.232 0.584
#> GSM537422 1 0.0237 0.940 0.996 0.000 0.004
#> GSM537423 2 0.0424 0.893 0.000 0.992 0.008
#> GSM537427 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537430 3 0.5016 0.638 0.000 0.240 0.760
#> GSM537336 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537337 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537348 1 0.1170 0.935 0.976 0.008 0.016
#> GSM537349 2 0.0000 0.891 0.000 1.000 0.000
#> GSM537356 1 0.0237 0.941 0.996 0.004 0.000
#> GSM537361 1 0.0237 0.940 0.996 0.000 0.004
#> GSM537374 3 0.2959 0.783 0.000 0.100 0.900
#> GSM537377 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537378 2 0.0892 0.893 0.000 0.980 0.020
#> GSM537379 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537383 2 0.0424 0.893 0.000 0.992 0.008
#> GSM537388 2 0.5058 0.688 0.000 0.756 0.244
#> GSM537395 3 0.1289 0.824 0.000 0.032 0.968
#> GSM537400 3 0.0592 0.826 0.012 0.000 0.988
#> GSM537404 1 0.7962 0.307 0.576 0.072 0.352
#> GSM537409 2 0.4452 0.752 0.000 0.808 0.192
#> GSM537418 1 0.0237 0.941 0.996 0.004 0.000
#> GSM537425 1 0.0424 0.939 0.992 0.000 0.008
#> GSM537333 1 0.0424 0.939 0.992 0.000 0.008
#> GSM537342 3 0.0000 0.825 0.000 0.000 1.000
#> GSM537347 1 0.0237 0.940 0.996 0.000 0.004
#> GSM537350 1 0.0424 0.940 0.992 0.008 0.000
#> GSM537362 1 0.0237 0.941 0.996 0.004 0.000
#> GSM537363 1 0.7508 0.645 0.696 0.148 0.156
#> GSM537368 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537376 3 0.0237 0.827 0.000 0.004 0.996
#> GSM537381 1 0.0237 0.941 0.996 0.004 0.000
#> GSM537386 2 0.0892 0.884 0.000 0.980 0.020
#> GSM537398 1 0.0237 0.941 0.996 0.004 0.000
#> GSM537402 3 0.0237 0.827 0.000 0.004 0.996
#> GSM537405 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537371 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537421 3 0.3412 0.777 0.000 0.124 0.876
#> GSM537424 1 0.0237 0.940 0.996 0.000 0.004
#> GSM537432 1 0.0747 0.935 0.984 0.000 0.016
#> GSM537331 3 0.7918 0.410 0.076 0.328 0.596
#> GSM537332 2 0.0747 0.893 0.000 0.984 0.016
#> GSM537334 3 0.5760 0.519 0.328 0.000 0.672
#> GSM537338 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537353 2 0.1753 0.883 0.000 0.952 0.048
#> GSM537357 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537358 2 0.2878 0.853 0.000 0.904 0.096
#> GSM537375 3 0.5760 0.508 0.000 0.328 0.672
#> GSM537389 2 0.0592 0.886 0.000 0.988 0.012
#> GSM537390 2 0.0424 0.893 0.000 0.992 0.008
#> GSM537393 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537399 1 0.0848 0.938 0.984 0.008 0.008
#> GSM537407 1 0.4897 0.789 0.812 0.172 0.016
#> GSM537408 2 0.2165 0.871 0.000 0.936 0.064
#> GSM537428 3 0.0829 0.826 0.012 0.004 0.984
#> GSM537354 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537410 3 0.2959 0.783 0.000 0.100 0.900
#> GSM537413 2 0.3941 0.783 0.000 0.844 0.156
#> GSM537396 2 0.7328 0.312 0.364 0.596 0.040
#> GSM537397 3 0.6527 0.317 0.404 0.008 0.588
#> GSM537330 1 0.3193 0.872 0.896 0.100 0.004
#> GSM537369 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537373 1 0.8179 0.375 0.564 0.352 0.084
#> GSM537401 3 0.7121 0.232 0.428 0.024 0.548
#> GSM537343 1 0.2031 0.923 0.952 0.032 0.016
#> GSM537367 1 0.8675 0.476 0.596 0.220 0.184
#> GSM537382 3 0.0000 0.825 0.000 0.000 1.000
#> GSM537385 2 0.4062 0.792 0.000 0.836 0.164
#> GSM537391 1 0.1170 0.935 0.976 0.008 0.016
#> GSM537419 2 0.0592 0.893 0.000 0.988 0.012
#> GSM537420 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537429 1 0.1170 0.935 0.976 0.008 0.016
#> GSM537431 3 0.4748 0.726 0.144 0.024 0.832
#> GSM537387 1 0.0983 0.936 0.980 0.004 0.016
#> GSM537414 1 0.3879 0.801 0.848 0.000 0.152
#> GSM537433 1 0.1529 0.923 0.960 0.040 0.000
#> GSM537335 1 0.0592 0.938 0.988 0.000 0.012
#> GSM537339 1 0.1170 0.935 0.976 0.008 0.016
#> GSM537340 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537344 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537346 3 0.6126 0.339 0.000 0.400 0.600
#> GSM537351 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537352 3 0.0747 0.829 0.000 0.016 0.984
#> GSM537359 2 0.0747 0.890 0.000 0.984 0.016
#> GSM537360 2 0.1289 0.888 0.000 0.968 0.032
#> GSM537364 1 0.0000 0.941 1.000 0.000 0.000
#> GSM537365 1 0.4139 0.840 0.860 0.124 0.016
#> GSM537372 1 0.1170 0.935 0.976 0.008 0.016
#> GSM537384 1 0.0237 0.941 0.996 0.004 0.000
#> GSM537394 2 0.1711 0.875 0.032 0.960 0.008
#> GSM537403 2 0.6291 0.175 0.000 0.532 0.468
#> GSM537406 2 0.0000 0.891 0.000 1.000 0.000
#> GSM537411 2 0.6008 0.370 0.000 0.628 0.372
#> GSM537412 2 0.0892 0.893 0.000 0.980 0.020
#> GSM537416 3 0.1289 0.824 0.000 0.032 0.968
#> GSM537426 3 0.6045 0.325 0.000 0.380 0.620
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.4669 0.7521 0.780 0.000 0.168 0.052
#> GSM537345 1 0.0707 0.8816 0.980 0.000 0.020 0.000
#> GSM537355 4 0.3942 0.6400 0.236 0.000 0.000 0.764
#> GSM537366 1 0.3123 0.8000 0.844 0.000 0.156 0.000
#> GSM537370 3 0.3032 0.7042 0.124 0.000 0.868 0.008
#> GSM537380 3 0.4905 0.4122 0.000 0.364 0.632 0.004
#> GSM537392 2 0.4983 0.5157 0.000 0.704 0.024 0.272
#> GSM537415 2 0.0000 0.7361 0.000 1.000 0.000 0.000
#> GSM537417 4 0.5339 0.5545 0.272 0.040 0.000 0.688
#> GSM537422 1 0.0188 0.8837 0.996 0.000 0.000 0.004
#> GSM537423 2 0.0336 0.7354 0.000 0.992 0.008 0.000
#> GSM537427 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537430 4 0.1302 0.8119 0.000 0.044 0.000 0.956
#> GSM537336 1 0.0707 0.8816 0.980 0.000 0.020 0.000
#> GSM537337 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537348 1 0.3024 0.8061 0.852 0.000 0.148 0.000
#> GSM537349 2 0.0000 0.7361 0.000 1.000 0.000 0.000
#> GSM537356 3 0.3569 0.6891 0.196 0.000 0.804 0.000
#> GSM537361 1 0.3257 0.7646 0.844 0.000 0.152 0.004
#> GSM537374 4 0.2469 0.7618 0.000 0.108 0.000 0.892
#> GSM537377 1 0.0000 0.8840 1.000 0.000 0.000 0.000
#> GSM537378 2 0.0000 0.7361 0.000 1.000 0.000 0.000
#> GSM537379 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537383 2 0.0336 0.7354 0.000 0.992 0.008 0.000
#> GSM537388 2 0.5345 0.2221 0.000 0.560 0.012 0.428
#> GSM537395 4 0.0592 0.8257 0.000 0.016 0.000 0.984
#> GSM537400 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537404 3 0.4542 0.6271 0.020 0.028 0.808 0.144
#> GSM537409 2 0.3610 0.6271 0.000 0.800 0.000 0.200
#> GSM537418 1 0.0188 0.8839 0.996 0.000 0.004 0.000
#> GSM537425 1 0.0336 0.8831 0.992 0.000 0.000 0.008
#> GSM537333 1 0.0336 0.8831 0.992 0.000 0.000 0.008
#> GSM537342 4 0.0817 0.8213 0.000 0.000 0.024 0.976
#> GSM537347 1 0.0188 0.8837 0.996 0.000 0.000 0.004
#> GSM537350 3 0.4643 0.5925 0.344 0.000 0.656 0.000
#> GSM537362 1 0.0188 0.8839 0.996 0.000 0.004 0.000
#> GSM537363 1 0.5923 0.6120 0.652 0.012 0.296 0.040
#> GSM537368 1 0.0000 0.8840 1.000 0.000 0.000 0.000
#> GSM537376 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537381 1 0.0188 0.8839 0.996 0.000 0.004 0.000
#> GSM537386 3 0.3444 0.5978 0.000 0.184 0.816 0.000
#> GSM537398 1 0.0336 0.8840 0.992 0.000 0.008 0.000
#> GSM537402 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537405 1 0.0000 0.8840 1.000 0.000 0.000 0.000
#> GSM537371 1 0.0707 0.8816 0.980 0.000 0.020 0.000
#> GSM537421 4 0.5055 0.3830 0.000 0.368 0.008 0.624
#> GSM537424 1 0.0188 0.8837 0.996 0.000 0.000 0.004
#> GSM537432 3 0.4121 0.6861 0.184 0.000 0.796 0.020
#> GSM537331 4 0.6299 0.3655 0.080 0.320 0.000 0.600
#> GSM537332 2 0.3498 0.6255 0.000 0.832 0.160 0.008
#> GSM537334 4 0.4477 0.5257 0.312 0.000 0.000 0.688
#> GSM537338 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537353 3 0.4507 0.5725 0.000 0.224 0.756 0.020
#> GSM537357 1 0.0707 0.8816 0.980 0.000 0.020 0.000
#> GSM537358 2 0.6028 0.2663 0.000 0.584 0.364 0.052
#> GSM537375 4 0.3074 0.7291 0.000 0.152 0.000 0.848
#> GSM537389 2 0.4008 0.5010 0.000 0.756 0.244 0.000
#> GSM537390 2 0.0188 0.7360 0.000 0.996 0.004 0.000
#> GSM537393 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537399 3 0.2921 0.7113 0.140 0.000 0.860 0.000
#> GSM537407 1 0.5371 0.5440 0.616 0.020 0.364 0.000
#> GSM537408 2 0.5310 0.1861 0.000 0.576 0.412 0.012
#> GSM537428 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537354 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537410 4 0.7139 0.1952 0.000 0.360 0.140 0.500
#> GSM537413 2 0.2011 0.7096 0.000 0.920 0.000 0.080
#> GSM537396 3 0.5548 0.1149 0.012 0.448 0.536 0.004
#> GSM537397 3 0.4171 0.6807 0.116 0.000 0.824 0.060
#> GSM537330 1 0.2578 0.8534 0.912 0.036 0.052 0.000
#> GSM537369 1 0.0000 0.8840 1.000 0.000 0.000 0.000
#> GSM537373 2 0.7909 0.0533 0.348 0.460 0.176 0.016
#> GSM537401 3 0.4285 0.6205 0.028 0.004 0.804 0.164
#> GSM537343 3 0.4999 -0.3022 0.492 0.000 0.508 0.000
#> GSM537367 1 0.7013 0.4118 0.540 0.032 0.372 0.056
#> GSM537382 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537385 2 0.4967 0.1700 0.000 0.548 0.000 0.452
#> GSM537391 1 0.3486 0.7860 0.812 0.000 0.188 0.000
#> GSM537419 2 0.0672 0.7364 0.000 0.984 0.008 0.008
#> GSM537420 1 0.0817 0.8816 0.976 0.000 0.024 0.000
#> GSM537429 1 0.3266 0.7921 0.832 0.000 0.168 0.000
#> GSM537431 4 0.6397 0.5280 0.144 0.000 0.208 0.648
#> GSM537387 3 0.4193 0.5841 0.268 0.000 0.732 0.000
#> GSM537414 1 0.3219 0.7521 0.836 0.000 0.000 0.164
#> GSM537433 1 0.3142 0.7767 0.860 0.132 0.008 0.000
#> GSM537335 1 0.5217 0.1042 0.608 0.000 0.380 0.012
#> GSM537339 1 0.3266 0.7905 0.832 0.000 0.168 0.000
#> GSM537340 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537344 1 0.0707 0.8816 0.980 0.000 0.020 0.000
#> GSM537346 4 0.3852 0.6790 0.000 0.180 0.012 0.808
#> GSM537351 1 0.3123 0.7774 0.844 0.000 0.156 0.000
#> GSM537352 4 0.0000 0.8321 0.000 0.000 0.000 1.000
#> GSM537359 2 0.4907 0.2152 0.000 0.580 0.420 0.000
#> GSM537360 2 0.0921 0.7320 0.000 0.972 0.000 0.028
#> GSM537364 1 0.0707 0.8816 0.980 0.000 0.020 0.000
#> GSM537365 3 0.1389 0.6930 0.048 0.000 0.952 0.000
#> GSM537372 3 0.3975 0.6696 0.240 0.000 0.760 0.000
#> GSM537384 1 0.0188 0.8839 0.996 0.000 0.004 0.000
#> GSM537394 3 0.4643 0.3869 0.000 0.344 0.656 0.000
#> GSM537403 4 0.5376 0.1994 0.000 0.396 0.016 0.588
#> GSM537406 2 0.0817 0.7279 0.000 0.976 0.024 0.000
#> GSM537411 3 0.3625 0.6244 0.000 0.160 0.828 0.012
#> GSM537412 2 0.0000 0.7361 0.000 1.000 0.000 0.000
#> GSM537416 4 0.4164 0.5582 0.000 0.264 0.000 0.736
#> GSM537426 2 0.4967 0.1363 0.000 0.548 0.000 0.452
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 1 0.4677 0.7010 0.748 0.000 0.020 0.048 0.184
#> GSM537345 3 0.1341 0.7715 0.056 0.000 0.944 0.000 0.000
#> GSM537355 4 0.3424 0.6301 0.240 0.000 0.000 0.760 0.000
#> GSM537366 1 0.2852 0.7530 0.828 0.000 0.000 0.000 0.172
#> GSM537370 5 0.2722 0.6982 0.120 0.000 0.004 0.008 0.868
#> GSM537380 5 0.4671 0.4596 0.000 0.332 0.028 0.000 0.640
#> GSM537392 2 0.4818 0.5349 0.000 0.700 0.028 0.252 0.020
#> GSM537415 2 0.0000 0.7311 0.000 1.000 0.000 0.000 0.000
#> GSM537417 4 0.4599 0.5317 0.272 0.040 0.000 0.688 0.000
#> GSM537422 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537423 2 0.0579 0.7310 0.000 0.984 0.008 0.000 0.008
#> GSM537427 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537430 4 0.1121 0.8106 0.000 0.044 0.000 0.956 0.000
#> GSM537336 3 0.1341 0.7715 0.056 0.000 0.944 0.000 0.000
#> GSM537337 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537348 1 0.2773 0.7581 0.836 0.000 0.000 0.000 0.164
#> GSM537349 2 0.0794 0.7292 0.000 0.972 0.028 0.000 0.000
#> GSM537356 5 0.3074 0.6689 0.196 0.000 0.000 0.000 0.804
#> GSM537361 1 0.2563 0.7369 0.872 0.000 0.008 0.000 0.120
#> GSM537374 4 0.2127 0.7606 0.000 0.108 0.000 0.892 0.000
#> GSM537377 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537378 2 0.0000 0.7311 0.000 1.000 0.000 0.000 0.000
#> GSM537379 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537383 2 0.1082 0.7277 0.000 0.964 0.028 0.000 0.008
#> GSM537388 2 0.4622 0.1885 0.000 0.548 0.000 0.440 0.012
#> GSM537395 4 0.0510 0.8245 0.000 0.016 0.000 0.984 0.000
#> GSM537400 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537404 5 0.4026 0.6445 0.020 0.028 0.004 0.140 0.808
#> GSM537409 2 0.3109 0.6204 0.000 0.800 0.000 0.200 0.000
#> GSM537418 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537425 1 0.0162 0.8217 0.996 0.000 0.000 0.004 0.000
#> GSM537333 1 0.0162 0.8218 0.996 0.000 0.000 0.004 0.000
#> GSM537342 4 0.0703 0.8203 0.000 0.000 0.000 0.976 0.024
#> GSM537347 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537350 5 0.3857 0.5904 0.312 0.000 0.000 0.000 0.688
#> GSM537362 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537363 1 0.5177 0.6146 0.656 0.008 0.004 0.044 0.288
#> GSM537368 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537376 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537381 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537386 5 0.3389 0.6501 0.000 0.116 0.048 0.000 0.836
#> GSM537398 1 0.0162 0.8225 0.996 0.000 0.000 0.000 0.004
#> GSM537402 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537405 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537371 3 0.1341 0.7715 0.056 0.000 0.944 0.000 0.000
#> GSM537421 4 0.4276 0.3691 0.000 0.380 0.000 0.616 0.004
#> GSM537424 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537432 5 0.3734 0.6685 0.184 0.000 0.008 0.016 0.792
#> GSM537331 4 0.6114 0.3233 0.080 0.316 0.028 0.576 0.000
#> GSM537332 2 0.2942 0.6541 0.000 0.856 0.008 0.008 0.128
#> GSM537334 4 0.4562 0.4992 0.292 0.000 0.032 0.676 0.000
#> GSM537338 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537353 5 0.3759 0.6049 0.000 0.220 0.000 0.016 0.764
#> GSM537357 3 0.1341 0.7715 0.056 0.000 0.944 0.000 0.000
#> GSM537358 2 0.5253 0.2299 0.000 0.572 0.008 0.036 0.384
#> GSM537375 4 0.2605 0.7323 0.000 0.148 0.000 0.852 0.000
#> GSM537389 2 0.4054 0.4928 0.000 0.732 0.020 0.000 0.248
#> GSM537390 2 0.0162 0.7312 0.000 0.996 0.000 0.000 0.004
#> GSM537393 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537399 5 0.2280 0.7003 0.120 0.000 0.000 0.000 0.880
#> GSM537407 1 0.4565 0.5800 0.632 0.008 0.008 0.000 0.352
#> GSM537408 2 0.4522 0.1125 0.000 0.552 0.000 0.008 0.440
#> GSM537428 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537354 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537410 4 0.6309 0.1524 0.000 0.368 0.000 0.472 0.160
#> GSM537413 2 0.2388 0.7125 0.000 0.900 0.028 0.072 0.000
#> GSM537396 5 0.4970 0.2355 0.008 0.392 0.020 0.000 0.580
#> GSM537397 5 0.3100 0.6726 0.064 0.000 0.020 0.040 0.876
#> GSM537330 1 0.2998 0.7866 0.884 0.036 0.028 0.000 0.052
#> GSM537369 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537373 2 0.7306 -0.0418 0.336 0.440 0.020 0.012 0.192
#> GSM537401 5 0.3333 0.6524 0.028 0.000 0.020 0.096 0.856
#> GSM537343 5 0.4658 -0.3513 0.484 0.000 0.012 0.000 0.504
#> GSM537367 1 0.6322 0.4471 0.540 0.032 0.012 0.052 0.364
#> GSM537382 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537385 2 0.4937 0.2158 0.000 0.544 0.028 0.428 0.000
#> GSM537391 1 0.4031 0.7197 0.772 0.000 0.044 0.000 0.184
#> GSM537419 2 0.1369 0.7301 0.000 0.956 0.028 0.008 0.008
#> GSM537420 3 0.4283 0.2239 0.456 0.000 0.544 0.000 0.000
#> GSM537429 1 0.3550 0.7357 0.796 0.000 0.020 0.000 0.184
#> GSM537431 4 0.6082 0.4686 0.144 0.000 0.016 0.616 0.224
#> GSM537387 5 0.5589 0.3500 0.080 0.000 0.372 0.000 0.548
#> GSM537414 1 0.2773 0.6858 0.836 0.000 0.000 0.164 0.000
#> GSM537433 1 0.2629 0.7098 0.860 0.136 0.000 0.000 0.004
#> GSM537335 1 0.4380 0.0994 0.616 0.000 0.000 0.008 0.376
#> GSM537339 1 0.3550 0.7342 0.796 0.000 0.020 0.000 0.184
#> GSM537340 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537344 1 0.2929 0.6905 0.820 0.000 0.180 0.000 0.000
#> GSM537346 4 0.3597 0.6716 0.000 0.180 0.008 0.800 0.012
#> GSM537351 3 0.5043 0.4271 0.356 0.000 0.600 0.000 0.044
#> GSM537352 4 0.0000 0.8309 0.000 0.000 0.000 1.000 0.000
#> GSM537359 2 0.5261 0.1538 0.000 0.528 0.048 0.000 0.424
#> GSM537360 2 0.0794 0.7287 0.000 0.972 0.000 0.028 0.000
#> GSM537364 1 0.4262 0.0254 0.560 0.000 0.440 0.000 0.000
#> GSM537365 5 0.1251 0.6923 0.036 0.000 0.008 0.000 0.956
#> GSM537372 5 0.3196 0.6579 0.192 0.000 0.004 0.000 0.804
#> GSM537384 1 0.0000 0.8233 1.000 0.000 0.000 0.000 0.000
#> GSM537394 5 0.4774 0.3456 0.000 0.360 0.028 0.000 0.612
#> GSM537403 4 0.4769 0.2059 0.000 0.392 0.004 0.588 0.016
#> GSM537406 2 0.0794 0.7227 0.000 0.972 0.000 0.000 0.028
#> GSM537411 5 0.3170 0.6539 0.000 0.160 0.004 0.008 0.828
#> GSM537412 2 0.0000 0.7311 0.000 1.000 0.000 0.000 0.000
#> GSM537416 4 0.3636 0.5516 0.000 0.272 0.000 0.728 0.000
#> GSM537426 2 0.4273 0.1335 0.000 0.552 0.000 0.448 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.4439 -0.17069 0.000 0.000 0.000 0.432 0.540 0.028
#> GSM537345 1 0.0000 0.73618 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537355 6 0.3076 0.59454 0.000 0.000 0.000 0.240 0.000 0.760
#> GSM537366 4 0.3198 0.64164 0.000 0.000 0.000 0.740 0.260 0.000
#> GSM537370 3 0.3490 0.36361 0.000 0.000 0.724 0.008 0.268 0.000
#> GSM537380 2 0.5777 0.13994 0.000 0.548 0.204 0.000 0.240 0.008
#> GSM537392 2 0.2848 0.59958 0.000 0.848 0.024 0.000 0.004 0.124
#> GSM537415 2 0.2854 0.68199 0.000 0.792 0.000 0.000 0.208 0.000
#> GSM537417 6 0.4655 0.55233 0.000 0.112 0.000 0.208 0.000 0.680
#> GSM537422 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537423 2 0.2118 0.69858 0.000 0.888 0.008 0.000 0.104 0.000
#> GSM537427 6 0.0146 0.81702 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM537430 6 0.2003 0.76051 0.000 0.116 0.000 0.000 0.000 0.884
#> GSM537336 1 0.0000 0.73618 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537337 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537348 4 0.2730 0.68665 0.000 0.000 0.000 0.808 0.192 0.000
#> GSM537349 2 0.0146 0.67684 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM537356 3 0.4663 0.38516 0.000 0.000 0.660 0.088 0.252 0.000
#> GSM537361 3 0.3797 0.15334 0.000 0.000 0.580 0.420 0.000 0.000
#> GSM537374 6 0.1663 0.77262 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM537377 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537378 2 0.2854 0.68199 0.000 0.792 0.000 0.000 0.208 0.000
#> GSM537379 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537383 2 0.0405 0.67498 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM537388 2 0.4594 0.00356 0.000 0.484 0.000 0.000 0.036 0.480
#> GSM537395 6 0.0146 0.81726 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM537400 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537404 3 0.2965 0.43463 0.000 0.008 0.856 0.012 0.108 0.016
#> GSM537409 2 0.5351 0.54358 0.000 0.592 0.000 0.000 0.208 0.200
#> GSM537418 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537425 4 0.0508 0.79324 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM537333 4 0.0291 0.79600 0.000 0.000 0.004 0.992 0.000 0.004
#> GSM537342 6 0.2491 0.75139 0.000 0.000 0.020 0.000 0.112 0.868
#> GSM537347 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537350 3 0.5993 0.12963 0.000 0.000 0.392 0.376 0.232 0.000
#> GSM537362 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537363 4 0.5887 0.16371 0.000 0.000 0.356 0.484 0.148 0.012
#> GSM537368 4 0.0790 0.78391 0.000 0.000 0.000 0.968 0.032 0.000
#> GSM537376 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537381 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537386 5 0.4734 0.23302 0.000 0.208 0.120 0.000 0.672 0.000
#> GSM537398 4 0.0632 0.78917 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM537402 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537405 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537371 1 0.0000 0.73618 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537421 6 0.5790 0.34234 0.000 0.220 0.012 0.000 0.208 0.560
#> GSM537424 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537432 3 0.0865 0.44564 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM537331 6 0.5464 0.08047 0.000 0.452 0.000 0.076 0.016 0.456
#> GSM537332 3 0.5469 0.19386 0.000 0.224 0.600 0.000 0.168 0.008
#> GSM537334 6 0.6070 0.39563 0.000 0.212 0.000 0.180 0.040 0.568
#> GSM537338 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537353 3 0.5288 0.35235 0.000 0.164 0.596 0.000 0.240 0.000
#> GSM537357 1 0.0000 0.73618 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537358 2 0.6214 0.14116 0.000 0.488 0.344 0.000 0.124 0.044
#> GSM537375 6 0.2854 0.68621 0.000 0.208 0.000 0.000 0.000 0.792
#> GSM537389 2 0.2178 0.63825 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM537390 2 0.2994 0.68248 0.000 0.788 0.004 0.000 0.208 0.000
#> GSM537393 6 0.0291 0.81600 0.000 0.000 0.004 0.000 0.004 0.992
#> GSM537399 3 0.5409 0.22859 0.000 0.000 0.540 0.136 0.324 0.000
#> GSM537407 3 0.5287 0.24563 0.000 0.000 0.584 0.272 0.144 0.000
#> GSM537408 3 0.5498 0.00539 0.000 0.408 0.464 0.000 0.128 0.000
#> GSM537428 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537354 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537410 5 0.6155 -0.08512 0.000 0.216 0.008 0.000 0.412 0.364
#> GSM537413 2 0.2013 0.66169 0.000 0.908 0.008 0.000 0.008 0.076
#> GSM537396 5 0.3835 0.30577 0.000 0.112 0.112 0.000 0.776 0.000
#> GSM537397 5 0.4735 0.06131 0.000 0.000 0.392 0.008 0.564 0.036
#> GSM537330 4 0.4354 0.51554 0.000 0.240 0.000 0.692 0.068 0.000
#> GSM537369 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537373 5 0.3961 0.29178 0.000 0.124 0.000 0.112 0.764 0.000
#> GSM537401 5 0.4464 0.19961 0.000 0.000 0.284 0.012 0.668 0.036
#> GSM537343 3 0.5279 0.27243 0.000 0.000 0.604 0.200 0.196 0.000
#> GSM537367 3 0.5380 0.26509 0.004 0.000 0.600 0.164 0.232 0.000
#> GSM537382 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537385 2 0.4063 0.43649 0.000 0.692 0.008 0.000 0.020 0.280
#> GSM537391 4 0.3823 0.33442 0.000 0.000 0.000 0.564 0.436 0.000
#> GSM537419 2 0.1701 0.69640 0.000 0.920 0.008 0.000 0.072 0.000
#> GSM537420 1 0.3810 0.23107 0.572 0.000 0.000 0.428 0.000 0.000
#> GSM537429 4 0.3804 0.35623 0.000 0.000 0.000 0.576 0.424 0.000
#> GSM537431 5 0.7089 0.19588 0.000 0.000 0.140 0.128 0.404 0.328
#> GSM537387 5 0.6915 0.04045 0.260 0.000 0.272 0.060 0.408 0.000
#> GSM537414 4 0.2491 0.65705 0.000 0.000 0.000 0.836 0.000 0.164
#> GSM537433 4 0.3610 0.62777 0.000 0.052 0.004 0.792 0.152 0.000
#> GSM537335 4 0.5045 0.29259 0.000 0.000 0.232 0.648 0.112 0.008
#> GSM537339 4 0.3828 0.32562 0.000 0.000 0.000 0.560 0.440 0.000
#> GSM537340 6 0.0363 0.81365 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM537344 4 0.2631 0.66222 0.180 0.000 0.000 0.820 0.000 0.000
#> GSM537346 6 0.3665 0.62037 0.000 0.252 0.020 0.000 0.000 0.728
#> GSM537351 1 0.5386 0.34725 0.524 0.000 0.124 0.352 0.000 0.000
#> GSM537352 6 0.0000 0.81801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537359 2 0.5265 0.02253 0.000 0.500 0.100 0.000 0.400 0.000
#> GSM537360 2 0.3374 0.67714 0.000 0.772 0.000 0.000 0.208 0.020
#> GSM537364 4 0.4523 -0.01170 0.452 0.000 0.000 0.516 0.032 0.000
#> GSM537365 3 0.0291 0.44216 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM537372 3 0.6060 0.06808 0.000 0.000 0.392 0.264 0.344 0.000
#> GSM537384 4 0.0000 0.79789 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537394 3 0.3795 0.26338 0.000 0.364 0.632 0.000 0.004 0.000
#> GSM537403 6 0.5728 0.37232 0.000 0.272 0.052 0.000 0.084 0.592
#> GSM537406 2 0.3563 0.58683 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM537411 3 0.4757 0.30104 0.000 0.084 0.636 0.000 0.280 0.000
#> GSM537412 2 0.2854 0.68199 0.000 0.792 0.000 0.000 0.208 0.000
#> GSM537416 6 0.5117 0.50538 0.000 0.116 0.016 0.000 0.208 0.660
#> GSM537426 6 0.5711 0.16452 0.000 0.276 0.000 0.000 0.208 0.516
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> SD:pam 100 0.0482 0.386 2
#> SD:pam 92 0.4567 0.623 3
#> SD:pam 87 0.7596 0.847 4
#> SD:pam 80 0.5766 0.639 5
#> SD:pam 60 0.5342 0.119 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 104 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.900 0.907 0.966 0.2849 0.724 0.724
#> 3 3 0.335 0.480 0.730 0.8748 0.895 0.857
#> 4 4 0.295 0.425 0.621 0.2732 0.581 0.370
#> 5 5 0.471 0.472 0.680 0.1113 0.802 0.420
#> 6 6 0.564 0.497 0.699 0.0537 0.891 0.557
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
#> GSM537341 2 0.0000 0.9721 0.000 1.000
#> GSM537345 1 0.0000 0.9066 1.000 0.000
#> GSM537355 2 0.0000 0.9721 0.000 1.000
#> GSM537366 2 0.0000 0.9721 0.000 1.000
#> GSM537370 2 0.0000 0.9721 0.000 1.000
#> GSM537380 2 0.0000 0.9721 0.000 1.000
#> GSM537392 2 0.0000 0.9721 0.000 1.000
#> GSM537415 2 0.0000 0.9721 0.000 1.000
#> GSM537417 2 0.0000 0.9721 0.000 1.000
#> GSM537422 2 0.4939 0.8545 0.108 0.892
#> GSM537423 2 0.0000 0.9721 0.000 1.000
#> GSM537427 2 0.0000 0.9721 0.000 1.000
#> GSM537430 2 0.0000 0.9721 0.000 1.000
#> GSM537336 1 0.0000 0.9066 1.000 0.000
#> GSM537337 2 0.0000 0.9721 0.000 1.000
#> GSM537348 2 0.7883 0.6544 0.236 0.764
#> GSM537349 2 0.0000 0.9721 0.000 1.000
#> GSM537356 2 0.0000 0.9721 0.000 1.000
#> GSM537361 1 0.9977 0.1707 0.528 0.472
#> GSM537374 2 0.0000 0.9721 0.000 1.000
#> GSM537377 1 0.0000 0.9066 1.000 0.000
#> GSM537378 2 0.0000 0.9721 0.000 1.000
#> GSM537379 2 0.0000 0.9721 0.000 1.000
#> GSM537383 2 0.0000 0.9721 0.000 1.000
#> GSM537388 2 0.0000 0.9721 0.000 1.000
#> GSM537395 2 0.0000 0.9721 0.000 1.000
#> GSM537400 2 0.0000 0.9721 0.000 1.000
#> GSM537404 2 0.0000 0.9721 0.000 1.000
#> GSM537409 2 0.0000 0.9721 0.000 1.000
#> GSM537418 2 0.1414 0.9535 0.020 0.980
#> GSM537425 2 0.1184 0.9571 0.016 0.984
#> GSM537333 2 0.0000 0.9721 0.000 1.000
#> GSM537342 2 0.0000 0.9721 0.000 1.000
#> GSM537347 2 0.0000 0.9721 0.000 1.000
#> GSM537350 2 0.5059 0.8499 0.112 0.888
#> GSM537362 2 0.0000 0.9721 0.000 1.000
#> GSM537363 2 0.9635 0.2973 0.388 0.612
#> GSM537368 1 0.0000 0.9066 1.000 0.000
#> GSM537376 2 0.0000 0.9721 0.000 1.000
#> GSM537381 1 0.0376 0.9048 0.996 0.004
#> GSM537386 2 0.0000 0.9721 0.000 1.000
#> GSM537398 2 0.9983 -0.0342 0.476 0.524
#> GSM537402 2 0.0000 0.9721 0.000 1.000
#> GSM537405 1 0.5059 0.8197 0.888 0.112
#> GSM537371 1 0.0000 0.9066 1.000 0.000
#> GSM537421 2 0.0000 0.9721 0.000 1.000
#> GSM537424 2 0.9815 0.1866 0.420 0.580
#> GSM537432 2 0.0000 0.9721 0.000 1.000
#> GSM537331 2 0.0000 0.9721 0.000 1.000
#> GSM537332 2 0.0000 0.9721 0.000 1.000
#> GSM537334 2 0.0000 0.9721 0.000 1.000
#> GSM537338 2 0.0000 0.9721 0.000 1.000
#> GSM537353 2 0.0000 0.9721 0.000 1.000
#> GSM537357 1 0.0000 0.9066 1.000 0.000
#> GSM537358 2 0.0000 0.9721 0.000 1.000
#> GSM537375 2 0.0000 0.9721 0.000 1.000
#> GSM537389 2 0.0000 0.9721 0.000 1.000
#> GSM537390 2 0.0000 0.9721 0.000 1.000
#> GSM537393 2 0.0000 0.9721 0.000 1.000
#> GSM537399 2 0.0000 0.9721 0.000 1.000
#> GSM537407 2 0.0000 0.9721 0.000 1.000
#> GSM537408 2 0.0000 0.9721 0.000 1.000
#> GSM537428 2 0.0000 0.9721 0.000 1.000
#> GSM537354 2 0.0000 0.9721 0.000 1.000
#> GSM537410 2 0.0000 0.9721 0.000 1.000
#> GSM537413 2 0.0000 0.9721 0.000 1.000
#> GSM537396 2 0.0000 0.9721 0.000 1.000
#> GSM537397 2 0.0000 0.9721 0.000 1.000
#> GSM537330 2 0.0000 0.9721 0.000 1.000
#> GSM537369 1 0.0000 0.9066 1.000 0.000
#> GSM537373 2 0.0000 0.9721 0.000 1.000
#> GSM537401 2 0.0000 0.9721 0.000 1.000
#> GSM537343 2 0.2043 0.9412 0.032 0.968
#> GSM537367 2 0.0000 0.9721 0.000 1.000
#> GSM537382 2 0.0000 0.9721 0.000 1.000
#> GSM537385 2 0.0000 0.9721 0.000 1.000
#> GSM537391 1 0.9970 0.1827 0.532 0.468
#> GSM537419 2 0.0000 0.9721 0.000 1.000
#> GSM537420 1 0.0000 0.9066 1.000 0.000
#> GSM537429 2 0.0000 0.9721 0.000 1.000
#> GSM537431 2 0.0000 0.9721 0.000 1.000
#> GSM537387 1 0.0376 0.9048 0.996 0.004
#> GSM537414 2 0.0000 0.9721 0.000 1.000
#> GSM537433 2 0.0000 0.9721 0.000 1.000
#> GSM537335 2 0.0000 0.9721 0.000 1.000
#> GSM537339 2 0.0000 0.9721 0.000 1.000
#> GSM537340 2 0.0000 0.9721 0.000 1.000
#> GSM537344 1 0.0000 0.9066 1.000 0.000
#> GSM537346 2 0.0000 0.9721 0.000 1.000
#> GSM537351 1 0.0000 0.9066 1.000 0.000
#> GSM537352 2 0.0000 0.9721 0.000 1.000
#> GSM537359 2 0.0000 0.9721 0.000 1.000
#> GSM537360 2 0.0000 0.9721 0.000 1.000
#> GSM537364 1 0.0000 0.9066 1.000 0.000
#> GSM537365 2 0.0000 0.9721 0.000 1.000
#> GSM537372 2 0.9286 0.4148 0.344 0.656
#> GSM537384 1 0.9522 0.4427 0.628 0.372
#> GSM537394 2 0.0000 0.9721 0.000 1.000
#> GSM537403 2 0.0000 0.9721 0.000 1.000
#> GSM537406 2 0.0000 0.9721 0.000 1.000
#> GSM537411 2 0.0000 0.9721 0.000 1.000
#> GSM537412 2 0.0000 0.9721 0.000 1.000
#> GSM537416 2 0.0000 0.9721 0.000 1.000
#> GSM537426 2 0.0000 0.9721 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.7759 -0.61030 0.048 0.480 0.472
#> GSM537345 1 0.0747 0.82271 0.984 0.000 0.016
#> GSM537355 2 0.2448 0.59821 0.000 0.924 0.076
#> GSM537366 2 0.8382 0.27735 0.084 0.492 0.424
#> GSM537370 2 0.6287 0.25040 0.024 0.704 0.272
#> GSM537380 2 0.5178 0.34128 0.000 0.744 0.256
#> GSM537392 2 0.5327 0.30528 0.000 0.728 0.272
#> GSM537415 2 0.5291 0.59022 0.000 0.732 0.268
#> GSM537417 2 0.6416 0.54253 0.008 0.616 0.376
#> GSM537422 2 0.8775 0.42806 0.116 0.500 0.384
#> GSM537423 2 0.0892 0.60372 0.000 0.980 0.020
#> GSM537427 2 0.5254 0.33834 0.000 0.736 0.264
#> GSM537430 2 0.4062 0.47986 0.000 0.836 0.164
#> GSM537336 1 0.0237 0.82391 0.996 0.000 0.004
#> GSM537337 2 0.4702 0.45450 0.000 0.788 0.212
#> GSM537348 3 0.9734 0.62443 0.292 0.260 0.448
#> GSM537349 2 0.4605 0.43389 0.000 0.796 0.204
#> GSM537356 2 0.8703 -0.30754 0.144 0.572 0.284
#> GSM537361 1 0.9271 0.12789 0.528 0.244 0.228
#> GSM537374 2 0.5529 0.31879 0.000 0.704 0.296
#> GSM537377 1 0.0892 0.82164 0.980 0.000 0.020
#> GSM537378 2 0.0424 0.61133 0.000 0.992 0.008
#> GSM537379 2 0.5797 0.59881 0.008 0.712 0.280
#> GSM537383 2 0.4504 0.43543 0.000 0.804 0.196
#> GSM537388 2 0.5254 0.34884 0.000 0.736 0.264
#> GSM537395 2 0.1964 0.62038 0.000 0.944 0.056
#> GSM537400 2 0.6099 0.60282 0.032 0.740 0.228
#> GSM537404 2 0.5216 0.60078 0.000 0.740 0.260
#> GSM537409 2 0.5948 0.54791 0.000 0.640 0.360
#> GSM537418 2 0.7668 -0.10897 0.460 0.496 0.044
#> GSM537425 2 0.8395 0.47887 0.096 0.548 0.356
#> GSM537333 2 0.6715 0.57199 0.028 0.660 0.312
#> GSM537342 2 0.3619 0.62838 0.000 0.864 0.136
#> GSM537347 2 0.2590 0.59854 0.004 0.924 0.072
#> GSM537350 2 0.9357 -0.52940 0.236 0.516 0.248
#> GSM537362 2 0.6208 0.50613 0.076 0.772 0.152
#> GSM537363 2 0.8915 0.16244 0.404 0.472 0.124
#> GSM537368 1 0.0237 0.82391 0.996 0.000 0.004
#> GSM537376 2 0.2165 0.62691 0.000 0.936 0.064
#> GSM537381 1 0.0747 0.82169 0.984 0.000 0.016
#> GSM537386 2 0.4702 0.45655 0.000 0.788 0.212
#> GSM537398 1 0.9730 -0.44522 0.420 0.228 0.352
#> GSM537402 2 0.2165 0.59015 0.000 0.936 0.064
#> GSM537405 1 0.1289 0.82059 0.968 0.000 0.032
#> GSM537371 1 0.0237 0.82391 0.996 0.000 0.004
#> GSM537421 2 0.5760 0.56120 0.000 0.672 0.328
#> GSM537424 1 0.9086 -0.00291 0.552 0.220 0.228
#> GSM537432 2 0.4551 0.62513 0.020 0.840 0.140
#> GSM537331 2 0.6200 0.19078 0.012 0.676 0.312
#> GSM537332 2 0.5678 0.57572 0.000 0.684 0.316
#> GSM537334 2 0.6448 0.19568 0.016 0.656 0.328
#> GSM537338 2 0.5754 0.27734 0.004 0.700 0.296
#> GSM537353 2 0.3192 0.62996 0.000 0.888 0.112
#> GSM537357 1 0.0000 0.82422 1.000 0.000 0.000
#> GSM537358 2 0.1163 0.59995 0.000 0.972 0.028
#> GSM537375 2 0.3192 0.60883 0.000 0.888 0.112
#> GSM537389 2 0.4002 0.49219 0.000 0.840 0.160
#> GSM537390 2 0.5363 0.59527 0.000 0.724 0.276
#> GSM537393 2 0.3482 0.63015 0.000 0.872 0.128
#> GSM537399 2 0.7271 -0.14372 0.040 0.608 0.352
#> GSM537407 2 0.6662 0.59263 0.052 0.716 0.232
#> GSM537408 2 0.1585 0.60389 0.008 0.964 0.028
#> GSM537428 2 0.5291 0.33683 0.000 0.732 0.268
#> GSM537354 2 0.2959 0.58139 0.000 0.900 0.100
#> GSM537410 2 0.5678 0.56888 0.000 0.684 0.316
#> GSM537413 2 0.3192 0.63051 0.000 0.888 0.112
#> GSM537396 2 0.6601 0.08800 0.028 0.676 0.296
#> GSM537397 3 0.8100 0.67767 0.068 0.420 0.512
#> GSM537330 2 0.2711 0.62395 0.000 0.912 0.088
#> GSM537369 1 0.1411 0.81962 0.964 0.000 0.036
#> GSM537373 2 0.2096 0.62719 0.004 0.944 0.052
#> GSM537401 2 0.6994 -0.13081 0.028 0.612 0.360
#> GSM537343 2 0.8808 0.36605 0.132 0.536 0.332
#> GSM537367 2 0.7513 0.52335 0.052 0.604 0.344
#> GSM537382 2 0.1163 0.62065 0.000 0.972 0.028
#> GSM537385 2 0.5254 0.32404 0.000 0.736 0.264
#> GSM537391 3 0.9914 0.56596 0.348 0.272 0.380
#> GSM537419 2 0.1411 0.59556 0.000 0.964 0.036
#> GSM537420 1 0.1643 0.81634 0.956 0.000 0.044
#> GSM537429 2 0.3502 0.55873 0.020 0.896 0.084
#> GSM537431 2 0.6818 0.54803 0.024 0.628 0.348
#> GSM537387 1 0.2772 0.79399 0.916 0.004 0.080
#> GSM537414 2 0.7410 0.51425 0.040 0.576 0.384
#> GSM537433 2 0.6962 0.55731 0.036 0.648 0.316
#> GSM537335 2 0.6934 0.08874 0.028 0.624 0.348
#> GSM537339 3 0.8228 0.69456 0.076 0.412 0.512
#> GSM537340 2 0.7001 0.53896 0.032 0.628 0.340
#> GSM537344 1 0.0892 0.82331 0.980 0.000 0.020
#> GSM537346 2 0.2796 0.62388 0.000 0.908 0.092
#> GSM537351 1 0.0592 0.82313 0.988 0.000 0.012
#> GSM537352 2 0.4291 0.49432 0.000 0.820 0.180
#> GSM537359 2 0.4452 0.45165 0.000 0.808 0.192
#> GSM537360 2 0.5621 0.57298 0.000 0.692 0.308
#> GSM537364 1 0.0424 0.82362 0.992 0.000 0.008
#> GSM537365 2 0.4575 0.62290 0.004 0.812 0.184
#> GSM537372 1 0.9515 -0.22023 0.480 0.216 0.304
#> GSM537384 1 0.6341 0.59574 0.716 0.032 0.252
#> GSM537394 2 0.1529 0.61701 0.000 0.960 0.040
#> GSM537403 2 0.5859 0.55278 0.000 0.656 0.344
#> GSM537406 2 0.3896 0.62941 0.008 0.864 0.128
#> GSM537411 2 0.3941 0.52566 0.000 0.844 0.156
#> GSM537412 2 0.5835 0.55281 0.000 0.660 0.340
#> GSM537416 2 0.5882 0.55093 0.000 0.652 0.348
#> GSM537426 2 0.5431 0.58638 0.000 0.716 0.284
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.6984 0.43337 0.144 0.268 0.004 0.584
#> GSM537345 1 0.1970 0.64969 0.932 0.000 0.008 0.060
#> GSM537355 3 0.7666 -0.34388 0.000 0.388 0.400 0.212
#> GSM537366 4 0.8994 0.04303 0.220 0.072 0.292 0.416
#> GSM537370 2 0.7423 -0.01379 0.136 0.484 0.008 0.372
#> GSM537380 2 0.1022 0.65086 0.000 0.968 0.000 0.032
#> GSM537392 2 0.1022 0.65057 0.000 0.968 0.000 0.032
#> GSM537415 2 0.4607 0.37822 0.004 0.716 0.276 0.004
#> GSM537417 3 0.3926 0.62077 0.016 0.160 0.820 0.004
#> GSM537422 3 0.6839 0.52330 0.208 0.076 0.664 0.052
#> GSM537423 2 0.1059 0.65021 0.000 0.972 0.016 0.012
#> GSM537427 2 0.7991 0.36448 0.012 0.464 0.292 0.232
#> GSM537430 2 0.4244 0.62495 0.000 0.800 0.032 0.168
#> GSM537336 1 0.0000 0.67667 1.000 0.000 0.000 0.000
#> GSM537337 2 0.7740 0.33907 0.000 0.416 0.348 0.236
#> GSM537348 4 0.4098 0.32597 0.204 0.012 0.000 0.784
#> GSM537349 2 0.0592 0.64873 0.000 0.984 0.000 0.016
#> GSM537356 4 0.7339 0.18576 0.348 0.112 0.016 0.524
#> GSM537361 1 0.7900 0.21463 0.452 0.024 0.380 0.144
#> GSM537374 4 0.7904 -0.00901 0.004 0.244 0.328 0.424
#> GSM537377 1 0.1970 0.64969 0.932 0.000 0.008 0.060
#> GSM537378 2 0.2530 0.64187 0.008 0.912 0.072 0.008
#> GSM537379 3 0.5071 0.59226 0.016 0.184 0.764 0.036
#> GSM537383 2 0.1022 0.65233 0.000 0.968 0.000 0.032
#> GSM537388 2 0.5783 0.51594 0.000 0.708 0.172 0.120
#> GSM537395 2 0.6142 0.53722 0.000 0.676 0.184 0.140
#> GSM537400 3 0.7137 0.57966 0.164 0.116 0.660 0.060
#> GSM537404 3 0.6767 0.61406 0.044 0.288 0.620 0.048
#> GSM537409 3 0.5038 0.54268 0.012 0.336 0.652 0.000
#> GSM537418 1 0.8747 0.20381 0.464 0.068 0.196 0.272
#> GSM537425 3 0.9084 0.45449 0.180 0.168 0.484 0.168
#> GSM537333 3 0.6136 0.63040 0.136 0.136 0.712 0.016
#> GSM537342 2 0.6271 0.01981 0.048 0.528 0.420 0.004
#> GSM537347 3 0.7720 -0.08858 0.012 0.284 0.512 0.192
#> GSM537350 4 0.7845 0.19984 0.320 0.280 0.000 0.400
#> GSM537362 4 0.9643 0.21103 0.240 0.144 0.252 0.364
#> GSM537363 1 0.9424 -0.03837 0.416 0.228 0.220 0.136
#> GSM537368 1 0.2149 0.68082 0.912 0.000 0.000 0.088
#> GSM537376 2 0.6681 0.38736 0.000 0.588 0.292 0.120
#> GSM537381 1 0.3161 0.67276 0.864 0.000 0.012 0.124
#> GSM537386 2 0.0188 0.64855 0.000 0.996 0.000 0.004
#> GSM537398 4 0.5530 0.14466 0.360 0.020 0.004 0.616
#> GSM537402 2 0.5994 0.56291 0.004 0.704 0.148 0.144
#> GSM537405 1 0.4252 0.57081 0.744 0.000 0.004 0.252
#> GSM537371 1 0.0000 0.67667 1.000 0.000 0.000 0.000
#> GSM537421 3 0.5772 0.62879 0.068 0.260 0.672 0.000
#> GSM537424 4 0.5244 -0.05245 0.436 0.008 0.000 0.556
#> GSM537432 3 0.7236 0.64196 0.084 0.208 0.640 0.068
#> GSM537331 2 0.9192 0.05506 0.080 0.380 0.244 0.296
#> GSM537332 2 0.5811 -0.30410 0.012 0.508 0.468 0.012
#> GSM537334 4 0.9157 0.13298 0.080 0.220 0.336 0.364
#> GSM537338 4 0.8343 -0.09377 0.016 0.284 0.324 0.376
#> GSM537353 2 0.5323 0.24437 0.008 0.592 0.396 0.004
#> GSM537357 1 0.0376 0.67322 0.992 0.000 0.004 0.004
#> GSM537358 2 0.0524 0.64874 0.000 0.988 0.008 0.004
#> GSM537375 3 0.7315 0.04053 0.004 0.232 0.556 0.208
#> GSM537389 2 0.0592 0.64873 0.000 0.984 0.000 0.016
#> GSM537390 2 0.3819 0.54212 0.004 0.816 0.172 0.008
#> GSM537393 3 0.7186 0.26316 0.012 0.384 0.504 0.100
#> GSM537399 4 0.7028 0.38284 0.148 0.304 0.000 0.548
#> GSM537407 3 0.9716 0.35602 0.196 0.272 0.356 0.176
#> GSM537408 2 0.3026 0.62331 0.056 0.900 0.032 0.012
#> GSM537428 2 0.7974 0.29403 0.004 0.404 0.328 0.264
#> GSM537354 2 0.7649 0.34690 0.000 0.456 0.312 0.232
#> GSM537410 3 0.5500 0.32041 0.016 0.464 0.520 0.000
#> GSM537413 2 0.2983 0.61169 0.004 0.880 0.108 0.008
#> GSM537396 2 0.4184 0.56885 0.100 0.836 0.008 0.056
#> GSM537397 4 0.5767 0.43795 0.152 0.136 0.000 0.712
#> GSM537330 2 0.5217 0.42934 0.000 0.608 0.380 0.012
#> GSM537369 1 0.3356 0.65026 0.824 0.000 0.000 0.176
#> GSM537373 2 0.6498 0.47406 0.056 0.712 0.132 0.100
#> GSM537401 4 0.8561 0.20359 0.140 0.352 0.068 0.440
#> GSM537343 1 0.9641 0.03390 0.324 0.128 0.292 0.256
#> GSM537367 3 0.8174 0.59014 0.088 0.208 0.568 0.136
#> GSM537382 2 0.7053 0.42882 0.008 0.588 0.264 0.140
#> GSM537385 2 0.2149 0.64460 0.000 0.912 0.000 0.088
#> GSM537391 4 0.5289 0.17721 0.344 0.020 0.000 0.636
#> GSM537419 2 0.0336 0.65013 0.000 0.992 0.008 0.000
#> GSM537420 1 0.3610 0.62942 0.800 0.000 0.000 0.200
#> GSM537429 2 0.8835 0.31030 0.104 0.492 0.236 0.168
#> GSM537431 3 0.7157 0.63740 0.096 0.160 0.664 0.080
#> GSM537387 1 0.5050 0.34264 0.588 0.004 0.000 0.408
#> GSM537414 3 0.6698 0.58846 0.156 0.100 0.692 0.052
#> GSM537433 3 0.9417 0.47082 0.144 0.252 0.416 0.188
#> GSM537335 4 0.8934 0.29616 0.096 0.148 0.336 0.420
#> GSM537339 4 0.4541 0.41481 0.144 0.060 0.000 0.796
#> GSM537340 3 0.5593 0.64859 0.080 0.212 0.708 0.000
#> GSM537344 1 0.3266 0.65540 0.832 0.000 0.000 0.168
#> GSM537346 2 0.5502 0.52222 0.012 0.652 0.320 0.016
#> GSM537351 1 0.1929 0.67966 0.940 0.000 0.024 0.036
#> GSM537352 2 0.7366 0.43646 0.000 0.524 0.252 0.224
#> GSM537359 2 0.0188 0.64855 0.000 0.996 0.000 0.004
#> GSM537360 3 0.5337 0.39712 0.012 0.424 0.564 0.000
#> GSM537364 1 0.0000 0.67667 1.000 0.000 0.000 0.000
#> GSM537365 3 0.8513 0.52603 0.064 0.324 0.464 0.148
#> GSM537372 4 0.4748 0.25654 0.268 0.016 0.000 0.716
#> GSM537384 4 0.4720 0.16202 0.324 0.004 0.000 0.672
#> GSM537394 2 0.2744 0.63530 0.024 0.912 0.052 0.012
#> GSM537403 3 0.4744 0.56293 0.012 0.284 0.704 0.000
#> GSM537406 2 0.3374 0.61338 0.028 0.880 0.080 0.012
#> GSM537411 2 0.6011 0.55373 0.000 0.688 0.132 0.180
#> GSM537412 3 0.5231 0.48965 0.012 0.384 0.604 0.000
#> GSM537416 3 0.4675 0.61465 0.020 0.244 0.736 0.000
#> GSM537426 2 0.5055 0.22844 0.008 0.624 0.368 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.3775 0.6152 0.000 0.060 0.016 0.092 0.832
#> GSM537345 1 0.2922 0.6930 0.880 0.000 0.016 0.024 0.080
#> GSM537355 4 0.4398 0.6597 0.000 0.240 0.040 0.720 0.000
#> GSM537366 5 0.6951 0.2279 0.088 0.012 0.356 0.044 0.500
#> GSM537370 5 0.6841 0.2361 0.000 0.284 0.040 0.144 0.532
#> GSM537380 2 0.0794 0.6892 0.000 0.972 0.000 0.028 0.000
#> GSM537392 2 0.0703 0.6886 0.000 0.976 0.000 0.024 0.000
#> GSM537415 2 0.4887 0.5287 0.000 0.720 0.148 0.132 0.000
#> GSM537417 3 0.5352 -0.1258 0.000 0.052 0.480 0.468 0.000
#> GSM537422 3 0.3349 0.5709 0.008 0.012 0.848 0.120 0.012
#> GSM537423 2 0.0609 0.6899 0.000 0.980 0.000 0.020 0.000
#> GSM537427 4 0.4949 0.6126 0.000 0.288 0.000 0.656 0.056
#> GSM537430 2 0.4173 0.3456 0.000 0.688 0.012 0.300 0.000
#> GSM537336 1 0.0324 0.7536 0.992 0.000 0.004 0.000 0.004
#> GSM537337 4 0.4997 0.6518 0.000 0.248 0.016 0.692 0.044
#> GSM537348 5 0.0290 0.6569 0.008 0.000 0.000 0.000 0.992
#> GSM537349 2 0.0609 0.6886 0.000 0.980 0.000 0.020 0.000
#> GSM537356 5 0.5186 0.5183 0.124 0.004 0.156 0.004 0.712
#> GSM537361 3 0.6340 0.2892 0.140 0.004 0.620 0.028 0.208
#> GSM537374 4 0.4532 0.6253 0.000 0.096 0.008 0.768 0.128
#> GSM537377 1 0.3452 0.6793 0.852 0.000 0.032 0.024 0.092
#> GSM537378 2 0.1704 0.6731 0.000 0.928 0.004 0.068 0.000
#> GSM537379 4 0.5646 0.3332 0.000 0.076 0.356 0.564 0.004
#> GSM537383 2 0.0794 0.6881 0.000 0.972 0.000 0.028 0.000
#> GSM537388 2 0.3508 0.4757 0.000 0.748 0.000 0.252 0.000
#> GSM537395 2 0.5454 -0.0968 0.000 0.532 0.064 0.404 0.000
#> GSM537400 3 0.4254 0.5507 0.004 0.048 0.792 0.144 0.012
#> GSM537404 3 0.6891 0.4864 0.000 0.088 0.584 0.208 0.120
#> GSM537409 3 0.6748 -0.0227 0.000 0.368 0.372 0.260 0.000
#> GSM537418 5 0.7103 0.1722 0.116 0.020 0.384 0.024 0.456
#> GSM537425 3 0.5885 0.3473 0.060 0.040 0.624 0.000 0.276
#> GSM537333 3 0.4231 0.5308 0.000 0.060 0.776 0.160 0.004
#> GSM537342 4 0.7244 0.3405 0.000 0.260 0.300 0.416 0.024
#> GSM537347 4 0.5263 0.6280 0.000 0.128 0.152 0.708 0.012
#> GSM537350 5 0.6501 0.5205 0.132 0.056 0.108 0.032 0.672
#> GSM537362 3 0.6649 0.3986 0.008 0.040 0.612 0.164 0.176
#> GSM537363 3 0.7864 0.1777 0.240 0.052 0.476 0.024 0.208
#> GSM537368 1 0.3963 0.7153 0.808 0.000 0.084 0.004 0.104
#> GSM537376 4 0.6244 0.5045 0.000 0.336 0.160 0.504 0.000
#> GSM537381 1 0.5152 0.6150 0.696 0.000 0.104 0.004 0.196
#> GSM537386 2 0.0404 0.6905 0.000 0.988 0.000 0.012 0.000
#> GSM537398 5 0.4007 0.6104 0.076 0.000 0.028 0.072 0.824
#> GSM537402 2 0.5728 -0.2848 0.000 0.484 0.084 0.432 0.000
#> GSM537405 5 0.5862 0.1650 0.336 0.000 0.100 0.004 0.560
#> GSM537371 1 0.0324 0.7536 0.992 0.000 0.004 0.000 0.004
#> GSM537421 3 0.6011 0.1566 0.000 0.108 0.528 0.360 0.004
#> GSM537424 5 0.3361 0.6259 0.080 0.000 0.036 0.024 0.860
#> GSM537432 3 0.4829 0.5064 0.000 0.068 0.724 0.200 0.008
#> GSM537331 4 0.5285 0.5741 0.000 0.288 0.000 0.632 0.080
#> GSM537332 2 0.5421 0.4549 0.000 0.628 0.276 0.096 0.000
#> GSM537334 4 0.4543 0.5935 0.000 0.064 0.016 0.768 0.152
#> GSM537338 4 0.4776 0.6517 0.000 0.168 0.012 0.744 0.076
#> GSM537353 4 0.6418 0.3477 0.000 0.412 0.172 0.416 0.000
#> GSM537357 1 0.0486 0.7518 0.988 0.000 0.004 0.004 0.004
#> GSM537358 2 0.1364 0.6906 0.000 0.952 0.036 0.012 0.000
#> GSM537375 4 0.4700 0.5712 0.000 0.088 0.184 0.728 0.000
#> GSM537389 2 0.0510 0.6897 0.000 0.984 0.000 0.016 0.000
#> GSM537390 2 0.2331 0.6812 0.000 0.900 0.080 0.020 0.000
#> GSM537393 4 0.5599 0.5014 0.000 0.120 0.260 0.620 0.000
#> GSM537399 5 0.7185 0.4349 0.028 0.232 0.132 0.040 0.568
#> GSM537407 3 0.6541 0.2999 0.076 0.052 0.592 0.008 0.272
#> GSM537408 2 0.2994 0.6694 0.016 0.888 0.056 0.032 0.008
#> GSM537428 4 0.4972 0.6312 0.000 0.260 0.000 0.672 0.068
#> GSM537354 4 0.5043 0.6698 0.000 0.208 0.100 0.692 0.000
#> GSM537410 2 0.6807 0.0205 0.000 0.364 0.336 0.300 0.000
#> GSM537413 2 0.3003 0.6693 0.000 0.864 0.092 0.044 0.000
#> GSM537396 2 0.6322 0.4962 0.016 0.680 0.072 0.112 0.120
#> GSM537397 5 0.0992 0.6604 0.000 0.024 0.000 0.008 0.968
#> GSM537330 4 0.5802 0.4698 0.000 0.388 0.096 0.516 0.000
#> GSM537369 1 0.5641 0.4737 0.596 0.000 0.088 0.004 0.312
#> GSM537373 2 0.8623 -0.0248 0.016 0.400 0.184 0.196 0.204
#> GSM537401 5 0.6201 0.1009 0.000 0.140 0.008 0.292 0.560
#> GSM537343 3 0.7031 0.0350 0.156 0.008 0.492 0.024 0.320
#> GSM537367 3 0.6420 0.4354 0.060 0.036 0.664 0.056 0.184
#> GSM537382 4 0.6486 0.5133 0.000 0.308 0.212 0.480 0.000
#> GSM537385 2 0.0794 0.6880 0.000 0.972 0.000 0.028 0.000
#> GSM537391 5 0.2831 0.6076 0.116 0.008 0.004 0.004 0.868
#> GSM537419 2 0.1992 0.6826 0.000 0.924 0.044 0.032 0.000
#> GSM537420 1 0.5843 0.2731 0.508 0.000 0.084 0.004 0.404
#> GSM537429 4 0.7860 0.5431 0.000 0.264 0.192 0.440 0.104
#> GSM537431 3 0.4271 0.5969 0.000 0.036 0.808 0.092 0.064
#> GSM537387 5 0.4045 0.1631 0.356 0.000 0.000 0.000 0.644
#> GSM537414 3 0.3653 0.5689 0.000 0.036 0.828 0.124 0.012
#> GSM537433 3 0.7595 0.2372 0.076 0.060 0.512 0.052 0.300
#> GSM537335 4 0.4427 0.5603 0.000 0.040 0.020 0.768 0.172
#> GSM537339 5 0.0451 0.6606 0.000 0.008 0.004 0.000 0.988
#> GSM537340 3 0.4612 0.5070 0.000 0.052 0.736 0.204 0.008
#> GSM537344 1 0.5190 0.5959 0.680 0.000 0.088 0.004 0.228
#> GSM537346 2 0.5204 0.0451 0.000 0.560 0.048 0.392 0.000
#> GSM537351 1 0.3299 0.7403 0.848 0.000 0.108 0.004 0.040
#> GSM537352 4 0.5426 0.6102 0.000 0.312 0.020 0.624 0.044
#> GSM537359 2 0.1478 0.6855 0.000 0.936 0.064 0.000 0.000
#> GSM537360 2 0.6714 0.1335 0.000 0.424 0.296 0.280 0.000
#> GSM537364 1 0.1430 0.7563 0.944 0.000 0.052 0.000 0.004
#> GSM537365 3 0.6292 0.4556 0.008 0.116 0.648 0.040 0.188
#> GSM537372 5 0.1211 0.6583 0.016 0.000 0.024 0.000 0.960
#> GSM537384 5 0.0703 0.6560 0.024 0.000 0.000 0.000 0.976
#> GSM537394 2 0.2989 0.6588 0.000 0.868 0.060 0.072 0.000
#> GSM537403 4 0.6202 0.1479 0.000 0.144 0.372 0.484 0.000
#> GSM537406 2 0.3043 0.6527 0.016 0.884 0.028 0.064 0.008
#> GSM537411 2 0.5491 -0.2554 0.000 0.492 0.052 0.452 0.004
#> GSM537412 2 0.6581 0.1443 0.000 0.452 0.324 0.224 0.000
#> GSM537416 3 0.5606 0.2315 0.000 0.088 0.568 0.344 0.000
#> GSM537426 2 0.5752 0.4331 0.000 0.620 0.172 0.208 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.3172 0.57392 0.000 0.012 0.152 0.000 0.820 0.016
#> GSM537345 1 0.2376 0.69862 0.888 0.000 0.044 0.000 0.068 0.000
#> GSM537355 6 0.3133 0.74439 0.000 0.212 0.000 0.008 0.000 0.780
#> GSM537366 3 0.4755 0.46000 0.008 0.000 0.632 0.056 0.304 0.000
#> GSM537370 5 0.5685 0.35214 0.000 0.188 0.164 0.004 0.620 0.024
#> GSM537380 2 0.0777 0.74790 0.000 0.972 0.004 0.000 0.000 0.024
#> GSM537392 2 0.0777 0.74790 0.000 0.972 0.004 0.000 0.000 0.024
#> GSM537415 2 0.4927 0.41869 0.000 0.648 0.104 0.244 0.000 0.004
#> GSM537417 4 0.4614 0.24240 0.000 0.004 0.032 0.548 0.000 0.416
#> GSM537422 4 0.1949 0.41625 0.000 0.000 0.088 0.904 0.004 0.004
#> GSM537423 2 0.1167 0.75068 0.000 0.960 0.008 0.012 0.000 0.020
#> GSM537427 6 0.3429 0.73782 0.004 0.252 0.004 0.000 0.000 0.740
#> GSM537430 6 0.4098 0.35829 0.000 0.496 0.000 0.008 0.000 0.496
#> GSM537336 1 0.0603 0.75878 0.980 0.000 0.016 0.000 0.004 0.000
#> GSM537337 6 0.3231 0.74320 0.000 0.200 0.000 0.016 0.000 0.784
#> GSM537348 5 0.0146 0.65619 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM537349 2 0.0632 0.74840 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM537356 5 0.4685 0.19085 0.040 0.000 0.388 0.004 0.568 0.000
#> GSM537361 4 0.7114 -0.36565 0.068 0.008 0.356 0.444 0.096 0.028
#> GSM537374 6 0.1637 0.65066 0.004 0.056 0.004 0.000 0.004 0.932
#> GSM537377 1 0.2951 0.67720 0.856 0.000 0.044 0.008 0.092 0.000
#> GSM537378 2 0.1858 0.73686 0.000 0.924 0.012 0.012 0.000 0.052
#> GSM537379 6 0.5011 0.15181 0.000 0.064 0.004 0.392 0.000 0.540
#> GSM537383 2 0.0891 0.74814 0.000 0.968 0.008 0.000 0.000 0.024
#> GSM537388 2 0.3747 0.04923 0.000 0.604 0.000 0.000 0.000 0.396
#> GSM537395 6 0.4245 0.71345 0.000 0.280 0.016 0.020 0.000 0.684
#> GSM537400 4 0.3676 0.45161 0.000 0.060 0.028 0.824 0.004 0.084
#> GSM537404 3 0.6451 0.26662 0.000 0.032 0.504 0.340 0.036 0.088
#> GSM537409 4 0.6201 0.26857 0.000 0.308 0.180 0.488 0.000 0.024
#> GSM537418 3 0.7224 0.32306 0.060 0.004 0.392 0.144 0.376 0.024
#> GSM537425 3 0.5917 0.61165 0.004 0.000 0.520 0.272 0.200 0.004
#> GSM537333 4 0.3066 0.45881 0.000 0.060 0.024 0.860 0.000 0.056
#> GSM537342 4 0.7929 0.36550 0.000 0.140 0.280 0.336 0.024 0.220
#> GSM537347 6 0.3886 0.64470 0.000 0.080 0.004 0.140 0.000 0.776
#> GSM537350 5 0.5776 0.06525 0.104 0.008 0.432 0.000 0.448 0.008
#> GSM537362 4 0.6242 0.16096 0.012 0.060 0.024 0.544 0.032 0.328
#> GSM537363 3 0.5300 0.55666 0.136 0.004 0.716 0.072 0.056 0.016
#> GSM537368 1 0.4147 0.70111 0.716 0.000 0.224 0.000 0.060 0.000
#> GSM537376 6 0.6426 0.58552 0.000 0.284 0.140 0.064 0.000 0.512
#> GSM537381 1 0.4827 0.63074 0.632 0.000 0.296 0.008 0.064 0.000
#> GSM537386 2 0.0976 0.75025 0.000 0.968 0.008 0.008 0.000 0.016
#> GSM537398 5 0.3614 0.59937 0.040 0.000 0.012 0.012 0.820 0.116
#> GSM537402 6 0.6093 0.49429 0.000 0.376 0.116 0.036 0.000 0.472
#> GSM537405 5 0.6064 0.00968 0.292 0.000 0.224 0.008 0.476 0.000
#> GSM537371 1 0.0603 0.75878 0.980 0.000 0.016 0.000 0.004 0.000
#> GSM537421 4 0.6730 0.45638 0.000 0.100 0.168 0.512 0.000 0.220
#> GSM537424 5 0.2645 0.62105 0.044 0.000 0.000 0.056 0.884 0.016
#> GSM537432 4 0.5464 0.42717 0.000 0.060 0.120 0.668 0.000 0.152
#> GSM537331 6 0.3194 0.71538 0.004 0.172 0.012 0.000 0.004 0.808
#> GSM537332 2 0.6279 0.43303 0.000 0.580 0.132 0.192 0.000 0.096
#> GSM537334 6 0.1419 0.59466 0.004 0.012 0.016 0.000 0.016 0.952
#> GSM537338 6 0.2828 0.72371 0.004 0.140 0.004 0.004 0.004 0.844
#> GSM537353 2 0.6592 -0.21904 0.000 0.424 0.036 0.220 0.000 0.320
#> GSM537357 1 0.0603 0.75878 0.980 0.000 0.016 0.000 0.004 0.000
#> GSM537358 2 0.1726 0.73961 0.000 0.932 0.012 0.012 0.000 0.044
#> GSM537375 6 0.3663 0.62882 0.000 0.068 0.000 0.148 0.000 0.784
#> GSM537389 2 0.0858 0.74758 0.000 0.968 0.004 0.000 0.000 0.028
#> GSM537390 2 0.3488 0.68459 0.000 0.820 0.060 0.108 0.000 0.012
#> GSM537393 6 0.4395 0.61252 0.000 0.080 0.016 0.164 0.000 0.740
#> GSM537399 5 0.7530 -0.07851 0.020 0.240 0.308 0.020 0.376 0.036
#> GSM537407 3 0.5362 0.66202 0.008 0.008 0.664 0.188 0.124 0.008
#> GSM537408 2 0.2951 0.69498 0.000 0.844 0.128 0.020 0.004 0.004
#> GSM537428 6 0.3221 0.74393 0.000 0.220 0.000 0.004 0.004 0.772
#> GSM537354 6 0.3835 0.73049 0.000 0.164 0.004 0.060 0.000 0.772
#> GSM537410 2 0.6963 -0.22924 0.000 0.348 0.244 0.348 0.000 0.060
#> GSM537413 2 0.3423 0.70460 0.000 0.828 0.084 0.076 0.000 0.012
#> GSM537396 2 0.5732 0.21861 0.000 0.528 0.372 0.012 0.060 0.028
#> GSM537397 5 0.1411 0.64387 0.000 0.004 0.060 0.000 0.936 0.000
#> GSM537330 6 0.5182 0.38590 0.000 0.428 0.016 0.052 0.000 0.504
#> GSM537369 1 0.5702 0.42865 0.512 0.000 0.196 0.000 0.292 0.000
#> GSM537373 3 0.6558 -0.06206 0.000 0.420 0.428 0.032 0.056 0.064
#> GSM537401 5 0.5157 0.47765 0.000 0.080 0.148 0.000 0.700 0.072
#> GSM537343 3 0.5861 0.61118 0.044 0.000 0.620 0.148 0.184 0.004
#> GSM537367 3 0.3787 0.59976 0.004 0.000 0.784 0.156 0.052 0.004
#> GSM537382 6 0.6666 0.57607 0.000 0.256 0.152 0.088 0.000 0.504
#> GSM537385 2 0.0937 0.74387 0.000 0.960 0.000 0.000 0.000 0.040
#> GSM537391 5 0.1124 0.64926 0.036 0.000 0.008 0.000 0.956 0.000
#> GSM537419 2 0.1949 0.73900 0.000 0.924 0.020 0.020 0.000 0.036
#> GSM537420 5 0.5896 -0.19844 0.376 0.000 0.204 0.000 0.420 0.000
#> GSM537429 6 0.6421 0.65882 0.004 0.228 0.020 0.064 0.096 0.588
#> GSM537431 4 0.4352 0.34803 0.000 0.008 0.260 0.696 0.008 0.028
#> GSM537387 5 0.3541 0.37264 0.260 0.000 0.012 0.000 0.728 0.000
#> GSM537414 4 0.2586 0.41935 0.000 0.008 0.080 0.880 0.000 0.032
#> GSM537433 3 0.4925 0.58152 0.008 0.004 0.672 0.092 0.224 0.000
#> GSM537335 6 0.2779 0.48676 0.004 0.004 0.016 0.000 0.120 0.856
#> GSM537339 5 0.1700 0.63504 0.000 0.000 0.080 0.000 0.916 0.004
#> GSM537340 4 0.4974 0.46245 0.000 0.008 0.220 0.660 0.000 0.112
#> GSM537344 1 0.5008 0.65301 0.640 0.000 0.212 0.000 0.148 0.000
#> GSM537346 2 0.5503 -0.07585 0.000 0.504 0.044 0.044 0.000 0.408
#> GSM537351 1 0.3984 0.60873 0.648 0.000 0.336 0.000 0.016 0.000
#> GSM537352 6 0.3323 0.74080 0.000 0.240 0.000 0.008 0.000 0.752
#> GSM537359 2 0.1850 0.74230 0.000 0.924 0.052 0.016 0.000 0.008
#> GSM537360 4 0.6645 0.14366 0.000 0.372 0.128 0.424 0.000 0.076
#> GSM537364 1 0.2234 0.75543 0.872 0.000 0.124 0.000 0.004 0.000
#> GSM537365 3 0.6170 0.58228 0.000 0.040 0.620 0.212 0.068 0.060
#> GSM537372 5 0.0405 0.65631 0.004 0.000 0.008 0.000 0.988 0.000
#> GSM537384 5 0.0260 0.65655 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM537394 2 0.3729 0.69186 0.000 0.820 0.052 0.032 0.004 0.092
#> GSM537403 4 0.6466 0.32732 0.000 0.056 0.140 0.468 0.000 0.336
#> GSM537406 2 0.3870 0.61376 0.000 0.764 0.192 0.032 0.008 0.004
#> GSM537411 6 0.5033 0.47663 0.000 0.424 0.036 0.020 0.000 0.520
#> GSM537412 4 0.6111 0.12532 0.000 0.372 0.184 0.432 0.000 0.012
#> GSM537416 4 0.6214 0.47281 0.000 0.036 0.204 0.536 0.000 0.224
#> GSM537426 2 0.5759 0.18253 0.000 0.520 0.124 0.340 0.000 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> SD:mclust 97 0.847 0.662 2
#> SD:mclust 68 0.611 0.841 3
#> SD:mclust 53 0.974 0.660 4
#> SD:mclust 63 0.732 0.836 5
#> SD:mclust 60 0.204 0.855 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 104 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 0.918 0.920 0.967 0.4768 0.522 0.522
#> 3 3 0.357 0.416 0.691 0.3734 0.778 0.587
#> 4 4 0.462 0.586 0.759 0.1355 0.771 0.438
#> 5 5 0.539 0.532 0.725 0.0703 0.869 0.547
#> 6 6 0.554 0.416 0.650 0.0414 0.889 0.537
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
#> GSM537341 2 0.6343 0.798 0.160 0.840
#> GSM537345 1 0.0000 0.952 1.000 0.000
#> GSM537355 2 0.0000 0.973 0.000 1.000
#> GSM537366 1 0.2043 0.932 0.968 0.032
#> GSM537370 2 0.0000 0.973 0.000 1.000
#> GSM537380 2 0.0000 0.973 0.000 1.000
#> GSM537392 2 0.0000 0.973 0.000 1.000
#> GSM537415 2 0.0000 0.973 0.000 1.000
#> GSM537417 2 0.0000 0.973 0.000 1.000
#> GSM537422 1 0.0000 0.952 1.000 0.000
#> GSM537423 2 0.0000 0.973 0.000 1.000
#> GSM537427 2 0.0000 0.973 0.000 1.000
#> GSM537430 2 0.0000 0.973 0.000 1.000
#> GSM537336 1 0.0000 0.952 1.000 0.000
#> GSM537337 2 0.0000 0.973 0.000 1.000
#> GSM537348 1 0.0000 0.952 1.000 0.000
#> GSM537349 2 0.0000 0.973 0.000 1.000
#> GSM537356 1 0.0672 0.948 0.992 0.008
#> GSM537361 1 0.0000 0.952 1.000 0.000
#> GSM537374 2 0.0000 0.973 0.000 1.000
#> GSM537377 1 0.0000 0.952 1.000 0.000
#> GSM537378 2 0.0000 0.973 0.000 1.000
#> GSM537379 2 0.0000 0.973 0.000 1.000
#> GSM537383 2 0.0000 0.973 0.000 1.000
#> GSM537388 2 0.0000 0.973 0.000 1.000
#> GSM537395 2 0.0000 0.973 0.000 1.000
#> GSM537400 1 0.0000 0.952 1.000 0.000
#> GSM537404 2 0.0672 0.966 0.008 0.992
#> GSM537409 2 0.0000 0.973 0.000 1.000
#> GSM537418 1 0.0000 0.952 1.000 0.000
#> GSM537425 1 0.0938 0.946 0.988 0.012
#> GSM537333 1 0.9866 0.282 0.568 0.432
#> GSM537342 2 0.0000 0.973 0.000 1.000
#> GSM537347 2 0.0000 0.973 0.000 1.000
#> GSM537350 1 0.0000 0.952 1.000 0.000
#> GSM537362 1 0.0000 0.952 1.000 0.000
#> GSM537363 1 0.7674 0.723 0.776 0.224
#> GSM537368 1 0.0000 0.952 1.000 0.000
#> GSM537376 2 0.0000 0.973 0.000 1.000
#> GSM537381 1 0.0000 0.952 1.000 0.000
#> GSM537386 2 0.0000 0.973 0.000 1.000
#> GSM537398 1 0.0000 0.952 1.000 0.000
#> GSM537402 2 0.0000 0.973 0.000 1.000
#> GSM537405 1 0.0000 0.952 1.000 0.000
#> GSM537371 1 0.0000 0.952 1.000 0.000
#> GSM537421 2 0.1633 0.951 0.024 0.976
#> GSM537424 1 0.0000 0.952 1.000 0.000
#> GSM537432 2 0.9922 0.144 0.448 0.552
#> GSM537331 2 0.0000 0.973 0.000 1.000
#> GSM537332 2 0.0000 0.973 0.000 1.000
#> GSM537334 2 0.0000 0.973 0.000 1.000
#> GSM537338 2 0.0000 0.973 0.000 1.000
#> GSM537353 2 0.0000 0.973 0.000 1.000
#> GSM537357 1 0.0000 0.952 1.000 0.000
#> GSM537358 2 0.0000 0.973 0.000 1.000
#> GSM537375 2 0.0000 0.973 0.000 1.000
#> GSM537389 2 0.0000 0.973 0.000 1.000
#> GSM537390 2 0.0000 0.973 0.000 1.000
#> GSM537393 2 0.0000 0.973 0.000 1.000
#> GSM537399 1 0.9710 0.337 0.600 0.400
#> GSM537407 1 0.0672 0.949 0.992 0.008
#> GSM537408 2 0.0000 0.973 0.000 1.000
#> GSM537428 2 0.0000 0.973 0.000 1.000
#> GSM537354 2 0.0000 0.973 0.000 1.000
#> GSM537410 2 0.0000 0.973 0.000 1.000
#> GSM537413 2 0.0000 0.973 0.000 1.000
#> GSM537396 2 0.0000 0.973 0.000 1.000
#> GSM537397 1 0.0938 0.946 0.988 0.012
#> GSM537330 2 0.0000 0.973 0.000 1.000
#> GSM537369 1 0.0000 0.952 1.000 0.000
#> GSM537373 2 0.0376 0.969 0.004 0.996
#> GSM537401 2 0.0672 0.966 0.008 0.992
#> GSM537343 1 0.0000 0.952 1.000 0.000
#> GSM537367 1 0.4298 0.885 0.912 0.088
#> GSM537382 2 0.0000 0.973 0.000 1.000
#> GSM537385 2 0.0000 0.973 0.000 1.000
#> GSM537391 1 0.0000 0.952 1.000 0.000
#> GSM537419 2 0.0000 0.973 0.000 1.000
#> GSM537420 1 0.0000 0.952 1.000 0.000
#> GSM537429 2 0.7453 0.724 0.212 0.788
#> GSM537431 1 0.8443 0.636 0.728 0.272
#> GSM537387 1 0.0000 0.952 1.000 0.000
#> GSM537414 1 0.3733 0.900 0.928 0.072
#> GSM537433 1 0.7528 0.732 0.784 0.216
#> GSM537335 2 0.8713 0.576 0.292 0.708
#> GSM537339 1 0.1184 0.944 0.984 0.016
#> GSM537340 2 0.9608 0.336 0.384 0.616
#> GSM537344 1 0.0000 0.952 1.000 0.000
#> GSM537346 2 0.0000 0.973 0.000 1.000
#> GSM537351 1 0.0000 0.952 1.000 0.000
#> GSM537352 2 0.0000 0.973 0.000 1.000
#> GSM537359 2 0.0000 0.973 0.000 1.000
#> GSM537360 2 0.0000 0.973 0.000 1.000
#> GSM537364 1 0.0000 0.952 1.000 0.000
#> GSM537365 2 0.4022 0.895 0.080 0.920
#> GSM537372 1 0.0000 0.952 1.000 0.000
#> GSM537384 1 0.0000 0.952 1.000 0.000
#> GSM537394 2 0.0000 0.973 0.000 1.000
#> GSM537403 2 0.0000 0.973 0.000 1.000
#> GSM537406 2 0.0000 0.973 0.000 1.000
#> GSM537411 2 0.0000 0.973 0.000 1.000
#> GSM537412 2 0.0000 0.973 0.000 1.000
#> GSM537416 2 0.0000 0.973 0.000 1.000
#> GSM537426 2 0.0000 0.973 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 3 0.6181 0.41328 0.104 0.116 0.780
#> GSM537345 1 0.4121 0.72567 0.832 0.000 0.168
#> GSM537355 2 0.1753 0.52793 0.000 0.952 0.048
#> GSM537366 1 0.5551 0.66292 0.768 0.020 0.212
#> GSM537370 3 0.3816 0.42455 0.000 0.148 0.852
#> GSM537380 3 0.5098 0.39559 0.000 0.248 0.752
#> GSM537392 3 0.5363 0.37657 0.000 0.276 0.724
#> GSM537415 2 0.5760 0.40487 0.000 0.672 0.328
#> GSM537417 2 0.1647 0.52253 0.004 0.960 0.036
#> GSM537422 1 0.7665 0.31472 0.500 0.456 0.044
#> GSM537423 2 0.6079 0.29261 0.000 0.612 0.388
#> GSM537427 2 0.6267 -0.18242 0.000 0.548 0.452
#> GSM537430 2 0.6274 -0.10177 0.000 0.544 0.456
#> GSM537336 1 0.0000 0.76888 1.000 0.000 0.000
#> GSM537337 2 0.5098 0.34701 0.000 0.752 0.248
#> GSM537348 1 0.6079 0.52675 0.612 0.000 0.388
#> GSM537349 2 0.6309 0.03405 0.000 0.504 0.496
#> GSM537356 1 0.3340 0.74007 0.880 0.000 0.120
#> GSM537361 1 0.4045 0.72946 0.872 0.104 0.024
#> GSM537374 3 0.5733 0.32252 0.000 0.324 0.676
#> GSM537377 1 0.5894 0.68115 0.752 0.028 0.220
#> GSM537378 2 0.5178 0.48150 0.000 0.744 0.256
#> GSM537379 2 0.3918 0.45191 0.004 0.856 0.140
#> GSM537383 3 0.6140 0.23339 0.000 0.404 0.596
#> GSM537388 2 0.5621 0.14164 0.000 0.692 0.308
#> GSM537395 2 0.3879 0.44285 0.000 0.848 0.152
#> GSM537400 1 0.8533 0.41590 0.536 0.360 0.104
#> GSM537404 3 0.7487 0.26916 0.040 0.408 0.552
#> GSM537409 2 0.1031 0.55518 0.000 0.976 0.024
#> GSM537418 1 0.0424 0.76897 0.992 0.000 0.008
#> GSM537425 1 0.1620 0.76797 0.964 0.024 0.012
#> GSM537333 2 0.9187 0.02931 0.196 0.532 0.272
#> GSM537342 2 0.5244 0.49537 0.004 0.756 0.240
#> GSM537347 3 0.6299 0.22512 0.000 0.476 0.524
#> GSM537350 1 0.4931 0.66253 0.768 0.000 0.232
#> GSM537362 1 0.7395 0.35973 0.492 0.032 0.476
#> GSM537363 1 0.7451 0.52228 0.636 0.060 0.304
#> GSM537368 1 0.0424 0.76909 0.992 0.000 0.008
#> GSM537376 2 0.5621 0.44425 0.000 0.692 0.308
#> GSM537381 1 0.0424 0.76892 0.992 0.000 0.008
#> GSM537386 3 0.5327 0.37400 0.000 0.272 0.728
#> GSM537398 1 0.7585 0.35362 0.484 0.040 0.476
#> GSM537402 2 0.6309 0.07924 0.000 0.504 0.496
#> GSM537405 1 0.0237 0.76907 0.996 0.000 0.004
#> GSM537371 1 0.1163 0.76791 0.972 0.000 0.028
#> GSM537421 2 0.4233 0.55371 0.004 0.836 0.160
#> GSM537424 1 0.5253 0.70893 0.792 0.020 0.188
#> GSM537432 2 0.9768 -0.07683 0.264 0.440 0.296
#> GSM537331 3 0.6111 0.29259 0.000 0.396 0.604
#> GSM537332 2 0.2860 0.56578 0.004 0.912 0.084
#> GSM537334 3 0.6228 0.25866 0.004 0.372 0.624
#> GSM537338 3 0.5968 0.29255 0.000 0.364 0.636
#> GSM537353 2 0.4702 0.52693 0.000 0.788 0.212
#> GSM537357 1 0.1031 0.76828 0.976 0.000 0.024
#> GSM537358 3 0.6305 0.00922 0.000 0.484 0.516
#> GSM537375 3 0.6299 0.12989 0.000 0.476 0.524
#> GSM537389 3 0.6309 -0.11582 0.000 0.496 0.504
#> GSM537390 2 0.5058 0.50446 0.000 0.756 0.244
#> GSM537393 2 0.2959 0.49675 0.000 0.900 0.100
#> GSM537399 1 0.7919 0.12136 0.480 0.056 0.464
#> GSM537407 1 0.5928 0.58709 0.696 0.008 0.296
#> GSM537408 3 0.6192 0.07423 0.000 0.420 0.580
#> GSM537428 3 0.6225 0.27054 0.000 0.432 0.568
#> GSM537354 2 0.5733 0.21965 0.000 0.676 0.324
#> GSM537410 2 0.5690 0.44434 0.004 0.708 0.288
#> GSM537413 2 0.5621 0.42824 0.000 0.692 0.308
#> GSM537396 3 0.6427 0.23912 0.012 0.348 0.640
#> GSM537397 3 0.5948 -0.04518 0.360 0.000 0.640
#> GSM537330 2 0.4796 0.30448 0.000 0.780 0.220
#> GSM537369 1 0.0892 0.76759 0.980 0.000 0.020
#> GSM537373 2 0.6398 0.27233 0.004 0.580 0.416
#> GSM537401 3 0.2774 0.41886 0.008 0.072 0.920
#> GSM537343 1 0.4605 0.68562 0.796 0.000 0.204
#> GSM537367 1 0.9806 0.14384 0.420 0.328 0.252
#> GSM537382 2 0.2261 0.56474 0.000 0.932 0.068
#> GSM537385 3 0.6267 0.13263 0.000 0.452 0.548
#> GSM537391 1 0.6111 0.52198 0.604 0.000 0.396
#> GSM537419 3 0.6267 0.01213 0.000 0.452 0.548
#> GSM537420 1 0.1163 0.76623 0.972 0.000 0.028
#> GSM537429 2 0.6180 0.24013 0.024 0.716 0.260
#> GSM537431 1 0.7528 0.53838 0.648 0.280 0.072
#> GSM537387 1 0.3412 0.74275 0.876 0.000 0.124
#> GSM537414 1 0.7534 0.35027 0.532 0.428 0.040
#> GSM537433 1 0.9724 0.22452 0.452 0.268 0.280
#> GSM537335 3 0.6794 0.28867 0.028 0.324 0.648
#> GSM537339 3 0.6470 -0.01809 0.356 0.012 0.632
#> GSM537340 2 0.8576 0.35261 0.160 0.600 0.240
#> GSM537344 1 0.0592 0.76856 0.988 0.000 0.012
#> GSM537346 2 0.5905 0.03565 0.000 0.648 0.352
#> GSM537351 1 0.0237 0.76867 0.996 0.000 0.004
#> GSM537352 2 0.1163 0.53931 0.000 0.972 0.028
#> GSM537359 3 0.5465 0.35154 0.000 0.288 0.712
#> GSM537360 2 0.4931 0.51235 0.000 0.768 0.232
#> GSM537364 1 0.0592 0.76904 0.988 0.000 0.012
#> GSM537365 3 0.8504 0.29329 0.216 0.172 0.612
#> GSM537372 1 0.4555 0.71380 0.800 0.000 0.200
#> GSM537384 1 0.2537 0.75777 0.920 0.000 0.080
#> GSM537394 3 0.5497 0.35276 0.000 0.292 0.708
#> GSM537403 2 0.0424 0.54819 0.000 0.992 0.008
#> GSM537406 2 0.6180 0.28322 0.000 0.584 0.416
#> GSM537411 3 0.5733 0.33161 0.000 0.324 0.676
#> GSM537412 2 0.4931 0.50245 0.000 0.768 0.232
#> GSM537416 2 0.2301 0.56464 0.004 0.936 0.060
#> GSM537426 2 0.3267 0.56136 0.000 0.884 0.116
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.5725 0.6037 0.160 0.748 0.044 0.048
#> GSM537345 1 0.5838 0.2134 0.524 0.000 0.032 0.444
#> GSM537355 3 0.5573 0.6059 0.000 0.052 0.676 0.272
#> GSM537366 1 0.3031 0.7733 0.896 0.072 0.016 0.016
#> GSM537370 2 0.3171 0.7100 0.016 0.876 0.004 0.104
#> GSM537380 2 0.1557 0.7441 0.000 0.944 0.000 0.056
#> GSM537392 2 0.1474 0.7471 0.000 0.948 0.000 0.052
#> GSM537415 3 0.4967 0.2534 0.000 0.452 0.548 0.000
#> GSM537417 3 0.4748 0.6113 0.000 0.016 0.716 0.268
#> GSM537422 3 0.5724 0.5777 0.144 0.000 0.716 0.140
#> GSM537423 2 0.4307 0.6243 0.000 0.784 0.192 0.024
#> GSM537427 4 0.5873 0.2322 0.000 0.416 0.036 0.548
#> GSM537430 2 0.5833 0.5543 0.000 0.692 0.096 0.212
#> GSM537336 1 0.2227 0.7823 0.928 0.000 0.036 0.036
#> GSM537337 4 0.5906 0.2875 0.000 0.064 0.292 0.644
#> GSM537348 4 0.5323 0.3628 0.352 0.020 0.000 0.628
#> GSM537349 2 0.3108 0.7382 0.000 0.872 0.112 0.016
#> GSM537356 1 0.3266 0.7623 0.868 0.108 0.000 0.024
#> GSM537361 1 0.5841 0.5561 0.692 0.004 0.076 0.228
#> GSM537374 4 0.3831 0.6359 0.000 0.204 0.004 0.792
#> GSM537377 4 0.5228 0.3503 0.312 0.000 0.024 0.664
#> GSM537378 2 0.5925 -0.0396 0.000 0.512 0.452 0.036
#> GSM537379 3 0.5695 0.5289 0.000 0.040 0.624 0.336
#> GSM537383 2 0.2699 0.7423 0.000 0.904 0.028 0.068
#> GSM537388 2 0.7554 0.1823 0.000 0.488 0.268 0.244
#> GSM537395 3 0.7592 0.4547 0.000 0.268 0.480 0.252
#> GSM537400 3 0.7393 0.4565 0.116 0.044 0.612 0.228
#> GSM537404 2 0.7854 0.5078 0.148 0.612 0.100 0.140
#> GSM537409 3 0.2586 0.7030 0.000 0.048 0.912 0.040
#> GSM537418 1 0.1721 0.7895 0.952 0.008 0.012 0.028
#> GSM537425 1 0.3198 0.7699 0.884 0.004 0.080 0.032
#> GSM537333 3 0.5156 0.5496 0.012 0.012 0.696 0.280
#> GSM537342 3 0.3016 0.6852 0.004 0.120 0.872 0.004
#> GSM537347 4 0.6230 0.5067 0.004 0.256 0.088 0.652
#> GSM537350 1 0.4920 0.4581 0.628 0.368 0.000 0.004
#> GSM537362 4 0.4461 0.5902 0.156 0.012 0.028 0.804
#> GSM537363 1 0.5241 0.6864 0.760 0.068 0.164 0.008
#> GSM537368 1 0.2335 0.7792 0.920 0.000 0.020 0.060
#> GSM537376 3 0.6136 0.4675 0.000 0.356 0.584 0.060
#> GSM537381 1 0.0524 0.7875 0.988 0.008 0.000 0.004
#> GSM537386 2 0.2669 0.7479 0.004 0.912 0.052 0.032
#> GSM537398 4 0.3264 0.6610 0.096 0.024 0.004 0.876
#> GSM537402 2 0.6039 0.4021 0.000 0.596 0.348 0.056
#> GSM537405 1 0.2256 0.7803 0.924 0.000 0.020 0.056
#> GSM537371 1 0.3485 0.7500 0.856 0.000 0.028 0.116
#> GSM537421 3 0.3093 0.6988 0.004 0.092 0.884 0.020
#> GSM537424 1 0.5320 0.2890 0.572 0.000 0.012 0.416
#> GSM537432 3 0.6955 0.4773 0.072 0.044 0.632 0.252
#> GSM537331 4 0.4776 0.5590 0.000 0.272 0.016 0.712
#> GSM537332 3 0.6242 0.6180 0.020 0.168 0.704 0.108
#> GSM537334 4 0.3168 0.6634 0.000 0.056 0.060 0.884
#> GSM537338 4 0.2988 0.6782 0.000 0.112 0.012 0.876
#> GSM537353 3 0.5496 0.4544 0.000 0.372 0.604 0.024
#> GSM537357 1 0.3056 0.7697 0.888 0.000 0.040 0.072
#> GSM537358 2 0.2002 0.7518 0.000 0.936 0.020 0.044
#> GSM537375 4 0.3383 0.6350 0.000 0.052 0.076 0.872
#> GSM537389 2 0.2675 0.7307 0.000 0.892 0.100 0.008
#> GSM537390 2 0.5807 0.3367 0.000 0.596 0.364 0.040
#> GSM537393 3 0.5744 0.6168 0.000 0.068 0.676 0.256
#> GSM537399 1 0.7062 0.0929 0.468 0.448 0.032 0.052
#> GSM537407 1 0.5726 0.6744 0.728 0.196 0.052 0.024
#> GSM537408 2 0.1543 0.7441 0.032 0.956 0.008 0.004
#> GSM537428 4 0.4882 0.5599 0.000 0.272 0.020 0.708
#> GSM537354 4 0.5298 0.4154 0.000 0.048 0.244 0.708
#> GSM537410 3 0.3539 0.6632 0.000 0.176 0.820 0.004
#> GSM537413 3 0.4889 0.4265 0.000 0.360 0.636 0.004
#> GSM537396 2 0.3160 0.7263 0.060 0.892 0.040 0.008
#> GSM537397 2 0.6756 0.3545 0.188 0.612 0.000 0.200
#> GSM537330 3 0.7394 0.4096 0.000 0.244 0.520 0.236
#> GSM537369 1 0.0469 0.7871 0.988 0.012 0.000 0.000
#> GSM537373 2 0.6071 0.5484 0.084 0.684 0.224 0.008
#> GSM537401 2 0.5451 0.6296 0.044 0.748 0.024 0.184
#> GSM537343 1 0.3508 0.7498 0.848 0.136 0.004 0.012
#> GSM537367 1 0.5998 0.6082 0.684 0.056 0.244 0.016
#> GSM537382 3 0.3813 0.6904 0.000 0.148 0.828 0.024
#> GSM537385 2 0.2759 0.7513 0.000 0.904 0.044 0.052
#> GSM537391 4 0.5879 0.3077 0.368 0.028 0.008 0.596
#> GSM537419 2 0.1820 0.7534 0.000 0.944 0.036 0.020
#> GSM537420 1 0.0992 0.7867 0.976 0.012 0.004 0.008
#> GSM537429 3 0.7363 0.4770 0.012 0.192 0.576 0.220
#> GSM537431 3 0.5798 0.5055 0.220 0.040 0.712 0.028
#> GSM537387 1 0.4587 0.6824 0.776 0.004 0.028 0.192
#> GSM537414 3 0.7200 0.4515 0.196 0.004 0.572 0.228
#> GSM537433 1 0.4686 0.7124 0.780 0.184 0.020 0.016
#> GSM537335 4 0.2862 0.6794 0.012 0.076 0.012 0.900
#> GSM537339 4 0.7412 0.2903 0.152 0.360 0.004 0.484
#> GSM537340 3 0.5424 0.6635 0.028 0.180 0.752 0.040
#> GSM537344 1 0.0000 0.7872 1.000 0.000 0.000 0.000
#> GSM537346 2 0.7767 0.3154 0.020 0.536 0.192 0.252
#> GSM537351 1 0.1833 0.7855 0.944 0.000 0.032 0.024
#> GSM537352 3 0.6504 0.6448 0.000 0.148 0.636 0.216
#> GSM537359 2 0.0469 0.7497 0.000 0.988 0.000 0.012
#> GSM537360 3 0.5671 0.3938 0.000 0.400 0.572 0.028
#> GSM537364 1 0.2722 0.7750 0.904 0.000 0.032 0.064
#> GSM537365 1 0.7312 0.2002 0.476 0.420 0.076 0.028
#> GSM537372 1 0.3812 0.7414 0.832 0.140 0.000 0.028
#> GSM537384 1 0.2216 0.7745 0.908 0.000 0.000 0.092
#> GSM537394 2 0.2443 0.7425 0.008 0.924 0.044 0.024
#> GSM537403 3 0.3820 0.6994 0.000 0.064 0.848 0.088
#> GSM537406 2 0.3791 0.6223 0.000 0.796 0.200 0.004
#> GSM537411 2 0.5288 0.5763 0.000 0.720 0.056 0.224
#> GSM537412 3 0.2469 0.6904 0.000 0.108 0.892 0.000
#> GSM537416 3 0.1975 0.6994 0.000 0.048 0.936 0.016
#> GSM537426 3 0.2918 0.6907 0.000 0.116 0.876 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.6001 0.6095 0.172 0.684 0.008 0.076 0.060
#> GSM537345 5 0.5104 0.3457 0.308 0.000 0.000 0.060 0.632
#> GSM537355 3 0.4996 0.3628 0.000 0.020 0.688 0.256 0.036
#> GSM537366 1 0.3551 0.7813 0.868 0.036 0.040 0.028 0.028
#> GSM537370 2 0.2733 0.7330 0.016 0.888 0.016 0.000 0.080
#> GSM537380 2 0.1921 0.7362 0.000 0.932 0.012 0.012 0.044
#> GSM537392 2 0.2251 0.7331 0.000 0.916 0.024 0.008 0.052
#> GSM537415 4 0.5380 0.4078 0.000 0.360 0.036 0.588 0.016
#> GSM537417 3 0.2171 0.6136 0.000 0.000 0.912 0.064 0.024
#> GSM537422 4 0.7684 0.0442 0.176 0.000 0.360 0.388 0.076
#> GSM537423 2 0.3960 0.6880 0.000 0.824 0.032 0.100 0.044
#> GSM537427 5 0.6732 0.2778 0.000 0.320 0.228 0.004 0.448
#> GSM537430 3 0.6194 0.1752 0.000 0.420 0.480 0.020 0.080
#> GSM537336 1 0.3416 0.7777 0.840 0.000 0.000 0.072 0.088
#> GSM537337 5 0.7508 0.3212 0.000 0.056 0.224 0.268 0.452
#> GSM537348 5 0.4827 0.5204 0.292 0.008 0.024 0.004 0.672
#> GSM537349 2 0.3556 0.6995 0.004 0.824 0.004 0.144 0.024
#> GSM537356 1 0.3518 0.7831 0.856 0.064 0.044 0.000 0.036
#> GSM537361 3 0.4608 0.2873 0.336 0.000 0.640 0.000 0.024
#> GSM537374 5 0.4592 0.6029 0.000 0.140 0.100 0.004 0.756
#> GSM537377 5 0.3691 0.5960 0.164 0.000 0.004 0.028 0.804
#> GSM537378 2 0.7324 -0.0455 0.000 0.436 0.224 0.304 0.036
#> GSM537379 3 0.1399 0.6160 0.000 0.000 0.952 0.020 0.028
#> GSM537383 2 0.3572 0.6799 0.000 0.832 0.120 0.008 0.040
#> GSM537388 3 0.6902 -0.0380 0.000 0.420 0.424 0.112 0.044
#> GSM537395 3 0.7471 0.1536 0.000 0.204 0.476 0.256 0.064
#> GSM537400 3 0.6402 0.4182 0.064 0.004 0.644 0.180 0.108
#> GSM537404 3 0.6872 0.3930 0.200 0.196 0.564 0.004 0.036
#> GSM537409 4 0.4275 0.4971 0.000 0.020 0.284 0.696 0.000
#> GSM537418 1 0.2555 0.8135 0.908 0.004 0.016 0.024 0.048
#> GSM537425 1 0.5138 0.7504 0.768 0.028 0.112 0.060 0.032
#> GSM537333 3 0.3492 0.5134 0.000 0.000 0.796 0.188 0.016
#> GSM537342 4 0.2304 0.6465 0.000 0.068 0.020 0.908 0.004
#> GSM537347 3 0.1960 0.6066 0.004 0.020 0.928 0.000 0.048
#> GSM537350 2 0.5372 0.1413 0.460 0.500 0.004 0.008 0.028
#> GSM537362 5 0.3110 0.6270 0.112 0.000 0.028 0.004 0.856
#> GSM537363 4 0.6036 -0.1634 0.460 0.032 0.008 0.468 0.032
#> GSM537368 1 0.2450 0.7984 0.896 0.000 0.000 0.028 0.076
#> GSM537376 4 0.4832 0.5926 0.000 0.200 0.000 0.712 0.088
#> GSM537381 1 0.1787 0.8041 0.936 0.004 0.044 0.000 0.016
#> GSM537386 2 0.3798 0.7199 0.004 0.840 0.088 0.040 0.028
#> GSM537398 5 0.4226 0.6266 0.060 0.000 0.176 0.000 0.764
#> GSM537402 4 0.5178 0.1987 0.000 0.404 0.012 0.560 0.024
#> GSM537405 1 0.2811 0.7986 0.876 0.000 0.012 0.012 0.100
#> GSM537371 1 0.3037 0.7872 0.860 0.000 0.000 0.040 0.100
#> GSM537421 4 0.2390 0.6300 0.000 0.032 0.044 0.912 0.012
#> GSM537424 1 0.5689 0.5272 0.616 0.000 0.248 0.000 0.136
#> GSM537432 4 0.7289 -0.0136 0.028 0.036 0.408 0.432 0.096
#> GSM537331 5 0.5797 0.4990 0.000 0.132 0.276 0.000 0.592
#> GSM537332 3 0.1924 0.6262 0.000 0.008 0.924 0.064 0.004
#> GSM537334 5 0.4674 0.3829 0.000 0.016 0.416 0.000 0.568
#> GSM537338 5 0.3929 0.6115 0.000 0.028 0.208 0.000 0.764
#> GSM537353 4 0.7278 0.3450 0.000 0.336 0.188 0.436 0.040
#> GSM537357 1 0.3704 0.7687 0.820 0.000 0.000 0.088 0.092
#> GSM537358 2 0.3120 0.7122 0.000 0.864 0.084 0.004 0.048
#> GSM537375 5 0.5891 0.5813 0.000 0.052 0.132 0.132 0.684
#> GSM537389 2 0.3002 0.7075 0.000 0.856 0.000 0.116 0.028
#> GSM537390 3 0.6472 0.1883 0.000 0.396 0.488 0.076 0.040
#> GSM537393 3 0.6322 0.4249 0.000 0.068 0.636 0.200 0.096
#> GSM537399 1 0.6711 0.0390 0.448 0.104 0.412 0.000 0.036
#> GSM537407 1 0.5544 0.6923 0.728 0.084 0.136 0.012 0.040
#> GSM537408 2 0.1565 0.7393 0.016 0.952 0.004 0.008 0.020
#> GSM537428 3 0.6312 -0.1421 0.000 0.156 0.452 0.000 0.392
#> GSM537354 5 0.6868 0.4023 0.000 0.068 0.108 0.272 0.552
#> GSM537410 4 0.3405 0.6346 0.004 0.136 0.008 0.836 0.016
#> GSM537413 4 0.4883 0.3459 0.000 0.372 0.024 0.600 0.004
#> GSM537396 2 0.4979 0.6642 0.100 0.768 0.008 0.092 0.032
#> GSM537397 2 0.6070 0.4295 0.256 0.592 0.008 0.000 0.144
#> GSM537330 3 0.1644 0.6278 0.000 0.008 0.940 0.048 0.004
#> GSM537369 1 0.0324 0.8062 0.992 0.004 0.000 0.000 0.004
#> GSM537373 2 0.6344 0.4772 0.092 0.608 0.008 0.260 0.032
#> GSM537401 2 0.6638 0.5386 0.092 0.608 0.008 0.060 0.232
#> GSM537343 1 0.3763 0.7494 0.812 0.152 0.004 0.008 0.024
#> GSM537367 1 0.6498 0.2596 0.512 0.040 0.020 0.388 0.040
#> GSM537382 4 0.3724 0.6439 0.000 0.068 0.052 0.844 0.036
#> GSM537385 2 0.3952 0.7059 0.000 0.812 0.024 0.132 0.032
#> GSM537391 5 0.4620 0.5769 0.236 0.028 0.000 0.016 0.720
#> GSM537419 2 0.1997 0.7356 0.000 0.924 0.000 0.040 0.036
#> GSM537420 1 0.1334 0.8057 0.960 0.020 0.004 0.012 0.004
#> GSM537429 3 0.2845 0.6133 0.000 0.020 0.876 0.096 0.008
#> GSM537431 4 0.6729 -0.0277 0.056 0.008 0.420 0.460 0.056
#> GSM537387 1 0.5099 0.4672 0.612 0.000 0.000 0.052 0.336
#> GSM537414 3 0.1686 0.6181 0.028 0.000 0.944 0.020 0.008
#> GSM537433 1 0.4671 0.7429 0.784 0.100 0.088 0.008 0.020
#> GSM537335 5 0.4324 0.6016 0.012 0.020 0.232 0.000 0.736
#> GSM537339 5 0.7210 0.4477 0.188 0.204 0.052 0.008 0.548
#> GSM537340 4 0.4714 0.6080 0.004 0.140 0.028 0.772 0.056
#> GSM537344 1 0.0324 0.8063 0.992 0.004 0.000 0.004 0.000
#> GSM537346 3 0.2193 0.6057 0.000 0.060 0.912 0.000 0.028
#> GSM537351 1 0.3727 0.7820 0.832 0.000 0.012 0.060 0.096
#> GSM537352 4 0.7333 0.3943 0.000 0.152 0.228 0.528 0.092
#> GSM537359 2 0.0912 0.7400 0.000 0.972 0.000 0.016 0.012
#> GSM537360 4 0.6614 0.4531 0.000 0.316 0.100 0.540 0.044
#> GSM537364 1 0.3533 0.7817 0.836 0.000 0.004 0.056 0.104
#> GSM537365 3 0.7184 0.2327 0.328 0.120 0.500 0.024 0.028
#> GSM537372 1 0.2582 0.7910 0.892 0.080 0.004 0.000 0.024
#> GSM537384 1 0.1270 0.8029 0.948 0.000 0.000 0.000 0.052
#> GSM537394 2 0.4164 0.5568 0.008 0.748 0.228 0.008 0.008
#> GSM537403 4 0.3730 0.6004 0.000 0.028 0.168 0.800 0.004
#> GSM537406 2 0.4095 0.6229 0.004 0.764 0.008 0.208 0.016
#> GSM537411 2 0.6746 0.4088 0.000 0.584 0.116 0.068 0.232
#> GSM537412 4 0.2588 0.6436 0.000 0.060 0.048 0.892 0.000
#> GSM537416 4 0.2873 0.6021 0.000 0.000 0.120 0.860 0.020
#> GSM537426 4 0.3317 0.6379 0.000 0.056 0.088 0.852 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 4 0.7504 0.15808 0.348 0.192 0.008 0.372 0.052 0.028
#> GSM537345 5 0.6165 0.01474 0.248 0.000 0.004 0.004 0.460 0.284
#> GSM537355 3 0.5100 0.36950 0.000 0.008 0.576 0.364 0.024 0.028
#> GSM537366 1 0.4731 0.51714 0.704 0.004 0.032 0.228 0.020 0.012
#> GSM537370 2 0.1592 0.67472 0.004 0.944 0.000 0.012 0.024 0.016
#> GSM537380 2 0.1148 0.67308 0.000 0.960 0.000 0.004 0.020 0.016
#> GSM537392 2 0.0725 0.67360 0.000 0.976 0.000 0.000 0.012 0.012
#> GSM537415 4 0.4926 0.38783 0.000 0.188 0.016 0.700 0.008 0.088
#> GSM537417 3 0.3823 0.64130 0.004 0.016 0.820 0.092 0.012 0.056
#> GSM537422 6 0.7976 0.24537 0.120 0.000 0.272 0.124 0.076 0.408
#> GSM537423 2 0.3155 0.62587 0.000 0.828 0.004 0.140 0.004 0.024
#> GSM537427 2 0.5156 0.40943 0.000 0.616 0.064 0.016 0.300 0.004
#> GSM537430 2 0.5996 0.30454 0.000 0.512 0.372 0.060 0.040 0.016
#> GSM537336 1 0.4970 0.56955 0.652 0.000 0.004 0.008 0.080 0.256
#> GSM537337 5 0.8147 0.17505 0.000 0.112 0.132 0.116 0.420 0.220
#> GSM537348 5 0.5981 0.08087 0.436 0.000 0.036 0.052 0.456 0.020
#> GSM537349 4 0.4300 0.18925 0.000 0.456 0.000 0.528 0.004 0.012
#> GSM537356 1 0.3194 0.64914 0.868 0.012 0.048 0.048 0.008 0.016
#> GSM537361 3 0.3124 0.58719 0.164 0.000 0.816 0.004 0.004 0.012
#> GSM537374 5 0.4807 0.43747 0.000 0.228 0.076 0.016 0.680 0.000
#> GSM537377 5 0.4266 0.37426 0.088 0.000 0.004 0.000 0.736 0.172
#> GSM537378 2 0.6752 0.13928 0.000 0.420 0.116 0.384 0.008 0.072
#> GSM537379 3 0.2980 0.66783 0.000 0.028 0.876 0.020 0.020 0.056
#> GSM537383 2 0.2201 0.67370 0.000 0.912 0.024 0.048 0.012 0.004
#> GSM537388 4 0.6702 0.32979 0.000 0.196 0.236 0.512 0.036 0.020
#> GSM537395 2 0.7101 0.37775 0.000 0.508 0.200 0.180 0.020 0.092
#> GSM537400 6 0.5386 0.31767 0.036 0.012 0.288 0.000 0.044 0.620
#> GSM537404 3 0.7093 0.12446 0.396 0.088 0.420 0.044 0.020 0.032
#> GSM537409 4 0.5192 0.23652 0.000 0.004 0.132 0.640 0.004 0.220
#> GSM537418 1 0.4716 0.66790 0.768 0.000 0.040 0.072 0.032 0.088
#> GSM537425 1 0.5830 0.59408 0.668 0.024 0.100 0.016 0.024 0.168
#> GSM537333 3 0.5002 0.32704 0.000 0.004 0.640 0.076 0.008 0.272
#> GSM537342 4 0.3543 0.29939 0.000 0.004 0.004 0.720 0.000 0.272
#> GSM537347 3 0.1912 0.66467 0.008 0.012 0.928 0.004 0.044 0.004
#> GSM537350 1 0.5497 0.18133 0.528 0.396 0.008 0.044 0.016 0.008
#> GSM537362 5 0.3618 0.50825 0.044 0.000 0.088 0.000 0.824 0.044
#> GSM537363 4 0.6113 0.18538 0.328 0.004 0.004 0.508 0.016 0.140
#> GSM537368 1 0.4464 0.62379 0.728 0.000 0.004 0.004 0.096 0.168
#> GSM537376 6 0.6444 0.22922 0.000 0.316 0.004 0.164 0.036 0.480
#> GSM537381 1 0.2001 0.67675 0.920 0.000 0.044 0.016 0.000 0.020
#> GSM537386 2 0.5611 0.50731 0.020 0.700 0.088 0.140 0.020 0.032
#> GSM537398 5 0.3460 0.50143 0.020 0.000 0.220 0.000 0.760 0.000
#> GSM537402 4 0.4596 0.44183 0.000 0.128 0.000 0.728 0.016 0.128
#> GSM537405 1 0.5278 0.58767 0.664 0.000 0.028 0.004 0.096 0.208
#> GSM537371 1 0.4719 0.59182 0.680 0.000 0.004 0.000 0.100 0.216
#> GSM537421 6 0.4545 0.12001 0.000 0.016 0.008 0.404 0.004 0.568
#> GSM537424 1 0.5585 0.16928 0.484 0.000 0.404 0.000 0.100 0.012
#> GSM537432 6 0.4933 0.42231 0.012 0.040 0.220 0.016 0.012 0.700
#> GSM537331 5 0.4593 0.49894 0.000 0.084 0.208 0.000 0.700 0.008
#> GSM537332 3 0.2437 0.68285 0.000 0.008 0.896 0.068 0.008 0.020
#> GSM537334 5 0.4063 0.28670 0.000 0.004 0.420 0.000 0.572 0.004
#> GSM537338 5 0.3551 0.53033 0.000 0.060 0.148 0.000 0.792 0.000
#> GSM537353 2 0.6347 0.39761 0.000 0.580 0.064 0.192 0.008 0.156
#> GSM537357 1 0.5107 0.53784 0.616 0.000 0.004 0.004 0.088 0.288
#> GSM537358 2 0.1611 0.67858 0.000 0.944 0.024 0.012 0.012 0.008
#> GSM537375 5 0.6798 0.33338 0.000 0.112 0.100 0.028 0.564 0.196
#> GSM537389 2 0.4418 0.07590 0.000 0.548 0.004 0.432 0.008 0.008
#> GSM537390 2 0.6574 0.31872 0.000 0.476 0.316 0.160 0.008 0.040
#> GSM537393 3 0.7705 0.23727 0.000 0.184 0.480 0.120 0.064 0.152
#> GSM537399 1 0.6168 0.15407 0.516 0.060 0.364 0.032 0.008 0.020
#> GSM537407 1 0.6570 0.50791 0.624 0.064 0.104 0.020 0.028 0.160
#> GSM537408 2 0.1592 0.66617 0.004 0.944 0.000 0.016 0.012 0.024
#> GSM537428 5 0.5785 0.13268 0.000 0.152 0.416 0.000 0.428 0.004
#> GSM537354 5 0.7763 0.05013 0.000 0.176 0.036 0.112 0.404 0.272
#> GSM537410 4 0.2417 0.43160 0.000 0.012 0.004 0.888 0.008 0.088
#> GSM537413 4 0.6742 0.15810 0.000 0.348 0.016 0.384 0.016 0.236
#> GSM537396 4 0.6940 0.18642 0.232 0.336 0.008 0.392 0.016 0.016
#> GSM537397 2 0.6558 -0.01256 0.396 0.428 0.004 0.024 0.132 0.016
#> GSM537330 3 0.3454 0.64606 0.000 0.008 0.804 0.160 0.004 0.024
#> GSM537369 1 0.0798 0.67722 0.976 0.000 0.004 0.004 0.012 0.004
#> GSM537373 4 0.5245 0.43272 0.144 0.140 0.004 0.688 0.012 0.012
#> GSM537401 5 0.7972 0.13317 0.176 0.216 0.008 0.196 0.388 0.016
#> GSM537343 1 0.3869 0.66555 0.828 0.072 0.008 0.012 0.032 0.048
#> GSM537367 1 0.5556 0.40852 0.608 0.004 0.012 0.292 0.024 0.060
#> GSM537382 6 0.4649 0.34599 0.000 0.036 0.008 0.256 0.016 0.684
#> GSM537385 4 0.4317 0.38783 0.016 0.336 0.000 0.636 0.000 0.012
#> GSM537391 5 0.4419 0.46719 0.172 0.004 0.004 0.016 0.748 0.056
#> GSM537419 2 0.2420 0.65962 0.012 0.900 0.000 0.060 0.008 0.020
#> GSM537420 1 0.2689 0.67231 0.888 0.000 0.008 0.056 0.016 0.032
#> GSM537429 3 0.3980 0.63694 0.004 0.008 0.788 0.148 0.012 0.040
#> GSM537431 6 0.5982 0.44412 0.036 0.056 0.168 0.048 0.020 0.672
#> GSM537387 1 0.6336 0.37703 0.452 0.000 0.008 0.008 0.228 0.304
#> GSM537414 3 0.1261 0.67998 0.024 0.000 0.952 0.000 0.000 0.024
#> GSM537433 1 0.3776 0.66483 0.832 0.020 0.088 0.012 0.020 0.028
#> GSM537335 5 0.3820 0.41164 0.000 0.004 0.332 0.000 0.660 0.004
#> GSM537339 5 0.6999 0.21516 0.364 0.036 0.052 0.072 0.460 0.016
#> GSM537340 6 0.5655 0.40623 0.000 0.140 0.008 0.176 0.032 0.644
#> GSM537344 1 0.1204 0.67955 0.960 0.000 0.004 0.004 0.016 0.016
#> GSM537346 3 0.2849 0.66527 0.008 0.072 0.880 0.008 0.020 0.012
#> GSM537351 1 0.5464 0.43212 0.524 0.000 0.012 0.000 0.092 0.372
#> GSM537352 6 0.7426 0.10626 0.000 0.336 0.052 0.176 0.044 0.392
#> GSM537359 2 0.3389 0.61110 0.004 0.844 0.008 0.020 0.028 0.096
#> GSM537360 4 0.6317 0.21017 0.000 0.284 0.036 0.536 0.012 0.132
#> GSM537364 1 0.5516 0.47845 0.556 0.000 0.016 0.000 0.100 0.328
#> GSM537365 3 0.7238 0.34415 0.188 0.152 0.524 0.004 0.028 0.104
#> GSM537372 1 0.2542 0.66482 0.900 0.044 0.004 0.024 0.004 0.024
#> GSM537384 1 0.3711 0.63781 0.832 0.000 0.032 0.032 0.080 0.024
#> GSM537394 2 0.2350 0.67154 0.000 0.900 0.068 0.008 0.008 0.016
#> GSM537403 4 0.5649 -0.03184 0.000 0.008 0.104 0.492 0.004 0.392
#> GSM537406 4 0.4860 0.40686 0.036 0.288 0.004 0.652 0.012 0.008
#> GSM537411 2 0.5056 0.58609 0.000 0.728 0.064 0.028 0.144 0.036
#> GSM537412 4 0.3441 0.39480 0.000 0.012 0.024 0.812 0.004 0.148
#> GSM537416 4 0.5117 -0.00769 0.000 0.004 0.068 0.480 0.000 0.448
#> GSM537426 4 0.4346 0.31937 0.000 0.016 0.024 0.712 0.008 0.240
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> SD:NMF 100 0.230 0.553 2
#> SD:NMF 43 0.155 0.233 3
#> SD:NMF 75 0.353 0.498 4
#> SD:NMF 67 0.467 0.388 5
#> SD:NMF 42 0.641 0.730 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 104 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 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 0.323 0.774 0.868 0.3249 0.751 0.751
#> 3 3 0.109 0.528 0.617 0.6558 0.827 0.774
#> 4 4 0.199 0.355 0.620 0.1827 0.771 0.632
#> 5 5 0.194 0.385 0.616 0.0796 0.886 0.737
#> 6 6 0.271 0.419 0.602 0.0646 0.892 0.706
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
#> GSM537341 2 0.969 0.428 0.396 0.604
#> GSM537345 1 0.141 0.801 0.980 0.020
#> GSM537355 2 0.278 0.865 0.048 0.952
#> GSM537366 2 0.921 0.548 0.336 0.664
#> GSM537370 2 0.706 0.794 0.192 0.808
#> GSM537380 2 0.184 0.862 0.028 0.972
#> GSM537392 2 0.184 0.862 0.028 0.972
#> GSM537415 2 0.118 0.861 0.016 0.984
#> GSM537417 2 0.767 0.736 0.224 0.776
#> GSM537422 2 0.788 0.713 0.236 0.764
#> GSM537423 2 0.141 0.861 0.020 0.980
#> GSM537427 2 0.184 0.865 0.028 0.972
#> GSM537430 2 0.163 0.864 0.024 0.976
#> GSM537336 1 0.343 0.820 0.936 0.064
#> GSM537337 2 0.358 0.860 0.068 0.932
#> GSM537348 2 0.971 0.416 0.400 0.600
#> GSM537349 2 0.141 0.862 0.020 0.980
#> GSM537356 2 0.946 0.516 0.364 0.636
#> GSM537361 2 0.866 0.645 0.288 0.712
#> GSM537374 2 0.518 0.846 0.116 0.884
#> GSM537377 1 0.141 0.801 0.980 0.020
#> GSM537378 2 0.118 0.861 0.016 0.984
#> GSM537379 2 0.541 0.844 0.124 0.876
#> GSM537383 2 0.141 0.861 0.020 0.980
#> GSM537388 2 0.311 0.865 0.056 0.944
#> GSM537395 2 0.343 0.861 0.064 0.936
#> GSM537400 2 0.634 0.821 0.160 0.840
#> GSM537404 2 0.808 0.694 0.248 0.752
#> GSM537409 2 0.141 0.854 0.020 0.980
#> GSM537418 2 0.973 0.352 0.404 0.596
#> GSM537425 2 0.781 0.710 0.232 0.768
#> GSM537333 2 0.574 0.839 0.136 0.864
#> GSM537342 2 0.242 0.864 0.040 0.960
#> GSM537347 2 0.506 0.850 0.112 0.888
#> GSM537350 2 0.625 0.813 0.156 0.844
#> GSM537362 1 0.932 0.491 0.652 0.348
#> GSM537363 2 0.563 0.828 0.132 0.868
#> GSM537368 1 0.373 0.821 0.928 0.072
#> GSM537376 2 0.416 0.859 0.084 0.916
#> GSM537381 2 0.980 0.288 0.416 0.584
#> GSM537386 2 0.141 0.860 0.020 0.980
#> GSM537398 2 0.996 0.212 0.464 0.536
#> GSM537402 2 0.224 0.868 0.036 0.964
#> GSM537405 1 0.827 0.702 0.740 0.260
#> GSM537371 1 0.358 0.821 0.932 0.068
#> GSM537421 2 0.327 0.852 0.060 0.940
#> GSM537424 2 0.506 0.850 0.112 0.888
#> GSM537432 2 0.242 0.864 0.040 0.960
#> GSM537331 2 0.443 0.854 0.092 0.908
#> GSM537332 2 0.118 0.863 0.016 0.984
#> GSM537334 2 0.767 0.755 0.224 0.776
#> GSM537338 2 0.388 0.857 0.076 0.924
#> GSM537353 2 0.224 0.864 0.036 0.964
#> GSM537357 1 0.343 0.820 0.936 0.064
#> GSM537358 2 0.163 0.864 0.024 0.976
#> GSM537375 2 0.278 0.866 0.048 0.952
#> GSM537389 2 0.141 0.862 0.020 0.980
#> GSM537390 2 0.141 0.860 0.020 0.980
#> GSM537393 2 0.260 0.865 0.044 0.956
#> GSM537399 2 0.634 0.811 0.160 0.840
#> GSM537407 2 0.745 0.746 0.212 0.788
#> GSM537408 2 0.204 0.866 0.032 0.968
#> GSM537428 2 0.373 0.863 0.072 0.928
#> GSM537354 2 0.343 0.861 0.064 0.936
#> GSM537410 2 0.242 0.864 0.040 0.960
#> GSM537413 2 0.141 0.854 0.020 0.980
#> GSM537396 2 0.242 0.866 0.040 0.960
#> GSM537397 2 0.767 0.761 0.224 0.776
#> GSM537330 2 0.358 0.865 0.068 0.932
#> GSM537369 1 0.697 0.791 0.812 0.188
#> GSM537373 2 0.295 0.865 0.052 0.948
#> GSM537401 2 0.969 0.429 0.396 0.604
#> GSM537343 2 0.644 0.799 0.164 0.836
#> GSM537367 2 0.745 0.747 0.212 0.788
#> GSM537382 2 0.506 0.850 0.112 0.888
#> GSM537385 2 0.295 0.866 0.052 0.948
#> GSM537391 1 0.971 0.362 0.600 0.400
#> GSM537419 2 0.118 0.861 0.016 0.984
#> GSM537420 1 0.697 0.791 0.812 0.188
#> GSM537429 2 0.402 0.865 0.080 0.920
#> GSM537431 2 0.469 0.849 0.100 0.900
#> GSM537387 1 0.971 0.362 0.600 0.400
#> GSM537414 2 0.760 0.742 0.220 0.780
#> GSM537433 2 0.844 0.653 0.272 0.728
#> GSM537335 2 0.767 0.755 0.224 0.776
#> GSM537339 2 0.971 0.419 0.400 0.600
#> GSM537340 2 0.358 0.861 0.068 0.932
#> GSM537344 1 0.697 0.791 0.812 0.188
#> GSM537346 2 0.494 0.849 0.108 0.892
#> GSM537351 1 0.671 0.794 0.824 0.176
#> GSM537352 2 0.373 0.862 0.072 0.928
#> GSM537359 2 0.141 0.854 0.020 0.980
#> GSM537360 2 0.163 0.865 0.024 0.976
#> GSM537364 1 0.343 0.820 0.936 0.064
#> GSM537365 2 0.430 0.861 0.088 0.912
#> GSM537372 2 0.936 0.543 0.352 0.648
#> GSM537384 2 0.969 0.432 0.396 0.604
#> GSM537394 2 0.141 0.863 0.020 0.980
#> GSM537403 2 0.430 0.854 0.088 0.912
#> GSM537406 2 0.184 0.864 0.028 0.972
#> GSM537411 2 0.260 0.865 0.044 0.956
#> GSM537412 2 0.141 0.854 0.020 0.980
#> GSM537416 2 0.204 0.859 0.032 0.968
#> GSM537426 2 0.141 0.854 0.020 0.980
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.552 0.5664 0.728 0.268 0.004
#> GSM537345 1 0.651 -0.7721 0.520 0.004 0.476
#> GSM537355 2 0.667 0.5096 0.276 0.688 0.036
#> GSM537366 2 0.947 0.3092 0.276 0.496 0.228
#> GSM537370 2 0.820 0.2087 0.400 0.524 0.076
#> GSM537380 2 0.492 0.6889 0.072 0.844 0.084
#> GSM537392 2 0.492 0.6902 0.072 0.844 0.084
#> GSM537415 2 0.331 0.7054 0.028 0.908 0.064
#> GSM537417 2 0.825 0.5325 0.100 0.588 0.312
#> GSM537422 2 0.855 0.5009 0.116 0.560 0.324
#> GSM537423 2 0.292 0.7034 0.032 0.924 0.044
#> GSM537427 2 0.400 0.7044 0.060 0.884 0.056
#> GSM537430 2 0.446 0.7106 0.080 0.864 0.056
#> GSM537336 3 0.623 0.8545 0.436 0.000 0.564
#> GSM537337 2 0.711 0.5378 0.260 0.680 0.060
#> GSM537348 1 0.569 0.5692 0.724 0.268 0.008
#> GSM537349 2 0.380 0.6976 0.052 0.892 0.056
#> GSM537356 1 0.691 0.4943 0.656 0.308 0.036
#> GSM537361 2 0.905 0.4295 0.164 0.532 0.304
#> GSM537374 2 0.722 0.5031 0.296 0.652 0.052
#> GSM537377 1 0.651 -0.7721 0.520 0.004 0.476
#> GSM537378 2 0.331 0.7054 0.028 0.908 0.064
#> GSM537379 2 0.734 0.6713 0.140 0.708 0.152
#> GSM537383 2 0.400 0.6953 0.060 0.884 0.056
#> GSM537388 2 0.691 0.4485 0.308 0.656 0.036
#> GSM537395 2 0.738 0.5552 0.252 0.672 0.076
#> GSM537400 2 0.839 0.6023 0.156 0.620 0.224
#> GSM537404 2 0.870 0.4872 0.144 0.572 0.284
#> GSM537409 2 0.544 0.6382 0.004 0.736 0.260
#> GSM537418 2 0.976 0.1503 0.312 0.436 0.252
#> GSM537425 2 0.846 0.5298 0.132 0.596 0.272
#> GSM537333 2 0.797 0.6316 0.116 0.644 0.240
#> GSM537342 2 0.471 0.7118 0.044 0.848 0.108
#> GSM537347 2 0.681 0.6311 0.220 0.716 0.064
#> GSM537350 2 0.619 0.6546 0.176 0.764 0.060
#> GSM537362 1 0.903 -0.0116 0.556 0.200 0.244
#> GSM537363 2 0.753 0.6439 0.084 0.664 0.252
#> GSM537368 3 0.676 0.8520 0.436 0.012 0.552
#> GSM537376 2 0.721 0.6778 0.128 0.716 0.156
#> GSM537381 2 0.978 0.1234 0.324 0.428 0.248
#> GSM537386 2 0.507 0.7105 0.052 0.832 0.116
#> GSM537398 1 0.654 0.5400 0.732 0.212 0.056
#> GSM537402 2 0.602 0.6851 0.140 0.784 0.076
#> GSM537405 3 0.939 0.4887 0.392 0.172 0.436
#> GSM537371 3 0.663 0.8532 0.440 0.008 0.552
#> GSM537421 2 0.663 0.6241 0.036 0.692 0.272
#> GSM537424 2 0.681 0.6311 0.220 0.716 0.064
#> GSM537432 2 0.557 0.7039 0.108 0.812 0.080
#> GSM537331 2 0.737 0.2424 0.400 0.564 0.036
#> GSM537332 2 0.511 0.7076 0.036 0.820 0.144
#> GSM537334 1 0.748 0.0605 0.504 0.460 0.036
#> GSM537338 2 0.704 0.5508 0.252 0.688 0.060
#> GSM537353 2 0.573 0.7039 0.108 0.804 0.088
#> GSM537357 3 0.623 0.8545 0.436 0.000 0.564
#> GSM537358 2 0.419 0.7048 0.056 0.876 0.068
#> GSM537375 2 0.673 0.6981 0.132 0.748 0.120
#> GSM537389 2 0.369 0.6987 0.048 0.896 0.056
#> GSM537390 2 0.336 0.7088 0.016 0.900 0.084
#> GSM537393 2 0.552 0.6948 0.120 0.812 0.068
#> GSM537399 2 0.684 0.6557 0.180 0.732 0.088
#> GSM537407 2 0.823 0.5589 0.144 0.632 0.224
#> GSM537408 2 0.438 0.7084 0.064 0.868 0.068
#> GSM537428 2 0.629 0.6130 0.236 0.728 0.036
#> GSM537354 2 0.738 0.5552 0.252 0.672 0.076
#> GSM537410 2 0.471 0.7118 0.044 0.848 0.108
#> GSM537413 2 0.584 0.5772 0.004 0.688 0.308
#> GSM537396 2 0.437 0.7106 0.076 0.868 0.056
#> GSM537397 2 0.791 0.0622 0.448 0.496 0.056
#> GSM537330 2 0.742 0.5053 0.288 0.648 0.064
#> GSM537369 1 0.634 -0.4024 0.672 0.016 0.312
#> GSM537373 2 0.509 0.7110 0.072 0.836 0.092
#> GSM537401 1 0.573 0.5642 0.720 0.272 0.008
#> GSM537343 2 0.780 0.6076 0.112 0.660 0.228
#> GSM537367 2 0.823 0.5710 0.136 0.628 0.236
#> GSM537382 2 0.772 0.6517 0.164 0.680 0.156
#> GSM537385 2 0.706 0.5039 0.276 0.672 0.052
#> GSM537391 1 0.558 0.3444 0.812 0.104 0.084
#> GSM537419 2 0.397 0.7093 0.044 0.884 0.072
#> GSM537420 1 0.634 -0.4024 0.672 0.016 0.312
#> GSM537429 2 0.764 0.4770 0.296 0.632 0.072
#> GSM537431 2 0.723 0.5267 0.036 0.600 0.364
#> GSM537387 1 0.558 0.3444 0.812 0.104 0.084
#> GSM537414 2 0.849 0.5297 0.128 0.588 0.284
#> GSM537433 2 0.885 0.4575 0.156 0.560 0.284
#> GSM537335 1 0.748 0.0605 0.504 0.460 0.036
#> GSM537339 1 0.548 0.5702 0.732 0.264 0.004
#> GSM537340 2 0.659 0.6715 0.056 0.728 0.216
#> GSM537344 1 0.634 -0.4024 0.672 0.016 0.312
#> GSM537346 2 0.589 0.7050 0.096 0.796 0.108
#> GSM537351 3 0.823 0.7193 0.384 0.080 0.536
#> GSM537352 2 0.730 0.5696 0.252 0.676 0.072
#> GSM537359 2 0.598 0.6422 0.028 0.744 0.228
#> GSM537360 2 0.489 0.7177 0.060 0.844 0.096
#> GSM537364 3 0.639 0.8517 0.412 0.004 0.584
#> GSM537365 2 0.653 0.7001 0.124 0.760 0.116
#> GSM537372 1 0.674 0.4800 0.656 0.316 0.028
#> GSM537384 1 0.663 0.5559 0.692 0.272 0.036
#> GSM537394 2 0.457 0.7169 0.068 0.860 0.072
#> GSM537403 2 0.645 0.6840 0.056 0.740 0.204
#> GSM537406 2 0.428 0.7097 0.056 0.872 0.072
#> GSM537411 2 0.603 0.7034 0.116 0.788 0.096
#> GSM537412 2 0.546 0.6034 0.000 0.712 0.288
#> GSM537416 2 0.613 0.5990 0.016 0.700 0.284
#> GSM537426 2 0.546 0.6034 0.000 0.712 0.288
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.519 0.7766 0.036 0.208 0.012 0.744
#> GSM537345 1 0.468 0.6589 0.768 0.000 0.040 0.192
#> GSM537355 2 0.561 0.3077 0.000 0.652 0.044 0.304
#> GSM537366 2 0.986 -0.2161 0.184 0.320 0.236 0.260
#> GSM537370 2 0.764 -0.1620 0.056 0.448 0.064 0.432
#> GSM537380 2 0.455 0.4458 0.000 0.804 0.092 0.104
#> GSM537392 2 0.455 0.4436 0.000 0.804 0.092 0.104
#> GSM537415 2 0.346 0.4390 0.000 0.864 0.096 0.040
#> GSM537417 3 0.886 0.4259 0.168 0.348 0.408 0.076
#> GSM537422 3 0.897 0.4444 0.184 0.320 0.416 0.080
#> GSM537423 2 0.316 0.4709 0.000 0.884 0.064 0.052
#> GSM537427 2 0.373 0.4873 0.004 0.860 0.076 0.060
#> GSM537430 2 0.436 0.4791 0.000 0.816 0.084 0.100
#> GSM537336 1 0.272 0.7091 0.904 0.000 0.032 0.064
#> GSM537337 2 0.667 0.3711 0.004 0.604 0.108 0.284
#> GSM537348 4 0.524 0.7768 0.040 0.204 0.012 0.744
#> GSM537349 2 0.309 0.4614 0.000 0.888 0.056 0.056
#> GSM537356 4 0.650 0.7313 0.052 0.236 0.044 0.668
#> GSM537361 3 0.955 0.3869 0.208 0.304 0.356 0.132
#> GSM537374 2 0.687 0.2787 0.004 0.552 0.104 0.340
#> GSM537377 1 0.468 0.6589 0.768 0.000 0.040 0.192
#> GSM537378 2 0.346 0.4390 0.000 0.864 0.096 0.040
#> GSM537379 2 0.808 0.0977 0.048 0.536 0.260 0.156
#> GSM537383 2 0.355 0.4692 0.000 0.864 0.068 0.068
#> GSM537388 2 0.563 0.2139 0.000 0.624 0.036 0.340
#> GSM537395 2 0.684 0.3882 0.004 0.592 0.124 0.280
#> GSM537400 3 0.885 0.3488 0.072 0.356 0.400 0.172
#> GSM537404 2 0.926 -0.3528 0.204 0.368 0.332 0.096
#> GSM537409 3 0.615 0.3794 0.000 0.464 0.488 0.048
#> GSM537418 1 0.994 -0.3583 0.304 0.244 0.240 0.212
#> GSM537425 2 0.897 -0.3745 0.176 0.388 0.356 0.080
#> GSM537333 3 0.819 0.4074 0.052 0.356 0.468 0.124
#> GSM537342 2 0.561 0.2929 0.004 0.692 0.252 0.052
#> GSM537347 2 0.702 0.4177 0.024 0.632 0.128 0.216
#> GSM537350 2 0.688 0.3875 0.056 0.672 0.088 0.184
#> GSM537362 1 0.859 0.1649 0.400 0.084 0.116 0.400
#> GSM537363 3 0.807 0.4490 0.088 0.344 0.496 0.072
#> GSM537368 1 0.209 0.7123 0.928 0.004 0.004 0.064
#> GSM537376 2 0.781 0.1100 0.024 0.528 0.280 0.168
#> GSM537381 3 0.999 0.2900 0.248 0.244 0.268 0.240
#> GSM537386 2 0.533 0.3977 0.000 0.740 0.172 0.088
#> GSM537398 4 0.598 0.7117 0.108 0.164 0.012 0.716
#> GSM537402 2 0.576 0.4795 0.004 0.720 0.108 0.168
#> GSM537405 1 0.635 0.5162 0.728 0.108 0.092 0.072
#> GSM537371 1 0.190 0.7121 0.932 0.000 0.004 0.064
#> GSM537421 3 0.695 0.4351 0.020 0.404 0.512 0.064
#> GSM537424 2 0.702 0.4177 0.024 0.632 0.128 0.216
#> GSM537432 2 0.635 0.3498 0.004 0.668 0.192 0.136
#> GSM537331 2 0.590 -0.0756 0.000 0.532 0.036 0.432
#> GSM537332 2 0.613 0.1394 0.008 0.644 0.288 0.060
#> GSM537334 4 0.679 0.4351 0.012 0.396 0.068 0.524
#> GSM537338 2 0.662 0.3904 0.004 0.612 0.108 0.276
#> GSM537353 2 0.635 0.3340 0.000 0.652 0.208 0.140
#> GSM537357 1 0.272 0.7091 0.904 0.000 0.032 0.064
#> GSM537358 2 0.411 0.4624 0.000 0.832 0.084 0.084
#> GSM537375 2 0.691 0.3182 0.000 0.584 0.252 0.164
#> GSM537389 2 0.301 0.4611 0.000 0.892 0.056 0.052
#> GSM537390 2 0.390 0.4121 0.000 0.832 0.132 0.036
#> GSM537393 2 0.634 0.4080 0.004 0.672 0.172 0.152
#> GSM537399 2 0.766 0.3254 0.060 0.608 0.136 0.196
#> GSM537407 2 0.897 -0.1789 0.148 0.468 0.264 0.120
#> GSM537408 2 0.455 0.4444 0.000 0.804 0.104 0.092
#> GSM537428 2 0.594 0.4496 0.000 0.664 0.080 0.256
#> GSM537354 2 0.684 0.3882 0.004 0.592 0.124 0.280
#> GSM537410 2 0.561 0.2929 0.004 0.692 0.252 0.052
#> GSM537413 2 0.673 -0.1450 0.000 0.496 0.412 0.092
#> GSM537396 2 0.466 0.4427 0.000 0.796 0.112 0.092
#> GSM537397 4 0.733 0.2571 0.048 0.424 0.052 0.476
#> GSM537330 2 0.634 0.3029 0.000 0.608 0.088 0.304
#> GSM537369 1 0.675 0.4807 0.512 0.016 0.056 0.416
#> GSM537373 2 0.596 0.3271 0.004 0.688 0.220 0.088
#> GSM537401 4 0.531 0.7761 0.040 0.212 0.012 0.736
#> GSM537343 2 0.880 -0.1142 0.120 0.488 0.264 0.128
#> GSM537367 2 0.889 -0.3459 0.140 0.412 0.352 0.096
#> GSM537382 2 0.821 0.0426 0.036 0.492 0.288 0.184
#> GSM537385 2 0.561 0.2981 0.000 0.652 0.044 0.304
#> GSM537391 4 0.633 0.3941 0.196 0.076 0.032 0.696
#> GSM537419 2 0.360 0.4688 0.000 0.860 0.084 0.056
#> GSM537420 1 0.675 0.4807 0.512 0.016 0.056 0.416
#> GSM537429 2 0.691 0.2280 0.008 0.564 0.100 0.328
#> GSM537431 3 0.706 0.3579 0.040 0.272 0.612 0.076
#> GSM537387 4 0.633 0.3941 0.196 0.076 0.032 0.696
#> GSM537414 3 0.931 0.3985 0.164 0.344 0.368 0.124
#> GSM537433 2 0.933 -0.3253 0.216 0.368 0.316 0.100
#> GSM537335 4 0.679 0.4351 0.012 0.396 0.068 0.524
#> GSM537339 4 0.527 0.7767 0.040 0.208 0.012 0.740
#> GSM537340 3 0.750 0.3933 0.040 0.436 0.452 0.072
#> GSM537344 1 0.675 0.4807 0.512 0.016 0.056 0.416
#> GSM537346 2 0.657 0.3672 0.044 0.700 0.144 0.112
#> GSM537351 1 0.437 0.6282 0.800 0.008 0.168 0.024
#> GSM537352 2 0.737 0.3683 0.012 0.560 0.156 0.272
#> GSM537359 2 0.675 0.0476 0.000 0.560 0.328 0.112
#> GSM537360 2 0.553 0.3690 0.000 0.712 0.212 0.076
#> GSM537364 1 0.194 0.7049 0.936 0.000 0.052 0.012
#> GSM537365 2 0.740 0.2223 0.044 0.616 0.216 0.124
#> GSM537372 4 0.630 0.7274 0.044 0.240 0.040 0.676
#> GSM537384 4 0.615 0.7679 0.064 0.208 0.028 0.700
#> GSM537394 2 0.478 0.4314 0.000 0.788 0.116 0.096
#> GSM537403 2 0.741 -0.1800 0.060 0.504 0.388 0.048
#> GSM537406 2 0.443 0.4390 0.000 0.808 0.124 0.068
#> GSM537411 2 0.633 0.3660 0.004 0.672 0.176 0.148
#> GSM537412 3 0.616 0.4212 0.000 0.416 0.532 0.052
#> GSM537416 3 0.641 0.4384 0.008 0.392 0.548 0.052
#> GSM537426 3 0.616 0.4212 0.000 0.416 0.532 0.052
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.349 0.6003 0.000 0.188 0.000 0.016 0.796
#> GSM537345 1 0.481 0.6850 0.740 0.000 0.056 0.020 0.184
#> GSM537355 2 0.562 0.4085 0.000 0.612 0.012 0.072 0.304
#> GSM537366 4 0.871 0.3372 0.112 0.256 0.024 0.344 0.264
#> GSM537370 2 0.700 0.0712 0.016 0.428 0.020 0.120 0.416
#> GSM537380 2 0.439 0.5230 0.000 0.804 0.052 0.060 0.084
#> GSM537392 2 0.432 0.5219 0.000 0.808 0.048 0.060 0.084
#> GSM537415 2 0.344 0.5252 0.000 0.856 0.040 0.080 0.024
#> GSM537417 4 0.679 0.5015 0.100 0.220 0.040 0.612 0.028
#> GSM537422 4 0.683 0.4872 0.112 0.196 0.040 0.620 0.032
#> GSM537423 2 0.315 0.5511 0.000 0.876 0.028 0.056 0.040
#> GSM537427 2 0.352 0.5665 0.000 0.844 0.008 0.084 0.064
#> GSM537430 2 0.459 0.5566 0.000 0.768 0.012 0.128 0.092
#> GSM537336 1 0.378 0.7619 0.840 0.000 0.040 0.044 0.076
#> GSM537337 2 0.643 0.4491 0.000 0.548 0.012 0.164 0.276
#> GSM537348 5 0.361 0.6019 0.004 0.184 0.000 0.016 0.796
#> GSM537349 2 0.218 0.5438 0.000 0.924 0.020 0.024 0.032
#> GSM537356 5 0.506 0.5344 0.012 0.212 0.004 0.060 0.712
#> GSM537361 4 0.782 0.4791 0.128 0.200 0.040 0.544 0.088
#> GSM537374 2 0.691 0.3789 0.004 0.512 0.036 0.128 0.320
#> GSM537377 1 0.481 0.6850 0.740 0.000 0.056 0.020 0.184
#> GSM537378 2 0.344 0.5252 0.000 0.856 0.040 0.080 0.024
#> GSM537379 2 0.736 0.0847 0.028 0.440 0.032 0.388 0.112
#> GSM537383 2 0.300 0.5473 0.000 0.884 0.028 0.036 0.052
#> GSM537388 2 0.554 0.3484 0.000 0.592 0.012 0.056 0.340
#> GSM537395 2 0.668 0.4452 0.000 0.532 0.020 0.180 0.268
#> GSM537400 4 0.797 0.3991 0.032 0.228 0.100 0.512 0.128
#> GSM537404 4 0.773 0.4652 0.136 0.272 0.024 0.500 0.068
#> GSM537409 4 0.664 0.2228 0.000 0.332 0.236 0.432 0.000
#> GSM537418 4 0.925 0.3524 0.248 0.164 0.052 0.328 0.208
#> GSM537425 4 0.812 0.4754 0.100 0.260 0.076 0.496 0.068
#> GSM537333 4 0.754 0.4059 0.020 0.208 0.132 0.552 0.088
#> GSM537342 2 0.538 0.3631 0.000 0.656 0.048 0.272 0.024
#> GSM537347 2 0.655 0.4341 0.012 0.576 0.008 0.188 0.216
#> GSM537350 2 0.642 0.4394 0.028 0.652 0.024 0.140 0.156
#> GSM537362 5 0.831 -0.1773 0.364 0.052 0.072 0.124 0.388
#> GSM537363 4 0.766 0.3629 0.060 0.228 0.124 0.544 0.044
#> GSM537368 1 0.296 0.7938 0.884 0.004 0.008 0.044 0.060
#> GSM537376 2 0.766 0.1407 0.008 0.432 0.072 0.348 0.140
#> GSM537381 4 0.920 0.3726 0.148 0.160 0.076 0.380 0.236
#> GSM537386 2 0.552 0.4760 0.000 0.720 0.128 0.092 0.060
#> GSM537398 5 0.538 0.5946 0.060 0.144 0.016 0.040 0.740
#> GSM537402 2 0.548 0.5463 0.000 0.712 0.036 0.112 0.140
#> GSM537405 1 0.606 0.5258 0.676 0.080 0.016 0.188 0.040
#> GSM537371 1 0.280 0.7940 0.888 0.000 0.008 0.044 0.060
#> GSM537421 4 0.729 0.0668 0.004 0.260 0.228 0.476 0.032
#> GSM537424 2 0.655 0.4341 0.012 0.576 0.008 0.188 0.216
#> GSM537432 2 0.643 0.4212 0.000 0.608 0.048 0.228 0.116
#> GSM537331 2 0.550 0.1602 0.000 0.520 0.012 0.040 0.428
#> GSM537332 2 0.605 0.1389 0.000 0.536 0.056 0.376 0.032
#> GSM537334 5 0.655 0.2069 0.004 0.368 0.036 0.080 0.512
#> GSM537338 2 0.639 0.4591 0.000 0.556 0.012 0.164 0.268
#> GSM537353 2 0.656 0.4093 0.000 0.592 0.052 0.240 0.116
#> GSM537357 1 0.378 0.7619 0.840 0.000 0.040 0.044 0.076
#> GSM537358 2 0.412 0.5415 0.000 0.816 0.032 0.096 0.056
#> GSM537375 2 0.727 0.3606 0.000 0.512 0.072 0.264 0.152
#> GSM537389 2 0.210 0.5436 0.000 0.928 0.020 0.024 0.028
#> GSM537390 2 0.393 0.5043 0.000 0.816 0.048 0.120 0.016
#> GSM537393 2 0.631 0.4791 0.000 0.604 0.028 0.232 0.136
#> GSM537399 2 0.724 0.3419 0.028 0.560 0.032 0.196 0.184
#> GSM537407 2 0.827 -0.2643 0.092 0.388 0.048 0.372 0.100
#> GSM537408 2 0.448 0.5202 0.000 0.788 0.040 0.124 0.048
#> GSM537428 2 0.549 0.5186 0.000 0.644 0.004 0.100 0.252
#> GSM537354 2 0.668 0.4452 0.000 0.532 0.020 0.180 0.268
#> GSM537410 2 0.538 0.3631 0.000 0.656 0.048 0.272 0.024
#> GSM537413 3 0.573 0.4892 0.000 0.364 0.560 0.064 0.012
#> GSM537396 2 0.446 0.5101 0.000 0.792 0.040 0.116 0.052
#> GSM537397 5 0.662 0.0275 0.012 0.404 0.016 0.096 0.472
#> GSM537330 2 0.636 0.4104 0.000 0.564 0.020 0.128 0.288
#> GSM537369 5 0.774 -0.1892 0.340 0.012 0.188 0.048 0.412
#> GSM537373 2 0.584 0.3853 0.000 0.648 0.048 0.244 0.060
#> GSM537401 5 0.362 0.5987 0.000 0.192 0.000 0.020 0.788
#> GSM537343 2 0.839 -0.1550 0.076 0.432 0.088 0.316 0.088
#> GSM537367 4 0.725 0.4434 0.088 0.300 0.020 0.528 0.064
#> GSM537382 2 0.774 0.0523 0.012 0.400 0.060 0.372 0.156
#> GSM537385 2 0.531 0.4258 0.000 0.648 0.020 0.044 0.288
#> GSM537391 5 0.526 0.4597 0.096 0.056 0.064 0.020 0.764
#> GSM537419 2 0.378 0.5516 0.000 0.828 0.020 0.112 0.040
#> GSM537420 5 0.774 -0.1892 0.340 0.012 0.188 0.048 0.412
#> GSM537429 2 0.684 0.3351 0.000 0.512 0.036 0.140 0.312
#> GSM537431 3 0.667 0.4236 0.020 0.108 0.532 0.328 0.012
#> GSM537387 5 0.526 0.4597 0.096 0.056 0.064 0.020 0.764
#> GSM537414 4 0.775 0.4789 0.112 0.236 0.044 0.536 0.072
#> GSM537433 4 0.803 0.4493 0.144 0.272 0.032 0.476 0.076
#> GSM537335 5 0.655 0.2069 0.004 0.368 0.036 0.080 0.512
#> GSM537339 5 0.365 0.6011 0.004 0.188 0.000 0.016 0.792
#> GSM537340 4 0.735 0.2798 0.012 0.280 0.176 0.496 0.036
#> GSM537344 5 0.774 -0.1892 0.340 0.012 0.188 0.048 0.412
#> GSM537346 2 0.610 0.4140 0.012 0.628 0.016 0.248 0.096
#> GSM537351 1 0.480 0.6598 0.720 0.000 0.060 0.212 0.008
#> GSM537352 2 0.693 0.4125 0.004 0.504 0.016 0.216 0.260
#> GSM537359 2 0.681 -0.4320 0.000 0.452 0.408 0.076 0.064
#> GSM537360 2 0.597 0.4536 0.000 0.640 0.044 0.240 0.076
#> GSM537364 1 0.276 0.7695 0.880 0.000 0.024 0.092 0.004
#> GSM537365 2 0.669 0.2966 0.028 0.568 0.016 0.288 0.100
#> GSM537372 5 0.521 0.5261 0.008 0.224 0.012 0.056 0.700
#> GSM537384 5 0.473 0.5861 0.024 0.196 0.004 0.032 0.744
#> GSM537394 2 0.453 0.5143 0.000 0.784 0.032 0.124 0.060
#> GSM537403 4 0.626 0.1645 0.032 0.400 0.028 0.516 0.024
#> GSM537406 2 0.406 0.5108 0.000 0.808 0.040 0.128 0.024
#> GSM537411 2 0.626 0.4329 0.000 0.616 0.036 0.232 0.116
#> GSM537412 4 0.676 -0.0776 0.000 0.272 0.336 0.392 0.000
#> GSM537416 4 0.695 -0.1090 0.004 0.248 0.320 0.424 0.004
#> GSM537426 4 0.676 -0.0776 0.000 0.272 0.336 0.392 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.310 0.6737 0.004 0.140 0.016 0.000 0.832 0.008
#> GSM537345 1 0.493 0.5579 0.688 0.000 0.004 0.008 0.176 0.124
#> GSM537355 2 0.556 0.2428 0.000 0.552 0.076 0.016 0.348 0.008
#> GSM537366 3 0.839 0.3672 0.120 0.176 0.348 0.020 0.292 0.044
#> GSM537370 5 0.693 0.1136 0.012 0.372 0.136 0.016 0.428 0.036
#> GSM537380 2 0.395 0.5375 0.000 0.812 0.012 0.048 0.092 0.036
#> GSM537392 2 0.383 0.5387 0.000 0.820 0.012 0.048 0.088 0.032
#> GSM537415 2 0.298 0.5473 0.000 0.864 0.056 0.060 0.020 0.000
#> GSM537417 3 0.604 0.4692 0.108 0.108 0.676 0.056 0.044 0.008
#> GSM537422 3 0.588 0.4534 0.116 0.088 0.692 0.056 0.036 0.012
#> GSM537423 2 0.234 0.5674 0.000 0.904 0.036 0.024 0.036 0.000
#> GSM537427 2 0.352 0.5748 0.000 0.824 0.088 0.008 0.076 0.004
#> GSM537430 2 0.477 0.5526 0.000 0.724 0.136 0.020 0.116 0.004
#> GSM537336 1 0.330 0.6736 0.824 0.000 0.028 0.008 0.004 0.136
#> GSM537337 2 0.631 0.3079 0.000 0.468 0.196 0.012 0.316 0.008
#> GSM537348 5 0.317 0.6722 0.008 0.136 0.016 0.000 0.832 0.008
#> GSM537349 2 0.190 0.5585 0.000 0.928 0.004 0.032 0.028 0.008
#> GSM537356 5 0.484 0.6560 0.016 0.144 0.068 0.012 0.744 0.016
#> GSM537361 3 0.640 0.5088 0.148 0.100 0.628 0.008 0.096 0.020
#> GSM537374 2 0.655 0.2501 0.008 0.476 0.148 0.004 0.332 0.032
#> GSM537377 1 0.493 0.5579 0.688 0.000 0.004 0.008 0.176 0.124
#> GSM537378 2 0.298 0.5473 0.000 0.864 0.056 0.060 0.020 0.000
#> GSM537379 3 0.715 0.0750 0.036 0.340 0.444 0.040 0.132 0.008
#> GSM537383 2 0.257 0.5612 0.000 0.896 0.012 0.032 0.048 0.012
#> GSM537388 2 0.532 0.1615 0.000 0.540 0.056 0.012 0.384 0.008
#> GSM537395 2 0.646 0.3204 0.000 0.456 0.220 0.016 0.300 0.008
#> GSM537400 3 0.731 0.4128 0.044 0.120 0.572 0.092 0.144 0.028
#> GSM537404 3 0.733 0.5051 0.140 0.188 0.544 0.024 0.068 0.036
#> GSM537409 4 0.612 0.2174 0.000 0.232 0.368 0.396 0.004 0.000
#> GSM537418 3 0.811 0.4123 0.264 0.108 0.372 0.008 0.208 0.040
#> GSM537425 3 0.821 0.4226 0.108 0.172 0.492 0.100 0.080 0.048
#> GSM537333 3 0.714 0.3920 0.032 0.092 0.600 0.116 0.108 0.052
#> GSM537342 2 0.623 0.3243 0.000 0.564 0.272 0.108 0.032 0.024
#> GSM537347 2 0.665 0.3697 0.020 0.500 0.216 0.012 0.244 0.008
#> GSM537350 2 0.695 0.3963 0.024 0.584 0.132 0.032 0.172 0.056
#> GSM537362 5 0.774 -0.2380 0.324 0.020 0.148 0.008 0.380 0.120
#> GSM537363 3 0.783 -0.0114 0.052 0.092 0.500 0.232 0.060 0.064
#> GSM537368 1 0.252 0.7330 0.892 0.000 0.032 0.000 0.056 0.020
#> GSM537376 3 0.721 0.0753 0.008 0.332 0.428 0.060 0.156 0.016
#> GSM537381 3 0.860 0.4376 0.144 0.112 0.396 0.012 0.192 0.144
#> GSM537386 2 0.566 0.4906 0.000 0.700 0.080 0.108 0.064 0.048
#> GSM537398 5 0.430 0.6197 0.072 0.112 0.024 0.000 0.780 0.012
#> GSM537402 2 0.587 0.5296 0.000 0.644 0.144 0.052 0.148 0.012
#> GSM537405 1 0.506 0.5132 0.700 0.040 0.200 0.008 0.048 0.004
#> GSM537371 1 0.245 0.7330 0.896 0.000 0.028 0.000 0.056 0.020
#> GSM537421 4 0.751 0.4108 0.004 0.152 0.328 0.412 0.060 0.044
#> GSM537424 2 0.665 0.3697 0.020 0.500 0.216 0.012 0.244 0.008
#> GSM537432 2 0.646 0.3569 0.000 0.528 0.284 0.048 0.128 0.012
#> GSM537331 5 0.519 0.0445 0.000 0.468 0.040 0.012 0.472 0.008
#> GSM537332 2 0.634 -0.0223 0.000 0.432 0.428 0.072 0.052 0.016
#> GSM537334 5 0.603 0.3842 0.012 0.316 0.076 0.004 0.556 0.036
#> GSM537338 2 0.620 0.3325 0.000 0.480 0.196 0.008 0.308 0.008
#> GSM537353 2 0.656 0.3440 0.000 0.512 0.300 0.048 0.124 0.016
#> GSM537357 1 0.330 0.6736 0.824 0.000 0.028 0.008 0.004 0.136
#> GSM537358 2 0.396 0.5602 0.000 0.816 0.060 0.028 0.076 0.020
#> GSM537375 2 0.738 0.2699 0.000 0.424 0.288 0.100 0.172 0.016
#> GSM537389 2 0.182 0.5584 0.000 0.932 0.004 0.032 0.024 0.008
#> GSM537390 2 0.359 0.5249 0.000 0.820 0.092 0.068 0.020 0.000
#> GSM537393 2 0.625 0.4277 0.000 0.540 0.256 0.028 0.168 0.008
#> GSM537399 2 0.730 0.2709 0.024 0.496 0.204 0.016 0.208 0.052
#> GSM537407 3 0.810 0.3574 0.092 0.304 0.400 0.020 0.128 0.056
#> GSM537408 2 0.481 0.5335 0.000 0.764 0.092 0.040 0.056 0.048
#> GSM537428 2 0.555 0.4110 0.000 0.592 0.104 0.008 0.284 0.012
#> GSM537354 2 0.646 0.3204 0.000 0.456 0.220 0.016 0.300 0.008
#> GSM537410 2 0.623 0.3243 0.000 0.564 0.272 0.108 0.032 0.024
#> GSM537413 4 0.605 0.3453 0.000 0.264 0.012 0.580 0.040 0.104
#> GSM537396 2 0.507 0.5048 0.000 0.744 0.096 0.056 0.068 0.036
#> GSM537397 5 0.649 0.2891 0.012 0.348 0.092 0.016 0.500 0.032
#> GSM537330 2 0.616 0.2637 0.000 0.500 0.152 0.012 0.324 0.012
#> GSM537369 6 0.516 1.0000 0.136 0.004 0.012 0.000 0.180 0.668
#> GSM537373 2 0.652 0.3359 0.000 0.572 0.236 0.096 0.068 0.028
#> GSM537401 5 0.322 0.6755 0.004 0.144 0.020 0.000 0.824 0.008
#> GSM537343 2 0.858 -0.2589 0.084 0.368 0.308 0.056 0.104 0.080
#> GSM537367 3 0.716 0.4956 0.092 0.192 0.568 0.064 0.068 0.016
#> GSM537382 3 0.710 0.1938 0.008 0.284 0.464 0.056 0.176 0.012
#> GSM537385 2 0.531 0.2864 0.000 0.604 0.032 0.036 0.316 0.012
#> GSM537391 5 0.518 0.2157 0.072 0.044 0.000 0.000 0.668 0.216
#> GSM537419 2 0.414 0.5648 0.000 0.792 0.108 0.032 0.060 0.008
#> GSM537420 6 0.516 1.0000 0.136 0.004 0.012 0.000 0.180 0.668
#> GSM537429 2 0.662 0.1399 0.004 0.456 0.148 0.016 0.352 0.024
#> GSM537431 4 0.663 0.2205 0.004 0.016 0.232 0.552 0.052 0.144
#> GSM537387 5 0.518 0.2157 0.072 0.044 0.000 0.000 0.668 0.216
#> GSM537414 3 0.642 0.5085 0.124 0.116 0.640 0.012 0.080 0.028
#> GSM537433 3 0.789 0.4885 0.144 0.180 0.500 0.028 0.096 0.052
#> GSM537335 5 0.603 0.3842 0.012 0.316 0.076 0.004 0.556 0.036
#> GSM537339 5 0.321 0.6734 0.008 0.140 0.016 0.000 0.828 0.008
#> GSM537340 3 0.750 -0.2292 0.016 0.136 0.440 0.316 0.056 0.036
#> GSM537344 6 0.516 1.0000 0.136 0.004 0.012 0.000 0.180 0.668
#> GSM537346 2 0.601 0.3622 0.012 0.564 0.272 0.004 0.136 0.012
#> GSM537351 1 0.479 0.6143 0.732 0.000 0.160 0.060 0.008 0.040
#> GSM537352 2 0.679 0.2501 0.000 0.388 0.280 0.032 0.296 0.004
#> GSM537359 4 0.727 0.2488 0.000 0.356 0.016 0.392 0.100 0.136
#> GSM537360 2 0.604 0.4333 0.000 0.588 0.256 0.060 0.088 0.008
#> GSM537364 1 0.246 0.7145 0.896 0.000 0.064 0.016 0.004 0.020
#> GSM537365 2 0.658 0.1871 0.036 0.484 0.352 0.008 0.104 0.016
#> GSM537372 5 0.483 0.6536 0.012 0.156 0.060 0.012 0.740 0.020
#> GSM537384 5 0.427 0.6733 0.032 0.140 0.032 0.004 0.780 0.012
#> GSM537394 2 0.492 0.5013 0.000 0.720 0.168 0.016 0.072 0.024
#> GSM537403 3 0.617 0.3445 0.024 0.268 0.580 0.080 0.048 0.000
#> GSM537406 2 0.458 0.5239 0.000 0.776 0.096 0.060 0.032 0.036
#> GSM537411 2 0.628 0.3610 0.000 0.520 0.308 0.024 0.132 0.016
#> GSM537412 4 0.544 0.5318 0.000 0.184 0.244 0.572 0.000 0.000
#> GSM537416 4 0.636 0.5165 0.000 0.168 0.276 0.520 0.020 0.016
#> GSM537426 4 0.544 0.5318 0.000 0.184 0.244 0.572 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> CV:hclust 93 0.85601 0.7674 2
#> CV:hclust 80 0.71699 0.8256 3
#> CV:hclust 17 0.37416 0.8282 4
#> CV:hclust 37 0.62987 0.4068 5
#> CV:hclust 44 0.00963 0.0519 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 104 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 0.824 0.902 0.958 0.4826 0.518 0.518
#> 3 3 0.382 0.568 0.759 0.3308 0.769 0.584
#> 4 4 0.424 0.463 0.651 0.1365 0.848 0.618
#> 5 5 0.517 0.442 0.654 0.0776 0.843 0.511
#> 6 6 0.572 0.502 0.668 0.0458 0.902 0.579
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
#> GSM537341 2 0.9044 0.5331 0.320 0.680
#> GSM537345 1 0.0000 0.9487 1.000 0.000
#> GSM537355 2 0.0000 0.9580 0.000 1.000
#> GSM537366 1 0.0376 0.9473 0.996 0.004
#> GSM537370 2 0.0000 0.9580 0.000 1.000
#> GSM537380 2 0.0000 0.9580 0.000 1.000
#> GSM537392 2 0.0000 0.9580 0.000 1.000
#> GSM537415 2 0.0000 0.9580 0.000 1.000
#> GSM537417 2 0.8081 0.6605 0.248 0.752
#> GSM537422 1 0.2778 0.9213 0.952 0.048
#> GSM537423 2 0.0000 0.9580 0.000 1.000
#> GSM537427 2 0.0000 0.9580 0.000 1.000
#> GSM537430 2 0.0000 0.9580 0.000 1.000
#> GSM537336 1 0.0000 0.9487 1.000 0.000
#> GSM537337 2 0.0000 0.9580 0.000 1.000
#> GSM537348 1 0.0000 0.9487 1.000 0.000
#> GSM537349 2 0.0000 0.9580 0.000 1.000
#> GSM537356 1 0.0376 0.9473 0.996 0.004
#> GSM537361 1 0.0000 0.9487 1.000 0.000
#> GSM537374 2 0.0000 0.9580 0.000 1.000
#> GSM537377 1 0.0000 0.9487 1.000 0.000
#> GSM537378 2 0.0000 0.9580 0.000 1.000
#> GSM537379 2 0.0000 0.9580 0.000 1.000
#> GSM537383 2 0.0000 0.9580 0.000 1.000
#> GSM537388 2 0.0000 0.9580 0.000 1.000
#> GSM537395 2 0.0000 0.9580 0.000 1.000
#> GSM537400 1 0.7453 0.7489 0.788 0.212
#> GSM537404 2 1.0000 -0.0176 0.496 0.504
#> GSM537409 2 0.0000 0.9580 0.000 1.000
#> GSM537418 1 0.0000 0.9487 1.000 0.000
#> GSM537425 1 0.0938 0.9435 0.988 0.012
#> GSM537333 1 0.9209 0.5211 0.664 0.336
#> GSM537342 2 0.0000 0.9580 0.000 1.000
#> GSM537347 2 0.7883 0.6803 0.236 0.764
#> GSM537350 1 0.0000 0.9487 1.000 0.000
#> GSM537362 1 0.0000 0.9487 1.000 0.000
#> GSM537363 1 0.2948 0.9185 0.948 0.052
#> GSM537368 1 0.0000 0.9487 1.000 0.000
#> GSM537376 2 0.0000 0.9580 0.000 1.000
#> GSM537381 1 0.0000 0.9487 1.000 0.000
#> GSM537386 2 0.0000 0.9580 0.000 1.000
#> GSM537398 1 0.0000 0.9487 1.000 0.000
#> GSM537402 2 0.0000 0.9580 0.000 1.000
#> GSM537405 1 0.0000 0.9487 1.000 0.000
#> GSM537371 1 0.0000 0.9487 1.000 0.000
#> GSM537421 2 0.1633 0.9392 0.024 0.976
#> GSM537424 1 0.0000 0.9487 1.000 0.000
#> GSM537432 2 0.8861 0.5585 0.304 0.696
#> GSM537331 2 0.0000 0.9580 0.000 1.000
#> GSM537332 2 0.0000 0.9580 0.000 1.000
#> GSM537334 2 0.0000 0.9580 0.000 1.000
#> GSM537338 2 0.0000 0.9580 0.000 1.000
#> GSM537353 2 0.0000 0.9580 0.000 1.000
#> GSM537357 1 0.0000 0.9487 1.000 0.000
#> GSM537358 2 0.0000 0.9580 0.000 1.000
#> GSM537375 2 0.0000 0.9580 0.000 1.000
#> GSM537389 2 0.0000 0.9580 0.000 1.000
#> GSM537390 2 0.0000 0.9580 0.000 1.000
#> GSM537393 2 0.0000 0.9580 0.000 1.000
#> GSM537399 1 0.6148 0.8203 0.848 0.152
#> GSM537407 1 0.0376 0.9473 0.996 0.004
#> GSM537408 2 0.0000 0.9580 0.000 1.000
#> GSM537428 2 0.0000 0.9580 0.000 1.000
#> GSM537354 2 0.0000 0.9580 0.000 1.000
#> GSM537410 2 0.0000 0.9580 0.000 1.000
#> GSM537413 2 0.0000 0.9580 0.000 1.000
#> GSM537396 2 0.2778 0.9192 0.048 0.952
#> GSM537397 1 0.6973 0.7725 0.812 0.188
#> GSM537330 2 0.0000 0.9580 0.000 1.000
#> GSM537369 1 0.0000 0.9487 1.000 0.000
#> GSM537373 2 0.3733 0.8970 0.072 0.928
#> GSM537401 2 0.4815 0.8647 0.104 0.896
#> GSM537343 1 0.0000 0.9487 1.000 0.000
#> GSM537367 1 0.3431 0.9091 0.936 0.064
#> GSM537382 2 0.0000 0.9580 0.000 1.000
#> GSM537385 2 0.0000 0.9580 0.000 1.000
#> GSM537391 1 0.0000 0.9487 1.000 0.000
#> GSM537419 2 0.0000 0.9580 0.000 1.000
#> GSM537420 1 0.0000 0.9487 1.000 0.000
#> GSM537429 2 0.6048 0.8089 0.148 0.852
#> GSM537431 1 0.7815 0.7192 0.768 0.232
#> GSM537387 1 0.0000 0.9487 1.000 0.000
#> GSM537414 1 0.4815 0.8736 0.896 0.104
#> GSM537433 1 0.1843 0.9348 0.972 0.028
#> GSM537335 2 0.1843 0.9366 0.028 0.972
#> GSM537339 1 0.3733 0.9002 0.928 0.072
#> GSM537340 2 0.9686 0.3350 0.396 0.604
#> GSM537344 1 0.0000 0.9487 1.000 0.000
#> GSM537346 2 0.0000 0.9580 0.000 1.000
#> GSM537351 1 0.0000 0.9487 1.000 0.000
#> GSM537352 2 0.0000 0.9580 0.000 1.000
#> GSM537359 2 0.0000 0.9580 0.000 1.000
#> GSM537360 2 0.0000 0.9580 0.000 1.000
#> GSM537364 1 0.0000 0.9487 1.000 0.000
#> GSM537365 1 0.9866 0.2618 0.568 0.432
#> GSM537372 1 0.0000 0.9487 1.000 0.000
#> GSM537384 1 0.0000 0.9487 1.000 0.000
#> GSM537394 2 0.0000 0.9580 0.000 1.000
#> GSM537403 2 0.0000 0.9580 0.000 1.000
#> GSM537406 2 0.0000 0.9580 0.000 1.000
#> GSM537411 2 0.0000 0.9580 0.000 1.000
#> GSM537412 2 0.0000 0.9580 0.000 1.000
#> GSM537416 2 0.0672 0.9520 0.008 0.992
#> GSM537426 2 0.0000 0.9580 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.9969 0.0155 0.320 0.372 0.308
#> GSM537345 1 0.2165 0.7784 0.936 0.000 0.064
#> GSM537355 2 0.5216 0.6713 0.000 0.740 0.260
#> GSM537366 3 0.7292 -0.1373 0.472 0.028 0.500
#> GSM537370 2 0.7987 0.4685 0.092 0.616 0.292
#> GSM537380 2 0.1860 0.7448 0.000 0.948 0.052
#> GSM537392 2 0.1031 0.7550 0.000 0.976 0.024
#> GSM537415 2 0.2878 0.7340 0.000 0.904 0.096
#> GSM537417 3 0.7906 0.5919 0.124 0.220 0.656
#> GSM537422 3 0.6276 0.5388 0.224 0.040 0.736
#> GSM537423 2 0.0892 0.7558 0.000 0.980 0.020
#> GSM537427 2 0.4121 0.7202 0.000 0.832 0.168
#> GSM537430 2 0.0592 0.7586 0.000 0.988 0.012
#> GSM537336 1 0.3340 0.7713 0.880 0.000 0.120
#> GSM537337 2 0.5115 0.6931 0.004 0.768 0.228
#> GSM537348 1 0.6475 0.6687 0.692 0.028 0.280
#> GSM537349 2 0.1163 0.7540 0.000 0.972 0.028
#> GSM537356 1 0.5945 0.7081 0.740 0.024 0.236
#> GSM537361 3 0.5848 0.4507 0.268 0.012 0.720
#> GSM537374 2 0.5932 0.6744 0.056 0.780 0.164
#> GSM537377 1 0.2261 0.7780 0.932 0.000 0.068
#> GSM537378 2 0.0892 0.7558 0.000 0.980 0.020
#> GSM537379 3 0.6345 0.2141 0.004 0.400 0.596
#> GSM537383 2 0.0592 0.7567 0.000 0.988 0.012
#> GSM537388 2 0.3038 0.7515 0.000 0.896 0.104
#> GSM537395 2 0.3340 0.7321 0.000 0.880 0.120
#> GSM537400 3 0.5357 0.5878 0.116 0.064 0.820
#> GSM537404 3 0.6634 0.6076 0.144 0.104 0.752
#> GSM537409 3 0.6307 0.0579 0.000 0.488 0.512
#> GSM537418 1 0.6148 0.5498 0.640 0.004 0.356
#> GSM537425 3 0.5831 0.4422 0.284 0.008 0.708
#> GSM537333 3 0.5153 0.5931 0.100 0.068 0.832
#> GSM537342 3 0.6299 -0.0179 0.000 0.476 0.524
#> GSM537347 3 0.6303 0.4602 0.032 0.248 0.720
#> GSM537350 1 0.5366 0.7371 0.776 0.016 0.208
#> GSM537362 1 0.5315 0.7526 0.772 0.012 0.216
#> GSM537363 3 0.7363 0.4388 0.280 0.064 0.656
#> GSM537368 1 0.3340 0.7714 0.880 0.000 0.120
#> GSM537376 2 0.4750 0.7126 0.000 0.784 0.216
#> GSM537381 1 0.4002 0.7606 0.840 0.000 0.160
#> GSM537386 2 0.2356 0.7410 0.000 0.928 0.072
#> GSM537398 1 0.6126 0.6800 0.712 0.020 0.268
#> GSM537402 2 0.3267 0.7529 0.000 0.884 0.116
#> GSM537405 1 0.3619 0.7704 0.864 0.000 0.136
#> GSM537371 1 0.3192 0.7741 0.888 0.000 0.112
#> GSM537421 3 0.7130 0.0990 0.024 0.432 0.544
#> GSM537424 1 0.4235 0.7560 0.824 0.000 0.176
#> GSM537432 3 0.5122 0.5473 0.012 0.200 0.788
#> GSM537331 2 0.6673 0.6403 0.056 0.720 0.224
#> GSM537332 3 0.6215 0.2762 0.000 0.428 0.572
#> GSM537334 2 0.7157 0.6027 0.056 0.668 0.276
#> GSM537338 2 0.6986 0.6278 0.056 0.688 0.256
#> GSM537353 2 0.3267 0.7336 0.000 0.884 0.116
#> GSM537357 1 0.3116 0.7754 0.892 0.000 0.108
#> GSM537358 2 0.1163 0.7557 0.000 0.972 0.028
#> GSM537375 2 0.6603 0.6121 0.020 0.648 0.332
#> GSM537389 2 0.1529 0.7524 0.000 0.960 0.040
#> GSM537390 2 0.2165 0.7472 0.000 0.936 0.064
#> GSM537393 2 0.5016 0.6920 0.000 0.760 0.240
#> GSM537399 3 0.9065 -0.0677 0.364 0.144 0.492
#> GSM537407 3 0.6501 0.3714 0.316 0.020 0.664
#> GSM537408 2 0.2261 0.7457 0.000 0.932 0.068
#> GSM537428 2 0.5656 0.6582 0.008 0.728 0.264
#> GSM537354 2 0.5201 0.6933 0.004 0.760 0.236
#> GSM537410 2 0.6244 0.0775 0.000 0.560 0.440
#> GSM537413 2 0.2448 0.7469 0.000 0.924 0.076
#> GSM537396 2 0.5823 0.6279 0.064 0.792 0.144
#> GSM537397 1 0.7742 0.5994 0.632 0.080 0.288
#> GSM537330 2 0.6267 0.1292 0.000 0.548 0.452
#> GSM537369 1 0.2165 0.7884 0.936 0.000 0.064
#> GSM537373 2 0.7571 0.2861 0.052 0.592 0.356
#> GSM537401 2 0.9374 0.2902 0.192 0.492 0.316
#> GSM537343 3 0.6931 -0.0218 0.456 0.016 0.528
#> GSM537367 3 0.5506 0.5309 0.220 0.016 0.764
#> GSM537382 2 0.6095 0.5140 0.000 0.608 0.392
#> GSM537385 2 0.1964 0.7558 0.000 0.944 0.056
#> GSM537391 1 0.5178 0.6985 0.808 0.028 0.164
#> GSM537419 2 0.1163 0.7560 0.000 0.972 0.028
#> GSM537420 1 0.2261 0.7880 0.932 0.000 0.068
#> GSM537429 3 0.7075 -0.2516 0.020 0.488 0.492
#> GSM537431 3 0.5730 0.5942 0.144 0.060 0.796
#> GSM537387 1 0.3816 0.7282 0.852 0.000 0.148
#> GSM537414 3 0.6488 0.5676 0.192 0.064 0.744
#> GSM537433 3 0.6684 0.4161 0.292 0.032 0.676
#> GSM537335 2 0.8825 0.3872 0.132 0.532 0.336
#> GSM537339 1 0.7683 0.6089 0.640 0.080 0.280
#> GSM537340 3 0.8637 0.5054 0.128 0.308 0.564
#> GSM537344 1 0.2261 0.7880 0.932 0.000 0.068
#> GSM537346 2 0.6291 -0.0323 0.000 0.532 0.468
#> GSM537351 1 0.6079 0.3121 0.612 0.000 0.388
#> GSM537352 2 0.4931 0.6973 0.000 0.768 0.232
#> GSM537359 2 0.2356 0.7408 0.000 0.928 0.072
#> GSM537360 2 0.3038 0.7336 0.000 0.896 0.104
#> GSM537364 1 0.3752 0.7558 0.856 0.000 0.144
#> GSM537365 3 0.6546 0.5851 0.148 0.096 0.756
#> GSM537372 1 0.5414 0.7281 0.772 0.016 0.212
#> GSM537384 1 0.4968 0.7429 0.800 0.012 0.188
#> GSM537394 2 0.3340 0.7290 0.000 0.880 0.120
#> GSM537403 3 0.6180 0.2775 0.000 0.416 0.584
#> GSM537406 2 0.3267 0.7302 0.000 0.884 0.116
#> GSM537411 2 0.4235 0.7354 0.000 0.824 0.176
#> GSM537412 2 0.6267 0.0296 0.000 0.548 0.452
#> GSM537416 3 0.5882 0.3810 0.000 0.348 0.652
#> GSM537426 2 0.4399 0.6912 0.000 0.812 0.188
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.5948 0.5047 0.092 0.196 0.008 0.704
#> GSM537345 1 0.1970 0.7661 0.932 0.000 0.008 0.060
#> GSM537355 2 0.7717 0.3704 0.000 0.444 0.304 0.252
#> GSM537366 3 0.7785 0.1315 0.212 0.004 0.440 0.344
#> GSM537370 4 0.5334 0.1913 0.004 0.364 0.012 0.620
#> GSM537380 2 0.1584 0.6692 0.000 0.952 0.012 0.036
#> GSM537392 2 0.1584 0.6692 0.000 0.952 0.012 0.036
#> GSM537415 2 0.4491 0.6013 0.000 0.800 0.060 0.140
#> GSM537417 3 0.3448 0.6102 0.028 0.060 0.884 0.028
#> GSM537422 3 0.3573 0.6072 0.132 0.004 0.848 0.016
#> GSM537423 2 0.0000 0.6771 0.000 1.000 0.000 0.000
#> GSM537427 2 0.6172 0.5156 0.000 0.632 0.084 0.284
#> GSM537430 2 0.2271 0.6689 0.000 0.916 0.008 0.076
#> GSM537336 1 0.0524 0.7917 0.988 0.000 0.008 0.004
#> GSM537337 2 0.7285 0.4614 0.000 0.516 0.176 0.308
#> GSM537348 4 0.5391 0.3811 0.320 0.012 0.012 0.656
#> GSM537349 2 0.1488 0.6734 0.000 0.956 0.012 0.032
#> GSM537356 4 0.7763 0.1086 0.332 0.000 0.248 0.420
#> GSM537361 3 0.5056 0.5593 0.164 0.000 0.760 0.076
#> GSM537374 2 0.5923 0.3881 0.000 0.580 0.044 0.376
#> GSM537377 1 0.1970 0.7661 0.932 0.000 0.008 0.060
#> GSM537378 2 0.0469 0.6786 0.000 0.988 0.000 0.012
#> GSM537379 3 0.5151 0.5377 0.000 0.140 0.760 0.100
#> GSM537383 2 0.1022 0.6728 0.000 0.968 0.000 0.032
#> GSM537388 2 0.6116 0.5629 0.000 0.668 0.112 0.220
#> GSM537395 2 0.5972 0.6149 0.000 0.692 0.176 0.132
#> GSM537400 3 0.4389 0.6081 0.060 0.012 0.828 0.100
#> GSM537404 3 0.5580 0.5972 0.068 0.048 0.772 0.112
#> GSM537409 3 0.7505 0.1648 0.000 0.324 0.476 0.200
#> GSM537418 3 0.7904 -0.0814 0.340 0.000 0.360 0.300
#> GSM537425 3 0.6163 0.5015 0.160 0.000 0.676 0.164
#> GSM537333 3 0.3915 0.6083 0.052 0.008 0.852 0.088
#> GSM537342 3 0.7668 0.1902 0.000 0.252 0.460 0.288
#> GSM537347 3 0.5575 0.5057 0.004 0.104 0.736 0.156
#> GSM537350 4 0.7872 0.0673 0.416 0.028 0.128 0.428
#> GSM537362 1 0.7386 -0.0262 0.464 0.000 0.168 0.368
#> GSM537363 3 0.7562 0.4625 0.156 0.012 0.516 0.316
#> GSM537368 1 0.0657 0.7920 0.984 0.000 0.012 0.004
#> GSM537376 2 0.7741 0.3779 0.000 0.440 0.264 0.296
#> GSM537381 1 0.7715 0.1095 0.436 0.000 0.324 0.240
#> GSM537386 2 0.2399 0.6667 0.000 0.920 0.032 0.048
#> GSM537398 4 0.5640 0.3878 0.308 0.012 0.024 0.656
#> GSM537402 2 0.6508 0.5814 0.000 0.640 0.168 0.192
#> GSM537405 1 0.1256 0.7845 0.964 0.000 0.028 0.008
#> GSM537371 1 0.0592 0.7913 0.984 0.000 0.016 0.000
#> GSM537421 3 0.7837 0.1939 0.004 0.244 0.452 0.300
#> GSM537424 4 0.6121 0.2321 0.396 0.000 0.052 0.552
#> GSM537432 3 0.5809 0.5405 0.004 0.076 0.696 0.224
#> GSM537331 2 0.7037 0.2709 0.000 0.464 0.120 0.416
#> GSM537332 3 0.4669 0.5739 0.000 0.200 0.764 0.036
#> GSM537334 4 0.7492 -0.2181 0.000 0.388 0.180 0.432
#> GSM537338 2 0.7304 0.2983 0.000 0.448 0.152 0.400
#> GSM537353 2 0.5257 0.6428 0.000 0.752 0.104 0.144
#> GSM537357 1 0.0524 0.7909 0.988 0.000 0.004 0.008
#> GSM537358 2 0.0895 0.6780 0.000 0.976 0.020 0.004
#> GSM537375 2 0.7806 0.3848 0.000 0.408 0.260 0.332
#> GSM537389 2 0.1488 0.6734 0.000 0.956 0.012 0.032
#> GSM537390 2 0.3301 0.6488 0.000 0.876 0.048 0.076
#> GSM537393 2 0.7319 0.5036 0.000 0.532 0.248 0.220
#> GSM537399 4 0.7738 0.0190 0.116 0.028 0.388 0.468
#> GSM537407 3 0.6506 0.4725 0.144 0.004 0.652 0.200
#> GSM537408 2 0.2996 0.6489 0.000 0.892 0.064 0.044
#> GSM537428 2 0.7146 0.4072 0.000 0.516 0.148 0.336
#> GSM537354 2 0.7329 0.4701 0.000 0.516 0.188 0.296
#> GSM537410 2 0.7591 0.0286 0.000 0.432 0.368 0.200
#> GSM537413 2 0.4227 0.6206 0.000 0.820 0.060 0.120
#> GSM537396 2 0.5728 0.1615 0.004 0.544 0.020 0.432
#> GSM537397 4 0.6327 0.4868 0.196 0.120 0.008 0.676
#> GSM537330 3 0.5599 0.4478 0.000 0.276 0.672 0.052
#> GSM537369 1 0.3529 0.7076 0.836 0.000 0.012 0.152
#> GSM537373 4 0.7737 -0.1160 0.004 0.372 0.196 0.428
#> GSM537401 4 0.4986 0.4740 0.044 0.216 0.000 0.740
#> GSM537343 3 0.7367 0.3464 0.212 0.004 0.548 0.236
#> GSM537367 3 0.5772 0.5679 0.100 0.004 0.716 0.180
#> GSM537382 3 0.7846 -0.0772 0.000 0.272 0.392 0.336
#> GSM537385 2 0.3790 0.6307 0.000 0.820 0.016 0.164
#> GSM537391 4 0.5653 0.1460 0.448 0.016 0.004 0.532
#> GSM537419 2 0.0804 0.6756 0.000 0.980 0.012 0.008
#> GSM537420 1 0.3479 0.7122 0.840 0.000 0.012 0.148
#> GSM537429 4 0.7900 -0.0951 0.000 0.300 0.332 0.368
#> GSM537431 3 0.4026 0.6148 0.048 0.012 0.848 0.092
#> GSM537387 1 0.3172 0.6824 0.840 0.000 0.000 0.160
#> GSM537414 3 0.3625 0.6025 0.120 0.004 0.852 0.024
#> GSM537433 3 0.6598 0.4798 0.140 0.008 0.652 0.200
#> GSM537335 4 0.7271 0.2239 0.012 0.248 0.160 0.580
#> GSM537339 4 0.6226 0.4842 0.200 0.120 0.004 0.676
#> GSM537340 3 0.8411 0.2891 0.040 0.240 0.480 0.240
#> GSM537344 1 0.3479 0.7122 0.840 0.000 0.012 0.148
#> GSM537346 3 0.6037 0.4212 0.000 0.304 0.628 0.068
#> GSM537351 1 0.4175 0.5884 0.776 0.000 0.212 0.012
#> GSM537352 2 0.7278 0.4833 0.000 0.528 0.188 0.284
#> GSM537359 2 0.1913 0.6679 0.000 0.940 0.020 0.040
#> GSM537360 2 0.5110 0.5883 0.000 0.764 0.104 0.132
#> GSM537364 1 0.1545 0.7770 0.952 0.000 0.040 0.008
#> GSM537365 3 0.6178 0.5569 0.068 0.044 0.720 0.168
#> GSM537372 4 0.5732 0.3094 0.364 0.004 0.028 0.604
#> GSM537384 4 0.5548 0.2696 0.388 0.000 0.024 0.588
#> GSM537394 2 0.4633 0.5506 0.000 0.780 0.172 0.048
#> GSM537403 3 0.6537 0.4561 0.000 0.164 0.636 0.200
#> GSM537406 2 0.4605 0.5945 0.000 0.796 0.072 0.132
#> GSM537411 2 0.6514 0.5844 0.000 0.636 0.152 0.212
#> GSM537412 2 0.7527 0.0625 0.000 0.452 0.356 0.192
#> GSM537416 3 0.5598 0.5462 0.000 0.076 0.704 0.220
#> GSM537426 2 0.6917 0.4309 0.000 0.592 0.208 0.200
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.2386 0.57139 0.016 0.048 0.008 0.012 0.916
#> GSM537345 1 0.1605 0.84530 0.944 0.000 0.012 0.004 0.040
#> GSM537355 2 0.8393 0.00656 0.000 0.328 0.168 0.300 0.204
#> GSM537366 3 0.6715 0.28449 0.076 0.008 0.488 0.040 0.388
#> GSM537370 5 0.4722 0.48909 0.000 0.148 0.032 0.056 0.764
#> GSM537380 2 0.1281 0.64548 0.000 0.956 0.012 0.000 0.032
#> GSM537392 2 0.0865 0.64632 0.000 0.972 0.004 0.000 0.024
#> GSM537415 2 0.4548 0.41735 0.004 0.712 0.012 0.256 0.016
#> GSM537417 3 0.4003 0.59808 0.012 0.036 0.796 0.156 0.000
#> GSM537422 3 0.4522 0.60656 0.060 0.000 0.744 0.192 0.004
#> GSM537423 2 0.0609 0.64568 0.000 0.980 0.000 0.020 0.000
#> GSM537427 2 0.7548 0.21291 0.000 0.460 0.068 0.196 0.276
#> GSM537430 2 0.4131 0.56377 0.000 0.804 0.016 0.120 0.060
#> GSM537336 1 0.0671 0.86226 0.980 0.000 0.016 0.004 0.000
#> GSM537337 2 0.8259 0.01497 0.000 0.340 0.124 0.292 0.244
#> GSM537348 5 0.2984 0.52813 0.124 0.004 0.016 0.000 0.856
#> GSM537349 2 0.1978 0.63855 0.000 0.928 0.004 0.044 0.024
#> GSM537356 5 0.6613 0.10851 0.120 0.004 0.316 0.024 0.536
#> GSM537361 3 0.3241 0.66864 0.040 0.000 0.872 0.052 0.036
#> GSM537374 5 0.7375 -0.07092 0.004 0.380 0.052 0.144 0.420
#> GSM537377 1 0.1787 0.84426 0.936 0.000 0.016 0.004 0.044
#> GSM537378 2 0.0609 0.64571 0.000 0.980 0.000 0.020 0.000
#> GSM537379 3 0.5982 0.33054 0.000 0.080 0.636 0.244 0.040
#> GSM537383 2 0.0609 0.64651 0.000 0.980 0.000 0.000 0.020
#> GSM537388 2 0.7653 0.28743 0.000 0.496 0.112 0.192 0.200
#> GSM537395 2 0.6852 0.19665 0.000 0.528 0.116 0.304 0.052
#> GSM537400 3 0.5229 0.37456 0.028 0.000 0.568 0.392 0.012
#> GSM537404 3 0.4013 0.66826 0.016 0.008 0.828 0.080 0.068
#> GSM537409 4 0.5474 0.51260 0.004 0.184 0.092 0.700 0.020
#> GSM537418 3 0.6701 0.19612 0.144 0.000 0.476 0.020 0.360
#> GSM537425 3 0.5126 0.64463 0.052 0.000 0.744 0.064 0.140
#> GSM537333 3 0.5001 0.38516 0.016 0.004 0.592 0.380 0.008
#> GSM537342 4 0.4506 0.54498 0.000 0.076 0.096 0.792 0.036
#> GSM537347 3 0.3937 0.62038 0.000 0.040 0.832 0.064 0.064
#> GSM537350 5 0.7385 0.21983 0.200 0.024 0.228 0.028 0.520
#> GSM537362 5 0.7701 0.18673 0.328 0.000 0.140 0.104 0.428
#> GSM537363 4 0.6878 -0.05360 0.056 0.000 0.332 0.508 0.104
#> GSM537368 1 0.1026 0.86299 0.968 0.000 0.024 0.004 0.004
#> GSM537376 4 0.5177 0.48684 0.000 0.188 0.040 0.720 0.052
#> GSM537381 3 0.6536 0.31166 0.192 0.000 0.520 0.008 0.280
#> GSM537386 2 0.3126 0.62348 0.000 0.876 0.024 0.040 0.060
#> GSM537398 5 0.3264 0.52629 0.132 0.000 0.024 0.004 0.840
#> GSM537402 4 0.5903 0.23849 0.000 0.364 0.032 0.556 0.048
#> GSM537405 1 0.1502 0.85555 0.940 0.000 0.056 0.000 0.004
#> GSM537371 1 0.1026 0.86133 0.968 0.000 0.024 0.004 0.004
#> GSM537421 4 0.3455 0.53005 0.004 0.068 0.084 0.844 0.000
#> GSM537424 5 0.5440 0.41695 0.184 0.000 0.156 0.000 0.660
#> GSM537432 4 0.5322 0.17685 0.000 0.028 0.336 0.612 0.024
#> GSM537331 5 0.7752 0.01202 0.000 0.332 0.116 0.132 0.420
#> GSM537332 3 0.5652 0.53344 0.004 0.124 0.684 0.172 0.016
#> GSM537334 5 0.8036 0.08370 0.004 0.268 0.132 0.156 0.440
#> GSM537338 5 0.8252 0.01748 0.004 0.284 0.132 0.188 0.392
#> GSM537353 2 0.5194 0.13285 0.000 0.552 0.024 0.412 0.012
#> GSM537357 1 0.0833 0.86227 0.976 0.000 0.016 0.004 0.004
#> GSM537358 2 0.1701 0.63930 0.000 0.944 0.016 0.028 0.012
#> GSM537375 4 0.8013 0.13484 0.000 0.232 0.116 0.424 0.228
#> GSM537389 2 0.2067 0.63765 0.000 0.924 0.004 0.044 0.028
#> GSM537390 2 0.2523 0.62308 0.004 0.908 0.024 0.052 0.012
#> GSM537393 4 0.8021 0.01715 0.000 0.340 0.128 0.372 0.160
#> GSM537399 5 0.5434 -0.22381 0.020 0.008 0.476 0.012 0.484
#> GSM537407 3 0.4312 0.63181 0.040 0.000 0.780 0.020 0.160
#> GSM537408 2 0.2822 0.61576 0.000 0.888 0.064 0.012 0.036
#> GSM537428 2 0.8222 0.04961 0.000 0.336 0.136 0.196 0.332
#> GSM537354 2 0.8231 -0.02750 0.000 0.328 0.124 0.324 0.224
#> GSM537410 4 0.5618 0.52734 0.004 0.172 0.092 0.700 0.032
#> GSM537413 2 0.4809 0.39546 0.004 0.708 0.012 0.244 0.032
#> GSM537396 5 0.7243 0.03055 0.008 0.368 0.028 0.164 0.432
#> GSM537397 5 0.2797 0.56979 0.048 0.020 0.012 0.020 0.900
#> GSM537330 3 0.5858 0.48557 0.000 0.160 0.680 0.116 0.044
#> GSM537369 1 0.5579 0.64295 0.672 0.000 0.108 0.016 0.204
#> GSM537373 4 0.8143 0.20710 0.008 0.256 0.076 0.368 0.292
#> GSM537401 5 0.2260 0.57153 0.016 0.048 0.004 0.012 0.920
#> GSM537343 3 0.5241 0.55055 0.072 0.000 0.692 0.016 0.220
#> GSM537367 3 0.5401 0.50503 0.032 0.000 0.636 0.300 0.032
#> GSM537382 4 0.5860 0.51270 0.000 0.112 0.116 0.696 0.076
#> GSM537385 2 0.5244 0.52533 0.000 0.716 0.016 0.132 0.136
#> GSM537391 5 0.4017 0.39552 0.248 0.000 0.012 0.004 0.736
#> GSM537419 2 0.1483 0.64322 0.000 0.952 0.012 0.028 0.008
#> GSM537420 1 0.5579 0.64295 0.672 0.000 0.108 0.016 0.204
#> GSM537429 5 0.8192 -0.02868 0.000 0.112 0.296 0.248 0.344
#> GSM537431 3 0.5160 0.36314 0.016 0.004 0.564 0.404 0.012
#> GSM537387 1 0.3205 0.76187 0.816 0.000 0.004 0.004 0.176
#> GSM537414 3 0.3063 0.64873 0.036 0.000 0.864 0.096 0.004
#> GSM537433 3 0.4566 0.63300 0.032 0.000 0.768 0.040 0.160
#> GSM537335 5 0.6532 0.38137 0.004 0.108 0.132 0.108 0.648
#> GSM537339 5 0.2584 0.56924 0.052 0.032 0.008 0.004 0.904
#> GSM537340 4 0.4481 0.50238 0.016 0.056 0.132 0.788 0.008
#> GSM537344 1 0.5594 0.64590 0.672 0.000 0.112 0.016 0.200
#> GSM537346 3 0.5233 0.49315 0.000 0.204 0.708 0.052 0.036
#> GSM537351 1 0.2771 0.78081 0.860 0.000 0.128 0.012 0.000
#> GSM537352 4 0.7996 0.00156 0.000 0.324 0.096 0.368 0.212
#> GSM537359 2 0.2599 0.63231 0.000 0.904 0.028 0.024 0.044
#> GSM537360 2 0.5458 0.23433 0.004 0.588 0.032 0.360 0.016
#> GSM537364 1 0.1205 0.85218 0.956 0.000 0.040 0.004 0.000
#> GSM537365 3 0.4634 0.66068 0.008 0.012 0.780 0.096 0.104
#> GSM537372 5 0.4488 0.46793 0.164 0.004 0.064 0.004 0.764
#> GSM537384 5 0.4798 0.44163 0.192 0.004 0.068 0.004 0.732
#> GSM537394 2 0.4332 0.51055 0.000 0.780 0.160 0.028 0.032
#> GSM537403 4 0.5007 0.41731 0.000 0.052 0.244 0.692 0.012
#> GSM537406 2 0.5421 0.36264 0.004 0.664 0.024 0.264 0.044
#> GSM537411 4 0.6899 0.10236 0.000 0.392 0.036 0.444 0.128
#> GSM537412 4 0.5931 0.37527 0.004 0.296 0.056 0.612 0.032
#> GSM537416 4 0.4488 0.38630 0.004 0.020 0.208 0.748 0.020
#> GSM537426 4 0.5358 0.36025 0.004 0.308 0.024 0.636 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.202 0.6685 0.000 0.028 0.000 0.016 0.920 0.036
#> GSM537345 1 0.154 0.8369 0.936 0.000 0.008 0.004 0.052 0.000
#> GSM537355 6 0.774 0.3889 0.000 0.180 0.076 0.156 0.108 0.480
#> GSM537366 3 0.552 0.2941 0.040 0.004 0.512 0.040 0.404 0.000
#> GSM537370 5 0.571 0.4501 0.004 0.156 0.032 0.016 0.664 0.128
#> GSM537380 2 0.154 0.7489 0.000 0.940 0.016 0.000 0.004 0.040
#> GSM537392 2 0.158 0.7464 0.000 0.936 0.012 0.000 0.004 0.048
#> GSM537415 2 0.452 0.4469 0.000 0.632 0.004 0.328 0.004 0.032
#> GSM537417 3 0.464 0.5471 0.000 0.008 0.648 0.040 0.004 0.300
#> GSM537422 3 0.529 0.5881 0.056 0.000 0.700 0.092 0.008 0.144
#> GSM537423 2 0.172 0.7593 0.000 0.932 0.004 0.036 0.000 0.028
#> GSM537427 6 0.585 0.5244 0.000 0.332 0.000 0.008 0.164 0.496
#> GSM537430 2 0.446 0.2531 0.000 0.628 0.000 0.036 0.004 0.332
#> GSM537336 1 0.196 0.8398 0.928 0.000 0.012 0.016 0.032 0.012
#> GSM537337 6 0.595 0.6004 0.000 0.196 0.000 0.048 0.160 0.596
#> GSM537348 5 0.215 0.6862 0.040 0.004 0.004 0.004 0.916 0.032
#> GSM537349 2 0.196 0.7489 0.000 0.912 0.000 0.072 0.008 0.008
#> GSM537356 5 0.524 0.2149 0.060 0.000 0.312 0.028 0.600 0.000
#> GSM537361 3 0.438 0.6390 0.036 0.000 0.784 0.028 0.040 0.112
#> GSM537374 6 0.613 0.4492 0.000 0.252 0.000 0.004 0.324 0.420
#> GSM537377 1 0.205 0.8344 0.912 0.000 0.028 0.004 0.056 0.000
#> GSM537378 2 0.221 0.7554 0.000 0.904 0.004 0.048 0.000 0.044
#> GSM537379 6 0.509 -0.2005 0.000 0.020 0.440 0.024 0.008 0.508
#> GSM537383 2 0.119 0.7574 0.000 0.956 0.004 0.008 0.000 0.032
#> GSM537388 6 0.740 0.3157 0.000 0.328 0.016 0.164 0.100 0.392
#> GSM537395 6 0.482 0.4399 0.000 0.364 0.000 0.064 0.000 0.572
#> GSM537400 3 0.671 0.1783 0.024 0.000 0.400 0.236 0.008 0.332
#> GSM537404 3 0.385 0.6438 0.000 0.004 0.816 0.044 0.060 0.076
#> GSM537409 4 0.448 0.5862 0.004 0.092 0.036 0.764 0.000 0.104
#> GSM537418 3 0.579 0.2674 0.072 0.000 0.528 0.024 0.364 0.012
#> GSM537425 3 0.504 0.6352 0.032 0.000 0.740 0.068 0.116 0.044
#> GSM537333 3 0.659 0.2294 0.020 0.000 0.400 0.212 0.008 0.360
#> GSM537342 4 0.594 0.5822 0.008 0.044 0.080 0.668 0.036 0.164
#> GSM537347 3 0.432 0.5541 0.000 0.004 0.668 0.004 0.028 0.296
#> GSM537350 5 0.713 0.2843 0.068 0.044 0.260 0.044 0.540 0.044
#> GSM537362 5 0.794 0.1572 0.224 0.000 0.148 0.028 0.372 0.228
#> GSM537363 4 0.707 0.3221 0.032 0.008 0.252 0.528 0.088 0.092
#> GSM537368 1 0.193 0.8416 0.920 0.000 0.032 0.004 0.044 0.000
#> GSM537376 6 0.638 -0.2052 0.000 0.124 0.024 0.400 0.016 0.436
#> GSM537381 3 0.644 0.3712 0.124 0.000 0.544 0.028 0.272 0.032
#> GSM537386 2 0.274 0.7455 0.000 0.888 0.028 0.028 0.008 0.048
#> GSM537398 5 0.296 0.6762 0.044 0.000 0.024 0.004 0.872 0.056
#> GSM537402 4 0.663 0.3206 0.000 0.256 0.020 0.496 0.024 0.204
#> GSM537405 1 0.286 0.8143 0.868 0.000 0.092 0.012 0.020 0.008
#> GSM537371 1 0.133 0.8405 0.948 0.000 0.020 0.000 0.032 0.000
#> GSM537421 4 0.564 0.4462 0.012 0.028 0.040 0.564 0.008 0.348
#> GSM537424 5 0.515 0.5476 0.108 0.000 0.156 0.012 0.700 0.024
#> GSM537432 6 0.662 -0.3120 0.012 0.008 0.172 0.340 0.016 0.452
#> GSM537331 6 0.627 0.4311 0.000 0.164 0.020 0.004 0.356 0.456
#> GSM537332 3 0.628 0.3950 0.000 0.068 0.568 0.236 0.004 0.124
#> GSM537334 6 0.557 0.4424 0.000 0.100 0.016 0.000 0.348 0.536
#> GSM537338 6 0.585 0.5310 0.000 0.116 0.012 0.016 0.288 0.568
#> GSM537353 2 0.629 -0.1101 0.004 0.432 0.012 0.168 0.004 0.380
#> GSM537357 1 0.194 0.8391 0.928 0.000 0.008 0.016 0.036 0.012
#> GSM537358 2 0.227 0.7440 0.000 0.904 0.020 0.008 0.004 0.064
#> GSM537375 6 0.576 0.5386 0.004 0.100 0.000 0.092 0.148 0.656
#> GSM537389 2 0.201 0.7484 0.000 0.908 0.000 0.076 0.008 0.008
#> GSM537390 2 0.317 0.7215 0.000 0.836 0.012 0.120 0.000 0.032
#> GSM537393 6 0.601 0.5201 0.000 0.176 0.012 0.112 0.068 0.632
#> GSM537399 3 0.562 0.2290 0.008 0.024 0.500 0.004 0.416 0.048
#> GSM537407 3 0.345 0.6179 0.008 0.000 0.816 0.008 0.140 0.028
#> GSM537408 2 0.284 0.7191 0.000 0.860 0.100 0.000 0.008 0.032
#> GSM537428 6 0.580 0.5897 0.000 0.192 0.016 0.000 0.224 0.568
#> GSM537354 6 0.595 0.5961 0.000 0.188 0.000 0.056 0.152 0.604
#> GSM537410 4 0.564 0.5972 0.004 0.100 0.068 0.700 0.024 0.104
#> GSM537413 2 0.440 0.5332 0.004 0.688 0.020 0.268 0.000 0.020
#> GSM537396 5 0.713 -0.0898 0.000 0.264 0.032 0.288 0.392 0.024
#> GSM537397 5 0.261 0.6835 0.024 0.016 0.016 0.004 0.900 0.040
#> GSM537330 3 0.704 0.3934 0.000 0.076 0.500 0.148 0.024 0.252
#> GSM537369 1 0.634 0.6490 0.612 0.000 0.104 0.052 0.192 0.040
#> GSM537373 4 0.753 0.3700 0.004 0.128 0.092 0.480 0.248 0.048
#> GSM537401 5 0.219 0.6609 0.000 0.032 0.004 0.008 0.912 0.044
#> GSM537343 3 0.382 0.5767 0.016 0.000 0.772 0.008 0.188 0.016
#> GSM537367 3 0.465 0.4507 0.016 0.000 0.668 0.276 0.036 0.004
#> GSM537382 4 0.653 0.3540 0.000 0.084 0.040 0.500 0.036 0.340
#> GSM537385 2 0.652 0.2687 0.000 0.544 0.004 0.180 0.068 0.204
#> GSM537391 5 0.469 0.5289 0.184 0.000 0.024 0.028 0.732 0.032
#> GSM537419 2 0.177 0.7595 0.000 0.924 0.004 0.060 0.000 0.012
#> GSM537420 1 0.634 0.6490 0.612 0.000 0.104 0.052 0.192 0.040
#> GSM537429 6 0.827 0.1720 0.000 0.088 0.144 0.180 0.188 0.400
#> GSM537431 3 0.664 0.1792 0.020 0.000 0.416 0.308 0.008 0.248
#> GSM537387 1 0.437 0.6996 0.720 0.000 0.012 0.024 0.228 0.016
#> GSM537414 3 0.462 0.6036 0.036 0.000 0.716 0.036 0.004 0.208
#> GSM537433 3 0.352 0.6131 0.012 0.000 0.808 0.028 0.148 0.004
#> GSM537335 5 0.500 -0.2391 0.000 0.032 0.020 0.000 0.476 0.472
#> GSM537339 5 0.214 0.6792 0.016 0.020 0.000 0.004 0.916 0.044
#> GSM537340 4 0.635 0.4026 0.016 0.036 0.068 0.492 0.012 0.376
#> GSM537344 1 0.634 0.6490 0.612 0.000 0.104 0.052 0.192 0.040
#> GSM537346 3 0.573 0.4572 0.000 0.156 0.568 0.000 0.016 0.260
#> GSM537351 1 0.376 0.7497 0.820 0.000 0.096 0.016 0.016 0.052
#> GSM537352 6 0.643 0.5377 0.000 0.172 0.008 0.124 0.108 0.588
#> GSM537359 2 0.238 0.7431 0.000 0.900 0.056 0.004 0.008 0.032
#> GSM537360 4 0.589 0.0237 0.000 0.404 0.004 0.420 0.000 0.172
#> GSM537364 1 0.196 0.8226 0.928 0.000 0.032 0.012 0.012 0.016
#> GSM537365 3 0.364 0.6412 0.000 0.012 0.832 0.040 0.084 0.032
#> GSM537372 5 0.281 0.6582 0.092 0.000 0.036 0.008 0.864 0.000
#> GSM537384 5 0.303 0.6554 0.092 0.000 0.040 0.008 0.856 0.004
#> GSM537394 2 0.412 0.6341 0.000 0.760 0.168 0.004 0.008 0.060
#> GSM537403 4 0.591 0.5468 0.004 0.024 0.112 0.616 0.016 0.228
#> GSM537406 2 0.564 0.2483 0.000 0.540 0.036 0.372 0.028 0.024
#> GSM537411 6 0.752 0.2729 0.000 0.276 0.024 0.232 0.076 0.392
#> GSM537412 4 0.453 0.5652 0.004 0.156 0.032 0.752 0.004 0.052
#> GSM537416 4 0.515 0.5212 0.012 0.012 0.116 0.696 0.004 0.160
#> GSM537426 4 0.458 0.5522 0.004 0.168 0.020 0.740 0.004 0.064
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> CV:kmeans 101 0.300 0.627 2
#> CV:kmeans 77 0.572 0.263 3
#> CV:kmeans 57 0.768 0.820 4
#> CV:kmeans 54 0.909 0.749 5
#> CV:kmeans 64 0.142 0.764 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 104 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.864 0.901 0.962 0.5020 0.498 0.498
#> 3 3 0.424 0.335 0.626 0.3275 0.730 0.508
#> 4 4 0.479 0.519 0.709 0.1253 0.753 0.400
#> 5 5 0.547 0.425 0.646 0.0675 0.832 0.450
#> 6 6 0.592 0.479 0.666 0.0410 0.893 0.537
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
#> GSM537341 1 0.9732 0.328 0.596 0.404
#> GSM537345 1 0.0000 0.956 1.000 0.000
#> GSM537355 2 0.0000 0.961 0.000 1.000
#> GSM537366 1 0.0000 0.956 1.000 0.000
#> GSM537370 2 0.9635 0.336 0.388 0.612
#> GSM537380 2 0.0000 0.961 0.000 1.000
#> GSM537392 2 0.0000 0.961 0.000 1.000
#> GSM537415 2 0.0000 0.961 0.000 1.000
#> GSM537417 2 0.9732 0.314 0.404 0.596
#> GSM537422 1 0.0000 0.956 1.000 0.000
#> GSM537423 2 0.0000 0.961 0.000 1.000
#> GSM537427 2 0.0000 0.961 0.000 1.000
#> GSM537430 2 0.0000 0.961 0.000 1.000
#> GSM537336 1 0.0000 0.956 1.000 0.000
#> GSM537337 2 0.0000 0.961 0.000 1.000
#> GSM537348 1 0.0000 0.956 1.000 0.000
#> GSM537349 2 0.0000 0.961 0.000 1.000
#> GSM537356 1 0.0000 0.956 1.000 0.000
#> GSM537361 1 0.0000 0.956 1.000 0.000
#> GSM537374 2 0.0000 0.961 0.000 1.000
#> GSM537377 1 0.0000 0.956 1.000 0.000
#> GSM537378 2 0.0000 0.961 0.000 1.000
#> GSM537379 2 0.0000 0.961 0.000 1.000
#> GSM537383 2 0.0000 0.961 0.000 1.000
#> GSM537388 2 0.0000 0.961 0.000 1.000
#> GSM537395 2 0.0000 0.961 0.000 1.000
#> GSM537400 1 0.0000 0.956 1.000 0.000
#> GSM537404 1 0.5946 0.806 0.856 0.144
#> GSM537409 2 0.0000 0.961 0.000 1.000
#> GSM537418 1 0.0000 0.956 1.000 0.000
#> GSM537425 1 0.0000 0.956 1.000 0.000
#> GSM537333 1 0.0672 0.950 0.992 0.008
#> GSM537342 2 0.0376 0.957 0.004 0.996
#> GSM537347 1 0.4690 0.858 0.900 0.100
#> GSM537350 1 0.0000 0.956 1.000 0.000
#> GSM537362 1 0.0000 0.956 1.000 0.000
#> GSM537363 1 0.0000 0.956 1.000 0.000
#> GSM537368 1 0.0000 0.956 1.000 0.000
#> GSM537376 2 0.0000 0.961 0.000 1.000
#> GSM537381 1 0.0000 0.956 1.000 0.000
#> GSM537386 2 0.0000 0.961 0.000 1.000
#> GSM537398 1 0.0000 0.956 1.000 0.000
#> GSM537402 2 0.0000 0.961 0.000 1.000
#> GSM537405 1 0.0000 0.956 1.000 0.000
#> GSM537371 1 0.0000 0.956 1.000 0.000
#> GSM537421 2 0.6801 0.758 0.180 0.820
#> GSM537424 1 0.0000 0.956 1.000 0.000
#> GSM537432 1 0.0672 0.950 0.992 0.008
#> GSM537331 2 0.0000 0.961 0.000 1.000
#> GSM537332 2 0.0000 0.961 0.000 1.000
#> GSM537334 2 0.0000 0.961 0.000 1.000
#> GSM537338 2 0.0000 0.961 0.000 1.000
#> GSM537353 2 0.0000 0.961 0.000 1.000
#> GSM537357 1 0.0000 0.956 1.000 0.000
#> GSM537358 2 0.0000 0.961 0.000 1.000
#> GSM537375 2 0.0000 0.961 0.000 1.000
#> GSM537389 2 0.0000 0.961 0.000 1.000
#> GSM537390 2 0.0000 0.961 0.000 1.000
#> GSM537393 2 0.0000 0.961 0.000 1.000
#> GSM537399 1 0.0000 0.956 1.000 0.000
#> GSM537407 1 0.0000 0.956 1.000 0.000
#> GSM537408 2 0.0000 0.961 0.000 1.000
#> GSM537428 2 0.0000 0.961 0.000 1.000
#> GSM537354 2 0.0000 0.961 0.000 1.000
#> GSM537410 2 0.0000 0.961 0.000 1.000
#> GSM537413 2 0.0000 0.961 0.000 1.000
#> GSM537396 2 0.6712 0.765 0.176 0.824
#> GSM537397 1 0.0672 0.949 0.992 0.008
#> GSM537330 2 0.0000 0.961 0.000 1.000
#> GSM537369 1 0.0000 0.956 1.000 0.000
#> GSM537373 2 0.9686 0.322 0.396 0.604
#> GSM537401 1 0.9732 0.328 0.596 0.404
#> GSM537343 1 0.0000 0.956 1.000 0.000
#> GSM537367 1 0.0000 0.956 1.000 0.000
#> GSM537382 2 0.0000 0.961 0.000 1.000
#> GSM537385 2 0.0000 0.961 0.000 1.000
#> GSM537391 1 0.0000 0.956 1.000 0.000
#> GSM537419 2 0.0000 0.961 0.000 1.000
#> GSM537420 1 0.0000 0.956 1.000 0.000
#> GSM537429 1 0.9815 0.284 0.580 0.420
#> GSM537431 1 0.0672 0.950 0.992 0.008
#> GSM537387 1 0.0000 0.956 1.000 0.000
#> GSM537414 1 0.0000 0.956 1.000 0.000
#> GSM537433 1 0.0000 0.956 1.000 0.000
#> GSM537335 1 0.9881 0.241 0.564 0.436
#> GSM537339 1 0.0000 0.956 1.000 0.000
#> GSM537340 2 0.9732 0.314 0.404 0.596
#> GSM537344 1 0.0000 0.956 1.000 0.000
#> GSM537346 2 0.0000 0.961 0.000 1.000
#> GSM537351 1 0.0000 0.956 1.000 0.000
#> GSM537352 2 0.0000 0.961 0.000 1.000
#> GSM537359 2 0.0000 0.961 0.000 1.000
#> GSM537360 2 0.0000 0.961 0.000 1.000
#> GSM537364 1 0.0000 0.956 1.000 0.000
#> GSM537365 1 0.0000 0.956 1.000 0.000
#> GSM537372 1 0.0000 0.956 1.000 0.000
#> GSM537384 1 0.0000 0.956 1.000 0.000
#> GSM537394 2 0.0000 0.961 0.000 1.000
#> GSM537403 2 0.0000 0.961 0.000 1.000
#> GSM537406 2 0.0000 0.961 0.000 1.000
#> GSM537411 2 0.0000 0.961 0.000 1.000
#> GSM537412 2 0.0000 0.961 0.000 1.000
#> GSM537416 2 0.2778 0.916 0.048 0.952
#> GSM537426 2 0.0000 0.961 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.6033 0.59766 0.660 0.336 0.004
#> GSM537345 1 0.2711 0.74753 0.912 0.088 0.000
#> GSM537355 2 0.6286 0.16565 0.000 0.536 0.464
#> GSM537366 1 0.1453 0.75890 0.968 0.008 0.024
#> GSM537370 2 0.8008 0.31878 0.192 0.656 0.152
#> GSM537380 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537392 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537415 3 0.6302 -0.42312 0.000 0.480 0.520
#> GSM537417 3 0.9267 0.12258 0.316 0.180 0.504
#> GSM537422 3 0.9254 0.09666 0.332 0.172 0.496
#> GSM537423 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537427 2 0.3116 0.49757 0.000 0.892 0.108
#> GSM537430 2 0.6308 0.41376 0.000 0.508 0.492
#> GSM537336 1 0.0892 0.75855 0.980 0.000 0.020
#> GSM537337 2 0.1411 0.47471 0.000 0.964 0.036
#> GSM537348 1 0.5706 0.61483 0.680 0.320 0.000
#> GSM537349 3 0.6309 -0.44333 0.000 0.500 0.500
#> GSM537356 1 0.1182 0.76152 0.976 0.012 0.012
#> GSM537361 1 0.7838 0.20253 0.488 0.052 0.460
#> GSM537374 2 0.4353 0.47667 0.008 0.836 0.156
#> GSM537377 1 0.3375 0.74631 0.892 0.100 0.008
#> GSM537378 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537379 3 0.7232 0.21702 0.028 0.428 0.544
#> GSM537383 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537388 2 0.5678 0.40952 0.000 0.684 0.316
#> GSM537395 2 0.5760 0.40707 0.000 0.672 0.328
#> GSM537400 3 0.9405 0.18169 0.204 0.300 0.496
#> GSM537404 3 0.8408 0.08457 0.344 0.100 0.556
#> GSM537409 3 0.4842 0.25510 0.000 0.224 0.776
#> GSM537418 1 0.0237 0.76190 0.996 0.000 0.004
#> GSM537425 1 0.6235 0.34045 0.564 0.000 0.436
#> GSM537333 3 0.9405 0.12543 0.300 0.204 0.496
#> GSM537342 3 0.6402 0.16076 0.040 0.236 0.724
#> GSM537347 3 0.9790 0.11337 0.260 0.308 0.432
#> GSM537350 1 0.0424 0.76167 0.992 0.008 0.000
#> GSM537362 1 0.4413 0.72073 0.832 0.160 0.008
#> GSM537363 1 0.6209 0.42574 0.628 0.004 0.368
#> GSM537368 1 0.0747 0.75972 0.984 0.000 0.016
#> GSM537376 2 0.5859 0.45502 0.000 0.656 0.344
#> GSM537381 1 0.0592 0.76082 0.988 0.000 0.012
#> GSM537386 2 0.6309 0.40796 0.000 0.500 0.500
#> GSM537398 1 0.5706 0.61483 0.680 0.320 0.000
#> GSM537402 3 0.6307 -0.42110 0.000 0.488 0.512
#> GSM537405 1 0.0747 0.75989 0.984 0.000 0.016
#> GSM537371 1 0.0747 0.75989 0.984 0.000 0.016
#> GSM537421 3 0.8836 -0.00128 0.120 0.388 0.492
#> GSM537424 1 0.3116 0.74251 0.892 0.108 0.000
#> GSM537432 3 0.9206 0.12583 0.188 0.288 0.524
#> GSM537331 2 0.0747 0.46806 0.000 0.984 0.016
#> GSM537332 3 0.3686 0.28783 0.000 0.140 0.860
#> GSM537334 2 0.1170 0.45227 0.008 0.976 0.016
#> GSM537338 2 0.0829 0.46047 0.004 0.984 0.012
#> GSM537353 3 0.6308 -0.42414 0.000 0.492 0.508
#> GSM537357 1 0.0592 0.76054 0.988 0.000 0.012
#> GSM537358 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537375 2 0.3682 0.38521 0.008 0.876 0.116
#> GSM537389 3 0.6309 -0.44333 0.000 0.500 0.500
#> GSM537390 3 0.6307 -0.43101 0.000 0.488 0.512
#> GSM537393 2 0.3816 0.45799 0.000 0.852 0.148
#> GSM537399 1 0.4505 0.74145 0.860 0.092 0.048
#> GSM537407 1 0.6180 0.37052 0.584 0.000 0.416
#> GSM537408 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537428 2 0.0592 0.46620 0.000 0.988 0.012
#> GSM537354 2 0.1753 0.47332 0.000 0.952 0.048
#> GSM537410 3 0.3454 0.16381 0.008 0.104 0.888
#> GSM537413 3 0.6307 -0.43046 0.000 0.488 0.512
#> GSM537396 3 0.9616 -0.24653 0.236 0.296 0.468
#> GSM537397 1 0.5733 0.61159 0.676 0.324 0.000
#> GSM537330 3 0.4750 0.27824 0.000 0.216 0.784
#> GSM537369 1 0.0000 0.76202 1.000 0.000 0.000
#> GSM537373 3 0.7181 -0.00425 0.468 0.024 0.508
#> GSM537401 1 0.6318 0.57285 0.636 0.356 0.008
#> GSM537343 1 0.3941 0.67197 0.844 0.000 0.156
#> GSM537367 1 0.6518 0.24987 0.512 0.004 0.484
#> GSM537382 2 0.6140 0.01448 0.000 0.596 0.404
#> GSM537385 2 0.6291 0.41638 0.000 0.532 0.468
#> GSM537391 1 0.5650 0.61881 0.688 0.312 0.000
#> GSM537419 2 0.6309 0.40759 0.000 0.500 0.500
#> GSM537420 1 0.0000 0.76202 1.000 0.000 0.000
#> GSM537429 2 0.9858 -0.10730 0.256 0.396 0.348
#> GSM537431 3 0.8973 0.03707 0.364 0.136 0.500
#> GSM537387 1 0.5650 0.61881 0.688 0.312 0.000
#> GSM537414 3 0.9281 0.08259 0.340 0.172 0.488
#> GSM537433 1 0.6252 0.32830 0.556 0.000 0.444
#> GSM537335 2 0.6318 -0.10545 0.356 0.636 0.008
#> GSM537339 1 0.5810 0.60106 0.664 0.336 0.000
#> GSM537340 3 0.8455 0.25139 0.296 0.120 0.584
#> GSM537344 1 0.0000 0.76202 1.000 0.000 0.000
#> GSM537346 3 0.6018 0.19837 0.008 0.308 0.684
#> GSM537351 1 0.5882 0.46478 0.652 0.000 0.348
#> GSM537352 2 0.2261 0.48655 0.000 0.932 0.068
#> GSM537359 2 0.6309 0.41263 0.000 0.504 0.496
#> GSM537360 3 0.6295 -0.41601 0.000 0.472 0.528
#> GSM537364 1 0.1860 0.74518 0.948 0.000 0.052
#> GSM537365 1 0.6521 0.24213 0.504 0.004 0.492
#> GSM537372 1 0.3551 0.73354 0.868 0.132 0.000
#> GSM537384 1 0.3340 0.73809 0.880 0.120 0.000
#> GSM537394 3 0.6180 -0.34851 0.000 0.416 0.584
#> GSM537403 3 0.4473 0.30703 0.008 0.164 0.828
#> GSM537406 3 0.6302 -0.42204 0.000 0.480 0.520
#> GSM537411 2 0.5291 0.47717 0.000 0.732 0.268
#> GSM537412 3 0.2796 0.17062 0.000 0.092 0.908
#> GSM537416 3 0.6974 0.33851 0.104 0.168 0.728
#> GSM537426 3 0.6252 -0.38070 0.000 0.444 0.556
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.3854 0.62836 0.844 0.016 0.016 0.124
#> GSM537345 1 0.4284 0.73285 0.764 0.000 0.224 0.012
#> GSM537355 4 0.7024 0.34836 0.020 0.344 0.080 0.556
#> GSM537366 1 0.4877 0.57159 0.664 0.000 0.328 0.008
#> GSM537370 1 0.8145 -0.35792 0.388 0.300 0.008 0.304
#> GSM537380 2 0.2408 0.70983 0.000 0.896 0.000 0.104
#> GSM537392 2 0.2469 0.70656 0.000 0.892 0.000 0.108
#> GSM537415 2 0.2281 0.70719 0.000 0.904 0.000 0.096
#> GSM537417 3 0.4331 0.51677 0.000 0.000 0.712 0.288
#> GSM537422 3 0.2376 0.65129 0.016 0.000 0.916 0.068
#> GSM537423 2 0.2011 0.72416 0.000 0.920 0.000 0.080
#> GSM537427 4 0.6123 0.41243 0.056 0.372 0.000 0.572
#> GSM537430 2 0.3801 0.58110 0.000 0.780 0.000 0.220
#> GSM537336 1 0.4819 0.67089 0.652 0.000 0.344 0.004
#> GSM537337 4 0.3903 0.60134 0.012 0.156 0.008 0.824
#> GSM537348 1 0.2271 0.67938 0.916 0.000 0.008 0.076
#> GSM537349 2 0.0707 0.73541 0.000 0.980 0.000 0.020
#> GSM537356 1 0.3498 0.71765 0.832 0.000 0.160 0.008
#> GSM537361 3 0.2521 0.62023 0.064 0.000 0.912 0.024
#> GSM537374 4 0.7444 0.41558 0.148 0.336 0.008 0.508
#> GSM537377 1 0.4502 0.72857 0.748 0.000 0.236 0.016
#> GSM537378 2 0.1940 0.72711 0.000 0.924 0.000 0.076
#> GSM537379 4 0.5472 -0.07038 0.000 0.016 0.440 0.544
#> GSM537383 2 0.2345 0.71204 0.000 0.900 0.000 0.100
#> GSM537388 2 0.5876 -0.04793 0.020 0.528 0.008 0.444
#> GSM537395 4 0.4917 0.47154 0.000 0.336 0.008 0.656
#> GSM537400 3 0.4567 0.51349 0.008 0.000 0.716 0.276
#> GSM537404 3 0.4578 0.63954 0.044 0.056 0.832 0.068
#> GSM537409 2 0.7641 0.02605 0.000 0.416 0.208 0.376
#> GSM537418 1 0.4699 0.69707 0.676 0.000 0.320 0.004
#> GSM537425 3 0.3448 0.56095 0.168 0.000 0.828 0.004
#> GSM537333 3 0.4343 0.53376 0.004 0.000 0.732 0.264
#> GSM537342 4 0.8561 0.20207 0.072 0.284 0.156 0.488
#> GSM537347 3 0.7209 0.39960 0.116 0.024 0.596 0.264
#> GSM537350 1 0.3306 0.72340 0.840 0.004 0.156 0.000
#> GSM537362 1 0.5327 0.70401 0.720 0.000 0.220 0.060
#> GSM537363 3 0.7976 -0.03049 0.400 0.040 0.444 0.116
#> GSM537368 1 0.4655 0.69758 0.684 0.000 0.312 0.004
#> GSM537376 4 0.5966 0.32834 0.000 0.316 0.060 0.624
#> GSM537381 1 0.5080 0.55441 0.576 0.000 0.420 0.004
#> GSM537386 2 0.1674 0.73398 0.004 0.952 0.012 0.032
#> GSM537398 1 0.3149 0.68684 0.880 0.000 0.032 0.088
#> GSM537402 2 0.5344 0.49789 0.000 0.668 0.032 0.300
#> GSM537405 1 0.4837 0.66864 0.648 0.000 0.348 0.004
#> GSM537371 1 0.4677 0.69533 0.680 0.000 0.316 0.004
#> GSM537421 4 0.7320 0.33244 0.008 0.252 0.176 0.564
#> GSM537424 1 0.3790 0.73836 0.820 0.000 0.164 0.016
#> GSM537432 3 0.6863 0.00525 0.040 0.032 0.468 0.460
#> GSM537331 4 0.6879 0.52882 0.132 0.232 0.012 0.624
#> GSM537332 3 0.7082 0.21376 0.000 0.368 0.500 0.132
#> GSM537334 4 0.6435 0.57744 0.136 0.144 0.024 0.696
#> GSM537338 4 0.5912 0.58309 0.116 0.148 0.012 0.724
#> GSM537353 2 0.5731 0.10375 0.000 0.544 0.028 0.428
#> GSM537357 1 0.4632 0.70094 0.688 0.000 0.308 0.004
#> GSM537358 2 0.2197 0.72216 0.000 0.916 0.004 0.080
#> GSM537375 4 0.4601 0.60249 0.020 0.104 0.056 0.820
#> GSM537389 2 0.0895 0.73582 0.000 0.976 0.004 0.020
#> GSM537390 2 0.1209 0.73735 0.000 0.964 0.004 0.032
#> GSM537393 4 0.5358 0.56680 0.004 0.220 0.052 0.724
#> GSM537399 1 0.5810 0.27406 0.624 0.020 0.340 0.016
#> GSM537407 3 0.2760 0.58417 0.128 0.000 0.872 0.000
#> GSM537408 2 0.2376 0.73151 0.000 0.916 0.016 0.068
#> GSM537428 4 0.6246 0.55933 0.092 0.216 0.012 0.680
#> GSM537354 4 0.3854 0.60391 0.008 0.152 0.012 0.828
#> GSM537410 2 0.7403 0.33187 0.016 0.556 0.140 0.288
#> GSM537413 2 0.2489 0.71049 0.000 0.912 0.020 0.068
#> GSM537396 2 0.6177 0.53677 0.172 0.704 0.016 0.108
#> GSM537397 1 0.2402 0.67825 0.912 0.000 0.012 0.076
#> GSM537330 3 0.8014 0.03977 0.008 0.380 0.384 0.228
#> GSM537369 1 0.4122 0.72939 0.760 0.000 0.236 0.004
#> GSM537373 2 0.8188 0.38231 0.196 0.568 0.084 0.152
#> GSM537401 1 0.4987 0.54554 0.772 0.036 0.016 0.176
#> GSM537343 3 0.4543 0.21280 0.324 0.000 0.676 0.000
#> GSM537367 3 0.3801 0.62622 0.064 0.004 0.856 0.076
#> GSM537382 4 0.5277 0.46013 0.008 0.116 0.108 0.768
#> GSM537385 2 0.4128 0.62276 0.020 0.808 0.004 0.168
#> GSM537391 1 0.3471 0.70551 0.868 0.000 0.060 0.072
#> GSM537419 2 0.1389 0.73825 0.000 0.952 0.000 0.048
#> GSM537420 1 0.4220 0.72539 0.748 0.000 0.248 0.004
#> GSM537429 4 0.9777 0.19786 0.240 0.216 0.188 0.356
#> GSM537431 3 0.3196 0.61714 0.008 0.000 0.856 0.136
#> GSM537387 1 0.3581 0.72456 0.852 0.000 0.116 0.032
#> GSM537414 3 0.3074 0.61578 0.000 0.000 0.848 0.152
#> GSM537433 3 0.3632 0.56728 0.156 0.004 0.832 0.008
#> GSM537335 4 0.6942 0.41021 0.344 0.068 0.024 0.564
#> GSM537339 1 0.3171 0.65345 0.876 0.004 0.016 0.104
#> GSM537340 4 0.8229 0.22352 0.032 0.200 0.284 0.484
#> GSM537344 1 0.4313 0.72082 0.736 0.000 0.260 0.004
#> GSM537346 3 0.7557 0.18431 0.000 0.284 0.484 0.232
#> GSM537351 3 0.4313 0.30872 0.260 0.000 0.736 0.004
#> GSM537352 4 0.4049 0.59382 0.008 0.180 0.008 0.804
#> GSM537359 2 0.2048 0.73189 0.000 0.928 0.008 0.064
#> GSM537360 2 0.4204 0.65831 0.000 0.788 0.020 0.192
#> GSM537364 1 0.5147 0.49565 0.536 0.000 0.460 0.004
#> GSM537365 3 0.3396 0.64351 0.068 0.024 0.884 0.024
#> GSM537372 1 0.1284 0.71187 0.964 0.000 0.024 0.012
#> GSM537384 1 0.1767 0.72091 0.944 0.000 0.044 0.012
#> GSM537394 2 0.4485 0.63661 0.000 0.796 0.152 0.052
#> GSM537403 4 0.7473 -0.10985 0.008 0.140 0.380 0.472
#> GSM537406 2 0.2799 0.69432 0.000 0.884 0.008 0.108
#> GSM537411 4 0.6283 0.18939 0.020 0.444 0.024 0.512
#> GSM537412 2 0.6390 0.46705 0.000 0.644 0.132 0.224
#> GSM537416 3 0.7281 0.09344 0.004 0.128 0.448 0.420
#> GSM537426 2 0.5365 0.52849 0.000 0.692 0.044 0.264
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.4551 0.2967 0.216 0.020 0.004 0.020 0.740
#> GSM537345 1 0.1788 0.7485 0.932 0.000 0.008 0.004 0.056
#> GSM537355 3 0.8467 -0.1589 0.000 0.168 0.304 0.240 0.288
#> GSM537366 1 0.6711 0.4872 0.532 0.004 0.168 0.016 0.280
#> GSM537370 5 0.6289 0.2834 0.056 0.276 0.016 0.040 0.612
#> GSM537380 2 0.1442 0.7340 0.000 0.952 0.004 0.012 0.032
#> GSM537392 2 0.1498 0.7323 0.000 0.952 0.008 0.016 0.024
#> GSM537415 2 0.3544 0.5982 0.000 0.788 0.008 0.200 0.004
#> GSM537417 3 0.3743 0.5227 0.052 0.000 0.840 0.080 0.028
#> GSM537422 3 0.5656 0.5572 0.284 0.000 0.612 0.100 0.004
#> GSM537423 2 0.0693 0.7349 0.000 0.980 0.000 0.012 0.008
#> GSM537427 2 0.7514 0.0313 0.000 0.428 0.124 0.092 0.356
#> GSM537430 2 0.5105 0.5768 0.000 0.744 0.052 0.060 0.144
#> GSM537336 1 0.0865 0.7533 0.972 0.000 0.024 0.004 0.000
#> GSM537337 5 0.8024 0.0750 0.000 0.096 0.256 0.260 0.388
#> GSM537348 5 0.4135 0.0904 0.340 0.000 0.004 0.000 0.656
#> GSM537349 2 0.1885 0.7300 0.000 0.932 0.004 0.044 0.020
#> GSM537356 1 0.5205 0.5017 0.592 0.000 0.044 0.004 0.360
#> GSM537361 3 0.4598 0.5124 0.312 0.000 0.664 0.008 0.016
#> GSM537374 5 0.7126 0.2390 0.004 0.244 0.128 0.072 0.552
#> GSM537377 1 0.2206 0.7435 0.912 0.000 0.016 0.004 0.068
#> GSM537378 2 0.1588 0.7336 0.000 0.948 0.008 0.028 0.016
#> GSM537379 3 0.5358 0.2653 0.000 0.008 0.692 0.156 0.144
#> GSM537383 2 0.1195 0.7354 0.000 0.960 0.000 0.012 0.028
#> GSM537388 2 0.8198 0.0688 0.000 0.380 0.188 0.144 0.288
#> GSM537395 2 0.8189 -0.0478 0.000 0.396 0.192 0.264 0.148
#> GSM537400 3 0.6716 0.3726 0.148 0.000 0.508 0.320 0.024
#> GSM537404 3 0.5757 0.5767 0.128 0.032 0.712 0.112 0.016
#> GSM537409 4 0.5873 0.4708 0.000 0.204 0.136 0.644 0.016
#> GSM537418 1 0.3085 0.7451 0.868 0.000 0.068 0.004 0.060
#> GSM537425 3 0.6168 0.3149 0.396 0.000 0.512 0.044 0.048
#> GSM537333 3 0.5963 0.4470 0.084 0.000 0.636 0.244 0.036
#> GSM537342 4 0.3792 0.4857 0.016 0.032 0.060 0.852 0.040
#> GSM537347 3 0.3950 0.4952 0.016 0.024 0.824 0.016 0.120
#> GSM537350 1 0.5142 0.5777 0.660 0.004 0.036 0.012 0.288
#> GSM537362 1 0.4483 0.6455 0.768 0.000 0.064 0.012 0.156
#> GSM537363 4 0.7007 0.0409 0.348 0.000 0.108 0.484 0.060
#> GSM537368 1 0.0854 0.7597 0.976 0.000 0.012 0.004 0.008
#> GSM537376 4 0.5831 0.4971 0.004 0.088 0.084 0.708 0.116
#> GSM537381 1 0.4049 0.6984 0.792 0.000 0.124 0.000 0.084
#> GSM537386 2 0.2807 0.7206 0.000 0.892 0.032 0.020 0.056
#> GSM537398 5 0.4576 0.0868 0.376 0.000 0.016 0.000 0.608
#> GSM537402 4 0.5262 0.1526 0.000 0.408 0.012 0.552 0.028
#> GSM537405 1 0.1205 0.7456 0.956 0.000 0.040 0.004 0.000
#> GSM537371 1 0.0771 0.7542 0.976 0.000 0.020 0.004 0.000
#> GSM537421 4 0.4303 0.5074 0.020 0.048 0.072 0.824 0.036
#> GSM537424 1 0.3427 0.6878 0.796 0.000 0.012 0.000 0.192
#> GSM537432 4 0.7465 0.1996 0.068 0.012 0.260 0.520 0.140
#> GSM537331 5 0.7174 0.2915 0.000 0.196 0.224 0.056 0.524
#> GSM537332 3 0.6476 0.3045 0.000 0.232 0.564 0.188 0.016
#> GSM537334 5 0.6376 0.3148 0.000 0.056 0.260 0.084 0.600
#> GSM537338 5 0.7086 0.2495 0.000 0.060 0.264 0.148 0.528
#> GSM537353 2 0.6573 0.1305 0.000 0.528 0.048 0.340 0.084
#> GSM537357 1 0.0324 0.7584 0.992 0.000 0.004 0.004 0.000
#> GSM537358 2 0.1095 0.7353 0.000 0.968 0.012 0.008 0.012
#> GSM537375 4 0.7552 0.0157 0.000 0.040 0.288 0.376 0.296
#> GSM537389 2 0.1549 0.7306 0.000 0.944 0.000 0.040 0.016
#> GSM537390 2 0.1364 0.7297 0.000 0.952 0.012 0.036 0.000
#> GSM537393 4 0.8447 0.0780 0.000 0.160 0.272 0.312 0.256
#> GSM537399 5 0.7108 -0.1869 0.168 0.024 0.400 0.004 0.404
#> GSM537407 3 0.6132 0.4868 0.260 0.004 0.620 0.032 0.084
#> GSM537408 2 0.2342 0.7189 0.000 0.916 0.040 0.020 0.024
#> GSM537428 5 0.7711 0.2443 0.000 0.184 0.236 0.104 0.476
#> GSM537354 5 0.8015 0.0260 0.000 0.088 0.256 0.292 0.364
#> GSM537410 4 0.4765 0.4957 0.008 0.144 0.080 0.760 0.008
#> GSM537413 2 0.2629 0.6898 0.000 0.880 0.012 0.104 0.004
#> GSM537396 2 0.7024 0.0844 0.004 0.420 0.008 0.232 0.336
#> GSM537397 5 0.4623 0.0849 0.340 0.012 0.000 0.008 0.640
#> GSM537330 3 0.6972 0.2639 0.000 0.156 0.588 0.156 0.100
#> GSM537369 1 0.2074 0.7432 0.896 0.000 0.000 0.000 0.104
#> GSM537373 4 0.8139 0.2144 0.036 0.284 0.044 0.420 0.216
#> GSM537401 5 0.4159 0.3596 0.160 0.020 0.000 0.032 0.788
#> GSM537343 1 0.6388 -0.0789 0.460 0.004 0.428 0.016 0.092
#> GSM537367 3 0.6392 0.3146 0.120 0.000 0.472 0.396 0.012
#> GSM537382 4 0.5254 0.4836 0.004 0.048 0.100 0.748 0.100
#> GSM537385 2 0.5891 0.5572 0.000 0.684 0.052 0.132 0.132
#> GSM537391 5 0.4650 -0.1773 0.468 0.000 0.000 0.012 0.520
#> GSM537419 2 0.1430 0.7314 0.000 0.944 0.000 0.052 0.004
#> GSM537420 1 0.2074 0.7435 0.896 0.000 0.000 0.000 0.104
#> GSM537429 5 0.8603 0.0842 0.040 0.088 0.264 0.204 0.404
#> GSM537431 3 0.6194 0.4354 0.112 0.004 0.556 0.320 0.008
#> GSM537387 1 0.3661 0.5660 0.724 0.000 0.000 0.000 0.276
#> GSM537414 3 0.3934 0.5926 0.160 0.000 0.796 0.036 0.008
#> GSM537433 3 0.6572 0.4728 0.276 0.016 0.588 0.032 0.088
#> GSM537335 5 0.5373 0.3729 0.012 0.028 0.220 0.040 0.700
#> GSM537339 5 0.3790 0.2673 0.248 0.000 0.004 0.004 0.744
#> GSM537340 4 0.6161 0.4474 0.056 0.072 0.136 0.700 0.036
#> GSM537344 1 0.1908 0.7476 0.908 0.000 0.000 0.000 0.092
#> GSM537346 3 0.5036 0.4026 0.000 0.216 0.708 0.016 0.060
#> GSM537351 1 0.4227 0.3311 0.692 0.000 0.292 0.016 0.000
#> GSM537352 4 0.8147 0.0572 0.000 0.144 0.168 0.368 0.320
#> GSM537359 2 0.2362 0.7279 0.000 0.916 0.024 0.028 0.032
#> GSM537360 2 0.6177 0.1811 0.000 0.552 0.056 0.348 0.044
#> GSM537364 1 0.2286 0.6836 0.888 0.000 0.108 0.004 0.000
#> GSM537365 3 0.6774 0.5695 0.144 0.052 0.656 0.092 0.056
#> GSM537372 1 0.4294 0.3507 0.532 0.000 0.000 0.000 0.468
#> GSM537384 1 0.4192 0.4667 0.596 0.000 0.000 0.000 0.404
#> GSM537394 2 0.3770 0.6534 0.000 0.824 0.124 0.032 0.020
#> GSM537403 4 0.4989 0.3724 0.004 0.048 0.244 0.696 0.008
#> GSM537406 2 0.4561 0.4721 0.000 0.688 0.012 0.284 0.016
#> GSM537411 4 0.7761 0.1676 0.000 0.348 0.068 0.368 0.216
#> GSM537412 4 0.5790 0.3732 0.004 0.312 0.088 0.592 0.004
#> GSM537416 4 0.4506 0.4277 0.008 0.036 0.192 0.756 0.008
#> GSM537426 4 0.5291 0.3149 0.000 0.348 0.052 0.596 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.3210 0.5928 0.096 0.000 0.000 0.020 0.844 0.040
#> GSM537345 1 0.1843 0.7314 0.912 0.000 0.004 0.000 0.080 0.004
#> GSM537355 6 0.8194 0.2900 0.004 0.108 0.136 0.208 0.112 0.432
#> GSM537366 5 0.6931 0.0197 0.356 0.000 0.232 0.040 0.364 0.008
#> GSM537370 5 0.7003 0.2408 0.020 0.248 0.072 0.044 0.552 0.064
#> GSM537380 2 0.1862 0.7716 0.000 0.932 0.024 0.004 0.020 0.020
#> GSM537392 2 0.2368 0.7669 0.000 0.908 0.028 0.008 0.020 0.036
#> GSM537415 2 0.3968 0.6309 0.000 0.752 0.012 0.208 0.020 0.008
#> GSM537417 3 0.5396 0.5199 0.048 0.004 0.656 0.056 0.004 0.232
#> GSM537422 3 0.6274 0.5003 0.292 0.000 0.536 0.120 0.008 0.044
#> GSM537423 2 0.1719 0.7791 0.000 0.932 0.000 0.032 0.004 0.032
#> GSM537427 6 0.5688 0.4250 0.000 0.332 0.012 0.004 0.112 0.540
#> GSM537430 2 0.4375 0.5381 0.000 0.700 0.020 0.000 0.032 0.248
#> GSM537336 1 0.1511 0.7518 0.940 0.000 0.044 0.012 0.004 0.000
#> GSM537337 6 0.3398 0.5849 0.000 0.032 0.008 0.044 0.068 0.848
#> GSM537348 5 0.3572 0.5788 0.204 0.000 0.000 0.000 0.764 0.032
#> GSM537349 2 0.2815 0.7615 0.000 0.884 0.016 0.056 0.024 0.020
#> GSM537356 5 0.5405 0.2345 0.412 0.000 0.076 0.008 0.500 0.004
#> GSM537361 3 0.4203 0.6000 0.204 0.000 0.744 0.012 0.016 0.024
#> GSM537374 6 0.5984 0.5211 0.000 0.176 0.012 0.012 0.232 0.568
#> GSM537377 1 0.2463 0.7306 0.888 0.000 0.024 0.004 0.080 0.004
#> GSM537378 2 0.2518 0.7721 0.000 0.892 0.008 0.036 0.004 0.060
#> GSM537379 6 0.5076 -0.0305 0.000 0.004 0.412 0.056 0.004 0.524
#> GSM537383 2 0.1483 0.7740 0.000 0.944 0.008 0.000 0.012 0.036
#> GSM537388 6 0.7671 0.3944 0.000 0.212 0.044 0.120 0.164 0.460
#> GSM537395 6 0.5280 0.3594 0.000 0.352 0.004 0.084 0.004 0.556
#> GSM537400 4 0.7789 -0.0277 0.132 0.000 0.304 0.384 0.036 0.144
#> GSM537404 3 0.4939 0.5873 0.088 0.012 0.764 0.056 0.024 0.056
#> GSM537409 4 0.5929 0.5086 0.000 0.160 0.116 0.644 0.012 0.068
#> GSM537418 1 0.4572 0.6788 0.756 0.000 0.080 0.024 0.128 0.012
#> GSM537425 3 0.6019 0.4555 0.300 0.000 0.572 0.060 0.040 0.028
#> GSM537333 3 0.7475 0.1922 0.116 0.000 0.416 0.320 0.032 0.116
#> GSM537342 4 0.4023 0.5396 0.000 0.012 0.008 0.792 0.096 0.092
#> GSM537347 3 0.5269 0.4823 0.012 0.016 0.692 0.028 0.048 0.204
#> GSM537350 1 0.6874 -0.1378 0.428 0.028 0.116 0.040 0.384 0.004
#> GSM537362 1 0.5403 0.5399 0.688 0.000 0.040 0.012 0.144 0.116
#> GSM537363 4 0.6770 0.1506 0.288 0.008 0.100 0.508 0.092 0.004
#> GSM537368 1 0.0767 0.7628 0.976 0.000 0.012 0.004 0.008 0.000
#> GSM537376 4 0.5597 0.2655 0.004 0.040 0.012 0.508 0.024 0.412
#> GSM537381 1 0.4833 0.6012 0.692 0.000 0.168 0.004 0.132 0.004
#> GSM537386 2 0.3634 0.7500 0.000 0.836 0.036 0.036 0.076 0.016
#> GSM537398 5 0.5339 0.4982 0.280 0.000 0.020 0.000 0.608 0.092
#> GSM537402 4 0.6876 0.3244 0.000 0.292 0.028 0.500 0.064 0.116
#> GSM537405 1 0.1692 0.7580 0.932 0.000 0.048 0.008 0.012 0.000
#> GSM537371 1 0.1078 0.7630 0.964 0.000 0.012 0.008 0.016 0.000
#> GSM537421 4 0.5304 0.4660 0.012 0.040 0.036 0.668 0.008 0.236
#> GSM537424 1 0.4670 0.5199 0.680 0.000 0.044 0.004 0.256 0.016
#> GSM537432 4 0.7488 0.3151 0.044 0.008 0.152 0.464 0.060 0.272
#> GSM537331 6 0.6488 0.5382 0.000 0.108 0.056 0.020 0.268 0.548
#> GSM537332 3 0.6712 0.3257 0.000 0.156 0.560 0.200 0.040 0.044
#> GSM537334 6 0.4852 0.5737 0.000 0.012 0.060 0.008 0.244 0.676
#> GSM537338 6 0.3145 0.6009 0.000 0.012 0.012 0.004 0.144 0.828
#> GSM537353 2 0.6814 0.1168 0.000 0.464 0.032 0.192 0.020 0.292
#> GSM537357 1 0.1036 0.7622 0.964 0.000 0.004 0.008 0.024 0.000
#> GSM537358 2 0.2647 0.7623 0.000 0.892 0.040 0.012 0.012 0.044
#> GSM537375 6 0.4286 0.4748 0.000 0.016 0.036 0.140 0.032 0.776
#> GSM537389 2 0.2641 0.7610 0.000 0.888 0.008 0.064 0.028 0.012
#> GSM537390 2 0.1950 0.7796 0.000 0.924 0.028 0.032 0.000 0.016
#> GSM537393 6 0.5428 0.4801 0.000 0.108 0.048 0.116 0.024 0.704
#> GSM537399 3 0.6051 0.0988 0.052 0.032 0.456 0.020 0.436 0.004
#> GSM537407 3 0.4511 0.5876 0.136 0.008 0.760 0.020 0.072 0.004
#> GSM537408 2 0.3464 0.7067 0.000 0.812 0.140 0.016 0.032 0.000
#> GSM537428 6 0.5716 0.6001 0.004 0.068 0.084 0.016 0.148 0.680
#> GSM537354 6 0.3512 0.5701 0.000 0.032 0.012 0.056 0.056 0.844
#> GSM537410 4 0.5216 0.5377 0.000 0.124 0.036 0.720 0.092 0.028
#> GSM537413 2 0.3398 0.7288 0.000 0.824 0.040 0.120 0.016 0.000
#> GSM537396 5 0.6770 -0.0202 0.008 0.256 0.016 0.232 0.472 0.016
#> GSM537397 5 0.3988 0.5863 0.192 0.008 0.012 0.004 0.764 0.020
#> GSM537330 3 0.7656 0.3063 0.000 0.092 0.492 0.140 0.088 0.188
#> GSM537369 1 0.2973 0.6923 0.836 0.000 0.024 0.000 0.136 0.004
#> GSM537373 4 0.6988 0.2838 0.024 0.136 0.028 0.456 0.340 0.016
#> GSM537401 5 0.3675 0.5489 0.064 0.000 0.004 0.020 0.820 0.092
#> GSM537343 3 0.5716 0.1805 0.416 0.008 0.496 0.016 0.052 0.012
#> GSM537367 3 0.6033 0.3383 0.108 0.004 0.520 0.340 0.024 0.004
#> GSM537382 4 0.5740 0.3775 0.012 0.012 0.012 0.608 0.088 0.268
#> GSM537385 2 0.7466 0.2126 0.000 0.480 0.032 0.124 0.156 0.208
#> GSM537391 5 0.4638 0.3925 0.368 0.000 0.000 0.004 0.588 0.040
#> GSM537419 2 0.2587 0.7776 0.000 0.896 0.028 0.048 0.012 0.016
#> GSM537420 1 0.3067 0.7047 0.840 0.000 0.028 0.004 0.124 0.004
#> GSM537429 5 0.8660 -0.2432 0.032 0.028 0.176 0.228 0.312 0.224
#> GSM537431 3 0.6817 0.1456 0.084 0.004 0.448 0.380 0.032 0.052
#> GSM537387 1 0.3766 0.4307 0.684 0.000 0.000 0.000 0.304 0.012
#> GSM537414 3 0.5101 0.5849 0.160 0.000 0.704 0.032 0.008 0.096
#> GSM537433 3 0.4865 0.5868 0.156 0.008 0.736 0.032 0.060 0.008
#> GSM537335 6 0.5164 0.4179 0.004 0.000 0.060 0.008 0.376 0.552
#> GSM537339 5 0.3894 0.5927 0.152 0.000 0.000 0.004 0.772 0.072
#> GSM537340 4 0.6805 0.4137 0.072 0.040 0.064 0.560 0.012 0.252
#> GSM537344 1 0.2604 0.7225 0.872 0.000 0.028 0.000 0.096 0.004
#> GSM537346 3 0.5796 0.4003 0.004 0.176 0.620 0.004 0.024 0.172
#> GSM537351 1 0.3568 0.5906 0.788 0.000 0.172 0.032 0.008 0.000
#> GSM537352 6 0.5693 0.4585 0.000 0.072 0.016 0.188 0.064 0.660
#> GSM537359 2 0.3058 0.7467 0.000 0.848 0.108 0.024 0.020 0.000
#> GSM537360 2 0.6616 0.2265 0.000 0.504 0.048 0.288 0.012 0.148
#> GSM537364 1 0.2294 0.7260 0.896 0.000 0.076 0.020 0.008 0.000
#> GSM537365 3 0.5297 0.5489 0.056 0.056 0.732 0.076 0.080 0.000
#> GSM537372 5 0.3782 0.4011 0.360 0.000 0.004 0.000 0.636 0.000
#> GSM537384 5 0.3996 0.1033 0.484 0.000 0.004 0.000 0.512 0.000
#> GSM537394 2 0.4562 0.6104 0.000 0.712 0.220 0.032 0.032 0.004
#> GSM537403 4 0.5511 0.4941 0.000 0.032 0.108 0.700 0.044 0.116
#> GSM537406 2 0.5192 0.4491 0.000 0.620 0.012 0.292 0.068 0.008
#> GSM537411 6 0.7381 0.0524 0.000 0.312 0.020 0.244 0.060 0.364
#> GSM537412 4 0.6073 0.3843 0.000 0.284 0.100 0.568 0.024 0.024
#> GSM537416 4 0.4395 0.4918 0.004 0.016 0.156 0.756 0.004 0.064
#> GSM537426 4 0.6326 0.3075 0.000 0.324 0.076 0.528 0.024 0.048
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> CV:skmeans 96 0.202 0.3529 2
#> CV:skmeans 30 NA NA 3
#> CV:skmeans 71 0.491 0.0899 4
#> CV:skmeans 43 0.992 0.0427 5
#> CV:skmeans 56 0.451 0.2795 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 104 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 0.716 0.866 0.940 0.4981 0.498 0.498
#> 3 3 0.750 0.833 0.927 0.3075 0.780 0.587
#> 4 4 0.604 0.616 0.818 0.1168 0.924 0.786
#> 5 5 0.621 0.592 0.796 0.0511 0.906 0.696
#> 6 6 0.684 0.663 0.824 0.0549 0.912 0.662
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM537341 1 0.0000 0.9293 1.000 0.000
#> GSM537345 1 0.0000 0.9293 1.000 0.000
#> GSM537355 1 0.3274 0.8976 0.940 0.060
#> GSM537366 1 0.0000 0.9293 1.000 0.000
#> GSM537370 1 0.3584 0.8932 0.932 0.068
#> GSM537380 2 0.0000 0.9381 0.000 1.000
#> GSM537392 2 0.0000 0.9381 0.000 1.000
#> GSM537415 2 0.0000 0.9381 0.000 1.000
#> GSM537417 1 0.8661 0.6093 0.712 0.288
#> GSM537422 1 0.0000 0.9293 1.000 0.000
#> GSM537423 2 0.0000 0.9381 0.000 1.000
#> GSM537427 2 0.0000 0.9381 0.000 1.000
#> GSM537430 2 0.0672 0.9336 0.008 0.992
#> GSM537336 1 0.0000 0.9293 1.000 0.000
#> GSM537337 2 0.0000 0.9381 0.000 1.000
#> GSM537348 1 0.0000 0.9293 1.000 0.000
#> GSM537349 2 0.0000 0.9381 0.000 1.000
#> GSM537356 1 0.0000 0.9293 1.000 0.000
#> GSM537361 1 0.0000 0.9293 1.000 0.000
#> GSM537374 2 0.8207 0.6627 0.256 0.744
#> GSM537377 1 0.0000 0.9293 1.000 0.000
#> GSM537378 2 0.0000 0.9381 0.000 1.000
#> GSM537379 1 0.9286 0.5076 0.656 0.344
#> GSM537383 2 0.0000 0.9381 0.000 1.000
#> GSM537388 2 0.0000 0.9381 0.000 1.000
#> GSM537395 2 0.0000 0.9381 0.000 1.000
#> GSM537400 1 0.3274 0.8976 0.940 0.060
#> GSM537404 1 0.9358 0.4782 0.648 0.352
#> GSM537409 2 0.0000 0.9381 0.000 1.000
#> GSM537418 1 0.0000 0.9293 1.000 0.000
#> GSM537425 1 0.0000 0.9293 1.000 0.000
#> GSM537333 1 0.0000 0.9293 1.000 0.000
#> GSM537342 2 0.1843 0.9211 0.028 0.972
#> GSM537347 1 0.0000 0.9293 1.000 0.000
#> GSM537350 1 0.0000 0.9293 1.000 0.000
#> GSM537362 1 0.0000 0.9293 1.000 0.000
#> GSM537363 1 0.8909 0.5765 0.692 0.308
#> GSM537368 1 0.0000 0.9293 1.000 0.000
#> GSM537376 2 0.0000 0.9381 0.000 1.000
#> GSM537381 1 0.0000 0.9293 1.000 0.000
#> GSM537386 2 0.0000 0.9381 0.000 1.000
#> GSM537398 1 0.0000 0.9293 1.000 0.000
#> GSM537402 2 0.2948 0.9028 0.052 0.948
#> GSM537405 1 0.0000 0.9293 1.000 0.000
#> GSM537371 1 0.0000 0.9293 1.000 0.000
#> GSM537421 2 0.5946 0.8126 0.144 0.856
#> GSM537424 1 0.0000 0.9293 1.000 0.000
#> GSM537432 1 0.2603 0.9079 0.956 0.044
#> GSM537331 2 0.8861 0.5477 0.304 0.696
#> GSM537332 2 0.0938 0.9306 0.012 0.988
#> GSM537334 1 0.4690 0.8669 0.900 0.100
#> GSM537338 2 0.8016 0.6806 0.244 0.756
#> GSM537353 2 0.0000 0.9381 0.000 1.000
#> GSM537357 1 0.0000 0.9293 1.000 0.000
#> GSM537358 2 0.0000 0.9381 0.000 1.000
#> GSM537375 2 0.9358 0.4517 0.352 0.648
#> GSM537389 2 0.0000 0.9381 0.000 1.000
#> GSM537390 2 0.0000 0.9381 0.000 1.000
#> GSM537393 2 0.8443 0.6296 0.272 0.728
#> GSM537399 1 0.0000 0.9293 1.000 0.000
#> GSM537407 1 0.8909 0.5627 0.692 0.308
#> GSM537408 2 0.0000 0.9381 0.000 1.000
#> GSM537428 1 0.5629 0.8372 0.868 0.132
#> GSM537354 2 0.0000 0.9381 0.000 1.000
#> GSM537410 2 0.0000 0.9381 0.000 1.000
#> GSM537413 2 0.0000 0.9381 0.000 1.000
#> GSM537396 2 0.9754 0.3097 0.408 0.592
#> GSM537397 1 0.0938 0.9246 0.988 0.012
#> GSM537330 1 0.2423 0.9085 0.960 0.040
#> GSM537369 1 0.0000 0.9293 1.000 0.000
#> GSM537373 1 1.0000 0.0135 0.504 0.496
#> GSM537401 1 0.8327 0.6621 0.736 0.264
#> GSM537343 1 0.2236 0.9124 0.964 0.036
#> GSM537367 1 0.6531 0.7912 0.832 0.168
#> GSM537382 2 0.6973 0.7611 0.188 0.812
#> GSM537385 2 0.0000 0.9381 0.000 1.000
#> GSM537391 1 0.0376 0.9277 0.996 0.004
#> GSM537419 2 0.0000 0.9381 0.000 1.000
#> GSM537420 1 0.0000 0.9293 1.000 0.000
#> GSM537429 1 0.0000 0.9293 1.000 0.000
#> GSM537431 1 0.8661 0.6229 0.712 0.288
#> GSM537387 1 0.0000 0.9293 1.000 0.000
#> GSM537414 1 0.1843 0.9166 0.972 0.028
#> GSM537433 1 0.2778 0.9047 0.952 0.048
#> GSM537335 1 0.0000 0.9293 1.000 0.000
#> GSM537339 1 0.0000 0.9293 1.000 0.000
#> GSM537340 2 0.8081 0.6708 0.248 0.752
#> GSM537344 1 0.0000 0.9293 1.000 0.000
#> GSM537346 2 0.0000 0.9381 0.000 1.000
#> GSM537351 1 0.0000 0.9293 1.000 0.000
#> GSM537352 2 0.0000 0.9381 0.000 1.000
#> GSM537359 2 0.0000 0.9381 0.000 1.000
#> GSM537360 2 0.0000 0.9381 0.000 1.000
#> GSM537364 1 0.0000 0.9293 1.000 0.000
#> GSM537365 1 0.5629 0.8275 0.868 0.132
#> GSM537372 1 0.0000 0.9293 1.000 0.000
#> GSM537384 1 0.0000 0.9293 1.000 0.000
#> GSM537394 2 0.1843 0.9190 0.028 0.972
#> GSM537403 2 0.0000 0.9381 0.000 1.000
#> GSM537406 2 0.0000 0.9381 0.000 1.000
#> GSM537411 2 0.0000 0.9381 0.000 1.000
#> GSM537412 2 0.0000 0.9381 0.000 1.000
#> GSM537416 2 0.3584 0.8887 0.068 0.932
#> GSM537426 2 0.0000 0.9381 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.0892 0.9323 0.980 0.000 0.020
#> GSM537345 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537355 3 0.5760 0.5064 0.328 0.000 0.672
#> GSM537366 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537370 3 0.5992 0.6119 0.268 0.016 0.716
#> GSM537380 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537392 2 0.0424 0.8900 0.000 0.992 0.008
#> GSM537415 2 0.0747 0.8868 0.000 0.984 0.016
#> GSM537417 1 0.5873 0.5508 0.684 0.004 0.312
#> GSM537422 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537423 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537427 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537430 3 0.1163 0.8735 0.000 0.028 0.972
#> GSM537336 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537337 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537348 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537349 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537356 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537361 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537374 3 0.4346 0.7251 0.000 0.184 0.816
#> GSM537377 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537378 2 0.0747 0.8868 0.000 0.984 0.016
#> GSM537379 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537383 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537388 2 0.3340 0.8185 0.000 0.880 0.120
#> GSM537395 3 0.4062 0.7496 0.000 0.164 0.836
#> GSM537400 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537404 1 0.7665 0.3981 0.600 0.060 0.340
#> GSM537409 2 0.4555 0.7201 0.000 0.800 0.200
#> GSM537418 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537425 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537333 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537342 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537347 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537350 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537362 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537363 1 0.5178 0.7678 0.808 0.028 0.164
#> GSM537368 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537376 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537381 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537386 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537398 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537402 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537405 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537371 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537421 3 0.1411 0.8719 0.000 0.036 0.964
#> GSM537424 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537432 1 0.4062 0.7914 0.836 0.000 0.164
#> GSM537331 3 0.6523 0.6594 0.048 0.228 0.724
#> GSM537332 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537334 3 0.2959 0.8171 0.100 0.000 0.900
#> GSM537338 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537353 2 0.0892 0.8843 0.000 0.980 0.020
#> GSM537357 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537358 2 0.0424 0.8900 0.000 0.992 0.008
#> GSM537375 3 0.3826 0.7952 0.008 0.124 0.868
#> GSM537389 2 0.0237 0.8914 0.000 0.996 0.004
#> GSM537390 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537393 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537399 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537407 1 0.5706 0.5382 0.680 0.320 0.000
#> GSM537408 2 0.0237 0.8914 0.000 0.996 0.004
#> GSM537428 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537354 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537410 3 0.0592 0.8814 0.000 0.012 0.988
#> GSM537413 2 0.5621 0.5741 0.000 0.692 0.308
#> GSM537396 2 0.5363 0.5798 0.276 0.724 0.000
#> GSM537397 1 0.6079 0.3336 0.612 0.000 0.388
#> GSM537330 1 0.0237 0.9428 0.996 0.004 0.000
#> GSM537369 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537373 2 0.6859 0.2210 0.420 0.564 0.016
#> GSM537401 3 0.8866 0.4998 0.248 0.180 0.572
#> GSM537343 1 0.1411 0.9215 0.964 0.036 0.000
#> GSM537367 1 0.5514 0.7704 0.800 0.156 0.044
#> GSM537382 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537385 2 0.3816 0.7923 0.000 0.852 0.148
#> GSM537391 1 0.0424 0.9404 0.992 0.000 0.008
#> GSM537419 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537420 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537429 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537431 3 0.4521 0.7378 0.180 0.004 0.816
#> GSM537387 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537414 1 0.4452 0.7535 0.808 0.000 0.192
#> GSM537433 1 0.1964 0.9058 0.944 0.056 0.000
#> GSM537335 1 0.1031 0.9294 0.976 0.000 0.024
#> GSM537339 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537340 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537344 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537346 3 0.6274 0.1016 0.000 0.456 0.544
#> GSM537351 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537352 3 0.0000 0.8856 0.000 0.000 1.000
#> GSM537359 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537360 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537364 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537365 1 0.1964 0.9049 0.944 0.056 0.000
#> GSM537372 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537384 1 0.0000 0.9451 1.000 0.000 0.000
#> GSM537394 2 0.0237 0.8905 0.004 0.996 0.000
#> GSM537403 2 0.5397 0.6124 0.000 0.720 0.280
#> GSM537406 2 0.0000 0.8921 0.000 1.000 0.000
#> GSM537411 2 0.6280 0.0969 0.000 0.540 0.460
#> GSM537412 2 0.0747 0.8868 0.000 0.984 0.016
#> GSM537416 3 0.1860 0.8579 0.000 0.052 0.948
#> GSM537426 2 0.5178 0.6572 0.000 0.744 0.256
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.4973 0.55227 0.644 0.000 0.348 0.008
#> GSM537345 1 0.2011 0.80955 0.920 0.000 0.080 0.000
#> GSM537355 4 0.4564 0.40015 0.328 0.000 0.000 0.672
#> GSM537366 1 0.3837 0.72705 0.776 0.000 0.224 0.000
#> GSM537370 3 0.4914 0.44555 0.044 0.000 0.748 0.208
#> GSM537380 2 0.3726 0.64545 0.000 0.788 0.212 0.000
#> GSM537392 2 0.5184 0.61653 0.000 0.732 0.212 0.056
#> GSM537415 2 0.0000 0.69221 0.000 1.000 0.000 0.000
#> GSM537417 1 0.4655 0.48474 0.684 0.000 0.004 0.312
#> GSM537422 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537423 2 0.3569 0.65585 0.000 0.804 0.196 0.000
#> GSM537427 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537430 4 0.0817 0.81482 0.000 0.024 0.000 0.976
#> GSM537336 1 0.2081 0.80981 0.916 0.000 0.084 0.000
#> GSM537337 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537348 1 0.3569 0.71768 0.804 0.000 0.196 0.000
#> GSM537349 2 0.0000 0.69221 0.000 1.000 0.000 0.000
#> GSM537356 1 0.4500 0.52045 0.684 0.000 0.316 0.000
#> GSM537361 1 0.4948 0.19491 0.560 0.000 0.440 0.000
#> GSM537374 4 0.3444 0.66556 0.000 0.184 0.000 0.816
#> GSM537377 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537378 2 0.0000 0.69221 0.000 1.000 0.000 0.000
#> GSM537379 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537383 2 0.3400 0.66388 0.000 0.820 0.180 0.000
#> GSM537388 2 0.4697 0.45716 0.000 0.644 0.000 0.356
#> GSM537395 4 0.2334 0.76994 0.000 0.088 0.004 0.908
#> GSM537400 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537404 3 0.7417 0.41388 0.140 0.028 0.592 0.240
#> GSM537409 2 0.3610 0.55586 0.000 0.800 0.000 0.200
#> GSM537418 1 0.0188 0.82896 0.996 0.000 0.004 0.000
#> GSM537425 1 0.2149 0.79622 0.912 0.000 0.088 0.000
#> GSM537333 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537342 4 0.0188 0.82501 0.000 0.000 0.004 0.996
#> GSM537347 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537350 1 0.3219 0.76624 0.836 0.000 0.164 0.000
#> GSM537362 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537363 1 0.7174 0.43435 0.576 0.012 0.280 0.132
#> GSM537368 1 0.0336 0.82878 0.992 0.000 0.008 0.000
#> GSM537376 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537381 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537386 2 0.4866 0.41110 0.000 0.596 0.404 0.000
#> GSM537398 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537402 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537405 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537371 1 0.3219 0.77729 0.836 0.000 0.164 0.000
#> GSM537421 4 0.5284 0.59039 0.000 0.264 0.040 0.696
#> GSM537424 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537432 1 0.7301 -0.03700 0.484 0.000 0.356 0.160
#> GSM537331 4 0.5022 0.63936 0.048 0.192 0.004 0.756
#> GSM537332 3 0.4941 0.04120 0.000 0.436 0.564 0.000
#> GSM537334 4 0.2973 0.73833 0.096 0.000 0.020 0.884
#> GSM537338 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537353 3 0.5602 -0.11412 0.000 0.472 0.508 0.020
#> GSM537357 1 0.2011 0.80955 0.920 0.000 0.080 0.000
#> GSM537358 2 0.5056 0.61415 0.000 0.732 0.224 0.044
#> GSM537375 4 0.2976 0.73798 0.008 0.120 0.000 0.872
#> GSM537389 2 0.0188 0.69086 0.000 0.996 0.004 0.000
#> GSM537390 2 0.3074 0.67388 0.000 0.848 0.152 0.000
#> GSM537393 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537399 1 0.2589 0.76970 0.884 0.000 0.116 0.000
#> GSM537407 3 0.5657 0.31388 0.312 0.044 0.644 0.000
#> GSM537408 2 0.4193 0.58848 0.000 0.732 0.268 0.000
#> GSM537428 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537354 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537410 4 0.6890 0.45562 0.000 0.268 0.152 0.580
#> GSM537413 2 0.3907 0.52838 0.000 0.768 0.000 0.232
#> GSM537396 2 0.6912 0.13910 0.152 0.576 0.272 0.000
#> GSM537397 3 0.7853 0.01566 0.364 0.000 0.368 0.268
#> GSM537330 1 0.1389 0.82145 0.952 0.000 0.048 0.000
#> GSM537369 1 0.2081 0.79719 0.916 0.000 0.084 0.000
#> GSM537373 2 0.7309 0.03395 0.200 0.528 0.272 0.000
#> GSM537401 4 0.8828 0.03813 0.232 0.060 0.272 0.436
#> GSM537343 3 0.4804 0.15764 0.384 0.000 0.616 0.000
#> GSM537367 3 0.5136 0.48265 0.188 0.056 0.752 0.004
#> GSM537382 4 0.0469 0.82148 0.000 0.000 0.012 0.988
#> GSM537385 2 0.4776 0.43072 0.000 0.624 0.000 0.376
#> GSM537391 1 0.4585 0.59841 0.668 0.000 0.332 0.000
#> GSM537419 2 0.3649 0.65112 0.000 0.796 0.204 0.000
#> GSM537420 1 0.1792 0.81481 0.932 0.000 0.068 0.000
#> GSM537429 1 0.3688 0.70307 0.792 0.000 0.208 0.000
#> GSM537431 4 0.7590 0.08682 0.180 0.004 0.344 0.472
#> GSM537387 1 0.4790 0.55858 0.620 0.000 0.380 0.000
#> GSM537414 1 0.3528 0.67280 0.808 0.000 0.000 0.192
#> GSM537433 1 0.3324 0.73953 0.852 0.136 0.012 0.000
#> GSM537335 1 0.0817 0.82532 0.976 0.000 0.000 0.024
#> GSM537339 1 0.4193 0.63114 0.732 0.000 0.268 0.000
#> GSM537340 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537344 1 0.1389 0.82180 0.952 0.000 0.048 0.000
#> GSM537346 4 0.7439 0.00791 0.000 0.296 0.204 0.500
#> GSM537351 1 0.3569 0.72677 0.804 0.000 0.196 0.000
#> GSM537352 4 0.0000 0.82654 0.000 0.000 0.000 1.000
#> GSM537359 2 0.4500 0.56685 0.000 0.684 0.316 0.000
#> GSM537360 2 0.0000 0.69221 0.000 1.000 0.000 0.000
#> GSM537364 1 0.1389 0.82387 0.952 0.000 0.048 0.000
#> GSM537365 3 0.2921 0.51090 0.140 0.000 0.860 0.000
#> GSM537372 1 0.2814 0.78606 0.868 0.000 0.132 0.000
#> GSM537384 1 0.0000 0.82941 1.000 0.000 0.000 0.000
#> GSM537394 3 0.4941 0.03835 0.000 0.436 0.564 0.000
#> GSM537403 2 0.7706 0.28607 0.000 0.436 0.328 0.236
#> GSM537406 2 0.0000 0.69221 0.000 1.000 0.000 0.000
#> GSM537411 3 0.5203 0.34396 0.000 0.232 0.720 0.048
#> GSM537412 2 0.0000 0.69221 0.000 1.000 0.000 0.000
#> GSM537416 4 0.4406 0.56892 0.000 0.300 0.000 0.700
#> GSM537426 2 0.4072 0.51754 0.000 0.748 0.000 0.252
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 1 0.5268 0.4492 0.588 0.000 0.368 0.020 0.024
#> GSM537345 5 0.1197 0.7943 0.048 0.000 0.000 0.000 0.952
#> GSM537355 4 0.3932 0.4002 0.328 0.000 0.000 0.672 0.000
#> GSM537366 1 0.4086 0.6246 0.704 0.000 0.284 0.000 0.012
#> GSM537370 3 0.6571 0.3838 0.032 0.200 0.584 0.184 0.000
#> GSM537380 2 0.0794 0.6716 0.000 0.972 0.028 0.000 0.000
#> GSM537392 2 0.1981 0.6595 0.000 0.924 0.028 0.048 0.000
#> GSM537415 2 0.3513 0.6716 0.000 0.800 0.180 0.000 0.020
#> GSM537417 1 0.4502 0.4033 0.668 0.000 0.008 0.312 0.012
#> GSM537422 1 0.0162 0.7968 0.996 0.000 0.000 0.000 0.004
#> GSM537423 2 0.0566 0.6807 0.000 0.984 0.012 0.000 0.004
#> GSM537427 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537430 4 0.0992 0.8311 0.000 0.024 0.000 0.968 0.008
#> GSM537336 5 0.1282 0.7923 0.044 0.000 0.004 0.000 0.952
#> GSM537337 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537348 1 0.3388 0.6819 0.792 0.000 0.200 0.000 0.008
#> GSM537349 2 0.3513 0.6716 0.000 0.800 0.180 0.000 0.020
#> GSM537356 1 0.4457 0.2621 0.620 0.000 0.368 0.000 0.012
#> GSM537361 3 0.4747 0.1229 0.488 0.000 0.496 0.000 0.016
#> GSM537374 4 0.2966 0.6840 0.000 0.184 0.000 0.816 0.000
#> GSM537377 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537378 2 0.3513 0.6716 0.000 0.800 0.180 0.000 0.020
#> GSM537379 4 0.0162 0.8436 0.000 0.000 0.000 0.996 0.004
#> GSM537383 2 0.0798 0.6839 0.000 0.976 0.016 0.000 0.008
#> GSM537388 2 0.4211 0.4774 0.000 0.636 0.000 0.360 0.004
#> GSM537395 4 0.1908 0.7845 0.000 0.092 0.000 0.908 0.000
#> GSM537400 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537404 3 0.7891 0.3763 0.108 0.208 0.472 0.208 0.004
#> GSM537409 2 0.6308 0.5442 0.000 0.600 0.180 0.200 0.020
#> GSM537418 1 0.0162 0.7971 0.996 0.000 0.004 0.000 0.000
#> GSM537425 1 0.2189 0.7505 0.904 0.000 0.084 0.000 0.012
#> GSM537333 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537342 4 0.0290 0.8424 0.000 0.000 0.008 0.992 0.000
#> GSM537347 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537350 1 0.3318 0.7158 0.808 0.000 0.180 0.000 0.012
#> GSM537362 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537363 1 0.6934 0.1771 0.504 0.012 0.344 0.108 0.032
#> GSM537368 1 0.0693 0.7933 0.980 0.000 0.012 0.000 0.008
#> GSM537376 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537381 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537386 2 0.4467 0.3846 0.000 0.640 0.344 0.000 0.016
#> GSM537398 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537402 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537405 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537371 5 0.1082 0.7750 0.028 0.000 0.008 0.000 0.964
#> GSM537421 4 0.5532 0.5629 0.000 0.092 0.224 0.668 0.016
#> GSM537424 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537432 3 0.6441 0.2670 0.420 0.000 0.424 0.152 0.004
#> GSM537331 4 0.4325 0.6553 0.048 0.192 0.004 0.756 0.000
#> GSM537332 2 0.4273 0.0678 0.000 0.552 0.448 0.000 0.000
#> GSM537334 4 0.2464 0.7585 0.096 0.000 0.016 0.888 0.000
#> GSM537338 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537353 2 0.4686 0.1966 0.000 0.596 0.384 0.020 0.000
#> GSM537357 5 0.1197 0.7943 0.048 0.000 0.000 0.000 0.952
#> GSM537358 2 0.1992 0.6593 0.000 0.924 0.032 0.044 0.000
#> GSM537375 4 0.2833 0.7519 0.004 0.120 0.000 0.864 0.012
#> GSM537389 2 0.3621 0.6670 0.000 0.788 0.192 0.000 0.020
#> GSM537390 2 0.1205 0.6863 0.000 0.956 0.040 0.000 0.004
#> GSM537393 4 0.0162 0.8436 0.000 0.000 0.000 0.996 0.004
#> GSM537399 1 0.2424 0.6954 0.868 0.000 0.132 0.000 0.000
#> GSM537407 3 0.3766 0.4228 0.268 0.004 0.728 0.000 0.000
#> GSM537408 2 0.1671 0.6448 0.000 0.924 0.076 0.000 0.000
#> GSM537428 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537354 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537410 4 0.6064 0.4502 0.000 0.076 0.316 0.580 0.028
#> GSM537413 2 0.6398 0.5248 0.000 0.576 0.180 0.228 0.016
#> GSM537396 3 0.6861 -0.0253 0.160 0.344 0.472 0.000 0.024
#> GSM537397 3 0.7328 0.1046 0.328 0.000 0.368 0.280 0.024
#> GSM537330 1 0.1124 0.7920 0.960 0.000 0.036 0.000 0.004
#> GSM537369 1 0.2006 0.7553 0.916 0.000 0.072 0.000 0.012
#> GSM537373 3 0.6898 0.0647 0.192 0.308 0.480 0.000 0.020
#> GSM537401 4 0.7062 0.0962 0.216 0.008 0.292 0.472 0.012
#> GSM537343 3 0.4147 0.3800 0.316 0.008 0.676 0.000 0.000
#> GSM537367 3 0.4013 0.4505 0.140 0.032 0.808 0.004 0.016
#> GSM537382 4 0.0324 0.8421 0.000 0.000 0.004 0.992 0.004
#> GSM537385 2 0.4380 0.4420 0.000 0.616 0.000 0.376 0.008
#> GSM537391 1 0.4161 0.5799 0.704 0.000 0.280 0.000 0.016
#> GSM537419 2 0.0794 0.6749 0.000 0.972 0.028 0.000 0.000
#> GSM537420 1 0.4227 0.2177 0.580 0.000 0.000 0.000 0.420
#> GSM537429 1 0.3388 0.6823 0.792 0.000 0.200 0.000 0.008
#> GSM537431 4 0.6957 0.0552 0.180 0.004 0.336 0.464 0.016
#> GSM537387 5 0.4547 0.6077 0.192 0.000 0.072 0.000 0.736
#> GSM537414 1 0.3196 0.6171 0.804 0.000 0.000 0.192 0.004
#> GSM537433 1 0.3567 0.6912 0.836 0.068 0.092 0.000 0.004
#> GSM537335 1 0.0703 0.7893 0.976 0.000 0.000 0.024 0.000
#> GSM537339 1 0.4086 0.5791 0.704 0.000 0.284 0.000 0.012
#> GSM537340 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537344 1 0.2471 0.7387 0.864 0.000 0.000 0.000 0.136
#> GSM537346 2 0.5051 0.0420 0.000 0.488 0.024 0.484 0.004
#> GSM537351 5 0.4627 0.1500 0.444 0.000 0.012 0.000 0.544
#> GSM537352 4 0.0000 0.8446 0.000 0.000 0.000 1.000 0.000
#> GSM537359 2 0.2305 0.6355 0.000 0.896 0.092 0.000 0.012
#> GSM537360 2 0.3318 0.6726 0.000 0.808 0.180 0.000 0.012
#> GSM537364 1 0.4327 0.3128 0.632 0.000 0.008 0.000 0.360
#> GSM537365 3 0.5104 0.4283 0.100 0.196 0.700 0.000 0.004
#> GSM537372 1 0.2864 0.7413 0.852 0.000 0.136 0.000 0.012
#> GSM537384 1 0.0000 0.7976 1.000 0.000 0.000 0.000 0.000
#> GSM537394 2 0.4268 0.0809 0.000 0.556 0.444 0.000 0.000
#> GSM537403 2 0.5979 0.3588 0.000 0.588 0.192 0.220 0.000
#> GSM537406 2 0.3318 0.6709 0.000 0.800 0.192 0.000 0.008
#> GSM537411 3 0.5170 0.1437 0.000 0.412 0.552 0.028 0.008
#> GSM537412 2 0.3513 0.6716 0.000 0.800 0.180 0.000 0.020
#> GSM537416 4 0.5304 0.5678 0.000 0.112 0.176 0.700 0.012
#> GSM537426 2 0.6490 0.5066 0.000 0.544 0.180 0.264 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.1493 0.6828 0.056 0.000 0.004 0.000 0.936 0.004
#> GSM537345 4 0.0000 0.7591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537355 6 0.3531 0.4613 0.328 0.000 0.000 0.000 0.000 0.672
#> GSM537366 1 0.4938 0.4289 0.568 0.000 0.076 0.000 0.356 0.000
#> GSM537370 3 0.3885 0.4002 0.004 0.000 0.684 0.000 0.300 0.012
#> GSM537380 2 0.3686 0.7366 0.000 0.748 0.220 0.000 0.032 0.000
#> GSM537392 2 0.3529 0.7552 0.000 0.788 0.176 0.000 0.028 0.008
#> GSM537415 2 0.0146 0.7768 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM537417 1 0.5313 0.4214 0.608 0.016 0.036 0.000 0.028 0.312
#> GSM537422 1 0.1334 0.8022 0.948 0.000 0.032 0.000 0.020 0.000
#> GSM537423 2 0.2981 0.7670 0.000 0.820 0.160 0.000 0.020 0.000
#> GSM537427 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537430 6 0.0937 0.8521 0.000 0.040 0.000 0.000 0.000 0.960
#> GSM537336 4 0.0000 0.7591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537337 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537348 1 0.3706 0.2977 0.620 0.000 0.000 0.000 0.380 0.000
#> GSM537349 2 0.0547 0.7790 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM537356 3 0.5328 0.4083 0.308 0.000 0.560 0.000 0.132 0.000
#> GSM537361 3 0.3985 0.5982 0.100 0.000 0.760 0.000 0.140 0.000
#> GSM537374 6 0.2664 0.7198 0.000 0.184 0.000 0.000 0.000 0.816
#> GSM537377 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537378 2 0.0146 0.7768 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM537379 6 0.1334 0.8437 0.000 0.000 0.032 0.000 0.020 0.948
#> GSM537383 2 0.2790 0.7717 0.000 0.840 0.140 0.000 0.020 0.000
#> GSM537388 2 0.3409 0.6150 0.000 0.700 0.000 0.000 0.000 0.300
#> GSM537395 6 0.1858 0.8129 0.000 0.092 0.004 0.000 0.000 0.904
#> GSM537400 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537404 3 0.4148 0.6058 0.072 0.008 0.796 0.000 0.036 0.088
#> GSM537409 2 0.3074 0.6479 0.000 0.792 0.004 0.000 0.004 0.200
#> GSM537418 1 0.0291 0.8194 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM537425 1 0.3616 0.6889 0.792 0.000 0.076 0.000 0.132 0.000
#> GSM537333 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537342 6 0.1297 0.8472 0.000 0.000 0.012 0.000 0.040 0.948
#> GSM537347 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537350 1 0.4122 0.6406 0.724 0.000 0.064 0.000 0.212 0.000
#> GSM537362 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537363 3 0.6266 0.2762 0.328 0.004 0.488 0.008 0.160 0.012
#> GSM537368 1 0.1010 0.8103 0.960 0.000 0.004 0.000 0.036 0.000
#> GSM537376 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537381 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537386 5 0.3103 0.6346 0.000 0.064 0.100 0.000 0.836 0.000
#> GSM537398 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537402 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537405 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537371 4 0.0000 0.7591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537421 6 0.4546 0.6750 0.000 0.192 0.032 0.000 0.052 0.724
#> GSM537424 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537432 3 0.3834 0.5540 0.244 0.000 0.728 0.000 0.024 0.004
#> GSM537331 6 0.4209 0.6769 0.048 0.196 0.000 0.000 0.016 0.740
#> GSM537332 3 0.2762 0.5422 0.000 0.196 0.804 0.000 0.000 0.000
#> GSM537334 6 0.3777 0.6811 0.056 0.004 0.000 0.000 0.164 0.776
#> GSM537338 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537353 3 0.3903 0.3163 0.000 0.304 0.680 0.000 0.004 0.012
#> GSM537357 4 0.0000 0.7591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537358 2 0.3562 0.7542 0.000 0.784 0.180 0.000 0.028 0.008
#> GSM537375 6 0.3650 0.7594 0.004 0.136 0.032 0.000 0.020 0.808
#> GSM537389 2 0.0790 0.7762 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM537390 2 0.2442 0.7750 0.000 0.852 0.144 0.000 0.004 0.000
#> GSM537393 6 0.1334 0.8437 0.000 0.000 0.032 0.000 0.020 0.948
#> GSM537399 1 0.2743 0.7034 0.828 0.000 0.164 0.000 0.008 0.000
#> GSM537407 3 0.4956 0.5211 0.116 0.004 0.652 0.000 0.228 0.000
#> GSM537408 2 0.3161 0.7396 0.000 0.776 0.216 0.000 0.008 0.000
#> GSM537428 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537354 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537410 6 0.6161 0.4687 0.000 0.112 0.080 0.000 0.232 0.576
#> GSM537413 2 0.3965 0.6085 0.000 0.720 0.008 0.000 0.024 0.248
#> GSM537396 5 0.2981 0.6610 0.020 0.160 0.000 0.000 0.820 0.000
#> GSM537397 5 0.3176 0.6676 0.056 0.000 0.048 0.000 0.856 0.040
#> GSM537330 1 0.2894 0.7589 0.852 0.004 0.036 0.000 0.108 0.000
#> GSM537369 1 0.3361 0.7097 0.816 0.000 0.076 0.000 0.108 0.000
#> GSM537373 5 0.3020 0.6577 0.012 0.156 0.008 0.000 0.824 0.000
#> GSM537401 5 0.3514 0.6761 0.088 0.000 0.000 0.000 0.804 0.108
#> GSM537343 3 0.4613 0.5356 0.260 0.000 0.660 0.000 0.080 0.000
#> GSM537367 3 0.3230 0.5874 0.012 0.000 0.776 0.000 0.212 0.000
#> GSM537382 6 0.0458 0.8582 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM537385 2 0.4122 0.6077 0.000 0.680 0.008 0.000 0.020 0.292
#> GSM537391 5 0.2912 0.6761 0.216 0.000 0.000 0.000 0.784 0.000
#> GSM537419 2 0.2597 0.7638 0.000 0.824 0.176 0.000 0.000 0.000
#> GSM537420 1 0.4178 0.2302 0.560 0.000 0.004 0.428 0.008 0.000
#> GSM537429 5 0.3810 0.3388 0.428 0.000 0.000 0.000 0.572 0.000
#> GSM537431 5 0.7052 0.3223 0.144 0.000 0.128 0.000 0.436 0.292
#> GSM537387 4 0.5175 0.0639 0.088 0.000 0.000 0.492 0.420 0.000
#> GSM537414 1 0.3393 0.6455 0.784 0.000 0.020 0.000 0.004 0.192
#> GSM537433 1 0.3907 0.7237 0.800 0.108 0.056 0.000 0.036 0.000
#> GSM537335 1 0.0632 0.8133 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM537339 5 0.2912 0.6761 0.216 0.000 0.000 0.000 0.784 0.000
#> GSM537340 6 0.0260 0.8602 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM537344 1 0.2320 0.7533 0.864 0.000 0.004 0.132 0.000 0.000
#> GSM537346 6 0.6036 0.0879 0.000 0.284 0.204 0.000 0.012 0.500
#> GSM537351 4 0.4361 0.0785 0.424 0.000 0.024 0.552 0.000 0.000
#> GSM537352 6 0.0000 0.8618 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537359 2 0.5682 0.4349 0.000 0.504 0.180 0.000 0.316 0.000
#> GSM537360 2 0.0777 0.7788 0.000 0.972 0.024 0.000 0.004 0.000
#> GSM537364 1 0.4026 0.3384 0.612 0.000 0.000 0.376 0.012 0.000
#> GSM537365 3 0.0632 0.6296 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM537372 1 0.4425 0.6406 0.712 0.000 0.112 0.000 0.176 0.000
#> GSM537384 1 0.0000 0.8205 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537394 3 0.3198 0.4557 0.000 0.260 0.740 0.000 0.000 0.000
#> GSM537403 2 0.6289 0.2850 0.000 0.404 0.372 0.000 0.016 0.208
#> GSM537406 2 0.0717 0.7781 0.000 0.976 0.008 0.000 0.016 0.000
#> GSM537411 3 0.4012 0.5466 0.000 0.076 0.748 0.000 0.176 0.000
#> GSM537412 2 0.0260 0.7763 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM537416 6 0.3329 0.6883 0.000 0.236 0.004 0.000 0.004 0.756
#> GSM537426 2 0.3337 0.5975 0.000 0.736 0.000 0.000 0.004 0.260
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> CV:pam 100 0.0385 0.477 2
#> CV:pam 98 0.2537 0.524 3
#> CV:pam 79 0.1188 0.593 4
#> CV:pam 73 0.3134 0.209 5
#> CV:pam 85 0.0470 0.191 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 104 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.896 0.923 0.962 0.316 0.675 0.675
#> 3 3 0.262 0.393 0.733 0.719 0.795 0.708
#> 4 4 0.316 0.334 0.650 0.241 0.659 0.411
#> 5 5 0.427 0.369 0.628 0.109 0.851 0.554
#> 6 6 0.519 0.445 0.659 0.053 0.912 0.649
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
#> GSM537341 2 0.0000 0.977 0.000 1.000
#> GSM537345 1 0.1633 0.904 0.976 0.024
#> GSM537355 2 0.0376 0.974 0.004 0.996
#> GSM537366 2 0.0672 0.970 0.008 0.992
#> GSM537370 2 0.0000 0.977 0.000 1.000
#> GSM537380 2 0.1414 0.960 0.020 0.980
#> GSM537392 2 0.1414 0.960 0.020 0.980
#> GSM537415 2 0.0000 0.977 0.000 1.000
#> GSM537417 2 0.0000 0.977 0.000 1.000
#> GSM537422 2 0.9522 0.312 0.372 0.628
#> GSM537423 2 0.0000 0.977 0.000 1.000
#> GSM537427 2 0.0000 0.977 0.000 1.000
#> GSM537430 2 0.0000 0.977 0.000 1.000
#> GSM537336 1 0.1633 0.904 0.976 0.024
#> GSM537337 2 0.0000 0.977 0.000 1.000
#> GSM537348 1 0.9732 0.475 0.596 0.404
#> GSM537349 2 0.1414 0.960 0.020 0.980
#> GSM537356 2 0.2043 0.946 0.032 0.968
#> GSM537361 1 0.8555 0.706 0.720 0.280
#> GSM537374 2 0.0000 0.977 0.000 1.000
#> GSM537377 1 0.1633 0.904 0.976 0.024
#> GSM537378 2 0.0000 0.977 0.000 1.000
#> GSM537379 2 0.0000 0.977 0.000 1.000
#> GSM537383 2 0.0000 0.977 0.000 1.000
#> GSM537388 2 0.1414 0.960 0.020 0.980
#> GSM537395 2 0.0000 0.977 0.000 1.000
#> GSM537400 2 0.0000 0.977 0.000 1.000
#> GSM537404 2 0.0000 0.977 0.000 1.000
#> GSM537409 2 0.0000 0.977 0.000 1.000
#> GSM537418 2 0.7745 0.656 0.228 0.772
#> GSM537425 2 0.9491 0.325 0.368 0.632
#> GSM537333 2 0.0000 0.977 0.000 1.000
#> GSM537342 2 0.0000 0.977 0.000 1.000
#> GSM537347 2 0.0000 0.977 0.000 1.000
#> GSM537350 2 0.4298 0.878 0.088 0.912
#> GSM537362 2 0.0376 0.974 0.004 0.996
#> GSM537363 2 0.9608 0.274 0.384 0.616
#> GSM537368 1 0.1633 0.904 0.976 0.024
#> GSM537376 2 0.0000 0.977 0.000 1.000
#> GSM537381 1 0.1633 0.904 0.976 0.024
#> GSM537386 2 0.1414 0.960 0.020 0.980
#> GSM537398 1 0.8016 0.760 0.756 0.244
#> GSM537402 2 0.0000 0.977 0.000 1.000
#> GSM537405 1 0.3431 0.888 0.936 0.064
#> GSM537371 1 0.1633 0.904 0.976 0.024
#> GSM537421 2 0.0000 0.977 0.000 1.000
#> GSM537424 1 0.9393 0.581 0.644 0.356
#> GSM537432 2 0.0000 0.977 0.000 1.000
#> GSM537331 2 0.0000 0.977 0.000 1.000
#> GSM537332 2 0.0000 0.977 0.000 1.000
#> GSM537334 2 0.0000 0.977 0.000 1.000
#> GSM537338 2 0.0376 0.974 0.004 0.996
#> GSM537353 2 0.0000 0.977 0.000 1.000
#> GSM537357 1 0.1633 0.904 0.976 0.024
#> GSM537358 2 0.0000 0.977 0.000 1.000
#> GSM537375 2 0.0000 0.977 0.000 1.000
#> GSM537389 2 0.1414 0.960 0.020 0.980
#> GSM537390 2 0.0000 0.977 0.000 1.000
#> GSM537393 2 0.0000 0.977 0.000 1.000
#> GSM537399 2 0.0000 0.977 0.000 1.000
#> GSM537407 2 0.0000 0.977 0.000 1.000
#> GSM537408 2 0.0000 0.977 0.000 1.000
#> GSM537428 2 0.0376 0.974 0.004 0.996
#> GSM537354 2 0.0000 0.977 0.000 1.000
#> GSM537410 2 0.0000 0.977 0.000 1.000
#> GSM537413 2 0.0000 0.977 0.000 1.000
#> GSM537396 2 0.0000 0.977 0.000 1.000
#> GSM537397 2 0.0000 0.977 0.000 1.000
#> GSM537330 2 0.0000 0.977 0.000 1.000
#> GSM537369 1 0.1633 0.904 0.976 0.024
#> GSM537373 2 0.0000 0.977 0.000 1.000
#> GSM537401 2 0.0000 0.977 0.000 1.000
#> GSM537343 2 0.0672 0.970 0.008 0.992
#> GSM537367 2 0.0000 0.977 0.000 1.000
#> GSM537382 2 0.0000 0.977 0.000 1.000
#> GSM537385 2 0.1414 0.960 0.020 0.980
#> GSM537391 1 0.7139 0.792 0.804 0.196
#> GSM537419 2 0.0000 0.977 0.000 1.000
#> GSM537420 1 0.1633 0.904 0.976 0.024
#> GSM537429 2 0.0000 0.977 0.000 1.000
#> GSM537431 2 0.0000 0.977 0.000 1.000
#> GSM537387 1 0.1633 0.904 0.976 0.024
#> GSM537414 2 0.0000 0.977 0.000 1.000
#> GSM537433 2 0.0000 0.977 0.000 1.000
#> GSM537335 2 0.0000 0.977 0.000 1.000
#> GSM537339 2 0.0000 0.977 0.000 1.000
#> GSM537340 2 0.0000 0.977 0.000 1.000
#> GSM537344 1 0.1633 0.904 0.976 0.024
#> GSM537346 2 0.0376 0.974 0.004 0.996
#> GSM537351 1 0.1633 0.904 0.976 0.024
#> GSM537352 2 0.0000 0.977 0.000 1.000
#> GSM537359 2 0.0000 0.977 0.000 1.000
#> GSM537360 2 0.0000 0.977 0.000 1.000
#> GSM537364 1 0.1633 0.904 0.976 0.024
#> GSM537365 2 0.0000 0.977 0.000 1.000
#> GSM537372 1 0.8081 0.754 0.752 0.248
#> GSM537384 1 0.7299 0.796 0.796 0.204
#> GSM537394 2 0.0000 0.977 0.000 1.000
#> GSM537403 2 0.0000 0.977 0.000 1.000
#> GSM537406 2 0.0000 0.977 0.000 1.000
#> GSM537411 2 0.0000 0.977 0.000 1.000
#> GSM537412 2 0.0000 0.977 0.000 1.000
#> GSM537416 2 0.0000 0.977 0.000 1.000
#> GSM537426 2 0.0000 0.977 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 3 0.9964 0.557151 0.336 0.296 0.368
#> GSM537345 1 0.3459 0.708038 0.892 0.012 0.096
#> GSM537355 2 0.5623 0.424161 0.004 0.716 0.280
#> GSM537366 2 0.8665 -0.096967 0.412 0.484 0.104
#> GSM537370 2 0.9591 -0.355895 0.232 0.472 0.296
#> GSM537380 2 0.6235 0.058984 0.000 0.564 0.436
#> GSM537392 2 0.6274 -0.002736 0.000 0.544 0.456
#> GSM537415 2 0.0424 0.596398 0.000 0.992 0.008
#> GSM537417 2 0.7228 0.361764 0.188 0.708 0.104
#> GSM537422 1 0.8779 -0.004820 0.472 0.416 0.112
#> GSM537423 2 0.3551 0.537618 0.000 0.868 0.132
#> GSM537427 2 0.6647 0.062271 0.012 0.592 0.396
#> GSM537430 2 0.4629 0.467394 0.004 0.808 0.188
#> GSM537336 1 0.0661 0.728517 0.988 0.008 0.004
#> GSM537337 2 0.6836 0.086653 0.016 0.572 0.412
#> GSM537348 1 0.7234 0.551336 0.640 0.048 0.312
#> GSM537349 2 0.6079 0.192796 0.000 0.612 0.388
#> GSM537356 1 0.8068 0.073561 0.596 0.316 0.088
#> GSM537361 1 0.7945 0.414874 0.652 0.224 0.124
#> GSM537374 2 0.8202 -0.174476 0.080 0.544 0.376
#> GSM537377 1 0.3539 0.708798 0.888 0.012 0.100
#> GSM537378 2 0.3116 0.554338 0.000 0.892 0.108
#> GSM537379 2 0.5816 0.447674 0.156 0.788 0.056
#> GSM537383 2 0.5216 0.370790 0.000 0.740 0.260
#> GSM537388 2 0.6682 -0.097735 0.008 0.504 0.488
#> GSM537395 2 0.3349 0.570103 0.004 0.888 0.108
#> GSM537400 2 0.7918 0.222917 0.256 0.640 0.104
#> GSM537404 2 0.2846 0.580649 0.020 0.924 0.056
#> GSM537409 2 0.2486 0.581722 0.008 0.932 0.060
#> GSM537418 1 0.6067 0.424512 0.736 0.236 0.028
#> GSM537425 1 0.8515 -0.010727 0.476 0.432 0.092
#> GSM537333 2 0.7814 0.249218 0.244 0.652 0.104
#> GSM537342 2 0.1525 0.594913 0.032 0.964 0.004
#> GSM537347 2 0.6388 0.472734 0.064 0.752 0.184
#> GSM537350 1 0.7665 0.264357 0.648 0.268 0.084
#> GSM537362 2 0.8543 -0.206260 0.408 0.496 0.096
#> GSM537363 1 0.8126 0.170802 0.564 0.356 0.080
#> GSM537368 1 0.0829 0.729698 0.984 0.012 0.004
#> GSM537376 2 0.0983 0.595243 0.004 0.980 0.016
#> GSM537381 1 0.0829 0.729698 0.984 0.012 0.004
#> GSM537386 2 0.5926 0.262557 0.000 0.644 0.356
#> GSM537398 1 0.6062 0.638061 0.708 0.016 0.276
#> GSM537402 2 0.3267 0.547678 0.000 0.884 0.116
#> GSM537405 1 0.2187 0.725024 0.948 0.024 0.028
#> GSM537371 1 0.0661 0.728517 0.988 0.008 0.004
#> GSM537421 2 0.2810 0.583701 0.036 0.928 0.036
#> GSM537424 1 0.6093 0.664529 0.776 0.068 0.156
#> GSM537432 2 0.5627 0.415063 0.188 0.780 0.032
#> GSM537331 3 0.9028 0.438990 0.132 0.432 0.436
#> GSM537332 2 0.1163 0.591562 0.000 0.972 0.028
#> GSM537334 3 0.9203 0.537528 0.156 0.368 0.476
#> GSM537338 3 0.8602 0.420407 0.100 0.408 0.492
#> GSM537353 2 0.0237 0.595895 0.000 0.996 0.004
#> GSM537357 1 0.0424 0.729010 0.992 0.008 0.000
#> GSM537358 2 0.2878 0.564139 0.000 0.904 0.096
#> GSM537375 2 0.6490 0.431489 0.036 0.708 0.256
#> GSM537389 2 0.6079 0.192796 0.000 0.612 0.388
#> GSM537390 2 0.0424 0.594654 0.000 0.992 0.008
#> GSM537393 2 0.2550 0.593680 0.012 0.932 0.056
#> GSM537399 2 0.9267 -0.159617 0.316 0.504 0.180
#> GSM537407 2 0.8318 0.000942 0.392 0.524 0.084
#> GSM537408 2 0.4316 0.566981 0.044 0.868 0.088
#> GSM537428 2 0.7029 -0.011161 0.020 0.540 0.440
#> GSM537354 2 0.6387 0.373172 0.020 0.680 0.300
#> GSM537410 2 0.1765 0.586612 0.004 0.956 0.040
#> GSM537413 2 0.1163 0.593694 0.000 0.972 0.028
#> GSM537396 2 0.9606 -0.285667 0.288 0.472 0.240
#> GSM537397 1 0.9811 -0.539359 0.380 0.240 0.380
#> GSM537330 2 0.3349 0.569352 0.004 0.888 0.108
#> GSM537369 1 0.1877 0.730466 0.956 0.012 0.032
#> GSM537373 2 0.5519 0.502822 0.120 0.812 0.068
#> GSM537401 2 0.9941 -0.517976 0.292 0.384 0.324
#> GSM537343 2 0.8460 -0.086569 0.440 0.472 0.088
#> GSM537367 2 0.8347 -0.015783 0.404 0.512 0.084
#> GSM537382 2 0.2063 0.594582 0.008 0.948 0.044
#> GSM537385 2 0.6244 0.046227 0.000 0.560 0.440
#> GSM537391 1 0.6195 0.610857 0.704 0.020 0.276
#> GSM537419 2 0.3340 0.546031 0.000 0.880 0.120
#> GSM537420 1 0.1751 0.730537 0.960 0.012 0.028
#> GSM537429 2 0.7975 0.312266 0.160 0.660 0.180
#> GSM537431 2 0.7557 0.255813 0.264 0.656 0.080
#> GSM537387 1 0.4692 0.688043 0.820 0.012 0.168
#> GSM537414 2 0.8229 0.197770 0.256 0.620 0.124
#> GSM537433 2 0.8175 0.097740 0.336 0.576 0.088
#> GSM537335 3 0.9833 0.590224 0.300 0.276 0.424
#> GSM537339 3 0.8852 0.074571 0.396 0.120 0.484
#> GSM537340 2 0.5961 0.466781 0.136 0.788 0.076
#> GSM537344 1 0.1015 0.730902 0.980 0.012 0.008
#> GSM537346 2 0.4002 0.523230 0.000 0.840 0.160
#> GSM537351 1 0.2116 0.719748 0.948 0.012 0.040
#> GSM537352 2 0.6051 0.372840 0.012 0.696 0.292
#> GSM537359 2 0.5891 0.452056 0.036 0.764 0.200
#> GSM537360 2 0.0000 0.595530 0.000 1.000 0.000
#> GSM537364 1 0.0661 0.728517 0.988 0.008 0.004
#> GSM537365 2 0.5295 0.460128 0.156 0.808 0.036
#> GSM537372 1 0.5852 0.680552 0.776 0.044 0.180
#> GSM537384 1 0.5574 0.684780 0.784 0.032 0.184
#> GSM537394 2 0.1289 0.594362 0.000 0.968 0.032
#> GSM537403 2 0.2680 0.577226 0.008 0.924 0.068
#> GSM537406 2 0.3589 0.576561 0.048 0.900 0.052
#> GSM537411 2 0.3851 0.526558 0.004 0.860 0.136
#> GSM537412 2 0.1878 0.584870 0.004 0.952 0.044
#> GSM537416 2 0.2902 0.577763 0.016 0.920 0.064
#> GSM537426 2 0.0000 0.595530 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.7005 0.4155 0.680 0.136 0.104 0.080
#> GSM537345 3 0.5203 0.6595 0.348 0.000 0.636 0.016
#> GSM537355 4 0.5404 0.2815 0.000 0.476 0.012 0.512
#> GSM537366 1 0.4340 0.4809 0.836 0.044 0.024 0.096
#> GSM537370 2 0.8236 0.0396 0.368 0.456 0.056 0.120
#> GSM537380 2 0.4604 0.4978 0.004 0.784 0.176 0.036
#> GSM537392 2 0.4604 0.4978 0.004 0.784 0.176 0.036
#> GSM537415 2 0.1909 0.5757 0.004 0.940 0.008 0.048
#> GSM537417 4 0.6923 0.4792 0.052 0.340 0.036 0.572
#> GSM537422 4 0.7646 0.2148 0.308 0.100 0.044 0.548
#> GSM537423 2 0.0895 0.5857 0.000 0.976 0.020 0.004
#> GSM537427 4 0.6021 0.1803 0.016 0.476 0.016 0.492
#> GSM537430 2 0.3450 0.4808 0.000 0.836 0.008 0.156
#> GSM537336 3 0.4992 0.7879 0.476 0.000 0.524 0.000
#> GSM537337 4 0.6044 0.3172 0.000 0.428 0.044 0.528
#> GSM537348 1 0.4868 0.4177 0.748 0.000 0.212 0.040
#> GSM537349 2 0.4561 0.4999 0.004 0.788 0.172 0.036
#> GSM537356 1 0.2418 0.4431 0.928 0.032 0.024 0.016
#> GSM537361 1 0.6656 0.2891 0.612 0.020 0.068 0.300
#> GSM537374 4 0.7171 0.3681 0.040 0.252 0.092 0.616
#> GSM537377 3 0.5453 0.6197 0.388 0.000 0.592 0.020
#> GSM537378 2 0.0804 0.5844 0.000 0.980 0.008 0.012
#> GSM537379 4 0.6388 0.4416 0.044 0.392 0.012 0.552
#> GSM537383 2 0.2635 0.5660 0.000 0.904 0.076 0.020
#> GSM537388 2 0.5452 0.4551 0.000 0.736 0.156 0.108
#> GSM537395 2 0.5039 -0.1147 0.000 0.592 0.004 0.404
#> GSM537400 4 0.7845 0.4407 0.180 0.212 0.040 0.568
#> GSM537404 4 0.6417 0.3640 0.072 0.388 0.000 0.540
#> GSM537409 2 0.4741 0.1536 0.004 0.668 0.000 0.328
#> GSM537418 1 0.3981 0.4243 0.860 0.036 0.036 0.068
#> GSM537425 1 0.6100 0.3749 0.640 0.048 0.012 0.300
#> GSM537333 4 0.6948 0.4839 0.084 0.236 0.040 0.640
#> GSM537342 2 0.6007 -0.1365 0.044 0.548 0.000 0.408
#> GSM537347 4 0.5685 0.3404 0.024 0.460 0.000 0.516
#> GSM537350 1 0.2553 0.4474 0.916 0.060 0.016 0.008
#> GSM537362 4 0.8370 0.0449 0.344 0.088 0.096 0.472
#> GSM537363 1 0.5715 0.3991 0.732 0.084 0.012 0.172
#> GSM537368 1 0.4866 -0.5752 0.596 0.000 0.404 0.000
#> GSM537376 2 0.4955 -0.1650 0.000 0.556 0.000 0.444
#> GSM537381 1 0.4730 -0.4867 0.636 0.000 0.364 0.000
#> GSM537386 2 0.4334 0.5124 0.008 0.808 0.156 0.028
#> GSM537398 1 0.6058 0.2799 0.624 0.000 0.308 0.068
#> GSM537402 2 0.3401 0.4813 0.008 0.840 0.000 0.152
#> GSM537405 1 0.4008 0.1717 0.820 0.000 0.148 0.032
#> GSM537371 3 0.5158 0.7891 0.472 0.000 0.524 0.004
#> GSM537421 4 0.6054 0.3570 0.028 0.444 0.008 0.520
#> GSM537424 1 0.4887 0.3802 0.756 0.004 0.204 0.036
#> GSM537432 4 0.6995 0.4475 0.120 0.324 0.004 0.552
#> GSM537331 4 0.8111 0.1628 0.076 0.396 0.080 0.448
#> GSM537332 2 0.2589 0.5307 0.000 0.884 0.000 0.116
#> GSM537334 4 0.7705 0.3851 0.092 0.208 0.092 0.608
#> GSM537338 4 0.7371 0.3695 0.060 0.236 0.088 0.616
#> GSM537353 2 0.4872 0.0107 0.004 0.640 0.000 0.356
#> GSM537357 3 0.4992 0.7879 0.476 0.000 0.524 0.000
#> GSM537358 2 0.0188 0.5857 0.000 0.996 0.004 0.000
#> GSM537375 4 0.5984 0.3867 0.008 0.404 0.028 0.560
#> GSM537389 2 0.4604 0.4978 0.004 0.784 0.176 0.036
#> GSM537390 2 0.1474 0.5706 0.000 0.948 0.000 0.052
#> GSM537393 4 0.5508 0.2944 0.016 0.476 0.000 0.508
#> GSM537399 1 0.5009 0.3864 0.700 0.280 0.016 0.004
#> GSM537407 1 0.6127 0.4287 0.692 0.148 0.004 0.156
#> GSM537408 2 0.2732 0.5568 0.076 0.904 0.008 0.012
#> GSM537428 2 0.5696 -0.2795 0.000 0.496 0.024 0.480
#> GSM537354 4 0.6333 0.3546 0.004 0.416 0.052 0.528
#> GSM537410 2 0.4136 0.4494 0.016 0.788 0.000 0.196
#> GSM537413 2 0.0376 0.5859 0.004 0.992 0.000 0.004
#> GSM537396 2 0.5097 0.1654 0.428 0.568 0.000 0.004
#> GSM537397 1 0.5971 0.4474 0.740 0.088 0.136 0.036
#> GSM537330 2 0.4564 0.1397 0.000 0.672 0.000 0.328
#> GSM537369 1 0.4905 -0.5150 0.632 0.000 0.364 0.004
#> GSM537373 2 0.6506 0.0246 0.456 0.472 0.000 0.072
#> GSM537401 1 0.7526 0.3888 0.636 0.164 0.112 0.088
#> GSM537343 1 0.5754 0.4536 0.760 0.104 0.040 0.096
#> GSM537367 1 0.7049 0.3411 0.548 0.152 0.000 0.300
#> GSM537382 2 0.5288 -0.2645 0.008 0.520 0.000 0.472
#> GSM537385 2 0.4604 0.4978 0.004 0.784 0.176 0.036
#> GSM537391 1 0.5546 0.3479 0.664 0.000 0.292 0.044
#> GSM537419 2 0.0657 0.5863 0.004 0.984 0.012 0.000
#> GSM537420 1 0.4428 -0.2543 0.720 0.000 0.276 0.004
#> GSM537429 2 0.6658 -0.3383 0.084 0.472 0.000 0.444
#> GSM537431 4 0.7994 0.3241 0.284 0.264 0.008 0.444
#> GSM537387 3 0.5512 0.3122 0.492 0.000 0.492 0.016
#> GSM537414 4 0.7611 0.4772 0.128 0.252 0.040 0.580
#> GSM537433 1 0.6846 0.3791 0.600 0.184 0.000 0.216
#> GSM537335 4 0.8204 0.3814 0.156 0.112 0.152 0.580
#> GSM537339 1 0.5542 0.4123 0.716 0.004 0.216 0.064
#> GSM537340 4 0.7145 0.4450 0.100 0.332 0.016 0.552
#> GSM537344 1 0.4964 -0.5326 0.616 0.000 0.380 0.004
#> GSM537346 2 0.4331 0.2787 0.000 0.712 0.000 0.288
#> GSM537351 3 0.5861 0.7257 0.480 0.000 0.488 0.032
#> GSM537352 4 0.5938 0.2682 0.000 0.476 0.036 0.488
#> GSM537359 2 0.2990 0.5687 0.044 0.904 0.016 0.036
#> GSM537360 2 0.3982 0.3651 0.004 0.776 0.000 0.220
#> GSM537364 3 0.5158 0.7891 0.472 0.000 0.524 0.004
#> GSM537365 2 0.7849 -0.1529 0.352 0.380 0.000 0.268
#> GSM537372 1 0.3232 0.4217 0.872 0.004 0.108 0.016
#> GSM537384 1 0.3836 0.4049 0.816 0.000 0.168 0.016
#> GSM537394 2 0.0779 0.5852 0.004 0.980 0.000 0.016
#> GSM537403 4 0.5165 0.3184 0.004 0.484 0.000 0.512
#> GSM537406 2 0.2684 0.5686 0.060 0.912 0.016 0.012
#> GSM537411 2 0.4040 0.3122 0.000 0.752 0.000 0.248
#> GSM537412 2 0.3768 0.4442 0.008 0.808 0.000 0.184
#> GSM537416 4 0.5648 0.3752 0.012 0.428 0.008 0.552
#> GSM537426 2 0.1716 0.5637 0.000 0.936 0.000 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.5720 0.5790 0.060 0.144 0.032 0.040 0.724
#> GSM537345 1 0.6280 -0.0140 0.532 0.000 0.056 0.048 0.364
#> GSM537355 4 0.5752 0.6110 0.000 0.240 0.148 0.612 0.000
#> GSM537366 1 0.7014 0.1480 0.404 0.020 0.192 0.000 0.384
#> GSM537370 5 0.8354 0.2385 0.048 0.300 0.076 0.144 0.432
#> GSM537380 2 0.2780 0.5862 0.000 0.896 0.032 0.040 0.032
#> GSM537392 2 0.2780 0.5862 0.000 0.896 0.032 0.040 0.032
#> GSM537415 2 0.3840 0.5456 0.000 0.772 0.208 0.012 0.008
#> GSM537417 3 0.6383 0.0246 0.012 0.116 0.532 0.336 0.004
#> GSM537422 3 0.4817 0.4138 0.044 0.084 0.780 0.088 0.004
#> GSM537423 2 0.0671 0.6081 0.000 0.980 0.000 0.016 0.004
#> GSM537427 4 0.4152 0.5802 0.000 0.296 0.000 0.692 0.012
#> GSM537430 2 0.3607 0.4431 0.000 0.752 0.004 0.244 0.000
#> GSM537336 1 0.0162 0.6162 0.996 0.000 0.004 0.000 0.000
#> GSM537337 4 0.4343 0.6752 0.000 0.176 0.044 0.768 0.012
#> GSM537348 5 0.2011 0.6534 0.088 0.000 0.000 0.004 0.908
#> GSM537349 2 0.2617 0.5877 0.000 0.904 0.028 0.036 0.032
#> GSM537356 5 0.6587 -0.1952 0.412 0.012 0.144 0.000 0.432
#> GSM537361 3 0.6387 -0.1906 0.380 0.000 0.492 0.016 0.112
#> GSM537374 4 0.3641 0.6431 0.000 0.120 0.000 0.820 0.060
#> GSM537377 1 0.6344 -0.0243 0.524 0.000 0.060 0.048 0.368
#> GSM537378 2 0.1282 0.6024 0.000 0.952 0.000 0.044 0.004
#> GSM537379 4 0.6922 0.4058 0.004 0.280 0.268 0.444 0.004
#> GSM537383 2 0.1869 0.5983 0.000 0.936 0.016 0.036 0.012
#> GSM537388 2 0.4443 0.4286 0.000 0.724 0.028 0.240 0.008
#> GSM537395 2 0.5114 -0.2311 0.000 0.492 0.036 0.472 0.000
#> GSM537400 3 0.5183 0.3856 0.016 0.156 0.728 0.096 0.004
#> GSM537404 3 0.6826 0.3493 0.008 0.132 0.608 0.188 0.064
#> GSM537409 2 0.6041 0.2559 0.000 0.516 0.356 0.128 0.000
#> GSM537418 1 0.6887 0.2719 0.432 0.008 0.320 0.000 0.240
#> GSM537425 3 0.7470 -0.1957 0.360 0.052 0.400 0.000 0.188
#> GSM537333 3 0.5649 0.3306 0.016 0.212 0.672 0.096 0.004
#> GSM537342 3 0.8087 0.0307 0.036 0.284 0.372 0.280 0.028
#> GSM537347 4 0.6688 0.4538 0.008 0.268 0.228 0.496 0.000
#> GSM537350 1 0.5847 0.1421 0.488 0.012 0.064 0.000 0.436
#> GSM537362 3 0.8442 0.2290 0.088 0.232 0.472 0.148 0.060
#> GSM537363 1 0.7570 0.2523 0.416 0.044 0.324 0.004 0.212
#> GSM537368 1 0.1628 0.6225 0.936 0.000 0.008 0.000 0.056
#> GSM537376 2 0.6968 -0.2967 0.000 0.380 0.252 0.360 0.008
#> GSM537381 1 0.2561 0.6161 0.884 0.000 0.020 0.000 0.096
#> GSM537386 2 0.1788 0.6115 0.000 0.932 0.056 0.004 0.008
#> GSM537398 5 0.3757 0.5936 0.156 0.000 0.024 0.012 0.808
#> GSM537402 2 0.4157 0.3609 0.000 0.716 0.020 0.264 0.000
#> GSM537405 1 0.6062 0.3859 0.564 0.000 0.168 0.000 0.268
#> GSM537371 1 0.0162 0.6162 0.996 0.000 0.004 0.000 0.000
#> GSM537421 3 0.7167 -0.1432 0.008 0.296 0.372 0.320 0.004
#> GSM537424 5 0.2956 0.6411 0.140 0.004 0.000 0.008 0.848
#> GSM537432 3 0.6875 0.1919 0.024 0.284 0.504 0.188 0.000
#> GSM537331 4 0.3897 0.5232 0.012 0.104 0.000 0.820 0.064
#> GSM537332 2 0.5594 0.4353 0.000 0.608 0.284 0.108 0.000
#> GSM537334 4 0.2275 0.5706 0.012 0.012 0.000 0.912 0.064
#> GSM537338 4 0.2037 0.5739 0.004 0.012 0.000 0.920 0.064
#> GSM537353 2 0.6605 0.0512 0.000 0.492 0.184 0.316 0.008
#> GSM537357 1 0.0324 0.6139 0.992 0.000 0.004 0.004 0.000
#> GSM537358 2 0.1915 0.6115 0.000 0.928 0.040 0.032 0.000
#> GSM537375 4 0.5236 0.6607 0.004 0.184 0.120 0.692 0.000
#> GSM537389 2 0.2701 0.5867 0.000 0.900 0.032 0.036 0.032
#> GSM537390 2 0.2848 0.5796 0.000 0.840 0.156 0.004 0.000
#> GSM537393 4 0.5941 0.5831 0.000 0.256 0.160 0.584 0.000
#> GSM537399 5 0.8329 0.1820 0.208 0.216 0.132 0.012 0.432
#> GSM537407 3 0.7583 -0.2333 0.364 0.052 0.368 0.000 0.216
#> GSM537408 2 0.5423 0.4658 0.136 0.728 0.092 0.040 0.004
#> GSM537428 4 0.4286 0.5392 0.000 0.340 0.004 0.652 0.004
#> GSM537354 4 0.4852 0.6677 0.000 0.184 0.100 0.716 0.000
#> GSM537410 2 0.6524 0.2751 0.012 0.488 0.356 0.144 0.000
#> GSM537413 2 0.2020 0.6068 0.000 0.900 0.100 0.000 0.000
#> GSM537396 2 0.8892 0.0162 0.184 0.428 0.152 0.056 0.180
#> GSM537397 5 0.4169 0.6448 0.072 0.060 0.020 0.020 0.828
#> GSM537330 2 0.6180 -0.1886 0.000 0.456 0.104 0.432 0.008
#> GSM537369 1 0.2563 0.6045 0.872 0.000 0.008 0.000 0.120
#> GSM537373 2 0.9337 -0.1574 0.156 0.328 0.240 0.064 0.212
#> GSM537401 5 0.6506 0.5403 0.048 0.132 0.032 0.120 0.668
#> GSM537343 1 0.7004 0.2895 0.456 0.016 0.272 0.000 0.256
#> GSM537367 3 0.7422 -0.0970 0.328 0.064 0.448 0.000 0.160
#> GSM537382 2 0.6714 -0.1961 0.004 0.448 0.220 0.328 0.000
#> GSM537385 2 0.2780 0.5862 0.000 0.896 0.032 0.040 0.032
#> GSM537391 5 0.2930 0.6164 0.164 0.000 0.000 0.004 0.832
#> GSM537419 2 0.2853 0.5992 0.000 0.876 0.052 0.072 0.000
#> GSM537420 1 0.3487 0.5428 0.780 0.000 0.008 0.000 0.212
#> GSM537429 2 0.7884 -0.0762 0.024 0.484 0.100 0.284 0.108
#> GSM537431 3 0.5332 0.4594 0.080 0.080 0.764 0.036 0.040
#> GSM537387 5 0.4219 0.1980 0.416 0.000 0.000 0.000 0.584
#> GSM537414 3 0.4764 0.3944 0.016 0.104 0.768 0.108 0.004
#> GSM537433 3 0.7863 -0.1766 0.340 0.076 0.360 0.000 0.224
#> GSM537335 4 0.3731 0.4788 0.032 0.012 0.004 0.828 0.124
#> GSM537339 5 0.2917 0.6558 0.076 0.004 0.012 0.024 0.884
#> GSM537340 3 0.6401 0.2513 0.020 0.172 0.584 0.224 0.000
#> GSM537344 1 0.2358 0.6121 0.888 0.000 0.008 0.000 0.104
#> GSM537346 2 0.6550 0.0358 0.000 0.456 0.156 0.380 0.008
#> GSM537351 1 0.0451 0.6182 0.988 0.000 0.008 0.000 0.004
#> GSM537352 4 0.4922 0.6497 0.000 0.256 0.056 0.684 0.004
#> GSM537359 2 0.2805 0.6048 0.004 0.864 0.124 0.004 0.004
#> GSM537360 2 0.5783 0.3424 0.000 0.612 0.228 0.160 0.000
#> GSM537364 1 0.0162 0.6162 0.996 0.000 0.004 0.000 0.000
#> GSM537365 3 0.7791 0.3138 0.096 0.164 0.536 0.024 0.180
#> GSM537372 5 0.3132 0.5893 0.172 0.000 0.008 0.000 0.820
#> GSM537384 5 0.2286 0.6508 0.108 0.000 0.004 0.000 0.888
#> GSM537394 2 0.4624 0.5697 0.000 0.744 0.144 0.112 0.000
#> GSM537403 4 0.6646 0.0850 0.000 0.224 0.380 0.396 0.000
#> GSM537406 2 0.4802 0.4880 0.100 0.760 0.124 0.004 0.012
#> GSM537411 2 0.4401 0.2730 0.000 0.656 0.016 0.328 0.000
#> GSM537412 2 0.4820 0.3763 0.000 0.632 0.332 0.036 0.000
#> GSM537416 3 0.6658 0.0340 0.004 0.208 0.460 0.328 0.000
#> GSM537426 2 0.3280 0.5762 0.000 0.812 0.176 0.012 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.3807 0.6963 0.000 0.028 0.228 0.000 0.740 0.004
#> GSM537345 1 0.4979 0.1663 0.596 0.000 0.032 0.012 0.348 0.012
#> GSM537355 6 0.5704 0.5804 0.000 0.300 0.008 0.124 0.008 0.560
#> GSM537366 3 0.4816 0.4966 0.048 0.040 0.708 0.004 0.200 0.000
#> GSM537370 5 0.6958 0.2622 0.000 0.188 0.264 0.016 0.476 0.056
#> GSM537380 2 0.2528 0.5494 0.040 0.900 0.032 0.016 0.012 0.000
#> GSM537392 2 0.2671 0.5502 0.040 0.896 0.032 0.016 0.012 0.004
#> GSM537415 2 0.4409 0.4510 0.004 0.672 0.016 0.292 0.012 0.004
#> GSM537417 4 0.4688 0.2991 0.000 0.036 0.012 0.612 0.000 0.340
#> GSM537422 4 0.2732 0.4405 0.024 0.000 0.060 0.884 0.004 0.028
#> GSM537423 2 0.0405 0.5813 0.000 0.988 0.000 0.008 0.000 0.004
#> GSM537427 6 0.3500 0.6805 0.004 0.220 0.004 0.004 0.004 0.764
#> GSM537430 2 0.4438 0.2248 0.000 0.636 0.012 0.016 0.004 0.332
#> GSM537336 1 0.3534 0.7799 0.740 0.000 0.244 0.000 0.016 0.000
#> GSM537337 6 0.3953 0.6857 0.000 0.180 0.008 0.036 0.008 0.768
#> GSM537348 5 0.1411 0.7961 0.004 0.000 0.060 0.000 0.936 0.000
#> GSM537349 2 0.2239 0.5555 0.040 0.912 0.028 0.016 0.004 0.000
#> GSM537356 3 0.4867 0.4178 0.068 0.016 0.660 0.000 0.256 0.000
#> GSM537361 4 0.5890 -0.3258 0.128 0.000 0.416 0.440 0.016 0.000
#> GSM537374 6 0.3073 0.6583 0.020 0.140 0.004 0.000 0.004 0.832
#> GSM537377 1 0.5597 0.1145 0.556 0.000 0.032 0.048 0.352 0.012
#> GSM537378 2 0.1880 0.5809 0.004 0.932 0.004 0.020 0.008 0.032
#> GSM537379 6 0.6141 0.3152 0.000 0.268 0.008 0.264 0.000 0.460
#> GSM537383 2 0.2207 0.5671 0.020 0.908 0.004 0.008 0.000 0.060
#> GSM537388 2 0.4130 0.4017 0.024 0.736 0.012 0.008 0.000 0.220
#> GSM537395 6 0.5328 0.5269 0.000 0.352 0.016 0.064 0.004 0.564
#> GSM537400 4 0.4653 0.4500 0.008 0.136 0.040 0.756 0.004 0.056
#> GSM537404 3 0.6949 -0.2497 0.000 0.116 0.436 0.332 0.004 0.112
#> GSM537409 4 0.6405 -0.0482 0.008 0.372 0.040 0.484 0.012 0.084
#> GSM537418 3 0.4531 0.5115 0.060 0.056 0.752 0.000 0.132 0.000
#> GSM537425 3 0.3705 0.5812 0.056 0.040 0.836 0.048 0.020 0.000
#> GSM537333 4 0.5051 0.3969 0.000 0.140 0.044 0.704 0.000 0.112
#> GSM537342 4 0.7932 0.3031 0.004 0.184 0.284 0.308 0.008 0.212
#> GSM537347 6 0.6230 0.2971 0.000 0.288 0.004 0.236 0.008 0.464
#> GSM537350 3 0.5990 0.1234 0.224 0.008 0.500 0.000 0.268 0.000
#> GSM537362 4 0.8380 0.1308 0.052 0.168 0.076 0.436 0.056 0.212
#> GSM537363 3 0.2288 0.5650 0.072 0.028 0.896 0.000 0.004 0.000
#> GSM537368 1 0.4146 0.7753 0.676 0.000 0.288 0.000 0.036 0.000
#> GSM537376 2 0.7643 -0.1699 0.000 0.328 0.132 0.224 0.008 0.308
#> GSM537381 1 0.4466 0.7538 0.620 0.000 0.336 0.000 0.044 0.000
#> GSM537386 2 0.2924 0.5705 0.024 0.864 0.028 0.084 0.000 0.000
#> GSM537398 5 0.3614 0.7477 0.080 0.000 0.052 0.008 0.832 0.028
#> GSM537402 2 0.6091 0.2960 0.000 0.604 0.088 0.068 0.012 0.228
#> GSM537405 3 0.6890 -0.1285 0.224 0.000 0.476 0.092 0.208 0.000
#> GSM537371 1 0.3673 0.7797 0.736 0.000 0.244 0.004 0.016 0.000
#> GSM537421 4 0.7695 0.2267 0.004 0.220 0.152 0.368 0.004 0.252
#> GSM537424 5 0.3542 0.7625 0.068 0.016 0.068 0.012 0.836 0.000
#> GSM537432 4 0.7762 0.3367 0.004 0.260 0.236 0.356 0.008 0.136
#> GSM537331 6 0.3142 0.5181 0.092 0.028 0.004 0.004 0.016 0.856
#> GSM537332 2 0.6602 0.2452 0.004 0.460 0.036 0.360 0.012 0.128
#> GSM537334 6 0.2306 0.5227 0.092 0.000 0.004 0.000 0.016 0.888
#> GSM537338 6 0.2062 0.5282 0.088 0.000 0.004 0.000 0.008 0.900
#> GSM537353 2 0.6904 0.1823 0.004 0.480 0.044 0.200 0.012 0.260
#> GSM537357 1 0.3301 0.7554 0.788 0.000 0.188 0.000 0.024 0.000
#> GSM537358 2 0.2793 0.5764 0.000 0.872 0.024 0.080 0.000 0.024
#> GSM537375 6 0.4750 0.6716 0.000 0.236 0.012 0.056 0.008 0.688
#> GSM537389 2 0.2222 0.5545 0.040 0.912 0.032 0.012 0.004 0.000
#> GSM537390 2 0.4453 0.4544 0.004 0.672 0.024 0.288 0.008 0.004
#> GSM537393 6 0.5452 0.6012 0.000 0.268 0.024 0.088 0.004 0.616
#> GSM537399 3 0.5856 0.3354 0.008 0.200 0.528 0.000 0.264 0.000
#> GSM537407 3 0.2681 0.5912 0.048 0.044 0.888 0.008 0.012 0.000
#> GSM537408 2 0.4395 0.3709 0.004 0.664 0.300 0.016 0.000 0.016
#> GSM537428 6 0.4008 0.6509 0.000 0.308 0.000 0.016 0.004 0.672
#> GSM537354 6 0.4300 0.6877 0.000 0.192 0.016 0.040 0.008 0.744
#> GSM537410 2 0.7498 0.1060 0.004 0.404 0.184 0.296 0.012 0.100
#> GSM537413 2 0.3562 0.5411 0.004 0.784 0.036 0.176 0.000 0.000
#> GSM537396 3 0.6068 0.4113 0.008 0.308 0.544 0.016 0.116 0.008
#> GSM537397 5 0.2768 0.7672 0.000 0.012 0.156 0.000 0.832 0.000
#> GSM537330 2 0.6294 -0.0426 0.004 0.448 0.008 0.152 0.012 0.376
#> GSM537369 1 0.4814 0.7471 0.616 0.000 0.304 0.000 0.080 0.000
#> GSM537373 3 0.4786 0.4308 0.004 0.276 0.668 0.016 0.020 0.016
#> GSM537401 5 0.4370 0.6703 0.000 0.028 0.236 0.004 0.712 0.020
#> GSM537343 3 0.3728 0.5088 0.116 0.024 0.812 0.004 0.044 0.000
#> GSM537367 3 0.3049 0.5883 0.052 0.044 0.868 0.032 0.004 0.000
#> GSM537382 2 0.7514 -0.0658 0.000 0.388 0.144 0.172 0.008 0.288
#> GSM537385 2 0.2671 0.5502 0.040 0.896 0.032 0.016 0.012 0.004
#> GSM537391 5 0.2563 0.7731 0.072 0.000 0.052 0.000 0.876 0.000
#> GSM537419 2 0.3547 0.5605 0.000 0.828 0.036 0.088 0.000 0.048
#> GSM537420 1 0.5379 0.6770 0.536 0.000 0.336 0.000 0.128 0.000
#> GSM537429 2 0.7564 -0.1038 0.000 0.428 0.044 0.100 0.128 0.300
#> GSM537431 3 0.5678 -0.1107 0.004 0.072 0.464 0.440 0.004 0.016
#> GSM537387 5 0.4668 0.3539 0.316 0.000 0.064 0.000 0.620 0.000
#> GSM537414 4 0.2403 0.4748 0.000 0.020 0.040 0.900 0.000 0.040
#> GSM537433 3 0.3230 0.5989 0.040 0.064 0.860 0.016 0.020 0.000
#> GSM537335 6 0.3532 0.4612 0.092 0.000 0.016 0.004 0.060 0.828
#> GSM537339 5 0.2092 0.7897 0.000 0.000 0.124 0.000 0.876 0.000
#> GSM537340 4 0.5988 0.4694 0.000 0.052 0.164 0.596 0.000 0.188
#> GSM537344 1 0.4467 0.7603 0.632 0.000 0.320 0.000 0.048 0.000
#> GSM537346 2 0.6884 0.0270 0.004 0.376 0.024 0.220 0.012 0.364
#> GSM537351 1 0.3983 0.6982 0.640 0.000 0.348 0.004 0.008 0.000
#> GSM537352 6 0.4488 0.6846 0.000 0.204 0.020 0.048 0.004 0.724
#> GSM537359 2 0.3728 0.5308 0.004 0.788 0.140 0.068 0.000 0.000
#> GSM537360 2 0.6538 0.1837 0.004 0.484 0.044 0.348 0.012 0.108
#> GSM537364 1 0.3673 0.7797 0.736 0.000 0.244 0.004 0.016 0.000
#> GSM537365 3 0.4881 0.3878 0.008 0.152 0.696 0.140 0.004 0.000
#> GSM537372 5 0.1863 0.8015 0.000 0.000 0.104 0.000 0.896 0.000
#> GSM537384 5 0.2361 0.7963 0.028 0.000 0.088 0.000 0.884 0.000
#> GSM537394 2 0.5184 0.5237 0.004 0.708 0.084 0.136 0.000 0.068
#> GSM537403 4 0.6672 0.2563 0.004 0.120 0.048 0.476 0.012 0.340
#> GSM537406 2 0.3875 0.3712 0.004 0.700 0.280 0.016 0.000 0.000
#> GSM537411 2 0.5693 -0.0243 0.000 0.520 0.040 0.056 0.004 0.380
#> GSM537412 2 0.5214 0.1848 0.004 0.496 0.040 0.444 0.012 0.004
#> GSM537416 4 0.6814 0.4119 0.000 0.092 0.156 0.504 0.004 0.244
#> GSM537426 2 0.4637 0.4722 0.004 0.680 0.036 0.264 0.012 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> CV:mclust 100 0.754 0.439 2
#> CV:mclust 54 0.901 0.871 3
#> CV:mclust 22 0.783 0.632 4
#> CV:mclust 49 0.220 0.478 5
#> CV:mclust 54 0.132 0.591 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 104 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 0.900 0.931 0.971 0.4803 0.522 0.522
#> 3 3 0.377 0.567 0.772 0.3461 0.809 0.647
#> 4 4 0.500 0.647 0.776 0.1504 0.790 0.494
#> 5 5 0.555 0.574 0.739 0.0715 0.867 0.542
#> 6 6 0.577 0.477 0.669 0.0434 0.891 0.541
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
#> GSM537341 2 0.6343 0.8050 0.160 0.840
#> GSM537345 1 0.0000 0.9683 1.000 0.000
#> GSM537355 2 0.0000 0.9696 0.000 1.000
#> GSM537366 1 0.2603 0.9377 0.956 0.044
#> GSM537370 2 0.0000 0.9696 0.000 1.000
#> GSM537380 2 0.0000 0.9696 0.000 1.000
#> GSM537392 2 0.0000 0.9696 0.000 1.000
#> GSM537415 2 0.0000 0.9696 0.000 1.000
#> GSM537417 2 0.0000 0.9696 0.000 1.000
#> GSM537422 1 0.0000 0.9683 1.000 0.000
#> GSM537423 2 0.0000 0.9696 0.000 1.000
#> GSM537427 2 0.0000 0.9696 0.000 1.000
#> GSM537430 2 0.0000 0.9696 0.000 1.000
#> GSM537336 1 0.0000 0.9683 1.000 0.000
#> GSM537337 2 0.0000 0.9696 0.000 1.000
#> GSM537348 1 0.0000 0.9683 1.000 0.000
#> GSM537349 2 0.0000 0.9696 0.000 1.000
#> GSM537356 1 0.0376 0.9664 0.996 0.004
#> GSM537361 1 0.0000 0.9683 1.000 0.000
#> GSM537374 2 0.0000 0.9696 0.000 1.000
#> GSM537377 1 0.0000 0.9683 1.000 0.000
#> GSM537378 2 0.0000 0.9696 0.000 1.000
#> GSM537379 2 0.0000 0.9696 0.000 1.000
#> GSM537383 2 0.0000 0.9696 0.000 1.000
#> GSM537388 2 0.0000 0.9696 0.000 1.000
#> GSM537395 2 0.0000 0.9696 0.000 1.000
#> GSM537400 1 0.0376 0.9664 0.996 0.004
#> GSM537404 2 0.1184 0.9573 0.016 0.984
#> GSM537409 2 0.0000 0.9696 0.000 1.000
#> GSM537418 1 0.0000 0.9683 1.000 0.000
#> GSM537425 1 0.0938 0.9618 0.988 0.012
#> GSM537333 1 0.8081 0.6905 0.752 0.248
#> GSM537342 2 0.0000 0.9696 0.000 1.000
#> GSM537347 2 0.0672 0.9636 0.008 0.992
#> GSM537350 1 0.0000 0.9683 1.000 0.000
#> GSM537362 1 0.0000 0.9683 1.000 0.000
#> GSM537363 1 0.6148 0.8284 0.848 0.152
#> GSM537368 1 0.0000 0.9683 1.000 0.000
#> GSM537376 2 0.0000 0.9696 0.000 1.000
#> GSM537381 1 0.0000 0.9683 1.000 0.000
#> GSM537386 2 0.0000 0.9696 0.000 1.000
#> GSM537398 1 0.0000 0.9683 1.000 0.000
#> GSM537402 2 0.0000 0.9696 0.000 1.000
#> GSM537405 1 0.0000 0.9683 1.000 0.000
#> GSM537371 1 0.0000 0.9683 1.000 0.000
#> GSM537421 2 0.1633 0.9500 0.024 0.976
#> GSM537424 1 0.0000 0.9683 1.000 0.000
#> GSM537432 2 0.9608 0.3749 0.384 0.616
#> GSM537331 2 0.0000 0.9696 0.000 1.000
#> GSM537332 2 0.0000 0.9696 0.000 1.000
#> GSM537334 2 0.0000 0.9696 0.000 1.000
#> GSM537338 2 0.0000 0.9696 0.000 1.000
#> GSM537353 2 0.0000 0.9696 0.000 1.000
#> GSM537357 1 0.0000 0.9683 1.000 0.000
#> GSM537358 2 0.0000 0.9696 0.000 1.000
#> GSM537375 2 0.0000 0.9696 0.000 1.000
#> GSM537389 2 0.0000 0.9696 0.000 1.000
#> GSM537390 2 0.0000 0.9696 0.000 1.000
#> GSM537393 2 0.0000 0.9696 0.000 1.000
#> GSM537399 1 0.7602 0.7241 0.780 0.220
#> GSM537407 1 0.1184 0.9587 0.984 0.016
#> GSM537408 2 0.0000 0.9696 0.000 1.000
#> GSM537428 2 0.0000 0.9696 0.000 1.000
#> GSM537354 2 0.0000 0.9696 0.000 1.000
#> GSM537410 2 0.0000 0.9696 0.000 1.000
#> GSM537413 2 0.0000 0.9696 0.000 1.000
#> GSM537396 2 0.0376 0.9667 0.004 0.996
#> GSM537397 1 0.0000 0.9683 1.000 0.000
#> GSM537330 2 0.0000 0.9696 0.000 1.000
#> GSM537369 1 0.0000 0.9683 1.000 0.000
#> GSM537373 2 0.0672 0.9637 0.008 0.992
#> GSM537401 2 0.2778 0.9286 0.048 0.952
#> GSM537343 1 0.0000 0.9683 1.000 0.000
#> GSM537367 1 0.0672 0.9644 0.992 0.008
#> GSM537382 2 0.0000 0.9696 0.000 1.000
#> GSM537385 2 0.0000 0.9696 0.000 1.000
#> GSM537391 1 0.0000 0.9683 1.000 0.000
#> GSM537419 2 0.0000 0.9696 0.000 1.000
#> GSM537420 1 0.0000 0.9683 1.000 0.000
#> GSM537429 2 0.6438 0.7989 0.164 0.836
#> GSM537431 1 0.5737 0.8434 0.864 0.136
#> GSM537387 1 0.0000 0.9683 1.000 0.000
#> GSM537414 1 0.3733 0.9142 0.928 0.072
#> GSM537433 1 0.8327 0.6535 0.736 0.264
#> GSM537335 2 0.9993 0.0507 0.484 0.516
#> GSM537339 1 0.0938 0.9619 0.988 0.012
#> GSM537340 2 0.9608 0.3635 0.384 0.616
#> GSM537344 1 0.0000 0.9683 1.000 0.000
#> GSM537346 2 0.0000 0.9696 0.000 1.000
#> GSM537351 1 0.0000 0.9683 1.000 0.000
#> GSM537352 2 0.0000 0.9696 0.000 1.000
#> GSM537359 2 0.0000 0.9696 0.000 1.000
#> GSM537360 2 0.0000 0.9696 0.000 1.000
#> GSM537364 1 0.0000 0.9683 1.000 0.000
#> GSM537365 2 0.6247 0.8085 0.156 0.844
#> GSM537372 1 0.0000 0.9683 1.000 0.000
#> GSM537384 1 0.0000 0.9683 1.000 0.000
#> GSM537394 2 0.0000 0.9696 0.000 1.000
#> GSM537403 2 0.0000 0.9696 0.000 1.000
#> GSM537406 2 0.0000 0.9696 0.000 1.000
#> GSM537411 2 0.0000 0.9696 0.000 1.000
#> GSM537412 2 0.0000 0.9696 0.000 1.000
#> GSM537416 2 0.0000 0.9696 0.000 1.000
#> GSM537426 2 0.0000 0.9696 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.8872 0.2247 0.132 0.520 0.348
#> GSM537345 1 0.5905 0.3823 0.648 0.000 0.352
#> GSM537355 2 0.6008 0.5108 0.000 0.628 0.372
#> GSM537366 1 0.5847 0.6839 0.780 0.172 0.048
#> GSM537370 2 0.6298 0.3781 0.004 0.608 0.388
#> GSM537380 2 0.4452 0.6815 0.000 0.808 0.192
#> GSM537392 2 0.4235 0.6914 0.000 0.824 0.176
#> GSM537415 2 0.0237 0.7284 0.000 0.996 0.004
#> GSM537417 2 0.6111 0.4811 0.000 0.604 0.396
#> GSM537422 1 0.7940 0.3872 0.592 0.076 0.332
#> GSM537423 2 0.3116 0.7384 0.000 0.892 0.108
#> GSM537427 3 0.6260 -0.2621 0.000 0.448 0.552
#> GSM537430 2 0.5591 0.6585 0.000 0.696 0.304
#> GSM537336 1 0.0747 0.7828 0.984 0.000 0.016
#> GSM537337 3 0.4121 0.5006 0.000 0.168 0.832
#> GSM537348 3 0.6308 0.0598 0.492 0.000 0.508
#> GSM537349 2 0.2959 0.7356 0.000 0.900 0.100
#> GSM537356 1 0.3692 0.7624 0.896 0.056 0.048
#> GSM537361 1 0.4504 0.6543 0.804 0.000 0.196
#> GSM537374 3 0.4002 0.5503 0.000 0.160 0.840
#> GSM537377 3 0.6309 -0.0389 0.496 0.000 0.504
#> GSM537378 2 0.3619 0.7387 0.000 0.864 0.136
#> GSM537379 3 0.6111 -0.0421 0.000 0.396 0.604
#> GSM537383 2 0.4235 0.7206 0.000 0.824 0.176
#> GSM537388 2 0.6192 0.5321 0.000 0.580 0.420
#> GSM537395 2 0.6260 0.4907 0.000 0.552 0.448
#> GSM537400 1 0.7932 0.2971 0.552 0.064 0.384
#> GSM537404 2 0.6349 0.6867 0.092 0.768 0.140
#> GSM537409 2 0.5138 0.6443 0.000 0.748 0.252
#> GSM537418 1 0.0661 0.7852 0.988 0.004 0.008
#> GSM537425 1 0.2297 0.7813 0.944 0.020 0.036
#> GSM537333 3 0.9730 0.0739 0.352 0.228 0.420
#> GSM537342 2 0.1529 0.7239 0.000 0.960 0.040
#> GSM537347 3 0.5815 0.1514 0.004 0.304 0.692
#> GSM537350 1 0.4519 0.7309 0.852 0.032 0.116
#> GSM537362 3 0.6045 0.2807 0.380 0.000 0.620
#> GSM537363 1 0.6685 0.6070 0.708 0.244 0.048
#> GSM537368 1 0.0747 0.7828 0.984 0.000 0.016
#> GSM537376 2 0.5621 0.6421 0.000 0.692 0.308
#> GSM537381 1 0.0592 0.7848 0.988 0.000 0.012
#> GSM537386 2 0.3482 0.7180 0.000 0.872 0.128
#> GSM537398 3 0.6008 0.2832 0.372 0.000 0.628
#> GSM537402 2 0.3482 0.7210 0.000 0.872 0.128
#> GSM537405 1 0.0892 0.7823 0.980 0.000 0.020
#> GSM537371 1 0.1860 0.7715 0.948 0.000 0.052
#> GSM537421 2 0.3573 0.7261 0.004 0.876 0.120
#> GSM537424 1 0.5905 0.4025 0.648 0.000 0.352
#> GSM537432 2 0.8948 0.3855 0.224 0.568 0.208
#> GSM537331 3 0.3941 0.5335 0.000 0.156 0.844
#> GSM537332 2 0.4796 0.6714 0.000 0.780 0.220
#> GSM537334 3 0.2261 0.5889 0.000 0.068 0.932
#> GSM537338 3 0.2711 0.5870 0.000 0.088 0.912
#> GSM537353 2 0.3686 0.7313 0.000 0.860 0.140
#> GSM537357 1 0.1031 0.7810 0.976 0.000 0.024
#> GSM537358 2 0.4178 0.7306 0.000 0.828 0.172
#> GSM537375 3 0.2878 0.5824 0.000 0.096 0.904
#> GSM537389 2 0.2796 0.7246 0.000 0.908 0.092
#> GSM537390 2 0.2356 0.7356 0.000 0.928 0.072
#> GSM537393 2 0.6299 0.3720 0.000 0.524 0.476
#> GSM537399 1 0.7672 0.4533 0.684 0.160 0.156
#> GSM537407 1 0.5956 0.6789 0.768 0.188 0.044
#> GSM537408 2 0.3989 0.7022 0.012 0.864 0.124
#> GSM537428 3 0.4842 0.4192 0.000 0.224 0.776
#> GSM537354 3 0.2878 0.5808 0.000 0.096 0.904
#> GSM537410 2 0.1529 0.7185 0.000 0.960 0.040
#> GSM537413 2 0.1163 0.7257 0.000 0.972 0.028
#> GSM537396 2 0.6264 0.6404 0.068 0.764 0.168
#> GSM537397 3 0.7328 0.2615 0.364 0.040 0.596
#> GSM537330 2 0.5621 0.6102 0.000 0.692 0.308
#> GSM537369 1 0.0592 0.7839 0.988 0.000 0.012
#> GSM537373 2 0.4475 0.6690 0.064 0.864 0.072
#> GSM537401 3 0.7207 0.2250 0.032 0.384 0.584
#> GSM537343 1 0.4964 0.7283 0.836 0.116 0.048
#> GSM537367 1 0.6937 0.6011 0.680 0.272 0.048
#> GSM537382 2 0.5016 0.6554 0.000 0.760 0.240
#> GSM537385 2 0.4291 0.7148 0.000 0.820 0.180
#> GSM537391 3 0.6302 0.0823 0.480 0.000 0.520
#> GSM537419 2 0.3116 0.7206 0.000 0.892 0.108
#> GSM537420 1 0.1163 0.7821 0.972 0.000 0.028
#> GSM537429 2 0.6566 0.5207 0.012 0.612 0.376
#> GSM537431 1 0.6007 0.6529 0.764 0.192 0.044
#> GSM537387 1 0.4750 0.6015 0.784 0.000 0.216
#> GSM537414 1 0.8836 0.2026 0.492 0.120 0.388
#> GSM537433 1 0.7190 0.5363 0.636 0.320 0.044
#> GSM537335 3 0.2636 0.5954 0.020 0.048 0.932
#> GSM537339 3 0.6912 0.3339 0.344 0.028 0.628
#> GSM537340 2 0.9457 0.1222 0.312 0.484 0.204
#> GSM537344 1 0.0424 0.7840 0.992 0.000 0.008
#> GSM537346 2 0.5810 0.5822 0.000 0.664 0.336
#> GSM537351 1 0.0747 0.7842 0.984 0.000 0.016
#> GSM537352 2 0.6308 0.3475 0.000 0.508 0.492
#> GSM537359 2 0.4172 0.6980 0.004 0.840 0.156
#> GSM537360 2 0.3192 0.7432 0.000 0.888 0.112
#> GSM537364 1 0.1163 0.7802 0.972 0.000 0.028
#> GSM537365 2 0.8211 0.1020 0.404 0.520 0.076
#> GSM537372 1 0.2804 0.7748 0.924 0.016 0.060
#> GSM537384 1 0.2448 0.7532 0.924 0.000 0.076
#> GSM537394 2 0.3425 0.7227 0.004 0.884 0.112
#> GSM537403 2 0.5465 0.6086 0.000 0.712 0.288
#> GSM537406 2 0.2860 0.7094 0.004 0.912 0.084
#> GSM537411 2 0.5397 0.6379 0.000 0.720 0.280
#> GSM537412 2 0.1529 0.7238 0.000 0.960 0.040
#> GSM537416 2 0.4796 0.6663 0.000 0.780 0.220
#> GSM537426 2 0.3551 0.7172 0.000 0.868 0.132
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.3464 0.7024 0.124 0.856 0.004 0.016
#> GSM537345 4 0.5288 0.0744 0.472 0.000 0.008 0.520
#> GSM537355 3 0.4553 0.7165 0.000 0.040 0.780 0.180
#> GSM537366 1 0.3377 0.7907 0.848 0.140 0.000 0.012
#> GSM537370 2 0.3662 0.7479 0.012 0.860 0.024 0.104
#> GSM537380 2 0.1820 0.7787 0.000 0.944 0.020 0.036
#> GSM537392 2 0.2197 0.7784 0.000 0.928 0.024 0.048
#> GSM537415 2 0.4907 0.1213 0.000 0.580 0.420 0.000
#> GSM537417 3 0.3668 0.7076 0.000 0.004 0.808 0.188
#> GSM537422 3 0.5647 0.6217 0.164 0.000 0.720 0.116
#> GSM537423 2 0.3570 0.7417 0.000 0.860 0.092 0.048
#> GSM537427 4 0.6162 0.4143 0.000 0.304 0.076 0.620
#> GSM537430 2 0.6295 0.4572 0.000 0.616 0.296 0.088
#> GSM537336 1 0.1820 0.8200 0.944 0.000 0.020 0.036
#> GSM537337 4 0.4578 0.6497 0.000 0.052 0.160 0.788
#> GSM537348 4 0.4290 0.6629 0.212 0.016 0.000 0.772
#> GSM537349 2 0.1913 0.7750 0.000 0.940 0.040 0.020
#> GSM537356 1 0.3143 0.8113 0.888 0.080 0.008 0.024
#> GSM537361 1 0.5277 0.6748 0.752 0.000 0.116 0.132
#> GSM537374 4 0.3401 0.7072 0.000 0.152 0.008 0.840
#> GSM537377 4 0.4253 0.6474 0.208 0.000 0.016 0.776
#> GSM537378 2 0.5950 0.1135 0.000 0.544 0.416 0.040
#> GSM537379 3 0.5256 0.6534 0.000 0.040 0.700 0.260
#> GSM537383 2 0.3497 0.7515 0.000 0.860 0.104 0.036
#> GSM537388 2 0.6915 0.4487 0.000 0.592 0.212 0.196
#> GSM537395 3 0.7486 0.4302 0.000 0.272 0.500 0.228
#> GSM537400 3 0.4354 0.6970 0.088 0.004 0.824 0.084
#> GSM537404 2 0.7390 0.5551 0.208 0.624 0.116 0.052
#> GSM537409 3 0.2319 0.7534 0.000 0.040 0.924 0.036
#> GSM537418 1 0.1854 0.8220 0.940 0.000 0.048 0.012
#> GSM537425 1 0.4569 0.7184 0.760 0.008 0.220 0.012
#> GSM537333 3 0.3100 0.7264 0.028 0.004 0.888 0.080
#> GSM537342 3 0.4160 0.6923 0.012 0.192 0.792 0.004
#> GSM537347 4 0.7198 0.1734 0.000 0.160 0.320 0.520
#> GSM537350 1 0.5060 0.3937 0.584 0.412 0.000 0.004
#> GSM537362 4 0.3529 0.7011 0.152 0.000 0.012 0.836
#> GSM537363 1 0.5346 0.7483 0.768 0.104 0.116 0.012
#> GSM537368 1 0.1635 0.8200 0.948 0.000 0.008 0.044
#> GSM537376 3 0.7830 0.2495 0.000 0.268 0.400 0.332
#> GSM537381 1 0.0712 0.8250 0.984 0.004 0.008 0.004
#> GSM537386 2 0.1305 0.7779 0.000 0.960 0.036 0.004
#> GSM537398 4 0.2799 0.7394 0.108 0.000 0.008 0.884
#> GSM537402 2 0.4232 0.7149 0.004 0.816 0.144 0.036
#> GSM537405 1 0.2089 0.8179 0.932 0.000 0.020 0.048
#> GSM537371 1 0.2101 0.8148 0.928 0.000 0.012 0.060
#> GSM537421 3 0.2342 0.7369 0.000 0.080 0.912 0.008
#> GSM537424 1 0.5460 0.4507 0.632 0.000 0.028 0.340
#> GSM537432 3 0.2636 0.7303 0.020 0.012 0.916 0.052
#> GSM537331 4 0.4713 0.6716 0.000 0.172 0.052 0.776
#> GSM537332 3 0.5272 0.6854 0.000 0.172 0.744 0.084
#> GSM537334 4 0.1970 0.7381 0.000 0.008 0.060 0.932
#> GSM537338 4 0.2131 0.7471 0.000 0.032 0.036 0.932
#> GSM537353 3 0.5923 0.3899 0.000 0.376 0.580 0.044
#> GSM537357 1 0.2002 0.8180 0.936 0.000 0.020 0.044
#> GSM537358 2 0.3266 0.7634 0.000 0.876 0.084 0.040
#> GSM537375 4 0.3081 0.7359 0.000 0.048 0.064 0.888
#> GSM537389 2 0.1489 0.7735 0.000 0.952 0.044 0.004
#> GSM537390 2 0.5699 0.2937 0.000 0.588 0.380 0.032
#> GSM537393 3 0.4595 0.7109 0.000 0.040 0.776 0.184
#> GSM537399 1 0.6724 0.2978 0.532 0.400 0.036 0.032
#> GSM537407 1 0.4890 0.7728 0.792 0.136 0.060 0.012
#> GSM537408 2 0.1362 0.7727 0.012 0.964 0.020 0.004
#> GSM537428 4 0.4746 0.6660 0.000 0.168 0.056 0.776
#> GSM537354 4 0.4100 0.7163 0.000 0.076 0.092 0.832
#> GSM537410 3 0.5223 0.4143 0.004 0.408 0.584 0.004
#> GSM537413 3 0.5473 0.3198 0.004 0.408 0.576 0.012
#> GSM537396 2 0.2433 0.7498 0.060 0.920 0.008 0.012
#> GSM537397 2 0.7619 0.2671 0.248 0.524 0.008 0.220
#> GSM537330 3 0.6346 0.5709 0.000 0.244 0.640 0.116
#> GSM537369 1 0.0804 0.8230 0.980 0.012 0.000 0.008
#> GSM537373 2 0.3071 0.7432 0.068 0.888 0.044 0.000
#> GSM537401 2 0.4914 0.6074 0.044 0.748 0.000 0.208
#> GSM537343 1 0.3982 0.7791 0.824 0.152 0.012 0.012
#> GSM537367 1 0.5167 0.7585 0.780 0.092 0.116 0.012
#> GSM537382 3 0.4485 0.7330 0.000 0.152 0.796 0.052
#> GSM537385 2 0.2227 0.7752 0.000 0.928 0.036 0.036
#> GSM537391 4 0.4539 0.5851 0.272 0.008 0.000 0.720
#> GSM537419 2 0.1767 0.7771 0.000 0.944 0.044 0.012
#> GSM537420 1 0.1042 0.8231 0.972 0.020 0.000 0.008
#> GSM537429 3 0.6001 0.6491 0.004 0.176 0.700 0.120
#> GSM537431 3 0.3718 0.6383 0.168 0.000 0.820 0.012
#> GSM537387 1 0.3539 0.7244 0.820 0.000 0.004 0.176
#> GSM537414 3 0.4583 0.7006 0.076 0.004 0.808 0.112
#> GSM537433 1 0.4810 0.7627 0.788 0.160 0.036 0.016
#> GSM537335 4 0.1639 0.7449 0.004 0.008 0.036 0.952
#> GSM537339 4 0.5167 0.7091 0.108 0.132 0.000 0.760
#> GSM537340 3 0.3855 0.7315 0.040 0.092 0.856 0.012
#> GSM537344 1 0.0779 0.8231 0.980 0.016 0.000 0.004
#> GSM537346 2 0.7475 -0.0800 0.000 0.420 0.404 0.176
#> GSM537351 1 0.2411 0.8157 0.920 0.000 0.040 0.040
#> GSM537352 3 0.6627 0.5548 0.000 0.112 0.588 0.300
#> GSM537359 2 0.1706 0.7665 0.000 0.948 0.036 0.016
#> GSM537360 3 0.5815 0.2486 0.000 0.428 0.540 0.032
#> GSM537364 1 0.2399 0.8136 0.920 0.000 0.032 0.048
#> GSM537365 1 0.7825 0.3046 0.480 0.356 0.140 0.024
#> GSM537372 1 0.2924 0.8046 0.884 0.100 0.000 0.016
#> GSM537384 1 0.1807 0.8169 0.940 0.008 0.000 0.052
#> GSM537394 2 0.1576 0.7740 0.000 0.948 0.048 0.004
#> GSM537403 3 0.3601 0.7486 0.000 0.056 0.860 0.084
#> GSM537406 2 0.0921 0.7730 0.000 0.972 0.028 0.000
#> GSM537411 2 0.6583 0.5840 0.000 0.632 0.176 0.192
#> GSM537412 3 0.3584 0.7119 0.004 0.152 0.836 0.008
#> GSM537416 3 0.1396 0.7462 0.004 0.032 0.960 0.004
#> GSM537426 3 0.2918 0.7367 0.000 0.116 0.876 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.5940 0.5191 0.244 0.636 0.000 0.088 0.032
#> GSM537345 5 0.4771 0.4983 0.208 0.000 0.008 0.060 0.724
#> GSM537355 3 0.4382 0.5038 0.000 0.012 0.736 0.228 0.024
#> GSM537366 1 0.2928 0.7886 0.888 0.060 0.008 0.036 0.008
#> GSM537370 2 0.2886 0.7013 0.012 0.884 0.036 0.000 0.068
#> GSM537380 2 0.1753 0.7097 0.000 0.936 0.032 0.000 0.032
#> GSM537392 2 0.2054 0.7087 0.000 0.920 0.052 0.000 0.028
#> GSM537415 4 0.4875 0.4113 0.000 0.400 0.020 0.576 0.004
#> GSM537417 3 0.1790 0.6454 0.004 0.004 0.940 0.036 0.016
#> GSM537422 3 0.8098 0.0939 0.256 0.000 0.364 0.280 0.100
#> GSM537423 2 0.2654 0.6908 0.000 0.888 0.064 0.048 0.000
#> GSM537427 5 0.6897 0.2331 0.000 0.304 0.292 0.004 0.400
#> GSM537430 3 0.5315 0.1350 0.000 0.432 0.528 0.016 0.024
#> GSM537336 1 0.4053 0.7930 0.816 0.000 0.024 0.056 0.104
#> GSM537337 5 0.6311 0.6095 0.000 0.044 0.256 0.096 0.604
#> GSM537348 5 0.4241 0.6293 0.264 0.008 0.012 0.000 0.716
#> GSM537349 2 0.3166 0.6741 0.000 0.856 0.020 0.112 0.012
#> GSM537356 1 0.2896 0.7926 0.888 0.068 0.016 0.004 0.024
#> GSM537361 3 0.4518 0.3192 0.320 0.000 0.660 0.004 0.016
#> GSM537374 5 0.4325 0.6568 0.000 0.192 0.048 0.004 0.756
#> GSM537377 5 0.3207 0.6702 0.056 0.000 0.024 0.048 0.872
#> GSM537378 2 0.6669 0.1613 0.000 0.460 0.324 0.212 0.004
#> GSM537379 3 0.1869 0.6357 0.000 0.012 0.936 0.016 0.036
#> GSM537383 2 0.3488 0.6370 0.000 0.804 0.180 0.008 0.008
#> GSM537388 2 0.6765 0.2162 0.000 0.476 0.380 0.100 0.044
#> GSM537395 3 0.6370 0.4441 0.000 0.224 0.620 0.096 0.060
#> GSM537400 3 0.6236 0.3653 0.032 0.000 0.600 0.264 0.104
#> GSM537404 3 0.6461 0.4637 0.240 0.160 0.580 0.004 0.016
#> GSM537409 4 0.4415 0.5656 0.000 0.028 0.236 0.728 0.008
#> GSM537418 1 0.3742 0.8048 0.840 0.004 0.016 0.088 0.052
#> GSM537425 1 0.6202 0.6905 0.676 0.016 0.136 0.132 0.040
#> GSM537333 3 0.4314 0.4567 0.004 0.000 0.700 0.280 0.016
#> GSM537342 4 0.2804 0.7057 0.000 0.092 0.012 0.880 0.016
#> GSM537347 3 0.1682 0.6285 0.004 0.012 0.940 0.000 0.044
#> GSM537350 2 0.4595 0.1103 0.488 0.504 0.000 0.004 0.004
#> GSM537362 5 0.2095 0.6830 0.060 0.000 0.008 0.012 0.920
#> GSM537363 4 0.5340 0.3254 0.336 0.044 0.000 0.608 0.012
#> GSM537368 1 0.3364 0.7999 0.848 0.000 0.020 0.020 0.112
#> GSM537376 4 0.5416 0.5977 0.000 0.248 0.004 0.652 0.096
#> GSM537381 1 0.1124 0.8156 0.960 0.000 0.036 0.004 0.000
#> GSM537386 2 0.2696 0.7093 0.000 0.892 0.072 0.024 0.012
#> GSM537398 5 0.3574 0.7072 0.028 0.000 0.168 0.000 0.804
#> GSM537402 4 0.4744 0.4371 0.004 0.364 0.004 0.616 0.012
#> GSM537405 1 0.3802 0.7984 0.824 0.000 0.036 0.020 0.120
#> GSM537371 1 0.4191 0.7805 0.792 0.000 0.020 0.040 0.148
#> GSM537421 4 0.2355 0.6819 0.000 0.036 0.024 0.916 0.024
#> GSM537424 1 0.5404 0.5255 0.620 0.000 0.292 0.000 0.088
#> GSM537432 4 0.6248 0.2376 0.008 0.016 0.352 0.544 0.080
#> GSM537331 5 0.5644 0.5681 0.000 0.100 0.316 0.000 0.584
#> GSM537332 3 0.2275 0.6560 0.000 0.012 0.912 0.064 0.012
#> GSM537334 5 0.4714 0.5301 0.000 0.016 0.372 0.004 0.608
#> GSM537338 5 0.4254 0.6932 0.000 0.040 0.220 0.000 0.740
#> GSM537353 2 0.6687 -0.1367 0.000 0.420 0.248 0.332 0.000
#> GSM537357 1 0.4628 0.7747 0.772 0.000 0.020 0.084 0.124
#> GSM537358 2 0.3522 0.6083 0.000 0.780 0.212 0.004 0.004
#> GSM537375 5 0.4998 0.7116 0.000 0.052 0.160 0.044 0.744
#> GSM537389 2 0.2575 0.6764 0.000 0.884 0.012 0.100 0.004
#> GSM537390 3 0.5119 0.2501 0.000 0.388 0.576 0.028 0.008
#> GSM537393 3 0.5030 0.5854 0.000 0.044 0.748 0.144 0.064
#> GSM537399 1 0.6097 0.3207 0.576 0.104 0.304 0.000 0.016
#> GSM537407 1 0.4844 0.7621 0.796 0.052 0.076 0.040 0.036
#> GSM537408 2 0.1828 0.7103 0.004 0.936 0.032 0.000 0.028
#> GSM537428 3 0.5718 -0.2478 0.000 0.084 0.496 0.000 0.420
#> GSM537354 5 0.5774 0.6781 0.000 0.088 0.120 0.088 0.704
#> GSM537410 4 0.4363 0.6177 0.008 0.244 0.004 0.728 0.016
#> GSM537413 4 0.4851 0.5693 0.000 0.276 0.032 0.680 0.012
#> GSM537396 2 0.4509 0.6224 0.152 0.772 0.000 0.056 0.020
#> GSM537397 2 0.6292 0.3818 0.308 0.548 0.012 0.000 0.132
#> GSM537330 3 0.2589 0.6482 0.000 0.008 0.888 0.092 0.012
#> GSM537369 1 0.0566 0.8107 0.984 0.012 0.004 0.000 0.000
#> GSM537373 2 0.5737 0.4793 0.112 0.652 0.000 0.220 0.016
#> GSM537401 2 0.6003 0.5587 0.120 0.660 0.000 0.040 0.180
#> GSM537343 1 0.2635 0.7969 0.888 0.088 0.008 0.000 0.016
#> GSM537367 1 0.5654 0.4832 0.620 0.040 0.004 0.308 0.028
#> GSM537382 4 0.3905 0.6975 0.000 0.080 0.036 0.832 0.052
#> GSM537385 2 0.4034 0.6477 0.012 0.812 0.024 0.136 0.016
#> GSM537391 5 0.3935 0.6361 0.220 0.012 0.000 0.008 0.760
#> GSM537419 2 0.1306 0.7071 0.000 0.960 0.016 0.016 0.008
#> GSM537420 1 0.1469 0.8070 0.948 0.036 0.000 0.016 0.000
#> GSM537429 3 0.3067 0.6199 0.004 0.000 0.844 0.140 0.012
#> GSM537431 4 0.5621 0.3039 0.044 0.000 0.320 0.608 0.028
#> GSM537387 1 0.5274 0.5737 0.612 0.000 0.012 0.040 0.336
#> GSM537414 3 0.2575 0.6388 0.036 0.000 0.904 0.044 0.016
#> GSM537433 1 0.4341 0.7695 0.816 0.056 0.084 0.012 0.032
#> GSM537335 5 0.3628 0.6958 0.000 0.012 0.216 0.000 0.772
#> GSM537339 5 0.5825 0.6214 0.172 0.132 0.020 0.004 0.672
#> GSM537340 4 0.5292 0.6628 0.004 0.132 0.044 0.740 0.080
#> GSM537344 1 0.1016 0.8140 0.972 0.008 0.012 0.004 0.004
#> GSM537346 3 0.2388 0.6314 0.004 0.076 0.904 0.004 0.012
#> GSM537351 1 0.5105 0.7667 0.744 0.000 0.052 0.060 0.144
#> GSM537352 4 0.7477 0.3450 0.000 0.144 0.264 0.496 0.096
#> GSM537359 2 0.1533 0.7047 0.004 0.952 0.004 0.016 0.024
#> GSM537360 4 0.6050 0.3632 0.000 0.404 0.104 0.488 0.004
#> GSM537364 1 0.4874 0.7695 0.756 0.000 0.040 0.056 0.148
#> GSM537365 3 0.7253 0.0744 0.400 0.116 0.432 0.020 0.032
#> GSM537372 1 0.2692 0.7870 0.884 0.092 0.008 0.000 0.016
#> GSM537384 1 0.1404 0.8119 0.956 0.008 0.004 0.004 0.028
#> GSM537394 2 0.3174 0.6862 0.004 0.844 0.132 0.000 0.020
#> GSM537403 4 0.4426 0.6154 0.000 0.028 0.188 0.760 0.024
#> GSM537406 2 0.3463 0.6474 0.020 0.836 0.000 0.128 0.016
#> GSM537411 2 0.7204 0.4017 0.000 0.556 0.124 0.116 0.204
#> GSM537412 4 0.2938 0.7009 0.000 0.084 0.032 0.876 0.008
#> GSM537416 4 0.2312 0.6620 0.000 0.012 0.060 0.912 0.016
#> GSM537426 4 0.3133 0.6977 0.000 0.080 0.052 0.864 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 4 0.6523 0.3351 0.332 0.132 0.004 0.488 0.032 0.012
#> GSM537345 5 0.5543 0.2137 0.204 0.000 0.000 0.000 0.556 0.240
#> GSM537355 3 0.4682 0.3371 0.000 0.004 0.600 0.360 0.012 0.024
#> GSM537366 1 0.4310 0.4275 0.688 0.008 0.028 0.272 0.000 0.004
#> GSM537370 2 0.1748 0.7040 0.008 0.940 0.004 0.008 0.024 0.016
#> GSM537380 2 0.1419 0.7103 0.000 0.952 0.004 0.012 0.016 0.016
#> GSM537392 2 0.0912 0.7111 0.000 0.972 0.012 0.004 0.008 0.004
#> GSM537415 4 0.4941 0.4219 0.000 0.188 0.016 0.684 0.000 0.112
#> GSM537417 3 0.3416 0.6157 0.008 0.028 0.860 0.036 0.020 0.048
#> GSM537422 3 0.7567 0.0294 0.124 0.000 0.400 0.036 0.112 0.328
#> GSM537423 2 0.3217 0.6889 0.000 0.848 0.020 0.100 0.008 0.024
#> GSM537427 2 0.5980 0.4844 0.000 0.608 0.092 0.040 0.240 0.020
#> GSM537430 2 0.6073 0.4765 0.000 0.572 0.292 0.072 0.036 0.028
#> GSM537336 1 0.5419 0.5742 0.608 0.000 0.008 0.004 0.124 0.256
#> GSM537337 5 0.7335 0.4100 0.000 0.072 0.164 0.060 0.512 0.192
#> GSM537348 5 0.4979 0.4335 0.336 0.004 0.024 0.024 0.608 0.004
#> GSM537349 4 0.3714 0.4158 0.000 0.340 0.004 0.656 0.000 0.000
#> GSM537356 1 0.2917 0.6757 0.888 0.016 0.036 0.028 0.020 0.012
#> GSM537361 3 0.3678 0.5768 0.180 0.000 0.780 0.000 0.020 0.020
#> GSM537374 5 0.3947 0.5340 0.000 0.212 0.008 0.036 0.744 0.000
#> GSM537377 5 0.3792 0.4981 0.048 0.000 0.012 0.000 0.784 0.156
#> GSM537378 2 0.6725 0.3165 0.000 0.476 0.124 0.320 0.008 0.072
#> GSM537379 3 0.2698 0.6253 0.004 0.032 0.896 0.012 0.024 0.032
#> GSM537383 2 0.2577 0.7101 0.000 0.892 0.040 0.052 0.008 0.008
#> GSM537388 4 0.6457 0.4092 0.000 0.160 0.212 0.564 0.048 0.016
#> GSM537395 2 0.7191 0.4566 0.000 0.512 0.244 0.116 0.068 0.060
#> GSM537400 6 0.5655 0.3002 0.028 0.004 0.288 0.004 0.080 0.596
#> GSM537404 3 0.7437 0.3071 0.332 0.108 0.448 0.028 0.040 0.044
#> GSM537409 4 0.5412 0.1589 0.000 0.000 0.192 0.600 0.004 0.204
#> GSM537418 1 0.5220 0.6570 0.724 0.012 0.052 0.048 0.016 0.148
#> GSM537425 1 0.6374 0.5372 0.588 0.020 0.124 0.024 0.016 0.228
#> GSM537333 3 0.4447 0.4023 0.004 0.000 0.680 0.044 0.004 0.268
#> GSM537342 4 0.4012 0.2304 0.004 0.004 0.008 0.708 0.008 0.268
#> GSM537347 3 0.1912 0.6296 0.012 0.004 0.928 0.004 0.044 0.008
#> GSM537350 1 0.5133 0.2249 0.552 0.380 0.000 0.048 0.020 0.000
#> GSM537362 5 0.3114 0.5759 0.040 0.000 0.048 0.000 0.860 0.052
#> GSM537363 4 0.5767 0.2104 0.300 0.004 0.000 0.516 0.000 0.180
#> GSM537368 1 0.4750 0.6359 0.700 0.000 0.004 0.004 0.120 0.172
#> GSM537376 6 0.6146 0.4219 0.000 0.208 0.000 0.196 0.040 0.556
#> GSM537381 1 0.1900 0.6877 0.916 0.000 0.068 0.008 0.000 0.008
#> GSM537386 2 0.5285 0.5010 0.012 0.684 0.092 0.188 0.004 0.020
#> GSM537398 5 0.3139 0.6019 0.028 0.000 0.160 0.000 0.812 0.000
#> GSM537402 4 0.3627 0.4726 0.000 0.080 0.000 0.792 0.000 0.128
#> GSM537405 1 0.5172 0.6289 0.672 0.000 0.024 0.000 0.132 0.172
#> GSM537371 1 0.5034 0.6176 0.664 0.000 0.008 0.000 0.148 0.180
#> GSM537421 6 0.4331 0.4968 0.004 0.016 0.012 0.276 0.004 0.688
#> GSM537424 3 0.5726 0.0760 0.416 0.000 0.456 0.000 0.116 0.012
#> GSM537432 6 0.4657 0.5413 0.008 0.048 0.124 0.032 0.020 0.768
#> GSM537331 5 0.4551 0.5444 0.000 0.064 0.260 0.004 0.672 0.000
#> GSM537332 3 0.2620 0.6256 0.008 0.012 0.884 0.084 0.004 0.008
#> GSM537334 5 0.4307 0.4187 0.000 0.008 0.376 0.008 0.604 0.004
#> GSM537338 5 0.3908 0.6050 0.000 0.048 0.152 0.008 0.784 0.008
#> GSM537353 2 0.5454 0.5887 0.000 0.688 0.060 0.132 0.008 0.112
#> GSM537357 1 0.5474 0.5522 0.584 0.000 0.004 0.004 0.132 0.276
#> GSM537358 2 0.2330 0.7104 0.000 0.908 0.040 0.024 0.004 0.024
#> GSM537375 5 0.6843 0.4255 0.000 0.076 0.116 0.032 0.544 0.232
#> GSM537389 2 0.3979 0.0433 0.000 0.540 0.000 0.456 0.000 0.004
#> GSM537390 2 0.6349 0.4152 0.000 0.508 0.308 0.136 0.004 0.044
#> GSM537393 3 0.7152 0.3249 0.000 0.140 0.548 0.060 0.084 0.168
#> GSM537399 1 0.5500 0.2617 0.580 0.036 0.340 0.008 0.016 0.020
#> GSM537407 1 0.5959 0.5893 0.684 0.064 0.100 0.020 0.020 0.112
#> GSM537408 2 0.1481 0.7043 0.008 0.952 0.004 0.008 0.012 0.016
#> GSM537428 5 0.5716 0.2987 0.000 0.112 0.392 0.008 0.484 0.004
#> GSM537354 5 0.7392 0.2815 0.000 0.116 0.052 0.096 0.488 0.248
#> GSM537410 4 0.2454 0.4468 0.004 0.016 0.000 0.876 0.000 0.104
#> GSM537413 4 0.6611 0.1901 0.000 0.332 0.024 0.404 0.004 0.236
#> GSM537396 4 0.6257 0.3470 0.216 0.292 0.000 0.476 0.008 0.008
#> GSM537397 2 0.6507 0.1097 0.368 0.460 0.004 0.024 0.128 0.016
#> GSM537330 3 0.3484 0.5716 0.000 0.000 0.784 0.188 0.016 0.012
#> GSM537369 1 0.0551 0.6939 0.984 0.004 0.000 0.008 0.000 0.004
#> GSM537373 4 0.4380 0.5063 0.136 0.108 0.000 0.744 0.000 0.012
#> GSM537401 5 0.7789 0.1503 0.208 0.188 0.004 0.208 0.384 0.008
#> GSM537343 1 0.3211 0.6860 0.856 0.088 0.004 0.012 0.012 0.028
#> GSM537367 1 0.4992 0.5653 0.712 0.008 0.004 0.168 0.020 0.088
#> GSM537382 6 0.5021 0.4963 0.000 0.020 0.008 0.320 0.036 0.616
#> GSM537385 4 0.3763 0.5285 0.008 0.184 0.012 0.780 0.008 0.008
#> GSM537391 5 0.4176 0.5708 0.176 0.008 0.000 0.008 0.756 0.052
#> GSM537419 2 0.1812 0.6998 0.000 0.924 0.004 0.060 0.004 0.008
#> GSM537420 1 0.1860 0.6945 0.928 0.004 0.000 0.036 0.004 0.028
#> GSM537429 3 0.3678 0.5397 0.000 0.000 0.752 0.220 0.004 0.024
#> GSM537431 6 0.5109 0.4505 0.024 0.012 0.204 0.048 0.012 0.700
#> GSM537387 1 0.6233 0.3930 0.432 0.000 0.004 0.004 0.252 0.308
#> GSM537414 3 0.1864 0.6386 0.040 0.000 0.924 0.004 0.000 0.032
#> GSM537433 1 0.4771 0.6242 0.760 0.032 0.132 0.020 0.012 0.044
#> GSM537335 5 0.3463 0.5658 0.000 0.008 0.240 0.004 0.748 0.000
#> GSM537339 5 0.5323 0.4947 0.292 0.008 0.036 0.036 0.624 0.004
#> GSM537340 6 0.5025 0.5457 0.000 0.096 0.004 0.128 0.052 0.720
#> GSM537344 1 0.0865 0.6997 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM537346 3 0.2658 0.6282 0.012 0.044 0.896 0.008 0.032 0.008
#> GSM537351 1 0.6261 0.4354 0.476 0.004 0.036 0.000 0.124 0.360
#> GSM537352 6 0.7775 0.3115 0.000 0.232 0.064 0.188 0.080 0.436
#> GSM537359 2 0.2583 0.6666 0.008 0.896 0.000 0.044 0.020 0.032
#> GSM537360 4 0.6819 0.0633 0.000 0.348 0.048 0.420 0.008 0.176
#> GSM537364 1 0.5770 0.5027 0.532 0.000 0.016 0.000 0.132 0.320
#> GSM537365 3 0.7404 0.2632 0.220 0.272 0.412 0.000 0.020 0.076
#> GSM537372 1 0.2014 0.6893 0.920 0.052 0.000 0.008 0.012 0.008
#> GSM537384 1 0.2821 0.6813 0.880 0.000 0.028 0.008 0.064 0.020
#> GSM537394 2 0.1583 0.7095 0.004 0.948 0.012 0.012 0.008 0.016
#> GSM537403 6 0.5800 0.3322 0.000 0.004 0.100 0.412 0.016 0.468
#> GSM537406 4 0.4268 0.5043 0.040 0.272 0.000 0.684 0.004 0.000
#> GSM537411 2 0.4814 0.6484 0.000 0.764 0.032 0.056 0.076 0.072
#> GSM537412 4 0.3837 0.3564 0.000 0.000 0.052 0.752 0.000 0.196
#> GSM537416 6 0.5203 0.3383 0.000 0.004 0.068 0.352 0.008 0.568
#> GSM537426 4 0.4788 0.1892 0.000 0.004 0.072 0.636 0.000 0.288
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> CV:NMF 101 0.300 0.560 2
#> CV:NMF 76 0.380 0.735 3
#> CV:NMF 84 0.690 0.699 4
#> CV:NMF 76 0.502 0.441 5
#> CV:NMF 53 0.520 0.409 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 104 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 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 0.364 0.716 0.860 0.3115 0.751 0.751
#> 3 3 0.148 0.521 0.707 0.7784 0.673 0.580
#> 4 4 0.184 0.496 0.597 0.1957 0.841 0.680
#> 5 5 0.263 0.407 0.589 0.0807 0.879 0.680
#> 6 6 0.329 0.347 0.579 0.0533 0.970 0.897
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
#> GSM537341 2 0.9170 0.5436 0.332 0.668
#> GSM537345 1 0.0376 0.7233 0.996 0.004
#> GSM537355 2 0.0672 0.8407 0.008 0.992
#> GSM537366 2 0.9393 0.4852 0.356 0.644
#> GSM537370 2 0.9170 0.5437 0.332 0.668
#> GSM537380 2 0.0376 0.8421 0.004 0.996
#> GSM537392 2 0.0376 0.8421 0.004 0.996
#> GSM537415 2 0.0000 0.8404 0.000 1.000
#> GSM537417 2 0.7602 0.7246 0.220 0.780
#> GSM537422 2 0.8443 0.6487 0.272 0.728
#> GSM537423 2 0.0000 0.8404 0.000 1.000
#> GSM537427 2 0.0938 0.8425 0.012 0.988
#> GSM537430 2 0.2948 0.8407 0.052 0.948
#> GSM537336 1 0.0938 0.7258 0.988 0.012
#> GSM537337 2 0.2603 0.8474 0.044 0.956
#> GSM537348 2 0.9170 0.5436 0.332 0.668
#> GSM537349 2 0.0376 0.8406 0.004 0.996
#> GSM537356 2 0.9393 0.4933 0.356 0.644
#> GSM537361 2 0.9209 0.5364 0.336 0.664
#> GSM537374 2 0.4022 0.8355 0.080 0.920
#> GSM537377 1 0.0376 0.7233 0.996 0.004
#> GSM537378 2 0.0000 0.8404 0.000 1.000
#> GSM537379 2 0.5946 0.8098 0.144 0.856
#> GSM537383 2 0.0376 0.8421 0.004 0.996
#> GSM537388 2 0.0376 0.8406 0.004 0.996
#> GSM537395 2 0.2423 0.8470 0.040 0.960
#> GSM537400 2 0.6531 0.7917 0.168 0.832
#> GSM537404 2 0.9427 0.4879 0.360 0.640
#> GSM537409 2 0.0672 0.8405 0.008 0.992
#> GSM537418 2 0.9922 0.2134 0.448 0.552
#> GSM537425 2 0.9732 0.3667 0.404 0.596
#> GSM537333 2 0.5059 0.8274 0.112 0.888
#> GSM537342 2 0.2778 0.8461 0.048 0.952
#> GSM537347 2 0.8016 0.7090 0.244 0.756
#> GSM537350 2 0.5737 0.8038 0.136 0.864
#> GSM537362 1 0.9954 0.1559 0.540 0.460
#> GSM537363 2 0.6048 0.7898 0.148 0.852
#> GSM537368 1 0.0938 0.7258 0.988 0.012
#> GSM537376 2 0.4815 0.8333 0.104 0.896
#> GSM537381 2 0.9977 0.0955 0.472 0.528
#> GSM537386 2 0.0672 0.8429 0.008 0.992
#> GSM537398 2 0.9580 0.4197 0.380 0.620
#> GSM537402 2 0.1843 0.8472 0.028 0.972
#> GSM537405 1 0.2043 0.7232 0.968 0.032
#> GSM537371 1 0.0938 0.7258 0.988 0.012
#> GSM537421 2 0.5059 0.8103 0.112 0.888
#> GSM537424 2 0.8016 0.7090 0.244 0.756
#> GSM537432 2 0.5294 0.8227 0.120 0.880
#> GSM537331 2 0.0672 0.8407 0.008 0.992
#> GSM537332 2 0.1633 0.8446 0.024 0.976
#> GSM537334 2 0.3879 0.8389 0.076 0.924
#> GSM537338 2 0.4298 0.8361 0.088 0.912
#> GSM537353 2 0.2043 0.8472 0.032 0.968
#> GSM537357 1 0.0938 0.7258 0.988 0.012
#> GSM537358 2 0.0672 0.8424 0.008 0.992
#> GSM537375 2 0.4431 0.8343 0.092 0.908
#> GSM537389 2 0.0376 0.8406 0.004 0.996
#> GSM537390 2 0.0000 0.8404 0.000 1.000
#> GSM537393 2 0.2778 0.8464 0.048 0.952
#> GSM537399 2 0.4690 0.8210 0.100 0.900
#> GSM537407 2 0.8955 0.5865 0.312 0.688
#> GSM537408 2 0.3584 0.8421 0.068 0.932
#> GSM537428 2 0.2603 0.8457 0.044 0.956
#> GSM537354 2 0.2423 0.8470 0.040 0.960
#> GSM537410 2 0.2778 0.8461 0.048 0.952
#> GSM537413 2 0.0376 0.8396 0.004 0.996
#> GSM537396 2 0.2603 0.8460 0.044 0.956
#> GSM537397 2 0.9209 0.5352 0.336 0.664
#> GSM537330 2 0.3733 0.8429 0.072 0.928
#> GSM537369 1 0.9358 0.4751 0.648 0.352
#> GSM537373 2 0.2948 0.8456 0.052 0.948
#> GSM537401 2 0.9170 0.5436 0.332 0.668
#> GSM537343 2 0.8386 0.6640 0.268 0.732
#> GSM537367 2 0.8386 0.6471 0.268 0.732
#> GSM537382 2 0.4562 0.8366 0.096 0.904
#> GSM537385 2 0.0938 0.8426 0.012 0.988
#> GSM537391 1 1.0000 0.0353 0.504 0.496
#> GSM537419 2 0.0376 0.8420 0.004 0.996
#> GSM537420 1 0.9358 0.4751 0.648 0.352
#> GSM537429 2 0.2603 0.8462 0.044 0.956
#> GSM537431 2 0.6438 0.7976 0.164 0.836
#> GSM537387 1 1.0000 0.0353 0.504 0.496
#> GSM537414 2 0.7815 0.7075 0.232 0.768
#> GSM537433 2 0.9358 0.4909 0.352 0.648
#> GSM537335 2 0.3879 0.8389 0.076 0.924
#> GSM537339 2 0.9170 0.5436 0.332 0.668
#> GSM537340 2 0.5059 0.8129 0.112 0.888
#> GSM537344 1 0.9358 0.4751 0.648 0.352
#> GSM537346 2 0.0938 0.8447 0.012 0.988
#> GSM537351 1 0.9866 0.2792 0.568 0.432
#> GSM537352 2 0.2423 0.8469 0.040 0.960
#> GSM537359 2 0.0376 0.8421 0.004 0.996
#> GSM537360 2 0.0938 0.8442 0.012 0.988
#> GSM537364 1 0.1633 0.7251 0.976 0.024
#> GSM537365 2 0.7139 0.7541 0.196 0.804
#> GSM537372 2 0.9323 0.5078 0.348 0.652
#> GSM537384 2 0.9170 0.5453 0.332 0.668
#> GSM537394 2 0.1184 0.8458 0.016 0.984
#> GSM537403 2 0.2948 0.8443 0.052 0.948
#> GSM537406 2 0.2603 0.8460 0.044 0.956
#> GSM537411 2 0.4939 0.8265 0.108 0.892
#> GSM537412 2 0.0672 0.8405 0.008 0.992
#> GSM537416 2 0.1633 0.8462 0.024 0.976
#> GSM537426 2 0.0672 0.8405 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.8261 0.4478 0.260 0.616 0.124
#> GSM537345 1 0.0983 0.7210 0.980 0.004 0.016
#> GSM537355 2 0.3375 0.6638 0.008 0.892 0.100
#> GSM537366 3 0.9760 0.5315 0.280 0.276 0.444
#> GSM537370 2 0.8203 0.4480 0.268 0.616 0.116
#> GSM537380 2 0.2261 0.6591 0.000 0.932 0.068
#> GSM537392 2 0.2165 0.6584 0.000 0.936 0.064
#> GSM537415 2 0.5465 0.4211 0.000 0.712 0.288
#> GSM537417 3 0.7633 0.6333 0.132 0.184 0.684
#> GSM537422 3 0.7510 0.5707 0.184 0.124 0.692
#> GSM537423 2 0.3482 0.6399 0.000 0.872 0.128
#> GSM537427 2 0.3213 0.6697 0.008 0.900 0.092
#> GSM537430 2 0.4469 0.6660 0.028 0.852 0.120
#> GSM537336 1 0.1753 0.7287 0.952 0.000 0.048
#> GSM537337 2 0.5122 0.6106 0.012 0.788 0.200
#> GSM537348 2 0.8202 0.4501 0.260 0.620 0.120
#> GSM537349 2 0.2496 0.6591 0.004 0.928 0.068
#> GSM537356 2 0.8572 0.3923 0.288 0.580 0.132
#> GSM537361 3 0.9487 0.5701 0.244 0.260 0.496
#> GSM537374 2 0.5136 0.6564 0.044 0.824 0.132
#> GSM537377 1 0.0983 0.7210 0.980 0.004 0.016
#> GSM537378 2 0.5465 0.4211 0.000 0.712 0.288
#> GSM537379 2 0.7376 0.5468 0.076 0.672 0.252
#> GSM537383 2 0.2261 0.6577 0.000 0.932 0.068
#> GSM537388 2 0.2400 0.6623 0.004 0.932 0.064
#> GSM537395 2 0.4963 0.6045 0.008 0.792 0.200
#> GSM537400 2 0.8527 0.1352 0.096 0.504 0.400
#> GSM537404 3 0.9488 0.5637 0.256 0.248 0.496
#> GSM537409 3 0.5098 0.5649 0.000 0.248 0.752
#> GSM537418 3 0.9752 0.4275 0.352 0.232 0.416
#> GSM537425 3 0.9399 0.5304 0.292 0.208 0.500
#> GSM537333 3 0.7363 0.4338 0.040 0.372 0.588
#> GSM537342 2 0.5815 0.4767 0.004 0.692 0.304
#> GSM537347 2 0.9125 0.1242 0.164 0.516 0.320
#> GSM537350 2 0.7106 0.5386 0.076 0.700 0.224
#> GSM537362 1 0.9608 -0.0985 0.468 0.300 0.232
#> GSM537363 3 0.7880 0.5965 0.096 0.268 0.636
#> GSM537368 1 0.1860 0.7286 0.948 0.000 0.052
#> GSM537376 2 0.7364 0.4647 0.056 0.640 0.304
#> GSM537381 3 0.9550 0.3934 0.368 0.196 0.436
#> GSM537386 2 0.4002 0.6406 0.000 0.840 0.160
#> GSM537398 2 0.8415 0.3847 0.320 0.572 0.108
#> GSM537402 2 0.3528 0.6742 0.016 0.892 0.092
#> GSM537405 1 0.2446 0.7248 0.936 0.012 0.052
#> GSM537371 1 0.1753 0.7280 0.952 0.000 0.048
#> GSM537421 3 0.7022 0.6001 0.068 0.232 0.700
#> GSM537424 2 0.9174 0.0582 0.164 0.504 0.332
#> GSM537432 2 0.7536 0.4695 0.068 0.640 0.292
#> GSM537331 2 0.2486 0.6611 0.008 0.932 0.060
#> GSM537332 2 0.6386 0.1133 0.004 0.584 0.412
#> GSM537334 2 0.4423 0.6503 0.048 0.864 0.088
#> GSM537338 2 0.5637 0.6366 0.040 0.788 0.172
#> GSM537353 2 0.5681 0.5554 0.016 0.748 0.236
#> GSM537357 1 0.1753 0.7287 0.952 0.000 0.048
#> GSM537358 2 0.3272 0.6600 0.004 0.892 0.104
#> GSM537375 2 0.6057 0.6206 0.044 0.760 0.196
#> GSM537389 2 0.2496 0.6591 0.004 0.928 0.068
#> GSM537390 2 0.5621 0.3929 0.000 0.692 0.308
#> GSM537393 2 0.6161 0.5133 0.020 0.708 0.272
#> GSM537399 2 0.6887 0.5744 0.076 0.720 0.204
#> GSM537407 3 0.9570 0.4701 0.204 0.348 0.448
#> GSM537408 2 0.5506 0.5948 0.016 0.764 0.220
#> GSM537428 2 0.4342 0.6651 0.024 0.856 0.120
#> GSM537354 2 0.4963 0.6045 0.008 0.792 0.200
#> GSM537410 2 0.5815 0.4767 0.004 0.692 0.304
#> GSM537413 2 0.4235 0.6148 0.000 0.824 0.176
#> GSM537396 2 0.4883 0.6000 0.004 0.788 0.208
#> GSM537397 2 0.8263 0.4435 0.268 0.612 0.120
#> GSM537330 2 0.5746 0.6286 0.040 0.780 0.180
#> GSM537369 1 0.8727 0.3409 0.572 0.280 0.148
#> GSM537373 2 0.5659 0.5448 0.012 0.740 0.248
#> GSM537401 2 0.8261 0.4478 0.260 0.616 0.124
#> GSM537343 3 0.9260 0.4451 0.160 0.376 0.464
#> GSM537367 3 0.8171 0.6070 0.184 0.172 0.644
#> GSM537382 2 0.7260 0.4412 0.048 0.636 0.316
#> GSM537385 2 0.2774 0.6657 0.008 0.920 0.072
#> GSM537391 2 0.8793 0.0561 0.436 0.452 0.112
#> GSM537419 2 0.2261 0.6627 0.000 0.932 0.068
#> GSM537420 1 0.8727 0.3409 0.572 0.280 0.148
#> GSM537429 2 0.4094 0.6606 0.028 0.872 0.100
#> GSM537431 3 0.7481 0.4944 0.064 0.296 0.640
#> GSM537387 2 0.8793 0.0561 0.436 0.452 0.112
#> GSM537414 3 0.8920 0.5313 0.144 0.324 0.532
#> GSM537433 3 0.9283 0.5663 0.260 0.216 0.524
#> GSM537335 2 0.4423 0.6503 0.048 0.864 0.088
#> GSM537339 2 0.8261 0.4478 0.260 0.616 0.124
#> GSM537340 3 0.6976 0.5986 0.064 0.236 0.700
#> GSM537344 1 0.8727 0.3409 0.572 0.280 0.148
#> GSM537346 2 0.4233 0.6409 0.004 0.836 0.160
#> GSM537351 3 0.6678 -0.0346 0.480 0.008 0.512
#> GSM537352 2 0.5220 0.5965 0.012 0.780 0.208
#> GSM537359 2 0.2959 0.6550 0.000 0.900 0.100
#> GSM537360 2 0.6527 0.1149 0.008 0.588 0.404
#> GSM537364 1 0.1964 0.7247 0.944 0.000 0.056
#> GSM537365 3 0.9071 0.3117 0.136 0.432 0.432
#> GSM537372 2 0.8380 0.4299 0.276 0.600 0.124
#> GSM537384 2 0.8430 0.4353 0.260 0.604 0.136
#> GSM537394 2 0.4228 0.6478 0.008 0.844 0.148
#> GSM537403 3 0.6667 0.4806 0.016 0.368 0.616
#> GSM537406 2 0.4883 0.6000 0.004 0.788 0.208
#> GSM537411 2 0.6796 0.5803 0.056 0.708 0.236
#> GSM537412 3 0.5178 0.5625 0.000 0.256 0.744
#> GSM537416 3 0.5058 0.5761 0.000 0.244 0.756
#> GSM537426 3 0.5178 0.5625 0.000 0.256 0.744
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.557 0.6852 0.684 0.272 0.008 0.036
#> GSM537345 4 0.380 0.7494 0.220 0.000 0.000 0.780
#> GSM537355 2 0.607 0.4741 0.268 0.648 0.084 0.000
#> GSM537366 3 0.962 0.5066 0.248 0.160 0.384 0.208
#> GSM537370 1 0.570 0.6662 0.664 0.288 0.004 0.044
#> GSM537380 2 0.239 0.5786 0.036 0.928 0.024 0.012
#> GSM537392 2 0.207 0.5810 0.028 0.940 0.024 0.008
#> GSM537415 2 0.442 0.4511 0.008 0.736 0.256 0.000
#> GSM537417 3 0.688 0.6211 0.092 0.080 0.688 0.140
#> GSM537422 3 0.643 0.5742 0.076 0.040 0.696 0.188
#> GSM537423 2 0.227 0.5774 0.004 0.912 0.084 0.000
#> GSM537427 2 0.538 0.5915 0.160 0.748 0.088 0.004
#> GSM537430 2 0.624 0.4875 0.276 0.632 0.092 0.000
#> GSM537336 4 0.172 0.8602 0.048 0.000 0.008 0.944
#> GSM537337 2 0.709 0.5447 0.212 0.588 0.196 0.004
#> GSM537348 1 0.586 0.6801 0.672 0.272 0.012 0.044
#> GSM537349 2 0.259 0.5872 0.080 0.904 0.016 0.000
#> GSM537356 1 0.677 0.6515 0.640 0.244 0.024 0.092
#> GSM537361 3 0.911 0.5774 0.220 0.096 0.440 0.244
#> GSM537374 2 0.632 0.4526 0.300 0.612 0.088 0.000
#> GSM537377 4 0.380 0.7494 0.220 0.000 0.000 0.780
#> GSM537378 2 0.442 0.4511 0.008 0.736 0.256 0.000
#> GSM537379 2 0.804 0.3505 0.336 0.448 0.200 0.016
#> GSM537383 2 0.136 0.5848 0.020 0.964 0.012 0.004
#> GSM537388 2 0.534 0.4717 0.260 0.696 0.044 0.000
#> GSM537395 2 0.706 0.5502 0.200 0.592 0.204 0.004
#> GSM537400 3 0.893 0.0365 0.332 0.260 0.356 0.052
#> GSM537404 3 0.939 0.5720 0.212 0.136 0.424 0.228
#> GSM537409 3 0.340 0.5409 0.004 0.164 0.832 0.000
#> GSM537418 3 0.931 0.4670 0.304 0.084 0.344 0.268
#> GSM537425 3 0.941 0.5224 0.208 0.120 0.392 0.280
#> GSM537333 3 0.750 0.4991 0.208 0.172 0.592 0.028
#> GSM537342 2 0.668 0.3215 0.124 0.592 0.284 0.000
#> GSM537347 2 0.943 -0.0276 0.304 0.340 0.256 0.100
#> GSM537350 2 0.688 0.3763 0.232 0.624 0.132 0.012
#> GSM537362 1 0.918 0.1656 0.440 0.120 0.176 0.264
#> GSM537363 3 0.717 0.5679 0.092 0.140 0.668 0.100
#> GSM537368 4 0.205 0.8588 0.064 0.000 0.008 0.928
#> GSM537376 2 0.842 0.3585 0.284 0.416 0.276 0.024
#> GSM537381 3 0.926 0.4258 0.240 0.084 0.340 0.336
#> GSM537386 2 0.551 0.5905 0.128 0.744 0.124 0.004
#> GSM537398 1 0.685 0.6630 0.620 0.232 0.008 0.140
#> GSM537402 2 0.558 0.5537 0.220 0.712 0.064 0.004
#> GSM537405 4 0.220 0.8568 0.080 0.000 0.004 0.916
#> GSM537371 4 0.197 0.8595 0.060 0.000 0.008 0.932
#> GSM537421 3 0.562 0.5736 0.040 0.112 0.768 0.080
#> GSM537424 2 0.946 -0.0763 0.304 0.328 0.268 0.100
#> GSM537432 2 0.834 0.3162 0.340 0.396 0.244 0.020
#> GSM537331 2 0.554 0.4026 0.320 0.644 0.036 0.000
#> GSM537332 2 0.724 0.0884 0.112 0.476 0.404 0.008
#> GSM537334 2 0.607 0.1530 0.452 0.504 0.044 0.000
#> GSM537338 2 0.702 0.4373 0.320 0.540 0.140 0.000
#> GSM537353 2 0.733 0.5372 0.160 0.584 0.240 0.016
#> GSM537357 4 0.172 0.8602 0.048 0.000 0.008 0.944
#> GSM537358 2 0.367 0.6055 0.044 0.860 0.092 0.004
#> GSM537375 2 0.731 0.4307 0.304 0.532 0.160 0.004
#> GSM537389 2 0.280 0.5858 0.092 0.892 0.016 0.000
#> GSM537390 2 0.498 0.4149 0.016 0.680 0.304 0.000
#> GSM537393 2 0.726 0.4981 0.176 0.556 0.264 0.004
#> GSM537399 2 0.696 0.4825 0.220 0.620 0.148 0.012
#> GSM537407 3 0.978 0.5007 0.232 0.228 0.356 0.184
#> GSM537408 2 0.592 0.4522 0.176 0.696 0.128 0.000
#> GSM537428 2 0.635 0.5284 0.252 0.636 0.112 0.000
#> GSM537354 2 0.706 0.5502 0.200 0.592 0.204 0.004
#> GSM537410 2 0.668 0.3215 0.124 0.592 0.284 0.000
#> GSM537413 2 0.573 0.4254 0.088 0.732 0.168 0.012
#> GSM537396 2 0.557 0.4714 0.152 0.728 0.120 0.000
#> GSM537397 1 0.545 0.6840 0.680 0.276 0.000 0.044
#> GSM537330 2 0.729 0.3814 0.336 0.524 0.132 0.008
#> GSM537369 1 0.601 0.1745 0.616 0.024 0.020 0.340
#> GSM537373 2 0.661 0.4177 0.132 0.648 0.212 0.008
#> GSM537401 1 0.557 0.6852 0.684 0.272 0.008 0.036
#> GSM537343 3 0.962 0.4551 0.232 0.264 0.364 0.140
#> GSM537367 3 0.767 0.6079 0.080 0.124 0.620 0.176
#> GSM537382 2 0.835 0.3303 0.268 0.412 0.300 0.020
#> GSM537385 2 0.439 0.5103 0.236 0.752 0.012 0.000
#> GSM537391 1 0.639 0.6520 0.652 0.156 0.000 0.192
#> GSM537419 2 0.230 0.5901 0.044 0.928 0.024 0.004
#> GSM537420 1 0.601 0.1745 0.616 0.024 0.020 0.340
#> GSM537429 2 0.597 0.4143 0.332 0.612 0.056 0.000
#> GSM537431 3 0.783 0.4950 0.172 0.160 0.600 0.068
#> GSM537387 1 0.639 0.6520 0.652 0.156 0.000 0.192
#> GSM537414 3 0.887 0.5569 0.204 0.160 0.504 0.132
#> GSM537433 3 0.917 0.5674 0.172 0.132 0.456 0.240
#> GSM537335 2 0.607 0.1530 0.452 0.504 0.044 0.000
#> GSM537339 1 0.557 0.6852 0.684 0.272 0.008 0.036
#> GSM537340 3 0.566 0.5731 0.048 0.108 0.768 0.076
#> GSM537344 1 0.601 0.1745 0.616 0.024 0.020 0.340
#> GSM537346 2 0.523 0.5749 0.128 0.756 0.116 0.000
#> GSM537351 4 0.718 -0.0475 0.136 0.000 0.404 0.460
#> GSM537352 2 0.710 0.5560 0.180 0.584 0.232 0.004
#> GSM537359 2 0.507 0.5048 0.124 0.788 0.072 0.016
#> GSM537360 2 0.619 0.2051 0.044 0.540 0.412 0.004
#> GSM537364 4 0.213 0.8558 0.076 0.000 0.004 0.920
#> GSM537365 3 0.947 0.3571 0.212 0.260 0.396 0.132
#> GSM537372 1 0.554 0.6921 0.688 0.264 0.004 0.044
#> GSM537384 1 0.626 0.6667 0.656 0.272 0.028 0.044
#> GSM537394 2 0.543 0.5829 0.132 0.740 0.128 0.000
#> GSM537403 3 0.575 0.4373 0.036 0.264 0.684 0.016
#> GSM537406 2 0.552 0.4746 0.152 0.732 0.116 0.000
#> GSM537411 2 0.779 0.3282 0.352 0.452 0.188 0.008
#> GSM537412 3 0.322 0.5396 0.000 0.164 0.836 0.000
#> GSM537416 3 0.365 0.5539 0.016 0.152 0.832 0.000
#> GSM537426 3 0.322 0.5396 0.000 0.164 0.836 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.358 0.6065 0.008 0.168 0.016 0.000 0.808
#> GSM537345 1 0.402 0.6796 0.792 0.000 0.004 0.052 0.152
#> GSM537355 2 0.610 0.3505 0.000 0.588 0.084 0.028 0.300
#> GSM537366 3 0.790 0.4558 0.060 0.120 0.540 0.072 0.208
#> GSM537370 5 0.425 0.5924 0.012 0.192 0.024 0.004 0.768
#> GSM537380 2 0.261 0.5619 0.000 0.896 0.004 0.044 0.056
#> GSM537392 2 0.216 0.5631 0.000 0.920 0.004 0.040 0.036
#> GSM537415 2 0.503 0.4662 0.000 0.716 0.184 0.092 0.008
#> GSM537417 3 0.629 -0.0181 0.036 0.056 0.628 0.256 0.024
#> GSM537422 3 0.647 -0.0842 0.072 0.024 0.600 0.276 0.028
#> GSM537423 2 0.309 0.5722 0.000 0.876 0.068 0.036 0.020
#> GSM537427 2 0.545 0.5423 0.000 0.708 0.088 0.036 0.168
#> GSM537430 2 0.640 0.3390 0.000 0.544 0.112 0.024 0.320
#> GSM537336 1 0.437 0.8160 0.772 0.000 0.164 0.012 0.052
#> GSM537337 2 0.749 0.4145 0.004 0.504 0.192 0.068 0.232
#> GSM537348 5 0.385 0.6044 0.008 0.168 0.028 0.000 0.796
#> GSM537349 2 0.281 0.5603 0.000 0.876 0.012 0.012 0.100
#> GSM537356 5 0.505 0.5758 0.012 0.148 0.112 0.000 0.728
#> GSM537361 3 0.502 0.4357 0.072 0.044 0.784 0.028 0.072
#> GSM537374 2 0.652 0.2911 0.000 0.524 0.120 0.024 0.332
#> GSM537377 1 0.402 0.6796 0.792 0.000 0.004 0.052 0.152
#> GSM537378 2 0.503 0.4662 0.000 0.716 0.184 0.092 0.008
#> GSM537379 2 0.800 0.1329 0.016 0.356 0.264 0.044 0.320
#> GSM537383 2 0.165 0.5688 0.000 0.944 0.004 0.020 0.032
#> GSM537388 2 0.537 0.3660 0.000 0.632 0.048 0.016 0.304
#> GSM537395 2 0.748 0.4246 0.004 0.512 0.192 0.072 0.220
#> GSM537400 3 0.875 0.0808 0.036 0.128 0.360 0.160 0.316
#> GSM537404 3 0.677 0.5088 0.064 0.104 0.652 0.036 0.144
#> GSM537409 4 0.605 0.6509 0.004 0.112 0.360 0.524 0.000
#> GSM537418 3 0.721 0.4498 0.132 0.048 0.592 0.036 0.192
#> GSM537425 3 0.750 0.4615 0.116 0.080 0.604 0.068 0.132
#> GSM537333 3 0.752 0.0268 0.024 0.108 0.540 0.248 0.080
#> GSM537342 2 0.723 0.3041 0.016 0.556 0.244 0.124 0.060
#> GSM537347 3 0.777 0.2325 0.016 0.288 0.404 0.032 0.260
#> GSM537350 2 0.694 0.3403 0.016 0.604 0.152 0.052 0.176
#> GSM537362 5 0.870 -0.0286 0.216 0.044 0.276 0.084 0.380
#> GSM537363 4 0.793 0.5121 0.076 0.084 0.296 0.488 0.056
#> GSM537368 1 0.376 0.8168 0.800 0.000 0.156 0.000 0.044
#> GSM537376 5 0.855 -0.1369 0.012 0.308 0.248 0.116 0.316
#> GSM537381 3 0.708 0.4437 0.148 0.052 0.592 0.020 0.188
#> GSM537386 2 0.576 0.5400 0.000 0.688 0.164 0.044 0.104
#> GSM537398 5 0.545 0.5783 0.128 0.128 0.024 0.004 0.716
#> GSM537402 2 0.613 0.4820 0.008 0.640 0.080 0.036 0.236
#> GSM537405 1 0.400 0.8167 0.800 0.000 0.152 0.024 0.024
#> GSM537371 1 0.361 0.8191 0.808 0.000 0.156 0.000 0.036
#> GSM537421 4 0.684 0.6086 0.056 0.064 0.300 0.560 0.020
#> GSM537424 3 0.759 0.2746 0.016 0.272 0.432 0.024 0.256
#> GSM537432 5 0.831 -0.0803 0.008 0.308 0.244 0.096 0.344
#> GSM537331 2 0.545 0.2539 0.000 0.572 0.044 0.012 0.372
#> GSM537332 2 0.728 0.0221 0.004 0.408 0.400 0.144 0.044
#> GSM537334 5 0.600 0.0861 0.000 0.404 0.072 0.016 0.508
#> GSM537338 2 0.731 0.2536 0.004 0.452 0.168 0.040 0.336
#> GSM537353 2 0.761 0.4721 0.004 0.508 0.228 0.096 0.164
#> GSM537357 1 0.437 0.8160 0.772 0.000 0.164 0.012 0.052
#> GSM537358 2 0.404 0.5909 0.000 0.824 0.084 0.040 0.052
#> GSM537375 2 0.746 0.2387 0.004 0.440 0.188 0.044 0.324
#> GSM537389 2 0.297 0.5560 0.000 0.864 0.012 0.012 0.112
#> GSM537390 2 0.546 0.4329 0.000 0.668 0.216 0.108 0.008
#> GSM537393 2 0.774 0.4301 0.004 0.492 0.232 0.104 0.168
#> GSM537399 2 0.695 0.3850 0.004 0.556 0.224 0.040 0.176
#> GSM537407 3 0.651 0.5005 0.044 0.176 0.644 0.016 0.120
#> GSM537408 2 0.630 0.4252 0.012 0.668 0.144 0.052 0.124
#> GSM537428 2 0.648 0.3858 0.000 0.548 0.128 0.024 0.300
#> GSM537354 2 0.751 0.4248 0.004 0.508 0.196 0.072 0.220
#> GSM537410 2 0.723 0.3041 0.016 0.556 0.244 0.124 0.060
#> GSM537413 2 0.591 0.3282 0.016 0.656 0.032 0.244 0.052
#> GSM537396 2 0.600 0.4574 0.016 0.704 0.124 0.060 0.096
#> GSM537397 5 0.400 0.6019 0.016 0.180 0.020 0.000 0.784
#> GSM537330 2 0.718 0.2046 0.000 0.448 0.200 0.032 0.320
#> GSM537369 5 0.630 0.1752 0.244 0.008 0.096 0.032 0.620
#> GSM537373 2 0.685 0.3889 0.024 0.616 0.212 0.076 0.072
#> GSM537401 5 0.358 0.6065 0.008 0.168 0.016 0.000 0.808
#> GSM537343 3 0.687 0.4676 0.044 0.216 0.596 0.016 0.128
#> GSM537367 3 0.727 0.1790 0.036 0.100 0.580 0.224 0.060
#> GSM537382 5 0.859 -0.1347 0.008 0.292 0.268 0.136 0.296
#> GSM537385 2 0.487 0.4374 0.000 0.688 0.044 0.008 0.260
#> GSM537391 5 0.446 0.5379 0.116 0.068 0.020 0.004 0.792
#> GSM537419 2 0.286 0.5732 0.000 0.892 0.036 0.032 0.040
#> GSM537420 5 0.630 0.1752 0.244 0.008 0.096 0.032 0.620
#> GSM537429 2 0.613 0.2771 0.000 0.544 0.104 0.012 0.340
#> GSM537431 4 0.747 0.2276 0.036 0.084 0.292 0.524 0.064
#> GSM537387 5 0.446 0.5379 0.116 0.068 0.020 0.004 0.792
#> GSM537414 3 0.688 0.2952 0.032 0.096 0.644 0.140 0.088
#> GSM537433 3 0.708 0.4560 0.072 0.100 0.644 0.076 0.108
#> GSM537335 5 0.600 0.0861 0.000 0.404 0.072 0.016 0.508
#> GSM537339 5 0.358 0.6065 0.008 0.168 0.016 0.000 0.808
#> GSM537340 4 0.689 0.6035 0.052 0.056 0.340 0.528 0.024
#> GSM537344 5 0.630 0.1752 0.244 0.008 0.096 0.032 0.620
#> GSM537346 2 0.550 0.5302 0.000 0.696 0.180 0.028 0.096
#> GSM537351 1 0.754 0.1575 0.332 0.000 0.316 0.316 0.036
#> GSM537352 2 0.771 0.4245 0.004 0.496 0.192 0.096 0.212
#> GSM537359 2 0.578 0.4438 0.016 0.700 0.028 0.168 0.088
#> GSM537360 2 0.704 0.2445 0.004 0.512 0.292 0.156 0.036
#> GSM537364 1 0.390 0.8146 0.804 0.000 0.152 0.028 0.016
#> GSM537365 3 0.724 0.3850 0.048 0.224 0.584 0.048 0.096
#> GSM537372 5 0.398 0.6063 0.016 0.168 0.024 0.000 0.792
#> GSM537384 5 0.434 0.5974 0.008 0.172 0.052 0.000 0.768
#> GSM537394 2 0.564 0.5381 0.004 0.700 0.172 0.036 0.088
#> GSM537403 3 0.727 -0.2896 0.004 0.224 0.396 0.356 0.020
#> GSM537406 2 0.588 0.4633 0.016 0.712 0.124 0.052 0.096
#> GSM537411 5 0.780 -0.0874 0.004 0.352 0.212 0.060 0.372
#> GSM537412 4 0.601 0.6704 0.004 0.112 0.344 0.540 0.000
#> GSM537416 4 0.614 0.6583 0.004 0.104 0.368 0.520 0.004
#> GSM537426 4 0.601 0.6704 0.004 0.112 0.344 0.540 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.207 0.63334 0.000 0.100 0.008 0.000 0.892 0.000
#> GSM537345 1 0.583 0.54154 0.640 0.000 0.124 0.000 0.096 0.140
#> GSM537355 2 0.646 0.31464 0.000 0.524 0.088 0.064 0.308 0.016
#> GSM537366 4 0.895 -0.33773 0.176 0.092 0.236 0.260 0.224 0.012
#> GSM537370 5 0.279 0.61959 0.000 0.124 0.016 0.000 0.852 0.008
#> GSM537380 2 0.304 0.53468 0.000 0.868 0.012 0.012 0.052 0.056
#> GSM537392 2 0.256 0.53274 0.000 0.896 0.012 0.012 0.028 0.052
#> GSM537415 2 0.475 0.45212 0.000 0.688 0.048 0.240 0.012 0.012
#> GSM537417 4 0.734 0.25653 0.100 0.048 0.276 0.504 0.032 0.040
#> GSM537422 4 0.691 0.28583 0.132 0.012 0.260 0.528 0.036 0.032
#> GSM537423 2 0.306 0.53650 0.000 0.864 0.028 0.080 0.016 0.012
#> GSM537427 2 0.535 0.51112 0.000 0.680 0.072 0.064 0.180 0.004
#> GSM537430 2 0.637 0.30172 0.000 0.500 0.136 0.036 0.320 0.008
#> GSM537336 1 0.212 0.75699 0.920 0.000 0.016 0.008 0.036 0.020
#> GSM537337 2 0.715 0.37192 0.000 0.460 0.180 0.116 0.240 0.004
#> GSM537348 5 0.242 0.63116 0.008 0.100 0.012 0.000 0.880 0.000
#> GSM537349 2 0.286 0.53785 0.000 0.856 0.008 0.012 0.116 0.008
#> GSM537356 5 0.403 0.57177 0.060 0.084 0.040 0.004 0.808 0.004
#> GSM537361 3 0.688 0.35533 0.188 0.016 0.564 0.160 0.048 0.024
#> GSM537374 2 0.637 0.26131 0.000 0.496 0.140 0.028 0.324 0.012
#> GSM537377 1 0.583 0.54154 0.640 0.000 0.124 0.000 0.096 0.140
#> GSM537378 2 0.475 0.45212 0.000 0.688 0.048 0.240 0.012 0.012
#> GSM537379 2 0.786 0.12432 0.008 0.336 0.224 0.096 0.316 0.020
#> GSM537383 2 0.176 0.54509 0.000 0.936 0.004 0.012 0.028 0.020
#> GSM537388 2 0.554 0.31294 0.000 0.568 0.044 0.048 0.336 0.004
#> GSM537395 2 0.711 0.38804 0.000 0.472 0.180 0.120 0.224 0.004
#> GSM537400 3 0.843 -0.02574 0.008 0.096 0.352 0.144 0.292 0.108
#> GSM537404 3 0.861 0.37977 0.180 0.092 0.376 0.208 0.132 0.012
#> GSM537409 4 0.229 0.42176 0.000 0.072 0.028 0.896 0.000 0.004
#> GSM537418 3 0.847 0.38584 0.156 0.032 0.440 0.136 0.164 0.072
#> GSM537425 3 0.899 0.34945 0.224 0.072 0.348 0.184 0.120 0.052
#> GSM537333 3 0.712 -0.09057 0.008 0.048 0.484 0.312 0.048 0.100
#> GSM537342 2 0.719 0.27058 0.000 0.492 0.184 0.228 0.048 0.048
#> GSM537347 3 0.874 0.31438 0.076 0.252 0.304 0.096 0.248 0.024
#> GSM537350 2 0.700 0.30449 0.000 0.552 0.168 0.056 0.164 0.060
#> GSM537362 3 0.740 0.01221 0.024 0.020 0.416 0.032 0.312 0.196
#> GSM537363 4 0.725 0.01239 0.040 0.044 0.184 0.552 0.032 0.148
#> GSM537368 1 0.144 0.76373 0.948 0.000 0.012 0.004 0.032 0.004
#> GSM537376 5 0.814 -0.11497 0.004 0.276 0.236 0.172 0.292 0.020
#> GSM537381 3 0.803 0.37264 0.260 0.024 0.392 0.148 0.164 0.012
#> GSM537386 2 0.592 0.47677 0.000 0.624 0.224 0.040 0.088 0.024
#> GSM537398 5 0.438 0.58027 0.116 0.076 0.028 0.000 0.772 0.008
#> GSM537402 2 0.628 0.44460 0.000 0.584 0.092 0.048 0.248 0.028
#> GSM537405 1 0.135 0.75993 0.952 0.000 0.020 0.000 0.008 0.020
#> GSM537371 1 0.128 0.76650 0.956 0.000 0.012 0.004 0.024 0.004
#> GSM537421 4 0.609 0.12382 0.024 0.044 0.152 0.648 0.008 0.124
#> GSM537424 3 0.867 0.34907 0.076 0.240 0.316 0.096 0.252 0.020
#> GSM537432 5 0.825 -0.04293 0.004 0.264 0.232 0.128 0.328 0.044
#> GSM537331 2 0.579 0.19888 0.000 0.508 0.052 0.036 0.392 0.012
#> GSM537332 2 0.742 0.00726 0.004 0.336 0.308 0.288 0.036 0.028
#> GSM537334 5 0.605 0.14104 0.000 0.340 0.088 0.028 0.528 0.016
#> GSM537338 2 0.706 0.22980 0.000 0.420 0.176 0.064 0.328 0.012
#> GSM537353 2 0.741 0.44120 0.000 0.488 0.196 0.140 0.148 0.028
#> GSM537357 1 0.212 0.75699 0.920 0.000 0.016 0.008 0.036 0.020
#> GSM537358 2 0.395 0.55456 0.000 0.816 0.072 0.056 0.044 0.012
#> GSM537375 2 0.732 0.20970 0.000 0.412 0.172 0.080 0.316 0.020
#> GSM537389 2 0.308 0.53001 0.000 0.836 0.008 0.012 0.136 0.008
#> GSM537390 2 0.497 0.42432 0.000 0.640 0.072 0.276 0.004 0.008
#> GSM537393 2 0.734 0.40579 0.000 0.480 0.168 0.184 0.152 0.016
#> GSM537399 2 0.700 0.30387 0.000 0.488 0.260 0.056 0.172 0.024
#> GSM537407 3 0.821 0.40640 0.144 0.132 0.456 0.148 0.112 0.008
#> GSM537408 2 0.647 0.38459 0.000 0.616 0.168 0.056 0.100 0.060
#> GSM537428 2 0.641 0.32769 0.000 0.492 0.124 0.064 0.320 0.000
#> GSM537354 2 0.714 0.38666 0.000 0.468 0.184 0.120 0.224 0.004
#> GSM537410 2 0.719 0.27058 0.000 0.492 0.184 0.228 0.048 0.048
#> GSM537413 2 0.591 0.11101 0.000 0.592 0.028 0.132 0.008 0.240
#> GSM537396 2 0.625 0.41159 0.000 0.648 0.136 0.076 0.076 0.064
#> GSM537397 5 0.231 0.62954 0.000 0.108 0.004 0.000 0.880 0.008
#> GSM537330 2 0.727 0.18002 0.000 0.380 0.208 0.072 0.328 0.012
#> GSM537369 5 0.671 0.20269 0.100 0.004 0.136 0.004 0.548 0.208
#> GSM537373 2 0.719 0.33923 0.008 0.548 0.172 0.156 0.072 0.044
#> GSM537401 5 0.207 0.63334 0.000 0.100 0.008 0.000 0.892 0.000
#> GSM537343 3 0.819 0.37635 0.108 0.168 0.428 0.164 0.132 0.000
#> GSM537367 4 0.761 0.14240 0.144 0.076 0.200 0.504 0.068 0.008
#> GSM537382 5 0.816 -0.10803 0.004 0.256 0.240 0.208 0.276 0.016
#> GSM537385 2 0.517 0.40326 0.000 0.628 0.060 0.012 0.288 0.012
#> GSM537391 5 0.345 0.53190 0.060 0.016 0.024 0.000 0.848 0.052
#> GSM537419 2 0.285 0.55402 0.000 0.884 0.040 0.016 0.040 0.020
#> GSM537420 5 0.671 0.20269 0.100 0.004 0.136 0.004 0.548 0.208
#> GSM537429 2 0.616 0.22535 0.000 0.476 0.116 0.032 0.372 0.004
#> GSM537431 6 0.722 0.00000 0.016 0.024 0.228 0.228 0.032 0.472
#> GSM537387 5 0.345 0.53190 0.060 0.016 0.024 0.000 0.848 0.052
#> GSM537414 3 0.711 0.20537 0.088 0.040 0.560 0.224 0.056 0.032
#> GSM537433 4 0.870 -0.30660 0.184 0.088 0.296 0.304 0.112 0.016
#> GSM537335 5 0.605 0.14104 0.000 0.340 0.088 0.028 0.528 0.016
#> GSM537339 5 0.207 0.63334 0.000 0.100 0.008 0.000 0.892 0.000
#> GSM537340 4 0.601 0.16979 0.020 0.036 0.180 0.648 0.012 0.104
#> GSM537344 5 0.671 0.20269 0.100 0.004 0.136 0.004 0.548 0.208
#> GSM537346 2 0.591 0.46037 0.000 0.628 0.224 0.048 0.076 0.024
#> GSM537351 1 0.753 -0.21172 0.376 0.000 0.176 0.124 0.012 0.312
#> GSM537352 2 0.729 0.38006 0.000 0.448 0.184 0.148 0.216 0.004
#> GSM537359 2 0.533 0.23375 0.000 0.628 0.036 0.016 0.036 0.284
#> GSM537360 2 0.624 0.23022 0.000 0.472 0.108 0.380 0.024 0.016
#> GSM537364 1 0.118 0.75700 0.956 0.000 0.020 0.000 0.000 0.024
#> GSM537365 3 0.817 0.33545 0.108 0.176 0.444 0.188 0.072 0.012
#> GSM537372 5 0.232 0.63285 0.000 0.100 0.008 0.000 0.884 0.008
#> GSM537384 5 0.310 0.61964 0.008 0.104 0.028 0.004 0.852 0.004
#> GSM537394 2 0.570 0.47155 0.000 0.648 0.220 0.044 0.060 0.028
#> GSM537403 4 0.597 0.32785 0.004 0.172 0.172 0.616 0.016 0.020
#> GSM537406 2 0.615 0.41753 0.000 0.656 0.136 0.068 0.076 0.064
#> GSM537411 5 0.764 -0.03238 0.000 0.300 0.216 0.100 0.364 0.020
#> GSM537412 4 0.198 0.41962 0.000 0.068 0.016 0.912 0.000 0.004
#> GSM537416 4 0.248 0.41077 0.000 0.060 0.024 0.896 0.004 0.016
#> GSM537426 4 0.198 0.41962 0.000 0.068 0.016 0.912 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> MAD:hclust 89 0.3302 0.766 2
#> MAD:hclust 66 0.5933 0.405 3
#> MAD:hclust 58 0.3618 0.935 4
#> MAD:hclust 41 0.0896 0.693 5
#> MAD:hclust 29 0.0940 0.390 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 104 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 0.824 0.911 0.961 0.4859 0.510 0.510
#> 3 3 0.357 0.488 0.688 0.3451 0.719 0.500
#> 4 4 0.406 0.372 0.650 0.1264 0.812 0.519
#> 5 5 0.497 0.430 0.623 0.0727 0.845 0.502
#> 6 6 0.580 0.453 0.652 0.0466 0.902 0.583
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
#> GSM537341 2 0.8144 0.660 0.252 0.748
#> GSM537345 1 0.0000 0.941 1.000 0.000
#> GSM537355 2 0.0000 0.969 0.000 1.000
#> GSM537366 1 0.0000 0.941 1.000 0.000
#> GSM537370 2 0.6973 0.764 0.188 0.812
#> GSM537380 2 0.0000 0.969 0.000 1.000
#> GSM537392 2 0.0000 0.969 0.000 1.000
#> GSM537415 2 0.0000 0.969 0.000 1.000
#> GSM537417 2 0.0672 0.963 0.008 0.992
#> GSM537422 1 0.0376 0.939 0.996 0.004
#> GSM537423 2 0.0000 0.969 0.000 1.000
#> GSM537427 2 0.0000 0.969 0.000 1.000
#> GSM537430 2 0.0000 0.969 0.000 1.000
#> GSM537336 1 0.0000 0.941 1.000 0.000
#> GSM537337 2 0.0000 0.969 0.000 1.000
#> GSM537348 1 0.0000 0.941 1.000 0.000
#> GSM537349 2 0.0000 0.969 0.000 1.000
#> GSM537356 1 0.0000 0.941 1.000 0.000
#> GSM537361 1 0.0000 0.941 1.000 0.000
#> GSM537374 2 0.0000 0.969 0.000 1.000
#> GSM537377 1 0.0000 0.941 1.000 0.000
#> GSM537378 2 0.0000 0.969 0.000 1.000
#> GSM537379 2 0.0000 0.969 0.000 1.000
#> GSM537383 2 0.0000 0.969 0.000 1.000
#> GSM537388 2 0.0000 0.969 0.000 1.000
#> GSM537395 2 0.0000 0.969 0.000 1.000
#> GSM537400 1 0.7376 0.749 0.792 0.208
#> GSM537404 1 0.7815 0.709 0.768 0.232
#> GSM537409 2 0.0000 0.969 0.000 1.000
#> GSM537418 1 0.0000 0.941 1.000 0.000
#> GSM537425 1 0.0000 0.941 1.000 0.000
#> GSM537333 2 0.9922 0.118 0.448 0.552
#> GSM537342 2 0.1184 0.956 0.016 0.984
#> GSM537347 2 0.7376 0.733 0.208 0.792
#> GSM537350 1 0.0000 0.941 1.000 0.000
#> GSM537362 1 0.0376 0.939 0.996 0.004
#> GSM537363 1 0.4562 0.868 0.904 0.096
#> GSM537368 1 0.0000 0.941 1.000 0.000
#> GSM537376 2 0.0000 0.969 0.000 1.000
#> GSM537381 1 0.0000 0.941 1.000 0.000
#> GSM537386 2 0.0000 0.969 0.000 1.000
#> GSM537398 1 0.0000 0.941 1.000 0.000
#> GSM537402 2 0.0000 0.969 0.000 1.000
#> GSM537405 1 0.0000 0.941 1.000 0.000
#> GSM537371 1 0.0000 0.941 1.000 0.000
#> GSM537421 2 0.5294 0.849 0.120 0.880
#> GSM537424 1 0.0000 0.941 1.000 0.000
#> GSM537432 1 0.9358 0.493 0.648 0.352
#> GSM537331 2 0.0000 0.969 0.000 1.000
#> GSM537332 2 0.0000 0.969 0.000 1.000
#> GSM537334 2 0.0000 0.969 0.000 1.000
#> GSM537338 2 0.0000 0.969 0.000 1.000
#> GSM537353 2 0.0000 0.969 0.000 1.000
#> GSM537357 1 0.0000 0.941 1.000 0.000
#> GSM537358 2 0.0000 0.969 0.000 1.000
#> GSM537375 2 0.0000 0.969 0.000 1.000
#> GSM537389 2 0.0000 0.969 0.000 1.000
#> GSM537390 2 0.0000 0.969 0.000 1.000
#> GSM537393 2 0.0000 0.969 0.000 1.000
#> GSM537399 1 0.7950 0.707 0.760 0.240
#> GSM537407 1 0.0000 0.941 1.000 0.000
#> GSM537408 2 0.0000 0.969 0.000 1.000
#> GSM537428 2 0.0000 0.969 0.000 1.000
#> GSM537354 2 0.0000 0.969 0.000 1.000
#> GSM537410 2 0.0000 0.969 0.000 1.000
#> GSM537413 2 0.0000 0.969 0.000 1.000
#> GSM537396 2 0.0000 0.969 0.000 1.000
#> GSM537397 1 0.7453 0.747 0.788 0.212
#> GSM537330 2 0.0000 0.969 0.000 1.000
#> GSM537369 1 0.0000 0.941 1.000 0.000
#> GSM537373 2 0.0000 0.969 0.000 1.000
#> GSM537401 2 0.6531 0.792 0.168 0.832
#> GSM537343 1 0.0000 0.941 1.000 0.000
#> GSM537367 1 0.0376 0.939 0.996 0.004
#> GSM537382 2 0.0000 0.969 0.000 1.000
#> GSM537385 2 0.0000 0.969 0.000 1.000
#> GSM537391 1 0.3114 0.902 0.944 0.056
#> GSM537419 2 0.0000 0.969 0.000 1.000
#> GSM537420 1 0.0000 0.941 1.000 0.000
#> GSM537429 2 0.6247 0.808 0.156 0.844
#> GSM537431 1 0.8499 0.648 0.724 0.276
#> GSM537387 1 0.0000 0.941 1.000 0.000
#> GSM537414 1 0.0376 0.939 0.996 0.004
#> GSM537433 1 0.0000 0.941 1.000 0.000
#> GSM537335 2 0.3879 0.900 0.076 0.924
#> GSM537339 1 0.0376 0.939 0.996 0.004
#> GSM537340 1 0.9896 0.254 0.560 0.440
#> GSM537344 1 0.0000 0.941 1.000 0.000
#> GSM537346 2 0.0000 0.969 0.000 1.000
#> GSM537351 1 0.0000 0.941 1.000 0.000
#> GSM537352 2 0.0000 0.969 0.000 1.000
#> GSM537359 2 0.0000 0.969 0.000 1.000
#> GSM537360 2 0.0000 0.969 0.000 1.000
#> GSM537364 1 0.0000 0.941 1.000 0.000
#> GSM537365 1 0.7528 0.739 0.784 0.216
#> GSM537372 1 0.0000 0.941 1.000 0.000
#> GSM537384 1 0.0000 0.941 1.000 0.000
#> GSM537394 2 0.0000 0.969 0.000 1.000
#> GSM537403 2 0.0000 0.969 0.000 1.000
#> GSM537406 2 0.0000 0.969 0.000 1.000
#> GSM537411 2 0.0000 0.969 0.000 1.000
#> GSM537412 2 0.0000 0.969 0.000 1.000
#> GSM537416 2 0.3584 0.907 0.068 0.932
#> GSM537426 2 0.0000 0.969 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.6521 0.3300 0.248 0.712 0.040
#> GSM537345 1 0.2446 0.7829 0.936 0.012 0.052
#> GSM537355 2 0.5560 0.5818 0.000 0.700 0.300
#> GSM537366 1 0.6529 0.7403 0.760 0.116 0.124
#> GSM537370 2 0.5334 0.4463 0.120 0.820 0.060
#> GSM537380 2 0.3619 0.6099 0.000 0.864 0.136
#> GSM537392 2 0.4931 0.6127 0.000 0.768 0.232
#> GSM537415 3 0.6079 0.2536 0.000 0.388 0.612
#> GSM537417 3 0.4413 0.5060 0.024 0.124 0.852
#> GSM537422 3 0.6427 0.0750 0.348 0.012 0.640
#> GSM537423 2 0.5859 0.5190 0.000 0.656 0.344
#> GSM537427 2 0.4796 0.6189 0.000 0.780 0.220
#> GSM537430 2 0.5016 0.6119 0.000 0.760 0.240
#> GSM537336 1 0.2066 0.7804 0.940 0.000 0.060
#> GSM537337 2 0.5968 0.4803 0.000 0.636 0.364
#> GSM537348 1 0.6556 0.6796 0.692 0.276 0.032
#> GSM537349 2 0.5465 0.5818 0.000 0.712 0.288
#> GSM537356 1 0.4662 0.7643 0.844 0.124 0.032
#> GSM537361 1 0.7067 0.5304 0.596 0.028 0.376
#> GSM537374 2 0.3375 0.5760 0.008 0.892 0.100
#> GSM537377 1 0.2599 0.7831 0.932 0.016 0.052
#> GSM537378 2 0.5926 0.4980 0.000 0.644 0.356
#> GSM537379 3 0.4796 0.4420 0.000 0.220 0.780
#> GSM537383 2 0.5363 0.5912 0.000 0.724 0.276
#> GSM537388 2 0.4974 0.6113 0.000 0.764 0.236
#> GSM537395 2 0.6267 0.3441 0.000 0.548 0.452
#> GSM537400 3 0.8576 0.2883 0.160 0.240 0.600
#> GSM537404 3 0.8028 0.1628 0.288 0.096 0.616
#> GSM537409 3 0.4399 0.4996 0.000 0.188 0.812
#> GSM537418 1 0.3134 0.7853 0.916 0.032 0.052
#> GSM537425 1 0.7853 0.4962 0.556 0.060 0.384
#> GSM537333 3 0.8021 0.3644 0.124 0.232 0.644
#> GSM537342 3 0.5465 0.4181 0.000 0.288 0.712
#> GSM537347 2 0.6835 0.2766 0.040 0.676 0.284
#> GSM537350 1 0.3832 0.7726 0.880 0.100 0.020
#> GSM537362 1 0.8122 0.6393 0.648 0.184 0.168
#> GSM537363 1 0.8280 0.3933 0.516 0.080 0.404
#> GSM537368 1 0.1860 0.7822 0.948 0.000 0.052
#> GSM537376 3 0.6302 -0.1636 0.000 0.480 0.520
#> GSM537381 1 0.1711 0.7893 0.960 0.008 0.032
#> GSM537386 2 0.4047 0.5806 0.004 0.848 0.148
#> GSM537398 1 0.7124 0.6547 0.672 0.272 0.056
#> GSM537402 2 0.6126 0.4649 0.000 0.600 0.400
#> GSM537405 1 0.1964 0.7817 0.944 0.000 0.056
#> GSM537371 1 0.1964 0.7812 0.944 0.000 0.056
#> GSM537421 3 0.5524 0.5113 0.040 0.164 0.796
#> GSM537424 1 0.4045 0.7734 0.872 0.104 0.024
#> GSM537432 3 0.8367 0.3181 0.136 0.252 0.612
#> GSM537331 2 0.2356 0.5927 0.000 0.928 0.072
#> GSM537332 3 0.4062 0.4958 0.000 0.164 0.836
#> GSM537334 2 0.4164 0.5483 0.008 0.848 0.144
#> GSM537338 2 0.3619 0.5815 0.000 0.864 0.136
#> GSM537353 3 0.5968 0.3154 0.000 0.364 0.636
#> GSM537357 1 0.1964 0.7812 0.944 0.000 0.056
#> GSM537358 2 0.5560 0.5732 0.000 0.700 0.300
#> GSM537375 3 0.5859 0.3513 0.000 0.344 0.656
#> GSM537389 2 0.5465 0.5791 0.000 0.712 0.288
#> GSM537390 3 0.6244 0.1078 0.000 0.440 0.560
#> GSM537393 3 0.5859 0.2986 0.000 0.344 0.656
#> GSM537399 2 0.9021 0.0384 0.264 0.552 0.184
#> GSM537407 1 0.9062 0.3640 0.452 0.136 0.412
#> GSM537408 2 0.5216 0.5923 0.000 0.740 0.260
#> GSM537428 2 0.4291 0.6179 0.000 0.820 0.180
#> GSM537354 3 0.6308 -0.1401 0.000 0.492 0.508
#> GSM537410 3 0.5254 0.4477 0.000 0.264 0.736
#> GSM537413 2 0.5948 0.5193 0.000 0.640 0.360
#> GSM537396 2 0.6606 0.5871 0.048 0.716 0.236
#> GSM537397 2 0.7360 -0.2523 0.440 0.528 0.032
#> GSM537330 2 0.6307 0.3076 0.000 0.512 0.488
#> GSM537369 1 0.0237 0.7873 0.996 0.004 0.000
#> GSM537373 3 0.6855 0.3739 0.032 0.316 0.652
#> GSM537401 2 0.5823 0.4281 0.144 0.792 0.064
#> GSM537343 1 0.7491 0.5551 0.620 0.056 0.324
#> GSM537367 3 0.6373 0.2298 0.268 0.028 0.704
#> GSM537382 2 0.6309 0.1784 0.000 0.500 0.500
#> GSM537385 2 0.4931 0.6103 0.000 0.768 0.232
#> GSM537391 1 0.6369 0.6192 0.668 0.316 0.016
#> GSM537419 2 0.5529 0.5741 0.000 0.704 0.296
#> GSM537420 1 0.0237 0.7873 0.996 0.004 0.000
#> GSM537429 2 0.5746 0.5008 0.040 0.780 0.180
#> GSM537431 3 0.8392 0.3107 0.148 0.236 0.616
#> GSM537387 1 0.4531 0.7313 0.824 0.168 0.008
#> GSM537414 3 0.7107 0.0408 0.340 0.036 0.624
#> GSM537433 1 0.8440 0.3887 0.492 0.088 0.420
#> GSM537335 2 0.5787 0.4881 0.068 0.796 0.136
#> GSM537339 1 0.7729 0.4359 0.516 0.436 0.048
#> GSM537340 3 0.7205 0.4762 0.192 0.100 0.708
#> GSM537344 1 0.0237 0.7873 0.996 0.004 0.000
#> GSM537346 2 0.6192 0.3664 0.000 0.580 0.420
#> GSM537351 1 0.5706 0.5826 0.680 0.000 0.320
#> GSM537352 2 0.6008 0.4663 0.000 0.628 0.372
#> GSM537359 2 0.3816 0.6056 0.000 0.852 0.148
#> GSM537360 3 0.5882 0.3397 0.000 0.348 0.652
#> GSM537364 1 0.2066 0.7804 0.940 0.000 0.060
#> GSM537365 3 0.9119 0.1264 0.224 0.228 0.548
#> GSM537372 1 0.6452 0.6891 0.704 0.264 0.032
#> GSM537384 1 0.4683 0.7625 0.836 0.140 0.024
#> GSM537394 2 0.5845 0.4345 0.004 0.688 0.308
#> GSM537403 3 0.4235 0.5025 0.000 0.176 0.824
#> GSM537406 3 0.6309 -0.1310 0.000 0.496 0.504
#> GSM537411 2 0.5517 0.5140 0.004 0.728 0.268
#> GSM537412 3 0.4796 0.4862 0.000 0.220 0.780
#> GSM537416 3 0.3234 0.5199 0.020 0.072 0.908
#> GSM537426 3 0.5706 0.3777 0.000 0.320 0.680
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.6456 0.3507 0.088 0.236 0.016 0.660
#> GSM537345 1 0.1302 0.6902 0.956 0.000 0.000 0.044
#> GSM537355 2 0.6025 0.5700 0.000 0.688 0.172 0.140
#> GSM537366 1 0.7714 0.1291 0.432 0.004 0.192 0.372
#> GSM537370 4 0.5326 0.2618 0.012 0.308 0.012 0.668
#> GSM537380 2 0.2256 0.6500 0.000 0.924 0.020 0.056
#> GSM537392 2 0.1488 0.6520 0.000 0.956 0.012 0.032
#> GSM537415 3 0.5294 0.1099 0.000 0.484 0.508 0.008
#> GSM537417 3 0.5824 0.4473 0.016 0.076 0.724 0.184
#> GSM537422 3 0.6480 0.3562 0.192 0.004 0.656 0.148
#> GSM537423 2 0.2402 0.6205 0.000 0.912 0.076 0.012
#> GSM537427 2 0.4462 0.6232 0.000 0.804 0.064 0.132
#> GSM537430 2 0.2586 0.6552 0.000 0.912 0.040 0.048
#> GSM537336 1 0.0672 0.6958 0.984 0.000 0.008 0.008
#> GSM537337 2 0.6855 0.3860 0.000 0.572 0.292 0.136
#> GSM537348 4 0.4957 0.1701 0.320 0.012 0.000 0.668
#> GSM537349 2 0.1305 0.6453 0.000 0.960 0.036 0.004
#> GSM537356 1 0.5696 0.2073 0.492 0.000 0.024 0.484
#> GSM537361 4 0.7845 0.1637 0.304 0.000 0.292 0.404
#> GSM537374 2 0.6510 0.4319 0.000 0.540 0.080 0.380
#> GSM537377 1 0.1302 0.6902 0.956 0.000 0.000 0.044
#> GSM537378 2 0.3047 0.5978 0.000 0.872 0.116 0.012
#> GSM537379 3 0.6958 0.4153 0.004 0.156 0.596 0.244
#> GSM537383 2 0.0779 0.6531 0.000 0.980 0.004 0.016
#> GSM537388 2 0.4071 0.6392 0.000 0.832 0.064 0.104
#> GSM537395 2 0.5035 0.5479 0.000 0.748 0.196 0.056
#> GSM537400 3 0.7429 0.1221 0.112 0.016 0.484 0.388
#> GSM537404 4 0.7133 0.0824 0.100 0.008 0.436 0.456
#> GSM537409 3 0.4137 0.5137 0.000 0.208 0.780 0.012
#> GSM537418 1 0.6727 0.2729 0.520 0.000 0.096 0.384
#> GSM537425 4 0.7908 0.0815 0.336 0.000 0.304 0.360
#> GSM537333 3 0.7280 0.1567 0.084 0.024 0.508 0.384
#> GSM537342 3 0.5325 0.4770 0.000 0.204 0.728 0.068
#> GSM537347 4 0.7506 0.1104 0.004 0.204 0.272 0.520
#> GSM537350 1 0.7269 0.2661 0.492 0.068 0.032 0.408
#> GSM537362 4 0.7062 0.1427 0.360 0.008 0.104 0.528
#> GSM537363 3 0.7524 -0.0708 0.388 0.012 0.468 0.132
#> GSM537368 1 0.0707 0.7011 0.980 0.000 0.000 0.020
#> GSM537376 3 0.6678 0.0650 0.000 0.412 0.500 0.088
#> GSM537381 1 0.6677 0.3374 0.552 0.000 0.100 0.348
#> GSM537386 2 0.4937 0.5841 0.000 0.764 0.064 0.172
#> GSM537398 4 0.5369 0.2237 0.296 0.016 0.012 0.676
#> GSM537402 2 0.5569 0.4171 0.000 0.660 0.296 0.044
#> GSM537405 1 0.0707 0.7002 0.980 0.000 0.000 0.020
#> GSM537371 1 0.0188 0.6990 0.996 0.000 0.000 0.004
#> GSM537421 3 0.5298 0.5354 0.052 0.132 0.780 0.036
#> GSM537424 4 0.5693 -0.1984 0.472 0.000 0.024 0.504
#> GSM537432 3 0.6813 0.1811 0.072 0.012 0.536 0.380
#> GSM537331 2 0.5631 0.5667 0.000 0.696 0.072 0.232
#> GSM537332 3 0.7394 0.3545 0.000 0.244 0.520 0.236
#> GSM537334 2 0.6758 0.3870 0.000 0.504 0.096 0.400
#> GSM537338 2 0.6587 0.4823 0.000 0.576 0.100 0.324
#> GSM537353 2 0.5776 -0.0564 0.000 0.504 0.468 0.028
#> GSM537357 1 0.0376 0.6994 0.992 0.000 0.004 0.004
#> GSM537358 2 0.2036 0.6432 0.000 0.936 0.032 0.032
#> GSM537375 3 0.6867 0.1996 0.000 0.324 0.552 0.124
#> GSM537389 2 0.1635 0.6438 0.000 0.948 0.044 0.008
#> GSM537390 2 0.5249 0.3936 0.000 0.708 0.248 0.044
#> GSM537393 3 0.6969 -0.0452 0.000 0.436 0.452 0.112
#> GSM537399 4 0.6093 0.3590 0.064 0.068 0.128 0.740
#> GSM537407 4 0.7454 0.2839 0.144 0.012 0.316 0.528
#> GSM537408 2 0.3601 0.6150 0.000 0.860 0.056 0.084
#> GSM537428 2 0.5421 0.5901 0.000 0.724 0.076 0.200
#> GSM537354 2 0.6784 0.2381 0.000 0.528 0.368 0.104
#> GSM537410 3 0.5085 0.4541 0.000 0.260 0.708 0.032
#> GSM537413 2 0.3659 0.5690 0.000 0.840 0.136 0.024
#> GSM537396 2 0.6648 0.3598 0.000 0.612 0.140 0.248
#> GSM537397 4 0.6674 0.3062 0.200 0.136 0.012 0.652
#> GSM537330 2 0.7866 -0.0387 0.000 0.384 0.336 0.280
#> GSM537369 1 0.4245 0.6372 0.784 0.000 0.020 0.196
#> GSM537373 3 0.6933 0.3340 0.000 0.300 0.560 0.140
#> GSM537401 4 0.5880 0.3386 0.048 0.264 0.012 0.676
#> GSM537343 4 0.7919 0.2167 0.216 0.012 0.296 0.476
#> GSM537367 3 0.4752 0.4267 0.068 0.008 0.800 0.124
#> GSM537382 3 0.7058 0.1457 0.000 0.344 0.520 0.136
#> GSM537385 2 0.3051 0.6509 0.000 0.884 0.028 0.088
#> GSM537391 4 0.7034 0.1496 0.344 0.088 0.016 0.552
#> GSM537419 2 0.1767 0.6445 0.000 0.944 0.044 0.012
#> GSM537420 1 0.4284 0.6347 0.780 0.000 0.020 0.200
#> GSM537429 2 0.7837 0.1786 0.020 0.424 0.144 0.412
#> GSM537431 3 0.7243 0.1533 0.084 0.024 0.524 0.368
#> GSM537387 1 0.4770 0.4715 0.700 0.000 0.012 0.288
#> GSM537414 3 0.7807 0.0491 0.216 0.008 0.480 0.296
#> GSM537433 4 0.8054 0.1658 0.240 0.008 0.368 0.384
#> GSM537335 4 0.6886 -0.0764 0.008 0.368 0.088 0.536
#> GSM537339 4 0.5813 0.2617 0.260 0.060 0.004 0.676
#> GSM537340 3 0.5724 0.5419 0.096 0.108 0.760 0.036
#> GSM537344 1 0.4204 0.6398 0.788 0.000 0.020 0.192
#> GSM537346 2 0.7738 0.0731 0.000 0.440 0.260 0.300
#> GSM537351 1 0.5257 0.4262 0.752 0.000 0.104 0.144
#> GSM537352 2 0.6726 0.3960 0.000 0.584 0.292 0.124
#> GSM537359 2 0.2660 0.6414 0.000 0.908 0.036 0.056
#> GSM537360 3 0.5366 0.2046 0.000 0.440 0.548 0.012
#> GSM537364 1 0.1042 0.6800 0.972 0.000 0.008 0.020
#> GSM537365 4 0.6934 0.2347 0.064 0.024 0.360 0.552
#> GSM537372 4 0.4889 0.0814 0.360 0.000 0.004 0.636
#> GSM537384 4 0.5112 -0.1107 0.436 0.000 0.004 0.560
#> GSM537394 2 0.7058 0.2644 0.000 0.560 0.168 0.272
#> GSM537403 3 0.3972 0.5088 0.000 0.204 0.788 0.008
#> GSM537406 2 0.5911 0.1683 0.000 0.584 0.372 0.044
#> GSM537411 2 0.7399 0.3961 0.000 0.512 0.208 0.280
#> GSM537412 3 0.4485 0.4919 0.000 0.248 0.740 0.012
#> GSM537416 3 0.3607 0.5518 0.012 0.088 0.868 0.032
#> GSM537426 3 0.5189 0.3239 0.000 0.372 0.616 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.2804 0.57958 0.012 0.096 0.004 0.008 0.880
#> GSM537345 1 0.1331 0.79476 0.952 0.000 0.008 0.000 0.040
#> GSM537355 2 0.7344 0.43194 0.000 0.536 0.100 0.160 0.204
#> GSM537366 5 0.8146 0.00227 0.232 0.004 0.284 0.096 0.384
#> GSM537370 5 0.3739 0.54267 0.000 0.116 0.052 0.008 0.824
#> GSM537380 2 0.2353 0.61052 0.000 0.908 0.028 0.004 0.060
#> GSM537392 2 0.1830 0.61354 0.000 0.932 0.028 0.000 0.040
#> GSM537415 4 0.4810 0.32302 0.000 0.400 0.012 0.580 0.008
#> GSM537417 3 0.5994 0.13384 0.004 0.060 0.516 0.404 0.016
#> GSM537422 4 0.6068 -0.12057 0.104 0.000 0.424 0.468 0.004
#> GSM537423 2 0.2124 0.58631 0.000 0.900 0.000 0.096 0.004
#> GSM537427 2 0.5738 0.54493 0.000 0.696 0.064 0.080 0.160
#> GSM537430 2 0.4022 0.59821 0.000 0.828 0.052 0.064 0.056
#> GSM537336 1 0.0324 0.80440 0.992 0.000 0.004 0.004 0.000
#> GSM537337 2 0.7290 0.24796 0.000 0.476 0.076 0.324 0.124
#> GSM537348 5 0.3730 0.56208 0.136 0.016 0.028 0.000 0.820
#> GSM537349 2 0.2166 0.59835 0.000 0.912 0.004 0.072 0.012
#> GSM537356 5 0.6131 0.35518 0.248 0.004 0.152 0.004 0.592
#> GSM537361 3 0.4178 0.58202 0.088 0.000 0.808 0.020 0.084
#> GSM537374 2 0.7507 0.18786 0.000 0.412 0.144 0.076 0.368
#> GSM537377 1 0.1444 0.79469 0.948 0.000 0.012 0.000 0.040
#> GSM537378 2 0.2733 0.57255 0.000 0.872 0.004 0.112 0.012
#> GSM537379 3 0.6717 0.05809 0.000 0.124 0.472 0.376 0.028
#> GSM537383 2 0.1377 0.61608 0.000 0.956 0.004 0.020 0.020
#> GSM537388 2 0.6358 0.52728 0.000 0.632 0.072 0.092 0.204
#> GSM537395 2 0.5470 0.47222 0.000 0.680 0.068 0.224 0.028
#> GSM537400 3 0.5610 0.44892 0.012 0.012 0.648 0.272 0.056
#> GSM537404 3 0.5873 0.58177 0.028 0.012 0.692 0.120 0.148
#> GSM537409 4 0.3449 0.58981 0.000 0.088 0.064 0.844 0.004
#> GSM537418 5 0.7255 0.13900 0.280 0.000 0.324 0.020 0.376
#> GSM537425 3 0.7002 0.47381 0.180 0.000 0.580 0.092 0.148
#> GSM537333 3 0.5590 0.44205 0.012 0.012 0.644 0.280 0.052
#> GSM537342 4 0.4777 0.57158 0.000 0.060 0.048 0.772 0.120
#> GSM537347 3 0.5335 0.48988 0.000 0.088 0.700 0.020 0.192
#> GSM537350 5 0.7536 0.21529 0.240 0.036 0.144 0.044 0.536
#> GSM537362 5 0.7964 0.30935 0.240 0.004 0.236 0.088 0.432
#> GSM537363 4 0.7789 0.12294 0.232 0.004 0.204 0.472 0.088
#> GSM537368 1 0.0912 0.80537 0.972 0.000 0.012 0.000 0.016
#> GSM537376 4 0.6379 0.35968 0.000 0.252 0.044 0.600 0.104
#> GSM537381 3 0.6961 0.03305 0.312 0.000 0.412 0.008 0.268
#> GSM537386 2 0.6149 0.45241 0.000 0.648 0.196 0.052 0.104
#> GSM537398 5 0.4549 0.56258 0.132 0.016 0.068 0.004 0.780
#> GSM537402 2 0.6439 0.05085 0.000 0.456 0.024 0.424 0.096
#> GSM537405 1 0.1990 0.79709 0.928 0.000 0.040 0.004 0.028
#> GSM537371 1 0.0324 0.80440 0.992 0.000 0.004 0.000 0.004
#> GSM537421 4 0.4065 0.54530 0.024 0.048 0.116 0.812 0.000
#> GSM537424 5 0.5512 0.38909 0.276 0.000 0.104 0.000 0.620
#> GSM537432 3 0.5756 0.39730 0.008 0.012 0.608 0.312 0.060
#> GSM537331 2 0.6626 0.43851 0.000 0.564 0.068 0.080 0.288
#> GSM537332 3 0.6355 0.38656 0.000 0.140 0.588 0.248 0.024
#> GSM537334 5 0.7675 -0.18743 0.000 0.368 0.152 0.088 0.392
#> GSM537338 2 0.7468 0.32955 0.000 0.452 0.088 0.128 0.332
#> GSM537353 4 0.5451 0.21337 0.000 0.424 0.032 0.528 0.016
#> GSM537357 1 0.0486 0.80549 0.988 0.000 0.004 0.004 0.004
#> GSM537358 2 0.2684 0.60345 0.000 0.900 0.032 0.044 0.024
#> GSM537375 4 0.7238 0.28928 0.000 0.236 0.172 0.524 0.068
#> GSM537389 2 0.2511 0.59215 0.000 0.892 0.004 0.088 0.016
#> GSM537390 2 0.4753 0.40506 0.000 0.708 0.032 0.244 0.016
#> GSM537393 4 0.7230 0.09042 0.000 0.352 0.156 0.444 0.048
#> GSM537399 3 0.5389 0.21949 0.020 0.024 0.532 0.000 0.424
#> GSM537407 3 0.5201 0.54624 0.036 0.016 0.740 0.040 0.168
#> GSM537408 2 0.4673 0.52538 0.000 0.776 0.096 0.028 0.100
#> GSM537428 2 0.6608 0.49128 0.000 0.592 0.072 0.092 0.244
#> GSM537354 4 0.7147 -0.07729 0.000 0.396 0.076 0.432 0.096
#> GSM537410 4 0.4608 0.59062 0.000 0.104 0.036 0.784 0.076
#> GSM537413 2 0.4271 0.49569 0.000 0.772 0.040 0.176 0.012
#> GSM537396 5 0.7384 -0.12852 0.000 0.372 0.040 0.204 0.384
#> GSM537397 5 0.3722 0.58664 0.056 0.060 0.016 0.016 0.852
#> GSM537330 3 0.7047 0.38714 0.000 0.240 0.556 0.096 0.108
#> GSM537369 1 0.5708 0.55176 0.640 0.000 0.092 0.016 0.252
#> GSM537373 4 0.6225 0.51962 0.000 0.132 0.044 0.640 0.184
#> GSM537401 5 0.2964 0.56841 0.008 0.108 0.004 0.012 0.868
#> GSM537343 3 0.5852 0.51772 0.060 0.012 0.688 0.048 0.192
#> GSM537367 4 0.6083 0.19732 0.044 0.004 0.312 0.592 0.048
#> GSM537382 4 0.6746 0.36197 0.000 0.228 0.056 0.580 0.136
#> GSM537385 2 0.4814 0.58484 0.000 0.764 0.032 0.076 0.128
#> GSM537391 5 0.5310 0.41060 0.240 0.020 0.024 0.024 0.692
#> GSM537419 2 0.2291 0.59865 0.000 0.908 0.008 0.072 0.012
#> GSM537420 1 0.5708 0.55176 0.640 0.000 0.092 0.016 0.252
#> GSM537429 5 0.7813 0.12016 0.000 0.200 0.272 0.092 0.436
#> GSM537431 3 0.4957 0.48146 0.012 0.004 0.700 0.244 0.040
#> GSM537387 1 0.4856 0.33358 0.584 0.000 0.004 0.020 0.392
#> GSM537414 3 0.4170 0.58524 0.056 0.004 0.812 0.108 0.020
#> GSM537433 3 0.7730 0.46785 0.104 0.012 0.524 0.172 0.188
#> GSM537335 5 0.7141 0.21847 0.000 0.236 0.152 0.072 0.540
#> GSM537339 5 0.3697 0.58290 0.092 0.028 0.032 0.004 0.844
#> GSM537340 4 0.4657 0.50831 0.048 0.040 0.140 0.772 0.000
#> GSM537344 1 0.5708 0.55176 0.640 0.000 0.092 0.016 0.252
#> GSM537346 3 0.5569 0.39662 0.000 0.292 0.624 0.012 0.072
#> GSM537351 1 0.3940 0.57367 0.756 0.000 0.220 0.024 0.000
#> GSM537352 2 0.7358 0.24446 0.000 0.472 0.080 0.320 0.128
#> GSM537359 2 0.4419 0.54534 0.000 0.800 0.084 0.040 0.076
#> GSM537360 4 0.4972 0.41236 0.000 0.352 0.032 0.612 0.004
#> GSM537364 1 0.0794 0.79395 0.972 0.000 0.028 0.000 0.000
#> GSM537365 3 0.4463 0.58005 0.024 0.012 0.792 0.036 0.136
#> GSM537372 5 0.4066 0.51407 0.188 0.000 0.044 0.000 0.768
#> GSM537384 5 0.4425 0.45588 0.244 0.000 0.040 0.000 0.716
#> GSM537394 2 0.5990 -0.09899 0.000 0.468 0.448 0.016 0.068
#> GSM537403 4 0.4074 0.58842 0.000 0.064 0.068 0.824 0.044
#> GSM537406 2 0.6322 -0.09040 0.000 0.468 0.020 0.420 0.092
#> GSM537411 2 0.8105 0.24263 0.000 0.396 0.116 0.256 0.232
#> GSM537412 4 0.3634 0.59314 0.000 0.136 0.040 0.820 0.004
#> GSM537416 4 0.4052 0.47543 0.004 0.028 0.204 0.764 0.000
#> GSM537426 4 0.3399 0.59479 0.000 0.172 0.012 0.812 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.2189 0.69231 0.000 0.032 0.000 0.004 0.904 0.060
#> GSM537345 1 0.2344 0.78462 0.892 0.000 0.004 0.000 0.076 0.028
#> GSM537355 6 0.7083 0.16103 0.000 0.372 0.028 0.108 0.076 0.416
#> GSM537366 3 0.7550 0.19537 0.092 0.000 0.384 0.144 0.344 0.036
#> GSM537370 5 0.3714 0.65892 0.004 0.052 0.060 0.004 0.832 0.048
#> GSM537380 2 0.2214 0.64155 0.000 0.912 0.028 0.004 0.012 0.044
#> GSM537392 2 0.1991 0.64072 0.000 0.920 0.024 0.000 0.012 0.044
#> GSM537415 4 0.5183 0.41471 0.000 0.328 0.012 0.584 0.000 0.076
#> GSM537417 3 0.6208 0.25513 0.004 0.008 0.444 0.204 0.000 0.340
#> GSM537422 3 0.6871 0.27656 0.088 0.000 0.460 0.308 0.004 0.140
#> GSM537423 2 0.1716 0.64164 0.000 0.932 0.004 0.036 0.000 0.028
#> GSM537427 2 0.4891 0.19096 0.000 0.576 0.000 0.004 0.060 0.360
#> GSM537430 2 0.4346 0.29865 0.000 0.632 0.004 0.000 0.028 0.336
#> GSM537336 1 0.1448 0.80140 0.948 0.000 0.012 0.000 0.024 0.016
#> GSM537337 6 0.6009 0.39689 0.000 0.288 0.000 0.132 0.036 0.544
#> GSM537348 5 0.1693 0.70261 0.044 0.000 0.004 0.000 0.932 0.020
#> GSM537349 2 0.2076 0.64085 0.000 0.912 0.000 0.060 0.016 0.012
#> GSM537356 5 0.4556 0.58716 0.096 0.000 0.120 0.000 0.748 0.036
#> GSM537361 3 0.3594 0.63459 0.072 0.000 0.836 0.008 0.044 0.040
#> GSM537374 6 0.6869 0.42740 0.000 0.212 0.060 0.004 0.268 0.456
#> GSM537377 1 0.2344 0.78462 0.892 0.000 0.004 0.000 0.076 0.028
#> GSM537378 2 0.3047 0.59908 0.000 0.848 0.004 0.064 0.000 0.084
#> GSM537379 6 0.6008 0.21575 0.004 0.024 0.272 0.148 0.000 0.552
#> GSM537383 2 0.1555 0.64132 0.000 0.940 0.000 0.012 0.008 0.040
#> GSM537388 2 0.6250 0.13143 0.000 0.504 0.004 0.076 0.072 0.344
#> GSM537395 2 0.5223 -0.03883 0.000 0.508 0.004 0.068 0.004 0.416
#> GSM537400 3 0.6550 0.41253 0.032 0.000 0.520 0.184 0.016 0.248
#> GSM537404 3 0.4938 0.61877 0.024 0.008 0.760 0.048 0.068 0.092
#> GSM537409 4 0.3774 0.60039 0.000 0.072 0.024 0.808 0.000 0.096
#> GSM537418 5 0.6626 0.08335 0.104 0.000 0.352 0.012 0.468 0.064
#> GSM537425 3 0.6617 0.56793 0.120 0.000 0.620 0.076 0.092 0.092
#> GSM537333 3 0.6434 0.42883 0.028 0.000 0.536 0.184 0.016 0.236
#> GSM537342 4 0.4237 0.57481 0.000 0.024 0.020 0.788 0.052 0.116
#> GSM537347 3 0.5211 0.49543 0.004 0.028 0.668 0.004 0.068 0.228
#> GSM537350 5 0.7970 0.41121 0.088 0.064 0.116 0.064 0.528 0.140
#> GSM537362 6 0.7375 0.01036 0.100 0.000 0.172 0.012 0.352 0.364
#> GSM537363 4 0.6390 0.36879 0.144 0.000 0.116 0.624 0.036 0.080
#> GSM537368 1 0.1194 0.80150 0.956 0.000 0.008 0.000 0.032 0.004
#> GSM537376 4 0.6884 0.01294 0.004 0.148 0.016 0.396 0.040 0.396
#> GSM537381 3 0.6277 0.32550 0.144 0.000 0.528 0.004 0.284 0.040
#> GSM537386 2 0.5770 0.47637 0.000 0.664 0.184 0.040 0.048 0.064
#> GSM537398 5 0.3035 0.68556 0.040 0.000 0.024 0.000 0.860 0.076
#> GSM537402 4 0.6919 0.05544 0.004 0.344 0.004 0.408 0.048 0.192
#> GSM537405 1 0.2039 0.79839 0.916 0.000 0.020 0.000 0.052 0.012
#> GSM537371 1 0.1464 0.80318 0.944 0.000 0.004 0.000 0.036 0.016
#> GSM537421 4 0.5132 0.45479 0.008 0.008 0.064 0.636 0.004 0.280
#> GSM537424 5 0.4581 0.59883 0.088 0.000 0.132 0.000 0.744 0.036
#> GSM537432 3 0.6711 0.29478 0.024 0.000 0.436 0.176 0.020 0.344
#> GSM537331 2 0.6089 -0.10581 0.000 0.448 0.008 0.004 0.172 0.368
#> GSM537332 3 0.5400 0.41433 0.000 0.092 0.628 0.248 0.000 0.032
#> GSM537334 6 0.6869 0.43680 0.000 0.180 0.072 0.004 0.276 0.468
#> GSM537338 6 0.6205 0.45347 0.000 0.224 0.008 0.028 0.184 0.556
#> GSM537353 6 0.6890 -0.00201 0.004 0.332 0.016 0.296 0.012 0.340
#> GSM537357 1 0.1464 0.80318 0.944 0.000 0.004 0.000 0.036 0.016
#> GSM537358 2 0.2345 0.64021 0.000 0.904 0.028 0.012 0.004 0.052
#> GSM537375 6 0.5466 0.42111 0.000 0.112 0.036 0.212 0.000 0.640
#> GSM537389 2 0.3294 0.61742 0.000 0.848 0.000 0.064 0.040 0.048
#> GSM537390 2 0.4727 0.46230 0.000 0.700 0.040 0.216 0.000 0.044
#> GSM537393 6 0.5851 0.45658 0.004 0.140 0.056 0.168 0.000 0.632
#> GSM537399 3 0.4978 0.37734 0.012 0.012 0.612 0.000 0.328 0.036
#> GSM537407 3 0.3558 0.60504 0.020 0.008 0.832 0.004 0.104 0.032
#> GSM537408 2 0.3442 0.58506 0.000 0.824 0.124 0.004 0.016 0.032
#> GSM537428 2 0.5677 -0.13912 0.000 0.440 0.004 0.004 0.116 0.436
#> GSM537354 6 0.5806 0.45533 0.000 0.232 0.000 0.200 0.012 0.556
#> GSM537410 4 0.2995 0.60501 0.000 0.048 0.012 0.864 0.004 0.072
#> GSM537413 2 0.3451 0.57386 0.000 0.804 0.012 0.156 0.000 0.028
#> GSM537396 5 0.7684 -0.04209 0.004 0.264 0.024 0.280 0.356 0.072
#> GSM537397 5 0.2189 0.70313 0.016 0.016 0.004 0.000 0.912 0.052
#> GSM537330 3 0.6388 0.37498 0.000 0.088 0.568 0.076 0.016 0.252
#> GSM537369 1 0.6286 0.34830 0.504 0.000 0.092 0.000 0.328 0.076
#> GSM537373 4 0.5216 0.55735 0.004 0.072 0.016 0.728 0.064 0.116
#> GSM537401 5 0.2189 0.68134 0.000 0.032 0.000 0.004 0.904 0.060
#> GSM537343 3 0.5236 0.56559 0.044 0.012 0.724 0.012 0.136 0.072
#> GSM537367 4 0.5302 0.24164 0.040 0.000 0.300 0.612 0.004 0.044
#> GSM537382 4 0.6819 0.12318 0.000 0.144 0.016 0.460 0.052 0.328
#> GSM537385 2 0.5590 0.42323 0.000 0.648 0.004 0.076 0.064 0.208
#> GSM537391 5 0.4394 0.58954 0.144 0.008 0.020 0.000 0.760 0.068
#> GSM537419 2 0.1526 0.64848 0.000 0.944 0.008 0.036 0.004 0.008
#> GSM537420 1 0.6324 0.34239 0.500 0.000 0.096 0.000 0.328 0.076
#> GSM537429 6 0.8340 0.19999 0.000 0.108 0.220 0.080 0.272 0.320
#> GSM537431 3 0.6229 0.45205 0.032 0.000 0.572 0.184 0.012 0.200
#> GSM537387 5 0.4395 0.14906 0.396 0.000 0.008 0.000 0.580 0.016
#> GSM537414 3 0.4359 0.61277 0.052 0.000 0.776 0.044 0.008 0.120
#> GSM537433 3 0.6692 0.54003 0.076 0.008 0.620 0.136 0.104 0.056
#> GSM537335 5 0.6358 -0.26348 0.000 0.104 0.064 0.000 0.444 0.388
#> GSM537339 5 0.1697 0.70605 0.020 0.004 0.004 0.000 0.936 0.036
#> GSM537340 4 0.5672 0.44340 0.036 0.012 0.056 0.608 0.004 0.284
#> GSM537344 1 0.6314 0.35031 0.504 0.000 0.096 0.000 0.324 0.076
#> GSM537346 3 0.4768 0.46370 0.000 0.236 0.676 0.000 0.012 0.076
#> GSM537351 1 0.4174 0.58349 0.772 0.000 0.148 0.024 0.004 0.052
#> GSM537352 6 0.6395 0.35431 0.000 0.304 0.004 0.144 0.044 0.504
#> GSM537359 2 0.3512 0.61033 0.000 0.836 0.088 0.008 0.024 0.044
#> GSM537360 4 0.5683 0.46013 0.000 0.212 0.020 0.596 0.000 0.172
#> GSM537364 1 0.0984 0.79048 0.968 0.000 0.008 0.000 0.012 0.012
#> GSM537365 3 0.2805 0.62378 0.012 0.012 0.884 0.004 0.064 0.024
#> GSM537372 5 0.1738 0.69649 0.052 0.000 0.016 0.000 0.928 0.004
#> GSM537384 5 0.2308 0.67878 0.076 0.000 0.016 0.000 0.896 0.012
#> GSM537394 2 0.5252 0.10468 0.000 0.512 0.420 0.004 0.016 0.048
#> GSM537403 4 0.2519 0.61005 0.000 0.020 0.020 0.888 0.000 0.072
#> GSM537406 4 0.5807 0.35149 0.000 0.332 0.016 0.552 0.020 0.080
#> GSM537411 6 0.8296 0.41547 0.004 0.208 0.072 0.132 0.188 0.396
#> GSM537412 4 0.3782 0.59950 0.000 0.088 0.024 0.808 0.000 0.080
#> GSM537416 4 0.4382 0.53278 0.004 0.000 0.104 0.740 0.004 0.148
#> GSM537426 4 0.3815 0.59754 0.000 0.088 0.016 0.800 0.000 0.096
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> MAD:kmeans 101 0.3614 0.5860 2
#> MAD:kmeans 56 0.2626 0.0470 3
#> MAD:kmeans 37 0.0387 0.0577 4
#> MAD:kmeans 50 0.5521 0.1921 5
#> MAD:kmeans 49 0.1787 0.3921 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 104 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.882 0.915 0.965 0.5030 0.498 0.498
#> 3 3 0.417 0.379 0.680 0.3268 0.808 0.631
#> 4 4 0.485 0.455 0.674 0.1245 0.767 0.444
#> 5 5 0.540 0.430 0.667 0.0671 0.900 0.637
#> 6 6 0.592 0.428 0.659 0.0412 0.900 0.567
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
#> GSM537341 1 0.8386 0.6410 0.732 0.268
#> GSM537345 1 0.0000 0.9656 1.000 0.000
#> GSM537355 2 0.0000 0.9594 0.000 1.000
#> GSM537366 1 0.0000 0.9656 1.000 0.000
#> GSM537370 1 0.8861 0.5802 0.696 0.304
#> GSM537380 2 0.0000 0.9594 0.000 1.000
#> GSM537392 2 0.0000 0.9594 0.000 1.000
#> GSM537415 2 0.0000 0.9594 0.000 1.000
#> GSM537417 2 0.7745 0.7001 0.228 0.772
#> GSM537422 1 0.0000 0.9656 1.000 0.000
#> GSM537423 2 0.0000 0.9594 0.000 1.000
#> GSM537427 2 0.0000 0.9594 0.000 1.000
#> GSM537430 2 0.0000 0.9594 0.000 1.000
#> GSM537336 1 0.0000 0.9656 1.000 0.000
#> GSM537337 2 0.0000 0.9594 0.000 1.000
#> GSM537348 1 0.0000 0.9656 1.000 0.000
#> GSM537349 2 0.0000 0.9594 0.000 1.000
#> GSM537356 1 0.0000 0.9656 1.000 0.000
#> GSM537361 1 0.0000 0.9656 1.000 0.000
#> GSM537374 2 0.0000 0.9594 0.000 1.000
#> GSM537377 1 0.0000 0.9656 1.000 0.000
#> GSM537378 2 0.0000 0.9594 0.000 1.000
#> GSM537379 2 0.0000 0.9594 0.000 1.000
#> GSM537383 2 0.0000 0.9594 0.000 1.000
#> GSM537388 2 0.0000 0.9594 0.000 1.000
#> GSM537395 2 0.0000 0.9594 0.000 1.000
#> GSM537400 1 0.0000 0.9656 1.000 0.000
#> GSM537404 1 0.0000 0.9656 1.000 0.000
#> GSM537409 2 0.0000 0.9594 0.000 1.000
#> GSM537418 1 0.0000 0.9656 1.000 0.000
#> GSM537425 1 0.0000 0.9656 1.000 0.000
#> GSM537333 1 0.2603 0.9272 0.956 0.044
#> GSM537342 2 0.5519 0.8391 0.128 0.872
#> GSM537347 1 0.5519 0.8412 0.872 0.128
#> GSM537350 1 0.0000 0.9656 1.000 0.000
#> GSM537362 1 0.0000 0.9656 1.000 0.000
#> GSM537363 1 0.0938 0.9561 0.988 0.012
#> GSM537368 1 0.0000 0.9656 1.000 0.000
#> GSM537376 2 0.0000 0.9594 0.000 1.000
#> GSM537381 1 0.0000 0.9656 1.000 0.000
#> GSM537386 2 0.0000 0.9594 0.000 1.000
#> GSM537398 1 0.0000 0.9656 1.000 0.000
#> GSM537402 2 0.0000 0.9594 0.000 1.000
#> GSM537405 1 0.0000 0.9656 1.000 0.000
#> GSM537371 1 0.0000 0.9656 1.000 0.000
#> GSM537421 2 0.9044 0.5502 0.320 0.680
#> GSM537424 1 0.0000 0.9656 1.000 0.000
#> GSM537432 1 0.0000 0.9656 1.000 0.000
#> GSM537331 2 0.0000 0.9594 0.000 1.000
#> GSM537332 2 0.0000 0.9594 0.000 1.000
#> GSM537334 2 0.0000 0.9594 0.000 1.000
#> GSM537338 2 0.0000 0.9594 0.000 1.000
#> GSM537353 2 0.0000 0.9594 0.000 1.000
#> GSM537357 1 0.0000 0.9656 1.000 0.000
#> GSM537358 2 0.0000 0.9594 0.000 1.000
#> GSM537375 2 0.0000 0.9594 0.000 1.000
#> GSM537389 2 0.0000 0.9594 0.000 1.000
#> GSM537390 2 0.0000 0.9594 0.000 1.000
#> GSM537393 2 0.0000 0.9594 0.000 1.000
#> GSM537399 1 0.0672 0.9595 0.992 0.008
#> GSM537407 1 0.0000 0.9656 1.000 0.000
#> GSM537408 2 0.0000 0.9594 0.000 1.000
#> GSM537428 2 0.0000 0.9594 0.000 1.000
#> GSM537354 2 0.0000 0.9594 0.000 1.000
#> GSM537410 2 0.4161 0.8845 0.084 0.916
#> GSM537413 2 0.0000 0.9594 0.000 1.000
#> GSM537396 2 0.0000 0.9594 0.000 1.000
#> GSM537397 1 0.3584 0.9051 0.932 0.068
#> GSM537330 2 0.0000 0.9594 0.000 1.000
#> GSM537369 1 0.0000 0.9656 1.000 0.000
#> GSM537373 2 0.5408 0.8439 0.124 0.876
#> GSM537401 1 0.8955 0.5640 0.688 0.312
#> GSM537343 1 0.0000 0.9656 1.000 0.000
#> GSM537367 1 0.0000 0.9656 1.000 0.000
#> GSM537382 2 0.0376 0.9563 0.004 0.996
#> GSM537385 2 0.0000 0.9594 0.000 1.000
#> GSM537391 1 0.0000 0.9656 1.000 0.000
#> GSM537419 2 0.0000 0.9594 0.000 1.000
#> GSM537420 1 0.0000 0.9656 1.000 0.000
#> GSM537429 2 0.9988 0.0105 0.480 0.520
#> GSM537431 1 0.0000 0.9656 1.000 0.000
#> GSM537387 1 0.0000 0.9656 1.000 0.000
#> GSM537414 1 0.0000 0.9656 1.000 0.000
#> GSM537433 1 0.0000 0.9656 1.000 0.000
#> GSM537335 1 0.9608 0.4080 0.616 0.384
#> GSM537339 1 0.0000 0.9656 1.000 0.000
#> GSM537340 2 0.9881 0.2722 0.436 0.564
#> GSM537344 1 0.0000 0.9656 1.000 0.000
#> GSM537346 2 0.0376 0.9563 0.004 0.996
#> GSM537351 1 0.0000 0.9656 1.000 0.000
#> GSM537352 2 0.0000 0.9594 0.000 1.000
#> GSM537359 2 0.0000 0.9594 0.000 1.000
#> GSM537360 2 0.0000 0.9594 0.000 1.000
#> GSM537364 1 0.0000 0.9656 1.000 0.000
#> GSM537365 1 0.0000 0.9656 1.000 0.000
#> GSM537372 1 0.0000 0.9656 1.000 0.000
#> GSM537384 1 0.0000 0.9656 1.000 0.000
#> GSM537394 2 0.0000 0.9594 0.000 1.000
#> GSM537403 2 0.0000 0.9594 0.000 1.000
#> GSM537406 2 0.0000 0.9594 0.000 1.000
#> GSM537411 2 0.0000 0.9594 0.000 1.000
#> GSM537412 2 0.0000 0.9594 0.000 1.000
#> GSM537416 2 0.8861 0.5802 0.304 0.696
#> GSM537426 2 0.0000 0.9594 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.7063 0.1204 0.516 0.020 0.464
#> GSM537345 1 0.0592 0.7861 0.988 0.000 0.012
#> GSM537355 2 0.6295 0.2722 0.000 0.528 0.472
#> GSM537366 1 0.1453 0.7841 0.968 0.008 0.024
#> GSM537370 3 0.8869 0.1832 0.380 0.124 0.496
#> GSM537380 2 0.6235 0.1891 0.000 0.564 0.436
#> GSM537392 2 0.6045 0.2876 0.000 0.620 0.380
#> GSM537415 2 0.3038 0.4415 0.000 0.896 0.104
#> GSM537417 3 0.8743 -0.0461 0.108 0.440 0.452
#> GSM537422 3 0.9641 0.0904 0.296 0.240 0.464
#> GSM537423 2 0.4291 0.4290 0.000 0.820 0.180
#> GSM537427 2 0.6260 0.2158 0.000 0.552 0.448
#> GSM537430 2 0.6008 0.2990 0.000 0.628 0.372
#> GSM537336 1 0.0892 0.7855 0.980 0.000 0.020
#> GSM537337 2 0.4121 0.4328 0.000 0.832 0.168
#> GSM537348 1 0.3941 0.7068 0.844 0.000 0.156
#> GSM537349 2 0.5948 0.3182 0.000 0.640 0.360
#> GSM537356 1 0.0424 0.7878 0.992 0.000 0.008
#> GSM537361 1 0.5397 0.6051 0.720 0.000 0.280
#> GSM537374 3 0.6299 -0.0901 0.000 0.476 0.524
#> GSM537377 1 0.0747 0.7853 0.984 0.000 0.016
#> GSM537378 2 0.4002 0.4364 0.000 0.840 0.160
#> GSM537379 2 0.6274 0.1492 0.000 0.544 0.456
#> GSM537383 2 0.6026 0.2904 0.000 0.624 0.376
#> GSM537388 2 0.6140 0.2868 0.000 0.596 0.404
#> GSM537395 2 0.4178 0.4447 0.000 0.828 0.172
#> GSM537400 3 0.8153 0.1497 0.240 0.128 0.632
#> GSM537404 1 0.8018 0.3166 0.520 0.064 0.416
#> GSM537409 2 0.6252 0.1854 0.000 0.556 0.444
#> GSM537418 1 0.0237 0.7891 0.996 0.000 0.004
#> GSM537425 1 0.5363 0.6092 0.724 0.000 0.276
#> GSM537333 3 0.8162 0.2166 0.192 0.164 0.644
#> GSM537342 2 0.7666 0.2519 0.076 0.636 0.288
#> GSM537347 3 0.6431 0.2579 0.156 0.084 0.760
#> GSM537350 1 0.0237 0.7883 0.996 0.000 0.004
#> GSM537362 1 0.4555 0.6968 0.800 0.000 0.200
#> GSM537363 1 0.7885 0.3894 0.580 0.068 0.352
#> GSM537368 1 0.0424 0.7884 0.992 0.000 0.008
#> GSM537376 2 0.4555 0.4187 0.000 0.800 0.200
#> GSM537381 1 0.0592 0.7884 0.988 0.000 0.012
#> GSM537386 2 0.6307 0.1107 0.000 0.512 0.488
#> GSM537398 1 0.4291 0.6827 0.820 0.000 0.180
#> GSM537402 2 0.5882 0.4059 0.000 0.652 0.348
#> GSM537405 1 0.0237 0.7889 0.996 0.000 0.004
#> GSM537371 1 0.0237 0.7889 0.996 0.000 0.004
#> GSM537421 2 0.8655 0.1024 0.108 0.512 0.380
#> GSM537424 1 0.1289 0.7796 0.968 0.000 0.032
#> GSM537432 3 0.8303 0.2165 0.196 0.172 0.632
#> GSM537331 3 0.6274 -0.0701 0.000 0.456 0.544
#> GSM537332 2 0.6244 0.1850 0.000 0.560 0.440
#> GSM537334 3 0.6476 -0.0628 0.004 0.448 0.548
#> GSM537338 3 0.6267 -0.0709 0.000 0.452 0.548
#> GSM537353 2 0.3412 0.4292 0.000 0.876 0.124
#> GSM537357 1 0.0237 0.7889 0.996 0.000 0.004
#> GSM537358 2 0.5529 0.3708 0.000 0.704 0.296
#> GSM537375 2 0.5785 0.2982 0.000 0.668 0.332
#> GSM537389 2 0.5810 0.3428 0.000 0.664 0.336
#> GSM537390 2 0.3482 0.4507 0.000 0.872 0.128
#> GSM537393 2 0.4750 0.3974 0.000 0.784 0.216
#> GSM537399 1 0.6359 0.4602 0.628 0.008 0.364
#> GSM537407 1 0.5363 0.6150 0.724 0.000 0.276
#> GSM537408 2 0.5905 0.3303 0.000 0.648 0.352
#> GSM537428 3 0.6305 -0.1282 0.000 0.484 0.516
#> GSM537354 2 0.3551 0.4301 0.000 0.868 0.132
#> GSM537410 2 0.6490 0.2482 0.012 0.628 0.360
#> GSM537413 2 0.5733 0.3827 0.000 0.676 0.324
#> GSM537396 2 0.8362 0.2125 0.088 0.528 0.384
#> GSM537397 1 0.6467 0.3451 0.604 0.008 0.388
#> GSM537330 3 0.5327 0.0327 0.000 0.272 0.728
#> GSM537369 1 0.0000 0.7887 1.000 0.000 0.000
#> GSM537373 2 0.8569 0.1921 0.196 0.608 0.196
#> GSM537401 3 0.8039 0.0698 0.428 0.064 0.508
#> GSM537343 1 0.4702 0.6689 0.788 0.000 0.212
#> GSM537367 3 0.9717 0.1056 0.248 0.304 0.448
#> GSM537382 2 0.5397 0.3777 0.000 0.720 0.280
#> GSM537385 2 0.6062 0.3001 0.000 0.616 0.384
#> GSM537391 1 0.5785 0.5225 0.696 0.004 0.300
#> GSM537419 2 0.5733 0.3497 0.000 0.676 0.324
#> GSM537420 1 0.0000 0.7887 1.000 0.000 0.000
#> GSM537429 3 0.8054 0.2073 0.356 0.076 0.568
#> GSM537431 3 0.8250 0.1723 0.232 0.140 0.628
#> GSM537387 1 0.3340 0.7322 0.880 0.000 0.120
#> GSM537414 1 0.7984 0.2627 0.496 0.060 0.444
#> GSM537433 1 0.6294 0.5800 0.692 0.020 0.288
#> GSM537335 3 0.9151 0.1937 0.228 0.228 0.544
#> GSM537339 1 0.6129 0.4762 0.668 0.008 0.324
#> GSM537340 2 0.9093 0.0162 0.140 0.460 0.400
#> GSM537344 1 0.0000 0.7887 1.000 0.000 0.000
#> GSM537346 3 0.6008 -0.0563 0.000 0.372 0.628
#> GSM537351 1 0.5623 0.6025 0.716 0.004 0.280
#> GSM537352 2 0.3816 0.4360 0.000 0.852 0.148
#> GSM537359 2 0.6252 0.1916 0.000 0.556 0.444
#> GSM537360 2 0.3686 0.4214 0.000 0.860 0.140
#> GSM537364 1 0.1031 0.7842 0.976 0.000 0.024
#> GSM537365 1 0.7379 0.4884 0.584 0.040 0.376
#> GSM537372 1 0.3267 0.7360 0.884 0.000 0.116
#> GSM537384 1 0.1753 0.7736 0.952 0.000 0.048
#> GSM537394 3 0.6215 -0.0816 0.000 0.428 0.572
#> GSM537403 2 0.6252 0.1855 0.000 0.556 0.444
#> GSM537406 2 0.2878 0.4545 0.000 0.904 0.096
#> GSM537411 3 0.6126 -0.1192 0.000 0.400 0.600
#> GSM537412 2 0.6180 0.1967 0.000 0.584 0.416
#> GSM537416 3 0.8135 -0.0889 0.068 0.448 0.484
#> GSM537426 2 0.4796 0.3824 0.000 0.780 0.220
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.7615 0.50101 0.592 0.124 0.236 0.048
#> GSM537345 1 0.1557 0.72450 0.944 0.000 0.056 0.000
#> GSM537355 2 0.6522 0.48162 0.000 0.632 0.144 0.224
#> GSM537366 1 0.4036 0.65409 0.836 0.000 0.076 0.088
#> GSM537370 1 0.8906 0.08855 0.348 0.324 0.280 0.048
#> GSM537380 2 0.1174 0.66127 0.000 0.968 0.020 0.012
#> GSM537392 2 0.0336 0.65941 0.000 0.992 0.000 0.008
#> GSM537415 4 0.5016 0.41766 0.000 0.396 0.004 0.600
#> GSM537417 3 0.6011 0.11952 0.004 0.032 0.516 0.448
#> GSM537422 3 0.7399 0.27392 0.164 0.000 0.420 0.416
#> GSM537423 2 0.2530 0.62985 0.000 0.896 0.004 0.100
#> GSM537427 2 0.5266 0.56039 0.000 0.752 0.108 0.140
#> GSM537430 2 0.3323 0.63324 0.000 0.876 0.060 0.064
#> GSM537336 1 0.2142 0.70436 0.928 0.000 0.056 0.016
#> GSM537337 4 0.7006 0.00474 0.000 0.428 0.116 0.456
#> GSM537348 1 0.4900 0.63447 0.732 0.000 0.236 0.032
#> GSM537349 2 0.2081 0.64699 0.000 0.916 0.000 0.084
#> GSM537356 1 0.1474 0.72045 0.948 0.000 0.052 0.000
#> GSM537361 3 0.5420 0.37977 0.352 0.000 0.624 0.024
#> GSM537374 2 0.7162 0.27032 0.000 0.472 0.392 0.136
#> GSM537377 1 0.1867 0.72487 0.928 0.000 0.072 0.000
#> GSM537378 2 0.3768 0.53465 0.000 0.808 0.008 0.184
#> GSM537379 3 0.6102 0.13281 0.000 0.048 0.532 0.420
#> GSM537383 2 0.0657 0.66045 0.000 0.984 0.004 0.012
#> GSM537388 2 0.5568 0.56070 0.000 0.728 0.120 0.152
#> GSM537395 2 0.6058 0.34256 0.000 0.632 0.072 0.296
#> GSM537400 3 0.5413 0.49723 0.048 0.004 0.712 0.236
#> GSM537404 3 0.7214 0.49970 0.236 0.024 0.608 0.132
#> GSM537409 4 0.4100 0.62686 0.000 0.128 0.048 0.824
#> GSM537418 1 0.1557 0.71667 0.944 0.000 0.056 0.000
#> GSM537425 1 0.6661 -0.17033 0.460 0.000 0.456 0.084
#> GSM537333 3 0.5490 0.49990 0.052 0.004 0.708 0.236
#> GSM537342 4 0.3659 0.62480 0.032 0.084 0.016 0.868
#> GSM537347 3 0.4061 0.49171 0.016 0.092 0.848 0.044
#> GSM537350 1 0.2418 0.71680 0.928 0.024 0.032 0.016
#> GSM537362 1 0.5666 0.53279 0.616 0.000 0.348 0.036
#> GSM537363 1 0.7468 0.04025 0.484 0.012 0.128 0.376
#> GSM537368 1 0.1706 0.71470 0.948 0.000 0.036 0.016
#> GSM537376 4 0.4422 0.52722 0.000 0.256 0.008 0.736
#> GSM537381 1 0.3257 0.62560 0.844 0.000 0.152 0.004
#> GSM537386 2 0.5548 0.54075 0.000 0.716 0.200 0.084
#> GSM537398 1 0.5105 0.59531 0.696 0.000 0.276 0.028
#> GSM537402 2 0.5088 0.22967 0.000 0.572 0.004 0.424
#> GSM537405 1 0.1398 0.71711 0.956 0.000 0.040 0.004
#> GSM537371 1 0.1489 0.71538 0.952 0.000 0.044 0.004
#> GSM537421 4 0.3610 0.60201 0.020 0.060 0.044 0.876
#> GSM537424 1 0.2125 0.72335 0.920 0.000 0.076 0.004
#> GSM537432 3 0.5776 0.45660 0.040 0.012 0.676 0.272
#> GSM537331 2 0.6634 0.48075 0.000 0.624 0.212 0.164
#> GSM537332 3 0.7517 0.22875 0.000 0.304 0.484 0.212
#> GSM537334 3 0.7349 -0.22246 0.000 0.384 0.456 0.160
#> GSM537338 2 0.7456 0.34005 0.000 0.488 0.316 0.196
#> GSM537353 4 0.5408 0.42699 0.000 0.408 0.016 0.576
#> GSM537357 1 0.1545 0.71599 0.952 0.000 0.040 0.008
#> GSM537358 2 0.2255 0.64698 0.000 0.920 0.012 0.068
#> GSM537375 4 0.6245 0.43680 0.000 0.168 0.164 0.668
#> GSM537389 2 0.2216 0.64450 0.000 0.908 0.000 0.092
#> GSM537390 2 0.5442 0.32501 0.000 0.672 0.040 0.288
#> GSM537393 4 0.7068 0.33652 0.000 0.296 0.156 0.548
#> GSM537399 3 0.4914 0.21018 0.312 0.012 0.676 0.000
#> GSM537407 3 0.5599 0.37420 0.352 0.000 0.616 0.032
#> GSM537408 2 0.3229 0.63316 0.000 0.880 0.072 0.048
#> GSM537428 2 0.6352 0.50331 0.000 0.656 0.188 0.156
#> GSM537354 4 0.6779 0.26772 0.000 0.324 0.116 0.560
#> GSM537410 4 0.3946 0.62522 0.004 0.172 0.012 0.812
#> GSM537413 2 0.3545 0.58145 0.000 0.828 0.008 0.164
#> GSM537396 2 0.6348 0.43380 0.048 0.676 0.040 0.236
#> GSM537397 1 0.6511 0.58362 0.668 0.076 0.228 0.028
#> GSM537330 3 0.6732 0.21589 0.000 0.336 0.556 0.108
#> GSM537369 1 0.0000 0.72471 1.000 0.000 0.000 0.000
#> GSM537373 4 0.6855 0.43047 0.076 0.296 0.024 0.604
#> GSM537401 1 0.8822 0.28340 0.448 0.228 0.260 0.064
#> GSM537343 1 0.6332 -0.13608 0.488 0.000 0.452 0.060
#> GSM537367 4 0.7534 -0.25575 0.192 0.000 0.360 0.448
#> GSM537382 4 0.4682 0.51409 0.004 0.212 0.024 0.760
#> GSM537385 2 0.4100 0.63080 0.000 0.824 0.048 0.128
#> GSM537391 1 0.5837 0.62426 0.720 0.040 0.204 0.036
#> GSM537419 2 0.2053 0.64984 0.000 0.924 0.004 0.072
#> GSM537420 1 0.0000 0.72471 1.000 0.000 0.000 0.000
#> GSM537429 3 0.9577 0.03465 0.204 0.296 0.360 0.140
#> GSM537431 3 0.5708 0.51236 0.076 0.004 0.708 0.212
#> GSM537387 1 0.4121 0.66280 0.796 0.000 0.184 0.020
#> GSM537414 3 0.6084 0.49738 0.252 0.000 0.656 0.092
#> GSM537433 3 0.7140 0.24124 0.404 0.000 0.464 0.132
#> GSM537335 3 0.8797 -0.09308 0.084 0.308 0.452 0.156
#> GSM537339 1 0.5695 0.60599 0.692 0.016 0.256 0.036
#> GSM537340 4 0.4923 0.58855 0.028 0.084 0.080 0.808
#> GSM537344 1 0.0336 0.72408 0.992 0.000 0.008 0.000
#> GSM537346 3 0.5538 0.30846 0.000 0.320 0.644 0.036
#> GSM537351 1 0.6799 -0.18954 0.464 0.000 0.440 0.096
#> GSM537352 4 0.6702 0.02216 0.000 0.436 0.088 0.476
#> GSM537359 2 0.2222 0.65064 0.000 0.924 0.016 0.060
#> GSM537360 4 0.5436 0.49100 0.000 0.356 0.024 0.620
#> GSM537364 1 0.3107 0.67370 0.884 0.000 0.080 0.036
#> GSM537365 3 0.5944 0.47764 0.252 0.012 0.680 0.056
#> GSM537372 1 0.4253 0.65901 0.776 0.000 0.208 0.016
#> GSM537384 1 0.3105 0.69717 0.856 0.000 0.140 0.004
#> GSM537394 2 0.5576 0.08323 0.000 0.536 0.444 0.020
#> GSM537403 4 0.3858 0.62077 0.000 0.100 0.056 0.844
#> GSM537406 2 0.5151 -0.10189 0.000 0.532 0.004 0.464
#> GSM537411 2 0.7659 0.26076 0.000 0.460 0.296 0.244
#> GSM537412 4 0.4245 0.61787 0.000 0.196 0.020 0.784
#> GSM537416 4 0.4132 0.46693 0.008 0.012 0.176 0.804
#> GSM537426 4 0.4360 0.59974 0.000 0.248 0.008 0.744
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.4516 0.4652 0.204 0.032 0.004 0.012 0.748
#> GSM537345 1 0.1197 0.7206 0.952 0.000 0.000 0.000 0.048
#> GSM537355 2 0.8257 0.2010 0.000 0.376 0.184 0.160 0.280
#> GSM537366 1 0.6165 0.5601 0.628 0.000 0.124 0.032 0.216
#> GSM537370 5 0.6381 0.4455 0.124 0.180 0.044 0.008 0.644
#> GSM537380 2 0.1211 0.6977 0.000 0.960 0.024 0.000 0.016
#> GSM537392 2 0.1278 0.6983 0.000 0.960 0.020 0.004 0.016
#> GSM537415 4 0.4473 0.2504 0.000 0.412 0.008 0.580 0.000
#> GSM537417 3 0.5972 0.2047 0.020 0.020 0.592 0.328 0.040
#> GSM537422 3 0.6946 0.3468 0.332 0.000 0.360 0.304 0.004
#> GSM537423 2 0.1569 0.6904 0.000 0.944 0.004 0.044 0.008
#> GSM537427 2 0.6567 0.4003 0.000 0.568 0.128 0.036 0.268
#> GSM537430 2 0.4679 0.6184 0.000 0.768 0.072 0.024 0.136
#> GSM537336 1 0.1116 0.7245 0.964 0.000 0.028 0.004 0.004
#> GSM537337 4 0.8520 0.1697 0.000 0.220 0.200 0.312 0.268
#> GSM537348 5 0.4135 0.3188 0.340 0.000 0.004 0.000 0.656
#> GSM537349 2 0.1830 0.6869 0.000 0.924 0.000 0.068 0.008
#> GSM537356 1 0.4457 0.4020 0.620 0.000 0.012 0.000 0.368
#> GSM537361 3 0.4260 0.4829 0.308 0.000 0.680 0.004 0.008
#> GSM537374 5 0.6727 0.2615 0.000 0.216 0.192 0.032 0.560
#> GSM537377 1 0.1628 0.7152 0.936 0.000 0.008 0.000 0.056
#> GSM537378 2 0.3155 0.6342 0.000 0.848 0.016 0.128 0.008
#> GSM537379 3 0.6847 0.0482 0.000 0.028 0.512 0.292 0.168
#> GSM537383 2 0.1461 0.6985 0.000 0.952 0.016 0.004 0.028
#> GSM537388 2 0.7280 0.3635 0.000 0.508 0.160 0.068 0.264
#> GSM537395 2 0.6966 0.3714 0.000 0.592 0.128 0.160 0.120
#> GSM537400 3 0.6759 0.5290 0.112 0.004 0.604 0.208 0.072
#> GSM537404 3 0.6546 0.5335 0.212 0.040 0.636 0.084 0.028
#> GSM537409 4 0.3133 0.5617 0.000 0.080 0.052 0.864 0.004
#> GSM537418 1 0.2730 0.7270 0.892 0.000 0.056 0.008 0.044
#> GSM537425 1 0.6376 -0.0241 0.484 0.000 0.408 0.036 0.072
#> GSM537333 3 0.6721 0.5211 0.088 0.004 0.604 0.220 0.084
#> GSM537342 4 0.4000 0.5431 0.016 0.036 0.028 0.836 0.084
#> GSM537347 3 0.4766 0.5053 0.000 0.060 0.748 0.020 0.172
#> GSM537350 1 0.5637 0.4622 0.624 0.028 0.040 0.004 0.304
#> GSM537362 1 0.5917 0.2083 0.552 0.000 0.104 0.004 0.340
#> GSM537363 4 0.6190 -0.0655 0.444 0.004 0.060 0.468 0.024
#> GSM537368 1 0.0854 0.7314 0.976 0.000 0.008 0.004 0.012
#> GSM537376 4 0.5744 0.5417 0.000 0.164 0.052 0.692 0.092
#> GSM537381 1 0.4096 0.6229 0.772 0.000 0.176 0.000 0.052
#> GSM537386 2 0.5783 0.5391 0.000 0.680 0.184 0.044 0.092
#> GSM537398 5 0.4731 0.3649 0.328 0.000 0.032 0.000 0.640
#> GSM537402 4 0.6146 0.0190 0.000 0.444 0.024 0.464 0.068
#> GSM537405 1 0.1179 0.7307 0.964 0.000 0.016 0.004 0.016
#> GSM537371 1 0.0854 0.7308 0.976 0.000 0.012 0.004 0.008
#> GSM537421 4 0.2891 0.5486 0.012 0.032 0.044 0.896 0.016
#> GSM537424 1 0.3333 0.6274 0.788 0.000 0.004 0.000 0.208
#> GSM537432 3 0.7958 0.4294 0.120 0.012 0.472 0.264 0.132
#> GSM537331 5 0.7201 -0.1329 0.000 0.380 0.176 0.036 0.408
#> GSM537332 3 0.5779 0.4102 0.000 0.172 0.616 0.212 0.000
#> GSM537334 5 0.6321 0.3236 0.000 0.120 0.236 0.036 0.608
#> GSM537338 5 0.7265 0.2110 0.000 0.180 0.204 0.080 0.536
#> GSM537353 4 0.6031 0.1856 0.000 0.440 0.036 0.480 0.044
#> GSM537357 1 0.0451 0.7305 0.988 0.000 0.000 0.004 0.008
#> GSM537358 2 0.1483 0.6966 0.000 0.952 0.028 0.008 0.012
#> GSM537375 4 0.7549 0.3024 0.000 0.068 0.248 0.468 0.216
#> GSM537389 2 0.2408 0.6717 0.000 0.892 0.004 0.096 0.008
#> GSM537390 2 0.4021 0.5737 0.000 0.780 0.052 0.168 0.000
#> GSM537393 4 0.8242 0.2919 0.000 0.168 0.248 0.396 0.188
#> GSM537399 3 0.6241 0.3524 0.076 0.028 0.568 0.004 0.324
#> GSM537407 3 0.5258 0.4930 0.260 0.024 0.676 0.004 0.036
#> GSM537408 2 0.2046 0.6825 0.000 0.916 0.068 0.000 0.016
#> GSM537428 2 0.7257 0.1090 0.000 0.404 0.188 0.036 0.372
#> GSM537354 4 0.8162 0.2970 0.000 0.156 0.200 0.416 0.228
#> GSM537410 4 0.3818 0.5650 0.012 0.084 0.028 0.844 0.032
#> GSM537413 2 0.2563 0.6554 0.000 0.872 0.008 0.120 0.000
#> GSM537396 2 0.7030 0.1968 0.008 0.488 0.012 0.256 0.236
#> GSM537397 5 0.4477 0.3910 0.288 0.016 0.000 0.008 0.688
#> GSM537330 3 0.6727 0.4186 0.000 0.172 0.612 0.096 0.120
#> GSM537369 1 0.2233 0.7019 0.892 0.000 0.004 0.000 0.104
#> GSM537373 4 0.6795 0.4551 0.056 0.200 0.032 0.628 0.084
#> GSM537401 5 0.3905 0.4873 0.164 0.020 0.004 0.012 0.800
#> GSM537343 1 0.6022 -0.1192 0.476 0.024 0.452 0.008 0.040
#> GSM537367 4 0.6378 -0.0205 0.160 0.000 0.296 0.536 0.008
#> GSM537382 4 0.5739 0.5273 0.000 0.112 0.056 0.700 0.132
#> GSM537385 2 0.5235 0.5984 0.000 0.724 0.032 0.080 0.164
#> GSM537391 5 0.4504 0.1930 0.428 0.000 0.000 0.008 0.564
#> GSM537419 2 0.0963 0.6924 0.000 0.964 0.000 0.036 0.000
#> GSM537420 1 0.2249 0.7073 0.896 0.000 0.008 0.000 0.096
#> GSM537429 5 0.8905 0.1094 0.112 0.112 0.244 0.104 0.428
#> GSM537431 3 0.5974 0.5558 0.080 0.012 0.668 0.208 0.032
#> GSM537387 1 0.4359 0.1172 0.584 0.000 0.004 0.000 0.412
#> GSM537414 3 0.4847 0.5301 0.268 0.000 0.684 0.040 0.008
#> GSM537433 3 0.7220 0.0956 0.404 0.012 0.436 0.056 0.092
#> GSM537335 5 0.5078 0.3791 0.000 0.052 0.204 0.028 0.716
#> GSM537339 5 0.3861 0.4049 0.284 0.000 0.004 0.000 0.712
#> GSM537340 4 0.4573 0.5247 0.048 0.064 0.064 0.808 0.016
#> GSM537344 1 0.1764 0.7208 0.928 0.000 0.008 0.000 0.064
#> GSM537346 3 0.4726 0.4229 0.000 0.256 0.696 0.004 0.044
#> GSM537351 1 0.4829 0.2513 0.660 0.000 0.300 0.036 0.004
#> GSM537352 4 0.8327 0.1736 0.000 0.256 0.140 0.348 0.256
#> GSM537359 2 0.2069 0.6889 0.000 0.924 0.052 0.012 0.012
#> GSM537360 4 0.5537 0.4146 0.000 0.308 0.052 0.620 0.020
#> GSM537364 1 0.1798 0.6977 0.928 0.000 0.064 0.004 0.004
#> GSM537365 3 0.5544 0.5649 0.180 0.044 0.716 0.020 0.040
#> GSM537372 5 0.4397 0.0966 0.432 0.000 0.004 0.000 0.564
#> GSM537384 1 0.4219 0.3093 0.584 0.000 0.000 0.000 0.416
#> GSM537394 2 0.4893 0.2178 0.000 0.580 0.396 0.008 0.016
#> GSM537403 4 0.3125 0.5575 0.004 0.040 0.056 0.880 0.020
#> GSM537406 2 0.5782 0.0474 0.004 0.520 0.016 0.416 0.044
#> GSM537411 5 0.7880 -0.0297 0.000 0.340 0.092 0.188 0.380
#> GSM537412 4 0.3326 0.5702 0.000 0.152 0.024 0.824 0.000
#> GSM537416 4 0.2389 0.4970 0.004 0.000 0.116 0.880 0.000
#> GSM537426 4 0.3475 0.5663 0.000 0.180 0.012 0.804 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.3268 0.6380 0.068 0.012 0.004 0.036 0.860 0.020
#> GSM537345 1 0.1387 0.7505 0.932 0.000 0.000 0.000 0.068 0.000
#> GSM537355 6 0.7930 0.2535 0.004 0.252 0.020 0.172 0.172 0.380
#> GSM537366 1 0.6844 0.3085 0.504 0.000 0.160 0.096 0.236 0.004
#> GSM537370 5 0.5099 0.4925 0.024 0.164 0.056 0.016 0.724 0.016
#> GSM537380 2 0.1862 0.6713 0.000 0.932 0.020 0.004 0.020 0.024
#> GSM537392 2 0.1893 0.6699 0.000 0.928 0.024 0.004 0.008 0.036
#> GSM537415 4 0.6191 0.2250 0.000 0.372 0.004 0.424 0.008 0.192
#> GSM537417 6 0.5883 -0.0394 0.008 0.000 0.380 0.136 0.004 0.472
#> GSM537422 3 0.7907 0.2811 0.292 0.000 0.340 0.200 0.024 0.144
#> GSM537423 2 0.3079 0.6567 0.000 0.844 0.000 0.096 0.004 0.056
#> GSM537427 2 0.5648 0.0790 0.000 0.516 0.000 0.012 0.116 0.356
#> GSM537430 2 0.5445 0.4224 0.000 0.632 0.024 0.024 0.052 0.268
#> GSM537336 1 0.1364 0.7523 0.952 0.000 0.012 0.020 0.016 0.000
#> GSM537337 6 0.5119 0.4531 0.000 0.116 0.000 0.124 0.056 0.704
#> GSM537348 5 0.2964 0.6543 0.204 0.000 0.000 0.000 0.792 0.004
#> GSM537349 2 0.2807 0.6709 0.000 0.868 0.000 0.088 0.016 0.028
#> GSM537356 5 0.4937 0.0475 0.468 0.000 0.052 0.004 0.476 0.000
#> GSM537361 3 0.3304 0.6118 0.172 0.000 0.804 0.004 0.008 0.012
#> GSM537374 6 0.6789 0.2857 0.000 0.168 0.068 0.000 0.360 0.404
#> GSM537377 1 0.1588 0.7482 0.924 0.000 0.004 0.000 0.072 0.000
#> GSM537378 2 0.4565 0.5727 0.000 0.716 0.000 0.108 0.008 0.168
#> GSM537379 6 0.4632 0.2853 0.000 0.004 0.220 0.056 0.016 0.704
#> GSM537383 2 0.1873 0.6723 0.000 0.924 0.000 0.020 0.008 0.048
#> GSM537388 2 0.7629 -0.0753 0.000 0.364 0.008 0.180 0.160 0.288
#> GSM537395 2 0.5419 0.0226 0.000 0.468 0.000 0.100 0.004 0.428
#> GSM537400 3 0.7134 0.4498 0.084 0.004 0.564 0.160 0.060 0.128
#> GSM537404 3 0.6581 0.5306 0.148 0.032 0.624 0.064 0.016 0.116
#> GSM537409 4 0.4970 0.4909 0.000 0.032 0.040 0.672 0.008 0.248
#> GSM537418 1 0.2939 0.7491 0.864 0.000 0.044 0.004 0.080 0.008
#> GSM537425 3 0.6491 0.1830 0.416 0.000 0.440 0.052 0.048 0.044
#> GSM537333 3 0.6989 0.4406 0.044 0.004 0.560 0.176 0.064 0.152
#> GSM537342 4 0.3193 0.4857 0.004 0.008 0.016 0.860 0.036 0.076
#> GSM537347 3 0.5556 0.4498 0.004 0.048 0.668 0.004 0.100 0.176
#> GSM537350 1 0.7735 0.1691 0.464 0.068 0.084 0.116 0.260 0.008
#> GSM537362 1 0.6252 0.1784 0.540 0.000 0.048 0.004 0.280 0.128
#> GSM537363 4 0.6070 0.0608 0.396 0.000 0.056 0.488 0.036 0.024
#> GSM537368 1 0.1167 0.7615 0.960 0.000 0.012 0.020 0.008 0.000
#> GSM537376 4 0.5795 0.1858 0.000 0.048 0.024 0.496 0.024 0.408
#> GSM537381 1 0.3958 0.6257 0.752 0.000 0.200 0.004 0.040 0.004
#> GSM537386 2 0.5432 0.5280 0.000 0.676 0.196 0.032 0.072 0.024
#> GSM537398 5 0.3915 0.6227 0.236 0.000 0.016 0.000 0.732 0.016
#> GSM537402 4 0.6273 0.1175 0.000 0.340 0.004 0.496 0.044 0.116
#> GSM537405 1 0.1401 0.7680 0.948 0.000 0.020 0.004 0.028 0.000
#> GSM537371 1 0.0837 0.7657 0.972 0.000 0.004 0.004 0.020 0.000
#> GSM537421 4 0.6091 0.3290 0.012 0.012 0.064 0.468 0.024 0.420
#> GSM537424 1 0.3735 0.6021 0.748 0.000 0.020 0.000 0.224 0.008
#> GSM537432 3 0.8028 0.3562 0.096 0.012 0.456 0.128 0.080 0.228
#> GSM537331 6 0.6901 0.1933 0.000 0.328 0.016 0.020 0.292 0.344
#> GSM537332 3 0.5392 0.3791 0.000 0.080 0.624 0.268 0.008 0.020
#> GSM537334 6 0.6124 0.2463 0.000 0.052 0.068 0.008 0.408 0.464
#> GSM537338 6 0.5273 0.4997 0.000 0.116 0.008 0.012 0.208 0.656
#> GSM537353 6 0.7172 -0.0640 0.000 0.332 0.040 0.244 0.020 0.364
#> GSM537357 1 0.1049 0.7650 0.960 0.000 0.000 0.008 0.032 0.000
#> GSM537358 2 0.2058 0.6746 0.000 0.924 0.024 0.012 0.012 0.028
#> GSM537375 6 0.4010 0.3677 0.000 0.028 0.036 0.092 0.032 0.812
#> GSM537389 2 0.3577 0.6527 0.000 0.812 0.004 0.136 0.028 0.020
#> GSM537390 2 0.5363 0.5230 0.000 0.688 0.048 0.172 0.012 0.080
#> GSM537393 6 0.4842 0.3342 0.000 0.076 0.048 0.096 0.024 0.756
#> GSM537399 3 0.5417 0.3671 0.036 0.048 0.604 0.000 0.304 0.008
#> GSM537407 3 0.3613 0.6055 0.112 0.028 0.824 0.012 0.024 0.000
#> GSM537408 2 0.3423 0.6317 0.000 0.836 0.104 0.024 0.028 0.008
#> GSM537428 6 0.6441 0.2166 0.000 0.344 0.012 0.008 0.216 0.420
#> GSM537354 6 0.4611 0.4156 0.000 0.076 0.004 0.136 0.036 0.748
#> GSM537410 4 0.1856 0.5188 0.000 0.028 0.008 0.932 0.008 0.024
#> GSM537413 2 0.2900 0.6689 0.000 0.856 0.016 0.112 0.004 0.012
#> GSM537396 4 0.6807 0.0533 0.000 0.328 0.028 0.408 0.224 0.012
#> GSM537397 5 0.3689 0.6628 0.184 0.004 0.004 0.024 0.780 0.004
#> GSM537330 3 0.7428 0.3331 0.000 0.084 0.516 0.136 0.076 0.188
#> GSM537369 1 0.2331 0.7402 0.888 0.000 0.032 0.000 0.080 0.000
#> GSM537373 4 0.4631 0.4686 0.024 0.092 0.024 0.776 0.076 0.008
#> GSM537401 5 0.2767 0.6203 0.048 0.016 0.000 0.020 0.888 0.028
#> GSM537343 3 0.5722 0.3072 0.384 0.040 0.528 0.012 0.028 0.008
#> GSM537367 4 0.6815 0.0340 0.116 0.004 0.316 0.488 0.016 0.060
#> GSM537382 4 0.5697 0.3253 0.008 0.028 0.024 0.632 0.052 0.256
#> GSM537385 2 0.6495 0.4114 0.000 0.584 0.008 0.156 0.116 0.136
#> GSM537391 5 0.4315 0.4255 0.384 0.000 0.004 0.012 0.596 0.004
#> GSM537419 2 0.1889 0.6799 0.000 0.920 0.000 0.056 0.004 0.020
#> GSM537420 1 0.2393 0.7408 0.884 0.000 0.020 0.000 0.092 0.004
#> GSM537429 5 0.9062 -0.0398 0.048 0.080 0.172 0.176 0.356 0.168
#> GSM537431 3 0.5891 0.5026 0.040 0.004 0.672 0.156 0.048 0.080
#> GSM537387 1 0.3944 -0.0241 0.568 0.000 0.000 0.000 0.428 0.004
#> GSM537414 3 0.5163 0.5883 0.200 0.000 0.688 0.020 0.020 0.072
#> GSM537433 3 0.6738 0.3868 0.300 0.028 0.512 0.124 0.020 0.016
#> GSM537335 5 0.5422 0.0174 0.000 0.024 0.056 0.008 0.580 0.332
#> GSM537339 5 0.2806 0.6731 0.136 0.000 0.000 0.004 0.844 0.016
#> GSM537340 4 0.7596 0.2720 0.084 0.040 0.072 0.388 0.028 0.388
#> GSM537344 1 0.2106 0.7491 0.904 0.000 0.032 0.000 0.064 0.000
#> GSM537346 3 0.4221 0.4861 0.004 0.188 0.744 0.000 0.008 0.056
#> GSM537351 1 0.4356 0.5024 0.740 0.000 0.196 0.032 0.024 0.008
#> GSM537352 6 0.6371 0.3950 0.000 0.148 0.000 0.200 0.088 0.564
#> GSM537359 2 0.2487 0.6581 0.000 0.892 0.068 0.004 0.028 0.008
#> GSM537360 4 0.6379 0.3105 0.000 0.228 0.004 0.420 0.012 0.336
#> GSM537364 1 0.2063 0.7349 0.912 0.000 0.060 0.020 0.008 0.000
#> GSM537365 3 0.4115 0.5966 0.080 0.048 0.812 0.008 0.040 0.012
#> GSM537372 5 0.3724 0.5822 0.268 0.000 0.012 0.004 0.716 0.000
#> GSM537384 5 0.3982 0.2016 0.460 0.000 0.004 0.000 0.536 0.000
#> GSM537394 2 0.4815 0.1379 0.000 0.516 0.444 0.004 0.028 0.008
#> GSM537403 4 0.3185 0.5163 0.000 0.008 0.016 0.836 0.012 0.128
#> GSM537406 4 0.4992 0.2368 0.000 0.332 0.012 0.608 0.036 0.012
#> GSM537411 2 0.8519 -0.1803 0.000 0.312 0.120 0.100 0.252 0.216
#> GSM537412 4 0.5254 0.4976 0.000 0.076 0.024 0.656 0.008 0.236
#> GSM537416 4 0.5819 0.4285 0.000 0.000 0.128 0.564 0.028 0.280
#> GSM537426 4 0.5263 0.4842 0.000 0.112 0.008 0.644 0.008 0.228
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> MAD:skmeans 101 0.2396 0.455 2
#> MAD:skmeans 30 NA NA 3
#> MAD:skmeans 56 0.0782 0.292 4
#> MAD:skmeans 48 0.3557 0.359 5
#> MAD:skmeans 44 0.5242 0.371 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 104 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 0.733 0.876 0.945 0.4988 0.498 0.498
#> 3 3 0.696 0.616 0.842 0.2833 0.825 0.666
#> 4 4 0.675 0.713 0.862 0.1429 0.753 0.443
#> 5 5 0.668 0.631 0.813 0.0580 0.961 0.855
#> 6 6 0.676 0.507 0.744 0.0452 0.941 0.760
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
#> GSM537341 1 0.0000 0.948 1.000 0.000
#> GSM537345 1 0.0000 0.948 1.000 0.000
#> GSM537355 1 0.2603 0.921 0.956 0.044
#> GSM537366 1 0.0000 0.948 1.000 0.000
#> GSM537370 1 0.0000 0.948 1.000 0.000
#> GSM537380 2 0.0000 0.929 0.000 1.000
#> GSM537392 2 0.0000 0.929 0.000 1.000
#> GSM537415 2 0.0000 0.929 0.000 1.000
#> GSM537417 2 0.9732 0.365 0.404 0.596
#> GSM537422 1 0.0000 0.948 1.000 0.000
#> GSM537423 2 0.0000 0.929 0.000 1.000
#> GSM537427 2 0.0000 0.929 0.000 1.000
#> GSM537430 2 0.2603 0.901 0.044 0.956
#> GSM537336 1 0.0000 0.948 1.000 0.000
#> GSM537337 2 0.0000 0.929 0.000 1.000
#> GSM537348 1 0.0000 0.948 1.000 0.000
#> GSM537349 2 0.0000 0.929 0.000 1.000
#> GSM537356 1 0.0000 0.948 1.000 0.000
#> GSM537361 1 0.0000 0.948 1.000 0.000
#> GSM537374 1 0.9635 0.379 0.612 0.388
#> GSM537377 1 0.0000 0.948 1.000 0.000
#> GSM537378 2 0.0000 0.929 0.000 1.000
#> GSM537379 2 0.9881 0.276 0.436 0.564
#> GSM537383 2 0.0000 0.929 0.000 1.000
#> GSM537388 2 0.0672 0.925 0.008 0.992
#> GSM537395 2 0.0000 0.929 0.000 1.000
#> GSM537400 1 0.3114 0.912 0.944 0.056
#> GSM537404 1 0.4431 0.885 0.908 0.092
#> GSM537409 2 0.0000 0.929 0.000 1.000
#> GSM537418 1 0.0000 0.948 1.000 0.000
#> GSM537425 1 0.0376 0.946 0.996 0.004
#> GSM537333 1 0.0000 0.948 1.000 0.000
#> GSM537342 2 0.1633 0.914 0.024 0.976
#> GSM537347 1 0.0000 0.948 1.000 0.000
#> GSM537350 1 0.0000 0.948 1.000 0.000
#> GSM537362 1 0.0000 0.948 1.000 0.000
#> GSM537363 1 0.7139 0.761 0.804 0.196
#> GSM537368 1 0.0000 0.948 1.000 0.000
#> GSM537376 2 0.0000 0.929 0.000 1.000
#> GSM537381 1 0.0000 0.948 1.000 0.000
#> GSM537386 2 0.0000 0.929 0.000 1.000
#> GSM537398 1 0.0000 0.948 1.000 0.000
#> GSM537402 2 0.8763 0.595 0.296 0.704
#> GSM537405 1 0.0000 0.948 1.000 0.000
#> GSM537371 1 0.0000 0.948 1.000 0.000
#> GSM537421 2 0.6712 0.774 0.176 0.824
#> GSM537424 1 0.0000 0.948 1.000 0.000
#> GSM537432 1 0.0000 0.948 1.000 0.000
#> GSM537331 2 0.8763 0.595 0.296 0.704
#> GSM537332 2 0.0000 0.929 0.000 1.000
#> GSM537334 1 0.4939 0.870 0.892 0.108
#> GSM537338 2 0.0672 0.925 0.008 0.992
#> GSM537353 2 0.0000 0.929 0.000 1.000
#> GSM537357 1 0.0000 0.948 1.000 0.000
#> GSM537358 2 0.0000 0.929 0.000 1.000
#> GSM537375 2 0.8081 0.678 0.248 0.752
#> GSM537389 2 0.0000 0.929 0.000 1.000
#> GSM537390 2 0.0000 0.929 0.000 1.000
#> GSM537393 2 0.6712 0.777 0.176 0.824
#> GSM537399 1 0.0000 0.948 1.000 0.000
#> GSM537407 1 0.4939 0.863 0.892 0.108
#> GSM537408 2 0.0000 0.929 0.000 1.000
#> GSM537428 1 0.3584 0.904 0.932 0.068
#> GSM537354 2 0.0000 0.929 0.000 1.000
#> GSM537410 2 0.0000 0.929 0.000 1.000
#> GSM537413 2 0.0000 0.929 0.000 1.000
#> GSM537396 1 0.8763 0.585 0.704 0.296
#> GSM537397 1 0.8763 0.550 0.704 0.296
#> GSM537330 1 0.4431 0.878 0.908 0.092
#> GSM537369 1 0.0000 0.948 1.000 0.000
#> GSM537373 1 0.6343 0.812 0.840 0.160
#> GSM537401 1 0.2236 0.927 0.964 0.036
#> GSM537343 1 0.0000 0.948 1.000 0.000
#> GSM537367 1 0.8386 0.647 0.732 0.268
#> GSM537382 2 0.9775 0.316 0.412 0.588
#> GSM537385 2 0.0376 0.927 0.004 0.996
#> GSM537391 1 0.0000 0.948 1.000 0.000
#> GSM537419 2 0.0000 0.929 0.000 1.000
#> GSM537420 1 0.0000 0.948 1.000 0.000
#> GSM537429 1 0.0000 0.948 1.000 0.000
#> GSM537431 1 0.5737 0.840 0.864 0.136
#> GSM537387 1 0.0000 0.948 1.000 0.000
#> GSM537414 1 0.0000 0.948 1.000 0.000
#> GSM537433 1 0.0672 0.944 0.992 0.008
#> GSM537335 1 0.0376 0.946 0.996 0.004
#> GSM537339 1 0.0000 0.948 1.000 0.000
#> GSM537340 2 0.6438 0.787 0.164 0.836
#> GSM537344 1 0.0000 0.948 1.000 0.000
#> GSM537346 2 0.0672 0.925 0.008 0.992
#> GSM537351 1 0.0000 0.948 1.000 0.000
#> GSM537352 2 0.0000 0.929 0.000 1.000
#> GSM537359 2 0.0000 0.929 0.000 1.000
#> GSM537360 2 0.0000 0.929 0.000 1.000
#> GSM537364 1 0.0000 0.948 1.000 0.000
#> GSM537365 1 0.8081 0.655 0.752 0.248
#> GSM537372 1 0.0000 0.948 1.000 0.000
#> GSM537384 1 0.0000 0.948 1.000 0.000
#> GSM537394 2 0.3114 0.888 0.056 0.944
#> GSM537403 2 0.0000 0.929 0.000 1.000
#> GSM537406 2 0.0000 0.929 0.000 1.000
#> GSM537411 2 0.0000 0.929 0.000 1.000
#> GSM537412 2 0.0000 0.929 0.000 1.000
#> GSM537416 2 0.9393 0.450 0.356 0.644
#> GSM537426 2 0.0000 0.929 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.2200 0.8761 0.940 0.056 0.004
#> GSM537345 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537355 1 0.9929 -0.2831 0.392 0.296 0.312
#> GSM537366 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537370 1 0.2703 0.8716 0.928 0.056 0.016
#> GSM537380 2 0.6111 0.6664 0.000 0.604 0.396
#> GSM537392 2 0.0237 0.3225 0.000 0.996 0.004
#> GSM537415 2 0.6308 0.6410 0.000 0.508 0.492
#> GSM537417 1 0.8494 0.4318 0.608 0.236 0.156
#> GSM537422 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537423 2 0.6225 0.6644 0.000 0.568 0.432
#> GSM537427 3 0.6280 0.6857 0.000 0.460 0.540
#> GSM537430 3 0.6309 0.6644 0.000 0.496 0.504
#> GSM537336 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537337 3 0.6252 0.6933 0.000 0.444 0.556
#> GSM537348 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537349 2 0.6126 0.6686 0.000 0.600 0.400
#> GSM537356 1 0.0424 0.9065 0.992 0.000 0.008
#> GSM537361 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537374 3 0.7464 0.6770 0.040 0.400 0.560
#> GSM537377 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537378 2 0.6235 0.6644 0.000 0.564 0.436
#> GSM537379 3 0.7487 0.6776 0.040 0.408 0.552
#> GSM537383 2 0.6111 0.6691 0.000 0.604 0.396
#> GSM537388 2 0.3192 0.1550 0.000 0.888 0.112
#> GSM537395 2 0.6062 -0.5163 0.000 0.616 0.384
#> GSM537400 3 0.6225 0.6935 0.000 0.432 0.568
#> GSM537404 1 0.5722 0.7516 0.800 0.132 0.068
#> GSM537409 3 0.6302 -0.6479 0.000 0.480 0.520
#> GSM537418 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537425 1 0.0747 0.9016 0.984 0.016 0.000
#> GSM537333 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537342 3 0.6225 0.6935 0.000 0.432 0.568
#> GSM537347 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537350 1 0.0424 0.9065 0.992 0.000 0.008
#> GSM537362 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537363 1 0.5423 0.7765 0.820 0.084 0.096
#> GSM537368 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537376 3 0.6235 0.6941 0.000 0.436 0.564
#> GSM537381 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537386 2 0.8000 0.6216 0.064 0.528 0.408
#> GSM537398 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537402 3 0.6168 0.6906 0.000 0.412 0.588
#> GSM537405 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537371 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537421 3 0.1289 0.1484 0.000 0.032 0.968
#> GSM537424 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537432 1 0.2152 0.8825 0.948 0.036 0.016
#> GSM537331 2 0.6143 -0.4088 0.012 0.684 0.304
#> GSM537332 2 0.6225 0.6644 0.000 0.568 0.432
#> GSM537334 1 0.6473 0.5715 0.668 0.312 0.020
#> GSM537338 3 0.6244 0.6939 0.000 0.440 0.560
#> GSM537353 3 0.6126 -0.5318 0.000 0.400 0.600
#> GSM537357 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537358 2 0.1411 0.2872 0.000 0.964 0.036
#> GSM537375 3 0.7484 0.6615 0.036 0.460 0.504
#> GSM537389 2 0.6168 0.6672 0.000 0.588 0.412
#> GSM537390 2 0.6225 0.6644 0.000 0.568 0.432
#> GSM537393 3 0.5882 0.6353 0.000 0.348 0.652
#> GSM537399 1 0.0424 0.9065 0.992 0.000 0.008
#> GSM537407 1 0.0237 0.9078 0.996 0.000 0.004
#> GSM537408 2 0.3412 0.4566 0.000 0.876 0.124
#> GSM537428 3 0.8328 0.6437 0.084 0.396 0.520
#> GSM537354 3 0.6252 0.6933 0.000 0.444 0.556
#> GSM537410 3 0.0747 0.1758 0.000 0.016 0.984
#> GSM537413 2 0.6235 0.6635 0.000 0.564 0.436
#> GSM537396 1 0.8652 0.1114 0.492 0.104 0.404
#> GSM537397 3 0.8100 0.1454 0.420 0.068 0.512
#> GSM537330 1 0.2165 0.8687 0.936 0.064 0.000
#> GSM537369 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537373 1 0.6442 0.3270 0.564 0.004 0.432
#> GSM537401 1 0.7844 0.4722 0.624 0.292 0.084
#> GSM537343 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537367 1 0.7909 0.5774 0.664 0.148 0.188
#> GSM537382 3 0.6442 0.6938 0.004 0.432 0.564
#> GSM537385 2 0.0237 0.3208 0.000 0.996 0.004
#> GSM537391 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537419 2 0.6111 0.6691 0.000 0.604 0.396
#> GSM537420 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537429 1 0.1647 0.8869 0.960 0.036 0.004
#> GSM537431 1 0.8983 0.0267 0.480 0.388 0.132
#> GSM537387 1 0.1999 0.8840 0.952 0.036 0.012
#> GSM537414 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537433 1 0.0892 0.9001 0.980 0.000 0.020
#> GSM537335 1 0.0848 0.9040 0.984 0.008 0.008
#> GSM537339 1 0.0747 0.9018 0.984 0.016 0.000
#> GSM537340 3 0.6111 0.6761 0.000 0.396 0.604
#> GSM537344 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537346 2 0.6026 -0.5433 0.000 0.624 0.376
#> GSM537351 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537352 3 0.6260 0.6925 0.000 0.448 0.552
#> GSM537359 2 0.6045 0.6599 0.000 0.620 0.380
#> GSM537360 3 0.6299 -0.6458 0.000 0.476 0.524
#> GSM537364 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537365 1 0.6663 0.6853 0.748 0.096 0.156
#> GSM537372 1 0.0424 0.9065 0.992 0.000 0.008
#> GSM537384 1 0.0000 0.9091 1.000 0.000 0.000
#> GSM537394 2 0.8779 0.4315 0.260 0.576 0.164
#> GSM537403 2 0.4002 0.1519 0.000 0.840 0.160
#> GSM537406 2 0.6309 0.6370 0.000 0.504 0.496
#> GSM537411 3 0.5560 0.1148 0.000 0.300 0.700
#> GSM537412 2 0.6309 0.6382 0.000 0.504 0.496
#> GSM537416 3 0.4128 0.4024 0.012 0.132 0.856
#> GSM537426 2 0.6079 0.4513 0.000 0.612 0.388
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.2197 0.8691 0.916 0.000 0.080 0.004
#> GSM537345 1 0.0188 0.9063 0.996 0.000 0.004 0.000
#> GSM537355 4 0.4830 0.3237 0.392 0.000 0.000 0.608
#> GSM537366 1 0.0921 0.8993 0.972 0.000 0.028 0.000
#> GSM537370 3 0.2999 0.7451 0.132 0.000 0.864 0.004
#> GSM537380 3 0.3052 0.6594 0.000 0.136 0.860 0.004
#> GSM537392 4 0.7070 0.3449 0.000 0.348 0.136 0.516
#> GSM537415 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537417 1 0.5698 0.4189 0.608 0.036 0.000 0.356
#> GSM537422 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537423 2 0.1022 0.7466 0.000 0.968 0.032 0.000
#> GSM537427 4 0.0817 0.8397 0.000 0.000 0.024 0.976
#> GSM537430 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537336 1 0.1716 0.8749 0.936 0.000 0.064 0.000
#> GSM537337 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537348 1 0.1022 0.8979 0.968 0.000 0.032 0.000
#> GSM537349 2 0.2714 0.7089 0.000 0.884 0.112 0.004
#> GSM537356 3 0.4304 0.7011 0.284 0.000 0.716 0.000
#> GSM537361 1 0.2760 0.8320 0.872 0.000 0.128 0.000
#> GSM537374 4 0.0188 0.8445 0.004 0.000 0.000 0.996
#> GSM537377 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537378 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537379 4 0.0188 0.8445 0.004 0.000 0.000 0.996
#> GSM537383 2 0.2714 0.7089 0.000 0.884 0.112 0.004
#> GSM537388 4 0.5186 0.5087 0.000 0.344 0.016 0.640
#> GSM537395 4 0.4134 0.6381 0.000 0.260 0.000 0.740
#> GSM537400 4 0.0921 0.8372 0.000 0.000 0.028 0.972
#> GSM537404 3 0.3836 0.7424 0.128 0.004 0.840 0.028
#> GSM537409 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537418 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537425 1 0.1297 0.8962 0.964 0.000 0.020 0.016
#> GSM537333 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537342 4 0.1118 0.8346 0.000 0.000 0.036 0.964
#> GSM537347 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537350 3 0.4222 0.7136 0.272 0.000 0.728 0.000
#> GSM537362 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537363 1 0.4419 0.7668 0.792 0.012 0.180 0.016
#> GSM537368 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537376 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537381 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537386 3 0.3626 0.6143 0.000 0.184 0.812 0.004
#> GSM537398 1 0.0188 0.9062 0.996 0.000 0.004 0.000
#> GSM537402 4 0.1297 0.8335 0.000 0.016 0.020 0.964
#> GSM537405 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537371 1 0.0188 0.9063 0.996 0.000 0.004 0.000
#> GSM537421 2 0.5368 0.4311 0.000 0.636 0.024 0.340
#> GSM537424 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537432 3 0.3356 0.7374 0.176 0.000 0.824 0.000
#> GSM537331 4 0.5462 0.6711 0.000 0.152 0.112 0.736
#> GSM537332 2 0.2469 0.6987 0.000 0.892 0.108 0.000
#> GSM537334 1 0.6178 0.5409 0.660 0.000 0.112 0.228
#> GSM537338 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537353 3 0.3636 0.6595 0.000 0.172 0.820 0.008
#> GSM537357 1 0.0188 0.9063 0.996 0.000 0.004 0.000
#> GSM537358 3 0.7180 0.1989 0.000 0.348 0.504 0.148
#> GSM537375 4 0.0376 0.8449 0.004 0.004 0.000 0.992
#> GSM537389 2 0.4920 0.3533 0.000 0.628 0.368 0.004
#> GSM537390 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537393 4 0.4361 0.6174 0.000 0.208 0.020 0.772
#> GSM537399 3 0.3942 0.7281 0.236 0.000 0.764 0.000
#> GSM537407 1 0.3649 0.7659 0.796 0.000 0.204 0.000
#> GSM537408 3 0.5746 0.2846 0.000 0.348 0.612 0.040
#> GSM537428 4 0.1118 0.8283 0.036 0.000 0.000 0.964
#> GSM537354 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537410 2 0.4819 0.4382 0.000 0.652 0.004 0.344
#> GSM537413 2 0.2466 0.7193 0.000 0.900 0.096 0.004
#> GSM537396 2 0.6209 -0.0971 0.052 0.492 0.456 0.000
#> GSM537397 3 0.5006 0.7104 0.104 0.000 0.772 0.124
#> GSM537330 1 0.2281 0.8339 0.904 0.000 0.096 0.000
#> GSM537369 1 0.0592 0.9022 0.984 0.000 0.016 0.000
#> GSM537373 2 0.5331 0.3806 0.332 0.644 0.024 0.000
#> GSM537401 3 0.5593 0.7057 0.212 0.000 0.708 0.080
#> GSM537343 1 0.3569 0.7698 0.804 0.000 0.196 0.000
#> GSM537367 2 0.8385 0.0798 0.384 0.400 0.180 0.036
#> GSM537382 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537385 4 0.6819 0.3795 0.000 0.348 0.112 0.540
#> GSM537391 1 0.0707 0.9006 0.980 0.000 0.020 0.000
#> GSM537419 2 0.2714 0.7089 0.000 0.884 0.112 0.004
#> GSM537420 1 0.0188 0.9063 0.996 0.000 0.004 0.000
#> GSM537429 1 0.1305 0.8919 0.960 0.000 0.036 0.004
#> GSM537431 1 0.6700 0.0833 0.480 0.000 0.088 0.432
#> GSM537387 3 0.4584 0.6835 0.300 0.000 0.696 0.004
#> GSM537414 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537433 1 0.2973 0.7807 0.856 0.144 0.000 0.000
#> GSM537335 1 0.5040 0.2110 0.628 0.000 0.364 0.008
#> GSM537339 1 0.1474 0.8899 0.948 0.000 0.052 0.000
#> GSM537340 4 0.2111 0.8164 0.000 0.044 0.024 0.932
#> GSM537344 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537346 4 0.2843 0.7952 0.000 0.020 0.088 0.892
#> GSM537351 1 0.2281 0.8516 0.904 0.000 0.096 0.000
#> GSM537352 4 0.0000 0.8455 0.000 0.000 0.000 1.000
#> GSM537359 3 0.5132 0.1646 0.000 0.448 0.548 0.004
#> GSM537360 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537364 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537365 3 0.2871 0.7107 0.032 0.072 0.896 0.000
#> GSM537372 3 0.4222 0.7136 0.272 0.000 0.728 0.000
#> GSM537384 1 0.0000 0.9070 1.000 0.000 0.000 0.000
#> GSM537394 3 0.1109 0.7048 0.004 0.028 0.968 0.000
#> GSM537403 4 0.4781 0.5321 0.000 0.336 0.004 0.660
#> GSM537406 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537411 3 0.1297 0.7026 0.000 0.020 0.964 0.016
#> GSM537412 2 0.0000 0.7551 0.000 1.000 0.000 0.000
#> GSM537416 2 0.5493 0.1847 0.000 0.528 0.016 0.456
#> GSM537426 2 0.3356 0.6153 0.000 0.824 0.000 0.176
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 1 0.3102 0.7199 0.860 0.000 0.056 0.000 0.084
#> GSM537345 3 0.3816 0.9169 0.304 0.000 0.696 0.000 0.000
#> GSM537355 4 0.4161 0.2447 0.392 0.000 0.000 0.608 0.000
#> GSM537366 1 0.1484 0.7704 0.944 0.000 0.008 0.000 0.048
#> GSM537370 5 0.2054 0.7313 0.052 0.000 0.028 0.000 0.920
#> GSM537380 5 0.5232 0.6000 0.000 0.084 0.268 0.000 0.648
#> GSM537392 4 0.7359 0.1041 0.000 0.316 0.268 0.388 0.028
#> GSM537415 2 0.0000 0.7333 0.000 1.000 0.000 0.000 0.000
#> GSM537417 1 0.5202 0.1842 0.596 0.056 0.000 0.348 0.000
#> GSM537422 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537423 2 0.2377 0.6934 0.000 0.872 0.128 0.000 0.000
#> GSM537427 4 0.1671 0.7987 0.000 0.000 0.076 0.924 0.000
#> GSM537430 4 0.0290 0.8238 0.000 0.008 0.000 0.992 0.000
#> GSM537336 3 0.3796 0.9136 0.300 0.000 0.700 0.000 0.000
#> GSM537337 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537348 1 0.1484 0.7716 0.944 0.000 0.008 0.000 0.048
#> GSM537349 2 0.3885 0.6082 0.000 0.724 0.268 0.000 0.008
#> GSM537356 5 0.3602 0.6724 0.180 0.000 0.024 0.000 0.796
#> GSM537361 1 0.2488 0.7157 0.872 0.000 0.004 0.000 0.124
#> GSM537374 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537377 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537378 2 0.0000 0.7333 0.000 1.000 0.000 0.000 0.000
#> GSM537379 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537383 2 0.3885 0.6082 0.000 0.724 0.268 0.000 0.008
#> GSM537388 4 0.4713 0.5473 0.000 0.280 0.044 0.676 0.000
#> GSM537395 4 0.3366 0.6454 0.000 0.232 0.000 0.768 0.000
#> GSM537400 4 0.0703 0.8190 0.000 0.000 0.000 0.976 0.024
#> GSM537404 5 0.1836 0.7305 0.032 0.000 0.000 0.036 0.932
#> GSM537409 2 0.0000 0.7333 0.000 1.000 0.000 0.000 0.000
#> GSM537418 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537425 1 0.1469 0.7707 0.948 0.000 0.000 0.016 0.036
#> GSM537333 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537342 4 0.2079 0.7945 0.000 0.000 0.020 0.916 0.064
#> GSM537347 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537350 5 0.2891 0.6801 0.176 0.000 0.000 0.000 0.824
#> GSM537362 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537363 1 0.5373 0.4832 0.712 0.012 0.084 0.012 0.180
#> GSM537368 1 0.2563 0.6645 0.872 0.000 0.120 0.000 0.008
#> GSM537376 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537381 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537386 5 0.6133 0.5004 0.000 0.164 0.292 0.000 0.544
#> GSM537398 1 0.0771 0.7779 0.976 0.000 0.020 0.000 0.004
#> GSM537402 4 0.1522 0.8030 0.000 0.012 0.000 0.944 0.044
#> GSM537405 1 0.0162 0.7804 0.996 0.000 0.004 0.000 0.000
#> GSM537371 3 0.3837 0.9146 0.308 0.000 0.692 0.000 0.000
#> GSM537421 2 0.5045 0.4376 0.000 0.636 0.000 0.308 0.056
#> GSM537424 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537432 5 0.2286 0.7192 0.108 0.000 0.004 0.000 0.888
#> GSM537331 4 0.6017 0.4799 0.000 0.120 0.292 0.580 0.008
#> GSM537332 2 0.2011 0.6960 0.000 0.908 0.004 0.000 0.088
#> GSM537334 1 0.6594 0.1237 0.516 0.000 0.260 0.216 0.008
#> GSM537338 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537353 5 0.2608 0.7257 0.000 0.088 0.020 0.004 0.888
#> GSM537357 3 0.3816 0.9169 0.304 0.000 0.696 0.000 0.000
#> GSM537358 5 0.6485 0.3518 0.000 0.308 0.016 0.144 0.532
#> GSM537375 4 0.0290 0.8238 0.000 0.008 0.000 0.992 0.000
#> GSM537389 2 0.6314 0.1812 0.000 0.512 0.184 0.000 0.304
#> GSM537390 2 0.0000 0.7333 0.000 1.000 0.000 0.000 0.000
#> GSM537393 4 0.4453 0.5947 0.000 0.212 0.020 0.744 0.024
#> GSM537399 5 0.2074 0.7199 0.104 0.000 0.000 0.000 0.896
#> GSM537407 1 0.3579 0.5792 0.756 0.000 0.004 0.000 0.240
#> GSM537408 5 0.5918 0.4247 0.000 0.308 0.044 0.048 0.600
#> GSM537428 4 0.0880 0.8123 0.032 0.000 0.000 0.968 0.000
#> GSM537354 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537410 2 0.4535 0.4899 0.000 0.684 0.024 0.288 0.004
#> GSM537413 2 0.3756 0.6221 0.000 0.744 0.248 0.000 0.008
#> GSM537396 5 0.5677 0.1886 0.020 0.432 0.040 0.000 0.508
#> GSM537397 5 0.3553 0.7283 0.072 0.000 0.048 0.028 0.852
#> GSM537330 1 0.3750 0.4836 0.756 0.000 0.232 0.000 0.012
#> GSM537369 1 0.0794 0.7752 0.972 0.000 0.000 0.000 0.028
#> GSM537373 2 0.5181 0.3516 0.272 0.668 0.032 0.000 0.028
#> GSM537401 5 0.5282 0.6810 0.132 0.000 0.056 0.076 0.736
#> GSM537343 1 0.3876 0.6065 0.776 0.000 0.032 0.000 0.192
#> GSM537367 2 0.9023 -0.1041 0.232 0.348 0.184 0.032 0.204
#> GSM537382 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537385 4 0.6979 0.1621 0.000 0.292 0.292 0.408 0.008
#> GSM537391 1 0.2707 0.7172 0.876 0.000 0.100 0.000 0.024
#> GSM537419 2 0.3835 0.6146 0.000 0.732 0.260 0.000 0.008
#> GSM537420 1 0.4138 -0.1891 0.616 0.000 0.384 0.000 0.000
#> GSM537429 1 0.1493 0.7687 0.948 0.000 0.028 0.000 0.024
#> GSM537431 1 0.5912 -0.0246 0.480 0.000 0.004 0.428 0.088
#> GSM537387 5 0.4616 0.5939 0.036 0.000 0.288 0.000 0.676
#> GSM537414 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537433 1 0.2732 0.6128 0.840 0.160 0.000 0.000 0.000
#> GSM537335 1 0.4473 0.0968 0.580 0.000 0.000 0.008 0.412
#> GSM537339 1 0.2278 0.7545 0.908 0.000 0.032 0.000 0.060
#> GSM537340 4 0.2291 0.7852 0.000 0.036 0.000 0.908 0.056
#> GSM537344 1 0.2020 0.7114 0.900 0.000 0.100 0.000 0.000
#> GSM537346 4 0.3943 0.7132 0.000 0.028 0.156 0.800 0.016
#> GSM537351 3 0.4826 0.5726 0.472 0.000 0.508 0.000 0.020
#> GSM537352 4 0.0000 0.8247 0.000 0.000 0.000 1.000 0.000
#> GSM537359 5 0.6321 0.2295 0.000 0.376 0.160 0.000 0.464
#> GSM537360 2 0.0162 0.7326 0.000 0.996 0.000 0.004 0.000
#> GSM537364 1 0.2074 0.6962 0.896 0.000 0.104 0.000 0.000
#> GSM537365 5 0.0833 0.7260 0.016 0.004 0.004 0.000 0.976
#> GSM537372 5 0.2929 0.6775 0.180 0.000 0.000 0.000 0.820
#> GSM537384 1 0.0000 0.7812 1.000 0.000 0.000 0.000 0.000
#> GSM537394 5 0.4453 0.6234 0.000 0.048 0.228 0.000 0.724
#> GSM537403 4 0.4220 0.5446 0.000 0.300 0.008 0.688 0.004
#> GSM537406 2 0.0290 0.7311 0.000 0.992 0.008 0.000 0.000
#> GSM537411 5 0.3627 0.7084 0.000 0.032 0.120 0.016 0.832
#> GSM537412 2 0.0000 0.7333 0.000 1.000 0.000 0.000 0.000
#> GSM537416 2 0.4982 0.2429 0.000 0.556 0.000 0.412 0.032
#> GSM537426 2 0.3003 0.5985 0.000 0.812 0.000 0.188 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.5103 0.5234 0.000 0.120 0.276 0.000 0.604 0.000
#> GSM537345 1 0.0000 0.8638 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537355 6 0.3737 0.3304 0.000 0.000 0.000 0.000 0.392 0.608
#> GSM537366 5 0.3104 0.7135 0.000 0.016 0.184 0.000 0.800 0.000
#> GSM537370 2 0.5104 0.0862 0.000 0.540 0.372 0.000 0.088 0.000
#> GSM537380 2 0.2019 0.2359 0.000 0.900 0.012 0.088 0.000 0.000
#> GSM537392 2 0.5906 -0.1225 0.000 0.424 0.000 0.208 0.000 0.368
#> GSM537415 4 0.0000 0.7176 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537417 5 0.5832 0.2876 0.000 0.000 0.004 0.196 0.508 0.292
#> GSM537422 5 0.0146 0.7923 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM537423 4 0.2454 0.6638 0.000 0.160 0.000 0.840 0.000 0.000
#> GSM537427 6 0.1610 0.8233 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM537430 6 0.1957 0.8033 0.000 0.000 0.000 0.112 0.000 0.888
#> GSM537336 1 0.0000 0.8638 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537337 6 0.0000 0.8532 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537348 5 0.3150 0.7329 0.000 0.052 0.120 0.000 0.828 0.000
#> GSM537349 4 0.4010 0.4556 0.000 0.408 0.008 0.584 0.000 0.000
#> GSM537356 2 0.5657 0.0462 0.000 0.436 0.412 0.000 0.152 0.000
#> GSM537361 5 0.3634 0.4303 0.000 0.000 0.356 0.000 0.644 0.000
#> GSM537374 6 0.0260 0.8532 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM537377 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537378 4 0.0000 0.7176 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537379 6 0.0146 0.8533 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM537383 4 0.3789 0.4543 0.000 0.416 0.000 0.584 0.000 0.000
#> GSM537388 6 0.3364 0.7834 0.000 0.068 0.012 0.088 0.000 0.832
#> GSM537395 6 0.1387 0.8292 0.000 0.000 0.000 0.068 0.000 0.932
#> GSM537400 6 0.0458 0.8511 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM537404 3 0.4268 -0.0238 0.000 0.428 0.556 0.000 0.012 0.004
#> GSM537409 4 0.0000 0.7176 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537418 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537425 5 0.1773 0.7812 0.000 0.016 0.036 0.000 0.932 0.016
#> GSM537333 5 0.0260 0.7913 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM537342 6 0.4011 0.6812 0.000 0.060 0.204 0.000 0.000 0.736
#> GSM537347 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537350 2 0.5842 0.0878 0.000 0.448 0.356 0.000 0.196 0.000
#> GSM537362 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537363 5 0.6503 0.2587 0.072 0.088 0.364 0.000 0.468 0.008
#> GSM537368 5 0.3349 0.6098 0.244 0.008 0.000 0.000 0.748 0.000
#> GSM537376 6 0.0000 0.8532 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537381 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537386 2 0.3563 0.1779 0.000 0.796 0.132 0.072 0.000 0.000
#> GSM537398 5 0.1757 0.7689 0.000 0.076 0.008 0.000 0.916 0.000
#> GSM537402 6 0.1820 0.8304 0.000 0.044 0.012 0.016 0.000 0.928
#> GSM537405 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537371 1 0.0000 0.8638 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537421 4 0.3432 0.6324 0.000 0.052 0.000 0.800 0.000 0.148
#> GSM537424 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537432 3 0.4503 0.1341 0.000 0.232 0.684 0.000 0.084 0.000
#> GSM537331 6 0.4542 0.2573 0.000 0.480 0.008 0.012 0.004 0.496
#> GSM537332 4 0.3965 0.3367 0.000 0.008 0.388 0.604 0.000 0.000
#> GSM537334 2 0.5949 -0.0161 0.000 0.416 0.000 0.000 0.364 0.220
#> GSM537338 6 0.0000 0.8532 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537353 3 0.5723 -0.1070 0.000 0.392 0.460 0.144 0.000 0.004
#> GSM537357 1 0.0000 0.8638 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537358 2 0.6655 0.1633 0.000 0.448 0.348 0.096 0.000 0.108
#> GSM537375 6 0.2100 0.8026 0.000 0.000 0.004 0.112 0.000 0.884
#> GSM537389 4 0.5503 0.3620 0.000 0.276 0.172 0.552 0.000 0.000
#> GSM537390 4 0.0000 0.7176 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537393 6 0.3960 0.6163 0.000 0.040 0.004 0.220 0.000 0.736
#> GSM537399 3 0.4882 -0.0806 0.000 0.428 0.512 0.000 0.060 0.000
#> GSM537407 3 0.4338 -0.3032 0.000 0.020 0.496 0.000 0.484 0.000
#> GSM537408 2 0.5495 0.1332 0.000 0.524 0.368 0.096 0.000 0.012
#> GSM537428 6 0.0790 0.8443 0.000 0.000 0.000 0.000 0.032 0.968
#> GSM537354 6 0.0000 0.8532 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537410 4 0.4168 0.6177 0.000 0.016 0.144 0.764 0.000 0.076
#> GSM537413 4 0.3695 0.4966 0.000 0.376 0.000 0.624 0.000 0.000
#> GSM537396 3 0.5617 -0.0188 0.000 0.096 0.532 0.352 0.020 0.000
#> GSM537397 2 0.4764 0.0893 0.000 0.548 0.408 0.000 0.008 0.036
#> GSM537330 5 0.4932 0.3239 0.000 0.372 0.072 0.000 0.556 0.000
#> GSM537369 5 0.0520 0.7903 0.000 0.008 0.008 0.000 0.984 0.000
#> GSM537373 4 0.5179 0.4454 0.000 0.076 0.284 0.620 0.020 0.000
#> GSM537401 3 0.5587 -0.1464 0.000 0.416 0.488 0.000 0.064 0.032
#> GSM537343 3 0.4037 -0.1095 0.000 0.012 0.608 0.000 0.380 0.000
#> GSM537367 3 0.4897 -0.0797 0.004 0.048 0.604 0.336 0.008 0.000
#> GSM537382 6 0.0458 0.8519 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM537385 2 0.6279 -0.1096 0.000 0.468 0.036 0.148 0.000 0.348
#> GSM537391 5 0.4327 0.6960 0.040 0.072 0.120 0.000 0.768 0.000
#> GSM537419 4 0.3695 0.4985 0.000 0.376 0.000 0.624 0.000 0.000
#> GSM537420 5 0.3756 0.3500 0.400 0.000 0.000 0.000 0.600 0.000
#> GSM537429 5 0.2867 0.7435 0.000 0.040 0.112 0.000 0.848 0.000
#> GSM537431 5 0.6481 0.2418 0.000 0.064 0.128 0.000 0.468 0.340
#> GSM537387 2 0.6889 0.0454 0.248 0.352 0.348 0.000 0.052 0.000
#> GSM537414 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537433 5 0.3738 0.6047 0.000 0.000 0.040 0.208 0.752 0.000
#> GSM537335 5 0.5099 0.3043 0.000 0.120 0.228 0.000 0.644 0.008
#> GSM537339 5 0.4014 0.6855 0.000 0.096 0.148 0.000 0.756 0.000
#> GSM537340 6 0.2265 0.8155 0.000 0.052 0.000 0.052 0.000 0.896
#> GSM537344 5 0.1863 0.7613 0.104 0.000 0.000 0.000 0.896 0.000
#> GSM537346 6 0.5378 0.4784 0.000 0.264 0.012 0.120 0.000 0.604
#> GSM537351 1 0.5442 0.3497 0.556 0.004 0.128 0.000 0.312 0.000
#> GSM537352 6 0.0000 0.8532 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537359 2 0.4737 0.2172 0.000 0.676 0.192 0.132 0.000 0.000
#> GSM537360 4 0.0790 0.7114 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM537364 5 0.2048 0.7424 0.120 0.000 0.000 0.000 0.880 0.000
#> GSM537365 3 0.3428 0.1069 0.000 0.304 0.696 0.000 0.000 0.000
#> GSM537372 2 0.5799 0.0898 0.000 0.448 0.368 0.000 0.184 0.000
#> GSM537384 5 0.0000 0.7928 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537394 2 0.5294 0.0194 0.000 0.532 0.356 0.112 0.000 0.000
#> GSM537403 6 0.3875 0.7398 0.000 0.004 0.124 0.092 0.000 0.780
#> GSM537406 4 0.1957 0.6670 0.000 0.000 0.112 0.888 0.000 0.000
#> GSM537411 2 0.3620 0.0668 0.000 0.648 0.352 0.000 0.000 0.000
#> GSM537412 4 0.0000 0.7176 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537416 4 0.4832 0.4916 0.000 0.036 0.028 0.640 0.000 0.296
#> GSM537426 4 0.3390 0.5107 0.000 0.000 0.000 0.704 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 disease.state(p) other(p) k
#> MAD:pam 99 0.00945 0.421 2
#> MAD:pam 79 0.40193 0.922 3
#> MAD:pam 88 0.44939 0.909 4
#> MAD:pam 83 0.64674 0.922 5
#> MAD:pam 59 0.20032 0.466 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 104 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.844 0.892 0.954 0.3298 0.652 0.652
#> 3 3 0.275 0.535 0.745 0.7629 0.647 0.480
#> 4 4 0.285 0.446 0.645 0.1771 0.742 0.407
#> 5 5 0.442 0.430 0.681 0.1075 0.868 0.558
#> 6 6 0.517 0.434 0.662 0.0399 0.924 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
#> GSM537341 2 0.0000 0.9762 0.000 1.000
#> GSM537345 1 0.0000 0.8490 1.000 0.000
#> GSM537355 2 0.0000 0.9762 0.000 1.000
#> GSM537366 2 0.0938 0.9643 0.012 0.988
#> GSM537370 2 0.0000 0.9762 0.000 1.000
#> GSM537380 2 0.0000 0.9762 0.000 1.000
#> GSM537392 2 0.0000 0.9762 0.000 1.000
#> GSM537415 2 0.0000 0.9762 0.000 1.000
#> GSM537417 2 0.0000 0.9762 0.000 1.000
#> GSM537422 1 0.9710 0.4426 0.600 0.400
#> GSM537423 2 0.0000 0.9762 0.000 1.000
#> GSM537427 2 0.0000 0.9762 0.000 1.000
#> GSM537430 2 0.0000 0.9762 0.000 1.000
#> GSM537336 1 0.0000 0.8490 1.000 0.000
#> GSM537337 2 0.0000 0.9762 0.000 1.000
#> GSM537348 2 0.9881 0.0578 0.436 0.564
#> GSM537349 2 0.0000 0.9762 0.000 1.000
#> GSM537356 2 0.5737 0.8110 0.136 0.864
#> GSM537361 1 0.2423 0.8362 0.960 0.040
#> GSM537374 2 0.0000 0.9762 0.000 1.000
#> GSM537377 1 0.0000 0.8490 1.000 0.000
#> GSM537378 2 0.0000 0.9762 0.000 1.000
#> GSM537379 2 0.0000 0.9762 0.000 1.000
#> GSM537383 2 0.0000 0.9762 0.000 1.000
#> GSM537388 2 0.0000 0.9762 0.000 1.000
#> GSM537395 2 0.0000 0.9762 0.000 1.000
#> GSM537400 2 0.0376 0.9725 0.004 0.996
#> GSM537404 2 0.0000 0.9762 0.000 1.000
#> GSM537409 2 0.0000 0.9762 0.000 1.000
#> GSM537418 1 0.9881 0.3704 0.564 0.436
#> GSM537425 1 0.9850 0.3826 0.572 0.428
#> GSM537333 2 0.0000 0.9762 0.000 1.000
#> GSM537342 2 0.0000 0.9762 0.000 1.000
#> GSM537347 2 0.0000 0.9762 0.000 1.000
#> GSM537350 2 0.8327 0.5727 0.264 0.736
#> GSM537362 2 0.0672 0.9686 0.008 0.992
#> GSM537363 1 0.9732 0.4348 0.596 0.404
#> GSM537368 1 0.0000 0.8490 1.000 0.000
#> GSM537376 2 0.0000 0.9762 0.000 1.000
#> GSM537381 1 0.0672 0.8474 0.992 0.008
#> GSM537386 2 0.0000 0.9762 0.000 1.000
#> GSM537398 1 0.9909 0.3299 0.556 0.444
#> GSM537402 2 0.0000 0.9762 0.000 1.000
#> GSM537405 1 0.0376 0.8484 0.996 0.004
#> GSM537371 1 0.0000 0.8490 1.000 0.000
#> GSM537421 2 0.0000 0.9762 0.000 1.000
#> GSM537424 1 0.9896 0.3414 0.560 0.440
#> GSM537432 2 0.0000 0.9762 0.000 1.000
#> GSM537331 2 0.0000 0.9762 0.000 1.000
#> GSM537332 2 0.0000 0.9762 0.000 1.000
#> GSM537334 2 0.0000 0.9762 0.000 1.000
#> GSM537338 2 0.0000 0.9762 0.000 1.000
#> GSM537353 2 0.0000 0.9762 0.000 1.000
#> GSM537357 1 0.0000 0.8490 1.000 0.000
#> GSM537358 2 0.0000 0.9762 0.000 1.000
#> GSM537375 2 0.0000 0.9762 0.000 1.000
#> GSM537389 2 0.0000 0.9762 0.000 1.000
#> GSM537390 2 0.0000 0.9762 0.000 1.000
#> GSM537393 2 0.0000 0.9762 0.000 1.000
#> GSM537399 2 0.1184 0.9603 0.016 0.984
#> GSM537407 2 0.0376 0.9725 0.004 0.996
#> GSM537408 2 0.0000 0.9762 0.000 1.000
#> GSM537428 2 0.0000 0.9762 0.000 1.000
#> GSM537354 2 0.0000 0.9762 0.000 1.000
#> GSM537410 2 0.0000 0.9762 0.000 1.000
#> GSM537413 2 0.0000 0.9762 0.000 1.000
#> GSM537396 2 0.0000 0.9762 0.000 1.000
#> GSM537397 2 0.5842 0.8008 0.140 0.860
#> GSM537330 2 0.0000 0.9762 0.000 1.000
#> GSM537369 1 0.0000 0.8490 1.000 0.000
#> GSM537373 2 0.0000 0.9762 0.000 1.000
#> GSM537401 2 0.0000 0.9762 0.000 1.000
#> GSM537343 2 0.6973 0.7226 0.188 0.812
#> GSM537367 2 0.0000 0.9762 0.000 1.000
#> GSM537382 2 0.0000 0.9762 0.000 1.000
#> GSM537385 2 0.0000 0.9762 0.000 1.000
#> GSM537391 2 0.9427 0.3233 0.360 0.640
#> GSM537419 2 0.0000 0.9762 0.000 1.000
#> GSM537420 1 0.0000 0.8490 1.000 0.000
#> GSM537429 2 0.0000 0.9762 0.000 1.000
#> GSM537431 2 0.0000 0.9762 0.000 1.000
#> GSM537387 1 0.4690 0.8041 0.900 0.100
#> GSM537414 2 0.0000 0.9762 0.000 1.000
#> GSM537433 2 0.0000 0.9762 0.000 1.000
#> GSM537335 2 0.0000 0.9762 0.000 1.000
#> GSM537339 2 0.0000 0.9762 0.000 1.000
#> GSM537340 2 0.1633 0.9511 0.024 0.976
#> GSM537344 1 0.0000 0.8490 1.000 0.000
#> GSM537346 2 0.0000 0.9762 0.000 1.000
#> GSM537351 1 0.0000 0.8490 1.000 0.000
#> GSM537352 2 0.0000 0.9762 0.000 1.000
#> GSM537359 2 0.0000 0.9762 0.000 1.000
#> GSM537360 2 0.0000 0.9762 0.000 1.000
#> GSM537364 1 0.0000 0.8490 1.000 0.000
#> GSM537365 2 0.0000 0.9762 0.000 1.000
#> GSM537372 1 0.9460 0.5047 0.636 0.364
#> GSM537384 1 0.4022 0.8167 0.920 0.080
#> GSM537394 2 0.0000 0.9762 0.000 1.000
#> GSM537403 2 0.0000 0.9762 0.000 1.000
#> GSM537406 2 0.0000 0.9762 0.000 1.000
#> GSM537411 2 0.0000 0.9762 0.000 1.000
#> GSM537412 2 0.0000 0.9762 0.000 1.000
#> GSM537416 2 0.0000 0.9762 0.000 1.000
#> GSM537426 2 0.0000 0.9762 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.5377 0.6041 0.112 0.820 0.068
#> GSM537345 1 0.1170 0.8479 0.976 0.016 0.008
#> GSM537355 2 0.6682 -0.1070 0.008 0.504 0.488
#> GSM537366 1 0.8895 0.1775 0.484 0.124 0.392
#> GSM537370 2 0.5731 0.6162 0.108 0.804 0.088
#> GSM537380 2 0.5690 0.5947 0.004 0.708 0.288
#> GSM537392 2 0.5591 0.5809 0.000 0.696 0.304
#> GSM537415 3 0.1411 0.6519 0.000 0.036 0.964
#> GSM537417 3 0.2301 0.6189 0.060 0.004 0.936
#> GSM537422 3 0.6735 -0.0626 0.424 0.012 0.564
#> GSM537423 3 0.5591 0.5191 0.000 0.304 0.696
#> GSM537427 2 0.4178 0.6336 0.000 0.828 0.172
#> GSM537430 2 0.6026 0.4868 0.000 0.624 0.376
#> GSM537336 1 0.0848 0.8486 0.984 0.008 0.008
#> GSM537337 2 0.6553 0.2577 0.008 0.580 0.412
#> GSM537348 2 0.6339 0.0588 0.360 0.632 0.008
#> GSM537349 2 0.6302 0.2020 0.000 0.520 0.480
#> GSM537356 1 0.5974 0.7312 0.784 0.148 0.068
#> GSM537361 1 0.1015 0.8482 0.980 0.008 0.012
#> GSM537374 2 0.4121 0.6402 0.000 0.832 0.168
#> GSM537377 1 0.1170 0.8479 0.976 0.016 0.008
#> GSM537378 3 0.5465 0.5399 0.000 0.288 0.712
#> GSM537379 3 0.5582 0.6324 0.088 0.100 0.812
#> GSM537383 2 0.6095 0.4637 0.000 0.608 0.392
#> GSM537388 2 0.5859 0.4739 0.000 0.656 0.344
#> GSM537395 3 0.6018 0.4979 0.008 0.308 0.684
#> GSM537400 3 0.8996 0.0942 0.140 0.356 0.504
#> GSM537404 3 0.4602 0.6542 0.016 0.152 0.832
#> GSM537409 3 0.0237 0.6360 0.000 0.004 0.996
#> GSM537418 1 0.4058 0.7865 0.880 0.044 0.076
#> GSM537425 1 0.6910 0.3424 0.584 0.020 0.396
#> GSM537333 3 0.7918 0.3548 0.104 0.256 0.640
#> GSM537342 3 0.3918 0.6559 0.004 0.140 0.856
#> GSM537347 2 0.8375 0.3836 0.092 0.540 0.368
#> GSM537350 1 0.9718 -0.0494 0.452 0.288 0.260
#> GSM537362 2 0.8576 0.5255 0.160 0.600 0.240
#> GSM537363 1 0.6750 0.4582 0.640 0.024 0.336
#> GSM537368 1 0.0661 0.8490 0.988 0.004 0.008
#> GSM537376 3 0.5864 0.5275 0.008 0.288 0.704
#> GSM537381 1 0.0848 0.8486 0.984 0.008 0.008
#> GSM537386 2 0.5835 0.5464 0.000 0.660 0.340
#> GSM537398 2 0.6608 -0.0420 0.432 0.560 0.008
#> GSM537402 3 0.6527 0.2159 0.008 0.404 0.588
#> GSM537405 1 0.1015 0.8486 0.980 0.012 0.008
#> GSM537371 1 0.0661 0.8490 0.988 0.004 0.008
#> GSM537421 3 0.1170 0.6489 0.008 0.016 0.976
#> GSM537424 1 0.6264 0.6652 0.716 0.256 0.028
#> GSM537432 3 0.8586 0.1338 0.104 0.376 0.520
#> GSM537331 2 0.3715 0.6336 0.004 0.868 0.128
#> GSM537332 3 0.2261 0.6615 0.000 0.068 0.932
#> GSM537334 2 0.3551 0.6302 0.000 0.868 0.132
#> GSM537338 2 0.3816 0.6345 0.000 0.852 0.148
#> GSM537353 3 0.4121 0.6445 0.000 0.168 0.832
#> GSM537357 1 0.0661 0.8490 0.988 0.004 0.008
#> GSM537358 3 0.5948 0.3981 0.000 0.360 0.640
#> GSM537375 3 0.6404 0.4854 0.012 0.344 0.644
#> GSM537389 3 0.6302 -0.0760 0.000 0.480 0.520
#> GSM537390 3 0.2625 0.6621 0.000 0.084 0.916
#> GSM537393 3 0.5247 0.6055 0.008 0.224 0.768
#> GSM537399 2 0.7361 0.5826 0.124 0.704 0.172
#> GSM537407 3 0.8112 0.4967 0.160 0.192 0.648
#> GSM537408 3 0.5859 0.4365 0.000 0.344 0.656
#> GSM537428 2 0.4452 0.6297 0.000 0.808 0.192
#> GSM537354 3 0.6434 0.4445 0.008 0.380 0.612
#> GSM537410 3 0.1163 0.6522 0.000 0.028 0.972
#> GSM537413 3 0.4974 0.5969 0.000 0.236 0.764
#> GSM537396 2 0.8474 0.2442 0.092 0.504 0.404
#> GSM537397 2 0.4744 0.5740 0.136 0.836 0.028
#> GSM537330 3 0.5835 0.4534 0.000 0.340 0.660
#> GSM537369 1 0.1711 0.8442 0.960 0.032 0.008
#> GSM537373 3 0.5216 0.5754 0.000 0.260 0.740
#> GSM537401 2 0.6322 0.6273 0.108 0.772 0.120
#> GSM537343 3 0.8287 0.4056 0.256 0.128 0.616
#> GSM537367 3 0.4280 0.5673 0.124 0.020 0.856
#> GSM537382 3 0.5760 0.4681 0.000 0.328 0.672
#> GSM537385 2 0.6062 0.4582 0.000 0.616 0.384
#> GSM537391 2 0.5896 0.3210 0.292 0.700 0.008
#> GSM537419 3 0.6154 0.2432 0.000 0.408 0.592
#> GSM537420 1 0.2280 0.8380 0.940 0.052 0.008
#> GSM537429 2 0.8372 0.4553 0.100 0.564 0.336
#> GSM537431 3 0.6726 0.5305 0.120 0.132 0.748
#> GSM537387 1 0.5541 0.6954 0.740 0.252 0.008
#> GSM537414 3 0.4059 0.5620 0.128 0.012 0.860
#> GSM537433 3 0.4636 0.5877 0.116 0.036 0.848
#> GSM537335 2 0.4139 0.6367 0.016 0.860 0.124
#> GSM537339 2 0.4609 0.5738 0.128 0.844 0.028
#> GSM537340 3 0.1832 0.6242 0.036 0.008 0.956
#> GSM537344 1 0.0848 0.8488 0.984 0.008 0.008
#> GSM537346 3 0.5733 0.4975 0.000 0.324 0.676
#> GSM537351 1 0.0848 0.8486 0.984 0.008 0.008
#> GSM537352 2 0.6625 0.2012 0.008 0.552 0.440
#> GSM537359 2 0.6260 0.3209 0.000 0.552 0.448
#> GSM537360 3 0.1031 0.6500 0.000 0.024 0.976
#> GSM537364 1 0.0848 0.8486 0.984 0.008 0.008
#> GSM537365 3 0.7345 0.5645 0.108 0.192 0.700
#> GSM537372 1 0.6510 0.5731 0.624 0.364 0.012
#> GSM537384 1 0.3784 0.8097 0.864 0.132 0.004
#> GSM537394 3 0.6111 0.3101 0.000 0.396 0.604
#> GSM537403 3 0.0000 0.6389 0.000 0.000 1.000
#> GSM537406 3 0.3551 0.6576 0.000 0.132 0.868
#> GSM537411 2 0.6008 0.5074 0.000 0.628 0.372
#> GSM537412 3 0.0000 0.6389 0.000 0.000 1.000
#> GSM537416 3 0.0661 0.6376 0.008 0.004 0.988
#> GSM537426 3 0.1411 0.6541 0.000 0.036 0.964
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.7536 0.45392 0.140 0.276 0.024 0.560
#> GSM537345 1 0.1792 0.69770 0.932 0.000 0.000 0.068
#> GSM537355 2 0.7870 0.17244 0.000 0.392 0.308 0.300
#> GSM537366 1 0.7396 0.49676 0.560 0.024 0.116 0.300
#> GSM537370 4 0.7260 0.54553 0.140 0.196 0.036 0.628
#> GSM537380 2 0.1042 0.66047 0.000 0.972 0.008 0.020
#> GSM537392 2 0.0779 0.66237 0.000 0.980 0.004 0.016
#> GSM537415 3 0.5190 0.41635 0.004 0.396 0.596 0.004
#> GSM537417 3 0.5511 0.63195 0.084 0.196 0.720 0.000
#> GSM537422 3 0.6307 0.32846 0.312 0.012 0.620 0.056
#> GSM537423 2 0.1042 0.65886 0.000 0.972 0.020 0.008
#> GSM537427 2 0.8115 0.24292 0.024 0.492 0.236 0.248
#> GSM537430 2 0.5661 0.57522 0.000 0.700 0.080 0.220
#> GSM537336 1 0.0804 0.71567 0.980 0.000 0.008 0.012
#> GSM537337 2 0.7836 0.17467 0.000 0.408 0.288 0.304
#> GSM537348 4 0.4944 0.34971 0.220 0.032 0.004 0.744
#> GSM537349 2 0.0188 0.65859 0.000 0.996 0.000 0.004
#> GSM537356 1 0.5069 0.56011 0.664 0.000 0.016 0.320
#> GSM537361 1 0.5767 0.57630 0.712 0.000 0.136 0.152
#> GSM537374 4 0.7613 0.36268 0.012 0.212 0.236 0.540
#> GSM537377 1 0.1940 0.69721 0.924 0.000 0.000 0.076
#> GSM537378 2 0.3285 0.65895 0.020 0.884 0.080 0.016
#> GSM537379 3 0.7199 0.57318 0.060 0.228 0.632 0.080
#> GSM537383 2 0.1488 0.66991 0.000 0.956 0.012 0.032
#> GSM537388 2 0.5480 0.50230 0.000 0.736 0.124 0.140
#> GSM537395 2 0.5423 0.62685 0.000 0.740 0.116 0.144
#> GSM537400 3 0.9014 -0.07463 0.292 0.056 0.352 0.300
#> GSM537404 3 0.8574 0.49255 0.100 0.292 0.492 0.116
#> GSM537409 3 0.4737 0.54615 0.004 0.296 0.696 0.004
#> GSM537418 1 0.4471 0.64064 0.768 0.004 0.016 0.212
#> GSM537425 1 0.8786 -0.18676 0.392 0.140 0.384 0.084
#> GSM537333 3 0.8736 0.32234 0.176 0.100 0.508 0.216
#> GSM537342 2 0.6615 0.04791 0.084 0.512 0.404 0.000
#> GSM537347 4 0.8883 0.11857 0.060 0.324 0.216 0.400
#> GSM537350 1 0.7850 0.17423 0.484 0.324 0.016 0.176
#> GSM537362 4 0.8548 0.39272 0.284 0.076 0.148 0.492
#> GSM537363 1 0.7755 0.38573 0.612 0.168 0.144 0.076
#> GSM537368 1 0.0524 0.71964 0.988 0.000 0.008 0.004
#> GSM537376 2 0.6216 0.54038 0.000 0.652 0.240 0.108
#> GSM537381 1 0.1151 0.72183 0.968 0.000 0.008 0.024
#> GSM537386 2 0.3653 0.58625 0.000 0.844 0.028 0.128
#> GSM537398 4 0.6302 0.23822 0.348 0.036 0.020 0.596
#> GSM537402 2 0.5812 0.61287 0.000 0.708 0.136 0.156
#> GSM537405 1 0.3271 0.69566 0.856 0.000 0.012 0.132
#> GSM537371 1 0.0672 0.71672 0.984 0.000 0.008 0.008
#> GSM537421 3 0.5783 0.62358 0.088 0.220 0.692 0.000
#> GSM537424 4 0.5679 -0.18327 0.488 0.004 0.016 0.492
#> GSM537432 3 0.9361 -0.05160 0.236 0.096 0.348 0.320
#> GSM537331 4 0.8965 0.35315 0.072 0.288 0.216 0.424
#> GSM537332 2 0.6276 -0.13729 0.040 0.520 0.432 0.008
#> GSM537334 4 0.8670 0.45751 0.076 0.176 0.260 0.488
#> GSM537338 4 0.7921 0.34282 0.016 0.224 0.260 0.500
#> GSM537353 2 0.6235 0.30218 0.048 0.588 0.356 0.008
#> GSM537357 1 0.1151 0.71390 0.968 0.000 0.008 0.024
#> GSM537358 2 0.1305 0.66089 0.000 0.960 0.036 0.004
#> GSM537375 4 0.8268 -0.13623 0.012 0.288 0.340 0.360
#> GSM537389 2 0.0657 0.66188 0.000 0.984 0.004 0.012
#> GSM537390 2 0.4884 0.44188 0.008 0.708 0.276 0.008
#> GSM537393 3 0.8136 0.26798 0.020 0.328 0.448 0.204
#> GSM537399 4 0.7827 0.43206 0.164 0.272 0.028 0.536
#> GSM537407 1 0.9766 -0.06473 0.364 0.204 0.208 0.224
#> GSM537408 2 0.3607 0.62260 0.096 0.864 0.032 0.008
#> GSM537428 4 0.7808 0.00948 0.000 0.360 0.252 0.388
#> GSM537354 2 0.7760 0.30209 0.000 0.408 0.352 0.240
#> GSM537410 3 0.6014 0.57640 0.060 0.292 0.644 0.004
#> GSM537413 2 0.2777 0.64657 0.004 0.888 0.104 0.004
#> GSM537396 2 0.3892 0.61457 0.104 0.852 0.020 0.024
#> GSM537397 4 0.6749 0.47159 0.172 0.180 0.008 0.640
#> GSM537330 2 0.7529 0.42235 0.016 0.564 0.224 0.196
#> GSM537369 1 0.1867 0.71735 0.928 0.000 0.000 0.072
#> GSM537373 2 0.6439 0.48754 0.100 0.680 0.200 0.020
#> GSM537401 4 0.8158 0.54409 0.128 0.184 0.108 0.580
#> GSM537343 1 0.8853 0.24818 0.468 0.104 0.284 0.144
#> GSM537367 3 0.7715 0.51779 0.212 0.108 0.604 0.076
#> GSM537382 2 0.6785 0.55705 0.012 0.640 0.208 0.140
#> GSM537385 2 0.2125 0.66256 0.000 0.920 0.004 0.076
#> GSM537391 4 0.5624 0.32766 0.280 0.052 0.000 0.668
#> GSM537419 2 0.0921 0.66206 0.000 0.972 0.028 0.000
#> GSM537420 1 0.3498 0.68474 0.832 0.000 0.008 0.160
#> GSM537429 2 0.8731 0.19128 0.100 0.476 0.132 0.292
#> GSM537431 3 0.8669 0.34034 0.240 0.064 0.480 0.216
#> GSM537387 1 0.5127 0.52364 0.668 0.008 0.008 0.316
#> GSM537414 3 0.6394 0.39589 0.284 0.024 0.640 0.052
#> GSM537433 3 0.8991 0.46246 0.244 0.184 0.468 0.104
#> GSM537335 4 0.8583 0.52214 0.104 0.124 0.264 0.508
#> GSM537339 4 0.4807 0.46035 0.152 0.052 0.008 0.788
#> GSM537340 3 0.5705 0.63288 0.108 0.180 0.712 0.000
#> GSM537344 1 0.1867 0.71735 0.928 0.000 0.000 0.072
#> GSM537346 2 0.5998 0.60494 0.052 0.740 0.144 0.064
#> GSM537351 1 0.1661 0.71108 0.944 0.000 0.052 0.004
#> GSM537352 2 0.7430 0.38115 0.000 0.512 0.228 0.260
#> GSM537359 2 0.0844 0.65444 0.004 0.980 0.012 0.004
#> GSM537360 3 0.4917 0.50958 0.004 0.328 0.664 0.004
#> GSM537364 1 0.0672 0.71672 0.984 0.000 0.008 0.008
#> GSM537365 2 0.9669 -0.33616 0.152 0.344 0.296 0.208
#> GSM537372 4 0.5178 -0.08519 0.392 0.004 0.004 0.600
#> GSM537384 1 0.5500 0.44512 0.564 0.004 0.012 0.420
#> GSM537394 2 0.4715 0.63628 0.040 0.824 0.064 0.072
#> GSM537403 3 0.4462 0.55889 0.004 0.256 0.736 0.004
#> GSM537406 2 0.4275 0.61371 0.064 0.836 0.088 0.012
#> GSM537411 2 0.7218 0.15347 0.000 0.444 0.140 0.416
#> GSM537412 3 0.4809 0.53619 0.004 0.308 0.684 0.004
#> GSM537416 3 0.5219 0.61993 0.056 0.216 0.728 0.000
#> GSM537426 3 0.5284 0.28338 0.004 0.436 0.556 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.4043 0.60581 0.016 0.064 0.072 0.016 0.832
#> GSM537345 1 0.4998 0.41941 0.716 0.000 0.108 0.004 0.172
#> GSM537355 3 0.6030 0.39437 0.000 0.340 0.568 0.052 0.040
#> GSM537366 1 0.7795 0.14891 0.372 0.024 0.020 0.292 0.292
#> GSM537370 5 0.6444 0.28546 0.032 0.260 0.084 0.016 0.608
#> GSM537380 2 0.1908 0.65221 0.000 0.908 0.092 0.000 0.000
#> GSM537392 2 0.2230 0.63910 0.000 0.884 0.116 0.000 0.000
#> GSM537415 4 0.4367 0.36653 0.000 0.372 0.008 0.620 0.000
#> GSM537417 4 0.2967 0.60208 0.012 0.016 0.104 0.868 0.000
#> GSM537422 4 0.3623 0.60066 0.072 0.052 0.028 0.848 0.000
#> GSM537423 2 0.2017 0.65658 0.000 0.912 0.080 0.008 0.000
#> GSM537427 3 0.5824 0.35141 0.000 0.392 0.520 0.004 0.084
#> GSM537430 2 0.4576 0.07321 0.000 0.536 0.456 0.004 0.004
#> GSM537336 1 0.0771 0.61617 0.976 0.000 0.020 0.000 0.004
#> GSM537337 3 0.6031 0.41364 0.000 0.336 0.568 0.028 0.068
#> GSM537348 5 0.1943 0.64252 0.056 0.000 0.020 0.000 0.924
#> GSM537349 2 0.1792 0.65299 0.000 0.916 0.084 0.000 0.000
#> GSM537356 5 0.4931 0.20879 0.372 0.012 0.000 0.016 0.600
#> GSM537361 1 0.6326 0.43985 0.652 0.020 0.200 0.092 0.036
#> GSM537374 3 0.4335 0.56249 0.000 0.072 0.760 0.000 0.168
#> GSM537377 1 0.5258 0.41209 0.704 0.000 0.108 0.012 0.176
#> GSM537378 2 0.3370 0.63757 0.000 0.824 0.148 0.028 0.000
#> GSM537379 4 0.5842 0.20745 0.008 0.072 0.428 0.492 0.000
#> GSM537383 2 0.1965 0.65122 0.000 0.904 0.096 0.000 0.000
#> GSM537388 2 0.4135 0.32433 0.000 0.656 0.340 0.004 0.000
#> GSM537395 2 0.6292 0.29316 0.000 0.532 0.260 0.208 0.000
#> GSM537400 4 0.7451 0.37398 0.060 0.108 0.272 0.532 0.028
#> GSM537404 4 0.8259 0.49289 0.096 0.208 0.188 0.476 0.032
#> GSM537409 4 0.3370 0.60091 0.000 0.148 0.028 0.824 0.000
#> GSM537418 5 0.6237 0.00324 0.448 0.024 0.028 0.028 0.472
#> GSM537425 4 0.7542 0.24008 0.312 0.132 0.020 0.484 0.052
#> GSM537333 4 0.5796 0.37257 0.020 0.056 0.320 0.600 0.004
#> GSM537342 4 0.7561 0.35314 0.068 0.296 0.148 0.480 0.008
#> GSM537347 3 0.7140 0.45147 0.008 0.168 0.588 0.144 0.092
#> GSM537350 5 0.6261 0.05228 0.424 0.056 0.016 0.016 0.488
#> GSM537362 3 0.8886 0.10413 0.076 0.076 0.364 0.304 0.180
#> GSM537363 1 0.6953 0.30688 0.584 0.144 0.016 0.216 0.040
#> GSM537368 1 0.0609 0.61926 0.980 0.000 0.000 0.000 0.020
#> GSM537376 2 0.6710 0.06757 0.000 0.424 0.272 0.304 0.000
#> GSM537381 1 0.1732 0.59900 0.920 0.000 0.000 0.000 0.080
#> GSM537386 2 0.1267 0.64588 0.000 0.960 0.024 0.012 0.004
#> GSM537398 5 0.5006 0.53622 0.180 0.000 0.116 0.000 0.704
#> GSM537402 2 0.6289 0.32363 0.000 0.584 0.272 0.120 0.024
#> GSM537405 1 0.4264 0.23233 0.620 0.004 0.000 0.000 0.376
#> GSM537371 1 0.0771 0.61617 0.976 0.000 0.020 0.000 0.004
#> GSM537421 4 0.4207 0.60464 0.020 0.056 0.124 0.800 0.000
#> GSM537424 5 0.4701 0.53459 0.236 0.000 0.060 0.000 0.704
#> GSM537432 4 0.6983 0.31712 0.020 0.108 0.324 0.520 0.028
#> GSM537331 3 0.6217 0.43463 0.004 0.272 0.572 0.004 0.148
#> GSM537332 2 0.5365 -0.03491 0.004 0.512 0.044 0.440 0.000
#> GSM537334 3 0.4269 0.54498 0.004 0.044 0.772 0.004 0.176
#> GSM537338 3 0.4714 0.56593 0.000 0.100 0.744 0.004 0.152
#> GSM537353 2 0.6519 0.19726 0.000 0.456 0.204 0.340 0.000
#> GSM537357 1 0.2278 0.58281 0.908 0.000 0.060 0.000 0.032
#> GSM537358 2 0.3051 0.65731 0.000 0.864 0.060 0.076 0.000
#> GSM537375 3 0.5706 0.29365 0.012 0.096 0.632 0.260 0.000
#> GSM537389 2 0.1892 0.65536 0.000 0.916 0.080 0.004 0.000
#> GSM537390 2 0.4527 0.54873 0.000 0.692 0.036 0.272 0.000
#> GSM537393 3 0.6113 0.04519 0.008 0.108 0.528 0.356 0.000
#> GSM537399 5 0.8868 0.24706 0.156 0.208 0.160 0.052 0.424
#> GSM537407 1 0.9366 0.09828 0.364 0.120 0.176 0.232 0.108
#> GSM537408 2 0.3403 0.59171 0.064 0.868 0.028 0.032 0.008
#> GSM537428 3 0.5867 0.40519 0.000 0.352 0.548 0.004 0.096
#> GSM537354 3 0.6402 0.39154 0.000 0.252 0.536 0.208 0.004
#> GSM537410 4 0.6401 0.56318 0.052 0.248 0.084 0.612 0.004
#> GSM537413 2 0.3010 0.62987 0.000 0.824 0.004 0.172 0.000
#> GSM537396 2 0.4930 0.55114 0.068 0.792 0.060 0.036 0.044
#> GSM537397 5 0.2450 0.64291 0.032 0.028 0.028 0.000 0.912
#> GSM537330 3 0.6511 0.01733 0.004 0.404 0.428 0.164 0.000
#> GSM537369 1 0.4088 0.25022 0.632 0.000 0.000 0.000 0.368
#> GSM537373 2 0.7033 0.39648 0.072 0.620 0.100 0.176 0.032
#> GSM537401 5 0.6699 0.07488 0.016 0.216 0.196 0.008 0.564
#> GSM537343 1 0.7606 0.27858 0.428 0.028 0.032 0.364 0.148
#> GSM537367 4 0.6434 0.50699 0.160 0.140 0.016 0.648 0.036
#> GSM537382 2 0.7318 0.05220 0.004 0.436 0.288 0.248 0.024
#> GSM537385 2 0.2536 0.63146 0.000 0.868 0.128 0.000 0.004
#> GSM537391 5 0.2828 0.63987 0.104 0.004 0.020 0.000 0.872
#> GSM537419 2 0.2569 0.66140 0.000 0.892 0.068 0.040 0.000
#> GSM537420 1 0.4291 0.01420 0.536 0.000 0.000 0.000 0.464
#> GSM537429 2 0.7583 -0.23191 0.004 0.400 0.392 0.112 0.092
#> GSM537431 4 0.7377 0.48615 0.056 0.148 0.212 0.560 0.024
#> GSM537387 5 0.3849 0.54781 0.232 0.000 0.016 0.000 0.752
#> GSM537414 4 0.5969 0.55241 0.072 0.080 0.156 0.688 0.004
#> GSM537433 4 0.7608 0.09352 0.316 0.076 0.024 0.488 0.096
#> GSM537335 3 0.4106 0.51166 0.004 0.028 0.772 0.004 0.192
#> GSM537339 5 0.1772 0.64158 0.032 0.008 0.020 0.000 0.940
#> GSM537340 4 0.2529 0.62276 0.024 0.032 0.036 0.908 0.000
#> GSM537344 1 0.3177 0.49918 0.792 0.000 0.000 0.000 0.208
#> GSM537346 2 0.5506 0.39855 0.004 0.648 0.240 0.108 0.000
#> GSM537351 1 0.1220 0.62043 0.964 0.004 0.008 0.020 0.004
#> GSM537352 3 0.5975 0.39502 0.000 0.352 0.556 0.020 0.072
#> GSM537359 2 0.1117 0.64294 0.000 0.964 0.020 0.016 0.000
#> GSM537360 4 0.4134 0.55716 0.000 0.224 0.032 0.744 0.000
#> GSM537364 1 0.0000 0.61964 1.000 0.000 0.000 0.000 0.000
#> GSM537365 4 0.9346 0.32813 0.156 0.228 0.248 0.308 0.060
#> GSM537372 5 0.2929 0.57961 0.180 0.000 0.000 0.000 0.820
#> GSM537384 5 0.2625 0.63178 0.108 0.000 0.016 0.000 0.876
#> GSM537394 2 0.3464 0.61435 0.000 0.836 0.096 0.068 0.000
#> GSM537403 4 0.4166 0.59399 0.004 0.116 0.088 0.792 0.000
#> GSM537406 2 0.3503 0.58691 0.060 0.864 0.020 0.044 0.012
#> GSM537411 3 0.7111 0.31304 0.004 0.312 0.512 0.108 0.064
#> GSM537412 4 0.4024 0.56068 0.000 0.220 0.028 0.752 0.000
#> GSM537416 4 0.3033 0.61551 0.016 0.032 0.076 0.876 0.000
#> GSM537426 4 0.4712 0.49223 0.000 0.268 0.048 0.684 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.2100 0.6336 0.000 0.008 0.048 0.004 0.916 0.024
#> GSM537345 1 0.3183 0.5728 0.828 0.000 0.060 0.000 0.112 0.000
#> GSM537355 6 0.3903 0.5482 0.000 0.304 0.012 0.004 0.000 0.680
#> GSM537366 3 0.7277 0.3792 0.100 0.000 0.360 0.252 0.288 0.000
#> GSM537370 5 0.4853 0.4561 0.008 0.172 0.068 0.004 0.724 0.024
#> GSM537380 2 0.1296 0.6625 0.000 0.952 0.012 0.004 0.000 0.032
#> GSM537392 2 0.1584 0.6465 0.000 0.928 0.008 0.000 0.000 0.064
#> GSM537415 4 0.3390 0.4512 0.000 0.296 0.000 0.704 0.000 0.000
#> GSM537417 4 0.4386 0.5359 0.012 0.032 0.116 0.776 0.000 0.064
#> GSM537422 4 0.4838 0.4168 0.064 0.032 0.172 0.724 0.004 0.004
#> GSM537423 2 0.1477 0.6590 0.000 0.940 0.004 0.008 0.000 0.048
#> GSM537427 6 0.3942 0.4841 0.000 0.368 0.004 0.000 0.004 0.624
#> GSM537430 6 0.3789 0.4073 0.000 0.416 0.000 0.000 0.000 0.584
#> GSM537336 1 0.1152 0.6697 0.952 0.000 0.044 0.000 0.004 0.000
#> GSM537337 6 0.3928 0.5492 0.000 0.300 0.008 0.004 0.004 0.684
#> GSM537348 5 0.0000 0.6636 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537349 2 0.1285 0.6549 0.000 0.944 0.004 0.000 0.000 0.052
#> GSM537356 5 0.5185 0.2079 0.104 0.000 0.300 0.004 0.592 0.000
#> GSM537361 3 0.6855 0.0593 0.388 0.000 0.428 0.072 0.028 0.084
#> GSM537374 6 0.2004 0.5531 0.004 0.036 0.028 0.004 0.004 0.924
#> GSM537377 1 0.3270 0.5671 0.820 0.000 0.060 0.000 0.120 0.000
#> GSM537378 2 0.3313 0.6039 0.000 0.812 0.004 0.036 0.000 0.148
#> GSM537379 6 0.7074 -0.0144 0.012 0.056 0.212 0.296 0.000 0.424
#> GSM537383 2 0.1444 0.6498 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM537388 2 0.3944 -0.0392 0.000 0.568 0.004 0.000 0.000 0.428
#> GSM537395 6 0.5714 0.1718 0.000 0.424 0.004 0.140 0.000 0.432
#> GSM537400 4 0.7612 0.2259 0.036 0.080 0.312 0.388 0.000 0.184
#> GSM537404 3 0.7612 0.1486 0.020 0.104 0.436 0.312 0.024 0.104
#> GSM537409 4 0.2442 0.5625 0.000 0.144 0.004 0.852 0.000 0.000
#> GSM537418 5 0.6561 -0.1147 0.188 0.000 0.368 0.024 0.412 0.008
#> GSM537425 3 0.6779 0.4740 0.160 0.012 0.428 0.356 0.044 0.000
#> GSM537333 4 0.7241 0.2970 0.012 0.076 0.284 0.420 0.000 0.208
#> GSM537342 4 0.7769 0.3882 0.028 0.168 0.104 0.520 0.052 0.128
#> GSM537347 6 0.5942 0.4871 0.000 0.108 0.136 0.112 0.004 0.640
#> GSM537350 3 0.6271 -0.0502 0.248 0.004 0.400 0.004 0.344 0.000
#> GSM537362 6 0.8629 0.0574 0.068 0.088 0.240 0.196 0.036 0.372
#> GSM537363 3 0.7376 0.3256 0.360 0.032 0.380 0.160 0.068 0.000
#> GSM537368 1 0.3807 0.5997 0.756 0.000 0.192 0.000 0.052 0.000
#> GSM537376 2 0.6303 -0.2246 0.000 0.400 0.016 0.212 0.000 0.372
#> GSM537381 1 0.5042 0.5021 0.648 0.000 0.212 0.004 0.136 0.000
#> GSM537386 2 0.1890 0.6623 0.000 0.924 0.008 0.024 0.000 0.044
#> GSM537398 5 0.4541 0.5717 0.156 0.000 0.016 0.024 0.752 0.052
#> GSM537402 2 0.6158 -0.0996 0.000 0.464 0.028 0.144 0.000 0.364
#> GSM537405 5 0.5974 0.0112 0.316 0.000 0.212 0.004 0.468 0.000
#> GSM537371 1 0.1010 0.6689 0.960 0.000 0.036 0.000 0.004 0.000
#> GSM537421 4 0.4245 0.5655 0.012 0.068 0.052 0.796 0.000 0.072
#> GSM537424 5 0.4176 0.5739 0.164 0.000 0.020 0.048 0.764 0.004
#> GSM537432 4 0.7801 0.1930 0.040 0.092 0.208 0.384 0.000 0.276
#> GSM537331 6 0.4120 0.5234 0.004 0.152 0.068 0.004 0.004 0.768
#> GSM537332 2 0.4697 0.3640 0.000 0.584 0.044 0.368 0.000 0.004
#> GSM537334 6 0.2075 0.5124 0.004 0.004 0.076 0.004 0.004 0.908
#> GSM537338 6 0.3878 0.5737 0.004 0.220 0.020 0.004 0.004 0.748
#> GSM537353 2 0.6324 0.1242 0.000 0.436 0.032 0.372 0.000 0.160
#> GSM537357 1 0.0405 0.6490 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM537358 2 0.2794 0.6543 0.000 0.840 0.004 0.144 0.000 0.012
#> GSM537375 6 0.5440 0.5051 0.012 0.056 0.140 0.100 0.000 0.692
#> GSM537389 2 0.1075 0.6565 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM537390 2 0.3774 0.5601 0.000 0.664 0.008 0.328 0.000 0.000
#> GSM537393 6 0.6187 0.4072 0.012 0.056 0.140 0.188 0.000 0.604
#> GSM537399 5 0.7472 -0.0523 0.048 0.292 0.272 0.004 0.360 0.024
#> GSM537407 3 0.7678 0.5215 0.156 0.024 0.532 0.124 0.108 0.056
#> GSM537408 2 0.4703 0.6007 0.028 0.732 0.168 0.064 0.008 0.000
#> GSM537428 6 0.3892 0.5010 0.000 0.352 0.000 0.004 0.004 0.640
#> GSM537354 6 0.4852 0.5681 0.000 0.244 0.016 0.072 0.000 0.668
#> GSM537410 4 0.5712 0.5159 0.028 0.164 0.052 0.692 0.040 0.024
#> GSM537413 2 0.3271 0.6302 0.000 0.760 0.008 0.232 0.000 0.000
#> GSM537396 2 0.5546 0.5619 0.028 0.668 0.212 0.064 0.016 0.012
#> GSM537397 5 0.0146 0.6650 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM537330 6 0.7079 0.1201 0.000 0.364 0.116 0.148 0.000 0.372
#> GSM537369 1 0.6030 0.1766 0.424 0.000 0.208 0.004 0.364 0.000
#> GSM537373 2 0.8349 0.3001 0.036 0.448 0.172 0.184 0.068 0.092
#> GSM537401 5 0.5031 0.3757 0.000 0.144 0.004 0.004 0.668 0.180
#> GSM537343 3 0.7094 0.5023 0.196 0.008 0.516 0.172 0.100 0.008
#> GSM537367 4 0.6188 -0.0554 0.048 0.024 0.308 0.552 0.068 0.000
#> GSM537382 2 0.6391 -0.2044 0.000 0.416 0.016 0.204 0.004 0.360
#> GSM537385 2 0.2266 0.6205 0.000 0.880 0.012 0.000 0.000 0.108
#> GSM537391 5 0.1588 0.6539 0.072 0.000 0.000 0.004 0.924 0.000
#> GSM537419 2 0.2126 0.6689 0.000 0.904 0.004 0.072 0.000 0.020
#> GSM537420 5 0.5874 0.1069 0.292 0.000 0.204 0.004 0.500 0.000
#> GSM537429 6 0.5582 0.3700 0.000 0.388 0.000 0.112 0.008 0.492
#> GSM537431 4 0.7306 0.2177 0.048 0.072 0.268 0.492 0.004 0.116
#> GSM537387 5 0.2738 0.5775 0.176 0.000 0.004 0.000 0.820 0.000
#> GSM537414 4 0.6917 0.1970 0.032 0.064 0.364 0.444 0.000 0.096
#> GSM537433 3 0.7152 0.4695 0.128 0.012 0.392 0.368 0.100 0.000
#> GSM537335 6 0.3731 0.4101 0.004 0.000 0.076 0.004 0.116 0.800
#> GSM537339 5 0.0291 0.6647 0.000 0.000 0.004 0.004 0.992 0.000
#> GSM537340 4 0.3010 0.5387 0.020 0.040 0.060 0.872 0.004 0.004
#> GSM537344 1 0.5465 0.4314 0.572 0.000 0.208 0.000 0.220 0.000
#> GSM537346 2 0.5212 0.4836 0.000 0.672 0.036 0.100 0.000 0.192
#> GSM537351 1 0.3411 0.5928 0.756 0.000 0.232 0.008 0.004 0.000
#> GSM537352 6 0.4119 0.5181 0.000 0.336 0.000 0.016 0.004 0.644
#> GSM537359 2 0.2594 0.6693 0.000 0.880 0.060 0.056 0.004 0.000
#> GSM537360 4 0.3250 0.5474 0.000 0.196 0.012 0.788 0.000 0.004
#> GSM537364 1 0.2595 0.6450 0.836 0.000 0.160 0.000 0.004 0.000
#> GSM537365 3 0.7556 0.3815 0.040 0.088 0.536 0.192 0.036 0.108
#> GSM537372 5 0.0260 0.6661 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM537384 5 0.0777 0.6648 0.024 0.000 0.000 0.004 0.972 0.000
#> GSM537394 2 0.3475 0.6436 0.000 0.812 0.028 0.140 0.000 0.020
#> GSM537403 4 0.3020 0.5624 0.000 0.080 0.000 0.844 0.000 0.076
#> GSM537406 2 0.5128 0.5485 0.028 0.692 0.184 0.088 0.008 0.000
#> GSM537411 6 0.6245 0.4178 0.000 0.264 0.060 0.132 0.000 0.544
#> GSM537412 4 0.2882 0.5493 0.000 0.180 0.008 0.812 0.000 0.000
#> GSM537416 4 0.3037 0.5644 0.012 0.032 0.028 0.872 0.000 0.056
#> GSM537426 4 0.3215 0.5181 0.000 0.240 0.004 0.756 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> MAD:mclust 96 0.6146 0.667 2
#> MAD:mclust 67 0.8471 0.327 3
#> MAD:mclust 55 0.5474 0.706 4
#> MAD:mclust 51 0.5896 0.747 5
#> MAD:mclust 59 0.0393 0.514 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 104 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 0.861 0.906 0.961 0.4804 0.522 0.522
#> 3 3 0.369 0.519 0.729 0.3614 0.713 0.496
#> 4 4 0.428 0.502 0.717 0.1327 0.788 0.467
#> 5 5 0.523 0.520 0.731 0.0678 0.860 0.528
#> 6 6 0.536 0.348 0.604 0.0472 0.864 0.474
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
#> GSM537341 2 0.671 0.781 0.176 0.824
#> GSM537345 1 0.000 0.955 1.000 0.000
#> GSM537355 2 0.000 0.960 0.000 1.000
#> GSM537366 1 0.000 0.955 1.000 0.000
#> GSM537370 2 0.184 0.938 0.028 0.972
#> GSM537380 2 0.000 0.960 0.000 1.000
#> GSM537392 2 0.000 0.960 0.000 1.000
#> GSM537415 2 0.000 0.960 0.000 1.000
#> GSM537417 2 0.000 0.960 0.000 1.000
#> GSM537422 1 0.000 0.955 1.000 0.000
#> GSM537423 2 0.000 0.960 0.000 1.000
#> GSM537427 2 0.000 0.960 0.000 1.000
#> GSM537430 2 0.000 0.960 0.000 1.000
#> GSM537336 1 0.000 0.955 1.000 0.000
#> GSM537337 2 0.000 0.960 0.000 1.000
#> GSM537348 1 0.000 0.955 1.000 0.000
#> GSM537349 2 0.000 0.960 0.000 1.000
#> GSM537356 1 0.000 0.955 1.000 0.000
#> GSM537361 1 0.000 0.955 1.000 0.000
#> GSM537374 2 0.000 0.960 0.000 1.000
#> GSM537377 1 0.000 0.955 1.000 0.000
#> GSM537378 2 0.000 0.960 0.000 1.000
#> GSM537379 2 0.000 0.960 0.000 1.000
#> GSM537383 2 0.000 0.960 0.000 1.000
#> GSM537388 2 0.000 0.960 0.000 1.000
#> GSM537395 2 0.000 0.960 0.000 1.000
#> GSM537400 1 0.373 0.897 0.928 0.072
#> GSM537404 2 0.295 0.917 0.052 0.948
#> GSM537409 2 0.000 0.960 0.000 1.000
#> GSM537418 1 0.000 0.955 1.000 0.000
#> GSM537425 1 0.000 0.955 1.000 0.000
#> GSM537333 2 0.975 0.297 0.408 0.592
#> GSM537342 2 0.541 0.839 0.124 0.876
#> GSM537347 2 0.552 0.838 0.128 0.872
#> GSM537350 1 0.000 0.955 1.000 0.000
#> GSM537362 1 0.000 0.955 1.000 0.000
#> GSM537363 1 0.722 0.739 0.800 0.200
#> GSM537368 1 0.000 0.955 1.000 0.000
#> GSM537376 2 0.000 0.960 0.000 1.000
#> GSM537381 1 0.000 0.955 1.000 0.000
#> GSM537386 2 0.000 0.960 0.000 1.000
#> GSM537398 1 0.000 0.955 1.000 0.000
#> GSM537402 2 0.000 0.960 0.000 1.000
#> GSM537405 1 0.000 0.955 1.000 0.000
#> GSM537371 1 0.000 0.955 1.000 0.000
#> GSM537421 2 0.961 0.363 0.384 0.616
#> GSM537424 1 0.000 0.955 1.000 0.000
#> GSM537432 1 0.958 0.382 0.620 0.380
#> GSM537331 2 0.000 0.960 0.000 1.000
#> GSM537332 2 0.000 0.960 0.000 1.000
#> GSM537334 2 0.000 0.960 0.000 1.000
#> GSM537338 2 0.000 0.960 0.000 1.000
#> GSM537353 2 0.000 0.960 0.000 1.000
#> GSM537357 1 0.000 0.955 1.000 0.000
#> GSM537358 2 0.000 0.960 0.000 1.000
#> GSM537375 2 0.000 0.960 0.000 1.000
#> GSM537389 2 0.000 0.960 0.000 1.000
#> GSM537390 2 0.000 0.960 0.000 1.000
#> GSM537393 2 0.000 0.960 0.000 1.000
#> GSM537399 2 0.991 0.200 0.444 0.556
#> GSM537407 1 0.469 0.868 0.900 0.100
#> GSM537408 2 0.000 0.960 0.000 1.000
#> GSM537428 2 0.000 0.960 0.000 1.000
#> GSM537354 2 0.000 0.960 0.000 1.000
#> GSM537410 2 0.000 0.960 0.000 1.000
#> GSM537413 2 0.000 0.960 0.000 1.000
#> GSM537396 2 0.000 0.960 0.000 1.000
#> GSM537397 1 0.311 0.912 0.944 0.056
#> GSM537330 2 0.000 0.960 0.000 1.000
#> GSM537369 1 0.000 0.955 1.000 0.000
#> GSM537373 2 0.000 0.960 0.000 1.000
#> GSM537401 2 0.204 0.935 0.032 0.968
#> GSM537343 1 0.118 0.945 0.984 0.016
#> GSM537367 1 0.184 0.936 0.972 0.028
#> GSM537382 2 0.000 0.960 0.000 1.000
#> GSM537385 2 0.000 0.960 0.000 1.000
#> GSM537391 1 0.000 0.955 1.000 0.000
#> GSM537419 2 0.000 0.960 0.000 1.000
#> GSM537420 1 0.000 0.955 1.000 0.000
#> GSM537429 2 0.775 0.700 0.228 0.772
#> GSM537431 1 0.955 0.393 0.624 0.376
#> GSM537387 1 0.000 0.955 1.000 0.000
#> GSM537414 1 0.000 0.955 1.000 0.000
#> GSM537433 1 0.184 0.936 0.972 0.028
#> GSM537335 2 0.373 0.899 0.072 0.928
#> GSM537339 1 0.000 0.955 1.000 0.000
#> GSM537340 1 0.973 0.326 0.596 0.404
#> GSM537344 1 0.000 0.955 1.000 0.000
#> GSM537346 2 0.000 0.960 0.000 1.000
#> GSM537351 1 0.000 0.955 1.000 0.000
#> GSM537352 2 0.000 0.960 0.000 1.000
#> GSM537359 2 0.000 0.960 0.000 1.000
#> GSM537360 2 0.000 0.960 0.000 1.000
#> GSM537364 1 0.000 0.955 1.000 0.000
#> GSM537365 2 0.871 0.588 0.292 0.708
#> GSM537372 1 0.000 0.955 1.000 0.000
#> GSM537384 1 0.000 0.955 1.000 0.000
#> GSM537394 2 0.000 0.960 0.000 1.000
#> GSM537403 2 0.000 0.960 0.000 1.000
#> GSM537406 2 0.000 0.960 0.000 1.000
#> GSM537411 2 0.000 0.960 0.000 1.000
#> GSM537412 2 0.000 0.960 0.000 1.000
#> GSM537416 2 0.000 0.960 0.000 1.000
#> GSM537426 2 0.000 0.960 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 3 0.5105 0.5508 0.048 0.124 0.828
#> GSM537345 1 0.4605 0.7173 0.796 0.000 0.204
#> GSM537355 2 0.4796 0.6231 0.000 0.780 0.220
#> GSM537366 1 0.6148 0.7116 0.776 0.076 0.148
#> GSM537370 3 0.5331 0.5292 0.024 0.184 0.792
#> GSM537380 3 0.5363 0.4765 0.000 0.276 0.724
#> GSM537392 3 0.5650 0.4451 0.000 0.312 0.688
#> GSM537415 2 0.3267 0.6834 0.000 0.884 0.116
#> GSM537417 2 0.3377 0.6677 0.012 0.896 0.092
#> GSM537422 1 0.7616 0.5166 0.636 0.292 0.072
#> GSM537423 2 0.4178 0.6673 0.000 0.828 0.172
#> GSM537427 3 0.6244 0.3627 0.000 0.440 0.560
#> GSM537430 2 0.6305 -0.2926 0.000 0.516 0.484
#> GSM537336 1 0.0237 0.8112 0.996 0.000 0.004
#> GSM537337 2 0.4399 0.6456 0.000 0.812 0.188
#> GSM537348 3 0.6309 -0.1822 0.496 0.000 0.504
#> GSM537349 2 0.6291 0.0909 0.000 0.532 0.468
#> GSM537356 1 0.3500 0.7848 0.880 0.004 0.116
#> GSM537361 1 0.2301 0.8032 0.936 0.004 0.060
#> GSM537374 3 0.5016 0.5161 0.000 0.240 0.760
#> GSM537377 1 0.5216 0.6658 0.740 0.000 0.260
#> GSM537378 2 0.2356 0.7081 0.000 0.928 0.072
#> GSM537379 2 0.4931 0.6208 0.004 0.784 0.212
#> GSM537383 3 0.6291 0.2894 0.000 0.468 0.532
#> GSM537388 2 0.6079 0.0657 0.000 0.612 0.388
#> GSM537395 2 0.3116 0.6827 0.000 0.892 0.108
#> GSM537400 1 0.8465 0.2348 0.528 0.096 0.376
#> GSM537404 3 0.8505 0.4327 0.144 0.256 0.600
#> GSM537409 2 0.0237 0.7083 0.000 0.996 0.004
#> GSM537418 1 0.1289 0.8121 0.968 0.000 0.032
#> GSM537425 1 0.1482 0.8107 0.968 0.020 0.012
#> GSM537333 3 0.9077 0.3408 0.152 0.340 0.508
#> GSM537342 2 0.3481 0.6939 0.052 0.904 0.044
#> GSM537347 3 0.7091 0.5076 0.040 0.320 0.640
#> GSM537350 1 0.5517 0.6527 0.728 0.004 0.268
#> GSM537362 3 0.6704 0.1306 0.376 0.016 0.608
#> GSM537363 1 0.6922 0.6531 0.720 0.080 0.200
#> GSM537368 1 0.0000 0.8113 1.000 0.000 0.000
#> GSM537376 2 0.4504 0.6714 0.000 0.804 0.196
#> GSM537381 1 0.1129 0.8111 0.976 0.004 0.020
#> GSM537386 3 0.5431 0.4634 0.000 0.284 0.716
#> GSM537398 3 0.6204 0.0117 0.424 0.000 0.576
#> GSM537402 2 0.6079 0.3601 0.000 0.612 0.388
#> GSM537405 1 0.1643 0.8095 0.956 0.000 0.044
#> GSM537371 1 0.1031 0.8096 0.976 0.000 0.024
#> GSM537421 2 0.5075 0.6161 0.068 0.836 0.096
#> GSM537424 1 0.4346 0.7354 0.816 0.000 0.184
#> GSM537432 3 0.8690 0.1459 0.440 0.104 0.456
#> GSM537331 3 0.5291 0.5112 0.000 0.268 0.732
#> GSM537332 2 0.1832 0.7145 0.008 0.956 0.036
#> GSM537334 3 0.4931 0.4956 0.000 0.232 0.768
#> GSM537338 3 0.5016 0.5057 0.000 0.240 0.760
#> GSM537353 2 0.1753 0.7154 0.000 0.952 0.048
#> GSM537357 1 0.0424 0.8110 0.992 0.000 0.008
#> GSM537358 2 0.5859 0.4116 0.000 0.656 0.344
#> GSM537375 2 0.6680 0.0540 0.008 0.508 0.484
#> GSM537389 2 0.6079 0.4219 0.000 0.612 0.388
#> GSM537390 2 0.2356 0.7124 0.000 0.928 0.072
#> GSM537393 2 0.3879 0.6604 0.000 0.848 0.152
#> GSM537399 3 0.6217 0.4355 0.264 0.024 0.712
#> GSM537407 1 0.6476 0.3132 0.548 0.004 0.448
#> GSM537408 2 0.6664 0.2610 0.008 0.528 0.464
#> GSM537428 3 0.5905 0.4682 0.000 0.352 0.648
#> GSM537354 2 0.4121 0.6218 0.000 0.832 0.168
#> GSM537410 2 0.3370 0.6988 0.024 0.904 0.072
#> GSM537413 2 0.4605 0.6534 0.000 0.796 0.204
#> GSM537396 3 0.6570 0.3952 0.024 0.308 0.668
#> GSM537397 3 0.5397 0.3756 0.280 0.000 0.720
#> GSM537330 2 0.6252 -0.2012 0.000 0.556 0.444
#> GSM537369 1 0.1964 0.8091 0.944 0.000 0.056
#> GSM537373 2 0.5506 0.5798 0.016 0.764 0.220
#> GSM537401 3 0.3896 0.5545 0.008 0.128 0.864
#> GSM537343 1 0.4784 0.7121 0.796 0.004 0.200
#> GSM537367 2 0.9129 0.0249 0.372 0.480 0.148
#> GSM537382 2 0.1529 0.7063 0.000 0.960 0.040
#> GSM537385 3 0.6260 0.2469 0.000 0.448 0.552
#> GSM537391 3 0.6154 0.0897 0.408 0.000 0.592
#> GSM537419 2 0.6302 0.1962 0.000 0.520 0.480
#> GSM537420 1 0.2165 0.8053 0.936 0.000 0.064
#> GSM537429 3 0.8608 0.3544 0.100 0.412 0.488
#> GSM537431 1 0.9103 0.0132 0.476 0.144 0.380
#> GSM537387 1 0.3816 0.7509 0.852 0.000 0.148
#> GSM537414 1 0.6007 0.6447 0.764 0.192 0.044
#> GSM537433 1 0.9458 0.2018 0.448 0.368 0.184
#> GSM537335 3 0.4912 0.5161 0.008 0.196 0.796
#> GSM537339 3 0.5810 0.2945 0.336 0.000 0.664
#> GSM537340 2 0.6984 0.4786 0.192 0.720 0.088
#> GSM537344 1 0.1643 0.8097 0.956 0.000 0.044
#> GSM537346 3 0.6521 0.2864 0.004 0.496 0.500
#> GSM537351 1 0.1337 0.8110 0.972 0.012 0.016
#> GSM537352 2 0.1964 0.7022 0.000 0.944 0.056
#> GSM537359 3 0.5465 0.4526 0.000 0.288 0.712
#> GSM537360 2 0.2066 0.7114 0.000 0.940 0.060
#> GSM537364 1 0.0747 0.8094 0.984 0.000 0.016
#> GSM537365 3 0.8665 0.3140 0.384 0.108 0.508
#> GSM537372 1 0.5291 0.6476 0.732 0.000 0.268
#> GSM537384 1 0.3340 0.7811 0.880 0.000 0.120
#> GSM537394 3 0.5690 0.4470 0.004 0.288 0.708
#> GSM537403 2 0.1337 0.7040 0.016 0.972 0.012
#> GSM537406 2 0.5201 0.5717 0.004 0.760 0.236
#> GSM537411 3 0.6062 0.4099 0.000 0.384 0.616
#> GSM537412 2 0.2200 0.7097 0.004 0.940 0.056
#> GSM537416 2 0.1999 0.6986 0.036 0.952 0.012
#> GSM537426 2 0.1643 0.7127 0.000 0.956 0.044
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.2909 0.55776 0.036 0.904 0.008 0.052
#> GSM537345 1 0.5721 0.16259 0.548 0.004 0.020 0.428
#> GSM537355 3 0.6412 0.52539 0.000 0.088 0.592 0.320
#> GSM537366 1 0.4370 0.70244 0.772 0.212 0.008 0.008
#> GSM537370 2 0.4008 0.55349 0.020 0.832 0.012 0.136
#> GSM537380 2 0.4274 0.61902 0.000 0.820 0.072 0.108
#> GSM537392 2 0.4764 0.61550 0.000 0.788 0.088 0.124
#> GSM537415 3 0.3428 0.67030 0.000 0.144 0.844 0.012
#> GSM537417 3 0.5531 0.64783 0.024 0.048 0.744 0.184
#> GSM537422 3 0.6043 0.38417 0.312 0.008 0.632 0.048
#> GSM537423 2 0.6449 0.10382 0.000 0.480 0.452 0.068
#> GSM537427 4 0.7469 -0.00113 0.000 0.368 0.180 0.452
#> GSM537430 2 0.7823 0.16642 0.000 0.408 0.272 0.320
#> GSM537336 1 0.1182 0.74294 0.968 0.000 0.016 0.016
#> GSM537337 3 0.5792 0.38619 0.000 0.032 0.552 0.416
#> GSM537348 4 0.7281 0.11593 0.380 0.152 0.000 0.468
#> GSM537349 2 0.4737 0.60147 0.000 0.728 0.252 0.020
#> GSM537356 1 0.3870 0.70955 0.788 0.208 0.000 0.004
#> GSM537361 1 0.4841 0.67843 0.792 0.044 0.016 0.148
#> GSM537374 4 0.4322 0.53334 0.000 0.152 0.044 0.804
#> GSM537377 4 0.5636 0.10191 0.424 0.000 0.024 0.552
#> GSM537378 3 0.4989 0.64162 0.000 0.164 0.764 0.072
#> GSM537379 3 0.6023 0.53534 0.000 0.060 0.612 0.328
#> GSM537383 2 0.6885 0.46591 0.000 0.596 0.196 0.208
#> GSM537388 2 0.7102 0.40049 0.000 0.548 0.288 0.164
#> GSM537395 3 0.5767 0.62914 0.000 0.152 0.712 0.136
#> GSM537400 4 0.9312 0.27748 0.304 0.088 0.252 0.356
#> GSM537404 2 0.7712 0.21235 0.248 0.552 0.024 0.176
#> GSM537409 3 0.2002 0.70966 0.000 0.044 0.936 0.020
#> GSM537418 1 0.1610 0.75515 0.952 0.032 0.000 0.016
#> GSM537425 1 0.3048 0.74392 0.900 0.028 0.016 0.056
#> GSM537333 3 0.7375 0.00328 0.080 0.028 0.452 0.440
#> GSM537342 3 0.4292 0.65754 0.072 0.088 0.832 0.008
#> GSM537347 4 0.7336 0.27246 0.024 0.332 0.100 0.544
#> GSM537350 2 0.6021 0.00702 0.368 0.592 0.020 0.020
#> GSM537362 4 0.4008 0.56184 0.136 0.020 0.012 0.832
#> GSM537363 1 0.6339 0.60302 0.688 0.140 0.160 0.012
#> GSM537368 1 0.0804 0.75365 0.980 0.012 0.000 0.008
#> GSM537376 3 0.5527 0.60740 0.000 0.168 0.728 0.104
#> GSM537381 1 0.2542 0.75508 0.904 0.084 0.000 0.012
#> GSM537386 2 0.4401 0.59617 0.000 0.812 0.076 0.112
#> GSM537398 4 0.5076 0.54467 0.172 0.072 0.000 0.756
#> GSM537402 2 0.6626 0.38905 0.000 0.544 0.364 0.092
#> GSM537405 1 0.1811 0.75208 0.948 0.028 0.004 0.020
#> GSM537371 1 0.1635 0.73560 0.948 0.000 0.008 0.044
#> GSM537421 3 0.3411 0.67511 0.048 0.008 0.880 0.064
#> GSM537424 1 0.4567 0.61133 0.740 0.016 0.000 0.244
#> GSM537432 4 0.8877 0.23688 0.232 0.060 0.288 0.420
#> GSM537331 4 0.5383 0.39976 0.000 0.292 0.036 0.672
#> GSM537332 3 0.5830 0.63647 0.008 0.172 0.720 0.100
#> GSM537334 4 0.3873 0.56553 0.000 0.096 0.060 0.844
#> GSM537338 4 0.3099 0.56728 0.000 0.104 0.020 0.876
#> GSM537353 3 0.4037 0.69110 0.000 0.112 0.832 0.056
#> GSM537357 1 0.2644 0.71398 0.908 0.000 0.032 0.060
#> GSM537358 2 0.5833 0.58635 0.000 0.692 0.212 0.096
#> GSM537375 4 0.4220 0.34500 0.000 0.004 0.248 0.748
#> GSM537389 2 0.4808 0.60616 0.000 0.736 0.236 0.028
#> GSM537390 3 0.5272 0.64157 0.000 0.172 0.744 0.084
#> GSM537393 3 0.5851 0.62234 0.000 0.084 0.680 0.236
#> GSM537399 2 0.5361 0.37366 0.208 0.724 0.000 0.068
#> GSM537407 1 0.5427 0.48974 0.568 0.416 0.000 0.016
#> GSM537408 2 0.3225 0.62257 0.016 0.892 0.060 0.032
#> GSM537428 4 0.6156 0.28274 0.000 0.344 0.064 0.592
#> GSM537354 3 0.5033 0.53488 0.004 0.008 0.664 0.324
#> GSM537410 3 0.2164 0.69817 0.004 0.068 0.924 0.004
#> GSM537413 3 0.5097 0.14724 0.000 0.428 0.568 0.004
#> GSM537396 2 0.2613 0.60789 0.024 0.916 0.052 0.008
#> GSM537397 2 0.5228 0.43183 0.124 0.756 0.000 0.120
#> GSM537330 3 0.7884 0.03633 0.000 0.312 0.384 0.304
#> GSM537369 1 0.2546 0.75237 0.900 0.092 0.000 0.008
#> GSM537373 2 0.6109 0.29765 0.032 0.580 0.376 0.012
#> GSM537401 2 0.5415 0.32241 0.012 0.668 0.016 0.304
#> GSM537343 1 0.4889 0.58591 0.636 0.360 0.000 0.004
#> GSM537367 3 0.7501 0.12843 0.344 0.152 0.496 0.008
#> GSM537382 3 0.3344 0.68433 0.004 0.108 0.868 0.020
#> GSM537385 2 0.4746 0.63051 0.000 0.776 0.168 0.056
#> GSM537391 4 0.7202 0.34115 0.296 0.152 0.004 0.548
#> GSM537419 2 0.4224 0.63876 0.000 0.812 0.144 0.044
#> GSM537420 1 0.3658 0.74068 0.836 0.144 0.000 0.020
#> GSM537429 3 0.8975 -0.06289 0.064 0.348 0.368 0.220
#> GSM537431 1 0.9625 -0.19908 0.336 0.152 0.320 0.192
#> GSM537387 1 0.5076 0.52773 0.712 0.024 0.004 0.260
#> GSM537414 1 0.8354 -0.08507 0.380 0.024 0.376 0.220
#> GSM537433 1 0.5835 0.64975 0.688 0.244 0.060 0.008
#> GSM537335 4 0.3677 0.56178 0.008 0.148 0.008 0.836
#> GSM537339 4 0.7197 0.26761 0.140 0.392 0.000 0.468
#> GSM537340 3 0.4553 0.66560 0.076 0.012 0.820 0.092
#> GSM537344 1 0.2266 0.75338 0.912 0.084 0.000 0.004
#> GSM537346 2 0.7655 0.34949 0.024 0.560 0.172 0.244
#> GSM537351 1 0.1059 0.74507 0.972 0.000 0.016 0.012
#> GSM537352 3 0.3581 0.70931 0.000 0.032 0.852 0.116
#> GSM537359 2 0.2943 0.63395 0.000 0.892 0.076 0.032
#> GSM537360 3 0.4022 0.69205 0.000 0.096 0.836 0.068
#> GSM537364 1 0.1042 0.74312 0.972 0.000 0.008 0.020
#> GSM537365 1 0.7913 0.31588 0.484 0.336 0.024 0.156
#> GSM537372 1 0.4642 0.68472 0.740 0.240 0.000 0.020
#> GSM537384 1 0.3820 0.74658 0.848 0.088 0.000 0.064
#> GSM537394 2 0.3774 0.58834 0.008 0.844 0.020 0.128
#> GSM537403 3 0.1362 0.70680 0.004 0.020 0.964 0.012
#> GSM537406 2 0.5148 0.38143 0.004 0.640 0.348 0.008
#> GSM537411 2 0.7314 0.27263 0.000 0.496 0.168 0.336
#> GSM537412 3 0.1807 0.70574 0.000 0.052 0.940 0.008
#> GSM537416 3 0.1677 0.69286 0.040 0.000 0.948 0.012
#> GSM537426 3 0.1970 0.70427 0.000 0.060 0.932 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.3584 0.62418 0.104 0.840 0.000 0.040 0.016
#> GSM537345 5 0.4703 0.35710 0.340 0.000 0.000 0.028 0.632
#> GSM537355 3 0.6021 0.22101 0.000 0.024 0.584 0.312 0.080
#> GSM537366 1 0.2736 0.80541 0.888 0.084 0.016 0.008 0.004
#> GSM537370 2 0.3006 0.65395 0.040 0.884 0.060 0.008 0.008
#> GSM537380 2 0.2688 0.68119 0.000 0.896 0.056 0.036 0.012
#> GSM537392 2 0.2995 0.67400 0.000 0.872 0.088 0.032 0.008
#> GSM537415 4 0.4587 0.65495 0.000 0.160 0.096 0.744 0.000
#> GSM537417 3 0.3821 0.52280 0.008 0.004 0.780 0.200 0.008
#> GSM537422 4 0.6828 0.18090 0.356 0.000 0.152 0.468 0.024
#> GSM537423 2 0.5532 0.38796 0.000 0.616 0.104 0.280 0.000
#> GSM537427 5 0.7599 0.13984 0.000 0.272 0.260 0.052 0.416
#> GSM537430 3 0.5380 0.48343 0.000 0.188 0.708 0.056 0.048
#> GSM537336 1 0.2726 0.78531 0.884 0.000 0.000 0.064 0.052
#> GSM537337 4 0.7457 0.33951 0.000 0.064 0.176 0.460 0.300
#> GSM537348 5 0.5746 0.47899 0.280 0.076 0.020 0.000 0.624
#> GSM537349 2 0.3495 0.66625 0.000 0.816 0.032 0.152 0.000
#> GSM537356 1 0.2392 0.79945 0.888 0.104 0.004 0.000 0.004
#> GSM537361 1 0.4273 0.31393 0.552 0.000 0.448 0.000 0.000
#> GSM537374 5 0.5741 0.23847 0.000 0.096 0.360 0.000 0.544
#> GSM537377 5 0.3060 0.56377 0.128 0.000 0.000 0.024 0.848
#> GSM537378 4 0.6361 0.36375 0.000 0.176 0.340 0.484 0.000
#> GSM537379 3 0.2037 0.60432 0.000 0.004 0.920 0.064 0.012
#> GSM537383 2 0.6089 0.16488 0.000 0.504 0.408 0.060 0.028
#> GSM537388 2 0.6351 0.41855 0.000 0.548 0.296 0.144 0.012
#> GSM537395 4 0.6426 0.31167 0.000 0.156 0.368 0.472 0.004
#> GSM537400 3 0.6077 0.42787 0.196 0.004 0.656 0.108 0.036
#> GSM537404 3 0.6920 0.14088 0.328 0.236 0.428 0.004 0.004
#> GSM537409 4 0.4106 0.56040 0.000 0.020 0.256 0.724 0.000
#> GSM537418 1 0.1875 0.81895 0.940 0.008 0.008 0.016 0.028
#> GSM537425 1 0.3238 0.78681 0.848 0.004 0.124 0.020 0.004
#> GSM537333 3 0.3669 0.57687 0.012 0.000 0.800 0.176 0.012
#> GSM537342 4 0.2774 0.68351 0.020 0.080 0.008 0.888 0.004
#> GSM537347 3 0.1580 0.59389 0.016 0.016 0.952 0.004 0.012
#> GSM537350 2 0.3521 0.51632 0.232 0.764 0.000 0.004 0.000
#> GSM537362 5 0.4194 0.41880 0.012 0.000 0.276 0.004 0.708
#> GSM537363 4 0.5947 -0.00720 0.420 0.092 0.000 0.484 0.004
#> GSM537368 1 0.1522 0.81002 0.944 0.000 0.000 0.012 0.044
#> GSM537376 4 0.3995 0.65671 0.000 0.152 0.000 0.788 0.060
#> GSM537381 1 0.1668 0.81843 0.940 0.028 0.032 0.000 0.000
#> GSM537386 2 0.5632 0.34682 0.016 0.600 0.336 0.040 0.008
#> GSM537398 5 0.4724 0.54898 0.080 0.008 0.168 0.000 0.744
#> GSM537402 4 0.4870 -0.02431 0.000 0.448 0.004 0.532 0.016
#> GSM537405 1 0.2705 0.81216 0.900 0.012 0.036 0.004 0.048
#> GSM537371 1 0.2325 0.79494 0.904 0.000 0.000 0.028 0.068
#> GSM537421 4 0.1917 0.67132 0.016 0.008 0.004 0.936 0.036
#> GSM537424 1 0.4281 0.71529 0.768 0.004 0.172 0.000 0.056
#> GSM537432 3 0.6491 0.43871 0.056 0.000 0.588 0.264 0.092
#> GSM537331 5 0.6059 0.39533 0.000 0.184 0.244 0.000 0.572
#> GSM537332 3 0.1282 0.61060 0.000 0.000 0.952 0.044 0.004
#> GSM537334 3 0.5190 0.00376 0.000 0.028 0.540 0.008 0.424
#> GSM537338 5 0.3281 0.54922 0.000 0.060 0.092 0.000 0.848
#> GSM537353 4 0.5731 0.56789 0.000 0.180 0.196 0.624 0.000
#> GSM537357 1 0.3346 0.75762 0.844 0.000 0.000 0.092 0.064
#> GSM537358 2 0.4643 0.59021 0.000 0.736 0.192 0.068 0.004
#> GSM537375 5 0.5812 0.32427 0.004 0.028 0.064 0.264 0.640
#> GSM537389 2 0.3209 0.63810 0.000 0.812 0.008 0.180 0.000
#> GSM537390 3 0.5339 0.47169 0.000 0.152 0.672 0.176 0.000
#> GSM537393 3 0.5584 0.44840 0.000 0.060 0.668 0.236 0.036
#> GSM537399 2 0.6879 0.05005 0.328 0.400 0.268 0.000 0.004
#> GSM537407 1 0.4932 0.70711 0.744 0.116 0.128 0.008 0.004
#> GSM537408 2 0.1617 0.68161 0.020 0.948 0.020 0.012 0.000
#> GSM537428 3 0.6672 0.01866 0.000 0.288 0.440 0.000 0.272
#> GSM537354 4 0.5980 0.49911 0.000 0.052 0.052 0.616 0.280
#> GSM537410 4 0.1956 0.69168 0.000 0.076 0.008 0.916 0.000
#> GSM537413 2 0.5733 0.12680 0.000 0.476 0.084 0.440 0.000
#> GSM537396 2 0.2529 0.66048 0.056 0.900 0.000 0.040 0.004
#> GSM537397 2 0.3771 0.57761 0.156 0.804 0.004 0.000 0.036
#> GSM537330 3 0.0963 0.60826 0.000 0.000 0.964 0.036 0.000
#> GSM537369 1 0.1731 0.81371 0.932 0.060 0.004 0.000 0.004
#> GSM537373 2 0.4973 0.23608 0.024 0.564 0.000 0.408 0.004
#> GSM537401 2 0.5556 0.43162 0.036 0.660 0.008 0.032 0.264
#> GSM537343 1 0.4490 0.61373 0.692 0.284 0.016 0.004 0.004
#> GSM537367 4 0.5572 0.47319 0.220 0.112 0.004 0.660 0.004
#> GSM537382 4 0.3115 0.67815 0.000 0.108 0.012 0.860 0.020
#> GSM537385 2 0.3332 0.67958 0.000 0.844 0.028 0.120 0.008
#> GSM537391 5 0.3184 0.58225 0.068 0.052 0.000 0.012 0.868
#> GSM537419 2 0.2046 0.68772 0.000 0.916 0.016 0.068 0.000
#> GSM537420 1 0.3530 0.69723 0.784 0.204 0.000 0.012 0.000
#> GSM537429 3 0.5233 0.56458 0.020 0.068 0.724 0.180 0.008
#> GSM537431 3 0.6964 0.36725 0.188 0.012 0.508 0.280 0.012
#> GSM537387 5 0.4907 -0.03077 0.484 0.000 0.000 0.024 0.492
#> GSM537414 3 0.3106 0.53048 0.140 0.000 0.840 0.020 0.000
#> GSM537433 1 0.3413 0.79053 0.844 0.100 0.052 0.000 0.004
#> GSM537335 5 0.4961 0.32363 0.004 0.028 0.372 0.000 0.596
#> GSM537339 5 0.6817 0.44693 0.136 0.252 0.052 0.000 0.560
#> GSM537340 4 0.3775 0.66394 0.032 0.044 0.024 0.856 0.044
#> GSM537344 1 0.1041 0.81663 0.964 0.032 0.000 0.000 0.004
#> GSM537346 3 0.1251 0.59836 0.008 0.036 0.956 0.000 0.000
#> GSM537351 1 0.2578 0.80260 0.904 0.000 0.016 0.040 0.040
#> GSM537352 4 0.5605 0.64251 0.000 0.128 0.128 0.704 0.040
#> GSM537359 2 0.1469 0.68783 0.000 0.948 0.016 0.036 0.000
#> GSM537360 4 0.5978 0.58507 0.000 0.188 0.172 0.628 0.012
#> GSM537364 1 0.2696 0.79367 0.892 0.000 0.012 0.024 0.072
#> GSM537365 1 0.5114 0.07197 0.488 0.036 0.476 0.000 0.000
#> GSM537372 1 0.2806 0.77173 0.844 0.152 0.004 0.000 0.000
#> GSM537384 1 0.2740 0.80454 0.888 0.044 0.004 0.000 0.064
#> GSM537394 2 0.4895 0.07514 0.012 0.528 0.452 0.008 0.000
#> GSM537403 4 0.1960 0.68215 0.000 0.020 0.048 0.928 0.004
#> GSM537406 2 0.4015 0.52653 0.008 0.724 0.000 0.264 0.004
#> GSM537411 3 0.8158 0.10831 0.000 0.320 0.356 0.124 0.200
#> GSM537412 4 0.2659 0.68995 0.000 0.052 0.060 0.888 0.000
#> GSM537416 4 0.1942 0.66503 0.012 0.000 0.068 0.920 0.000
#> GSM537426 4 0.2694 0.69208 0.000 0.076 0.040 0.884 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 6 0.849 -0.17217 0.236 0.276 0.004 0.120 0.072 0.292
#> GSM537345 5 0.605 0.04012 0.288 0.000 0.004 0.000 0.460 0.248
#> GSM537355 3 0.670 0.06865 0.000 0.028 0.428 0.404 0.080 0.060
#> GSM537366 1 0.501 0.55683 0.720 0.004 0.036 0.088 0.004 0.148
#> GSM537370 2 0.277 0.54864 0.044 0.884 0.004 0.000 0.024 0.044
#> GSM537380 2 0.226 0.57233 0.000 0.912 0.012 0.008 0.040 0.028
#> GSM537392 2 0.152 0.57335 0.000 0.948 0.020 0.008 0.016 0.008
#> GSM537415 4 0.327 0.45081 0.000 0.076 0.060 0.844 0.000 0.020
#> GSM537417 3 0.520 0.37934 0.000 0.008 0.636 0.280 0.040 0.036
#> GSM537422 4 0.761 -0.04386 0.180 0.000 0.184 0.364 0.004 0.268
#> GSM537423 2 0.383 0.48289 0.000 0.752 0.012 0.216 0.004 0.016
#> GSM537427 2 0.605 0.33352 0.000 0.584 0.108 0.024 0.260 0.024
#> GSM537430 3 0.615 0.00237 0.000 0.404 0.452 0.060 0.084 0.000
#> GSM537336 1 0.397 0.64998 0.708 0.000 0.004 0.012 0.008 0.268
#> GSM537337 5 0.761 -0.05117 0.000 0.080 0.064 0.352 0.388 0.116
#> GSM537348 5 0.627 0.32609 0.308 0.016 0.020 0.000 0.516 0.140
#> GSM537349 2 0.623 0.15754 0.000 0.512 0.028 0.316 0.008 0.136
#> GSM537356 1 0.242 0.67321 0.888 0.008 0.012 0.000 0.004 0.088
#> GSM537361 3 0.467 0.20217 0.324 0.000 0.620 0.000 0.004 0.052
#> GSM537374 5 0.536 0.38322 0.000 0.136 0.272 0.004 0.588 0.000
#> GSM537377 5 0.420 0.42187 0.084 0.000 0.000 0.000 0.728 0.188
#> GSM537378 4 0.576 0.30287 0.000 0.220 0.188 0.576 0.000 0.016
#> GSM537379 3 0.468 0.46811 0.000 0.028 0.740 0.164 0.052 0.016
#> GSM537383 2 0.387 0.55769 0.000 0.808 0.116 0.040 0.024 0.012
#> GSM537388 4 0.790 0.09634 0.000 0.280 0.204 0.328 0.016 0.172
#> GSM537395 2 0.650 0.31646 0.000 0.540 0.148 0.256 0.024 0.032
#> GSM537400 3 0.650 -0.13011 0.076 0.032 0.444 0.032 0.004 0.412
#> GSM537404 1 0.753 0.26935 0.496 0.136 0.248 0.044 0.032 0.044
#> GSM537409 4 0.287 0.43051 0.000 0.008 0.140 0.840 0.000 0.012
#> GSM537418 1 0.390 0.70207 0.804 0.000 0.036 0.016 0.020 0.124
#> GSM537425 1 0.495 0.65018 0.700 0.000 0.164 0.028 0.000 0.108
#> GSM537333 3 0.422 0.38009 0.004 0.004 0.744 0.068 0.000 0.180
#> GSM537342 4 0.409 0.30944 0.008 0.012 0.000 0.656 0.000 0.324
#> GSM537347 3 0.276 0.50266 0.008 0.024 0.888 0.008 0.060 0.012
#> GSM537350 2 0.600 0.09775 0.412 0.444 0.000 0.028 0.000 0.116
#> GSM537362 5 0.430 0.47450 0.008 0.004 0.216 0.000 0.724 0.048
#> GSM537363 4 0.606 0.05097 0.388 0.004 0.000 0.396 0.000 0.212
#> GSM537368 1 0.299 0.69747 0.824 0.000 0.004 0.004 0.008 0.160
#> GSM537376 6 0.677 -0.03747 0.000 0.208 0.004 0.292 0.048 0.448
#> GSM537381 1 0.288 0.71143 0.860 0.000 0.080 0.004 0.000 0.056
#> GSM537386 2 0.728 0.10926 0.032 0.420 0.344 0.036 0.012 0.156
#> GSM537398 5 0.388 0.52101 0.056 0.004 0.148 0.000 0.784 0.008
#> GSM537402 4 0.628 0.23584 0.000 0.192 0.000 0.528 0.040 0.240
#> GSM537405 1 0.393 0.67722 0.760 0.000 0.040 0.000 0.012 0.188
#> GSM537371 1 0.373 0.67504 0.748 0.000 0.008 0.000 0.020 0.224
#> GSM537421 4 0.463 0.20068 0.000 0.004 0.008 0.588 0.024 0.376
#> GSM537424 1 0.582 0.37785 0.536 0.000 0.320 0.000 0.120 0.024
#> GSM537432 6 0.692 -0.02484 0.036 0.032 0.396 0.088 0.016 0.432
#> GSM537331 5 0.528 0.45758 0.000 0.188 0.160 0.000 0.640 0.012
#> GSM537332 3 0.243 0.51908 0.004 0.004 0.892 0.072 0.000 0.028
#> GSM537334 5 0.427 0.27771 0.000 0.012 0.428 0.004 0.556 0.000
#> GSM537338 5 0.332 0.52575 0.000 0.072 0.056 0.000 0.844 0.028
#> GSM537353 2 0.632 0.15116 0.000 0.484 0.076 0.360 0.004 0.076
#> GSM537357 1 0.448 0.61689 0.660 0.000 0.004 0.016 0.020 0.300
#> GSM537358 2 0.289 0.56600 0.000 0.876 0.056 0.040 0.004 0.024
#> GSM537375 5 0.605 0.31419 0.000 0.012 0.068 0.188 0.624 0.108
#> GSM537389 4 0.634 0.04266 0.004 0.384 0.008 0.404 0.004 0.196
#> GSM537390 2 0.652 0.08442 0.000 0.408 0.308 0.264 0.004 0.016
#> GSM537393 3 0.712 0.18837 0.000 0.204 0.428 0.296 0.056 0.016
#> GSM537399 1 0.699 0.07502 0.416 0.108 0.360 0.000 0.008 0.108
#> GSM537407 1 0.606 0.43408 0.568 0.048 0.244 0.000 0.000 0.140
#> GSM537408 2 0.199 0.56742 0.016 0.920 0.008 0.004 0.000 0.052
#> GSM537428 5 0.619 0.28786 0.000 0.240 0.284 0.000 0.464 0.012
#> GSM537354 4 0.727 0.16605 0.000 0.076 0.032 0.456 0.292 0.144
#> GSM537410 4 0.299 0.41833 0.000 0.024 0.000 0.824 0.000 0.152
#> GSM537413 2 0.609 0.32300 0.000 0.568 0.052 0.244 0.000 0.136
#> GSM537396 2 0.762 0.01860 0.192 0.344 0.000 0.168 0.004 0.292
#> GSM537397 2 0.700 0.15733 0.316 0.468 0.004 0.012 0.108 0.092
#> GSM537330 3 0.442 0.49303 0.008 0.012 0.776 0.128 0.020 0.056
#> GSM537369 1 0.122 0.70970 0.956 0.004 0.004 0.000 0.004 0.032
#> GSM537373 4 0.695 0.19283 0.096 0.168 0.000 0.444 0.000 0.292
#> GSM537401 5 0.836 0.10617 0.132 0.248 0.000 0.084 0.356 0.180
#> GSM537343 1 0.333 0.68059 0.844 0.084 0.008 0.012 0.000 0.052
#> GSM537367 4 0.570 0.21624 0.284 0.004 0.008 0.560 0.000 0.144
#> GSM537382 6 0.551 -0.13276 0.000 0.048 0.016 0.412 0.016 0.508
#> GSM537385 2 0.753 -0.05153 0.044 0.360 0.028 0.328 0.008 0.232
#> GSM537391 5 0.555 0.42634 0.112 0.048 0.000 0.012 0.676 0.152
#> GSM537419 2 0.266 0.56698 0.008 0.888 0.004 0.060 0.004 0.036
#> GSM537420 1 0.417 0.59623 0.768 0.064 0.000 0.024 0.000 0.144
#> GSM537429 3 0.668 0.25238 0.008 0.032 0.536 0.220 0.016 0.188
#> GSM537431 6 0.715 -0.02719 0.064 0.068 0.392 0.072 0.000 0.404
#> GSM537387 1 0.609 0.18682 0.384 0.000 0.000 0.000 0.332 0.284
#> GSM537414 3 0.326 0.49782 0.088 0.000 0.852 0.024 0.016 0.020
#> GSM537433 1 0.275 0.70263 0.868 0.000 0.080 0.004 0.000 0.048
#> GSM537335 5 0.432 0.39771 0.000 0.016 0.336 0.000 0.636 0.012
#> GSM537339 5 0.694 0.42603 0.252 0.068 0.076 0.000 0.536 0.068
#> GSM537340 4 0.600 0.14370 0.000 0.100 0.012 0.504 0.020 0.364
#> GSM537344 1 0.147 0.70981 0.932 0.000 0.000 0.000 0.004 0.064
#> GSM537346 3 0.306 0.48787 0.004 0.120 0.844 0.000 0.008 0.024
#> GSM537351 1 0.477 0.57433 0.608 0.004 0.036 0.004 0.004 0.344
#> GSM537352 4 0.763 0.02650 0.000 0.244 0.032 0.344 0.068 0.312
#> GSM537359 2 0.220 0.56009 0.004 0.904 0.012 0.008 0.000 0.072
#> GSM537360 4 0.487 0.37407 0.000 0.208 0.068 0.696 0.004 0.024
#> GSM537364 1 0.464 0.62261 0.664 0.000 0.024 0.004 0.024 0.284
#> GSM537365 3 0.612 0.10432 0.352 0.056 0.508 0.000 0.004 0.080
#> GSM537372 1 0.240 0.68312 0.892 0.028 0.000 0.000 0.008 0.072
#> GSM537384 1 0.449 0.60259 0.752 0.000 0.032 0.000 0.104 0.112
#> GSM537394 2 0.339 0.49321 0.000 0.784 0.192 0.000 0.004 0.020
#> GSM537403 4 0.444 0.37390 0.000 0.020 0.052 0.720 0.000 0.208
#> GSM537406 4 0.693 0.19405 0.072 0.236 0.000 0.436 0.000 0.256
#> GSM537411 2 0.670 0.36922 0.000 0.580 0.196 0.052 0.116 0.056
#> GSM537412 4 0.252 0.45295 0.000 0.016 0.048 0.892 0.000 0.044
#> GSM537416 4 0.401 0.34341 0.000 0.004 0.040 0.728 0.000 0.228
#> GSM537426 4 0.235 0.44803 0.000 0.028 0.028 0.904 0.000 0.040
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> MAD:NMF 98 0.348 0.263 2
#> MAD:NMF 63 0.714 0.928 3
#> MAD:NMF 67 0.451 0.620 4
#> MAD:NMF 64 0.373 0.609 5
#> MAD:NMF 31 0.707 0.702 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 104 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 0.641 0.901 0.941 0.4787 0.514 0.514
#> 3 3 0.502 0.491 0.782 0.2898 0.911 0.827
#> 4 4 0.522 0.556 0.639 0.1395 0.825 0.626
#> 5 5 0.637 0.735 0.804 0.0854 0.815 0.492
#> 6 6 0.680 0.722 0.815 0.0354 0.980 0.903
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
#> GSM537341 2 0.2423 0.925 0.040 0.960
#> GSM537345 1 0.0000 0.948 1.000 0.000
#> GSM537355 2 0.6048 0.862 0.148 0.852
#> GSM537366 1 0.0000 0.948 1.000 0.000
#> GSM537370 2 0.0376 0.931 0.004 0.996
#> GSM537380 2 0.1633 0.930 0.024 0.976
#> GSM537392 2 0.1633 0.930 0.024 0.976
#> GSM537415 1 0.3431 0.929 0.936 0.064
#> GSM537417 1 0.0672 0.947 0.992 0.008
#> GSM537422 1 0.0000 0.948 1.000 0.000
#> GSM537423 1 0.6048 0.855 0.852 0.148
#> GSM537427 2 0.0376 0.931 0.004 0.996
#> GSM537430 2 0.0000 0.929 0.000 1.000
#> GSM537336 1 0.0000 0.948 1.000 0.000
#> GSM537337 1 0.6048 0.855 0.852 0.148
#> GSM537348 2 0.1633 0.930 0.024 0.976
#> GSM537349 2 0.1633 0.930 0.024 0.976
#> GSM537356 2 0.8763 0.652 0.296 0.704
#> GSM537361 2 0.7376 0.781 0.208 0.792
#> GSM537374 2 0.0000 0.929 0.000 1.000
#> GSM537377 2 0.5059 0.893 0.112 0.888
#> GSM537378 2 0.4939 0.897 0.108 0.892
#> GSM537379 2 0.4690 0.901 0.100 0.900
#> GSM537383 2 0.0000 0.929 0.000 1.000
#> GSM537388 2 0.0376 0.931 0.004 0.996
#> GSM537395 1 0.6048 0.855 0.852 0.148
#> GSM537400 2 0.0376 0.931 0.004 0.996
#> GSM537404 1 0.0672 0.947 0.992 0.008
#> GSM537409 1 0.3431 0.929 0.936 0.064
#> GSM537418 1 0.3584 0.927 0.932 0.068
#> GSM537425 1 0.0000 0.948 1.000 0.000
#> GSM537333 2 0.0000 0.929 0.000 1.000
#> GSM537342 1 0.1633 0.944 0.976 0.024
#> GSM537347 2 0.4562 0.903 0.096 0.904
#> GSM537350 1 0.0376 0.947 0.996 0.004
#> GSM537362 2 0.4562 0.903 0.096 0.904
#> GSM537363 1 0.0000 0.948 1.000 0.000
#> GSM537368 1 0.0000 0.948 1.000 0.000
#> GSM537376 2 0.4815 0.898 0.104 0.896
#> GSM537381 2 0.2603 0.926 0.044 0.956
#> GSM537386 2 0.0000 0.929 0.000 1.000
#> GSM537398 2 0.0000 0.929 0.000 1.000
#> GSM537402 2 0.8763 0.651 0.296 0.704
#> GSM537405 1 0.0672 0.947 0.992 0.008
#> GSM537371 1 0.0000 0.948 1.000 0.000
#> GSM537421 1 0.0000 0.948 1.000 0.000
#> GSM537424 2 0.6048 0.862 0.148 0.852
#> GSM537432 2 0.0376 0.931 0.004 0.996
#> GSM537331 2 0.0000 0.929 0.000 1.000
#> GSM537332 2 0.0376 0.931 0.004 0.996
#> GSM537334 2 0.0000 0.929 0.000 1.000
#> GSM537338 2 0.1633 0.930 0.024 0.976
#> GSM537353 1 0.7602 0.748 0.780 0.220
#> GSM537357 1 0.0000 0.948 1.000 0.000
#> GSM537358 2 0.7602 0.760 0.220 0.780
#> GSM537375 2 0.4690 0.901 0.100 0.900
#> GSM537389 2 0.0376 0.931 0.004 0.996
#> GSM537390 2 0.0376 0.931 0.004 0.996
#> GSM537393 2 0.0376 0.931 0.004 0.996
#> GSM537399 2 0.0000 0.929 0.000 1.000
#> GSM537407 2 0.3274 0.918 0.060 0.940
#> GSM537408 1 0.0672 0.947 0.992 0.008
#> GSM537428 2 0.7602 0.760 0.220 0.780
#> GSM537354 1 0.4815 0.899 0.896 0.104
#> GSM537410 1 0.1633 0.944 0.976 0.024
#> GSM537413 2 0.0376 0.931 0.004 0.996
#> GSM537396 2 0.0376 0.931 0.004 0.996
#> GSM537397 2 0.2948 0.923 0.052 0.948
#> GSM537330 2 0.0000 0.929 0.000 1.000
#> GSM537369 2 0.9983 0.148 0.476 0.524
#> GSM537373 1 0.7376 0.766 0.792 0.208
#> GSM537401 2 0.1633 0.930 0.024 0.976
#> GSM537343 1 0.4562 0.907 0.904 0.096
#> GSM537367 1 0.0000 0.948 1.000 0.000
#> GSM537382 2 0.4815 0.898 0.104 0.896
#> GSM537385 2 0.2603 0.924 0.044 0.956
#> GSM537391 2 0.1633 0.930 0.024 0.976
#> GSM537419 2 0.7453 0.787 0.212 0.788
#> GSM537420 1 0.4939 0.879 0.892 0.108
#> GSM537429 2 0.0376 0.931 0.004 0.996
#> GSM537431 2 0.0672 0.931 0.008 0.992
#> GSM537387 2 0.0938 0.930 0.012 0.988
#> GSM537414 1 0.3431 0.929 0.936 0.064
#> GSM537433 1 0.0000 0.948 1.000 0.000
#> GSM537335 2 0.0000 0.929 0.000 1.000
#> GSM537339 2 0.0376 0.931 0.004 0.996
#> GSM537340 1 0.0000 0.948 1.000 0.000
#> GSM537344 1 0.4562 0.907 0.904 0.096
#> GSM537346 2 0.0376 0.931 0.004 0.996
#> GSM537351 1 0.0000 0.948 1.000 0.000
#> GSM537352 1 0.6247 0.845 0.844 0.156
#> GSM537359 2 0.6623 0.822 0.172 0.828
#> GSM537360 1 0.1633 0.944 0.976 0.024
#> GSM537364 1 0.0000 0.948 1.000 0.000
#> GSM537365 2 0.7139 0.796 0.196 0.804
#> GSM537372 2 0.4939 0.896 0.108 0.892
#> GSM537384 2 0.5059 0.893 0.112 0.888
#> GSM537394 2 0.0376 0.931 0.004 0.996
#> GSM537403 1 0.0000 0.948 1.000 0.000
#> GSM537406 1 0.0000 0.948 1.000 0.000
#> GSM537411 2 0.3274 0.918 0.060 0.940
#> GSM537412 1 0.0000 0.948 1.000 0.000
#> GSM537416 1 0.3431 0.929 0.936 0.064
#> GSM537426 1 0.3431 0.929 0.936 0.064
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.2261 0.51168 0.068 0.932 0.000
#> GSM537345 3 0.0237 0.88870 0.004 0.000 0.996
#> GSM537355 2 0.7274 -0.11422 0.452 0.520 0.028
#> GSM537366 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537370 2 0.0424 0.52798 0.008 0.992 0.000
#> GSM537380 2 0.1643 0.52068 0.044 0.956 0.000
#> GSM537392 2 0.1753 0.52035 0.048 0.952 0.000
#> GSM537415 3 0.5285 0.85310 0.112 0.064 0.824
#> GSM537417 3 0.2625 0.88130 0.084 0.000 0.916
#> GSM537422 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537423 3 0.6856 0.76418 0.132 0.128 0.740
#> GSM537427 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537430 2 0.6291 0.02905 0.468 0.532 0.000
#> GSM537336 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537337 3 0.6856 0.76418 0.132 0.128 0.740
#> GSM537348 2 0.3619 0.46745 0.136 0.864 0.000
#> GSM537349 2 0.1643 0.52068 0.044 0.956 0.000
#> GSM537356 2 0.9357 -0.26825 0.392 0.440 0.168
#> GSM537361 1 0.8925 0.35207 0.464 0.412 0.124
#> GSM537374 1 0.5968 0.13890 0.636 0.364 0.000
#> GSM537377 2 0.6763 -0.02561 0.436 0.552 0.012
#> GSM537378 2 0.6129 0.16466 0.324 0.668 0.008
#> GSM537379 2 0.6330 0.06330 0.396 0.600 0.004
#> GSM537383 2 0.2711 0.45194 0.088 0.912 0.000
#> GSM537388 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537395 3 0.6856 0.76418 0.132 0.128 0.740
#> GSM537400 2 0.6225 0.02455 0.432 0.568 0.000
#> GSM537404 3 0.2625 0.88130 0.084 0.000 0.916
#> GSM537409 3 0.5285 0.85310 0.112 0.064 0.824
#> GSM537418 3 0.5153 0.85513 0.100 0.068 0.832
#> GSM537425 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537333 2 0.6291 0.02905 0.468 0.532 0.000
#> GSM537342 3 0.3637 0.87884 0.084 0.024 0.892
#> GSM537347 2 0.6468 -0.02627 0.444 0.552 0.004
#> GSM537350 3 0.2486 0.88691 0.060 0.008 0.932
#> GSM537362 2 0.6460 -0.02001 0.440 0.556 0.004
#> GSM537363 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537368 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537376 2 0.6451 -0.00617 0.436 0.560 0.004
#> GSM537381 2 0.4452 0.39238 0.192 0.808 0.000
#> GSM537386 2 0.6295 0.02581 0.472 0.528 0.000
#> GSM537398 2 0.6286 0.03133 0.464 0.536 0.000
#> GSM537402 2 0.9460 -0.29126 0.396 0.424 0.180
#> GSM537405 3 0.2796 0.87915 0.092 0.000 0.908
#> GSM537371 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537421 3 0.0592 0.88919 0.012 0.000 0.988
#> GSM537424 2 0.7274 -0.11422 0.452 0.520 0.028
#> GSM537432 2 0.3879 0.44777 0.152 0.848 0.000
#> GSM537331 2 0.6295 0.02581 0.472 0.528 0.000
#> GSM537332 2 0.0424 0.52795 0.008 0.992 0.000
#> GSM537334 2 0.6295 0.02581 0.472 0.528 0.000
#> GSM537338 2 0.3482 0.47443 0.128 0.872 0.000
#> GSM537353 3 0.7963 0.62837 0.152 0.188 0.660
#> GSM537357 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537358 2 0.9144 -0.35787 0.408 0.448 0.144
#> GSM537375 2 0.6330 0.06330 0.396 0.600 0.004
#> GSM537389 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537390 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537393 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537399 1 0.5968 0.13890 0.636 0.364 0.000
#> GSM537407 1 0.6476 0.28136 0.548 0.448 0.004
#> GSM537408 3 0.2651 0.88660 0.060 0.012 0.928
#> GSM537428 2 0.9144 -0.35787 0.408 0.448 0.144
#> GSM537354 3 0.5944 0.82217 0.120 0.088 0.792
#> GSM537410 3 0.3637 0.87884 0.084 0.024 0.892
#> GSM537413 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537396 2 0.1411 0.52116 0.036 0.964 0.000
#> GSM537397 2 0.4475 0.43265 0.144 0.840 0.016
#> GSM537330 2 0.2711 0.45194 0.088 0.912 0.000
#> GSM537369 1 0.9938 0.21831 0.368 0.280 0.352
#> GSM537373 3 0.7633 0.66095 0.132 0.184 0.684
#> GSM537401 2 0.3752 0.45943 0.144 0.856 0.000
#> GSM537343 3 0.5793 0.83336 0.116 0.084 0.800
#> GSM537367 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537382 2 0.6247 0.09120 0.376 0.620 0.004
#> GSM537385 2 0.2356 0.50872 0.072 0.928 0.000
#> GSM537391 2 0.3686 0.46332 0.140 0.860 0.000
#> GSM537419 2 0.8556 -0.21499 0.416 0.488 0.096
#> GSM537420 3 0.5008 0.79838 0.180 0.016 0.804
#> GSM537429 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537431 1 0.4974 0.32088 0.764 0.236 0.000
#> GSM537387 2 0.1399 0.52341 0.028 0.968 0.004
#> GSM537414 3 0.5060 0.85747 0.100 0.064 0.836
#> GSM537433 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537335 2 0.6295 0.02581 0.472 0.528 0.000
#> GSM537339 2 0.0424 0.52798 0.008 0.992 0.000
#> GSM537340 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537344 3 0.5793 0.83336 0.116 0.084 0.800
#> GSM537346 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537351 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537352 3 0.6981 0.75366 0.136 0.132 0.732
#> GSM537359 1 0.8628 0.32014 0.472 0.428 0.100
#> GSM537360 3 0.3550 0.88072 0.080 0.024 0.896
#> GSM537364 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537365 1 0.8786 0.34049 0.464 0.424 0.112
#> GSM537372 2 0.6659 -0.06212 0.460 0.532 0.008
#> GSM537384 2 0.6529 0.08923 0.368 0.620 0.012
#> GSM537394 2 0.0000 0.52862 0.000 1.000 0.000
#> GSM537403 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537406 3 0.1163 0.88768 0.028 0.000 0.972
#> GSM537411 1 0.6483 0.27428 0.544 0.452 0.004
#> GSM537412 3 0.0000 0.88824 0.000 0.000 1.000
#> GSM537416 3 0.5285 0.85310 0.112 0.064 0.824
#> GSM537426 3 0.5285 0.85310 0.112 0.064 0.824
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.7784 0.5936 0.364 0.392 0.000 NA
#> GSM537345 3 0.0524 0.7575 0.004 0.000 0.988 NA
#> GSM537355 1 0.2048 0.6157 0.928 0.008 0.000 NA
#> GSM537366 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537370 2 0.7640 0.6678 0.296 0.464 0.000 NA
#> GSM537380 2 0.7761 0.6278 0.340 0.416 0.000 NA
#> GSM537392 2 0.7766 0.6247 0.344 0.412 0.000 NA
#> GSM537415 3 0.6946 0.6754 0.116 0.000 0.504 NA
#> GSM537417 3 0.4746 0.7390 0.056 0.000 0.776 NA
#> GSM537422 3 0.0469 0.7571 0.000 0.000 0.988 NA
#> GSM537423 3 0.7583 0.5880 0.196 0.000 0.420 NA
#> GSM537427 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537430 2 0.0336 0.4278 0.008 0.992 0.000 NA
#> GSM537336 3 0.0188 0.7563 0.000 0.000 0.996 NA
#> GSM537337 3 0.7583 0.5880 0.196 0.000 0.420 NA
#> GSM537348 1 0.7490 -0.2563 0.476 0.328 0.000 NA
#> GSM537349 2 0.7761 0.6278 0.340 0.416 0.000 NA
#> GSM537356 1 0.4289 0.5694 0.796 0.000 0.032 NA
#> GSM537361 1 0.5649 0.5464 0.620 0.000 0.036 NA
#> GSM537374 2 0.5630 0.1014 0.136 0.724 0.000 NA
#> GSM537377 1 0.1724 0.6071 0.948 0.032 0.000 NA
#> GSM537378 1 0.4824 0.4411 0.780 0.144 0.000 NA
#> GSM537379 1 0.3392 0.5546 0.872 0.072 0.000 NA
#> GSM537383 2 0.7039 0.6269 0.256 0.568 0.000 NA
#> GSM537388 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537395 3 0.7583 0.5880 0.196 0.000 0.420 NA
#> GSM537400 2 0.5731 0.3679 0.116 0.712 0.000 NA
#> GSM537404 3 0.4874 0.7377 0.056 0.000 0.764 NA
#> GSM537409 3 0.6946 0.6754 0.116 0.000 0.504 NA
#> GSM537418 3 0.6859 0.6785 0.108 0.000 0.512 NA
#> GSM537425 3 0.0469 0.7577 0.000 0.000 0.988 NA
#> GSM537333 2 0.0336 0.4278 0.008 0.992 0.000 NA
#> GSM537342 3 0.5998 0.7275 0.088 0.000 0.664 NA
#> GSM537347 1 0.1820 0.6090 0.944 0.036 0.000 NA
#> GSM537350 3 0.5172 0.7176 0.036 0.000 0.704 NA
#> GSM537362 1 0.2919 0.5911 0.896 0.044 0.000 NA
#> GSM537363 3 0.0000 0.7558 0.000 0.000 1.000 NA
#> GSM537368 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537376 1 0.1798 0.6009 0.944 0.040 0.000 NA
#> GSM537381 1 0.6548 -0.0109 0.608 0.276 0.000 NA
#> GSM537386 2 0.0000 0.4232 0.000 1.000 0.000 NA
#> GSM537398 2 0.0469 0.4287 0.012 0.988 0.000 NA
#> GSM537402 1 0.5583 0.5655 0.664 0.004 0.036 NA
#> GSM537405 3 0.5030 0.7348 0.060 0.000 0.752 NA
#> GSM537371 3 0.0188 0.7563 0.000 0.000 0.996 NA
#> GSM537421 3 0.3052 0.7607 0.004 0.000 0.860 NA
#> GSM537424 1 0.2048 0.6157 0.928 0.008 0.000 NA
#> GSM537432 1 0.7895 -0.4609 0.376 0.316 0.000 NA
#> GSM537331 2 0.0000 0.4232 0.000 1.000 0.000 NA
#> GSM537332 2 0.7694 0.6613 0.308 0.448 0.000 NA
#> GSM537334 2 0.0000 0.4232 0.000 1.000 0.000 NA
#> GSM537338 1 0.7485 -0.2807 0.472 0.336 0.000 NA
#> GSM537353 3 0.7862 0.4953 0.280 0.000 0.388 NA
#> GSM537357 3 0.0188 0.7563 0.000 0.000 0.996 NA
#> GSM537358 1 0.5535 0.5395 0.560 0.020 0.000 NA
#> GSM537375 1 0.3392 0.5546 0.872 0.072 0.000 NA
#> GSM537389 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537390 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537393 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537399 2 0.5630 0.1014 0.136 0.724 0.000 NA
#> GSM537407 1 0.5693 0.5406 0.688 0.072 0.000 NA
#> GSM537408 3 0.5200 0.7153 0.036 0.000 0.700 NA
#> GSM537428 1 0.5526 0.5413 0.564 0.020 0.000 NA
#> GSM537354 3 0.7215 0.6601 0.152 0.000 0.500 NA
#> GSM537410 3 0.5998 0.7275 0.088 0.000 0.664 NA
#> GSM537413 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537396 2 0.7761 0.6267 0.340 0.416 0.000 NA
#> GSM537397 1 0.7299 -0.2520 0.512 0.312 0.000 NA
#> GSM537330 2 0.7039 0.6269 0.256 0.568 0.000 NA
#> GSM537369 1 0.6756 0.2768 0.612 0.000 0.188 NA
#> GSM537373 3 0.7812 0.5248 0.264 0.000 0.408 NA
#> GSM537401 1 0.7490 -0.2383 0.476 0.328 0.000 NA
#> GSM537343 3 0.7228 0.6637 0.156 0.000 0.504 NA
#> GSM537367 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537382 1 0.2805 0.5620 0.888 0.100 0.000 NA
#> GSM537385 2 0.7786 0.5867 0.368 0.388 0.000 NA
#> GSM537391 1 0.7479 -0.2451 0.480 0.324 0.000 NA
#> GSM537419 1 0.4188 0.5907 0.752 0.004 0.000 NA
#> GSM537420 3 0.6746 0.6151 0.124 0.000 0.580 NA
#> GSM537429 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537431 1 0.7901 0.2879 0.356 0.348 0.000 NA
#> GSM537387 2 0.7634 0.5985 0.352 0.436 0.000 NA
#> GSM537414 3 0.6813 0.6806 0.104 0.000 0.516 NA
#> GSM537433 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537335 2 0.0000 0.4232 0.000 1.000 0.000 NA
#> GSM537339 2 0.7640 0.6678 0.296 0.464 0.000 NA
#> GSM537340 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537344 3 0.7228 0.6637 0.156 0.000 0.504 NA
#> GSM537346 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537351 3 0.0000 0.7558 0.000 0.000 1.000 NA
#> GSM537352 3 0.7621 0.5816 0.204 0.000 0.420 NA
#> GSM537359 1 0.6042 0.5188 0.560 0.048 0.000 NA
#> GSM537360 3 0.5609 0.7396 0.088 0.000 0.712 NA
#> GSM537364 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537365 1 0.5220 0.5545 0.632 0.000 0.016 NA
#> GSM537372 1 0.1820 0.6142 0.944 0.036 0.000 NA
#> GSM537384 1 0.3935 0.5224 0.840 0.100 0.000 NA
#> GSM537394 2 0.7659 0.6693 0.296 0.460 0.000 NA
#> GSM537403 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537406 3 0.2814 0.7591 0.000 0.000 0.868 NA
#> GSM537411 1 0.5662 0.5423 0.692 0.072 0.000 NA
#> GSM537412 3 0.0336 0.7539 0.000 0.000 0.992 NA
#> GSM537416 3 0.6946 0.6754 0.116 0.000 0.504 NA
#> GSM537426 3 0.6946 0.6754 0.116 0.000 0.504 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.1544 0.8350 0.000 0.932 0.000 0.000 0.068
#> GSM537345 1 0.1270 0.8699 0.948 0.000 0.000 0.052 0.000
#> GSM537355 5 0.4782 0.6959 0.000 0.236 0.008 0.048 0.708
#> GSM537366 1 0.0290 0.8785 0.992 0.000 0.000 0.008 0.000
#> GSM537370 2 0.0451 0.8551 0.000 0.988 0.004 0.000 0.008
#> GSM537380 2 0.1121 0.8449 0.000 0.956 0.000 0.000 0.044
#> GSM537392 2 0.1197 0.8446 0.000 0.952 0.000 0.000 0.048
#> GSM537415 4 0.1153 0.8725 0.024 0.004 0.008 0.964 0.000
#> GSM537417 1 0.5034 0.7422 0.752 0.000 0.048 0.132 0.068
#> GSM537422 1 0.0794 0.8782 0.972 0.000 0.000 0.028 0.000
#> GSM537423 4 0.3268 0.8475 0.020 0.060 0.004 0.872 0.044
#> GSM537427 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537430 3 0.4150 0.7926 0.000 0.388 0.612 0.000 0.000
#> GSM537336 1 0.1121 0.8732 0.956 0.000 0.000 0.044 0.000
#> GSM537337 4 0.3201 0.8502 0.020 0.056 0.004 0.876 0.044
#> GSM537348 2 0.3684 0.6103 0.000 0.720 0.000 0.000 0.280
#> GSM537349 2 0.1121 0.8449 0.000 0.956 0.000 0.000 0.044
#> GSM537356 5 0.6662 0.6549 0.012 0.180 0.028 0.176 0.604
#> GSM537361 5 0.4463 0.5738 0.024 0.000 0.076 0.112 0.788
#> GSM537374 3 0.4541 0.6439 0.000 0.140 0.760 0.004 0.096
#> GSM537377 5 0.4524 0.6724 0.000 0.280 0.020 0.008 0.692
#> GSM537378 5 0.5098 0.3704 0.000 0.480 0.012 0.016 0.492
#> GSM537379 5 0.4781 0.5580 0.000 0.388 0.012 0.008 0.592
#> GSM537383 2 0.2230 0.7221 0.000 0.884 0.116 0.000 0.000
#> GSM537388 2 0.0290 0.8525 0.000 0.992 0.008 0.000 0.000
#> GSM537395 4 0.3268 0.8475 0.020 0.060 0.004 0.872 0.044
#> GSM537400 3 0.4930 0.5784 0.000 0.424 0.548 0.000 0.028
#> GSM537404 1 0.5200 0.7274 0.736 0.000 0.048 0.148 0.068
#> GSM537409 4 0.1153 0.8725 0.024 0.004 0.008 0.964 0.000
#> GSM537418 4 0.1573 0.8738 0.036 0.004 0.008 0.948 0.004
#> GSM537425 1 0.1341 0.8688 0.944 0.000 0.000 0.056 0.000
#> GSM537333 3 0.4161 0.7884 0.000 0.392 0.608 0.000 0.000
#> GSM537342 4 0.3817 0.7162 0.252 0.004 0.000 0.740 0.004
#> GSM537347 5 0.4134 0.6782 0.000 0.264 0.008 0.008 0.720
#> GSM537350 1 0.5908 0.6461 0.644 0.000 0.100 0.228 0.028
#> GSM537362 5 0.4557 0.6305 0.000 0.324 0.012 0.008 0.656
#> GSM537363 1 0.0609 0.8785 0.980 0.000 0.000 0.020 0.000
#> GSM537368 1 0.0290 0.8785 0.992 0.000 0.000 0.008 0.000
#> GSM537376 5 0.4443 0.6569 0.000 0.300 0.012 0.008 0.680
#> GSM537381 2 0.4236 0.3558 0.000 0.664 0.004 0.004 0.328
#> GSM537386 3 0.4126 0.7986 0.000 0.380 0.620 0.000 0.000
#> GSM537398 3 0.4299 0.7904 0.000 0.388 0.608 0.000 0.004
#> GSM537402 5 0.7289 0.5937 0.016 0.128 0.144 0.120 0.592
#> GSM537405 1 0.5477 0.7088 0.712 0.000 0.052 0.164 0.072
#> GSM537371 1 0.1121 0.8732 0.956 0.000 0.000 0.044 0.000
#> GSM537421 1 0.4251 0.3854 0.624 0.000 0.004 0.372 0.000
#> GSM537424 5 0.4782 0.6959 0.000 0.236 0.008 0.048 0.708
#> GSM537432 2 0.3994 0.6810 0.000 0.792 0.140 0.000 0.068
#> GSM537331 3 0.4126 0.7986 0.000 0.380 0.620 0.000 0.000
#> GSM537332 2 0.0404 0.8545 0.000 0.988 0.000 0.000 0.012
#> GSM537334 3 0.4126 0.7986 0.000 0.380 0.620 0.000 0.000
#> GSM537338 2 0.3586 0.6321 0.000 0.736 0.000 0.000 0.264
#> GSM537353 4 0.5257 0.7643 0.052 0.072 0.008 0.752 0.116
#> GSM537357 1 0.1121 0.8732 0.956 0.000 0.000 0.044 0.000
#> GSM537358 5 0.6505 0.5091 0.008 0.056 0.220 0.092 0.624
#> GSM537375 5 0.4781 0.5580 0.000 0.388 0.012 0.008 0.592
#> GSM537389 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537390 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537393 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537399 3 0.4541 0.6439 0.000 0.140 0.760 0.004 0.096
#> GSM537407 5 0.2352 0.5751 0.000 0.004 0.092 0.008 0.896
#> GSM537408 1 0.5984 0.6423 0.640 0.000 0.100 0.228 0.032
#> GSM537428 5 0.6564 0.5123 0.008 0.060 0.220 0.092 0.620
#> GSM537354 4 0.3191 0.8685 0.064 0.024 0.004 0.876 0.032
#> GSM537410 4 0.3817 0.7162 0.252 0.004 0.000 0.740 0.004
#> GSM537413 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537396 2 0.1251 0.8437 0.000 0.956 0.008 0.000 0.036
#> GSM537397 2 0.4193 0.6100 0.000 0.748 0.000 0.040 0.212
#> GSM537330 2 0.2230 0.7221 0.000 0.884 0.116 0.000 0.000
#> GSM537369 5 0.7674 0.3627 0.060 0.120 0.024 0.332 0.464
#> GSM537373 4 0.5280 0.7725 0.068 0.060 0.008 0.752 0.112
#> GSM537401 2 0.3861 0.5951 0.000 0.712 0.004 0.000 0.284
#> GSM537343 4 0.3967 0.8488 0.100 0.032 0.004 0.828 0.036
#> GSM537367 1 0.0290 0.8785 0.992 0.000 0.000 0.008 0.000
#> GSM537382 5 0.4710 0.5972 0.000 0.364 0.012 0.008 0.616
#> GSM537385 2 0.1608 0.8317 0.000 0.928 0.000 0.000 0.072
#> GSM537391 2 0.3707 0.6024 0.000 0.716 0.000 0.000 0.284
#> GSM537419 5 0.6511 0.6274 0.008 0.172 0.144 0.044 0.632
#> GSM537420 1 0.7211 0.5345 0.548 0.000 0.184 0.184 0.084
#> GSM537429 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537431 3 0.4425 0.0703 0.000 0.000 0.544 0.004 0.452
#> GSM537387 2 0.1830 0.8239 0.000 0.924 0.000 0.008 0.068
#> GSM537414 4 0.1412 0.8723 0.036 0.004 0.008 0.952 0.000
#> GSM537433 1 0.0290 0.8785 0.992 0.000 0.000 0.008 0.000
#> GSM537335 3 0.4126 0.7986 0.000 0.380 0.620 0.000 0.000
#> GSM537339 2 0.0451 0.8551 0.000 0.988 0.004 0.000 0.008
#> GSM537340 1 0.0290 0.8785 0.992 0.000 0.000 0.008 0.000
#> GSM537344 4 0.3967 0.8488 0.100 0.032 0.004 0.828 0.036
#> GSM537346 2 0.0290 0.8525 0.000 0.992 0.008 0.000 0.000
#> GSM537351 1 0.0794 0.8774 0.972 0.000 0.000 0.028 0.000
#> GSM537352 4 0.3412 0.8449 0.020 0.060 0.004 0.864 0.052
#> GSM537359 5 0.5226 0.4758 0.008 0.024 0.216 0.044 0.708
#> GSM537360 4 0.3969 0.5904 0.304 0.004 0.000 0.692 0.000
#> GSM537364 1 0.0290 0.8785 0.992 0.000 0.000 0.008 0.000
#> GSM537365 5 0.4553 0.5869 0.008 0.012 0.076 0.120 0.784
#> GSM537372 5 0.4549 0.6882 0.000 0.244 0.032 0.008 0.716
#> GSM537384 5 0.5006 0.5221 0.000 0.408 0.020 0.008 0.564
#> GSM537394 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> GSM537403 1 0.0404 0.8782 0.988 0.000 0.000 0.012 0.000
#> GSM537406 1 0.3497 0.7977 0.828 0.000 0.020 0.140 0.012
#> GSM537411 5 0.2477 0.5783 0.000 0.008 0.092 0.008 0.892
#> GSM537412 1 0.0404 0.8782 0.988 0.000 0.000 0.012 0.000
#> GSM537416 4 0.1153 0.8725 0.024 0.004 0.008 0.964 0.000
#> GSM537426 4 0.1153 0.8725 0.024 0.004 0.008 0.964 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 2 0.1387 0.845 0.000 0.932 0.000 0.000 0.068 0.000
#> GSM537345 4 0.1152 0.891 0.044 0.000 0.000 0.952 0.000 0.004
#> GSM537355 5 0.4467 0.689 0.016 0.232 0.040 0.000 0.708 0.004
#> GSM537366 4 0.0000 0.906 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537370 2 0.0520 0.862 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM537380 2 0.1007 0.854 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM537392 2 0.1075 0.854 0.000 0.952 0.000 0.000 0.048 0.000
#> GSM537415 1 0.0000 0.839 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537417 3 0.3975 0.658 0.008 0.000 0.600 0.392 0.000 0.000
#> GSM537422 4 0.1088 0.890 0.016 0.000 0.024 0.960 0.000 0.000
#> GSM537423 1 0.3306 0.824 0.848 0.056 0.052 0.000 0.044 0.000
#> GSM537427 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537430 6 0.3446 0.813 0.000 0.308 0.000 0.000 0.000 0.692
#> GSM537336 4 0.1010 0.897 0.036 0.000 0.000 0.960 0.000 0.004
#> GSM537337 1 0.3245 0.826 0.852 0.052 0.052 0.000 0.044 0.000
#> GSM537348 2 0.3426 0.613 0.000 0.720 0.000 0.000 0.276 0.004
#> GSM537349 2 0.1007 0.854 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM537356 5 0.5859 0.633 0.112 0.144 0.084 0.000 0.652 0.008
#> GSM537361 5 0.4218 0.506 0.008 0.000 0.184 0.000 0.740 0.068
#> GSM537374 6 0.3575 0.629 0.000 0.092 0.056 0.000 0.028 0.824
#> GSM537377 5 0.3596 0.678 0.000 0.244 0.008 0.000 0.740 0.008
#> GSM537378 5 0.4308 0.396 0.008 0.452 0.000 0.000 0.532 0.008
#> GSM537379 5 0.3861 0.580 0.000 0.352 0.000 0.000 0.640 0.008
#> GSM537383 2 0.2416 0.698 0.000 0.844 0.000 0.000 0.000 0.156
#> GSM537388 2 0.0260 0.861 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM537395 1 0.3306 0.824 0.848 0.056 0.052 0.000 0.044 0.000
#> GSM537400 6 0.4176 0.533 0.000 0.404 0.016 0.000 0.000 0.580
#> GSM537404 3 0.3934 0.685 0.008 0.000 0.616 0.376 0.000 0.000
#> GSM537409 1 0.0000 0.839 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537418 1 0.0767 0.842 0.976 0.000 0.008 0.012 0.004 0.000
#> GSM537425 4 0.1219 0.886 0.048 0.000 0.000 0.948 0.000 0.004
#> GSM537333 6 0.3464 0.809 0.000 0.312 0.000 0.000 0.000 0.688
#> GSM537342 1 0.4233 0.700 0.736 0.000 0.080 0.180 0.000 0.004
#> GSM537347 5 0.3445 0.669 0.000 0.260 0.000 0.000 0.732 0.008
#> GSM537350 3 0.3542 0.771 0.052 0.000 0.788 0.160 0.000 0.000
#> GSM537362 5 0.3741 0.614 0.000 0.320 0.000 0.000 0.672 0.008
#> GSM537363 4 0.0405 0.906 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM537368 4 0.0000 0.906 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537376 5 0.3468 0.665 0.000 0.264 0.000 0.000 0.728 0.008
#> GSM537381 2 0.3833 0.340 0.000 0.648 0.000 0.000 0.344 0.008
#> GSM537386 6 0.3371 0.823 0.000 0.292 0.000 0.000 0.000 0.708
#> GSM537398 6 0.3547 0.817 0.000 0.300 0.000 0.000 0.004 0.696
#> GSM537402 5 0.6810 0.485 0.064 0.128 0.284 0.000 0.504 0.020
#> GSM537405 3 0.3827 0.741 0.008 0.000 0.680 0.308 0.000 0.004
#> GSM537371 4 0.1010 0.897 0.036 0.000 0.000 0.960 0.000 0.004
#> GSM537421 4 0.3717 0.299 0.384 0.000 0.000 0.616 0.000 0.000
#> GSM537424 5 0.4467 0.689 0.016 0.232 0.040 0.000 0.708 0.004
#> GSM537432 2 0.3572 0.700 0.000 0.792 0.016 0.000 0.024 0.168
#> GSM537331 6 0.3371 0.823 0.000 0.292 0.000 0.000 0.000 0.708
#> GSM537332 2 0.0363 0.863 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM537334 6 0.3371 0.823 0.000 0.292 0.000 0.000 0.000 0.708
#> GSM537338 2 0.3337 0.635 0.000 0.736 0.000 0.000 0.260 0.004
#> GSM537353 1 0.4907 0.738 0.728 0.068 0.096 0.000 0.108 0.000
#> GSM537357 4 0.1010 0.897 0.036 0.000 0.000 0.960 0.000 0.004
#> GSM537358 5 0.6580 0.382 0.032 0.056 0.316 0.000 0.516 0.080
#> GSM537375 5 0.3861 0.580 0.000 0.352 0.000 0.000 0.640 0.008
#> GSM537389 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537390 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537393 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537399 6 0.3575 0.629 0.000 0.092 0.056 0.000 0.028 0.824
#> GSM537407 5 0.2575 0.555 0.000 0.004 0.044 0.000 0.880 0.072
#> GSM537408 3 0.3506 0.770 0.052 0.000 0.792 0.156 0.000 0.000
#> GSM537428 5 0.6618 0.388 0.032 0.060 0.312 0.000 0.516 0.080
#> GSM537354 1 0.2878 0.840 0.884 0.020 0.024 0.040 0.032 0.000
#> GSM537410 1 0.4233 0.700 0.736 0.000 0.080 0.180 0.000 0.004
#> GSM537413 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537396 2 0.1176 0.852 0.000 0.956 0.000 0.000 0.020 0.024
#> GSM537397 2 0.3794 0.607 0.040 0.744 0.000 0.000 0.216 0.000
#> GSM537330 2 0.2416 0.698 0.000 0.844 0.000 0.000 0.000 0.156
#> GSM537369 5 0.6801 0.384 0.264 0.084 0.128 0.000 0.512 0.012
#> GSM537373 1 0.5130 0.739 0.724 0.056 0.104 0.012 0.104 0.000
#> GSM537401 2 0.3468 0.597 0.000 0.712 0.000 0.000 0.284 0.004
#> GSM537343 1 0.4365 0.803 0.784 0.028 0.124 0.032 0.028 0.004
#> GSM537367 4 0.0000 0.906 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537382 5 0.3774 0.614 0.000 0.328 0.000 0.000 0.664 0.008
#> GSM537385 2 0.1444 0.842 0.000 0.928 0.000 0.000 0.072 0.000
#> GSM537391 2 0.3448 0.606 0.000 0.716 0.000 0.000 0.280 0.004
#> GSM537419 5 0.5937 0.552 0.000 0.172 0.264 0.000 0.544 0.020
#> GSM537420 3 0.0713 0.663 0.000 0.000 0.972 0.028 0.000 0.000
#> GSM537429 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537431 6 0.5166 0.135 0.000 0.000 0.100 0.000 0.348 0.552
#> GSM537387 2 0.1787 0.834 0.008 0.920 0.000 0.000 0.068 0.004
#> GSM537414 1 0.0508 0.839 0.984 0.000 0.004 0.012 0.000 0.000
#> GSM537433 4 0.0000 0.906 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537335 6 0.3371 0.823 0.000 0.292 0.000 0.000 0.000 0.708
#> GSM537339 2 0.0520 0.862 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM537340 4 0.0000 0.906 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537344 1 0.4365 0.803 0.784 0.028 0.124 0.032 0.028 0.004
#> GSM537346 2 0.0260 0.861 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM537351 4 0.0632 0.903 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM537352 1 0.3433 0.821 0.840 0.056 0.056 0.000 0.048 0.000
#> GSM537359 5 0.5380 0.339 0.000 0.024 0.304 0.000 0.592 0.080
#> GSM537360 1 0.3309 0.556 0.720 0.000 0.000 0.280 0.000 0.000
#> GSM537364 4 0.0000 0.906 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537365 5 0.4717 0.533 0.028 0.012 0.152 0.000 0.740 0.068
#> GSM537372 5 0.3510 0.692 0.000 0.204 0.008 0.000 0.772 0.016
#> GSM537384 5 0.4193 0.528 0.000 0.384 0.008 0.000 0.600 0.008
#> GSM537394 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537403 4 0.0146 0.905 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM537406 4 0.4032 -0.221 0.008 0.000 0.420 0.572 0.000 0.000
#> GSM537411 5 0.2687 0.559 0.000 0.008 0.044 0.000 0.876 0.072
#> GSM537412 4 0.0146 0.905 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM537416 1 0.0000 0.839 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537426 1 0.0000 0.839 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> ATC:hclust 103 0.347 0.374 2
#> ATC:hclust 61 0.603 0.295 3
#> ATC:hclust 83 0.638 0.516 4
#> ATC:hclust 98 0.318 0.829 5
#> ATC:hclust 94 0.347 0.879 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 104 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 0.998 0.999 0.4960 0.504 0.504
#> 3 3 0.631 0.740 0.856 0.2921 0.751 0.550
#> 4 4 0.595 0.641 0.753 0.1443 0.839 0.582
#> 5 5 0.718 0.733 0.847 0.0742 0.856 0.529
#> 6 6 0.742 0.694 0.813 0.0453 0.902 0.587
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
#> GSM537341 2 0.0000 1.000 0.000 1.000
#> GSM537345 1 0.0000 0.998 1.000 0.000
#> GSM537355 1 0.0000 0.998 1.000 0.000
#> GSM537366 1 0.0000 0.998 1.000 0.000
#> GSM537370 2 0.0000 1.000 0.000 1.000
#> GSM537380 2 0.0000 1.000 0.000 1.000
#> GSM537392 2 0.0000 1.000 0.000 1.000
#> GSM537415 1 0.0000 0.998 1.000 0.000
#> GSM537417 1 0.0000 0.998 1.000 0.000
#> GSM537422 1 0.0000 0.998 1.000 0.000
#> GSM537423 1 0.3879 0.918 0.924 0.076
#> GSM537427 2 0.0000 1.000 0.000 1.000
#> GSM537430 2 0.0000 1.000 0.000 1.000
#> GSM537336 1 0.0000 0.998 1.000 0.000
#> GSM537337 2 0.0000 1.000 0.000 1.000
#> GSM537348 2 0.0000 1.000 0.000 1.000
#> GSM537349 2 0.0000 1.000 0.000 1.000
#> GSM537356 1 0.0000 0.998 1.000 0.000
#> GSM537361 1 0.0000 0.998 1.000 0.000
#> GSM537374 2 0.0000 1.000 0.000 1.000
#> GSM537377 2 0.0000 1.000 0.000 1.000
#> GSM537378 2 0.0000 1.000 0.000 1.000
#> GSM537379 2 0.0000 1.000 0.000 1.000
#> GSM537383 2 0.0000 1.000 0.000 1.000
#> GSM537388 2 0.0000 1.000 0.000 1.000
#> GSM537395 2 0.0000 1.000 0.000 1.000
#> GSM537400 2 0.0000 1.000 0.000 1.000
#> GSM537404 1 0.0000 0.998 1.000 0.000
#> GSM537409 1 0.0000 0.998 1.000 0.000
#> GSM537418 1 0.0000 0.998 1.000 0.000
#> GSM537425 1 0.0000 0.998 1.000 0.000
#> GSM537333 2 0.0000 1.000 0.000 1.000
#> GSM537342 1 0.0000 0.998 1.000 0.000
#> GSM537347 2 0.0000 1.000 0.000 1.000
#> GSM537350 1 0.0000 0.998 1.000 0.000
#> GSM537362 2 0.0000 1.000 0.000 1.000
#> GSM537363 1 0.0000 0.998 1.000 0.000
#> GSM537368 1 0.0000 0.998 1.000 0.000
#> GSM537376 2 0.0000 1.000 0.000 1.000
#> GSM537381 2 0.0000 1.000 0.000 1.000
#> GSM537386 2 0.0000 1.000 0.000 1.000
#> GSM537398 2 0.0000 1.000 0.000 1.000
#> GSM537402 2 0.0000 1.000 0.000 1.000
#> GSM537405 1 0.0000 0.998 1.000 0.000
#> GSM537371 1 0.0000 0.998 1.000 0.000
#> GSM537421 1 0.0000 0.998 1.000 0.000
#> GSM537424 2 0.0000 1.000 0.000 1.000
#> GSM537432 2 0.0000 1.000 0.000 1.000
#> GSM537331 2 0.0000 1.000 0.000 1.000
#> GSM537332 2 0.0000 1.000 0.000 1.000
#> GSM537334 2 0.0000 1.000 0.000 1.000
#> GSM537338 2 0.0000 1.000 0.000 1.000
#> GSM537353 1 0.0376 0.995 0.996 0.004
#> GSM537357 1 0.0000 0.998 1.000 0.000
#> GSM537358 2 0.0000 1.000 0.000 1.000
#> GSM537375 2 0.0000 1.000 0.000 1.000
#> GSM537389 2 0.0000 1.000 0.000 1.000
#> GSM537390 2 0.0000 1.000 0.000 1.000
#> GSM537393 2 0.0000 1.000 0.000 1.000
#> GSM537399 2 0.0000 1.000 0.000 1.000
#> GSM537407 2 0.0000 1.000 0.000 1.000
#> GSM537408 1 0.0000 0.998 1.000 0.000
#> GSM537428 2 0.0000 1.000 0.000 1.000
#> GSM537354 1 0.0000 0.998 1.000 0.000
#> GSM537410 1 0.0000 0.998 1.000 0.000
#> GSM537413 2 0.0000 1.000 0.000 1.000
#> GSM537396 2 0.0000 1.000 0.000 1.000
#> GSM537397 2 0.0000 1.000 0.000 1.000
#> GSM537330 2 0.0000 1.000 0.000 1.000
#> GSM537369 1 0.0000 0.998 1.000 0.000
#> GSM537373 1 0.0000 0.998 1.000 0.000
#> GSM537401 2 0.0000 1.000 0.000 1.000
#> GSM537343 1 0.0000 0.998 1.000 0.000
#> GSM537367 1 0.0000 0.998 1.000 0.000
#> GSM537382 2 0.0000 1.000 0.000 1.000
#> GSM537385 2 0.0000 1.000 0.000 1.000
#> GSM537391 2 0.0000 1.000 0.000 1.000
#> GSM537419 2 0.0000 1.000 0.000 1.000
#> GSM537420 1 0.0000 0.998 1.000 0.000
#> GSM537429 2 0.0000 1.000 0.000 1.000
#> GSM537431 2 0.0000 1.000 0.000 1.000
#> GSM537387 2 0.0000 1.000 0.000 1.000
#> GSM537414 1 0.0000 0.998 1.000 0.000
#> GSM537433 1 0.0000 0.998 1.000 0.000
#> GSM537335 2 0.0000 1.000 0.000 1.000
#> GSM537339 2 0.0000 1.000 0.000 1.000
#> GSM537340 1 0.0000 0.998 1.000 0.000
#> GSM537344 1 0.0000 0.998 1.000 0.000
#> GSM537346 2 0.0000 1.000 0.000 1.000
#> GSM537351 1 0.0000 0.998 1.000 0.000
#> GSM537352 1 0.0000 0.998 1.000 0.000
#> GSM537359 2 0.0000 1.000 0.000 1.000
#> GSM537360 1 0.0000 0.998 1.000 0.000
#> GSM537364 1 0.0000 0.998 1.000 0.000
#> GSM537365 1 0.0376 0.995 0.996 0.004
#> GSM537372 2 0.0000 1.000 0.000 1.000
#> GSM537384 2 0.0000 1.000 0.000 1.000
#> GSM537394 2 0.0000 1.000 0.000 1.000
#> GSM537403 1 0.0000 0.998 1.000 0.000
#> GSM537406 1 0.0000 0.998 1.000 0.000
#> GSM537411 2 0.0000 1.000 0.000 1.000
#> GSM537412 1 0.0000 0.998 1.000 0.000
#> GSM537416 1 0.0000 0.998 1.000 0.000
#> GSM537426 1 0.0000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.5591 0.76624 0.696 0.304 0.000
#> GSM537345 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537355 2 0.1163 0.77602 0.000 0.972 0.028
#> GSM537366 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537370 1 0.2261 0.78932 0.932 0.068 0.000
#> GSM537380 1 0.5397 0.77138 0.720 0.280 0.000
#> GSM537392 1 0.5529 0.76063 0.704 0.296 0.000
#> GSM537415 2 0.5859 0.46811 0.000 0.656 0.344
#> GSM537417 3 0.0424 0.99022 0.000 0.008 0.992
#> GSM537422 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537423 2 0.0829 0.77039 0.004 0.984 0.012
#> GSM537427 1 0.5529 0.76063 0.704 0.296 0.000
#> GSM537430 1 0.0237 0.77637 0.996 0.004 0.000
#> GSM537336 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537337 2 0.0892 0.76160 0.020 0.980 0.000
#> GSM537348 1 0.5882 0.74247 0.652 0.348 0.000
#> GSM537349 1 0.5882 0.72325 0.652 0.348 0.000
#> GSM537356 2 0.1411 0.77683 0.000 0.964 0.036
#> GSM537361 2 0.6252 0.25105 0.000 0.556 0.444
#> GSM537374 1 0.0892 0.77636 0.980 0.020 0.000
#> GSM537377 1 0.5835 0.74801 0.660 0.340 0.000
#> GSM537378 2 0.6274 -0.30369 0.456 0.544 0.000
#> GSM537379 1 0.5560 0.77507 0.700 0.300 0.000
#> GSM537383 1 0.0892 0.78195 0.980 0.020 0.000
#> GSM537388 1 0.0892 0.78195 0.980 0.020 0.000
#> GSM537395 2 0.0892 0.76160 0.020 0.980 0.000
#> GSM537400 1 0.0237 0.77637 0.996 0.004 0.000
#> GSM537404 3 0.0892 0.97849 0.000 0.020 0.980
#> GSM537409 2 0.1753 0.77540 0.000 0.952 0.048
#> GSM537418 2 0.1753 0.77540 0.000 0.952 0.048
#> GSM537425 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537333 1 0.0892 0.77636 0.980 0.020 0.000
#> GSM537342 2 0.6062 0.39172 0.000 0.616 0.384
#> GSM537347 1 0.5785 0.75312 0.668 0.332 0.000
#> GSM537350 3 0.0592 0.98665 0.000 0.012 0.988
#> GSM537362 1 0.5733 0.75874 0.676 0.324 0.000
#> GSM537363 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537368 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537376 1 0.5529 0.77671 0.704 0.296 0.000
#> GSM537381 1 0.6204 0.64857 0.576 0.424 0.000
#> GSM537386 1 0.0000 0.77590 1.000 0.000 0.000
#> GSM537398 1 0.0892 0.77636 0.980 0.020 0.000
#> GSM537402 2 0.0000 0.76495 0.000 1.000 0.000
#> GSM537405 3 0.0892 0.97849 0.000 0.020 0.980
#> GSM537371 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537421 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537424 2 0.0000 0.76495 0.000 1.000 0.000
#> GSM537432 1 0.0892 0.77636 0.980 0.020 0.000
#> GSM537331 1 0.0000 0.77590 1.000 0.000 0.000
#> GSM537332 1 0.3038 0.79267 0.896 0.104 0.000
#> GSM537334 1 0.0237 0.77637 0.996 0.004 0.000
#> GSM537338 1 0.4346 0.79405 0.816 0.184 0.000
#> GSM537353 2 0.0592 0.77076 0.000 0.988 0.012
#> GSM537357 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537358 2 0.0000 0.76495 0.000 1.000 0.000
#> GSM537375 1 0.5560 0.77507 0.700 0.300 0.000
#> GSM537389 2 0.6215 -0.20438 0.428 0.572 0.000
#> GSM537390 2 0.6260 -0.26919 0.448 0.552 0.000
#> GSM537393 2 0.4750 0.49128 0.216 0.784 0.000
#> GSM537399 1 0.0892 0.77636 0.980 0.020 0.000
#> GSM537407 1 0.5926 0.72198 0.644 0.356 0.000
#> GSM537408 2 0.1289 0.77664 0.000 0.968 0.032
#> GSM537428 2 0.0000 0.76495 0.000 1.000 0.000
#> GSM537354 2 0.5859 0.46811 0.000 0.656 0.344
#> GSM537410 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537413 1 0.5465 0.76397 0.712 0.288 0.000
#> GSM537396 1 0.6252 0.59344 0.556 0.444 0.000
#> GSM537397 1 0.6267 0.57455 0.548 0.452 0.000
#> GSM537330 1 0.0892 0.78195 0.980 0.020 0.000
#> GSM537369 2 0.1529 0.77672 0.000 0.960 0.040
#> GSM537373 2 0.1289 0.77664 0.000 0.968 0.032
#> GSM537401 1 0.5785 0.75427 0.668 0.332 0.000
#> GSM537343 2 0.4178 0.69394 0.000 0.828 0.172
#> GSM537367 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537382 1 0.5591 0.76365 0.696 0.304 0.000
#> GSM537385 1 0.5785 0.74189 0.668 0.332 0.000
#> GSM537391 1 0.6235 0.62622 0.564 0.436 0.000
#> GSM537419 2 0.0237 0.76231 0.004 0.996 0.000
#> GSM537420 2 0.6260 0.24415 0.000 0.552 0.448
#> GSM537429 1 0.2625 0.79115 0.916 0.084 0.000
#> GSM537431 1 0.1031 0.77706 0.976 0.024 0.000
#> GSM537387 1 0.6126 0.66661 0.600 0.400 0.000
#> GSM537414 2 0.5810 0.48199 0.000 0.664 0.336
#> GSM537433 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537335 1 0.0000 0.77590 1.000 0.000 0.000
#> GSM537339 1 0.4974 0.78617 0.764 0.236 0.000
#> GSM537340 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537344 2 0.4504 0.67370 0.000 0.804 0.196
#> GSM537346 1 0.0892 0.78195 0.980 0.020 0.000
#> GSM537351 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537352 2 0.1289 0.77664 0.000 0.968 0.032
#> GSM537359 2 0.5926 0.00921 0.356 0.644 0.000
#> GSM537360 2 0.6305 0.15034 0.000 0.516 0.484
#> GSM537364 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537365 2 0.0424 0.76938 0.000 0.992 0.008
#> GSM537372 1 0.5810 0.75079 0.664 0.336 0.000
#> GSM537384 1 0.5810 0.75412 0.664 0.336 0.000
#> GSM537394 1 0.0592 0.78049 0.988 0.012 0.000
#> GSM537403 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537406 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537411 1 0.1031 0.77706 0.976 0.024 0.000
#> GSM537412 3 0.0000 0.99682 0.000 0.000 1.000
#> GSM537416 2 0.5859 0.46811 0.000 0.656 0.344
#> GSM537426 2 0.1753 0.77540 0.000 0.952 0.048
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.5093 0.5537 0.012 0.640 0.000 0.348
#> GSM537345 3 0.1388 0.9560 0.012 0.028 0.960 0.000
#> GSM537355 1 0.4072 0.7655 0.748 0.252 0.000 0.000
#> GSM537366 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537370 2 0.5147 0.3560 0.004 0.536 0.000 0.460
#> GSM537380 2 0.5980 0.4533 0.044 0.560 0.000 0.396
#> GSM537392 2 0.6296 0.4463 0.064 0.548 0.000 0.388
#> GSM537415 1 0.2469 0.7309 0.892 0.000 0.108 0.000
#> GSM537417 3 0.2021 0.9344 0.040 0.024 0.936 0.000
#> GSM537422 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537423 1 0.2704 0.7534 0.876 0.124 0.000 0.000
#> GSM537427 2 0.6296 0.4463 0.064 0.548 0.000 0.388
#> GSM537430 4 0.0000 0.7188 0.000 0.000 0.000 1.000
#> GSM537336 3 0.0592 0.9587 0.000 0.016 0.984 0.000
#> GSM537337 1 0.3172 0.7299 0.840 0.160 0.000 0.000
#> GSM537348 2 0.5532 0.5098 0.068 0.704 0.000 0.228
#> GSM537349 2 0.6156 0.5058 0.064 0.592 0.000 0.344
#> GSM537356 1 0.4730 0.7087 0.636 0.364 0.000 0.000
#> GSM537361 1 0.6219 0.6182 0.520 0.432 0.044 0.004
#> GSM537374 4 0.1792 0.6954 0.000 0.068 0.000 0.932
#> GSM537377 2 0.5042 0.4461 0.096 0.768 0.000 0.136
#> GSM537378 2 0.7114 0.4549 0.232 0.564 0.000 0.204
#> GSM537379 2 0.4957 0.5459 0.016 0.684 0.000 0.300
#> GSM537383 4 0.4730 0.2096 0.000 0.364 0.000 0.636
#> GSM537388 4 0.4746 0.1900 0.000 0.368 0.000 0.632
#> GSM537395 1 0.3266 0.7242 0.832 0.168 0.000 0.000
#> GSM537400 4 0.0000 0.7188 0.000 0.000 0.000 1.000
#> GSM537404 3 0.5476 0.7006 0.120 0.144 0.736 0.000
#> GSM537409 1 0.2473 0.7284 0.908 0.080 0.012 0.000
#> GSM537418 1 0.1452 0.7577 0.956 0.036 0.008 0.000
#> GSM537425 3 0.1388 0.9560 0.012 0.028 0.960 0.000
#> GSM537333 4 0.1474 0.7042 0.000 0.052 0.000 0.948
#> GSM537342 1 0.4562 0.7080 0.792 0.056 0.152 0.000
#> GSM537347 2 0.5265 0.4514 0.092 0.748 0.000 0.160
#> GSM537350 3 0.2197 0.9274 0.048 0.024 0.928 0.000
#> GSM537362 2 0.5744 0.5088 0.068 0.676 0.000 0.256
#> GSM537363 3 0.0336 0.9607 0.000 0.008 0.992 0.000
#> GSM537368 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537376 2 0.5062 0.5457 0.020 0.680 0.000 0.300
#> GSM537381 2 0.4500 0.5604 0.032 0.776 0.000 0.192
#> GSM537386 4 0.0188 0.7175 0.000 0.004 0.000 0.996
#> GSM537398 4 0.1716 0.6979 0.000 0.064 0.000 0.936
#> GSM537402 1 0.4454 0.7379 0.692 0.308 0.000 0.000
#> GSM537405 3 0.4906 0.7605 0.084 0.140 0.776 0.000
#> GSM537371 3 0.1284 0.9570 0.012 0.024 0.964 0.000
#> GSM537421 3 0.2222 0.9288 0.060 0.016 0.924 0.000
#> GSM537424 1 0.4999 0.5591 0.508 0.492 0.000 0.000
#> GSM537432 4 0.4040 0.4889 0.000 0.248 0.000 0.752
#> GSM537331 4 0.0188 0.7175 0.000 0.004 0.000 0.996
#> GSM537332 2 0.5158 0.3281 0.004 0.524 0.000 0.472
#> GSM537334 4 0.0000 0.7188 0.000 0.000 0.000 1.000
#> GSM537338 2 0.5253 0.4890 0.016 0.624 0.000 0.360
#> GSM537353 1 0.2469 0.7807 0.892 0.108 0.000 0.000
#> GSM537357 3 0.1059 0.9578 0.012 0.016 0.972 0.000
#> GSM537358 1 0.4843 0.6696 0.604 0.396 0.000 0.000
#> GSM537375 2 0.5308 0.5500 0.036 0.684 0.000 0.280
#> GSM537389 2 0.7031 0.4171 0.288 0.556 0.000 0.156
#> GSM537390 2 0.7137 0.4372 0.256 0.556 0.000 0.188
#> GSM537393 2 0.6264 0.3689 0.376 0.560 0.000 0.064
#> GSM537399 4 0.1792 0.6954 0.000 0.068 0.000 0.932
#> GSM537407 2 0.6170 0.2729 0.136 0.672 0.000 0.192
#> GSM537408 1 0.3975 0.7700 0.760 0.240 0.000 0.000
#> GSM537428 1 0.4972 0.6106 0.544 0.456 0.000 0.000
#> GSM537354 1 0.2408 0.7340 0.896 0.000 0.104 0.000
#> GSM537410 3 0.1610 0.9446 0.032 0.016 0.952 0.000
#> GSM537413 2 0.6296 0.4463 0.064 0.548 0.000 0.388
#> GSM537396 2 0.4988 0.5791 0.036 0.728 0.000 0.236
#> GSM537397 2 0.5387 0.5711 0.048 0.696 0.000 0.256
#> GSM537330 4 0.4713 0.2165 0.000 0.360 0.000 0.640
#> GSM537369 1 0.4193 0.7624 0.732 0.268 0.000 0.000
#> GSM537373 1 0.2704 0.7812 0.876 0.124 0.000 0.000
#> GSM537401 2 0.5596 0.5118 0.068 0.696 0.000 0.236
#> GSM537343 1 0.3945 0.7737 0.780 0.216 0.004 0.000
#> GSM537367 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537382 2 0.5423 0.5389 0.028 0.640 0.000 0.332
#> GSM537385 2 0.5453 0.5477 0.032 0.648 0.000 0.320
#> GSM537391 2 0.5091 0.5138 0.068 0.752 0.000 0.180
#> GSM537419 2 0.4877 -0.0501 0.328 0.664 0.000 0.008
#> GSM537420 1 0.6114 0.6254 0.524 0.428 0.048 0.000
#> GSM537429 2 0.5288 0.3286 0.008 0.520 0.000 0.472
#> GSM537431 4 0.3942 0.5002 0.000 0.236 0.000 0.764
#> GSM537387 2 0.5021 0.5817 0.036 0.724 0.000 0.240
#> GSM537414 1 0.2408 0.7340 0.896 0.000 0.104 0.000
#> GSM537433 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537335 4 0.0469 0.7118 0.000 0.012 0.000 0.988
#> GSM537339 2 0.5257 0.3878 0.008 0.548 0.000 0.444
#> GSM537340 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537344 1 0.4792 0.7406 0.680 0.312 0.008 0.000
#> GSM537346 4 0.4477 0.3361 0.000 0.312 0.000 0.688
#> GSM537351 3 0.0937 0.9589 0.012 0.012 0.976 0.000
#> GSM537352 1 0.1474 0.7776 0.948 0.052 0.000 0.000
#> GSM537359 2 0.5851 0.1632 0.236 0.680 0.000 0.084
#> GSM537360 1 0.3400 0.6704 0.820 0.000 0.180 0.000
#> GSM537364 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537365 1 0.4804 0.6919 0.616 0.384 0.000 0.000
#> GSM537372 2 0.4834 0.4314 0.096 0.784 0.000 0.120
#> GSM537384 2 0.5358 0.5535 0.048 0.700 0.000 0.252
#> GSM537394 4 0.4925 -0.0978 0.000 0.428 0.000 0.572
#> GSM537403 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537406 3 0.0188 0.9613 0.000 0.004 0.996 0.000
#> GSM537411 4 0.4978 0.3738 0.012 0.324 0.000 0.664
#> GSM537412 3 0.0000 0.9621 0.000 0.000 1.000 0.000
#> GSM537416 1 0.2469 0.7309 0.892 0.000 0.108 0.000
#> GSM537426 1 0.2473 0.7284 0.908 0.080 0.012 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.4031 0.6957 0.000 0.788 0.048 0.004 0.160
#> GSM537345 1 0.2941 0.8776 0.884 0.000 0.020 0.032 0.064
#> GSM537355 5 0.4118 0.2894 0.000 0.000 0.004 0.336 0.660
#> GSM537366 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537370 2 0.1788 0.8193 0.000 0.932 0.056 0.004 0.008
#> GSM537380 2 0.1408 0.8248 0.000 0.948 0.044 0.008 0.000
#> GSM537392 2 0.1725 0.8230 0.000 0.936 0.044 0.020 0.000
#> GSM537415 4 0.0794 0.8583 0.028 0.000 0.000 0.972 0.000
#> GSM537417 1 0.4037 0.7816 0.784 0.000 0.012 0.028 0.176
#> GSM537422 1 0.0000 0.9071 1.000 0.000 0.000 0.000 0.000
#> GSM537423 4 0.1981 0.8468 0.000 0.016 0.000 0.920 0.064
#> GSM537427 2 0.1725 0.8230 0.000 0.936 0.044 0.020 0.000
#> GSM537430 3 0.1410 0.9292 0.000 0.060 0.940 0.000 0.000
#> GSM537336 1 0.0290 0.9062 0.992 0.000 0.008 0.000 0.000
#> GSM537337 4 0.2104 0.8352 0.000 0.060 0.000 0.916 0.024
#> GSM537348 5 0.5808 0.4199 0.000 0.320 0.100 0.004 0.576
#> GSM537349 2 0.1646 0.8257 0.000 0.944 0.032 0.020 0.004
#> GSM537356 5 0.3354 0.5713 0.004 0.004 0.012 0.152 0.828
#> GSM537361 5 0.2032 0.6596 0.004 0.000 0.020 0.052 0.924
#> GSM537374 3 0.1282 0.9251 0.000 0.044 0.952 0.004 0.000
#> GSM537377 5 0.5030 0.6100 0.000 0.220 0.080 0.004 0.696
#> GSM537378 2 0.2102 0.8122 0.000 0.916 0.004 0.068 0.012
#> GSM537379 2 0.5287 0.4926 0.000 0.656 0.068 0.008 0.268
#> GSM537383 2 0.2605 0.7602 0.000 0.852 0.148 0.000 0.000
#> GSM537388 2 0.2911 0.7558 0.000 0.852 0.136 0.004 0.008
#> GSM537395 4 0.2208 0.8268 0.000 0.072 0.000 0.908 0.020
#> GSM537400 3 0.2116 0.9178 0.000 0.076 0.912 0.004 0.008
#> GSM537404 1 0.5651 0.3488 0.512 0.000 0.016 0.044 0.428
#> GSM537409 4 0.0771 0.8580 0.004 0.020 0.000 0.976 0.000
#> GSM537418 4 0.0854 0.8592 0.004 0.012 0.000 0.976 0.008
#> GSM537425 1 0.2507 0.8873 0.908 0.000 0.020 0.028 0.044
#> GSM537333 3 0.1430 0.9281 0.000 0.052 0.944 0.000 0.004
#> GSM537342 4 0.3456 0.8054 0.036 0.000 0.012 0.844 0.108
#> GSM537347 5 0.5036 0.6196 0.000 0.200 0.092 0.004 0.704
#> GSM537350 1 0.3928 0.7817 0.788 0.000 0.008 0.028 0.176
#> GSM537362 5 0.5778 0.4235 0.000 0.324 0.096 0.004 0.576
#> GSM537363 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537368 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537376 2 0.5514 0.4308 0.000 0.620 0.064 0.012 0.304
#> GSM537381 2 0.5624 0.3494 0.000 0.580 0.060 0.012 0.348
#> GSM537386 3 0.1792 0.9191 0.000 0.084 0.916 0.000 0.000
#> GSM537398 3 0.1197 0.9267 0.000 0.048 0.952 0.000 0.000
#> GSM537402 5 0.4301 0.4907 0.000 0.028 0.000 0.260 0.712
#> GSM537405 1 0.5511 0.4298 0.548 0.000 0.012 0.044 0.396
#> GSM537371 1 0.1725 0.8942 0.936 0.000 0.020 0.000 0.044
#> GSM537421 1 0.2439 0.8438 0.876 0.000 0.004 0.120 0.000
#> GSM537424 5 0.3553 0.7092 0.000 0.084 0.020 0.048 0.848
#> GSM537432 3 0.5295 0.5427 0.000 0.280 0.648 0.008 0.064
#> GSM537331 3 0.1851 0.9162 0.000 0.088 0.912 0.000 0.000
#> GSM537332 2 0.1983 0.8144 0.000 0.924 0.060 0.008 0.008
#> GSM537334 3 0.1410 0.9292 0.000 0.060 0.940 0.000 0.000
#> GSM537338 2 0.6030 0.2994 0.000 0.548 0.104 0.008 0.340
#> GSM537353 4 0.3366 0.7183 0.000 0.000 0.000 0.768 0.232
#> GSM537357 1 0.1168 0.8995 0.960 0.000 0.008 0.032 0.000
#> GSM537358 5 0.3248 0.6807 0.000 0.040 0.004 0.104 0.852
#> GSM537375 2 0.5590 0.4126 0.000 0.608 0.076 0.008 0.308
#> GSM537389 2 0.2127 0.7910 0.000 0.892 0.000 0.108 0.000
#> GSM537390 2 0.2020 0.7961 0.000 0.900 0.000 0.100 0.000
#> GSM537393 2 0.3010 0.7401 0.000 0.824 0.000 0.172 0.004
#> GSM537399 3 0.1571 0.9203 0.000 0.060 0.936 0.004 0.000
#> GSM537407 5 0.3197 0.7062 0.000 0.052 0.076 0.008 0.864
#> GSM537408 5 0.4425 0.0939 0.000 0.000 0.008 0.392 0.600
#> GSM537428 5 0.2696 0.7030 0.000 0.040 0.012 0.052 0.896
#> GSM537354 4 0.0865 0.8605 0.024 0.000 0.000 0.972 0.004
#> GSM537410 1 0.3053 0.8642 0.872 0.000 0.008 0.044 0.076
#> GSM537413 2 0.1725 0.8230 0.000 0.936 0.044 0.020 0.000
#> GSM537396 2 0.2312 0.8064 0.000 0.912 0.016 0.012 0.060
#> GSM537397 2 0.1731 0.8156 0.000 0.940 0.008 0.012 0.040
#> GSM537330 2 0.2911 0.7563 0.000 0.852 0.136 0.004 0.008
#> GSM537369 4 0.5148 0.3209 0.004 0.012 0.012 0.508 0.464
#> GSM537373 4 0.3521 0.7228 0.000 0.000 0.004 0.764 0.232
#> GSM537401 5 0.5808 0.4199 0.000 0.320 0.100 0.004 0.576
#> GSM537343 4 0.4779 0.3685 0.004 0.000 0.012 0.536 0.448
#> GSM537367 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537382 2 0.0693 0.8235 0.000 0.980 0.000 0.008 0.012
#> GSM537385 2 0.1419 0.8222 0.000 0.956 0.016 0.012 0.016
#> GSM537391 5 0.5468 0.4212 0.000 0.332 0.060 0.008 0.600
#> GSM537419 5 0.3142 0.7082 0.000 0.076 0.004 0.056 0.864
#> GSM537420 5 0.1857 0.6601 0.004 0.000 0.008 0.060 0.928
#> GSM537429 2 0.1983 0.8144 0.000 0.924 0.060 0.008 0.008
#> GSM537431 3 0.3663 0.7662 0.000 0.044 0.820 0.004 0.132
#> GSM537387 2 0.2444 0.7975 0.000 0.904 0.016 0.012 0.068
#> GSM537414 4 0.0865 0.8605 0.024 0.000 0.000 0.972 0.004
#> GSM537433 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537335 3 0.1851 0.9162 0.000 0.088 0.912 0.000 0.000
#> GSM537339 2 0.1341 0.8226 0.000 0.944 0.056 0.000 0.000
#> GSM537340 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537344 5 0.3962 0.4363 0.004 0.000 0.012 0.240 0.744
#> GSM537346 2 0.3328 0.7084 0.000 0.812 0.176 0.004 0.008
#> GSM537351 1 0.1281 0.8993 0.956 0.000 0.012 0.032 0.000
#> GSM537352 4 0.1908 0.8393 0.000 0.000 0.000 0.908 0.092
#> GSM537359 5 0.2804 0.7091 0.000 0.048 0.044 0.016 0.892
#> GSM537360 4 0.1518 0.8465 0.048 0.000 0.004 0.944 0.004
#> GSM537364 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537365 5 0.3177 0.5551 0.000 0.000 0.000 0.208 0.792
#> GSM537372 5 0.4219 0.6699 0.000 0.156 0.072 0.000 0.772
#> GSM537384 2 0.5654 0.3797 0.000 0.592 0.076 0.008 0.324
#> GSM537394 2 0.2408 0.7973 0.000 0.892 0.096 0.004 0.008
#> GSM537403 1 0.0609 0.9074 0.980 0.000 0.020 0.000 0.000
#> GSM537406 1 0.1648 0.8976 0.940 0.000 0.020 0.000 0.040
#> GSM537411 5 0.5471 0.2031 0.000 0.052 0.428 0.004 0.516
#> GSM537412 1 0.0510 0.9081 0.984 0.000 0.016 0.000 0.000
#> GSM537416 4 0.0794 0.8583 0.028 0.000 0.000 0.972 0.000
#> GSM537426 4 0.0771 0.8580 0.004 0.020 0.000 0.976 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.4264 0.1259 0.000 0.492 0.000 0.000 0.492 0.016
#> GSM537345 4 0.5057 0.6586 0.012 0.004 0.304 0.632 0.024 0.024
#> GSM537355 3 0.5769 0.5894 0.188 0.004 0.552 0.000 0.252 0.004
#> GSM537366 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537370 2 0.1798 0.8875 0.000 0.932 0.020 0.000 0.020 0.028
#> GSM537380 2 0.0748 0.8916 0.004 0.976 0.000 0.000 0.016 0.004
#> GSM537392 2 0.0551 0.8924 0.008 0.984 0.000 0.000 0.004 0.004
#> GSM537415 1 0.0363 0.8545 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537417 3 0.4364 0.0604 0.012 0.000 0.556 0.424 0.008 0.000
#> GSM537422 4 0.2422 0.8498 0.000 0.004 0.056 0.900 0.016 0.024
#> GSM537423 1 0.2705 0.8081 0.872 0.004 0.072 0.000 0.052 0.000
#> GSM537427 2 0.0551 0.8924 0.008 0.984 0.000 0.000 0.004 0.004
#> GSM537430 6 0.1471 0.9249 0.000 0.064 0.000 0.000 0.004 0.932
#> GSM537336 4 0.3090 0.8379 0.000 0.004 0.092 0.856 0.024 0.024
#> GSM537337 1 0.2767 0.8155 0.880 0.028 0.044 0.000 0.048 0.000
#> GSM537348 5 0.2728 0.6569 0.000 0.100 0.004 0.000 0.864 0.032
#> GSM537349 2 0.0806 0.8891 0.008 0.972 0.000 0.000 0.020 0.000
#> GSM537356 3 0.3975 0.6582 0.040 0.000 0.716 0.000 0.244 0.000
#> GSM537361 3 0.3547 0.6048 0.000 0.000 0.696 0.000 0.300 0.004
#> GSM537374 6 0.1320 0.9212 0.000 0.036 0.000 0.000 0.016 0.948
#> GSM537377 5 0.4411 0.6175 0.000 0.084 0.160 0.000 0.740 0.016
#> GSM537378 2 0.2911 0.8516 0.036 0.876 0.052 0.000 0.032 0.004
#> GSM537379 5 0.5770 0.2355 0.000 0.412 0.112 0.000 0.460 0.016
#> GSM537383 2 0.1075 0.8811 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM537388 2 0.3467 0.8527 0.000 0.832 0.068 0.000 0.024 0.076
#> GSM537395 1 0.3568 0.7756 0.828 0.084 0.044 0.000 0.044 0.000
#> GSM537400 6 0.1760 0.9120 0.000 0.048 0.020 0.000 0.004 0.928
#> GSM537404 3 0.4260 0.6023 0.012 0.000 0.740 0.184 0.064 0.000
#> GSM537409 1 0.0146 0.8521 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM537418 1 0.0363 0.8545 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537425 4 0.4738 0.7365 0.012 0.004 0.240 0.696 0.024 0.024
#> GSM537333 6 0.1549 0.9206 0.000 0.044 0.000 0.000 0.020 0.936
#> GSM537342 1 0.4166 0.3194 0.584 0.000 0.404 0.004 0.004 0.004
#> GSM537347 5 0.3152 0.6463 0.000 0.084 0.032 0.000 0.852 0.032
#> GSM537350 3 0.4543 -0.0885 0.012 0.000 0.492 0.484 0.008 0.004
#> GSM537362 5 0.2586 0.6569 0.000 0.100 0.000 0.000 0.868 0.032
#> GSM537363 4 0.0291 0.8656 0.000 0.000 0.004 0.992 0.000 0.004
#> GSM537368 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537376 5 0.5743 0.2983 0.000 0.384 0.112 0.000 0.488 0.016
#> GSM537381 5 0.6056 0.4025 0.000 0.284 0.140 0.000 0.540 0.036
#> GSM537386 6 0.1845 0.9235 0.000 0.072 0.008 0.000 0.004 0.916
#> GSM537398 6 0.1564 0.9188 0.000 0.040 0.000 0.000 0.024 0.936
#> GSM537402 5 0.5853 -0.3177 0.108 0.016 0.396 0.000 0.476 0.004
#> GSM537405 3 0.3958 0.5497 0.012 0.000 0.740 0.220 0.028 0.000
#> GSM537371 4 0.4560 0.7379 0.004 0.004 0.244 0.700 0.024 0.024
#> GSM537421 4 0.5724 0.6522 0.220 0.004 0.084 0.644 0.024 0.024
#> GSM537424 5 0.2236 0.5863 0.016 0.016 0.048 0.000 0.912 0.008
#> GSM537432 6 0.5949 0.5445 0.000 0.200 0.084 0.000 0.104 0.612
#> GSM537331 6 0.1845 0.9235 0.000 0.072 0.008 0.000 0.004 0.916
#> GSM537332 2 0.3444 0.8582 0.000 0.836 0.076 0.000 0.032 0.056
#> GSM537334 6 0.1787 0.9246 0.000 0.068 0.008 0.000 0.004 0.920
#> GSM537338 5 0.4239 0.6336 0.000 0.176 0.036 0.000 0.752 0.036
#> GSM537353 1 0.4686 0.5499 0.676 0.004 0.232 0.000 0.088 0.000
#> GSM537357 4 0.3631 0.8306 0.016 0.004 0.100 0.832 0.024 0.024
#> GSM537358 5 0.4958 0.1399 0.040 0.016 0.308 0.000 0.628 0.008
#> GSM537375 5 0.5588 0.3046 0.000 0.400 0.092 0.000 0.492 0.016
#> GSM537389 2 0.1327 0.8726 0.064 0.936 0.000 0.000 0.000 0.000
#> GSM537390 2 0.1267 0.8756 0.060 0.940 0.000 0.000 0.000 0.000
#> GSM537393 2 0.1814 0.8478 0.100 0.900 0.000 0.000 0.000 0.000
#> GSM537399 6 0.1226 0.9203 0.000 0.040 0.004 0.000 0.004 0.952
#> GSM537407 5 0.2904 0.5478 0.000 0.008 0.112 0.000 0.852 0.028
#> GSM537408 3 0.5914 0.5504 0.232 0.004 0.544 0.000 0.212 0.008
#> GSM537428 5 0.4306 0.3197 0.024 0.012 0.248 0.000 0.708 0.008
#> GSM537354 1 0.0363 0.8545 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537410 4 0.4788 0.4279 0.024 0.000 0.368 0.588 0.016 0.004
#> GSM537413 2 0.0551 0.8924 0.008 0.984 0.000 0.000 0.004 0.004
#> GSM537396 2 0.5031 0.7245 0.008 0.712 0.084 0.000 0.160 0.036
#> GSM537397 2 0.2999 0.8674 0.000 0.860 0.032 0.000 0.084 0.024
#> GSM537330 2 0.3246 0.8562 0.000 0.844 0.068 0.000 0.016 0.072
#> GSM537369 3 0.4354 0.5570 0.216 0.000 0.704 0.000 0.080 0.000
#> GSM537373 1 0.4913 0.2871 0.564 0.000 0.364 0.000 0.072 0.000
#> GSM537401 5 0.2839 0.6569 0.000 0.100 0.008 0.000 0.860 0.032
#> GSM537343 3 0.4728 0.5224 0.256 0.000 0.652 0.000 0.092 0.000
#> GSM537367 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537382 2 0.3857 0.8048 0.000 0.792 0.112 0.000 0.084 0.012
#> GSM537385 2 0.1531 0.8677 0.000 0.928 0.000 0.000 0.068 0.004
#> GSM537391 5 0.3114 0.6382 0.008 0.068 0.032 0.000 0.864 0.028
#> GSM537419 5 0.4040 0.4226 0.024 0.024 0.188 0.000 0.760 0.004
#> GSM537420 3 0.4067 0.5991 0.012 0.000 0.680 0.000 0.296 0.012
#> GSM537429 2 0.3145 0.8653 0.000 0.856 0.068 0.000 0.032 0.044
#> GSM537431 6 0.3582 0.7009 0.000 0.008 0.024 0.000 0.192 0.776
#> GSM537387 2 0.2700 0.7951 0.000 0.836 0.004 0.000 0.156 0.004
#> GSM537414 1 0.0363 0.8545 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537433 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537335 6 0.1845 0.9235 0.000 0.072 0.008 0.000 0.004 0.916
#> GSM537339 2 0.0993 0.8931 0.000 0.964 0.000 0.000 0.024 0.012
#> GSM537340 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537344 3 0.4293 0.6764 0.084 0.000 0.716 0.000 0.200 0.000
#> GSM537346 2 0.3809 0.8119 0.000 0.796 0.064 0.000 0.016 0.124
#> GSM537351 4 0.3721 0.8295 0.012 0.004 0.116 0.820 0.024 0.024
#> GSM537352 1 0.2499 0.8131 0.880 0.000 0.072 0.000 0.048 0.000
#> GSM537359 5 0.4374 0.3234 0.008 0.012 0.260 0.000 0.696 0.024
#> GSM537360 1 0.0837 0.8474 0.972 0.000 0.020 0.004 0.000 0.004
#> GSM537364 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537365 3 0.5582 0.4720 0.100 0.004 0.516 0.000 0.372 0.008
#> GSM537372 5 0.2944 0.6277 0.000 0.056 0.052 0.000 0.868 0.024
#> GSM537384 5 0.5577 0.3083 0.000 0.392 0.092 0.000 0.500 0.016
#> GSM537394 2 0.3744 0.8467 0.000 0.816 0.076 0.000 0.036 0.072
#> GSM537403 4 0.0260 0.8637 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM537406 4 0.2668 0.7191 0.000 0.000 0.168 0.828 0.000 0.004
#> GSM537411 5 0.3424 0.5524 0.000 0.004 0.020 0.000 0.780 0.196
#> GSM537412 4 0.0000 0.8663 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537416 1 0.0363 0.8545 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537426 1 0.0000 0.8508 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> ATC:kmeans 104 0.2435 0.228 2
#> ATC:kmeans 91 0.6482 0.748 3
#> ATC:kmeans 78 0.0956 0.795 4
#> ATC:kmeans 85 0.5267 0.504 5
#> ATC:kmeans 87 0.4410 0.555 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 104 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.994 0.997 0.5011 0.500 0.500
#> 3 3 0.735 0.856 0.873 0.2898 0.814 0.638
#> 4 4 0.817 0.839 0.899 0.1267 0.892 0.698
#> 5 5 0.798 0.824 0.894 0.0583 0.952 0.825
#> 6 6 0.776 0.636 0.835 0.0377 0.972 0.883
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
#> GSM537341 2 0.0000 0.995 0.000 1.000
#> GSM537345 1 0.0000 0.999 1.000 0.000
#> GSM537355 1 0.0000 0.999 1.000 0.000
#> GSM537366 1 0.0000 0.999 1.000 0.000
#> GSM537370 2 0.0000 0.995 0.000 1.000
#> GSM537380 2 0.0000 0.995 0.000 1.000
#> GSM537392 2 0.0000 0.995 0.000 1.000
#> GSM537415 1 0.0000 0.999 1.000 0.000
#> GSM537417 1 0.0000 0.999 1.000 0.000
#> GSM537422 1 0.0000 0.999 1.000 0.000
#> GSM537423 1 0.0000 0.999 1.000 0.000
#> GSM537427 2 0.0000 0.995 0.000 1.000
#> GSM537430 2 0.0000 0.995 0.000 1.000
#> GSM537336 1 0.0000 0.999 1.000 0.000
#> GSM537337 1 0.0376 0.995 0.996 0.004
#> GSM537348 2 0.0000 0.995 0.000 1.000
#> GSM537349 2 0.0000 0.995 0.000 1.000
#> GSM537356 1 0.0000 0.999 1.000 0.000
#> GSM537361 1 0.0000 0.999 1.000 0.000
#> GSM537374 2 0.0000 0.995 0.000 1.000
#> GSM537377 2 0.0000 0.995 0.000 1.000
#> GSM537378 2 0.0000 0.995 0.000 1.000
#> GSM537379 2 0.0000 0.995 0.000 1.000
#> GSM537383 2 0.0000 0.995 0.000 1.000
#> GSM537388 2 0.0000 0.995 0.000 1.000
#> GSM537395 2 0.5294 0.868 0.120 0.880
#> GSM537400 2 0.0000 0.995 0.000 1.000
#> GSM537404 1 0.0000 0.999 1.000 0.000
#> GSM537409 1 0.0000 0.999 1.000 0.000
#> GSM537418 1 0.0000 0.999 1.000 0.000
#> GSM537425 1 0.0000 0.999 1.000 0.000
#> GSM537333 2 0.0000 0.995 0.000 1.000
#> GSM537342 1 0.0000 0.999 1.000 0.000
#> GSM537347 2 0.0000 0.995 0.000 1.000
#> GSM537350 1 0.0000 0.999 1.000 0.000
#> GSM537362 2 0.0000 0.995 0.000 1.000
#> GSM537363 1 0.0000 0.999 1.000 0.000
#> GSM537368 1 0.0000 0.999 1.000 0.000
#> GSM537376 2 0.0000 0.995 0.000 1.000
#> GSM537381 2 0.0000 0.995 0.000 1.000
#> GSM537386 2 0.0000 0.995 0.000 1.000
#> GSM537398 2 0.0000 0.995 0.000 1.000
#> GSM537402 1 0.2043 0.967 0.968 0.032
#> GSM537405 1 0.0000 0.999 1.000 0.000
#> GSM537371 1 0.0000 0.999 1.000 0.000
#> GSM537421 1 0.0000 0.999 1.000 0.000
#> GSM537424 2 0.4161 0.911 0.084 0.916
#> GSM537432 2 0.0000 0.995 0.000 1.000
#> GSM537331 2 0.0000 0.995 0.000 1.000
#> GSM537332 2 0.0000 0.995 0.000 1.000
#> GSM537334 2 0.0000 0.995 0.000 1.000
#> GSM537338 2 0.0000 0.995 0.000 1.000
#> GSM537353 1 0.0000 0.999 1.000 0.000
#> GSM537357 1 0.0000 0.999 1.000 0.000
#> GSM537358 2 0.4022 0.915 0.080 0.920
#> GSM537375 2 0.0000 0.995 0.000 1.000
#> GSM537389 2 0.0000 0.995 0.000 1.000
#> GSM537390 2 0.0000 0.995 0.000 1.000
#> GSM537393 2 0.0000 0.995 0.000 1.000
#> GSM537399 2 0.0000 0.995 0.000 1.000
#> GSM537407 2 0.0000 0.995 0.000 1.000
#> GSM537408 1 0.0000 0.999 1.000 0.000
#> GSM537428 2 0.0000 0.995 0.000 1.000
#> GSM537354 1 0.0000 0.999 1.000 0.000
#> GSM537410 1 0.0000 0.999 1.000 0.000
#> GSM537413 2 0.0000 0.995 0.000 1.000
#> GSM537396 2 0.0000 0.995 0.000 1.000
#> GSM537397 2 0.0000 0.995 0.000 1.000
#> GSM537330 2 0.0000 0.995 0.000 1.000
#> GSM537369 1 0.0000 0.999 1.000 0.000
#> GSM537373 1 0.0000 0.999 1.000 0.000
#> GSM537401 2 0.0000 0.995 0.000 1.000
#> GSM537343 1 0.0000 0.999 1.000 0.000
#> GSM537367 1 0.0000 0.999 1.000 0.000
#> GSM537382 2 0.0000 0.995 0.000 1.000
#> GSM537385 2 0.0000 0.995 0.000 1.000
#> GSM537391 2 0.0000 0.995 0.000 1.000
#> GSM537419 2 0.0000 0.995 0.000 1.000
#> GSM537420 1 0.0000 0.999 1.000 0.000
#> GSM537429 2 0.0000 0.995 0.000 1.000
#> GSM537431 2 0.0000 0.995 0.000 1.000
#> GSM537387 2 0.0000 0.995 0.000 1.000
#> GSM537414 1 0.0000 0.999 1.000 0.000
#> GSM537433 1 0.0000 0.999 1.000 0.000
#> GSM537335 2 0.0000 0.995 0.000 1.000
#> GSM537339 2 0.0000 0.995 0.000 1.000
#> GSM537340 1 0.0000 0.999 1.000 0.000
#> GSM537344 1 0.0000 0.999 1.000 0.000
#> GSM537346 2 0.0000 0.995 0.000 1.000
#> GSM537351 1 0.0000 0.999 1.000 0.000
#> GSM537352 1 0.0000 0.999 1.000 0.000
#> GSM537359 2 0.0000 0.995 0.000 1.000
#> GSM537360 1 0.0000 0.999 1.000 0.000
#> GSM537364 1 0.0000 0.999 1.000 0.000
#> GSM537365 1 0.0000 0.999 1.000 0.000
#> GSM537372 2 0.0000 0.995 0.000 1.000
#> GSM537384 2 0.0000 0.995 0.000 1.000
#> GSM537394 2 0.0000 0.995 0.000 1.000
#> GSM537403 1 0.0000 0.999 1.000 0.000
#> GSM537406 1 0.0000 0.999 1.000 0.000
#> GSM537411 2 0.0000 0.995 0.000 1.000
#> GSM537412 1 0.0000 0.999 1.000 0.000
#> GSM537416 1 0.0000 0.999 1.000 0.000
#> GSM537426 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.2165 0.8944 0.936 0.064 0.000
#> GSM537345 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537355 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537366 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537370 2 0.5363 0.8218 0.276 0.724 0.000
#> GSM537380 2 0.5138 0.8320 0.252 0.748 0.000
#> GSM537392 2 0.5058 0.8327 0.244 0.756 0.000
#> GSM537415 3 0.5138 0.7850 0.000 0.252 0.748
#> GSM537417 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537422 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537423 2 0.5905 0.0786 0.000 0.648 0.352
#> GSM537427 2 0.5016 0.8321 0.240 0.760 0.000
#> GSM537430 1 0.0237 0.9485 0.996 0.004 0.000
#> GSM537336 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537337 2 0.0000 0.6903 0.000 1.000 0.000
#> GSM537348 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537349 2 0.5058 0.8327 0.244 0.756 0.000
#> GSM537356 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537361 3 0.0237 0.9382 0.004 0.000 0.996
#> GSM537374 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537377 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537378 2 0.3482 0.7809 0.128 0.872 0.000
#> GSM537379 1 0.3686 0.7933 0.860 0.140 0.000
#> GSM537383 2 0.5291 0.8274 0.268 0.732 0.000
#> GSM537388 2 0.5882 0.7447 0.348 0.652 0.000
#> GSM537395 2 0.0000 0.6903 0.000 1.000 0.000
#> GSM537400 1 0.0237 0.9485 0.996 0.004 0.000
#> GSM537404 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537409 3 0.6140 0.5722 0.000 0.404 0.596
#> GSM537418 3 0.5138 0.7850 0.000 0.252 0.748
#> GSM537425 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537333 1 0.0237 0.9485 0.996 0.004 0.000
#> GSM537342 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537347 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537350 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537362 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537363 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537368 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537376 1 0.0747 0.9394 0.984 0.016 0.000
#> GSM537381 1 0.3267 0.8279 0.884 0.116 0.000
#> GSM537386 1 0.0237 0.9485 0.996 0.004 0.000
#> GSM537398 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537402 2 0.7153 0.3309 0.048 0.652 0.300
#> GSM537405 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537371 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537421 3 0.0424 0.9372 0.000 0.008 0.992
#> GSM537424 1 0.3481 0.8364 0.904 0.052 0.044
#> GSM537432 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537331 1 0.0237 0.9485 0.996 0.004 0.000
#> GSM537332 2 0.5859 0.7497 0.344 0.656 0.000
#> GSM537334 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537338 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537353 3 0.5178 0.7812 0.000 0.256 0.744
#> GSM537357 3 0.0237 0.9390 0.000 0.004 0.996
#> GSM537358 2 0.6629 0.5545 0.360 0.624 0.016
#> GSM537375 1 0.3412 0.8172 0.876 0.124 0.000
#> GSM537389 2 0.2959 0.7631 0.100 0.900 0.000
#> GSM537390 2 0.3482 0.7809 0.128 0.872 0.000
#> GSM537393 2 0.1643 0.7241 0.044 0.956 0.000
#> GSM537399 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537407 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537408 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537428 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537354 3 0.5138 0.7850 0.000 0.252 0.748
#> GSM537410 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537413 2 0.5016 0.8321 0.240 0.760 0.000
#> GSM537396 2 0.5465 0.8137 0.288 0.712 0.000
#> GSM537397 2 0.5098 0.8326 0.248 0.752 0.000
#> GSM537330 2 0.6062 0.6776 0.384 0.616 0.000
#> GSM537369 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537373 3 0.1964 0.9125 0.000 0.056 0.944
#> GSM537401 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537343 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537367 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537382 2 0.5327 0.8246 0.272 0.728 0.000
#> GSM537385 2 0.5138 0.8320 0.252 0.748 0.000
#> GSM537391 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537419 1 0.5905 0.1437 0.648 0.352 0.000
#> GSM537420 3 0.0237 0.9382 0.004 0.000 0.996
#> GSM537429 2 0.5291 0.8271 0.268 0.732 0.000
#> GSM537431 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537387 2 0.5254 0.8289 0.264 0.736 0.000
#> GSM537414 3 0.5058 0.7913 0.000 0.244 0.756
#> GSM537433 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537335 1 0.0237 0.9485 0.996 0.004 0.000
#> GSM537339 2 0.5254 0.8289 0.264 0.736 0.000
#> GSM537340 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537344 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537346 2 0.5785 0.7635 0.332 0.668 0.000
#> GSM537351 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537352 3 0.5138 0.7850 0.000 0.252 0.748
#> GSM537359 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537360 3 0.4291 0.8363 0.000 0.180 0.820
#> GSM537364 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537365 3 0.0475 0.9374 0.004 0.004 0.992
#> GSM537372 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537384 1 0.3686 0.7941 0.860 0.140 0.000
#> GSM537394 1 0.2796 0.8530 0.908 0.092 0.000
#> GSM537403 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537406 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537411 1 0.0000 0.9499 1.000 0.000 0.000
#> GSM537412 3 0.0000 0.9406 0.000 0.000 1.000
#> GSM537416 3 0.5138 0.7850 0.000 0.252 0.748
#> GSM537426 3 0.6140 0.5722 0.000 0.404 0.596
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 1 0.5000 0.39701 0.500 0.500 0.000 0.000
#> GSM537345 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537355 3 0.1388 0.94613 0.012 0.000 0.960 0.028
#> GSM537366 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537370 2 0.0336 0.94057 0.008 0.992 0.000 0.000
#> GSM537380 2 0.0188 0.94168 0.004 0.996 0.000 0.000
#> GSM537392 2 0.0188 0.94145 0.000 0.996 0.000 0.004
#> GSM537415 4 0.1211 0.95014 0.000 0.000 0.040 0.960
#> GSM537417 3 0.0336 0.96609 0.000 0.000 0.992 0.008
#> GSM537422 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537423 4 0.1042 0.93770 0.000 0.008 0.020 0.972
#> GSM537427 2 0.0188 0.94145 0.000 0.996 0.000 0.004
#> GSM537430 1 0.4331 0.73557 0.712 0.288 0.000 0.000
#> GSM537336 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537337 4 0.1211 0.91664 0.000 0.040 0.000 0.960
#> GSM537348 1 0.1867 0.78088 0.928 0.072 0.000 0.000
#> GSM537349 2 0.0188 0.94145 0.000 0.996 0.000 0.004
#> GSM537356 3 0.0336 0.96609 0.000 0.000 0.992 0.008
#> GSM537361 3 0.1733 0.93582 0.024 0.000 0.948 0.028
#> GSM537374 1 0.3172 0.78846 0.840 0.160 0.000 0.000
#> GSM537377 1 0.3356 0.78644 0.824 0.176 0.000 0.000
#> GSM537378 2 0.0779 0.93150 0.004 0.980 0.000 0.016
#> GSM537379 1 0.4981 0.47600 0.536 0.464 0.000 0.000
#> GSM537383 2 0.0469 0.94000 0.012 0.988 0.000 0.000
#> GSM537388 2 0.1211 0.91713 0.040 0.960 0.000 0.000
#> GSM537395 4 0.1940 0.88947 0.000 0.076 0.000 0.924
#> GSM537400 1 0.4277 0.74338 0.720 0.280 0.000 0.000
#> GSM537404 3 0.0469 0.96371 0.000 0.000 0.988 0.012
#> GSM537409 4 0.1211 0.95014 0.000 0.000 0.040 0.960
#> GSM537418 4 0.1211 0.95014 0.000 0.000 0.040 0.960
#> GSM537425 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537333 1 0.4164 0.75029 0.736 0.264 0.000 0.000
#> GSM537342 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537347 1 0.1824 0.77721 0.936 0.060 0.000 0.004
#> GSM537350 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537362 1 0.2011 0.78224 0.920 0.080 0.000 0.000
#> GSM537363 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537368 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537376 1 0.4643 0.67810 0.656 0.344 0.000 0.000
#> GSM537381 1 0.4933 0.54749 0.568 0.432 0.000 0.000
#> GSM537386 1 0.4331 0.73557 0.712 0.288 0.000 0.000
#> GSM537398 1 0.3311 0.78688 0.828 0.172 0.000 0.000
#> GSM537402 4 0.9419 0.39332 0.228 0.168 0.176 0.428
#> GSM537405 3 0.0336 0.96609 0.000 0.000 0.992 0.008
#> GSM537371 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537421 3 0.4454 0.53402 0.000 0.000 0.692 0.308
#> GSM537424 1 0.2894 0.73731 0.900 0.020 0.008 0.072
#> GSM537432 1 0.4193 0.75140 0.732 0.268 0.000 0.000
#> GSM537331 1 0.4661 0.67897 0.652 0.348 0.000 0.000
#> GSM537332 2 0.1716 0.89278 0.064 0.936 0.000 0.000
#> GSM537334 1 0.3649 0.77783 0.796 0.204 0.000 0.000
#> GSM537338 1 0.3172 0.78846 0.840 0.160 0.000 0.000
#> GSM537353 4 0.1211 0.95014 0.000 0.000 0.040 0.960
#> GSM537357 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537358 1 0.7065 -0.00842 0.472 0.404 0.000 0.124
#> GSM537375 1 0.4916 0.55561 0.576 0.424 0.000 0.000
#> GSM537389 2 0.1211 0.91107 0.000 0.960 0.000 0.040
#> GSM537390 2 0.0707 0.93017 0.000 0.980 0.000 0.020
#> GSM537393 2 0.3610 0.71477 0.000 0.800 0.000 0.200
#> GSM537399 1 0.3356 0.78715 0.824 0.176 0.000 0.000
#> GSM537407 1 0.0895 0.73990 0.976 0.004 0.000 0.020
#> GSM537408 3 0.2500 0.91221 0.040 0.000 0.916 0.044
#> GSM537428 1 0.1452 0.72955 0.956 0.008 0.000 0.036
#> GSM537354 4 0.1211 0.95014 0.000 0.000 0.040 0.960
#> GSM537410 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537413 2 0.0188 0.94145 0.000 0.996 0.000 0.004
#> GSM537396 2 0.2216 0.87324 0.092 0.908 0.000 0.000
#> GSM537397 2 0.0000 0.94129 0.000 1.000 0.000 0.000
#> GSM537330 2 0.1557 0.90391 0.056 0.944 0.000 0.000
#> GSM537369 3 0.0188 0.96792 0.000 0.000 0.996 0.004
#> GSM537373 3 0.4222 0.61276 0.000 0.000 0.728 0.272
#> GSM537401 1 0.1716 0.77890 0.936 0.064 0.000 0.000
#> GSM537343 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537367 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537382 2 0.0592 0.93863 0.016 0.984 0.000 0.000
#> GSM537385 2 0.0336 0.94145 0.008 0.992 0.000 0.000
#> GSM537391 1 0.1474 0.77329 0.948 0.052 0.000 0.000
#> GSM537419 1 0.5535 0.38728 0.656 0.304 0.000 0.040
#> GSM537420 3 0.2983 0.88884 0.068 0.000 0.892 0.040
#> GSM537429 2 0.0188 0.94142 0.004 0.996 0.000 0.000
#> GSM537431 1 0.1109 0.75930 0.968 0.028 0.000 0.004
#> GSM537387 2 0.0188 0.94168 0.004 0.996 0.000 0.000
#> GSM537414 4 0.1557 0.93893 0.000 0.000 0.056 0.944
#> GSM537433 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537335 1 0.4967 0.50391 0.548 0.452 0.000 0.000
#> GSM537339 2 0.0336 0.94153 0.008 0.992 0.000 0.000
#> GSM537340 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537344 3 0.0188 0.96792 0.000 0.000 0.996 0.004
#> GSM537346 2 0.1389 0.90964 0.048 0.952 0.000 0.000
#> GSM537351 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537352 4 0.1302 0.94827 0.000 0.000 0.044 0.956
#> GSM537359 1 0.1452 0.72955 0.956 0.008 0.000 0.036
#> GSM537360 4 0.1389 0.94523 0.000 0.000 0.048 0.952
#> GSM537364 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537365 3 0.2385 0.91716 0.028 0.000 0.920 0.052
#> GSM537372 1 0.1576 0.77245 0.948 0.048 0.000 0.004
#> GSM537384 1 0.4961 0.50955 0.552 0.448 0.000 0.000
#> GSM537394 2 0.4585 0.26960 0.332 0.668 0.000 0.000
#> GSM537403 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537406 3 0.0188 0.96789 0.000 0.000 0.996 0.004
#> GSM537411 1 0.1576 0.77245 0.948 0.048 0.000 0.004
#> GSM537412 3 0.0000 0.96939 0.000 0.000 1.000 0.000
#> GSM537416 4 0.1211 0.95014 0.000 0.000 0.040 0.960
#> GSM537426 4 0.1211 0.95014 0.000 0.000 0.040 0.960
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 3 0.4268 0.5055 0.000 0.344 0.648 0.000 0.008
#> GSM537345 1 0.0162 0.9448 0.996 0.000 0.000 0.000 0.004
#> GSM537355 1 0.3750 0.6380 0.756 0.000 0.000 0.012 0.232
#> GSM537366 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537370 2 0.2540 0.8481 0.000 0.888 0.088 0.000 0.024
#> GSM537380 2 0.0290 0.8732 0.000 0.992 0.008 0.000 0.000
#> GSM537392 2 0.0290 0.8732 0.000 0.992 0.008 0.000 0.000
#> GSM537415 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
#> GSM537417 1 0.0404 0.9403 0.988 0.000 0.000 0.012 0.000
#> GSM537422 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537423 4 0.1492 0.9411 0.008 0.004 0.000 0.948 0.040
#> GSM537427 2 0.0290 0.8732 0.000 0.992 0.008 0.000 0.000
#> GSM537430 3 0.2304 0.8247 0.000 0.100 0.892 0.000 0.008
#> GSM537336 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537337 4 0.1410 0.9183 0.000 0.060 0.000 0.940 0.000
#> GSM537348 3 0.1670 0.8221 0.000 0.012 0.936 0.000 0.052
#> GSM537349 2 0.0290 0.8732 0.000 0.992 0.008 0.000 0.000
#> GSM537356 1 0.0671 0.9367 0.980 0.000 0.000 0.016 0.004
#> GSM537361 1 0.2953 0.7878 0.844 0.000 0.000 0.012 0.144
#> GSM537374 3 0.1281 0.8328 0.000 0.032 0.956 0.000 0.012
#> GSM537377 3 0.2848 0.7875 0.000 0.004 0.840 0.000 0.156
#> GSM537378 2 0.1682 0.8436 0.000 0.944 0.012 0.012 0.032
#> GSM537379 3 0.4889 0.7366 0.000 0.136 0.720 0.000 0.144
#> GSM537383 2 0.1121 0.8715 0.000 0.956 0.044 0.000 0.000
#> GSM537388 2 0.4607 0.7025 0.000 0.720 0.228 0.004 0.048
#> GSM537395 4 0.2848 0.7995 0.000 0.156 0.000 0.840 0.004
#> GSM537400 3 0.3413 0.8083 0.000 0.100 0.844 0.004 0.052
#> GSM537404 1 0.0798 0.9348 0.976 0.000 0.000 0.016 0.008
#> GSM537409 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
#> GSM537418 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
#> GSM537425 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537333 3 0.1981 0.8343 0.000 0.048 0.924 0.000 0.028
#> GSM537342 1 0.0162 0.9440 0.996 0.000 0.000 0.004 0.000
#> GSM537347 3 0.2124 0.8016 0.000 0.004 0.900 0.000 0.096
#> GSM537350 1 0.0451 0.9413 0.988 0.000 0.000 0.008 0.004
#> GSM537362 3 0.1764 0.8172 0.000 0.008 0.928 0.000 0.064
#> GSM537363 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537368 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537376 3 0.4569 0.7620 0.000 0.104 0.748 0.000 0.148
#> GSM537381 3 0.4837 0.7376 0.000 0.092 0.728 0.004 0.176
#> GSM537386 3 0.2286 0.8191 0.000 0.108 0.888 0.000 0.004
#> GSM537398 3 0.1281 0.8328 0.000 0.032 0.956 0.000 0.012
#> GSM537402 5 0.4766 0.6834 0.028 0.100 0.016 0.068 0.788
#> GSM537405 1 0.0671 0.9367 0.980 0.000 0.000 0.016 0.004
#> GSM537371 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537421 1 0.3274 0.6770 0.780 0.000 0.000 0.220 0.000
#> GSM537424 3 0.5157 0.6813 0.012 0.008 0.724 0.076 0.180
#> GSM537432 3 0.3133 0.8174 0.000 0.080 0.864 0.004 0.052
#> GSM537331 3 0.2763 0.7970 0.000 0.148 0.848 0.000 0.004
#> GSM537332 2 0.4952 0.6279 0.000 0.672 0.272 0.004 0.052
#> GSM537334 3 0.1205 0.8338 0.000 0.040 0.956 0.000 0.004
#> GSM537338 3 0.1331 0.8339 0.000 0.040 0.952 0.000 0.008
#> GSM537353 4 0.0898 0.9673 0.020 0.000 0.000 0.972 0.008
#> GSM537357 1 0.0162 0.9440 0.996 0.000 0.000 0.004 0.000
#> GSM537358 5 0.3523 0.7581 0.000 0.032 0.140 0.004 0.824
#> GSM537375 3 0.4364 0.7623 0.000 0.088 0.764 0.000 0.148
#> GSM537389 2 0.0703 0.8568 0.000 0.976 0.000 0.024 0.000
#> GSM537390 2 0.0451 0.8699 0.000 0.988 0.004 0.008 0.000
#> GSM537393 2 0.2424 0.7544 0.000 0.868 0.000 0.132 0.000
#> GSM537399 3 0.1205 0.8342 0.000 0.040 0.956 0.000 0.004
#> GSM537407 3 0.3534 0.6283 0.000 0.000 0.744 0.000 0.256
#> GSM537408 5 0.4147 0.5380 0.316 0.000 0.000 0.008 0.676
#> GSM537428 5 0.2891 0.7433 0.000 0.000 0.176 0.000 0.824
#> GSM537354 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
#> GSM537410 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537413 2 0.0162 0.8725 0.000 0.996 0.004 0.000 0.000
#> GSM537396 2 0.5769 0.6268 0.000 0.628 0.144 0.004 0.224
#> GSM537397 2 0.1646 0.8680 0.000 0.944 0.020 0.004 0.032
#> GSM537330 2 0.4578 0.6806 0.000 0.712 0.244 0.004 0.040
#> GSM537369 1 0.0854 0.9348 0.976 0.000 0.004 0.008 0.012
#> GSM537373 1 0.3838 0.5693 0.716 0.000 0.000 0.280 0.004
#> GSM537401 3 0.1740 0.8203 0.000 0.012 0.932 0.000 0.056
#> GSM537343 1 0.0162 0.9448 0.996 0.000 0.000 0.000 0.004
#> GSM537367 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537382 2 0.4417 0.7789 0.000 0.772 0.100 0.004 0.124
#> GSM537385 2 0.0404 0.8737 0.000 0.988 0.012 0.000 0.000
#> GSM537391 3 0.4846 0.6415 0.000 0.056 0.696 0.004 0.244
#> GSM537419 5 0.4069 0.7482 0.000 0.076 0.136 0.000 0.788
#> GSM537420 5 0.4682 0.2942 0.420 0.000 0.000 0.016 0.564
#> GSM537429 2 0.2878 0.8440 0.000 0.880 0.068 0.004 0.048
#> GSM537431 3 0.1892 0.8085 0.000 0.004 0.916 0.000 0.080
#> GSM537387 2 0.1195 0.8722 0.000 0.960 0.028 0.000 0.012
#> GSM537414 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
#> GSM537433 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537335 3 0.3171 0.7726 0.000 0.176 0.816 0.000 0.008
#> GSM537339 2 0.1205 0.8689 0.000 0.956 0.040 0.000 0.004
#> GSM537340 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537344 1 0.0671 0.9367 0.980 0.000 0.000 0.016 0.004
#> GSM537346 2 0.4567 0.6961 0.000 0.720 0.232 0.004 0.044
#> GSM537351 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537352 4 0.0865 0.9673 0.024 0.000 0.000 0.972 0.004
#> GSM537359 5 0.2929 0.7433 0.000 0.000 0.180 0.000 0.820
#> GSM537360 4 0.0880 0.9591 0.032 0.000 0.000 0.968 0.000
#> GSM537364 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537365 1 0.4449 0.3530 0.636 0.000 0.004 0.008 0.352
#> GSM537372 3 0.2136 0.8046 0.000 0.008 0.904 0.000 0.088
#> GSM537384 3 0.4170 0.7609 0.000 0.080 0.780 0.000 0.140
#> GSM537394 3 0.5439 -0.0337 0.000 0.464 0.484 0.004 0.048
#> GSM537403 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537406 1 0.0290 0.9424 0.992 0.000 0.000 0.000 0.008
#> GSM537411 3 0.2286 0.7926 0.000 0.004 0.888 0.000 0.108
#> GSM537412 1 0.0000 0.9460 1.000 0.000 0.000 0.000 0.000
#> GSM537416 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
#> GSM537426 4 0.0609 0.9708 0.020 0.000 0.000 0.980 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.4077 0.3574 0.044 0.228 0.004 0.000 0.724 0.000
#> GSM537345 4 0.1333 0.8920 0.048 0.000 0.008 0.944 0.000 0.000
#> GSM537355 4 0.4708 0.4921 0.068 0.000 0.260 0.664 0.000 0.008
#> GSM537366 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537370 2 0.4791 0.6152 0.080 0.664 0.008 0.000 0.248 0.000
#> GSM537380 2 0.0000 0.7715 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537392 2 0.0000 0.7715 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537415 6 0.0260 0.9342 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM537417 4 0.0862 0.8950 0.016 0.000 0.004 0.972 0.000 0.008
#> GSM537422 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537423 6 0.2658 0.8454 0.016 0.000 0.112 0.008 0.000 0.864
#> GSM537427 2 0.0000 0.7715 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537430 5 0.1584 0.5556 0.008 0.064 0.000 0.000 0.928 0.000
#> GSM537336 4 0.0858 0.8980 0.028 0.000 0.004 0.968 0.000 0.000
#> GSM537337 6 0.1461 0.9012 0.016 0.044 0.000 0.000 0.000 0.940
#> GSM537348 5 0.3385 0.4495 0.172 0.004 0.028 0.000 0.796 0.000
#> GSM537349 2 0.0000 0.7715 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537356 4 0.3353 0.8092 0.156 0.000 0.028 0.808 0.000 0.008
#> GSM537361 4 0.5262 0.4987 0.156 0.000 0.204 0.632 0.000 0.008
#> GSM537374 5 0.1167 0.5571 0.020 0.008 0.012 0.000 0.960 0.000
#> GSM537377 1 0.3955 0.6008 0.560 0.000 0.004 0.000 0.436 0.000
#> GSM537378 2 0.1958 0.7180 0.100 0.896 0.000 0.000 0.004 0.000
#> GSM537379 5 0.5270 -0.4841 0.404 0.100 0.000 0.000 0.496 0.000
#> GSM537383 2 0.1500 0.7628 0.012 0.936 0.000 0.000 0.052 0.000
#> GSM537388 2 0.5941 0.3497 0.140 0.468 0.016 0.000 0.376 0.000
#> GSM537395 6 0.4304 0.5067 0.020 0.336 0.008 0.000 0.000 0.636
#> GSM537400 5 0.3105 0.5032 0.108 0.036 0.012 0.000 0.844 0.000
#> GSM537404 4 0.2177 0.8676 0.052 0.000 0.032 0.908 0.000 0.008
#> GSM537409 6 0.0260 0.9342 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM537418 6 0.0260 0.9342 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM537425 4 0.0858 0.8999 0.028 0.000 0.004 0.968 0.000 0.000
#> GSM537333 5 0.1549 0.5461 0.044 0.020 0.000 0.000 0.936 0.000
#> GSM537342 4 0.0806 0.8994 0.020 0.000 0.000 0.972 0.000 0.008
#> GSM537347 5 0.4215 0.2556 0.196 0.000 0.080 0.000 0.724 0.000
#> GSM537350 4 0.0520 0.8999 0.008 0.000 0.000 0.984 0.000 0.008
#> GSM537362 5 0.3642 0.3378 0.204 0.000 0.036 0.000 0.760 0.000
#> GSM537363 4 0.0508 0.9015 0.012 0.000 0.004 0.984 0.000 0.000
#> GSM537368 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537376 5 0.4905 -0.4528 0.408 0.064 0.000 0.000 0.528 0.000
#> GSM537381 1 0.4821 0.3146 0.540 0.028 0.016 0.000 0.416 0.000
#> GSM537386 5 0.1625 0.5570 0.012 0.060 0.000 0.000 0.928 0.000
#> GSM537398 5 0.1353 0.5588 0.024 0.012 0.012 0.000 0.952 0.000
#> GSM537402 3 0.2813 0.7623 0.024 0.068 0.880 0.012 0.000 0.016
#> GSM537405 4 0.1901 0.8799 0.040 0.000 0.028 0.924 0.000 0.008
#> GSM537371 4 0.1049 0.8967 0.032 0.000 0.008 0.960 0.000 0.000
#> GSM537421 4 0.3482 0.5359 0.000 0.000 0.000 0.684 0.000 0.316
#> GSM537424 1 0.5424 0.5085 0.552 0.004 0.052 0.000 0.364 0.028
#> GSM537432 5 0.3476 0.4610 0.148 0.020 0.024 0.000 0.808 0.000
#> GSM537331 5 0.2209 0.5462 0.024 0.072 0.004 0.000 0.900 0.000
#> GSM537332 5 0.6149 -0.2317 0.148 0.396 0.024 0.000 0.432 0.000
#> GSM537334 5 0.0914 0.5625 0.016 0.016 0.000 0.000 0.968 0.000
#> GSM537338 5 0.1458 0.5649 0.020 0.016 0.016 0.000 0.948 0.000
#> GSM537353 6 0.1605 0.9163 0.016 0.000 0.032 0.012 0.000 0.940
#> GSM537357 4 0.1116 0.8965 0.028 0.000 0.004 0.960 0.000 0.008
#> GSM537358 3 0.1406 0.7799 0.016 0.008 0.952 0.000 0.020 0.004
#> GSM537375 5 0.5151 -0.5918 0.444 0.084 0.000 0.000 0.472 0.000
#> GSM537389 2 0.0405 0.7682 0.008 0.988 0.000 0.000 0.000 0.004
#> GSM537390 2 0.0405 0.7696 0.008 0.988 0.000 0.000 0.000 0.004
#> GSM537393 2 0.1549 0.7371 0.020 0.936 0.000 0.000 0.000 0.044
#> GSM537399 5 0.1409 0.5597 0.032 0.012 0.008 0.000 0.948 0.000
#> GSM537407 5 0.5643 -0.0581 0.216 0.000 0.248 0.000 0.536 0.000
#> GSM537408 3 0.3624 0.6499 0.016 0.000 0.756 0.220 0.000 0.008
#> GSM537428 3 0.2680 0.7452 0.056 0.000 0.868 0.000 0.076 0.000
#> GSM537354 6 0.0405 0.9334 0.004 0.000 0.000 0.008 0.000 0.988
#> GSM537410 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537413 2 0.0146 0.7712 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM537396 2 0.7206 0.3337 0.156 0.408 0.140 0.000 0.296 0.000
#> GSM537397 2 0.4559 0.6877 0.128 0.736 0.020 0.000 0.116 0.000
#> GSM537330 2 0.5312 0.3502 0.080 0.504 0.008 0.000 0.408 0.000
#> GSM537369 4 0.2971 0.8247 0.144 0.000 0.020 0.832 0.000 0.004
#> GSM537373 4 0.4623 0.5588 0.068 0.000 0.004 0.664 0.000 0.264
#> GSM537401 5 0.3090 0.4541 0.140 0.004 0.028 0.000 0.828 0.000
#> GSM537343 4 0.2152 0.8732 0.068 0.000 0.024 0.904 0.000 0.004
#> GSM537367 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537382 2 0.5520 0.4424 0.312 0.532 0.000 0.000 0.156 0.000
#> GSM537385 2 0.0891 0.7698 0.008 0.968 0.000 0.000 0.024 0.000
#> GSM537391 5 0.5041 0.3152 0.160 0.012 0.156 0.000 0.672 0.000
#> GSM537419 3 0.3662 0.7399 0.056 0.072 0.824 0.000 0.048 0.000
#> GSM537420 3 0.5333 0.5120 0.112 0.000 0.588 0.292 0.000 0.008
#> GSM537429 2 0.5580 0.5696 0.148 0.596 0.016 0.000 0.240 0.000
#> GSM537431 5 0.3072 0.4824 0.076 0.000 0.084 0.000 0.840 0.000
#> GSM537387 2 0.2772 0.7452 0.040 0.864 0.004 0.000 0.092 0.000
#> GSM537414 6 0.0806 0.9257 0.008 0.000 0.000 0.020 0.000 0.972
#> GSM537433 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537335 5 0.2344 0.5400 0.028 0.076 0.004 0.000 0.892 0.000
#> GSM537339 2 0.2776 0.7418 0.032 0.860 0.004 0.000 0.104 0.000
#> GSM537340 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537344 4 0.2699 0.8499 0.108 0.000 0.020 0.864 0.000 0.008
#> GSM537346 2 0.5391 0.4036 0.092 0.520 0.008 0.000 0.380 0.000
#> GSM537351 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537352 6 0.1364 0.9248 0.020 0.000 0.016 0.012 0.000 0.952
#> GSM537359 3 0.1930 0.7701 0.036 0.000 0.916 0.000 0.048 0.000
#> GSM537360 6 0.1285 0.8985 0.004 0.000 0.000 0.052 0.000 0.944
#> GSM537364 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537365 4 0.5961 -0.0660 0.120 0.000 0.396 0.460 0.000 0.024
#> GSM537372 5 0.4771 0.1045 0.248 0.000 0.100 0.000 0.652 0.000
#> GSM537384 1 0.4819 0.6150 0.528 0.056 0.000 0.000 0.416 0.000
#> GSM537394 5 0.5616 0.2267 0.144 0.224 0.024 0.000 0.608 0.000
#> GSM537403 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537406 4 0.0146 0.9025 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM537411 5 0.4001 0.3307 0.128 0.000 0.112 0.000 0.760 0.000
#> GSM537412 4 0.0000 0.9031 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537416 6 0.0260 0.9342 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM537426 6 0.0260 0.9342 0.000 0.000 0.000 0.008 0.000 0.992
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> ATC:skmeans 104 0.322 0.2762 2
#> ATC:skmeans 101 0.158 0.1168 3
#> ATC:skmeans 98 0.305 0.1427 4
#> ATC:skmeans 101 0.446 0.1315 5
#> ATC:skmeans 79 0.472 0.0557 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 104 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 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 0.812 0.920 0.960 0.4634 0.518 0.518
#> 3 3 0.883 0.893 0.956 0.3723 0.728 0.529
#> 4 4 0.785 0.738 0.871 0.1375 0.872 0.673
#> 5 5 0.793 0.865 0.906 0.0747 0.893 0.654
#> 6 6 0.886 0.862 0.939 0.0480 0.958 0.812
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
#> GSM537341 2 0.000 0.989 0.000 1.000
#> GSM537345 1 0.000 0.907 1.000 0.000
#> GSM537355 2 0.584 0.820 0.140 0.860
#> GSM537366 1 0.000 0.907 1.000 0.000
#> GSM537370 2 0.000 0.989 0.000 1.000
#> GSM537380 2 0.000 0.989 0.000 1.000
#> GSM537392 2 0.000 0.989 0.000 1.000
#> GSM537415 1 0.000 0.907 1.000 0.000
#> GSM537417 1 0.000 0.907 1.000 0.000
#> GSM537422 1 0.000 0.907 1.000 0.000
#> GSM537423 2 0.000 0.989 0.000 1.000
#> GSM537427 2 0.000 0.989 0.000 1.000
#> GSM537430 2 0.000 0.989 0.000 1.000
#> GSM537336 1 0.000 0.907 1.000 0.000
#> GSM537337 2 0.000 0.989 0.000 1.000
#> GSM537348 2 0.000 0.989 0.000 1.000
#> GSM537349 2 0.000 0.989 0.000 1.000
#> GSM537356 2 0.563 0.831 0.132 0.868
#> GSM537361 1 0.775 0.761 0.772 0.228
#> GSM537374 2 0.000 0.989 0.000 1.000
#> GSM537377 2 0.000 0.989 0.000 1.000
#> GSM537378 2 0.000 0.989 0.000 1.000
#> GSM537379 2 0.000 0.989 0.000 1.000
#> GSM537383 2 0.000 0.989 0.000 1.000
#> GSM537388 2 0.000 0.989 0.000 1.000
#> GSM537395 2 0.000 0.989 0.000 1.000
#> GSM537400 2 0.000 0.989 0.000 1.000
#> GSM537404 1 0.000 0.907 1.000 0.000
#> GSM537409 1 0.775 0.761 0.772 0.228
#> GSM537418 1 0.775 0.761 0.772 0.228
#> GSM537425 1 0.000 0.907 1.000 0.000
#> GSM537333 2 0.000 0.989 0.000 1.000
#> GSM537342 1 0.615 0.820 0.848 0.152
#> GSM537347 2 0.000 0.989 0.000 1.000
#> GSM537350 1 0.000 0.907 1.000 0.000
#> GSM537362 2 0.000 0.989 0.000 1.000
#> GSM537363 1 0.000 0.907 1.000 0.000
#> GSM537368 1 0.000 0.907 1.000 0.000
#> GSM537376 2 0.000 0.989 0.000 1.000
#> GSM537381 2 0.000 0.989 0.000 1.000
#> GSM537386 2 0.000 0.989 0.000 1.000
#> GSM537398 2 0.000 0.989 0.000 1.000
#> GSM537402 2 0.456 0.879 0.096 0.904
#> GSM537405 1 0.000 0.907 1.000 0.000
#> GSM537371 1 0.000 0.907 1.000 0.000
#> GSM537421 1 0.000 0.907 1.000 0.000
#> GSM537424 2 0.000 0.989 0.000 1.000
#> GSM537432 2 0.000 0.989 0.000 1.000
#> GSM537331 2 0.000 0.989 0.000 1.000
#> GSM537332 2 0.000 0.989 0.000 1.000
#> GSM537334 2 0.000 0.989 0.000 1.000
#> GSM537338 2 0.000 0.989 0.000 1.000
#> GSM537353 2 0.738 0.709 0.208 0.792
#> GSM537357 1 0.000 0.907 1.000 0.000
#> GSM537358 2 0.000 0.989 0.000 1.000
#> GSM537375 2 0.000 0.989 0.000 1.000
#> GSM537389 2 0.000 0.989 0.000 1.000
#> GSM537390 2 0.000 0.989 0.000 1.000
#> GSM537393 2 0.000 0.989 0.000 1.000
#> GSM537399 2 0.000 0.989 0.000 1.000
#> GSM537407 2 0.000 0.989 0.000 1.000
#> GSM537408 1 0.795 0.747 0.760 0.240
#> GSM537428 2 0.000 0.989 0.000 1.000
#> GSM537354 1 0.000 0.907 1.000 0.000
#> GSM537410 1 0.000 0.907 1.000 0.000
#> GSM537413 2 0.000 0.989 0.000 1.000
#> GSM537396 2 0.000 0.989 0.000 1.000
#> GSM537397 2 0.000 0.989 0.000 1.000
#> GSM537330 2 0.000 0.989 0.000 1.000
#> GSM537369 1 0.999 0.219 0.516 0.484
#> GSM537373 1 0.983 0.400 0.576 0.424
#> GSM537401 2 0.000 0.989 0.000 1.000
#> GSM537343 1 0.775 0.761 0.772 0.228
#> GSM537367 1 0.000 0.907 1.000 0.000
#> GSM537382 2 0.000 0.989 0.000 1.000
#> GSM537385 2 0.000 0.989 0.000 1.000
#> GSM537391 2 0.000 0.989 0.000 1.000
#> GSM537419 2 0.000 0.989 0.000 1.000
#> GSM537420 1 0.775 0.761 0.772 0.228
#> GSM537429 2 0.000 0.989 0.000 1.000
#> GSM537431 2 0.000 0.989 0.000 1.000
#> GSM537387 2 0.000 0.989 0.000 1.000
#> GSM537414 1 0.000 0.907 1.000 0.000
#> GSM537433 1 0.000 0.907 1.000 0.000
#> GSM537335 2 0.000 0.989 0.000 1.000
#> GSM537339 2 0.000 0.989 0.000 1.000
#> GSM537340 1 0.000 0.907 1.000 0.000
#> GSM537344 1 0.781 0.756 0.768 0.232
#> GSM537346 2 0.000 0.989 0.000 1.000
#> GSM537351 1 0.000 0.907 1.000 0.000
#> GSM537352 1 0.921 0.609 0.664 0.336
#> GSM537359 2 0.000 0.989 0.000 1.000
#> GSM537360 1 0.000 0.907 1.000 0.000
#> GSM537364 1 0.000 0.907 1.000 0.000
#> GSM537365 1 0.929 0.595 0.656 0.344
#> GSM537372 2 0.000 0.989 0.000 1.000
#> GSM537384 2 0.000 0.989 0.000 1.000
#> GSM537394 2 0.000 0.989 0.000 1.000
#> GSM537403 1 0.000 0.907 1.000 0.000
#> GSM537406 1 0.000 0.907 1.000 0.000
#> GSM537411 2 0.000 0.989 0.000 1.000
#> GSM537412 1 0.000 0.907 1.000 0.000
#> GSM537416 1 0.000 0.907 1.000 0.000
#> GSM537426 1 0.775 0.761 0.772 0.228
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537345 3 0.2625 0.894 0.000 0.084 0.916
#> GSM537355 2 0.1643 0.900 0.044 0.956 0.000
#> GSM537366 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537370 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537380 1 0.1289 0.926 0.968 0.032 0.000
#> GSM537392 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537415 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537417 3 0.2625 0.896 0.000 0.084 0.916
#> GSM537422 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537423 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537427 1 0.3192 0.848 0.888 0.112 0.000
#> GSM537430 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537336 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537337 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537348 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537349 1 0.5859 0.471 0.656 0.344 0.000
#> GSM537356 2 0.0592 0.925 0.012 0.988 0.000
#> GSM537361 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537374 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537377 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537378 2 0.5835 0.485 0.340 0.660 0.000
#> GSM537379 1 0.5810 0.471 0.664 0.336 0.000
#> GSM537383 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537388 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537395 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537400 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537404 2 0.2356 0.878 0.000 0.928 0.072
#> GSM537409 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537418 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537425 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537333 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537342 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537347 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537350 2 0.3482 0.815 0.000 0.872 0.128
#> GSM537362 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537363 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537368 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537376 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537381 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537386 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537398 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537402 2 0.0237 0.930 0.004 0.996 0.000
#> GSM537405 3 0.6079 0.389 0.000 0.388 0.612
#> GSM537371 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537421 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537424 2 0.4842 0.702 0.224 0.776 0.000
#> GSM537432 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537331 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537332 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537334 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537338 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537353 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537357 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537358 2 0.0747 0.922 0.016 0.984 0.000
#> GSM537375 1 0.6026 0.373 0.624 0.376 0.000
#> GSM537389 2 0.4399 0.752 0.188 0.812 0.000
#> GSM537390 2 0.4842 0.705 0.224 0.776 0.000
#> GSM537393 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537399 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537407 1 0.2356 0.890 0.928 0.072 0.000
#> GSM537408 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537428 2 0.2625 0.861 0.084 0.916 0.000
#> GSM537354 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537410 2 0.4235 0.751 0.000 0.824 0.176
#> GSM537413 1 0.2261 0.895 0.932 0.068 0.000
#> GSM537396 1 0.0424 0.945 0.992 0.008 0.000
#> GSM537397 1 0.5882 0.462 0.652 0.348 0.000
#> GSM537330 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537369 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537373 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537401 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537343 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537367 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537382 1 0.1031 0.933 0.976 0.024 0.000
#> GSM537385 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537391 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537419 2 0.6126 0.329 0.400 0.600 0.000
#> GSM537420 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537429 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537431 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537387 1 0.5882 0.462 0.652 0.348 0.000
#> GSM537414 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537433 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537335 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537339 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537340 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537344 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537346 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537351 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537352 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537359 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537360 2 0.0892 0.920 0.000 0.980 0.020
#> GSM537364 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537365 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537372 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537384 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537394 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537403 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537406 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537411 1 0.0000 0.951 1.000 0.000 0.000
#> GSM537412 3 0.0000 0.970 0.000 0.000 1.000
#> GSM537416 2 0.0000 0.932 0.000 1.000 0.000
#> GSM537426 2 0.0000 0.932 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.4543 0.6300 0.000 0.676 0.000 0.324
#> GSM537345 3 0.2081 0.8778 0.084 0.000 0.916 0.000
#> GSM537355 1 0.1389 0.8901 0.952 0.048 0.000 0.000
#> GSM537366 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537370 2 0.4948 0.4549 0.000 0.560 0.000 0.440
#> GSM537380 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537392 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537415 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537417 3 0.2081 0.8790 0.084 0.000 0.916 0.000
#> GSM537422 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537423 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537427 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537430 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537336 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537337 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537348 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537349 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537356 1 0.0469 0.9190 0.988 0.012 0.000 0.000
#> GSM537361 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537374 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537377 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537378 2 0.4730 0.1722 0.364 0.636 0.000 0.000
#> GSM537379 2 0.7923 0.1538 0.336 0.340 0.000 0.324
#> GSM537383 2 0.1867 0.5800 0.000 0.928 0.000 0.072
#> GSM537388 2 0.2704 0.5535 0.000 0.876 0.000 0.124
#> GSM537395 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537400 4 0.4304 0.3318 0.000 0.284 0.000 0.716
#> GSM537404 1 0.1867 0.8748 0.928 0.000 0.072 0.000
#> GSM537409 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537418 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537425 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537333 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537342 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537347 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537350 1 0.2760 0.8126 0.872 0.000 0.128 0.000
#> GSM537362 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537363 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537368 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537376 2 0.4543 0.6300 0.000 0.676 0.000 0.324
#> GSM537381 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537386 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537398 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537402 1 0.0188 0.9241 0.996 0.004 0.000 0.000
#> GSM537405 3 0.4817 0.3685 0.388 0.000 0.612 0.000
#> GSM537371 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537421 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537424 1 0.3764 0.6677 0.784 0.216 0.000 0.000
#> GSM537432 2 0.4994 0.4193 0.000 0.520 0.000 0.480
#> GSM537331 4 0.0188 0.9180 0.000 0.004 0.000 0.996
#> GSM537332 2 0.4661 0.6104 0.000 0.652 0.000 0.348
#> GSM537334 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537338 2 0.4543 0.6300 0.000 0.676 0.000 0.324
#> GSM537353 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537357 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537358 1 0.0707 0.9130 0.980 0.000 0.000 0.020
#> GSM537375 1 0.7878 -0.2837 0.384 0.292 0.000 0.324
#> GSM537389 2 0.4994 -0.1962 0.480 0.520 0.000 0.000
#> GSM537390 2 0.4955 -0.0871 0.444 0.556 0.000 0.000
#> GSM537393 1 0.4564 0.5792 0.672 0.328 0.000 0.000
#> GSM537399 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537407 2 0.6316 0.5555 0.080 0.596 0.000 0.324
#> GSM537408 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537428 1 0.2216 0.8412 0.908 0.000 0.000 0.092
#> GSM537354 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537410 1 0.3356 0.7529 0.824 0.000 0.176 0.000
#> GSM537413 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537396 2 0.4978 0.6247 0.012 0.664 0.000 0.324
#> GSM537397 2 0.2973 0.5799 0.144 0.856 0.000 0.000
#> GSM537330 2 0.3356 0.4946 0.000 0.824 0.000 0.176
#> GSM537369 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537373 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537401 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537343 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537367 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537382 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537385 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537391 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537419 1 0.4866 0.2494 0.596 0.404 0.000 0.000
#> GSM537420 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537429 2 0.3123 0.5175 0.000 0.844 0.000 0.156
#> GSM537431 2 0.4996 0.4121 0.000 0.516 0.000 0.484
#> GSM537387 2 0.0000 0.6247 0.000 1.000 0.000 0.000
#> GSM537414 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537433 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537335 4 0.0000 0.9227 0.000 0.000 0.000 1.000
#> GSM537339 2 0.1302 0.6331 0.000 0.956 0.000 0.044
#> GSM537340 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537344 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537346 2 0.4522 0.4641 0.000 0.680 0.000 0.320
#> GSM537351 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537352 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537359 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537360 1 0.0707 0.9152 0.980 0.000 0.020 0.000
#> GSM537364 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537365 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537372 2 0.4720 0.6299 0.004 0.672 0.000 0.324
#> GSM537384 2 0.4543 0.6300 0.000 0.676 0.000 0.324
#> GSM537394 2 0.4994 0.4193 0.000 0.520 0.000 0.480
#> GSM537403 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537406 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537411 4 0.3688 0.5877 0.000 0.208 0.000 0.792
#> GSM537412 3 0.0000 0.9636 0.000 0.000 1.000 0.000
#> GSM537416 1 0.0000 0.9263 1.000 0.000 0.000 0.000
#> GSM537426 1 0.0000 0.9263 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 5 0.0290 0.899 0.000 0.008 0.000 0.000 0.992
#> GSM537345 1 0.2017 0.870 0.912 0.008 0.000 0.080 0.000
#> GSM537355 4 0.1270 0.905 0.000 0.000 0.000 0.948 0.052
#> GSM537366 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537370 5 0.5422 0.597 0.000 0.212 0.132 0.000 0.656
#> GSM537380 2 0.3534 0.822 0.000 0.744 0.000 0.000 0.256
#> GSM537392 2 0.2732 0.872 0.000 0.840 0.000 0.000 0.160
#> GSM537415 4 0.2732 0.862 0.000 0.160 0.000 0.840 0.000
#> GSM537417 1 0.2505 0.862 0.888 0.020 0.000 0.092 0.000
#> GSM537422 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537423 4 0.0579 0.923 0.000 0.008 0.000 0.984 0.008
#> GSM537427 2 0.2732 0.872 0.000 0.840 0.000 0.000 0.160
#> GSM537430 3 0.0794 0.968 0.000 0.000 0.972 0.000 0.028
#> GSM537336 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537337 4 0.0000 0.923 0.000 0.000 0.000 1.000 0.000
#> GSM537348 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537349 2 0.2732 0.872 0.000 0.840 0.000 0.000 0.160
#> GSM537356 4 0.0609 0.920 0.000 0.000 0.000 0.980 0.020
#> GSM537361 4 0.0290 0.922 0.000 0.000 0.000 0.992 0.008
#> GSM537374 3 0.0000 0.991 0.000 0.000 1.000 0.000 0.000
#> GSM537377 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537378 2 0.2732 0.872 0.000 0.840 0.000 0.000 0.160
#> GSM537379 5 0.1892 0.838 0.000 0.004 0.000 0.080 0.916
#> GSM537383 2 0.3152 0.857 0.000 0.840 0.024 0.000 0.136
#> GSM537388 2 0.5167 0.736 0.000 0.684 0.116 0.000 0.200
#> GSM537395 4 0.0000 0.923 0.000 0.000 0.000 1.000 0.000
#> GSM537400 5 0.3796 0.606 0.000 0.000 0.300 0.000 0.700
#> GSM537404 4 0.2228 0.879 0.076 0.012 0.000 0.908 0.004
#> GSM537409 4 0.2605 0.866 0.000 0.148 0.000 0.852 0.000
#> GSM537418 4 0.2605 0.866 0.000 0.148 0.000 0.852 0.000
#> GSM537425 1 0.0771 0.934 0.976 0.020 0.000 0.004 0.000
#> GSM537333 3 0.0162 0.989 0.000 0.000 0.996 0.000 0.004
#> GSM537342 4 0.0609 0.920 0.000 0.020 0.000 0.980 0.000
#> GSM537347 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537350 4 0.2969 0.823 0.128 0.020 0.000 0.852 0.000
#> GSM537362 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537363 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537368 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537376 5 0.0880 0.886 0.000 0.032 0.000 0.000 0.968
#> GSM537381 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537386 3 0.0000 0.991 0.000 0.000 1.000 0.000 0.000
#> GSM537398 3 0.0609 0.977 0.000 0.000 0.980 0.000 0.020
#> GSM537402 4 0.0404 0.922 0.000 0.000 0.000 0.988 0.012
#> GSM537405 1 0.4607 0.437 0.616 0.012 0.000 0.368 0.004
#> GSM537371 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537421 1 0.3013 0.831 0.832 0.160 0.000 0.008 0.000
#> GSM537424 4 0.1965 0.872 0.000 0.000 0.000 0.904 0.096
#> GSM537432 5 0.2471 0.808 0.000 0.000 0.136 0.000 0.864
#> GSM537331 3 0.0000 0.991 0.000 0.000 1.000 0.000 0.000
#> GSM537332 5 0.0510 0.899 0.000 0.000 0.016 0.000 0.984
#> GSM537334 3 0.0000 0.991 0.000 0.000 1.000 0.000 0.000
#> GSM537338 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> GSM537353 4 0.0000 0.923 0.000 0.000 0.000 1.000 0.000
#> GSM537357 1 0.2629 0.848 0.860 0.136 0.000 0.004 0.000
#> GSM537358 4 0.1341 0.898 0.000 0.000 0.000 0.944 0.056
#> GSM537375 5 0.2305 0.823 0.000 0.012 0.000 0.092 0.896
#> GSM537389 2 0.2848 0.870 0.000 0.840 0.000 0.004 0.156
#> GSM537390 2 0.2732 0.872 0.000 0.840 0.000 0.000 0.160
#> GSM537393 2 0.2732 0.698 0.000 0.840 0.000 0.160 0.000
#> GSM537399 5 0.4256 0.333 0.000 0.000 0.436 0.000 0.564
#> GSM537407 5 0.0609 0.894 0.000 0.000 0.000 0.020 0.980
#> GSM537408 4 0.0000 0.923 0.000 0.000 0.000 1.000 0.000
#> GSM537428 4 0.3242 0.710 0.000 0.000 0.000 0.784 0.216
#> GSM537354 4 0.0404 0.922 0.000 0.012 0.000 0.988 0.000
#> GSM537410 4 0.3488 0.768 0.168 0.024 0.000 0.808 0.000
#> GSM537413 2 0.2732 0.872 0.000 0.840 0.000 0.000 0.160
#> GSM537396 5 0.0671 0.895 0.000 0.004 0.000 0.016 0.980
#> GSM537397 2 0.6724 0.442 0.000 0.420 0.000 0.284 0.296
#> GSM537330 2 0.3193 0.749 0.000 0.840 0.132 0.000 0.028
#> GSM537369 4 0.0290 0.922 0.000 0.000 0.000 0.992 0.008
#> GSM537373 4 0.0290 0.922 0.000 0.000 0.000 0.992 0.008
#> GSM537401 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537343 4 0.0290 0.922 0.000 0.000 0.000 0.992 0.008
#> GSM537367 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537382 2 0.2891 0.868 0.000 0.824 0.000 0.000 0.176
#> GSM537385 2 0.3774 0.781 0.000 0.704 0.000 0.000 0.296
#> GSM537391 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537419 4 0.3586 0.666 0.000 0.000 0.000 0.736 0.264
#> GSM537420 4 0.0693 0.921 0.000 0.012 0.000 0.980 0.008
#> GSM537429 2 0.3229 0.754 0.000 0.840 0.128 0.000 0.032
#> GSM537431 5 0.2471 0.808 0.000 0.000 0.136 0.000 0.864
#> GSM537387 2 0.3366 0.842 0.000 0.768 0.000 0.000 0.232
#> GSM537414 4 0.2605 0.866 0.000 0.148 0.000 0.852 0.000
#> GSM537433 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537335 3 0.0000 0.991 0.000 0.000 1.000 0.000 0.000
#> GSM537339 2 0.3612 0.796 0.000 0.732 0.000 0.000 0.268
#> GSM537340 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537344 4 0.0290 0.922 0.000 0.000 0.000 0.992 0.008
#> GSM537346 2 0.5854 0.530 0.000 0.596 0.152 0.000 0.252
#> GSM537351 1 0.0898 0.933 0.972 0.020 0.000 0.008 0.000
#> GSM537352 4 0.0000 0.923 0.000 0.000 0.000 1.000 0.000
#> GSM537359 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537360 4 0.3123 0.855 0.012 0.160 0.000 0.828 0.000
#> GSM537364 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537365 4 0.0000 0.923 0.000 0.000 0.000 1.000 0.000
#> GSM537372 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM537384 5 0.0290 0.900 0.000 0.008 0.000 0.000 0.992
#> GSM537394 5 0.2966 0.802 0.000 0.016 0.136 0.000 0.848
#> GSM537403 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537406 1 0.0404 0.939 0.988 0.012 0.000 0.000 0.000
#> GSM537411 5 0.2424 0.827 0.000 0.000 0.132 0.000 0.868
#> GSM537412 1 0.0000 0.944 1.000 0.000 0.000 0.000 0.000
#> GSM537416 4 0.2732 0.862 0.000 0.160 0.000 0.840 0.000
#> GSM537426 4 0.2605 0.866 0.000 0.148 0.000 0.852 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537345 4 0.2778 0.74604 0.008 0.000 0.168 0.824 0.000 0.000
#> GSM537355 3 0.1007 0.91484 0.000 0.000 0.956 0.000 0.044 0.000
#> GSM537366 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537370 5 0.3672 0.43637 0.000 0.368 0.000 0.000 0.632 0.000
#> GSM537380 2 0.3175 0.71273 0.000 0.744 0.000 0.000 0.256 0.000
#> GSM537392 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537415 1 0.0000 0.91343 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537417 4 0.2309 0.83879 0.028 0.000 0.084 0.888 0.000 0.000
#> GSM537422 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537423 3 0.0458 0.93618 0.016 0.000 0.984 0.000 0.000 0.000
#> GSM537427 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537430 6 0.0713 0.96334 0.000 0.000 0.000 0.000 0.028 0.972
#> GSM537336 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537337 3 0.0146 0.94033 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM537348 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537349 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537356 3 0.0260 0.93804 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM537361 3 0.0000 0.94028 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM537374 6 0.0000 0.98947 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537377 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537378 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537379 5 0.1556 0.86445 0.000 0.000 0.080 0.000 0.920 0.000
#> GSM537383 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537388 2 0.3468 0.70100 0.000 0.728 0.000 0.000 0.264 0.008
#> GSM537395 3 0.0146 0.94033 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM537400 5 0.3409 0.58336 0.000 0.000 0.000 0.000 0.700 0.300
#> GSM537404 3 0.3141 0.73041 0.012 0.000 0.788 0.200 0.000 0.000
#> GSM537409 1 0.0363 0.91301 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537418 1 0.0363 0.91301 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537425 4 0.0713 0.92270 0.028 0.000 0.000 0.972 0.000 0.000
#> GSM537333 6 0.0146 0.98745 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM537342 3 0.0713 0.93193 0.028 0.000 0.972 0.000 0.000 0.000
#> GSM537347 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537350 3 0.2748 0.81772 0.024 0.000 0.848 0.128 0.000 0.000
#> GSM537362 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537363 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537368 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537376 5 0.0865 0.90731 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM537381 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537386 6 0.0000 0.98947 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537398 6 0.0547 0.97407 0.000 0.000 0.000 0.000 0.020 0.980
#> GSM537402 3 0.0146 0.93984 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM537405 4 0.4152 0.21209 0.012 0.000 0.440 0.548 0.000 0.000
#> GSM537371 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537421 1 0.0000 0.91343 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537424 3 0.1663 0.87633 0.000 0.000 0.912 0.000 0.088 0.000
#> GSM537432 5 0.0260 0.92295 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM537331 6 0.0000 0.98947 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537332 5 0.0146 0.92440 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM537334 6 0.0000 0.98947 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537338 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537353 3 0.0146 0.94033 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM537357 1 0.3868 0.00269 0.508 0.000 0.000 0.492 0.000 0.000
#> GSM537358 3 0.1141 0.90817 0.000 0.000 0.948 0.000 0.052 0.000
#> GSM537375 5 0.1858 0.85128 0.000 0.004 0.092 0.000 0.904 0.000
#> GSM537389 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537390 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537393 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537399 5 0.3823 0.29317 0.000 0.000 0.000 0.000 0.564 0.436
#> GSM537407 5 0.0547 0.91582 0.000 0.000 0.020 0.000 0.980 0.000
#> GSM537408 3 0.0000 0.94028 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM537428 3 0.2883 0.71538 0.000 0.000 0.788 0.000 0.212 0.000
#> GSM537354 3 0.0547 0.93477 0.020 0.000 0.980 0.000 0.000 0.000
#> GSM537410 3 0.2331 0.86724 0.032 0.000 0.888 0.080 0.000 0.000
#> GSM537413 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537396 5 0.0603 0.91660 0.000 0.004 0.016 0.000 0.980 0.000
#> GSM537397 2 0.6040 0.33760 0.000 0.420 0.284 0.000 0.296 0.000
#> GSM537330 2 0.0260 0.85973 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM537369 3 0.0000 0.94028 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM537373 3 0.0000 0.94028 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM537401 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537343 3 0.0000 0.94028 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM537367 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537382 2 0.0790 0.85296 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM537385 2 0.3390 0.66190 0.000 0.704 0.000 0.000 0.296 0.000
#> GSM537391 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537419 3 0.3198 0.65463 0.000 0.000 0.740 0.000 0.260 0.000
#> GSM537420 3 0.0363 0.93648 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM537429 2 0.0000 0.86345 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537431 5 0.0260 0.92295 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM537387 2 0.2883 0.75044 0.000 0.788 0.000 0.000 0.212 0.000
#> GSM537414 1 0.0363 0.91301 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM537433 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537335 6 0.0000 0.98947 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM537339 2 0.2491 0.77488 0.000 0.836 0.000 0.000 0.164 0.000
#> GSM537340 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537344 3 0.0000 0.94028 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM537346 2 0.3323 0.64992 0.000 0.752 0.000 0.000 0.240 0.008
#> GSM537351 4 0.0713 0.92270 0.028 0.000 0.000 0.972 0.000 0.000
#> GSM537352 3 0.0146 0.94033 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM537359 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537360 1 0.0000 0.91343 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537364 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537365 3 0.0146 0.94033 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM537372 5 0.0000 0.92537 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM537384 5 0.0260 0.92209 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM537394 5 0.2302 0.82115 0.000 0.120 0.000 0.000 0.872 0.008
#> GSM537403 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537406 4 0.0363 0.93272 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM537411 5 0.2178 0.82583 0.000 0.000 0.000 0.000 0.868 0.132
#> GSM537412 4 0.0000 0.93931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM537416 1 0.0000 0.91343 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM537426 1 0.0363 0.91301 0.988 0.000 0.012 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> ATC:pam 102 0.0815 0.120 2
#> ATC:pam 96 0.7193 0.629 3
#> ATC:pam 90 0.3490 0.777 4
#> ATC:pam 101 0.6185 0.786 5
#> ATC:pam 99 0.4969 0.646 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 104 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 0.355 0.394 0.806 0.3212 0.779 0.779
#> 3 3 0.530 0.707 0.805 0.7614 0.586 0.496
#> 4 4 0.642 0.807 0.891 0.0719 0.940 0.871
#> 5 5 0.682 0.623 0.764 0.1411 0.827 0.607
#> 6 6 0.636 0.423 0.713 0.1115 0.834 0.512
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
#> GSM537341 2 0.1184 0.6839 0.016 0.984
#> GSM537345 2 0.9933 -0.2889 0.452 0.548
#> GSM537355 2 0.0672 0.6878 0.008 0.992
#> GSM537366 1 0.9993 0.4167 0.516 0.484
#> GSM537370 2 0.2043 0.6761 0.032 0.968
#> GSM537380 2 0.1414 0.6809 0.020 0.980
#> GSM537392 2 0.9552 0.0683 0.376 0.624
#> GSM537415 1 0.8327 0.4527 0.736 0.264
#> GSM537417 2 0.9850 -0.2254 0.428 0.572
#> GSM537422 1 0.9996 0.4085 0.512 0.488
#> GSM537423 2 0.1414 0.6860 0.020 0.980
#> GSM537427 2 0.9909 -0.0601 0.444 0.556
#> GSM537430 2 0.0672 0.6883 0.008 0.992
#> GSM537336 2 0.9933 -0.2889 0.452 0.548
#> GSM537337 2 0.9896 -0.0606 0.440 0.560
#> GSM537348 2 0.0376 0.6885 0.004 0.996
#> GSM537349 2 0.2948 0.6550 0.052 0.948
#> GSM537356 2 0.5629 0.5309 0.132 0.868
#> GSM537361 2 0.7139 0.4152 0.196 0.804
#> GSM537374 2 0.0938 0.6886 0.012 0.988
#> GSM537377 2 0.0376 0.6875 0.004 0.996
#> GSM537378 2 0.8016 0.3069 0.244 0.756
#> GSM537379 2 0.0376 0.6889 0.004 0.996
#> GSM537383 2 0.0672 0.6881 0.008 0.992
#> GSM537388 2 0.1184 0.6871 0.016 0.984
#> GSM537395 2 0.9896 -0.0546 0.440 0.560
#> GSM537400 2 0.1184 0.6871 0.016 0.984
#> GSM537404 2 0.9580 -0.0917 0.380 0.620
#> GSM537409 1 0.9896 0.2228 0.560 0.440
#> GSM537418 1 0.9129 0.4032 0.672 0.328
#> GSM537425 2 0.9933 -0.2889 0.452 0.548
#> GSM537333 2 0.0376 0.6885 0.004 0.996
#> GSM537342 2 0.8813 0.1769 0.300 0.700
#> GSM537347 2 0.0376 0.6885 0.004 0.996
#> GSM537350 2 0.9963 -0.2973 0.464 0.536
#> GSM537362 2 0.0672 0.6883 0.008 0.992
#> GSM537363 2 0.9954 -0.3128 0.460 0.540
#> GSM537368 1 0.9993 0.4167 0.516 0.484
#> GSM537376 2 0.0672 0.6883 0.008 0.992
#> GSM537381 2 0.0938 0.6858 0.012 0.988
#> GSM537386 2 0.1184 0.6871 0.016 0.984
#> GSM537398 2 0.0376 0.6885 0.004 0.996
#> GSM537402 2 0.0672 0.6878 0.008 0.992
#> GSM537405 2 0.9850 -0.2254 0.428 0.572
#> GSM537371 2 0.9933 -0.2889 0.452 0.548
#> GSM537421 2 0.9866 -0.2314 0.432 0.568
#> GSM537424 2 0.0376 0.6875 0.004 0.996
#> GSM537432 2 0.1414 0.6859 0.020 0.980
#> GSM537331 2 0.1184 0.6871 0.016 0.984
#> GSM537332 2 0.1843 0.6809 0.028 0.972
#> GSM537334 2 0.0938 0.6867 0.012 0.988
#> GSM537338 2 0.0672 0.6887 0.008 0.992
#> GSM537353 2 0.0672 0.6878 0.008 0.992
#> GSM537357 2 0.9866 -0.2314 0.432 0.568
#> GSM537358 2 0.1184 0.6867 0.016 0.984
#> GSM537375 2 0.0376 0.6875 0.004 0.996
#> GSM537389 2 0.9933 -0.0700 0.452 0.548
#> GSM537390 2 0.9933 -0.0708 0.452 0.548
#> GSM537393 2 0.9909 -0.0664 0.444 0.556
#> GSM537399 2 0.1184 0.6871 0.016 0.984
#> GSM537407 2 0.0672 0.6878 0.008 0.992
#> GSM537408 2 0.6712 0.4735 0.176 0.824
#> GSM537428 2 0.1184 0.6867 0.016 0.984
#> GSM537354 1 0.8386 0.4529 0.732 0.268
#> GSM537410 2 0.9933 -0.2661 0.452 0.548
#> GSM537413 2 0.9909 -0.0634 0.444 0.556
#> GSM537396 2 0.1414 0.6860 0.020 0.980
#> GSM537397 2 0.1414 0.6833 0.020 0.980
#> GSM537330 2 0.1414 0.6809 0.020 0.980
#> GSM537369 2 0.6148 0.4973 0.152 0.848
#> GSM537373 2 0.0376 0.6875 0.004 0.996
#> GSM537401 2 0.0938 0.6886 0.012 0.988
#> GSM537343 2 0.8713 0.1869 0.292 0.708
#> GSM537367 1 0.9988 0.4145 0.520 0.480
#> GSM537382 2 0.2043 0.6724 0.032 0.968
#> GSM537385 2 0.1633 0.6778 0.024 0.976
#> GSM537391 2 0.1184 0.6867 0.016 0.984
#> GSM537419 2 0.1184 0.6867 0.016 0.984
#> GSM537420 2 0.8861 0.1798 0.304 0.696
#> GSM537429 2 0.9922 -0.0639 0.448 0.552
#> GSM537431 2 0.1184 0.6867 0.016 0.984
#> GSM537387 2 0.0000 0.6884 0.000 1.000
#> GSM537414 2 0.9000 0.1454 0.316 0.684
#> GSM537433 1 0.9993 0.4167 0.516 0.484
#> GSM537335 2 0.0376 0.6885 0.004 0.996
#> GSM537339 2 0.0938 0.6865 0.012 0.988
#> GSM537340 1 0.9993 0.4167 0.516 0.484
#> GSM537344 2 0.8713 0.1869 0.292 0.708
#> GSM537346 2 0.1843 0.6791 0.028 0.972
#> GSM537351 2 0.9963 -0.2973 0.464 0.536
#> GSM537352 2 0.4815 0.5773 0.104 0.896
#> GSM537359 2 0.1184 0.6867 0.016 0.984
#> GSM537360 2 0.9922 -0.2504 0.448 0.552
#> GSM537364 1 0.9983 0.4068 0.524 0.476
#> GSM537365 2 0.1184 0.6867 0.016 0.984
#> GSM537372 2 0.0376 0.6875 0.004 0.996
#> GSM537384 2 0.0376 0.6875 0.004 0.996
#> GSM537394 2 0.1184 0.6871 0.016 0.984
#> GSM537403 2 0.9963 -0.2973 0.464 0.536
#> GSM537406 2 0.9963 -0.2973 0.464 0.536
#> GSM537411 2 0.0000 0.6884 0.000 1.000
#> GSM537412 2 0.9963 -0.2973 0.464 0.536
#> GSM537416 1 0.8386 0.4528 0.732 0.268
#> GSM537426 1 0.9815 0.2687 0.580 0.420
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 2 0.0000 0.8846 0.000 1.000 0.000
#> GSM537345 1 0.2537 0.6134 0.920 0.080 0.000
#> GSM537355 1 0.9292 0.5033 0.516 0.284 0.200
#> GSM537366 3 0.6678 0.7752 0.480 0.008 0.512
#> GSM537370 2 0.3686 0.8511 0.000 0.860 0.140
#> GSM537380 2 0.2066 0.8820 0.000 0.940 0.060
#> GSM537392 2 0.4974 0.7921 0.000 0.764 0.236
#> GSM537415 1 0.6879 0.5455 0.556 0.016 0.428
#> GSM537417 1 0.2356 0.6105 0.928 0.072 0.000
#> GSM537422 1 0.5156 -0.0842 0.776 0.008 0.216
#> GSM537423 1 0.8812 0.5671 0.516 0.124 0.360
#> GSM537427 2 0.5678 0.7183 0.000 0.684 0.316
#> GSM537430 2 0.1015 0.8803 0.008 0.980 0.012
#> GSM537336 1 0.3550 0.5893 0.896 0.080 0.024
#> GSM537337 3 0.9716 -0.2383 0.228 0.344 0.428
#> GSM537348 2 0.0237 0.8841 0.004 0.996 0.000
#> GSM537349 2 0.4291 0.8289 0.000 0.820 0.180
#> GSM537356 1 0.8034 0.4908 0.584 0.336 0.080
#> GSM537361 1 0.4994 0.5980 0.816 0.160 0.024
#> GSM537374 2 0.1015 0.8803 0.008 0.980 0.012
#> GSM537377 2 0.2050 0.8814 0.020 0.952 0.028
#> GSM537378 2 0.5728 0.7576 0.008 0.720 0.272
#> GSM537379 2 0.2200 0.8848 0.004 0.940 0.056
#> GSM537383 2 0.2537 0.8766 0.000 0.920 0.080
#> GSM537388 2 0.1411 0.8860 0.000 0.964 0.036
#> GSM537395 2 0.7232 0.5376 0.028 0.544 0.428
#> GSM537400 2 0.0661 0.8821 0.004 0.988 0.008
#> GSM537404 1 0.2356 0.6105 0.928 0.072 0.000
#> GSM537409 1 0.7438 0.5395 0.536 0.036 0.428
#> GSM537418 1 0.7337 0.5414 0.540 0.032 0.428
#> GSM537425 1 0.2537 0.6134 0.920 0.080 0.000
#> GSM537333 2 0.0661 0.8821 0.004 0.988 0.008
#> GSM537342 1 0.6372 0.6391 0.764 0.084 0.152
#> GSM537347 2 0.0661 0.8821 0.004 0.988 0.008
#> GSM537350 1 0.2590 0.6123 0.924 0.072 0.004
#> GSM537362 2 0.0237 0.8841 0.004 0.996 0.000
#> GSM537363 1 0.7509 -0.1962 0.636 0.064 0.300
#> GSM537368 3 0.6678 0.7752 0.480 0.008 0.512
#> GSM537376 2 0.0475 0.8833 0.004 0.992 0.004
#> GSM537381 2 0.3851 0.8535 0.004 0.860 0.136
#> GSM537386 2 0.2200 0.8577 0.004 0.940 0.056
#> GSM537398 2 0.0848 0.8818 0.008 0.984 0.008
#> GSM537402 2 0.7966 0.6046 0.128 0.652 0.220
#> GSM537405 1 0.2356 0.6105 0.928 0.072 0.000
#> GSM537371 1 0.2537 0.6134 0.920 0.080 0.000
#> GSM537421 1 0.2902 0.6126 0.920 0.064 0.016
#> GSM537424 2 0.4280 0.8472 0.020 0.856 0.124
#> GSM537432 2 0.0237 0.8852 0.000 0.996 0.004
#> GSM537331 2 0.2200 0.8577 0.004 0.940 0.056
#> GSM537332 2 0.3686 0.8488 0.000 0.860 0.140
#> GSM537334 2 0.2384 0.8560 0.008 0.936 0.056
#> GSM537338 2 0.0475 0.8833 0.004 0.992 0.004
#> GSM537353 1 0.9268 0.5509 0.512 0.188 0.300
#> GSM537357 1 0.2902 0.6131 0.920 0.064 0.016
#> GSM537358 2 0.3572 0.8716 0.040 0.900 0.060
#> GSM537375 2 0.1267 0.8862 0.004 0.972 0.024
#> GSM537389 2 0.6264 0.6401 0.004 0.616 0.380
#> GSM537390 2 0.6189 0.6593 0.004 0.632 0.364
#> GSM537393 2 0.6298 0.6302 0.004 0.608 0.388
#> GSM537399 2 0.1015 0.8820 0.012 0.980 0.008
#> GSM537407 2 0.1482 0.8803 0.020 0.968 0.012
#> GSM537408 1 0.9048 0.5258 0.548 0.268 0.184
#> GSM537428 2 0.1919 0.8833 0.020 0.956 0.024
#> GSM537354 1 0.6994 0.5480 0.556 0.020 0.424
#> GSM537410 1 0.2590 0.6135 0.924 0.072 0.004
#> GSM537413 2 0.5968 0.6618 0.000 0.636 0.364
#> GSM537396 2 0.6180 0.7495 0.024 0.716 0.260
#> GSM537397 2 0.4235 0.8301 0.000 0.824 0.176
#> GSM537330 2 0.1964 0.8829 0.000 0.944 0.056
#> GSM537369 1 0.9111 0.5602 0.548 0.212 0.240
#> GSM537373 1 0.8875 0.5768 0.528 0.136 0.336
#> GSM537401 2 0.0475 0.8833 0.004 0.992 0.004
#> GSM537343 1 0.5944 0.6400 0.792 0.088 0.120
#> GSM537367 3 0.6518 0.7711 0.484 0.004 0.512
#> GSM537382 2 0.4555 0.8158 0.000 0.800 0.200
#> GSM537385 2 0.2448 0.8788 0.000 0.924 0.076
#> GSM537391 2 0.1182 0.8867 0.012 0.976 0.012
#> GSM537419 2 0.1585 0.8854 0.008 0.964 0.028
#> GSM537420 1 0.4526 0.6223 0.856 0.104 0.040
#> GSM537429 2 0.5785 0.6871 0.000 0.668 0.332
#> GSM537431 2 0.1482 0.8803 0.020 0.968 0.012
#> GSM537387 2 0.3619 0.8532 0.000 0.864 0.136
#> GSM537414 1 0.7919 0.5851 0.556 0.064 0.380
#> GSM537433 3 0.6678 0.7752 0.480 0.008 0.512
#> GSM537335 2 0.2301 0.8578 0.004 0.936 0.060
#> GSM537339 2 0.4062 0.8376 0.000 0.836 0.164
#> GSM537340 3 0.6678 0.7752 0.480 0.008 0.512
#> GSM537344 1 0.5096 0.6352 0.836 0.084 0.080
#> GSM537346 2 0.2711 0.8736 0.000 0.912 0.088
#> GSM537351 1 0.2590 0.6123 0.924 0.072 0.004
#> GSM537352 1 0.8635 0.5804 0.532 0.112 0.356
#> GSM537359 2 0.1999 0.8785 0.036 0.952 0.012
#> GSM537360 1 0.8073 0.6008 0.576 0.080 0.344
#> GSM537364 3 0.7476 0.7179 0.452 0.036 0.512
#> GSM537365 1 0.8538 0.4459 0.520 0.380 0.100
#> GSM537372 2 0.1015 0.8805 0.012 0.980 0.008
#> GSM537384 2 0.1832 0.8860 0.008 0.956 0.036
#> GSM537394 2 0.0892 0.8870 0.000 0.980 0.020
#> GSM537403 1 0.3370 0.5919 0.904 0.072 0.024
#> GSM537406 1 0.2590 0.6123 0.924 0.072 0.004
#> GSM537411 2 0.0848 0.8818 0.008 0.984 0.008
#> GSM537412 1 0.6982 0.1477 0.708 0.072 0.220
#> GSM537416 1 0.6879 0.5455 0.556 0.016 0.428
#> GSM537426 1 0.7517 0.5493 0.540 0.040 0.420
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 2 0.0188 0.9164 0.000 0.996 0.000 0.004
#> GSM537345 3 0.3787 0.8048 0.124 0.000 0.840 0.036
#> GSM537355 3 0.3448 0.7343 0.000 0.168 0.828 0.004
#> GSM537366 1 0.0000 0.8504 1.000 0.000 0.000 0.000
#> GSM537370 2 0.0524 0.9164 0.000 0.988 0.008 0.004
#> GSM537380 2 0.0376 0.9167 0.000 0.992 0.004 0.004
#> GSM537392 2 0.1488 0.9062 0.000 0.956 0.032 0.012
#> GSM537415 3 0.1211 0.7917 0.000 0.000 0.960 0.040
#> GSM537417 3 0.3547 0.8092 0.144 0.016 0.840 0.000
#> GSM537422 3 0.5334 0.4295 0.400 0.008 0.588 0.004
#> GSM537423 3 0.5188 0.4726 0.000 0.240 0.716 0.044
#> GSM537427 2 0.2730 0.8660 0.000 0.896 0.088 0.016
#> GSM537430 2 0.2999 0.8174 0.000 0.864 0.004 0.132
#> GSM537336 3 0.5085 0.6795 0.256 0.008 0.716 0.020
#> GSM537337 2 0.5267 0.6159 0.000 0.712 0.240 0.048
#> GSM537348 2 0.0000 0.9164 0.000 1.000 0.000 0.000
#> GSM537349 2 0.0804 0.9147 0.000 0.980 0.008 0.012
#> GSM537356 3 0.3157 0.7654 0.004 0.144 0.852 0.000
#> GSM537361 3 0.4286 0.8125 0.072 0.056 0.844 0.028
#> GSM537374 2 0.4655 0.5137 0.000 0.684 0.004 0.312
#> GSM537377 2 0.2845 0.8469 0.000 0.896 0.076 0.028
#> GSM537378 2 0.2222 0.8874 0.000 0.924 0.060 0.016
#> GSM537379 2 0.0000 0.9164 0.000 1.000 0.000 0.000
#> GSM537383 2 0.0376 0.9167 0.000 0.992 0.004 0.004
#> GSM537388 2 0.0188 0.9164 0.000 0.996 0.000 0.004
#> GSM537395 2 0.4100 0.7871 0.000 0.824 0.128 0.048
#> GSM537400 2 0.2888 0.8288 0.000 0.872 0.004 0.124
#> GSM537404 3 0.4038 0.8163 0.108 0.016 0.844 0.032
#> GSM537409 3 0.1474 0.7851 0.000 0.000 0.948 0.052
#> GSM537418 3 0.1807 0.7887 0.000 0.008 0.940 0.052
#> GSM537425 3 0.3932 0.8042 0.128 0.004 0.836 0.032
#> GSM537333 2 0.0779 0.9123 0.000 0.980 0.004 0.016
#> GSM537342 3 0.1724 0.8206 0.032 0.020 0.948 0.000
#> GSM537347 2 0.1398 0.9009 0.000 0.956 0.004 0.040
#> GSM537350 3 0.4185 0.8159 0.080 0.016 0.844 0.060
#> GSM537362 2 0.0000 0.9164 0.000 1.000 0.000 0.000
#> GSM537363 1 0.5704 -0.2314 0.496 0.008 0.484 0.012
#> GSM537368 1 0.0000 0.8504 1.000 0.000 0.000 0.000
#> GSM537376 2 0.0188 0.9157 0.000 0.996 0.004 0.000
#> GSM537381 2 0.0376 0.9166 0.000 0.992 0.004 0.004
#> GSM537386 4 0.3539 0.9605 0.000 0.176 0.004 0.820
#> GSM537398 2 0.3831 0.7277 0.000 0.792 0.004 0.204
#> GSM537402 2 0.1767 0.8936 0.000 0.944 0.044 0.012
#> GSM537405 3 0.4001 0.8160 0.112 0.016 0.844 0.028
#> GSM537371 3 0.3842 0.8028 0.128 0.000 0.836 0.036
#> GSM537421 3 0.3263 0.8201 0.100 0.012 0.876 0.012
#> GSM537424 2 0.0804 0.9137 0.000 0.980 0.008 0.012
#> GSM537432 2 0.0188 0.9164 0.000 0.996 0.000 0.004
#> GSM537331 4 0.3539 0.9605 0.000 0.176 0.004 0.820
#> GSM537332 2 0.0376 0.9170 0.000 0.992 0.004 0.004
#> GSM537334 4 0.3870 0.8846 0.000 0.208 0.004 0.788
#> GSM537338 2 0.0188 0.9164 0.000 0.996 0.000 0.004
#> GSM537353 3 0.5778 0.0328 0.000 0.472 0.500 0.028
#> GSM537357 3 0.3601 0.8189 0.100 0.012 0.864 0.024
#> GSM537358 2 0.2908 0.8630 0.000 0.896 0.040 0.064
#> GSM537375 2 0.0188 0.9157 0.000 0.996 0.004 0.000
#> GSM537389 2 0.3280 0.8291 0.000 0.860 0.124 0.016
#> GSM537390 2 0.3280 0.8291 0.000 0.860 0.124 0.016
#> GSM537393 2 0.3280 0.8291 0.000 0.860 0.124 0.016
#> GSM537399 2 0.4655 0.5137 0.000 0.684 0.004 0.312
#> GSM537407 2 0.3885 0.7878 0.000 0.844 0.092 0.064
#> GSM537408 3 0.3665 0.8131 0.016 0.052 0.872 0.060
#> GSM537428 2 0.0657 0.9149 0.000 0.984 0.004 0.012
#> GSM537354 3 0.1545 0.7952 0.000 0.008 0.952 0.040
#> GSM537410 3 0.3547 0.8092 0.144 0.016 0.840 0.000
#> GSM537413 2 0.3224 0.8348 0.000 0.864 0.120 0.016
#> GSM537396 2 0.2412 0.8719 0.000 0.908 0.084 0.008
#> GSM537397 2 0.0937 0.9123 0.000 0.976 0.012 0.012
#> GSM537330 2 0.0376 0.9167 0.000 0.992 0.004 0.004
#> GSM537369 3 0.2999 0.7748 0.000 0.132 0.864 0.004
#> GSM537373 3 0.2751 0.7981 0.000 0.056 0.904 0.040
#> GSM537401 2 0.0000 0.9164 0.000 1.000 0.000 0.000
#> GSM537343 3 0.3574 0.8196 0.064 0.056 0.872 0.008
#> GSM537367 1 0.0000 0.8504 1.000 0.000 0.000 0.000
#> GSM537382 2 0.0672 0.9156 0.000 0.984 0.008 0.008
#> GSM537385 2 0.0000 0.9164 0.000 1.000 0.000 0.000
#> GSM537391 2 0.0188 0.9157 0.000 0.996 0.004 0.000
#> GSM537419 2 0.0376 0.9163 0.000 0.992 0.004 0.004
#> GSM537420 3 0.4172 0.8127 0.044 0.020 0.844 0.092
#> GSM537429 2 0.2976 0.8403 0.000 0.872 0.120 0.008
#> GSM537431 2 0.3791 0.7398 0.000 0.796 0.004 0.200
#> GSM537387 2 0.0376 0.9167 0.000 0.992 0.004 0.004
#> GSM537414 3 0.1356 0.7981 0.000 0.008 0.960 0.032
#> GSM537433 1 0.0000 0.8504 1.000 0.000 0.000 0.000
#> GSM537335 4 0.3539 0.9605 0.000 0.176 0.004 0.820
#> GSM537339 2 0.0524 0.9164 0.000 0.988 0.008 0.004
#> GSM537340 1 0.0188 0.8474 0.996 0.000 0.000 0.004
#> GSM537344 3 0.3781 0.8200 0.104 0.024 0.856 0.016
#> GSM537346 2 0.0376 0.9167 0.000 0.992 0.004 0.004
#> GSM537351 3 0.3730 0.8084 0.144 0.016 0.836 0.004
#> GSM537352 3 0.2589 0.7965 0.000 0.044 0.912 0.044
#> GSM537359 2 0.2737 0.8657 0.000 0.888 0.008 0.104
#> GSM537360 3 0.0657 0.8089 0.000 0.012 0.984 0.004
#> GSM537364 1 0.0376 0.8398 0.992 0.004 0.004 0.000
#> GSM537365 3 0.4872 0.3998 0.000 0.356 0.640 0.004
#> GSM537372 2 0.1576 0.8966 0.000 0.948 0.004 0.048
#> GSM537384 2 0.0188 0.9157 0.000 0.996 0.004 0.000
#> GSM537394 2 0.0188 0.9164 0.000 0.996 0.000 0.004
#> GSM537403 3 0.5306 0.6872 0.236 0.008 0.720 0.036
#> GSM537406 3 0.4327 0.8134 0.084 0.016 0.836 0.064
#> GSM537411 2 0.3157 0.8067 0.000 0.852 0.004 0.144
#> GSM537412 3 0.5463 0.1373 0.488 0.008 0.500 0.004
#> GSM537416 3 0.1211 0.7917 0.000 0.000 0.960 0.040
#> GSM537426 3 0.1389 0.7873 0.000 0.000 0.952 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.1211 0.85331 0.000 0.960 0.016 0.024 0.000
#> GSM537345 1 0.4192 0.80531 0.596 0.000 0.000 0.000 0.404
#> GSM537355 5 0.5844 0.23916 0.000 0.244 0.000 0.156 0.600
#> GSM537366 1 0.0000 0.62566 1.000 0.000 0.000 0.000 0.000
#> GSM537370 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537380 2 0.1202 0.86068 0.000 0.960 0.004 0.032 0.004
#> GSM537392 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537415 4 0.2006 0.72031 0.000 0.012 0.000 0.916 0.072
#> GSM537417 5 0.4305 -0.65794 0.488 0.000 0.000 0.000 0.512
#> GSM537422 1 0.4138 0.80975 0.616 0.000 0.000 0.000 0.384
#> GSM537423 4 0.6486 0.21497 0.000 0.236 0.000 0.492 0.272
#> GSM537427 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537430 2 0.4384 0.39162 0.000 0.660 0.324 0.016 0.000
#> GSM537336 1 0.4182 0.80769 0.600 0.000 0.000 0.000 0.400
#> GSM537337 2 0.5111 0.01937 0.000 0.500 0.000 0.464 0.036
#> GSM537348 2 0.1997 0.84211 0.000 0.932 0.016 0.028 0.024
#> GSM537349 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537356 5 0.0771 0.43204 0.000 0.004 0.000 0.020 0.976
#> GSM537361 5 0.1012 0.42967 0.000 0.000 0.012 0.020 0.968
#> GSM537374 3 0.4538 0.52247 0.000 0.364 0.620 0.016 0.000
#> GSM537377 2 0.4598 0.53867 0.000 0.664 0.008 0.016 0.312
#> GSM537378 2 0.1281 0.85963 0.000 0.956 0.000 0.032 0.012
#> GSM537379 2 0.0566 0.85959 0.000 0.984 0.012 0.004 0.000
#> GSM537383 2 0.1168 0.86058 0.000 0.960 0.008 0.032 0.000
#> GSM537388 2 0.0880 0.86137 0.000 0.968 0.000 0.032 0.000
#> GSM537395 2 0.1851 0.82545 0.000 0.912 0.000 0.088 0.000
#> GSM537400 2 0.4522 0.39664 0.000 0.660 0.316 0.024 0.000
#> GSM537404 5 0.3048 0.17243 0.176 0.000 0.004 0.000 0.820
#> GSM537409 4 0.2006 0.72031 0.000 0.012 0.000 0.916 0.072
#> GSM537418 4 0.2069 0.71991 0.000 0.012 0.000 0.912 0.076
#> GSM537425 1 0.4182 0.80769 0.600 0.000 0.000 0.000 0.400
#> GSM537333 2 0.1314 0.85195 0.000 0.960 0.012 0.016 0.012
#> GSM537342 4 0.4387 0.53456 0.000 0.012 0.000 0.640 0.348
#> GSM537347 2 0.3849 0.72281 0.000 0.800 0.020 0.016 0.164
#> GSM537350 1 0.4622 0.73980 0.548 0.000 0.000 0.012 0.440
#> GSM537362 2 0.2875 0.81361 0.000 0.888 0.020 0.032 0.060
#> GSM537363 1 0.4114 0.80614 0.624 0.000 0.000 0.000 0.376
#> GSM537368 1 0.0000 0.62566 1.000 0.000 0.000 0.000 0.000
#> GSM537376 2 0.1386 0.85011 0.000 0.952 0.016 0.032 0.000
#> GSM537381 2 0.0404 0.85952 0.000 0.988 0.000 0.000 0.012
#> GSM537386 3 0.0290 0.61401 0.000 0.000 0.992 0.008 0.000
#> GSM537398 3 0.4538 0.52247 0.000 0.364 0.620 0.016 0.000
#> GSM537402 2 0.4718 0.12567 0.000 0.540 0.000 0.016 0.444
#> GSM537405 5 0.3274 0.04781 0.220 0.000 0.000 0.000 0.780
#> GSM537371 1 0.4182 0.80769 0.600 0.000 0.000 0.000 0.400
#> GSM537421 1 0.4517 0.80325 0.600 0.012 0.000 0.000 0.388
#> GSM537424 2 0.5049 0.22035 0.000 0.548 0.012 0.016 0.424
#> GSM537432 2 0.0955 0.85308 0.000 0.968 0.004 0.028 0.000
#> GSM537331 3 0.0290 0.61401 0.000 0.000 0.992 0.008 0.000
#> GSM537332 2 0.0880 0.86137 0.000 0.968 0.000 0.032 0.000
#> GSM537334 3 0.1544 0.63130 0.000 0.068 0.932 0.000 0.000
#> GSM537338 2 0.1300 0.85124 0.000 0.956 0.016 0.028 0.000
#> GSM537353 5 0.6804 0.03598 0.000 0.332 0.000 0.300 0.368
#> GSM537357 1 0.4182 0.80769 0.600 0.000 0.000 0.000 0.400
#> GSM537358 2 0.4806 0.42506 0.000 0.640 0.004 0.028 0.328
#> GSM537375 2 0.1372 0.85080 0.000 0.956 0.004 0.016 0.024
#> GSM537389 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537390 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537393 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537399 3 0.4787 0.51760 0.000 0.364 0.608 0.028 0.000
#> GSM537407 5 0.5219 0.09469 0.000 0.420 0.020 0.016 0.544
#> GSM537408 5 0.4192 0.22043 0.000 0.032 0.000 0.232 0.736
#> GSM537428 5 0.5124 -0.09532 0.000 0.484 0.004 0.028 0.484
#> GSM537354 4 0.2006 0.72031 0.000 0.012 0.000 0.916 0.072
#> GSM537410 1 0.4150 0.80930 0.612 0.000 0.000 0.000 0.388
#> GSM537413 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537396 2 0.0703 0.85605 0.000 0.976 0.000 0.024 0.000
#> GSM537397 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537330 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537369 5 0.2966 0.28021 0.000 0.000 0.000 0.184 0.816
#> GSM537373 4 0.6504 0.23361 0.000 0.196 0.000 0.448 0.356
#> GSM537401 2 0.1891 0.84459 0.000 0.936 0.016 0.032 0.016
#> GSM537343 5 0.3177 0.23086 0.000 0.000 0.000 0.208 0.792
#> GSM537367 1 0.0000 0.62566 1.000 0.000 0.000 0.000 0.000
#> GSM537382 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537385 2 0.0771 0.86208 0.000 0.976 0.004 0.020 0.000
#> GSM537391 2 0.1461 0.84870 0.000 0.952 0.004 0.028 0.016
#> GSM537419 2 0.4295 0.62043 0.000 0.724 0.004 0.024 0.248
#> GSM537420 5 0.0880 0.42932 0.000 0.000 0.000 0.032 0.968
#> GSM537429 2 0.1043 0.86038 0.000 0.960 0.000 0.040 0.000
#> GSM537431 2 0.6722 0.09633 0.000 0.520 0.308 0.028 0.144
#> GSM537387 2 0.1012 0.86164 0.000 0.968 0.000 0.020 0.012
#> GSM537414 4 0.3949 0.55748 0.000 0.000 0.000 0.668 0.332
#> GSM537433 1 0.0000 0.62566 1.000 0.000 0.000 0.000 0.000
#> GSM537335 3 0.0290 0.61401 0.000 0.000 0.992 0.008 0.000
#> GSM537339 2 0.1281 0.85963 0.000 0.956 0.000 0.032 0.012
#> GSM537340 1 0.0000 0.62566 1.000 0.000 0.000 0.000 0.000
#> GSM537344 5 0.0703 0.42850 0.000 0.000 0.000 0.024 0.976
#> GSM537346 2 0.0880 0.86053 0.000 0.968 0.000 0.032 0.000
#> GSM537351 1 0.4517 0.80604 0.600 0.000 0.000 0.012 0.388
#> GSM537352 4 0.6425 0.28719 0.000 0.188 0.000 0.476 0.336
#> GSM537359 5 0.5225 -0.00676 0.000 0.456 0.008 0.028 0.508
#> GSM537360 4 0.4088 0.49790 0.000 0.000 0.000 0.632 0.368
#> GSM537364 1 0.0290 0.62907 0.992 0.000 0.000 0.000 0.008
#> GSM537365 5 0.4583 0.33037 0.000 0.296 0.000 0.032 0.672
#> GSM537372 2 0.4658 0.57358 0.000 0.684 0.016 0.016 0.284
#> GSM537384 2 0.1710 0.84424 0.000 0.940 0.004 0.016 0.040
#> GSM537394 2 0.0510 0.85985 0.000 0.984 0.000 0.016 0.000
#> GSM537403 1 0.4517 0.80604 0.600 0.000 0.000 0.012 0.388
#> GSM537406 1 0.4517 0.80604 0.600 0.000 0.000 0.012 0.388
#> GSM537411 2 0.3768 0.73145 0.000 0.808 0.020 0.016 0.156
#> GSM537412 1 0.4101 0.80739 0.628 0.000 0.000 0.000 0.372
#> GSM537416 4 0.2006 0.72031 0.000 0.012 0.000 0.916 0.072
#> GSM537426 4 0.1608 0.71328 0.000 0.000 0.000 0.928 0.072
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 2 0.3758 0.6157 0.016 0.700 0.000 0.000 0.284 0.000
#> GSM537345 4 0.3804 -0.5763 0.000 0.000 0.424 0.576 0.000 0.000
#> GSM537355 5 0.7024 0.0855 0.236 0.036 0.068 0.140 0.520 0.000
#> GSM537366 4 0.3823 0.3889 0.000 0.000 0.436 0.564 0.000 0.000
#> GSM537370 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537380 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537392 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537415 1 0.0713 0.7870 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM537417 4 0.4465 -0.7753 0.000 0.000 0.460 0.512 0.028 0.000
#> GSM537422 4 0.0260 0.4289 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM537423 1 0.4327 0.7290 0.764 0.016 0.032 0.028 0.160 0.000
#> GSM537427 2 0.0146 0.7436 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM537430 6 0.6015 -0.0205 0.000 0.240 0.000 0.000 0.376 0.384
#> GSM537336 4 0.0260 0.4289 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM537337 1 0.4512 0.6815 0.756 0.116 0.008 0.020 0.100 0.000
#> GSM537348 2 0.4262 0.1601 0.016 0.508 0.000 0.000 0.476 0.000
#> GSM537349 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537356 4 0.6167 -0.7486 0.004 0.004 0.392 0.392 0.208 0.000
#> GSM537361 4 0.6062 -0.6635 0.000 0.000 0.320 0.404 0.276 0.000
#> GSM537374 6 0.4685 0.2904 0.004 0.040 0.000 0.000 0.388 0.568
#> GSM537377 5 0.3686 0.5611 0.000 0.220 0.032 0.000 0.748 0.000
#> GSM537378 2 0.0146 0.7436 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM537379 2 0.3175 0.6528 0.000 0.744 0.000 0.000 0.256 0.000
#> GSM537383 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537388 2 0.2697 0.6957 0.000 0.812 0.000 0.000 0.188 0.000
#> GSM537395 2 0.5190 0.2207 0.392 0.524 0.004 0.000 0.080 0.000
#> GSM537400 6 0.6001 0.0438 0.000 0.240 0.000 0.000 0.348 0.412
#> GSM537404 3 0.4978 0.9289 0.000 0.000 0.500 0.432 0.068 0.000
#> GSM537409 1 0.0713 0.7870 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM537418 1 0.0713 0.7870 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM537425 4 0.3578 -0.3445 0.000 0.000 0.340 0.660 0.000 0.000
#> GSM537333 2 0.3950 0.3363 0.000 0.564 0.000 0.000 0.432 0.004
#> GSM537342 1 0.4974 0.0652 0.528 0.000 0.024 0.420 0.028 0.000
#> GSM537347 5 0.3426 0.5144 0.000 0.276 0.004 0.000 0.720 0.000
#> GSM537350 4 0.3354 0.1777 0.000 0.000 0.168 0.796 0.036 0.000
#> GSM537362 5 0.4181 -0.1035 0.012 0.476 0.000 0.000 0.512 0.000
#> GSM537363 4 0.0260 0.4289 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM537368 4 0.3823 0.3889 0.000 0.000 0.436 0.564 0.000 0.000
#> GSM537376 2 0.3998 0.5334 0.016 0.644 0.000 0.000 0.340 0.000
#> GSM537381 2 0.3464 0.5886 0.000 0.688 0.000 0.000 0.312 0.000
#> GSM537386 6 0.0260 0.5591 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM537398 5 0.4779 -0.2418 0.000 0.040 0.004 0.000 0.488 0.468
#> GSM537402 5 0.4770 0.5302 0.064 0.156 0.032 0.012 0.736 0.000
#> GSM537405 3 0.4936 0.9252 0.000 0.000 0.500 0.436 0.064 0.000
#> GSM537371 4 0.3747 -0.5024 0.000 0.000 0.396 0.604 0.000 0.000
#> GSM537421 4 0.1007 0.4001 0.044 0.000 0.000 0.956 0.000 0.000
#> GSM537424 5 0.3139 0.5961 0.000 0.160 0.028 0.000 0.812 0.000
#> GSM537432 2 0.3620 0.5236 0.000 0.648 0.000 0.000 0.352 0.000
#> GSM537331 6 0.0260 0.5591 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM537332 2 0.2996 0.6744 0.000 0.772 0.000 0.000 0.228 0.000
#> GSM537334 6 0.1442 0.5780 0.004 0.012 0.000 0.000 0.040 0.944
#> GSM537338 2 0.4076 0.4879 0.016 0.620 0.000 0.000 0.364 0.000
#> GSM537353 1 0.5774 0.5783 0.604 0.036 0.052 0.028 0.280 0.000
#> GSM537357 4 0.0291 0.4223 0.004 0.000 0.004 0.992 0.000 0.000
#> GSM537358 5 0.2949 0.5964 0.000 0.140 0.028 0.000 0.832 0.000
#> GSM537375 2 0.3717 0.4640 0.000 0.616 0.000 0.000 0.384 0.000
#> GSM537389 2 0.0146 0.7430 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM537390 2 0.0146 0.7436 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM537393 2 0.0146 0.7436 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM537399 6 0.4746 0.2374 0.004 0.040 0.000 0.000 0.424 0.532
#> GSM537407 5 0.2333 0.5461 0.000 0.040 0.060 0.000 0.896 0.004
#> GSM537408 4 0.7534 -0.2848 0.260 0.028 0.060 0.332 0.320 0.000
#> GSM537428 5 0.2088 0.5780 0.000 0.068 0.028 0.000 0.904 0.000
#> GSM537354 1 0.0713 0.7870 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM537410 4 0.1141 0.3848 0.000 0.000 0.052 0.948 0.000 0.000
#> GSM537413 2 0.0146 0.7436 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM537396 2 0.3699 0.5505 0.000 0.660 0.004 0.000 0.336 0.000
#> GSM537397 2 0.2491 0.7046 0.000 0.836 0.000 0.000 0.164 0.000
#> GSM537330 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537369 4 0.7371 -0.6206 0.132 0.004 0.280 0.404 0.180 0.000
#> GSM537373 1 0.4952 0.7126 0.728 0.024 0.036 0.052 0.160 0.000
#> GSM537401 5 0.4250 -0.0197 0.016 0.456 0.000 0.000 0.528 0.000
#> GSM537343 4 0.6650 -0.7866 0.052 0.004 0.384 0.412 0.148 0.000
#> GSM537367 4 0.3823 0.3889 0.000 0.000 0.436 0.564 0.000 0.000
#> GSM537382 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537385 2 0.3136 0.6734 0.004 0.768 0.000 0.000 0.228 0.000
#> GSM537391 5 0.3747 0.2514 0.000 0.396 0.000 0.000 0.604 0.000
#> GSM537419 5 0.2595 0.6030 0.000 0.160 0.004 0.000 0.836 0.000
#> GSM537420 5 0.5784 -0.6336 0.000 0.000 0.176 0.408 0.416 0.000
#> GSM537429 2 0.0146 0.7436 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM537431 5 0.4392 0.4447 0.004 0.052 0.056 0.000 0.772 0.116
#> GSM537387 2 0.3101 0.6633 0.000 0.756 0.000 0.000 0.244 0.000
#> GSM537414 1 0.3052 0.6340 0.780 0.000 0.000 0.216 0.004 0.000
#> GSM537433 4 0.3823 0.3889 0.000 0.000 0.436 0.564 0.000 0.000
#> GSM537335 6 0.0260 0.5591 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM537339 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537340 4 0.3823 0.3889 0.000 0.000 0.436 0.564 0.000 0.000
#> GSM537344 3 0.5573 0.8664 0.000 0.004 0.460 0.416 0.120 0.000
#> GSM537346 2 0.0000 0.7451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM537351 4 0.1003 0.4247 0.000 0.000 0.016 0.964 0.020 0.000
#> GSM537352 1 0.4469 0.7270 0.756 0.016 0.036 0.032 0.160 0.000
#> GSM537359 5 0.1367 0.5616 0.000 0.044 0.012 0.000 0.944 0.000
#> GSM537360 1 0.3774 0.1880 0.592 0.000 0.000 0.408 0.000 0.000
#> GSM537364 4 0.3797 0.3909 0.000 0.000 0.420 0.580 0.000 0.000
#> GSM537365 5 0.2867 0.5199 0.004 0.032 0.076 0.016 0.872 0.000
#> GSM537372 5 0.3417 0.5667 0.000 0.132 0.052 0.000 0.812 0.004
#> GSM537384 2 0.3765 0.4174 0.000 0.596 0.000 0.000 0.404 0.000
#> GSM537394 2 0.3101 0.6629 0.000 0.756 0.000 0.000 0.244 0.000
#> GSM537403 4 0.0891 0.4341 0.000 0.000 0.008 0.968 0.024 0.000
#> GSM537406 4 0.1088 0.4269 0.000 0.000 0.016 0.960 0.024 0.000
#> GSM537411 5 0.3851 0.5579 0.000 0.160 0.056 0.000 0.776 0.008
#> GSM537412 4 0.0993 0.4347 0.000 0.000 0.012 0.964 0.024 0.000
#> GSM537416 1 0.0713 0.7870 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM537426 1 0.0713 0.7870 0.972 0.000 0.000 0.028 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> ATC:mclust 56 NA NA 2
#> ATC:mclust 98 0.422 0.709 3
#> ATC:mclust 98 0.513 0.817 4
#> ATC:mclust 77 0.473 0.512 5
#> ATC:mclust 59 0.685 0.793 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 104 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 0.756 0.875 0.947 0.4848 0.507 0.507
#> 3 3 0.476 0.532 0.790 0.3521 0.662 0.423
#> 4 4 0.446 0.491 0.722 0.1314 0.739 0.373
#> 5 5 0.545 0.479 0.699 0.0668 0.868 0.544
#> 6 6 0.570 0.373 0.632 0.0402 0.904 0.593
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
#> GSM537341 2 0.482 0.8624 0.104 0.896
#> GSM537345 1 0.000 0.9502 1.000 0.000
#> GSM537355 1 0.000 0.9502 1.000 0.000
#> GSM537366 1 0.000 0.9502 1.000 0.000
#> GSM537370 2 0.000 0.9277 0.000 1.000
#> GSM537380 2 0.000 0.9277 0.000 1.000
#> GSM537392 2 0.000 0.9277 0.000 1.000
#> GSM537415 1 0.000 0.9502 1.000 0.000
#> GSM537417 1 0.000 0.9502 1.000 0.000
#> GSM537422 1 0.000 0.9502 1.000 0.000
#> GSM537423 1 0.000 0.9502 1.000 0.000
#> GSM537427 2 0.000 0.9277 0.000 1.000
#> GSM537430 2 0.000 0.9277 0.000 1.000
#> GSM537336 1 0.000 0.9502 1.000 0.000
#> GSM537337 1 0.000 0.9502 1.000 0.000
#> GSM537348 2 0.917 0.5343 0.332 0.668
#> GSM537349 2 0.184 0.9157 0.028 0.972
#> GSM537356 1 0.000 0.9502 1.000 0.000
#> GSM537361 1 0.000 0.9502 1.000 0.000
#> GSM537374 2 0.000 0.9277 0.000 1.000
#> GSM537377 1 0.000 0.9502 1.000 0.000
#> GSM537378 1 0.808 0.6574 0.752 0.248
#> GSM537379 2 0.260 0.9071 0.044 0.956
#> GSM537383 2 0.000 0.9277 0.000 1.000
#> GSM537388 2 0.000 0.9277 0.000 1.000
#> GSM537395 1 0.000 0.9502 1.000 0.000
#> GSM537400 2 0.000 0.9277 0.000 1.000
#> GSM537404 1 0.000 0.9502 1.000 0.000
#> GSM537409 1 0.000 0.9502 1.000 0.000
#> GSM537418 1 0.000 0.9502 1.000 0.000
#> GSM537425 1 0.000 0.9502 1.000 0.000
#> GSM537333 2 0.000 0.9277 0.000 1.000
#> GSM537342 1 0.000 0.9502 1.000 0.000
#> GSM537347 2 0.343 0.8941 0.064 0.936
#> GSM537350 1 0.000 0.9502 1.000 0.000
#> GSM537362 2 0.118 0.9211 0.016 0.984
#> GSM537363 1 0.000 0.9502 1.000 0.000
#> GSM537368 1 0.000 0.9502 1.000 0.000
#> GSM537376 2 0.000 0.9277 0.000 1.000
#> GSM537381 2 0.999 0.0926 0.484 0.516
#> GSM537386 2 0.000 0.9277 0.000 1.000
#> GSM537398 2 0.000 0.9277 0.000 1.000
#> GSM537402 1 0.000 0.9502 1.000 0.000
#> GSM537405 1 0.000 0.9502 1.000 0.000
#> GSM537371 1 0.000 0.9502 1.000 0.000
#> GSM537421 1 0.000 0.9502 1.000 0.000
#> GSM537424 1 0.000 0.9502 1.000 0.000
#> GSM537432 2 0.000 0.9277 0.000 1.000
#> GSM537331 2 0.000 0.9277 0.000 1.000
#> GSM537332 2 0.000 0.9277 0.000 1.000
#> GSM537334 2 0.000 0.9277 0.000 1.000
#> GSM537338 2 0.000 0.9277 0.000 1.000
#> GSM537353 1 0.000 0.9502 1.000 0.000
#> GSM537357 1 0.000 0.9502 1.000 0.000
#> GSM537358 1 0.936 0.4462 0.648 0.352
#> GSM537375 2 0.552 0.8381 0.128 0.872
#> GSM537389 2 0.971 0.3719 0.400 0.600
#> GSM537390 2 0.327 0.8970 0.060 0.940
#> GSM537393 1 0.943 0.4262 0.640 0.360
#> GSM537399 2 0.000 0.9277 0.000 1.000
#> GSM537407 1 0.913 0.5029 0.672 0.328
#> GSM537408 1 0.000 0.9502 1.000 0.000
#> GSM537428 1 0.327 0.8968 0.940 0.060
#> GSM537354 1 0.000 0.9502 1.000 0.000
#> GSM537410 1 0.000 0.9502 1.000 0.000
#> GSM537413 2 0.000 0.9277 0.000 1.000
#> GSM537396 2 0.730 0.7477 0.204 0.796
#> GSM537397 2 0.925 0.5179 0.340 0.660
#> GSM537330 2 0.000 0.9277 0.000 1.000
#> GSM537369 1 0.000 0.9502 1.000 0.000
#> GSM537373 1 0.000 0.9502 1.000 0.000
#> GSM537401 2 0.552 0.8392 0.128 0.872
#> GSM537343 1 0.000 0.9502 1.000 0.000
#> GSM537367 1 0.000 0.9502 1.000 0.000
#> GSM537382 2 0.242 0.9094 0.040 0.960
#> GSM537385 2 0.494 0.8588 0.108 0.892
#> GSM537391 2 0.961 0.4147 0.384 0.616
#> GSM537419 1 0.541 0.8302 0.876 0.124
#> GSM537420 1 0.000 0.9502 1.000 0.000
#> GSM537429 2 0.000 0.9277 0.000 1.000
#> GSM537431 2 0.000 0.9277 0.000 1.000
#> GSM537387 1 0.943 0.4262 0.640 0.360
#> GSM537414 1 0.000 0.9502 1.000 0.000
#> GSM537433 1 0.000 0.9502 1.000 0.000
#> GSM537335 2 0.000 0.9277 0.000 1.000
#> GSM537339 2 0.000 0.9277 0.000 1.000
#> GSM537340 1 0.000 0.9502 1.000 0.000
#> GSM537344 1 0.000 0.9502 1.000 0.000
#> GSM537346 2 0.000 0.9277 0.000 1.000
#> GSM537351 1 0.000 0.9502 1.000 0.000
#> GSM537352 1 0.000 0.9502 1.000 0.000
#> GSM537359 1 0.961 0.3608 0.616 0.384
#> GSM537360 1 0.000 0.9502 1.000 0.000
#> GSM537364 1 0.000 0.9502 1.000 0.000
#> GSM537365 1 0.000 0.9502 1.000 0.000
#> GSM537372 1 0.615 0.7962 0.848 0.152
#> GSM537384 1 0.802 0.6649 0.756 0.244
#> GSM537394 2 0.000 0.9277 0.000 1.000
#> GSM537403 1 0.000 0.9502 1.000 0.000
#> GSM537406 1 0.000 0.9502 1.000 0.000
#> GSM537411 2 0.000 0.9277 0.000 1.000
#> GSM537412 1 0.000 0.9502 1.000 0.000
#> GSM537416 1 0.000 0.9502 1.000 0.000
#> GSM537426 1 0.000 0.9502 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM537341 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537345 3 0.5733 0.56476 0.000 0.324 0.676
#> GSM537355 3 0.6180 0.48644 0.000 0.416 0.584
#> GSM537366 3 0.6260 0.41891 0.000 0.448 0.552
#> GSM537370 1 0.5497 0.58345 0.708 0.292 0.000
#> GSM537380 1 0.2959 0.79613 0.900 0.100 0.000
#> GSM537392 1 0.5785 0.52209 0.668 0.332 0.000
#> GSM537415 2 0.0237 0.67065 0.000 0.996 0.004
#> GSM537417 3 0.0237 0.64418 0.000 0.004 0.996
#> GSM537422 3 0.5905 0.56516 0.000 0.352 0.648
#> GSM537423 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537427 2 0.5968 0.23347 0.364 0.636 0.000
#> GSM537430 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537336 2 0.6026 0.08701 0.000 0.624 0.376
#> GSM537337 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537348 1 0.3973 0.80304 0.880 0.032 0.088
#> GSM537349 1 0.5810 0.51639 0.664 0.336 0.000
#> GSM537356 3 0.1163 0.64742 0.000 0.028 0.972
#> GSM537361 3 0.0237 0.64248 0.004 0.000 0.996
#> GSM537374 1 0.4178 0.74948 0.828 0.000 0.172
#> GSM537377 3 0.0237 0.64248 0.004 0.000 0.996
#> GSM537378 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537379 1 0.1289 0.84007 0.968 0.032 0.000
#> GSM537383 1 0.1643 0.83398 0.956 0.044 0.000
#> GSM537388 1 0.1753 0.83149 0.952 0.048 0.000
#> GSM537395 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537400 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537404 3 0.0000 0.64331 0.000 0.000 1.000
#> GSM537409 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537418 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537425 3 0.6126 0.50617 0.000 0.400 0.600
#> GSM537333 1 0.1753 0.83175 0.952 0.000 0.048
#> GSM537342 2 0.5706 0.23641 0.000 0.680 0.320
#> GSM537347 3 0.5785 0.22190 0.332 0.000 0.668
#> GSM537350 3 0.6126 0.51149 0.000 0.400 0.600
#> GSM537362 1 0.3192 0.79709 0.888 0.000 0.112
#> GSM537363 3 0.5706 0.59155 0.000 0.320 0.680
#> GSM537368 3 0.5760 0.58659 0.000 0.328 0.672
#> GSM537376 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537381 2 0.6309 -0.00688 0.496 0.504 0.000
#> GSM537386 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537398 1 0.6168 0.41968 0.588 0.000 0.412
#> GSM537402 2 0.6168 -0.07336 0.000 0.588 0.412
#> GSM537405 3 0.0000 0.64331 0.000 0.000 1.000
#> GSM537371 3 0.5859 0.57267 0.000 0.344 0.656
#> GSM537421 2 0.5098 0.38617 0.000 0.752 0.248
#> GSM537424 3 0.6309 0.25971 0.000 0.496 0.504
#> GSM537432 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537331 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537332 1 0.5650 0.55498 0.688 0.312 0.000
#> GSM537334 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537338 1 0.0000 0.84588 1.000 0.000 0.000
#> GSM537353 2 0.0592 0.66612 0.000 0.988 0.012
#> GSM537357 2 0.5529 0.29154 0.000 0.704 0.296
#> GSM537358 1 0.9457 0.12525 0.484 0.204 0.312
#> GSM537375 1 0.4411 0.77163 0.844 0.016 0.140
#> GSM537389 2 0.1411 0.65726 0.036 0.964 0.000
#> GSM537390 2 0.3482 0.60929 0.128 0.872 0.000
#> GSM537393 2 0.0000 0.67153 0.000 1.000 0.000
#> GSM537399 1 0.1753 0.83241 0.952 0.000 0.048
#> GSM537407 3 0.3752 0.55793 0.144 0.000 0.856
#> GSM537408 3 0.6252 0.43093 0.000 0.444 0.556
#> GSM537428 3 0.4289 0.61925 0.092 0.040 0.868
#> GSM537354 2 0.0237 0.67065 0.000 0.996 0.004
#> GSM537410 2 0.6305 -0.27013 0.000 0.516 0.484
#> GSM537413 2 0.6291 -0.07324 0.468 0.532 0.000
#> GSM537396 2 0.5529 0.38832 0.296 0.704 0.000
#> GSM537397 2 0.5058 0.49065 0.244 0.756 0.000
#> GSM537330 1 0.1411 0.83742 0.964 0.036 0.000
#> GSM537369 2 0.6095 0.04776 0.000 0.608 0.392
#> GSM537373 2 0.3941 0.52412 0.000 0.844 0.156
#> GSM537401 1 0.5098 0.66907 0.752 0.000 0.248
#> GSM537343 2 0.6286 -0.20091 0.000 0.536 0.464
#> GSM537367 3 0.6154 0.50006 0.000 0.408 0.592
#> GSM537382 2 0.6225 0.06228 0.432 0.568 0.000
#> GSM537385 1 0.1643 0.83501 0.956 0.044 0.000
#> GSM537391 1 0.5236 0.69507 0.804 0.028 0.168
#> GSM537419 3 0.7381 0.50929 0.244 0.080 0.676
#> GSM537420 3 0.0237 0.64248 0.004 0.000 0.996
#> GSM537429 2 0.6295 -0.08237 0.472 0.528 0.000
#> GSM537431 3 0.6126 0.01315 0.400 0.000 0.600
#> GSM537387 2 0.4974 0.54922 0.236 0.764 0.000
#> GSM537414 2 0.0237 0.67065 0.000 0.996 0.004
#> GSM537433 3 0.6045 0.53949 0.000 0.380 0.620
#> GSM537335 1 0.0237 0.84523 0.996 0.004 0.000
#> GSM537339 2 0.6204 0.08768 0.424 0.576 0.000
#> GSM537340 3 0.6204 0.47072 0.000 0.424 0.576
#> GSM537344 3 0.4062 0.64327 0.000 0.164 0.836
#> GSM537346 1 0.4750 0.68052 0.784 0.216 0.000
#> GSM537351 2 0.6280 -0.19566 0.000 0.540 0.460
#> GSM537352 2 0.0747 0.66342 0.000 0.984 0.016
#> GSM537359 3 0.4452 0.49056 0.192 0.000 0.808
#> GSM537360 2 0.0237 0.67065 0.000 0.996 0.004
#> GSM537364 3 0.4931 0.62736 0.000 0.232 0.768
#> GSM537365 3 0.3941 0.64320 0.000 0.156 0.844
#> GSM537372 3 0.2261 0.61492 0.068 0.000 0.932
#> GSM537384 1 0.9980 0.02037 0.364 0.324 0.312
#> GSM537394 1 0.0424 0.84467 0.992 0.008 0.000
#> GSM537403 3 0.6026 0.54245 0.000 0.376 0.624
#> GSM537406 3 0.5905 0.56667 0.000 0.352 0.648
#> GSM537411 3 0.5785 0.21235 0.332 0.000 0.668
#> GSM537412 2 0.6215 -0.09864 0.000 0.572 0.428
#> GSM537416 2 0.0237 0.67065 0.000 0.996 0.004
#> GSM537426 2 0.0000 0.67153 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM537341 4 0.4957 0.55903 0.000 0.320 0.012 0.668
#> GSM537345 1 0.2926 0.61505 0.888 0.012 0.096 0.004
#> GSM537355 3 0.5757 0.60128 0.076 0.240 0.684 0.000
#> GSM537366 3 0.6454 0.46931 0.344 0.084 0.572 0.000
#> GSM537370 2 0.5959 0.15469 0.044 0.568 0.000 0.388
#> GSM537380 2 0.5928 0.07987 0.032 0.564 0.004 0.400
#> GSM537392 2 0.3616 0.62547 0.036 0.852 0.000 0.112
#> GSM537415 1 0.3975 0.53662 0.760 0.240 0.000 0.000
#> GSM537417 3 0.2546 0.64656 0.060 0.000 0.912 0.028
#> GSM537422 1 0.4452 0.42242 0.732 0.008 0.260 0.000
#> GSM537423 2 0.5407 0.50474 0.108 0.740 0.152 0.000
#> GSM537427 2 0.4130 0.63031 0.064 0.828 0.000 0.108
#> GSM537430 4 0.2706 0.73674 0.000 0.080 0.020 0.900
#> GSM537336 1 0.3215 0.62442 0.876 0.032 0.092 0.000
#> GSM537337 2 0.4925 0.16027 0.428 0.572 0.000 0.000
#> GSM537348 4 0.4321 0.67474 0.144 0.040 0.004 0.812
#> GSM537349 2 0.3734 0.63458 0.044 0.856 0.004 0.096
#> GSM537356 3 0.6133 0.50915 0.268 0.000 0.644 0.088
#> GSM537361 3 0.5136 0.52773 0.056 0.004 0.752 0.188
#> GSM537374 4 0.0804 0.73043 0.000 0.008 0.012 0.980
#> GSM537377 4 0.7950 0.12216 0.324 0.008 0.228 0.440
#> GSM537378 1 0.4399 0.51456 0.760 0.224 0.000 0.016
#> GSM537379 4 0.4436 0.65129 0.148 0.052 0.000 0.800
#> GSM537383 4 0.4891 0.60448 0.012 0.308 0.000 0.680
#> GSM537388 2 0.4049 0.50205 0.000 0.780 0.008 0.212
#> GSM537395 2 0.4585 0.35408 0.332 0.668 0.000 0.000
#> GSM537400 4 0.3837 0.68945 0.000 0.224 0.000 0.776
#> GSM537404 3 0.4948 0.57847 0.100 0.000 0.776 0.124
#> GSM537409 1 0.4998 0.00863 0.512 0.488 0.000 0.000
#> GSM537418 1 0.2281 0.64310 0.904 0.096 0.000 0.000
#> GSM537425 1 0.3494 0.55359 0.824 0.004 0.172 0.000
#> GSM537333 4 0.2262 0.71672 0.040 0.012 0.016 0.932
#> GSM537342 1 0.7599 -0.08725 0.424 0.200 0.376 0.000
#> GSM537347 4 0.5425 0.58378 0.052 0.004 0.228 0.716
#> GSM537350 3 0.5941 0.59174 0.072 0.276 0.652 0.000
#> GSM537362 4 0.3289 0.68089 0.120 0.004 0.012 0.864
#> GSM537363 3 0.6137 0.31465 0.448 0.048 0.504 0.000
#> GSM537368 3 0.5646 0.59311 0.272 0.056 0.672 0.000
#> GSM537376 4 0.3504 0.72759 0.012 0.116 0.012 0.860
#> GSM537381 1 0.6602 0.17635 0.552 0.092 0.000 0.356
#> GSM537386 4 0.3266 0.71364 0.000 0.168 0.000 0.832
#> GSM537398 4 0.1545 0.72223 0.000 0.008 0.040 0.952
#> GSM537402 2 0.5827 0.02636 0.036 0.568 0.396 0.000
#> GSM537405 3 0.2675 0.63786 0.048 0.000 0.908 0.044
#> GSM537371 1 0.3208 0.58364 0.848 0.004 0.148 0.000
#> GSM537421 1 0.3525 0.63555 0.860 0.100 0.040 0.000
#> GSM537424 1 0.3547 0.60375 0.864 0.000 0.072 0.064
#> GSM537432 4 0.4283 0.67299 0.000 0.256 0.004 0.740
#> GSM537331 4 0.3486 0.70113 0.000 0.188 0.000 0.812
#> GSM537332 2 0.5154 0.32077 0.012 0.660 0.004 0.324
#> GSM537334 4 0.0469 0.73161 0.000 0.012 0.000 0.988
#> GSM537338 4 0.3219 0.71517 0.000 0.164 0.000 0.836
#> GSM537353 1 0.6212 0.34889 0.560 0.380 0.060 0.000
#> GSM537357 1 0.2282 0.63940 0.924 0.024 0.052 0.000
#> GSM537358 2 0.5560 0.39086 0.016 0.684 0.276 0.024
#> GSM537375 4 0.6524 0.42191 0.308 0.052 0.024 0.616
#> GSM537389 2 0.4567 0.44811 0.276 0.716 0.000 0.008
#> GSM537390 2 0.4951 0.54395 0.212 0.744 0.000 0.044
#> GSM537393 1 0.4844 0.44584 0.688 0.300 0.000 0.012
#> GSM537399 4 0.2814 0.72995 0.000 0.132 0.000 0.868
#> GSM537407 3 0.4360 0.47809 0.008 0.000 0.744 0.248
#> GSM537408 2 0.5750 -0.04334 0.028 0.532 0.440 0.000
#> GSM537428 3 0.4734 0.64826 0.028 0.160 0.792 0.020
#> GSM537354 1 0.4855 0.36638 0.644 0.352 0.004 0.000
#> GSM537410 3 0.6330 0.61087 0.144 0.200 0.656 0.000
#> GSM537413 2 0.3796 0.63557 0.056 0.848 0.000 0.096
#> GSM537396 2 0.3421 0.62239 0.020 0.876 0.016 0.088
#> GSM537397 2 0.4530 0.61036 0.048 0.808 0.008 0.136
#> GSM537330 4 0.3870 0.69435 0.004 0.208 0.000 0.788
#> GSM537369 1 0.2402 0.62547 0.912 0.012 0.076 0.000
#> GSM537373 1 0.4093 0.63409 0.832 0.096 0.072 0.000
#> GSM537401 4 0.4022 0.72531 0.000 0.096 0.068 0.836
#> GSM537343 1 0.3196 0.59508 0.856 0.008 0.136 0.000
#> GSM537367 3 0.5850 0.60585 0.244 0.080 0.676 0.000
#> GSM537382 2 0.6921 0.50033 0.160 0.580 0.000 0.260
#> GSM537385 2 0.3870 0.59520 0.008 0.820 0.008 0.164
#> GSM537391 2 0.7670 0.14452 0.000 0.420 0.216 0.364
#> GSM537419 3 0.6140 0.26797 0.008 0.400 0.556 0.036
#> GSM537420 3 0.1256 0.65412 0.008 0.028 0.964 0.000
#> GSM537429 2 0.4508 0.55566 0.036 0.780 0.000 0.184
#> GSM537431 4 0.5560 0.39274 0.000 0.024 0.392 0.584
#> GSM537387 1 0.5376 0.53801 0.736 0.176 0.000 0.088
#> GSM537414 1 0.1661 0.64369 0.944 0.052 0.004 0.000
#> GSM537433 3 0.5859 0.57489 0.284 0.064 0.652 0.000
#> GSM537335 4 0.3569 0.69878 0.000 0.196 0.000 0.804
#> GSM537339 1 0.7663 -0.15899 0.408 0.212 0.000 0.380
#> GSM537340 1 0.6334 -0.21855 0.484 0.060 0.456 0.000
#> GSM537344 1 0.6240 0.00877 0.568 0.000 0.368 0.064
#> GSM537346 4 0.5126 0.27304 0.004 0.444 0.000 0.552
#> GSM537351 3 0.7188 0.23120 0.428 0.136 0.436 0.000
#> GSM537352 1 0.7108 0.30381 0.512 0.348 0.140 0.000
#> GSM537359 3 0.2300 0.63959 0.000 0.048 0.924 0.028
#> GSM537360 2 0.6222 0.06087 0.412 0.532 0.056 0.000
#> GSM537364 3 0.4149 0.66249 0.152 0.036 0.812 0.000
#> GSM537365 3 0.6040 0.60301 0.240 0.060 0.684 0.016
#> GSM537372 3 0.5619 0.32855 0.040 0.000 0.640 0.320
#> GSM537384 1 0.6122 0.25507 0.576 0.012 0.032 0.380
#> GSM537394 4 0.4866 0.42905 0.000 0.404 0.000 0.596
#> GSM537403 3 0.5208 0.65272 0.080 0.172 0.748 0.000
#> GSM537406 3 0.5599 0.57191 0.048 0.288 0.664 0.000
#> GSM537411 4 0.5064 0.45798 0.004 0.004 0.360 0.632
#> GSM537412 3 0.6823 0.56064 0.196 0.200 0.604 0.000
#> GSM537416 1 0.3266 0.60196 0.832 0.168 0.000 0.000
#> GSM537426 2 0.4655 0.38797 0.312 0.684 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM537341 2 0.7100 -0.02176 0.012 0.472 0.308 0.012 0.196
#> GSM537345 1 0.1518 0.71934 0.952 0.000 0.020 0.012 0.016
#> GSM537355 4 0.4892 0.00385 0.016 0.484 0.000 0.496 0.004
#> GSM537366 4 0.5846 0.28320 0.380 0.004 0.000 0.528 0.088
#> GSM537370 5 0.6251 0.12830 0.008 0.152 0.280 0.000 0.560
#> GSM537380 2 0.2549 0.68649 0.008 0.904 0.060 0.004 0.024
#> GSM537392 2 0.1365 0.69245 0.000 0.952 0.004 0.004 0.040
#> GSM537415 1 0.4723 0.61877 0.736 0.136 0.000 0.000 0.128
#> GSM537417 4 0.1872 0.61751 0.020 0.000 0.052 0.928 0.000
#> GSM537422 1 0.4612 0.65772 0.756 0.004 0.000 0.124 0.116
#> GSM537423 2 0.3976 0.66969 0.024 0.824 0.000 0.084 0.068
#> GSM537427 2 0.1117 0.69759 0.000 0.964 0.016 0.000 0.020
#> GSM537430 3 0.3550 0.61505 0.000 0.064 0.848 0.016 0.072
#> GSM537336 1 0.2361 0.71966 0.892 0.000 0.000 0.012 0.096
#> GSM537337 2 0.4141 0.61833 0.236 0.736 0.000 0.000 0.028
#> GSM537348 3 0.5521 0.53282 0.124 0.004 0.656 0.000 0.216
#> GSM537349 2 0.0867 0.69862 0.000 0.976 0.008 0.008 0.008
#> GSM537356 4 0.6818 0.41470 0.252 0.000 0.200 0.524 0.024
#> GSM537361 4 0.4297 0.47621 0.036 0.000 0.236 0.728 0.000
#> GSM537374 3 0.3317 0.60839 0.000 0.004 0.804 0.004 0.188
#> GSM537377 3 0.6088 0.03557 0.396 0.004 0.520 0.056 0.024
#> GSM537378 2 0.5905 0.35383 0.392 0.532 0.044 0.000 0.032
#> GSM537379 3 0.3870 0.50179 0.148 0.024 0.808 0.000 0.020
#> GSM537383 2 0.5071 0.42382 0.008 0.660 0.284 0.000 0.048
#> GSM537388 2 0.4974 0.48102 0.000 0.696 0.092 0.000 0.212
#> GSM537395 2 0.2505 0.69441 0.092 0.888 0.000 0.000 0.020
#> GSM537400 3 0.4696 0.50184 0.000 0.024 0.616 0.000 0.360
#> GSM537404 4 0.4369 0.52660 0.052 0.000 0.208 0.740 0.000
#> GSM537409 1 0.6477 0.13440 0.464 0.340 0.000 0.000 0.196
#> GSM537418 1 0.2574 0.70974 0.876 0.012 0.000 0.000 0.112
#> GSM537425 1 0.2728 0.72149 0.896 0.008 0.012 0.068 0.016
#> GSM537333 3 0.1059 0.59263 0.020 0.004 0.968 0.008 0.000
#> GSM537342 1 0.6561 0.27306 0.520 0.016 0.000 0.312 0.152
#> GSM537347 3 0.4676 0.45474 0.028 0.004 0.720 0.236 0.012
#> GSM537350 4 0.4592 0.59345 0.028 0.016 0.000 0.724 0.232
#> GSM537362 3 0.2593 0.59704 0.048 0.004 0.904 0.008 0.036
#> GSM537363 1 0.5284 0.19678 0.568 0.000 0.000 0.376 0.056
#> GSM537368 4 0.5658 0.40224 0.332 0.000 0.000 0.572 0.096
#> GSM537376 3 0.4865 0.52967 0.020 0.160 0.756 0.008 0.056
#> GSM537381 5 0.6355 -0.00436 0.140 0.004 0.408 0.000 0.448
#> GSM537386 3 0.4987 0.57335 0.000 0.080 0.684 0.000 0.236
#> GSM537398 3 0.2408 0.61626 0.000 0.004 0.892 0.008 0.096
#> GSM537402 2 0.4142 0.53392 0.004 0.728 0.000 0.252 0.016
#> GSM537405 4 0.2313 0.61919 0.040 0.000 0.044 0.912 0.004
#> GSM537371 1 0.2625 0.71824 0.900 0.000 0.028 0.056 0.016
#> GSM537421 1 0.3292 0.69699 0.836 0.016 0.000 0.008 0.140
#> GSM537424 1 0.3592 0.65772 0.832 0.012 0.132 0.008 0.016
#> GSM537432 5 0.3707 0.40065 0.008 0.004 0.220 0.000 0.768
#> GSM537331 3 0.5447 0.54696 0.000 0.112 0.640 0.000 0.248
#> GSM537332 5 0.3190 0.50942 0.008 0.012 0.140 0.000 0.840
#> GSM537334 3 0.2305 0.62087 0.000 0.012 0.896 0.000 0.092
#> GSM537338 3 0.4597 0.57568 0.000 0.044 0.696 0.000 0.260
#> GSM537353 2 0.5808 0.47744 0.272 0.624 0.000 0.084 0.020
#> GSM537357 1 0.1571 0.72489 0.936 0.000 0.000 0.004 0.060
#> GSM537358 2 0.6425 0.04862 0.004 0.448 0.008 0.424 0.116
#> GSM537375 3 0.5175 0.38121 0.252 0.028 0.688 0.008 0.024
#> GSM537389 2 0.2616 0.69468 0.100 0.880 0.000 0.000 0.020
#> GSM537390 2 0.1717 0.70298 0.052 0.936 0.004 0.000 0.008
#> GSM537393 2 0.5343 0.47500 0.344 0.604 0.032 0.000 0.020
#> GSM537399 3 0.3876 0.55507 0.000 0.000 0.684 0.000 0.316
#> GSM537407 4 0.4045 0.15955 0.000 0.000 0.356 0.644 0.000
#> GSM537408 4 0.5032 0.06570 0.000 0.448 0.000 0.520 0.032
#> GSM537428 4 0.4851 0.45333 0.020 0.000 0.008 0.620 0.352
#> GSM537354 2 0.5631 0.18254 0.424 0.500 0.000 0.000 0.076
#> GSM537410 4 0.4997 0.62459 0.156 0.024 0.000 0.740 0.080
#> GSM537413 2 0.1211 0.69760 0.000 0.960 0.016 0.000 0.024
#> GSM537396 5 0.2581 0.56295 0.028 0.048 0.020 0.000 0.904
#> GSM537397 2 0.5677 0.14090 0.008 0.516 0.060 0.000 0.416
#> GSM537330 3 0.5274 0.54674 0.000 0.192 0.676 0.000 0.132
#> GSM537369 1 0.2032 0.71667 0.924 0.004 0.020 0.000 0.052
#> GSM537373 1 0.3566 0.72238 0.852 0.064 0.000 0.056 0.028
#> GSM537401 3 0.5358 0.57637 0.004 0.060 0.668 0.012 0.256
#> GSM537343 1 0.2165 0.72677 0.920 0.004 0.016 0.056 0.004
#> GSM537367 4 0.5320 0.52499 0.264 0.008 0.000 0.656 0.072
#> GSM537382 2 0.6344 0.56779 0.084 0.652 0.140 0.000 0.124
#> GSM537385 2 0.1967 0.69995 0.000 0.932 0.020 0.036 0.012
#> GSM537391 5 0.4195 0.53343 0.020 0.016 0.112 0.036 0.816
#> GSM537419 2 0.4130 0.48739 0.000 0.696 0.000 0.292 0.012
#> GSM537420 4 0.2674 0.62327 0.012 0.000 0.000 0.868 0.120
#> GSM537429 5 0.4248 0.48711 0.008 0.096 0.104 0.000 0.792
#> GSM537431 3 0.6006 0.45673 0.000 0.000 0.584 0.220 0.196
#> GSM537387 1 0.6129 0.46879 0.668 0.156 0.096 0.000 0.080
#> GSM537414 1 0.2228 0.70250 0.920 0.044 0.020 0.000 0.016
#> GSM537433 4 0.5509 0.46397 0.304 0.004 0.000 0.612 0.080
#> GSM537335 3 0.5447 0.54186 0.000 0.112 0.640 0.000 0.248
#> GSM537339 3 0.8327 0.17719 0.240 0.176 0.380 0.000 0.204
#> GSM537340 1 0.5623 0.16820 0.540 0.004 0.000 0.388 0.068
#> GSM537344 1 0.4718 0.56816 0.736 0.000 0.048 0.200 0.016
#> GSM537346 3 0.6742 0.23906 0.000 0.296 0.412 0.000 0.292
#> GSM537351 5 0.5594 0.21282 0.284 0.000 0.000 0.108 0.608
#> GSM537352 1 0.6292 0.33925 0.516 0.024 0.000 0.088 0.372
#> GSM537359 4 0.1701 0.62029 0.000 0.016 0.012 0.944 0.028
#> GSM537360 5 0.5990 0.27849 0.264 0.072 0.000 0.040 0.624
#> GSM537364 4 0.4934 0.60093 0.188 0.000 0.000 0.708 0.104
#> GSM537365 5 0.8189 -0.04426 0.252 0.000 0.116 0.268 0.364
#> GSM537372 3 0.5368 -0.00440 0.036 0.000 0.480 0.476 0.008
#> GSM537384 1 0.5170 0.13955 0.512 0.012 0.456 0.000 0.020
#> GSM537394 3 0.5757 0.34779 0.000 0.088 0.496 0.000 0.416
#> GSM537403 4 0.4605 0.62814 0.048 0.020 0.000 0.756 0.176
#> GSM537406 4 0.4492 0.59908 0.004 0.056 0.000 0.744 0.196
#> GSM537411 3 0.4507 0.38761 0.000 0.004 0.644 0.340 0.012
#> GSM537412 4 0.6761 0.50695 0.228 0.096 0.000 0.588 0.088
#> GSM537416 1 0.3705 0.68645 0.816 0.064 0.000 0.000 0.120
#> GSM537426 5 0.5864 0.29452 0.236 0.164 0.000 0.000 0.600
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM537341 2 0.530 0.4176 0.024 0.736 0.040 0.020 0.068 0.112
#> GSM537345 5 0.204 0.5776 0.004 0.000 0.016 0.072 0.908 0.000
#> GSM537355 1 0.624 0.2084 0.488 0.260 0.008 0.236 0.008 0.000
#> GSM537366 1 0.655 0.1286 0.388 0.000 0.024 0.324 0.264 0.000
#> GSM537370 2 0.748 -0.0580 0.004 0.448 0.240 0.016 0.104 0.188
#> GSM537380 2 0.168 0.5952 0.000 0.940 0.004 0.020 0.012 0.024
#> GSM537392 2 0.219 0.6423 0.004 0.892 0.004 0.096 0.000 0.004
#> GSM537415 4 0.376 0.4820 0.000 0.036 0.016 0.784 0.164 0.000
#> GSM537417 1 0.455 0.5152 0.740 0.000 0.004 0.144 0.016 0.096
#> GSM537422 4 0.545 -0.0252 0.052 0.000 0.032 0.496 0.420 0.000
#> GSM537423 2 0.546 0.5350 0.068 0.600 0.040 0.292 0.000 0.000
#> GSM537427 2 0.223 0.6423 0.000 0.872 0.000 0.124 0.000 0.004
#> GSM537430 6 0.259 0.6107 0.016 0.024 0.024 0.024 0.008 0.904
#> GSM537336 5 0.385 0.5477 0.016 0.000 0.044 0.160 0.780 0.000
#> GSM537337 2 0.532 0.3454 0.000 0.508 0.012 0.408 0.072 0.000
#> GSM537348 5 0.604 0.3160 0.008 0.104 0.024 0.024 0.620 0.220
#> GSM537349 2 0.312 0.6400 0.020 0.816 0.004 0.160 0.000 0.000
#> GSM537356 5 0.423 0.4276 0.224 0.012 0.004 0.016 0.732 0.012
#> GSM537361 1 0.497 0.2468 0.596 0.000 0.000 0.020 0.044 0.340
#> GSM537374 6 0.173 0.6143 0.008 0.000 0.064 0.000 0.004 0.924
#> GSM537377 6 0.670 0.2373 0.052 0.000 0.024 0.112 0.316 0.496
#> GSM537378 4 0.541 0.2318 0.000 0.184 0.016 0.672 0.104 0.024
#> GSM537379 6 0.505 0.4808 0.000 0.004 0.024 0.228 0.072 0.672
#> GSM537383 2 0.663 0.2652 0.004 0.464 0.004 0.212 0.028 0.288
#> GSM537388 2 0.468 0.5172 0.008 0.724 0.192 0.040 0.000 0.036
#> GSM537395 2 0.361 0.5883 0.004 0.708 0.004 0.284 0.000 0.000
#> GSM537400 6 0.438 0.4108 0.000 0.024 0.368 0.004 0.000 0.604
#> GSM537404 1 0.375 0.5151 0.816 0.000 0.004 0.024 0.060 0.096
#> GSM537409 4 0.292 0.4841 0.000 0.096 0.024 0.860 0.020 0.000
#> GSM537418 5 0.435 0.1752 0.000 0.000 0.024 0.420 0.556 0.000
#> GSM537425 5 0.491 0.4079 0.036 0.000 0.016 0.312 0.628 0.008
#> GSM537333 6 0.210 0.5974 0.000 0.004 0.012 0.052 0.016 0.916
#> GSM537342 5 0.743 0.0306 0.328 0.020 0.100 0.156 0.396 0.000
#> GSM537347 6 0.475 0.5082 0.168 0.012 0.008 0.044 0.028 0.740
#> GSM537350 1 0.536 0.4535 0.624 0.032 0.284 0.012 0.048 0.000
#> GSM537362 6 0.368 0.6129 0.008 0.104 0.024 0.008 0.028 0.828
#> GSM537363 5 0.595 0.3716 0.244 0.000 0.032 0.156 0.568 0.000
#> GSM537368 1 0.668 0.1847 0.452 0.000 0.060 0.176 0.312 0.000
#> GSM537376 6 0.562 0.5079 0.024 0.012 0.052 0.100 0.104 0.708
#> GSM537381 3 0.625 0.0647 0.000 0.000 0.508 0.056 0.116 0.320
#> GSM537386 6 0.531 0.5292 0.004 0.264 0.108 0.008 0.000 0.616
#> GSM537398 6 0.338 0.6169 0.004 0.080 0.044 0.016 0.008 0.848
#> GSM537402 2 0.432 0.2426 0.384 0.596 0.012 0.004 0.004 0.000
#> GSM537405 1 0.292 0.5369 0.860 0.000 0.008 0.012 0.104 0.016
#> GSM537371 5 0.282 0.5737 0.016 0.000 0.004 0.124 0.852 0.004
#> GSM537421 4 0.414 0.3467 0.004 0.000 0.032 0.692 0.272 0.000
#> GSM537424 5 0.682 0.0761 0.008 0.000 0.032 0.356 0.376 0.228
#> GSM537432 3 0.277 0.5072 0.000 0.000 0.816 0.000 0.004 0.180
#> GSM537331 6 0.546 0.4873 0.004 0.324 0.100 0.008 0.000 0.564
#> GSM537332 3 0.336 0.4988 0.000 0.000 0.780 0.024 0.000 0.196
#> GSM537334 6 0.251 0.6222 0.000 0.092 0.020 0.008 0.000 0.880
#> GSM537338 6 0.603 0.5272 0.008 0.232 0.120 0.016 0.020 0.604
#> GSM537353 2 0.717 -0.0450 0.148 0.432 0.000 0.152 0.268 0.000
#> GSM537357 5 0.349 0.5160 0.000 0.000 0.020 0.224 0.756 0.000
#> GSM537358 1 0.557 -0.0282 0.468 0.428 0.088 0.016 0.000 0.000
#> GSM537375 6 0.550 0.4421 0.000 0.012 0.016 0.216 0.116 0.640
#> GSM537389 2 0.387 0.5721 0.000 0.688 0.004 0.296 0.012 0.000
#> GSM537390 4 0.409 -0.3083 0.000 0.464 0.000 0.528 0.000 0.008
#> GSM537393 4 0.411 0.3378 0.000 0.196 0.008 0.752 0.032 0.012
#> GSM537399 6 0.337 0.5080 0.000 0.000 0.292 0.000 0.000 0.708
#> GSM537407 1 0.399 -0.1344 0.532 0.000 0.000 0.004 0.000 0.464
#> GSM537408 1 0.491 -0.0463 0.496 0.460 0.028 0.012 0.004 0.000
#> GSM537428 1 0.519 0.4563 0.636 0.004 0.284 0.048 0.020 0.008
#> GSM537354 4 0.423 0.4842 0.004 0.076 0.008 0.756 0.156 0.000
#> GSM537410 1 0.559 0.4566 0.628 0.000 0.040 0.216 0.116 0.000
#> GSM537413 2 0.335 0.6117 0.000 0.748 0.008 0.244 0.000 0.000
#> GSM537396 3 0.170 0.5957 0.004 0.040 0.936 0.012 0.000 0.008
#> GSM537397 2 0.582 0.3283 0.008 0.648 0.208 0.016 0.080 0.040
#> GSM537330 6 0.584 0.5090 0.004 0.040 0.080 0.244 0.012 0.620
#> GSM537369 5 0.155 0.5633 0.004 0.000 0.020 0.036 0.940 0.000
#> GSM537373 5 0.550 0.4343 0.072 0.052 0.004 0.228 0.644 0.000
#> GSM537401 6 0.717 0.4064 0.020 0.304 0.084 0.016 0.092 0.484
#> GSM537343 5 0.353 0.5505 0.032 0.000 0.000 0.180 0.784 0.004
#> GSM537367 1 0.618 0.3277 0.512 0.000 0.024 0.256 0.208 0.000
#> GSM537382 4 0.845 -0.1318 0.000 0.164 0.116 0.368 0.132 0.220
#> GSM537385 2 0.401 0.6322 0.076 0.772 0.004 0.144 0.000 0.004
#> GSM537391 3 0.404 0.5712 0.028 0.064 0.820 0.004 0.032 0.052
#> GSM537419 2 0.524 0.3664 0.332 0.580 0.008 0.076 0.000 0.004
#> GSM537420 1 0.343 0.4962 0.764 0.000 0.216 0.000 0.020 0.000
#> GSM537429 3 0.356 0.5557 0.000 0.076 0.824 0.012 0.004 0.084
#> GSM537431 6 0.469 0.5359 0.100 0.000 0.196 0.000 0.008 0.696
#> GSM537387 5 0.474 0.4891 0.004 0.120 0.044 0.032 0.764 0.036
#> GSM537414 4 0.409 0.2348 0.000 0.004 0.012 0.632 0.352 0.000
#> GSM537433 1 0.642 0.2582 0.464 0.000 0.028 0.276 0.232 0.000
#> GSM537335 6 0.550 0.4869 0.004 0.324 0.104 0.008 0.000 0.560
#> GSM537339 5 0.672 0.1788 0.004 0.292 0.028 0.024 0.500 0.152
#> GSM537340 4 0.637 -0.0135 0.264 0.000 0.016 0.424 0.296 0.000
#> GSM537344 5 0.176 0.5760 0.052 0.000 0.008 0.012 0.928 0.000
#> GSM537346 6 0.742 0.2397 0.004 0.228 0.280 0.096 0.004 0.388
#> GSM537351 3 0.564 0.3399 0.060 0.000 0.644 0.180 0.116 0.000
#> GSM537352 5 0.717 0.2624 0.084 0.020 0.248 0.168 0.480 0.000
#> GSM537359 1 0.171 0.5288 0.932 0.024 0.040 0.000 0.004 0.000
#> GSM537360 4 0.516 0.2076 0.004 0.008 0.352 0.572 0.064 0.000
#> GSM537364 1 0.605 0.3950 0.584 0.000 0.088 0.088 0.240 0.000
#> GSM537365 3 0.880 -0.2058 0.204 0.000 0.248 0.212 0.228 0.108
#> GSM537372 1 0.627 0.2170 0.456 0.008 0.000 0.004 0.256 0.276
#> GSM537384 5 0.581 0.1020 0.000 0.000 0.020 0.124 0.524 0.332
#> GSM537394 6 0.600 0.2374 0.000 0.176 0.384 0.008 0.000 0.432
#> GSM537403 1 0.530 0.5248 0.680 0.000 0.100 0.164 0.056 0.000
#> GSM537406 1 0.471 0.5026 0.708 0.036 0.216 0.032 0.008 0.000
#> GSM537411 6 0.471 0.3338 0.396 0.024 0.004 0.004 0.004 0.568
#> GSM537412 4 0.607 0.0660 0.328 0.016 0.020 0.528 0.108 0.000
#> GSM537416 4 0.362 0.4334 0.000 0.012 0.008 0.748 0.232 0.000
#> GSM537426 3 0.531 0.1969 0.000 0.064 0.548 0.368 0.020 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) other(p) k
#> ATC:NMF 97 0.27444 0.5000 2
#> ATC:NMF 74 0.66041 0.5695 3
#> ATC:NMF 68 0.01695 0.0502 4
#> ATC:NMF 61 0.02573 0.0623 5
#> ATC:NMF 40 0.00311 0.0251 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