Date: 2019-12-25 21:25:40 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 154 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 154
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| CV:skmeans | 2 | 1.000 | 0.998 | 0.999 | ** | |
| CV:mclust | 2 | 1.000 | 0.995 | 0.991 | ** | |
| MAD:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:skmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.996 | 0.996 | ** | |
| ATC:pam | 2 | 1.000 | 0.999 | 0.999 | ** | |
| ATC:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:NMF | 2 | 1.000 | 0.977 | 0.988 | ** | |
| SD:skmeans | 3 | 0.967 | 0.924 | 0.958 | ** | |
| MAD:pam | 2 | 0.945 | 0.942 | 0.976 | * | |
| SD:NMF | 4 | 0.935 | 0.913 | 0.961 | * | |
| ATC:skmeans | 6 | 0.912 | 0.890 | 0.920 | * | 2,5 |
| SD:mclust | 5 | 0.904 | 0.879 | 0.935 | * | 2 |
| SD:kmeans | 5 | 0.891 | 0.841 | 0.907 | ||
| CV:kmeans | 2 | 0.865 | 0.949 | 0.952 | ||
| SD:hclust | 4 | 0.488 | 0.522 | 0.689 | ||
| SD:pam | 2 | 0.457 | 0.786 | 0.896 | ||
| CV:hclust | 2 | 0.314 | 0.848 | 0.766 | ||
| CV:pam | 2 | 0.071 | 0.558 | 0.780 |
**: 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 0.672 0.814 0.919 0.492 0.501 0.501
#> CV:NMF 2 1.000 0.977 0.988 0.501 0.500 0.500
#> MAD:NMF 2 1.000 1.000 1.000 0.501 0.500 0.500
#> ATC:NMF 2 1.000 1.000 1.000 0.501 0.500 0.500
#> SD:skmeans 2 0.471 0.609 0.782 0.497 0.500 0.500
#> CV:skmeans 2 1.000 0.998 0.999 0.501 0.500 0.500
#> MAD:skmeans 2 1.000 1.000 1.000 0.501 0.500 0.500
#> ATC:skmeans 2 1.000 1.000 1.000 0.501 0.500 0.500
#> SD:mclust 2 1.000 1.000 1.000 0.501 0.500 0.500
#> CV:mclust 2 1.000 0.995 0.991 0.495 0.500 0.500
#> MAD:mclust 2 1.000 1.000 1.000 0.501 0.500 0.500
#> ATC:mclust 2 1.000 1.000 1.000 0.501 0.500 0.500
#> SD:kmeans 2 0.495 0.491 0.792 0.490 0.499 0.499
#> CV:kmeans 2 0.865 0.949 0.952 0.498 0.500 0.500
#> MAD:kmeans 2 1.000 1.000 1.000 0.501 0.500 0.500
#> ATC:kmeans 2 1.000 0.996 0.996 0.501 0.500 0.500
#> SD:pam 2 0.457 0.786 0.896 0.491 0.511 0.511
#> CV:pam 2 0.071 0.558 0.780 0.489 0.500 0.500
#> MAD:pam 2 0.945 0.942 0.976 0.503 0.497 0.497
#> ATC:pam 2 1.000 0.999 0.999 0.501 0.500 0.500
#> SD:hclust 2 0.114 0.437 0.644 0.416 0.502 0.502
#> CV:hclust 2 0.314 0.848 0.766 0.335 0.499 0.499
#> MAD:hclust 2 1.000 1.000 1.000 0.501 0.500 0.500
#> ATC:hclust 2 1.000 1.000 1.000 0.501 0.500 0.500
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.655 0.857 0.904 0.323 0.672 0.443
#> CV:NMF 3 0.617 0.691 0.826 0.290 0.827 0.666
#> MAD:NMF 3 0.774 0.856 0.935 0.271 0.806 0.634
#> ATC:NMF 3 0.778 0.841 0.907 0.243 0.857 0.719
#> SD:skmeans 3 0.967 0.924 0.958 0.349 0.733 0.513
#> CV:skmeans 3 0.526 0.712 0.814 0.304 0.855 0.710
#> MAD:skmeans 3 0.898 0.907 0.947 0.287 0.854 0.709
#> ATC:skmeans 3 0.875 0.849 0.917 0.281 0.860 0.720
#> SD:mclust 3 0.714 0.815 0.858 0.263 0.860 0.720
#> CV:mclust 3 0.855 0.882 0.928 0.264 0.874 0.748
#> MAD:mclust 3 0.797 0.891 0.908 0.267 0.856 0.711
#> ATC:mclust 3 0.747 0.884 0.863 0.209 0.894 0.787
#> SD:kmeans 3 0.485 0.533 0.689 0.340 0.613 0.371
#> CV:kmeans 3 0.636 0.717 0.807 0.256 0.870 0.744
#> MAD:kmeans 3 0.696 0.667 0.773 0.250 0.880 0.760
#> ATC:kmeans 3 0.657 0.735 0.765 0.248 0.886 0.772
#> SD:pam 3 0.615 0.801 0.883 0.356 0.770 0.574
#> CV:pam 3 0.231 0.565 0.753 0.321 0.718 0.493
#> MAD:pam 3 0.666 0.744 0.851 0.263 0.849 0.700
#> ATC:pam 3 0.653 0.636 0.744 0.233 0.901 0.801
#> SD:hclust 3 0.307 0.285 0.555 0.503 0.620 0.379
#> CV:hclust 3 0.160 0.791 0.787 0.414 0.981 0.963
#> MAD:hclust 3 0.780 0.752 0.876 0.228 0.905 0.809
#> ATC:hclust 3 0.772 0.703 0.829 0.270 0.856 0.711
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.935 0.913 0.961 0.1129 0.880 0.681
#> CV:NMF 4 0.806 0.822 0.921 0.1478 0.813 0.535
#> MAD:NMF 4 0.607 0.622 0.777 0.1373 0.807 0.535
#> ATC:NMF 4 0.699 0.685 0.856 0.1486 0.870 0.672
#> SD:skmeans 4 0.846 0.867 0.930 0.1196 0.851 0.590
#> CV:skmeans 4 0.501 0.592 0.763 0.1476 0.828 0.557
#> MAD:skmeans 4 0.714 0.763 0.877 0.1562 0.884 0.679
#> ATC:skmeans 4 0.743 0.832 0.823 0.1138 0.917 0.779
#> SD:mclust 4 0.751 0.844 0.893 0.1604 0.809 0.525
#> CV:mclust 4 0.665 0.743 0.778 0.1297 0.839 0.591
#> MAD:mclust 4 0.676 0.809 0.824 0.0881 0.934 0.820
#> ATC:mclust 4 0.617 0.670 0.781 0.1236 0.860 0.676
#> SD:kmeans 4 0.665 0.686 0.808 0.1301 0.786 0.478
#> CV:kmeans 4 0.617 0.692 0.787 0.1456 0.854 0.634
#> MAD:kmeans 4 0.604 0.726 0.760 0.1249 0.826 0.568
#> ATC:kmeans 4 0.606 0.601 0.642 0.1205 0.865 0.658
#> SD:pam 4 0.751 0.766 0.893 0.1264 0.846 0.588
#> CV:pam 4 0.300 0.423 0.671 0.1186 0.906 0.730
#> MAD:pam 4 0.556 0.624 0.787 0.1488 0.902 0.735
#> ATC:pam 4 0.699 0.768 0.867 0.1824 0.796 0.536
#> SD:hclust 4 0.488 0.522 0.689 0.1326 0.725 0.380
#> CV:hclust 4 0.148 0.734 0.779 0.1600 0.950 0.899
#> MAD:hclust 4 0.608 0.519 0.778 0.1236 0.892 0.741
#> ATC:hclust 4 0.854 0.795 0.884 0.0811 0.908 0.757
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.692 0.668 0.815 0.0763 0.892 0.657
#> CV:NMF 5 0.644 0.618 0.787 0.0594 0.924 0.720
#> MAD:NMF 5 0.617 0.669 0.810 0.0815 0.841 0.516
#> ATC:NMF 5 0.651 0.479 0.735 0.0829 0.842 0.538
#> SD:skmeans 5 0.839 0.814 0.902 0.0630 0.898 0.632
#> CV:skmeans 5 0.506 0.524 0.678 0.0671 0.929 0.731
#> MAD:skmeans 5 0.653 0.566 0.770 0.0630 0.929 0.736
#> ATC:skmeans 5 0.906 0.929 0.945 0.1149 0.888 0.638
#> SD:mclust 5 0.904 0.879 0.935 0.0787 0.928 0.726
#> CV:mclust 5 0.761 0.757 0.863 0.0774 0.938 0.778
#> MAD:mclust 5 0.667 0.807 0.848 0.1230 0.880 0.626
#> ATC:mclust 5 0.727 0.727 0.807 0.1076 0.861 0.603
#> SD:kmeans 5 0.891 0.841 0.907 0.0724 0.886 0.603
#> CV:kmeans 5 0.631 0.581 0.742 0.0796 0.904 0.663
#> MAD:kmeans 5 0.574 0.746 0.759 0.0754 0.931 0.742
#> ATC:kmeans 5 0.586 0.778 0.750 0.0849 0.889 0.618
#> SD:pam 5 0.691 0.606 0.785 0.0596 0.925 0.723
#> CV:pam 5 0.375 0.375 0.608 0.0695 0.925 0.742
#> MAD:pam 5 0.621 0.609 0.786 0.0739 0.887 0.633
#> ATC:pam 5 0.806 0.856 0.916 0.0900 0.900 0.654
#> SD:hclust 5 0.619 0.476 0.660 0.0712 0.892 0.659
#> CV:hclust 5 0.167 0.722 0.772 0.0740 0.978 0.950
#> MAD:hclust 5 0.612 0.690 0.804 0.0916 0.880 0.649
#> ATC:hclust 5 0.739 0.683 0.820 0.0833 0.925 0.757
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.653 0.542 0.722 0.0453 0.939 0.757
#> CV:NMF 6 0.625 0.518 0.723 0.0416 0.939 0.739
#> MAD:NMF 6 0.613 0.560 0.758 0.0413 0.899 0.607
#> ATC:NMF 6 0.799 0.760 0.826 0.0549 0.876 0.547
#> SD:skmeans 6 0.772 0.735 0.834 0.0365 0.966 0.837
#> CV:skmeans 6 0.510 0.407 0.596 0.0404 0.958 0.807
#> MAD:skmeans 6 0.640 0.572 0.718 0.0387 0.927 0.691
#> ATC:skmeans 6 0.912 0.890 0.920 0.0397 0.954 0.778
#> SD:mclust 6 0.866 0.862 0.914 0.0417 0.963 0.819
#> CV:mclust 6 0.870 0.843 0.927 0.0577 0.921 0.694
#> MAD:mclust 6 0.682 0.686 0.757 0.0325 0.949 0.790
#> ATC:mclust 6 0.888 0.883 0.923 0.0554 0.941 0.764
#> SD:kmeans 6 0.843 0.793 0.874 0.0390 0.964 0.825
#> CV:kmeans 6 0.669 0.632 0.751 0.0452 0.920 0.658
#> MAD:kmeans 6 0.677 0.601 0.707 0.0572 0.970 0.875
#> ATC:kmeans 6 0.638 0.779 0.779 0.0588 0.959 0.802
#> SD:pam 6 0.728 0.636 0.797 0.0428 0.893 0.558
#> CV:pam 6 0.453 0.411 0.626 0.0404 0.888 0.582
#> MAD:pam 6 0.654 0.582 0.742 0.0397 0.944 0.761
#> ATC:pam 6 0.813 0.799 0.893 0.0467 0.943 0.734
#> SD:hclust 6 0.665 0.630 0.767 0.0526 0.904 0.648
#> CV:hclust 6 0.267 0.614 0.753 0.0633 0.971 0.932
#> MAD:hclust 6 0.654 0.627 0.779 0.0432 0.988 0.950
#> ATC:hclust 6 0.703 0.639 0.784 0.0406 0.979 0.912
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 genotype/variation(p) disease.state(p) k
#> SD:NMF 137 0.00558 0.416 2
#> CV:NMF 154 0.92460 0.476 2
#> MAD:NMF 154 0.92460 0.476 2
#> ATC:NMF 154 0.92460 0.476 2
#> SD:skmeans 130 0.33296 0.674 2
#> CV:skmeans 154 0.92460 0.476 2
#> MAD:skmeans 154 0.92460 0.476 2
#> ATC:skmeans 154 0.92460 0.476 2
#> SD:mclust 154 0.92460 0.476 2
#> CV:mclust 154 0.92460 0.476 2
#> MAD:mclust 154 0.92460 0.476 2
#> ATC:mclust 154 0.92460 0.476 2
#> SD:kmeans 89 0.03230 0.900 2
#> CV:kmeans 154 0.92460 0.476 2
#> MAD:kmeans 154 0.92460 0.476 2
#> ATC:kmeans 154 0.92460 0.476 2
#> SD:pam 140 0.00260 0.470 2
#> CV:pam 108 0.43667 0.865 2
#> MAD:pam 148 0.87248 0.432 2
#> ATC:pam 154 0.92460 0.476 2
#> SD:hclust 83 0.05127 NA 2
#> CV:hclust 148 0.97895 0.393 2
#> MAD:hclust 154 0.92460 0.476 2
#> ATC:hclust 154 0.92460 0.476 2
test_to_known_factors(res_list, k = 3)
#> n genotype/variation(p) disease.state(p) k
#> SD:NMF 147 0.1666 5.65e-01 3
#> CV:NMF 131 0.8680 7.58e-05 3
#> MAD:NMF 146 0.8759 8.25e-01 3
#> ATC:NMF 144 0.4620 7.58e-01 3
#> SD:skmeans 150 0.0552 4.40e-01 3
#> CV:skmeans 143 0.0820 3.09e-01 3
#> MAD:skmeans 149 0.3667 2.17e-01 3
#> ATC:skmeans 152 0.9963 9.42e-02 3
#> SD:mclust 136 0.0629 1.95e-01 3
#> CV:mclust 146 0.3128 4.44e-01 3
#> MAD:mclust 152 0.1677 1.85e-01 3
#> ATC:mclust 149 0.3466 9.16e-01 3
#> SD:kmeans 101 0.0713 3.53e-01 3
#> CV:kmeans 127 0.9967 4.83e-01 3
#> MAD:kmeans 138 0.1671 1.61e-01 3
#> ATC:kmeans 140 0.8500 1.85e-02 3
#> SD:pam 144 0.0752 3.13e-01 3
#> CV:pam 110 0.8572 4.37e-01 3
#> MAD:pam 134 0.0584 1.34e-01 3
#> ATC:pam 135 0.5681 9.58e-01 3
#> SD:hclust 56 0.6957 1.00e+00 3
#> CV:hclust 138 0.9639 4.48e-01 3
#> MAD:hclust 132 0.2015 4.87e-01 3
#> ATC:hclust 112 0.7485 3.10e-02 3
test_to_known_factors(res_list, k = 4)
#> n genotype/variation(p) disease.state(p) k
#> SD:NMF 149 0.38910 0.1018 4
#> CV:NMF 141 0.42002 0.2630 4
#> MAD:NMF 121 0.23638 0.5983 4
#> ATC:NMF 125 0.47299 0.5238 4
#> SD:skmeans 145 0.09311 0.2428 4
#> CV:skmeans 112 0.20357 0.6120 4
#> MAD:skmeans 136 0.42974 0.5596 4
#> ATC:skmeans 151 0.45997 0.0471 4
#> SD:mclust 141 0.01074 0.3975 4
#> CV:mclust 142 0.01173 0.7597 4
#> MAD:mclust 146 0.32680 0.0374 4
#> ATC:mclust 133 0.52773 0.1888 4
#> SD:kmeans 128 0.09862 0.2121 4
#> CV:kmeans 130 0.20363 0.6215 4
#> MAD:kmeans 143 0.32881 0.5489 4
#> ATC:kmeans 134 0.57599 0.1615 4
#> SD:pam 136 0.25363 0.2451 4
#> CV:pam 62 0.92553 NA 4
#> MAD:pam 122 0.00435 0.3617 4
#> ATC:pam 148 0.48219 0.2880 4
#> SD:hclust 106 0.10429 0.6135 4
#> CV:hclust 137 0.83830 0.8121 4
#> MAD:hclust 114 0.24414 0.6882 4
#> ATC:hclust 129 0.47179 0.0675 4
test_to_known_factors(res_list, k = 5)
#> n genotype/variation(p) disease.state(p) k
#> SD:NMF 125 0.363100 2.22e-01 5
#> CV:NMF 115 0.000033 2.46e-01 5
#> MAD:NMF 127 0.315244 8.78e-01 5
#> ATC:NMF 77 0.216659 6.90e-01 5
#> SD:skmeans 144 0.031149 9.76e-02 5
#> CV:skmeans 97 0.546038 3.75e-03 5
#> MAD:skmeans 102 0.574004 1.05e-05 5
#> ATC:skmeans 151 0.387587 1.79e-01 5
#> SD:mclust 150 0.101739 3.78e-01 5
#> CV:mclust 139 0.035663 9.54e-01 5
#> MAD:mclust 144 0.269558 1.80e-01 5
#> ATC:mclust 136 0.566245 8.88e-02 5
#> SD:kmeans 145 0.092081 1.02e-01 5
#> CV:kmeans 109 0.415960 3.86e-03 5
#> MAD:kmeans 142 0.451189 2.01e-01 5
#> ATC:kmeans 146 0.394626 2.12e-01 5
#> SD:pam 102 0.162526 NA 5
#> CV:pam 56 0.707307 NA 5
#> MAD:pam 118 0.103712 2.25e-01 5
#> ATC:pam 150 0.356920 2.59e-01 5
#> SD:hclust 73 0.232362 3.13e-03 5
#> CV:hclust 133 0.854664 8.23e-01 5
#> MAD:hclust 132 0.410501 1.46e-01 5
#> ATC:hclust 121 0.530923 2.06e-01 5
test_to_known_factors(res_list, k = 6)
#> n genotype/variation(p) disease.state(p) k
#> SD:NMF 94 2.45e-01 0.15970 6
#> CV:NMF 96 2.92e-07 0.25041 6
#> MAD:NMF 101 4.89e-01 0.74006 6
#> ATC:NMF 136 8.48e-01 0.69167 6
#> SD:skmeans 132 7.07e-02 0.31154 6
#> CV:skmeans 72 5.31e-01 0.08727 6
#> MAD:skmeans 111 4.10e-01 0.10708 6
#> ATC:skmeans 152 3.83e-01 0.38761 6
#> SD:mclust 148 2.05e-01 0.68607 6
#> CV:mclust 145 2.91e-01 0.07320 6
#> MAD:mclust 136 4.81e-01 0.16483 6
#> ATC:mclust 149 2.12e-01 0.18573 6
#> SD:kmeans 136 1.51e-01 0.33389 6
#> CV:kmeans 123 3.69e-01 0.04280 6
#> MAD:kmeans 120 1.93e-01 0.20842 6
#> ATC:kmeans 144 2.43e-01 0.42046 6
#> SD:pam 121 1.66e-12 0.09597 6
#> CV:pam 76 2.03e-01 NA 6
#> MAD:pam 119 5.39e-02 0.06159 6
#> ATC:pam 147 5.71e-01 0.49968 6
#> SD:hclust 120 1.51e-02 0.02306 6
#> CV:hclust 123 7.26e-01 0.81966 6
#> MAD:hclust 123 7.30e-02 0.42751 6
#> ATC:hclust 122 7.40e-01 0.00823 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.114 0.437 0.644 0.4155 0.502 0.502
#> 3 3 0.307 0.285 0.555 0.5032 0.620 0.379
#> 4 4 0.488 0.522 0.689 0.1326 0.725 0.380
#> 5 5 0.619 0.476 0.660 0.0712 0.892 0.659
#> 6 6 0.665 0.630 0.767 0.0526 0.904 0.648
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
#> GSM564615 1 0.278 0.5360 0.952 0.048
#> GSM564616 2 0.998 0.0346 0.472 0.528
#> GSM564617 2 0.998 0.0346 0.472 0.528
#> GSM564618 1 0.996 0.1267 0.536 0.464
#> GSM564619 1 0.913 0.5675 0.672 0.328
#> GSM564620 1 0.900 0.5642 0.684 0.316
#> GSM564621 1 0.866 0.5795 0.712 0.288
#> GSM564622 2 0.966 0.2441 0.392 0.608
#> GSM564623 1 0.991 0.1740 0.556 0.444
#> GSM564624 2 1.000 -0.0462 0.500 0.500
#> GSM564625 1 0.900 0.5642 0.684 0.316
#> GSM564626 1 0.921 0.5602 0.664 0.336
#> GSM564627 1 0.814 0.5961 0.748 0.252
#> GSM564628 2 0.998 0.0346 0.472 0.528
#> GSM564629 1 0.900 0.5637 0.684 0.316
#> GSM564630 2 0.998 0.0346 0.472 0.528
#> GSM564609 2 0.634 0.5469 0.160 0.840
#> GSM564610 1 0.925 0.5561 0.660 0.340
#> GSM564611 1 0.946 0.5233 0.636 0.364
#> GSM564612 2 0.163 0.5833 0.024 0.976
#> GSM564613 2 0.722 0.5160 0.200 0.800
#> GSM564614 1 0.311 0.5236 0.944 0.056
#> GSM564631 2 0.141 0.5813 0.020 0.980
#> GSM564632 2 0.866 0.4296 0.288 0.712
#> GSM564633 2 0.224 0.5848 0.036 0.964
#> GSM564634 2 0.767 0.5045 0.224 0.776
#> GSM564635 2 0.141 0.5813 0.020 0.980
#> GSM564636 2 0.343 0.5862 0.064 0.936
#> GSM564637 1 0.997 0.1225 0.532 0.468
#> GSM564638 2 0.343 0.5862 0.064 0.936
#> GSM564639 2 0.163 0.5833 0.024 0.976
#> GSM564640 2 0.909 0.3793 0.324 0.676
#> GSM564641 2 0.242 0.5861 0.040 0.960
#> GSM564642 2 0.904 0.3837 0.320 0.680
#> GSM564643 2 0.730 0.5208 0.204 0.796
#> GSM564644 2 0.909 0.3793 0.324 0.676
#> GSM564645 2 0.141 0.5813 0.020 0.980
#> GSM564647 2 0.388 0.5844 0.076 0.924
#> GSM564648 2 0.900 0.3890 0.316 0.684
#> GSM564649 2 0.141 0.5813 0.020 0.980
#> GSM564650 1 0.999 0.0913 0.520 0.480
#> GSM564651 2 0.886 0.4053 0.304 0.696
#> GSM564652 2 0.909 0.3793 0.324 0.676
#> GSM564653 2 0.909 0.3793 0.324 0.676
#> GSM564654 2 0.184 0.5847 0.028 0.972
#> GSM564655 2 0.966 0.2513 0.392 0.608
#> GSM564656 2 0.141 0.5813 0.020 0.980
#> GSM564657 2 0.163 0.5833 0.024 0.976
#> GSM564658 2 0.909 0.3793 0.324 0.676
#> GSM564659 2 0.494 0.5780 0.108 0.892
#> GSM564660 1 0.985 0.2106 0.572 0.428
#> GSM564661 2 0.909 0.3793 0.324 0.676
#> GSM564662 2 0.141 0.5813 0.020 0.980
#> GSM564663 2 0.904 0.3837 0.320 0.680
#> GSM564664 2 0.909 0.3793 0.324 0.676
#> GSM564665 2 0.605 0.5591 0.148 0.852
#> GSM564666 1 0.973 0.2586 0.596 0.404
#> GSM564667 2 0.141 0.5813 0.020 0.980
#> GSM564668 2 0.653 0.5429 0.168 0.832
#> GSM564669 2 0.184 0.5848 0.028 0.972
#> GSM564670 2 0.584 0.5643 0.140 0.860
#> GSM564671 1 0.827 0.3957 0.740 0.260
#> GSM564672 2 0.141 0.5813 0.020 0.980
#> GSM564673 2 0.909 0.3793 0.324 0.676
#> GSM564674 2 0.904 0.3837 0.320 0.680
#> GSM564675 1 0.969 0.2853 0.604 0.396
#> GSM564676 2 0.909 0.3793 0.324 0.676
#> GSM564677 2 0.921 0.3592 0.336 0.664
#> GSM564678 2 0.909 0.3793 0.324 0.676
#> GSM564679 2 0.909 0.3793 0.324 0.676
#> GSM564680 2 0.184 0.5848 0.028 0.972
#> GSM564682 2 0.242 0.5861 0.040 0.960
#> GSM564683 2 0.141 0.5813 0.020 0.980
#> GSM564684 1 0.827 0.3955 0.740 0.260
#> GSM564685 2 0.141 0.5813 0.020 0.980
#> GSM564686 1 0.844 0.3899 0.728 0.272
#> GSM564687 2 0.909 0.3793 0.324 0.676
#> GSM564688 2 0.909 0.3793 0.324 0.676
#> GSM564689 1 1.000 0.0669 0.512 0.488
#> GSM564690 2 0.913 0.3736 0.328 0.672
#> GSM564691 2 0.373 0.5865 0.072 0.928
#> GSM564692 2 0.886 0.4061 0.304 0.696
#> GSM564694 2 0.987 0.1318 0.432 0.568
#> GSM564695 1 0.993 0.1610 0.548 0.452
#> GSM564696 2 0.469 0.5755 0.100 0.900
#> GSM564697 2 0.991 0.1159 0.444 0.556
#> GSM564698 2 0.295 0.5864 0.052 0.948
#> GSM564700 1 0.844 0.3899 0.728 0.272
#> GSM564701 2 0.909 0.3793 0.324 0.676
#> GSM564702 2 0.917 0.3682 0.332 0.668
#> GSM564703 2 0.775 0.3970 0.228 0.772
#> GSM564704 1 0.760 0.5925 0.780 0.220
#> GSM564705 1 0.946 0.5233 0.636 0.364
#> GSM564706 2 0.788 0.3895 0.236 0.764
#> GSM564707 1 0.943 0.5307 0.640 0.360
#> GSM564708 2 0.788 0.3861 0.236 0.764
#> GSM564709 1 0.625 0.5893 0.844 0.156
#> GSM564710 1 0.943 0.5307 0.640 0.360
#> GSM564711 2 0.917 0.2763 0.332 0.668
#> GSM564712 1 0.943 0.5307 0.640 0.360
#> GSM564713 2 0.833 0.3587 0.264 0.736
#> GSM564714 2 0.850 0.3495 0.276 0.724
#> GSM564715 1 0.943 0.5307 0.640 0.360
#> GSM564716 1 0.913 0.5676 0.672 0.328
#> GSM564717 1 0.946 0.5233 0.636 0.364
#> GSM564718 2 0.996 0.0310 0.464 0.536
#> GSM564719 1 0.946 0.5233 0.636 0.364
#> GSM564720 1 0.946 0.5233 0.636 0.364
#> GSM564721 1 0.929 0.5540 0.656 0.344
#> GSM564722 1 0.949 0.3814 0.632 0.368
#> GSM564723 1 0.943 0.5307 0.640 0.360
#> GSM564724 2 0.983 0.1204 0.424 0.576
#> GSM564725 1 0.909 0.5716 0.676 0.324
#> GSM564726 1 0.456 0.5514 0.904 0.096
#> GSM564727 1 0.529 0.5806 0.880 0.120
#> GSM564728 1 0.343 0.5297 0.936 0.064
#> GSM564729 1 0.278 0.5249 0.952 0.048
#> GSM564730 1 0.891 0.5841 0.692 0.308
#> GSM564731 1 0.936 0.5096 0.648 0.352
#> GSM564732 1 0.482 0.5701 0.896 0.104
#> GSM564733 2 0.998 -0.0512 0.472 0.528
#> GSM564734 1 0.662 0.5954 0.828 0.172
#> GSM564735 1 0.850 0.5086 0.724 0.276
#> GSM564736 2 0.850 0.3461 0.276 0.724
#> GSM564737 1 0.943 0.5307 0.640 0.360
#> GSM564738 2 0.997 0.0427 0.468 0.532
#> GSM564739 2 0.795 0.3892 0.240 0.760
#> GSM564740 1 0.518 0.4998 0.884 0.116
#> GSM564741 2 0.990 0.1051 0.440 0.560
#> GSM564742 2 0.781 0.3972 0.232 0.768
#> GSM564743 1 0.891 0.5841 0.692 0.308
#> GSM564744 1 0.943 0.5307 0.640 0.360
#> GSM564745 1 0.871 0.5910 0.708 0.292
#> GSM564746 1 0.925 0.5467 0.660 0.340
#> GSM564747 1 0.966 0.3182 0.608 0.392
#> GSM564748 2 0.781 0.3972 0.232 0.768
#> GSM564749 1 0.946 0.5233 0.636 0.364
#> GSM564750 1 0.295 0.5301 0.948 0.052
#> GSM564751 2 0.775 0.3970 0.228 0.772
#> GSM564752 1 0.295 0.5301 0.948 0.052
#> GSM564753 2 0.775 0.3970 0.228 0.772
#> GSM564754 1 0.844 0.5924 0.728 0.272
#> GSM564755 1 0.494 0.5043 0.892 0.108
#> GSM564756 1 0.929 0.5490 0.656 0.344
#> GSM564757 1 0.204 0.5280 0.968 0.032
#> GSM564758 1 0.343 0.5263 0.936 0.064
#> GSM564759 2 0.788 0.3895 0.236 0.764
#> GSM564760 1 0.753 0.5971 0.784 0.216
#> GSM564761 1 0.936 0.5421 0.648 0.352
#> GSM564762 1 0.909 0.5707 0.676 0.324
#> GSM564681 1 0.904 0.3679 0.680 0.320
#> GSM564693 2 0.904 0.3841 0.320 0.680
#> GSM564646 1 0.821 0.3950 0.744 0.256
#> GSM564699 1 0.850 0.3865 0.724 0.276
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.5500 0.5531 0.816 0.084 0.100
#> GSM564616 2 0.4912 0.6046 0.196 0.796 0.008
#> GSM564617 2 0.4963 0.6034 0.200 0.792 0.008
#> GSM564618 2 0.6113 0.5207 0.300 0.688 0.012
#> GSM564619 1 0.9964 0.3770 0.356 0.292 0.352
#> GSM564620 1 0.9874 0.4400 0.412 0.304 0.284
#> GSM564621 1 0.9667 0.4737 0.464 0.264 0.272
#> GSM564622 2 0.6271 0.6816 0.140 0.772 0.088
#> GSM564623 2 0.6307 0.4808 0.328 0.660 0.012
#> GSM564624 2 0.5619 0.5764 0.244 0.744 0.012
#> GSM564625 1 0.9874 0.4400 0.412 0.304 0.284
#> GSM564626 3 0.9947 -0.3707 0.336 0.288 0.376
#> GSM564627 1 0.9604 0.4909 0.476 0.256 0.268
#> GSM564628 2 0.4912 0.6046 0.196 0.796 0.008
#> GSM564629 1 0.9889 0.4373 0.408 0.300 0.292
#> GSM564630 2 0.4912 0.6046 0.196 0.796 0.008
#> GSM564609 2 0.5928 0.5023 0.008 0.696 0.296
#> GSM564610 3 0.9942 -0.3596 0.332 0.288 0.380
#> GSM564611 3 0.9877 -0.3132 0.296 0.292 0.412
#> GSM564612 3 0.6299 -0.1102 0.000 0.476 0.524
#> GSM564613 2 0.5551 0.5969 0.020 0.768 0.212
#> GSM564614 1 0.2651 0.5129 0.928 0.012 0.060
#> GSM564631 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564632 2 0.7966 0.5628 0.128 0.652 0.220
#> GSM564633 3 0.6513 -0.1210 0.004 0.476 0.520
#> GSM564634 2 0.6067 0.5743 0.028 0.736 0.236
#> GSM564635 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564636 2 0.7074 0.1643 0.020 0.500 0.480
#> GSM564637 2 0.6570 0.5268 0.308 0.668 0.024
#> GSM564638 2 0.7074 0.1643 0.020 0.500 0.480
#> GSM564639 3 0.6291 -0.0975 0.000 0.468 0.532
#> GSM564640 2 0.0000 0.7378 0.000 1.000 0.000
#> GSM564641 3 0.6302 -0.1313 0.000 0.480 0.520
#> GSM564642 2 0.0237 0.7379 0.000 0.996 0.004
#> GSM564643 2 0.6668 0.5458 0.040 0.696 0.264
#> GSM564644 2 0.0237 0.7373 0.000 0.996 0.004
#> GSM564645 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564647 2 0.6410 0.3009 0.004 0.576 0.420
#> GSM564648 2 0.0829 0.7368 0.004 0.984 0.012
#> GSM564649 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564650 2 0.5325 0.5674 0.248 0.748 0.004
#> GSM564651 2 0.1399 0.7310 0.004 0.968 0.028
#> GSM564652 2 0.0237 0.7372 0.004 0.996 0.000
#> GSM564653 2 0.0000 0.7378 0.000 1.000 0.000
#> GSM564654 3 0.6299 -0.1100 0.000 0.476 0.524
#> GSM564655 2 0.7398 0.6400 0.180 0.700 0.120
#> GSM564656 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564657 3 0.6299 -0.1102 0.000 0.476 0.524
#> GSM564658 2 0.0424 0.7383 0.008 0.992 0.000
#> GSM564659 2 0.6398 0.3818 0.008 0.620 0.372
#> GSM564660 2 0.6298 0.4136 0.388 0.608 0.004
#> GSM564661 2 0.0000 0.7378 0.000 1.000 0.000
#> GSM564662 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564663 2 0.0237 0.7379 0.000 0.996 0.004
#> GSM564664 2 0.0661 0.7348 0.004 0.988 0.008
#> GSM564665 2 0.6912 0.4272 0.028 0.628 0.344
#> GSM564666 2 0.7278 0.2926 0.456 0.516 0.028
#> GSM564667 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564668 2 0.5986 0.5178 0.012 0.704 0.284
#> GSM564669 3 0.6295 -0.1053 0.000 0.472 0.528
#> GSM564670 2 0.6228 0.4690 0.012 0.672 0.316
#> GSM564671 1 0.6442 0.0416 0.564 0.432 0.004
#> GSM564672 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564673 2 0.0237 0.7383 0.004 0.996 0.000
#> GSM564674 2 0.0237 0.7379 0.000 0.996 0.004
#> GSM564675 2 0.6467 0.3947 0.388 0.604 0.008
#> GSM564676 2 0.0661 0.7349 0.004 0.988 0.008
#> GSM564677 2 0.1267 0.7374 0.024 0.972 0.004
#> GSM564678 2 0.0237 0.7373 0.000 0.996 0.004
#> GSM564679 2 0.0237 0.7373 0.000 0.996 0.004
#> GSM564680 3 0.6295 -0.1053 0.000 0.472 0.528
#> GSM564682 3 0.6302 -0.1313 0.000 0.480 0.520
#> GSM564683 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564684 1 0.6451 0.0323 0.560 0.436 0.004
#> GSM564685 3 0.6280 -0.0853 0.000 0.460 0.540
#> GSM564686 1 0.6140 0.0460 0.596 0.404 0.000
#> GSM564687 2 0.0000 0.7378 0.000 1.000 0.000
#> GSM564688 2 0.0000 0.7378 0.000 1.000 0.000
#> GSM564689 2 0.5016 0.5765 0.240 0.760 0.000
#> GSM564690 2 0.0661 0.7361 0.004 0.988 0.008
#> GSM564691 2 0.6267 0.2421 0.000 0.548 0.452
#> GSM564692 2 0.1453 0.7326 0.008 0.968 0.024
#> GSM564694 2 0.7634 0.6158 0.232 0.668 0.100
#> GSM564695 2 0.7207 0.4202 0.384 0.584 0.032
#> GSM564696 2 0.7570 0.3038 0.044 0.552 0.404
#> GSM564697 2 0.4755 0.6693 0.184 0.808 0.008
#> GSM564698 2 0.6518 0.1681 0.004 0.512 0.484
#> GSM564700 1 0.6140 0.0460 0.596 0.404 0.000
#> GSM564701 2 0.0237 0.7376 0.004 0.996 0.000
#> GSM564702 2 0.0747 0.7367 0.016 0.984 0.000
#> GSM564703 3 0.2564 0.3181 0.036 0.028 0.936
#> GSM564704 1 0.9322 0.4812 0.504 0.192 0.304
#> GSM564705 3 0.9863 -0.3104 0.300 0.284 0.416
#> GSM564706 3 0.2681 0.3156 0.040 0.028 0.932
#> GSM564707 3 0.9895 -0.3204 0.312 0.284 0.404
#> GSM564708 3 0.2550 0.3143 0.040 0.024 0.936
#> GSM564709 1 0.8657 0.5240 0.592 0.164 0.244
#> GSM564710 3 0.9885 -0.3151 0.308 0.284 0.408
#> GSM564711 3 0.4748 0.2456 0.144 0.024 0.832
#> GSM564712 3 0.9885 -0.3151 0.308 0.284 0.408
#> GSM564713 3 0.3461 0.2984 0.076 0.024 0.900
#> GSM564714 3 0.3966 0.2791 0.100 0.024 0.876
#> GSM564715 3 0.9885 -0.3151 0.308 0.284 0.408
#> GSM564716 3 0.9908 -0.3758 0.360 0.268 0.372
#> GSM564717 3 0.9863 -0.3104 0.300 0.284 0.416
#> GSM564718 3 0.6451 0.0875 0.292 0.024 0.684
#> GSM564719 3 0.9863 -0.3104 0.300 0.284 0.416
#> GSM564720 3 0.9874 -0.3119 0.304 0.284 0.412
#> GSM564721 3 0.9936 -0.3582 0.336 0.284 0.380
#> GSM564722 3 0.8535 -0.2265 0.404 0.096 0.500
#> GSM564723 3 0.9885 -0.3151 0.308 0.284 0.408
#> GSM564724 3 0.6148 0.1439 0.244 0.028 0.728
#> GSM564725 1 0.9909 0.3598 0.368 0.268 0.364
#> GSM564726 1 0.4931 0.4839 0.784 0.004 0.212
#> GSM564727 1 0.7620 0.5450 0.684 0.128 0.188
#> GSM564728 1 0.3234 0.5216 0.908 0.020 0.072
#> GSM564729 1 0.4469 0.5427 0.864 0.060 0.076
#> GSM564730 1 0.9907 0.3935 0.376 0.268 0.356
#> GSM564731 3 0.9454 -0.3513 0.388 0.180 0.432
#> GSM564732 1 0.7829 0.5513 0.672 0.164 0.164
#> GSM564733 3 0.7694 -0.0355 0.292 0.076 0.632
#> GSM564734 1 0.9083 0.5143 0.548 0.196 0.256
#> GSM564735 1 0.6798 0.3047 0.584 0.016 0.400
#> GSM564736 3 0.3886 0.2862 0.096 0.024 0.880
#> GSM564737 3 0.9885 -0.3151 0.308 0.284 0.408
#> GSM564738 3 0.6473 0.0910 0.312 0.020 0.668
#> GSM564739 3 0.3028 0.3076 0.048 0.032 0.920
#> GSM564740 1 0.3694 0.4919 0.896 0.052 0.052
#> GSM564741 3 0.6195 0.1361 0.276 0.020 0.704
#> GSM564742 3 0.2564 0.3180 0.036 0.028 0.936
#> GSM564743 1 0.9907 0.3935 0.376 0.268 0.356
#> GSM564744 3 0.9885 -0.3151 0.308 0.284 0.408
#> GSM564745 1 0.9787 0.4217 0.412 0.240 0.348
#> GSM564746 1 0.9939 0.4170 0.388 0.300 0.312
#> GSM564747 3 0.8318 -0.1819 0.392 0.084 0.524
#> GSM564748 3 0.2681 0.3171 0.040 0.028 0.932
#> GSM564749 3 0.9863 -0.3104 0.300 0.284 0.416
#> GSM564750 1 0.3918 0.5061 0.856 0.004 0.140
#> GSM564751 3 0.2564 0.3181 0.036 0.028 0.936
#> GSM564752 1 0.3918 0.5061 0.856 0.004 0.140
#> GSM564753 3 0.2443 0.3188 0.032 0.028 0.940
#> GSM564754 1 0.9776 0.4395 0.424 0.244 0.332
#> GSM564755 1 0.4569 0.5031 0.860 0.068 0.072
#> GSM564756 3 0.9914 -0.3417 0.328 0.280 0.392
#> GSM564757 1 0.5181 0.5529 0.832 0.084 0.084
#> GSM564758 1 0.2902 0.5130 0.920 0.016 0.064
#> GSM564759 3 0.2681 0.3156 0.040 0.028 0.932
#> GSM564760 1 0.9248 0.4879 0.516 0.188 0.296
#> GSM564761 3 0.9919 -0.3438 0.324 0.284 0.392
#> GSM564762 1 0.9871 0.3669 0.376 0.256 0.368
#> GSM564681 2 0.6299 0.1589 0.476 0.524 0.000
#> GSM564693 2 0.0237 0.7378 0.000 0.996 0.004
#> GSM564646 1 0.6225 0.0349 0.568 0.432 0.000
#> GSM564699 1 0.6470 0.0916 0.632 0.356 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.5859 0.3988 0.284 0.064 0.000 0.652
#> GSM564616 2 0.7325 0.6510 0.096 0.612 0.244 0.048
#> GSM564617 2 0.7343 0.6505 0.092 0.612 0.244 0.052
#> GSM564618 2 0.7097 0.6090 0.040 0.644 0.200 0.116
#> GSM564619 1 0.2722 0.8192 0.904 0.064 0.000 0.032
#> GSM564620 1 0.4415 0.7553 0.804 0.140 0.000 0.056
#> GSM564621 1 0.5266 0.7257 0.752 0.140 0.000 0.108
#> GSM564622 2 0.6969 0.6015 0.056 0.528 0.388 0.028
#> GSM564623 2 0.7201 0.5942 0.040 0.640 0.180 0.140
#> GSM564624 2 0.7042 0.6377 0.060 0.640 0.232 0.068
#> GSM564625 1 0.4465 0.7529 0.800 0.144 0.000 0.056
#> GSM564626 1 0.1820 0.8334 0.944 0.036 0.000 0.020
#> GSM564627 1 0.5594 0.7028 0.724 0.112 0.000 0.164
#> GSM564628 2 0.7325 0.6510 0.096 0.612 0.244 0.048
#> GSM564629 1 0.4465 0.7512 0.800 0.144 0.000 0.056
#> GSM564630 2 0.7325 0.6510 0.096 0.612 0.244 0.048
#> GSM564609 3 0.4755 0.0944 0.012 0.260 0.724 0.004
#> GSM564610 1 0.1297 0.8374 0.964 0.020 0.000 0.016
#> GSM564611 1 0.0779 0.8339 0.980 0.016 0.004 0.000
#> GSM564612 3 0.0707 0.6006 0.000 0.020 0.980 0.000
#> GSM564613 3 0.5252 -0.1974 0.020 0.336 0.644 0.000
#> GSM564614 4 0.3978 0.6168 0.108 0.056 0.000 0.836
#> GSM564631 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564632 3 0.6523 -0.1734 0.004 0.332 0.584 0.080
#> GSM564633 3 0.1022 0.5930 0.000 0.032 0.968 0.000
#> GSM564634 3 0.5783 -0.1393 0.032 0.324 0.636 0.008
#> GSM564635 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564636 3 0.2255 0.5630 0.000 0.068 0.920 0.012
#> GSM564637 2 0.7733 0.5870 0.036 0.576 0.212 0.176
#> GSM564638 3 0.2255 0.5630 0.000 0.068 0.920 0.012
#> GSM564639 3 0.0592 0.6016 0.000 0.016 0.984 0.000
#> GSM564640 2 0.6834 0.6877 0.100 0.476 0.424 0.000
#> GSM564641 3 0.1822 0.5942 0.004 0.044 0.944 0.008
#> GSM564642 2 0.6792 0.6851 0.096 0.476 0.428 0.000
#> GSM564643 3 0.5520 -0.0481 0.020 0.304 0.664 0.012
#> GSM564644 2 0.6918 0.6871 0.108 0.472 0.420 0.000
#> GSM564645 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564647 3 0.2999 0.4578 0.000 0.132 0.864 0.004
#> GSM564648 2 0.6752 0.6728 0.092 0.468 0.440 0.000
#> GSM564649 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564650 2 0.7690 0.6277 0.068 0.608 0.200 0.124
#> GSM564651 2 0.6660 0.6568 0.084 0.464 0.452 0.000
#> GSM564652 2 0.6878 0.6861 0.104 0.472 0.424 0.000
#> GSM564653 2 0.6741 0.6869 0.092 0.484 0.424 0.000
#> GSM564654 3 0.0921 0.5958 0.000 0.028 0.972 0.000
#> GSM564655 3 0.7843 -0.4675 0.028 0.380 0.464 0.128
#> GSM564656 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564657 3 0.0707 0.6006 0.000 0.020 0.980 0.000
#> GSM564658 2 0.6908 0.6873 0.092 0.480 0.424 0.004
#> GSM564659 3 0.3907 0.3494 0.008 0.180 0.808 0.004
#> GSM564660 2 0.7402 0.4993 0.012 0.564 0.172 0.252
#> GSM564661 2 0.6788 0.6872 0.096 0.480 0.424 0.000
#> GSM564662 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564663 2 0.6792 0.6851 0.096 0.476 0.428 0.000
#> GSM564664 2 0.6957 0.6850 0.112 0.472 0.416 0.000
#> GSM564665 3 0.4479 0.2904 0.008 0.224 0.760 0.008
#> GSM564666 2 0.7459 0.3631 0.004 0.500 0.168 0.328
#> GSM564667 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564668 3 0.5033 0.0490 0.020 0.268 0.708 0.004
#> GSM564669 3 0.0707 0.5995 0.000 0.020 0.980 0.000
#> GSM564670 3 0.4475 0.2006 0.008 0.240 0.748 0.004
#> GSM564671 2 0.6296 0.1083 0.020 0.552 0.028 0.400
#> GSM564672 3 0.0188 0.6061 0.000 0.004 0.996 0.000
#> GSM564673 2 0.6741 0.6869 0.092 0.484 0.424 0.000
#> GSM564674 2 0.6792 0.6851 0.096 0.476 0.428 0.000
#> GSM564675 2 0.7638 0.5248 0.032 0.580 0.168 0.220
#> GSM564676 2 0.7037 0.6815 0.120 0.464 0.416 0.000
#> GSM564677 2 0.7292 0.6855 0.096 0.468 0.420 0.016
#> GSM564678 2 0.6918 0.6871 0.108 0.472 0.420 0.000
#> GSM564679 2 0.6918 0.6871 0.108 0.472 0.420 0.000
#> GSM564680 3 0.0707 0.5995 0.000 0.020 0.980 0.000
#> GSM564682 3 0.1909 0.5921 0.004 0.048 0.940 0.008
#> GSM564683 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564684 2 0.6287 0.1187 0.020 0.556 0.028 0.396
#> GSM564685 3 0.0000 0.6074 0.000 0.000 1.000 0.000
#> GSM564686 2 0.6570 0.0487 0.016 0.504 0.044 0.436
#> GSM564687 2 0.6788 0.6872 0.096 0.480 0.424 0.000
#> GSM564688 2 0.6741 0.6869 0.092 0.484 0.424 0.000
#> GSM564689 2 0.7531 0.6327 0.064 0.620 0.200 0.116
#> GSM564690 2 0.6994 0.6866 0.116 0.472 0.412 0.000
#> GSM564691 3 0.2281 0.5130 0.000 0.096 0.904 0.000
#> GSM564692 2 0.6825 0.6625 0.084 0.464 0.448 0.004
#> GSM564694 2 0.7003 0.5346 0.008 0.536 0.356 0.100
#> GSM564695 2 0.7643 0.4848 0.012 0.532 0.200 0.256
#> GSM564696 3 0.3910 0.4448 0.000 0.156 0.820 0.024
#> GSM564697 2 0.7683 0.6452 0.040 0.532 0.324 0.104
#> GSM564698 3 0.1940 0.5523 0.000 0.076 0.924 0.000
#> GSM564700 2 0.6570 0.0487 0.016 0.504 0.044 0.436
#> GSM564701 2 0.6878 0.6871 0.104 0.472 0.424 0.000
#> GSM564702 2 0.7161 0.6901 0.104 0.476 0.412 0.008
#> GSM564703 3 0.8982 0.1695 0.136 0.240 0.480 0.144
#> GSM564704 1 0.5346 0.6064 0.692 0.032 0.004 0.272
#> GSM564705 1 0.0336 0.8358 0.992 0.008 0.000 0.000
#> GSM564706 3 0.9033 0.1628 0.144 0.236 0.476 0.144
#> GSM564707 1 0.0188 0.8382 0.996 0.004 0.000 0.000
#> GSM564708 3 0.9053 0.1602 0.144 0.240 0.472 0.144
#> GSM564709 1 0.5807 0.4405 0.596 0.040 0.000 0.364
#> GSM564710 1 0.0000 0.8382 1.000 0.000 0.000 0.000
#> GSM564711 3 0.9534 -0.0298 0.140 0.224 0.392 0.244
#> GSM564712 1 0.0000 0.8382 1.000 0.000 0.000 0.000
#> GSM564713 3 0.9238 0.1151 0.168 0.220 0.456 0.156
#> GSM564714 3 0.9413 0.0713 0.148 0.232 0.424 0.196
#> GSM564715 1 0.0000 0.8382 1.000 0.000 0.000 0.000
#> GSM564716 1 0.2197 0.8332 0.928 0.024 0.000 0.048
#> GSM564717 1 0.0469 0.8346 0.988 0.012 0.000 0.000
#> GSM564718 4 0.9670 0.2644 0.160 0.204 0.276 0.360
#> GSM564719 1 0.0469 0.8346 0.988 0.012 0.000 0.000
#> GSM564720 1 0.0188 0.8371 0.996 0.004 0.000 0.000
#> GSM564721 1 0.1584 0.8369 0.952 0.012 0.000 0.036
#> GSM564722 4 0.9442 0.3244 0.292 0.168 0.148 0.392
#> GSM564723 1 0.0000 0.8382 1.000 0.000 0.000 0.000
#> GSM564724 3 0.9789 -0.2491 0.172 0.212 0.308 0.308
#> GSM564725 1 0.2089 0.8356 0.932 0.020 0.000 0.048
#> GSM564726 4 0.4142 0.6334 0.080 0.064 0.012 0.844
#> GSM564727 1 0.6273 0.1545 0.488 0.056 0.000 0.456
#> GSM564728 4 0.4071 0.6263 0.104 0.064 0.000 0.832
#> GSM564729 4 0.5292 0.5100 0.216 0.060 0.000 0.724
#> GSM564730 1 0.2706 0.8172 0.900 0.020 0.000 0.080
#> GSM564731 1 0.7603 0.4455 0.616 0.104 0.076 0.204
#> GSM564732 1 0.6327 0.1900 0.496 0.060 0.000 0.444
#> GSM564733 1 0.8569 0.1357 0.528 0.108 0.224 0.140
#> GSM564734 1 0.5535 0.5529 0.656 0.040 0.000 0.304
#> GSM564735 4 0.7309 0.5638 0.100 0.116 0.124 0.660
#> GSM564736 3 0.9361 0.0773 0.172 0.216 0.440 0.172
#> GSM564737 1 0.0000 0.8382 1.000 0.000 0.000 0.000
#> GSM564738 4 0.9253 0.2536 0.100 0.212 0.284 0.404
#> GSM564739 3 0.9134 0.1407 0.168 0.232 0.464 0.136
#> GSM564740 4 0.4057 0.6168 0.032 0.152 0.000 0.816
#> GSM564741 4 0.9333 0.1941 0.104 0.208 0.316 0.372
#> GSM564742 3 0.8996 0.1701 0.136 0.236 0.480 0.148
#> GSM564743 1 0.2706 0.8172 0.900 0.020 0.000 0.080
#> GSM564744 1 0.0000 0.8382 1.000 0.000 0.000 0.000
#> GSM564745 1 0.3542 0.7900 0.852 0.028 0.000 0.120
#> GSM564746 1 0.3787 0.7786 0.840 0.124 0.000 0.036
#> GSM564747 4 0.9545 0.3109 0.292 0.160 0.172 0.376
#> GSM564748 3 0.9017 0.1661 0.136 0.240 0.476 0.148
#> GSM564749 1 0.0469 0.8346 0.988 0.012 0.000 0.000
#> GSM564750 4 0.3349 0.6462 0.064 0.052 0.004 0.880
#> GSM564751 3 0.8998 0.1710 0.140 0.236 0.480 0.144
#> GSM564752 4 0.3349 0.6462 0.064 0.052 0.004 0.880
#> GSM564753 3 0.8962 0.1733 0.136 0.236 0.484 0.144
#> GSM564754 1 0.3926 0.7522 0.820 0.016 0.004 0.160
#> GSM564755 4 0.4829 0.6127 0.068 0.156 0.000 0.776
#> GSM564756 1 0.0895 0.8388 0.976 0.004 0.000 0.020
#> GSM564757 4 0.5906 0.3844 0.292 0.064 0.000 0.644
#> GSM564758 4 0.3996 0.6240 0.104 0.060 0.000 0.836
#> GSM564759 3 0.9033 0.1628 0.144 0.236 0.476 0.144
#> GSM564760 1 0.5113 0.6368 0.712 0.036 0.000 0.252
#> GSM564761 1 0.1042 0.8379 0.972 0.020 0.000 0.008
#> GSM564762 1 0.3150 0.8142 0.888 0.036 0.004 0.072
#> GSM564681 2 0.7415 0.3134 0.028 0.532 0.096 0.344
#> GSM564693 2 0.6745 0.6845 0.092 0.480 0.428 0.000
#> GSM564646 2 0.6198 0.1111 0.016 0.556 0.028 0.400
#> GSM564699 4 0.6443 -0.0203 0.004 0.468 0.056 0.472
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.4457 0.57035 0.208 0.004 0.000 0.740 0.048
#> GSM564616 2 0.5975 0.43001 0.068 0.668 0.020 0.028 0.216
#> GSM564617 2 0.5918 0.42617 0.064 0.672 0.020 0.028 0.216
#> GSM564618 2 0.6374 0.18183 0.024 0.600 0.024 0.068 0.284
#> GSM564619 1 0.2952 0.81659 0.892 0.028 0.012 0.016 0.052
#> GSM564620 1 0.4871 0.74569 0.784 0.040 0.020 0.048 0.108
#> GSM564621 1 0.5700 0.69562 0.724 0.036 0.020 0.112 0.108
#> GSM564622 2 0.6041 0.51251 0.024 0.668 0.120 0.012 0.176
#> GSM564623 2 0.6596 0.09659 0.020 0.572 0.024 0.088 0.296
#> GSM564624 2 0.5861 0.34388 0.036 0.656 0.024 0.032 0.252
#> GSM564625 1 0.4943 0.74283 0.780 0.044 0.020 0.048 0.108
#> GSM564626 1 0.1975 0.83385 0.936 0.020 0.004 0.016 0.024
#> GSM564627 1 0.5779 0.66260 0.696 0.028 0.012 0.168 0.096
#> GSM564628 2 0.5975 0.43001 0.068 0.668 0.020 0.028 0.216
#> GSM564629 1 0.4871 0.74548 0.784 0.048 0.020 0.040 0.108
#> GSM564630 2 0.5975 0.43001 0.068 0.668 0.020 0.028 0.216
#> GSM564609 2 0.5087 0.16815 0.008 0.588 0.376 0.000 0.028
#> GSM564610 1 0.1617 0.84040 0.948 0.020 0.000 0.012 0.020
#> GSM564611 1 0.1483 0.83610 0.952 0.028 0.008 0.000 0.012
#> GSM564612 3 0.4074 0.47524 0.000 0.364 0.636 0.000 0.000
#> GSM564613 2 0.4936 0.40230 0.008 0.684 0.260 0.000 0.048
#> GSM564614 4 0.1364 0.58945 0.036 0.000 0.000 0.952 0.012
#> GSM564631 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564632 2 0.7033 0.31315 0.000 0.528 0.284 0.068 0.120
#> GSM564633 3 0.4430 0.46953 0.000 0.360 0.628 0.000 0.012
#> GSM564634 2 0.5911 0.37459 0.024 0.616 0.276 0.000 0.084
#> GSM564635 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564636 3 0.5292 0.41792 0.000 0.368 0.580 0.004 0.048
#> GSM564637 2 0.6640 0.00789 0.012 0.544 0.024 0.100 0.320
#> GSM564638 3 0.5280 0.42334 0.000 0.364 0.584 0.004 0.048
#> GSM564639 3 0.4045 0.48083 0.000 0.356 0.644 0.000 0.000
#> GSM564640 2 0.1270 0.68885 0.052 0.948 0.000 0.000 0.000
#> GSM564641 3 0.5094 0.46410 0.000 0.352 0.600 0.000 0.048
#> GSM564642 2 0.1484 0.68886 0.048 0.944 0.008 0.000 0.000
#> GSM564643 2 0.5815 0.27029 0.008 0.580 0.340 0.008 0.064
#> GSM564644 2 0.1502 0.68792 0.056 0.940 0.000 0.000 0.004
#> GSM564645 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564647 3 0.5042 0.25204 0.000 0.460 0.512 0.004 0.024
#> GSM564648 2 0.2053 0.68489 0.048 0.924 0.024 0.000 0.004
#> GSM564649 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564650 2 0.5765 0.19567 0.028 0.624 0.000 0.064 0.284
#> GSM564651 2 0.2152 0.68229 0.044 0.920 0.032 0.000 0.004
#> GSM564652 2 0.1341 0.68877 0.056 0.944 0.000 0.000 0.000
#> GSM564653 2 0.1408 0.68725 0.044 0.948 0.000 0.000 0.008
#> GSM564654 3 0.4088 0.47001 0.000 0.368 0.632 0.000 0.000
#> GSM564655 2 0.7266 0.39104 0.012 0.576 0.144 0.084 0.184
#> GSM564656 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564657 3 0.4074 0.47524 0.000 0.364 0.636 0.000 0.000
#> GSM564658 2 0.1800 0.68757 0.048 0.932 0.000 0.000 0.020
#> GSM564659 2 0.5029 -0.08636 0.000 0.528 0.444 0.004 0.024
#> GSM564660 2 0.6461 -0.27566 0.000 0.496 0.008 0.152 0.344
#> GSM564661 2 0.1197 0.68802 0.048 0.952 0.000 0.000 0.000
#> GSM564662 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564663 2 0.1484 0.68886 0.048 0.944 0.008 0.000 0.000
#> GSM564664 2 0.1731 0.68578 0.060 0.932 0.004 0.000 0.004
#> GSM564665 2 0.5995 0.02729 0.004 0.504 0.392 0.000 0.100
#> GSM564666 2 0.7328 -0.45823 0.000 0.396 0.032 0.224 0.348
#> GSM564667 3 0.4135 0.49417 0.000 0.340 0.656 0.000 0.004
#> GSM564668 2 0.5052 0.20045 0.008 0.600 0.364 0.000 0.028
#> GSM564669 3 0.4060 0.47683 0.000 0.360 0.640 0.000 0.000
#> GSM564670 2 0.5128 0.11354 0.000 0.580 0.380 0.004 0.036
#> GSM564671 5 0.6959 0.71585 0.004 0.336 0.000 0.320 0.340
#> GSM564672 3 0.3999 0.49109 0.000 0.344 0.656 0.000 0.000
#> GSM564673 2 0.1357 0.68803 0.048 0.948 0.000 0.000 0.004
#> GSM564674 2 0.1484 0.68886 0.048 0.944 0.008 0.000 0.000
#> GSM564675 2 0.6876 -0.18870 0.012 0.512 0.012 0.164 0.300
#> GSM564676 2 0.1704 0.68345 0.068 0.928 0.000 0.000 0.004
#> GSM564677 2 0.2158 0.67739 0.052 0.920 0.000 0.008 0.020
#> GSM564678 2 0.1502 0.68792 0.056 0.940 0.000 0.000 0.004
#> GSM564679 2 0.1502 0.68792 0.056 0.940 0.000 0.000 0.004
#> GSM564680 3 0.4060 0.47683 0.000 0.360 0.640 0.000 0.000
#> GSM564682 3 0.5107 0.46032 0.000 0.356 0.596 0.000 0.048
#> GSM564683 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564684 5 0.6954 0.71553 0.004 0.336 0.000 0.312 0.348
#> GSM564685 3 0.3983 0.49444 0.000 0.340 0.660 0.000 0.000
#> GSM564686 5 0.6796 0.71795 0.000 0.292 0.000 0.336 0.372
#> GSM564687 2 0.1197 0.68802 0.048 0.952 0.000 0.000 0.000
#> GSM564688 2 0.1408 0.68725 0.044 0.948 0.000 0.000 0.008
#> GSM564689 2 0.5569 0.22362 0.024 0.636 0.000 0.056 0.284
#> GSM564690 2 0.1764 0.68495 0.064 0.928 0.000 0.000 0.008
#> GSM564691 3 0.4848 0.35506 0.000 0.420 0.556 0.000 0.024
#> GSM564692 2 0.2511 0.68420 0.044 0.908 0.024 0.000 0.024
#> GSM564694 2 0.6870 0.30842 0.000 0.564 0.136 0.060 0.240
#> GSM564695 2 0.7042 -0.25301 0.000 0.472 0.036 0.160 0.332
#> GSM564696 3 0.6547 0.15399 0.000 0.416 0.424 0.008 0.152
#> GSM564697 2 0.4990 0.47622 0.012 0.732 0.008 0.060 0.188
#> GSM564698 3 0.4557 0.39451 0.000 0.404 0.584 0.000 0.012
#> GSM564700 5 0.6796 0.71795 0.000 0.292 0.000 0.336 0.372
#> GSM564701 2 0.1430 0.68899 0.052 0.944 0.000 0.000 0.004
#> GSM564702 2 0.1914 0.68478 0.056 0.928 0.000 0.008 0.008
#> GSM564703 3 0.4856 0.22168 0.020 0.000 0.584 0.004 0.392
#> GSM564704 1 0.5155 0.49664 0.652 0.000 0.008 0.288 0.052
#> GSM564705 1 0.1306 0.83662 0.960 0.016 0.008 0.000 0.016
#> GSM564706 3 0.4937 0.21687 0.024 0.000 0.580 0.004 0.392
#> GSM564707 1 0.1018 0.83961 0.968 0.016 0.000 0.000 0.016
#> GSM564708 3 0.4884 0.21120 0.020 0.000 0.572 0.004 0.404
#> GSM564709 1 0.5541 0.26166 0.556 0.004 0.008 0.388 0.044
#> GSM564710 1 0.0912 0.83897 0.972 0.016 0.000 0.000 0.012
#> GSM564711 3 0.6301 0.07872 0.016 0.000 0.484 0.100 0.400
#> GSM564712 1 0.0807 0.84060 0.976 0.012 0.000 0.000 0.012
#> GSM564713 3 0.5742 0.17929 0.044 0.000 0.548 0.024 0.384
#> GSM564714 3 0.5868 0.14469 0.024 0.000 0.504 0.048 0.424
#> GSM564715 1 0.0912 0.83897 0.972 0.016 0.000 0.000 0.012
#> GSM564716 1 0.2688 0.82629 0.904 0.012 0.012 0.048 0.024
#> GSM564717 1 0.1799 0.83224 0.940 0.020 0.012 0.000 0.028
#> GSM564718 3 0.7530 -0.18009 0.044 0.000 0.368 0.232 0.356
#> GSM564719 1 0.1617 0.83445 0.948 0.020 0.012 0.000 0.020
#> GSM564720 1 0.0968 0.83864 0.972 0.012 0.004 0.000 0.012
#> GSM564721 1 0.2555 0.83395 0.908 0.016 0.004 0.048 0.024
#> GSM564722 4 0.8500 0.36019 0.188 0.000 0.220 0.296 0.296
#> GSM564723 1 0.0807 0.83925 0.976 0.012 0.000 0.000 0.012
#> GSM564724 3 0.7407 -0.12591 0.048 0.000 0.392 0.184 0.376
#> GSM564725 1 0.2484 0.83113 0.912 0.012 0.008 0.048 0.020
#> GSM564726 4 0.4178 0.54490 0.000 0.004 0.100 0.792 0.104
#> GSM564727 4 0.5553 0.12742 0.424 0.004 0.004 0.520 0.048
#> GSM564728 4 0.1648 0.58573 0.020 0.000 0.000 0.940 0.040
#> GSM564729 4 0.3708 0.59177 0.136 0.004 0.000 0.816 0.044
#> GSM564730 1 0.2699 0.80724 0.880 0.008 0.000 0.100 0.012
#> GSM564731 1 0.7295 0.29984 0.552 0.000 0.116 0.164 0.168
#> GSM564732 4 0.5507 0.05668 0.436 0.004 0.004 0.512 0.044
#> GSM564733 1 0.7791 0.10002 0.468 0.004 0.192 0.088 0.248
#> GSM564734 1 0.5102 0.42849 0.620 0.000 0.008 0.336 0.036
#> GSM564735 4 0.6330 0.39554 0.008 0.004 0.240 0.580 0.168
#> GSM564736 3 0.6127 0.14817 0.052 0.000 0.532 0.040 0.376
#> GSM564737 1 0.0807 0.83925 0.976 0.012 0.000 0.000 0.012
#> GSM564738 5 0.6748 -0.29562 0.000 0.000 0.368 0.260 0.372
#> GSM564739 3 0.5374 0.20358 0.052 0.000 0.568 0.004 0.376
#> GSM564740 4 0.3845 0.34815 0.000 0.012 0.004 0.760 0.224
#> GSM564741 3 0.6672 -0.11928 0.000 0.000 0.392 0.232 0.376
#> GSM564742 3 0.4960 0.22487 0.020 0.000 0.584 0.008 0.388
#> GSM564743 1 0.2699 0.80724 0.880 0.008 0.000 0.100 0.012
#> GSM564744 1 0.0807 0.84060 0.976 0.012 0.000 0.000 0.012
#> GSM564745 1 0.3151 0.76799 0.836 0.000 0.000 0.144 0.020
#> GSM564746 1 0.4302 0.77441 0.820 0.044 0.020 0.028 0.088
#> GSM564747 4 0.8568 0.35261 0.216 0.000 0.224 0.280 0.280
#> GSM564748 3 0.4980 0.21949 0.020 0.000 0.576 0.008 0.396
#> GSM564749 1 0.1518 0.83479 0.952 0.020 0.012 0.000 0.016
#> GSM564750 4 0.2972 0.57358 0.000 0.004 0.040 0.872 0.084
#> GSM564751 3 0.4917 0.22342 0.024 0.000 0.588 0.004 0.384
#> GSM564752 4 0.2913 0.57433 0.000 0.004 0.040 0.876 0.080
#> GSM564753 3 0.4846 0.22737 0.020 0.000 0.588 0.004 0.388
#> GSM564754 1 0.3844 0.71870 0.788 0.004 0.000 0.180 0.028
#> GSM564755 4 0.4924 0.36046 0.028 0.024 0.004 0.712 0.232
#> GSM564756 1 0.1299 0.84001 0.960 0.012 0.000 0.020 0.008
#> GSM564757 4 0.4450 0.56656 0.216 0.004 0.000 0.736 0.044
#> GSM564758 4 0.2040 0.58426 0.032 0.008 0.000 0.928 0.032
#> GSM564759 3 0.4937 0.21635 0.024 0.000 0.580 0.004 0.392
#> GSM564760 1 0.5402 0.53999 0.656 0.008 0.008 0.272 0.056
#> GSM564761 1 0.1393 0.83976 0.956 0.024 0.000 0.008 0.012
#> GSM564762 1 0.3500 0.80084 0.864 0.016 0.016 0.064 0.040
#> GSM564681 2 0.6824 -0.57188 0.004 0.428 0.000 0.260 0.308
#> GSM564693 2 0.1569 0.68828 0.044 0.944 0.004 0.000 0.008
#> GSM564646 5 0.6820 0.71804 0.000 0.332 0.000 0.316 0.352
#> GSM564699 5 0.7069 0.66214 0.000 0.256 0.012 0.364 0.368
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.4534 0.6331 0.132 0.752 0.020 0.008 0.000 0.088
#> GSM564616 5 0.5815 0.3786 0.024 0.016 0.100 0.020 0.664 0.176
#> GSM564617 5 0.5895 0.3726 0.024 0.020 0.100 0.020 0.660 0.176
#> GSM564618 5 0.6809 -0.1034 0.016 0.036 0.096 0.024 0.480 0.348
#> GSM564619 1 0.3469 0.8019 0.856 0.044 0.036 0.004 0.036 0.024
#> GSM564620 1 0.5384 0.7300 0.740 0.060 0.084 0.016 0.044 0.056
#> GSM564621 1 0.6233 0.6603 0.660 0.148 0.084 0.020 0.036 0.052
#> GSM564622 5 0.6433 0.2783 0.012 0.012 0.212 0.016 0.548 0.200
#> GSM564623 5 0.6848 -0.1962 0.016 0.036 0.096 0.024 0.452 0.376
#> GSM564624 5 0.6471 0.1644 0.020 0.024 0.096 0.024 0.564 0.272
#> GSM564625 1 0.5446 0.7273 0.736 0.060 0.084 0.016 0.048 0.056
#> GSM564626 1 0.2472 0.8185 0.904 0.032 0.020 0.000 0.032 0.012
#> GSM564627 1 0.6267 0.6264 0.632 0.204 0.060 0.020 0.028 0.056
#> GSM564628 5 0.5815 0.3786 0.024 0.016 0.100 0.020 0.664 0.176
#> GSM564629 1 0.5474 0.7254 0.736 0.056 0.084 0.020 0.052 0.052
#> GSM564630 5 0.5815 0.3786 0.024 0.016 0.100 0.020 0.664 0.176
#> GSM564609 3 0.4649 0.3800 0.000 0.000 0.492 0.000 0.468 0.040
#> GSM564610 1 0.1700 0.8263 0.936 0.024 0.012 0.000 0.028 0.000
#> GSM564611 1 0.1598 0.8208 0.940 0.000 0.004 0.008 0.040 0.008
#> GSM564612 3 0.2838 0.8742 0.000 0.000 0.808 0.004 0.188 0.000
#> GSM564613 5 0.5193 0.0258 0.000 0.008 0.348 0.004 0.572 0.068
#> GSM564614 2 0.3380 0.6729 0.004 0.812 0.024 0.008 0.000 0.152
#> GSM564631 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564632 3 0.6530 0.2213 0.000 0.008 0.392 0.012 0.344 0.244
#> GSM564633 3 0.3371 0.8722 0.000 0.004 0.796 0.008 0.180 0.012
#> GSM564634 5 0.5834 -0.1202 0.004 0.004 0.384 0.028 0.508 0.072
#> GSM564635 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564636 3 0.4475 0.8370 0.000 0.016 0.744 0.020 0.180 0.040
#> GSM564637 6 0.4526 0.4113 0.000 0.000 0.032 0.000 0.456 0.512
#> GSM564638 3 0.4443 0.8390 0.000 0.016 0.748 0.020 0.176 0.040
#> GSM564639 3 0.2882 0.8746 0.000 0.000 0.812 0.008 0.180 0.000
#> GSM564640 5 0.0405 0.7171 0.008 0.000 0.004 0.000 0.988 0.000
#> GSM564641 3 0.4356 0.8450 0.000 0.004 0.744 0.044 0.184 0.024
#> GSM564642 5 0.0508 0.7167 0.004 0.000 0.012 0.000 0.984 0.000
#> GSM564643 5 0.5149 -0.2705 0.000 0.000 0.440 0.000 0.476 0.084
#> GSM564644 5 0.0363 0.7160 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM564645 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564647 3 0.4366 0.7210 0.000 0.004 0.644 0.004 0.324 0.024
#> GSM564648 5 0.1338 0.7064 0.004 0.000 0.032 0.004 0.952 0.008
#> GSM564649 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564650 5 0.4049 -0.1302 0.004 0.000 0.004 0.000 0.580 0.412
#> GSM564651 5 0.1226 0.7020 0.004 0.000 0.040 0.000 0.952 0.004
#> GSM564652 5 0.0653 0.7171 0.012 0.000 0.004 0.000 0.980 0.004
#> GSM564653 5 0.0603 0.7143 0.000 0.000 0.004 0.000 0.980 0.016
#> GSM564654 3 0.3043 0.8691 0.000 0.000 0.792 0.008 0.200 0.000
#> GSM564655 5 0.6507 -0.0999 0.000 0.004 0.248 0.016 0.404 0.328
#> GSM564656 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564657 3 0.2838 0.8742 0.000 0.000 0.808 0.004 0.188 0.000
#> GSM564658 5 0.0982 0.7134 0.004 0.000 0.004 0.004 0.968 0.020
#> GSM564659 3 0.4495 0.6049 0.000 0.004 0.580 0.000 0.388 0.028
#> GSM564660 6 0.4466 0.6590 0.000 0.004 0.016 0.012 0.340 0.628
#> GSM564661 5 0.0291 0.7162 0.004 0.000 0.004 0.000 0.992 0.000
#> GSM564662 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564663 5 0.0508 0.7167 0.004 0.000 0.012 0.000 0.984 0.000
#> GSM564664 5 0.0837 0.7116 0.020 0.000 0.004 0.000 0.972 0.004
#> GSM564665 3 0.5740 0.4837 0.000 0.000 0.504 0.036 0.384 0.076
#> GSM564666 6 0.5611 0.7096 0.000 0.048 0.056 0.016 0.244 0.636
#> GSM564667 3 0.2841 0.8741 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564668 5 0.4651 -0.3881 0.000 0.000 0.480 0.000 0.480 0.040
#> GSM564669 3 0.2915 0.8747 0.000 0.000 0.808 0.008 0.184 0.000
#> GSM564670 3 0.4930 0.4280 0.000 0.004 0.496 0.000 0.448 0.052
#> GSM564671 6 0.5101 0.7544 0.004 0.108 0.004 0.004 0.220 0.660
#> GSM564672 3 0.2778 0.8749 0.000 0.000 0.824 0.008 0.168 0.000
#> GSM564673 5 0.0551 0.7161 0.004 0.000 0.004 0.000 0.984 0.008
#> GSM564674 5 0.0508 0.7167 0.004 0.000 0.012 0.000 0.984 0.000
#> GSM564675 6 0.5751 0.5682 0.012 0.024 0.048 0.008 0.376 0.532
#> GSM564676 5 0.0777 0.7103 0.024 0.000 0.000 0.000 0.972 0.004
#> GSM564677 5 0.2125 0.6696 0.016 0.004 0.004 0.000 0.908 0.068
#> GSM564678 5 0.0363 0.7160 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM564679 5 0.0363 0.7160 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM564680 3 0.2915 0.8747 0.000 0.000 0.808 0.008 0.184 0.000
#> GSM564682 3 0.4464 0.8445 0.000 0.008 0.740 0.044 0.184 0.024
#> GSM564683 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564684 6 0.4968 0.7571 0.004 0.100 0.004 0.004 0.212 0.676
#> GSM564685 3 0.2841 0.8746 0.000 0.000 0.824 0.012 0.164 0.000
#> GSM564686 6 0.3705 0.7280 0.000 0.056 0.000 0.008 0.144 0.792
#> GSM564687 5 0.0291 0.7162 0.004 0.000 0.004 0.000 0.992 0.000
#> GSM564688 5 0.0603 0.7143 0.000 0.000 0.004 0.000 0.980 0.016
#> GSM564689 5 0.3862 -0.0301 0.000 0.000 0.004 0.000 0.608 0.388
#> GSM564690 5 0.0777 0.7112 0.024 0.000 0.000 0.000 0.972 0.004
#> GSM564691 3 0.3965 0.8107 0.000 0.004 0.716 0.004 0.256 0.020
#> GSM564692 5 0.1774 0.7034 0.004 0.004 0.024 0.004 0.936 0.028
#> GSM564694 5 0.6089 -0.2133 0.000 0.008 0.168 0.004 0.424 0.396
#> GSM564695 6 0.5147 0.6390 0.000 0.008 0.056 0.012 0.320 0.604
#> GSM564696 3 0.6471 0.5836 0.000 0.008 0.532 0.092 0.284 0.084
#> GSM564697 5 0.3984 0.1871 0.000 0.000 0.016 0.000 0.648 0.336
#> GSM564698 3 0.3894 0.8334 0.000 0.004 0.732 0.008 0.240 0.016
#> GSM564700 6 0.3763 0.7279 0.000 0.060 0.000 0.008 0.144 0.788
#> GSM564701 5 0.0508 0.7170 0.012 0.000 0.004 0.000 0.984 0.000
#> GSM564702 5 0.1078 0.7131 0.012 0.000 0.008 0.000 0.964 0.016
#> GSM564703 4 0.2146 0.8227 0.000 0.000 0.116 0.880 0.000 0.004
#> GSM564704 1 0.4907 0.4858 0.620 0.328 0.008 0.020 0.004 0.020
#> GSM564705 1 0.1509 0.8200 0.948 0.000 0.008 0.008 0.024 0.012
#> GSM564706 4 0.2476 0.8236 0.004 0.004 0.120 0.868 0.000 0.004
#> GSM564707 1 0.1180 0.8241 0.960 0.000 0.004 0.004 0.024 0.008
#> GSM564708 4 0.2170 0.8228 0.000 0.000 0.100 0.888 0.000 0.012
#> GSM564709 1 0.5285 0.2237 0.500 0.432 0.004 0.020 0.000 0.044
#> GSM564710 1 0.1036 0.8234 0.964 0.000 0.000 0.004 0.024 0.008
#> GSM564711 4 0.4070 0.7906 0.004 0.096 0.084 0.792 0.000 0.024
#> GSM564712 1 0.1210 0.8254 0.960 0.008 0.000 0.004 0.020 0.008
#> GSM564713 4 0.3306 0.8109 0.032 0.020 0.104 0.840 0.000 0.004
#> GSM564714 4 0.3684 0.8062 0.000 0.048 0.112 0.812 0.000 0.028
#> GSM564715 1 0.1036 0.8234 0.964 0.000 0.000 0.004 0.024 0.008
#> GSM564716 1 0.3234 0.8135 0.868 0.060 0.020 0.020 0.020 0.012
#> GSM564717 1 0.2402 0.8040 0.908 0.000 0.028 0.012 0.024 0.028
#> GSM564718 4 0.5472 0.6413 0.024 0.204 0.052 0.672 0.000 0.048
#> GSM564719 1 0.1971 0.8124 0.928 0.000 0.016 0.008 0.024 0.024
#> GSM564720 1 0.1096 0.8230 0.964 0.000 0.004 0.004 0.020 0.008
#> GSM564721 1 0.2947 0.8214 0.880 0.056 0.012 0.004 0.024 0.024
#> GSM564722 4 0.7145 0.1995 0.144 0.292 0.024 0.464 0.000 0.076
#> GSM564723 1 0.0951 0.8237 0.968 0.000 0.000 0.004 0.020 0.008
#> GSM564724 4 0.5234 0.6792 0.024 0.176 0.068 0.700 0.000 0.032
#> GSM564725 1 0.2955 0.8183 0.880 0.060 0.020 0.008 0.020 0.012
#> GSM564726 2 0.4870 0.5764 0.000 0.696 0.016 0.168 0.000 0.120
#> GSM564727 2 0.5589 0.1959 0.356 0.548 0.024 0.008 0.000 0.064
#> GSM564728 2 0.3263 0.6731 0.000 0.816 0.016 0.016 0.000 0.152
#> GSM564729 2 0.4077 0.6628 0.080 0.788 0.020 0.004 0.000 0.108
#> GSM564730 1 0.3308 0.7840 0.824 0.140 0.008 0.000 0.016 0.012
#> GSM564731 1 0.6837 0.2896 0.492 0.196 0.016 0.256 0.004 0.036
#> GSM564732 2 0.5407 0.1446 0.368 0.552 0.016 0.012 0.000 0.052
#> GSM564733 1 0.6728 0.0497 0.444 0.104 0.052 0.380 0.004 0.016
#> GSM564734 1 0.5057 0.3943 0.568 0.372 0.004 0.016 0.000 0.040
#> GSM564735 2 0.6024 0.1198 0.004 0.508 0.044 0.360 0.000 0.084
#> GSM564736 4 0.3703 0.8010 0.036 0.040 0.100 0.820 0.000 0.004
#> GSM564737 1 0.0951 0.8237 0.968 0.000 0.000 0.004 0.020 0.008
#> GSM564738 4 0.5133 0.6344 0.000 0.224 0.056 0.668 0.000 0.052
#> GSM564739 4 0.3005 0.8139 0.036 0.000 0.108 0.848 0.000 0.008
#> GSM564740 2 0.4765 0.4064 0.000 0.524 0.012 0.028 0.000 0.436
#> GSM564741 4 0.4932 0.6677 0.000 0.212 0.060 0.688 0.000 0.040
#> GSM564742 4 0.2146 0.8239 0.000 0.004 0.116 0.880 0.000 0.000
#> GSM564743 1 0.3308 0.7840 0.824 0.140 0.008 0.000 0.016 0.012
#> GSM564744 1 0.1210 0.8254 0.960 0.008 0.000 0.004 0.020 0.008
#> GSM564745 1 0.3951 0.7427 0.772 0.180 0.004 0.008 0.008 0.028
#> GSM564746 1 0.4824 0.7549 0.780 0.044 0.072 0.020 0.048 0.036
#> GSM564747 4 0.7170 0.1943 0.176 0.288 0.028 0.452 0.000 0.056
#> GSM564748 4 0.1957 0.8249 0.000 0.000 0.112 0.888 0.000 0.000
#> GSM564749 1 0.1785 0.8164 0.936 0.000 0.012 0.008 0.028 0.016
#> GSM564750 2 0.4289 0.6492 0.000 0.756 0.016 0.092 0.000 0.136
#> GSM564751 4 0.2333 0.8216 0.004 0.000 0.120 0.872 0.000 0.004
#> GSM564752 2 0.4200 0.6489 0.000 0.760 0.012 0.092 0.000 0.136
#> GSM564753 4 0.2191 0.8229 0.000 0.004 0.120 0.876 0.000 0.000
#> GSM564754 1 0.3891 0.7133 0.768 0.192 0.004 0.008 0.008 0.020
#> GSM564755 2 0.5157 0.4165 0.016 0.516 0.012 0.028 0.000 0.428
#> GSM564756 1 0.2146 0.8264 0.916 0.044 0.008 0.000 0.024 0.008
#> GSM564757 2 0.4364 0.6271 0.144 0.748 0.016 0.000 0.000 0.092
#> GSM564758 2 0.3712 0.6697 0.004 0.788 0.024 0.016 0.000 0.168
#> GSM564759 4 0.2476 0.8240 0.004 0.004 0.120 0.868 0.000 0.004
#> GSM564760 1 0.5660 0.5133 0.604 0.296 0.012 0.040 0.008 0.040
#> GSM564761 1 0.1893 0.8243 0.928 0.024 0.008 0.000 0.036 0.004
#> GSM564762 1 0.3829 0.7932 0.836 0.068 0.024 0.036 0.016 0.020
#> GSM564681 6 0.5380 0.7252 0.004 0.084 0.008 0.004 0.308 0.592
#> GSM564693 5 0.0717 0.7148 0.000 0.000 0.008 0.000 0.976 0.016
#> GSM564646 6 0.4820 0.7565 0.000 0.104 0.004 0.004 0.204 0.684
#> GSM564699 6 0.4491 0.6878 0.000 0.076 0.020 0.016 0.124 0.764
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 genotype/variation(p) disease.state(p) k
#> SD:hclust 83 0.0513 NA 2
#> SD:hclust 56 0.6957 1.00000 3
#> SD:hclust 106 0.1043 0.61352 4
#> SD:hclust 73 0.2324 0.00313 5
#> SD:hclust 120 0.0151 0.02306 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 154 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.495 0.491 0.792 0.4895 0.499 0.499
#> 3 3 0.485 0.533 0.689 0.3403 0.613 0.371
#> 4 4 0.665 0.686 0.808 0.1301 0.786 0.478
#> 5 5 0.891 0.841 0.907 0.0724 0.886 0.603
#> 6 6 0.843 0.793 0.874 0.0390 0.964 0.825
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM564615 1 0.0000 0.756 1.000 0.000
#> GSM564616 2 0.9988 0.424 0.480 0.520
#> GSM564617 2 0.9988 0.424 0.480 0.520
#> GSM564618 2 0.9998 0.404 0.492 0.508
#> GSM564619 1 0.0000 0.756 1.000 0.000
#> GSM564620 1 0.0000 0.756 1.000 0.000
#> GSM564621 1 0.0000 0.756 1.000 0.000
#> GSM564622 2 0.9977 0.436 0.472 0.528
#> GSM564623 1 1.0000 -0.395 0.504 0.496
#> GSM564624 2 0.9996 0.410 0.488 0.512
#> GSM564625 1 0.0000 0.756 1.000 0.000
#> GSM564626 1 0.0000 0.756 1.000 0.000
#> GSM564627 1 0.0000 0.756 1.000 0.000
#> GSM564628 2 0.9993 0.417 0.484 0.516
#> GSM564629 1 0.0000 0.756 1.000 0.000
#> GSM564630 2 0.9988 0.424 0.480 0.520
#> GSM564609 2 0.0000 0.618 0.000 1.000
#> GSM564610 1 0.0000 0.756 1.000 0.000
#> GSM564611 1 0.0376 0.754 0.996 0.004
#> GSM564612 2 0.0000 0.618 0.000 1.000
#> GSM564613 2 0.1414 0.614 0.020 0.980
#> GSM564614 1 0.0000 0.756 1.000 0.000
#> GSM564631 2 0.0000 0.618 0.000 1.000
#> GSM564632 2 0.1633 0.613 0.024 0.976
#> GSM564633 2 0.0000 0.618 0.000 1.000
#> GSM564634 2 0.9661 0.468 0.392 0.608
#> GSM564635 2 0.0000 0.618 0.000 1.000
#> GSM564636 2 0.0000 0.618 0.000 1.000
#> GSM564637 2 0.0376 0.617 0.004 0.996
#> GSM564638 2 0.0000 0.618 0.000 1.000
#> GSM564639 2 0.0000 0.618 0.000 1.000
#> GSM564640 2 0.9983 0.430 0.476 0.524
#> GSM564641 2 0.0000 0.618 0.000 1.000
#> GSM564642 2 0.9977 0.436 0.472 0.528
#> GSM564643 2 0.6343 0.566 0.160 0.840
#> GSM564644 2 0.9977 0.436 0.472 0.528
#> GSM564645 2 0.0000 0.618 0.000 1.000
#> GSM564647 2 0.0000 0.618 0.000 1.000
#> GSM564648 2 0.9977 0.436 0.472 0.528
#> GSM564649 2 0.0000 0.618 0.000 1.000
#> GSM564650 2 0.9983 0.430 0.476 0.524
#> GSM564651 2 0.9963 0.440 0.464 0.536
#> GSM564652 2 0.9983 0.430 0.476 0.524
#> GSM564653 2 0.9977 0.436 0.472 0.528
#> GSM564654 2 0.0000 0.618 0.000 1.000
#> GSM564655 2 0.0672 0.617 0.008 0.992
#> GSM564656 2 0.0000 0.618 0.000 1.000
#> GSM564657 2 0.0000 0.618 0.000 1.000
#> GSM564658 2 0.9977 0.436 0.472 0.528
#> GSM564659 2 0.0000 0.618 0.000 1.000
#> GSM564660 2 0.9996 0.412 0.488 0.512
#> GSM564661 2 0.9977 0.436 0.472 0.528
#> GSM564662 2 0.0000 0.618 0.000 1.000
#> GSM564663 2 0.9977 0.436 0.472 0.528
#> GSM564664 2 0.9977 0.436 0.472 0.528
#> GSM564665 2 0.0000 0.618 0.000 1.000
#> GSM564666 2 0.3431 0.600 0.064 0.936
#> GSM564667 2 0.0000 0.618 0.000 1.000
#> GSM564668 2 0.0000 0.618 0.000 1.000
#> GSM564669 2 0.0000 0.618 0.000 1.000
#> GSM564670 2 0.0000 0.618 0.000 1.000
#> GSM564671 1 1.0000 -0.395 0.504 0.496
#> GSM564672 2 0.0000 0.618 0.000 1.000
#> GSM564673 2 0.9977 0.436 0.472 0.528
#> GSM564674 2 0.9977 0.436 0.472 0.528
#> GSM564675 1 1.0000 -0.395 0.504 0.496
#> GSM564676 2 0.9977 0.436 0.472 0.528
#> GSM564677 2 0.9998 0.402 0.492 0.508
#> GSM564678 2 0.9977 0.436 0.472 0.528
#> GSM564679 2 0.9977 0.436 0.472 0.528
#> GSM564680 2 0.0000 0.618 0.000 1.000
#> GSM564682 2 0.0000 0.618 0.000 1.000
#> GSM564683 2 0.0000 0.618 0.000 1.000
#> GSM564684 1 1.0000 -0.395 0.504 0.496
#> GSM564685 2 0.0000 0.618 0.000 1.000
#> GSM564686 1 1.0000 -0.395 0.504 0.496
#> GSM564687 2 0.9977 0.436 0.472 0.528
#> GSM564688 2 0.9977 0.436 0.472 0.528
#> GSM564689 2 0.9988 0.424 0.480 0.520
#> GSM564690 2 0.9977 0.436 0.472 0.528
#> GSM564691 2 0.0000 0.618 0.000 1.000
#> GSM564692 2 0.9977 0.436 0.472 0.528
#> GSM564694 2 0.8813 0.508 0.300 0.700
#> GSM564695 2 0.0938 0.614 0.012 0.988
#> GSM564696 2 0.0000 0.618 0.000 1.000
#> GSM564697 2 0.9977 0.436 0.472 0.528
#> GSM564698 2 0.0000 0.618 0.000 1.000
#> GSM564700 1 1.0000 -0.395 0.504 0.496
#> GSM564701 2 0.9977 0.436 0.472 0.528
#> GSM564702 2 0.9996 0.410 0.488 0.512
#> GSM564703 2 1.0000 -0.221 0.496 0.504
#> GSM564704 1 0.0000 0.756 1.000 0.000
#> GSM564705 1 0.0376 0.754 0.996 0.004
#> GSM564706 2 1.0000 -0.221 0.496 0.504
#> GSM564707 1 0.0376 0.754 0.996 0.004
#> GSM564708 1 0.9998 0.221 0.508 0.492
#> GSM564709 1 0.0000 0.756 1.000 0.000
#> GSM564710 1 0.0376 0.754 0.996 0.004
#> GSM564711 1 0.9977 0.248 0.528 0.472
#> GSM564712 1 0.0000 0.756 1.000 0.000
#> GSM564713 1 0.9983 0.243 0.524 0.476
#> GSM564714 1 1.0000 0.205 0.500 0.500
#> GSM564715 1 0.0376 0.754 0.996 0.004
#> GSM564716 1 0.0000 0.756 1.000 0.000
#> GSM564717 1 0.0376 0.754 0.996 0.004
#> GSM564718 1 0.9977 0.248 0.528 0.472
#> GSM564719 1 0.0376 0.754 0.996 0.004
#> GSM564720 1 0.0376 0.754 0.996 0.004
#> GSM564721 1 0.0000 0.756 1.000 0.000
#> GSM564722 1 0.0000 0.756 1.000 0.000
#> GSM564723 1 0.0376 0.754 0.996 0.004
#> GSM564724 1 0.9983 0.243 0.524 0.476
#> GSM564725 1 0.0000 0.756 1.000 0.000
#> GSM564726 1 0.9977 0.248 0.528 0.472
#> GSM564727 1 0.0000 0.756 1.000 0.000
#> GSM564728 1 0.0000 0.756 1.000 0.000
#> GSM564729 1 0.0000 0.756 1.000 0.000
#> GSM564730 1 0.0000 0.756 1.000 0.000
#> GSM564731 1 0.1414 0.740 0.980 0.020
#> GSM564732 1 0.0000 0.756 1.000 0.000
#> GSM564733 1 0.5629 0.624 0.868 0.132
#> GSM564734 1 0.0000 0.756 1.000 0.000
#> GSM564735 1 0.9977 0.248 0.528 0.472
#> GSM564736 1 0.9983 0.243 0.524 0.476
#> GSM564737 1 0.0376 0.754 0.996 0.004
#> GSM564738 1 0.9983 0.243 0.524 0.476
#> GSM564739 1 0.9998 0.221 0.508 0.492
#> GSM564740 1 0.0000 0.756 1.000 0.000
#> GSM564741 1 0.9988 0.237 0.520 0.480
#> GSM564742 2 1.0000 -0.221 0.496 0.504
#> GSM564743 1 0.0000 0.756 1.000 0.000
#> GSM564744 1 0.0376 0.754 0.996 0.004
#> GSM564745 1 0.0000 0.756 1.000 0.000
#> GSM564746 1 0.0376 0.754 0.996 0.004
#> GSM564747 1 0.0938 0.747 0.988 0.012
#> GSM564748 2 1.0000 -0.221 0.496 0.504
#> GSM564749 1 0.0376 0.754 0.996 0.004
#> GSM564750 1 0.9977 0.248 0.528 0.472
#> GSM564751 2 1.0000 -0.221 0.496 0.504
#> GSM564752 1 0.9977 0.248 0.528 0.472
#> GSM564753 2 1.0000 -0.221 0.496 0.504
#> GSM564754 1 0.0000 0.756 1.000 0.000
#> GSM564755 1 0.0000 0.756 1.000 0.000
#> GSM564756 1 0.0000 0.756 1.000 0.000
#> GSM564757 1 0.0000 0.756 1.000 0.000
#> GSM564758 1 0.0000 0.756 1.000 0.000
#> GSM564759 2 1.0000 -0.221 0.496 0.504
#> GSM564760 1 0.0000 0.756 1.000 0.000
#> GSM564761 1 0.0376 0.754 0.996 0.004
#> GSM564762 1 0.0000 0.756 1.000 0.000
#> GSM564681 1 1.0000 -0.395 0.504 0.496
#> GSM564693 2 0.9977 0.436 0.472 0.528
#> GSM564646 1 1.0000 -0.395 0.504 0.496
#> GSM564699 2 0.0938 0.611 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.1711 0.6904 0.960 0.008 0.032
#> GSM564616 2 0.0661 0.6246 0.008 0.988 0.004
#> GSM564617 2 0.0661 0.6246 0.008 0.988 0.004
#> GSM564618 2 0.6659 -0.1032 0.460 0.532 0.008
#> GSM564619 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564620 2 0.8680 0.2594 0.424 0.472 0.104
#> GSM564621 1 0.6039 0.5750 0.788 0.108 0.104
#> GSM564622 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564623 1 0.6836 0.3108 0.572 0.412 0.016
#> GSM564624 2 0.0829 0.6182 0.012 0.984 0.004
#> GSM564625 1 0.7778 0.3345 0.656 0.240 0.104
#> GSM564626 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564627 1 0.4902 0.6314 0.844 0.064 0.092
#> GSM564628 2 0.0424 0.6230 0.008 0.992 0.000
#> GSM564629 2 0.8683 0.2513 0.428 0.468 0.104
#> GSM564630 2 0.0661 0.6246 0.008 0.988 0.004
#> GSM564609 3 0.4605 0.8564 0.000 0.204 0.796
#> GSM564610 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564611 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564612 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564613 2 0.5988 -0.0619 0.000 0.632 0.368
#> GSM564614 1 0.2682 0.6944 0.920 0.004 0.076
#> GSM564631 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564632 2 0.7382 -0.1327 0.456 0.512 0.032
#> GSM564633 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564634 2 0.0592 0.6213 0.000 0.988 0.012
#> GSM564635 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564636 3 0.4351 0.8719 0.004 0.168 0.828
#> GSM564637 2 0.7647 -0.1144 0.440 0.516 0.044
#> GSM564638 3 0.4121 0.8729 0.000 0.168 0.832
#> GSM564639 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564640 2 0.0475 0.6259 0.004 0.992 0.004
#> GSM564641 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564642 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564643 2 0.7278 -0.1305 0.456 0.516 0.028
#> GSM564644 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564645 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564647 3 0.5216 0.7972 0.000 0.260 0.740
#> GSM564648 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564649 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564650 2 0.6398 0.0679 0.372 0.620 0.008
#> GSM564651 2 0.0424 0.6240 0.000 0.992 0.008
#> GSM564652 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564653 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564654 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564655 2 0.9151 0.0677 0.292 0.528 0.180
#> GSM564656 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564657 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564658 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564659 3 0.5678 0.7225 0.000 0.316 0.684
#> GSM564660 2 0.6641 -0.0887 0.448 0.544 0.008
#> GSM564661 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564662 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564663 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564664 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564665 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564666 1 0.7250 0.3208 0.572 0.396 0.032
#> GSM564667 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564668 3 0.5465 0.7625 0.000 0.288 0.712
#> GSM564669 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564670 2 0.6215 -0.2338 0.000 0.572 0.428
#> GSM564671 1 0.6836 0.3108 0.572 0.412 0.016
#> GSM564672 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564673 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564674 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564675 1 0.6713 0.3049 0.572 0.416 0.012
#> GSM564676 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564677 2 0.0424 0.6230 0.008 0.992 0.000
#> GSM564678 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564679 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564680 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564682 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564683 3 0.4121 0.8729 0.000 0.168 0.832
#> GSM564684 1 0.6713 0.3049 0.572 0.416 0.012
#> GSM564685 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564686 1 0.6824 0.3161 0.576 0.408 0.016
#> GSM564687 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564688 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564689 2 0.0237 0.6245 0.004 0.996 0.000
#> GSM564690 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564691 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564692 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564694 2 0.7178 -0.1424 0.464 0.512 0.024
#> GSM564695 1 0.7240 0.2589 0.540 0.432 0.028
#> GSM564696 3 0.4062 0.8714 0.000 0.164 0.836
#> GSM564697 2 0.0000 0.6251 0.000 1.000 0.000
#> GSM564698 3 0.4291 0.8770 0.000 0.180 0.820
#> GSM564700 1 0.6824 0.3161 0.576 0.408 0.016
#> GSM564701 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564702 2 0.0424 0.6230 0.008 0.992 0.000
#> GSM564703 3 0.2173 0.7417 0.048 0.008 0.944
#> GSM564704 1 0.3618 0.6589 0.884 0.012 0.104
#> GSM564705 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564706 3 0.2063 0.7425 0.044 0.008 0.948
#> GSM564707 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564708 3 0.2066 0.7236 0.060 0.000 0.940
#> GSM564709 1 0.3293 0.6666 0.900 0.012 0.088
#> GSM564710 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564711 1 0.4291 0.6831 0.820 0.000 0.180
#> GSM564712 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564713 3 0.6180 0.0177 0.416 0.000 0.584
#> GSM564714 3 0.2356 0.7570 0.072 0.000 0.928
#> GSM564715 2 0.8680 0.2598 0.424 0.472 0.104
#> GSM564716 1 0.5815 0.5894 0.800 0.096 0.104
#> GSM564717 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564718 1 0.2945 0.6938 0.908 0.004 0.088
#> GSM564719 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564720 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564721 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564722 1 0.2860 0.6956 0.912 0.004 0.084
#> GSM564723 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564724 1 0.5138 0.6392 0.748 0.000 0.252
#> GSM564725 1 0.5657 0.5989 0.808 0.088 0.104
#> GSM564726 1 0.2945 0.6915 0.908 0.004 0.088
#> GSM564727 1 0.2173 0.6797 0.944 0.008 0.048
#> GSM564728 1 0.2682 0.6944 0.920 0.004 0.076
#> GSM564729 1 0.1453 0.6903 0.968 0.008 0.024
#> GSM564730 1 0.8691 -0.2254 0.452 0.444 0.104
#> GSM564731 1 0.5947 0.6543 0.776 0.052 0.172
#> GSM564732 1 0.3293 0.6666 0.900 0.012 0.088
#> GSM564733 1 0.6543 0.6298 0.748 0.076 0.176
#> GSM564734 1 0.5737 0.5943 0.804 0.092 0.104
#> GSM564735 1 0.5325 0.5581 0.748 0.004 0.248
#> GSM564736 3 0.6215 -0.0188 0.428 0.000 0.572
#> GSM564737 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564738 1 0.6209 0.3421 0.628 0.004 0.368
#> GSM564739 3 0.4750 0.4912 0.216 0.000 0.784
#> GSM564740 1 0.2682 0.6944 0.920 0.004 0.076
#> GSM564741 3 0.6247 0.3364 0.376 0.004 0.620
#> GSM564742 3 0.1015 0.7656 0.012 0.008 0.980
#> GSM564743 1 0.8518 0.0174 0.540 0.356 0.104
#> GSM564744 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564745 1 0.7664 0.3579 0.668 0.228 0.104
#> GSM564746 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564747 1 0.4409 0.6763 0.824 0.004 0.172
#> GSM564748 3 0.2063 0.7425 0.044 0.008 0.948
#> GSM564749 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564750 1 0.3193 0.6870 0.896 0.004 0.100
#> GSM564751 3 0.2173 0.7417 0.048 0.008 0.944
#> GSM564752 1 0.3193 0.6870 0.896 0.004 0.100
#> GSM564753 3 0.1015 0.7656 0.012 0.008 0.980
#> GSM564754 2 0.8683 0.2515 0.428 0.468 0.104
#> GSM564755 1 0.2682 0.6944 0.920 0.004 0.076
#> GSM564756 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564757 1 0.1751 0.6897 0.960 0.012 0.028
#> GSM564758 1 0.2682 0.6944 0.920 0.004 0.076
#> GSM564759 3 0.2063 0.7425 0.044 0.008 0.948
#> GSM564760 1 0.4253 0.6501 0.872 0.048 0.080
#> GSM564761 2 0.8675 0.2672 0.420 0.476 0.104
#> GSM564762 1 0.5492 0.6048 0.816 0.080 0.104
#> GSM564681 2 0.6664 -0.1090 0.464 0.528 0.008
#> GSM564693 2 0.0237 0.6268 0.000 0.996 0.004
#> GSM564646 1 0.6713 0.3049 0.572 0.416 0.012
#> GSM564699 1 0.7920 0.3521 0.572 0.360 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.7013 0.4343 0.292 0.152 0.000 0.556
#> GSM564616 2 0.4808 0.7569 0.236 0.736 0.028 0.000
#> GSM564617 2 0.4808 0.7569 0.236 0.736 0.028 0.000
#> GSM564618 2 0.3688 0.4295 0.000 0.792 0.000 0.208
#> GSM564619 1 0.0779 0.8772 0.980 0.004 0.000 0.016
#> GSM564620 1 0.1545 0.8703 0.952 0.008 0.000 0.040
#> GSM564621 1 0.4956 0.7513 0.776 0.116 0.000 0.108
#> GSM564622 2 0.4365 0.7489 0.188 0.784 0.028 0.000
#> GSM564623 2 0.4955 -0.0482 0.000 0.556 0.000 0.444
#> GSM564624 2 0.2198 0.6759 0.072 0.920 0.008 0.000
#> GSM564625 1 0.3978 0.8114 0.836 0.056 0.000 0.108
#> GSM564626 1 0.0779 0.8772 0.980 0.004 0.000 0.016
#> GSM564627 1 0.5217 0.7228 0.756 0.136 0.000 0.108
#> GSM564628 2 0.4399 0.7553 0.212 0.768 0.020 0.000
#> GSM564629 1 0.2124 0.8592 0.924 0.008 0.000 0.068
#> GSM564630 2 0.4964 0.7530 0.256 0.716 0.028 0.000
#> GSM564609 3 0.1211 0.8686 0.000 0.040 0.960 0.000
#> GSM564610 1 0.0895 0.8766 0.976 0.004 0.000 0.020
#> GSM564611 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564612 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564613 2 0.4121 0.6249 0.020 0.796 0.184 0.000
#> GSM564614 4 0.3448 0.7077 0.004 0.168 0.000 0.828
#> GSM564631 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564632 2 0.5272 0.3377 0.000 0.680 0.032 0.288
#> GSM564633 3 0.0469 0.8884 0.000 0.012 0.988 0.000
#> GSM564634 2 0.4833 0.7577 0.228 0.740 0.032 0.000
#> GSM564635 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564636 3 0.1854 0.8567 0.000 0.048 0.940 0.012
#> GSM564637 2 0.5557 0.3222 0.000 0.652 0.040 0.308
#> GSM564638 3 0.0376 0.8858 0.000 0.004 0.992 0.004
#> GSM564639 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564640 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564641 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564642 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564643 2 0.5062 0.2986 0.000 0.680 0.020 0.300
#> GSM564644 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564645 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564647 3 0.1557 0.8534 0.000 0.056 0.944 0.000
#> GSM564648 2 0.4775 0.7575 0.232 0.740 0.028 0.000
#> GSM564649 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564650 2 0.2164 0.5725 0.004 0.924 0.004 0.068
#> GSM564651 2 0.5085 0.7520 0.260 0.708 0.032 0.000
#> GSM564652 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564653 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564654 3 0.0469 0.8884 0.000 0.012 0.988 0.000
#> GSM564655 2 0.6621 0.4186 0.000 0.616 0.140 0.244
#> GSM564656 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564657 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564658 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564659 3 0.2814 0.7837 0.000 0.132 0.868 0.000
#> GSM564660 2 0.3751 0.4491 0.004 0.800 0.000 0.196
#> GSM564661 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564662 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564663 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564664 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564665 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564666 4 0.4907 0.3496 0.000 0.420 0.000 0.580
#> GSM564667 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564668 3 0.2704 0.7872 0.000 0.124 0.876 0.000
#> GSM564669 3 0.0469 0.8884 0.000 0.012 0.988 0.000
#> GSM564670 2 0.4663 0.5517 0.012 0.716 0.272 0.000
#> GSM564671 2 0.4985 -0.1112 0.000 0.532 0.000 0.468
#> GSM564672 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564673 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564674 2 0.4964 0.7539 0.256 0.716 0.028 0.000
#> GSM564675 2 0.4776 0.1333 0.000 0.624 0.000 0.376
#> GSM564676 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564677 2 0.4290 0.7542 0.212 0.772 0.016 0.000
#> GSM564678 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564679 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564680 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564682 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564683 3 0.0336 0.8805 0.000 0.000 0.992 0.008
#> GSM564684 2 0.4898 0.0324 0.000 0.584 0.000 0.416
#> GSM564685 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564686 2 0.4998 -0.1681 0.000 0.512 0.000 0.488
#> GSM564687 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564688 2 0.4671 0.7576 0.220 0.752 0.028 0.000
#> GSM564689 2 0.4399 0.7562 0.224 0.760 0.016 0.000
#> GSM564690 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564691 3 0.0336 0.8895 0.000 0.008 0.992 0.000
#> GSM564692 2 0.4775 0.7575 0.232 0.740 0.028 0.000
#> GSM564694 2 0.4372 0.3445 0.000 0.728 0.004 0.268
#> GSM564695 2 0.4996 -0.1318 0.000 0.516 0.000 0.484
#> GSM564696 3 0.0592 0.8752 0.000 0.000 0.984 0.016
#> GSM564697 2 0.3647 0.7271 0.152 0.832 0.016 0.000
#> GSM564698 3 0.0469 0.8884 0.000 0.012 0.988 0.000
#> GSM564700 2 0.4992 -0.1342 0.000 0.524 0.000 0.476
#> GSM564701 2 0.5022 0.7519 0.264 0.708 0.028 0.000
#> GSM564702 2 0.4671 0.7574 0.220 0.752 0.028 0.000
#> GSM564703 3 0.4799 0.6644 0.008 0.004 0.704 0.284
#> GSM564704 1 0.5063 0.7421 0.768 0.108 0.000 0.124
#> GSM564705 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564706 3 0.4647 0.6614 0.008 0.000 0.704 0.288
#> GSM564707 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564708 3 0.4825 0.6594 0.008 0.004 0.700 0.288
#> GSM564709 1 0.5480 0.6959 0.736 0.140 0.000 0.124
#> GSM564710 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564711 4 0.2882 0.6872 0.024 0.000 0.084 0.892
#> GSM564712 1 0.0336 0.8768 0.992 0.000 0.000 0.008
#> GSM564713 4 0.4922 0.3943 0.012 0.004 0.284 0.700
#> GSM564714 3 0.4877 0.4654 0.000 0.000 0.592 0.408
#> GSM564715 1 0.0336 0.8768 0.992 0.000 0.000 0.008
#> GSM564716 1 0.4581 0.7813 0.800 0.080 0.000 0.120
#> GSM564717 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564718 4 0.0336 0.7326 0.000 0.000 0.008 0.992
#> GSM564719 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564720 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564721 1 0.0188 0.8758 0.996 0.000 0.000 0.004
#> GSM564722 4 0.3655 0.7334 0.060 0.072 0.004 0.864
#> GSM564723 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564724 4 0.3863 0.5853 0.008 0.004 0.176 0.812
#> GSM564725 1 0.4786 0.7660 0.788 0.104 0.000 0.108
#> GSM564726 4 0.0524 0.7343 0.000 0.008 0.004 0.988
#> GSM564727 1 0.6149 0.6150 0.676 0.144 0.000 0.180
#> GSM564728 4 0.3356 0.7041 0.000 0.176 0.000 0.824
#> GSM564729 4 0.6980 0.4818 0.264 0.164 0.000 0.572
#> GSM564730 1 0.2751 0.8518 0.904 0.040 0.000 0.056
#> GSM564731 4 0.5281 -0.0295 0.464 0.000 0.008 0.528
#> GSM564732 1 0.5581 0.6863 0.728 0.140 0.000 0.132
#> GSM564733 4 0.5400 0.0939 0.428 0.004 0.008 0.560
#> GSM564734 1 0.4261 0.7969 0.820 0.068 0.000 0.112
#> GSM564735 4 0.0469 0.7325 0.000 0.000 0.012 0.988
#> GSM564736 4 0.4825 0.3879 0.008 0.004 0.288 0.700
#> GSM564737 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564738 4 0.0707 0.7309 0.000 0.000 0.020 0.980
#> GSM564739 3 0.7959 0.1495 0.284 0.004 0.420 0.292
#> GSM564740 4 0.3266 0.7063 0.000 0.168 0.000 0.832
#> GSM564741 4 0.4250 0.4274 0.000 0.000 0.276 0.724
#> GSM564742 3 0.4304 0.6728 0.000 0.000 0.716 0.284
#> GSM564743 1 0.3547 0.8292 0.864 0.064 0.000 0.072
#> GSM564744 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564745 1 0.4055 0.8081 0.832 0.060 0.000 0.108
#> GSM564746 1 0.0779 0.8772 0.980 0.004 0.000 0.016
#> GSM564747 4 0.5220 0.1048 0.424 0.000 0.008 0.568
#> GSM564748 3 0.4647 0.6614 0.008 0.000 0.704 0.288
#> GSM564749 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564750 4 0.0657 0.7346 0.000 0.012 0.004 0.984
#> GSM564751 3 0.4621 0.6660 0.008 0.000 0.708 0.284
#> GSM564752 4 0.2281 0.7280 0.000 0.096 0.000 0.904
#> GSM564753 3 0.4103 0.7006 0.000 0.000 0.744 0.256
#> GSM564754 1 0.0707 0.8770 0.980 0.000 0.000 0.020
#> GSM564755 4 0.3356 0.7041 0.000 0.176 0.000 0.824
#> GSM564756 1 0.0336 0.8768 0.992 0.000 0.000 0.008
#> GSM564757 4 0.6439 0.5892 0.176 0.176 0.000 0.648
#> GSM564758 4 0.3123 0.7116 0.000 0.156 0.000 0.844
#> GSM564759 3 0.4746 0.6407 0.008 0.000 0.688 0.304
#> GSM564760 1 0.6295 0.5786 0.656 0.132 0.000 0.212
#> GSM564761 1 0.0000 0.8746 1.000 0.000 0.000 0.000
#> GSM564762 1 0.4353 0.6973 0.756 0.012 0.000 0.232
#> GSM564681 2 0.2408 0.5370 0.000 0.896 0.000 0.104
#> GSM564693 2 0.4706 0.7577 0.224 0.748 0.028 0.000
#> GSM564646 2 0.4898 0.0324 0.000 0.584 0.000 0.416
#> GSM564699 4 0.4356 0.5925 0.000 0.292 0.000 0.708
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.6960 -0.176 0.328 0.004 0.328 0.340 0.000
#> GSM564616 2 0.1087 0.949 0.016 0.968 0.008 0.008 0.000
#> GSM564617 2 0.1087 0.949 0.016 0.968 0.008 0.008 0.000
#> GSM564618 3 0.1857 0.807 0.004 0.060 0.928 0.008 0.000
#> GSM564619 1 0.1369 0.945 0.956 0.008 0.008 0.028 0.000
#> GSM564620 1 0.1278 0.944 0.960 0.004 0.016 0.020 0.000
#> GSM564621 1 0.2387 0.925 0.908 0.004 0.040 0.048 0.000
#> GSM564622 2 0.2300 0.916 0.012 0.920 0.040 0.024 0.004
#> GSM564623 3 0.1205 0.814 0.000 0.040 0.956 0.004 0.000
#> GSM564624 2 0.4318 0.569 0.008 0.688 0.296 0.008 0.000
#> GSM564625 1 0.2157 0.932 0.920 0.004 0.036 0.040 0.000
#> GSM564626 1 0.1369 0.945 0.956 0.008 0.008 0.028 0.000
#> GSM564627 1 0.2664 0.917 0.892 0.004 0.040 0.064 0.000
#> GSM564628 2 0.1200 0.947 0.016 0.964 0.012 0.008 0.000
#> GSM564629 1 0.1461 0.943 0.952 0.004 0.016 0.028 0.000
#> GSM564630 2 0.0960 0.950 0.016 0.972 0.004 0.008 0.000
#> GSM564609 5 0.0912 0.972 0.000 0.012 0.000 0.016 0.972
#> GSM564610 1 0.0960 0.951 0.972 0.016 0.004 0.008 0.000
#> GSM564611 1 0.1059 0.951 0.968 0.020 0.004 0.008 0.000
#> GSM564612 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564613 2 0.4340 0.748 0.000 0.780 0.060 0.012 0.148
#> GSM564614 3 0.4802 0.224 0.012 0.004 0.504 0.480 0.000
#> GSM564631 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564632 3 0.2206 0.804 0.000 0.068 0.912 0.016 0.004
#> GSM564633 5 0.0451 0.983 0.000 0.004 0.000 0.008 0.988
#> GSM564634 2 0.0324 0.962 0.000 0.992 0.004 0.000 0.004
#> GSM564635 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564636 5 0.1883 0.928 0.000 0.008 0.048 0.012 0.932
#> GSM564637 3 0.1831 0.804 0.000 0.076 0.920 0.000 0.004
#> GSM564638 5 0.0566 0.982 0.000 0.004 0.000 0.012 0.984
#> GSM564639 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564640 2 0.0486 0.962 0.004 0.988 0.000 0.004 0.004
#> GSM564641 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564642 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564643 3 0.2012 0.809 0.000 0.060 0.920 0.020 0.000
#> GSM564644 2 0.0613 0.961 0.008 0.984 0.000 0.004 0.004
#> GSM564645 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564647 5 0.0912 0.971 0.000 0.016 0.000 0.012 0.972
#> GSM564648 2 0.0486 0.962 0.004 0.988 0.004 0.000 0.004
#> GSM564649 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564650 3 0.2929 0.719 0.000 0.180 0.820 0.000 0.000
#> GSM564651 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564652 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564653 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564654 5 0.0324 0.985 0.000 0.004 0.000 0.004 0.992
#> GSM564655 3 0.4513 0.546 0.000 0.284 0.688 0.004 0.024
#> GSM564656 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564657 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564658 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564659 5 0.2302 0.912 0.000 0.020 0.048 0.016 0.916
#> GSM564660 3 0.1608 0.807 0.000 0.072 0.928 0.000 0.000
#> GSM564661 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564662 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564663 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564664 2 0.0613 0.961 0.008 0.984 0.000 0.004 0.004
#> GSM564665 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564666 3 0.1281 0.813 0.000 0.032 0.956 0.012 0.000
#> GSM564667 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564668 5 0.1934 0.924 0.000 0.052 0.004 0.016 0.928
#> GSM564669 5 0.0324 0.985 0.000 0.004 0.000 0.004 0.992
#> GSM564670 2 0.4905 0.608 0.000 0.688 0.040 0.012 0.260
#> GSM564671 3 0.0955 0.811 0.000 0.028 0.968 0.004 0.000
#> GSM564672 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564673 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564674 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564675 3 0.1124 0.815 0.000 0.036 0.960 0.004 0.000
#> GSM564676 2 0.0613 0.961 0.008 0.984 0.000 0.004 0.004
#> GSM564677 2 0.0510 0.957 0.000 0.984 0.016 0.000 0.000
#> GSM564678 2 0.0613 0.961 0.008 0.984 0.000 0.004 0.004
#> GSM564679 2 0.0613 0.961 0.008 0.984 0.000 0.004 0.004
#> GSM564680 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564682 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564683 5 0.0000 0.983 0.000 0.000 0.000 0.000 1.000
#> GSM564684 3 0.1124 0.815 0.000 0.036 0.960 0.004 0.000
#> GSM564685 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM564686 3 0.1124 0.815 0.000 0.036 0.960 0.004 0.000
#> GSM564687 2 0.0324 0.963 0.004 0.992 0.000 0.000 0.004
#> GSM564688 2 0.0613 0.961 0.004 0.984 0.008 0.000 0.004
#> GSM564689 2 0.0775 0.961 0.004 0.980 0.008 0.004 0.004
#> GSM564690 2 0.0613 0.961 0.008 0.984 0.000 0.004 0.004
#> GSM564691 5 0.0324 0.985 0.000 0.004 0.000 0.004 0.992
#> GSM564692 2 0.0324 0.962 0.000 0.992 0.004 0.000 0.004
#> GSM564694 3 0.1981 0.808 0.000 0.064 0.920 0.016 0.000
#> GSM564695 3 0.1251 0.814 0.000 0.036 0.956 0.008 0.000
#> GSM564696 5 0.0404 0.976 0.000 0.000 0.000 0.012 0.988
#> GSM564697 2 0.1043 0.939 0.000 0.960 0.040 0.000 0.000
#> GSM564698 5 0.0451 0.983 0.000 0.004 0.000 0.008 0.988
#> GSM564700 3 0.1124 0.815 0.000 0.036 0.960 0.004 0.000
#> GSM564701 2 0.0451 0.962 0.008 0.988 0.000 0.000 0.004
#> GSM564702 2 0.0613 0.961 0.004 0.984 0.008 0.000 0.004
#> GSM564703 4 0.4331 0.519 0.004 0.000 0.000 0.596 0.400
#> GSM564704 1 0.2473 0.910 0.896 0.000 0.032 0.072 0.000
#> GSM564705 1 0.0771 0.951 0.976 0.020 0.004 0.000 0.000
#> GSM564706 4 0.4276 0.550 0.004 0.000 0.000 0.616 0.380
#> GSM564707 1 0.0771 0.951 0.976 0.020 0.004 0.000 0.000
#> GSM564708 4 0.4211 0.574 0.004 0.000 0.000 0.636 0.360
#> GSM564709 1 0.2491 0.910 0.896 0.000 0.036 0.068 0.000
#> GSM564710 1 0.0771 0.951 0.976 0.020 0.004 0.000 0.000
#> GSM564711 4 0.1386 0.729 0.000 0.000 0.032 0.952 0.016
#> GSM564712 1 0.1059 0.951 0.968 0.020 0.004 0.008 0.000
#> GSM564713 4 0.1043 0.738 0.000 0.000 0.000 0.960 0.040
#> GSM564714 4 0.2852 0.715 0.000 0.000 0.000 0.828 0.172
#> GSM564715 1 0.0771 0.951 0.976 0.020 0.004 0.000 0.000
#> GSM564716 1 0.2504 0.923 0.900 0.004 0.032 0.064 0.000
#> GSM564717 1 0.0932 0.950 0.972 0.020 0.004 0.004 0.000
#> GSM564718 4 0.1197 0.719 0.000 0.000 0.048 0.952 0.000
#> GSM564719 1 0.0932 0.950 0.972 0.020 0.004 0.004 0.000
#> GSM564720 1 0.0932 0.952 0.972 0.020 0.004 0.004 0.000
#> GSM564721 1 0.0671 0.952 0.980 0.016 0.004 0.000 0.000
#> GSM564722 4 0.1764 0.709 0.008 0.000 0.064 0.928 0.000
#> GSM564723 1 0.0932 0.952 0.972 0.020 0.004 0.004 0.000
#> GSM564724 4 0.1281 0.736 0.000 0.000 0.012 0.956 0.032
#> GSM564725 1 0.2504 0.923 0.900 0.004 0.032 0.064 0.000
#> GSM564726 4 0.1952 0.698 0.004 0.000 0.084 0.912 0.000
#> GSM564727 1 0.4150 0.792 0.772 0.004 0.044 0.180 0.000
#> GSM564728 3 0.4327 0.472 0.008 0.000 0.632 0.360 0.000
#> GSM564729 3 0.6957 0.128 0.316 0.004 0.344 0.336 0.000
#> GSM564730 1 0.0740 0.951 0.980 0.004 0.008 0.008 0.000
#> GSM564731 4 0.1768 0.709 0.072 0.000 0.000 0.924 0.004
#> GSM564732 1 0.3141 0.879 0.852 0.000 0.040 0.108 0.000
#> GSM564733 4 0.1357 0.715 0.048 0.000 0.000 0.948 0.004
#> GSM564734 1 0.1836 0.937 0.932 0.000 0.032 0.036 0.000
#> GSM564735 4 0.1341 0.716 0.000 0.000 0.056 0.944 0.000
#> GSM564736 4 0.1197 0.738 0.000 0.000 0.000 0.952 0.048
#> GSM564737 1 0.0932 0.952 0.972 0.020 0.004 0.004 0.000
#> GSM564738 4 0.1410 0.715 0.000 0.000 0.060 0.940 0.000
#> GSM564739 4 0.5604 0.609 0.132 0.000 0.000 0.628 0.240
#> GSM564740 3 0.4341 0.469 0.008 0.000 0.628 0.364 0.000
#> GSM564741 4 0.1701 0.737 0.000 0.000 0.016 0.936 0.048
#> GSM564742 4 0.4192 0.514 0.000 0.000 0.000 0.596 0.404
#> GSM564743 1 0.0740 0.951 0.980 0.004 0.008 0.008 0.000
#> GSM564744 1 0.1059 0.951 0.968 0.020 0.004 0.008 0.000
#> GSM564745 1 0.1469 0.944 0.948 0.000 0.016 0.036 0.000
#> GSM564746 1 0.1243 0.945 0.960 0.008 0.004 0.028 0.000
#> GSM564747 4 0.1282 0.720 0.044 0.000 0.000 0.952 0.004
#> GSM564748 4 0.4299 0.539 0.004 0.000 0.000 0.608 0.388
#> GSM564749 1 0.0932 0.950 0.972 0.020 0.004 0.004 0.000
#> GSM564750 4 0.2179 0.683 0.004 0.000 0.100 0.896 0.000
#> GSM564751 4 0.4331 0.519 0.004 0.000 0.000 0.596 0.400
#> GSM564752 4 0.4084 0.267 0.004 0.000 0.328 0.668 0.000
#> GSM564753 4 0.4192 0.514 0.000 0.000 0.000 0.596 0.404
#> GSM564754 1 0.0671 0.952 0.980 0.016 0.004 0.000 0.000
#> GSM564755 3 0.4327 0.472 0.008 0.000 0.632 0.360 0.000
#> GSM564756 1 0.0932 0.951 0.972 0.020 0.004 0.004 0.000
#> GSM564757 3 0.6666 0.275 0.208 0.004 0.456 0.332 0.000
#> GSM564758 3 0.4557 0.241 0.008 0.000 0.516 0.476 0.000
#> GSM564759 4 0.4238 0.565 0.004 0.000 0.000 0.628 0.368
#> GSM564760 1 0.4370 0.719 0.724 0.000 0.040 0.236 0.000
#> GSM564761 1 0.1059 0.952 0.968 0.020 0.004 0.008 0.000
#> GSM564762 1 0.1597 0.939 0.940 0.000 0.012 0.048 0.000
#> GSM564681 3 0.1851 0.797 0.000 0.088 0.912 0.000 0.000
#> GSM564693 2 0.0613 0.961 0.004 0.984 0.008 0.000 0.004
#> GSM564646 3 0.1124 0.815 0.000 0.036 0.960 0.004 0.000
#> GSM564699 3 0.1281 0.813 0.000 0.032 0.956 0.012 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.5235 0.6902 0.120 0.692 0.000 0.132 0.000 0.056
#> GSM564616 5 0.3231 0.8433 0.008 0.116 0.000 0.000 0.832 0.044
#> GSM564617 5 0.3231 0.8445 0.008 0.116 0.000 0.000 0.832 0.044
#> GSM564618 6 0.2095 0.8596 0.004 0.076 0.000 0.000 0.016 0.904
#> GSM564619 1 0.1610 0.8250 0.916 0.084 0.000 0.000 0.000 0.000
#> GSM564620 1 0.2664 0.7562 0.816 0.184 0.000 0.000 0.000 0.000
#> GSM564621 1 0.3867 0.0988 0.512 0.488 0.000 0.000 0.000 0.000
#> GSM564622 5 0.4586 0.7210 0.008 0.176 0.000 0.000 0.712 0.104
#> GSM564623 6 0.1728 0.8813 0.004 0.064 0.000 0.000 0.008 0.924
#> GSM564624 6 0.5414 0.4104 0.008 0.116 0.000 0.000 0.304 0.572
#> GSM564625 1 0.3797 0.3356 0.580 0.420 0.000 0.000 0.000 0.000
#> GSM564626 1 0.1610 0.8250 0.916 0.084 0.000 0.000 0.000 0.000
#> GSM564627 2 0.3864 -0.0624 0.480 0.520 0.000 0.000 0.000 0.000
#> GSM564628 5 0.3231 0.8433 0.008 0.116 0.000 0.000 0.832 0.044
#> GSM564629 1 0.2730 0.7513 0.808 0.192 0.000 0.000 0.000 0.000
#> GSM564630 5 0.3165 0.8479 0.008 0.116 0.000 0.000 0.836 0.040
#> GSM564609 3 0.3216 0.8702 0.000 0.108 0.840 0.004 0.008 0.040
#> GSM564610 1 0.0790 0.8553 0.968 0.032 0.000 0.000 0.000 0.000
#> GSM564611 1 0.0891 0.8524 0.968 0.024 0.000 0.000 0.008 0.000
#> GSM564612 3 0.0260 0.9530 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM564613 5 0.6828 0.4266 0.000 0.152 0.164 0.004 0.532 0.148
#> GSM564614 2 0.4734 0.6327 0.000 0.672 0.000 0.208 0.000 0.120
#> GSM564631 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564632 6 0.2255 0.8534 0.000 0.088 0.000 0.004 0.016 0.892
#> GSM564633 3 0.1082 0.9404 0.000 0.040 0.956 0.004 0.000 0.000
#> GSM564634 5 0.1765 0.8983 0.000 0.052 0.000 0.000 0.924 0.024
#> GSM564635 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564636 3 0.3612 0.8390 0.000 0.100 0.804 0.004 0.000 0.092
#> GSM564637 6 0.1723 0.8714 0.000 0.036 0.000 0.000 0.036 0.928
#> GSM564638 3 0.1219 0.9375 0.000 0.048 0.948 0.004 0.000 0.000
#> GSM564639 3 0.0146 0.9531 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564640 5 0.0713 0.9222 0.000 0.028 0.000 0.000 0.972 0.000
#> GSM564641 3 0.0713 0.9481 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM564642 5 0.0146 0.9267 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM564643 6 0.2356 0.8547 0.000 0.096 0.000 0.004 0.016 0.884
#> GSM564644 5 0.0790 0.9215 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564645 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564647 3 0.3922 0.8247 0.000 0.140 0.784 0.004 0.008 0.064
#> GSM564648 5 0.1151 0.9183 0.000 0.032 0.000 0.000 0.956 0.012
#> GSM564649 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564650 6 0.3134 0.7995 0.000 0.036 0.000 0.000 0.144 0.820
#> GSM564651 5 0.0632 0.9267 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM564652 5 0.0632 0.9257 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM564653 5 0.0363 0.9265 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM564654 3 0.0146 0.9531 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564655 6 0.4567 0.7071 0.000 0.096 0.012 0.004 0.156 0.732
#> GSM564656 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564657 3 0.0146 0.9531 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564658 5 0.0146 0.9263 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM564659 3 0.4640 0.7539 0.000 0.140 0.728 0.004 0.012 0.116
#> GSM564660 6 0.0914 0.8831 0.000 0.016 0.000 0.000 0.016 0.968
#> GSM564661 5 0.0363 0.9265 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM564662 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564663 5 0.0260 0.9265 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM564664 5 0.0790 0.9215 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564665 3 0.1155 0.9428 0.000 0.036 0.956 0.004 0.000 0.004
#> GSM564666 6 0.1049 0.8836 0.000 0.032 0.000 0.000 0.008 0.960
#> GSM564667 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564668 3 0.3977 0.8293 0.000 0.108 0.800 0.004 0.032 0.056
#> GSM564669 3 0.0146 0.9531 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564670 5 0.7153 0.2453 0.000 0.152 0.284 0.004 0.440 0.120
#> GSM564671 6 0.2113 0.8622 0.000 0.092 0.000 0.004 0.008 0.896
#> GSM564672 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564673 5 0.0458 0.9266 0.000 0.016 0.000 0.000 0.984 0.000
#> GSM564674 5 0.0363 0.9270 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM564675 6 0.0909 0.8839 0.000 0.020 0.000 0.000 0.012 0.968
#> GSM564676 5 0.0790 0.9215 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564677 5 0.0858 0.9253 0.000 0.028 0.000 0.000 0.968 0.004
#> GSM564678 5 0.0790 0.9215 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564679 5 0.0713 0.9222 0.000 0.028 0.000 0.000 0.972 0.000
#> GSM564680 3 0.0146 0.9531 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564682 3 0.1010 0.9443 0.000 0.036 0.960 0.000 0.000 0.004
#> GSM564683 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564684 6 0.1970 0.8674 0.000 0.092 0.000 0.000 0.008 0.900
#> GSM564685 3 0.0146 0.9529 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564686 6 0.2113 0.8622 0.000 0.092 0.000 0.004 0.008 0.896
#> GSM564687 5 0.0291 0.9272 0.000 0.004 0.000 0.000 0.992 0.004
#> GSM564688 5 0.0603 0.9266 0.000 0.016 0.000 0.000 0.980 0.004
#> GSM564689 5 0.0790 0.9215 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564690 5 0.0790 0.9215 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564691 3 0.0935 0.9470 0.000 0.032 0.964 0.004 0.000 0.000
#> GSM564692 5 0.1594 0.9066 0.000 0.052 0.000 0.000 0.932 0.016
#> GSM564694 6 0.2636 0.8362 0.000 0.120 0.000 0.004 0.016 0.860
#> GSM564695 6 0.1151 0.8843 0.000 0.032 0.000 0.000 0.012 0.956
#> GSM564696 3 0.1861 0.9282 0.000 0.036 0.928 0.020 0.000 0.016
#> GSM564697 5 0.2001 0.8986 0.000 0.040 0.000 0.000 0.912 0.048
#> GSM564698 3 0.1152 0.9388 0.000 0.044 0.952 0.004 0.000 0.000
#> GSM564700 6 0.2113 0.8622 0.000 0.092 0.000 0.004 0.008 0.896
#> GSM564701 5 0.0146 0.9263 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM564702 5 0.0777 0.9249 0.000 0.024 0.000 0.000 0.972 0.004
#> GSM564703 4 0.2902 0.7713 0.004 0.000 0.196 0.800 0.000 0.000
#> GSM564704 1 0.4452 0.1840 0.572 0.400 0.000 0.024 0.000 0.004
#> GSM564705 1 0.0260 0.8594 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM564706 4 0.2838 0.7768 0.004 0.000 0.188 0.808 0.000 0.000
#> GSM564707 1 0.0260 0.8594 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM564708 4 0.2946 0.7783 0.004 0.004 0.184 0.808 0.000 0.000
#> GSM564709 2 0.4407 0.1228 0.480 0.496 0.000 0.024 0.000 0.000
#> GSM564710 1 0.0260 0.8594 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM564711 4 0.0520 0.8049 0.000 0.008 0.008 0.984 0.000 0.000
#> GSM564712 1 0.1026 0.8573 0.968 0.012 0.000 0.008 0.008 0.004
#> GSM564713 4 0.0820 0.8071 0.000 0.016 0.012 0.972 0.000 0.000
#> GSM564714 4 0.2006 0.8022 0.000 0.004 0.104 0.892 0.000 0.000
#> GSM564715 1 0.0260 0.8594 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM564716 1 0.3878 0.4486 0.644 0.348 0.000 0.004 0.000 0.004
#> GSM564717 1 0.0717 0.8542 0.976 0.016 0.000 0.000 0.008 0.000
#> GSM564718 4 0.1265 0.7875 0.000 0.044 0.000 0.948 0.000 0.008
#> GSM564719 1 0.0806 0.8520 0.972 0.020 0.000 0.000 0.008 0.000
#> GSM564720 1 0.0520 0.8593 0.984 0.008 0.000 0.000 0.008 0.000
#> GSM564721 1 0.0260 0.8594 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM564722 4 0.4057 0.2459 0.000 0.388 0.000 0.600 0.000 0.012
#> GSM564723 1 0.0405 0.8593 0.988 0.004 0.000 0.000 0.008 0.000
#> GSM564724 4 0.0717 0.8045 0.000 0.016 0.008 0.976 0.000 0.000
#> GSM564725 1 0.3852 0.3641 0.612 0.384 0.000 0.004 0.000 0.000
#> GSM564726 4 0.4047 0.4774 0.000 0.296 0.000 0.676 0.000 0.028
#> GSM564727 2 0.4910 0.5009 0.312 0.620 0.000 0.052 0.000 0.016
#> GSM564728 2 0.4882 0.6395 0.000 0.660 0.000 0.188 0.000 0.152
#> GSM564729 2 0.5060 0.6899 0.084 0.712 0.000 0.132 0.000 0.072
#> GSM564730 1 0.1523 0.8465 0.940 0.044 0.000 0.008 0.000 0.008
#> GSM564731 4 0.1829 0.7869 0.036 0.028 0.000 0.928 0.000 0.008
#> GSM564732 2 0.4614 0.2980 0.416 0.548 0.000 0.032 0.000 0.004
#> GSM564733 4 0.1789 0.7899 0.032 0.044 0.000 0.924 0.000 0.000
#> GSM564734 1 0.3820 0.4710 0.660 0.332 0.000 0.004 0.000 0.004
#> GSM564735 4 0.2243 0.7454 0.000 0.112 0.004 0.880 0.000 0.004
#> GSM564736 4 0.0820 0.8071 0.000 0.016 0.012 0.972 0.000 0.000
#> GSM564737 1 0.0405 0.8593 0.988 0.004 0.000 0.000 0.008 0.000
#> GSM564738 4 0.1410 0.7892 0.000 0.044 0.004 0.944 0.000 0.008
#> GSM564739 4 0.3626 0.7624 0.084 0.012 0.092 0.812 0.000 0.000
#> GSM564740 2 0.5504 0.4982 0.000 0.560 0.000 0.188 0.000 0.252
#> GSM564741 4 0.0458 0.8082 0.000 0.000 0.016 0.984 0.000 0.000
#> GSM564742 4 0.2793 0.7691 0.000 0.000 0.200 0.800 0.000 0.000
#> GSM564743 1 0.1719 0.8405 0.928 0.056 0.000 0.008 0.000 0.008
#> GSM564744 1 0.1026 0.8572 0.968 0.012 0.000 0.008 0.008 0.004
#> GSM564745 1 0.2958 0.7603 0.824 0.160 0.000 0.008 0.000 0.008
#> GSM564746 1 0.1610 0.8231 0.916 0.084 0.000 0.000 0.000 0.000
#> GSM564747 4 0.0862 0.8012 0.016 0.008 0.000 0.972 0.000 0.004
#> GSM564748 4 0.2838 0.7768 0.004 0.000 0.188 0.808 0.000 0.000
#> GSM564749 1 0.0806 0.8520 0.972 0.020 0.000 0.000 0.008 0.000
#> GSM564750 4 0.4107 0.4935 0.000 0.280 0.000 0.684 0.000 0.036
#> GSM564751 4 0.2902 0.7713 0.004 0.000 0.196 0.800 0.000 0.000
#> GSM564752 4 0.5267 0.2354 0.000 0.320 0.000 0.560 0.000 0.120
#> GSM564753 4 0.2793 0.7691 0.000 0.000 0.200 0.800 0.000 0.000
#> GSM564754 1 0.0291 0.8593 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM564755 2 0.4946 0.6319 0.000 0.652 0.000 0.188 0.000 0.160
#> GSM564756 1 0.0924 0.8570 0.972 0.008 0.000 0.008 0.008 0.004
#> GSM564757 2 0.4977 0.6821 0.044 0.712 0.000 0.132 0.000 0.112
#> GSM564758 2 0.5013 0.6096 0.000 0.636 0.000 0.224 0.000 0.140
#> GSM564759 4 0.2809 0.7840 0.004 0.004 0.168 0.824 0.000 0.000
#> GSM564760 2 0.5135 0.4215 0.364 0.552 0.000 0.080 0.000 0.004
#> GSM564761 1 0.0622 0.8590 0.980 0.012 0.000 0.000 0.008 0.000
#> GSM564762 1 0.2932 0.7616 0.836 0.140 0.000 0.020 0.000 0.004
#> GSM564681 6 0.2230 0.8745 0.000 0.084 0.000 0.000 0.024 0.892
#> GSM564693 5 0.0603 0.9258 0.000 0.016 0.000 0.000 0.980 0.004
#> GSM564646 6 0.1970 0.8674 0.000 0.092 0.000 0.000 0.008 0.900
#> GSM564699 6 0.2062 0.8655 0.000 0.088 0.000 0.008 0.004 0.900
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 genotype/variation(p) disease.state(p) k
#> SD:kmeans 89 0.0323 0.900 2
#> SD:kmeans 101 0.0713 0.353 3
#> SD:kmeans 128 0.0986 0.212 4
#> SD:kmeans 145 0.0921 0.102 5
#> SD:kmeans 136 0.1509 0.334 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.471 0.609 0.782 0.4970 0.500 0.500
#> 3 3 0.967 0.924 0.958 0.3488 0.733 0.513
#> 4 4 0.846 0.867 0.930 0.1196 0.851 0.590
#> 5 5 0.839 0.814 0.902 0.0630 0.898 0.632
#> 6 6 0.772 0.735 0.834 0.0365 0.966 0.837
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
#> GSM564615 1 0.0000 0.716 1.000 0.000
#> GSM564616 2 0.9998 0.575 0.492 0.508
#> GSM564617 2 0.9998 0.575 0.492 0.508
#> GSM564618 2 0.9998 0.575 0.492 0.508
#> GSM564619 1 0.0000 0.716 1.000 0.000
#> GSM564620 1 0.0000 0.716 1.000 0.000
#> GSM564621 1 0.0000 0.716 1.000 0.000
#> GSM564622 2 0.9998 0.575 0.492 0.508
#> GSM564623 2 0.9998 0.575 0.492 0.508
#> GSM564624 2 0.9998 0.575 0.492 0.508
#> GSM564625 1 0.0000 0.716 1.000 0.000
#> GSM564626 1 0.0000 0.716 1.000 0.000
#> GSM564627 1 0.0000 0.716 1.000 0.000
#> GSM564628 2 0.9998 0.575 0.492 0.508
#> GSM564629 1 0.0000 0.716 1.000 0.000
#> GSM564630 2 0.9998 0.575 0.492 0.508
#> GSM564609 2 0.0000 0.617 0.000 1.000
#> GSM564610 1 0.0000 0.716 1.000 0.000
#> GSM564611 1 0.0000 0.716 1.000 0.000
#> GSM564612 2 0.0000 0.617 0.000 1.000
#> GSM564613 2 0.0000 0.617 0.000 1.000
#> GSM564614 1 0.3584 0.685 0.932 0.068
#> GSM564631 2 0.0000 0.617 0.000 1.000
#> GSM564632 2 0.0000 0.617 0.000 1.000
#> GSM564633 2 0.0000 0.617 0.000 1.000
#> GSM564634 2 0.0938 0.615 0.012 0.988
#> GSM564635 2 0.0000 0.617 0.000 1.000
#> GSM564636 2 0.0000 0.617 0.000 1.000
#> GSM564637 2 0.0000 0.617 0.000 1.000
#> GSM564638 2 0.0000 0.617 0.000 1.000
#> GSM564639 2 0.0000 0.617 0.000 1.000
#> GSM564640 2 0.9998 0.575 0.492 0.508
#> GSM564641 2 0.0000 0.617 0.000 1.000
#> GSM564642 2 0.9998 0.575 0.492 0.508
#> GSM564643 2 0.0000 0.617 0.000 1.000
#> GSM564644 2 0.9998 0.575 0.492 0.508
#> GSM564645 2 0.0000 0.617 0.000 1.000
#> GSM564647 2 0.0000 0.617 0.000 1.000
#> GSM564648 2 0.9998 0.575 0.492 0.508
#> GSM564649 2 0.0000 0.617 0.000 1.000
#> GSM564650 2 0.9998 0.575 0.492 0.508
#> GSM564651 2 0.8661 0.572 0.288 0.712
#> GSM564652 2 0.9998 0.575 0.492 0.508
#> GSM564653 2 0.9998 0.575 0.492 0.508
#> GSM564654 2 0.0000 0.617 0.000 1.000
#> GSM564655 2 0.0000 0.617 0.000 1.000
#> GSM564656 2 0.0000 0.617 0.000 1.000
#> GSM564657 2 0.0000 0.617 0.000 1.000
#> GSM564658 2 0.9998 0.575 0.492 0.508
#> GSM564659 2 0.0000 0.617 0.000 1.000
#> GSM564660 2 0.9998 0.575 0.492 0.508
#> GSM564661 2 0.9998 0.575 0.492 0.508
#> GSM564662 2 0.0000 0.617 0.000 1.000
#> GSM564663 2 0.9998 0.575 0.492 0.508
#> GSM564664 2 0.9998 0.575 0.492 0.508
#> GSM564665 2 0.0000 0.617 0.000 1.000
#> GSM564666 2 0.0000 0.617 0.000 1.000
#> GSM564667 2 0.0000 0.617 0.000 1.000
#> GSM564668 2 0.0000 0.617 0.000 1.000
#> GSM564669 2 0.0000 0.617 0.000 1.000
#> GSM564670 2 0.0000 0.617 0.000 1.000
#> GSM564671 2 0.9998 0.575 0.492 0.508
#> GSM564672 2 0.0000 0.617 0.000 1.000
#> GSM564673 2 0.9998 0.575 0.492 0.508
#> GSM564674 2 0.9998 0.575 0.492 0.508
#> GSM564675 2 0.9998 0.575 0.492 0.508
#> GSM564676 2 0.9998 0.575 0.492 0.508
#> GSM564677 2 0.9998 0.575 0.492 0.508
#> GSM564678 2 0.9998 0.575 0.492 0.508
#> GSM564679 2 0.9998 0.575 0.492 0.508
#> GSM564680 2 0.0000 0.617 0.000 1.000
#> GSM564682 2 0.0000 0.617 0.000 1.000
#> GSM564683 2 0.0000 0.617 0.000 1.000
#> GSM564684 2 0.9998 0.575 0.492 0.508
#> GSM564685 2 0.0000 0.617 0.000 1.000
#> GSM564686 2 0.9983 0.572 0.476 0.524
#> GSM564687 2 0.9998 0.575 0.492 0.508
#> GSM564688 2 0.9998 0.575 0.492 0.508
#> GSM564689 2 0.9998 0.575 0.492 0.508
#> GSM564690 2 0.9998 0.575 0.492 0.508
#> GSM564691 2 0.0000 0.617 0.000 1.000
#> GSM564692 2 0.9998 0.575 0.492 0.508
#> GSM564694 2 0.0000 0.617 0.000 1.000
#> GSM564695 2 0.0000 0.617 0.000 1.000
#> GSM564696 2 0.0000 0.617 0.000 1.000
#> GSM564697 2 0.9998 0.575 0.492 0.508
#> GSM564698 2 0.0000 0.617 0.000 1.000
#> GSM564700 2 0.9998 0.575 0.492 0.508
#> GSM564701 2 0.9998 0.575 0.492 0.508
#> GSM564702 2 0.9998 0.575 0.492 0.508
#> GSM564703 1 0.9998 0.464 0.508 0.492
#> GSM564704 1 0.0000 0.716 1.000 0.000
#> GSM564705 1 0.0000 0.716 1.000 0.000
#> GSM564706 1 0.9998 0.464 0.508 0.492
#> GSM564707 1 0.0000 0.716 1.000 0.000
#> GSM564708 1 0.9998 0.464 0.508 0.492
#> GSM564709 1 0.0000 0.716 1.000 0.000
#> GSM564710 1 0.0000 0.716 1.000 0.000
#> GSM564711 1 0.9998 0.464 0.508 0.492
#> GSM564712 1 0.0000 0.716 1.000 0.000
#> GSM564713 1 0.9998 0.464 0.508 0.492
#> GSM564714 1 0.9998 0.464 0.508 0.492
#> GSM564715 1 0.0000 0.716 1.000 0.000
#> GSM564716 1 0.0000 0.716 1.000 0.000
#> GSM564717 1 0.0000 0.716 1.000 0.000
#> GSM564718 1 0.9998 0.464 0.508 0.492
#> GSM564719 1 0.0000 0.716 1.000 0.000
#> GSM564720 1 0.0000 0.716 1.000 0.000
#> GSM564721 1 0.0000 0.716 1.000 0.000
#> GSM564722 1 0.6623 0.629 0.828 0.172
#> GSM564723 1 0.0000 0.716 1.000 0.000
#> GSM564724 1 0.9998 0.464 0.508 0.492
#> GSM564725 1 0.0000 0.716 1.000 0.000
#> GSM564726 1 0.9998 0.464 0.508 0.492
#> GSM564727 1 0.0000 0.716 1.000 0.000
#> GSM564728 1 0.5946 0.646 0.856 0.144
#> GSM564729 1 0.0000 0.716 1.000 0.000
#> GSM564730 1 0.0000 0.716 1.000 0.000
#> GSM564731 1 0.9970 0.473 0.532 0.468
#> GSM564732 1 0.0000 0.716 1.000 0.000
#> GSM564733 1 0.9998 0.464 0.508 0.492
#> GSM564734 1 0.0000 0.716 1.000 0.000
#> GSM564735 1 0.9998 0.464 0.508 0.492
#> GSM564736 1 0.9998 0.464 0.508 0.492
#> GSM564737 1 0.0000 0.716 1.000 0.000
#> GSM564738 1 0.9998 0.464 0.508 0.492
#> GSM564739 1 0.9998 0.464 0.508 0.492
#> GSM564740 1 0.5519 0.654 0.872 0.128
#> GSM564741 1 0.9998 0.464 0.508 0.492
#> GSM564742 1 0.9998 0.464 0.508 0.492
#> GSM564743 1 0.0000 0.716 1.000 0.000
#> GSM564744 1 0.0000 0.716 1.000 0.000
#> GSM564745 1 0.0000 0.716 1.000 0.000
#> GSM564746 1 0.0000 0.716 1.000 0.000
#> GSM564747 1 0.9993 0.467 0.516 0.484
#> GSM564748 1 0.9998 0.464 0.508 0.492
#> GSM564749 1 0.0000 0.716 1.000 0.000
#> GSM564750 1 0.9998 0.464 0.508 0.492
#> GSM564751 1 0.9998 0.464 0.508 0.492
#> GSM564752 1 0.9998 0.464 0.508 0.492
#> GSM564753 1 0.9998 0.464 0.508 0.492
#> GSM564754 1 0.0000 0.716 1.000 0.000
#> GSM564755 1 0.2043 0.702 0.968 0.032
#> GSM564756 1 0.0000 0.716 1.000 0.000
#> GSM564757 1 0.0000 0.716 1.000 0.000
#> GSM564758 1 0.8327 0.575 0.736 0.264
#> GSM564759 1 0.9998 0.464 0.508 0.492
#> GSM564760 1 0.0000 0.716 1.000 0.000
#> GSM564761 1 0.0000 0.716 1.000 0.000
#> GSM564762 1 0.0000 0.716 1.000 0.000
#> GSM564681 2 0.9998 0.575 0.492 0.508
#> GSM564693 2 0.9998 0.575 0.492 0.508
#> GSM564646 2 0.9998 0.575 0.492 0.508
#> GSM564699 2 0.0000 0.617 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564616 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564617 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564618 2 0.2165 0.929 0.064 0.936 0.000
#> GSM564619 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564620 1 0.2165 0.951 0.936 0.064 0.000
#> GSM564621 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564622 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564623 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564624 2 0.0424 0.950 0.008 0.992 0.000
#> GSM564625 1 0.0592 0.955 0.988 0.012 0.000
#> GSM564626 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564627 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564628 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564629 1 0.1860 0.953 0.948 0.052 0.000
#> GSM564630 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564609 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564610 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564611 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564612 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564613 2 0.4291 0.803 0.000 0.820 0.180
#> GSM564614 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564631 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564632 2 0.3713 0.900 0.032 0.892 0.076
#> GSM564633 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564634 2 0.1964 0.924 0.000 0.944 0.056
#> GSM564635 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564636 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564637 2 0.3528 0.892 0.016 0.892 0.092
#> GSM564638 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564639 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564640 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564641 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564642 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564643 2 0.2681 0.928 0.040 0.932 0.028
#> GSM564644 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564645 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564647 3 0.0592 0.954 0.000 0.012 0.988
#> GSM564648 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564649 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564650 2 0.2066 0.931 0.060 0.940 0.000
#> GSM564651 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564652 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564653 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564654 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564655 2 0.5785 0.552 0.000 0.668 0.332
#> GSM564656 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564657 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564658 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564659 3 0.1163 0.942 0.000 0.028 0.972
#> GSM564660 2 0.2066 0.931 0.060 0.940 0.000
#> GSM564661 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564662 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564663 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564664 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564665 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564666 2 0.4914 0.865 0.068 0.844 0.088
#> GSM564667 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564668 3 0.0892 0.949 0.000 0.020 0.980
#> GSM564669 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564670 2 0.6225 0.300 0.000 0.568 0.432
#> GSM564671 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564672 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564673 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564674 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564675 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564676 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564677 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564678 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564679 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564680 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564682 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564683 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564684 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564685 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564686 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564687 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564688 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564689 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564690 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564691 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564692 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564694 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564695 2 0.6811 0.697 0.064 0.716 0.220
#> GSM564696 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564697 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564698 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564700 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564701 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564702 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564703 3 0.0424 0.959 0.008 0.000 0.992
#> GSM564704 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564705 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564706 3 0.0424 0.959 0.008 0.000 0.992
#> GSM564707 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564708 3 0.0424 0.959 0.008 0.000 0.992
#> GSM564709 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564710 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564711 3 0.5835 0.527 0.340 0.000 0.660
#> GSM564712 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564713 3 0.1411 0.942 0.036 0.000 0.964
#> GSM564714 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564715 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564716 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564717 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564718 1 0.2878 0.866 0.904 0.000 0.096
#> GSM564719 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564720 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564721 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564722 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564723 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564724 3 0.3619 0.846 0.136 0.000 0.864
#> GSM564725 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564726 1 0.6215 0.160 0.572 0.000 0.428
#> GSM564727 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564728 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564729 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564730 1 0.1163 0.955 0.972 0.028 0.000
#> GSM564731 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564732 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564733 1 0.1289 0.942 0.968 0.000 0.032
#> GSM564734 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564735 3 0.2448 0.912 0.076 0.000 0.924
#> GSM564736 3 0.1411 0.942 0.036 0.000 0.964
#> GSM564737 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564738 3 0.1753 0.932 0.048 0.000 0.952
#> GSM564739 3 0.2537 0.901 0.080 0.000 0.920
#> GSM564740 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564741 3 0.0592 0.957 0.012 0.000 0.988
#> GSM564742 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564743 1 0.0747 0.955 0.984 0.016 0.000
#> GSM564744 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564745 1 0.0237 0.955 0.996 0.004 0.000
#> GSM564746 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564747 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564748 3 0.0237 0.961 0.004 0.000 0.996
#> GSM564749 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564750 3 0.6154 0.379 0.408 0.000 0.592
#> GSM564751 3 0.0424 0.959 0.008 0.000 0.992
#> GSM564752 3 0.5988 0.478 0.368 0.000 0.632
#> GSM564753 3 0.0000 0.962 0.000 0.000 1.000
#> GSM564754 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564755 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564756 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564757 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564758 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564759 3 0.0424 0.959 0.008 0.000 0.992
#> GSM564760 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564761 1 0.2261 0.951 0.932 0.068 0.000
#> GSM564762 1 0.0000 0.955 1.000 0.000 0.000
#> GSM564681 2 0.2165 0.929 0.064 0.936 0.000
#> GSM564693 2 0.0000 0.953 0.000 1.000 0.000
#> GSM564646 2 0.2261 0.927 0.068 0.932 0.000
#> GSM564699 3 0.5153 0.816 0.068 0.100 0.832
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.4898 0.2527 0.416 0.000 0.000 0.584
#> GSM564616 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564617 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564618 4 0.4585 0.5816 0.000 0.332 0.000 0.668
#> GSM564619 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564620 1 0.0524 0.9655 0.988 0.004 0.000 0.008
#> GSM564621 1 0.0817 0.9580 0.976 0.000 0.000 0.024
#> GSM564622 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564623 4 0.2704 0.8046 0.000 0.124 0.000 0.876
#> GSM564624 2 0.0336 0.9595 0.000 0.992 0.000 0.008
#> GSM564625 1 0.0336 0.9643 0.992 0.000 0.000 0.008
#> GSM564626 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564627 1 0.1302 0.9454 0.956 0.000 0.000 0.044
#> GSM564628 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564629 1 0.0376 0.9661 0.992 0.004 0.000 0.004
#> GSM564630 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564609 3 0.0469 0.9271 0.000 0.012 0.988 0.000
#> GSM564610 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564611 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564612 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564613 2 0.6147 0.3200 0.000 0.564 0.380 0.056
#> GSM564614 4 0.0469 0.8239 0.012 0.000 0.000 0.988
#> GSM564631 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564632 4 0.5613 0.7310 0.000 0.156 0.120 0.724
#> GSM564633 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564634 2 0.2149 0.8653 0.000 0.912 0.088 0.000
#> GSM564635 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564636 3 0.1118 0.9101 0.000 0.000 0.964 0.036
#> GSM564637 4 0.4514 0.7852 0.000 0.136 0.064 0.800
#> GSM564638 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564640 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564641 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564642 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564643 4 0.4804 0.7653 0.000 0.160 0.064 0.776
#> GSM564644 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564645 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564647 3 0.0336 0.9294 0.000 0.008 0.992 0.000
#> GSM564648 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564649 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564650 2 0.4761 0.2637 0.000 0.628 0.000 0.372
#> GSM564651 2 0.0336 0.9600 0.000 0.992 0.008 0.000
#> GSM564652 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564653 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564654 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564655 4 0.7235 0.4957 0.000 0.180 0.288 0.532
#> GSM564656 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564657 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564658 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564659 3 0.0336 0.9296 0.000 0.008 0.992 0.000
#> GSM564660 4 0.4697 0.5386 0.000 0.356 0.000 0.644
#> GSM564661 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564662 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564663 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564664 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564665 3 0.0188 0.9315 0.000 0.004 0.996 0.000
#> GSM564666 4 0.2002 0.8225 0.000 0.020 0.044 0.936
#> GSM564667 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564668 3 0.0336 0.9296 0.000 0.008 0.992 0.000
#> GSM564669 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564670 3 0.4830 0.3358 0.000 0.392 0.608 0.000
#> GSM564671 4 0.2530 0.8094 0.000 0.112 0.000 0.888
#> GSM564672 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564673 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564674 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM564675 4 0.3444 0.7670 0.000 0.184 0.000 0.816
#> GSM564676 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564677 2 0.0188 0.9626 0.000 0.996 0.000 0.004
#> GSM564678 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564679 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564680 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564682 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564683 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564684 4 0.3311 0.7764 0.000 0.172 0.000 0.828
#> GSM564685 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564686 4 0.2408 0.8121 0.000 0.104 0.000 0.896
#> GSM564687 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564688 2 0.0188 0.9626 0.000 0.996 0.000 0.004
#> GSM564689 2 0.0188 0.9626 0.000 0.996 0.000 0.004
#> GSM564690 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564691 3 0.0188 0.9314 0.000 0.004 0.996 0.000
#> GSM564692 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564694 4 0.4137 0.7433 0.000 0.208 0.012 0.780
#> GSM564695 4 0.3453 0.8102 0.000 0.052 0.080 0.868
#> GSM564696 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564697 2 0.0188 0.9626 0.000 0.996 0.000 0.004
#> GSM564698 3 0.0000 0.9332 0.000 0.000 1.000 0.000
#> GSM564700 4 0.2408 0.8121 0.000 0.104 0.000 0.896
#> GSM564701 2 0.0188 0.9654 0.004 0.996 0.000 0.000
#> GSM564702 2 0.0188 0.9626 0.000 0.996 0.000 0.004
#> GSM564703 3 0.2197 0.8967 0.004 0.000 0.916 0.080
#> GSM564704 1 0.0817 0.9582 0.976 0.000 0.000 0.024
#> GSM564705 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564706 3 0.2334 0.8920 0.004 0.000 0.908 0.088
#> GSM564707 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564708 3 0.2401 0.8895 0.004 0.000 0.904 0.092
#> GSM564709 1 0.1474 0.9403 0.948 0.000 0.000 0.052
#> GSM564710 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564711 4 0.6985 0.0217 0.116 0.000 0.404 0.480
#> GSM564712 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564713 3 0.3908 0.7741 0.004 0.000 0.784 0.212
#> GSM564714 3 0.2647 0.8710 0.000 0.000 0.880 0.120
#> GSM564715 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564716 1 0.1118 0.9520 0.964 0.000 0.000 0.036
#> GSM564717 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564718 4 0.0592 0.8226 0.016 0.000 0.000 0.984
#> GSM564719 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564720 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564721 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564722 4 0.3219 0.7125 0.164 0.000 0.000 0.836
#> GSM564723 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564724 3 0.5387 0.4212 0.016 0.000 0.584 0.400
#> GSM564725 1 0.0592 0.9615 0.984 0.000 0.000 0.016
#> GSM564726 4 0.0188 0.8236 0.004 0.000 0.000 0.996
#> GSM564727 1 0.3219 0.8233 0.836 0.000 0.000 0.164
#> GSM564728 4 0.0188 0.8236 0.004 0.000 0.000 0.996
#> GSM564729 4 0.4843 0.3183 0.396 0.000 0.000 0.604
#> GSM564730 1 0.0188 0.9655 0.996 0.000 0.000 0.004
#> GSM564731 1 0.3123 0.8406 0.844 0.000 0.000 0.156
#> GSM564732 1 0.1867 0.9231 0.928 0.000 0.000 0.072
#> GSM564733 1 0.4323 0.7594 0.776 0.000 0.020 0.204
#> GSM564734 1 0.0336 0.9643 0.992 0.000 0.000 0.008
#> GSM564735 4 0.2831 0.7455 0.004 0.000 0.120 0.876
#> GSM564736 3 0.4103 0.7179 0.000 0.000 0.744 0.256
#> GSM564737 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564738 4 0.2053 0.7890 0.004 0.000 0.072 0.924
#> GSM564739 3 0.5466 0.6737 0.220 0.000 0.712 0.068
#> GSM564740 4 0.0188 0.8236 0.004 0.000 0.000 0.996
#> GSM564741 3 0.4564 0.6014 0.000 0.000 0.672 0.328
#> GSM564742 3 0.1940 0.9006 0.000 0.000 0.924 0.076
#> GSM564743 1 0.0336 0.9643 0.992 0.000 0.000 0.008
#> GSM564744 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564745 1 0.0336 0.9643 0.992 0.000 0.000 0.008
#> GSM564746 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564747 1 0.3801 0.7568 0.780 0.000 0.000 0.220
#> GSM564748 3 0.2334 0.8920 0.004 0.000 0.908 0.088
#> GSM564749 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564750 4 0.0376 0.8233 0.004 0.000 0.004 0.992
#> GSM564751 3 0.2412 0.8930 0.008 0.000 0.908 0.084
#> GSM564752 4 0.0188 0.8236 0.004 0.000 0.000 0.996
#> GSM564753 3 0.1867 0.9024 0.000 0.000 0.928 0.072
#> GSM564754 1 0.0188 0.9650 0.996 0.000 0.000 0.004
#> GSM564755 4 0.0188 0.8236 0.004 0.000 0.000 0.996
#> GSM564756 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564757 4 0.3311 0.7258 0.172 0.000 0.000 0.828
#> GSM564758 4 0.0592 0.8238 0.016 0.000 0.000 0.984
#> GSM564759 3 0.2401 0.8896 0.004 0.000 0.904 0.092
#> GSM564760 1 0.3024 0.8475 0.852 0.000 0.000 0.148
#> GSM564761 1 0.0188 0.9668 0.996 0.004 0.000 0.000
#> GSM564762 1 0.1389 0.9431 0.952 0.000 0.000 0.048
#> GSM564681 4 0.4981 0.2828 0.000 0.464 0.000 0.536
#> GSM564693 2 0.0188 0.9626 0.000 0.996 0.000 0.004
#> GSM564646 4 0.3311 0.7769 0.000 0.172 0.000 0.828
#> GSM564699 4 0.1109 0.8240 0.000 0.004 0.028 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 1 0.6742 0.09401 0.408 0.000 0.316 0.276 0.000
#> GSM564616 2 0.1331 0.94453 0.000 0.952 0.040 0.008 0.000
#> GSM564617 2 0.1331 0.94618 0.000 0.952 0.040 0.008 0.000
#> GSM564618 3 0.1408 0.80237 0.000 0.044 0.948 0.008 0.000
#> GSM564619 1 0.0486 0.91380 0.988 0.004 0.004 0.004 0.000
#> GSM564620 1 0.0566 0.91377 0.984 0.004 0.000 0.012 0.000
#> GSM564621 1 0.2504 0.87414 0.896 0.000 0.040 0.064 0.000
#> GSM564622 2 0.2886 0.84301 0.000 0.844 0.148 0.008 0.000
#> GSM564623 3 0.0609 0.81488 0.000 0.000 0.980 0.020 0.000
#> GSM564624 2 0.4559 0.14634 0.000 0.512 0.480 0.008 0.000
#> GSM564625 1 0.1444 0.89858 0.948 0.000 0.012 0.040 0.000
#> GSM564626 1 0.0451 0.91400 0.988 0.004 0.000 0.008 0.000
#> GSM564627 1 0.3644 0.82189 0.824 0.000 0.096 0.080 0.000
#> GSM564628 2 0.1956 0.92612 0.000 0.916 0.076 0.008 0.000
#> GSM564629 1 0.0833 0.91147 0.976 0.004 0.004 0.016 0.000
#> GSM564630 2 0.0898 0.95093 0.000 0.972 0.020 0.008 0.000
#> GSM564609 5 0.0981 0.95206 0.000 0.008 0.012 0.008 0.972
#> GSM564610 1 0.0324 0.91433 0.992 0.004 0.000 0.004 0.000
#> GSM564611 1 0.0290 0.91366 0.992 0.008 0.000 0.000 0.000
#> GSM564612 5 0.0162 0.96728 0.000 0.000 0.000 0.004 0.996
#> GSM564613 5 0.5718 0.55565 0.000 0.168 0.160 0.012 0.660
#> GSM564614 3 0.5344 0.25632 0.052 0.000 0.500 0.448 0.000
#> GSM564631 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564632 3 0.3443 0.73793 0.000 0.012 0.840 0.028 0.120
#> GSM564633 5 0.0162 0.96705 0.000 0.000 0.000 0.004 0.996
#> GSM564634 2 0.3732 0.79895 0.000 0.820 0.056 0.004 0.120
#> GSM564635 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564636 5 0.1571 0.91117 0.000 0.000 0.060 0.004 0.936
#> GSM564637 3 0.4117 0.72219 0.000 0.048 0.804 0.020 0.128
#> GSM564638 5 0.0162 0.96705 0.000 0.000 0.000 0.004 0.996
#> GSM564639 5 0.0162 0.96728 0.000 0.000 0.000 0.004 0.996
#> GSM564640 2 0.0290 0.95456 0.000 0.992 0.008 0.000 0.000
#> GSM564641 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564642 2 0.0162 0.95465 0.000 0.996 0.004 0.000 0.000
#> GSM564643 3 0.2507 0.78019 0.000 0.012 0.900 0.016 0.072
#> GSM564644 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564645 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564647 5 0.0727 0.95732 0.000 0.004 0.012 0.004 0.980
#> GSM564648 2 0.0955 0.95100 0.000 0.968 0.028 0.004 0.000
#> GSM564649 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564650 3 0.3612 0.56906 0.000 0.268 0.732 0.000 0.000
#> GSM564651 2 0.0451 0.95305 0.000 0.988 0.004 0.000 0.008
#> GSM564652 2 0.0451 0.95470 0.000 0.988 0.008 0.004 0.000
#> GSM564653 2 0.0162 0.95461 0.000 0.996 0.004 0.000 0.000
#> GSM564654 5 0.0162 0.96728 0.000 0.000 0.000 0.004 0.996
#> GSM564655 3 0.6689 0.17961 0.000 0.096 0.464 0.040 0.400
#> GSM564656 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564657 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564658 2 0.0451 0.95421 0.000 0.988 0.008 0.004 0.000
#> GSM564659 5 0.1934 0.90983 0.000 0.004 0.052 0.016 0.928
#> GSM564660 3 0.1877 0.79603 0.000 0.064 0.924 0.012 0.000
#> GSM564661 2 0.0162 0.95461 0.000 0.996 0.004 0.000 0.000
#> GSM564662 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564663 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564664 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564665 5 0.0162 0.96651 0.000 0.000 0.004 0.000 0.996
#> GSM564666 3 0.1197 0.81121 0.000 0.000 0.952 0.048 0.000
#> GSM564667 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564668 5 0.0807 0.95549 0.000 0.000 0.012 0.012 0.976
#> GSM564669 5 0.0162 0.96728 0.000 0.000 0.000 0.004 0.996
#> GSM564670 5 0.4240 0.73401 0.000 0.148 0.048 0.016 0.788
#> GSM564671 3 0.0703 0.81360 0.000 0.000 0.976 0.024 0.000
#> GSM564672 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564673 2 0.0162 0.95461 0.000 0.996 0.004 0.000 0.000
#> GSM564674 2 0.0404 0.95431 0.000 0.988 0.012 0.000 0.000
#> GSM564675 3 0.1012 0.81515 0.000 0.012 0.968 0.020 0.000
#> GSM564676 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564677 2 0.1544 0.92792 0.000 0.932 0.068 0.000 0.000
#> GSM564678 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564679 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564680 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564682 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564683 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564684 3 0.0566 0.81510 0.000 0.004 0.984 0.012 0.000
#> GSM564685 5 0.0000 0.96827 0.000 0.000 0.000 0.000 1.000
#> GSM564686 3 0.0963 0.81242 0.000 0.000 0.964 0.036 0.000
#> GSM564687 2 0.0290 0.95487 0.000 0.992 0.008 0.000 0.000
#> GSM564688 2 0.1043 0.94512 0.000 0.960 0.040 0.000 0.000
#> GSM564689 2 0.1270 0.93682 0.000 0.948 0.052 0.000 0.000
#> GSM564690 2 0.0162 0.95449 0.000 0.996 0.004 0.000 0.000
#> GSM564691 5 0.0162 0.96705 0.000 0.000 0.000 0.004 0.996
#> GSM564692 2 0.0771 0.95189 0.000 0.976 0.020 0.004 0.000
#> GSM564694 3 0.1967 0.80546 0.000 0.036 0.932 0.012 0.020
#> GSM564695 3 0.1329 0.81484 0.000 0.004 0.956 0.032 0.008
#> GSM564696 5 0.0880 0.93918 0.000 0.000 0.000 0.032 0.968
#> GSM564697 2 0.2020 0.88919 0.000 0.900 0.100 0.000 0.000
#> GSM564698 5 0.0162 0.96728 0.000 0.000 0.000 0.004 0.996
#> GSM564700 3 0.0880 0.81300 0.000 0.000 0.968 0.032 0.000
#> GSM564701 2 0.0162 0.95461 0.000 0.996 0.004 0.000 0.000
#> GSM564702 2 0.1502 0.93788 0.000 0.940 0.056 0.004 0.000
#> GSM564703 4 0.4225 0.57287 0.004 0.000 0.000 0.632 0.364
#> GSM564704 1 0.3106 0.82881 0.840 0.000 0.020 0.140 0.000
#> GSM564705 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564706 4 0.4066 0.63078 0.004 0.000 0.000 0.672 0.324
#> GSM564707 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564708 4 0.3715 0.69498 0.004 0.000 0.000 0.736 0.260
#> GSM564709 1 0.3506 0.82698 0.832 0.000 0.064 0.104 0.000
#> GSM564710 1 0.0324 0.91433 0.992 0.004 0.000 0.004 0.000
#> GSM564711 4 0.0955 0.74496 0.000 0.000 0.004 0.968 0.028
#> GSM564712 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564713 4 0.1121 0.74962 0.000 0.000 0.000 0.956 0.044
#> GSM564714 4 0.3109 0.72398 0.000 0.000 0.000 0.800 0.200
#> GSM564715 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564716 1 0.3769 0.78546 0.788 0.000 0.032 0.180 0.000
#> GSM564717 1 0.0404 0.91256 0.988 0.012 0.000 0.000 0.000
#> GSM564718 4 0.0794 0.72983 0.000 0.000 0.028 0.972 0.000
#> GSM564719 1 0.0609 0.90827 0.980 0.020 0.000 0.000 0.000
#> GSM564720 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564721 1 0.0000 0.91411 1.000 0.000 0.000 0.000 0.000
#> GSM564722 4 0.2522 0.66949 0.012 0.000 0.108 0.880 0.000
#> GSM564723 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564724 4 0.0912 0.74043 0.000 0.000 0.012 0.972 0.016
#> GSM564725 1 0.2848 0.85430 0.868 0.000 0.028 0.104 0.000
#> GSM564726 4 0.2763 0.63207 0.004 0.000 0.148 0.848 0.000
#> GSM564727 1 0.5435 0.63736 0.660 0.000 0.152 0.188 0.000
#> GSM564728 3 0.4288 0.45058 0.004 0.000 0.612 0.384 0.000
#> GSM564729 1 0.6767 -0.00888 0.380 0.000 0.348 0.272 0.000
#> GSM564730 1 0.0324 0.91306 0.992 0.000 0.004 0.004 0.000
#> GSM564731 4 0.2439 0.69664 0.120 0.000 0.004 0.876 0.000
#> GSM564732 1 0.3994 0.79677 0.792 0.000 0.068 0.140 0.000
#> GSM564733 4 0.2017 0.70439 0.080 0.000 0.008 0.912 0.000
#> GSM564734 1 0.1430 0.89428 0.944 0.000 0.004 0.052 0.000
#> GSM564735 4 0.0771 0.73359 0.000 0.000 0.020 0.976 0.004
#> GSM564736 4 0.1043 0.74874 0.000 0.000 0.000 0.960 0.040
#> GSM564737 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564738 4 0.0865 0.73182 0.000 0.000 0.024 0.972 0.004
#> GSM564739 4 0.5268 0.66837 0.112 0.000 0.000 0.668 0.220
#> GSM564740 3 0.4196 0.49871 0.004 0.000 0.640 0.356 0.000
#> GSM564741 4 0.1430 0.74920 0.000 0.000 0.004 0.944 0.052
#> GSM564742 4 0.4015 0.59722 0.000 0.000 0.000 0.652 0.348
#> GSM564743 1 0.0324 0.91443 0.992 0.004 0.004 0.000 0.000
#> GSM564744 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564745 1 0.0566 0.91088 0.984 0.000 0.004 0.012 0.000
#> GSM564746 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564747 4 0.2411 0.70382 0.108 0.000 0.008 0.884 0.000
#> GSM564748 4 0.3966 0.61513 0.000 0.000 0.000 0.664 0.336
#> GSM564749 1 0.0404 0.91245 0.988 0.012 0.000 0.000 0.000
#> GSM564750 4 0.2929 0.58911 0.000 0.000 0.180 0.820 0.000
#> GSM564751 4 0.4114 0.55388 0.000 0.000 0.000 0.624 0.376
#> GSM564752 4 0.4264 0.15027 0.004 0.000 0.376 0.620 0.000
#> GSM564753 4 0.4138 0.53782 0.000 0.000 0.000 0.616 0.384
#> GSM564754 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564755 3 0.4182 0.50030 0.004 0.000 0.644 0.352 0.000
#> GSM564756 1 0.0162 0.91446 0.996 0.004 0.000 0.000 0.000
#> GSM564757 3 0.6523 0.30597 0.232 0.000 0.480 0.288 0.000
#> GSM564758 4 0.4731 -0.11871 0.016 0.000 0.456 0.528 0.000
#> GSM564759 4 0.3730 0.66898 0.000 0.000 0.000 0.712 0.288
#> GSM564760 1 0.5149 0.66900 0.680 0.000 0.104 0.216 0.000
#> GSM564761 1 0.0324 0.91433 0.992 0.004 0.000 0.004 0.000
#> GSM564762 1 0.3333 0.75574 0.788 0.000 0.004 0.208 0.000
#> GSM564681 3 0.1894 0.78917 0.000 0.072 0.920 0.008 0.000
#> GSM564693 2 0.1430 0.94047 0.000 0.944 0.052 0.004 0.000
#> GSM564646 3 0.0693 0.81517 0.000 0.012 0.980 0.008 0.000
#> GSM564699 3 0.1341 0.80750 0.000 0.000 0.944 0.056 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.6538 0.6706 0.124 0.556 0.000 0.156 0.000 0.164
#> GSM564616 5 0.3210 0.8610 0.000 0.152 0.000 0.000 0.812 0.036
#> GSM564617 5 0.3387 0.8541 0.000 0.164 0.000 0.000 0.796 0.040
#> GSM564618 6 0.3141 0.7770 0.000 0.112 0.000 0.004 0.048 0.836
#> GSM564619 1 0.2597 0.7674 0.824 0.176 0.000 0.000 0.000 0.000
#> GSM564620 1 0.3198 0.7177 0.740 0.260 0.000 0.000 0.000 0.000
#> GSM564621 1 0.4580 0.2573 0.488 0.484 0.000 0.016 0.000 0.012
#> GSM564622 5 0.5228 0.6141 0.000 0.192 0.000 0.000 0.612 0.196
#> GSM564623 6 0.1970 0.8012 0.000 0.092 0.000 0.008 0.000 0.900
#> GSM564624 6 0.5510 0.2496 0.000 0.144 0.000 0.000 0.340 0.516
#> GSM564625 1 0.4226 0.4847 0.580 0.404 0.000 0.012 0.000 0.004
#> GSM564626 1 0.2562 0.7708 0.828 0.172 0.000 0.000 0.000 0.000
#> GSM564627 2 0.4858 -0.1847 0.440 0.516 0.000 0.024 0.000 0.020
#> GSM564628 5 0.4108 0.8093 0.000 0.164 0.000 0.000 0.744 0.092
#> GSM564629 1 0.3330 0.6891 0.716 0.284 0.000 0.000 0.000 0.000
#> GSM564630 5 0.2971 0.8702 0.004 0.144 0.000 0.000 0.832 0.020
#> GSM564609 3 0.2407 0.9041 0.000 0.072 0.896 0.008 0.016 0.008
#> GSM564610 1 0.1765 0.8038 0.904 0.096 0.000 0.000 0.000 0.000
#> GSM564611 1 0.0508 0.8039 0.984 0.004 0.000 0.000 0.012 0.000
#> GSM564612 3 0.0260 0.9434 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM564613 3 0.7003 0.3571 0.000 0.144 0.516 0.008 0.156 0.176
#> GSM564614 2 0.6116 0.5638 0.020 0.512 0.000 0.268 0.000 0.200
#> GSM564631 3 0.0000 0.9429 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564632 6 0.3850 0.7584 0.000 0.084 0.076 0.008 0.020 0.812
#> GSM564633 3 0.0713 0.9404 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM564634 5 0.5390 0.6952 0.000 0.092 0.132 0.004 0.692 0.080
#> GSM564635 3 0.0146 0.9429 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564636 3 0.2649 0.8794 0.000 0.036 0.880 0.012 0.000 0.072
#> GSM564637 6 0.3162 0.7779 0.000 0.064 0.064 0.000 0.020 0.852
#> GSM564638 3 0.0777 0.9408 0.000 0.024 0.972 0.000 0.000 0.004
#> GSM564639 3 0.0405 0.9426 0.000 0.008 0.988 0.004 0.000 0.000
#> GSM564640 5 0.1410 0.9210 0.004 0.044 0.000 0.000 0.944 0.008
#> GSM564641 3 0.0692 0.9399 0.000 0.020 0.976 0.000 0.000 0.004
#> GSM564642 5 0.1296 0.9197 0.000 0.044 0.004 0.000 0.948 0.004
#> GSM564643 6 0.2852 0.7723 0.000 0.064 0.080 0.000 0.000 0.856
#> GSM564644 5 0.1340 0.9153 0.008 0.040 0.000 0.000 0.948 0.004
#> GSM564645 3 0.0146 0.9429 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564647 3 0.2208 0.9119 0.000 0.052 0.912 0.008 0.012 0.016
#> GSM564648 5 0.1895 0.9128 0.000 0.072 0.000 0.000 0.912 0.016
#> GSM564649 3 0.0146 0.9432 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564650 6 0.4032 0.6571 0.000 0.068 0.000 0.000 0.192 0.740
#> GSM564651 5 0.1621 0.9132 0.004 0.048 0.008 0.000 0.936 0.004
#> GSM564652 5 0.1625 0.9165 0.000 0.060 0.000 0.000 0.928 0.012
#> GSM564653 5 0.0547 0.9169 0.000 0.020 0.000 0.000 0.980 0.000
#> GSM564654 3 0.0692 0.9409 0.000 0.020 0.976 0.004 0.000 0.000
#> GSM564655 6 0.7268 0.4067 0.000 0.092 0.256 0.048 0.108 0.496
#> GSM564656 3 0.0520 0.9429 0.000 0.008 0.984 0.008 0.000 0.000
#> GSM564657 3 0.0146 0.9429 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564658 5 0.1531 0.9186 0.000 0.068 0.000 0.000 0.928 0.004
#> GSM564659 3 0.3301 0.8347 0.000 0.068 0.828 0.004 0.000 0.100
#> GSM564660 6 0.2393 0.8004 0.000 0.040 0.000 0.004 0.064 0.892
#> GSM564661 5 0.0692 0.9174 0.000 0.020 0.000 0.000 0.976 0.004
#> GSM564662 3 0.0146 0.9429 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564663 5 0.1152 0.9200 0.000 0.044 0.000 0.000 0.952 0.004
#> GSM564664 5 0.1003 0.9143 0.004 0.028 0.000 0.000 0.964 0.004
#> GSM564665 3 0.1553 0.9271 0.000 0.032 0.944 0.004 0.008 0.012
#> GSM564666 6 0.1528 0.8047 0.000 0.048 0.000 0.016 0.000 0.936
#> GSM564667 3 0.0146 0.9430 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM564668 3 0.2332 0.9090 0.000 0.060 0.904 0.008 0.016 0.012
#> GSM564669 3 0.0547 0.9423 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM564670 3 0.5495 0.6608 0.000 0.144 0.676 0.004 0.116 0.060
#> GSM564671 6 0.1444 0.7951 0.000 0.072 0.000 0.000 0.000 0.928
#> GSM564672 3 0.0146 0.9429 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564673 5 0.0790 0.9189 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564674 5 0.1471 0.9174 0.000 0.064 0.000 0.000 0.932 0.004
#> GSM564675 6 0.1334 0.8140 0.000 0.032 0.000 0.000 0.020 0.948
#> GSM564676 5 0.0951 0.9148 0.008 0.020 0.000 0.000 0.968 0.004
#> GSM564677 5 0.2843 0.8590 0.000 0.036 0.000 0.000 0.848 0.116
#> GSM564678 5 0.0922 0.9145 0.004 0.024 0.000 0.000 0.968 0.004
#> GSM564679 5 0.0692 0.9155 0.004 0.020 0.000 0.000 0.976 0.000
#> GSM564680 3 0.0146 0.9429 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564682 3 0.1340 0.9312 0.000 0.040 0.948 0.000 0.004 0.008
#> GSM564683 3 0.0146 0.9429 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564684 6 0.0937 0.8046 0.000 0.040 0.000 0.000 0.000 0.960
#> GSM564685 3 0.0146 0.9429 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564686 6 0.0937 0.8053 0.000 0.040 0.000 0.000 0.000 0.960
#> GSM564687 5 0.1462 0.9197 0.000 0.056 0.000 0.000 0.936 0.008
#> GSM564688 5 0.2376 0.8941 0.000 0.044 0.000 0.000 0.888 0.068
#> GSM564689 5 0.3076 0.8552 0.004 0.044 0.000 0.000 0.840 0.112
#> GSM564690 5 0.0922 0.9148 0.004 0.024 0.000 0.000 0.968 0.004
#> GSM564691 3 0.0748 0.9405 0.000 0.016 0.976 0.000 0.004 0.004
#> GSM564692 5 0.1967 0.9078 0.000 0.084 0.000 0.000 0.904 0.012
#> GSM564694 6 0.3340 0.7854 0.000 0.100 0.032 0.004 0.024 0.840
#> GSM564695 6 0.1882 0.8055 0.000 0.060 0.008 0.012 0.000 0.920
#> GSM564696 3 0.2698 0.8723 0.000 0.040 0.880 0.064 0.000 0.016
#> GSM564697 5 0.3588 0.7957 0.000 0.060 0.000 0.000 0.788 0.152
#> GSM564698 3 0.0790 0.9391 0.000 0.032 0.968 0.000 0.000 0.000
#> GSM564700 6 0.1204 0.8005 0.000 0.056 0.000 0.000 0.000 0.944
#> GSM564701 5 0.1003 0.9188 0.004 0.028 0.000 0.000 0.964 0.004
#> GSM564702 5 0.2672 0.8996 0.000 0.080 0.000 0.000 0.868 0.052
#> GSM564703 4 0.3644 0.6653 0.008 0.008 0.252 0.732 0.000 0.000
#> GSM564704 1 0.5506 0.1235 0.524 0.372 0.000 0.088 0.000 0.016
#> GSM564705 1 0.0146 0.8069 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564706 4 0.3564 0.6947 0.008 0.020 0.200 0.772 0.000 0.000
#> GSM564707 1 0.0603 0.8109 0.980 0.016 0.000 0.004 0.000 0.000
#> GSM564708 4 0.3277 0.7017 0.004 0.016 0.188 0.792 0.000 0.000
#> GSM564709 1 0.5878 -0.0588 0.484 0.396 0.000 0.072 0.000 0.048
#> GSM564710 1 0.0458 0.8112 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM564711 4 0.1728 0.7040 0.008 0.064 0.000 0.924 0.000 0.004
#> GSM564712 1 0.0547 0.8117 0.980 0.020 0.000 0.000 0.000 0.000
#> GSM564713 4 0.1320 0.7135 0.000 0.036 0.016 0.948 0.000 0.000
#> GSM564714 4 0.2723 0.7171 0.000 0.016 0.128 0.852 0.000 0.004
#> GSM564715 1 0.0937 0.8128 0.960 0.040 0.000 0.000 0.000 0.000
#> GSM564716 1 0.5280 0.3718 0.544 0.376 0.000 0.060 0.000 0.020
#> GSM564717 1 0.1116 0.8033 0.960 0.028 0.000 0.008 0.004 0.000
#> GSM564718 4 0.2805 0.6498 0.000 0.160 0.000 0.828 0.000 0.012
#> GSM564719 1 0.1148 0.7987 0.960 0.020 0.000 0.004 0.016 0.000
#> GSM564720 1 0.0260 0.8086 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM564721 1 0.1204 0.8129 0.944 0.056 0.000 0.000 0.000 0.000
#> GSM564722 4 0.5064 0.0925 0.004 0.372 0.000 0.552 0.000 0.072
#> GSM564723 1 0.0260 0.8095 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM564724 4 0.1908 0.6960 0.000 0.096 0.000 0.900 0.000 0.004
#> GSM564725 1 0.5241 0.3073 0.528 0.396 0.000 0.060 0.000 0.016
#> GSM564726 4 0.4700 0.3693 0.000 0.268 0.000 0.648 0.000 0.084
#> GSM564727 2 0.6203 0.4952 0.264 0.556 0.000 0.092 0.000 0.088
#> GSM564728 2 0.5919 0.5097 0.000 0.452 0.000 0.228 0.000 0.320
#> GSM564729 2 0.6571 0.6647 0.104 0.544 0.000 0.188 0.000 0.164
#> GSM564730 1 0.2320 0.7817 0.864 0.132 0.000 0.004 0.000 0.000
#> GSM564731 4 0.3787 0.5911 0.120 0.100 0.000 0.780 0.000 0.000
#> GSM564732 2 0.5755 0.2367 0.380 0.504 0.000 0.084 0.000 0.032
#> GSM564733 4 0.3624 0.6140 0.060 0.156 0.000 0.784 0.000 0.000
#> GSM564734 1 0.4726 0.4383 0.628 0.316 0.000 0.044 0.000 0.012
#> GSM564735 4 0.2118 0.6769 0.000 0.104 0.000 0.888 0.000 0.008
#> GSM564736 4 0.1398 0.7087 0.000 0.052 0.008 0.940 0.000 0.000
#> GSM564737 1 0.0458 0.8111 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM564738 4 0.2214 0.6834 0.000 0.092 0.004 0.892 0.000 0.012
#> GSM564739 4 0.4640 0.6425 0.128 0.020 0.124 0.728 0.000 0.000
#> GSM564740 6 0.6012 -0.4061 0.000 0.348 0.000 0.244 0.000 0.408
#> GSM564741 4 0.1572 0.7160 0.000 0.036 0.028 0.936 0.000 0.000
#> GSM564742 4 0.3518 0.6658 0.000 0.012 0.256 0.732 0.000 0.000
#> GSM564743 1 0.2615 0.7638 0.852 0.136 0.000 0.004 0.000 0.008
#> GSM564744 1 0.0632 0.8110 0.976 0.024 0.000 0.000 0.000 0.000
#> GSM564745 1 0.3420 0.6878 0.748 0.240 0.000 0.012 0.000 0.000
#> GSM564746 1 0.2597 0.7688 0.824 0.176 0.000 0.000 0.000 0.000
#> GSM564747 4 0.3731 0.6255 0.072 0.124 0.000 0.796 0.000 0.008
#> GSM564748 4 0.3570 0.6813 0.004 0.016 0.228 0.752 0.000 0.000
#> GSM564749 1 0.0622 0.8063 0.980 0.012 0.000 0.000 0.008 0.000
#> GSM564750 4 0.4639 0.3870 0.000 0.256 0.000 0.660 0.000 0.084
#> GSM564751 4 0.3882 0.6577 0.012 0.012 0.260 0.716 0.000 0.000
#> GSM564752 4 0.5751 -0.0333 0.000 0.276 0.000 0.508 0.000 0.216
#> GSM564753 4 0.3595 0.6376 0.000 0.008 0.288 0.704 0.000 0.000
#> GSM564754 1 0.1075 0.8132 0.952 0.048 0.000 0.000 0.000 0.000
#> GSM564755 2 0.5992 0.4814 0.000 0.420 0.000 0.240 0.000 0.340
#> GSM564756 1 0.1082 0.8084 0.956 0.040 0.000 0.000 0.004 0.000
#> GSM564757 2 0.6351 0.6521 0.060 0.544 0.000 0.172 0.000 0.224
#> GSM564758 2 0.6260 0.3999 0.008 0.412 0.000 0.300 0.000 0.280
#> GSM564759 4 0.3361 0.7009 0.004 0.020 0.188 0.788 0.000 0.000
#> GSM564760 2 0.6189 0.4226 0.308 0.516 0.000 0.132 0.000 0.044
#> GSM564761 1 0.1141 0.8095 0.948 0.052 0.000 0.000 0.000 0.000
#> GSM564762 1 0.5637 0.3435 0.556 0.252 0.000 0.188 0.000 0.004
#> GSM564681 6 0.2442 0.7909 0.000 0.048 0.000 0.000 0.068 0.884
#> GSM564693 5 0.2451 0.8986 0.000 0.056 0.000 0.000 0.884 0.060
#> GSM564646 6 0.1007 0.8036 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM564699 6 0.1528 0.8030 0.000 0.048 0.000 0.016 0.000 0.936
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 genotype/variation(p) disease.state(p) k
#> SD:skmeans 130 0.3330 0.6740 2
#> SD:skmeans 150 0.0552 0.4398 3
#> SD:skmeans 145 0.0931 0.2428 4
#> SD:skmeans 144 0.0311 0.0976 5
#> SD:skmeans 132 0.0707 0.3115 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 154 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 0.457 0.786 0.896 0.4906 0.511 0.511
#> 3 3 0.615 0.801 0.883 0.3556 0.770 0.574
#> 4 4 0.751 0.766 0.893 0.1264 0.846 0.588
#> 5 5 0.691 0.606 0.785 0.0596 0.925 0.723
#> 6 6 0.728 0.636 0.797 0.0428 0.893 0.558
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
#> GSM564615 1 0.9129 0.535 0.672 0.328
#> GSM564616 1 0.1184 0.878 0.984 0.016
#> GSM564617 1 0.0672 0.877 0.992 0.008
#> GSM564618 1 0.7883 0.701 0.764 0.236
#> GSM564619 1 0.0000 0.876 1.000 0.000
#> GSM564620 1 0.3879 0.853 0.924 0.076
#> GSM564621 1 0.4815 0.832 0.896 0.104
#> GSM564622 1 0.9922 0.211 0.552 0.448
#> GSM564623 1 0.6801 0.783 0.820 0.180
#> GSM564624 1 0.3431 0.865 0.936 0.064
#> GSM564625 1 0.4562 0.838 0.904 0.096
#> GSM564626 1 0.0000 0.876 1.000 0.000
#> GSM564627 1 0.3584 0.854 0.932 0.068
#> GSM564628 1 0.1414 0.877 0.980 0.020
#> GSM564629 1 0.0672 0.876 0.992 0.008
#> GSM564630 1 0.0672 0.877 0.992 0.008
#> GSM564609 2 0.5946 0.814 0.144 0.856
#> GSM564610 1 0.0000 0.876 1.000 0.000
#> GSM564611 1 0.0672 0.877 0.992 0.008
#> GSM564612 2 0.4562 0.838 0.096 0.904
#> GSM564613 1 0.9833 0.290 0.576 0.424
#> GSM564614 1 0.9988 0.060 0.520 0.480
#> GSM564631 2 0.0000 0.884 0.000 1.000
#> GSM564632 2 0.6531 0.799 0.168 0.832
#> GSM564633 2 0.0000 0.884 0.000 1.000
#> GSM564634 1 0.7950 0.693 0.760 0.240
#> GSM564635 2 0.0376 0.884 0.004 0.996
#> GSM564636 2 0.0000 0.884 0.000 1.000
#> GSM564637 2 0.7602 0.722 0.220 0.780
#> GSM564638 2 0.0000 0.884 0.000 1.000
#> GSM564639 2 0.0000 0.884 0.000 1.000
#> GSM564640 1 0.0672 0.877 0.992 0.008
#> GSM564641 2 0.2236 0.875 0.036 0.964
#> GSM564642 1 0.2778 0.871 0.952 0.048
#> GSM564643 2 0.4161 0.853 0.084 0.916
#> GSM564644 1 0.2423 0.874 0.960 0.040
#> GSM564645 2 0.0000 0.884 0.000 1.000
#> GSM564647 2 0.5946 0.805 0.144 0.856
#> GSM564648 1 0.3879 0.858 0.924 0.076
#> GSM564649 2 0.0672 0.883 0.008 0.992
#> GSM564650 1 0.8608 0.618 0.716 0.284
#> GSM564651 1 0.9909 0.245 0.556 0.444
#> GSM564652 1 0.2043 0.876 0.968 0.032
#> GSM564653 1 0.0938 0.877 0.988 0.012
#> GSM564654 2 0.0000 0.884 0.000 1.000
#> GSM564655 2 0.3114 0.876 0.056 0.944
#> GSM564656 2 0.0000 0.884 0.000 1.000
#> GSM564657 2 0.4022 0.850 0.080 0.920
#> GSM564658 1 0.0672 0.877 0.992 0.008
#> GSM564659 2 0.5059 0.833 0.112 0.888
#> GSM564660 1 0.3733 0.857 0.928 0.072
#> GSM564661 1 0.2043 0.875 0.968 0.032
#> GSM564662 2 0.0000 0.884 0.000 1.000
#> GSM564663 1 0.2043 0.875 0.968 0.032
#> GSM564664 1 0.6048 0.797 0.852 0.148
#> GSM564665 2 0.4161 0.849 0.084 0.916
#> GSM564666 2 0.8661 0.589 0.288 0.712
#> GSM564667 2 0.4431 0.841 0.092 0.908
#> GSM564668 2 0.2778 0.874 0.048 0.952
#> GSM564669 2 0.0000 0.884 0.000 1.000
#> GSM564670 2 0.7602 0.734 0.220 0.780
#> GSM564671 1 0.8555 0.668 0.720 0.280
#> GSM564672 2 0.0000 0.884 0.000 1.000
#> GSM564673 1 0.4815 0.842 0.896 0.104
#> GSM564674 1 0.0672 0.877 0.992 0.008
#> GSM564675 1 0.8443 0.640 0.728 0.272
#> GSM564676 1 0.1184 0.877 0.984 0.016
#> GSM564677 1 0.0938 0.877 0.988 0.012
#> GSM564678 1 0.0938 0.877 0.988 0.012
#> GSM564679 1 0.0672 0.877 0.992 0.008
#> GSM564680 2 0.0000 0.884 0.000 1.000
#> GSM564682 2 0.6712 0.768 0.176 0.824
#> GSM564683 2 0.0000 0.884 0.000 1.000
#> GSM564684 1 0.4161 0.857 0.916 0.084
#> GSM564685 2 0.0000 0.884 0.000 1.000
#> GSM564686 2 0.9522 0.380 0.372 0.628
#> GSM564687 1 0.3114 0.867 0.944 0.056
#> GSM564688 1 0.8327 0.650 0.736 0.264
#> GSM564689 1 0.3274 0.865 0.940 0.060
#> GSM564690 1 0.2948 0.870 0.948 0.052
#> GSM564691 2 0.7950 0.691 0.240 0.760
#> GSM564692 1 0.4815 0.838 0.896 0.104
#> GSM564694 1 0.9795 0.349 0.584 0.416
#> GSM564695 2 0.4298 0.858 0.088 0.912
#> GSM564696 2 0.0000 0.884 0.000 1.000
#> GSM564697 1 0.1414 0.878 0.980 0.020
#> GSM564698 2 0.0000 0.884 0.000 1.000
#> GSM564700 1 0.9732 0.434 0.596 0.404
#> GSM564701 1 0.1184 0.877 0.984 0.016
#> GSM564702 1 0.4022 0.855 0.920 0.080
#> GSM564703 2 0.2236 0.878 0.036 0.964
#> GSM564704 1 0.4562 0.836 0.904 0.096
#> GSM564705 1 0.0672 0.877 0.992 0.008
#> GSM564706 2 0.3274 0.868 0.060 0.940
#> GSM564707 1 0.3584 0.865 0.932 0.068
#> GSM564708 2 0.0672 0.884 0.008 0.992
#> GSM564709 1 0.4562 0.836 0.904 0.096
#> GSM564710 1 0.0672 0.877 0.992 0.008
#> GSM564711 2 0.6801 0.762 0.180 0.820
#> GSM564712 1 0.0000 0.876 1.000 0.000
#> GSM564713 2 0.4939 0.843 0.108 0.892
#> GSM564714 2 0.0000 0.884 0.000 1.000
#> GSM564715 1 0.3274 0.861 0.940 0.060
#> GSM564716 1 0.7299 0.748 0.796 0.204
#> GSM564717 1 0.0672 0.877 0.992 0.008
#> GSM564718 2 0.8267 0.654 0.260 0.740
#> GSM564719 1 0.0672 0.877 0.992 0.008
#> GSM564720 1 0.0000 0.876 1.000 0.000
#> GSM564721 1 0.0000 0.876 1.000 0.000
#> GSM564722 2 0.9944 0.203 0.456 0.544
#> GSM564723 1 0.0000 0.876 1.000 0.000
#> GSM564724 2 0.2043 0.879 0.032 0.968
#> GSM564725 1 0.6712 0.774 0.824 0.176
#> GSM564726 2 0.5737 0.817 0.136 0.864
#> GSM564727 1 0.6247 0.793 0.844 0.156
#> GSM564728 2 0.9522 0.416 0.372 0.628
#> GSM564729 1 0.9909 0.204 0.556 0.444
#> GSM564730 1 0.2236 0.868 0.964 0.036
#> GSM564731 1 0.9129 0.548 0.672 0.328
#> GSM564732 1 0.4562 0.836 0.904 0.096
#> GSM564733 2 0.9491 0.435 0.368 0.632
#> GSM564734 1 0.0000 0.876 1.000 0.000
#> GSM564735 2 0.4690 0.846 0.100 0.900
#> GSM564736 2 0.1184 0.882 0.016 0.984
#> GSM564737 1 0.0376 0.877 0.996 0.004
#> GSM564738 2 0.0672 0.881 0.008 0.992
#> GSM564739 2 0.5294 0.827 0.120 0.880
#> GSM564740 1 0.6623 0.784 0.828 0.172
#> GSM564741 2 0.0672 0.881 0.008 0.992
#> GSM564742 2 0.0000 0.884 0.000 1.000
#> GSM564743 1 0.0000 0.876 1.000 0.000
#> GSM564744 1 0.0000 0.876 1.000 0.000
#> GSM564745 1 0.0000 0.876 1.000 0.000
#> GSM564746 1 0.1184 0.877 0.984 0.016
#> GSM564747 2 0.9954 0.152 0.460 0.540
#> GSM564748 2 0.4022 0.856 0.080 0.920
#> GSM564749 1 0.0672 0.877 0.992 0.008
#> GSM564750 2 0.5946 0.809 0.144 0.856
#> GSM564751 2 0.2236 0.877 0.036 0.964
#> GSM564752 2 0.4298 0.854 0.088 0.912
#> GSM564753 2 0.0000 0.884 0.000 1.000
#> GSM564754 1 0.4431 0.839 0.908 0.092
#> GSM564755 1 0.9977 0.124 0.528 0.472
#> GSM564756 1 0.0000 0.876 1.000 0.000
#> GSM564757 1 0.6438 0.786 0.836 0.164
#> GSM564758 2 0.9977 0.119 0.472 0.528
#> GSM564759 2 0.0376 0.884 0.004 0.996
#> GSM564760 1 0.7056 0.758 0.808 0.192
#> GSM564761 1 0.0376 0.877 0.996 0.004
#> GSM564762 1 0.4431 0.839 0.908 0.092
#> GSM564681 1 0.1633 0.877 0.976 0.024
#> GSM564693 1 0.8207 0.680 0.744 0.256
#> GSM564646 1 0.5946 0.799 0.856 0.144
#> GSM564699 2 0.1184 0.882 0.016 0.984
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0892 0.8717 0.980 0.020 0.000
#> GSM564616 2 0.0892 0.8446 0.020 0.980 0.000
#> GSM564617 2 0.1129 0.8438 0.020 0.976 0.004
#> GSM564618 1 0.4452 0.7814 0.808 0.192 0.000
#> GSM564619 2 0.3116 0.8329 0.108 0.892 0.000
#> GSM564620 2 0.5905 0.3414 0.352 0.648 0.000
#> GSM564621 1 0.4654 0.8132 0.792 0.208 0.000
#> GSM564622 2 0.6062 0.5671 0.016 0.708 0.276
#> GSM564623 1 0.4062 0.8039 0.836 0.164 0.000
#> GSM564624 2 0.2682 0.8210 0.076 0.920 0.004
#> GSM564625 1 0.4887 0.8039 0.772 0.228 0.000
#> GSM564626 2 0.5905 0.5552 0.352 0.648 0.000
#> GSM564627 1 0.4346 0.8155 0.816 0.184 0.000
#> GSM564628 2 0.2301 0.8303 0.060 0.936 0.004
#> GSM564629 2 0.5178 0.6175 0.256 0.744 0.000
#> GSM564630 2 0.0237 0.8458 0.004 0.996 0.000
#> GSM564609 3 0.2625 0.8595 0.000 0.084 0.916
#> GSM564610 2 0.4654 0.7836 0.208 0.792 0.000
#> GSM564611 2 0.3941 0.8112 0.156 0.844 0.000
#> GSM564612 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564613 2 0.3921 0.7889 0.016 0.872 0.112
#> GSM564614 1 0.0424 0.8687 0.992 0.008 0.000
#> GSM564631 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564632 3 0.8494 0.4971 0.236 0.156 0.608
#> GSM564633 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564634 2 0.4963 0.7346 0.008 0.792 0.200
#> GSM564635 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564636 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564637 3 0.5798 0.7266 0.044 0.176 0.780
#> GSM564638 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564639 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564640 2 0.0475 0.8460 0.004 0.992 0.004
#> GSM564641 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564642 2 0.2063 0.8498 0.044 0.948 0.008
#> GSM564643 3 0.6062 0.7351 0.072 0.148 0.780
#> GSM564644 2 0.0829 0.8468 0.012 0.984 0.004
#> GSM564645 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564647 3 0.0237 0.9165 0.000 0.004 0.996
#> GSM564648 2 0.1751 0.8417 0.028 0.960 0.012
#> GSM564649 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564650 2 0.2682 0.8210 0.076 0.920 0.004
#> GSM564651 2 0.5138 0.6811 0.000 0.748 0.252
#> GSM564652 2 0.4733 0.8156 0.196 0.800 0.004
#> GSM564653 2 0.1315 0.8443 0.020 0.972 0.008
#> GSM564654 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564655 3 0.1751 0.8979 0.012 0.028 0.960
#> GSM564656 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564657 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564658 2 0.0000 0.8457 0.000 1.000 0.000
#> GSM564659 3 0.0892 0.9078 0.000 0.020 0.980
#> GSM564660 2 0.5948 0.4178 0.360 0.640 0.000
#> GSM564661 2 0.3918 0.8355 0.120 0.868 0.012
#> GSM564662 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564663 2 0.0829 0.8467 0.012 0.984 0.004
#> GSM564664 2 0.1031 0.8439 0.000 0.976 0.024
#> GSM564665 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564666 1 0.4233 0.8055 0.836 0.160 0.004
#> GSM564667 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564668 3 0.0592 0.9132 0.000 0.012 0.988
#> GSM564669 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564670 3 0.4978 0.7287 0.004 0.216 0.780
#> GSM564671 1 0.3879 0.8123 0.848 0.152 0.000
#> GSM564672 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564673 2 0.2031 0.8383 0.016 0.952 0.032
#> GSM564674 2 0.0983 0.8467 0.016 0.980 0.004
#> GSM564675 1 0.6600 0.4487 0.604 0.384 0.012
#> GSM564676 2 0.0237 0.8458 0.000 0.996 0.004
#> GSM564677 2 0.2796 0.8316 0.092 0.908 0.000
#> GSM564678 2 0.0829 0.8449 0.012 0.984 0.004
#> GSM564679 2 0.0475 0.8459 0.004 0.992 0.004
#> GSM564680 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564682 3 0.0237 0.9165 0.000 0.004 0.996
#> GSM564683 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564684 1 0.4121 0.8009 0.832 0.168 0.000
#> GSM564685 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564686 1 0.4172 0.8081 0.840 0.156 0.004
#> GSM564687 2 0.1399 0.8424 0.028 0.968 0.004
#> GSM564688 2 0.2680 0.8245 0.068 0.924 0.008
#> GSM564689 2 0.2682 0.8210 0.076 0.920 0.004
#> GSM564690 2 0.0661 0.8465 0.008 0.988 0.004
#> GSM564691 3 0.3482 0.8137 0.000 0.128 0.872
#> GSM564692 2 0.1999 0.8371 0.012 0.952 0.036
#> GSM564694 2 0.6286 0.7157 0.136 0.772 0.092
#> GSM564695 3 0.7885 0.5816 0.212 0.128 0.660
#> GSM564696 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564697 2 0.1399 0.8423 0.028 0.968 0.004
#> GSM564698 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564700 1 0.4062 0.8040 0.836 0.164 0.000
#> GSM564701 2 0.0237 0.8458 0.000 0.996 0.004
#> GSM564702 2 0.3644 0.8386 0.124 0.872 0.004
#> GSM564703 3 0.0424 0.9147 0.000 0.008 0.992
#> GSM564704 1 0.2448 0.8500 0.924 0.076 0.000
#> GSM564705 2 0.4235 0.8032 0.176 0.824 0.000
#> GSM564706 3 0.1491 0.9014 0.016 0.016 0.968
#> GSM564707 2 0.4178 0.8066 0.172 0.828 0.000
#> GSM564708 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564709 1 0.1643 0.8662 0.956 0.044 0.000
#> GSM564710 2 0.4555 0.7898 0.200 0.800 0.000
#> GSM564711 3 0.4937 0.7777 0.148 0.028 0.824
#> GSM564712 2 0.5254 0.7337 0.264 0.736 0.000
#> GSM564713 3 0.7285 0.4811 0.320 0.048 0.632
#> GSM564714 3 0.0592 0.9122 0.012 0.000 0.988
#> GSM564715 2 0.4654 0.7840 0.208 0.792 0.000
#> GSM564716 1 0.2165 0.8620 0.936 0.064 0.000
#> GSM564717 2 0.4062 0.8083 0.164 0.836 0.000
#> GSM564718 1 0.1620 0.8698 0.964 0.024 0.012
#> GSM564719 2 0.3941 0.8112 0.156 0.844 0.000
#> GSM564720 2 0.4062 0.8080 0.164 0.836 0.000
#> GSM564721 2 0.4291 0.8015 0.180 0.820 0.000
#> GSM564722 1 0.1315 0.8698 0.972 0.020 0.008
#> GSM564723 2 0.4235 0.8035 0.176 0.824 0.000
#> GSM564724 3 0.6309 -0.0568 0.496 0.000 0.504
#> GSM564725 1 0.1753 0.8642 0.952 0.048 0.000
#> GSM564726 1 0.1878 0.8637 0.952 0.004 0.044
#> GSM564727 1 0.1753 0.8649 0.952 0.048 0.000
#> GSM564728 1 0.1129 0.8695 0.976 0.020 0.004
#> GSM564729 1 0.0592 0.8697 0.988 0.012 0.000
#> GSM564730 2 0.5905 0.6354 0.352 0.648 0.000
#> GSM564731 1 0.2448 0.8490 0.924 0.076 0.000
#> GSM564732 1 0.1643 0.8656 0.956 0.044 0.000
#> GSM564733 3 0.8243 0.1762 0.420 0.076 0.504
#> GSM564734 2 0.6215 0.4332 0.428 0.572 0.000
#> GSM564735 1 0.4139 0.8197 0.860 0.016 0.124
#> GSM564736 3 0.4605 0.7142 0.204 0.000 0.796
#> GSM564737 2 0.4291 0.8022 0.180 0.820 0.000
#> GSM564738 1 0.5859 0.4883 0.656 0.000 0.344
#> GSM564739 3 0.3967 0.8343 0.072 0.044 0.884
#> GSM564740 1 0.1529 0.8660 0.960 0.040 0.000
#> GSM564741 3 0.4750 0.6931 0.216 0.000 0.784
#> GSM564742 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564743 2 0.5968 0.6174 0.364 0.636 0.000
#> GSM564744 2 0.4399 0.7973 0.188 0.812 0.000
#> GSM564745 1 0.2625 0.8448 0.916 0.084 0.000
#> GSM564746 2 0.1860 0.8402 0.052 0.948 0.000
#> GSM564747 1 0.6291 0.7363 0.768 0.080 0.152
#> GSM564748 3 0.1315 0.9041 0.008 0.020 0.972
#> GSM564749 2 0.4002 0.8097 0.160 0.840 0.000
#> GSM564750 1 0.3889 0.8439 0.884 0.032 0.084
#> GSM564751 3 0.0237 0.9164 0.004 0.000 0.996
#> GSM564752 1 0.1989 0.8598 0.948 0.004 0.048
#> GSM564753 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564754 1 0.5431 0.5483 0.716 0.284 0.000
#> GSM564755 1 0.0000 0.8684 1.000 0.000 0.000
#> GSM564756 2 0.4178 0.8046 0.172 0.828 0.000
#> GSM564757 1 0.0237 0.8686 0.996 0.004 0.000
#> GSM564758 1 0.2200 0.8651 0.940 0.056 0.004
#> GSM564759 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM564760 1 0.1989 0.8646 0.948 0.048 0.004
#> GSM564761 2 0.4346 0.8003 0.184 0.816 0.000
#> GSM564762 1 0.2356 0.8520 0.928 0.072 0.000
#> GSM564681 2 0.6295 0.1519 0.472 0.528 0.000
#> GSM564693 2 0.3141 0.8212 0.068 0.912 0.020
#> GSM564646 1 0.4062 0.8040 0.836 0.164 0.000
#> GSM564699 1 0.4784 0.7133 0.796 0.004 0.200
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.3726 0.6827 0.212 0.000 0.000 0.788
#> GSM564616 2 0.2813 0.8482 0.080 0.896 0.000 0.024
#> GSM564617 2 0.2882 0.8442 0.084 0.892 0.000 0.024
#> GSM564618 4 0.1854 0.8023 0.012 0.048 0.000 0.940
#> GSM564619 1 0.1118 0.8773 0.964 0.036 0.000 0.000
#> GSM564620 1 0.1389 0.8712 0.952 0.048 0.000 0.000
#> GSM564621 4 0.5823 0.4355 0.348 0.044 0.000 0.608
#> GSM564622 2 0.8284 0.2503 0.284 0.452 0.240 0.024
#> GSM564623 4 0.1398 0.8070 0.004 0.040 0.000 0.956
#> GSM564624 2 0.3300 0.7974 0.008 0.848 0.000 0.144
#> GSM564625 1 0.1635 0.8725 0.948 0.044 0.000 0.008
#> GSM564626 1 0.0592 0.8832 0.984 0.016 0.000 0.000
#> GSM564627 4 0.4711 0.6427 0.236 0.024 0.000 0.740
#> GSM564628 2 0.3570 0.8259 0.092 0.860 0.000 0.048
#> GSM564629 1 0.1398 0.8747 0.956 0.040 0.000 0.004
#> GSM564630 2 0.1411 0.8822 0.020 0.960 0.000 0.020
#> GSM564609 3 0.1305 0.8804 0.004 0.036 0.960 0.000
#> GSM564610 1 0.0376 0.8852 0.992 0.004 0.000 0.004
#> GSM564611 2 0.1867 0.8649 0.072 0.928 0.000 0.000
#> GSM564612 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564613 2 0.1639 0.8755 0.004 0.952 0.036 0.008
#> GSM564614 4 0.0188 0.8189 0.004 0.000 0.000 0.996
#> GSM564631 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564632 3 0.4967 0.2809 0.000 0.000 0.548 0.452
#> GSM564633 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564634 2 0.3494 0.7567 0.004 0.824 0.172 0.000
#> GSM564635 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564636 3 0.0188 0.9010 0.000 0.000 0.996 0.004
#> GSM564637 3 0.6548 0.4874 0.000 0.116 0.608 0.276
#> GSM564638 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564640 2 0.0000 0.8895 0.000 1.000 0.000 0.000
#> GSM564641 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564642 2 0.0188 0.8900 0.004 0.996 0.000 0.000
#> GSM564643 3 0.5476 0.5416 0.004 0.028 0.660 0.308
#> GSM564644 2 0.0336 0.8901 0.008 0.992 0.000 0.000
#> GSM564645 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564647 3 0.0469 0.8974 0.000 0.012 0.988 0.000
#> GSM564648 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564649 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564650 2 0.4621 0.6116 0.000 0.708 0.008 0.284
#> GSM564651 2 0.1792 0.8547 0.000 0.932 0.068 0.000
#> GSM564652 1 0.5126 0.1716 0.552 0.444 0.000 0.004
#> GSM564653 2 0.0000 0.8895 0.000 1.000 0.000 0.000
#> GSM564654 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564655 3 0.4194 0.7448 0.000 0.028 0.800 0.172
#> GSM564656 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564657 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564658 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564659 3 0.0937 0.8918 0.000 0.012 0.976 0.012
#> GSM564660 4 0.4961 0.0466 0.000 0.448 0.000 0.552
#> GSM564661 2 0.1118 0.8829 0.036 0.964 0.000 0.000
#> GSM564662 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564663 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564664 2 0.0336 0.8901 0.008 0.992 0.000 0.000
#> GSM564665 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564666 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564667 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564668 3 0.1557 0.8698 0.000 0.056 0.944 0.000
#> GSM564669 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564670 3 0.4277 0.7456 0.004 0.172 0.800 0.024
#> GSM564671 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564672 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564673 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564674 2 0.1792 0.8661 0.068 0.932 0.000 0.000
#> GSM564675 4 0.3973 0.6461 0.004 0.200 0.004 0.792
#> GSM564676 2 0.0469 0.8896 0.012 0.988 0.000 0.000
#> GSM564677 2 0.2976 0.8220 0.008 0.872 0.000 0.120
#> GSM564678 2 0.0336 0.8901 0.008 0.992 0.000 0.000
#> GSM564679 2 0.0336 0.8901 0.008 0.992 0.000 0.000
#> GSM564680 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564682 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564683 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564684 4 0.0336 0.8173 0.000 0.008 0.000 0.992
#> GSM564685 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564686 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564687 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564688 2 0.1211 0.8776 0.000 0.960 0.000 0.040
#> GSM564689 2 0.0336 0.8901 0.008 0.992 0.000 0.000
#> GSM564690 2 0.0336 0.8901 0.008 0.992 0.000 0.000
#> GSM564691 3 0.3024 0.7857 0.000 0.148 0.852 0.000
#> GSM564692 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564694 2 0.5853 0.3471 0.004 0.564 0.028 0.404
#> GSM564695 3 0.5203 0.3577 0.000 0.008 0.576 0.416
#> GSM564696 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564697 2 0.0707 0.8853 0.000 0.980 0.000 0.020
#> GSM564698 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564700 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564701 2 0.0188 0.8895 0.004 0.996 0.000 0.000
#> GSM564702 2 0.4991 0.3615 0.388 0.608 0.000 0.004
#> GSM564703 3 0.4500 0.5367 0.316 0.000 0.684 0.000
#> GSM564704 1 0.1474 0.8661 0.948 0.000 0.000 0.052
#> GSM564705 1 0.1022 0.8817 0.968 0.032 0.000 0.000
#> GSM564706 3 0.1716 0.8623 0.064 0.000 0.936 0.000
#> GSM564707 1 0.0188 0.8844 0.996 0.004 0.000 0.000
#> GSM564708 3 0.0817 0.8907 0.024 0.000 0.976 0.000
#> GSM564709 4 0.5296 0.0318 0.492 0.008 0.000 0.500
#> GSM564710 1 0.0188 0.8844 0.996 0.004 0.000 0.000
#> GSM564711 3 0.5604 0.0595 0.476 0.000 0.504 0.020
#> GSM564712 1 0.0188 0.8836 0.996 0.000 0.000 0.004
#> GSM564713 3 0.6635 0.5453 0.176 0.008 0.652 0.164
#> GSM564714 3 0.0469 0.8967 0.000 0.000 0.988 0.012
#> GSM564715 1 0.0188 0.8844 0.996 0.004 0.000 0.000
#> GSM564716 1 0.1305 0.8758 0.960 0.004 0.000 0.036
#> GSM564717 2 0.3528 0.7546 0.192 0.808 0.000 0.000
#> GSM564718 4 0.1716 0.7972 0.064 0.000 0.000 0.936
#> GSM564719 2 0.1389 0.8745 0.048 0.952 0.000 0.000
#> GSM564720 1 0.1474 0.8689 0.948 0.052 0.000 0.000
#> GSM564721 1 0.2345 0.8318 0.900 0.100 0.000 0.000
#> GSM564722 4 0.3402 0.7304 0.164 0.000 0.004 0.832
#> GSM564723 1 0.0524 0.8850 0.988 0.008 0.000 0.004
#> GSM564724 4 0.5296 0.0485 0.008 0.000 0.492 0.500
#> GSM564725 1 0.4720 0.4767 0.672 0.004 0.000 0.324
#> GSM564726 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564727 4 0.4277 0.5909 0.280 0.000 0.000 0.720
#> GSM564728 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564729 4 0.3801 0.6753 0.220 0.000 0.000 0.780
#> GSM564730 1 0.0336 0.8839 0.992 0.000 0.000 0.008
#> GSM564731 1 0.3172 0.7644 0.840 0.000 0.000 0.160
#> GSM564732 1 0.4522 0.5049 0.680 0.000 0.000 0.320
#> GSM564733 1 0.2197 0.8369 0.916 0.000 0.080 0.004
#> GSM564734 1 0.1256 0.8816 0.964 0.028 0.000 0.008
#> GSM564735 4 0.5894 0.6265 0.108 0.000 0.200 0.692
#> GSM564736 3 0.4399 0.6671 0.020 0.000 0.768 0.212
#> GSM564737 1 0.0188 0.8844 0.996 0.004 0.000 0.000
#> GSM564738 4 0.4564 0.4898 0.000 0.000 0.328 0.672
#> GSM564739 1 0.4454 0.5263 0.692 0.000 0.308 0.000
#> GSM564740 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564741 3 0.3907 0.6555 0.000 0.000 0.768 0.232
#> GSM564742 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564743 1 0.2737 0.8222 0.888 0.104 0.000 0.008
#> GSM564744 1 0.1398 0.8757 0.956 0.040 0.000 0.004
#> GSM564745 1 0.0469 0.8830 0.988 0.000 0.000 0.012
#> GSM564746 1 0.1211 0.8742 0.960 0.040 0.000 0.000
#> GSM564747 1 0.2759 0.8439 0.904 0.000 0.052 0.044
#> GSM564748 3 0.3726 0.7173 0.212 0.000 0.788 0.000
#> GSM564749 2 0.4746 0.4512 0.368 0.632 0.000 0.000
#> GSM564750 4 0.4231 0.7478 0.096 0.000 0.080 0.824
#> GSM564751 3 0.0188 0.9010 0.004 0.000 0.996 0.000
#> GSM564752 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564753 3 0.0000 0.9026 0.000 0.000 1.000 0.000
#> GSM564754 1 0.0188 0.8844 0.996 0.004 0.000 0.000
#> GSM564755 4 0.0000 0.8192 0.000 0.000 0.000 1.000
#> GSM564756 2 0.4989 0.1242 0.472 0.528 0.000 0.000
#> GSM564757 4 0.0817 0.8131 0.024 0.000 0.000 0.976
#> GSM564758 4 0.4406 0.5575 0.300 0.000 0.000 0.700
#> GSM564759 3 0.0336 0.8992 0.008 0.000 0.992 0.000
#> GSM564760 1 0.4382 0.5469 0.704 0.000 0.000 0.296
#> GSM564761 1 0.0469 0.8855 0.988 0.012 0.000 0.000
#> GSM564762 1 0.4955 0.1329 0.556 0.000 0.000 0.444
#> GSM564681 4 0.5174 0.2952 0.012 0.368 0.000 0.620
#> GSM564693 2 0.1576 0.8724 0.000 0.948 0.004 0.048
#> GSM564646 4 0.0188 0.8184 0.000 0.004 0.000 0.996
#> GSM564699 4 0.0188 0.8179 0.000 0.000 0.004 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.6032 0.2095 0.116 0.000 0.000 0.460 0.424
#> GSM564616 2 0.5709 0.2913 0.060 0.476 0.000 0.008 0.456
#> GSM564617 2 0.5829 0.4403 0.080 0.548 0.000 0.008 0.364
#> GSM564618 5 0.1883 0.4670 0.008 0.012 0.000 0.048 0.932
#> GSM564619 1 0.1990 0.8046 0.920 0.004 0.000 0.008 0.068
#> GSM564620 1 0.4727 0.5199 0.636 0.012 0.000 0.012 0.340
#> GSM564621 5 0.6461 0.0665 0.356 0.012 0.000 0.136 0.496
#> GSM564622 5 0.8409 0.1266 0.216 0.220 0.136 0.012 0.416
#> GSM564623 5 0.3093 0.3902 0.000 0.008 0.000 0.168 0.824
#> GSM564624 5 0.3861 0.2583 0.000 0.284 0.000 0.004 0.712
#> GSM564625 1 0.3734 0.7183 0.792 0.008 0.000 0.016 0.184
#> GSM564626 1 0.2929 0.7598 0.840 0.000 0.000 0.008 0.152
#> GSM564627 5 0.6829 -0.0176 0.200 0.012 0.000 0.332 0.456
#> GSM564628 5 0.5789 0.1829 0.104 0.304 0.000 0.004 0.588
#> GSM564629 1 0.4758 0.5942 0.676 0.012 0.000 0.024 0.288
#> GSM564630 2 0.4270 0.5840 0.004 0.656 0.000 0.004 0.336
#> GSM564609 3 0.2289 0.7958 0.000 0.012 0.904 0.004 0.080
#> GSM564610 1 0.1372 0.8211 0.956 0.004 0.000 0.024 0.016
#> GSM564611 2 0.1043 0.8102 0.040 0.960 0.000 0.000 0.000
#> GSM564612 3 0.0162 0.8492 0.000 0.000 0.996 0.004 0.000
#> GSM564613 2 0.2864 0.7840 0.000 0.864 0.024 0.000 0.112
#> GSM564614 4 0.4151 0.4432 0.004 0.000 0.000 0.652 0.344
#> GSM564631 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564632 5 0.5515 0.2655 0.000 0.000 0.260 0.112 0.628
#> GSM564633 3 0.0324 0.8487 0.000 0.000 0.992 0.004 0.004
#> GSM564634 2 0.3124 0.7118 0.004 0.844 0.136 0.000 0.016
#> GSM564635 3 0.0162 0.8493 0.000 0.000 0.996 0.000 0.004
#> GSM564636 3 0.0451 0.8474 0.000 0.000 0.988 0.004 0.008
#> GSM564637 3 0.6793 0.3367 0.000 0.172 0.560 0.040 0.228
#> GSM564638 3 0.0162 0.8493 0.000 0.000 0.996 0.000 0.004
#> GSM564639 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564640 2 0.0000 0.8244 0.000 1.000 0.000 0.000 0.000
#> GSM564641 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564642 2 0.0703 0.8242 0.000 0.976 0.000 0.000 0.024
#> GSM564643 5 0.4791 0.2682 0.000 0.008 0.336 0.020 0.636
#> GSM564644 2 0.0162 0.8246 0.004 0.996 0.000 0.000 0.000
#> GSM564645 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564647 3 0.0566 0.8451 0.000 0.012 0.984 0.000 0.004
#> GSM564648 2 0.3612 0.6685 0.000 0.732 0.000 0.000 0.268
#> GSM564649 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564650 5 0.4722 0.3615 0.000 0.368 0.000 0.024 0.608
#> GSM564651 2 0.0880 0.8114 0.000 0.968 0.032 0.000 0.000
#> GSM564652 1 0.6636 0.1784 0.440 0.192 0.000 0.004 0.364
#> GSM564653 2 0.0000 0.8244 0.000 1.000 0.000 0.000 0.000
#> GSM564654 3 0.0324 0.8487 0.000 0.000 0.992 0.004 0.004
#> GSM564655 3 0.3779 0.7608 0.000 0.032 0.836 0.040 0.092
#> GSM564656 3 0.0324 0.8487 0.000 0.000 0.992 0.004 0.004
#> GSM564657 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564658 2 0.1571 0.8130 0.004 0.936 0.000 0.000 0.060
#> GSM564659 3 0.0955 0.8383 0.000 0.000 0.968 0.004 0.028
#> GSM564660 5 0.3641 0.4766 0.000 0.120 0.000 0.060 0.820
#> GSM564661 2 0.0324 0.8250 0.004 0.992 0.000 0.000 0.004
#> GSM564662 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564663 2 0.1270 0.8162 0.000 0.948 0.000 0.000 0.052
#> GSM564664 2 0.0162 0.8246 0.004 0.996 0.000 0.000 0.000
#> GSM564665 3 0.0451 0.8485 0.000 0.000 0.988 0.008 0.004
#> GSM564666 4 0.4321 0.3857 0.004 0.000 0.000 0.600 0.396
#> GSM564667 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564668 3 0.2067 0.8142 0.000 0.044 0.924 0.004 0.028
#> GSM564669 3 0.0324 0.8487 0.000 0.000 0.992 0.004 0.004
#> GSM564670 3 0.5156 0.5315 0.000 0.092 0.688 0.004 0.216
#> GSM564671 5 0.4029 0.1914 0.004 0.000 0.000 0.316 0.680
#> GSM564672 3 0.0162 0.8493 0.000 0.000 0.996 0.000 0.004
#> GSM564673 2 0.3534 0.6782 0.000 0.744 0.000 0.000 0.256
#> GSM564674 2 0.4549 0.6853 0.048 0.728 0.000 0.004 0.220
#> GSM564675 5 0.2597 0.4641 0.000 0.024 0.000 0.092 0.884
#> GSM564676 2 0.0290 0.8234 0.008 0.992 0.000 0.000 0.000
#> GSM564677 2 0.4341 0.2809 0.000 0.592 0.000 0.004 0.404
#> GSM564678 2 0.0162 0.8246 0.004 0.996 0.000 0.000 0.000
#> GSM564679 2 0.0162 0.8246 0.004 0.996 0.000 0.000 0.000
#> GSM564680 3 0.0324 0.8487 0.000 0.000 0.992 0.004 0.004
#> GSM564682 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564683 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564684 5 0.3949 0.2213 0.004 0.000 0.000 0.300 0.696
#> GSM564685 3 0.0000 0.8494 0.000 0.000 1.000 0.000 0.000
#> GSM564686 5 0.4211 0.0922 0.004 0.000 0.000 0.360 0.636
#> GSM564687 2 0.1043 0.8198 0.000 0.960 0.000 0.000 0.040
#> GSM564688 2 0.3913 0.4717 0.000 0.676 0.000 0.000 0.324
#> GSM564689 2 0.0162 0.8239 0.000 0.996 0.000 0.000 0.004
#> GSM564690 2 0.0162 0.8246 0.004 0.996 0.000 0.000 0.000
#> GSM564691 3 0.2690 0.7274 0.000 0.156 0.844 0.000 0.000
#> GSM564692 2 0.3730 0.6480 0.000 0.712 0.000 0.000 0.288
#> GSM564694 5 0.4361 0.4778 0.000 0.140 0.032 0.040 0.788
#> GSM564695 5 0.5841 0.2502 0.000 0.000 0.256 0.148 0.596
#> GSM564696 3 0.3274 0.7239 0.000 0.000 0.780 0.220 0.000
#> GSM564697 2 0.0290 0.8223 0.000 0.992 0.000 0.000 0.008
#> GSM564698 3 0.0324 0.8487 0.000 0.000 0.992 0.004 0.004
#> GSM564700 5 0.4047 0.1834 0.004 0.000 0.000 0.320 0.676
#> GSM564701 2 0.1732 0.8060 0.000 0.920 0.000 0.000 0.080
#> GSM564702 5 0.6545 0.1906 0.260 0.228 0.000 0.004 0.508
#> GSM564703 3 0.6254 0.4034 0.160 0.000 0.500 0.340 0.000
#> GSM564704 1 0.1106 0.8205 0.964 0.000 0.000 0.024 0.012
#> GSM564705 1 0.1331 0.8198 0.952 0.040 0.000 0.008 0.000
#> GSM564706 3 0.4763 0.5903 0.032 0.000 0.632 0.336 0.000
#> GSM564707 1 0.0451 0.8196 0.988 0.004 0.000 0.008 0.000
#> GSM564708 3 0.2393 0.8136 0.016 0.000 0.900 0.080 0.004
#> GSM564709 1 0.6176 0.3111 0.540 0.000 0.000 0.288 0.172
#> GSM564710 1 0.0451 0.8202 0.988 0.004 0.000 0.008 0.000
#> GSM564711 4 0.6565 0.1466 0.244 0.000 0.224 0.524 0.008
#> GSM564712 1 0.0162 0.8194 0.996 0.000 0.000 0.004 0.000
#> GSM564713 3 0.5540 0.3166 0.060 0.000 0.536 0.400 0.004
#> GSM564714 3 0.4304 0.4139 0.000 0.000 0.516 0.484 0.000
#> GSM564715 1 0.0324 0.8200 0.992 0.004 0.000 0.004 0.000
#> GSM564716 1 0.1990 0.8176 0.928 0.004 0.000 0.040 0.028
#> GSM564717 2 0.2648 0.7202 0.152 0.848 0.000 0.000 0.000
#> GSM564718 4 0.1981 0.5645 0.028 0.000 0.000 0.924 0.048
#> GSM564719 2 0.0404 0.8214 0.012 0.988 0.000 0.000 0.000
#> GSM564720 1 0.1638 0.8073 0.932 0.064 0.000 0.004 0.000
#> GSM564721 1 0.2011 0.7957 0.908 0.088 0.000 0.004 0.000
#> GSM564722 4 0.3201 0.5700 0.052 0.000 0.000 0.852 0.096
#> GSM564723 1 0.0955 0.8198 0.968 0.028 0.000 0.004 0.000
#> GSM564724 4 0.4517 -0.0104 0.004 0.000 0.372 0.616 0.008
#> GSM564725 1 0.4028 0.6828 0.768 0.000 0.000 0.192 0.040
#> GSM564726 4 0.2068 0.5710 0.004 0.000 0.000 0.904 0.092
#> GSM564727 4 0.6614 0.2074 0.316 0.000 0.000 0.448 0.236
#> GSM564728 4 0.4238 0.4256 0.004 0.000 0.000 0.628 0.368
#> GSM564729 4 0.6002 0.2561 0.116 0.000 0.000 0.492 0.392
#> GSM564730 1 0.3391 0.7088 0.800 0.000 0.000 0.012 0.188
#> GSM564731 1 0.3074 0.7370 0.804 0.000 0.000 0.196 0.000
#> GSM564732 1 0.4367 0.6676 0.748 0.000 0.000 0.192 0.060
#> GSM564733 1 0.3073 0.7747 0.868 0.000 0.076 0.052 0.004
#> GSM564734 1 0.1731 0.8098 0.932 0.060 0.000 0.004 0.004
#> GSM564735 4 0.1419 0.5434 0.016 0.000 0.016 0.956 0.012
#> GSM564736 3 0.4704 0.3861 0.008 0.000 0.508 0.480 0.004
#> GSM564737 1 0.0324 0.8199 0.992 0.004 0.000 0.004 0.000
#> GSM564738 4 0.2616 0.4911 0.000 0.000 0.100 0.880 0.020
#> GSM564739 1 0.6490 0.3038 0.520 0.000 0.252 0.224 0.004
#> GSM564740 4 0.4211 0.4318 0.004 0.000 0.000 0.636 0.360
#> GSM564741 3 0.4449 0.3858 0.000 0.000 0.512 0.484 0.004
#> GSM564742 3 0.4015 0.6069 0.000 0.000 0.652 0.348 0.000
#> GSM564743 1 0.4782 0.6317 0.720 0.036 0.000 0.020 0.224
#> GSM564744 1 0.1502 0.8105 0.940 0.056 0.000 0.004 0.000
#> GSM564745 1 0.0798 0.8215 0.976 0.000 0.000 0.016 0.008
#> GSM564746 1 0.4030 0.6561 0.736 0.008 0.000 0.008 0.248
#> GSM564747 1 0.4355 0.6277 0.732 0.000 0.044 0.224 0.000
#> GSM564748 3 0.6235 0.4112 0.156 0.000 0.500 0.344 0.000
#> GSM564749 2 0.4161 0.3579 0.392 0.608 0.000 0.000 0.000
#> GSM564750 4 0.2747 0.5686 0.036 0.000 0.020 0.896 0.048
#> GSM564751 3 0.4118 0.6161 0.004 0.000 0.660 0.336 0.000
#> GSM564752 4 0.2389 0.5677 0.004 0.000 0.000 0.880 0.116
#> GSM564753 3 0.3774 0.6605 0.000 0.000 0.704 0.296 0.000
#> GSM564754 1 0.0324 0.8199 0.992 0.004 0.000 0.004 0.000
#> GSM564755 4 0.4251 0.4108 0.004 0.000 0.000 0.624 0.372
#> GSM564756 2 0.4273 0.1549 0.448 0.552 0.000 0.000 0.000
#> GSM564757 5 0.4655 -0.2014 0.012 0.000 0.000 0.476 0.512
#> GSM564758 4 0.5240 0.4325 0.216 0.000 0.000 0.672 0.112
#> GSM564759 3 0.4118 0.6211 0.004 0.000 0.660 0.336 0.000
#> GSM564760 1 0.4548 0.6668 0.748 0.000 0.000 0.156 0.096
#> GSM564761 1 0.0854 0.8224 0.976 0.012 0.000 0.008 0.004
#> GSM564762 1 0.4352 0.6086 0.720 0.000 0.000 0.244 0.036
#> GSM564681 5 0.2575 0.4846 0.016 0.036 0.000 0.044 0.904
#> GSM564693 5 0.3949 0.3028 0.000 0.300 0.000 0.004 0.696
#> GSM564646 5 0.3838 0.2494 0.004 0.000 0.000 0.280 0.716
#> GSM564699 4 0.4397 0.3341 0.000 0.000 0.004 0.564 0.432
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 6 0.4250 0.5765 0.084 0.000 0.000 0.088 0.048 0.780
#> GSM564616 5 0.2846 0.6738 0.016 0.140 0.000 0.000 0.840 0.004
#> GSM564617 5 0.3566 0.6304 0.024 0.224 0.000 0.000 0.752 0.000
#> GSM564618 5 0.2512 0.5559 0.008 0.000 0.000 0.008 0.868 0.116
#> GSM564619 1 0.2597 0.7418 0.824 0.000 0.000 0.000 0.176 0.000
#> GSM564620 5 0.3101 0.5778 0.244 0.000 0.000 0.000 0.756 0.000
#> GSM564621 5 0.4875 0.5402 0.108 0.000 0.000 0.024 0.704 0.164
#> GSM564622 5 0.4109 0.6520 0.064 0.052 0.044 0.028 0.812 0.000
#> GSM564623 6 0.4177 0.2797 0.000 0.000 0.000 0.012 0.468 0.520
#> GSM564624 5 0.2595 0.6305 0.000 0.084 0.000 0.000 0.872 0.044
#> GSM564625 1 0.3993 0.2907 0.592 0.000 0.000 0.000 0.400 0.008
#> GSM564626 1 0.3390 0.5683 0.704 0.000 0.000 0.000 0.296 0.000
#> GSM564627 5 0.6004 0.2622 0.084 0.000 0.000 0.060 0.536 0.320
#> GSM564628 5 0.2812 0.6739 0.048 0.096 0.000 0.000 0.856 0.000
#> GSM564629 5 0.3405 0.5349 0.272 0.000 0.000 0.004 0.724 0.000
#> GSM564630 5 0.3468 0.5738 0.004 0.284 0.000 0.000 0.712 0.000
#> GSM564609 3 0.3249 0.7718 0.000 0.004 0.824 0.044 0.128 0.000
#> GSM564610 1 0.2367 0.8249 0.900 0.004 0.000 0.020 0.064 0.012
#> GSM564611 2 0.0790 0.8197 0.032 0.968 0.000 0.000 0.000 0.000
#> GSM564612 3 0.1285 0.8777 0.000 0.000 0.944 0.052 0.004 0.000
#> GSM564613 2 0.3720 0.6328 0.000 0.760 0.020 0.000 0.208 0.012
#> GSM564614 6 0.3816 0.4890 0.000 0.000 0.000 0.296 0.016 0.688
#> GSM564631 3 0.0000 0.8844 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564632 6 0.5773 0.5347 0.000 0.000 0.144 0.044 0.196 0.616
#> GSM564633 3 0.1471 0.8735 0.000 0.000 0.932 0.064 0.004 0.000
#> GSM564634 2 0.3017 0.7294 0.000 0.844 0.084 0.000 0.072 0.000
#> GSM564635 3 0.0260 0.8849 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM564636 3 0.0551 0.8825 0.000 0.000 0.984 0.004 0.008 0.004
#> GSM564637 3 0.7265 0.1468 0.000 0.164 0.448 0.064 0.032 0.292
#> GSM564638 3 0.0405 0.8862 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM564639 3 0.1152 0.8803 0.000 0.000 0.952 0.044 0.004 0.000
#> GSM564640 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564641 3 0.0458 0.8782 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM564642 2 0.0632 0.8276 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM564643 6 0.6505 0.4009 0.000 0.000 0.208 0.064 0.204 0.524
#> GSM564644 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564645 3 0.0000 0.8844 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564647 3 0.0951 0.8771 0.000 0.020 0.968 0.004 0.008 0.000
#> GSM564648 5 0.3695 0.4487 0.000 0.376 0.000 0.000 0.624 0.000
#> GSM564649 3 0.0146 0.8837 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564650 6 0.5122 0.4954 0.000 0.180 0.000 0.000 0.192 0.628
#> GSM564651 2 0.0806 0.8213 0.000 0.972 0.020 0.008 0.000 0.000
#> GSM564652 5 0.7043 0.1640 0.344 0.112 0.000 0.000 0.396 0.148
#> GSM564653 2 0.0146 0.8344 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM564654 3 0.1471 0.8735 0.000 0.000 0.932 0.064 0.004 0.000
#> GSM564655 3 0.5376 0.6774 0.000 0.036 0.720 0.100 0.060 0.084
#> GSM564656 3 0.1007 0.8817 0.000 0.000 0.956 0.044 0.000 0.000
#> GSM564657 3 0.0508 0.8865 0.000 0.000 0.984 0.012 0.004 0.000
#> GSM564658 2 0.2191 0.7594 0.004 0.876 0.000 0.000 0.120 0.000
#> GSM564659 3 0.2250 0.8546 0.000 0.000 0.896 0.064 0.040 0.000
#> GSM564660 6 0.4389 0.5444 0.000 0.052 0.000 0.000 0.288 0.660
#> GSM564661 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564662 3 0.0000 0.8844 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564663 2 0.1814 0.7799 0.000 0.900 0.000 0.000 0.100 0.000
#> GSM564664 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564665 3 0.0632 0.8852 0.000 0.000 0.976 0.024 0.000 0.000
#> GSM564666 6 0.3494 0.5343 0.000 0.000 0.000 0.252 0.012 0.736
#> GSM564667 3 0.0000 0.8844 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564668 3 0.2918 0.8350 0.000 0.020 0.868 0.064 0.048 0.000
#> GSM564669 3 0.1471 0.8735 0.000 0.000 0.932 0.064 0.004 0.000
#> GSM564670 3 0.4800 0.4210 0.000 0.020 0.604 0.032 0.344 0.000
#> GSM564671 6 0.2793 0.6462 0.000 0.000 0.000 0.000 0.200 0.800
#> GSM564672 3 0.0363 0.8859 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM564673 5 0.3869 0.1444 0.000 0.500 0.000 0.000 0.500 0.000
#> GSM564674 2 0.4410 0.1239 0.028 0.560 0.000 0.000 0.412 0.000
#> GSM564675 5 0.3500 0.3600 0.000 0.000 0.000 0.028 0.768 0.204
#> GSM564676 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564677 2 0.5669 0.1740 0.000 0.504 0.000 0.000 0.176 0.320
#> GSM564678 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564679 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564680 3 0.1471 0.8735 0.000 0.000 0.932 0.064 0.004 0.000
#> GSM564682 3 0.0146 0.8838 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564683 3 0.0000 0.8844 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564684 6 0.2854 0.6431 0.000 0.000 0.000 0.000 0.208 0.792
#> GSM564685 3 0.0000 0.8844 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564686 6 0.2946 0.6550 0.000 0.000 0.000 0.012 0.176 0.812
#> GSM564687 2 0.1444 0.7999 0.000 0.928 0.000 0.000 0.072 0.000
#> GSM564688 2 0.5229 0.3724 0.000 0.604 0.000 0.000 0.156 0.240
#> GSM564689 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564690 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564691 3 0.2595 0.7300 0.000 0.160 0.836 0.000 0.004 0.000
#> GSM564692 5 0.3659 0.4643 0.000 0.364 0.000 0.000 0.636 0.000
#> GSM564694 6 0.5610 0.3615 0.000 0.056 0.008 0.024 0.440 0.472
#> GSM564695 6 0.6678 0.5475 0.000 0.000 0.132 0.128 0.212 0.528
#> GSM564696 3 0.3607 0.2084 0.000 0.000 0.652 0.348 0.000 0.000
#> GSM564697 2 0.0000 0.8354 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564698 3 0.1471 0.8735 0.000 0.000 0.932 0.064 0.004 0.000
#> GSM564700 6 0.2793 0.6462 0.000 0.000 0.000 0.000 0.200 0.800
#> GSM564701 2 0.2631 0.6974 0.000 0.820 0.000 0.000 0.180 0.000
#> GSM564702 5 0.5512 0.5154 0.116 0.064 0.000 0.000 0.664 0.156
#> GSM564703 4 0.3690 0.5860 0.008 0.000 0.308 0.684 0.000 0.000
#> GSM564704 1 0.1856 0.8279 0.920 0.000 0.000 0.000 0.032 0.048
#> GSM564705 1 0.2070 0.8200 0.908 0.044 0.000 0.000 0.048 0.000
#> GSM564706 4 0.3620 0.5675 0.000 0.000 0.352 0.648 0.000 0.000
#> GSM564707 1 0.1007 0.8258 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM564708 3 0.3521 0.5871 0.004 0.000 0.724 0.268 0.004 0.000
#> GSM564709 1 0.4962 0.5326 0.612 0.000 0.000 0.048 0.020 0.320
#> GSM564710 1 0.0458 0.8295 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM564711 4 0.3228 0.5782 0.056 0.000 0.080 0.848 0.004 0.012
#> GSM564712 1 0.0000 0.8285 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.5657 -0.0656 0.036 0.000 0.444 0.472 0.024 0.024
#> GSM564714 4 0.2730 0.6249 0.000 0.000 0.192 0.808 0.000 0.000
#> GSM564715 1 0.1480 0.8274 0.940 0.000 0.000 0.020 0.040 0.000
#> GSM564716 1 0.3136 0.7988 0.844 0.000 0.000 0.020 0.108 0.028
#> GSM564717 2 0.2562 0.6869 0.172 0.828 0.000 0.000 0.000 0.000
#> GSM564718 4 0.3697 0.3857 0.016 0.000 0.000 0.732 0.004 0.248
#> GSM564719 2 0.0363 0.8307 0.012 0.988 0.000 0.000 0.000 0.000
#> GSM564720 1 0.0790 0.8239 0.968 0.032 0.000 0.000 0.000 0.000
#> GSM564721 1 0.2462 0.7864 0.876 0.096 0.000 0.000 0.028 0.000
#> GSM564722 4 0.4880 0.0661 0.032 0.000 0.000 0.540 0.016 0.412
#> GSM564723 1 0.0458 0.8282 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM564724 4 0.3405 0.5887 0.000 0.000 0.272 0.724 0.000 0.004
#> GSM564725 1 0.4590 0.7234 0.728 0.000 0.000 0.024 0.080 0.168
#> GSM564726 4 0.3874 0.2318 0.000 0.000 0.000 0.636 0.008 0.356
#> GSM564727 6 0.5873 0.3070 0.260 0.000 0.000 0.088 0.064 0.588
#> GSM564728 6 0.3575 0.5015 0.000 0.000 0.000 0.284 0.008 0.708
#> GSM564729 6 0.4218 0.5710 0.064 0.000 0.000 0.108 0.048 0.780
#> GSM564730 1 0.4184 0.6672 0.752 0.000 0.000 0.004 0.124 0.120
#> GSM564731 1 0.3395 0.7685 0.812 0.000 0.000 0.136 0.004 0.048
#> GSM564732 1 0.4756 0.6739 0.688 0.000 0.000 0.032 0.048 0.232
#> GSM564733 1 0.3843 0.7258 0.792 0.000 0.064 0.132 0.008 0.004
#> GSM564734 1 0.0972 0.8283 0.964 0.028 0.000 0.000 0.000 0.008
#> GSM564735 4 0.2504 0.4752 0.000 0.000 0.004 0.856 0.004 0.136
#> GSM564736 4 0.3288 0.5885 0.000 0.000 0.276 0.724 0.000 0.000
#> GSM564737 1 0.0000 0.8285 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.3395 0.5000 0.000 0.000 0.048 0.812 0.004 0.136
#> GSM564739 4 0.6214 0.2161 0.328 0.000 0.144 0.492 0.036 0.000
#> GSM564740 6 0.3383 0.5180 0.000 0.000 0.000 0.268 0.004 0.728
#> GSM564741 4 0.3266 0.5936 0.000 0.000 0.272 0.728 0.000 0.000
#> GSM564742 4 0.3592 0.5753 0.000 0.000 0.344 0.656 0.000 0.000
#> GSM564743 1 0.5166 0.5824 0.676 0.004 0.000 0.016 0.148 0.156
#> GSM564744 1 0.0713 0.8253 0.972 0.028 0.000 0.000 0.000 0.000
#> GSM564745 1 0.1296 0.8296 0.952 0.000 0.000 0.012 0.032 0.004
#> GSM564746 5 0.3774 0.2841 0.408 0.000 0.000 0.000 0.592 0.000
#> GSM564747 1 0.3905 0.5136 0.668 0.000 0.016 0.316 0.000 0.000
#> GSM564748 4 0.4585 0.5987 0.068 0.000 0.284 0.648 0.000 0.000
#> GSM564749 2 0.3765 0.3389 0.404 0.596 0.000 0.000 0.000 0.000
#> GSM564750 4 0.4873 0.2824 0.020 0.000 0.028 0.628 0.008 0.316
#> GSM564751 4 0.3620 0.5675 0.000 0.000 0.352 0.648 0.000 0.000
#> GSM564752 4 0.3966 0.1012 0.000 0.000 0.000 0.552 0.004 0.444
#> GSM564753 4 0.3817 0.4408 0.000 0.000 0.432 0.568 0.000 0.000
#> GSM564754 1 0.0000 0.8285 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564755 6 0.3876 0.5107 0.000 0.000 0.000 0.276 0.024 0.700
#> GSM564756 2 0.3857 0.0894 0.468 0.532 0.000 0.000 0.000 0.000
#> GSM564757 6 0.2504 0.6251 0.004 0.000 0.000 0.088 0.028 0.880
#> GSM564758 4 0.5922 0.2969 0.196 0.000 0.000 0.556 0.020 0.228
#> GSM564759 4 0.3819 0.5126 0.000 0.000 0.372 0.624 0.000 0.004
#> GSM564760 1 0.4544 0.6981 0.716 0.000 0.000 0.036 0.040 0.208
#> GSM564761 1 0.1524 0.8227 0.932 0.008 0.000 0.000 0.060 0.000
#> GSM564762 1 0.3680 0.7155 0.756 0.000 0.000 0.020 0.008 0.216
#> GSM564681 6 0.4114 0.4955 0.008 0.000 0.000 0.008 0.356 0.628
#> GSM564693 5 0.4121 0.5592 0.000 0.136 0.000 0.000 0.748 0.116
#> GSM564646 6 0.2941 0.6375 0.000 0.000 0.000 0.000 0.220 0.780
#> GSM564699 6 0.3470 0.6140 0.000 0.000 0.000 0.152 0.052 0.796
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n genotype/variation(p) disease.state(p) k
#> SD:pam 140 2.60e-03 0.470 2
#> SD:pam 144 7.52e-02 0.313 3
#> SD:pam 136 2.54e-01 0.245 4
#> SD:pam 102 1.63e-01 NA 5
#> SD:pam 121 1.66e-12 0.096 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 154 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.714 0.815 0.858 0.2634 0.860 0.720
#> 4 4 0.751 0.844 0.893 0.1604 0.809 0.525
#> 5 5 0.904 0.879 0.935 0.0787 0.928 0.726
#> 6 6 0.866 0.862 0.914 0.0417 0.963 0.819
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564616 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564617 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564618 2 0.6062 0.342 0.000 0.616 0.384
#> GSM564619 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564620 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564621 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564622 2 0.2165 0.739 0.000 0.936 0.064
#> GSM564623 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564624 2 0.2537 0.727 0.000 0.920 0.080
#> GSM564625 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564626 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564627 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564628 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564629 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564630 2 0.0892 0.764 0.000 0.980 0.020
#> GSM564609 3 0.4654 0.907 0.000 0.208 0.792
#> GSM564610 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564611 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564612 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564613 3 0.5621 0.780 0.000 0.308 0.692
#> GSM564614 1 0.3551 0.938 0.868 0.000 0.132
#> GSM564631 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564632 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564633 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564634 3 0.5859 0.709 0.000 0.344 0.656
#> GSM564635 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564636 3 0.4399 0.925 0.000 0.188 0.812
#> GSM564637 2 0.6260 0.219 0.000 0.552 0.448
#> GSM564638 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564639 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564640 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564641 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564642 2 0.0424 0.767 0.000 0.992 0.008
#> GSM564643 2 0.6252 0.228 0.000 0.556 0.444
#> GSM564644 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564645 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564647 3 0.5497 0.807 0.000 0.292 0.708
#> GSM564648 2 0.0237 0.767 0.000 0.996 0.004
#> GSM564649 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564650 2 0.6045 0.348 0.000 0.620 0.380
#> GSM564651 2 0.1163 0.760 0.000 0.972 0.028
#> GSM564652 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564653 2 0.0237 0.767 0.000 0.996 0.004
#> GSM564654 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564655 3 0.4931 0.878 0.000 0.232 0.768
#> GSM564656 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564657 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564658 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564659 3 0.5098 0.865 0.000 0.248 0.752
#> GSM564660 2 0.6140 0.306 0.000 0.596 0.404
#> GSM564661 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564662 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564663 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564664 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564665 3 0.4605 0.911 0.000 0.204 0.796
#> GSM564666 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564667 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564668 3 0.4887 0.886 0.000 0.228 0.772
#> GSM564669 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564670 3 0.5591 0.787 0.000 0.304 0.696
#> GSM564671 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564672 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564673 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564674 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564675 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564676 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564677 2 0.0892 0.764 0.000 0.980 0.020
#> GSM564678 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564679 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564680 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564682 3 0.4504 0.911 0.000 0.196 0.804
#> GSM564683 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564684 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564685 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564686 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564687 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564688 2 0.0892 0.764 0.000 0.980 0.020
#> GSM564689 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564690 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564691 3 0.5178 0.852 0.000 0.256 0.744
#> GSM564692 2 0.0747 0.765 0.000 0.984 0.016
#> GSM564694 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564695 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564696 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564697 2 0.1411 0.756 0.000 0.964 0.036
#> GSM564698 3 0.3941 0.946 0.000 0.156 0.844
#> GSM564700 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564701 2 0.0892 0.764 0.000 0.980 0.020
#> GSM564702 2 0.0237 0.767 0.000 0.996 0.004
#> GSM564703 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564704 1 0.1643 0.943 0.956 0.000 0.044
#> GSM564705 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564706 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564707 1 0.2796 0.942 0.908 0.000 0.092
#> GSM564708 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564709 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564710 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564711 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564712 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564713 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564714 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564715 1 0.2796 0.942 0.908 0.000 0.092
#> GSM564716 1 0.2625 0.943 0.916 0.000 0.084
#> GSM564717 1 0.2625 0.943 0.916 0.000 0.084
#> GSM564718 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564719 1 0.2537 0.943 0.920 0.000 0.080
#> GSM564720 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564721 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564722 1 0.3619 0.938 0.864 0.000 0.136
#> GSM564723 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564724 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564725 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564726 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564727 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564728 1 0.3551 0.938 0.868 0.000 0.132
#> GSM564729 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564730 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564731 1 0.3816 0.936 0.852 0.000 0.148
#> GSM564732 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564733 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564734 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564735 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564736 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564737 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564738 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564739 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564740 1 0.3551 0.938 0.868 0.000 0.132
#> GSM564741 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564742 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564743 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564744 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564745 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564746 1 0.2796 0.942 0.908 0.000 0.092
#> GSM564747 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564748 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564749 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564750 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564751 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564752 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564753 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564754 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564755 1 0.3551 0.938 0.868 0.000 0.132
#> GSM564756 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564757 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564758 1 0.3551 0.938 0.868 0.000 0.132
#> GSM564759 1 0.3941 0.934 0.844 0.000 0.156
#> GSM564760 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564761 1 0.0000 0.941 1.000 0.000 0.000
#> GSM564762 1 0.2878 0.942 0.904 0.000 0.096
#> GSM564681 2 0.6045 0.348 0.000 0.620 0.380
#> GSM564693 2 0.0000 0.768 0.000 1.000 0.000
#> GSM564646 2 0.6267 0.210 0.000 0.548 0.452
#> GSM564699 2 0.6267 0.210 0.000 0.548 0.452
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564616 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564617 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564618 2 0.6961 0.0137 0.000 0.524 0.352 0.124
#> GSM564619 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564620 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564621 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564622 2 0.3384 0.8043 0.000 0.860 0.116 0.024
#> GSM564623 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564624 2 0.3266 0.8406 0.000 0.868 0.024 0.108
#> GSM564625 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564626 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564627 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564628 2 0.0188 0.9375 0.000 0.996 0.004 0.000
#> GSM564629 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564630 2 0.0336 0.9358 0.000 0.992 0.000 0.008
#> GSM564609 3 0.2871 0.7831 0.000 0.072 0.896 0.032
#> GSM564610 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564611 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564612 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564613 3 0.5159 0.7190 0.000 0.156 0.756 0.088
#> GSM564614 4 0.4877 0.6078 0.408 0.000 0.000 0.592
#> GSM564631 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564632 3 0.6949 0.5057 0.000 0.348 0.528 0.124
#> GSM564633 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564634 3 0.4974 0.6882 0.000 0.224 0.736 0.040
#> GSM564635 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564636 3 0.2222 0.7946 0.000 0.016 0.924 0.060
#> GSM564637 3 0.6911 0.5204 0.000 0.336 0.540 0.124
#> GSM564638 3 0.0000 0.8033 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564640 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564641 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564642 2 0.0779 0.9284 0.000 0.980 0.016 0.004
#> GSM564643 3 0.6937 0.5108 0.000 0.344 0.532 0.124
#> GSM564644 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564645 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564647 3 0.2227 0.7944 0.000 0.036 0.928 0.036
#> GSM564648 2 0.0188 0.9375 0.000 0.996 0.004 0.000
#> GSM564649 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564650 2 0.6746 0.2363 0.000 0.580 0.296 0.124
#> GSM564651 2 0.2988 0.8222 0.000 0.876 0.112 0.012
#> GSM564652 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564653 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564654 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564655 3 0.5361 0.7155 0.000 0.148 0.744 0.108
#> GSM564656 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564657 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564658 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564659 3 0.2871 0.7878 0.000 0.032 0.896 0.072
#> GSM564660 3 0.7078 0.3345 0.000 0.420 0.456 0.124
#> GSM564661 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564662 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564663 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564664 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564665 3 0.1256 0.8006 0.000 0.008 0.964 0.028
#> GSM564666 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564667 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564668 3 0.2675 0.7895 0.000 0.048 0.908 0.044
#> GSM564669 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564670 3 0.4336 0.7466 0.000 0.128 0.812 0.060
#> GSM564671 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564672 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564673 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564674 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564675 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564676 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564677 2 0.0804 0.9292 0.000 0.980 0.008 0.012
#> GSM564678 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564679 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564680 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564682 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564683 3 0.0000 0.8033 0.000 0.000 1.000 0.000
#> GSM564684 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564685 3 0.0336 0.8030 0.000 0.000 0.992 0.008
#> GSM564686 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564687 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564688 2 0.1284 0.9194 0.000 0.964 0.012 0.024
#> GSM564689 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564690 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564691 3 0.1356 0.8001 0.000 0.008 0.960 0.032
#> GSM564692 2 0.1520 0.9136 0.000 0.956 0.020 0.024
#> GSM564694 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564695 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564696 3 0.0592 0.8025 0.000 0.000 0.984 0.016
#> GSM564697 2 0.2385 0.8859 0.000 0.920 0.028 0.052
#> GSM564698 3 0.0188 0.8032 0.000 0.000 0.996 0.004
#> GSM564700 3 0.6972 0.4946 0.000 0.356 0.520 0.124
#> GSM564701 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564702 2 0.0188 0.9375 0.000 0.996 0.000 0.004
#> GSM564703 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564704 1 0.0469 0.9782 0.988 0.000 0.000 0.012
#> GSM564705 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564706 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564707 1 0.1022 0.9592 0.968 0.000 0.000 0.032
#> GSM564708 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564709 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564710 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564711 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564712 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564713 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564714 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564715 1 0.1211 0.9506 0.960 0.000 0.000 0.040
#> GSM564716 1 0.1867 0.9102 0.928 0.000 0.000 0.072
#> GSM564717 1 0.0921 0.9632 0.972 0.000 0.000 0.028
#> GSM564718 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564719 1 0.0707 0.9710 0.980 0.000 0.000 0.020
#> GSM564720 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564721 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564722 4 0.4804 0.6532 0.384 0.000 0.000 0.616
#> GSM564723 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564724 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564725 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564726 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564727 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564728 4 0.4830 0.6401 0.392 0.000 0.000 0.608
#> GSM564729 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564730 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564731 4 0.3266 0.9069 0.168 0.000 0.000 0.832
#> GSM564732 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564733 4 0.2921 0.9262 0.140 0.000 0.000 0.860
#> GSM564734 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564735 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564736 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564737 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564738 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564739 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564740 4 0.4830 0.6401 0.392 0.000 0.000 0.608
#> GSM564741 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564742 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564743 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564744 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564745 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564746 1 0.1716 0.9211 0.936 0.000 0.000 0.064
#> GSM564747 4 0.3266 0.9070 0.168 0.000 0.000 0.832
#> GSM564748 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564749 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564750 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564751 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564752 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564753 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564754 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564755 4 0.4830 0.6401 0.392 0.000 0.000 0.608
#> GSM564756 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564757 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564758 4 0.4830 0.6401 0.392 0.000 0.000 0.608
#> GSM564759 4 0.2814 0.9310 0.132 0.000 0.000 0.868
#> GSM564760 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564761 1 0.0000 0.9883 1.000 0.000 0.000 0.000
#> GSM564762 1 0.2469 0.8564 0.892 0.000 0.000 0.108
#> GSM564681 2 0.5874 0.5717 0.000 0.700 0.176 0.124
#> GSM564693 2 0.0000 0.9389 0.000 1.000 0.000 0.000
#> GSM564646 3 0.6993 0.4780 0.000 0.364 0.512 0.124
#> GSM564699 3 0.6937 0.5105 0.000 0.344 0.532 0.124
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 1 0.1121 0.9672 0.956 0.000 0.000 0.044 0.000
#> GSM564616 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564617 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564618 5 0.3452 0.6601 0.000 0.244 0.000 0.000 0.756
#> GSM564619 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564620 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564621 1 0.0609 0.9773 0.980 0.000 0.000 0.020 0.000
#> GSM564622 5 0.4436 0.3971 0.000 0.396 0.008 0.000 0.596
#> GSM564623 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564624 2 0.4161 0.3009 0.000 0.608 0.000 0.000 0.392
#> GSM564625 1 0.0510 0.9782 0.984 0.000 0.000 0.016 0.000
#> GSM564626 1 0.0162 0.9783 0.996 0.000 0.000 0.004 0.000
#> GSM564627 1 0.0609 0.9773 0.980 0.000 0.000 0.020 0.000
#> GSM564628 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564629 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564630 2 0.1341 0.8805 0.000 0.944 0.000 0.000 0.056
#> GSM564609 5 0.4108 0.6835 0.000 0.008 0.308 0.000 0.684
#> GSM564610 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564611 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564612 3 0.1851 0.8837 0.000 0.000 0.912 0.000 0.088
#> GSM564613 5 0.4003 0.7011 0.000 0.008 0.288 0.000 0.704
#> GSM564614 4 0.3210 0.7623 0.212 0.000 0.000 0.788 0.000
#> GSM564631 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564632 5 0.0404 0.8081 0.000 0.012 0.000 0.000 0.988
#> GSM564633 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564634 5 0.4360 0.7003 0.000 0.024 0.284 0.000 0.692
#> GSM564635 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564636 3 0.1478 0.9229 0.000 0.000 0.936 0.000 0.064
#> GSM564637 5 0.0404 0.8081 0.000 0.012 0.000 0.000 0.988
#> GSM564638 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564639 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564640 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564641 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564642 2 0.3789 0.6594 0.000 0.768 0.020 0.000 0.212
#> GSM564643 5 0.0404 0.8081 0.000 0.012 0.000 0.000 0.988
#> GSM564644 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564645 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564647 5 0.3990 0.6827 0.000 0.004 0.308 0.000 0.688
#> GSM564648 2 0.0404 0.9199 0.000 0.988 0.000 0.000 0.012
#> GSM564649 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564650 5 0.3534 0.6466 0.000 0.256 0.000 0.000 0.744
#> GSM564651 5 0.4934 0.4575 0.000 0.364 0.036 0.000 0.600
#> GSM564652 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564653 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564654 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564655 5 0.3980 0.7039 0.000 0.008 0.284 0.000 0.708
#> GSM564656 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564657 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564658 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564659 5 0.3990 0.6816 0.000 0.004 0.308 0.000 0.688
#> GSM564660 5 0.3424 0.6642 0.000 0.240 0.000 0.000 0.760
#> GSM564661 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564662 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564663 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564664 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564665 5 0.4126 0.5773 0.000 0.000 0.380 0.000 0.620
#> GSM564666 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564667 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564668 5 0.3969 0.6866 0.000 0.004 0.304 0.000 0.692
#> GSM564669 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564670 5 0.4046 0.6946 0.000 0.008 0.296 0.000 0.696
#> GSM564671 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564672 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564673 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564674 2 0.0162 0.9252 0.000 0.996 0.000 0.000 0.004
#> GSM564675 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564676 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564677 2 0.0162 0.9253 0.000 0.996 0.000 0.000 0.004
#> GSM564678 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564679 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564680 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564682 5 0.4219 0.5070 0.000 0.000 0.416 0.000 0.584
#> GSM564683 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564684 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564685 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564686 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564687 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564688 2 0.4306 -0.0949 0.000 0.508 0.000 0.000 0.492
#> GSM564689 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564690 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564691 5 0.4171 0.5479 0.000 0.000 0.396 0.000 0.604
#> GSM564692 2 0.3837 0.5120 0.000 0.692 0.000 0.000 0.308
#> GSM564694 5 0.0404 0.8081 0.000 0.012 0.000 0.000 0.988
#> GSM564695 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564696 3 0.1043 0.9466 0.000 0.000 0.960 0.000 0.040
#> GSM564697 2 0.3636 0.5840 0.000 0.728 0.000 0.000 0.272
#> GSM564698 3 0.0000 0.9888 0.000 0.000 1.000 0.000 0.000
#> GSM564700 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
#> GSM564701 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564702 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000
#> GSM564703 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564704 1 0.1270 0.9623 0.948 0.000 0.000 0.052 0.000
#> GSM564705 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564707 1 0.0880 0.9663 0.968 0.000 0.000 0.032 0.000
#> GSM564708 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564709 1 0.1043 0.9697 0.960 0.000 0.000 0.040 0.000
#> GSM564710 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564712 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564714 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564715 1 0.0880 0.9663 0.968 0.000 0.000 0.032 0.000
#> GSM564716 1 0.1341 0.9560 0.944 0.000 0.000 0.056 0.000
#> GSM564717 1 0.0794 0.9688 0.972 0.000 0.000 0.028 0.000
#> GSM564718 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564719 1 0.0794 0.9747 0.972 0.000 0.000 0.028 0.000
#> GSM564720 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564721 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564722 4 0.3266 0.7748 0.200 0.000 0.000 0.796 0.004
#> GSM564723 1 0.0290 0.9785 0.992 0.000 0.000 0.008 0.000
#> GSM564724 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564725 1 0.0703 0.9763 0.976 0.000 0.000 0.024 0.000
#> GSM564726 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564727 1 0.1043 0.9697 0.960 0.000 0.000 0.040 0.000
#> GSM564728 4 0.3700 0.7210 0.240 0.000 0.000 0.752 0.008
#> GSM564729 1 0.1121 0.9672 0.956 0.000 0.000 0.044 0.000
#> GSM564730 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564731 4 0.0963 0.9171 0.036 0.000 0.000 0.964 0.000
#> GSM564732 1 0.1043 0.9697 0.960 0.000 0.000 0.040 0.000
#> GSM564733 4 0.0794 0.9230 0.028 0.000 0.000 0.972 0.000
#> GSM564734 1 0.0963 0.9717 0.964 0.000 0.000 0.036 0.000
#> GSM564735 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564736 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564737 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564739 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564740 4 0.3700 0.7210 0.240 0.000 0.000 0.752 0.008
#> GSM564741 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564742 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564743 1 0.0510 0.9781 0.984 0.000 0.000 0.016 0.000
#> GSM564744 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564745 1 0.1043 0.9697 0.960 0.000 0.000 0.040 0.000
#> GSM564746 1 0.1121 0.9575 0.956 0.000 0.000 0.044 0.000
#> GSM564747 4 0.0162 0.9384 0.004 0.000 0.000 0.996 0.000
#> GSM564748 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564749 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564750 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564751 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564752 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564753 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564754 1 0.0404 0.9785 0.988 0.000 0.000 0.012 0.000
#> GSM564755 4 0.3700 0.7210 0.240 0.000 0.000 0.752 0.008
#> GSM564756 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564757 1 0.1121 0.9672 0.956 0.000 0.000 0.044 0.000
#> GSM564758 4 0.3700 0.7210 0.240 0.000 0.000 0.752 0.008
#> GSM564759 4 0.0000 0.9406 0.000 0.000 0.000 1.000 0.000
#> GSM564760 1 0.0963 0.9717 0.964 0.000 0.000 0.036 0.000
#> GSM564761 1 0.0000 0.9780 1.000 0.000 0.000 0.000 0.000
#> GSM564762 1 0.2074 0.9079 0.896 0.000 0.000 0.104 0.000
#> GSM564681 5 0.3612 0.6281 0.000 0.268 0.000 0.000 0.732
#> GSM564693 2 0.0609 0.9135 0.000 0.980 0.000 0.000 0.020
#> GSM564646 5 0.0963 0.8002 0.000 0.036 0.000 0.000 0.964
#> GSM564699 5 0.0290 0.8085 0.000 0.008 0.000 0.000 0.992
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.3175 0.8169 0.164 0.808 0.000 0.028 0.000 0.000
#> GSM564616 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564617 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564618 6 0.2378 0.8373 0.000 0.000 0.000 0.000 0.152 0.848
#> GSM564619 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564620 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564621 2 0.3862 0.4767 0.476 0.524 0.000 0.000 0.000 0.000
#> GSM564622 6 0.3915 0.3641 0.000 0.000 0.004 0.000 0.412 0.584
#> GSM564623 6 0.1327 0.8840 0.000 0.000 0.000 0.000 0.064 0.936
#> GSM564624 5 0.3076 0.6730 0.000 0.000 0.000 0.000 0.760 0.240
#> GSM564625 2 0.3659 0.7307 0.364 0.636 0.000 0.000 0.000 0.000
#> GSM564626 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564627 1 0.3810 -0.2005 0.572 0.428 0.000 0.000 0.000 0.000
#> GSM564628 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564629 1 0.0790 0.8889 0.968 0.032 0.000 0.000 0.000 0.000
#> GSM564630 5 0.1204 0.9004 0.000 0.000 0.000 0.000 0.944 0.056
#> GSM564609 6 0.1814 0.8685 0.000 0.000 0.100 0.000 0.000 0.900
#> GSM564610 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564611 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564612 3 0.2562 0.7785 0.000 0.000 0.828 0.000 0.000 0.172
#> GSM564613 6 0.1838 0.8830 0.000 0.000 0.068 0.000 0.016 0.916
#> GSM564614 4 0.3371 0.7840 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM564631 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564632 6 0.0146 0.8863 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM564633 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564634 6 0.2009 0.8823 0.000 0.000 0.068 0.000 0.024 0.908
#> GSM564635 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564636 3 0.1501 0.9082 0.000 0.000 0.924 0.000 0.000 0.076
#> GSM564637 6 0.0146 0.8863 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM564638 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564639 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564640 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564641 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564642 5 0.3017 0.7642 0.000 0.000 0.020 0.000 0.816 0.164
#> GSM564643 6 0.0146 0.8863 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM564644 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564645 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564647 6 0.2178 0.8475 0.000 0.000 0.132 0.000 0.000 0.868
#> GSM564648 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564649 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564650 6 0.2378 0.8373 0.000 0.000 0.000 0.000 0.152 0.848
#> GSM564651 5 0.4401 -0.0204 0.000 0.000 0.024 0.000 0.512 0.464
#> GSM564652 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564653 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564654 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564655 6 0.1387 0.8813 0.000 0.000 0.068 0.000 0.000 0.932
#> GSM564656 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564657 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564658 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564659 6 0.2300 0.8378 0.000 0.000 0.144 0.000 0.000 0.856
#> GSM564660 6 0.2340 0.8404 0.000 0.000 0.000 0.000 0.148 0.852
#> GSM564661 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564662 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564663 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564664 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564665 6 0.2730 0.7891 0.000 0.000 0.192 0.000 0.000 0.808
#> GSM564666 6 0.0000 0.8850 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM564667 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564668 6 0.1814 0.8686 0.000 0.000 0.100 0.000 0.000 0.900
#> GSM564669 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564670 6 0.1983 0.8815 0.000 0.000 0.072 0.000 0.020 0.908
#> GSM564671 6 0.1327 0.8840 0.000 0.000 0.000 0.000 0.064 0.936
#> GSM564672 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564673 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564674 5 0.0146 0.9390 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564675 6 0.1327 0.8840 0.000 0.000 0.000 0.000 0.064 0.936
#> GSM564676 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564677 5 0.0146 0.9391 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564678 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564679 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564680 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564682 6 0.3023 0.7375 0.000 0.000 0.232 0.000 0.000 0.768
#> GSM564683 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564684 6 0.2092 0.8544 0.000 0.000 0.000 0.000 0.124 0.876
#> GSM564685 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564686 6 0.1327 0.8840 0.000 0.000 0.000 0.000 0.064 0.936
#> GSM564687 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564688 5 0.3265 0.6466 0.000 0.000 0.004 0.000 0.748 0.248
#> GSM564689 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564690 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564691 6 0.2854 0.7784 0.000 0.000 0.208 0.000 0.000 0.792
#> GSM564692 5 0.2048 0.8365 0.000 0.000 0.000 0.000 0.880 0.120
#> GSM564694 6 0.0000 0.8850 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM564695 6 0.0000 0.8850 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM564696 3 0.1610 0.8971 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM564697 5 0.2762 0.7405 0.000 0.000 0.000 0.000 0.804 0.196
#> GSM564698 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564700 6 0.1327 0.8840 0.000 0.000 0.000 0.000 0.064 0.936
#> GSM564701 5 0.0260 0.9364 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM564702 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564703 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564704 2 0.3265 0.8713 0.248 0.748 0.000 0.004 0.000 0.000
#> GSM564705 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564707 1 0.0146 0.9135 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564708 4 0.0865 0.9129 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM564709 2 0.3198 0.8681 0.260 0.740 0.000 0.000 0.000 0.000
#> GSM564710 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.0363 0.9184 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM564712 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.0363 0.9182 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM564714 4 0.0260 0.9167 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM564715 1 0.0146 0.9135 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564716 1 0.5703 -0.4320 0.424 0.416 0.000 0.160 0.000 0.000
#> GSM564717 1 0.0146 0.9135 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564718 4 0.1663 0.9041 0.000 0.088 0.000 0.912 0.000 0.000
#> GSM564719 1 0.0458 0.9017 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM564720 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564721 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564722 4 0.3595 0.7755 0.008 0.288 0.000 0.704 0.000 0.000
#> GSM564723 1 0.0458 0.9038 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM564724 4 0.0363 0.9184 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM564725 2 0.3244 0.8616 0.268 0.732 0.000 0.000 0.000 0.000
#> GSM564726 4 0.0937 0.9172 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM564727 2 0.3076 0.8700 0.240 0.760 0.000 0.000 0.000 0.000
#> GSM564728 4 0.3371 0.7840 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM564729 2 0.3210 0.8199 0.168 0.804 0.000 0.028 0.000 0.000
#> GSM564730 1 0.2003 0.7856 0.884 0.116 0.000 0.000 0.000 0.000
#> GSM564731 4 0.2907 0.8579 0.020 0.152 0.000 0.828 0.000 0.000
#> GSM564732 2 0.3126 0.8713 0.248 0.752 0.000 0.000 0.000 0.000
#> GSM564733 4 0.2170 0.8935 0.012 0.100 0.000 0.888 0.000 0.000
#> GSM564734 2 0.3198 0.8680 0.260 0.740 0.000 0.000 0.000 0.000
#> GSM564735 4 0.0713 0.9184 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM564736 4 0.0363 0.9182 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM564737 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.0865 0.9177 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM564739 4 0.1007 0.9171 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM564740 4 0.3371 0.7840 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM564741 4 0.0790 0.9182 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564742 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564743 1 0.2416 0.7173 0.844 0.156 0.000 0.000 0.000 0.000
#> GSM564744 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564745 2 0.3244 0.8630 0.268 0.732 0.000 0.000 0.000 0.000
#> GSM564746 1 0.0508 0.9027 0.984 0.004 0.000 0.012 0.000 0.000
#> GSM564747 4 0.2692 0.8646 0.012 0.148 0.000 0.840 0.000 0.000
#> GSM564748 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564749 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564750 4 0.0865 0.9177 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM564751 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564752 4 0.0937 0.9172 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM564753 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564754 1 0.2996 0.5609 0.772 0.228 0.000 0.000 0.000 0.000
#> GSM564755 4 0.3371 0.7840 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM564756 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564757 2 0.3213 0.8111 0.160 0.808 0.000 0.032 0.000 0.000
#> GSM564758 4 0.3371 0.7840 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM564759 4 0.0790 0.9118 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM564760 2 0.3101 0.8694 0.244 0.756 0.000 0.000 0.000 0.000
#> GSM564761 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564762 2 0.4691 0.4778 0.108 0.672 0.000 0.220 0.000 0.000
#> GSM564681 6 0.2454 0.8291 0.000 0.000 0.000 0.000 0.160 0.840
#> GSM564693 5 0.0000 0.9412 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564646 6 0.2340 0.8399 0.000 0.000 0.000 0.000 0.148 0.852
#> GSM564699 6 0.0000 0.8850 0.000 0.000 0.000 0.000 0.000 1.000
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 genotype/variation(p) disease.state(p) k
#> SD:mclust 154 0.9246 0.476 2
#> SD:mclust 136 0.0629 0.195 3
#> SD:mclust 141 0.0107 0.397 4
#> SD:mclust 150 0.1017 0.378 5
#> SD:mclust 148 0.2049 0.686 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 154 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 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 0.672 0.814 0.919 0.4923 0.501 0.501
#> 3 3 0.655 0.857 0.904 0.3231 0.672 0.443
#> 4 4 0.935 0.913 0.961 0.1129 0.880 0.681
#> 5 5 0.692 0.668 0.815 0.0763 0.892 0.657
#> 6 6 0.653 0.542 0.722 0.0453 0.939 0.757
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
#> GSM564615 1 0.0376 0.9313 0.996 0.004
#> GSM564616 1 0.2603 0.9079 0.956 0.044
#> GSM564617 1 0.2778 0.9044 0.952 0.048
#> GSM564618 1 0.2236 0.9141 0.964 0.036
#> GSM564619 1 0.0376 0.9313 0.996 0.004
#> GSM564620 1 0.0000 0.9307 1.000 0.000
#> GSM564621 1 0.0376 0.9313 0.996 0.004
#> GSM564622 2 0.7299 0.7393 0.204 0.796
#> GSM564623 1 0.1184 0.9245 0.984 0.016
#> GSM564624 1 0.3114 0.8973 0.944 0.056
#> GSM564625 1 0.0376 0.9313 0.996 0.004
#> GSM564626 1 0.0376 0.9313 0.996 0.004
#> GSM564627 1 0.0000 0.9307 1.000 0.000
#> GSM564628 1 0.1843 0.9179 0.972 0.028
#> GSM564629 1 0.0376 0.9313 0.996 0.004
#> GSM564630 1 0.2043 0.9159 0.968 0.032
#> GSM564609 2 0.0376 0.8802 0.004 0.996
#> GSM564610 1 0.0000 0.9307 1.000 0.000
#> GSM564611 1 0.0672 0.9273 0.992 0.008
#> GSM564612 2 0.0000 0.8808 0.000 1.000
#> GSM564613 2 0.0376 0.8802 0.004 0.996
#> GSM564614 1 0.0376 0.9313 0.996 0.004
#> GSM564631 2 0.0672 0.8818 0.008 0.992
#> GSM564632 2 0.0672 0.8801 0.008 0.992
#> GSM564633 2 0.0672 0.8818 0.008 0.992
#> GSM564634 2 0.1633 0.8753 0.024 0.976
#> GSM564635 2 0.0000 0.8808 0.000 1.000
#> GSM564636 2 0.0672 0.8818 0.008 0.992
#> GSM564637 2 0.0376 0.8802 0.004 0.996
#> GSM564638 2 0.0938 0.8807 0.012 0.988
#> GSM564639 2 0.0672 0.8818 0.008 0.992
#> GSM564640 1 0.3114 0.8975 0.944 0.056
#> GSM564641 2 0.0672 0.8818 0.008 0.992
#> GSM564642 2 0.7299 0.7393 0.204 0.796
#> GSM564643 2 0.1184 0.8784 0.016 0.984
#> GSM564644 2 0.7376 0.7352 0.208 0.792
#> GSM564645 2 0.0672 0.8818 0.008 0.992
#> GSM564647 2 0.0376 0.8802 0.004 0.996
#> GSM564648 2 0.9754 0.4024 0.408 0.592
#> GSM564649 2 0.0000 0.8808 0.000 1.000
#> GSM564650 1 0.9963 0.0134 0.536 0.464
#> GSM564651 2 0.0672 0.8801 0.008 0.992
#> GSM564652 1 0.8499 0.5758 0.724 0.276
#> GSM564653 2 1.0000 0.1305 0.496 0.504
#> GSM564654 2 0.0000 0.8808 0.000 1.000
#> GSM564655 2 0.0376 0.8802 0.004 0.996
#> GSM564656 2 0.0672 0.8818 0.008 0.992
#> GSM564657 2 0.0672 0.8818 0.008 0.992
#> GSM564658 2 0.9833 0.3628 0.424 0.576
#> GSM564659 2 0.0376 0.8802 0.004 0.996
#> GSM564660 1 0.9286 0.4176 0.656 0.344
#> GSM564661 1 0.8763 0.5336 0.704 0.296
#> GSM564662 2 0.0672 0.8818 0.008 0.992
#> GSM564663 2 0.8207 0.6784 0.256 0.744
#> GSM564664 2 0.7674 0.7170 0.224 0.776
#> GSM564665 2 0.0376 0.8802 0.004 0.996
#> GSM564666 2 0.4939 0.8311 0.108 0.892
#> GSM564667 2 0.0672 0.8818 0.008 0.992
#> GSM564668 2 0.0376 0.8802 0.004 0.996
#> GSM564669 2 0.0672 0.8818 0.008 0.992
#> GSM564670 2 0.0376 0.8802 0.004 0.996
#> GSM564671 1 0.1184 0.9245 0.984 0.016
#> GSM564672 2 0.0672 0.8818 0.008 0.992
#> GSM564673 2 0.9993 0.1759 0.484 0.516
#> GSM564674 2 0.8661 0.6318 0.288 0.712
#> GSM564675 1 0.1414 0.9226 0.980 0.020
#> GSM564676 1 0.9977 -0.0293 0.528 0.472
#> GSM564677 1 0.1843 0.9179 0.972 0.028
#> GSM564678 2 0.9993 0.1772 0.484 0.516
#> GSM564679 1 0.4022 0.8743 0.920 0.080
#> GSM564680 2 0.0672 0.8818 0.008 0.992
#> GSM564682 2 0.0000 0.8808 0.000 1.000
#> GSM564683 2 0.0938 0.8807 0.012 0.988
#> GSM564684 1 0.1414 0.9226 0.980 0.020
#> GSM564685 2 0.0672 0.8818 0.008 0.992
#> GSM564686 1 0.1184 0.9245 0.984 0.016
#> GSM564687 2 0.9922 0.2931 0.448 0.552
#> GSM564688 2 0.7528 0.7265 0.216 0.784
#> GSM564689 1 0.2603 0.9075 0.956 0.044
#> GSM564690 2 0.9580 0.4698 0.380 0.620
#> GSM564691 2 0.0376 0.8802 0.004 0.996
#> GSM564692 2 0.7528 0.7265 0.216 0.784
#> GSM564694 2 0.6438 0.7772 0.164 0.836
#> GSM564695 2 0.1633 0.8755 0.024 0.976
#> GSM564696 2 0.0938 0.8807 0.012 0.988
#> GSM564697 1 0.9881 0.1236 0.564 0.436
#> GSM564698 2 0.0672 0.8818 0.008 0.992
#> GSM564700 1 0.1184 0.9245 0.984 0.016
#> GSM564701 2 0.9754 0.4040 0.408 0.592
#> GSM564702 1 0.2236 0.9131 0.964 0.036
#> GSM564703 2 0.3431 0.8535 0.064 0.936
#> GSM564704 1 0.0376 0.9313 0.996 0.004
#> GSM564705 1 0.0000 0.9307 1.000 0.000
#> GSM564706 2 0.2778 0.8637 0.048 0.952
#> GSM564707 1 0.0376 0.9313 0.996 0.004
#> GSM564708 2 0.5059 0.8169 0.112 0.888
#> GSM564709 1 0.0376 0.9313 0.996 0.004
#> GSM564710 1 0.0376 0.9292 0.996 0.004
#> GSM564711 1 0.5059 0.8270 0.888 0.112
#> GSM564712 1 0.0376 0.9313 0.996 0.004
#> GSM564713 1 0.9754 0.2905 0.592 0.408
#> GSM564714 2 0.1843 0.8738 0.028 0.972
#> GSM564715 1 0.0376 0.9313 0.996 0.004
#> GSM564716 1 0.0376 0.9313 0.996 0.004
#> GSM564717 1 0.0376 0.9313 0.996 0.004
#> GSM564718 1 0.1184 0.9242 0.984 0.016
#> GSM564719 1 0.0000 0.9307 1.000 0.000
#> GSM564720 1 0.0000 0.9307 1.000 0.000
#> GSM564721 1 0.0000 0.9307 1.000 0.000
#> GSM564722 1 0.0376 0.9313 0.996 0.004
#> GSM564723 1 0.0376 0.9313 0.996 0.004
#> GSM564724 1 0.7299 0.7053 0.796 0.204
#> GSM564725 1 0.0376 0.9313 0.996 0.004
#> GSM564726 1 0.2043 0.9113 0.968 0.032
#> GSM564727 1 0.0376 0.9313 0.996 0.004
#> GSM564728 1 0.0376 0.9313 0.996 0.004
#> GSM564729 1 0.0376 0.9313 0.996 0.004
#> GSM564730 1 0.0000 0.9307 1.000 0.000
#> GSM564731 1 0.0376 0.9313 0.996 0.004
#> GSM564732 1 0.0376 0.9313 0.996 0.004
#> GSM564733 1 0.0938 0.9270 0.988 0.012
#> GSM564734 1 0.0376 0.9313 0.996 0.004
#> GSM564735 1 0.9129 0.4875 0.672 0.328
#> GSM564736 1 0.9909 0.1776 0.556 0.444
#> GSM564737 1 0.0376 0.9313 0.996 0.004
#> GSM564738 2 0.7376 0.7072 0.208 0.792
#> GSM564739 2 0.9909 0.2101 0.444 0.556
#> GSM564740 1 0.0376 0.9313 0.996 0.004
#> GSM564741 2 0.4298 0.8347 0.088 0.912
#> GSM564742 2 0.1414 0.8774 0.020 0.980
#> GSM564743 1 0.0000 0.9307 1.000 0.000
#> GSM564744 1 0.0000 0.9307 1.000 0.000
#> GSM564745 1 0.0376 0.9313 0.996 0.004
#> GSM564746 1 0.0376 0.9313 0.996 0.004
#> GSM564747 1 0.0376 0.9313 0.996 0.004
#> GSM564748 2 0.1843 0.8738 0.028 0.972
#> GSM564749 1 0.0000 0.9307 1.000 0.000
#> GSM564750 1 0.6438 0.7606 0.836 0.164
#> GSM564751 2 0.2043 0.8724 0.032 0.968
#> GSM564752 1 0.6712 0.7443 0.824 0.176
#> GSM564753 2 0.1414 0.8774 0.020 0.980
#> GSM564754 1 0.0376 0.9313 0.996 0.004
#> GSM564755 1 0.0376 0.9313 0.996 0.004
#> GSM564756 1 0.0000 0.9307 1.000 0.000
#> GSM564757 1 0.0376 0.9313 0.996 0.004
#> GSM564758 1 0.0376 0.9313 0.996 0.004
#> GSM564759 2 0.2043 0.8722 0.032 0.968
#> GSM564760 1 0.0376 0.9313 0.996 0.004
#> GSM564761 1 0.0672 0.9273 0.992 0.008
#> GSM564762 1 0.0376 0.9313 0.996 0.004
#> GSM564681 1 0.1414 0.9226 0.980 0.020
#> GSM564693 2 0.9732 0.4133 0.404 0.596
#> GSM564646 1 0.1414 0.9226 0.980 0.020
#> GSM564699 2 0.0938 0.8807 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.1129 0.888 0.976 0.020 0.004
#> GSM564616 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564617 2 0.0983 0.877 0.016 0.980 0.004
#> GSM564618 2 0.3482 0.831 0.128 0.872 0.000
#> GSM564619 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564620 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564621 1 0.2537 0.921 0.920 0.080 0.000
#> GSM564622 2 0.3192 0.865 0.000 0.888 0.112
#> GSM564623 2 0.6345 0.478 0.400 0.596 0.004
#> GSM564624 2 0.2998 0.876 0.016 0.916 0.068
#> GSM564625 1 0.3192 0.923 0.888 0.112 0.000
#> GSM564626 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564627 1 0.2537 0.921 0.920 0.080 0.000
#> GSM564628 2 0.0983 0.877 0.016 0.980 0.004
#> GSM564629 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564630 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564609 2 0.3941 0.843 0.000 0.844 0.156
#> GSM564610 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564611 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564612 2 0.6168 0.445 0.000 0.588 0.412
#> GSM564613 2 0.3551 0.855 0.000 0.868 0.132
#> GSM564614 1 0.1525 0.883 0.964 0.032 0.004
#> GSM564631 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564632 2 0.3425 0.859 0.004 0.884 0.112
#> GSM564633 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564634 2 0.3551 0.855 0.000 0.868 0.132
#> GSM564635 3 0.0424 0.931 0.000 0.008 0.992
#> GSM564636 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564637 2 0.3551 0.855 0.000 0.868 0.132
#> GSM564638 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564639 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564640 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564641 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564642 2 0.2173 0.880 0.008 0.944 0.048
#> GSM564643 2 0.3715 0.856 0.004 0.868 0.128
#> GSM564644 2 0.1647 0.867 0.036 0.960 0.004
#> GSM564645 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564647 2 0.3941 0.843 0.000 0.844 0.156
#> GSM564648 2 0.1878 0.880 0.004 0.952 0.044
#> GSM564649 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564650 2 0.4217 0.861 0.032 0.868 0.100
#> GSM564651 2 0.3816 0.848 0.000 0.852 0.148
#> GSM564652 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564653 2 0.0829 0.878 0.012 0.984 0.004
#> GSM564654 3 0.1411 0.907 0.000 0.036 0.964
#> GSM564655 2 0.3941 0.844 0.000 0.844 0.156
#> GSM564656 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564657 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564658 2 0.1015 0.878 0.012 0.980 0.008
#> GSM564659 2 0.3879 0.845 0.000 0.848 0.152
#> GSM564660 2 0.3618 0.840 0.104 0.884 0.012
#> GSM564661 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564662 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564663 2 0.0747 0.877 0.016 0.984 0.000
#> GSM564664 2 0.1636 0.875 0.020 0.964 0.016
#> GSM564665 2 0.3941 0.843 0.000 0.844 0.156
#> GSM564666 2 0.5470 0.788 0.168 0.796 0.036
#> GSM564667 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564668 2 0.3941 0.843 0.000 0.844 0.156
#> GSM564669 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564670 2 0.3686 0.852 0.000 0.860 0.140
#> GSM564671 2 0.6451 0.387 0.436 0.560 0.004
#> GSM564672 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564673 2 0.0829 0.878 0.012 0.984 0.004
#> GSM564674 2 0.1765 0.880 0.004 0.956 0.040
#> GSM564675 2 0.4233 0.812 0.160 0.836 0.004
#> GSM564676 2 0.1031 0.874 0.024 0.976 0.000
#> GSM564677 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564678 2 0.1267 0.874 0.024 0.972 0.004
#> GSM564679 2 0.0892 0.875 0.020 0.980 0.000
#> GSM564680 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564682 2 0.4504 0.809 0.000 0.804 0.196
#> GSM564683 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564684 2 0.5623 0.697 0.280 0.716 0.004
#> GSM564685 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564686 2 0.6298 0.505 0.388 0.608 0.004
#> GSM564687 2 0.0829 0.878 0.012 0.984 0.004
#> GSM564688 2 0.3551 0.855 0.000 0.868 0.132
#> GSM564689 2 0.0848 0.879 0.008 0.984 0.008
#> GSM564690 2 0.1182 0.878 0.012 0.976 0.012
#> GSM564691 2 0.5327 0.714 0.000 0.728 0.272
#> GSM564692 2 0.2537 0.874 0.000 0.920 0.080
#> GSM564694 2 0.3551 0.855 0.000 0.868 0.132
#> GSM564695 2 0.4253 0.857 0.048 0.872 0.080
#> GSM564696 3 0.0237 0.934 0.000 0.004 0.996
#> GSM564697 2 0.0747 0.879 0.000 0.984 0.016
#> GSM564698 3 0.0892 0.922 0.000 0.020 0.980
#> GSM564700 2 0.6345 0.478 0.400 0.596 0.004
#> GSM564701 2 0.1765 0.865 0.040 0.956 0.004
#> GSM564702 2 0.0848 0.879 0.008 0.984 0.008
#> GSM564703 3 0.5588 0.763 0.124 0.068 0.808
#> GSM564704 1 0.1289 0.909 0.968 0.032 0.000
#> GSM564705 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564706 3 0.6018 0.526 0.308 0.008 0.684
#> GSM564707 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564708 1 0.6599 0.789 0.748 0.084 0.168
#> GSM564709 1 0.1529 0.912 0.960 0.040 0.000
#> GSM564710 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564711 1 0.1337 0.885 0.972 0.012 0.016
#> GSM564712 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564713 1 0.5285 0.613 0.752 0.004 0.244
#> GSM564714 3 0.1289 0.911 0.032 0.000 0.968
#> GSM564715 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564716 1 0.2448 0.920 0.924 0.076 0.000
#> GSM564717 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564718 1 0.1337 0.885 0.972 0.016 0.012
#> GSM564719 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564720 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564721 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564722 1 0.0983 0.888 0.980 0.016 0.004
#> GSM564723 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564724 1 0.1636 0.881 0.964 0.016 0.020
#> GSM564725 1 0.2878 0.923 0.904 0.096 0.000
#> GSM564726 1 0.1711 0.881 0.960 0.032 0.008
#> GSM564727 1 0.0983 0.888 0.980 0.016 0.004
#> GSM564728 1 0.1765 0.879 0.956 0.040 0.004
#> GSM564729 1 0.1129 0.887 0.976 0.020 0.004
#> GSM564730 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564731 1 0.3193 0.923 0.896 0.100 0.004
#> GSM564732 1 0.1031 0.907 0.976 0.024 0.000
#> GSM564733 1 0.0983 0.902 0.980 0.016 0.004
#> GSM564734 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564735 3 0.6721 0.443 0.380 0.016 0.604
#> GSM564736 1 0.6081 0.396 0.652 0.004 0.344
#> GSM564737 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564738 3 0.3995 0.831 0.116 0.016 0.868
#> GSM564739 1 0.7458 0.734 0.692 0.112 0.196
#> GSM564740 1 0.1765 0.879 0.956 0.040 0.004
#> GSM564741 3 0.3995 0.831 0.116 0.016 0.868
#> GSM564742 3 0.0000 0.932 0.000 0.000 1.000
#> GSM564743 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564744 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564745 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564746 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564747 1 0.0237 0.895 0.996 0.000 0.004
#> GSM564748 3 0.0000 0.932 0.000 0.000 1.000
#> GSM564749 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564750 1 0.1905 0.877 0.956 0.016 0.028
#> GSM564751 3 0.5554 0.770 0.112 0.076 0.812
#> GSM564752 1 0.4371 0.797 0.860 0.032 0.108
#> GSM564753 3 0.0000 0.932 0.000 0.000 1.000
#> GSM564754 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564755 1 0.1765 0.879 0.956 0.040 0.004
#> GSM564756 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564757 1 0.1647 0.882 0.960 0.036 0.004
#> GSM564758 1 0.1525 0.883 0.964 0.032 0.004
#> GSM564759 3 0.0237 0.930 0.000 0.004 0.996
#> GSM564760 1 0.0237 0.895 0.996 0.000 0.004
#> GSM564761 1 0.3267 0.923 0.884 0.116 0.000
#> GSM564762 1 0.2356 0.920 0.928 0.072 0.000
#> GSM564681 2 0.3551 0.829 0.132 0.868 0.000
#> GSM564693 2 0.2878 0.870 0.000 0.904 0.096
#> GSM564646 2 0.4682 0.787 0.192 0.804 0.004
#> GSM564699 3 0.8820 0.117 0.116 0.408 0.476
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.4072 0.666 0.252 0.000 0.000 0.748
#> GSM564616 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564617 2 0.0188 0.967 0.000 0.996 0.000 0.004
#> GSM564618 2 0.4072 0.679 0.000 0.748 0.000 0.252
#> GSM564619 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564620 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564621 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564622 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564623 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564624 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564625 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564626 1 0.0336 0.954 0.992 0.000 0.000 0.008
#> GSM564627 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564628 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564629 1 0.0336 0.954 0.992 0.000 0.000 0.008
#> GSM564630 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564609 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564610 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM564611 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564612 2 0.4624 0.504 0.000 0.660 0.340 0.000
#> GSM564613 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564614 4 0.3801 0.713 0.220 0.000 0.000 0.780
#> GSM564631 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564632 2 0.1867 0.911 0.000 0.928 0.000 0.072
#> GSM564633 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564634 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564635 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564636 3 0.0469 0.966 0.000 0.012 0.988 0.000
#> GSM564637 2 0.0469 0.961 0.000 0.988 0.000 0.012
#> GSM564638 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564640 2 0.0376 0.967 0.004 0.992 0.000 0.004
#> GSM564641 3 0.0188 0.974 0.000 0.004 0.996 0.000
#> GSM564642 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564643 2 0.2469 0.870 0.000 0.892 0.000 0.108
#> GSM564644 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564645 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564647 2 0.0469 0.962 0.000 0.988 0.012 0.000
#> GSM564648 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564649 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564650 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564651 2 0.0376 0.967 0.004 0.992 0.000 0.004
#> GSM564652 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564653 2 0.0376 0.967 0.004 0.992 0.000 0.004
#> GSM564654 3 0.0592 0.961 0.000 0.016 0.984 0.000
#> GSM564655 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564656 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564657 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564658 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564659 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564660 2 0.1792 0.915 0.000 0.932 0.000 0.068
#> GSM564661 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564662 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564663 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564664 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564665 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564666 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564667 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564668 2 0.0188 0.966 0.000 0.996 0.004 0.000
#> GSM564669 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564670 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564671 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564672 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564673 2 0.0376 0.967 0.004 0.992 0.000 0.004
#> GSM564674 2 0.0376 0.967 0.004 0.992 0.000 0.004
#> GSM564675 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564676 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564677 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564678 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564679 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564680 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564682 2 0.0707 0.957 0.000 0.980 0.020 0.000
#> GSM564683 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564684 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564685 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564686 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564687 2 0.0188 0.967 0.004 0.996 0.000 0.000
#> GSM564688 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564689 2 0.0188 0.967 0.004 0.996 0.000 0.000
#> GSM564690 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564691 2 0.1211 0.940 0.000 0.960 0.040 0.000
#> GSM564692 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564694 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564695 2 0.4866 0.352 0.000 0.596 0.000 0.404
#> GSM564696 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564697 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564698 3 0.0469 0.967 0.000 0.012 0.988 0.000
#> GSM564700 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564701 2 0.0524 0.965 0.004 0.988 0.000 0.008
#> GSM564702 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564703 1 0.4222 0.645 0.728 0.000 0.272 0.000
#> GSM564704 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564705 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564706 1 0.2408 0.869 0.896 0.000 0.104 0.000
#> GSM564707 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> GSM564708 1 0.0469 0.950 0.988 0.000 0.012 0.000
#> GSM564709 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564710 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> GSM564711 1 0.0657 0.949 0.984 0.000 0.012 0.004
#> GSM564712 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564713 1 0.4761 0.530 0.664 0.000 0.332 0.004
#> GSM564714 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564715 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564716 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564717 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564718 1 0.1118 0.934 0.964 0.000 0.000 0.036
#> GSM564719 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564720 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564721 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM564722 1 0.4830 0.330 0.608 0.000 0.000 0.392
#> GSM564723 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564724 1 0.2999 0.833 0.864 0.000 0.132 0.004
#> GSM564725 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564726 4 0.0336 0.907 0.008 0.000 0.000 0.992
#> GSM564727 1 0.3266 0.785 0.832 0.000 0.000 0.168
#> GSM564728 4 0.0336 0.907 0.008 0.000 0.000 0.992
#> GSM564729 4 0.4804 0.385 0.384 0.000 0.000 0.616
#> GSM564730 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM564731 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564732 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564733 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564734 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564735 4 0.5285 0.075 0.008 0.000 0.468 0.524
#> GSM564736 3 0.1109 0.944 0.028 0.000 0.968 0.004
#> GSM564737 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564738 3 0.5132 0.138 0.004 0.000 0.548 0.448
#> GSM564739 1 0.1022 0.936 0.968 0.000 0.032 0.000
#> GSM564740 4 0.0336 0.907 0.008 0.000 0.000 0.992
#> GSM564741 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564742 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564743 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564744 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564745 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564746 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564747 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564748 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564749 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564750 4 0.2216 0.847 0.092 0.000 0.000 0.908
#> GSM564751 1 0.2469 0.865 0.892 0.000 0.108 0.000
#> GSM564752 4 0.0336 0.907 0.008 0.000 0.000 0.992
#> GSM564753 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> GSM564754 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564755 4 0.0336 0.907 0.008 0.000 0.000 0.992
#> GSM564756 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM564757 4 0.0469 0.905 0.012 0.000 0.000 0.988
#> GSM564758 1 0.4406 0.568 0.700 0.000 0.000 0.300
#> GSM564759 3 0.1022 0.942 0.032 0.000 0.968 0.000
#> GSM564760 1 0.1022 0.936 0.968 0.000 0.000 0.032
#> GSM564761 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM564762 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM564681 2 0.2589 0.865 0.000 0.884 0.000 0.116
#> GSM564693 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> GSM564646 4 0.0469 0.908 0.000 0.012 0.000 0.988
#> GSM564699 4 0.0469 0.908 0.000 0.012 0.000 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 1 0.4836 0.4806 0.612 0.000 0.000 0.356 0.032
#> GSM564616 2 0.3242 0.6829 0.000 0.784 0.000 0.000 0.216
#> GSM564617 2 0.3274 0.6857 0.000 0.780 0.000 0.000 0.220
#> GSM564618 2 0.4781 0.6360 0.000 0.728 0.000 0.112 0.160
#> GSM564619 1 0.3074 0.7420 0.804 0.000 0.000 0.000 0.196
#> GSM564620 1 0.3003 0.7460 0.812 0.000 0.000 0.000 0.188
#> GSM564621 1 0.3300 0.7389 0.792 0.000 0.000 0.004 0.204
#> GSM564622 2 0.3508 0.6750 0.000 0.748 0.000 0.000 0.252
#> GSM564623 4 0.3229 0.7940 0.000 0.032 0.000 0.840 0.128
#> GSM564624 2 0.2329 0.7410 0.000 0.876 0.000 0.000 0.124
#> GSM564625 1 0.3074 0.7420 0.804 0.000 0.000 0.000 0.196
#> GSM564626 1 0.3074 0.7420 0.804 0.000 0.000 0.000 0.196
#> GSM564627 1 0.3300 0.7366 0.792 0.000 0.000 0.004 0.204
#> GSM564628 2 0.3210 0.6847 0.000 0.788 0.000 0.000 0.212
#> GSM564629 1 0.3300 0.7339 0.792 0.004 0.000 0.000 0.204
#> GSM564630 2 0.3305 0.6806 0.000 0.776 0.000 0.000 0.224
#> GSM564609 2 0.4519 0.6685 0.000 0.752 0.100 0.000 0.148
#> GSM564610 1 0.1121 0.7974 0.956 0.000 0.000 0.000 0.044
#> GSM564611 5 0.4294 0.0928 0.468 0.000 0.000 0.000 0.532
#> GSM564612 3 0.4477 0.5748 0.000 0.252 0.708 0.000 0.040
#> GSM564613 2 0.3231 0.7151 0.000 0.800 0.000 0.004 0.196
#> GSM564614 1 0.4949 0.3970 0.572 0.000 0.000 0.396 0.032
#> GSM564631 3 0.0162 0.8988 0.000 0.000 0.996 0.000 0.004
#> GSM564632 2 0.3736 0.7042 0.000 0.824 0.004 0.072 0.100
#> GSM564633 3 0.1484 0.8850 0.000 0.008 0.944 0.000 0.048
#> GSM564634 2 0.1410 0.7240 0.000 0.940 0.000 0.000 0.060
#> GSM564635 3 0.1357 0.8874 0.000 0.004 0.948 0.000 0.048
#> GSM564636 3 0.2341 0.8660 0.000 0.012 0.912 0.020 0.056
#> GSM564637 4 0.5176 0.0133 0.000 0.468 0.000 0.492 0.040
#> GSM564638 3 0.1942 0.8694 0.000 0.000 0.920 0.012 0.068
#> GSM564639 3 0.0880 0.8944 0.000 0.000 0.968 0.000 0.032
#> GSM564640 2 0.2561 0.6546 0.000 0.856 0.000 0.000 0.144
#> GSM564641 3 0.0671 0.8978 0.000 0.004 0.980 0.000 0.016
#> GSM564642 2 0.4256 -0.1680 0.000 0.564 0.000 0.000 0.436
#> GSM564643 2 0.4891 0.6217 0.000 0.740 0.012 0.152 0.096
#> GSM564644 2 0.4307 -0.4740 0.000 0.500 0.000 0.000 0.500
#> GSM564645 3 0.0510 0.8981 0.000 0.000 0.984 0.000 0.016
#> GSM564647 2 0.0566 0.7422 0.000 0.984 0.004 0.000 0.012
#> GSM564648 2 0.2690 0.7354 0.000 0.844 0.000 0.000 0.156
#> GSM564649 3 0.0404 0.8985 0.000 0.000 0.988 0.000 0.012
#> GSM564650 2 0.5316 0.3543 0.000 0.632 0.000 0.284 0.084
#> GSM564651 2 0.1638 0.7390 0.000 0.932 0.004 0.000 0.064
#> GSM564652 2 0.2074 0.7452 0.000 0.896 0.000 0.000 0.104
#> GSM564653 2 0.1671 0.7228 0.000 0.924 0.000 0.000 0.076
#> GSM564654 3 0.4226 0.6998 0.000 0.176 0.764 0.000 0.060
#> GSM564655 2 0.3735 0.6737 0.000 0.816 0.004 0.048 0.132
#> GSM564656 3 0.0609 0.8989 0.000 0.000 0.980 0.000 0.020
#> GSM564657 3 0.0000 0.8991 0.000 0.000 1.000 0.000 0.000
#> GSM564658 2 0.3480 0.4393 0.000 0.752 0.000 0.000 0.248
#> GSM564659 2 0.2438 0.7447 0.000 0.900 0.040 0.000 0.060
#> GSM564660 2 0.5053 0.3869 0.000 0.624 0.000 0.324 0.052
#> GSM564661 2 0.1341 0.7401 0.000 0.944 0.000 0.000 0.056
#> GSM564662 3 0.0000 0.8991 0.000 0.000 1.000 0.000 0.000
#> GSM564663 2 0.4074 0.0107 0.000 0.636 0.000 0.000 0.364
#> GSM564664 5 0.4278 0.4956 0.000 0.452 0.000 0.000 0.548
#> GSM564665 2 0.4886 0.5145 0.000 0.712 0.188 0.000 0.100
#> GSM564666 4 0.2233 0.8357 0.000 0.016 0.000 0.904 0.080
#> GSM564667 3 0.0000 0.8991 0.000 0.000 1.000 0.000 0.000
#> GSM564668 2 0.4559 0.6290 0.000 0.748 0.152 0.000 0.100
#> GSM564669 3 0.0703 0.8985 0.000 0.000 0.976 0.000 0.024
#> GSM564670 2 0.3039 0.7019 0.000 0.808 0.000 0.000 0.192
#> GSM564671 4 0.1430 0.8386 0.000 0.004 0.000 0.944 0.052
#> GSM564672 3 0.0609 0.8979 0.000 0.000 0.980 0.000 0.020
#> GSM564673 2 0.1608 0.7472 0.000 0.928 0.000 0.000 0.072
#> GSM564674 2 0.0794 0.7393 0.000 0.972 0.000 0.000 0.028
#> GSM564675 4 0.1608 0.8398 0.000 0.000 0.000 0.928 0.072
#> GSM564676 5 0.4249 0.5030 0.000 0.432 0.000 0.000 0.568
#> GSM564677 2 0.2813 0.6715 0.000 0.832 0.000 0.000 0.168
#> GSM564678 5 0.4278 0.4962 0.000 0.452 0.000 0.000 0.548
#> GSM564679 5 0.4307 0.4144 0.000 0.496 0.000 0.000 0.504
#> GSM564680 3 0.0609 0.8977 0.000 0.000 0.980 0.000 0.020
#> GSM564682 2 0.3647 0.6503 0.000 0.816 0.132 0.000 0.052
#> GSM564683 3 0.0162 0.8991 0.000 0.000 0.996 0.000 0.004
#> GSM564684 4 0.1386 0.8416 0.000 0.016 0.000 0.952 0.032
#> GSM564685 3 0.0000 0.8991 0.000 0.000 1.000 0.000 0.000
#> GSM564686 4 0.0404 0.8447 0.000 0.000 0.000 0.988 0.012
#> GSM564687 2 0.1341 0.7506 0.000 0.944 0.000 0.000 0.056
#> GSM564688 2 0.1478 0.7379 0.000 0.936 0.000 0.000 0.064
#> GSM564689 5 0.4304 0.4427 0.000 0.484 0.000 0.000 0.516
#> GSM564690 5 0.4291 0.4803 0.000 0.464 0.000 0.000 0.536
#> GSM564691 2 0.2505 0.7084 0.000 0.888 0.092 0.000 0.020
#> GSM564692 2 0.2929 0.7032 0.000 0.820 0.000 0.000 0.180
#> GSM564694 2 0.2464 0.7448 0.000 0.888 0.000 0.016 0.096
#> GSM564695 4 0.4164 0.7382 0.000 0.120 0.000 0.784 0.096
#> GSM564696 3 0.2700 0.8518 0.000 0.004 0.884 0.024 0.088
#> GSM564697 2 0.4268 -0.2965 0.000 0.556 0.000 0.000 0.444
#> GSM564698 3 0.3346 0.8056 0.000 0.092 0.844 0.000 0.064
#> GSM564700 4 0.0963 0.8423 0.000 0.000 0.000 0.964 0.036
#> GSM564701 2 0.1341 0.7390 0.000 0.944 0.000 0.000 0.056
#> GSM564702 2 0.1270 0.7496 0.000 0.948 0.000 0.000 0.052
#> GSM564703 1 0.4430 0.2130 0.540 0.000 0.456 0.000 0.004
#> GSM564704 1 0.0404 0.7948 0.988 0.000 0.000 0.000 0.012
#> GSM564705 1 0.2605 0.7254 0.852 0.000 0.000 0.000 0.148
#> GSM564706 3 0.4803 0.1853 0.444 0.000 0.536 0.000 0.020
#> GSM564707 1 0.0510 0.7938 0.984 0.000 0.000 0.000 0.016
#> GSM564708 1 0.2513 0.7621 0.876 0.000 0.116 0.000 0.008
#> GSM564709 1 0.2522 0.7734 0.896 0.000 0.000 0.052 0.052
#> GSM564710 1 0.0404 0.7942 0.988 0.000 0.000 0.000 0.012
#> GSM564711 1 0.3197 0.7179 0.836 0.000 0.140 0.000 0.024
#> GSM564712 1 0.3661 0.5561 0.724 0.000 0.000 0.000 0.276
#> GSM564713 1 0.4197 0.6419 0.728 0.000 0.244 0.000 0.028
#> GSM564714 3 0.2046 0.8699 0.000 0.000 0.916 0.016 0.068
#> GSM564715 1 0.0510 0.7938 0.984 0.000 0.000 0.000 0.016
#> GSM564716 1 0.3109 0.7420 0.800 0.000 0.000 0.000 0.200
#> GSM564717 1 0.4278 0.0990 0.548 0.000 0.000 0.000 0.452
#> GSM564718 1 0.4035 0.6900 0.784 0.000 0.000 0.156 0.060
#> GSM564719 5 0.4268 0.1496 0.444 0.000 0.000 0.000 0.556
#> GSM564720 1 0.3177 0.6537 0.792 0.000 0.000 0.000 0.208
#> GSM564721 1 0.0794 0.7946 0.972 0.000 0.000 0.000 0.028
#> GSM564722 4 0.4779 0.4015 0.340 0.000 0.000 0.628 0.032
#> GSM564723 1 0.0880 0.7938 0.968 0.000 0.000 0.000 0.032
#> GSM564724 1 0.4276 0.6345 0.724 0.000 0.244 0.000 0.032
#> GSM564725 1 0.3074 0.7438 0.804 0.000 0.000 0.000 0.196
#> GSM564726 4 0.1872 0.8413 0.020 0.000 0.000 0.928 0.052
#> GSM564727 1 0.4602 0.6543 0.708 0.000 0.000 0.240 0.052
#> GSM564728 4 0.0703 0.8418 0.000 0.000 0.000 0.976 0.024
#> GSM564729 1 0.5049 0.5646 0.644 0.000 0.000 0.296 0.060
#> GSM564730 1 0.2424 0.7527 0.868 0.000 0.000 0.000 0.132
#> GSM564731 1 0.0404 0.7958 0.988 0.000 0.000 0.000 0.012
#> GSM564732 1 0.0451 0.7972 0.988 0.000 0.000 0.004 0.008
#> GSM564733 1 0.0794 0.7986 0.972 0.000 0.000 0.000 0.028
#> GSM564734 1 0.0703 0.7958 0.976 0.000 0.000 0.000 0.024
#> GSM564735 3 0.6063 0.1812 0.016 0.000 0.520 0.384 0.080
#> GSM564736 1 0.4897 0.2022 0.516 0.000 0.460 0.000 0.024
#> GSM564737 1 0.1270 0.7895 0.948 0.000 0.000 0.000 0.052
#> GSM564738 4 0.5293 0.0414 0.000 0.000 0.460 0.492 0.048
#> GSM564739 1 0.1557 0.7918 0.940 0.000 0.052 0.000 0.008
#> GSM564740 4 0.1608 0.8379 0.000 0.000 0.000 0.928 0.072
#> GSM564741 3 0.0609 0.8961 0.000 0.000 0.980 0.000 0.020
#> GSM564742 3 0.0609 0.8961 0.000 0.000 0.980 0.000 0.020
#> GSM564743 1 0.4015 0.4154 0.652 0.000 0.000 0.000 0.348
#> GSM564744 1 0.1197 0.7912 0.952 0.000 0.000 0.000 0.048
#> GSM564745 1 0.0162 0.7947 0.996 0.000 0.000 0.000 0.004
#> GSM564746 1 0.3039 0.7451 0.808 0.000 0.000 0.000 0.192
#> GSM564747 1 0.1197 0.7956 0.952 0.000 0.000 0.000 0.048
#> GSM564748 3 0.0955 0.8940 0.004 0.000 0.968 0.000 0.028
#> GSM564749 5 0.4256 0.1545 0.436 0.000 0.000 0.000 0.564
#> GSM564750 4 0.3915 0.7760 0.096 0.000 0.004 0.812 0.088
#> GSM564751 3 0.4620 0.3370 0.392 0.000 0.592 0.000 0.016
#> GSM564752 4 0.1732 0.8363 0.000 0.000 0.000 0.920 0.080
#> GSM564753 3 0.0404 0.8975 0.000 0.000 0.988 0.000 0.012
#> GSM564754 1 0.1792 0.7764 0.916 0.000 0.000 0.000 0.084
#> GSM564755 4 0.0404 0.8457 0.000 0.000 0.000 0.988 0.012
#> GSM564756 1 0.3561 0.6036 0.740 0.000 0.000 0.000 0.260
#> GSM564757 4 0.1310 0.8376 0.020 0.000 0.000 0.956 0.024
#> GSM564758 1 0.5559 0.3128 0.544 0.000 0.000 0.380 0.076
#> GSM564759 3 0.2270 0.8418 0.076 0.000 0.904 0.000 0.020
#> GSM564760 1 0.2712 0.7726 0.880 0.000 0.000 0.088 0.032
#> GSM564761 1 0.0404 0.7969 0.988 0.000 0.000 0.000 0.012
#> GSM564762 1 0.0404 0.7971 0.988 0.000 0.000 0.000 0.012
#> GSM564681 4 0.5290 0.4792 0.000 0.300 0.000 0.624 0.076
#> GSM564693 2 0.1671 0.7401 0.000 0.924 0.000 0.000 0.076
#> GSM564646 4 0.1568 0.8392 0.000 0.020 0.000 0.944 0.036
#> GSM564699 4 0.1197 0.8429 0.000 0.000 0.000 0.952 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 1 0.5349 0.4314 0.540 0.012 0.000 0.376 0.004 0.068
#> GSM564616 5 0.0520 0.4906 0.000 0.008 0.000 0.000 0.984 0.008
#> GSM564617 5 0.0547 0.4952 0.000 0.020 0.000 0.000 0.980 0.000
#> GSM564618 5 0.2344 0.4618 0.000 0.004 0.000 0.048 0.896 0.052
#> GSM564619 1 0.4440 0.5610 0.596 0.012 0.000 0.000 0.376 0.016
#> GSM564620 1 0.4015 0.6997 0.744 0.012 0.000 0.000 0.208 0.036
#> GSM564621 1 0.3946 0.7045 0.748 0.012 0.000 0.000 0.208 0.032
#> GSM564622 5 0.2263 0.4553 0.000 0.016 0.000 0.000 0.884 0.100
#> GSM564623 5 0.5583 -0.1485 0.000 0.000 0.000 0.336 0.508 0.156
#> GSM564624 5 0.2119 0.4885 0.000 0.036 0.000 0.000 0.904 0.060
#> GSM564625 1 0.3449 0.7128 0.780 0.008 0.000 0.000 0.196 0.016
#> GSM564626 1 0.4113 0.6384 0.668 0.008 0.000 0.000 0.308 0.016
#> GSM564627 1 0.4839 0.4919 0.544 0.016 0.000 0.008 0.416 0.016
#> GSM564628 5 0.0508 0.4933 0.000 0.012 0.000 0.000 0.984 0.004
#> GSM564629 1 0.4712 0.4550 0.524 0.016 0.000 0.000 0.440 0.020
#> GSM564630 5 0.0909 0.4903 0.000 0.020 0.000 0.000 0.968 0.012
#> GSM564609 6 0.5650 0.4236 0.000 0.024 0.092 0.000 0.352 0.532
#> GSM564610 1 0.3052 0.7577 0.852 0.008 0.000 0.000 0.064 0.076
#> GSM564611 2 0.3583 0.4677 0.260 0.728 0.000 0.000 0.008 0.004
#> GSM564612 3 0.4485 0.6333 0.000 0.084 0.724 0.000 0.012 0.180
#> GSM564613 5 0.4507 0.3120 0.000 0.048 0.004 0.004 0.676 0.268
#> GSM564614 1 0.4197 0.6763 0.744 0.012 0.000 0.196 0.004 0.044
#> GSM564631 3 0.0260 0.8564 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM564632 6 0.4383 0.4201 0.000 0.032 0.004 0.016 0.240 0.708
#> GSM564633 3 0.3201 0.7278 0.000 0.000 0.780 0.000 0.012 0.208
#> GSM564634 5 0.6126 -0.0643 0.000 0.232 0.008 0.000 0.456 0.304
#> GSM564635 3 0.2883 0.7693 0.000 0.008 0.832 0.000 0.008 0.152
#> GSM564636 3 0.3570 0.7153 0.000 0.004 0.752 0.000 0.016 0.228
#> GSM564637 4 0.6340 0.3160 0.000 0.236 0.000 0.548 0.148 0.068
#> GSM564638 3 0.3702 0.7058 0.000 0.012 0.752 0.004 0.008 0.224
#> GSM564639 3 0.1556 0.8447 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM564640 2 0.5787 -0.1246 0.000 0.480 0.000 0.000 0.324 0.196
#> GSM564641 3 0.0632 0.8547 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM564642 2 0.5564 -0.2114 0.000 0.472 0.000 0.000 0.140 0.388
#> GSM564643 6 0.5936 0.4342 0.000 0.028 0.004 0.116 0.304 0.548
#> GSM564644 2 0.2053 0.6837 0.000 0.888 0.000 0.000 0.108 0.004
#> GSM564645 3 0.0937 0.8565 0.000 0.000 0.960 0.000 0.000 0.040
#> GSM564647 5 0.5783 0.1997 0.000 0.168 0.036 0.000 0.608 0.188
#> GSM564648 5 0.4703 -0.1418 0.000 0.048 0.000 0.000 0.544 0.408
#> GSM564649 3 0.0790 0.8564 0.000 0.000 0.968 0.000 0.000 0.032
#> GSM564650 4 0.7208 -0.2236 0.000 0.248 0.000 0.404 0.104 0.244
#> GSM564651 6 0.5763 0.3713 0.000 0.180 0.000 0.000 0.356 0.464
#> GSM564652 6 0.5334 0.4108 0.000 0.128 0.000 0.000 0.320 0.552
#> GSM564653 5 0.6082 -0.3101 0.000 0.272 0.000 0.000 0.372 0.356
#> GSM564654 6 0.4513 0.0284 0.000 0.004 0.440 0.000 0.024 0.532
#> GSM564655 6 0.6174 0.2319 0.000 0.172 0.008 0.024 0.248 0.548
#> GSM564656 3 0.1141 0.8551 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM564657 3 0.0865 0.8574 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM564658 2 0.4141 0.1277 0.000 0.556 0.000 0.000 0.432 0.012
#> GSM564659 5 0.5642 0.0297 0.000 0.124 0.016 0.000 0.560 0.300
#> GSM564660 6 0.6865 0.1620 0.000 0.068 0.000 0.264 0.224 0.444
#> GSM564661 6 0.5871 0.3939 0.000 0.220 0.000 0.000 0.312 0.468
#> GSM564662 3 0.0713 0.8585 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM564663 2 0.4524 0.3512 0.000 0.616 0.000 0.000 0.336 0.048
#> GSM564664 2 0.2039 0.6824 0.000 0.904 0.000 0.000 0.076 0.020
#> GSM564665 6 0.7454 0.3361 0.000 0.232 0.240 0.000 0.152 0.376
#> GSM564666 4 0.4927 0.6877 0.000 0.012 0.000 0.652 0.080 0.256
#> GSM564667 3 0.0520 0.8571 0.000 0.000 0.984 0.000 0.008 0.008
#> GSM564668 6 0.5685 0.4516 0.000 0.040 0.076 0.000 0.332 0.552
#> GSM564669 3 0.1610 0.8428 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM564670 5 0.2034 0.4971 0.000 0.024 0.004 0.000 0.912 0.060
#> GSM564671 4 0.2100 0.7064 0.000 0.004 0.000 0.884 0.000 0.112
#> GSM564672 3 0.1501 0.8474 0.000 0.000 0.924 0.000 0.000 0.076
#> GSM564673 6 0.5654 0.3296 0.000 0.152 0.000 0.000 0.404 0.444
#> GSM564674 5 0.5209 0.1783 0.000 0.168 0.000 0.000 0.612 0.220
#> GSM564675 4 0.4674 0.6996 0.000 0.008 0.000 0.680 0.076 0.236
#> GSM564676 2 0.1333 0.6780 0.000 0.944 0.000 0.000 0.048 0.008
#> GSM564677 6 0.6220 0.4110 0.000 0.272 0.000 0.016 0.236 0.476
#> GSM564678 2 0.1444 0.6888 0.000 0.928 0.000 0.000 0.072 0.000
#> GSM564679 2 0.1957 0.6825 0.000 0.888 0.000 0.000 0.112 0.000
#> GSM564680 3 0.1204 0.8533 0.000 0.000 0.944 0.000 0.000 0.056
#> GSM564682 5 0.6697 0.0351 0.000 0.184 0.324 0.000 0.436 0.056
#> GSM564683 3 0.0713 0.8575 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM564684 4 0.1320 0.7407 0.000 0.000 0.000 0.948 0.036 0.016
#> GSM564685 3 0.0458 0.8577 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM564686 4 0.0405 0.7454 0.000 0.004 0.000 0.988 0.000 0.008
#> GSM564687 5 0.4860 0.2346 0.000 0.128 0.000 0.000 0.656 0.216
#> GSM564688 5 0.5781 -0.2998 0.000 0.176 0.000 0.000 0.428 0.396
#> GSM564689 2 0.2170 0.6849 0.000 0.888 0.000 0.000 0.100 0.012
#> GSM564690 2 0.1588 0.6889 0.000 0.924 0.000 0.000 0.072 0.004
#> GSM564691 5 0.6590 -0.0262 0.000 0.180 0.384 0.000 0.392 0.044
#> GSM564692 5 0.2309 0.4721 0.000 0.028 0.000 0.000 0.888 0.084
#> GSM564694 5 0.2771 0.4890 0.000 0.060 0.000 0.004 0.868 0.068
#> GSM564695 4 0.5102 0.6571 0.000 0.056 0.000 0.612 0.024 0.308
#> GSM564696 3 0.3658 0.7335 0.000 0.028 0.772 0.000 0.008 0.192
#> GSM564697 2 0.3259 0.5958 0.000 0.772 0.000 0.000 0.216 0.012
#> GSM564698 3 0.4758 0.3993 0.000 0.000 0.580 0.000 0.060 0.360
#> GSM564700 4 0.0713 0.7442 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM564701 5 0.5176 0.2107 0.000 0.192 0.000 0.000 0.620 0.188
#> GSM564702 5 0.5312 -0.1301 0.000 0.112 0.000 0.000 0.524 0.364
#> GSM564703 1 0.4097 0.1265 0.504 0.000 0.488 0.000 0.000 0.008
#> GSM564704 1 0.0508 0.7526 0.984 0.004 0.000 0.000 0.000 0.012
#> GSM564705 1 0.3398 0.6271 0.740 0.252 0.000 0.000 0.000 0.008
#> GSM564706 3 0.3972 0.5333 0.300 0.004 0.680 0.000 0.000 0.016
#> GSM564707 1 0.0972 0.7536 0.964 0.028 0.000 0.000 0.000 0.008
#> GSM564708 1 0.2698 0.7467 0.872 0.008 0.096 0.000 0.004 0.020
#> GSM564709 1 0.4120 0.6762 0.748 0.052 0.000 0.188 0.000 0.012
#> GSM564710 1 0.0993 0.7561 0.964 0.012 0.000 0.000 0.000 0.024
#> GSM564711 1 0.3490 0.6920 0.784 0.000 0.176 0.000 0.000 0.040
#> GSM564712 1 0.3405 0.6127 0.724 0.272 0.000 0.000 0.000 0.004
#> GSM564713 1 0.4440 0.6800 0.728 0.008 0.188 0.000 0.004 0.072
#> GSM564714 3 0.1863 0.8199 0.000 0.000 0.896 0.000 0.000 0.104
#> GSM564715 1 0.0914 0.7552 0.968 0.016 0.000 0.000 0.000 0.016
#> GSM564716 1 0.4255 0.7033 0.732 0.008 0.000 0.000 0.196 0.064
#> GSM564717 1 0.3998 0.1509 0.504 0.492 0.000 0.000 0.000 0.004
#> GSM564718 1 0.4840 0.6253 0.696 0.000 0.012 0.148 0.000 0.144
#> GSM564719 2 0.3404 0.4833 0.248 0.744 0.000 0.000 0.004 0.004
#> GSM564720 1 0.3290 0.6254 0.744 0.252 0.000 0.000 0.000 0.004
#> GSM564721 1 0.1707 0.7564 0.928 0.056 0.000 0.000 0.004 0.012
#> GSM564722 4 0.4229 0.6100 0.200 0.008 0.000 0.732 0.000 0.060
#> GSM564723 1 0.2053 0.7385 0.888 0.108 0.000 0.000 0.000 0.004
#> GSM564724 1 0.5815 0.4445 0.540 0.004 0.308 0.012 0.000 0.136
#> GSM564725 1 0.3390 0.7261 0.804 0.008 0.000 0.000 0.160 0.028
#> GSM564726 4 0.4851 0.6866 0.096 0.012 0.000 0.680 0.000 0.212
#> GSM564727 1 0.4840 0.6605 0.696 0.008 0.000 0.220 0.052 0.024
#> GSM564728 4 0.0748 0.7429 0.004 0.004 0.000 0.976 0.000 0.016
#> GSM564729 1 0.5408 0.5885 0.616 0.008 0.000 0.240 0.004 0.132
#> GSM564730 1 0.4559 0.6461 0.712 0.156 0.000 0.000 0.004 0.128
#> GSM564731 1 0.1268 0.7570 0.952 0.008 0.004 0.000 0.000 0.036
#> GSM564732 1 0.1321 0.7574 0.952 0.004 0.000 0.020 0.000 0.024
#> GSM564733 1 0.3329 0.7206 0.768 0.008 0.000 0.000 0.004 0.220
#> GSM564734 1 0.1728 0.7555 0.924 0.064 0.000 0.004 0.000 0.008
#> GSM564735 3 0.6353 0.3721 0.032 0.012 0.544 0.168 0.000 0.244
#> GSM564736 1 0.5737 0.2234 0.460 0.008 0.416 0.000 0.004 0.112
#> GSM564737 1 0.1349 0.7511 0.940 0.056 0.000 0.000 0.000 0.004
#> GSM564738 4 0.5391 0.1273 0.000 0.000 0.432 0.456 0.000 0.112
#> GSM564739 1 0.1615 0.7561 0.928 0.004 0.064 0.000 0.000 0.004
#> GSM564740 4 0.3342 0.7229 0.000 0.012 0.000 0.760 0.000 0.228
#> GSM564741 3 0.0603 0.8569 0.000 0.000 0.980 0.004 0.000 0.016
#> GSM564742 3 0.0458 0.8550 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM564743 1 0.5359 0.5096 0.608 0.260 0.000 0.000 0.012 0.120
#> GSM564744 1 0.2711 0.7420 0.872 0.068 0.000 0.000 0.004 0.056
#> GSM564745 1 0.0622 0.7525 0.980 0.008 0.000 0.000 0.000 0.012
#> GSM564746 1 0.4577 0.5233 0.568 0.016 0.000 0.000 0.400 0.016
#> GSM564747 1 0.2849 0.7490 0.876 0.060 0.016 0.004 0.000 0.044
#> GSM564748 3 0.1334 0.8473 0.020 0.000 0.948 0.000 0.000 0.032
#> GSM564749 2 0.3645 0.4872 0.236 0.740 0.000 0.000 0.000 0.024
#> GSM564750 4 0.6145 0.6113 0.144 0.012 0.016 0.520 0.000 0.308
#> GSM564751 3 0.4274 0.1532 0.432 0.004 0.552 0.000 0.000 0.012
#> GSM564752 4 0.3445 0.7189 0.000 0.012 0.000 0.744 0.000 0.244
#> GSM564753 3 0.0146 0.8560 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM564754 1 0.2070 0.7428 0.896 0.092 0.000 0.000 0.000 0.012
#> GSM564755 4 0.2218 0.7522 0.000 0.012 0.000 0.884 0.000 0.104
#> GSM564756 1 0.5195 0.5456 0.616 0.208 0.000 0.000 0.000 0.176
#> GSM564757 4 0.1225 0.7386 0.036 0.000 0.000 0.952 0.000 0.012
#> GSM564758 1 0.6152 0.1549 0.456 0.008 0.000 0.276 0.000 0.260
#> GSM564759 3 0.2901 0.7635 0.128 0.000 0.840 0.000 0.000 0.032
#> GSM564760 1 0.4784 0.6709 0.700 0.008 0.000 0.184 0.004 0.104
#> GSM564761 1 0.1679 0.7572 0.936 0.016 0.000 0.000 0.036 0.012
#> GSM564762 1 0.2346 0.7496 0.868 0.008 0.000 0.000 0.000 0.124
#> GSM564681 4 0.5913 0.1917 0.000 0.056 0.000 0.532 0.076 0.336
#> GSM564693 6 0.5663 0.4377 0.000 0.136 0.000 0.008 0.332 0.524
#> GSM564646 4 0.1265 0.7393 0.000 0.000 0.000 0.948 0.008 0.044
#> GSM564699 4 0.2234 0.7512 0.000 0.004 0.000 0.872 0.000 0.124
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
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 genotype/variation(p) disease.state(p) k
#> SD:NMF 137 0.00558 0.416 2
#> SD:NMF 147 0.16656 0.565 3
#> SD:NMF 149 0.38910 0.102 4
#> SD:NMF 125 0.36310 0.222 5
#> SD:NMF 94 0.24503 0.160 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.314 0.848 0.766 0.3352 0.499 0.499
#> 3 3 0.160 0.791 0.787 0.4142 0.981 0.963
#> 4 4 0.148 0.734 0.779 0.1600 0.950 0.899
#> 5 5 0.167 0.722 0.772 0.0740 0.978 0.950
#> 6 6 0.267 0.614 0.753 0.0633 0.971 0.932
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
#> GSM564615 1 0.9087 0.7885 0.676 0.324
#> GSM564616 2 0.2603 0.9002 0.044 0.956
#> GSM564617 2 0.2423 0.9016 0.040 0.960
#> GSM564618 2 0.3274 0.8924 0.060 0.940
#> GSM564619 1 0.9881 0.9015 0.564 0.436
#> GSM564620 1 0.9850 0.8951 0.572 0.428
#> GSM564621 1 0.9815 0.8930 0.580 0.420
#> GSM564622 2 0.2423 0.9014 0.040 0.960
#> GSM564623 2 0.7745 0.5488 0.228 0.772
#> GSM564624 2 0.3114 0.8949 0.056 0.944
#> GSM564625 1 0.9522 0.8618 0.628 0.372
#> GSM564626 1 0.9866 0.8983 0.568 0.432
#> GSM564627 1 0.9795 0.8919 0.584 0.416
#> GSM564628 2 0.2778 0.8978 0.048 0.952
#> GSM564629 1 0.9795 0.8851 0.584 0.416
#> GSM564630 2 0.1843 0.9052 0.028 0.972
#> GSM564609 2 0.3733 0.8531 0.072 0.928
#> GSM564610 1 0.9963 0.8913 0.536 0.464
#> GSM564611 1 0.9996 0.8659 0.512 0.488
#> GSM564612 2 0.1633 0.9077 0.024 0.976
#> GSM564613 2 0.1633 0.9074 0.024 0.976
#> GSM564614 1 0.9087 0.7947 0.676 0.324
#> GSM564631 2 0.1184 0.9065 0.016 0.984
#> GSM564632 2 0.1633 0.9095 0.024 0.976
#> GSM564633 2 0.1414 0.9071 0.020 0.980
#> GSM564634 2 0.2948 0.8905 0.052 0.948
#> GSM564635 2 0.1843 0.9072 0.028 0.972
#> GSM564636 2 0.1633 0.9085 0.024 0.976
#> GSM564637 2 0.4298 0.8475 0.088 0.912
#> GSM564638 2 0.2778 0.8880 0.048 0.952
#> GSM564639 2 0.1414 0.9062 0.020 0.980
#> GSM564640 2 0.1414 0.9034 0.020 0.980
#> GSM564641 2 0.1843 0.9065 0.028 0.972
#> GSM564642 2 0.2603 0.8888 0.044 0.956
#> GSM564643 2 0.7139 0.6129 0.196 0.804
#> GSM564644 2 0.3431 0.8803 0.064 0.936
#> GSM564645 2 0.1633 0.9050 0.024 0.976
#> GSM564647 2 0.0672 0.9058 0.008 0.992
#> GSM564648 2 0.1184 0.9071 0.016 0.984
#> GSM564649 2 0.1414 0.9064 0.020 0.980
#> GSM564650 2 0.2778 0.8927 0.048 0.952
#> GSM564651 2 0.1633 0.9096 0.024 0.976
#> GSM564652 2 0.4690 0.8289 0.100 0.900
#> GSM564653 2 0.1414 0.9071 0.020 0.980
#> GSM564654 2 0.1414 0.9062 0.020 0.980
#> GSM564655 2 0.2043 0.9086 0.032 0.968
#> GSM564656 2 0.1414 0.9062 0.020 0.980
#> GSM564657 2 0.1843 0.9071 0.028 0.972
#> GSM564658 2 0.3114 0.8801 0.056 0.944
#> GSM564659 2 0.1184 0.9082 0.016 0.984
#> GSM564660 2 0.4022 0.8676 0.080 0.920
#> GSM564661 2 0.3431 0.8817 0.064 0.936
#> GSM564662 2 0.1414 0.9062 0.020 0.980
#> GSM564663 2 0.1414 0.9070 0.020 0.980
#> GSM564664 2 0.4815 0.8436 0.104 0.896
#> GSM564665 2 0.2948 0.9004 0.052 0.948
#> GSM564666 2 0.5294 0.7941 0.120 0.880
#> GSM564667 2 0.2043 0.9075 0.032 0.968
#> GSM564668 2 0.2236 0.9064 0.036 0.964
#> GSM564669 2 0.1184 0.9065 0.016 0.984
#> GSM564670 2 0.0938 0.9093 0.012 0.988
#> GSM564671 1 0.9977 0.6621 0.528 0.472
#> GSM564672 2 0.1184 0.9065 0.016 0.984
#> GSM564673 2 0.2043 0.9078 0.032 0.968
#> GSM564674 2 0.1414 0.9064 0.020 0.980
#> GSM564675 2 0.4022 0.8566 0.080 0.920
#> GSM564676 2 0.3431 0.8834 0.064 0.936
#> GSM564677 2 0.2043 0.9063 0.032 0.968
#> GSM564678 2 0.3431 0.8834 0.064 0.936
#> GSM564679 2 0.3733 0.8744 0.072 0.928
#> GSM564680 2 0.1184 0.9065 0.016 0.984
#> GSM564682 2 0.2043 0.9050 0.032 0.968
#> GSM564683 2 0.1414 0.9062 0.020 0.980
#> GSM564684 2 0.9427 0.0126 0.360 0.640
#> GSM564685 2 0.5059 0.8126 0.112 0.888
#> GSM564686 2 0.9170 0.1556 0.332 0.668
#> GSM564687 2 0.5629 0.7744 0.132 0.868
#> GSM564688 2 0.1184 0.9065 0.016 0.984
#> GSM564689 2 0.1633 0.9013 0.024 0.976
#> GSM564690 2 0.2948 0.8913 0.052 0.948
#> GSM564691 2 0.0938 0.9065 0.012 0.988
#> GSM564692 2 0.1633 0.9074 0.024 0.976
#> GSM564694 2 0.7219 0.6373 0.200 0.800
#> GSM564695 2 0.3114 0.8929 0.056 0.944
#> GSM564696 2 0.3584 0.8925 0.068 0.932
#> GSM564697 2 0.2778 0.8858 0.048 0.952
#> GSM564698 2 0.1184 0.9079 0.016 0.984
#> GSM564700 2 0.9000 0.2351 0.316 0.684
#> GSM564701 2 0.1843 0.9049 0.028 0.972
#> GSM564702 2 0.3114 0.8937 0.056 0.944
#> GSM564703 1 0.9963 0.8887 0.536 0.464
#> GSM564704 1 0.9909 0.9009 0.556 0.444
#> GSM564705 1 0.9970 0.8842 0.532 0.468
#> GSM564706 1 0.9977 0.8871 0.528 0.472
#> GSM564707 1 0.9944 0.8941 0.544 0.456
#> GSM564708 1 0.9833 0.7322 0.576 0.424
#> GSM564709 1 0.9815 0.9003 0.580 0.420
#> GSM564710 1 0.9954 0.8917 0.540 0.460
#> GSM564711 1 0.9963 0.8899 0.536 0.464
#> GSM564712 1 0.9963 0.8877 0.536 0.464
#> GSM564713 1 0.9983 0.8639 0.524 0.476
#> GSM564714 1 0.9944 0.8972 0.544 0.456
#> GSM564715 1 0.9954 0.8937 0.540 0.460
#> GSM564716 1 0.9732 0.8938 0.596 0.404
#> GSM564717 1 0.8909 0.7238 0.692 0.308
#> GSM564718 1 0.9866 0.8989 0.568 0.432
#> GSM564719 1 0.9954 0.8728 0.540 0.460
#> GSM564720 1 0.9922 0.8937 0.552 0.448
#> GSM564721 1 0.9795 0.8956 0.584 0.416
#> GSM564722 1 0.9909 0.9016 0.556 0.444
#> GSM564723 1 0.9977 0.8844 0.528 0.472
#> GSM564724 1 0.9922 0.8856 0.552 0.448
#> GSM564725 1 0.9795 0.8966 0.584 0.416
#> GSM564726 1 0.9286 0.8149 0.656 0.344
#> GSM564727 1 0.9248 0.8151 0.660 0.340
#> GSM564728 1 0.9129 0.7942 0.672 0.328
#> GSM564729 1 0.9044 0.7871 0.680 0.320
#> GSM564730 1 0.9909 0.8966 0.556 0.444
#> GSM564731 1 0.9909 0.8964 0.556 0.444
#> GSM564732 1 0.9661 0.8748 0.608 0.392
#> GSM564733 1 0.9732 0.8791 0.596 0.404
#> GSM564734 1 0.9775 0.8961 0.588 0.412
#> GSM564735 1 0.9754 0.8776 0.592 0.408
#> GSM564736 1 0.9833 0.8904 0.576 0.424
#> GSM564737 1 0.9977 0.8817 0.528 0.472
#> GSM564738 1 0.9933 0.8959 0.548 0.452
#> GSM564739 1 0.9954 0.8908 0.540 0.460
#> GSM564740 1 0.9661 0.8572 0.608 0.392
#> GSM564741 1 0.9933 0.8996 0.548 0.452
#> GSM564742 1 0.9963 0.8921 0.536 0.464
#> GSM564743 1 0.9909 0.8966 0.556 0.444
#> GSM564744 1 0.9922 0.8949 0.552 0.448
#> GSM564745 1 0.9881 0.8982 0.564 0.436
#> GSM564746 1 0.9866 0.8890 0.568 0.432
#> GSM564747 1 0.9896 0.9017 0.560 0.440
#> GSM564748 1 0.9954 0.8933 0.540 0.460
#> GSM564749 1 0.9970 0.8604 0.532 0.468
#> GSM564750 1 0.9427 0.8419 0.640 0.360
#> GSM564751 1 0.9963 0.8888 0.536 0.464
#> GSM564752 1 0.9393 0.8372 0.644 0.356
#> GSM564753 1 0.9963 0.8895 0.536 0.464
#> GSM564754 1 0.9933 0.8981 0.548 0.452
#> GSM564755 1 0.9286 0.8140 0.656 0.344
#> GSM564756 1 0.9881 0.8983 0.564 0.436
#> GSM564757 1 0.9170 0.7991 0.668 0.332
#> GSM564758 1 0.9358 0.4378 0.648 0.352
#> GSM564759 1 0.9954 0.8936 0.540 0.460
#> GSM564760 1 0.9491 0.8486 0.632 0.368
#> GSM564761 1 0.9963 0.8924 0.536 0.464
#> GSM564762 1 0.9881 0.9015 0.564 0.436
#> GSM564681 2 0.1633 0.9055 0.024 0.976
#> GSM564693 2 0.5519 0.7873 0.128 0.872
#> GSM564646 2 0.9427 -0.0167 0.360 0.640
#> GSM564699 2 0.8713 0.3141 0.292 0.708
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.9130 0.313 0.492 0.152 0.356
#> GSM564616 2 0.2486 0.890 0.060 0.932 0.008
#> GSM564617 2 0.2446 0.891 0.052 0.936 0.012
#> GSM564618 2 0.3213 0.883 0.060 0.912 0.028
#> GSM564619 1 0.5858 0.850 0.740 0.240 0.020
#> GSM564620 1 0.6319 0.829 0.732 0.228 0.040
#> GSM564621 1 0.7256 0.799 0.696 0.216 0.088
#> GSM564622 2 0.2339 0.894 0.048 0.940 0.012
#> GSM564623 2 0.7217 0.642 0.132 0.716 0.152
#> GSM564624 2 0.3237 0.883 0.056 0.912 0.032
#> GSM564625 1 0.7036 0.774 0.720 0.184 0.096
#> GSM564626 1 0.5858 0.841 0.740 0.240 0.020
#> GSM564627 1 0.6232 0.827 0.740 0.220 0.040
#> GSM564628 2 0.3141 0.884 0.068 0.912 0.020
#> GSM564629 1 0.6142 0.814 0.748 0.212 0.040
#> GSM564630 2 0.2063 0.894 0.044 0.948 0.008
#> GSM564609 2 0.3272 0.849 0.104 0.892 0.004
#> GSM564610 1 0.5656 0.846 0.728 0.264 0.008
#> GSM564611 1 0.5678 0.813 0.684 0.316 0.000
#> GSM564612 2 0.1585 0.896 0.028 0.964 0.008
#> GSM564613 2 0.2031 0.897 0.032 0.952 0.016
#> GSM564614 1 0.8900 0.301 0.512 0.132 0.356
#> GSM564631 2 0.1620 0.896 0.024 0.964 0.012
#> GSM564632 2 0.1267 0.898 0.024 0.972 0.004
#> GSM564633 2 0.2031 0.896 0.032 0.952 0.016
#> GSM564634 2 0.3472 0.851 0.040 0.904 0.056
#> GSM564635 2 0.2031 0.891 0.032 0.952 0.016
#> GSM564636 2 0.1774 0.897 0.024 0.960 0.016
#> GSM564637 2 0.3998 0.863 0.060 0.884 0.056
#> GSM564638 2 0.3129 0.868 0.088 0.904 0.008
#> GSM564639 2 0.2176 0.894 0.032 0.948 0.020
#> GSM564640 2 0.1482 0.892 0.020 0.968 0.012
#> GSM564641 2 0.1989 0.895 0.048 0.948 0.004
#> GSM564642 2 0.2550 0.866 0.024 0.936 0.040
#> GSM564643 2 0.6410 0.708 0.092 0.764 0.144
#> GSM564644 2 0.3472 0.843 0.040 0.904 0.056
#> GSM564645 2 0.2297 0.890 0.036 0.944 0.020
#> GSM564647 2 0.1585 0.895 0.028 0.964 0.008
#> GSM564648 2 0.1620 0.894 0.024 0.964 0.012
#> GSM564649 2 0.1620 0.895 0.024 0.964 0.012
#> GSM564650 2 0.2810 0.893 0.036 0.928 0.036
#> GSM564651 2 0.1905 0.897 0.028 0.956 0.016
#> GSM564652 2 0.4047 0.810 0.148 0.848 0.004
#> GSM564653 2 0.1832 0.894 0.036 0.956 0.008
#> GSM564654 2 0.1620 0.893 0.024 0.964 0.012
#> GSM564655 2 0.2926 0.896 0.040 0.924 0.036
#> GSM564656 2 0.2152 0.894 0.036 0.948 0.016
#> GSM564657 2 0.2339 0.895 0.048 0.940 0.012
#> GSM564658 2 0.3039 0.857 0.036 0.920 0.044
#> GSM564659 2 0.1711 0.897 0.032 0.960 0.008
#> GSM564660 2 0.3993 0.864 0.064 0.884 0.052
#> GSM564661 2 0.3669 0.862 0.064 0.896 0.040
#> GSM564662 2 0.2297 0.893 0.036 0.944 0.020
#> GSM564663 2 0.1765 0.896 0.040 0.956 0.004
#> GSM564664 2 0.4290 0.821 0.064 0.872 0.064
#> GSM564665 2 0.3572 0.865 0.060 0.900 0.040
#> GSM564666 2 0.5416 0.801 0.100 0.820 0.080
#> GSM564667 2 0.2269 0.895 0.040 0.944 0.016
#> GSM564668 2 0.2845 0.888 0.068 0.920 0.012
#> GSM564669 2 0.1620 0.895 0.024 0.964 0.012
#> GSM564670 2 0.1529 0.898 0.040 0.960 0.000
#> GSM564671 2 0.9930 -0.241 0.280 0.380 0.340
#> GSM564672 2 0.1620 0.895 0.024 0.964 0.012
#> GSM564673 2 0.2383 0.891 0.044 0.940 0.016
#> GSM564674 2 0.1765 0.895 0.040 0.956 0.004
#> GSM564675 2 0.3791 0.867 0.048 0.892 0.060
#> GSM564676 2 0.3207 0.875 0.084 0.904 0.012
#> GSM564677 2 0.2280 0.896 0.052 0.940 0.008
#> GSM564678 2 0.3207 0.875 0.084 0.904 0.012
#> GSM564679 2 0.3031 0.880 0.076 0.912 0.012
#> GSM564680 2 0.1620 0.895 0.024 0.964 0.012
#> GSM564682 2 0.2200 0.895 0.056 0.940 0.004
#> GSM564683 2 0.2031 0.893 0.032 0.952 0.016
#> GSM564684 2 0.8637 0.343 0.152 0.588 0.260
#> GSM564685 2 0.4966 0.771 0.060 0.840 0.100
#> GSM564686 2 0.8544 0.391 0.152 0.600 0.248
#> GSM564687 2 0.5442 0.686 0.056 0.812 0.132
#> GSM564688 2 0.1832 0.895 0.036 0.956 0.008
#> GSM564689 2 0.2031 0.896 0.032 0.952 0.016
#> GSM564690 2 0.2774 0.884 0.072 0.920 0.008
#> GSM564691 2 0.1315 0.896 0.020 0.972 0.008
#> GSM564692 2 0.1999 0.895 0.036 0.952 0.012
#> GSM564694 2 0.6252 0.730 0.084 0.772 0.144
#> GSM564695 2 0.2773 0.890 0.048 0.928 0.024
#> GSM564696 2 0.3780 0.864 0.044 0.892 0.064
#> GSM564697 2 0.3009 0.861 0.028 0.920 0.052
#> GSM564698 2 0.0892 0.895 0.020 0.980 0.000
#> GSM564700 2 0.8528 0.401 0.156 0.604 0.240
#> GSM564701 2 0.1585 0.896 0.028 0.964 0.008
#> GSM564702 2 0.2772 0.885 0.080 0.916 0.004
#> GSM564703 1 0.5443 0.844 0.736 0.260 0.004
#> GSM564704 1 0.6105 0.851 0.724 0.252 0.024
#> GSM564705 1 0.5690 0.830 0.708 0.288 0.004
#> GSM564706 1 0.5517 0.844 0.728 0.268 0.004
#> GSM564707 1 0.5553 0.841 0.724 0.272 0.004
#> GSM564708 1 0.8077 0.393 0.652 0.176 0.172
#> GSM564709 1 0.6211 0.851 0.736 0.228 0.036
#> GSM564710 1 0.5737 0.844 0.732 0.256 0.012
#> GSM564711 1 0.6929 0.839 0.688 0.260 0.052
#> GSM564712 1 0.5327 0.840 0.728 0.272 0.000
#> GSM564713 1 0.6998 0.797 0.664 0.292 0.044
#> GSM564714 1 0.6053 0.849 0.720 0.260 0.020
#> GSM564715 1 0.5480 0.845 0.732 0.264 0.004
#> GSM564716 1 0.6446 0.841 0.736 0.212 0.052
#> GSM564717 1 0.6621 0.632 0.752 0.148 0.100
#> GSM564718 1 0.6606 0.843 0.716 0.236 0.048
#> GSM564719 1 0.5553 0.828 0.724 0.272 0.004
#> GSM564720 1 0.5244 0.844 0.756 0.240 0.004
#> GSM564721 1 0.5366 0.841 0.776 0.208 0.016
#> GSM564722 1 0.6067 0.851 0.736 0.236 0.028
#> GSM564723 1 0.5848 0.842 0.720 0.268 0.012
#> GSM564724 1 0.7568 0.759 0.680 0.212 0.108
#> GSM564725 1 0.6168 0.843 0.740 0.224 0.036
#> GSM564726 1 0.8957 0.416 0.536 0.152 0.312
#> GSM564727 1 0.8752 0.468 0.568 0.148 0.284
#> GSM564728 1 0.8983 0.308 0.508 0.140 0.352
#> GSM564729 1 0.8973 0.279 0.500 0.136 0.364
#> GSM564730 1 0.5536 0.848 0.752 0.236 0.012
#> GSM564731 1 0.6211 0.848 0.736 0.228 0.036
#> GSM564732 1 0.7276 0.791 0.704 0.192 0.104
#> GSM564733 1 0.8331 0.736 0.628 0.208 0.164
#> GSM564734 1 0.6034 0.841 0.752 0.212 0.036
#> GSM564735 1 0.7988 0.759 0.656 0.200 0.144
#> GSM564736 1 0.6794 0.807 0.728 0.196 0.076
#> GSM564737 1 0.5465 0.833 0.712 0.288 0.000
#> GSM564738 1 0.7331 0.832 0.672 0.256 0.072
#> GSM564739 1 0.5291 0.843 0.732 0.268 0.000
#> GSM564740 1 0.8985 0.648 0.564 0.220 0.216
#> GSM564741 1 0.6875 0.846 0.700 0.244 0.056
#> GSM564742 1 0.5404 0.847 0.740 0.256 0.004
#> GSM564743 1 0.5578 0.847 0.748 0.240 0.012
#> GSM564744 1 0.5244 0.845 0.756 0.240 0.004
#> GSM564745 1 0.6056 0.844 0.744 0.224 0.032
#> GSM564746 1 0.5894 0.828 0.752 0.220 0.028
#> GSM564747 1 0.5378 0.850 0.756 0.236 0.008
#> GSM564748 1 0.5656 0.845 0.728 0.264 0.008
#> GSM564749 1 0.5763 0.822 0.716 0.276 0.008
#> GSM564750 1 0.8862 0.638 0.576 0.192 0.232
#> GSM564751 1 0.5618 0.847 0.732 0.260 0.008
#> GSM564752 1 0.8792 0.611 0.580 0.176 0.244
#> GSM564753 1 0.5692 0.844 0.724 0.268 0.008
#> GSM564754 1 0.5737 0.847 0.732 0.256 0.012
#> GSM564755 1 0.8848 0.478 0.560 0.156 0.284
#> GSM564756 1 0.5639 0.850 0.752 0.232 0.016
#> GSM564757 1 0.9120 0.362 0.504 0.156 0.340
#> GSM564758 3 0.5744 0.000 0.072 0.128 0.800
#> GSM564759 1 0.5541 0.848 0.740 0.252 0.008
#> GSM564760 1 0.8386 0.660 0.624 0.172 0.204
#> GSM564761 1 0.5397 0.841 0.720 0.280 0.000
#> GSM564762 1 0.6141 0.850 0.736 0.232 0.032
#> GSM564681 2 0.2200 0.895 0.056 0.940 0.004
#> GSM564693 2 0.4443 0.828 0.052 0.864 0.084
#> GSM564646 2 0.8824 0.316 0.168 0.572 0.260
#> GSM564699 2 0.8137 0.488 0.140 0.640 0.220
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.713 0.817 0.332 0.084 0.024 0.560
#> GSM564616 2 0.255 0.891 0.060 0.916 0.008 0.016
#> GSM564617 2 0.267 0.892 0.048 0.916 0.016 0.020
#> GSM564618 2 0.327 0.883 0.056 0.892 0.024 0.028
#> GSM564619 1 0.487 0.755 0.792 0.140 0.012 0.056
#> GSM564620 1 0.639 0.653 0.708 0.152 0.036 0.104
#> GSM564621 1 0.704 0.537 0.648 0.144 0.032 0.176
#> GSM564622 2 0.264 0.893 0.052 0.916 0.016 0.016
#> GSM564623 2 0.652 0.636 0.096 0.688 0.032 0.184
#> GSM564624 2 0.328 0.883 0.052 0.892 0.024 0.032
#> GSM564625 1 0.713 0.406 0.636 0.108 0.040 0.216
#> GSM564626 1 0.479 0.738 0.792 0.144 0.008 0.056
#> GSM564627 1 0.619 0.658 0.716 0.144 0.024 0.116
#> GSM564628 2 0.323 0.881 0.072 0.888 0.012 0.028
#> GSM564629 1 0.615 0.655 0.732 0.132 0.044 0.092
#> GSM564630 2 0.231 0.894 0.048 0.928 0.008 0.016
#> GSM564609 2 0.296 0.854 0.116 0.876 0.004 0.004
#> GSM564610 1 0.452 0.766 0.800 0.156 0.008 0.036
#> GSM564611 1 0.485 0.725 0.748 0.220 0.004 0.028
#> GSM564612 2 0.182 0.898 0.032 0.948 0.012 0.008
#> GSM564613 2 0.214 0.899 0.040 0.936 0.012 0.012
#> GSM564614 4 0.679 0.816 0.328 0.068 0.020 0.584
#> GSM564631 2 0.174 0.898 0.024 0.952 0.016 0.008
#> GSM564632 2 0.149 0.900 0.036 0.956 0.004 0.004
#> GSM564633 2 0.225 0.900 0.052 0.928 0.016 0.004
#> GSM564634 2 0.363 0.842 0.024 0.876 0.060 0.040
#> GSM564635 2 0.221 0.892 0.028 0.936 0.020 0.016
#> GSM564636 2 0.192 0.898 0.036 0.944 0.012 0.008
#> GSM564637 2 0.369 0.873 0.048 0.868 0.012 0.072
#> GSM564638 2 0.307 0.868 0.096 0.884 0.008 0.012
#> GSM564639 2 0.241 0.894 0.036 0.928 0.020 0.016
#> GSM564640 2 0.159 0.896 0.024 0.956 0.016 0.004
#> GSM564641 2 0.201 0.896 0.060 0.932 0.004 0.004
#> GSM564642 2 0.272 0.880 0.028 0.912 0.052 0.008
#> GSM564643 2 0.573 0.700 0.064 0.732 0.020 0.184
#> GSM564644 2 0.361 0.846 0.032 0.868 0.088 0.012
#> GSM564645 2 0.242 0.891 0.032 0.928 0.024 0.016
#> GSM564647 2 0.134 0.897 0.024 0.964 0.008 0.004
#> GSM564648 2 0.141 0.898 0.020 0.960 0.020 0.000
#> GSM564649 2 0.176 0.897 0.016 0.952 0.020 0.012
#> GSM564650 2 0.287 0.896 0.036 0.908 0.012 0.044
#> GSM564651 2 0.192 0.901 0.028 0.944 0.024 0.004
#> GSM564652 2 0.438 0.768 0.176 0.796 0.016 0.012
#> GSM564653 2 0.189 0.897 0.036 0.944 0.016 0.004
#> GSM564654 2 0.185 0.895 0.028 0.948 0.012 0.012
#> GSM564655 2 0.294 0.896 0.036 0.908 0.028 0.028
#> GSM564656 2 0.231 0.894 0.032 0.932 0.020 0.016
#> GSM564657 2 0.252 0.897 0.052 0.920 0.016 0.012
#> GSM564658 2 0.290 0.872 0.032 0.904 0.056 0.008
#> GSM564659 2 0.171 0.900 0.036 0.948 0.016 0.000
#> GSM564660 2 0.373 0.871 0.064 0.864 0.008 0.064
#> GSM564661 2 0.342 0.877 0.060 0.880 0.052 0.008
#> GSM564662 2 0.242 0.893 0.032 0.928 0.024 0.016
#> GSM564663 2 0.207 0.898 0.044 0.936 0.016 0.004
#> GSM564664 2 0.442 0.813 0.048 0.832 0.096 0.024
#> GSM564665 2 0.367 0.859 0.044 0.876 0.048 0.032
#> GSM564666 2 0.516 0.791 0.096 0.776 0.008 0.120
#> GSM564667 2 0.240 0.896 0.032 0.928 0.028 0.012
#> GSM564668 2 0.282 0.890 0.068 0.904 0.020 0.008
#> GSM564669 2 0.195 0.896 0.032 0.944 0.012 0.012
#> GSM564670 2 0.174 0.901 0.056 0.940 0.004 0.000
#> GSM564671 4 0.818 0.230 0.160 0.332 0.036 0.472
#> GSM564672 2 0.192 0.897 0.036 0.944 0.012 0.008
#> GSM564673 2 0.263 0.892 0.036 0.920 0.020 0.024
#> GSM564674 2 0.177 0.896 0.036 0.948 0.012 0.004
#> GSM564675 2 0.374 0.865 0.044 0.864 0.012 0.080
#> GSM564676 2 0.303 0.876 0.088 0.888 0.020 0.004
#> GSM564677 2 0.229 0.897 0.060 0.924 0.012 0.004
#> GSM564678 2 0.303 0.876 0.088 0.888 0.020 0.004
#> GSM564679 2 0.296 0.879 0.084 0.892 0.020 0.004
#> GSM564680 2 0.195 0.896 0.032 0.944 0.012 0.012
#> GSM564682 2 0.223 0.896 0.064 0.924 0.004 0.008
#> GSM564683 2 0.231 0.893 0.032 0.932 0.020 0.016
#> GSM564684 2 0.719 0.328 0.084 0.564 0.028 0.324
#> GSM564685 2 0.641 0.647 0.040 0.712 0.120 0.128
#> GSM564686 2 0.731 0.356 0.100 0.568 0.028 0.304
#> GSM564687 2 0.580 0.659 0.048 0.728 0.192 0.032
#> GSM564688 2 0.201 0.896 0.040 0.940 0.012 0.008
#> GSM564689 2 0.239 0.899 0.036 0.928 0.012 0.024
#> GSM564690 2 0.271 0.885 0.076 0.904 0.016 0.004
#> GSM564691 2 0.157 0.898 0.028 0.956 0.012 0.004
#> GSM564692 2 0.192 0.897 0.028 0.944 0.024 0.004
#> GSM564694 2 0.574 0.723 0.068 0.744 0.028 0.160
#> GSM564695 2 0.258 0.893 0.048 0.916 0.004 0.032
#> GSM564696 2 0.417 0.848 0.036 0.852 0.052 0.060
#> GSM564697 2 0.285 0.876 0.024 0.904 0.064 0.008
#> GSM564698 2 0.100 0.898 0.024 0.972 0.004 0.000
#> GSM564700 2 0.740 0.363 0.112 0.568 0.028 0.292
#> GSM564701 2 0.158 0.898 0.036 0.952 0.012 0.000
#> GSM564702 2 0.273 0.889 0.076 0.904 0.008 0.012
#> GSM564703 1 0.458 0.765 0.796 0.164 0.020 0.020
#> GSM564704 1 0.491 0.770 0.784 0.148 0.008 0.060
#> GSM564705 1 0.482 0.748 0.768 0.192 0.008 0.032
#> GSM564706 1 0.525 0.758 0.764 0.172 0.036 0.028
#> GSM564707 1 0.405 0.762 0.808 0.168 0.000 0.024
#> GSM564708 1 0.859 -0.229 0.392 0.040 0.208 0.360
#> GSM564709 1 0.538 0.750 0.752 0.144 0.004 0.100
#> GSM564710 1 0.469 0.761 0.796 0.152 0.012 0.040
#> GSM564711 1 0.680 0.692 0.680 0.160 0.044 0.116
#> GSM564712 1 0.433 0.759 0.804 0.164 0.008 0.024
#> GSM564713 1 0.727 0.587 0.624 0.208 0.036 0.132
#> GSM564714 1 0.538 0.747 0.752 0.172 0.012 0.064
#> GSM564715 1 0.472 0.764 0.788 0.168 0.016 0.028
#> GSM564716 1 0.523 0.714 0.756 0.124 0.000 0.120
#> GSM564717 1 0.717 0.306 0.648 0.044 0.140 0.168
#> GSM564718 1 0.611 0.699 0.732 0.124 0.036 0.108
#> GSM564719 1 0.504 0.735 0.768 0.176 0.012 0.044
#> GSM564720 1 0.450 0.754 0.808 0.140 0.008 0.044
#> GSM564721 1 0.450 0.759 0.816 0.124 0.012 0.048
#> GSM564722 1 0.566 0.746 0.740 0.156 0.012 0.092
#> GSM564723 1 0.481 0.757 0.784 0.168 0.016 0.032
#> GSM564724 1 0.784 0.228 0.556 0.104 0.060 0.280
#> GSM564725 1 0.564 0.713 0.732 0.152 0.004 0.112
#> GSM564726 4 0.698 0.781 0.348 0.076 0.020 0.556
#> GSM564727 4 0.699 0.717 0.424 0.072 0.016 0.488
#> GSM564728 4 0.655 0.822 0.336 0.072 0.008 0.584
#> GSM564729 4 0.656 0.810 0.324 0.068 0.012 0.596
#> GSM564730 1 0.393 0.757 0.832 0.128 0.000 0.040
#> GSM564731 1 0.546 0.741 0.768 0.128 0.024 0.080
#> GSM564732 1 0.660 0.506 0.652 0.116 0.012 0.220
#> GSM564733 1 0.732 0.176 0.560 0.128 0.016 0.296
#> GSM564734 1 0.561 0.722 0.736 0.136 0.004 0.124
#> GSM564735 1 0.739 0.259 0.580 0.132 0.024 0.264
#> GSM564736 1 0.671 0.560 0.660 0.124 0.020 0.196
#> GSM564737 1 0.441 0.752 0.788 0.184 0.004 0.024
#> GSM564738 1 0.674 0.635 0.668 0.168 0.024 0.140
#> GSM564739 1 0.440 0.765 0.800 0.168 0.012 0.020
#> GSM564740 1 0.766 -0.135 0.500 0.148 0.016 0.336
#> GSM564741 1 0.641 0.706 0.700 0.160 0.028 0.112
#> GSM564742 1 0.502 0.762 0.776 0.168 0.032 0.024
#> GSM564743 1 0.415 0.756 0.820 0.132 0.000 0.048
#> GSM564744 1 0.427 0.756 0.820 0.136 0.008 0.036
#> GSM564745 1 0.551 0.714 0.764 0.120 0.020 0.096
#> GSM564746 1 0.541 0.706 0.772 0.132 0.028 0.068
#> GSM564747 1 0.488 0.767 0.784 0.152 0.008 0.056
#> GSM564748 1 0.488 0.761 0.784 0.164 0.028 0.024
#> GSM564749 1 0.532 0.723 0.756 0.176 0.016 0.052
#> GSM564750 1 0.723 -0.251 0.500 0.116 0.008 0.376
#> GSM564751 1 0.538 0.762 0.756 0.176 0.040 0.028
#> GSM564752 1 0.720 -0.384 0.476 0.108 0.008 0.408
#> GSM564753 1 0.515 0.755 0.764 0.180 0.028 0.028
#> GSM564754 1 0.451 0.768 0.804 0.152 0.012 0.032
#> GSM564755 4 0.699 0.699 0.420 0.080 0.012 0.488
#> GSM564756 1 0.487 0.769 0.792 0.140 0.012 0.056
#> GSM564757 4 0.708 0.816 0.344 0.084 0.020 0.552
#> GSM564758 3 0.626 0.000 0.024 0.056 0.668 0.252
#> GSM564759 1 0.530 0.760 0.772 0.152 0.036 0.040
#> GSM564760 1 0.751 -0.228 0.504 0.116 0.020 0.360
#> GSM564761 1 0.436 0.759 0.792 0.180 0.004 0.024
#> GSM564762 1 0.522 0.748 0.772 0.140 0.012 0.076
#> GSM564681 2 0.222 0.896 0.056 0.928 0.008 0.008
#> GSM564693 2 0.400 0.835 0.028 0.844 0.016 0.112
#> GSM564646 2 0.749 0.267 0.108 0.540 0.028 0.324
#> GSM564699 2 0.732 0.444 0.120 0.592 0.028 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.518 0.7085 0.220 0.016 0.044 0.708 0.012
#> GSM564616 3 0.266 0.8800 0.060 0.004 0.900 0.012 0.024
#> GSM564617 3 0.249 0.8832 0.044 0.000 0.908 0.016 0.032
#> GSM564618 3 0.313 0.8742 0.060 0.004 0.880 0.024 0.032
#> GSM564619 1 0.502 0.7316 0.756 0.004 0.120 0.092 0.028
#> GSM564620 1 0.633 0.5980 0.664 0.004 0.116 0.132 0.084
#> GSM564621 1 0.688 0.4711 0.584 0.004 0.112 0.228 0.072
#> GSM564622 3 0.270 0.8826 0.052 0.004 0.900 0.012 0.032
#> GSM564623 3 0.606 0.6371 0.068 0.024 0.676 0.196 0.036
#> GSM564624 3 0.318 0.8745 0.056 0.008 0.880 0.024 0.032
#> GSM564625 1 0.694 0.2792 0.556 0.012 0.076 0.288 0.068
#> GSM564626 1 0.489 0.7211 0.772 0.008 0.108 0.084 0.028
#> GSM564627 1 0.625 0.6165 0.668 0.008 0.116 0.152 0.056
#> GSM564628 3 0.330 0.8709 0.068 0.008 0.872 0.024 0.028
#> GSM564629 1 0.614 0.5992 0.684 0.004 0.096 0.104 0.112
#> GSM564630 3 0.238 0.8849 0.044 0.004 0.916 0.012 0.024
#> GSM564609 3 0.266 0.8490 0.112 0.004 0.876 0.004 0.004
#> GSM564610 1 0.405 0.7611 0.812 0.004 0.128 0.036 0.020
#> GSM564611 1 0.464 0.7251 0.756 0.008 0.184 0.016 0.036
#> GSM564612 3 0.164 0.8884 0.028 0.008 0.948 0.004 0.012
#> GSM564613 3 0.189 0.8911 0.040 0.004 0.936 0.012 0.008
#> GSM564614 4 0.452 0.6888 0.200 0.008 0.028 0.752 0.012
#> GSM564631 3 0.161 0.8889 0.024 0.016 0.948 0.000 0.012
#> GSM564632 3 0.153 0.8917 0.040 0.004 0.948 0.004 0.004
#> GSM564633 3 0.213 0.8900 0.056 0.008 0.920 0.000 0.016
#> GSM564634 3 0.361 0.8334 0.016 0.036 0.856 0.016 0.076
#> GSM564635 3 0.239 0.8847 0.040 0.012 0.916 0.004 0.028
#> GSM564636 3 0.181 0.8889 0.036 0.008 0.940 0.004 0.012
#> GSM564637 3 0.319 0.8665 0.040 0.000 0.868 0.076 0.016
#> GSM564638 3 0.300 0.8638 0.088 0.012 0.876 0.020 0.004
#> GSM564639 3 0.230 0.8860 0.040 0.012 0.920 0.004 0.024
#> GSM564640 3 0.147 0.8871 0.016 0.008 0.956 0.004 0.016
#> GSM564641 3 0.186 0.8871 0.060 0.004 0.928 0.000 0.008
#> GSM564642 3 0.332 0.8698 0.036 0.052 0.872 0.004 0.036
#> GSM564643 3 0.509 0.6995 0.044 0.016 0.720 0.208 0.012
#> GSM564644 3 0.413 0.8238 0.024 0.084 0.824 0.008 0.060
#> GSM564645 3 0.231 0.8838 0.036 0.012 0.920 0.004 0.028
#> GSM564647 3 0.107 0.8900 0.016 0.004 0.968 0.000 0.012
#> GSM564648 3 0.142 0.8899 0.016 0.012 0.956 0.000 0.016
#> GSM564649 3 0.205 0.8894 0.028 0.012 0.932 0.004 0.024
#> GSM564650 3 0.248 0.8874 0.028 0.000 0.908 0.048 0.016
#> GSM564651 3 0.161 0.8926 0.024 0.016 0.948 0.000 0.012
#> GSM564652 3 0.393 0.7600 0.188 0.008 0.784 0.004 0.016
#> GSM564653 3 0.176 0.8877 0.028 0.012 0.944 0.004 0.012
#> GSM564654 3 0.175 0.8881 0.028 0.008 0.944 0.004 0.016
#> GSM564655 3 0.262 0.8889 0.036 0.008 0.908 0.016 0.032
#> GSM564656 3 0.211 0.8862 0.032 0.008 0.928 0.004 0.028
#> GSM564657 3 0.218 0.8889 0.048 0.008 0.920 0.000 0.024
#> GSM564658 3 0.290 0.8680 0.024 0.052 0.892 0.004 0.028
#> GSM564659 3 0.146 0.8923 0.032 0.008 0.952 0.000 0.008
#> GSM564660 3 0.358 0.8633 0.060 0.008 0.856 0.060 0.016
#> GSM564661 3 0.343 0.8685 0.060 0.048 0.864 0.004 0.024
#> GSM564662 3 0.231 0.8856 0.036 0.012 0.920 0.004 0.028
#> GSM564663 3 0.239 0.8910 0.044 0.016 0.916 0.004 0.020
#> GSM564664 3 0.504 0.7820 0.036 0.084 0.776 0.020 0.084
#> GSM564665 3 0.366 0.8491 0.044 0.024 0.856 0.012 0.064
#> GSM564666 3 0.491 0.7756 0.088 0.016 0.760 0.128 0.008
#> GSM564667 3 0.248 0.8883 0.036 0.012 0.912 0.004 0.036
#> GSM564668 3 0.255 0.8837 0.060 0.004 0.904 0.008 0.024
#> GSM564669 3 0.183 0.8871 0.032 0.008 0.940 0.004 0.016
#> GSM564670 3 0.160 0.8926 0.048 0.012 0.940 0.000 0.000
#> GSM564671 4 0.696 0.2154 0.092 0.044 0.312 0.536 0.016
#> GSM564672 3 0.176 0.8875 0.036 0.008 0.940 0.000 0.016
#> GSM564673 3 0.275 0.8824 0.032 0.032 0.904 0.012 0.020
#> GSM564674 3 0.197 0.8876 0.028 0.016 0.936 0.004 0.016
#> GSM564675 3 0.364 0.8564 0.044 0.008 0.852 0.076 0.020
#> GSM564676 3 0.318 0.8622 0.080 0.020 0.872 0.004 0.024
#> GSM564677 3 0.237 0.8875 0.060 0.012 0.912 0.004 0.012
#> GSM564678 3 0.318 0.8622 0.080 0.020 0.872 0.004 0.024
#> GSM564679 3 0.312 0.8668 0.076 0.020 0.876 0.004 0.024
#> GSM564680 3 0.183 0.8871 0.032 0.008 0.940 0.004 0.016
#> GSM564682 3 0.208 0.8867 0.064 0.004 0.920 0.004 0.008
#> GSM564683 3 0.220 0.8850 0.036 0.008 0.924 0.004 0.028
#> GSM564684 3 0.597 0.3605 0.044 0.024 0.552 0.372 0.008
#> GSM564685 3 0.637 0.5288 0.024 0.072 0.628 0.032 0.244
#> GSM564686 3 0.623 0.3650 0.052 0.028 0.552 0.356 0.012
#> GSM564687 3 0.683 0.4865 0.028 0.220 0.608 0.040 0.104
#> GSM564688 3 0.211 0.8884 0.036 0.008 0.928 0.004 0.024
#> GSM564689 3 0.246 0.8899 0.032 0.008 0.916 0.028 0.016
#> GSM564690 3 0.290 0.8718 0.068 0.016 0.888 0.004 0.024
#> GSM564691 3 0.151 0.8898 0.024 0.012 0.952 0.000 0.012
#> GSM564692 3 0.191 0.8891 0.020 0.016 0.940 0.008 0.016
#> GSM564694 3 0.563 0.7101 0.064 0.016 0.720 0.156 0.044
#> GSM564695 3 0.244 0.8881 0.040 0.008 0.912 0.036 0.004
#> GSM564696 3 0.448 0.8284 0.048 0.020 0.812 0.036 0.084
#> GSM564697 3 0.292 0.8699 0.016 0.052 0.892 0.008 0.032
#> GSM564698 3 0.103 0.8891 0.024 0.004 0.968 0.000 0.004
#> GSM564700 3 0.638 0.3718 0.060 0.032 0.552 0.344 0.012
#> GSM564701 3 0.179 0.8881 0.032 0.012 0.940 0.000 0.016
#> GSM564702 3 0.285 0.8789 0.076 0.008 0.888 0.012 0.016
#> GSM564703 1 0.477 0.7596 0.772 0.004 0.136 0.040 0.048
#> GSM564704 1 0.452 0.7638 0.780 0.000 0.124 0.076 0.020
#> GSM564705 1 0.435 0.7420 0.780 0.004 0.164 0.020 0.032
#> GSM564706 1 0.549 0.7424 0.716 0.000 0.148 0.052 0.084
#> GSM564707 1 0.391 0.7597 0.808 0.000 0.144 0.028 0.020
#> GSM564708 5 0.550 0.0000 0.164 0.020 0.020 0.076 0.720
#> GSM564709 1 0.532 0.7244 0.724 0.000 0.112 0.132 0.032
#> GSM564710 1 0.397 0.7559 0.820 0.008 0.124 0.028 0.020
#> GSM564711 1 0.679 0.6218 0.608 0.000 0.132 0.160 0.100
#> GSM564712 1 0.384 0.7509 0.812 0.004 0.148 0.020 0.016
#> GSM564713 1 0.732 0.5119 0.560 0.004 0.168 0.164 0.104
#> GSM564714 1 0.570 0.7228 0.704 0.004 0.152 0.096 0.044
#> GSM564715 1 0.419 0.7592 0.800 0.004 0.140 0.020 0.036
#> GSM564716 1 0.574 0.6761 0.692 0.004 0.096 0.172 0.036
#> GSM564717 1 0.642 0.0933 0.588 0.032 0.004 0.104 0.272
#> GSM564718 1 0.629 0.6391 0.656 0.000 0.096 0.152 0.096
#> GSM564719 1 0.492 0.7297 0.764 0.008 0.140 0.032 0.056
#> GSM564720 1 0.411 0.7495 0.816 0.008 0.116 0.020 0.040
#> GSM564721 1 0.458 0.7599 0.792 0.004 0.104 0.064 0.036
#> GSM564722 1 0.559 0.7204 0.716 0.004 0.120 0.120 0.040
#> GSM564723 1 0.408 0.7519 0.808 0.012 0.140 0.016 0.024
#> GSM564724 1 0.777 -0.0725 0.420 0.008 0.060 0.324 0.188
#> GSM564725 1 0.593 0.6573 0.672 0.008 0.116 0.180 0.024
#> GSM564726 4 0.533 0.6682 0.204 0.012 0.036 0.712 0.036
#> GSM564727 4 0.541 0.6919 0.316 0.004 0.044 0.624 0.012
#> GSM564728 4 0.456 0.7070 0.208 0.008 0.032 0.744 0.008
#> GSM564729 4 0.426 0.6830 0.196 0.008 0.028 0.764 0.004
#> GSM564730 1 0.354 0.7533 0.848 0.004 0.100 0.028 0.020
#> GSM564731 1 0.548 0.7239 0.736 0.004 0.096 0.080 0.084
#> GSM564732 1 0.615 0.3714 0.592 0.004 0.076 0.300 0.028
#> GSM564733 1 0.693 -0.1004 0.452 0.004 0.100 0.400 0.044
#> GSM564734 1 0.548 0.7000 0.704 0.000 0.112 0.156 0.028
#> GSM564735 1 0.696 0.0710 0.480 0.004 0.108 0.364 0.044
#> GSM564736 1 0.672 0.4263 0.564 0.004 0.096 0.284 0.052
#> GSM564737 1 0.401 0.7476 0.800 0.004 0.156 0.024 0.016
#> GSM564738 1 0.663 0.5677 0.612 0.004 0.136 0.196 0.052
#> GSM564739 1 0.459 0.7602 0.776 0.000 0.140 0.040 0.044
#> GSM564740 1 0.711 -0.2555 0.424 0.020 0.124 0.412 0.020
#> GSM564741 1 0.648 0.6647 0.644 0.004 0.140 0.144 0.068
#> GSM564742 1 0.521 0.7464 0.736 0.000 0.144 0.044 0.076
#> GSM564743 1 0.353 0.7512 0.848 0.004 0.100 0.032 0.016
#> GSM564744 1 0.351 0.7479 0.848 0.004 0.104 0.024 0.020
#> GSM564745 1 0.560 0.7097 0.732 0.008 0.092 0.096 0.072
#> GSM564746 1 0.548 0.6830 0.736 0.004 0.100 0.084 0.076
#> GSM564747 1 0.488 0.7570 0.764 0.000 0.120 0.076 0.040
#> GSM564748 1 0.499 0.7496 0.752 0.000 0.136 0.040 0.072
#> GSM564749 1 0.496 0.7201 0.760 0.008 0.144 0.032 0.056
#> GSM564750 4 0.639 0.3191 0.416 0.000 0.092 0.468 0.024
#> GSM564751 1 0.519 0.7528 0.732 0.000 0.148 0.032 0.088
#> GSM564752 4 0.620 0.4749 0.372 0.000 0.080 0.524 0.024
#> GSM564753 1 0.521 0.7384 0.732 0.000 0.156 0.044 0.068
#> GSM564754 1 0.406 0.7621 0.812 0.004 0.128 0.032 0.024
#> GSM564755 4 0.556 0.7054 0.300 0.012 0.044 0.632 0.012
#> GSM564756 1 0.464 0.7681 0.788 0.008 0.120 0.048 0.036
#> GSM564757 4 0.499 0.7180 0.232 0.012 0.040 0.708 0.008
#> GSM564758 2 0.295 0.0000 0.016 0.876 0.020 0.088 0.000
#> GSM564759 1 0.538 0.7412 0.732 0.000 0.124 0.068 0.076
#> GSM564760 4 0.684 0.3890 0.392 0.012 0.084 0.476 0.036
#> GSM564761 1 0.422 0.7539 0.792 0.004 0.152 0.036 0.016
#> GSM564762 1 0.535 0.7178 0.744 0.008 0.096 0.108 0.044
#> GSM564681 3 0.227 0.8873 0.048 0.008 0.920 0.008 0.016
#> GSM564693 3 0.356 0.8328 0.028 0.008 0.840 0.116 0.008
#> GSM564646 3 0.634 0.2829 0.056 0.028 0.524 0.380 0.012
#> GSM564699 3 0.655 0.4332 0.084 0.032 0.568 0.304 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.369 0.5817 0.100 0.004 0.024 0.828 0.032 0.012
#> GSM564616 3 0.308 0.7422 0.060 0.000 0.860 0.028 0.052 0.000
#> GSM564617 3 0.292 0.7562 0.052 0.000 0.868 0.020 0.060 0.000
#> GSM564618 3 0.344 0.7347 0.060 0.004 0.844 0.040 0.052 0.000
#> GSM564619 1 0.520 0.6750 0.712 0.004 0.100 0.100 0.084 0.000
#> GSM564620 1 0.679 0.4388 0.560 0.020 0.096 0.156 0.168 0.000
#> GSM564621 1 0.702 0.2554 0.488 0.016 0.092 0.272 0.132 0.000
#> GSM564622 3 0.308 0.7496 0.048 0.000 0.860 0.028 0.064 0.000
#> GSM564623 3 0.591 0.2836 0.052 0.008 0.636 0.192 0.108 0.004
#> GSM564624 3 0.352 0.7361 0.056 0.004 0.844 0.040 0.052 0.004
#> GSM564625 1 0.714 0.0198 0.436 0.012 0.056 0.336 0.148 0.012
#> GSM564626 1 0.552 0.6619 0.692 0.004 0.092 0.112 0.096 0.004
#> GSM564627 1 0.668 0.4755 0.572 0.012 0.092 0.184 0.136 0.004
#> GSM564628 3 0.362 0.7179 0.056 0.000 0.832 0.040 0.068 0.004
#> GSM564629 1 0.670 0.4355 0.588 0.048 0.072 0.112 0.180 0.000
#> GSM564630 3 0.274 0.7585 0.048 0.000 0.880 0.020 0.052 0.000
#> GSM564609 3 0.242 0.7284 0.096 0.008 0.884 0.004 0.008 0.000
#> GSM564610 1 0.390 0.7417 0.800 0.004 0.120 0.024 0.052 0.000
#> GSM564611 1 0.460 0.6966 0.736 0.012 0.172 0.008 0.068 0.004
#> GSM564612 3 0.166 0.7882 0.020 0.008 0.940 0.004 0.028 0.000
#> GSM564613 3 0.198 0.7896 0.036 0.008 0.928 0.016 0.008 0.004
#> GSM564614 4 0.281 0.5565 0.088 0.020 0.012 0.872 0.008 0.000
#> GSM564631 3 0.138 0.7861 0.016 0.004 0.952 0.004 0.024 0.000
#> GSM564632 3 0.128 0.7926 0.024 0.004 0.956 0.004 0.012 0.000
#> GSM564633 3 0.221 0.7886 0.048 0.012 0.908 0.000 0.032 0.000
#> GSM564634 3 0.395 0.6205 0.008 0.048 0.812 0.012 0.104 0.016
#> GSM564635 3 0.237 0.7713 0.024 0.024 0.908 0.008 0.036 0.000
#> GSM564636 3 0.171 0.7888 0.028 0.004 0.936 0.004 0.028 0.000
#> GSM564637 3 0.304 0.7552 0.024 0.000 0.860 0.076 0.040 0.000
#> GSM564638 3 0.310 0.7403 0.084 0.008 0.860 0.020 0.028 0.000
#> GSM564639 3 0.213 0.7761 0.028 0.016 0.920 0.008 0.028 0.000
#> GSM564640 3 0.180 0.7815 0.016 0.000 0.924 0.004 0.056 0.000
#> GSM564641 3 0.194 0.7845 0.052 0.008 0.920 0.000 0.020 0.000
#> GSM564642 3 0.338 0.6990 0.028 0.008 0.816 0.004 0.144 0.000
#> GSM564643 3 0.495 0.4195 0.028 0.000 0.696 0.208 0.060 0.008
#> GSM564644 3 0.378 0.4797 0.016 0.000 0.732 0.000 0.244 0.008
#> GSM564645 3 0.222 0.7704 0.024 0.024 0.916 0.008 0.028 0.000
#> GSM564647 3 0.148 0.7911 0.016 0.008 0.952 0.008 0.012 0.004
#> GSM564648 3 0.131 0.7881 0.016 0.004 0.952 0.000 0.028 0.000
#> GSM564649 3 0.210 0.7853 0.016 0.016 0.920 0.008 0.040 0.000
#> GSM564650 3 0.248 0.7843 0.024 0.000 0.896 0.044 0.036 0.000
#> GSM564651 3 0.171 0.7940 0.024 0.004 0.932 0.000 0.040 0.000
#> GSM564652 3 0.397 0.5760 0.160 0.000 0.768 0.008 0.064 0.000
#> GSM564653 3 0.186 0.7812 0.028 0.000 0.924 0.004 0.044 0.000
#> GSM564654 3 0.159 0.7809 0.016 0.012 0.944 0.004 0.024 0.000
#> GSM564655 3 0.244 0.7766 0.024 0.024 0.904 0.008 0.040 0.000
#> GSM564656 3 0.207 0.7742 0.016 0.024 0.924 0.012 0.024 0.000
#> GSM564657 3 0.207 0.7856 0.036 0.012 0.920 0.004 0.028 0.000
#> GSM564658 3 0.292 0.7139 0.020 0.000 0.844 0.000 0.128 0.008
#> GSM564659 3 0.131 0.7917 0.020 0.000 0.956 0.008 0.012 0.004
#> GSM564660 3 0.336 0.7379 0.044 0.004 0.848 0.068 0.036 0.000
#> GSM564661 3 0.349 0.7109 0.056 0.000 0.816 0.004 0.120 0.004
#> GSM564662 3 0.230 0.7721 0.024 0.024 0.912 0.008 0.032 0.000
#> GSM564663 3 0.260 0.7819 0.044 0.004 0.884 0.004 0.064 0.000
#> GSM564664 3 0.436 0.3043 0.024 0.008 0.684 0.000 0.276 0.008
#> GSM564665 3 0.380 0.6850 0.028 0.032 0.836 0.020 0.072 0.012
#> GSM564666 3 0.480 0.5591 0.064 0.000 0.736 0.140 0.056 0.004
#> GSM564667 3 0.245 0.7760 0.032 0.024 0.904 0.008 0.032 0.000
#> GSM564668 3 0.244 0.7725 0.040 0.024 0.904 0.008 0.024 0.000
#> GSM564669 3 0.158 0.7817 0.020 0.004 0.944 0.008 0.024 0.000
#> GSM564670 3 0.171 0.7924 0.040 0.000 0.932 0.004 0.024 0.000
#> GSM564671 4 0.612 -0.0036 0.040 0.004 0.280 0.580 0.076 0.020
#> GSM564672 3 0.155 0.7825 0.024 0.004 0.944 0.004 0.024 0.000
#> GSM564673 3 0.332 0.7513 0.036 0.008 0.860 0.012 0.064 0.020
#> GSM564674 3 0.217 0.7815 0.028 0.000 0.904 0.004 0.064 0.000
#> GSM564675 3 0.369 0.7075 0.036 0.000 0.820 0.080 0.064 0.000
#> GSM564676 3 0.316 0.7213 0.080 0.000 0.840 0.004 0.076 0.000
#> GSM564677 3 0.240 0.7795 0.060 0.000 0.892 0.004 0.044 0.000
#> GSM564678 3 0.316 0.7213 0.080 0.000 0.840 0.004 0.076 0.000
#> GSM564679 3 0.316 0.7234 0.072 0.000 0.840 0.004 0.084 0.000
#> GSM564680 3 0.158 0.7817 0.020 0.004 0.944 0.008 0.024 0.000
#> GSM564682 3 0.200 0.7835 0.056 0.008 0.916 0.000 0.020 0.000
#> GSM564683 3 0.222 0.7725 0.024 0.024 0.916 0.008 0.028 0.000
#> GSM564684 3 0.586 -0.0710 0.028 0.004 0.516 0.372 0.076 0.004
#> GSM564685 3 0.702 -0.7212 0.020 0.172 0.460 0.020 0.308 0.020
#> GSM564686 3 0.598 -0.0507 0.032 0.004 0.516 0.360 0.084 0.004
#> GSM564687 5 0.620 0.0000 0.012 0.012 0.384 0.004 0.464 0.124
#> GSM564688 3 0.212 0.7836 0.036 0.000 0.912 0.008 0.044 0.000
#> GSM564689 3 0.235 0.7835 0.028 0.000 0.904 0.028 0.040 0.000
#> GSM564690 3 0.289 0.7404 0.072 0.000 0.860 0.004 0.064 0.000
#> GSM564691 3 0.143 0.7907 0.016 0.008 0.948 0.000 0.028 0.000
#> GSM564692 3 0.200 0.7854 0.020 0.000 0.916 0.008 0.056 0.000
#> GSM564694 3 0.556 0.4384 0.048 0.024 0.700 0.148 0.072 0.008
#> GSM564695 3 0.263 0.7865 0.040 0.000 0.888 0.028 0.044 0.000
#> GSM564696 3 0.457 0.6097 0.036 0.056 0.784 0.032 0.088 0.004
#> GSM564697 3 0.290 0.7123 0.012 0.000 0.840 0.004 0.140 0.004
#> GSM564698 3 0.113 0.7895 0.012 0.008 0.964 0.004 0.012 0.000
#> GSM564700 3 0.608 -0.0503 0.036 0.004 0.520 0.352 0.080 0.008
#> GSM564701 3 0.197 0.7823 0.028 0.000 0.912 0.000 0.060 0.000
#> GSM564702 3 0.289 0.7741 0.064 0.000 0.868 0.016 0.052 0.000
#> GSM564703 1 0.487 0.7399 0.748 0.040 0.128 0.052 0.032 0.000
#> GSM564704 1 0.526 0.7343 0.708 0.012 0.128 0.104 0.048 0.000
#> GSM564705 1 0.428 0.7198 0.760 0.008 0.156 0.012 0.064 0.000
#> GSM564706 1 0.589 0.7129 0.664 0.096 0.148 0.064 0.028 0.000
#> GSM564707 1 0.411 0.7423 0.784 0.016 0.144 0.024 0.032 0.000
#> GSM564708 2 0.215 0.0000 0.044 0.916 0.008 0.024 0.008 0.000
#> GSM564709 1 0.505 0.7020 0.708 0.024 0.096 0.160 0.012 0.000
#> GSM564710 1 0.352 0.7421 0.816 0.000 0.116 0.012 0.056 0.000
#> GSM564711 1 0.714 0.5284 0.536 0.120 0.124 0.184 0.036 0.000
#> GSM564712 1 0.364 0.7343 0.796 0.000 0.144 0.008 0.052 0.000
#> GSM564713 1 0.735 0.4243 0.500 0.104 0.164 0.196 0.036 0.000
#> GSM564714 1 0.597 0.6876 0.652 0.052 0.148 0.120 0.028 0.000
#> GSM564715 1 0.449 0.7415 0.764 0.016 0.140 0.036 0.044 0.000
#> GSM564716 1 0.604 0.5936 0.628 0.048 0.084 0.208 0.032 0.000
#> GSM564717 1 0.676 0.1111 0.504 0.232 0.004 0.016 0.212 0.032
#> GSM564718 1 0.671 0.5474 0.576 0.116 0.092 0.188 0.028 0.000
#> GSM564719 1 0.460 0.7024 0.752 0.016 0.124 0.008 0.096 0.004
#> GSM564720 1 0.390 0.7264 0.800 0.016 0.104 0.004 0.076 0.000
#> GSM564721 1 0.455 0.7418 0.776 0.020 0.092 0.068 0.044 0.000
#> GSM564722 1 0.577 0.6729 0.672 0.040 0.104 0.148 0.036 0.000
#> GSM564723 1 0.377 0.7357 0.796 0.004 0.136 0.008 0.056 0.000
#> GSM564724 4 0.810 0.2565 0.308 0.192 0.044 0.356 0.088 0.012
#> GSM564725 1 0.637 0.5600 0.584 0.036 0.108 0.232 0.040 0.000
#> GSM564726 4 0.414 0.5390 0.112 0.068 0.004 0.792 0.012 0.012
#> GSM564727 4 0.427 0.6197 0.208 0.000 0.028 0.732 0.032 0.000
#> GSM564728 4 0.276 0.5863 0.100 0.012 0.016 0.868 0.004 0.000
#> GSM564729 4 0.266 0.5635 0.092 0.004 0.012 0.876 0.016 0.000
#> GSM564730 1 0.393 0.7330 0.816 0.020 0.088 0.028 0.048 0.000
#> GSM564731 1 0.573 0.6851 0.692 0.088 0.084 0.100 0.036 0.000
#> GSM564732 1 0.617 0.2239 0.508 0.024 0.064 0.364 0.040 0.000
#> GSM564733 4 0.681 0.2687 0.364 0.048 0.084 0.464 0.036 0.004
#> GSM564734 1 0.554 0.6786 0.668 0.028 0.104 0.180 0.020 0.000
#> GSM564735 4 0.679 0.1269 0.404 0.056 0.088 0.420 0.032 0.000
#> GSM564736 1 0.681 0.2395 0.476 0.064 0.088 0.340 0.032 0.000
#> GSM564737 1 0.392 0.7305 0.784 0.004 0.152 0.016 0.044 0.000
#> GSM564738 1 0.666 0.4751 0.556 0.064 0.116 0.236 0.028 0.000
#> GSM564739 1 0.481 0.7405 0.748 0.036 0.136 0.052 0.028 0.000
#> GSM564740 4 0.667 0.3589 0.348 0.016 0.096 0.480 0.056 0.004
#> GSM564741 1 0.662 0.6193 0.592 0.072 0.132 0.168 0.036 0.000
#> GSM564742 1 0.553 0.7150 0.692 0.080 0.144 0.064 0.020 0.000
#> GSM564743 1 0.362 0.7306 0.828 0.008 0.088 0.020 0.056 0.000
#> GSM564744 1 0.332 0.7288 0.840 0.004 0.092 0.012 0.052 0.000
#> GSM564745 1 0.615 0.6582 0.672 0.076 0.080 0.112 0.052 0.008
#> GSM564746 1 0.597 0.5771 0.668 0.036 0.076 0.096 0.124 0.000
#> GSM564747 1 0.504 0.7304 0.736 0.040 0.108 0.092 0.024 0.000
#> GSM564748 1 0.533 0.7224 0.708 0.076 0.140 0.056 0.020 0.000
#> GSM564749 1 0.458 0.6937 0.748 0.016 0.128 0.004 0.100 0.004
#> GSM564750 4 0.611 0.4403 0.348 0.028 0.072 0.524 0.028 0.000
#> GSM564751 1 0.563 0.7294 0.684 0.092 0.144 0.056 0.024 0.000
#> GSM564752 4 0.572 0.5359 0.304 0.028 0.056 0.588 0.024 0.000
#> GSM564753 1 0.561 0.7062 0.680 0.076 0.160 0.064 0.020 0.000
#> GSM564754 1 0.396 0.7462 0.796 0.004 0.124 0.032 0.044 0.000
#> GSM564755 4 0.438 0.6328 0.196 0.012 0.028 0.740 0.024 0.000
#> GSM564756 1 0.512 0.7393 0.748 0.032 0.104 0.052 0.056 0.008
#> GSM564757 4 0.356 0.6030 0.120 0.004 0.024 0.824 0.024 0.004
#> GSM564758 6 0.149 0.0000 0.004 0.000 0.000 0.024 0.028 0.944
#> GSM564759 1 0.577 0.7000 0.680 0.096 0.120 0.080 0.024 0.000
#> GSM564760 4 0.655 0.4802 0.304 0.032 0.064 0.544 0.040 0.016
#> GSM564761 1 0.417 0.7362 0.772 0.004 0.148 0.024 0.052 0.000
#> GSM564762 1 0.576 0.6667 0.672 0.048 0.092 0.156 0.032 0.000
#> GSM564681 3 0.219 0.7854 0.044 0.000 0.908 0.008 0.040 0.000
#> GSM564693 3 0.358 0.6845 0.024 0.000 0.820 0.104 0.052 0.000
#> GSM564646 3 0.608 -0.1235 0.036 0.004 0.488 0.384 0.084 0.004
#> GSM564699 3 0.629 0.0292 0.060 0.004 0.540 0.308 0.080 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n genotype/variation(p) disease.state(p) k
#> CV:hclust 148 0.979 0.393 2
#> CV:hclust 138 0.964 0.448 3
#> CV:hclust 137 0.838 0.812 4
#> CV:hclust 133 0.855 0.823 5
#> CV:hclust 123 0.726 0.820 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 154 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 0.865 0.949 0.952 0.4983 0.500 0.500
#> 3 3 0.636 0.717 0.807 0.2557 0.870 0.744
#> 4 4 0.617 0.692 0.787 0.1456 0.854 0.634
#> 5 5 0.631 0.581 0.742 0.0796 0.904 0.663
#> 6 6 0.669 0.632 0.751 0.0452 0.920 0.658
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
#> GSM564615 1 0.2603 0.955 0.956 0.044
#> GSM564616 2 0.3879 0.956 0.076 0.924
#> GSM564617 2 0.3879 0.956 0.076 0.924
#> GSM564618 2 0.4298 0.952 0.088 0.912
#> GSM564619 1 0.0376 0.955 0.996 0.004
#> GSM564620 1 0.0000 0.954 1.000 0.000
#> GSM564621 1 0.0000 0.954 1.000 0.000
#> GSM564622 2 0.3733 0.956 0.072 0.928
#> GSM564623 2 0.4690 0.917 0.100 0.900
#> GSM564624 2 0.3879 0.956 0.076 0.924
#> GSM564625 1 0.0000 0.954 1.000 0.000
#> GSM564626 1 0.0376 0.955 0.996 0.004
#> GSM564627 1 0.0000 0.954 1.000 0.000
#> GSM564628 2 0.5059 0.939 0.112 0.888
#> GSM564629 1 0.0000 0.954 1.000 0.000
#> GSM564630 2 0.3879 0.956 0.076 0.924
#> GSM564609 2 0.0000 0.956 0.000 1.000
#> GSM564610 1 0.0938 0.955 0.988 0.012
#> GSM564611 1 0.3584 0.928 0.932 0.068
#> GSM564612 2 0.1843 0.958 0.028 0.972
#> GSM564613 2 0.0938 0.958 0.012 0.988
#> GSM564614 1 0.2603 0.955 0.956 0.044
#> GSM564631 2 0.0000 0.956 0.000 1.000
#> GSM564632 2 0.0000 0.956 0.000 1.000
#> GSM564633 2 0.0000 0.956 0.000 1.000
#> GSM564634 2 0.2236 0.960 0.036 0.964
#> GSM564635 2 0.0000 0.956 0.000 1.000
#> GSM564636 2 0.0000 0.956 0.000 1.000
#> GSM564637 2 0.0000 0.956 0.000 1.000
#> GSM564638 2 0.0000 0.956 0.000 1.000
#> GSM564639 2 0.0000 0.956 0.000 1.000
#> GSM564640 2 0.3879 0.956 0.076 0.924
#> GSM564641 2 0.0376 0.957 0.004 0.996
#> GSM564642 2 0.3584 0.956 0.068 0.932
#> GSM564643 2 0.4298 0.918 0.088 0.912
#> GSM564644 2 0.3879 0.956 0.076 0.924
#> GSM564645 2 0.0000 0.956 0.000 1.000
#> GSM564647 2 0.1184 0.958 0.016 0.984
#> GSM564648 2 0.3584 0.957 0.068 0.932
#> GSM564649 2 0.0000 0.956 0.000 1.000
#> GSM564650 2 0.3431 0.958 0.064 0.936
#> GSM564651 2 0.2778 0.957 0.048 0.952
#> GSM564652 2 0.3879 0.956 0.076 0.924
#> GSM564653 2 0.3879 0.956 0.076 0.924
#> GSM564654 2 0.0000 0.956 0.000 1.000
#> GSM564655 2 0.0000 0.956 0.000 1.000
#> GSM564656 2 0.0000 0.956 0.000 1.000
#> GSM564657 2 0.0672 0.957 0.008 0.992
#> GSM564658 2 0.3879 0.956 0.076 0.924
#> GSM564659 2 0.0000 0.956 0.000 1.000
#> GSM564660 2 0.2043 0.957 0.032 0.968
#> GSM564661 2 0.3879 0.956 0.076 0.924
#> GSM564662 2 0.0000 0.956 0.000 1.000
#> GSM564663 2 0.3879 0.956 0.076 0.924
#> GSM564664 2 0.3879 0.956 0.076 0.924
#> GSM564665 2 0.0000 0.956 0.000 1.000
#> GSM564666 2 0.4298 0.918 0.088 0.912
#> GSM564667 2 0.0000 0.956 0.000 1.000
#> GSM564668 2 0.0000 0.956 0.000 1.000
#> GSM564669 2 0.0000 0.956 0.000 1.000
#> GSM564670 2 0.1633 0.958 0.024 0.976
#> GSM564671 2 0.5178 0.904 0.116 0.884
#> GSM564672 2 0.0000 0.956 0.000 1.000
#> GSM564673 2 0.3879 0.956 0.076 0.924
#> GSM564674 2 0.3879 0.956 0.076 0.924
#> GSM564675 2 0.3274 0.951 0.060 0.940
#> GSM564676 2 0.3879 0.956 0.076 0.924
#> GSM564677 2 0.3879 0.956 0.076 0.924
#> GSM564678 2 0.3879 0.956 0.076 0.924
#> GSM564679 2 0.3879 0.956 0.076 0.924
#> GSM564680 2 0.0000 0.956 0.000 1.000
#> GSM564682 2 0.1843 0.958 0.028 0.972
#> GSM564683 2 0.0000 0.956 0.000 1.000
#> GSM564684 2 0.4690 0.917 0.100 0.900
#> GSM564685 2 0.0000 0.956 0.000 1.000
#> GSM564686 2 0.4690 0.917 0.100 0.900
#> GSM564687 2 0.4161 0.953 0.084 0.916
#> GSM564688 2 0.3879 0.956 0.076 0.924
#> GSM564689 2 0.3879 0.956 0.076 0.924
#> GSM564690 2 0.3879 0.956 0.076 0.924
#> GSM564691 2 0.2043 0.957 0.032 0.968
#> GSM564692 2 0.3879 0.956 0.076 0.924
#> GSM564694 2 0.3733 0.932 0.072 0.928
#> GSM564695 2 0.0672 0.957 0.008 0.992
#> GSM564696 2 0.0000 0.956 0.000 1.000
#> GSM564697 2 0.3879 0.956 0.076 0.924
#> GSM564698 2 0.0000 0.956 0.000 1.000
#> GSM564700 2 0.4690 0.917 0.100 0.900
#> GSM564701 2 0.3879 0.956 0.076 0.924
#> GSM564702 2 0.3879 0.956 0.076 0.924
#> GSM564703 1 0.5629 0.923 0.868 0.132
#> GSM564704 1 0.0376 0.955 0.996 0.004
#> GSM564705 1 0.3584 0.928 0.932 0.068
#> GSM564706 1 0.5737 0.924 0.864 0.136
#> GSM564707 1 0.3584 0.928 0.932 0.068
#> GSM564708 1 0.3879 0.942 0.924 0.076
#> GSM564709 1 0.0376 0.955 0.996 0.004
#> GSM564710 1 0.2948 0.938 0.948 0.052
#> GSM564711 1 0.3431 0.951 0.936 0.064
#> GSM564712 1 0.3584 0.928 0.932 0.068
#> GSM564713 1 0.4022 0.943 0.920 0.080
#> GSM564714 1 0.5737 0.924 0.864 0.136
#> GSM564715 1 0.1414 0.953 0.980 0.020
#> GSM564716 1 0.1633 0.958 0.976 0.024
#> GSM564717 1 0.0672 0.955 0.992 0.008
#> GSM564718 1 0.3431 0.951 0.936 0.064
#> GSM564719 1 0.3431 0.931 0.936 0.064
#> GSM564720 1 0.1184 0.954 0.984 0.016
#> GSM564721 1 0.0672 0.955 0.992 0.008
#> GSM564722 1 0.1843 0.958 0.972 0.028
#> GSM564723 1 0.3584 0.928 0.932 0.068
#> GSM564724 1 0.4022 0.943 0.920 0.080
#> GSM564725 1 0.2236 0.956 0.964 0.036
#> GSM564726 1 0.3114 0.951 0.944 0.056
#> GSM564727 1 0.2043 0.956 0.968 0.032
#> GSM564728 1 0.2603 0.955 0.956 0.044
#> GSM564729 1 0.2603 0.955 0.956 0.044
#> GSM564730 1 0.0672 0.955 0.992 0.008
#> GSM564731 1 0.2948 0.956 0.948 0.052
#> GSM564732 1 0.1633 0.957 0.976 0.024
#> GSM564733 1 0.3733 0.947 0.928 0.072
#> GSM564734 1 0.0376 0.955 0.996 0.004
#> GSM564735 1 0.3879 0.942 0.924 0.076
#> GSM564736 1 0.4022 0.943 0.920 0.080
#> GSM564737 1 0.3584 0.928 0.932 0.068
#> GSM564738 1 0.4022 0.943 0.920 0.080
#> GSM564739 1 0.5178 0.924 0.884 0.116
#> GSM564740 1 0.2603 0.955 0.956 0.044
#> GSM564741 1 0.4022 0.943 0.920 0.080
#> GSM564742 1 0.5946 0.918 0.856 0.144
#> GSM564743 1 0.0672 0.955 0.992 0.008
#> GSM564744 1 0.1184 0.954 0.984 0.016
#> GSM564745 1 0.0000 0.954 1.000 0.000
#> GSM564746 1 0.0376 0.955 0.996 0.004
#> GSM564747 1 0.1843 0.959 0.972 0.028
#> GSM564748 1 0.5946 0.918 0.856 0.144
#> GSM564749 1 0.3274 0.933 0.940 0.060
#> GSM564750 1 0.3114 0.951 0.944 0.056
#> GSM564751 1 0.5408 0.929 0.876 0.124
#> GSM564752 1 0.3114 0.951 0.944 0.056
#> GSM564753 1 0.5737 0.924 0.864 0.136
#> GSM564754 1 0.0672 0.955 0.992 0.008
#> GSM564755 1 0.2603 0.955 0.956 0.044
#> GSM564756 1 0.0672 0.955 0.992 0.008
#> GSM564757 1 0.2603 0.955 0.956 0.044
#> GSM564758 1 0.2603 0.955 0.956 0.044
#> GSM564759 1 0.5629 0.926 0.868 0.132
#> GSM564760 1 0.2236 0.956 0.964 0.036
#> GSM564761 1 0.4161 0.916 0.916 0.084
#> GSM564762 1 0.2423 0.957 0.960 0.040
#> GSM564681 2 0.3879 0.956 0.076 0.924
#> GSM564693 2 0.3431 0.958 0.064 0.936
#> GSM564646 2 0.4690 0.917 0.100 0.900
#> GSM564699 2 0.3584 0.917 0.068 0.932
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564616 2 0.7475 0.77600 0.044 0.580 0.376
#> GSM564617 2 0.7475 0.77600 0.044 0.580 0.376
#> GSM564618 2 0.7256 0.72372 0.028 0.532 0.440
#> GSM564619 1 0.1267 0.86228 0.972 0.004 0.024
#> GSM564620 1 0.3267 0.81643 0.884 0.000 0.116
#> GSM564621 3 0.6309 0.18112 0.496 0.000 0.504
#> GSM564622 2 0.7209 0.78904 0.036 0.604 0.360
#> GSM564623 3 0.4195 0.38019 0.012 0.136 0.852
#> GSM564624 2 0.7475 0.77600 0.044 0.580 0.376
#> GSM564625 1 0.6079 0.27507 0.612 0.000 0.388
#> GSM564626 1 0.1525 0.86235 0.964 0.004 0.032
#> GSM564627 1 0.5465 0.53896 0.712 0.000 0.288
#> GSM564628 2 0.7346 0.72949 0.032 0.536 0.432
#> GSM564629 1 0.1989 0.85645 0.948 0.004 0.048
#> GSM564630 2 0.7475 0.77600 0.044 0.580 0.376
#> GSM564609 2 0.2443 0.75004 0.032 0.940 0.028
#> GSM564610 1 0.1170 0.86101 0.976 0.008 0.016
#> GSM564611 1 0.1999 0.84025 0.952 0.012 0.036
#> GSM564612 2 0.2982 0.75945 0.024 0.920 0.056
#> GSM564613 2 0.6090 0.80360 0.020 0.716 0.264
#> GSM564614 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564631 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564632 2 0.6369 0.79182 0.016 0.668 0.316
#> GSM564633 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564634 2 0.6541 0.80678 0.024 0.672 0.304
#> GSM564635 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564636 2 0.2651 0.75537 0.012 0.928 0.060
#> GSM564637 2 0.5810 0.79168 0.000 0.664 0.336
#> GSM564638 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564639 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564640 2 0.7190 0.80321 0.044 0.636 0.320
#> GSM564641 2 0.1170 0.73996 0.016 0.976 0.008
#> GSM564642 2 0.7112 0.80532 0.044 0.648 0.308
#> GSM564643 3 0.5220 0.21911 0.012 0.208 0.780
#> GSM564644 2 0.7260 0.80319 0.048 0.636 0.316
#> GSM564645 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564647 2 0.2804 0.76146 0.016 0.924 0.060
#> GSM564648 2 0.6962 0.80492 0.036 0.648 0.316
#> GSM564649 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564650 2 0.7481 0.78475 0.048 0.596 0.356
#> GSM564651 2 0.6793 0.80765 0.036 0.672 0.292
#> GSM564652 2 0.7260 0.80268 0.048 0.636 0.316
#> GSM564653 2 0.7190 0.80321 0.044 0.636 0.320
#> GSM564654 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564655 2 0.3120 0.75743 0.012 0.908 0.080
#> GSM564656 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564657 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564658 2 0.7310 0.80123 0.048 0.628 0.324
#> GSM564659 2 0.2680 0.76070 0.008 0.924 0.068
#> GSM564660 2 0.7123 0.77832 0.032 0.604 0.364
#> GSM564661 2 0.7285 0.80204 0.048 0.632 0.320
#> GSM564662 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564663 2 0.7234 0.80408 0.048 0.640 0.312
#> GSM564664 2 0.7260 0.80319 0.048 0.636 0.316
#> GSM564665 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564666 3 0.5884 0.00118 0.012 0.272 0.716
#> GSM564667 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564668 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564669 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564670 2 0.5939 0.79969 0.028 0.748 0.224
#> GSM564671 3 0.2947 0.48172 0.020 0.060 0.920
#> GSM564672 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564673 2 0.6962 0.80492 0.036 0.648 0.316
#> GSM564674 2 0.7262 0.79994 0.044 0.624 0.332
#> GSM564675 2 0.6859 0.74513 0.016 0.564 0.420
#> GSM564676 2 0.7285 0.80240 0.048 0.632 0.320
#> GSM564677 2 0.7470 0.79451 0.052 0.612 0.336
#> GSM564678 2 0.7285 0.80240 0.048 0.632 0.320
#> GSM564679 2 0.7310 0.80123 0.048 0.628 0.324
#> GSM564680 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564682 2 0.3009 0.75870 0.028 0.920 0.052
#> GSM564683 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564684 3 0.4326 0.37182 0.012 0.144 0.844
#> GSM564685 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564686 3 0.3845 0.41324 0.012 0.116 0.872
#> GSM564687 2 0.7310 0.80123 0.048 0.628 0.324
#> GSM564688 2 0.7138 0.80455 0.044 0.644 0.312
#> GSM564689 2 0.7401 0.79503 0.048 0.612 0.340
#> GSM564690 2 0.7285 0.80240 0.048 0.632 0.320
#> GSM564691 2 0.3370 0.76234 0.024 0.904 0.072
#> GSM564692 2 0.7065 0.80444 0.040 0.644 0.316
#> GSM564694 2 0.6683 0.60295 0.008 0.496 0.496
#> GSM564695 2 0.6867 0.79206 0.028 0.636 0.336
#> GSM564696 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564697 2 0.7357 0.79873 0.048 0.620 0.332
#> GSM564698 2 0.0592 0.72982 0.012 0.988 0.000
#> GSM564700 3 0.4261 0.37900 0.012 0.140 0.848
#> GSM564701 2 0.7012 0.80627 0.040 0.652 0.308
#> GSM564702 2 0.7551 0.77479 0.048 0.580 0.372
#> GSM564703 1 0.2682 0.83438 0.920 0.076 0.004
#> GSM564704 1 0.1620 0.86414 0.964 0.012 0.024
#> GSM564705 1 0.1337 0.85693 0.972 0.012 0.016
#> GSM564706 1 0.3933 0.81797 0.880 0.092 0.028
#> GSM564707 1 0.1170 0.86063 0.976 0.016 0.008
#> GSM564708 1 0.4821 0.79534 0.840 0.040 0.120
#> GSM564709 1 0.1453 0.86408 0.968 0.008 0.024
#> GSM564710 1 0.1015 0.86040 0.980 0.008 0.012
#> GSM564711 1 0.5137 0.79762 0.832 0.064 0.104
#> GSM564712 1 0.1182 0.85825 0.976 0.012 0.012
#> GSM564713 1 0.5371 0.77116 0.812 0.048 0.140
#> GSM564714 1 0.4172 0.80791 0.868 0.104 0.028
#> GSM564715 1 0.0424 0.86399 0.992 0.000 0.008
#> GSM564716 1 0.2448 0.84456 0.924 0.000 0.076
#> GSM564717 1 0.1170 0.86135 0.976 0.008 0.016
#> GSM564718 1 0.5276 0.78467 0.820 0.052 0.128
#> GSM564719 1 0.1482 0.85486 0.968 0.012 0.020
#> GSM564720 1 0.1337 0.85693 0.972 0.012 0.016
#> GSM564721 1 0.0848 0.86300 0.984 0.008 0.008
#> GSM564722 1 0.1860 0.85807 0.948 0.000 0.052
#> GSM564723 1 0.1182 0.85825 0.976 0.012 0.012
#> GSM564724 1 0.6079 0.75909 0.784 0.088 0.128
#> GSM564725 1 0.4974 0.66917 0.764 0.000 0.236
#> GSM564726 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564727 3 0.6215 0.38251 0.428 0.000 0.572
#> GSM564728 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564729 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564730 1 0.0661 0.86288 0.988 0.004 0.008
#> GSM564731 1 0.3337 0.84046 0.908 0.032 0.060
#> GSM564732 1 0.6095 0.28334 0.608 0.000 0.392
#> GSM564733 1 0.5377 0.78963 0.820 0.068 0.112
#> GSM564734 1 0.1399 0.86411 0.968 0.004 0.028
#> GSM564735 1 0.7181 0.48769 0.648 0.048 0.304
#> GSM564736 1 0.6630 0.65760 0.724 0.056 0.220
#> GSM564737 1 0.1182 0.85825 0.976 0.012 0.012
#> GSM564738 1 0.7745 0.54046 0.648 0.092 0.260
#> GSM564739 1 0.2165 0.84620 0.936 0.064 0.000
#> GSM564740 3 0.6079 0.45864 0.388 0.000 0.612
#> GSM564741 1 0.7995 0.44242 0.608 0.088 0.304
#> GSM564742 1 0.4121 0.80536 0.868 0.108 0.024
#> GSM564743 1 0.1015 0.86120 0.980 0.008 0.012
#> GSM564744 1 0.1015 0.86008 0.980 0.012 0.008
#> GSM564745 1 0.0892 0.86180 0.980 0.000 0.020
#> GSM564746 1 0.1765 0.86175 0.956 0.004 0.040
#> GSM564747 1 0.0747 0.86466 0.984 0.000 0.016
#> GSM564748 1 0.4015 0.81457 0.876 0.096 0.028
#> GSM564749 1 0.1482 0.85486 0.968 0.012 0.020
#> GSM564750 3 0.6468 0.31640 0.444 0.004 0.552
#> GSM564751 1 0.2860 0.82915 0.912 0.084 0.004
#> GSM564752 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564753 1 0.4342 0.79265 0.856 0.120 0.024
#> GSM564754 1 0.0237 0.86389 0.996 0.000 0.004
#> GSM564755 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564756 1 0.0829 0.86261 0.984 0.004 0.012
#> GSM564757 3 0.6095 0.45815 0.392 0.000 0.608
#> GSM564758 3 0.6079 0.46036 0.388 0.000 0.612
#> GSM564759 1 0.3850 0.82114 0.884 0.088 0.028
#> GSM564760 1 0.6282 0.32603 0.612 0.004 0.384
#> GSM564761 1 0.1482 0.85373 0.968 0.012 0.020
#> GSM564762 1 0.3038 0.82892 0.896 0.000 0.104
#> GSM564681 2 0.7475 0.77430 0.044 0.580 0.376
#> GSM564693 2 0.7030 0.76836 0.024 0.580 0.396
#> GSM564646 3 0.4326 0.37182 0.012 0.144 0.844
#> GSM564699 3 0.5450 0.16041 0.012 0.228 0.760
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.2542 0.6509 0.084 0.012 0.000 0.904
#> GSM564616 2 0.4801 0.7651 0.000 0.764 0.188 0.048
#> GSM564617 2 0.4720 0.7675 0.000 0.768 0.188 0.044
#> GSM564618 2 0.5292 0.7256 0.000 0.744 0.168 0.088
#> GSM564619 1 0.2797 0.7909 0.900 0.068 0.000 0.032
#> GSM564620 1 0.5352 0.6883 0.740 0.092 0.000 0.168
#> GSM564621 4 0.6446 0.2881 0.328 0.088 0.000 0.584
#> GSM564622 2 0.5102 0.7861 0.000 0.732 0.220 0.048
#> GSM564623 4 0.5387 0.4080 0.000 0.400 0.016 0.584
#> GSM564624 2 0.5030 0.7581 0.000 0.752 0.188 0.060
#> GSM564625 1 0.6733 0.2073 0.492 0.092 0.000 0.416
#> GSM564626 1 0.2739 0.7917 0.904 0.060 0.000 0.036
#> GSM564627 1 0.6336 0.4713 0.608 0.088 0.000 0.304
#> GSM564628 2 0.5100 0.7346 0.000 0.756 0.168 0.076
#> GSM564629 1 0.4022 0.7609 0.836 0.096 0.000 0.068
#> GSM564630 2 0.4839 0.7765 0.000 0.756 0.200 0.044
#> GSM564609 3 0.2730 0.8036 0.016 0.088 0.896 0.000
#> GSM564610 1 0.0188 0.8145 0.996 0.000 0.000 0.004
#> GSM564611 1 0.1389 0.7901 0.952 0.048 0.000 0.000
#> GSM564612 3 0.3351 0.7328 0.008 0.148 0.844 0.000
#> GSM564613 3 0.5263 -0.4069 0.000 0.448 0.544 0.008
#> GSM564614 4 0.2412 0.6497 0.084 0.008 0.000 0.908
#> GSM564631 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564632 2 0.6384 0.5461 0.000 0.496 0.440 0.064
#> GSM564633 3 0.0592 0.8807 0.000 0.016 0.984 0.000
#> GSM564634 2 0.5165 0.7835 0.004 0.604 0.388 0.004
#> GSM564635 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564636 3 0.2973 0.7468 0.000 0.144 0.856 0.000
#> GSM564637 2 0.5663 0.7921 0.000 0.676 0.264 0.060
#> GSM564638 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564640 2 0.5186 0.8224 0.016 0.640 0.344 0.000
#> GSM564641 3 0.1824 0.8402 0.004 0.060 0.936 0.000
#> GSM564642 2 0.5804 0.7989 0.032 0.604 0.360 0.004
#> GSM564643 4 0.5807 0.4112 0.000 0.344 0.044 0.612
#> GSM564644 2 0.5668 0.8166 0.032 0.636 0.328 0.004
#> GSM564645 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564647 3 0.3444 0.6730 0.000 0.184 0.816 0.000
#> GSM564648 2 0.4855 0.8199 0.004 0.644 0.352 0.000
#> GSM564649 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564650 2 0.5880 0.8077 0.012 0.676 0.264 0.048
#> GSM564651 2 0.5150 0.7784 0.008 0.596 0.396 0.000
#> GSM564652 2 0.5420 0.8194 0.024 0.624 0.352 0.000
#> GSM564653 2 0.5167 0.8235 0.016 0.644 0.340 0.000
#> GSM564654 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564655 3 0.1940 0.8143 0.000 0.076 0.924 0.000
#> GSM564656 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564657 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564658 2 0.5608 0.8212 0.032 0.648 0.316 0.004
#> GSM564659 3 0.3024 0.7399 0.000 0.148 0.852 0.000
#> GSM564660 2 0.5900 0.7518 0.000 0.664 0.260 0.076
#> GSM564661 2 0.5579 0.8238 0.028 0.640 0.328 0.004
#> GSM564662 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564663 2 0.5582 0.8156 0.032 0.620 0.348 0.000
#> GSM564664 2 0.5686 0.8139 0.032 0.632 0.332 0.004
#> GSM564665 3 0.0376 0.8836 0.000 0.004 0.992 0.004
#> GSM564666 4 0.6337 0.2877 0.000 0.380 0.068 0.552
#> GSM564667 3 0.0188 0.8871 0.000 0.000 0.996 0.004
#> GSM564668 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564669 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564670 3 0.4967 -0.3991 0.000 0.452 0.548 0.000
#> GSM564671 4 0.4955 0.5232 0.000 0.268 0.024 0.708
#> GSM564672 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564673 2 0.4995 0.8230 0.004 0.648 0.344 0.004
#> GSM564674 2 0.5110 0.8307 0.016 0.656 0.328 0.000
#> GSM564675 2 0.5672 0.7321 0.000 0.712 0.188 0.100
#> GSM564676 2 0.5511 0.8173 0.032 0.636 0.332 0.000
#> GSM564677 2 0.5272 0.8302 0.032 0.680 0.288 0.000
#> GSM564678 2 0.5511 0.8173 0.032 0.636 0.332 0.000
#> GSM564679 2 0.5473 0.8218 0.032 0.644 0.324 0.000
#> GSM564680 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564682 3 0.3659 0.7302 0.024 0.136 0.840 0.000
#> GSM564683 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564684 4 0.5442 0.4447 0.000 0.336 0.028 0.636
#> GSM564685 3 0.0524 0.8790 0.000 0.008 0.988 0.004
#> GSM564686 4 0.5460 0.4405 0.000 0.340 0.028 0.632
#> GSM564687 2 0.5519 0.8198 0.028 0.652 0.316 0.004
#> GSM564688 2 0.5159 0.8104 0.012 0.624 0.364 0.000
#> GSM564689 2 0.4776 0.8283 0.016 0.712 0.272 0.000
#> GSM564690 2 0.5511 0.8173 0.032 0.636 0.332 0.000
#> GSM564691 3 0.4059 0.6202 0.012 0.200 0.788 0.000
#> GSM564692 2 0.4889 0.8146 0.004 0.636 0.360 0.000
#> GSM564694 2 0.7402 0.3910 0.000 0.500 0.192 0.308
#> GSM564695 2 0.5573 0.7885 0.000 0.676 0.272 0.052
#> GSM564696 3 0.0376 0.8832 0.000 0.004 0.992 0.004
#> GSM564697 2 0.5108 0.8319 0.020 0.672 0.308 0.000
#> GSM564698 3 0.0000 0.8902 0.000 0.000 1.000 0.000
#> GSM564700 4 0.5478 0.4347 0.000 0.344 0.028 0.628
#> GSM564701 2 0.5269 0.8113 0.016 0.620 0.364 0.000
#> GSM564702 2 0.5793 0.7892 0.012 0.700 0.232 0.056
#> GSM564703 1 0.4672 0.7692 0.820 0.092 0.064 0.024
#> GSM564704 1 0.2596 0.8079 0.908 0.024 0.000 0.068
#> GSM564705 1 0.0469 0.8108 0.988 0.012 0.000 0.000
#> GSM564706 1 0.5741 0.7371 0.764 0.096 0.092 0.048
#> GSM564707 1 0.0336 0.8145 0.992 0.000 0.008 0.000
#> GSM564708 1 0.7229 0.5129 0.560 0.128 0.012 0.300
#> GSM564709 1 0.1488 0.8148 0.956 0.012 0.000 0.032
#> GSM564710 1 0.0524 0.8147 0.988 0.004 0.000 0.008
#> GSM564711 1 0.8039 0.5461 0.564 0.112 0.080 0.244
#> GSM564712 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> GSM564713 1 0.7746 0.4302 0.512 0.120 0.032 0.336
#> GSM564714 1 0.6535 0.6984 0.712 0.096 0.128 0.064
#> GSM564715 1 0.0188 0.8142 0.996 0.000 0.000 0.004
#> GSM564716 1 0.5170 0.6743 0.724 0.048 0.000 0.228
#> GSM564717 1 0.1042 0.8109 0.972 0.020 0.000 0.008
#> GSM564718 1 0.8065 0.4459 0.512 0.116 0.056 0.316
#> GSM564719 1 0.1118 0.7993 0.964 0.036 0.000 0.000
#> GSM564720 1 0.0336 0.8121 0.992 0.008 0.000 0.000
#> GSM564721 1 0.0524 0.8136 0.988 0.008 0.000 0.004
#> GSM564722 1 0.5092 0.7405 0.764 0.096 0.000 0.140
#> GSM564723 1 0.0188 0.8131 0.996 0.004 0.000 0.000
#> GSM564724 1 0.8858 0.3937 0.472 0.116 0.132 0.280
#> GSM564725 1 0.5937 0.5089 0.608 0.052 0.000 0.340
#> GSM564726 4 0.2984 0.6437 0.084 0.028 0.000 0.888
#> GSM564727 4 0.4079 0.5678 0.180 0.020 0.000 0.800
#> GSM564728 4 0.2412 0.6510 0.084 0.008 0.000 0.908
#> GSM564729 4 0.2412 0.6510 0.084 0.008 0.000 0.908
#> GSM564730 1 0.1297 0.8152 0.964 0.016 0.000 0.020
#> GSM564731 1 0.5277 0.7497 0.768 0.116 0.008 0.108
#> GSM564732 4 0.5856 0.0454 0.408 0.036 0.000 0.556
#> GSM564733 1 0.8032 0.4992 0.540 0.108 0.068 0.284
#> GSM564734 1 0.2413 0.8036 0.916 0.020 0.000 0.064
#> GSM564735 4 0.8018 -0.1365 0.388 0.108 0.048 0.456
#> GSM564736 1 0.8337 0.2663 0.432 0.116 0.064 0.388
#> GSM564737 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> GSM564738 4 0.8933 -0.1109 0.360 0.096 0.144 0.400
#> GSM564739 1 0.2317 0.8075 0.928 0.032 0.036 0.004
#> GSM564740 4 0.2996 0.6462 0.064 0.044 0.000 0.892
#> GSM564741 4 0.8843 -0.1080 0.364 0.096 0.132 0.408
#> GSM564742 1 0.6224 0.6996 0.724 0.096 0.140 0.040
#> GSM564743 1 0.0188 0.8138 0.996 0.004 0.000 0.000
#> GSM564744 1 0.0188 0.8138 0.996 0.004 0.000 0.000
#> GSM564745 1 0.1733 0.8141 0.948 0.024 0.000 0.028
#> GSM564746 1 0.2722 0.7901 0.904 0.064 0.000 0.032
#> GSM564747 1 0.2623 0.8052 0.908 0.064 0.000 0.028
#> GSM564748 1 0.6109 0.7138 0.736 0.096 0.124 0.044
#> GSM564749 1 0.0707 0.8078 0.980 0.020 0.000 0.000
#> GSM564750 4 0.5332 0.5020 0.184 0.080 0.000 0.736
#> GSM564751 1 0.4652 0.7667 0.820 0.076 0.084 0.020
#> GSM564752 4 0.3354 0.6346 0.084 0.044 0.000 0.872
#> GSM564753 1 0.6109 0.7132 0.736 0.096 0.124 0.044
#> GSM564754 1 0.0817 0.8147 0.976 0.000 0.000 0.024
#> GSM564755 4 0.2271 0.6518 0.076 0.008 0.000 0.916
#> GSM564756 1 0.1411 0.8177 0.960 0.020 0.000 0.020
#> GSM564757 4 0.2542 0.6509 0.084 0.012 0.000 0.904
#> GSM564758 4 0.3354 0.6439 0.084 0.044 0.000 0.872
#> GSM564759 1 0.5759 0.7427 0.764 0.104 0.076 0.056
#> GSM564760 4 0.6866 -0.0853 0.408 0.080 0.008 0.504
#> GSM564761 1 0.0336 0.8124 0.992 0.008 0.000 0.000
#> GSM564762 1 0.5894 0.6763 0.692 0.108 0.000 0.200
#> GSM564681 2 0.5532 0.7722 0.000 0.704 0.228 0.068
#> GSM564693 2 0.5559 0.7855 0.000 0.696 0.240 0.064
#> GSM564646 4 0.5478 0.4347 0.000 0.344 0.028 0.628
#> GSM564699 4 0.6140 0.3863 0.000 0.340 0.064 0.596
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 3 0.5951 -0.22016 0.012 0.072 0.460 0.456 0.000
#> GSM564616 2 0.5999 0.55485 0.000 0.644 0.232 0.052 0.072
#> GSM564617 2 0.5949 0.56043 0.000 0.644 0.236 0.044 0.076
#> GSM564618 3 0.6375 -0.12288 0.000 0.432 0.464 0.048 0.056
#> GSM564619 1 0.3690 0.68412 0.828 0.052 0.008 0.112 0.000
#> GSM564620 1 0.5990 0.40060 0.612 0.084 0.028 0.276 0.000
#> GSM564621 4 0.7647 0.39176 0.212 0.124 0.164 0.500 0.000
#> GSM564622 2 0.6286 0.63384 0.000 0.628 0.196 0.040 0.136
#> GSM564623 3 0.2628 0.56910 0.000 0.088 0.884 0.028 0.000
#> GSM564624 2 0.6210 0.47665 0.000 0.588 0.296 0.044 0.072
#> GSM564625 4 0.7478 0.38348 0.276 0.124 0.108 0.492 0.000
#> GSM564626 1 0.3597 0.68715 0.832 0.044 0.008 0.116 0.000
#> GSM564627 1 0.6930 -0.03824 0.460 0.084 0.068 0.388 0.000
#> GSM564628 2 0.6331 0.34872 0.000 0.548 0.340 0.060 0.052
#> GSM564629 1 0.4848 0.60012 0.736 0.076 0.012 0.176 0.000
#> GSM564630 2 0.5855 0.58529 0.000 0.664 0.212 0.048 0.076
#> GSM564609 5 0.3409 0.74010 0.008 0.156 0.008 0.004 0.824
#> GSM564610 1 0.1393 0.75571 0.956 0.012 0.008 0.024 0.000
#> GSM564611 1 0.1774 0.74143 0.932 0.052 0.000 0.016 0.000
#> GSM564612 5 0.3631 0.69698 0.008 0.192 0.004 0.004 0.792
#> GSM564613 5 0.5785 -0.17046 0.000 0.404 0.092 0.000 0.504
#> GSM564614 4 0.5937 0.18240 0.008 0.080 0.432 0.480 0.000
#> GSM564631 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564632 5 0.6806 -0.28190 0.000 0.296 0.352 0.000 0.352
#> GSM564633 5 0.0880 0.86630 0.000 0.032 0.000 0.000 0.968
#> GSM564634 2 0.4581 0.76317 0.000 0.696 0.004 0.032 0.268
#> GSM564635 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564636 5 0.4002 0.72704 0.000 0.120 0.084 0.000 0.796
#> GSM564637 2 0.6417 0.48106 0.000 0.496 0.336 0.004 0.164
#> GSM564638 5 0.0671 0.87468 0.000 0.016 0.000 0.004 0.980
#> GSM564639 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564640 2 0.3923 0.81310 0.008 0.756 0.004 0.004 0.228
#> GSM564641 5 0.2904 0.78719 0.008 0.112 0.008 0.004 0.868
#> GSM564642 2 0.4706 0.80452 0.020 0.724 0.000 0.032 0.224
#> GSM564643 3 0.2407 0.58799 0.000 0.088 0.896 0.004 0.012
#> GSM564644 2 0.4173 0.81562 0.012 0.756 0.000 0.020 0.212
#> GSM564645 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564647 5 0.3643 0.66529 0.000 0.212 0.008 0.004 0.776
#> GSM564648 2 0.4095 0.81141 0.000 0.752 0.024 0.004 0.220
#> GSM564649 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564650 2 0.6321 0.55536 0.012 0.564 0.296 0.004 0.124
#> GSM564651 2 0.4336 0.77970 0.008 0.700 0.000 0.012 0.280
#> GSM564652 2 0.4378 0.81469 0.012 0.740 0.012 0.008 0.228
#> GSM564653 2 0.4030 0.81257 0.012 0.752 0.004 0.004 0.228
#> GSM564654 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564655 5 0.1864 0.82761 0.000 0.068 0.004 0.004 0.924
#> GSM564656 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564657 5 0.0290 0.87801 0.000 0.008 0.000 0.000 0.992
#> GSM564658 2 0.4296 0.81508 0.012 0.756 0.004 0.020 0.208
#> GSM564659 5 0.3365 0.76281 0.000 0.120 0.044 0.000 0.836
#> GSM564660 3 0.6077 -0.16985 0.000 0.396 0.480 0.000 0.124
#> GSM564661 2 0.4296 0.81713 0.012 0.756 0.004 0.020 0.208
#> GSM564662 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564663 2 0.4303 0.81532 0.016 0.748 0.000 0.020 0.216
#> GSM564664 2 0.4328 0.81267 0.016 0.752 0.000 0.024 0.208
#> GSM564665 5 0.1074 0.86855 0.000 0.012 0.004 0.016 0.968
#> GSM564666 3 0.3513 0.57700 0.000 0.132 0.828 0.004 0.036
#> GSM564667 5 0.0324 0.87666 0.000 0.004 0.000 0.004 0.992
#> GSM564668 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564669 5 0.0162 0.87898 0.000 0.004 0.000 0.000 0.996
#> GSM564670 2 0.5012 0.44089 0.004 0.532 0.016 0.004 0.444
#> GSM564671 3 0.1668 0.53771 0.000 0.032 0.940 0.028 0.000
#> GSM564672 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564673 2 0.4849 0.80275 0.000 0.712 0.024 0.032 0.232
#> GSM564674 2 0.4372 0.81741 0.008 0.748 0.020 0.008 0.216
#> GSM564675 3 0.6264 -0.13106 0.000 0.412 0.484 0.024 0.080
#> GSM564676 2 0.4272 0.81585 0.016 0.752 0.000 0.020 0.212
#> GSM564677 2 0.4568 0.81453 0.024 0.764 0.020 0.012 0.180
#> GSM564678 2 0.4272 0.81585 0.016 0.752 0.000 0.020 0.212
#> GSM564679 2 0.4240 0.81537 0.020 0.756 0.000 0.016 0.208
#> GSM564680 5 0.0162 0.87898 0.000 0.004 0.000 0.000 0.996
#> GSM564682 5 0.3774 0.68601 0.012 0.196 0.004 0.004 0.784
#> GSM564683 5 0.0000 0.87960 0.000 0.000 0.000 0.000 1.000
#> GSM564684 3 0.1478 0.57485 0.000 0.064 0.936 0.000 0.000
#> GSM564685 5 0.0992 0.85864 0.000 0.024 0.000 0.008 0.968
#> GSM564686 3 0.1671 0.58441 0.000 0.076 0.924 0.000 0.000
#> GSM564687 2 0.4583 0.80491 0.012 0.740 0.000 0.044 0.204
#> GSM564688 2 0.4172 0.80395 0.012 0.732 0.004 0.004 0.248
#> GSM564689 2 0.4468 0.80168 0.012 0.776 0.044 0.008 0.160
#> GSM564690 2 0.4272 0.81585 0.016 0.752 0.000 0.020 0.212
#> GSM564691 5 0.4143 0.56432 0.008 0.260 0.004 0.004 0.724
#> GSM564692 2 0.3947 0.80816 0.008 0.748 0.008 0.000 0.236
#> GSM564694 3 0.6082 0.34593 0.000 0.204 0.640 0.032 0.124
#> GSM564695 2 0.6471 0.38021 0.000 0.464 0.384 0.008 0.144
#> GSM564696 5 0.0613 0.87355 0.000 0.004 0.004 0.008 0.984
#> GSM564697 2 0.4004 0.81992 0.012 0.776 0.004 0.012 0.196
#> GSM564698 5 0.0671 0.87460 0.000 0.016 0.004 0.000 0.980
#> GSM564700 3 0.1732 0.58630 0.000 0.080 0.920 0.000 0.000
#> GSM564701 2 0.4277 0.80595 0.008 0.732 0.008 0.008 0.244
#> GSM564702 2 0.6356 0.40090 0.012 0.508 0.380 0.008 0.092
#> GSM564703 1 0.5102 0.43547 0.620 0.008 0.000 0.336 0.036
#> GSM564704 1 0.3280 0.67348 0.824 0.004 0.012 0.160 0.000
#> GSM564705 1 0.0771 0.75917 0.976 0.020 0.000 0.004 0.000
#> GSM564706 1 0.5929 0.34287 0.556 0.008 0.008 0.360 0.068
#> GSM564707 1 0.0613 0.76060 0.984 0.008 0.000 0.004 0.004
#> GSM564708 4 0.5674 0.47911 0.232 0.080 0.020 0.664 0.004
#> GSM564709 1 0.1913 0.75323 0.932 0.016 0.008 0.044 0.000
#> GSM564710 1 0.0960 0.76055 0.972 0.008 0.004 0.016 0.000
#> GSM564711 4 0.6648 0.39058 0.280 0.044 0.016 0.584 0.076
#> GSM564712 1 0.0727 0.76120 0.980 0.012 0.004 0.004 0.000
#> GSM564713 4 0.4779 0.54179 0.220 0.016 0.008 0.728 0.028
#> GSM564714 1 0.5873 0.29595 0.532 0.004 0.000 0.372 0.092
#> GSM564715 1 0.0579 0.76031 0.984 0.008 0.000 0.008 0.000
#> GSM564716 1 0.5327 0.18104 0.552 0.032 0.012 0.404 0.000
#> GSM564717 1 0.2688 0.74115 0.896 0.056 0.012 0.036 0.000
#> GSM564718 4 0.5937 0.52291 0.216 0.044 0.016 0.672 0.052
#> GSM564719 1 0.1626 0.74663 0.940 0.044 0.000 0.016 0.000
#> GSM564720 1 0.0912 0.76080 0.972 0.016 0.000 0.012 0.000
#> GSM564721 1 0.0451 0.76050 0.988 0.008 0.000 0.004 0.000
#> GSM564722 1 0.4297 0.19096 0.528 0.000 0.000 0.472 0.000
#> GSM564723 1 0.0798 0.75972 0.976 0.016 0.000 0.008 0.000
#> GSM564724 4 0.6778 0.50321 0.184 0.044 0.020 0.620 0.132
#> GSM564725 4 0.5966 0.31531 0.392 0.032 0.048 0.528 0.000
#> GSM564726 4 0.6011 0.28946 0.012 0.084 0.380 0.524 0.000
#> GSM564727 4 0.6517 0.25465 0.040 0.080 0.396 0.484 0.000
#> GSM564728 3 0.5904 -0.21109 0.012 0.068 0.468 0.452 0.000
#> GSM564729 3 0.5904 -0.21586 0.012 0.068 0.464 0.456 0.000
#> GSM564730 1 0.2149 0.74931 0.924 0.028 0.012 0.036 0.000
#> GSM564731 1 0.6369 0.10626 0.456 0.064 0.020 0.448 0.012
#> GSM564732 4 0.7627 0.44856 0.212 0.100 0.196 0.492 0.000
#> GSM564733 4 0.4866 0.43652 0.284 0.000 0.000 0.664 0.052
#> GSM564734 1 0.2844 0.72196 0.876 0.028 0.004 0.092 0.000
#> GSM564735 4 0.4656 0.60031 0.156 0.000 0.052 0.764 0.028
#> GSM564736 4 0.4260 0.58862 0.164 0.000 0.008 0.776 0.052
#> GSM564737 1 0.0566 0.76023 0.984 0.012 0.000 0.004 0.000
#> GSM564738 4 0.5722 0.55516 0.168 0.000 0.028 0.680 0.124
#> GSM564739 1 0.3023 0.70062 0.860 0.004 0.000 0.112 0.024
#> GSM564740 4 0.5068 0.35498 0.016 0.016 0.388 0.580 0.000
#> GSM564741 4 0.5544 0.56053 0.172 0.000 0.024 0.692 0.112
#> GSM564742 1 0.6030 0.30753 0.532 0.004 0.000 0.352 0.112
#> GSM564743 1 0.1267 0.75765 0.960 0.012 0.004 0.024 0.000
#> GSM564744 1 0.0968 0.76041 0.972 0.012 0.004 0.012 0.000
#> GSM564745 1 0.3575 0.71000 0.848 0.056 0.020 0.076 0.000
#> GSM564746 1 0.3340 0.69632 0.852 0.044 0.008 0.096 0.000
#> GSM564747 1 0.3550 0.59530 0.760 0.004 0.000 0.236 0.000
#> GSM564748 1 0.5888 0.31705 0.540 0.004 0.000 0.360 0.096
#> GSM564749 1 0.1364 0.75192 0.952 0.036 0.000 0.012 0.000
#> GSM564750 4 0.5102 0.57386 0.084 0.016 0.164 0.732 0.004
#> GSM564751 1 0.5264 0.50250 0.660 0.008 0.000 0.264 0.068
#> GSM564752 4 0.5274 0.37496 0.012 0.040 0.336 0.612 0.000
#> GSM564753 1 0.5991 0.31347 0.536 0.004 0.000 0.352 0.108
#> GSM564754 1 0.0703 0.75968 0.976 0.000 0.000 0.024 0.000
#> GSM564755 3 0.5904 -0.21109 0.012 0.068 0.468 0.452 0.000
#> GSM564756 1 0.2095 0.75401 0.920 0.012 0.008 0.060 0.000
#> GSM564757 3 0.5951 -0.21533 0.012 0.072 0.464 0.452 0.000
#> GSM564758 4 0.6548 0.23478 0.016 0.128 0.400 0.456 0.000
#> GSM564759 1 0.5419 0.34395 0.564 0.004 0.004 0.384 0.044
#> GSM564760 4 0.6493 0.57536 0.148 0.072 0.132 0.644 0.004
#> GSM564761 1 0.0771 0.76110 0.976 0.020 0.000 0.004 0.000
#> GSM564762 4 0.5156 -0.00396 0.440 0.020 0.012 0.528 0.000
#> GSM564681 3 0.5691 -0.21759 0.000 0.444 0.476 0.000 0.080
#> GSM564693 2 0.6038 0.43224 0.000 0.516 0.372 0.004 0.108
#> GSM564646 3 0.1732 0.58630 0.000 0.080 0.920 0.000 0.000
#> GSM564699 3 0.2953 0.58544 0.000 0.100 0.868 0.004 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.6172 0.73511 0.000 0.412 0.000 0.280 0.004 0.304
#> GSM564616 5 0.5812 0.30538 0.000 0.156 0.020 0.004 0.588 0.232
#> GSM564617 5 0.5855 0.25169 0.000 0.144 0.020 0.004 0.572 0.260
#> GSM564618 6 0.5910 0.49637 0.000 0.144 0.012 0.008 0.288 0.548
#> GSM564619 1 0.4833 0.64111 0.660 0.268 0.000 0.032 0.040 0.000
#> GSM564620 1 0.5892 0.26184 0.444 0.436 0.000 0.076 0.044 0.000
#> GSM564621 2 0.5770 0.49579 0.136 0.660 0.000 0.144 0.036 0.024
#> GSM564622 5 0.5940 0.39386 0.000 0.148 0.044 0.004 0.608 0.196
#> GSM564623 6 0.3065 0.61557 0.000 0.100 0.000 0.004 0.052 0.844
#> GSM564624 5 0.6076 0.05538 0.000 0.144 0.020 0.004 0.508 0.324
#> GSM564625 2 0.5440 0.49210 0.124 0.692 0.000 0.128 0.036 0.020
#> GSM564626 1 0.4605 0.63401 0.668 0.276 0.000 0.028 0.028 0.000
#> GSM564627 2 0.6328 -0.00325 0.360 0.476 0.000 0.120 0.036 0.008
#> GSM564628 5 0.6090 -0.25130 0.000 0.152 0.012 0.004 0.420 0.412
#> GSM564629 1 0.5210 0.49455 0.556 0.372 0.000 0.036 0.036 0.000
#> GSM564630 5 0.5672 0.38518 0.000 0.152 0.024 0.004 0.620 0.200
#> GSM564609 3 0.3589 0.71985 0.004 0.008 0.768 0.012 0.208 0.000
#> GSM564610 1 0.2742 0.79852 0.876 0.072 0.000 0.036 0.016 0.000
#> GSM564611 1 0.1819 0.79673 0.932 0.024 0.000 0.008 0.032 0.004
#> GSM564612 3 0.2871 0.75294 0.000 0.000 0.804 0.004 0.192 0.000
#> GSM564613 3 0.6145 -0.02134 0.000 0.032 0.492 0.016 0.376 0.084
#> GSM564614 2 0.6004 0.73113 0.000 0.476 0.000 0.256 0.004 0.264
#> GSM564631 3 0.0291 0.89790 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM564632 6 0.6558 0.30605 0.000 0.024 0.328 0.012 0.180 0.456
#> GSM564633 3 0.1053 0.89279 0.000 0.012 0.964 0.004 0.020 0.000
#> GSM564634 5 0.4916 0.71172 0.000 0.088 0.164 0.020 0.716 0.012
#> GSM564635 3 0.0260 0.89577 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM564636 3 0.4010 0.76694 0.000 0.012 0.792 0.012 0.124 0.060
#> GSM564637 6 0.5408 0.07538 0.004 0.004 0.084 0.000 0.444 0.464
#> GSM564638 3 0.0984 0.89484 0.000 0.012 0.968 0.012 0.008 0.000
#> GSM564639 3 0.0146 0.89759 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564640 5 0.3296 0.79168 0.012 0.012 0.160 0.000 0.812 0.004
#> GSM564641 3 0.2708 0.82801 0.004 0.008 0.864 0.012 0.112 0.000
#> GSM564642 5 0.3883 0.78935 0.024 0.028 0.148 0.008 0.792 0.000
#> GSM564643 6 0.1629 0.64727 0.000 0.024 0.004 0.004 0.028 0.940
#> GSM564644 5 0.3899 0.78610 0.040 0.020 0.136 0.008 0.796 0.000
#> GSM564645 3 0.0508 0.89335 0.000 0.000 0.984 0.012 0.004 0.000
#> GSM564647 3 0.3437 0.68168 0.000 0.008 0.752 0.004 0.236 0.000
#> GSM564648 5 0.3885 0.77828 0.000 0.024 0.168 0.000 0.776 0.032
#> GSM564649 3 0.0260 0.89799 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM564650 5 0.5372 0.12300 0.008 0.004 0.076 0.000 0.524 0.388
#> GSM564651 5 0.3578 0.77121 0.008 0.008 0.220 0.000 0.760 0.004
#> GSM564652 5 0.4042 0.78235 0.016 0.024 0.144 0.016 0.792 0.008
#> GSM564653 5 0.3124 0.78863 0.012 0.004 0.164 0.000 0.816 0.004
#> GSM564654 3 0.0260 0.89674 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM564655 3 0.2058 0.85666 0.000 0.036 0.908 0.000 0.056 0.000
#> GSM564656 3 0.0363 0.89521 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM564657 3 0.0508 0.89744 0.000 0.000 0.984 0.004 0.012 0.000
#> GSM564658 5 0.4029 0.79111 0.036 0.020 0.132 0.012 0.796 0.004
#> GSM564659 3 0.3353 0.79222 0.000 0.012 0.824 0.008 0.136 0.020
#> GSM564660 6 0.5331 0.54804 0.000 0.020 0.072 0.012 0.256 0.640
#> GSM564661 5 0.3585 0.78796 0.020 0.016 0.136 0.008 0.816 0.004
#> GSM564662 3 0.0146 0.89701 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM564663 5 0.3300 0.79276 0.016 0.016 0.156 0.000 0.812 0.000
#> GSM564664 5 0.4168 0.77315 0.060 0.020 0.132 0.008 0.780 0.000
#> GSM564665 3 0.2351 0.85490 0.000 0.052 0.900 0.012 0.036 0.000
#> GSM564666 6 0.2613 0.66617 0.000 0.012 0.020 0.016 0.060 0.892
#> GSM564667 3 0.0291 0.89690 0.000 0.004 0.992 0.004 0.000 0.000
#> GSM564668 3 0.0363 0.89737 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM564669 3 0.0291 0.89790 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM564670 5 0.4952 0.39915 0.004 0.016 0.404 0.008 0.552 0.016
#> GSM564671 6 0.1268 0.60244 0.000 0.036 0.000 0.008 0.004 0.952
#> GSM564672 3 0.0260 0.89674 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM564673 5 0.4437 0.75991 0.000 0.056 0.156 0.016 0.756 0.016
#> GSM564674 5 0.3621 0.78844 0.012 0.016 0.144 0.000 0.808 0.020
#> GSM564675 6 0.5335 0.56563 0.000 0.072 0.020 0.008 0.276 0.624
#> GSM564676 5 0.3995 0.78762 0.044 0.020 0.136 0.004 0.792 0.004
#> GSM564677 5 0.4185 0.77219 0.024 0.020 0.112 0.008 0.800 0.036
#> GSM564678 5 0.3995 0.78762 0.044 0.020 0.136 0.004 0.792 0.004
#> GSM564679 5 0.3678 0.79200 0.028 0.020 0.132 0.004 0.812 0.004
#> GSM564680 3 0.0405 0.89778 0.000 0.000 0.988 0.004 0.008 0.000
#> GSM564682 3 0.3658 0.70471 0.008 0.008 0.760 0.008 0.216 0.000
#> GSM564683 3 0.0363 0.89521 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM564684 6 0.0964 0.62973 0.000 0.012 0.000 0.004 0.016 0.968
#> GSM564685 3 0.1620 0.87098 0.000 0.024 0.940 0.024 0.012 0.000
#> GSM564686 6 0.0951 0.63568 0.000 0.008 0.000 0.004 0.020 0.968
#> GSM564687 5 0.4490 0.74017 0.028 0.052 0.112 0.024 0.780 0.004
#> GSM564688 5 0.3243 0.76908 0.000 0.008 0.208 0.000 0.780 0.004
#> GSM564689 5 0.3835 0.78559 0.020 0.012 0.120 0.004 0.812 0.032
#> GSM564690 5 0.3995 0.78762 0.044 0.020 0.136 0.004 0.792 0.004
#> GSM564691 3 0.3595 0.58901 0.000 0.008 0.704 0.000 0.288 0.000
#> GSM564692 5 0.3304 0.78127 0.000 0.008 0.172 0.004 0.804 0.012
#> GSM564694 6 0.5021 0.65615 0.000 0.072 0.044 0.028 0.116 0.740
#> GSM564695 6 0.5649 0.19344 0.000 0.016 0.084 0.004 0.408 0.488
#> GSM564696 3 0.1483 0.87901 0.000 0.036 0.944 0.012 0.008 0.000
#> GSM564697 5 0.4057 0.78820 0.044 0.012 0.128 0.008 0.796 0.012
#> GSM564698 3 0.0862 0.89550 0.000 0.016 0.972 0.004 0.008 0.000
#> GSM564700 6 0.0951 0.63568 0.000 0.008 0.000 0.004 0.020 0.968
#> GSM564701 5 0.3089 0.78042 0.000 0.008 0.188 0.004 0.800 0.000
#> GSM564702 6 0.5895 0.13482 0.016 0.020 0.040 0.016 0.448 0.460
#> GSM564703 4 0.4787 0.49996 0.388 0.000 0.040 0.564 0.008 0.000
#> GSM564704 1 0.3512 0.59010 0.740 0.004 0.000 0.248 0.008 0.000
#> GSM564705 1 0.0881 0.80656 0.972 0.008 0.000 0.012 0.008 0.000
#> GSM564706 4 0.5103 0.60129 0.308 0.004 0.064 0.612 0.012 0.000
#> GSM564707 1 0.1719 0.79441 0.924 0.000 0.016 0.060 0.000 0.000
#> GSM564708 4 0.5667 0.47952 0.056 0.220 0.004 0.652 0.052 0.016
#> GSM564709 1 0.2775 0.79723 0.884 0.028 0.000 0.060 0.016 0.012
#> GSM564710 1 0.1483 0.81032 0.944 0.012 0.000 0.036 0.008 0.000
#> GSM564711 4 0.5275 0.63300 0.100 0.072 0.080 0.724 0.024 0.000
#> GSM564712 1 0.1693 0.80820 0.936 0.012 0.000 0.032 0.020 0.000
#> GSM564713 4 0.2985 0.62189 0.076 0.012 0.016 0.872 0.020 0.004
#> GSM564714 4 0.4867 0.59196 0.320 0.000 0.068 0.608 0.004 0.000
#> GSM564715 1 0.1219 0.80447 0.948 0.004 0.000 0.048 0.000 0.000
#> GSM564716 1 0.6522 0.05989 0.424 0.144 0.000 0.388 0.036 0.008
#> GSM564717 1 0.4127 0.73025 0.804 0.076 0.000 0.052 0.056 0.012
#> GSM564718 4 0.4268 0.62803 0.072 0.056 0.048 0.800 0.024 0.000
#> GSM564719 1 0.1844 0.79794 0.932 0.024 0.000 0.012 0.028 0.004
#> GSM564720 1 0.0893 0.81162 0.972 0.004 0.000 0.016 0.004 0.004
#> GSM564721 1 0.0984 0.80796 0.968 0.012 0.000 0.008 0.012 0.000
#> GSM564722 4 0.4015 0.58275 0.320 0.008 0.000 0.664 0.004 0.004
#> GSM564723 1 0.0767 0.80883 0.976 0.004 0.000 0.012 0.008 0.000
#> GSM564724 4 0.4823 0.58332 0.028 0.076 0.112 0.756 0.024 0.004
#> GSM564725 4 0.6791 0.13918 0.276 0.204 0.000 0.468 0.040 0.012
#> GSM564726 2 0.6086 0.66729 0.000 0.444 0.000 0.344 0.008 0.204
#> GSM564727 2 0.6001 0.70687 0.012 0.512 0.000 0.268 0.000 0.208
#> GSM564728 2 0.6261 0.73239 0.000 0.412 0.000 0.284 0.008 0.296
#> GSM564729 2 0.6156 0.73544 0.000 0.420 0.000 0.276 0.004 0.300
#> GSM564730 1 0.3751 0.77542 0.820 0.088 0.000 0.052 0.036 0.004
#> GSM564731 4 0.6159 0.58518 0.204 0.116 0.004 0.612 0.052 0.012
#> GSM564732 2 0.6773 0.57478 0.124 0.536 0.000 0.228 0.012 0.100
#> GSM564733 4 0.3590 0.63533 0.116 0.028 0.024 0.824 0.004 0.004
#> GSM564734 1 0.3025 0.78880 0.860 0.088 0.000 0.032 0.016 0.004
#> GSM564735 4 0.3035 0.57452 0.048 0.040 0.020 0.876 0.004 0.012
#> GSM564736 4 0.2981 0.58678 0.052 0.032 0.032 0.876 0.004 0.004
#> GSM564737 1 0.1010 0.80521 0.960 0.000 0.000 0.036 0.004 0.000
#> GSM564738 4 0.3816 0.59373 0.048 0.024 0.088 0.824 0.004 0.012
#> GSM564739 1 0.3780 0.52595 0.744 0.000 0.028 0.224 0.004 0.000
#> GSM564740 4 0.5406 -0.03190 0.004 0.104 0.000 0.588 0.008 0.296
#> GSM564741 4 0.3555 0.59891 0.052 0.028 0.084 0.832 0.000 0.004
#> GSM564742 4 0.4993 0.59290 0.316 0.000 0.080 0.600 0.004 0.000
#> GSM564743 1 0.2898 0.79847 0.876 0.056 0.000 0.036 0.028 0.004
#> GSM564744 1 0.2001 0.80594 0.924 0.012 0.000 0.032 0.028 0.004
#> GSM564745 1 0.5530 0.66806 0.688 0.144 0.000 0.092 0.060 0.016
#> GSM564746 1 0.4003 0.68794 0.736 0.224 0.000 0.020 0.020 0.000
#> GSM564747 1 0.3797 0.01321 0.580 0.000 0.000 0.420 0.000 0.000
#> GSM564748 4 0.4918 0.60129 0.308 0.000 0.076 0.612 0.004 0.000
#> GSM564749 1 0.1381 0.80221 0.952 0.020 0.000 0.004 0.020 0.004
#> GSM564750 4 0.4287 0.26611 0.008 0.188 0.004 0.748 0.008 0.044
#> GSM564751 4 0.5513 0.34497 0.444 0.004 0.076 0.464 0.012 0.000
#> GSM564752 4 0.5818 -0.42371 0.000 0.308 0.000 0.516 0.008 0.168
#> GSM564753 4 0.5021 0.58429 0.324 0.000 0.080 0.592 0.004 0.000
#> GSM564754 1 0.1327 0.79803 0.936 0.000 0.000 0.064 0.000 0.000
#> GSM564755 2 0.6172 0.73511 0.000 0.412 0.000 0.280 0.004 0.304
#> GSM564756 1 0.3790 0.77601 0.824 0.048 0.000 0.072 0.048 0.008
#> GSM564757 2 0.6172 0.73511 0.000 0.412 0.000 0.280 0.004 0.304
#> GSM564758 2 0.6737 0.57796 0.008 0.504 0.000 0.224 0.056 0.208
#> GSM564759 4 0.4571 0.60344 0.308 0.004 0.040 0.644 0.004 0.000
#> GSM564760 4 0.5555 -0.39105 0.028 0.440 0.004 0.480 0.004 0.044
#> GSM564761 1 0.1622 0.81130 0.940 0.016 0.000 0.028 0.016 0.000
#> GSM564762 4 0.5586 0.60561 0.224 0.104 0.000 0.636 0.024 0.012
#> GSM564681 6 0.4543 0.48366 0.000 0.012 0.028 0.000 0.336 0.624
#> GSM564693 5 0.5105 -0.04171 0.000 0.008 0.048 0.004 0.496 0.444
#> GSM564646 6 0.0951 0.63568 0.000 0.008 0.000 0.004 0.020 0.968
#> GSM564699 6 0.1874 0.64831 0.000 0.008 0.012 0.020 0.028 0.932
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 genotype/variation(p) disease.state(p) k
#> CV:kmeans 154 0.925 0.47591 2
#> CV:kmeans 127 0.997 0.48303 3
#> CV:kmeans 130 0.204 0.62147 4
#> CV:kmeans 109 0.416 0.00386 5
#> CV:kmeans 123 0.369 0.04280 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 154 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 1.000 0.998 0.999 0.5008 0.500 0.500
#> 3 3 0.526 0.712 0.814 0.3042 0.855 0.710
#> 4 4 0.501 0.592 0.763 0.1476 0.828 0.557
#> 5 5 0.506 0.524 0.678 0.0671 0.929 0.731
#> 6 6 0.510 0.407 0.596 0.0404 0.958 0.807
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
#> GSM564615 1 0.0000 0.999 1.000 0.000
#> GSM564616 2 0.0000 0.999 0.000 1.000
#> GSM564617 2 0.0000 0.999 0.000 1.000
#> GSM564618 2 0.0376 0.996 0.004 0.996
#> GSM564619 1 0.0000 0.999 1.000 0.000
#> GSM564620 1 0.0000 0.999 1.000 0.000
#> GSM564621 1 0.0000 0.999 1.000 0.000
#> GSM564622 2 0.0000 0.999 0.000 1.000
#> GSM564623 2 0.0376 0.996 0.004 0.996
#> GSM564624 2 0.0000 0.999 0.000 1.000
#> GSM564625 1 0.0000 0.999 1.000 0.000
#> GSM564626 1 0.0000 0.999 1.000 0.000
#> GSM564627 1 0.0000 0.999 1.000 0.000
#> GSM564628 2 0.0000 0.999 0.000 1.000
#> GSM564629 1 0.0000 0.999 1.000 0.000
#> GSM564630 2 0.0000 0.999 0.000 1.000
#> GSM564609 2 0.0000 0.999 0.000 1.000
#> GSM564610 1 0.0000 0.999 1.000 0.000
#> GSM564611 1 0.0376 0.997 0.996 0.004
#> GSM564612 2 0.0000 0.999 0.000 1.000
#> GSM564613 2 0.0000 0.999 0.000 1.000
#> GSM564614 1 0.0000 0.999 1.000 0.000
#> GSM564631 2 0.0000 0.999 0.000 1.000
#> GSM564632 2 0.0000 0.999 0.000 1.000
#> GSM564633 2 0.0000 0.999 0.000 1.000
#> GSM564634 2 0.0000 0.999 0.000 1.000
#> GSM564635 2 0.0000 0.999 0.000 1.000
#> GSM564636 2 0.0000 0.999 0.000 1.000
#> GSM564637 2 0.0000 0.999 0.000 1.000
#> GSM564638 2 0.0000 0.999 0.000 1.000
#> GSM564639 2 0.0000 0.999 0.000 1.000
#> GSM564640 2 0.0000 0.999 0.000 1.000
#> GSM564641 2 0.0000 0.999 0.000 1.000
#> GSM564642 2 0.0000 0.999 0.000 1.000
#> GSM564643 2 0.0000 0.999 0.000 1.000
#> GSM564644 2 0.0000 0.999 0.000 1.000
#> GSM564645 2 0.0000 0.999 0.000 1.000
#> GSM564647 2 0.0000 0.999 0.000 1.000
#> GSM564648 2 0.0000 0.999 0.000 1.000
#> GSM564649 2 0.0000 0.999 0.000 1.000
#> GSM564650 2 0.0000 0.999 0.000 1.000
#> GSM564651 2 0.0000 0.999 0.000 1.000
#> GSM564652 2 0.0000 0.999 0.000 1.000
#> GSM564653 2 0.0000 0.999 0.000 1.000
#> GSM564654 2 0.0000 0.999 0.000 1.000
#> GSM564655 2 0.0000 0.999 0.000 1.000
#> GSM564656 2 0.0000 0.999 0.000 1.000
#> GSM564657 2 0.0000 0.999 0.000 1.000
#> GSM564658 2 0.0000 0.999 0.000 1.000
#> GSM564659 2 0.0000 0.999 0.000 1.000
#> GSM564660 2 0.0000 0.999 0.000 1.000
#> GSM564661 2 0.0000 0.999 0.000 1.000
#> GSM564662 2 0.0000 0.999 0.000 1.000
#> GSM564663 2 0.0000 0.999 0.000 1.000
#> GSM564664 2 0.0000 0.999 0.000 1.000
#> GSM564665 2 0.0000 0.999 0.000 1.000
#> GSM564666 2 0.0376 0.996 0.004 0.996
#> GSM564667 2 0.0000 0.999 0.000 1.000
#> GSM564668 2 0.0000 0.999 0.000 1.000
#> GSM564669 2 0.0000 0.999 0.000 1.000
#> GSM564670 2 0.0000 0.999 0.000 1.000
#> GSM564671 2 0.2948 0.947 0.052 0.948
#> GSM564672 2 0.0000 0.999 0.000 1.000
#> GSM564673 2 0.0000 0.999 0.000 1.000
#> GSM564674 2 0.0000 0.999 0.000 1.000
#> GSM564675 2 0.0000 0.999 0.000 1.000
#> GSM564676 2 0.0000 0.999 0.000 1.000
#> GSM564677 2 0.0376 0.995 0.004 0.996
#> GSM564678 2 0.0000 0.999 0.000 1.000
#> GSM564679 2 0.0000 0.999 0.000 1.000
#> GSM564680 2 0.0000 0.999 0.000 1.000
#> GSM564682 2 0.0000 0.999 0.000 1.000
#> GSM564683 2 0.0000 0.999 0.000 1.000
#> GSM564684 2 0.0376 0.996 0.004 0.996
#> GSM564685 2 0.0000 0.999 0.000 1.000
#> GSM564686 2 0.0672 0.992 0.008 0.992
#> GSM564687 2 0.0000 0.999 0.000 1.000
#> GSM564688 2 0.0000 0.999 0.000 1.000
#> GSM564689 2 0.0000 0.999 0.000 1.000
#> GSM564690 2 0.0000 0.999 0.000 1.000
#> GSM564691 2 0.0000 0.999 0.000 1.000
#> GSM564692 2 0.0000 0.999 0.000 1.000
#> GSM564694 2 0.0376 0.996 0.004 0.996
#> GSM564695 2 0.0000 0.999 0.000 1.000
#> GSM564696 2 0.0000 0.999 0.000 1.000
#> GSM564697 2 0.0000 0.999 0.000 1.000
#> GSM564698 2 0.0000 0.999 0.000 1.000
#> GSM564700 2 0.0376 0.996 0.004 0.996
#> GSM564701 2 0.0000 0.999 0.000 1.000
#> GSM564702 2 0.0376 0.996 0.004 0.996
#> GSM564703 1 0.0376 0.997 0.996 0.004
#> GSM564704 1 0.0000 0.999 1.000 0.000
#> GSM564705 1 0.0000 0.999 1.000 0.000
#> GSM564706 1 0.0376 0.997 0.996 0.004
#> GSM564707 1 0.0376 0.997 0.996 0.004
#> GSM564708 1 0.0000 0.999 1.000 0.000
#> GSM564709 1 0.0000 0.999 1.000 0.000
#> GSM564710 1 0.0376 0.997 0.996 0.004
#> GSM564711 1 0.0000 0.999 1.000 0.000
#> GSM564712 1 0.0376 0.997 0.996 0.004
#> GSM564713 1 0.0000 0.999 1.000 0.000
#> GSM564714 1 0.0000 0.999 1.000 0.000
#> GSM564715 1 0.0376 0.997 0.996 0.004
#> GSM564716 1 0.0000 0.999 1.000 0.000
#> GSM564717 1 0.0000 0.999 1.000 0.000
#> GSM564718 1 0.0000 0.999 1.000 0.000
#> GSM564719 1 0.0000 0.999 1.000 0.000
#> GSM564720 1 0.0000 0.999 1.000 0.000
#> GSM564721 1 0.0000 0.999 1.000 0.000
#> GSM564722 1 0.0000 0.999 1.000 0.000
#> GSM564723 1 0.0000 0.999 1.000 0.000
#> GSM564724 1 0.0000 0.999 1.000 0.000
#> GSM564725 1 0.0000 0.999 1.000 0.000
#> GSM564726 1 0.0000 0.999 1.000 0.000
#> GSM564727 1 0.0000 0.999 1.000 0.000
#> GSM564728 1 0.0000 0.999 1.000 0.000
#> GSM564729 1 0.0000 0.999 1.000 0.000
#> GSM564730 1 0.0000 0.999 1.000 0.000
#> GSM564731 1 0.0000 0.999 1.000 0.000
#> GSM564732 1 0.0000 0.999 1.000 0.000
#> GSM564733 1 0.0000 0.999 1.000 0.000
#> GSM564734 1 0.0000 0.999 1.000 0.000
#> GSM564735 1 0.0000 0.999 1.000 0.000
#> GSM564736 1 0.0000 0.999 1.000 0.000
#> GSM564737 1 0.0000 0.999 1.000 0.000
#> GSM564738 1 0.0000 0.999 1.000 0.000
#> GSM564739 1 0.0376 0.997 0.996 0.004
#> GSM564740 1 0.0000 0.999 1.000 0.000
#> GSM564741 1 0.0000 0.999 1.000 0.000
#> GSM564742 1 0.0376 0.997 0.996 0.004
#> GSM564743 1 0.0000 0.999 1.000 0.000
#> GSM564744 1 0.0000 0.999 1.000 0.000
#> GSM564745 1 0.0000 0.999 1.000 0.000
#> GSM564746 1 0.0000 0.999 1.000 0.000
#> GSM564747 1 0.0000 0.999 1.000 0.000
#> GSM564748 1 0.0376 0.997 0.996 0.004
#> GSM564749 1 0.0000 0.999 1.000 0.000
#> GSM564750 1 0.0000 0.999 1.000 0.000
#> GSM564751 1 0.0376 0.997 0.996 0.004
#> GSM564752 1 0.0000 0.999 1.000 0.000
#> GSM564753 1 0.0000 0.999 1.000 0.000
#> GSM564754 1 0.0000 0.999 1.000 0.000
#> GSM564755 1 0.0000 0.999 1.000 0.000
#> GSM564756 1 0.0000 0.999 1.000 0.000
#> GSM564757 1 0.0000 0.999 1.000 0.000
#> GSM564758 1 0.0000 0.999 1.000 0.000
#> GSM564759 1 0.0000 0.999 1.000 0.000
#> GSM564760 1 0.0000 0.999 1.000 0.000
#> GSM564761 1 0.0376 0.997 0.996 0.004
#> GSM564762 1 0.0000 0.999 1.000 0.000
#> GSM564681 2 0.0000 0.999 0.000 1.000
#> GSM564693 2 0.0000 0.999 0.000 1.000
#> GSM564646 2 0.0376 0.996 0.004 0.996
#> GSM564699 2 0.0938 0.989 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.5465 0.7967 0.712 0.288 0.000
#> GSM564616 2 0.5244 0.7095 0.004 0.756 0.240
#> GSM564617 2 0.5327 0.7069 0.000 0.728 0.272
#> GSM564618 2 0.2959 0.6608 0.000 0.900 0.100
#> GSM564619 1 0.1289 0.8802 0.968 0.032 0.000
#> GSM564620 1 0.3551 0.8723 0.868 0.132 0.000
#> GSM564621 1 0.4605 0.8457 0.796 0.204 0.000
#> GSM564622 2 0.6215 0.5830 0.000 0.572 0.428
#> GSM564623 2 0.1585 0.5982 0.008 0.964 0.028
#> GSM564624 2 0.5363 0.7045 0.000 0.724 0.276
#> GSM564625 1 0.4235 0.8581 0.824 0.176 0.000
#> GSM564626 1 0.1289 0.8804 0.968 0.032 0.000
#> GSM564627 1 0.4346 0.8578 0.816 0.184 0.000
#> GSM564628 2 0.3619 0.6825 0.000 0.864 0.136
#> GSM564629 1 0.1289 0.8816 0.968 0.032 0.000
#> GSM564630 2 0.5465 0.7010 0.000 0.712 0.288
#> GSM564609 3 0.4349 0.7311 0.020 0.128 0.852
#> GSM564610 1 0.1289 0.8786 0.968 0.032 0.000
#> GSM564611 1 0.3234 0.8453 0.908 0.072 0.020
#> GSM564612 3 0.2625 0.7655 0.000 0.084 0.916
#> GSM564613 3 0.5948 0.2191 0.000 0.360 0.640
#> GSM564614 1 0.5497 0.7949 0.708 0.292 0.000
#> GSM564631 3 0.0000 0.8021 0.000 0.000 1.000
#> GSM564632 3 0.6509 -0.0869 0.004 0.472 0.524
#> GSM564633 3 0.0424 0.8030 0.000 0.008 0.992
#> GSM564634 3 0.6617 -0.2032 0.008 0.436 0.556
#> GSM564635 3 0.0237 0.8018 0.004 0.000 0.996
#> GSM564636 3 0.4121 0.7024 0.000 0.168 0.832
#> GSM564637 2 0.6330 0.6133 0.004 0.600 0.396
#> GSM564638 3 0.1860 0.7737 0.000 0.052 0.948
#> GSM564639 3 0.0000 0.8021 0.000 0.000 1.000
#> GSM564640 2 0.6045 0.6610 0.000 0.620 0.380
#> GSM564641 3 0.2116 0.7910 0.012 0.040 0.948
#> GSM564642 3 0.6881 -0.0414 0.020 0.388 0.592
#> GSM564643 2 0.4733 0.5648 0.004 0.800 0.196
#> GSM564644 2 0.6252 0.5741 0.000 0.556 0.444
#> GSM564645 3 0.0000 0.8021 0.000 0.000 1.000
#> GSM564647 3 0.5098 0.5149 0.000 0.248 0.752
#> GSM564648 2 0.6168 0.6231 0.000 0.588 0.412
#> GSM564649 3 0.0424 0.8027 0.000 0.008 0.992
#> GSM564650 2 0.5529 0.7078 0.000 0.704 0.296
#> GSM564651 3 0.6079 -0.0423 0.000 0.388 0.612
#> GSM564652 2 0.6617 0.5749 0.008 0.556 0.436
#> GSM564653 2 0.6168 0.6248 0.000 0.588 0.412
#> GSM564654 3 0.0237 0.8028 0.000 0.004 0.996
#> GSM564655 3 0.2878 0.7665 0.000 0.096 0.904
#> GSM564656 3 0.0424 0.8027 0.000 0.008 0.992
#> GSM564657 3 0.1170 0.7945 0.016 0.008 0.976
#> GSM564658 2 0.6717 0.6772 0.020 0.628 0.352
#> GSM564659 3 0.4654 0.6341 0.000 0.208 0.792
#> GSM564660 2 0.5325 0.6534 0.004 0.748 0.248
#> GSM564661 2 0.6553 0.6236 0.008 0.580 0.412
#> GSM564662 3 0.0000 0.8021 0.000 0.000 1.000
#> GSM564663 2 0.6724 0.6022 0.012 0.568 0.420
#> GSM564664 2 0.6669 0.4966 0.008 0.524 0.468
#> GSM564665 3 0.2796 0.7633 0.000 0.092 0.908
#> GSM564666 2 0.4110 0.5368 0.004 0.844 0.152
#> GSM564667 3 0.0475 0.8018 0.004 0.004 0.992
#> GSM564668 3 0.1031 0.8011 0.000 0.024 0.976
#> GSM564669 3 0.0892 0.7999 0.000 0.020 0.980
#> GSM564670 3 0.5327 0.4646 0.000 0.272 0.728
#> GSM564671 2 0.3263 0.5466 0.048 0.912 0.040
#> GSM564672 3 0.0424 0.8026 0.000 0.008 0.992
#> GSM564673 2 0.6647 0.5452 0.008 0.540 0.452
#> GSM564674 2 0.6033 0.6935 0.004 0.660 0.336
#> GSM564675 2 0.2878 0.6462 0.000 0.904 0.096
#> GSM564676 2 0.6565 0.6077 0.008 0.576 0.416
#> GSM564677 2 0.6541 0.6989 0.024 0.672 0.304
#> GSM564678 2 0.6836 0.6039 0.016 0.572 0.412
#> GSM564679 2 0.6379 0.6642 0.008 0.624 0.368
#> GSM564680 3 0.0237 0.8028 0.000 0.004 0.996
#> GSM564682 3 0.4411 0.7042 0.016 0.140 0.844
#> GSM564683 3 0.0237 0.8028 0.000 0.004 0.996
#> GSM564684 2 0.1950 0.5957 0.008 0.952 0.040
#> GSM564685 3 0.0592 0.8011 0.000 0.012 0.988
#> GSM564686 2 0.1950 0.5961 0.008 0.952 0.040
#> GSM564687 2 0.6529 0.6675 0.012 0.620 0.368
#> GSM564688 3 0.6302 -0.4014 0.000 0.480 0.520
#> GSM564689 2 0.5926 0.6801 0.000 0.644 0.356
#> GSM564690 2 0.6617 0.6399 0.012 0.600 0.388
#> GSM564691 3 0.3482 0.7155 0.000 0.128 0.872
#> GSM564692 2 0.6154 0.6370 0.000 0.592 0.408
#> GSM564694 2 0.5058 0.5384 0.000 0.756 0.244
#> GSM564695 2 0.6062 0.5018 0.000 0.616 0.384
#> GSM564696 3 0.2165 0.7775 0.000 0.064 0.936
#> GSM564697 2 0.6104 0.6811 0.004 0.648 0.348
#> GSM564698 3 0.2096 0.7861 0.004 0.052 0.944
#> GSM564700 2 0.1765 0.5986 0.004 0.956 0.040
#> GSM564701 3 0.6305 -0.4209 0.000 0.484 0.516
#> GSM564702 2 0.4663 0.6915 0.016 0.828 0.156
#> GSM564703 1 0.4351 0.7907 0.828 0.004 0.168
#> GSM564704 1 0.2878 0.8817 0.904 0.096 0.000
#> GSM564705 1 0.0829 0.8742 0.984 0.012 0.004
#> GSM564706 1 0.3896 0.8301 0.864 0.008 0.128
#> GSM564707 1 0.1751 0.8730 0.960 0.012 0.028
#> GSM564708 1 0.2998 0.8847 0.916 0.068 0.016
#> GSM564709 1 0.3644 0.8772 0.872 0.124 0.004
#> GSM564710 1 0.1015 0.8760 0.980 0.008 0.012
#> GSM564711 1 0.4964 0.8737 0.836 0.116 0.048
#> GSM564712 1 0.1491 0.8739 0.968 0.016 0.016
#> GSM564713 1 0.5377 0.8650 0.820 0.112 0.068
#> GSM564714 1 0.7368 0.5037 0.604 0.044 0.352
#> GSM564715 1 0.1015 0.8770 0.980 0.012 0.008
#> GSM564716 1 0.2537 0.8818 0.920 0.080 0.000
#> GSM564717 1 0.0592 0.8743 0.988 0.012 0.000
#> GSM564718 1 0.4371 0.8782 0.860 0.108 0.032
#> GSM564719 1 0.2063 0.8680 0.948 0.044 0.008
#> GSM564720 1 0.1031 0.8736 0.976 0.024 0.000
#> GSM564721 1 0.0892 0.8788 0.980 0.020 0.000
#> GSM564722 1 0.3715 0.8764 0.868 0.128 0.004
#> GSM564723 1 0.0892 0.8740 0.980 0.020 0.000
#> GSM564724 1 0.5585 0.8558 0.812 0.092 0.096
#> GSM564725 1 0.4121 0.8624 0.832 0.168 0.000
#> GSM564726 1 0.5656 0.7986 0.712 0.284 0.004
#> GSM564727 1 0.4842 0.8353 0.776 0.224 0.000
#> GSM564728 1 0.5497 0.7938 0.708 0.292 0.000
#> GSM564729 1 0.5465 0.7967 0.712 0.288 0.000
#> GSM564730 1 0.1289 0.8826 0.968 0.032 0.000
#> GSM564731 1 0.2280 0.8848 0.940 0.052 0.008
#> GSM564732 1 0.4291 0.8569 0.820 0.180 0.000
#> GSM564733 1 0.3983 0.8711 0.884 0.048 0.068
#> GSM564734 1 0.1753 0.8832 0.952 0.048 0.000
#> GSM564735 1 0.8623 0.7153 0.600 0.224 0.176
#> GSM564736 1 0.6886 0.8269 0.728 0.184 0.088
#> GSM564737 1 0.0747 0.8741 0.984 0.016 0.000
#> GSM564738 1 0.9636 0.5178 0.468 0.248 0.284
#> GSM564739 1 0.4110 0.8062 0.844 0.004 0.152
#> GSM564740 1 0.5982 0.7605 0.668 0.328 0.004
#> GSM564741 1 0.8017 0.7667 0.652 0.208 0.140
#> GSM564742 1 0.6209 0.4970 0.628 0.004 0.368
#> GSM564743 1 0.0747 0.8758 0.984 0.016 0.000
#> GSM564744 1 0.0424 0.8744 0.992 0.008 0.000
#> GSM564745 1 0.2537 0.8830 0.920 0.080 0.000
#> GSM564746 1 0.0892 0.8784 0.980 0.020 0.000
#> GSM564747 1 0.0424 0.8773 0.992 0.008 0.000
#> GSM564748 1 0.4575 0.7792 0.812 0.004 0.184
#> GSM564749 1 0.0592 0.8743 0.988 0.012 0.000
#> GSM564750 1 0.6053 0.8114 0.720 0.260 0.020
#> GSM564751 1 0.2261 0.8628 0.932 0.000 0.068
#> GSM564752 1 0.5397 0.8026 0.720 0.280 0.000
#> GSM564753 1 0.5929 0.5884 0.676 0.004 0.320
#> GSM564754 1 0.0424 0.8762 0.992 0.008 0.000
#> GSM564755 1 0.5465 0.7967 0.712 0.288 0.000
#> GSM564756 1 0.1163 0.8821 0.972 0.028 0.000
#> GSM564757 1 0.5465 0.7967 0.712 0.288 0.000
#> GSM564758 1 0.5928 0.7867 0.696 0.296 0.008
#> GSM564759 1 0.4233 0.7984 0.836 0.004 0.160
#> GSM564760 1 0.4399 0.8529 0.812 0.188 0.000
#> GSM564761 1 0.2269 0.8677 0.944 0.040 0.016
#> GSM564762 1 0.2165 0.8834 0.936 0.064 0.000
#> GSM564681 2 0.3816 0.6851 0.000 0.852 0.148
#> GSM564693 2 0.5178 0.7056 0.000 0.744 0.256
#> GSM564646 2 0.1315 0.5934 0.008 0.972 0.020
#> GSM564699 2 0.5167 0.4657 0.016 0.792 0.192
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.2843 0.6524 0.088 0.020 0.000 0.892
#> GSM564616 2 0.2521 0.7244 0.016 0.924 0.032 0.028
#> GSM564617 2 0.1452 0.7151 0.000 0.956 0.008 0.036
#> GSM564618 2 0.3836 0.6694 0.000 0.816 0.016 0.168
#> GSM564619 1 0.3450 0.7333 0.836 0.008 0.000 0.156
#> GSM564620 1 0.5723 0.3630 0.580 0.032 0.000 0.388
#> GSM564621 4 0.4872 0.5723 0.244 0.028 0.000 0.728
#> GSM564622 2 0.4744 0.6529 0.000 0.736 0.240 0.024
#> GSM564623 4 0.5296 -0.1489 0.000 0.496 0.008 0.496
#> GSM564624 2 0.2409 0.7232 0.004 0.924 0.032 0.040
#> GSM564625 4 0.5339 0.3388 0.384 0.016 0.000 0.600
#> GSM564626 1 0.3208 0.7416 0.848 0.004 0.000 0.148
#> GSM564627 4 0.5649 0.3317 0.392 0.028 0.000 0.580
#> GSM564628 2 0.3108 0.6987 0.000 0.872 0.016 0.112
#> GSM564629 1 0.5003 0.5714 0.676 0.016 0.000 0.308
#> GSM564630 2 0.2529 0.7267 0.008 0.920 0.048 0.024
#> GSM564609 3 0.6638 0.5686 0.064 0.248 0.652 0.036
#> GSM564610 1 0.2861 0.7584 0.888 0.016 0.000 0.096
#> GSM564611 1 0.2530 0.6956 0.896 0.100 0.000 0.004
#> GSM564612 3 0.4820 0.5667 0.012 0.296 0.692 0.000
#> GSM564613 2 0.6254 0.1486 0.012 0.508 0.448 0.032
#> GSM564614 4 0.2796 0.6536 0.092 0.016 0.000 0.892
#> GSM564631 3 0.1004 0.8399 0.004 0.024 0.972 0.000
#> GSM564632 3 0.7171 0.0236 0.000 0.400 0.464 0.136
#> GSM564633 3 0.1543 0.8378 0.008 0.032 0.956 0.004
#> GSM564634 2 0.5673 0.3077 0.008 0.536 0.444 0.012
#> GSM564635 3 0.0707 0.8396 0.000 0.020 0.980 0.000
#> GSM564636 3 0.5019 0.6566 0.004 0.240 0.728 0.028
#> GSM564637 2 0.6611 0.4942 0.012 0.584 0.336 0.068
#> GSM564638 3 0.2363 0.8206 0.000 0.056 0.920 0.024
#> GSM564639 3 0.0188 0.8377 0.000 0.004 0.996 0.000
#> GSM564640 2 0.3932 0.7298 0.032 0.836 0.128 0.004
#> GSM564641 3 0.4057 0.7832 0.036 0.120 0.836 0.008
#> GSM564642 2 0.6349 0.4105 0.056 0.548 0.392 0.004
#> GSM564643 2 0.7677 0.3316 0.000 0.456 0.248 0.296
#> GSM564644 2 0.5030 0.6931 0.060 0.752 0.188 0.000
#> GSM564645 3 0.0336 0.8382 0.000 0.008 0.992 0.000
#> GSM564647 3 0.4973 0.4466 0.008 0.348 0.644 0.000
#> GSM564648 2 0.5126 0.6797 0.008 0.728 0.236 0.028
#> GSM564649 3 0.1305 0.8415 0.004 0.036 0.960 0.000
#> GSM564650 2 0.4493 0.7333 0.020 0.828 0.092 0.060
#> GSM564651 2 0.6049 0.2986 0.028 0.524 0.440 0.008
#> GSM564652 2 0.6190 0.6855 0.068 0.692 0.216 0.024
#> GSM564653 2 0.4381 0.7218 0.028 0.804 0.160 0.008
#> GSM564654 3 0.0469 0.8395 0.000 0.012 0.988 0.000
#> GSM564655 3 0.3858 0.7749 0.004 0.116 0.844 0.036
#> GSM564656 3 0.0592 0.8388 0.000 0.016 0.984 0.000
#> GSM564657 3 0.2585 0.8218 0.032 0.048 0.916 0.004
#> GSM564658 2 0.4744 0.7259 0.056 0.788 0.152 0.004
#> GSM564659 3 0.5453 0.5099 0.000 0.304 0.660 0.036
#> GSM564660 2 0.6339 0.6158 0.004 0.672 0.168 0.156
#> GSM564661 2 0.4287 0.7303 0.032 0.808 0.156 0.004
#> GSM564662 3 0.0524 0.8379 0.004 0.008 0.988 0.000
#> GSM564663 2 0.5363 0.6900 0.056 0.728 0.212 0.004
#> GSM564664 2 0.5820 0.6440 0.080 0.680 0.240 0.000
#> GSM564665 3 0.3432 0.7686 0.004 0.140 0.848 0.008
#> GSM564666 2 0.7463 0.2576 0.000 0.440 0.176 0.384
#> GSM564667 3 0.0895 0.8406 0.004 0.020 0.976 0.000
#> GSM564668 3 0.1389 0.8387 0.000 0.048 0.952 0.000
#> GSM564669 3 0.1211 0.8390 0.000 0.040 0.960 0.000
#> GSM564670 3 0.6038 0.2120 0.012 0.416 0.548 0.024
#> GSM564671 4 0.5585 0.3102 0.012 0.316 0.020 0.652
#> GSM564672 3 0.0779 0.8413 0.004 0.016 0.980 0.000
#> GSM564673 2 0.5266 0.6648 0.012 0.704 0.264 0.020
#> GSM564674 2 0.4235 0.7387 0.024 0.828 0.128 0.020
#> GSM564675 2 0.4919 0.6386 0.000 0.752 0.048 0.200
#> GSM564676 2 0.4706 0.7113 0.072 0.788 0.140 0.000
#> GSM564677 2 0.5119 0.7326 0.068 0.800 0.092 0.040
#> GSM564678 2 0.4804 0.7044 0.072 0.780 0.148 0.000
#> GSM564679 2 0.4071 0.7222 0.064 0.832 0.104 0.000
#> GSM564680 3 0.1022 0.8413 0.000 0.032 0.968 0.000
#> GSM564682 3 0.5631 0.6095 0.076 0.224 0.700 0.000
#> GSM564683 3 0.0844 0.8375 0.004 0.012 0.980 0.004
#> GSM564684 4 0.5409 -0.1501 0.000 0.492 0.012 0.496
#> GSM564685 3 0.0779 0.8389 0.000 0.016 0.980 0.004
#> GSM564686 4 0.5865 0.0448 0.000 0.412 0.036 0.552
#> GSM564687 2 0.5393 0.7117 0.056 0.752 0.176 0.016
#> GSM564688 2 0.5038 0.5746 0.012 0.652 0.336 0.000
#> GSM564689 2 0.4118 0.7279 0.060 0.836 0.100 0.004
#> GSM564690 2 0.4686 0.7069 0.068 0.788 0.144 0.000
#> GSM564691 3 0.5174 0.3710 0.012 0.368 0.620 0.000
#> GSM564692 2 0.4687 0.7267 0.028 0.784 0.176 0.012
#> GSM564694 2 0.7793 0.3481 0.004 0.468 0.256 0.272
#> GSM564695 2 0.7858 0.3899 0.016 0.492 0.308 0.184
#> GSM564696 3 0.2048 0.8271 0.000 0.064 0.928 0.008
#> GSM564697 2 0.3857 0.7318 0.044 0.848 0.104 0.004
#> GSM564698 3 0.2821 0.8074 0.004 0.076 0.900 0.020
#> GSM564700 2 0.5781 0.1307 0.000 0.488 0.028 0.484
#> GSM564701 2 0.6131 0.5406 0.044 0.600 0.348 0.008
#> GSM564702 2 0.4423 0.7040 0.036 0.832 0.032 0.100
#> GSM564703 1 0.5724 0.6440 0.716 0.000 0.144 0.140
#> GSM564704 1 0.5588 0.3890 0.600 0.020 0.004 0.376
#> GSM564705 1 0.1042 0.7490 0.972 0.020 0.000 0.008
#> GSM564706 1 0.6608 0.5410 0.628 0.000 0.168 0.204
#> GSM564707 1 0.2218 0.7519 0.932 0.004 0.028 0.036
#> GSM564708 4 0.6652 0.1754 0.396 0.000 0.088 0.516
#> GSM564709 1 0.5437 0.4336 0.624 0.012 0.008 0.356
#> GSM564710 1 0.3053 0.7637 0.892 0.016 0.012 0.080
#> GSM564711 4 0.7044 0.2538 0.368 0.004 0.112 0.516
#> GSM564712 1 0.2302 0.7628 0.924 0.008 0.008 0.060
#> GSM564713 4 0.6491 0.4557 0.280 0.004 0.096 0.620
#> GSM564714 1 0.7714 0.2006 0.448 0.000 0.260 0.292
#> GSM564715 1 0.2011 0.7640 0.920 0.000 0.000 0.080
#> GSM564716 4 0.4985 0.0656 0.468 0.000 0.000 0.532
#> GSM564717 1 0.2831 0.7586 0.876 0.004 0.000 0.120
#> GSM564718 4 0.5524 0.4953 0.276 0.000 0.048 0.676
#> GSM564719 1 0.3370 0.7109 0.872 0.080 0.000 0.048
#> GSM564720 1 0.1985 0.7559 0.940 0.016 0.004 0.040
#> GSM564721 1 0.3052 0.7452 0.860 0.004 0.000 0.136
#> GSM564722 4 0.5746 0.2527 0.424 0.016 0.008 0.552
#> GSM564723 1 0.1356 0.7568 0.960 0.008 0.000 0.032
#> GSM564724 4 0.7012 0.3574 0.284 0.000 0.156 0.560
#> GSM564725 4 0.5047 0.4897 0.316 0.016 0.000 0.668
#> GSM564726 4 0.1807 0.6520 0.052 0.008 0.000 0.940
#> GSM564727 4 0.4059 0.6115 0.200 0.012 0.000 0.788
#> GSM564728 4 0.2101 0.6521 0.060 0.012 0.000 0.928
#> GSM564729 4 0.2675 0.6516 0.100 0.008 0.000 0.892
#> GSM564730 1 0.4283 0.6382 0.740 0.004 0.000 0.256
#> GSM564731 1 0.5543 0.2885 0.556 0.000 0.020 0.424
#> GSM564732 4 0.4699 0.4760 0.320 0.004 0.000 0.676
#> GSM564733 4 0.7196 0.0959 0.408 0.004 0.120 0.468
#> GSM564734 1 0.4936 0.4965 0.652 0.008 0.000 0.340
#> GSM564735 4 0.4840 0.6148 0.116 0.000 0.100 0.784
#> GSM564736 4 0.5151 0.5969 0.140 0.000 0.100 0.760
#> GSM564737 1 0.1256 0.7552 0.964 0.008 0.000 0.028
#> GSM564738 4 0.5906 0.5513 0.148 0.000 0.152 0.700
#> GSM564739 1 0.4812 0.7115 0.800 0.008 0.096 0.096
#> GSM564740 4 0.2699 0.6452 0.068 0.028 0.000 0.904
#> GSM564741 4 0.6110 0.5388 0.176 0.000 0.144 0.680
#> GSM564742 1 0.7113 0.3698 0.532 0.000 0.316 0.152
#> GSM564743 1 0.3552 0.7488 0.848 0.024 0.000 0.128
#> GSM564744 1 0.1635 0.7576 0.948 0.008 0.000 0.044
#> GSM564745 1 0.4560 0.5870 0.700 0.004 0.000 0.296
#> GSM564746 1 0.2799 0.7531 0.884 0.008 0.000 0.108
#> GSM564747 1 0.3751 0.7190 0.800 0.000 0.004 0.196
#> GSM564748 1 0.6617 0.5289 0.628 0.000 0.176 0.196
#> GSM564749 1 0.0895 0.7442 0.976 0.020 0.000 0.004
#> GSM564750 4 0.2860 0.6522 0.100 0.004 0.008 0.888
#> GSM564751 1 0.4487 0.7224 0.808 0.000 0.092 0.100
#> GSM564752 4 0.1743 0.6522 0.056 0.004 0.000 0.940
#> GSM564753 1 0.6728 0.4806 0.596 0.000 0.268 0.136
#> GSM564754 1 0.3356 0.7286 0.824 0.000 0.000 0.176
#> GSM564755 4 0.2021 0.6512 0.056 0.012 0.000 0.932
#> GSM564756 1 0.4779 0.6973 0.756 0.028 0.004 0.212
#> GSM564757 4 0.2741 0.6512 0.096 0.012 0.000 0.892
#> GSM564758 4 0.3319 0.6556 0.096 0.016 0.012 0.876
#> GSM564759 1 0.7133 0.4088 0.548 0.000 0.172 0.280
#> GSM564760 4 0.4295 0.5825 0.240 0.000 0.008 0.752
#> GSM564761 1 0.2123 0.7533 0.936 0.032 0.004 0.028
#> GSM564762 4 0.5317 0.0668 0.460 0.004 0.004 0.532
#> GSM564681 2 0.3037 0.7088 0.000 0.888 0.036 0.076
#> GSM564693 2 0.4444 0.7153 0.000 0.808 0.120 0.072
#> GSM564646 2 0.5399 0.1893 0.000 0.520 0.012 0.468
#> GSM564699 4 0.7595 -0.1565 0.000 0.372 0.200 0.428
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.326 0.66466 0.036 0.000 0.000 0.840 0.124
#> GSM564616 2 0.540 0.37828 0.016 0.576 0.020 0.008 0.380
#> GSM564617 2 0.481 0.38403 0.000 0.600 0.020 0.004 0.376
#> GSM564618 5 0.603 0.25840 0.000 0.376 0.028 0.060 0.536
#> GSM564619 1 0.521 0.57967 0.696 0.016 0.000 0.216 0.072
#> GSM564620 4 0.625 0.03755 0.432 0.016 0.000 0.460 0.092
#> GSM564621 4 0.594 0.50614 0.260 0.008 0.000 0.604 0.128
#> GSM564622 2 0.679 0.35264 0.016 0.492 0.156 0.004 0.332
#> GSM564623 5 0.556 0.59806 0.000 0.092 0.012 0.244 0.652
#> GSM564624 2 0.488 0.33195 0.000 0.576 0.028 0.000 0.396
#> GSM564625 4 0.496 0.39983 0.352 0.000 0.000 0.608 0.040
#> GSM564626 1 0.443 0.65355 0.784 0.020 0.000 0.132 0.064
#> GSM564627 4 0.597 0.40099 0.320 0.004 0.000 0.560 0.116
#> GSM564628 5 0.543 -0.09153 0.012 0.468 0.008 0.020 0.492
#> GSM564629 1 0.552 0.35421 0.572 0.000 0.000 0.348 0.080
#> GSM564630 2 0.475 0.52529 0.004 0.676 0.036 0.000 0.284
#> GSM564609 3 0.830 0.42733 0.076 0.192 0.508 0.076 0.148
#> GSM564610 1 0.448 0.66842 0.796 0.048 0.000 0.096 0.060
#> GSM564611 1 0.430 0.57384 0.728 0.244 0.000 0.008 0.020
#> GSM564612 3 0.536 0.43064 0.008 0.352 0.592 0.000 0.048
#> GSM564613 3 0.732 -0.03399 0.004 0.296 0.380 0.016 0.304
#> GSM564614 4 0.330 0.67137 0.028 0.004 0.000 0.840 0.128
#> GSM564631 3 0.199 0.78057 0.004 0.040 0.928 0.000 0.028
#> GSM564632 5 0.773 0.14207 0.004 0.196 0.340 0.060 0.400
#> GSM564633 3 0.281 0.78102 0.012 0.068 0.888 0.000 0.032
#> GSM564634 2 0.760 0.19068 0.036 0.404 0.372 0.016 0.172
#> GSM564635 3 0.251 0.77976 0.000 0.060 0.896 0.000 0.044
#> GSM564636 3 0.654 0.44608 0.004 0.172 0.556 0.012 0.256
#> GSM564637 2 0.749 0.06510 0.004 0.384 0.196 0.040 0.376
#> GSM564638 3 0.405 0.76133 0.008 0.048 0.824 0.020 0.100
#> GSM564639 3 0.140 0.77612 0.004 0.008 0.956 0.004 0.028
#> GSM564640 2 0.390 0.65846 0.016 0.820 0.052 0.000 0.112
#> GSM564641 3 0.589 0.68789 0.056 0.124 0.708 0.012 0.100
#> GSM564642 2 0.675 0.49049 0.040 0.556 0.256 0.000 0.148
#> GSM564643 5 0.684 0.56124 0.000 0.100 0.152 0.144 0.604
#> GSM564644 2 0.368 0.66280 0.016 0.840 0.072 0.000 0.072
#> GSM564645 3 0.141 0.77498 0.004 0.008 0.952 0.000 0.036
#> GSM564647 3 0.597 0.38249 0.000 0.320 0.548 0.000 0.132
#> GSM564648 2 0.634 0.43695 0.000 0.532 0.172 0.004 0.292
#> GSM564649 3 0.332 0.76458 0.000 0.116 0.840 0.000 0.044
#> GSM564650 2 0.592 0.30921 0.012 0.560 0.052 0.012 0.364
#> GSM564651 2 0.618 0.35342 0.004 0.536 0.340 0.004 0.116
#> GSM564652 2 0.746 0.44087 0.128 0.556 0.128 0.008 0.180
#> GSM564653 2 0.383 0.66103 0.004 0.816 0.068 0.000 0.112
#> GSM564654 3 0.211 0.78053 0.004 0.036 0.928 0.008 0.024
#> GSM564655 3 0.617 0.61426 0.008 0.180 0.652 0.028 0.132
#> GSM564656 3 0.172 0.77743 0.004 0.024 0.944 0.004 0.024
#> GSM564657 3 0.344 0.76878 0.024 0.104 0.848 0.000 0.024
#> GSM564658 2 0.482 0.65399 0.032 0.772 0.072 0.004 0.120
#> GSM564659 3 0.629 0.51013 0.004 0.200 0.592 0.008 0.196
#> GSM564660 5 0.711 0.30787 0.012 0.256 0.140 0.044 0.548
#> GSM564661 2 0.519 0.64561 0.032 0.732 0.088 0.000 0.148
#> GSM564662 3 0.112 0.77579 0.000 0.016 0.964 0.000 0.020
#> GSM564663 2 0.475 0.65763 0.032 0.772 0.092 0.000 0.104
#> GSM564664 2 0.464 0.63146 0.068 0.788 0.084 0.000 0.060
#> GSM564665 3 0.512 0.66868 0.000 0.172 0.712 0.008 0.108
#> GSM564666 5 0.648 0.58591 0.000 0.108 0.100 0.152 0.640
#> GSM564667 3 0.292 0.77862 0.008 0.072 0.880 0.000 0.040
#> GSM564668 3 0.374 0.75966 0.000 0.096 0.828 0.008 0.068
#> GSM564669 3 0.278 0.77674 0.000 0.048 0.880 0.000 0.072
#> GSM564670 3 0.725 -0.16251 0.020 0.376 0.384 0.004 0.216
#> GSM564671 5 0.600 0.44566 0.000 0.056 0.032 0.356 0.556
#> GSM564672 3 0.125 0.77837 0.000 0.036 0.956 0.000 0.008
#> GSM564673 2 0.718 0.46683 0.032 0.540 0.236 0.016 0.176
#> GSM564674 2 0.555 0.58580 0.012 0.672 0.076 0.008 0.232
#> GSM564675 5 0.646 0.41775 0.004 0.300 0.040 0.084 0.572
#> GSM564676 2 0.277 0.65439 0.024 0.896 0.040 0.000 0.040
#> GSM564677 2 0.603 0.56641 0.068 0.648 0.048 0.004 0.232
#> GSM564678 2 0.223 0.64813 0.032 0.920 0.036 0.000 0.012
#> GSM564679 2 0.252 0.65058 0.024 0.908 0.028 0.000 0.040
#> GSM564680 3 0.238 0.78095 0.000 0.048 0.904 0.000 0.048
#> GSM564682 3 0.695 0.40492 0.060 0.304 0.532 0.004 0.100
#> GSM564683 3 0.106 0.77472 0.004 0.008 0.968 0.000 0.020
#> GSM564684 5 0.531 0.59808 0.000 0.064 0.012 0.264 0.660
#> GSM564685 3 0.152 0.77771 0.004 0.012 0.952 0.004 0.028
#> GSM564686 5 0.549 0.57021 0.000 0.068 0.012 0.288 0.632
#> GSM564687 2 0.597 0.59634 0.016 0.680 0.132 0.020 0.152
#> GSM564688 2 0.659 0.51298 0.020 0.556 0.192 0.000 0.232
#> GSM564689 2 0.401 0.62335 0.016 0.796 0.020 0.004 0.164
#> GSM564690 2 0.276 0.65043 0.024 0.896 0.032 0.000 0.048
#> GSM564691 3 0.571 0.31506 0.004 0.384 0.536 0.000 0.076
#> GSM564692 2 0.513 0.63364 0.008 0.708 0.100 0.000 0.184
#> GSM564694 5 0.759 0.47278 0.016 0.100 0.192 0.144 0.548
#> GSM564695 5 0.804 0.20982 0.016 0.260 0.232 0.064 0.428
#> GSM564696 3 0.414 0.74865 0.004 0.064 0.804 0.008 0.120
#> GSM564697 2 0.458 0.64550 0.020 0.776 0.056 0.004 0.144
#> GSM564698 3 0.412 0.73814 0.008 0.044 0.796 0.004 0.148
#> GSM564700 5 0.548 0.60258 0.000 0.100 0.008 0.232 0.660
#> GSM564701 2 0.636 0.55226 0.024 0.596 0.224 0.000 0.156
#> GSM564702 5 0.706 0.00439 0.040 0.420 0.048 0.044 0.448
#> GSM564703 1 0.680 0.53544 0.620 0.008 0.112 0.176 0.084
#> GSM564704 1 0.700 0.18958 0.464 0.028 0.008 0.372 0.128
#> GSM564705 1 0.363 0.67215 0.848 0.076 0.000 0.036 0.040
#> GSM564706 1 0.841 0.16154 0.384 0.032 0.144 0.340 0.100
#> GSM564707 1 0.406 0.66823 0.832 0.032 0.008 0.064 0.064
#> GSM564708 4 0.744 0.40072 0.256 0.012 0.092 0.532 0.108
#> GSM564709 1 0.717 0.24090 0.476 0.056 0.004 0.348 0.116
#> GSM564710 1 0.481 0.67281 0.780 0.036 0.012 0.120 0.052
#> GSM564711 4 0.753 0.39835 0.232 0.012 0.112 0.536 0.108
#> GSM564712 1 0.344 0.67881 0.860 0.036 0.000 0.064 0.040
#> GSM564713 4 0.680 0.47726 0.220 0.000 0.088 0.588 0.104
#> GSM564714 1 0.871 0.05929 0.300 0.012 0.236 0.300 0.152
#> GSM564715 1 0.468 0.67596 0.788 0.024 0.012 0.108 0.068
#> GSM564716 4 0.573 0.38794 0.332 0.012 0.000 0.584 0.072
#> GSM564717 1 0.487 0.66710 0.760 0.064 0.000 0.136 0.040
#> GSM564718 4 0.644 0.53024 0.192 0.004 0.060 0.636 0.108
#> GSM564719 1 0.509 0.62204 0.724 0.188 0.000 0.056 0.032
#> GSM564720 1 0.394 0.67839 0.832 0.052 0.000 0.072 0.044
#> GSM564721 1 0.517 0.64570 0.732 0.048 0.000 0.164 0.056
#> GSM564722 4 0.765 0.24946 0.296 0.028 0.024 0.452 0.200
#> GSM564723 1 0.386 0.67909 0.836 0.060 0.000 0.068 0.036
#> GSM564724 4 0.697 0.48246 0.132 0.008 0.160 0.608 0.092
#> GSM564725 4 0.544 0.55752 0.248 0.008 0.000 0.656 0.088
#> GSM564726 4 0.311 0.66847 0.024 0.000 0.000 0.844 0.132
#> GSM564727 4 0.444 0.64878 0.140 0.000 0.000 0.760 0.100
#> GSM564728 4 0.271 0.65732 0.008 0.000 0.000 0.860 0.132
#> GSM564729 4 0.398 0.66329 0.060 0.004 0.000 0.800 0.136
#> GSM564730 1 0.539 0.47479 0.636 0.008 0.000 0.288 0.068
#> GSM564731 1 0.677 0.12116 0.452 0.012 0.028 0.420 0.088
#> GSM564732 4 0.567 0.53020 0.264 0.008 0.000 0.628 0.100
#> GSM564733 4 0.770 0.39015 0.228 0.020 0.148 0.524 0.080
#> GSM564734 1 0.577 0.42951 0.600 0.024 0.000 0.316 0.060
#> GSM564735 4 0.598 0.61589 0.092 0.004 0.092 0.696 0.116
#> GSM564736 4 0.555 0.61639 0.080 0.008 0.088 0.736 0.088
#> GSM564737 1 0.324 0.67445 0.868 0.020 0.000 0.068 0.044
#> GSM564738 4 0.720 0.49885 0.104 0.004 0.156 0.576 0.160
#> GSM564739 1 0.634 0.60812 0.680 0.020 0.080 0.140 0.080
#> GSM564740 4 0.437 0.59984 0.032 0.004 0.000 0.728 0.236
#> GSM564741 4 0.674 0.54921 0.108 0.008 0.116 0.636 0.132
#> GSM564742 1 0.861 0.24500 0.376 0.020 0.276 0.200 0.128
#> GSM564743 1 0.464 0.66669 0.788 0.056 0.000 0.084 0.072
#> GSM564744 1 0.333 0.67184 0.868 0.044 0.000 0.044 0.044
#> GSM564745 1 0.585 0.42087 0.584 0.016 0.000 0.324 0.076
#> GSM564746 1 0.434 0.66556 0.784 0.012 0.000 0.136 0.068
#> GSM564747 1 0.509 0.63598 0.728 0.016 0.008 0.188 0.060
#> GSM564748 1 0.793 0.35422 0.472 0.012 0.180 0.248 0.088
#> GSM564749 1 0.342 0.66629 0.852 0.096 0.000 0.028 0.024
#> GSM564750 4 0.440 0.66630 0.084 0.004 0.012 0.792 0.108
#> GSM564751 1 0.691 0.54862 0.616 0.016 0.092 0.188 0.088
#> GSM564752 4 0.292 0.67247 0.016 0.000 0.000 0.852 0.132
#> GSM564753 1 0.822 0.22116 0.400 0.020 0.296 0.212 0.072
#> GSM564754 1 0.406 0.64825 0.764 0.000 0.000 0.196 0.040
#> GSM564755 4 0.293 0.64018 0.004 0.000 0.000 0.832 0.164
#> GSM564756 1 0.756 0.44063 0.512 0.100 0.008 0.260 0.120
#> GSM564757 4 0.349 0.66510 0.036 0.008 0.000 0.836 0.120
#> GSM564758 4 0.504 0.65919 0.076 0.008 0.016 0.744 0.156
#> GSM564759 1 0.850 0.14141 0.360 0.016 0.200 0.308 0.116
#> GSM564760 4 0.469 0.65068 0.160 0.008 0.008 0.760 0.064
#> GSM564761 1 0.402 0.67780 0.828 0.060 0.000 0.064 0.048
#> GSM564762 4 0.653 0.27639 0.356 0.016 0.020 0.528 0.080
#> GSM564681 5 0.517 0.11832 0.004 0.416 0.008 0.020 0.552
#> GSM564693 5 0.682 0.13105 0.000 0.412 0.072 0.068 0.448
#> GSM564646 5 0.544 0.60339 0.000 0.080 0.012 0.248 0.660
#> GSM564699 5 0.676 0.58538 0.000 0.072 0.124 0.212 0.592
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.327 0.53562 0.036 0.024 0.000 0.840 0.000 0.100
#> GSM564616 5 0.711 0.23287 0.024 0.080 0.056 0.024 0.428 0.388
#> GSM564617 5 0.588 0.30503 0.012 0.068 0.020 0.004 0.484 0.412
#> GSM564618 6 0.572 0.35396 0.000 0.056 0.036 0.040 0.220 0.648
#> GSM564619 1 0.557 0.47899 0.640 0.140 0.000 0.180 0.000 0.040
#> GSM564620 4 0.738 0.02580 0.348 0.132 0.000 0.404 0.036 0.080
#> GSM564621 4 0.668 0.37750 0.248 0.100 0.008 0.556 0.016 0.072
#> GSM564622 6 0.791 -0.18171 0.012 0.128 0.132 0.028 0.332 0.368
#> GSM564623 6 0.484 0.54406 0.000 0.036 0.004 0.196 0.056 0.708
#> GSM564624 6 0.629 -0.15545 0.004 0.068 0.048 0.012 0.412 0.456
#> GSM564625 4 0.567 0.43964 0.220 0.116 0.000 0.620 0.000 0.044
#> GSM564626 1 0.581 0.45023 0.600 0.124 0.000 0.244 0.012 0.020
#> GSM564627 4 0.685 0.35376 0.232 0.144 0.004 0.520 0.004 0.096
#> GSM564628 6 0.633 0.13122 0.008 0.076 0.016 0.044 0.316 0.540
#> GSM564629 1 0.692 0.24305 0.424 0.216 0.004 0.300 0.000 0.056
#> GSM564630 5 0.614 0.44180 0.016 0.076 0.040 0.004 0.560 0.304
#> GSM564609 3 0.879 0.24790 0.060 0.152 0.396 0.060 0.212 0.120
#> GSM564610 1 0.597 0.50311 0.664 0.152 0.000 0.072 0.056 0.056
#> GSM564611 1 0.613 0.39831 0.600 0.132 0.008 0.028 0.220 0.012
#> GSM564612 3 0.556 0.50322 0.004 0.052 0.588 0.000 0.308 0.048
#> GSM564613 6 0.811 0.03922 0.012 0.148 0.272 0.016 0.216 0.336
#> GSM564614 4 0.435 0.54361 0.048 0.096 0.000 0.772 0.000 0.084
#> GSM564631 3 0.367 0.73808 0.000 0.076 0.820 0.000 0.072 0.032
#> GSM564632 6 0.836 0.22824 0.020 0.136 0.268 0.044 0.156 0.376
#> GSM564633 3 0.527 0.71638 0.008 0.120 0.720 0.008 0.060 0.084
#> GSM564634 5 0.835 0.25540 0.012 0.152 0.208 0.040 0.368 0.220
#> GSM564635 3 0.387 0.73782 0.000 0.076 0.808 0.000 0.076 0.040
#> GSM564636 3 0.711 0.35772 0.008 0.088 0.476 0.008 0.136 0.284
#> GSM564637 6 0.764 -0.00695 0.004 0.088 0.180 0.024 0.336 0.368
#> GSM564638 3 0.494 0.70043 0.004 0.116 0.744 0.024 0.024 0.088
#> GSM564639 3 0.187 0.72708 0.000 0.032 0.928 0.000 0.024 0.016
#> GSM564640 5 0.435 0.60508 0.016 0.032 0.040 0.000 0.772 0.140
#> GSM564641 3 0.732 0.52603 0.032 0.192 0.520 0.004 0.132 0.120
#> GSM564642 5 0.770 0.46046 0.076 0.124 0.208 0.000 0.480 0.112
#> GSM564643 6 0.692 0.50666 0.000 0.036 0.124 0.184 0.096 0.560
#> GSM564644 5 0.552 0.61052 0.044 0.064 0.088 0.000 0.712 0.092
#> GSM564645 3 0.203 0.73033 0.000 0.032 0.920 0.000 0.016 0.032
#> GSM564647 3 0.689 0.30127 0.000 0.100 0.464 0.000 0.276 0.160
#> GSM564648 5 0.719 0.40347 0.012 0.104 0.140 0.004 0.480 0.260
#> GSM564649 3 0.424 0.72917 0.000 0.076 0.780 0.000 0.096 0.048
#> GSM564650 5 0.660 0.14708 0.000 0.056 0.056 0.044 0.468 0.376
#> GSM564651 5 0.661 0.36904 0.008 0.048 0.308 0.000 0.484 0.152
#> GSM564652 5 0.849 0.40663 0.136 0.132 0.116 0.012 0.416 0.188
#> GSM564653 5 0.493 0.60621 0.012 0.064 0.056 0.000 0.736 0.132
#> GSM564654 3 0.253 0.73662 0.000 0.028 0.900 0.008 0.036 0.028
#> GSM564655 3 0.786 0.27486 0.012 0.140 0.436 0.028 0.136 0.248
#> GSM564656 3 0.312 0.73166 0.000 0.096 0.848 0.000 0.040 0.016
#> GSM564657 3 0.444 0.72810 0.016 0.068 0.780 0.004 0.108 0.024
#> GSM564658 5 0.626 0.57209 0.044 0.112 0.060 0.000 0.636 0.148
#> GSM564659 3 0.633 0.57225 0.000 0.068 0.596 0.012 0.156 0.168
#> GSM564660 6 0.728 0.37284 0.008 0.072 0.136 0.060 0.176 0.548
#> GSM564661 5 0.577 0.59854 0.056 0.060 0.068 0.008 0.704 0.104
#> GSM564662 3 0.186 0.73058 0.000 0.028 0.928 0.000 0.032 0.012
#> GSM564663 5 0.601 0.59145 0.028 0.072 0.104 0.000 0.656 0.140
#> GSM564664 5 0.551 0.59087 0.064 0.072 0.088 0.000 0.716 0.060
#> GSM564665 3 0.617 0.58187 0.000 0.100 0.616 0.008 0.172 0.104
#> GSM564666 6 0.623 0.53848 0.004 0.084 0.072 0.160 0.036 0.644
#> GSM564667 3 0.449 0.73623 0.004 0.080 0.780 0.008 0.080 0.048
#> GSM564668 3 0.555 0.67914 0.004 0.044 0.696 0.020 0.120 0.116
#> GSM564669 3 0.338 0.74028 0.000 0.048 0.848 0.004 0.044 0.056
#> GSM564670 5 0.835 0.19871 0.036 0.160 0.248 0.008 0.328 0.220
#> GSM564671 6 0.615 0.35885 0.008 0.068 0.016 0.348 0.032 0.528
#> GSM564672 3 0.235 0.73440 0.000 0.036 0.904 0.000 0.028 0.032
#> GSM564673 5 0.750 0.41486 0.020 0.104 0.156 0.008 0.464 0.248
#> GSM564674 5 0.694 0.43790 0.028 0.080 0.116 0.000 0.516 0.260
#> GSM564675 6 0.652 0.41990 0.008 0.088 0.060 0.056 0.160 0.628
#> GSM564676 5 0.387 0.60482 0.020 0.036 0.048 0.000 0.824 0.072
#> GSM564677 5 0.720 0.44268 0.044 0.104 0.048 0.024 0.532 0.248
#> GSM564678 5 0.364 0.59996 0.020 0.032 0.044 0.004 0.844 0.056
#> GSM564679 5 0.357 0.60642 0.016 0.056 0.024 0.000 0.840 0.064
#> GSM564680 3 0.320 0.74037 0.000 0.044 0.852 0.000 0.072 0.032
#> GSM564682 3 0.780 0.22661 0.048 0.164 0.400 0.000 0.288 0.100
#> GSM564683 3 0.174 0.72162 0.000 0.052 0.928 0.000 0.016 0.004
#> GSM564684 6 0.498 0.51206 0.000 0.036 0.004 0.284 0.032 0.644
#> GSM564685 3 0.306 0.72936 0.004 0.072 0.864 0.000 0.032 0.028
#> GSM564686 6 0.472 0.50733 0.000 0.044 0.000 0.276 0.020 0.660
#> GSM564687 5 0.689 0.54757 0.032 0.092 0.084 0.020 0.592 0.180
#> GSM564688 5 0.686 0.47476 0.016 0.064 0.220 0.004 0.532 0.164
#> GSM564689 5 0.464 0.56891 0.008 0.048 0.028 0.004 0.740 0.172
#> GSM564690 5 0.379 0.59994 0.020 0.052 0.024 0.004 0.832 0.068
#> GSM564691 3 0.614 0.13392 0.004 0.084 0.448 0.000 0.416 0.048
#> GSM564692 5 0.650 0.51416 0.008 0.084 0.108 0.004 0.572 0.224
#> GSM564694 6 0.773 0.47014 0.004 0.124 0.104 0.156 0.104 0.508
#> GSM564695 6 0.865 0.22990 0.008 0.144 0.156 0.112 0.212 0.368
#> GSM564696 3 0.588 0.63111 0.000 0.160 0.640 0.016 0.040 0.144
#> GSM564697 5 0.509 0.58410 0.012 0.064 0.036 0.000 0.700 0.188
#> GSM564698 3 0.641 0.63488 0.016 0.104 0.640 0.024 0.076 0.140
#> GSM564700 6 0.494 0.54191 0.000 0.032 0.004 0.240 0.048 0.676
#> GSM564701 5 0.709 0.50271 0.020 0.112 0.180 0.004 0.536 0.148
#> GSM564702 6 0.806 0.06464 0.076 0.088 0.036 0.068 0.332 0.400
#> GSM564703 1 0.740 -0.34965 0.416 0.316 0.128 0.124 0.012 0.004
#> GSM564704 1 0.773 0.06439 0.384 0.248 0.004 0.264 0.044 0.056
#> GSM564705 1 0.435 0.51010 0.784 0.116 0.004 0.024 0.056 0.016
#> GSM564706 2 0.801 0.46657 0.304 0.376 0.140 0.140 0.024 0.016
#> GSM564707 1 0.555 0.45240 0.684 0.188 0.016 0.060 0.036 0.016
#> GSM564708 4 0.749 0.05929 0.160 0.316 0.108 0.396 0.000 0.020
#> GSM564709 1 0.801 0.11597 0.404 0.160 0.004 0.276 0.076 0.080
#> GSM564710 1 0.517 0.51531 0.724 0.136 0.004 0.080 0.036 0.020
#> GSM564711 4 0.759 0.03724 0.196 0.300 0.112 0.376 0.000 0.016
#> GSM564712 1 0.452 0.51537 0.748 0.172 0.004 0.040 0.028 0.008
#> GSM564713 4 0.764 -0.12814 0.156 0.344 0.080 0.380 0.004 0.036
#> GSM564714 2 0.849 0.56848 0.204 0.384 0.172 0.168 0.016 0.056
#> GSM564715 1 0.568 0.49587 0.680 0.164 0.016 0.092 0.036 0.012
#> GSM564716 4 0.639 0.32173 0.252 0.200 0.000 0.512 0.004 0.032
#> GSM564717 1 0.676 0.38849 0.552 0.204 0.004 0.164 0.056 0.020
#> GSM564718 4 0.671 0.29082 0.128 0.292 0.048 0.508 0.000 0.024
#> GSM564719 1 0.609 0.34329 0.592 0.132 0.000 0.032 0.228 0.016
#> GSM564720 1 0.448 0.51797 0.760 0.144 0.004 0.032 0.056 0.004
#> GSM564721 1 0.637 0.43702 0.588 0.148 0.000 0.196 0.044 0.024
#> GSM564722 4 0.818 -0.02477 0.236 0.260 0.020 0.368 0.048 0.068
#> GSM564723 1 0.519 0.52604 0.724 0.128 0.000 0.068 0.056 0.024
#> GSM564724 4 0.732 0.09263 0.144 0.280 0.116 0.444 0.000 0.016
#> GSM564725 4 0.679 0.36895 0.260 0.152 0.008 0.516 0.008 0.056
#> GSM564726 4 0.373 0.52455 0.012 0.148 0.004 0.796 0.000 0.040
#> GSM564727 4 0.433 0.52786 0.136 0.076 0.000 0.760 0.000 0.028
#> GSM564728 4 0.266 0.53255 0.012 0.028 0.000 0.876 0.000 0.084
#> GSM564729 4 0.403 0.53925 0.048 0.064 0.000 0.796 0.000 0.092
#> GSM564730 1 0.558 0.42477 0.616 0.108 0.004 0.252 0.008 0.012
#> GSM564731 1 0.712 -0.05589 0.368 0.292 0.020 0.292 0.004 0.024
#> GSM564732 4 0.515 0.50325 0.176 0.112 0.000 0.680 0.000 0.032
#> GSM564733 4 0.772 -0.09995 0.196 0.228 0.136 0.420 0.008 0.012
#> GSM564734 1 0.624 0.27382 0.484 0.148 0.000 0.340 0.020 0.008
#> GSM564735 4 0.712 0.27404 0.100 0.252 0.076 0.516 0.000 0.056
#> GSM564736 4 0.676 0.24446 0.104 0.268 0.076 0.528 0.000 0.024
#> GSM564737 1 0.444 0.51728 0.764 0.144 0.004 0.060 0.016 0.012
#> GSM564738 4 0.768 0.01593 0.036 0.292 0.164 0.416 0.004 0.088
#> GSM564739 1 0.715 0.17811 0.540 0.228 0.100 0.088 0.020 0.024
#> GSM564740 4 0.611 0.41003 0.048 0.124 0.008 0.588 0.000 0.232
#> GSM564741 4 0.736 0.07605 0.144 0.232 0.116 0.484 0.004 0.020
#> GSM564742 2 0.787 0.56037 0.296 0.384 0.180 0.108 0.016 0.016
#> GSM564743 1 0.561 0.50262 0.688 0.120 0.000 0.116 0.024 0.052
#> GSM564744 1 0.384 0.52787 0.812 0.112 0.004 0.040 0.028 0.004
#> GSM564745 4 0.631 0.01756 0.408 0.148 0.000 0.416 0.008 0.020
#> GSM564746 1 0.511 0.49899 0.692 0.148 0.000 0.124 0.000 0.036
#> GSM564747 1 0.592 0.32967 0.604 0.212 0.000 0.148 0.016 0.020
#> GSM564748 1 0.768 -0.55572 0.344 0.328 0.168 0.148 0.004 0.008
#> GSM564749 1 0.421 0.51368 0.780 0.096 0.000 0.016 0.100 0.008
#> GSM564750 4 0.612 0.45512 0.080 0.188 0.028 0.632 0.000 0.072
#> GSM564751 1 0.688 -0.18893 0.444 0.348 0.096 0.104 0.004 0.004
#> GSM564752 4 0.385 0.52330 0.016 0.152 0.000 0.784 0.000 0.048
#> GSM564753 2 0.778 0.53457 0.304 0.320 0.232 0.132 0.004 0.008
#> GSM564754 1 0.522 0.46438 0.680 0.180 0.004 0.116 0.008 0.012
#> GSM564755 4 0.342 0.52243 0.012 0.044 0.000 0.820 0.000 0.124
#> GSM564756 1 0.823 0.12615 0.408 0.192 0.020 0.224 0.116 0.040
#> GSM564757 4 0.295 0.54237 0.040 0.028 0.000 0.868 0.000 0.064
#> GSM564758 4 0.632 0.49886 0.088 0.120 0.024 0.648 0.012 0.108
#> GSM564759 2 0.831 0.48617 0.232 0.368 0.112 0.232 0.036 0.020
#> GSM564760 4 0.519 0.47586 0.144 0.152 0.000 0.676 0.000 0.028
#> GSM564761 1 0.447 0.52793 0.780 0.100 0.000 0.044 0.056 0.020
#> GSM564762 4 0.755 -0.03631 0.300 0.268 0.024 0.356 0.012 0.040
#> GSM564681 6 0.556 0.33271 0.012 0.044 0.008 0.040 0.260 0.636
#> GSM564693 6 0.757 0.20321 0.000 0.052 0.124 0.088 0.324 0.412
#> GSM564646 6 0.516 0.53911 0.004 0.024 0.004 0.260 0.056 0.652
#> GSM564699 6 0.625 0.53960 0.000 0.060 0.056 0.196 0.064 0.624
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 genotype/variation(p) disease.state(p) k
#> CV:skmeans 154 0.925 0.47591 2
#> CV:skmeans 143 0.082 0.30933 3
#> CV:skmeans 112 0.204 0.61201 4
#> CV:skmeans 97 0.546 0.00375 5
#> CV:skmeans 72 0.531 0.08727 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 154 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 0.071 0.558 0.780 0.4885 0.500 0.500
#> 3 3 0.231 0.565 0.753 0.3212 0.718 0.493
#> 4 4 0.300 0.423 0.671 0.1186 0.906 0.730
#> 5 5 0.375 0.375 0.608 0.0695 0.925 0.742
#> 6 6 0.453 0.411 0.626 0.0404 0.888 0.582
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM564615 1 0.9815 0.2564 0.580 0.420
#> GSM564616 1 0.9896 0.2836 0.560 0.440
#> GSM564617 2 0.1414 0.7101 0.020 0.980
#> GSM564618 2 0.9522 0.2891 0.372 0.628
#> GSM564619 2 0.9286 0.4525 0.344 0.656
#> GSM564620 1 0.8443 0.6023 0.728 0.272
#> GSM564621 1 0.6801 0.6498 0.820 0.180
#> GSM564622 1 0.8207 0.5980 0.744 0.256
#> GSM564623 1 0.7453 0.6927 0.788 0.212
#> GSM564624 2 0.2423 0.7159 0.040 0.960
#> GSM564625 1 0.9358 0.3922 0.648 0.352
#> GSM564626 2 0.7056 0.6311 0.192 0.808
#> GSM564627 2 0.9044 0.4688 0.320 0.680
#> GSM564628 2 0.8207 0.5989 0.256 0.744
#> GSM564629 2 0.9286 0.4592 0.344 0.656
#> GSM564630 2 0.1633 0.7126 0.024 0.976
#> GSM564609 1 0.9850 0.3063 0.572 0.428
#> GSM564610 2 0.7139 0.6736 0.196 0.804
#> GSM564611 2 0.3733 0.6857 0.072 0.928
#> GSM564612 1 0.9977 0.2512 0.528 0.472
#> GSM564613 2 0.9323 0.4436 0.348 0.652
#> GSM564614 1 0.4690 0.6919 0.900 0.100
#> GSM564631 1 0.6973 0.6498 0.812 0.188
#> GSM564632 1 0.9129 0.6002 0.672 0.328
#> GSM564633 1 0.7299 0.6618 0.796 0.204
#> GSM564634 2 0.8955 0.5056 0.312 0.688
#> GSM564635 1 0.7883 0.5968 0.764 0.236
#> GSM564636 1 0.8207 0.6712 0.744 0.256
#> GSM564637 2 0.9909 0.2440 0.444 0.556
#> GSM564638 1 0.5946 0.6911 0.856 0.144
#> GSM564639 1 0.4022 0.7000 0.920 0.080
#> GSM564640 2 0.2948 0.7167 0.052 0.948
#> GSM564641 1 0.9795 0.2423 0.584 0.416
#> GSM564642 2 0.8207 0.5890 0.256 0.744
#> GSM564643 1 0.8144 0.5958 0.748 0.252
#> GSM564644 2 0.1184 0.7095 0.016 0.984
#> GSM564645 1 0.7602 0.6136 0.780 0.220
#> GSM564647 2 0.9954 0.1553 0.460 0.540
#> GSM564648 2 0.9522 0.3886 0.372 0.628
#> GSM564649 1 0.9954 0.1763 0.540 0.460
#> GSM564650 2 0.5737 0.7094 0.136 0.864
#> GSM564651 2 0.9044 0.4305 0.320 0.680
#> GSM564652 2 0.8909 0.4893 0.308 0.692
#> GSM564653 2 0.2603 0.7128 0.044 0.956
#> GSM564654 1 0.4690 0.7078 0.900 0.100
#> GSM564655 1 0.4815 0.6995 0.896 0.104
#> GSM564656 1 0.3733 0.7031 0.928 0.072
#> GSM564657 1 0.8144 0.6238 0.748 0.252
#> GSM564658 2 0.2043 0.7140 0.032 0.968
#> GSM564659 1 0.9129 0.5316 0.672 0.328
#> GSM564660 2 0.9850 0.2664 0.428 0.572
#> GSM564661 2 0.3733 0.7129 0.072 0.928
#> GSM564662 1 0.7815 0.6007 0.768 0.232
#> GSM564663 2 0.7219 0.6502 0.200 0.800
#> GSM564664 2 0.1843 0.7116 0.028 0.972
#> GSM564665 1 0.8909 0.5380 0.692 0.308
#> GSM564666 1 0.8499 0.6369 0.724 0.276
#> GSM564667 1 0.7883 0.6177 0.764 0.236
#> GSM564668 1 0.8144 0.6352 0.748 0.252
#> GSM564669 1 0.4161 0.7025 0.916 0.084
#> GSM564670 1 0.9963 0.2722 0.536 0.464
#> GSM564671 1 0.7815 0.6472 0.768 0.232
#> GSM564672 1 0.4161 0.7004 0.916 0.084
#> GSM564673 2 0.7376 0.6378 0.208 0.792
#> GSM564674 2 0.9393 0.4257 0.356 0.644
#> GSM564675 2 0.9833 0.1100 0.424 0.576
#> GSM564676 2 0.1184 0.7100 0.016 0.984
#> GSM564677 2 0.8386 0.5464 0.268 0.732
#> GSM564678 2 0.1184 0.7092 0.016 0.984
#> GSM564679 2 0.0938 0.7075 0.012 0.988
#> GSM564680 1 0.7219 0.6426 0.800 0.200
#> GSM564682 2 0.9775 0.2839 0.412 0.588
#> GSM564683 1 0.4431 0.6999 0.908 0.092
#> GSM564684 2 0.8763 0.4503 0.296 0.704
#> GSM564685 1 0.7950 0.5970 0.760 0.240
#> GSM564686 1 0.7299 0.6717 0.796 0.204
#> GSM564687 2 0.1414 0.7098 0.020 0.980
#> GSM564688 2 0.5629 0.6884 0.132 0.868
#> GSM564689 2 0.1633 0.7106 0.024 0.976
#> GSM564690 2 0.1843 0.7111 0.028 0.972
#> GSM564691 2 0.5294 0.7049 0.120 0.880
#> GSM564692 2 0.9000 0.4976 0.316 0.684
#> GSM564694 1 0.8813 0.6362 0.700 0.300
#> GSM564695 2 0.9988 -0.1126 0.480 0.520
#> GSM564696 1 0.3584 0.7025 0.932 0.068
#> GSM564697 2 0.2778 0.7142 0.048 0.952
#> GSM564698 1 0.7376 0.6871 0.792 0.208
#> GSM564700 1 0.7299 0.6469 0.796 0.204
#> GSM564701 2 0.9608 0.3724 0.384 0.616
#> GSM564702 2 0.9209 0.3211 0.336 0.664
#> GSM564703 1 0.7139 0.6953 0.804 0.196
#> GSM564704 1 0.9866 0.1245 0.568 0.432
#> GSM564705 2 0.4939 0.7102 0.108 0.892
#> GSM564706 1 0.6801 0.7087 0.820 0.180
#> GSM564707 1 0.8555 0.5126 0.720 0.280
#> GSM564708 1 0.2603 0.7010 0.956 0.044
#> GSM564709 2 0.9866 0.2225 0.432 0.568
#> GSM564710 2 0.7453 0.6193 0.212 0.788
#> GSM564711 1 0.3431 0.7070 0.936 0.064
#> GSM564712 2 0.9686 0.3772 0.396 0.604
#> GSM564713 1 0.0938 0.6938 0.988 0.012
#> GSM564714 1 0.0672 0.6931 0.992 0.008
#> GSM564715 2 0.9795 0.3499 0.416 0.584
#> GSM564716 1 0.9881 0.1608 0.564 0.436
#> GSM564717 2 0.2948 0.7114 0.052 0.948
#> GSM564718 1 0.6531 0.6668 0.832 0.168
#> GSM564719 2 0.3584 0.6844 0.068 0.932
#> GSM564720 2 0.3431 0.7081 0.064 0.936
#> GSM564721 2 0.5737 0.7001 0.136 0.864
#> GSM564722 2 0.9552 0.4131 0.376 0.624
#> GSM564723 2 0.4022 0.7131 0.080 0.920
#> GSM564724 1 0.0376 0.6919 0.996 0.004
#> GSM564725 1 0.5737 0.7065 0.864 0.136
#> GSM564726 1 0.7602 0.6041 0.780 0.220
#> GSM564727 1 0.9710 0.3062 0.600 0.400
#> GSM564728 1 0.8763 0.4834 0.704 0.296
#> GSM564729 1 0.8081 0.6042 0.752 0.248
#> GSM564730 2 0.4022 0.7116 0.080 0.920
#> GSM564731 1 0.9460 0.3650 0.636 0.364
#> GSM564732 1 0.9977 -0.0241 0.528 0.472
#> GSM564733 1 0.6973 0.6664 0.812 0.188
#> GSM564734 2 0.8144 0.5466 0.252 0.748
#> GSM564735 1 0.1184 0.6966 0.984 0.016
#> GSM564736 1 0.3431 0.7035 0.936 0.064
#> GSM564737 2 0.9286 0.5385 0.344 0.656
#> GSM564738 1 0.2236 0.7007 0.964 0.036
#> GSM564739 1 0.4562 0.7147 0.904 0.096
#> GSM564740 1 0.7453 0.6385 0.788 0.212
#> GSM564741 1 0.0376 0.6919 0.996 0.004
#> GSM564742 1 0.5946 0.6847 0.856 0.144
#> GSM564743 2 0.7139 0.6732 0.196 0.804
#> GSM564744 2 0.4562 0.7127 0.096 0.904
#> GSM564745 2 0.8955 0.4633 0.312 0.688
#> GSM564746 2 0.7745 0.6263 0.228 0.772
#> GSM564747 1 0.5519 0.6794 0.872 0.128
#> GSM564748 1 0.4298 0.6978 0.912 0.088
#> GSM564749 2 0.3584 0.6844 0.068 0.932
#> GSM564750 1 0.4690 0.7126 0.900 0.100
#> GSM564751 1 0.5737 0.6884 0.864 0.136
#> GSM564752 1 0.8861 0.4757 0.696 0.304
#> GSM564753 1 0.0938 0.6950 0.988 0.012
#> GSM564754 1 0.9608 0.2966 0.616 0.384
#> GSM564755 1 0.9129 0.4661 0.672 0.328
#> GSM564756 2 0.8861 0.5793 0.304 0.696
#> GSM564757 1 0.8207 0.5825 0.744 0.256
#> GSM564758 1 0.9248 0.4207 0.660 0.340
#> GSM564759 1 0.8207 0.6031 0.744 0.256
#> GSM564760 1 0.8443 0.5738 0.728 0.272
#> GSM564761 2 0.4939 0.7064 0.108 0.892
#> GSM564762 1 0.9580 0.3312 0.620 0.380
#> GSM564681 2 0.6148 0.6552 0.152 0.848
#> GSM564693 2 0.9608 0.1871 0.384 0.616
#> GSM564646 2 0.9866 0.0664 0.432 0.568
#> GSM564699 1 0.9833 0.2493 0.576 0.424
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.2902 0.6814 0.920 0.064 0.016
#> GSM564616 3 0.5763 0.5394 0.008 0.276 0.716
#> GSM564617 2 0.2446 0.7672 0.052 0.936 0.012
#> GSM564618 2 0.8472 0.3084 0.100 0.540 0.360
#> GSM564619 1 0.9329 0.2021 0.436 0.400 0.164
#> GSM564620 1 0.9314 0.4982 0.492 0.180 0.328
#> GSM564621 1 0.5053 0.6913 0.812 0.024 0.164
#> GSM564622 3 0.1015 0.7014 0.008 0.012 0.980
#> GSM564623 3 0.6625 0.6220 0.196 0.068 0.736
#> GSM564624 2 0.1999 0.7694 0.036 0.952 0.012
#> GSM564625 1 0.7272 0.6891 0.700 0.096 0.204
#> GSM564626 2 0.6200 0.4870 0.312 0.676 0.012
#> GSM564627 1 0.3573 0.6836 0.876 0.120 0.004
#> GSM564628 2 0.8199 0.1114 0.072 0.488 0.440
#> GSM564629 1 0.7056 0.5609 0.656 0.300 0.044
#> GSM564630 2 0.2116 0.7700 0.040 0.948 0.012
#> GSM564609 3 0.6724 0.1286 0.012 0.420 0.568
#> GSM564610 2 0.5965 0.7304 0.100 0.792 0.108
#> GSM564611 2 0.0747 0.7645 0.016 0.984 0.000
#> GSM564612 3 0.6067 0.5946 0.028 0.236 0.736
#> GSM564613 2 0.8691 0.3221 0.116 0.528 0.356
#> GSM564614 1 0.4605 0.6751 0.796 0.000 0.204
#> GSM564631 3 0.0000 0.6940 0.000 0.000 1.000
#> GSM564632 3 0.8072 0.5591 0.208 0.144 0.648
#> GSM564633 3 0.2527 0.7053 0.020 0.044 0.936
#> GSM564634 2 0.7190 0.4560 0.036 0.608 0.356
#> GSM564635 3 0.0475 0.6965 0.004 0.004 0.992
#> GSM564636 3 0.4232 0.6918 0.044 0.084 0.872
#> GSM564637 2 0.9424 0.1820 0.188 0.472 0.340
#> GSM564638 3 0.2414 0.7023 0.040 0.020 0.940
#> GSM564639 3 0.1163 0.6974 0.028 0.000 0.972
#> GSM564640 2 0.1315 0.7715 0.008 0.972 0.020
#> GSM564641 3 0.4700 0.6251 0.008 0.180 0.812
#> GSM564642 2 0.6282 0.5380 0.012 0.664 0.324
#> GSM564643 3 0.2590 0.6920 0.072 0.004 0.924
#> GSM564644 2 0.0237 0.7650 0.004 0.996 0.000
#> GSM564645 3 0.0424 0.6950 0.008 0.000 0.992
#> GSM564647 3 0.5845 0.4534 0.004 0.308 0.688
#> GSM564648 2 0.5988 0.4675 0.000 0.632 0.368
#> GSM564649 3 0.6416 0.4818 0.020 0.304 0.676
#> GSM564650 2 0.5889 0.7287 0.096 0.796 0.108
#> GSM564651 2 0.5873 0.5197 0.004 0.684 0.312
#> GSM564652 3 0.8250 0.2523 0.080 0.392 0.528
#> GSM564653 2 0.0848 0.7675 0.008 0.984 0.008
#> GSM564654 3 0.2703 0.6977 0.056 0.016 0.928
#> GSM564655 3 0.0661 0.6950 0.008 0.004 0.988
#> GSM564656 3 0.2682 0.6600 0.076 0.004 0.920
#> GSM564657 3 0.2846 0.7046 0.020 0.056 0.924
#> GSM564658 2 0.2200 0.7679 0.056 0.940 0.004
#> GSM564659 3 0.5179 0.6714 0.088 0.080 0.832
#> GSM564660 3 0.9377 0.1256 0.172 0.380 0.448
#> GSM564661 2 0.2434 0.7731 0.024 0.940 0.036
#> GSM564662 3 0.0237 0.6948 0.004 0.000 0.996
#> GSM564663 2 0.5585 0.6806 0.024 0.772 0.204
#> GSM564664 2 0.0237 0.7664 0.000 0.996 0.004
#> GSM564665 3 0.3425 0.6836 0.004 0.112 0.884
#> GSM564666 3 0.8468 0.4330 0.308 0.116 0.576
#> GSM564667 3 0.1905 0.7022 0.016 0.028 0.956
#> GSM564668 3 0.3875 0.6950 0.044 0.068 0.888
#> GSM564669 3 0.1015 0.6986 0.012 0.008 0.980
#> GSM564670 3 0.7382 0.5989 0.116 0.184 0.700
#> GSM564671 3 0.8571 0.3176 0.272 0.140 0.588
#> GSM564672 3 0.0237 0.6948 0.004 0.000 0.996
#> GSM564673 2 0.5122 0.6893 0.012 0.788 0.200
#> GSM564674 2 0.6696 0.4894 0.020 0.632 0.348
#> GSM564675 2 0.9353 -0.0493 0.168 0.444 0.388
#> GSM564676 2 0.0237 0.7650 0.004 0.996 0.000
#> GSM564677 2 0.5397 0.5916 0.000 0.720 0.280
#> GSM564678 2 0.0424 0.7657 0.008 0.992 0.000
#> GSM564679 2 0.0237 0.7650 0.004 0.996 0.000
#> GSM564680 3 0.0829 0.6973 0.012 0.004 0.984
#> GSM564682 3 0.7032 0.3184 0.028 0.368 0.604
#> GSM564683 3 0.0000 0.6940 0.000 0.000 1.000
#> GSM564684 3 0.9947 0.1404 0.292 0.328 0.380
#> GSM564685 3 0.0592 0.6989 0.000 0.012 0.988
#> GSM564686 1 0.7044 0.2958 0.620 0.032 0.348
#> GSM564687 2 0.0237 0.7658 0.004 0.996 0.000
#> GSM564688 2 0.5847 0.6886 0.048 0.780 0.172
#> GSM564689 2 0.1163 0.7658 0.028 0.972 0.000
#> GSM564690 2 0.0237 0.7650 0.004 0.996 0.000
#> GSM564691 2 0.5167 0.6878 0.016 0.792 0.192
#> GSM564692 2 0.6908 0.5403 0.036 0.656 0.308
#> GSM564694 3 0.6783 0.6383 0.140 0.116 0.744
#> GSM564695 3 0.6715 0.6085 0.056 0.228 0.716
#> GSM564696 3 0.2261 0.6716 0.068 0.000 0.932
#> GSM564697 2 0.1711 0.7721 0.008 0.960 0.032
#> GSM564698 3 0.4995 0.6733 0.092 0.068 0.840
#> GSM564700 1 0.6107 0.6715 0.764 0.052 0.184
#> GSM564701 3 0.6500 0.0328 0.004 0.464 0.532
#> GSM564702 2 0.8130 0.1792 0.072 0.528 0.400
#> GSM564703 3 0.8318 0.3505 0.284 0.116 0.600
#> GSM564704 1 0.7927 0.6868 0.664 0.160 0.176
#> GSM564705 2 0.2383 0.7651 0.044 0.940 0.016
#> GSM564706 3 0.7713 0.3287 0.284 0.080 0.636
#> GSM564707 1 0.8139 0.6431 0.616 0.108 0.276
#> GSM564708 3 0.5325 0.4764 0.248 0.004 0.748
#> GSM564709 2 0.9850 -0.0871 0.324 0.412 0.264
#> GSM564710 2 0.7531 0.5917 0.092 0.672 0.236
#> GSM564711 3 0.5706 0.2769 0.320 0.000 0.680
#> GSM564712 2 0.8562 0.3814 0.108 0.540 0.352
#> GSM564713 1 0.6275 0.5917 0.644 0.008 0.348
#> GSM564714 1 0.6235 0.5140 0.564 0.000 0.436
#> GSM564715 1 0.8076 0.6595 0.632 0.252 0.116
#> GSM564716 1 0.8972 0.6255 0.564 0.236 0.200
#> GSM564717 2 0.0983 0.7658 0.016 0.980 0.004
#> GSM564718 1 0.5689 0.7003 0.780 0.036 0.184
#> GSM564719 2 0.0424 0.7650 0.008 0.992 0.000
#> GSM564720 2 0.3933 0.7644 0.092 0.880 0.028
#> GSM564721 2 0.4609 0.7326 0.128 0.844 0.028
#> GSM564722 1 0.7741 0.6065 0.608 0.324 0.068
#> GSM564723 2 0.3623 0.7613 0.072 0.896 0.032
#> GSM564724 1 0.6299 0.4569 0.524 0.000 0.476
#> GSM564725 3 0.7542 0.1388 0.432 0.040 0.528
#> GSM564726 1 0.3875 0.6856 0.888 0.068 0.044
#> GSM564727 1 0.4174 0.6879 0.872 0.092 0.036
#> GSM564728 1 0.5174 0.6772 0.832 0.092 0.076
#> GSM564729 1 0.4937 0.6252 0.824 0.028 0.148
#> GSM564730 2 0.5955 0.7196 0.180 0.772 0.048
#> GSM564731 1 0.7651 0.6930 0.680 0.196 0.124
#> GSM564732 1 0.6856 0.7131 0.740 0.128 0.132
#> GSM564733 3 0.8779 -0.2808 0.416 0.112 0.472
#> GSM564734 1 0.6608 0.4070 0.560 0.432 0.008
#> GSM564735 1 0.6529 0.5763 0.620 0.012 0.368
#> GSM564736 1 0.7471 0.3654 0.516 0.036 0.448
#> GSM564737 2 0.7530 0.6098 0.084 0.664 0.252
#> GSM564738 1 0.7080 0.5389 0.564 0.024 0.412
#> GSM564739 3 0.6142 0.4913 0.212 0.040 0.748
#> GSM564740 1 0.2050 0.6696 0.952 0.020 0.028
#> GSM564741 1 0.6302 0.4562 0.520 0.000 0.480
#> GSM564742 1 0.8655 0.5312 0.512 0.108 0.380
#> GSM564743 2 0.6624 0.6668 0.248 0.708 0.044
#> GSM564744 2 0.4269 0.7600 0.052 0.872 0.076
#> GSM564745 1 0.6917 0.4708 0.608 0.368 0.024
#> GSM564746 2 0.7740 -0.0128 0.444 0.508 0.048
#> GSM564747 1 0.6935 0.5714 0.604 0.024 0.372
#> GSM564748 1 0.7948 0.4925 0.520 0.060 0.420
#> GSM564749 2 0.1411 0.7633 0.036 0.964 0.000
#> GSM564750 3 0.7129 0.1210 0.392 0.028 0.580
#> GSM564751 3 0.7188 -0.3732 0.488 0.024 0.488
#> GSM564752 1 0.4563 0.6813 0.852 0.112 0.036
#> GSM564753 1 0.6309 0.3854 0.500 0.000 0.500
#> GSM564754 1 0.7772 0.6842 0.672 0.132 0.196
#> GSM564755 1 0.2187 0.6663 0.948 0.024 0.028
#> GSM564756 2 0.5384 0.6934 0.024 0.788 0.188
#> GSM564757 1 0.3683 0.6907 0.896 0.060 0.044
#> GSM564758 1 0.5470 0.6892 0.796 0.168 0.036
#> GSM564759 1 0.8479 0.6132 0.580 0.120 0.300
#> GSM564760 1 0.7661 0.6892 0.684 0.144 0.172
#> GSM564761 2 0.3780 0.7674 0.044 0.892 0.064
#> GSM564762 1 0.6470 0.7128 0.760 0.092 0.148
#> GSM564681 2 0.7256 0.6450 0.124 0.712 0.164
#> GSM564693 3 0.8113 0.4178 0.088 0.324 0.588
#> GSM564646 3 0.9623 0.0414 0.204 0.384 0.412
#> GSM564699 1 0.7413 0.6512 0.692 0.204 0.104
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.5511 0.23503 0.500 0.016 0.000 0.484
#> GSM564616 3 0.7983 0.11232 0.020 0.204 0.496 0.280
#> GSM564617 2 0.5980 0.24931 0.040 0.592 0.004 0.364
#> GSM564618 4 0.8554 0.25206 0.040 0.332 0.208 0.420
#> GSM564619 4 0.9155 0.22770 0.340 0.232 0.076 0.352
#> GSM564620 1 0.9555 -0.20233 0.332 0.120 0.236 0.312
#> GSM564621 4 0.5327 0.27956 0.208 0.004 0.056 0.732
#> GSM564622 3 0.5245 0.58128 0.044 0.012 0.748 0.196
#> GSM564623 4 0.6548 0.25085 0.032 0.032 0.364 0.572
#> GSM564624 2 0.5349 0.34655 0.020 0.656 0.004 0.320
#> GSM564625 1 0.7283 -0.14783 0.480 0.032 0.068 0.420
#> GSM564626 4 0.8049 0.15537 0.352 0.288 0.004 0.356
#> GSM564627 4 0.5766 0.12582 0.404 0.032 0.000 0.564
#> GSM564628 4 0.8664 0.36488 0.056 0.260 0.220 0.464
#> GSM564629 1 0.7233 -0.21390 0.480 0.112 0.008 0.400
#> GSM564630 2 0.4360 0.60459 0.032 0.816 0.012 0.140
#> GSM564609 3 0.7149 0.14700 0.052 0.392 0.516 0.040
#> GSM564610 2 0.7263 0.55709 0.236 0.624 0.064 0.076
#> GSM564611 2 0.1798 0.66439 0.040 0.944 0.000 0.016
#> GSM564612 3 0.5403 0.54087 0.052 0.196 0.740 0.012
#> GSM564613 2 0.8684 0.19147 0.080 0.464 0.308 0.148
#> GSM564614 4 0.6522 0.07209 0.280 0.000 0.112 0.608
#> GSM564631 3 0.0921 0.67716 0.028 0.000 0.972 0.000
#> GSM564632 3 0.8243 0.37672 0.136 0.108 0.572 0.184
#> GSM564633 3 0.4192 0.67953 0.056 0.044 0.852 0.048
#> GSM564634 2 0.7940 0.33286 0.080 0.512 0.336 0.072
#> GSM564635 3 0.0524 0.68446 0.008 0.004 0.988 0.000
#> GSM564636 3 0.5810 0.61982 0.060 0.068 0.760 0.112
#> GSM564637 2 0.8643 0.09397 0.188 0.424 0.336 0.052
#> GSM564638 3 0.3515 0.67949 0.072 0.012 0.876 0.040
#> GSM564639 3 0.1854 0.67991 0.048 0.000 0.940 0.012
#> GSM564640 2 0.1209 0.67203 0.004 0.964 0.032 0.000
#> GSM564641 3 0.4749 0.62289 0.044 0.132 0.804 0.020
#> GSM564642 2 0.6253 0.44626 0.028 0.612 0.332 0.028
#> GSM564643 3 0.5204 0.53531 0.032 0.004 0.712 0.252
#> GSM564644 2 0.0188 0.66610 0.004 0.996 0.000 0.000
#> GSM564645 3 0.1545 0.68478 0.040 0.000 0.952 0.008
#> GSM564647 3 0.5680 0.47384 0.040 0.276 0.676 0.008
#> GSM564648 2 0.6841 0.35995 0.020 0.556 0.360 0.064
#> GSM564649 3 0.6206 0.49813 0.068 0.260 0.660 0.012
#> GSM564650 2 0.7492 0.43867 0.080 0.628 0.096 0.196
#> GSM564651 2 0.5146 0.47294 0.016 0.696 0.280 0.008
#> GSM564652 3 0.8096 0.17918 0.068 0.360 0.480 0.092
#> GSM564653 2 0.0895 0.66821 0.020 0.976 0.004 0.000
#> GSM564654 3 0.2928 0.67740 0.056 0.012 0.904 0.028
#> GSM564655 3 0.1305 0.67909 0.036 0.004 0.960 0.000
#> GSM564656 3 0.2530 0.65673 0.100 0.004 0.896 0.000
#> GSM564657 3 0.3070 0.68040 0.020 0.068 0.896 0.016
#> GSM564658 2 0.3104 0.66319 0.060 0.892 0.004 0.044
#> GSM564659 3 0.5419 0.60346 0.056 0.060 0.784 0.100
#> GSM564660 3 0.9161 -0.17967 0.080 0.220 0.352 0.348
#> GSM564661 2 0.2531 0.67119 0.020 0.924 0.032 0.024
#> GSM564662 3 0.0921 0.68302 0.028 0.000 0.972 0.000
#> GSM564663 2 0.5244 0.60222 0.052 0.756 0.180 0.012
#> GSM564664 2 0.0188 0.66748 0.000 0.996 0.004 0.000
#> GSM564665 3 0.3616 0.64664 0.036 0.112 0.852 0.000
#> GSM564666 4 0.8624 0.11323 0.144 0.068 0.384 0.404
#> GSM564667 3 0.1975 0.68888 0.028 0.016 0.944 0.012
#> GSM564668 3 0.3777 0.66368 0.060 0.052 0.868 0.020
#> GSM564669 3 0.1585 0.68101 0.040 0.004 0.952 0.004
#> GSM564670 3 0.7409 0.47523 0.064 0.148 0.640 0.148
#> GSM564671 4 0.8876 0.33072 0.120 0.120 0.308 0.452
#> GSM564672 3 0.1211 0.68318 0.040 0.000 0.960 0.000
#> GSM564673 2 0.4673 0.60747 0.024 0.780 0.184 0.012
#> GSM564674 2 0.7285 0.40517 0.040 0.568 0.316 0.076
#> GSM564675 2 0.8948 -0.30169 0.080 0.376 0.172 0.372
#> GSM564676 2 0.0188 0.66610 0.004 0.996 0.000 0.000
#> GSM564677 2 0.5947 0.49556 0.024 0.668 0.276 0.032
#> GSM564678 2 0.0592 0.66657 0.016 0.984 0.000 0.000
#> GSM564679 2 0.0188 0.66610 0.004 0.996 0.000 0.000
#> GSM564680 3 0.1585 0.68113 0.040 0.004 0.952 0.004
#> GSM564682 3 0.7054 0.35031 0.056 0.336 0.568 0.040
#> GSM564683 3 0.0817 0.67849 0.024 0.000 0.976 0.000
#> GSM564684 4 0.5358 0.45062 0.032 0.212 0.020 0.736
#> GSM564685 3 0.3194 0.68350 0.044 0.020 0.896 0.040
#> GSM564686 4 0.5995 0.39338 0.132 0.020 0.120 0.728
#> GSM564687 2 0.0188 0.66654 0.000 0.996 0.000 0.004
#> GSM564688 2 0.7115 0.44390 0.044 0.652 0.132 0.172
#> GSM564689 2 0.1545 0.66393 0.040 0.952 0.000 0.008
#> GSM564690 2 0.0469 0.66613 0.012 0.988 0.000 0.000
#> GSM564691 2 0.4655 0.57498 0.032 0.760 0.208 0.000
#> GSM564692 2 0.6848 0.49548 0.072 0.632 0.260 0.036
#> GSM564694 3 0.8104 -0.09423 0.064 0.092 0.428 0.416
#> GSM564695 3 0.7765 0.42550 0.064 0.172 0.604 0.160
#> GSM564696 3 0.3485 0.65599 0.116 0.000 0.856 0.028
#> GSM564697 2 0.1975 0.66971 0.012 0.944 0.028 0.016
#> GSM564698 3 0.4928 0.63055 0.072 0.040 0.812 0.076
#> GSM564700 4 0.7621 0.09654 0.360 0.036 0.096 0.508
#> GSM564701 3 0.7038 0.14640 0.024 0.404 0.508 0.064
#> GSM564702 2 0.8094 0.05540 0.024 0.472 0.316 0.188
#> GSM564703 3 0.7915 0.04530 0.396 0.092 0.460 0.052
#> GSM564704 1 0.5322 0.51988 0.780 0.072 0.120 0.028
#> GSM564705 2 0.5442 0.59581 0.164 0.748 0.008 0.080
#> GSM564706 3 0.6221 0.34362 0.316 0.076 0.608 0.000
#> GSM564707 1 0.5991 0.50661 0.736 0.056 0.156 0.052
#> GSM564708 3 0.5678 0.41469 0.316 0.000 0.640 0.044
#> GSM564709 2 0.9449 -0.12405 0.340 0.340 0.200 0.120
#> GSM564710 2 0.8770 0.40153 0.208 0.512 0.164 0.116
#> GSM564711 3 0.5075 0.28118 0.344 0.000 0.644 0.012
#> GSM564712 2 0.9118 0.29615 0.248 0.420 0.248 0.084
#> GSM564713 1 0.6002 0.48268 0.640 0.008 0.304 0.048
#> GSM564714 1 0.4830 0.46239 0.608 0.000 0.392 0.000
#> GSM564715 1 0.5926 0.42437 0.752 0.116 0.056 0.076
#> GSM564716 1 0.7436 0.42604 0.620 0.188 0.148 0.044
#> GSM564717 2 0.1305 0.66942 0.036 0.960 0.004 0.000
#> GSM564718 1 0.5452 0.51362 0.768 0.020 0.096 0.116
#> GSM564719 2 0.0336 0.66637 0.008 0.992 0.000 0.000
#> GSM564720 2 0.5816 0.59478 0.224 0.700 0.008 0.068
#> GSM564721 2 0.5777 0.59710 0.172 0.728 0.012 0.088
#> GSM564722 1 0.6534 0.39143 0.632 0.284 0.060 0.024
#> GSM564723 2 0.6216 0.56699 0.208 0.692 0.020 0.080
#> GSM564724 1 0.5080 0.41513 0.576 0.000 0.420 0.004
#> GSM564725 4 0.8429 0.21503 0.276 0.020 0.348 0.356
#> GSM564726 1 0.6389 0.34759 0.544 0.032 0.020 0.404
#> GSM564727 1 0.6442 0.33481 0.552 0.036 0.020 0.392
#> GSM564728 1 0.6886 0.35644 0.548 0.044 0.036 0.372
#> GSM564729 4 0.7242 -0.18492 0.420 0.020 0.084 0.476
#> GSM564730 2 0.8361 0.28134 0.220 0.476 0.036 0.268
#> GSM564731 1 0.7477 0.46731 0.632 0.188 0.100 0.080
#> GSM564732 1 0.7485 0.43912 0.616 0.080 0.080 0.224
#> GSM564733 1 0.6959 0.28547 0.504 0.100 0.392 0.004
#> GSM564734 1 0.5660 0.28251 0.632 0.336 0.008 0.024
#> GSM564735 1 0.5632 0.46696 0.624 0.000 0.340 0.036
#> GSM564736 1 0.6694 0.36165 0.536 0.024 0.396 0.044
#> GSM564737 2 0.8716 0.42067 0.232 0.508 0.156 0.104
#> GSM564738 1 0.5152 0.46207 0.608 0.004 0.384 0.004
#> GSM564739 3 0.6685 0.34492 0.308 0.028 0.608 0.056
#> GSM564740 1 0.5268 0.37826 0.592 0.000 0.012 0.396
#> GSM564741 1 0.5097 0.40804 0.568 0.000 0.428 0.004
#> GSM564742 1 0.6259 0.47952 0.616 0.084 0.300 0.000
#> GSM564743 2 0.7493 0.47040 0.240 0.556 0.012 0.192
#> GSM564744 2 0.6092 0.61432 0.160 0.728 0.044 0.068
#> GSM564745 1 0.8021 0.00131 0.436 0.268 0.008 0.288
#> GSM564746 1 0.7951 0.09434 0.496 0.340 0.040 0.124
#> GSM564747 1 0.5102 0.51456 0.716 0.012 0.256 0.016
#> GSM564748 1 0.5841 0.44328 0.584 0.024 0.384 0.008
#> GSM564749 2 0.4094 0.63456 0.116 0.828 0.000 0.056
#> GSM564750 3 0.6580 0.15757 0.364 0.004 0.556 0.076
#> GSM564751 1 0.5417 0.42838 0.596 0.012 0.388 0.004
#> GSM564752 1 0.6751 0.35306 0.568 0.060 0.020 0.352
#> GSM564753 1 0.4916 0.41361 0.576 0.000 0.424 0.000
#> GSM564754 1 0.5019 0.47771 0.804 0.040 0.100 0.056
#> GSM564755 1 0.5155 0.27090 0.528 0.004 0.000 0.468
#> GSM564756 2 0.5213 0.61510 0.032 0.768 0.168 0.032
#> GSM564757 1 0.5990 0.28541 0.524 0.020 0.012 0.444
#> GSM564758 1 0.7137 0.41180 0.620 0.108 0.032 0.240
#> GSM564759 1 0.5811 0.50648 0.708 0.076 0.208 0.008
#> GSM564760 1 0.5865 0.49513 0.736 0.084 0.156 0.024
#> GSM564761 2 0.5813 0.61529 0.136 0.752 0.044 0.068
#> GSM564762 1 0.5925 0.51949 0.752 0.052 0.112 0.084
#> GSM564681 4 0.7258 0.10904 0.052 0.416 0.044 0.488
#> GSM564693 3 0.8321 0.19848 0.068 0.292 0.508 0.132
#> GSM564646 4 0.7636 0.46215 0.048 0.168 0.180 0.604
#> GSM564699 1 0.8282 0.21502 0.516 0.172 0.052 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 5 0.4402 0.52127 0.056 0.008 0.000 0.172 0.764
#> GSM564616 3 0.6544 0.02810 0.308 0.196 0.492 0.004 0.000
#> GSM564617 2 0.5419 0.10994 0.440 0.520 0.008 0.016 0.016
#> GSM564618 1 0.6870 0.32900 0.536 0.280 0.148 0.004 0.032
#> GSM564619 1 0.8299 0.22642 0.412 0.160 0.024 0.308 0.096
#> GSM564620 1 0.8954 0.13254 0.376 0.092 0.192 0.272 0.068
#> GSM564621 5 0.6193 0.29421 0.312 0.000 0.036 0.076 0.576
#> GSM564622 3 0.5022 0.54834 0.268 0.012 0.684 0.012 0.024
#> GSM564623 1 0.5720 0.22048 0.656 0.008 0.120 0.004 0.212
#> GSM564624 2 0.4614 0.27219 0.356 0.628 0.008 0.004 0.004
#> GSM564625 1 0.7298 0.21769 0.548 0.012 0.064 0.236 0.140
#> GSM564626 1 0.7714 0.20349 0.420 0.104 0.000 0.336 0.140
#> GSM564627 1 0.7295 -0.23308 0.340 0.020 0.000 0.312 0.328
#> GSM564628 1 0.7944 0.34710 0.484 0.196 0.196 0.008 0.116
#> GSM564629 1 0.6720 0.22344 0.544 0.040 0.000 0.288 0.128
#> GSM564630 2 0.4084 0.50984 0.204 0.768 0.008 0.008 0.012
#> GSM564609 3 0.6216 0.22546 0.068 0.336 0.564 0.024 0.008
#> GSM564610 2 0.8081 0.33293 0.156 0.488 0.032 0.240 0.084
#> GSM564611 2 0.2444 0.59275 0.036 0.912 0.000 0.028 0.024
#> GSM564612 3 0.6461 0.55709 0.124 0.148 0.660 0.048 0.020
#> GSM564613 2 0.8245 0.06031 0.272 0.436 0.196 0.048 0.048
#> GSM564614 5 0.5929 0.43474 0.112 0.000 0.108 0.088 0.692
#> GSM564631 3 0.0510 0.65415 0.000 0.000 0.984 0.016 0.000
#> GSM564632 3 0.8057 0.37971 0.292 0.060 0.468 0.132 0.048
#> GSM564633 3 0.4587 0.66518 0.108 0.032 0.800 0.036 0.024
#> GSM564634 2 0.7831 0.16545 0.088 0.420 0.384 0.048 0.060
#> GSM564635 3 0.2032 0.67638 0.052 0.004 0.924 0.020 0.000
#> GSM564636 3 0.5195 0.61479 0.196 0.036 0.724 0.032 0.012
#> GSM564637 2 0.8011 0.04191 0.080 0.392 0.348 0.168 0.012
#> GSM564638 3 0.5517 0.65145 0.168 0.012 0.716 0.076 0.028
#> GSM564639 3 0.4426 0.65503 0.128 0.000 0.788 0.056 0.028
#> GSM564640 2 0.1644 0.60515 0.012 0.948 0.028 0.008 0.004
#> GSM564641 3 0.3844 0.60824 0.044 0.104 0.828 0.024 0.000
#> GSM564642 2 0.6174 0.34922 0.048 0.560 0.352 0.024 0.016
#> GSM564643 3 0.7027 0.29506 0.280 0.000 0.488 0.028 0.204
#> GSM564644 2 0.0162 0.60207 0.000 0.996 0.000 0.004 0.000
#> GSM564645 3 0.3103 0.67872 0.072 0.000 0.872 0.044 0.012
#> GSM564647 3 0.4481 0.48231 0.024 0.228 0.732 0.016 0.000
#> GSM564648 2 0.6777 0.22979 0.084 0.480 0.392 0.012 0.032
#> GSM564649 3 0.6271 0.49563 0.076 0.224 0.644 0.040 0.016
#> GSM564650 2 0.8277 0.16286 0.192 0.496 0.084 0.052 0.176
#> GSM564651 2 0.5127 0.41806 0.056 0.708 0.216 0.016 0.004
#> GSM564652 3 0.7917 0.16864 0.116 0.308 0.472 0.048 0.056
#> GSM564653 2 0.0898 0.60215 0.008 0.972 0.000 0.020 0.000
#> GSM564654 3 0.4825 0.65926 0.120 0.012 0.776 0.068 0.024
#> GSM564655 3 0.1153 0.65739 0.008 0.000 0.964 0.024 0.004
#> GSM564656 3 0.2124 0.61208 0.000 0.004 0.900 0.096 0.000
#> GSM564657 3 0.4296 0.67069 0.112 0.048 0.808 0.024 0.008
#> GSM564658 2 0.3072 0.58871 0.040 0.872 0.004 0.080 0.004
#> GSM564659 3 0.5743 0.60211 0.208 0.024 0.688 0.056 0.024
#> GSM564660 1 0.8852 0.26922 0.420 0.208 0.192 0.052 0.128
#> GSM564661 2 0.3049 0.59449 0.052 0.888 0.020 0.028 0.012
#> GSM564662 3 0.2395 0.67651 0.048 0.000 0.912 0.024 0.016
#> GSM564663 2 0.4934 0.52932 0.060 0.736 0.184 0.016 0.004
#> GSM564664 2 0.0000 0.60184 0.000 1.000 0.000 0.000 0.000
#> GSM564665 3 0.2804 0.62245 0.012 0.092 0.880 0.016 0.000
#> GSM564666 1 0.7922 0.21966 0.492 0.024 0.244 0.076 0.164
#> GSM564667 3 0.3293 0.67633 0.096 0.004 0.860 0.028 0.012
#> GSM564668 3 0.5722 0.63567 0.136 0.044 0.728 0.056 0.036
#> GSM564669 3 0.4045 0.65891 0.124 0.000 0.808 0.052 0.016
#> GSM564670 3 0.7226 0.50424 0.244 0.108 0.568 0.044 0.036
#> GSM564671 1 0.8662 0.16750 0.344 0.044 0.252 0.068 0.292
#> GSM564672 3 0.2520 0.67687 0.056 0.000 0.896 0.048 0.000
#> GSM564673 2 0.4570 0.50461 0.020 0.720 0.240 0.020 0.000
#> GSM564674 2 0.7133 0.28995 0.108 0.508 0.328 0.020 0.036
#> GSM564675 1 0.7850 0.34716 0.456 0.296 0.156 0.080 0.012
#> GSM564676 2 0.0162 0.60207 0.000 0.996 0.000 0.004 0.000
#> GSM564677 2 0.5438 0.40076 0.032 0.624 0.320 0.012 0.012
#> GSM564678 2 0.0771 0.60177 0.020 0.976 0.000 0.004 0.000
#> GSM564679 2 0.0162 0.60207 0.000 0.996 0.000 0.004 0.000
#> GSM564680 3 0.4207 0.65816 0.124 0.000 0.800 0.056 0.020
#> GSM564682 3 0.6938 0.33417 0.136 0.308 0.520 0.024 0.012
#> GSM564683 3 0.1211 0.66405 0.016 0.000 0.960 0.024 0.000
#> GSM564684 5 0.6138 0.04511 0.388 0.104 0.000 0.008 0.500
#> GSM564685 3 0.2784 0.66374 0.072 0.012 0.888 0.028 0.000
#> GSM564686 5 0.6746 0.09050 0.396 0.000 0.080 0.056 0.468
#> GSM564687 2 0.0324 0.60255 0.004 0.992 0.000 0.000 0.004
#> GSM564688 2 0.6869 0.33191 0.212 0.600 0.128 0.020 0.040
#> GSM564689 2 0.2026 0.59703 0.044 0.928 0.000 0.016 0.012
#> GSM564690 2 0.0566 0.60174 0.004 0.984 0.000 0.012 0.000
#> GSM564691 2 0.4924 0.49501 0.044 0.732 0.200 0.016 0.008
#> GSM564692 2 0.6614 0.41429 0.104 0.608 0.236 0.036 0.016
#> GSM564694 1 0.7447 0.14073 0.456 0.040 0.360 0.024 0.120
#> GSM564695 3 0.8154 0.33065 0.260 0.140 0.476 0.036 0.088
#> GSM564696 3 0.3336 0.61478 0.060 0.000 0.844 0.096 0.000
#> GSM564697 2 0.2409 0.59954 0.044 0.916 0.012 0.020 0.008
#> GSM564698 3 0.5723 0.61346 0.192 0.012 0.696 0.056 0.044
#> GSM564700 1 0.7706 -0.09979 0.380 0.008 0.044 0.212 0.356
#> GSM564701 3 0.6600 0.16874 0.128 0.384 0.472 0.008 0.008
#> GSM564702 2 0.8083 -0.02282 0.212 0.440 0.256 0.016 0.076
#> GSM564703 4 0.9061 0.07636 0.168 0.084 0.320 0.336 0.092
#> GSM564704 4 0.5961 0.48101 0.100 0.052 0.076 0.724 0.048
#> GSM564705 2 0.7737 0.26788 0.192 0.500 0.004 0.196 0.108
#> GSM564706 3 0.7088 0.24199 0.068 0.068 0.536 0.308 0.020
#> GSM564707 4 0.7212 0.40883 0.144 0.024 0.156 0.600 0.076
#> GSM564708 3 0.6454 0.27021 0.072 0.000 0.580 0.284 0.064
#> GSM564709 1 0.9720 0.15522 0.264 0.244 0.140 0.232 0.120
#> GSM564710 2 0.9384 -0.03907 0.292 0.296 0.076 0.196 0.140
#> GSM564711 3 0.5896 0.22042 0.052 0.000 0.604 0.304 0.040
#> GSM564712 2 0.9603 -0.00888 0.248 0.272 0.116 0.248 0.116
#> GSM564713 4 0.6402 0.48974 0.100 0.004 0.208 0.632 0.056
#> GSM564714 4 0.4449 0.49844 0.004 0.000 0.352 0.636 0.008
#> GSM564715 4 0.6617 0.18223 0.204 0.064 0.012 0.628 0.092
#> GSM564716 4 0.8765 0.27380 0.112 0.184 0.144 0.468 0.092
#> GSM564717 2 0.2943 0.58745 0.040 0.888 0.000 0.036 0.036
#> GSM564718 4 0.5404 0.43471 0.092 0.008 0.056 0.744 0.100
#> GSM564719 2 0.0404 0.60233 0.000 0.988 0.000 0.012 0.000
#> GSM564720 2 0.7833 0.28494 0.224 0.464 0.000 0.196 0.116
#> GSM564721 2 0.6736 0.42725 0.148 0.612 0.004 0.172 0.064
#> GSM564722 4 0.6036 0.39066 0.096 0.220 0.032 0.648 0.004
#> GSM564723 2 0.8299 0.18040 0.208 0.412 0.012 0.260 0.108
#> GSM564724 4 0.4760 0.45586 0.020 0.000 0.416 0.564 0.000
#> GSM564725 1 0.8708 0.18782 0.344 0.016 0.224 0.272 0.144
#> GSM564726 5 0.5967 0.32805 0.056 0.008 0.012 0.408 0.516
#> GSM564727 5 0.6050 0.42777 0.064 0.020 0.004 0.364 0.548
#> GSM564728 5 0.5535 0.35424 0.020 0.020 0.008 0.404 0.548
#> GSM564729 5 0.5524 0.53743 0.056 0.008 0.032 0.208 0.696
#> GSM564730 1 0.8349 -0.02063 0.348 0.324 0.004 0.152 0.172
#> GSM564731 4 0.6983 0.40541 0.032 0.168 0.088 0.628 0.084
#> GSM564732 4 0.7376 0.11661 0.060 0.052 0.076 0.564 0.248
#> GSM564733 4 0.7435 0.35255 0.076 0.060 0.328 0.500 0.036
#> GSM564734 4 0.7333 0.13729 0.144 0.284 0.000 0.496 0.076
#> GSM564735 4 0.6525 0.47671 0.132 0.000 0.208 0.608 0.052
#> GSM564736 4 0.7397 0.42001 0.156 0.012 0.280 0.504 0.048
#> GSM564737 2 0.9546 -0.01004 0.240 0.284 0.100 0.252 0.124
#> GSM564738 4 0.5821 0.50768 0.100 0.004 0.264 0.624 0.008
#> GSM564739 3 0.7531 0.14915 0.168 0.020 0.492 0.280 0.040
#> GSM564740 4 0.5949 0.00369 0.100 0.000 0.008 0.564 0.328
#> GSM564741 4 0.5331 0.47193 0.060 0.000 0.372 0.568 0.000
#> GSM564742 4 0.5622 0.52551 0.016 0.060 0.232 0.676 0.016
#> GSM564743 2 0.8079 0.21235 0.228 0.432 0.008 0.240 0.092
#> GSM564744 2 0.5841 0.51997 0.072 0.704 0.040 0.164 0.020
#> GSM564745 5 0.8547 -0.20977 0.280 0.208 0.000 0.220 0.292
#> GSM564746 4 0.8808 -0.06480 0.236 0.220 0.024 0.368 0.152
#> GSM564747 4 0.4267 0.52738 0.028 0.000 0.232 0.736 0.004
#> GSM564748 4 0.5498 0.43281 0.020 0.012 0.428 0.528 0.012
#> GSM564749 2 0.6371 0.42903 0.152 0.648 0.000 0.116 0.084
#> GSM564750 3 0.7869 0.10334 0.136 0.004 0.440 0.304 0.116
#> GSM564751 4 0.6035 0.47253 0.076 0.000 0.276 0.612 0.036
#> GSM564752 5 0.5402 0.38094 0.024 0.024 0.000 0.388 0.564
#> GSM564753 4 0.5192 0.47424 0.032 0.000 0.388 0.572 0.008
#> GSM564754 4 0.6680 0.21091 0.208 0.020 0.040 0.620 0.112
#> GSM564755 5 0.4527 0.52494 0.036 0.000 0.000 0.272 0.692
#> GSM564756 2 0.4782 0.54460 0.052 0.756 0.168 0.008 0.016
#> GSM564757 5 0.4511 0.53677 0.012 0.008 0.004 0.284 0.692
#> GSM564758 4 0.6858 0.14187 0.088 0.072 0.012 0.596 0.232
#> GSM564759 4 0.6587 0.52255 0.048 0.064 0.216 0.636 0.036
#> GSM564760 4 0.5796 0.45983 0.100 0.044 0.084 0.732 0.040
#> GSM564761 2 0.7636 0.32183 0.208 0.540 0.016 0.128 0.108
#> GSM564762 4 0.6132 0.46704 0.072 0.044 0.104 0.712 0.068
#> GSM564681 1 0.6997 0.23955 0.472 0.272 0.004 0.012 0.240
#> GSM564693 3 0.8958 0.17354 0.208 0.176 0.408 0.052 0.156
#> GSM564646 5 0.6649 0.00364 0.388 0.068 0.060 0.000 0.484
#> GSM564699 4 0.8723 -0.09577 0.236 0.120 0.024 0.356 0.264
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.2825 0.64206 0.028 0.880 0.000 0.032 0.004 0.056
#> GSM564616 3 0.6222 -0.11888 0.032 0.000 0.432 0.000 0.140 0.396
#> GSM564617 6 0.5356 0.08384 0.048 0.012 0.004 0.008 0.388 0.540
#> GSM564618 6 0.5873 0.38891 0.028 0.004 0.076 0.040 0.204 0.648
#> GSM564619 1 0.6912 0.39777 0.524 0.004 0.008 0.096 0.144 0.224
#> GSM564620 6 0.9250 0.15514 0.188 0.088 0.128 0.164 0.080 0.352
#> GSM564621 2 0.5748 0.40797 0.084 0.576 0.016 0.012 0.004 0.308
#> GSM564622 3 0.6417 0.31317 0.048 0.020 0.500 0.072 0.004 0.356
#> GSM564623 6 0.3684 0.41410 0.016 0.060 0.040 0.028 0.012 0.844
#> GSM564624 5 0.4890 0.05843 0.032 0.000 0.008 0.004 0.492 0.464
#> GSM564625 6 0.7263 -0.05460 0.392 0.108 0.036 0.060 0.008 0.396
#> GSM564626 1 0.3878 0.52760 0.816 0.024 0.000 0.016 0.052 0.092
#> GSM564627 2 0.7372 0.24721 0.180 0.412 0.000 0.092 0.016 0.300
#> GSM564628 6 0.6131 0.41354 0.052 0.016 0.144 0.008 0.140 0.640
#> GSM564629 1 0.6591 0.11809 0.476 0.076 0.000 0.052 0.032 0.364
#> GSM564630 5 0.4971 0.45448 0.052 0.008 0.008 0.004 0.648 0.280
#> GSM564609 3 0.5372 0.33354 0.012 0.004 0.620 0.008 0.280 0.076
#> GSM564610 1 0.5730 0.20495 0.512 0.008 0.024 0.024 0.404 0.028
#> GSM564611 5 0.1908 0.60862 0.096 0.000 0.000 0.000 0.900 0.004
#> GSM564612 3 0.7308 0.48413 0.048 0.028 0.560 0.172 0.132 0.060
#> GSM564613 5 0.8142 0.05040 0.020 0.028 0.136 0.180 0.412 0.224
#> GSM564614 2 0.3865 0.60310 0.012 0.812 0.080 0.016 0.000 0.080
#> GSM564631 3 0.0520 0.56713 0.008 0.000 0.984 0.008 0.000 0.000
#> GSM564632 3 0.7746 0.28866 0.024 0.020 0.384 0.280 0.052 0.240
#> GSM564633 3 0.5551 0.58265 0.040 0.016 0.716 0.076 0.032 0.120
#> GSM564634 3 0.7245 0.01611 0.036 0.040 0.472 0.032 0.328 0.092
#> GSM564635 3 0.2753 0.59887 0.016 0.004 0.876 0.088 0.004 0.012
#> GSM564636 3 0.5466 0.53791 0.020 0.004 0.676 0.084 0.024 0.192
#> GSM564637 3 0.7526 -0.03263 0.028 0.008 0.396 0.132 0.352 0.084
#> GSM564638 3 0.6315 0.55484 0.040 0.020 0.596 0.228 0.008 0.108
#> GSM564639 3 0.5419 0.55949 0.040 0.024 0.664 0.224 0.000 0.048
#> GSM564640 5 0.1478 0.66402 0.000 0.004 0.032 0.000 0.944 0.020
#> GSM564641 3 0.3675 0.55293 0.008 0.000 0.828 0.028 0.080 0.056
#> GSM564642 5 0.5612 0.21416 0.024 0.004 0.428 0.012 0.492 0.040
#> GSM564643 6 0.7623 0.00306 0.064 0.136 0.364 0.068 0.000 0.368
#> GSM564644 5 0.0146 0.66046 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564645 3 0.4373 0.59788 0.036 0.020 0.780 0.120 0.000 0.044
#> GSM564647 3 0.3379 0.50576 0.012 0.000 0.804 0.008 0.168 0.008
#> GSM564648 3 0.6114 -0.11184 0.024 0.016 0.456 0.004 0.424 0.076
#> GSM564649 3 0.5900 0.51159 0.020 0.012 0.660 0.064 0.188 0.056
#> GSM564650 5 0.8120 0.13232 0.036 0.148 0.076 0.056 0.456 0.228
#> GSM564651 5 0.5520 0.47406 0.028 0.016 0.168 0.052 0.700 0.036
#> GSM564652 3 0.7723 0.16703 0.088 0.028 0.448 0.024 0.284 0.128
#> GSM564653 5 0.1007 0.66163 0.008 0.004 0.000 0.004 0.968 0.016
#> GSM564654 3 0.5760 0.56902 0.044 0.024 0.668 0.188 0.008 0.068
#> GSM564655 3 0.1799 0.57620 0.024 0.008 0.936 0.016 0.000 0.016
#> GSM564656 3 0.1901 0.52755 0.008 0.000 0.912 0.076 0.004 0.000
#> GSM564657 3 0.5257 0.59607 0.036 0.004 0.724 0.140 0.052 0.044
#> GSM564658 5 0.3139 0.61145 0.120 0.000 0.000 0.008 0.836 0.036
#> GSM564659 3 0.6631 0.51984 0.044 0.032 0.596 0.212 0.020 0.096
#> GSM564660 6 0.8583 0.30623 0.032 0.052 0.160 0.184 0.172 0.400
#> GSM564661 5 0.2820 0.64919 0.016 0.000 0.016 0.052 0.884 0.032
#> GSM564662 3 0.3729 0.60080 0.036 0.020 0.828 0.088 0.000 0.028
#> GSM564663 5 0.4794 0.56484 0.032 0.004 0.204 0.012 0.716 0.032
#> GSM564664 5 0.0000 0.66001 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564665 3 0.2114 0.55864 0.012 0.000 0.904 0.008 0.076 0.000
#> GSM564666 6 0.7209 0.37407 0.032 0.068 0.184 0.156 0.016 0.544
#> GSM564667 3 0.3272 0.59600 0.016 0.000 0.820 0.144 0.000 0.020
#> GSM564668 3 0.6472 0.54135 0.044 0.028 0.612 0.220 0.036 0.060
#> GSM564669 3 0.5380 0.56333 0.044 0.024 0.676 0.208 0.000 0.048
#> GSM564670 3 0.7466 0.40698 0.020 0.012 0.484 0.200 0.088 0.196
#> GSM564671 6 0.8002 0.35701 0.048 0.196 0.204 0.076 0.024 0.452
#> GSM564672 3 0.3676 0.59887 0.032 0.012 0.828 0.092 0.000 0.036
#> GSM564673 5 0.4221 0.51753 0.004 0.000 0.284 0.008 0.684 0.020
#> GSM564674 5 0.6505 0.29952 0.028 0.016 0.344 0.004 0.484 0.124
#> GSM564675 6 0.6076 0.34544 0.024 0.000 0.072 0.044 0.280 0.580
#> GSM564676 5 0.0146 0.66046 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564677 5 0.4645 0.39603 0.012 0.008 0.364 0.000 0.600 0.016
#> GSM564678 5 0.0725 0.66055 0.012 0.000 0.000 0.000 0.976 0.012
#> GSM564679 5 0.0146 0.66046 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564680 3 0.5406 0.56129 0.044 0.024 0.672 0.212 0.000 0.048
#> GSM564682 3 0.7137 0.36771 0.036 0.008 0.500 0.092 0.288 0.076
#> GSM564683 3 0.1003 0.58343 0.004 0.000 0.964 0.028 0.000 0.004
#> GSM564684 6 0.5806 0.16119 0.040 0.400 0.000 0.024 0.032 0.504
#> GSM564685 3 0.3252 0.58676 0.016 0.004 0.856 0.028 0.012 0.084
#> GSM564686 6 0.6354 0.21746 0.036 0.352 0.032 0.076 0.000 0.504
#> GSM564687 5 0.0146 0.66075 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564688 5 0.6574 0.36393 0.032 0.024 0.128 0.012 0.568 0.236
#> GSM564689 5 0.2107 0.65364 0.024 0.012 0.000 0.008 0.920 0.036
#> GSM564690 5 0.0653 0.66123 0.004 0.004 0.000 0.000 0.980 0.012
#> GSM564691 5 0.4872 0.53477 0.016 0.008 0.208 0.032 0.712 0.024
#> GSM564692 5 0.6701 0.47356 0.044 0.008 0.196 0.088 0.596 0.068
#> GSM564694 6 0.6776 0.32519 0.036 0.028 0.252 0.088 0.032 0.564
#> GSM564695 3 0.8508 0.23968 0.032 0.068 0.400 0.124 0.124 0.252
#> GSM564696 3 0.3479 0.52107 0.008 0.000 0.820 0.084 0.000 0.088
#> GSM564697 5 0.2736 0.65243 0.028 0.008 0.008 0.008 0.888 0.060
#> GSM564698 3 0.6380 0.49805 0.032 0.020 0.568 0.244 0.004 0.132
#> GSM564700 6 0.6602 0.27691 0.036 0.224 0.020 0.188 0.000 0.532
#> GSM564701 3 0.7149 0.20840 0.032 0.004 0.444 0.076 0.348 0.096
#> GSM564702 5 0.8391 -0.07964 0.056 0.064 0.220 0.040 0.392 0.228
#> GSM564703 1 0.7634 0.11428 0.412 0.012 0.240 0.248 0.068 0.020
#> GSM564704 4 0.6553 0.35925 0.300 0.012 0.068 0.544 0.048 0.028
#> GSM564705 1 0.3714 0.51829 0.656 0.000 0.000 0.004 0.340 0.000
#> GSM564706 3 0.6194 0.17019 0.028 0.012 0.520 0.356 0.068 0.016
#> GSM564707 1 0.6108 -0.02247 0.448 0.004 0.144 0.388 0.016 0.000
#> GSM564708 3 0.6722 0.12921 0.188 0.032 0.492 0.268 0.000 0.020
#> GSM564709 1 0.8880 0.29814 0.384 0.036 0.116 0.140 0.204 0.120
#> GSM564710 1 0.5603 0.56665 0.684 0.016 0.032 0.076 0.176 0.016
#> GSM564711 3 0.5429 0.19091 0.072 0.028 0.608 0.288 0.000 0.004
#> GSM564712 1 0.5764 0.56633 0.676 0.004 0.080 0.064 0.156 0.020
#> GSM564713 4 0.4703 0.61092 0.056 0.016 0.136 0.756 0.004 0.032
#> GSM564714 4 0.4246 0.58080 0.012 0.000 0.340 0.636 0.000 0.012
#> GSM564715 1 0.4231 0.43231 0.696 0.004 0.004 0.264 0.032 0.000
#> GSM564716 1 0.8229 0.07821 0.348 0.036 0.136 0.296 0.172 0.012
#> GSM564717 5 0.2595 0.55454 0.160 0.000 0.000 0.000 0.836 0.004
#> GSM564718 4 0.5456 0.54487 0.112 0.084 0.040 0.712 0.000 0.052
#> GSM564719 5 0.0291 0.66111 0.004 0.000 0.000 0.000 0.992 0.004
#> GSM564720 1 0.5312 0.50682 0.620 0.016 0.000 0.028 0.296 0.040
#> GSM564721 5 0.5179 0.02899 0.384 0.004 0.000 0.032 0.552 0.028
#> GSM564722 4 0.4974 0.47243 0.048 0.004 0.008 0.712 0.188 0.040
#> GSM564723 1 0.4086 0.59108 0.724 0.004 0.004 0.024 0.240 0.004
#> GSM564724 4 0.4221 0.52628 0.008 0.000 0.396 0.588 0.000 0.008
#> GSM564725 6 0.8942 0.20069 0.276 0.116 0.160 0.144 0.016 0.288
#> GSM564726 2 0.5357 0.52426 0.004 0.556 0.008 0.360 0.004 0.068
#> GSM564727 2 0.4732 0.69475 0.024 0.716 0.000 0.204 0.016 0.040
#> GSM564728 2 0.4313 0.62020 0.004 0.664 0.000 0.304 0.008 0.020
#> GSM564729 2 0.3439 0.69600 0.036 0.848 0.008 0.076 0.004 0.028
#> GSM564730 1 0.6930 0.44383 0.512 0.036 0.000 0.052 0.260 0.140
#> GSM564731 4 0.7352 0.48208 0.112 0.056 0.084 0.560 0.168 0.020
#> GSM564732 4 0.8326 0.03142 0.232 0.272 0.096 0.340 0.040 0.020
#> GSM564733 4 0.6763 0.42370 0.076 0.016 0.260 0.560 0.044 0.044
#> GSM564734 1 0.6762 0.41058 0.444 0.012 0.000 0.276 0.240 0.028
#> GSM564735 4 0.3463 0.61165 0.000 0.008 0.160 0.800 0.000 0.032
#> GSM564736 4 0.5675 0.49001 0.032 0.024 0.220 0.656 0.008 0.060
#> GSM564737 1 0.4036 0.57817 0.788 0.008 0.060 0.004 0.132 0.008
#> GSM564738 4 0.3345 0.62251 0.000 0.000 0.204 0.776 0.000 0.020
#> GSM564739 3 0.7161 0.14731 0.332 0.012 0.428 0.160 0.008 0.060
#> GSM564740 4 0.5161 0.20819 0.000 0.252 0.004 0.620 0.000 0.124
#> GSM564741 4 0.4415 0.56147 0.016 0.000 0.300 0.660 0.000 0.024
#> GSM564742 4 0.6227 0.59372 0.092 0.012 0.220 0.608 0.060 0.008
#> GSM564743 5 0.7424 -0.02902 0.332 0.024 0.004 0.084 0.412 0.144
#> GSM564744 5 0.4500 0.43169 0.264 0.000 0.036 0.012 0.684 0.004
#> GSM564745 1 0.6667 0.50163 0.572 0.140 0.000 0.028 0.192 0.068
#> GSM564746 1 0.7594 0.51382 0.504 0.068 0.012 0.204 0.152 0.060
#> GSM564747 4 0.5246 0.61165 0.112 0.000 0.252 0.624 0.000 0.012
#> GSM564748 3 0.4852 -0.38874 0.056 0.000 0.492 0.452 0.000 0.000
#> GSM564749 5 0.4274 -0.09642 0.432 0.004 0.000 0.000 0.552 0.012
#> GSM564750 4 0.7152 0.03546 0.024 0.136 0.344 0.432 0.004 0.060
#> GSM564751 4 0.6129 0.45214 0.288 0.004 0.212 0.488 0.000 0.008
#> GSM564752 2 0.4423 0.59434 0.000 0.644 0.000 0.320 0.016 0.020
#> GSM564753 4 0.5097 0.52221 0.064 0.008 0.384 0.544 0.000 0.000
#> GSM564754 1 0.4107 0.38963 0.704 0.008 0.004 0.268 0.012 0.004
#> GSM564755 2 0.2667 0.71694 0.000 0.852 0.000 0.128 0.000 0.020
#> GSM564756 5 0.4327 0.58174 0.036 0.012 0.164 0.004 0.764 0.020
#> GSM564757 2 0.3539 0.69336 0.056 0.832 0.000 0.084 0.004 0.024
#> GSM564758 4 0.7538 0.21645 0.140 0.196 0.004 0.500 0.044 0.116
#> GSM564759 4 0.6959 0.58294 0.108 0.016 0.212 0.568 0.056 0.040
#> GSM564760 4 0.5821 0.57850 0.152 0.024 0.068 0.688 0.024 0.044
#> GSM564761 1 0.4343 0.42988 0.584 0.004 0.012 0.004 0.396 0.000
#> GSM564762 4 0.6196 0.57190 0.120 0.040 0.088 0.676 0.036 0.040
#> GSM564681 6 0.6673 0.39875 0.056 0.164 0.000 0.024 0.200 0.556
#> GSM564693 3 0.9494 0.04444 0.068 0.148 0.312 0.164 0.144 0.164
#> GSM564646 6 0.6572 0.26190 0.044 0.356 0.044 0.028 0.024 0.504
#> GSM564699 6 0.7463 0.17441 0.024 0.180 0.016 0.356 0.044 0.380
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 genotype/variation(p) disease.state(p) k
#> CV:pam 108 0.437 0.865 2
#> CV:pam 110 0.857 0.437 3
#> CV:pam 62 0.926 NA 4
#> CV:pam 56 0.707 NA 5
#> CV:pam 76 0.203 NA 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.991 0.4953 0.500 0.500
#> 3 3 0.855 0.882 0.928 0.2641 0.874 0.748
#> 4 4 0.665 0.743 0.778 0.1297 0.839 0.591
#> 5 5 0.761 0.757 0.863 0.0774 0.938 0.778
#> 6 6 0.870 0.843 0.927 0.0577 0.921 0.694
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
#> GSM564615 1 0.0000 0.999 1.000 0.000
#> GSM564616 2 0.1414 0.995 0.020 0.980
#> GSM564617 2 0.1414 0.995 0.020 0.980
#> GSM564618 2 0.1414 0.995 0.020 0.980
#> GSM564619 1 0.0000 0.999 1.000 0.000
#> GSM564620 1 0.0000 0.999 1.000 0.000
#> GSM564621 1 0.0000 0.999 1.000 0.000
#> GSM564622 2 0.1414 0.995 0.020 0.980
#> GSM564623 2 0.1414 0.995 0.020 0.980
#> GSM564624 2 0.1414 0.995 0.020 0.980
#> GSM564625 1 0.0000 0.999 1.000 0.000
#> GSM564626 1 0.0000 0.999 1.000 0.000
#> GSM564627 1 0.0000 0.999 1.000 0.000
#> GSM564628 2 0.1414 0.995 0.020 0.980
#> GSM564629 1 0.0000 0.999 1.000 0.000
#> GSM564630 2 0.1414 0.995 0.020 0.980
#> GSM564609 2 0.1414 0.995 0.020 0.980
#> GSM564610 1 0.0000 0.999 1.000 0.000
#> GSM564611 1 0.0376 0.996 0.996 0.004
#> GSM564612 2 0.1414 0.995 0.020 0.980
#> GSM564613 2 0.1414 0.995 0.020 0.980
#> GSM564614 1 0.0000 0.999 1.000 0.000
#> GSM564631 2 0.0000 0.984 0.000 1.000
#> GSM564632 2 0.1414 0.995 0.020 0.980
#> GSM564633 2 0.0672 0.989 0.008 0.992
#> GSM564634 2 0.1414 0.995 0.020 0.980
#> GSM564635 2 0.0000 0.984 0.000 1.000
#> GSM564636 2 0.1414 0.995 0.020 0.980
#> GSM564637 2 0.1414 0.995 0.020 0.980
#> GSM564638 2 0.0376 0.987 0.004 0.996
#> GSM564639 2 0.0000 0.984 0.000 1.000
#> GSM564640 2 0.1414 0.995 0.020 0.980
#> GSM564641 2 0.1414 0.995 0.020 0.980
#> GSM564642 2 0.1414 0.995 0.020 0.980
#> GSM564643 2 0.1414 0.995 0.020 0.980
#> GSM564644 2 0.1414 0.995 0.020 0.980
#> GSM564645 2 0.0000 0.984 0.000 1.000
#> GSM564647 2 0.1414 0.995 0.020 0.980
#> GSM564648 2 0.1414 0.995 0.020 0.980
#> GSM564649 2 0.0000 0.984 0.000 1.000
#> GSM564650 2 0.1414 0.995 0.020 0.980
#> GSM564651 2 0.1414 0.995 0.020 0.980
#> GSM564652 2 0.1414 0.995 0.020 0.980
#> GSM564653 2 0.1414 0.995 0.020 0.980
#> GSM564654 2 0.0000 0.984 0.000 1.000
#> GSM564655 2 0.0376 0.987 0.004 0.996
#> GSM564656 2 0.0000 0.984 0.000 1.000
#> GSM564657 2 0.1414 0.995 0.020 0.980
#> GSM564658 2 0.1414 0.995 0.020 0.980
#> GSM564659 2 0.1414 0.995 0.020 0.980
#> GSM564660 2 0.1414 0.995 0.020 0.980
#> GSM564661 2 0.1414 0.995 0.020 0.980
#> GSM564662 2 0.0000 0.984 0.000 1.000
#> GSM564663 2 0.1414 0.995 0.020 0.980
#> GSM564664 2 0.1633 0.992 0.024 0.976
#> GSM564665 2 0.0000 0.984 0.000 1.000
#> GSM564666 2 0.1414 0.995 0.020 0.980
#> GSM564667 2 0.0000 0.984 0.000 1.000
#> GSM564668 2 0.0000 0.984 0.000 1.000
#> GSM564669 2 0.0938 0.991 0.012 0.988
#> GSM564670 2 0.1414 0.995 0.020 0.980
#> GSM564671 2 0.1414 0.995 0.020 0.980
#> GSM564672 2 0.0000 0.984 0.000 1.000
#> GSM564673 2 0.1414 0.995 0.020 0.980
#> GSM564674 2 0.1414 0.995 0.020 0.980
#> GSM564675 2 0.1414 0.995 0.020 0.980
#> GSM564676 2 0.1414 0.995 0.020 0.980
#> GSM564677 2 0.1414 0.995 0.020 0.980
#> GSM564678 2 0.1414 0.995 0.020 0.980
#> GSM564679 2 0.1414 0.995 0.020 0.980
#> GSM564680 2 0.0000 0.984 0.000 1.000
#> GSM564682 2 0.1414 0.995 0.020 0.980
#> GSM564683 2 0.0000 0.984 0.000 1.000
#> GSM564684 2 0.1414 0.995 0.020 0.980
#> GSM564685 2 0.0000 0.984 0.000 1.000
#> GSM564686 2 0.1414 0.995 0.020 0.980
#> GSM564687 2 0.1633 0.992 0.024 0.976
#> GSM564688 2 0.1414 0.995 0.020 0.980
#> GSM564689 2 0.1414 0.995 0.020 0.980
#> GSM564690 2 0.1414 0.995 0.020 0.980
#> GSM564691 2 0.1414 0.995 0.020 0.980
#> GSM564692 2 0.1414 0.995 0.020 0.980
#> GSM564694 2 0.1414 0.995 0.020 0.980
#> GSM564695 2 0.1414 0.995 0.020 0.980
#> GSM564696 2 0.0376 0.987 0.004 0.996
#> GSM564697 2 0.1414 0.995 0.020 0.980
#> GSM564698 2 0.0672 0.989 0.008 0.992
#> GSM564700 2 0.1414 0.995 0.020 0.980
#> GSM564701 2 0.1414 0.995 0.020 0.980
#> GSM564702 2 0.1414 0.995 0.020 0.980
#> GSM564703 1 0.0000 0.999 1.000 0.000
#> GSM564704 1 0.0000 0.999 1.000 0.000
#> GSM564705 1 0.0000 0.999 1.000 0.000
#> GSM564706 1 0.0000 0.999 1.000 0.000
#> GSM564707 1 0.0000 0.999 1.000 0.000
#> GSM564708 1 0.0000 0.999 1.000 0.000
#> GSM564709 1 0.0000 0.999 1.000 0.000
#> GSM564710 1 0.0000 0.999 1.000 0.000
#> GSM564711 1 0.0000 0.999 1.000 0.000
#> GSM564712 1 0.0000 0.999 1.000 0.000
#> GSM564713 1 0.0000 0.999 1.000 0.000
#> GSM564714 1 0.0000 0.999 1.000 0.000
#> GSM564715 1 0.0000 0.999 1.000 0.000
#> GSM564716 1 0.0000 0.999 1.000 0.000
#> GSM564717 1 0.0000 0.999 1.000 0.000
#> GSM564718 1 0.0000 0.999 1.000 0.000
#> GSM564719 1 0.0000 0.999 1.000 0.000
#> GSM564720 1 0.0000 0.999 1.000 0.000
#> GSM564721 1 0.0000 0.999 1.000 0.000
#> GSM564722 1 0.0000 0.999 1.000 0.000
#> GSM564723 1 0.0000 0.999 1.000 0.000
#> GSM564724 1 0.0000 0.999 1.000 0.000
#> GSM564725 1 0.0000 0.999 1.000 0.000
#> GSM564726 1 0.0000 0.999 1.000 0.000
#> GSM564727 1 0.0000 0.999 1.000 0.000
#> GSM564728 1 0.0000 0.999 1.000 0.000
#> GSM564729 1 0.0000 0.999 1.000 0.000
#> GSM564730 1 0.0000 0.999 1.000 0.000
#> GSM564731 1 0.0000 0.999 1.000 0.000
#> GSM564732 1 0.0000 0.999 1.000 0.000
#> GSM564733 1 0.0000 0.999 1.000 0.000
#> GSM564734 1 0.0000 0.999 1.000 0.000
#> GSM564735 1 0.0000 0.999 1.000 0.000
#> GSM564736 1 0.0000 0.999 1.000 0.000
#> GSM564737 1 0.0000 0.999 1.000 0.000
#> GSM564738 1 0.1633 0.975 0.976 0.024
#> GSM564739 1 0.0376 0.996 0.996 0.004
#> GSM564740 1 0.0000 0.999 1.000 0.000
#> GSM564741 1 0.0000 0.999 1.000 0.000
#> GSM564742 1 0.0000 0.999 1.000 0.000
#> GSM564743 1 0.0000 0.999 1.000 0.000
#> GSM564744 1 0.0000 0.999 1.000 0.000
#> GSM564745 1 0.0000 0.999 1.000 0.000
#> GSM564746 1 0.0000 0.999 1.000 0.000
#> GSM564747 1 0.0000 0.999 1.000 0.000
#> GSM564748 1 0.0000 0.999 1.000 0.000
#> GSM564749 1 0.0000 0.999 1.000 0.000
#> GSM564750 1 0.0000 0.999 1.000 0.000
#> GSM564751 1 0.0000 0.999 1.000 0.000
#> GSM564752 1 0.0000 0.999 1.000 0.000
#> GSM564753 1 0.0000 0.999 1.000 0.000
#> GSM564754 1 0.0000 0.999 1.000 0.000
#> GSM564755 1 0.0000 0.999 1.000 0.000
#> GSM564756 1 0.0000 0.999 1.000 0.000
#> GSM564757 1 0.0000 0.999 1.000 0.000
#> GSM564758 1 0.0000 0.999 1.000 0.000
#> GSM564759 1 0.0000 0.999 1.000 0.000
#> GSM564760 1 0.0000 0.999 1.000 0.000
#> GSM564761 1 0.1184 0.984 0.984 0.016
#> GSM564762 1 0.0000 0.999 1.000 0.000
#> GSM564681 2 0.1414 0.995 0.020 0.980
#> GSM564693 2 0.1414 0.995 0.020 0.980
#> GSM564646 2 0.1414 0.995 0.020 0.980
#> GSM564699 2 0.1414 0.995 0.020 0.980
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.1964 0.960 0.944 0.000 0.056
#> GSM564616 2 0.0424 0.894 0.000 0.992 0.008
#> GSM564617 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564618 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564619 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564620 1 0.0000 0.978 1.000 0.000 0.000
#> GSM564621 1 0.0592 0.977 0.988 0.000 0.012
#> GSM564622 2 0.0000 0.893 0.000 1.000 0.000
#> GSM564623 2 0.2448 0.859 0.000 0.924 0.076
#> GSM564624 2 0.0000 0.893 0.000 1.000 0.000
#> GSM564625 1 0.0747 0.976 0.984 0.000 0.016
#> GSM564626 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564627 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564628 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564629 1 0.0592 0.978 0.988 0.000 0.012
#> GSM564630 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564609 3 0.5529 0.742 0.000 0.296 0.704
#> GSM564610 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564611 1 0.0475 0.978 0.992 0.004 0.004
#> GSM564612 3 0.6286 0.335 0.000 0.464 0.536
#> GSM564613 2 0.5529 0.510 0.000 0.704 0.296
#> GSM564614 1 0.1753 0.964 0.952 0.000 0.048
#> GSM564631 3 0.3482 0.922 0.000 0.128 0.872
#> GSM564632 2 0.5363 0.564 0.000 0.724 0.276
#> GSM564633 3 0.3941 0.908 0.000 0.156 0.844
#> GSM564634 2 0.6111 0.242 0.000 0.604 0.396
#> GSM564635 3 0.3038 0.918 0.000 0.104 0.896
#> GSM564636 2 0.6305 -0.151 0.000 0.516 0.484
#> GSM564637 2 0.1964 0.869 0.000 0.944 0.056
#> GSM564638 3 0.3816 0.913 0.000 0.148 0.852
#> GSM564639 3 0.3412 0.923 0.000 0.124 0.876
#> GSM564640 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564641 3 0.5098 0.815 0.000 0.248 0.752
#> GSM564642 2 0.3038 0.820 0.000 0.896 0.104
#> GSM564643 2 0.2537 0.855 0.000 0.920 0.080
#> GSM564644 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564645 3 0.3038 0.918 0.000 0.104 0.896
#> GSM564647 2 0.6180 0.126 0.000 0.584 0.416
#> GSM564648 2 0.0000 0.893 0.000 1.000 0.000
#> GSM564649 3 0.3340 0.923 0.000 0.120 0.880
#> GSM564650 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564651 2 0.5678 0.462 0.000 0.684 0.316
#> GSM564652 2 0.3267 0.806 0.000 0.884 0.116
#> GSM564653 2 0.0000 0.893 0.000 1.000 0.000
#> GSM564654 3 0.3267 0.921 0.000 0.116 0.884
#> GSM564655 3 0.3686 0.917 0.000 0.140 0.860
#> GSM564656 3 0.3038 0.918 0.000 0.104 0.896
#> GSM564657 3 0.4452 0.879 0.000 0.192 0.808
#> GSM564658 2 0.0592 0.892 0.000 0.988 0.012
#> GSM564659 2 0.6140 0.202 0.000 0.596 0.404
#> GSM564660 2 0.0592 0.892 0.000 0.988 0.012
#> GSM564661 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564662 3 0.3192 0.921 0.000 0.112 0.888
#> GSM564663 2 0.0424 0.893 0.000 0.992 0.008
#> GSM564664 2 0.1015 0.888 0.012 0.980 0.008
#> GSM564665 3 0.3340 0.921 0.000 0.120 0.880
#> GSM564666 2 0.2165 0.867 0.000 0.936 0.064
#> GSM564667 3 0.3116 0.919 0.000 0.108 0.892
#> GSM564668 3 0.3267 0.922 0.000 0.116 0.884
#> GSM564669 3 0.4974 0.835 0.000 0.236 0.764
#> GSM564670 2 0.6291 -0.102 0.000 0.532 0.468
#> GSM564671 2 0.2796 0.844 0.000 0.908 0.092
#> GSM564672 3 0.3619 0.919 0.000 0.136 0.864
#> GSM564673 2 0.1753 0.870 0.000 0.952 0.048
#> GSM564674 2 0.0424 0.893 0.000 0.992 0.008
#> GSM564675 2 0.1031 0.886 0.000 0.976 0.024
#> GSM564676 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564677 2 0.1129 0.890 0.004 0.976 0.020
#> GSM564678 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564679 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564680 3 0.4931 0.836 0.000 0.232 0.768
#> GSM564682 3 0.6045 0.582 0.000 0.380 0.620
#> GSM564683 3 0.3038 0.918 0.000 0.104 0.896
#> GSM564684 2 0.2537 0.855 0.000 0.920 0.080
#> GSM564685 3 0.3038 0.918 0.000 0.104 0.896
#> GSM564686 2 0.2537 0.855 0.000 0.920 0.080
#> GSM564687 2 0.0661 0.890 0.008 0.988 0.004
#> GSM564688 2 0.1163 0.885 0.000 0.972 0.028
#> GSM564689 2 0.0424 0.894 0.000 0.992 0.008
#> GSM564690 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564691 2 0.5785 0.410 0.000 0.668 0.332
#> GSM564692 2 0.0000 0.893 0.000 1.000 0.000
#> GSM564694 2 0.1529 0.881 0.000 0.960 0.040
#> GSM564695 2 0.4291 0.730 0.000 0.820 0.180
#> GSM564696 3 0.3038 0.918 0.000 0.104 0.896
#> GSM564697 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564698 3 0.3482 0.922 0.000 0.128 0.872
#> GSM564700 2 0.2537 0.855 0.000 0.920 0.080
#> GSM564701 2 0.0747 0.891 0.000 0.984 0.016
#> GSM564702 2 0.0592 0.893 0.000 0.988 0.012
#> GSM564703 1 0.1643 0.968 0.956 0.000 0.044
#> GSM564704 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564705 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564706 1 0.1643 0.968 0.956 0.000 0.044
#> GSM564707 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564708 1 0.2356 0.957 0.928 0.000 0.072
#> GSM564709 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564710 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564711 1 0.1753 0.967 0.952 0.000 0.048
#> GSM564712 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564713 1 0.1753 0.967 0.952 0.000 0.048
#> GSM564714 1 0.1753 0.967 0.952 0.000 0.048
#> GSM564715 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564716 1 0.0424 0.979 0.992 0.000 0.008
#> GSM564717 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564718 1 0.1860 0.965 0.948 0.000 0.052
#> GSM564719 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564720 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564721 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564722 1 0.0592 0.977 0.988 0.000 0.012
#> GSM564723 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564724 1 0.2261 0.957 0.932 0.000 0.068
#> GSM564725 1 0.0237 0.978 0.996 0.000 0.004
#> GSM564726 1 0.1860 0.966 0.948 0.000 0.052
#> GSM564727 1 0.0747 0.976 0.984 0.000 0.016
#> GSM564728 1 0.1964 0.960 0.944 0.000 0.056
#> GSM564729 1 0.1860 0.962 0.948 0.000 0.052
#> GSM564730 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564731 1 0.1643 0.968 0.956 0.000 0.044
#> GSM564732 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564733 1 0.1753 0.967 0.952 0.000 0.048
#> GSM564734 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564735 1 0.1753 0.969 0.952 0.000 0.048
#> GSM564736 1 0.1964 0.964 0.944 0.000 0.056
#> GSM564737 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564738 1 0.2774 0.950 0.920 0.008 0.072
#> GSM564739 1 0.1399 0.974 0.968 0.004 0.028
#> GSM564740 1 0.2590 0.948 0.924 0.004 0.072
#> GSM564741 1 0.2261 0.958 0.932 0.000 0.068
#> GSM564742 1 0.1643 0.968 0.956 0.000 0.044
#> GSM564743 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564744 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564745 1 0.0592 0.978 0.988 0.000 0.012
#> GSM564746 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564747 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564748 1 0.1643 0.968 0.956 0.000 0.044
#> GSM564749 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564750 1 0.1289 0.975 0.968 0.000 0.032
#> GSM564751 1 0.1643 0.968 0.956 0.000 0.044
#> GSM564752 1 0.1860 0.962 0.948 0.000 0.052
#> GSM564753 1 0.1753 0.967 0.952 0.000 0.048
#> GSM564754 1 0.0424 0.978 0.992 0.000 0.008
#> GSM564755 1 0.1964 0.960 0.944 0.000 0.056
#> GSM564756 1 0.0237 0.979 0.996 0.000 0.004
#> GSM564757 1 0.1964 0.960 0.944 0.000 0.056
#> GSM564758 1 0.1643 0.970 0.956 0.000 0.044
#> GSM564759 1 0.1753 0.967 0.952 0.000 0.048
#> GSM564760 1 0.0592 0.978 0.988 0.000 0.012
#> GSM564761 1 0.1015 0.974 0.980 0.008 0.012
#> GSM564762 1 0.0592 0.977 0.988 0.000 0.012
#> GSM564681 2 0.0237 0.894 0.000 0.996 0.004
#> GSM564693 2 0.0592 0.892 0.000 0.988 0.012
#> GSM564646 2 0.2537 0.855 0.000 0.920 0.080
#> GSM564699 2 0.2537 0.855 0.000 0.920 0.080
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.5085 0.743 0.616 0.008 0.000 0.376
#> GSM564616 2 0.2805 0.810 0.012 0.888 0.100 0.000
#> GSM564617 2 0.3166 0.810 0.016 0.868 0.116 0.000
#> GSM564618 2 0.3168 0.784 0.060 0.884 0.056 0.000
#> GSM564619 1 0.4933 0.922 0.568 0.000 0.000 0.432
#> GSM564620 1 0.4941 0.921 0.564 0.000 0.000 0.436
#> GSM564621 1 0.4888 0.897 0.588 0.000 0.000 0.412
#> GSM564622 2 0.3280 0.810 0.016 0.860 0.124 0.000
#> GSM564623 2 0.4722 0.618 0.300 0.692 0.008 0.000
#> GSM564624 2 0.3557 0.805 0.036 0.856 0.108 0.000
#> GSM564625 1 0.4888 0.898 0.588 0.000 0.000 0.412
#> GSM564626 1 0.4941 0.923 0.564 0.000 0.000 0.436
#> GSM564627 1 0.4933 0.915 0.568 0.000 0.000 0.432
#> GSM564628 2 0.3453 0.796 0.052 0.868 0.080 0.000
#> GSM564629 1 0.4933 0.922 0.568 0.000 0.000 0.432
#> GSM564630 2 0.3105 0.811 0.012 0.868 0.120 0.000
#> GSM564609 3 0.3806 0.778 0.020 0.156 0.824 0.000
#> GSM564610 1 0.4948 0.923 0.560 0.000 0.000 0.440
#> GSM564611 1 0.4961 0.918 0.552 0.000 0.000 0.448
#> GSM564612 3 0.4098 0.744 0.012 0.204 0.784 0.000
#> GSM564613 3 0.5088 0.351 0.004 0.424 0.572 0.000
#> GSM564614 1 0.5281 0.747 0.528 0.008 0.000 0.464
#> GSM564631 3 0.0524 0.858 0.000 0.008 0.988 0.004
#> GSM564632 3 0.5536 0.423 0.024 0.384 0.592 0.000
#> GSM564633 3 0.1305 0.853 0.000 0.036 0.960 0.004
#> GSM564634 3 0.4920 0.452 0.000 0.368 0.628 0.004
#> GSM564635 3 0.0376 0.857 0.000 0.004 0.992 0.004
#> GSM564636 3 0.4158 0.729 0.008 0.224 0.768 0.000
#> GSM564637 2 0.6158 0.577 0.080 0.628 0.292 0.000
#> GSM564638 3 0.1109 0.855 0.000 0.028 0.968 0.004
#> GSM564639 3 0.0376 0.857 0.000 0.004 0.992 0.004
#> GSM564640 2 0.3024 0.804 0.000 0.852 0.148 0.000
#> GSM564641 3 0.3757 0.781 0.020 0.152 0.828 0.000
#> GSM564642 2 0.5152 0.571 0.020 0.664 0.316 0.000
#> GSM564643 2 0.4868 0.612 0.304 0.684 0.012 0.000
#> GSM564644 2 0.3217 0.807 0.012 0.860 0.128 0.000
#> GSM564645 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564647 3 0.4511 0.669 0.008 0.268 0.724 0.000
#> GSM564648 2 0.3052 0.806 0.004 0.860 0.136 0.000
#> GSM564649 3 0.0000 0.856 0.000 0.000 1.000 0.000
#> GSM564650 2 0.2909 0.808 0.020 0.888 0.092 0.000
#> GSM564651 3 0.5105 0.273 0.004 0.432 0.564 0.000
#> GSM564652 2 0.5026 0.583 0.016 0.672 0.312 0.000
#> GSM564653 2 0.3032 0.811 0.008 0.868 0.124 0.000
#> GSM564654 3 0.0524 0.858 0.000 0.008 0.988 0.004
#> GSM564655 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564656 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564657 3 0.2081 0.831 0.000 0.084 0.916 0.000
#> GSM564658 2 0.3969 0.778 0.016 0.804 0.180 0.000
#> GSM564659 3 0.4401 0.683 0.004 0.272 0.724 0.000
#> GSM564660 2 0.3354 0.797 0.044 0.872 0.084 0.000
#> GSM564661 2 0.3249 0.805 0.008 0.852 0.140 0.000
#> GSM564662 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564663 2 0.3672 0.792 0.012 0.824 0.164 0.000
#> GSM564664 2 0.4362 0.791 0.024 0.808 0.156 0.012
#> GSM564665 3 0.0524 0.858 0.000 0.008 0.988 0.004
#> GSM564666 2 0.4690 0.630 0.276 0.712 0.012 0.000
#> GSM564667 3 0.0376 0.857 0.000 0.004 0.992 0.004
#> GSM564668 3 0.0376 0.857 0.000 0.004 0.992 0.004
#> GSM564669 3 0.2010 0.845 0.004 0.060 0.932 0.004
#> GSM564670 3 0.4630 0.677 0.016 0.252 0.732 0.000
#> GSM564671 2 0.5024 0.571 0.360 0.632 0.008 0.000
#> GSM564672 3 0.0524 0.858 0.000 0.008 0.988 0.004
#> GSM564673 2 0.3764 0.744 0.000 0.784 0.216 0.000
#> GSM564674 2 0.3300 0.805 0.008 0.848 0.144 0.000
#> GSM564675 2 0.4599 0.685 0.212 0.760 0.028 0.000
#> GSM564676 2 0.3105 0.809 0.012 0.868 0.120 0.000
#> GSM564677 2 0.4458 0.759 0.016 0.780 0.196 0.008
#> GSM564678 2 0.3105 0.809 0.012 0.868 0.120 0.000
#> GSM564679 2 0.3324 0.805 0.012 0.852 0.136 0.000
#> GSM564680 3 0.1109 0.856 0.000 0.028 0.968 0.004
#> GSM564682 3 0.4012 0.754 0.016 0.184 0.800 0.000
#> GSM564683 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564684 2 0.5007 0.574 0.356 0.636 0.008 0.000
#> GSM564685 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564686 2 0.5007 0.574 0.356 0.636 0.008 0.000
#> GSM564687 2 0.4000 0.801 0.016 0.828 0.144 0.012
#> GSM564688 2 0.4228 0.723 0.008 0.760 0.232 0.000
#> GSM564689 2 0.3625 0.794 0.012 0.828 0.160 0.000
#> GSM564690 2 0.3271 0.806 0.012 0.856 0.132 0.000
#> GSM564691 3 0.4973 0.546 0.008 0.348 0.644 0.000
#> GSM564692 2 0.3032 0.810 0.008 0.868 0.124 0.000
#> GSM564694 2 0.4900 0.664 0.236 0.732 0.032 0.000
#> GSM564695 2 0.6235 0.191 0.056 0.524 0.420 0.000
#> GSM564696 3 0.0188 0.855 0.000 0.000 0.996 0.004
#> GSM564697 2 0.3529 0.800 0.012 0.836 0.152 0.000
#> GSM564698 3 0.0779 0.858 0.000 0.016 0.980 0.004
#> GSM564700 2 0.5007 0.574 0.356 0.636 0.008 0.000
#> GSM564701 2 0.4253 0.750 0.016 0.776 0.208 0.000
#> GSM564702 2 0.3335 0.808 0.016 0.856 0.128 0.000
#> GSM564703 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564704 1 0.4981 0.905 0.536 0.000 0.000 0.464
#> GSM564705 1 0.4961 0.917 0.552 0.000 0.000 0.448
#> GSM564706 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564707 4 0.5155 -0.780 0.468 0.000 0.004 0.528
#> GSM564708 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564709 1 0.4981 0.904 0.536 0.000 0.000 0.464
#> GSM564710 1 0.5151 0.908 0.532 0.000 0.004 0.464
#> GSM564711 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564712 1 0.4967 0.915 0.548 0.000 0.000 0.452
#> GSM564713 4 0.1209 0.804 0.004 0.000 0.032 0.964
#> GSM564714 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564715 1 0.4981 0.905 0.536 0.000 0.000 0.464
#> GSM564716 1 0.4999 0.874 0.508 0.000 0.000 0.492
#> GSM564717 1 0.5132 0.920 0.548 0.000 0.004 0.448
#> GSM564718 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564719 1 0.4955 0.919 0.556 0.000 0.000 0.444
#> GSM564720 1 0.4948 0.922 0.560 0.000 0.000 0.440
#> GSM564721 1 0.4933 0.922 0.568 0.000 0.000 0.432
#> GSM564722 4 0.4817 -0.524 0.388 0.000 0.000 0.612
#> GSM564723 1 0.4941 0.921 0.564 0.000 0.000 0.436
#> GSM564724 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564725 1 0.4972 0.891 0.544 0.000 0.000 0.456
#> GSM564726 4 0.2654 0.712 0.108 0.004 0.000 0.888
#> GSM564727 1 0.4888 0.874 0.588 0.000 0.000 0.412
#> GSM564728 1 0.5212 0.735 0.572 0.008 0.000 0.420
#> GSM564729 1 0.5125 0.785 0.604 0.008 0.000 0.388
#> GSM564730 1 0.4933 0.923 0.568 0.000 0.000 0.432
#> GSM564731 4 0.1677 0.803 0.012 0.000 0.040 0.948
#> GSM564732 1 0.4916 0.904 0.576 0.000 0.000 0.424
#> GSM564733 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564734 1 0.4933 0.922 0.568 0.000 0.000 0.432
#> GSM564735 4 0.2032 0.786 0.036 0.000 0.028 0.936
#> GSM564736 4 0.1635 0.806 0.008 0.000 0.044 0.948
#> GSM564737 1 0.4961 0.913 0.552 0.000 0.000 0.448
#> GSM564738 4 0.1798 0.802 0.016 0.000 0.040 0.944
#> GSM564739 4 0.1733 0.784 0.024 0.000 0.028 0.948
#> GSM564740 4 0.5597 -0.520 0.464 0.020 0.000 0.516
#> GSM564741 4 0.1635 0.806 0.008 0.000 0.044 0.948
#> GSM564742 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564743 1 0.4948 0.924 0.560 0.000 0.000 0.440
#> GSM564744 1 0.4948 0.922 0.560 0.000 0.000 0.440
#> GSM564745 1 0.4933 0.921 0.568 0.000 0.000 0.432
#> GSM564746 1 0.4933 0.922 0.568 0.000 0.000 0.432
#> GSM564747 4 0.3907 0.270 0.232 0.000 0.000 0.768
#> GSM564748 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564749 1 0.4941 0.921 0.564 0.000 0.000 0.436
#> GSM564750 4 0.2345 0.712 0.100 0.000 0.000 0.900
#> GSM564751 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564752 4 0.3945 0.534 0.216 0.004 0.000 0.780
#> GSM564753 4 0.1302 0.811 0.000 0.000 0.044 0.956
#> GSM564754 1 0.4977 0.913 0.540 0.000 0.000 0.460
#> GSM564755 1 0.5203 0.729 0.576 0.008 0.000 0.416
#> GSM564756 1 0.4996 0.888 0.516 0.000 0.000 0.484
#> GSM564757 1 0.5085 0.743 0.616 0.008 0.000 0.376
#> GSM564758 4 0.3942 0.393 0.236 0.000 0.000 0.764
#> GSM564759 4 0.1211 0.806 0.000 0.000 0.040 0.960
#> GSM564760 4 0.4989 -0.792 0.472 0.000 0.000 0.528
#> GSM564761 1 0.5421 0.917 0.548 0.004 0.008 0.440
#> GSM564762 4 0.3528 0.448 0.192 0.000 0.000 0.808
#> GSM564681 2 0.4829 0.742 0.156 0.776 0.068 0.000
#> GSM564693 2 0.3216 0.766 0.076 0.880 0.044 0.000
#> GSM564646 2 0.4955 0.585 0.344 0.648 0.008 0.000
#> GSM564699 2 0.5152 0.597 0.316 0.664 0.020 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 5 0.6102 0.9469 0.232 0.000 0.000 0.200 0.568
#> GSM564616 2 0.0807 0.8544 0.000 0.976 0.012 0.000 0.012
#> GSM564617 2 0.0798 0.8525 0.000 0.976 0.008 0.000 0.016
#> GSM564618 2 0.1478 0.8427 0.000 0.936 0.000 0.000 0.064
#> GSM564619 1 0.0865 0.8682 0.972 0.000 0.000 0.024 0.004
#> GSM564620 1 0.1831 0.8477 0.920 0.000 0.000 0.076 0.004
#> GSM564621 1 0.3164 0.7982 0.852 0.000 0.000 0.104 0.044
#> GSM564622 2 0.1211 0.8518 0.000 0.960 0.024 0.000 0.016
#> GSM564623 2 0.4151 0.6819 0.000 0.652 0.004 0.000 0.344
#> GSM564624 2 0.1403 0.8516 0.000 0.952 0.024 0.000 0.024
#> GSM564625 1 0.4022 0.7199 0.796 0.000 0.000 0.100 0.104
#> GSM564626 1 0.0898 0.8673 0.972 0.000 0.000 0.020 0.008
#> GSM564627 1 0.2293 0.8390 0.900 0.000 0.000 0.084 0.016
#> GSM564628 2 0.1043 0.8484 0.000 0.960 0.000 0.000 0.040
#> GSM564629 1 0.1082 0.8679 0.964 0.000 0.000 0.028 0.008
#> GSM564630 2 0.1211 0.8531 0.000 0.960 0.024 0.000 0.016
#> GSM564609 3 0.3819 0.7062 0.000 0.228 0.756 0.000 0.016
#> GSM564610 1 0.0771 0.8683 0.976 0.000 0.000 0.020 0.004
#> GSM564611 1 0.0771 0.8654 0.976 0.000 0.000 0.020 0.004
#> GSM564612 3 0.3790 0.6657 0.000 0.272 0.724 0.000 0.004
#> GSM564613 2 0.4718 0.0706 0.000 0.540 0.444 0.000 0.016
#> GSM564614 5 0.6199 0.9447 0.236 0.000 0.000 0.212 0.552
#> GSM564631 3 0.0324 0.8425 0.000 0.004 0.992 0.000 0.004
#> GSM564632 3 0.5237 0.1232 0.000 0.468 0.488 0.000 0.044
#> GSM564633 3 0.1270 0.8329 0.000 0.052 0.948 0.000 0.000
#> GSM564634 3 0.4420 0.1917 0.000 0.448 0.548 0.000 0.004
#> GSM564635 3 0.0162 0.8402 0.000 0.000 0.996 0.000 0.004
#> GSM564636 3 0.3807 0.6940 0.000 0.240 0.748 0.000 0.012
#> GSM564637 2 0.5013 0.6463 0.000 0.696 0.204 0.000 0.100
#> GSM564638 3 0.1357 0.8336 0.000 0.048 0.948 0.000 0.004
#> GSM564639 3 0.0324 0.8425 0.000 0.004 0.992 0.000 0.004
#> GSM564640 2 0.0880 0.8524 0.000 0.968 0.032 0.000 0.000
#> GSM564641 3 0.3863 0.6819 0.000 0.248 0.740 0.000 0.012
#> GSM564642 2 0.3387 0.7496 0.012 0.828 0.148 0.000 0.012
#> GSM564643 2 0.4473 0.6245 0.000 0.580 0.008 0.000 0.412
#> GSM564644 2 0.0912 0.8525 0.000 0.972 0.016 0.000 0.012
#> GSM564645 3 0.0162 0.8402 0.000 0.000 0.996 0.000 0.004
#> GSM564647 3 0.4576 0.4535 0.000 0.376 0.608 0.000 0.016
#> GSM564648 2 0.1300 0.8505 0.000 0.956 0.028 0.000 0.016
#> GSM564649 3 0.0162 0.8430 0.000 0.004 0.996 0.000 0.000
#> GSM564650 2 0.0798 0.8547 0.000 0.976 0.008 0.000 0.016
#> GSM564651 2 0.4481 0.1909 0.000 0.576 0.416 0.000 0.008
#> GSM564652 2 0.2189 0.8193 0.000 0.904 0.084 0.000 0.012
#> GSM564653 2 0.0865 0.8544 0.000 0.972 0.024 0.000 0.004
#> GSM564654 3 0.0000 0.8416 0.000 0.000 1.000 0.000 0.000
#> GSM564655 3 0.0162 0.8421 0.000 0.000 0.996 0.000 0.004
#> GSM564656 3 0.0162 0.8402 0.000 0.000 0.996 0.000 0.004
#> GSM564657 3 0.2411 0.8039 0.000 0.108 0.884 0.000 0.008
#> GSM564658 2 0.1393 0.8501 0.008 0.956 0.024 0.000 0.012
#> GSM564659 3 0.4401 0.5613 0.000 0.328 0.656 0.000 0.016
#> GSM564660 2 0.1557 0.8462 0.000 0.940 0.008 0.000 0.052
#> GSM564661 2 0.0798 0.8534 0.000 0.976 0.016 0.000 0.008
#> GSM564662 3 0.0324 0.8425 0.000 0.004 0.992 0.000 0.004
#> GSM564663 2 0.1299 0.8507 0.008 0.960 0.020 0.000 0.012
#> GSM564664 2 0.1617 0.8496 0.020 0.948 0.020 0.000 0.012
#> GSM564665 3 0.0000 0.8416 0.000 0.000 1.000 0.000 0.000
#> GSM564666 2 0.4101 0.6964 0.000 0.664 0.004 0.000 0.332
#> GSM564667 3 0.0324 0.8425 0.000 0.004 0.992 0.000 0.004
#> GSM564668 3 0.0162 0.8430 0.000 0.004 0.996 0.000 0.000
#> GSM564669 3 0.1331 0.8354 0.000 0.040 0.952 0.000 0.008
#> GSM564670 2 0.4653 -0.0426 0.000 0.516 0.472 0.000 0.012
#> GSM564671 2 0.4430 0.5810 0.000 0.540 0.004 0.000 0.456
#> GSM564672 3 0.0162 0.8430 0.000 0.004 0.996 0.000 0.000
#> GSM564673 2 0.1197 0.8478 0.000 0.952 0.048 0.000 0.000
#> GSM564674 2 0.0955 0.8531 0.000 0.968 0.028 0.000 0.004
#> GSM564675 2 0.3048 0.7896 0.000 0.820 0.004 0.000 0.176
#> GSM564676 2 0.0912 0.8525 0.000 0.972 0.016 0.000 0.012
#> GSM564677 2 0.2186 0.8384 0.016 0.924 0.044 0.004 0.012
#> GSM564678 2 0.0807 0.8528 0.000 0.976 0.012 0.000 0.012
#> GSM564679 2 0.0912 0.8525 0.000 0.972 0.016 0.000 0.012
#> GSM564680 3 0.0404 0.8427 0.000 0.012 0.988 0.000 0.000
#> GSM564682 3 0.4734 0.5274 0.008 0.344 0.632 0.000 0.016
#> GSM564683 3 0.0162 0.8402 0.000 0.000 0.996 0.000 0.004
#> GSM564684 2 0.4196 0.6738 0.000 0.640 0.004 0.000 0.356
#> GSM564685 3 0.0162 0.8402 0.000 0.000 0.996 0.000 0.004
#> GSM564686 2 0.4211 0.6726 0.000 0.636 0.004 0.000 0.360
#> GSM564687 2 0.1574 0.8502 0.012 0.952 0.020 0.004 0.012
#> GSM564688 2 0.2068 0.8182 0.000 0.904 0.092 0.000 0.004
#> GSM564689 2 0.0992 0.8531 0.000 0.968 0.024 0.000 0.008
#> GSM564690 2 0.1012 0.8518 0.000 0.968 0.020 0.000 0.012
#> GSM564691 3 0.4902 0.2648 0.008 0.460 0.520 0.000 0.012
#> GSM564692 2 0.1124 0.8520 0.000 0.960 0.036 0.000 0.004
#> GSM564694 2 0.3821 0.7639 0.000 0.764 0.020 0.000 0.216
#> GSM564695 2 0.4967 0.5060 0.000 0.660 0.280 0.000 0.060
#> GSM564696 3 0.0000 0.8416 0.000 0.000 1.000 0.000 0.000
#> GSM564697 2 0.1173 0.8514 0.004 0.964 0.020 0.000 0.012
#> GSM564698 3 0.0703 0.8410 0.000 0.024 0.976 0.000 0.000
#> GSM564700 2 0.4196 0.6738 0.000 0.640 0.004 0.000 0.356
#> GSM564701 2 0.1651 0.8469 0.008 0.944 0.036 0.000 0.012
#> GSM564702 2 0.1216 0.8559 0.000 0.960 0.020 0.000 0.020
#> GSM564703 4 0.1195 0.8847 0.028 0.000 0.000 0.960 0.012
#> GSM564704 1 0.2561 0.7766 0.856 0.000 0.000 0.144 0.000
#> GSM564705 1 0.0000 0.8547 1.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.0992 0.8855 0.024 0.000 0.000 0.968 0.008
#> GSM564707 1 0.1792 0.8216 0.916 0.000 0.000 0.084 0.000
#> GSM564708 4 0.1211 0.8835 0.024 0.000 0.000 0.960 0.016
#> GSM564709 1 0.2997 0.7616 0.840 0.000 0.000 0.148 0.012
#> GSM564710 1 0.0703 0.8646 0.976 0.000 0.000 0.024 0.000
#> GSM564711 4 0.0865 0.8855 0.024 0.000 0.000 0.972 0.004
#> GSM564712 1 0.0609 0.8617 0.980 0.000 0.000 0.020 0.000
#> GSM564713 4 0.0992 0.8859 0.024 0.000 0.000 0.968 0.008
#> GSM564714 4 0.0992 0.8855 0.024 0.000 0.000 0.968 0.008
#> GSM564715 1 0.1282 0.8657 0.952 0.000 0.000 0.044 0.004
#> GSM564716 1 0.2612 0.8049 0.868 0.000 0.000 0.124 0.008
#> GSM564717 1 0.1205 0.8683 0.956 0.000 0.000 0.040 0.004
#> GSM564718 4 0.0992 0.8854 0.024 0.000 0.000 0.968 0.008
#> GSM564719 1 0.0290 0.8570 0.992 0.000 0.000 0.008 0.000
#> GSM564720 1 0.0000 0.8547 1.000 0.000 0.000 0.000 0.000
#> GSM564721 1 0.0798 0.8666 0.976 0.000 0.000 0.016 0.008
#> GSM564722 1 0.4718 0.2795 0.628 0.000 0.000 0.344 0.028
#> GSM564723 1 0.0290 0.8574 0.992 0.000 0.000 0.008 0.000
#> GSM564724 4 0.1211 0.8835 0.024 0.000 0.000 0.960 0.016
#> GSM564725 1 0.3304 0.7203 0.816 0.000 0.000 0.168 0.016
#> GSM564726 4 0.4065 0.4882 0.016 0.000 0.000 0.720 0.264
#> GSM564727 1 0.5798 0.1835 0.604 0.000 0.000 0.148 0.248
#> GSM564728 5 0.6171 0.9529 0.240 0.000 0.000 0.204 0.556
#> GSM564729 5 0.6174 0.9394 0.256 0.000 0.000 0.192 0.552
#> GSM564730 1 0.1281 0.8668 0.956 0.000 0.000 0.032 0.012
#> GSM564731 4 0.1364 0.8781 0.036 0.000 0.000 0.952 0.012
#> GSM564732 1 0.4280 0.6772 0.772 0.000 0.000 0.140 0.088
#> GSM564733 4 0.0865 0.8855 0.024 0.000 0.000 0.972 0.004
#> GSM564734 1 0.1364 0.8657 0.952 0.000 0.000 0.036 0.012
#> GSM564735 4 0.1399 0.8803 0.028 0.000 0.000 0.952 0.020
#> GSM564736 4 0.1106 0.8846 0.024 0.000 0.000 0.964 0.012
#> GSM564737 1 0.0162 0.8536 0.996 0.000 0.000 0.004 0.000
#> GSM564738 4 0.1564 0.8792 0.024 0.000 0.004 0.948 0.024
#> GSM564739 4 0.2358 0.8029 0.104 0.000 0.000 0.888 0.008
#> GSM564740 5 0.6518 0.7859 0.240 0.000 0.000 0.276 0.484
#> GSM564741 4 0.1211 0.8834 0.024 0.000 0.000 0.960 0.016
#> GSM564742 4 0.0992 0.8855 0.024 0.000 0.000 0.968 0.008
#> GSM564743 1 0.0693 0.8654 0.980 0.000 0.000 0.012 0.008
#> GSM564744 1 0.0703 0.8671 0.976 0.000 0.000 0.024 0.000
#> GSM564745 1 0.1670 0.8594 0.936 0.000 0.000 0.052 0.012
#> GSM564746 1 0.0771 0.8674 0.976 0.000 0.000 0.020 0.004
#> GSM564747 1 0.4557 0.0287 0.516 0.000 0.000 0.476 0.008
#> GSM564748 4 0.0992 0.8855 0.024 0.000 0.000 0.968 0.008
#> GSM564749 1 0.0162 0.8536 0.996 0.000 0.000 0.004 0.000
#> GSM564750 4 0.3110 0.7902 0.080 0.000 0.000 0.860 0.060
#> GSM564751 4 0.1082 0.8838 0.028 0.000 0.000 0.964 0.008
#> GSM564752 4 0.4825 0.0582 0.024 0.000 0.000 0.568 0.408
#> GSM564753 4 0.0992 0.8855 0.024 0.000 0.000 0.968 0.008
#> GSM564754 1 0.2017 0.8519 0.912 0.000 0.000 0.080 0.008
#> GSM564755 5 0.6171 0.9529 0.240 0.000 0.000 0.204 0.556
#> GSM564756 1 0.2127 0.8301 0.892 0.000 0.000 0.108 0.000
#> GSM564757 5 0.6087 0.9427 0.244 0.000 0.000 0.188 0.568
#> GSM564758 4 0.6433 -0.3804 0.184 0.000 0.000 0.464 0.352
#> GSM564759 4 0.0992 0.8855 0.024 0.000 0.000 0.968 0.008
#> GSM564760 1 0.5810 0.0943 0.580 0.000 0.000 0.296 0.124
#> GSM564761 1 0.1386 0.8591 0.952 0.016 0.000 0.032 0.000
#> GSM564762 4 0.4798 0.0575 0.396 0.000 0.000 0.580 0.024
#> GSM564681 2 0.1894 0.8415 0.000 0.920 0.008 0.000 0.072
#> GSM564693 2 0.1768 0.8395 0.000 0.924 0.004 0.000 0.072
#> GSM564646 2 0.4196 0.6738 0.000 0.640 0.004 0.000 0.356
#> GSM564699 2 0.5420 0.5532 0.000 0.524 0.060 0.000 0.416
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 2 0.0665 0.8679 0.008 0.980 0.000 0.008 0.000 0.004
#> GSM564616 5 0.0547 0.8998 0.000 0.000 0.000 0.000 0.980 0.020
#> GSM564617 5 0.0632 0.8939 0.000 0.000 0.000 0.000 0.976 0.024
#> GSM564618 5 0.3244 0.6233 0.000 0.000 0.000 0.000 0.732 0.268
#> GSM564619 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564620 1 0.0458 0.9260 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM564621 1 0.3804 0.2338 0.576 0.424 0.000 0.000 0.000 0.000
#> GSM564622 5 0.0547 0.9006 0.000 0.000 0.000 0.000 0.980 0.020
#> GSM564623 6 0.0937 0.9432 0.000 0.000 0.000 0.000 0.040 0.960
#> GSM564624 5 0.0937 0.8900 0.000 0.000 0.000 0.000 0.960 0.040
#> GSM564625 1 0.3795 0.4037 0.632 0.364 0.000 0.000 0.000 0.004
#> GSM564626 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564627 1 0.2793 0.7251 0.800 0.200 0.000 0.000 0.000 0.000
#> GSM564628 5 0.2883 0.7075 0.000 0.000 0.000 0.000 0.788 0.212
#> GSM564629 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564630 5 0.0458 0.8980 0.000 0.000 0.000 0.000 0.984 0.016
#> GSM564609 3 0.4621 0.6251 0.000 0.012 0.676 0.000 0.256 0.056
#> GSM564610 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564611 1 0.0291 0.9300 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM564612 3 0.4664 0.6118 0.000 0.012 0.668 0.000 0.264 0.056
#> GSM564613 5 0.3139 0.8134 0.000 0.012 0.084 0.000 0.848 0.056
#> GSM564614 2 0.0508 0.8682 0.004 0.984 0.000 0.012 0.000 0.000
#> GSM564631 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564632 5 0.5383 -0.0618 0.000 0.012 0.440 0.000 0.472 0.076
#> GSM564633 3 0.0972 0.8616 0.000 0.000 0.964 0.000 0.028 0.008
#> GSM564634 5 0.4531 0.6286 0.000 0.012 0.240 0.000 0.692 0.056
#> GSM564635 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564636 3 0.4395 0.6251 0.000 0.008 0.684 0.000 0.264 0.044
#> GSM564637 5 0.4200 0.7403 0.000 0.012 0.092 0.000 0.760 0.136
#> GSM564638 3 0.1151 0.8569 0.000 0.000 0.956 0.000 0.032 0.012
#> GSM564639 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564640 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564641 3 0.4474 0.6501 0.000 0.012 0.696 0.000 0.240 0.052
#> GSM564642 5 0.1606 0.8764 0.000 0.008 0.004 0.000 0.932 0.056
#> GSM564643 6 0.0291 0.9497 0.000 0.004 0.000 0.000 0.004 0.992
#> GSM564644 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564645 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564647 3 0.5152 0.2252 0.000 0.012 0.504 0.000 0.428 0.056
#> GSM564648 5 0.0692 0.8988 0.000 0.004 0.000 0.000 0.976 0.020
#> GSM564649 3 0.0363 0.8721 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM564650 5 0.0260 0.9016 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM564651 5 0.3190 0.8178 0.000 0.012 0.088 0.000 0.844 0.056
#> GSM564652 5 0.0603 0.8997 0.000 0.004 0.000 0.000 0.980 0.016
#> GSM564653 5 0.0146 0.9022 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564654 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564655 3 0.0551 0.8720 0.000 0.004 0.984 0.000 0.004 0.008
#> GSM564656 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564657 3 0.1078 0.8629 0.000 0.008 0.964 0.000 0.012 0.016
#> GSM564658 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564659 3 0.5028 0.4591 0.000 0.012 0.588 0.000 0.340 0.060
#> GSM564660 5 0.2048 0.8480 0.000 0.000 0.000 0.000 0.880 0.120
#> GSM564661 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564662 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564663 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564664 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564665 3 0.0508 0.8711 0.000 0.004 0.984 0.000 0.000 0.012
#> GSM564666 6 0.0291 0.9497 0.000 0.004 0.000 0.000 0.004 0.992
#> GSM564667 3 0.0146 0.8746 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM564668 3 0.0291 0.8737 0.000 0.004 0.992 0.000 0.000 0.004
#> GSM564669 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564670 5 0.4239 0.6669 0.000 0.012 0.196 0.000 0.736 0.056
#> GSM564671 6 0.0363 0.9545 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM564672 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564673 5 0.0692 0.8987 0.000 0.004 0.000 0.000 0.976 0.020
#> GSM564674 5 0.0363 0.9011 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM564675 6 0.2454 0.7975 0.000 0.000 0.000 0.000 0.160 0.840
#> GSM564676 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564677 5 0.0508 0.9017 0.004 0.000 0.000 0.000 0.984 0.012
#> GSM564678 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564679 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564680 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564682 3 0.5088 0.3584 0.000 0.012 0.548 0.000 0.384 0.056
#> GSM564683 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564684 6 0.0937 0.9432 0.000 0.000 0.000 0.000 0.040 0.960
#> GSM564685 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564686 6 0.0363 0.9545 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM564687 5 0.0260 0.8996 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM564688 5 0.0713 0.8968 0.000 0.000 0.000 0.000 0.972 0.028
#> GSM564689 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564690 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564691 5 0.5116 -0.0678 0.000 0.012 0.448 0.000 0.488 0.052
#> GSM564692 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564694 6 0.1194 0.9279 0.000 0.004 0.008 0.000 0.032 0.956
#> GSM564695 5 0.4933 0.6376 0.000 0.012 0.136 0.000 0.684 0.168
#> GSM564696 3 0.0000 0.8753 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564697 5 0.0000 0.9025 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564698 3 0.0665 0.8707 0.000 0.004 0.980 0.000 0.008 0.008
#> GSM564700 6 0.0458 0.9542 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM564701 5 0.0603 0.8999 0.000 0.004 0.000 0.000 0.980 0.016
#> GSM564702 5 0.1075 0.8852 0.000 0.000 0.000 0.000 0.952 0.048
#> GSM564703 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564704 1 0.1088 0.9102 0.960 0.024 0.000 0.016 0.000 0.000
#> GSM564705 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564707 1 0.0603 0.9252 0.980 0.016 0.000 0.004 0.000 0.000
#> GSM564708 4 0.0146 0.9700 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM564709 1 0.1176 0.9104 0.956 0.024 0.000 0.020 0.000 0.000
#> GSM564710 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564712 1 0.0146 0.9306 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM564713 4 0.0405 0.9656 0.008 0.004 0.000 0.988 0.000 0.000
#> GSM564714 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564715 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564716 1 0.0405 0.9287 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM564717 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564718 4 0.0146 0.9700 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM564719 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564720 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564721 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564722 1 0.4144 0.6464 0.728 0.072 0.000 0.200 0.000 0.000
#> GSM564723 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564724 4 0.0146 0.9700 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM564725 1 0.2633 0.8302 0.864 0.104 0.000 0.032 0.000 0.000
#> GSM564726 2 0.1957 0.8269 0.000 0.888 0.000 0.112 0.000 0.000
#> GSM564727 2 0.2243 0.8042 0.112 0.880 0.000 0.004 0.000 0.004
#> GSM564728 2 0.0405 0.8679 0.004 0.988 0.000 0.008 0.000 0.000
#> GSM564729 2 0.0551 0.8677 0.004 0.984 0.000 0.008 0.000 0.004
#> GSM564730 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564731 4 0.1245 0.9309 0.032 0.016 0.000 0.952 0.000 0.000
#> GSM564732 2 0.3717 0.3774 0.384 0.616 0.000 0.000 0.000 0.000
#> GSM564733 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564734 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564735 4 0.0717 0.9564 0.008 0.016 0.000 0.976 0.000 0.000
#> GSM564736 4 0.0146 0.9700 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM564737 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.0260 0.9678 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM564739 4 0.2501 0.8265 0.108 0.016 0.000 0.872 0.004 0.000
#> GSM564740 2 0.1732 0.8488 0.004 0.920 0.000 0.072 0.000 0.004
#> GSM564741 4 0.0146 0.9700 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM564742 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564743 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564744 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564745 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564746 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564747 1 0.3245 0.7380 0.800 0.028 0.000 0.172 0.000 0.000
#> GSM564748 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564749 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564750 4 0.4328 0.6289 0.100 0.180 0.000 0.720 0.000 0.000
#> GSM564751 4 0.0146 0.9683 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564752 2 0.1663 0.8400 0.000 0.912 0.000 0.088 0.000 0.000
#> GSM564753 4 0.0000 0.9704 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564754 1 0.0146 0.9314 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM564755 2 0.0405 0.8679 0.004 0.988 0.000 0.008 0.000 0.000
#> GSM564756 1 0.0777 0.9190 0.972 0.004 0.000 0.024 0.000 0.000
#> GSM564757 2 0.0665 0.8679 0.008 0.980 0.000 0.008 0.000 0.004
#> GSM564758 2 0.3626 0.7672 0.068 0.788 0.000 0.144 0.000 0.000
#> GSM564759 4 0.0146 0.9689 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564760 2 0.4915 0.6421 0.208 0.652 0.000 0.140 0.000 0.000
#> GSM564761 1 0.0777 0.9102 0.972 0.000 0.000 0.004 0.024 0.000
#> GSM564762 1 0.4420 0.4041 0.604 0.036 0.000 0.360 0.000 0.000
#> GSM564681 5 0.2823 0.7413 0.000 0.000 0.000 0.000 0.796 0.204
#> GSM564693 5 0.2562 0.7836 0.000 0.000 0.000 0.000 0.828 0.172
#> GSM564646 6 0.0937 0.9432 0.000 0.000 0.000 0.000 0.040 0.960
#> GSM564699 6 0.0291 0.9497 0.000 0.004 0.000 0.000 0.004 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 genotype/variation(p) disease.state(p) k
#> CV:mclust 154 0.9246 0.4759 2
#> CV:mclust 146 0.3128 0.4437 3
#> CV:mclust 142 0.0117 0.7597 4
#> CV:mclust 139 0.0357 0.9544 5
#> CV:mclust 145 0.2907 0.0732 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 154 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 1.000 0.977 0.988 0.5013 0.500 0.500
#> 3 3 0.617 0.691 0.826 0.2903 0.827 0.666
#> 4 4 0.806 0.822 0.921 0.1478 0.813 0.535
#> 5 5 0.644 0.618 0.787 0.0594 0.924 0.720
#> 6 6 0.625 0.518 0.723 0.0416 0.939 0.739
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
#> GSM564615 1 0.0000 0.995 1.000 0.000
#> GSM564616 2 0.0000 0.982 0.000 1.000
#> GSM564617 2 0.0000 0.982 0.000 1.000
#> GSM564618 2 0.5519 0.869 0.128 0.872
#> GSM564619 1 0.0000 0.995 1.000 0.000
#> GSM564620 1 0.0000 0.995 1.000 0.000
#> GSM564621 1 0.0000 0.995 1.000 0.000
#> GSM564622 2 0.0000 0.982 0.000 1.000
#> GSM564623 2 0.7219 0.777 0.200 0.800
#> GSM564624 2 0.0376 0.979 0.004 0.996
#> GSM564625 1 0.0000 0.995 1.000 0.000
#> GSM564626 1 0.0000 0.995 1.000 0.000
#> GSM564627 1 0.0000 0.995 1.000 0.000
#> GSM564628 2 0.4690 0.898 0.100 0.900
#> GSM564629 1 0.0000 0.995 1.000 0.000
#> GSM564630 2 0.0000 0.982 0.000 1.000
#> GSM564609 2 0.0000 0.982 0.000 1.000
#> GSM564610 1 0.0000 0.995 1.000 0.000
#> GSM564611 1 0.1843 0.973 0.972 0.028
#> GSM564612 2 0.0000 0.982 0.000 1.000
#> GSM564613 2 0.0000 0.982 0.000 1.000
#> GSM564614 1 0.0000 0.995 1.000 0.000
#> GSM564631 2 0.0000 0.982 0.000 1.000
#> GSM564632 2 0.0000 0.982 0.000 1.000
#> GSM564633 2 0.0000 0.982 0.000 1.000
#> GSM564634 2 0.0000 0.982 0.000 1.000
#> GSM564635 2 0.0000 0.982 0.000 1.000
#> GSM564636 2 0.0000 0.982 0.000 1.000
#> GSM564637 2 0.0000 0.982 0.000 1.000
#> GSM564638 2 0.0000 0.982 0.000 1.000
#> GSM564639 2 0.0000 0.982 0.000 1.000
#> GSM564640 2 0.0000 0.982 0.000 1.000
#> GSM564641 2 0.0000 0.982 0.000 1.000
#> GSM564642 2 0.0000 0.982 0.000 1.000
#> GSM564643 2 0.1414 0.967 0.020 0.980
#> GSM564644 2 0.0000 0.982 0.000 1.000
#> GSM564645 2 0.0000 0.982 0.000 1.000
#> GSM564647 2 0.0000 0.982 0.000 1.000
#> GSM564648 2 0.0000 0.982 0.000 1.000
#> GSM564649 2 0.0000 0.982 0.000 1.000
#> GSM564650 2 0.0000 0.982 0.000 1.000
#> GSM564651 2 0.0000 0.982 0.000 1.000
#> GSM564652 2 0.0000 0.982 0.000 1.000
#> GSM564653 2 0.0000 0.982 0.000 1.000
#> GSM564654 2 0.0000 0.982 0.000 1.000
#> GSM564655 2 0.0000 0.982 0.000 1.000
#> GSM564656 2 0.0000 0.982 0.000 1.000
#> GSM564657 2 0.0000 0.982 0.000 1.000
#> GSM564658 2 0.0000 0.982 0.000 1.000
#> GSM564659 2 0.0000 0.982 0.000 1.000
#> GSM564660 2 0.0000 0.982 0.000 1.000
#> GSM564661 2 0.0000 0.982 0.000 1.000
#> GSM564662 2 0.0000 0.982 0.000 1.000
#> GSM564663 2 0.0000 0.982 0.000 1.000
#> GSM564664 2 0.0000 0.982 0.000 1.000
#> GSM564665 2 0.0000 0.982 0.000 1.000
#> GSM564666 2 0.3114 0.939 0.056 0.944
#> GSM564667 2 0.0000 0.982 0.000 1.000
#> GSM564668 2 0.0000 0.982 0.000 1.000
#> GSM564669 2 0.0000 0.982 0.000 1.000
#> GSM564670 2 0.0000 0.982 0.000 1.000
#> GSM564671 2 0.8267 0.681 0.260 0.740
#> GSM564672 2 0.0000 0.982 0.000 1.000
#> GSM564673 2 0.0000 0.982 0.000 1.000
#> GSM564674 2 0.0000 0.982 0.000 1.000
#> GSM564675 2 0.0376 0.979 0.004 0.996
#> GSM564676 2 0.0000 0.982 0.000 1.000
#> GSM564677 2 0.0000 0.982 0.000 1.000
#> GSM564678 2 0.0000 0.982 0.000 1.000
#> GSM564679 2 0.0000 0.982 0.000 1.000
#> GSM564680 2 0.0000 0.982 0.000 1.000
#> GSM564682 2 0.0000 0.982 0.000 1.000
#> GSM564683 2 0.0000 0.982 0.000 1.000
#> GSM564684 2 0.2778 0.945 0.048 0.952
#> GSM564685 2 0.0000 0.982 0.000 1.000
#> GSM564686 2 0.6623 0.815 0.172 0.828
#> GSM564687 2 0.0000 0.982 0.000 1.000
#> GSM564688 2 0.0000 0.982 0.000 1.000
#> GSM564689 2 0.0000 0.982 0.000 1.000
#> GSM564690 2 0.0000 0.982 0.000 1.000
#> GSM564691 2 0.0000 0.982 0.000 1.000
#> GSM564692 2 0.0000 0.982 0.000 1.000
#> GSM564694 2 0.0000 0.982 0.000 1.000
#> GSM564695 2 0.0000 0.982 0.000 1.000
#> GSM564696 2 0.0000 0.982 0.000 1.000
#> GSM564697 2 0.0000 0.982 0.000 1.000
#> GSM564698 2 0.0000 0.982 0.000 1.000
#> GSM564700 2 0.5519 0.869 0.128 0.872
#> GSM564701 2 0.0000 0.982 0.000 1.000
#> GSM564702 2 0.4562 0.902 0.096 0.904
#> GSM564703 1 0.1184 0.984 0.984 0.016
#> GSM564704 1 0.0000 0.995 1.000 0.000
#> GSM564705 1 0.0000 0.995 1.000 0.000
#> GSM564706 1 0.3584 0.932 0.932 0.068
#> GSM564707 1 0.0000 0.995 1.000 0.000
#> GSM564708 1 0.0672 0.990 0.992 0.008
#> GSM564709 1 0.0000 0.995 1.000 0.000
#> GSM564710 1 0.0000 0.995 1.000 0.000
#> GSM564711 1 0.0376 0.993 0.996 0.004
#> GSM564712 1 0.0000 0.995 1.000 0.000
#> GSM564713 1 0.0000 0.995 1.000 0.000
#> GSM564714 1 0.0000 0.995 1.000 0.000
#> GSM564715 1 0.0000 0.995 1.000 0.000
#> GSM564716 1 0.0000 0.995 1.000 0.000
#> GSM564717 1 0.1843 0.973 0.972 0.028
#> GSM564718 1 0.0000 0.995 1.000 0.000
#> GSM564719 1 0.1414 0.980 0.980 0.020
#> GSM564720 1 0.0938 0.987 0.988 0.012
#> GSM564721 1 0.0000 0.995 1.000 0.000
#> GSM564722 1 0.0000 0.995 1.000 0.000
#> GSM564723 1 0.0938 0.987 0.988 0.012
#> GSM564724 1 0.0000 0.995 1.000 0.000
#> GSM564725 1 0.0000 0.995 1.000 0.000
#> GSM564726 1 0.0000 0.995 1.000 0.000
#> GSM564727 1 0.0000 0.995 1.000 0.000
#> GSM564728 1 0.0000 0.995 1.000 0.000
#> GSM564729 1 0.0000 0.995 1.000 0.000
#> GSM564730 1 0.0000 0.995 1.000 0.000
#> GSM564731 1 0.0000 0.995 1.000 0.000
#> GSM564732 1 0.0000 0.995 1.000 0.000
#> GSM564733 1 0.0000 0.995 1.000 0.000
#> GSM564734 1 0.0000 0.995 1.000 0.000
#> GSM564735 1 0.0000 0.995 1.000 0.000
#> GSM564736 1 0.0000 0.995 1.000 0.000
#> GSM564737 1 0.0000 0.995 1.000 0.000
#> GSM564738 1 0.0000 0.995 1.000 0.000
#> GSM564739 1 0.0000 0.995 1.000 0.000
#> GSM564740 1 0.0000 0.995 1.000 0.000
#> GSM564741 1 0.0000 0.995 1.000 0.000
#> GSM564742 1 0.3114 0.945 0.944 0.056
#> GSM564743 1 0.0000 0.995 1.000 0.000
#> GSM564744 1 0.0000 0.995 1.000 0.000
#> GSM564745 1 0.0000 0.995 1.000 0.000
#> GSM564746 1 0.0000 0.995 1.000 0.000
#> GSM564747 1 0.0000 0.995 1.000 0.000
#> GSM564748 1 0.1843 0.973 0.972 0.028
#> GSM564749 1 0.0376 0.993 0.996 0.004
#> GSM564750 1 0.0000 0.995 1.000 0.000
#> GSM564751 1 0.0672 0.990 0.992 0.008
#> GSM564752 1 0.0000 0.995 1.000 0.000
#> GSM564753 1 0.0938 0.987 0.988 0.012
#> GSM564754 1 0.0000 0.995 1.000 0.000
#> GSM564755 1 0.0000 0.995 1.000 0.000
#> GSM564756 1 0.0000 0.995 1.000 0.000
#> GSM564757 1 0.0000 0.995 1.000 0.000
#> GSM564758 1 0.2423 0.960 0.960 0.040
#> GSM564759 1 0.0000 0.995 1.000 0.000
#> GSM564760 1 0.0000 0.995 1.000 0.000
#> GSM564761 1 0.0000 0.995 1.000 0.000
#> GSM564762 1 0.0000 0.995 1.000 0.000
#> GSM564681 2 0.0000 0.982 0.000 1.000
#> GSM564693 2 0.0000 0.982 0.000 1.000
#> GSM564646 2 0.5294 0.877 0.120 0.880
#> GSM564699 2 0.6343 0.831 0.160 0.840
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 2 0.6008 0.40756 0.372 0.628 0.000
#> GSM564616 3 0.6252 0.59076 0.000 0.444 0.556
#> GSM564617 3 0.6225 0.61291 0.000 0.432 0.568
#> GSM564618 2 0.4233 0.48854 0.004 0.836 0.160
#> GSM564619 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564620 1 0.1860 0.86482 0.948 0.052 0.000
#> GSM564621 1 0.4605 0.67748 0.796 0.204 0.000
#> GSM564622 3 0.5926 0.72062 0.000 0.356 0.644
#> GSM564623 2 0.1399 0.60672 0.004 0.968 0.028
#> GSM564624 3 0.6299 0.52857 0.000 0.476 0.524
#> GSM564625 1 0.1860 0.86524 0.948 0.052 0.000
#> GSM564626 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564627 1 0.2625 0.83722 0.916 0.084 0.000
#> GSM564628 2 0.6282 -0.13503 0.004 0.612 0.384
#> GSM564629 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564630 3 0.6008 0.70102 0.000 0.372 0.628
#> GSM564609 3 0.1289 0.72229 0.000 0.032 0.968
#> GSM564610 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564611 1 0.1289 0.87704 0.968 0.032 0.000
#> GSM564612 3 0.2165 0.73483 0.000 0.064 0.936
#> GSM564613 3 0.5621 0.76625 0.000 0.308 0.692
#> GSM564614 1 0.6291 -0.02318 0.532 0.468 0.000
#> GSM564631 3 0.2165 0.68327 0.000 0.064 0.936
#> GSM564632 2 0.5431 0.24042 0.000 0.716 0.284
#> GSM564633 3 0.2448 0.67504 0.000 0.076 0.924
#> GSM564634 3 0.5529 0.77217 0.000 0.296 0.704
#> GSM564635 3 0.1411 0.70086 0.000 0.036 0.964
#> GSM564636 3 0.3116 0.70767 0.000 0.108 0.892
#> GSM564637 3 0.6095 0.65908 0.000 0.392 0.608
#> GSM564638 3 0.4110 0.58808 0.004 0.152 0.844
#> GSM564639 3 0.3038 0.64954 0.000 0.104 0.896
#> GSM564640 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564641 3 0.0424 0.71157 0.000 0.008 0.992
#> GSM564642 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564643 2 0.1753 0.58918 0.000 0.952 0.048
#> GSM564644 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564645 3 0.0747 0.70904 0.000 0.016 0.984
#> GSM564647 3 0.4654 0.76796 0.000 0.208 0.792
#> GSM564648 3 0.5810 0.74183 0.000 0.336 0.664
#> GSM564649 3 0.0000 0.71409 0.000 0.000 1.000
#> GSM564650 3 0.5760 0.75046 0.000 0.328 0.672
#> GSM564651 3 0.5431 0.77332 0.000 0.284 0.716
#> GSM564652 3 0.5785 0.76995 0.004 0.300 0.696
#> GSM564653 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564654 3 0.1289 0.70744 0.000 0.032 0.968
#> GSM564655 3 0.2448 0.67528 0.000 0.076 0.924
#> GSM564656 3 0.2625 0.66797 0.000 0.084 0.916
#> GSM564657 3 0.1163 0.70354 0.000 0.028 0.972
#> GSM564658 3 0.5591 0.76903 0.000 0.304 0.696
#> GSM564659 3 0.4654 0.75536 0.000 0.208 0.792
#> GSM564660 2 0.5882 -0.00612 0.000 0.652 0.348
#> GSM564661 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564662 3 0.0747 0.70907 0.000 0.016 0.984
#> GSM564663 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564664 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564665 3 0.1411 0.72935 0.000 0.036 0.964
#> GSM564666 2 0.0237 0.61297 0.000 0.996 0.004
#> GSM564667 3 0.0424 0.71157 0.000 0.008 0.992
#> GSM564668 3 0.2448 0.67576 0.000 0.076 0.924
#> GSM564669 3 0.4121 0.57138 0.000 0.168 0.832
#> GSM564670 3 0.5363 0.77343 0.000 0.276 0.724
#> GSM564671 2 0.1031 0.62427 0.024 0.976 0.000
#> GSM564672 3 0.3192 0.64248 0.000 0.112 0.888
#> GSM564673 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564674 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564675 2 0.4178 0.46574 0.000 0.828 0.172
#> GSM564676 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564677 3 0.6082 0.76683 0.012 0.296 0.692
#> GSM564678 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564679 3 0.5785 0.76971 0.004 0.300 0.696
#> GSM564680 3 0.3482 0.62341 0.000 0.128 0.872
#> GSM564682 3 0.2878 0.74555 0.000 0.096 0.904
#> GSM564683 3 0.3038 0.64927 0.000 0.104 0.896
#> GSM564684 2 0.2356 0.57302 0.000 0.928 0.072
#> GSM564685 3 0.1529 0.69836 0.000 0.040 0.960
#> GSM564686 2 0.0747 0.62124 0.016 0.984 0.000
#> GSM564687 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564688 3 0.5529 0.77230 0.000 0.296 0.704
#> GSM564689 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564690 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564691 3 0.4452 0.76512 0.000 0.192 0.808
#> GSM564692 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564694 2 0.5785 0.01873 0.000 0.668 0.332
#> GSM564695 3 0.5706 0.75708 0.000 0.320 0.680
#> GSM564696 3 0.3267 0.63699 0.000 0.116 0.884
#> GSM564697 3 0.5560 0.77130 0.000 0.300 0.700
#> GSM564698 3 0.3192 0.64251 0.000 0.112 0.888
#> GSM564700 2 0.0237 0.61589 0.004 0.996 0.000
#> GSM564701 3 0.5431 0.77334 0.000 0.284 0.716
#> GSM564702 2 0.5958 0.16904 0.008 0.692 0.300
#> GSM564703 1 0.3340 0.81995 0.880 0.000 0.120
#> GSM564704 1 0.0237 0.89487 0.996 0.004 0.000
#> GSM564705 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564706 1 0.4277 0.80122 0.852 0.016 0.132
#> GSM564707 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564708 1 0.3686 0.79835 0.860 0.000 0.140
#> GSM564709 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564710 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564711 1 0.2680 0.85751 0.924 0.008 0.068
#> GSM564712 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564713 1 0.3181 0.85497 0.912 0.024 0.064
#> GSM564714 1 0.7851 0.53249 0.644 0.100 0.256
#> GSM564715 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564716 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564717 1 0.0475 0.89360 0.992 0.004 0.004
#> GSM564718 1 0.3375 0.85422 0.908 0.044 0.048
#> GSM564719 1 0.0592 0.89088 0.988 0.012 0.000
#> GSM564720 1 0.0747 0.88841 0.984 0.016 0.000
#> GSM564721 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564722 1 0.0747 0.89027 0.984 0.016 0.000
#> GSM564723 1 0.0237 0.89490 0.996 0.004 0.000
#> GSM564724 1 0.7830 0.58720 0.668 0.136 0.196
#> GSM564725 1 0.3941 0.75269 0.844 0.156 0.000
#> GSM564726 2 0.5621 0.48650 0.308 0.692 0.000
#> GSM564727 1 0.5497 0.49992 0.708 0.292 0.000
#> GSM564728 2 0.5760 0.46421 0.328 0.672 0.000
#> GSM564729 2 0.6274 0.23388 0.456 0.544 0.000
#> GSM564730 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564731 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564732 1 0.0424 0.89325 0.992 0.008 0.000
#> GSM564733 1 0.4353 0.77895 0.836 0.008 0.156
#> GSM564734 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564735 2 0.8068 0.09377 0.456 0.480 0.064
#> GSM564736 1 0.6698 0.53265 0.684 0.280 0.036
#> GSM564737 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564738 2 0.8349 0.48079 0.156 0.624 0.220
#> GSM564739 1 0.0237 0.89524 0.996 0.000 0.004
#> GSM564740 2 0.5591 0.49108 0.304 0.696 0.000
#> GSM564741 1 0.7785 0.13187 0.528 0.420 0.052
#> GSM564742 1 0.5497 0.61219 0.708 0.000 0.292
#> GSM564743 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564744 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564745 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564746 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564747 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564748 1 0.4605 0.73009 0.796 0.000 0.204
#> GSM564749 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564750 2 0.6154 0.32876 0.408 0.592 0.000
#> GSM564751 1 0.2448 0.85483 0.924 0.000 0.076
#> GSM564752 2 0.5968 0.40706 0.364 0.636 0.000
#> GSM564753 1 0.6062 0.61672 0.708 0.016 0.276
#> GSM564754 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564755 2 0.5650 0.48294 0.312 0.688 0.000
#> GSM564756 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564757 2 0.6192 0.31867 0.420 0.580 0.000
#> GSM564758 2 0.6680 0.13966 0.484 0.508 0.008
#> GSM564759 1 0.4974 0.68662 0.764 0.000 0.236
#> GSM564760 1 0.3038 0.82190 0.896 0.104 0.000
#> GSM564761 1 0.0237 0.89486 0.996 0.004 0.000
#> GSM564762 1 0.0000 0.89629 1.000 0.000 0.000
#> GSM564681 2 0.4842 0.37188 0.000 0.776 0.224
#> GSM564693 3 0.6299 0.52903 0.000 0.476 0.524
#> GSM564646 2 0.0892 0.60788 0.000 0.980 0.020
#> GSM564699 2 0.2564 0.62343 0.036 0.936 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.0000 0.8589 0.000 0.000 0.000 1.000
#> GSM564616 2 0.0336 0.9326 0.000 0.992 0.000 0.008
#> GSM564617 2 0.0188 0.9334 0.000 0.996 0.000 0.004
#> GSM564618 4 0.5000 -0.0229 0.000 0.496 0.000 0.504
#> GSM564619 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564620 1 0.1743 0.9135 0.940 0.004 0.000 0.056
#> GSM564621 1 0.3052 0.8318 0.860 0.004 0.000 0.136
#> GSM564622 2 0.1109 0.9217 0.000 0.968 0.004 0.028
#> GSM564623 4 0.0336 0.8581 0.000 0.008 0.000 0.992
#> GSM564624 2 0.0817 0.9252 0.000 0.976 0.000 0.024
#> GSM564625 1 0.1305 0.9268 0.960 0.004 0.000 0.036
#> GSM564626 1 0.0188 0.9446 0.996 0.004 0.000 0.000
#> GSM564627 1 0.1824 0.9087 0.936 0.004 0.000 0.060
#> GSM564628 2 0.3266 0.7863 0.000 0.832 0.000 0.168
#> GSM564629 1 0.0188 0.9446 0.996 0.004 0.000 0.000
#> GSM564630 2 0.0000 0.9339 0.000 1.000 0.000 0.000
#> GSM564609 3 0.2704 0.7805 0.000 0.124 0.876 0.000
#> GSM564610 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564611 1 0.0921 0.9289 0.972 0.028 0.000 0.000
#> GSM564612 3 0.3942 0.6562 0.000 0.236 0.764 0.000
#> GSM564613 2 0.1388 0.9229 0.000 0.960 0.028 0.012
#> GSM564614 4 0.3791 0.7103 0.200 0.004 0.000 0.796
#> GSM564631 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564632 4 0.3972 0.6926 0.000 0.204 0.008 0.788
#> GSM564633 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564634 2 0.0469 0.9334 0.000 0.988 0.012 0.000
#> GSM564635 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564636 3 0.3208 0.7518 0.000 0.148 0.848 0.004
#> GSM564637 2 0.6232 0.4088 0.000 0.596 0.072 0.332
#> GSM564638 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564640 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564641 3 0.1389 0.8437 0.000 0.048 0.952 0.000
#> GSM564642 2 0.0376 0.9341 0.004 0.992 0.004 0.000
#> GSM564643 4 0.1004 0.8490 0.000 0.024 0.004 0.972
#> GSM564644 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564645 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564647 2 0.3172 0.7983 0.000 0.840 0.160 0.000
#> GSM564648 2 0.0376 0.9346 0.000 0.992 0.004 0.004
#> GSM564649 3 0.0188 0.8677 0.000 0.004 0.996 0.000
#> GSM564650 2 0.2593 0.8601 0.000 0.892 0.004 0.104
#> GSM564651 2 0.2011 0.8863 0.000 0.920 0.080 0.000
#> GSM564652 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564653 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564654 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564655 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564656 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564657 3 0.0376 0.8674 0.004 0.004 0.992 0.000
#> GSM564658 2 0.0000 0.9339 0.000 1.000 0.000 0.000
#> GSM564659 2 0.5488 0.1638 0.000 0.532 0.452 0.016
#> GSM564660 2 0.4585 0.5103 0.000 0.668 0.000 0.332
#> GSM564661 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564662 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564663 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564664 2 0.0376 0.9341 0.004 0.992 0.004 0.000
#> GSM564665 3 0.2921 0.7530 0.000 0.140 0.860 0.000
#> GSM564666 4 0.0188 0.8587 0.000 0.004 0.000 0.996
#> GSM564667 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564668 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564669 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564670 2 0.1722 0.9073 0.000 0.944 0.048 0.008
#> GSM564671 4 0.0000 0.8589 0.000 0.000 0.000 1.000
#> GSM564672 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564673 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564674 2 0.0336 0.9342 0.000 0.992 0.008 0.000
#> GSM564675 4 0.4356 0.5659 0.000 0.292 0.000 0.708
#> GSM564676 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564677 2 0.0564 0.9340 0.004 0.988 0.004 0.004
#> GSM564678 2 0.0376 0.9341 0.004 0.992 0.004 0.000
#> GSM564679 2 0.0188 0.9337 0.004 0.996 0.000 0.000
#> GSM564680 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564682 2 0.3801 0.7180 0.000 0.780 0.220 0.000
#> GSM564683 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564684 4 0.0188 0.8587 0.000 0.004 0.000 0.996
#> GSM564685 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564686 4 0.0000 0.8589 0.000 0.000 0.000 1.000
#> GSM564687 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564688 2 0.0469 0.9335 0.000 0.988 0.012 0.000
#> GSM564689 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564690 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564691 2 0.0707 0.9293 0.000 0.980 0.020 0.000
#> GSM564692 2 0.0000 0.9339 0.000 1.000 0.000 0.000
#> GSM564694 4 0.4483 0.5672 0.000 0.284 0.004 0.712
#> GSM564695 2 0.1209 0.9229 0.000 0.964 0.004 0.032
#> GSM564696 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564697 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> GSM564698 3 0.0000 0.8698 0.000 0.000 1.000 0.000
#> GSM564700 4 0.0188 0.8587 0.000 0.004 0.000 0.996
#> GSM564701 2 0.0817 0.9284 0.000 0.976 0.024 0.000
#> GSM564702 2 0.1792 0.8977 0.000 0.932 0.000 0.068
#> GSM564703 1 0.1022 0.9282 0.968 0.000 0.032 0.000
#> GSM564704 1 0.0921 0.9341 0.972 0.000 0.000 0.028
#> GSM564705 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564706 1 0.4985 0.0756 0.532 0.000 0.468 0.000
#> GSM564707 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564708 3 0.4981 0.1996 0.464 0.000 0.536 0.000
#> GSM564709 1 0.0707 0.9377 0.980 0.000 0.000 0.020
#> GSM564710 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564711 1 0.4431 0.5571 0.696 0.000 0.304 0.000
#> GSM564712 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564713 1 0.5112 0.3151 0.608 0.000 0.384 0.008
#> GSM564714 3 0.3257 0.7665 0.152 0.000 0.844 0.004
#> GSM564715 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564716 1 0.0188 0.9445 0.996 0.000 0.000 0.004
#> GSM564717 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564718 1 0.3808 0.7676 0.812 0.000 0.176 0.012
#> GSM564719 1 0.0336 0.9424 0.992 0.008 0.000 0.000
#> GSM564720 1 0.0592 0.9382 0.984 0.016 0.000 0.000
#> GSM564721 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564722 1 0.1716 0.9077 0.936 0.000 0.000 0.064
#> GSM564723 1 0.0336 0.9424 0.992 0.008 0.000 0.000
#> GSM564724 3 0.1489 0.8439 0.044 0.000 0.952 0.004
#> GSM564725 1 0.3219 0.7923 0.836 0.000 0.000 0.164
#> GSM564726 4 0.0000 0.8589 0.000 0.000 0.000 1.000
#> GSM564727 4 0.4925 0.2540 0.428 0.000 0.000 0.572
#> GSM564728 4 0.0336 0.8575 0.008 0.000 0.000 0.992
#> GSM564729 4 0.2704 0.7848 0.124 0.000 0.000 0.876
#> GSM564730 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564731 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564732 1 0.1004 0.9346 0.972 0.004 0.000 0.024
#> GSM564733 3 0.4790 0.4413 0.380 0.000 0.620 0.000
#> GSM564734 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564735 4 0.6258 0.4163 0.076 0.000 0.324 0.600
#> GSM564736 3 0.6469 0.5439 0.164 0.000 0.644 0.192
#> GSM564737 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564738 4 0.4933 0.2458 0.000 0.000 0.432 0.568
#> GSM564739 1 0.0336 0.9423 0.992 0.000 0.008 0.000
#> GSM564740 4 0.0000 0.8589 0.000 0.000 0.000 1.000
#> GSM564741 4 0.5130 0.5075 0.020 0.000 0.312 0.668
#> GSM564742 3 0.4866 0.3927 0.404 0.000 0.596 0.000
#> GSM564743 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564744 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564745 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564746 1 0.0188 0.9446 0.996 0.004 0.000 0.000
#> GSM564747 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564748 3 0.4972 0.2045 0.456 0.000 0.544 0.000
#> GSM564749 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564750 4 0.1118 0.8450 0.036 0.000 0.000 0.964
#> GSM564751 1 0.1940 0.8899 0.924 0.000 0.076 0.000
#> GSM564752 4 0.0469 0.8559 0.012 0.000 0.000 0.988
#> GSM564753 3 0.4477 0.5746 0.312 0.000 0.688 0.000
#> GSM564754 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564755 4 0.0188 0.8583 0.004 0.000 0.000 0.996
#> GSM564756 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564757 4 0.0188 0.8585 0.004 0.000 0.000 0.996
#> GSM564758 4 0.3638 0.7841 0.120 0.032 0.000 0.848
#> GSM564759 3 0.4776 0.4678 0.376 0.000 0.624 0.000
#> GSM564760 1 0.3649 0.7496 0.796 0.000 0.000 0.204
#> GSM564761 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564762 1 0.0000 0.9458 1.000 0.000 0.000 0.000
#> GSM564681 2 0.3626 0.7732 0.000 0.812 0.004 0.184
#> GSM564693 2 0.2714 0.8574 0.000 0.884 0.004 0.112
#> GSM564646 4 0.0188 0.8587 0.000 0.004 0.000 0.996
#> GSM564699 4 0.0000 0.8589 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.2491 0.74144 0.068 0.000 0.000 0.896 0.036
#> GSM564616 5 0.4251 0.54583 0.000 0.316 0.000 0.012 0.672
#> GSM564617 5 0.3983 0.52785 0.000 0.340 0.000 0.000 0.660
#> GSM564618 5 0.5849 0.51119 0.000 0.196 0.000 0.196 0.608
#> GSM564619 1 0.1732 0.86441 0.920 0.000 0.000 0.000 0.080
#> GSM564620 1 0.3563 0.75618 0.780 0.000 0.000 0.012 0.208
#> GSM564621 1 0.2782 0.84027 0.880 0.000 0.000 0.072 0.048
#> GSM564622 5 0.4128 0.57510 0.000 0.220 0.008 0.020 0.752
#> GSM564623 4 0.3607 0.59148 0.000 0.004 0.000 0.752 0.244
#> GSM564624 5 0.4555 0.52323 0.000 0.344 0.000 0.020 0.636
#> GSM564625 1 0.1750 0.86279 0.936 0.000 0.000 0.028 0.036
#> GSM564626 1 0.1341 0.86618 0.944 0.000 0.000 0.000 0.056
#> GSM564627 1 0.2304 0.86195 0.908 0.000 0.000 0.044 0.048
#> GSM564628 5 0.4706 0.56815 0.000 0.256 0.000 0.052 0.692
#> GSM564629 1 0.2783 0.84464 0.868 0.012 0.000 0.004 0.116
#> GSM564630 5 0.3983 0.52813 0.000 0.340 0.000 0.000 0.660
#> GSM564609 3 0.4850 0.56394 0.000 0.072 0.696 0.000 0.232
#> GSM564610 1 0.2127 0.85802 0.892 0.000 0.000 0.000 0.108
#> GSM564611 1 0.5014 0.40931 0.536 0.432 0.000 0.000 0.032
#> GSM564612 3 0.4254 0.60484 0.000 0.220 0.740 0.000 0.040
#> GSM564613 5 0.5360 0.50292 0.000 0.296 0.056 0.012 0.636
#> GSM564614 4 0.5065 0.25165 0.420 0.000 0.000 0.544 0.036
#> GSM564631 3 0.0290 0.79622 0.000 0.000 0.992 0.000 0.008
#> GSM564632 5 0.6011 0.05209 0.000 0.052 0.028 0.440 0.480
#> GSM564633 3 0.1608 0.78580 0.000 0.000 0.928 0.000 0.072
#> GSM564634 5 0.5099 0.30932 0.000 0.336 0.052 0.000 0.612
#> GSM564635 3 0.2389 0.75782 0.000 0.004 0.880 0.000 0.116
#> GSM564636 3 0.3021 0.74608 0.000 0.060 0.872 0.004 0.064
#> GSM564637 2 0.6756 0.04851 0.000 0.456 0.056 0.408 0.080
#> GSM564638 3 0.1251 0.78390 0.000 0.000 0.956 0.008 0.036
#> GSM564639 3 0.0290 0.79610 0.000 0.000 0.992 0.000 0.008
#> GSM564640 2 0.3003 0.60203 0.000 0.812 0.000 0.000 0.188
#> GSM564641 3 0.1741 0.77884 0.000 0.040 0.936 0.000 0.024
#> GSM564642 2 0.4249 0.52241 0.000 0.688 0.016 0.000 0.296
#> GSM564643 5 0.5251 0.34215 0.000 0.044 0.004 0.376 0.576
#> GSM564644 2 0.1341 0.67769 0.000 0.944 0.000 0.000 0.056
#> GSM564645 3 0.0404 0.79685 0.000 0.000 0.988 0.000 0.012
#> GSM564647 5 0.6157 0.34910 0.000 0.220 0.220 0.000 0.560
#> GSM564648 5 0.3424 0.53325 0.000 0.240 0.000 0.000 0.760
#> GSM564649 3 0.0510 0.79672 0.000 0.000 0.984 0.000 0.016
#> GSM564650 2 0.3771 0.51986 0.000 0.804 0.004 0.156 0.036
#> GSM564651 2 0.6113 0.26649 0.000 0.524 0.144 0.000 0.332
#> GSM564652 5 0.4547 0.15481 0.000 0.400 0.012 0.000 0.588
#> GSM564653 2 0.3730 0.51101 0.000 0.712 0.000 0.000 0.288
#> GSM564654 3 0.3053 0.72145 0.000 0.008 0.828 0.000 0.164
#> GSM564655 3 0.3656 0.70589 0.000 0.032 0.800 0.000 0.168
#> GSM564656 3 0.1043 0.79294 0.000 0.000 0.960 0.000 0.040
#> GSM564657 3 0.0162 0.79556 0.000 0.004 0.996 0.000 0.000
#> GSM564658 2 0.1851 0.63835 0.000 0.912 0.000 0.000 0.088
#> GSM564659 5 0.6366 0.31729 0.000 0.112 0.368 0.016 0.504
#> GSM564660 5 0.5083 0.55096 0.000 0.160 0.000 0.140 0.700
#> GSM564661 2 0.4249 0.08831 0.000 0.568 0.000 0.000 0.432
#> GSM564662 3 0.0162 0.79583 0.000 0.000 0.996 0.000 0.004
#> GSM564663 2 0.1732 0.66358 0.000 0.920 0.000 0.000 0.080
#> GSM564664 2 0.0963 0.68155 0.000 0.964 0.000 0.000 0.036
#> GSM564665 3 0.5904 0.37545 0.000 0.196 0.600 0.000 0.204
#> GSM564666 4 0.3452 0.61040 0.000 0.000 0.000 0.756 0.244
#> GSM564667 3 0.1341 0.78953 0.000 0.000 0.944 0.000 0.056
#> GSM564668 3 0.3462 0.68850 0.000 0.012 0.792 0.000 0.196
#> GSM564669 3 0.0609 0.79606 0.000 0.000 0.980 0.000 0.020
#> GSM564670 5 0.5512 0.51329 0.000 0.280 0.080 0.008 0.632
#> GSM564671 4 0.2561 0.71325 0.000 0.000 0.000 0.856 0.144
#> GSM564672 3 0.0880 0.79482 0.000 0.000 0.968 0.000 0.032
#> GSM564673 5 0.4666 0.20549 0.000 0.412 0.016 0.000 0.572
#> GSM564674 2 0.4045 0.42941 0.000 0.644 0.000 0.000 0.356
#> GSM564675 4 0.5785 -0.02572 0.000 0.092 0.000 0.504 0.404
#> GSM564676 2 0.0162 0.66991 0.000 0.996 0.000 0.000 0.004
#> GSM564677 2 0.4066 0.51093 0.000 0.672 0.004 0.000 0.324
#> GSM564678 2 0.0290 0.66937 0.000 0.992 0.000 0.000 0.008
#> GSM564679 2 0.0703 0.67954 0.000 0.976 0.000 0.000 0.024
#> GSM564680 3 0.0404 0.79638 0.000 0.000 0.988 0.000 0.012
#> GSM564682 2 0.6606 0.01376 0.000 0.444 0.328 0.000 0.228
#> GSM564683 3 0.0162 0.79563 0.000 0.000 0.996 0.000 0.004
#> GSM564684 4 0.2583 0.70307 0.000 0.004 0.000 0.864 0.132
#> GSM564685 3 0.0404 0.79668 0.000 0.000 0.988 0.000 0.012
#> GSM564686 4 0.1341 0.74467 0.000 0.000 0.000 0.944 0.056
#> GSM564687 2 0.4060 0.20084 0.000 0.640 0.000 0.000 0.360
#> GSM564688 2 0.4836 0.32919 0.000 0.628 0.036 0.000 0.336
#> GSM564689 2 0.0404 0.67757 0.000 0.988 0.000 0.000 0.012
#> GSM564690 2 0.0290 0.67662 0.000 0.992 0.000 0.000 0.008
#> GSM564691 2 0.4113 0.56330 0.000 0.784 0.076 0.000 0.140
#> GSM564692 5 0.4182 0.47633 0.000 0.352 0.004 0.000 0.644
#> GSM564694 5 0.5142 0.39896 0.000 0.052 0.000 0.348 0.600
#> GSM564695 5 0.6218 0.21828 0.000 0.440 0.008 0.108 0.444
#> GSM564696 3 0.0510 0.79660 0.000 0.000 0.984 0.000 0.016
#> GSM564697 2 0.0963 0.68074 0.000 0.964 0.000 0.000 0.036
#> GSM564698 3 0.3676 0.66312 0.000 0.004 0.760 0.004 0.232
#> GSM564700 4 0.1671 0.73698 0.000 0.000 0.000 0.924 0.076
#> GSM564701 5 0.5422 0.28183 0.004 0.372 0.056 0.000 0.568
#> GSM564702 5 0.5946 0.26719 0.000 0.380 0.000 0.112 0.508
#> GSM564703 1 0.1478 0.86120 0.936 0.000 0.064 0.000 0.000
#> GSM564704 1 0.2352 0.86494 0.912 0.008 0.000 0.048 0.032
#> GSM564705 1 0.1750 0.86647 0.936 0.036 0.000 0.000 0.028
#> GSM564706 3 0.5402 -0.00371 0.472 0.004 0.484 0.004 0.036
#> GSM564707 1 0.1753 0.86618 0.936 0.032 0.000 0.000 0.032
#> GSM564708 1 0.4905 0.42276 0.624 0.000 0.336 0.000 0.040
#> GSM564709 1 0.5262 0.77051 0.736 0.124 0.000 0.096 0.044
#> GSM564710 1 0.0794 0.86700 0.972 0.000 0.000 0.000 0.028
#> GSM564711 1 0.4660 0.68322 0.728 0.000 0.220 0.016 0.036
#> GSM564712 1 0.1836 0.86958 0.932 0.032 0.000 0.000 0.036
#> GSM564713 1 0.4462 0.77385 0.788 0.000 0.124 0.032 0.056
#> GSM564714 3 0.5198 0.64563 0.176 0.008 0.732 0.032 0.052
#> GSM564715 1 0.0609 0.86559 0.980 0.000 0.000 0.000 0.020
#> GSM564716 1 0.2152 0.86426 0.920 0.004 0.000 0.032 0.044
#> GSM564717 1 0.3445 0.82095 0.824 0.140 0.000 0.000 0.036
#> GSM564718 1 0.4996 0.75207 0.752 0.000 0.140 0.048 0.060
#> GSM564719 1 0.5088 0.39573 0.528 0.436 0.000 0.000 0.036
#> GSM564720 1 0.3710 0.81433 0.808 0.144 0.000 0.000 0.048
#> GSM564721 1 0.1808 0.86870 0.936 0.040 0.000 0.004 0.020
#> GSM564722 1 0.5939 0.38207 0.564 0.024 0.000 0.348 0.064
#> GSM564723 1 0.3035 0.83883 0.856 0.112 0.000 0.000 0.032
#> GSM564724 3 0.5646 0.45378 0.308 0.000 0.616 0.032 0.044
#> GSM564725 1 0.2491 0.84954 0.896 0.000 0.000 0.068 0.036
#> GSM564726 4 0.1444 0.75517 0.012 0.000 0.000 0.948 0.040
#> GSM564727 4 0.4420 0.16697 0.448 0.000 0.000 0.548 0.004
#> GSM564728 4 0.1211 0.75859 0.024 0.000 0.000 0.960 0.016
#> GSM564729 4 0.3016 0.70938 0.132 0.000 0.000 0.848 0.020
#> GSM564730 1 0.1907 0.87134 0.928 0.028 0.000 0.000 0.044
#> GSM564731 1 0.2130 0.87184 0.920 0.012 0.004 0.004 0.060
#> GSM564732 1 0.1965 0.85963 0.924 0.000 0.000 0.052 0.024
#> GSM564733 1 0.6078 0.05735 0.492 0.000 0.424 0.036 0.048
#> GSM564734 1 0.1934 0.87035 0.932 0.040 0.000 0.020 0.008
#> GSM564735 4 0.7311 0.09208 0.140 0.000 0.388 0.412 0.060
#> GSM564736 3 0.7899 -0.03738 0.312 0.000 0.328 0.292 0.068
#> GSM564737 1 0.0898 0.86696 0.972 0.008 0.000 0.000 0.020
#> GSM564738 4 0.5303 0.33880 0.004 0.000 0.372 0.576 0.048
#> GSM564739 1 0.1200 0.86991 0.964 0.008 0.012 0.000 0.016
#> GSM564740 4 0.1341 0.75233 0.000 0.000 0.000 0.944 0.056
#> GSM564741 4 0.5432 0.53820 0.036 0.000 0.264 0.660 0.040
#> GSM564742 3 0.5244 0.41020 0.368 0.004 0.588 0.004 0.036
#> GSM564743 1 0.3164 0.85135 0.852 0.044 0.000 0.000 0.104
#> GSM564744 1 0.2592 0.86104 0.892 0.056 0.000 0.000 0.052
#> GSM564745 1 0.1831 0.86846 0.920 0.004 0.000 0.000 0.076
#> GSM564746 1 0.0798 0.86768 0.976 0.008 0.000 0.000 0.016
#> GSM564747 1 0.1153 0.86848 0.964 0.008 0.000 0.004 0.024
#> GSM564748 3 0.5384 0.16047 0.440 0.004 0.516 0.004 0.036
#> GSM564749 1 0.3769 0.78815 0.788 0.180 0.000 0.000 0.032
#> GSM564750 4 0.2843 0.73504 0.076 0.000 0.000 0.876 0.048
#> GSM564751 1 0.1956 0.85321 0.916 0.000 0.076 0.000 0.008
#> GSM564752 4 0.2054 0.75209 0.028 0.000 0.000 0.920 0.052
#> GSM564753 3 0.4682 0.44631 0.356 0.000 0.620 0.000 0.024
#> GSM564754 1 0.0865 0.86745 0.972 0.004 0.000 0.000 0.024
#> GSM564755 4 0.0451 0.75537 0.004 0.000 0.000 0.988 0.008
#> GSM564756 1 0.4971 0.78624 0.748 0.128 0.000 0.024 0.100
#> GSM564757 4 0.0898 0.75781 0.020 0.000 0.000 0.972 0.008
#> GSM564758 4 0.5821 0.55063 0.228 0.012 0.000 0.636 0.124
#> GSM564759 3 0.5165 0.37740 0.376 0.000 0.576 0.000 0.048
#> GSM564760 1 0.3961 0.71760 0.760 0.000 0.000 0.212 0.028
#> GSM564761 1 0.1597 0.87126 0.940 0.012 0.000 0.000 0.048
#> GSM564762 1 0.2166 0.86511 0.912 0.004 0.000 0.012 0.072
#> GSM564681 5 0.6438 0.42239 0.000 0.212 0.000 0.292 0.496
#> GSM564693 5 0.6184 0.42196 0.000 0.276 0.004 0.160 0.560
#> GSM564646 4 0.1608 0.73886 0.000 0.000 0.000 0.928 0.072
#> GSM564699 4 0.1410 0.74734 0.000 0.000 0.000 0.940 0.060
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.2952 0.6789 0.068 0.000 0.000 0.864 0.052 0.016
#> GSM564616 6 0.1807 0.5154 0.000 0.060 0.000 0.000 0.020 0.920
#> GSM564617 6 0.2350 0.5057 0.000 0.076 0.000 0.000 0.036 0.888
#> GSM564618 6 0.2990 0.5124 0.000 0.036 0.000 0.084 0.020 0.860
#> GSM564619 1 0.3072 0.8211 0.840 0.000 0.000 0.000 0.084 0.076
#> GSM564620 1 0.4859 0.5396 0.596 0.000 0.000 0.016 0.040 0.348
#> GSM564621 1 0.3657 0.7962 0.824 0.000 0.000 0.072 0.044 0.060
#> GSM564622 6 0.2022 0.5029 0.000 0.024 0.008 0.000 0.052 0.916
#> GSM564623 6 0.4534 0.0878 0.000 0.000 0.000 0.472 0.032 0.496
#> GSM564624 6 0.2151 0.5170 0.000 0.072 0.000 0.008 0.016 0.904
#> GSM564625 1 0.3049 0.8103 0.864 0.000 0.000 0.040 0.052 0.044
#> GSM564626 1 0.2046 0.8194 0.908 0.000 0.000 0.000 0.032 0.060
#> GSM564627 1 0.3323 0.8052 0.836 0.000 0.000 0.032 0.028 0.104
#> GSM564628 6 0.1585 0.5191 0.000 0.036 0.000 0.012 0.012 0.940
#> GSM564629 1 0.4283 0.6484 0.672 0.000 0.004 0.000 0.036 0.288
#> GSM564630 6 0.1913 0.5084 0.000 0.080 0.000 0.000 0.012 0.908
#> GSM564609 3 0.6118 0.2912 0.000 0.068 0.540 0.000 0.300 0.092
#> GSM564610 1 0.3792 0.7923 0.780 0.000 0.000 0.000 0.112 0.108
#> GSM564611 2 0.4359 0.3450 0.296 0.664 0.000 0.000 0.032 0.008
#> GSM564612 3 0.4957 0.4684 0.000 0.244 0.668 0.000 0.040 0.048
#> GSM564613 6 0.4490 0.4097 0.000 0.016 0.028 0.008 0.252 0.696
#> GSM564614 4 0.5490 0.2265 0.404 0.000 0.000 0.508 0.052 0.036
#> GSM564631 3 0.1152 0.6870 0.000 0.000 0.952 0.000 0.044 0.004
#> GSM564632 5 0.6986 -0.1140 0.000 0.012 0.040 0.228 0.392 0.328
#> GSM564633 3 0.2597 0.6359 0.000 0.000 0.824 0.000 0.176 0.000
#> GSM564634 5 0.6411 0.4971 0.000 0.148 0.068 0.000 0.532 0.252
#> GSM564635 3 0.4048 0.4260 0.000 0.004 0.644 0.000 0.340 0.012
#> GSM564636 3 0.4838 0.5880 0.000 0.056 0.740 0.004 0.100 0.100
#> GSM564637 2 0.6793 0.0738 0.000 0.436 0.040 0.384 0.100 0.040
#> GSM564638 3 0.2633 0.6542 0.000 0.000 0.864 0.004 0.112 0.020
#> GSM564639 3 0.0260 0.6847 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM564640 2 0.4486 0.3695 0.000 0.696 0.000 0.000 0.208 0.096
#> GSM564641 3 0.3693 0.6436 0.004 0.060 0.828 0.000 0.048 0.060
#> GSM564642 5 0.5761 0.4556 0.000 0.344 0.040 0.000 0.536 0.080
#> GSM564643 5 0.7230 0.2791 0.000 0.032 0.048 0.256 0.448 0.216
#> GSM564644 2 0.1616 0.6575 0.000 0.932 0.000 0.000 0.048 0.020
#> GSM564645 3 0.0865 0.6857 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM564647 6 0.7062 -0.2484 0.000 0.072 0.240 0.000 0.328 0.360
#> GSM564648 6 0.5213 0.0895 0.000 0.064 0.004 0.008 0.376 0.548
#> GSM564649 3 0.1745 0.6817 0.000 0.000 0.920 0.000 0.068 0.012
#> GSM564650 2 0.4396 0.5386 0.000 0.768 0.008 0.132 0.060 0.032
#> GSM564651 5 0.6615 0.4203 0.000 0.296 0.156 0.000 0.480 0.068
#> GSM564652 5 0.5615 0.4672 0.016 0.184 0.000 0.000 0.600 0.200
#> GSM564653 2 0.5353 -0.2339 0.000 0.516 0.000 0.000 0.368 0.116
#> GSM564654 3 0.4092 0.4254 0.000 0.000 0.636 0.000 0.344 0.020
#> GSM564655 3 0.4704 0.3749 0.000 0.032 0.604 0.004 0.352 0.008
#> GSM564656 3 0.2191 0.6627 0.000 0.000 0.876 0.000 0.120 0.004
#> GSM564657 3 0.1168 0.6849 0.000 0.028 0.956 0.000 0.016 0.000
#> GSM564658 2 0.2831 0.5981 0.000 0.840 0.000 0.000 0.024 0.136
#> GSM564659 6 0.6477 0.0314 0.000 0.028 0.392 0.008 0.156 0.416
#> GSM564660 6 0.5014 0.3817 0.000 0.020 0.000 0.064 0.280 0.636
#> GSM564661 5 0.6056 0.3684 0.000 0.352 0.000 0.000 0.388 0.260
#> GSM564662 3 0.0777 0.6859 0.000 0.000 0.972 0.000 0.024 0.004
#> GSM564663 2 0.3645 0.5256 0.000 0.784 0.000 0.000 0.152 0.064
#> GSM564664 2 0.1074 0.6689 0.000 0.960 0.000 0.000 0.028 0.012
#> GSM564665 3 0.5602 0.0311 0.000 0.076 0.460 0.000 0.440 0.024
#> GSM564666 6 0.5392 0.0907 0.000 0.000 0.000 0.440 0.112 0.448
#> GSM564667 3 0.1908 0.6708 0.000 0.000 0.900 0.000 0.096 0.004
#> GSM564668 3 0.4699 0.3481 0.000 0.008 0.580 0.000 0.376 0.036
#> GSM564669 3 0.1285 0.6858 0.000 0.000 0.944 0.004 0.052 0.000
#> GSM564670 6 0.5537 0.3024 0.000 0.108 0.052 0.000 0.192 0.648
#> GSM564671 4 0.3979 0.5293 0.000 0.000 0.000 0.752 0.172 0.076
#> GSM564672 3 0.1267 0.6810 0.000 0.000 0.940 0.000 0.060 0.000
#> GSM564673 5 0.6337 0.4953 0.000 0.212 0.028 0.000 0.484 0.276
#> GSM564674 5 0.5996 0.3454 0.000 0.392 0.000 0.004 0.408 0.196
#> GSM564675 6 0.5681 0.3690 0.000 0.028 0.000 0.300 0.104 0.568
#> GSM564676 2 0.0405 0.6697 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM564677 5 0.5147 0.4266 0.008 0.364 0.000 0.000 0.556 0.072
#> GSM564678 2 0.0405 0.6686 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564679 2 0.1176 0.6669 0.000 0.956 0.000 0.000 0.024 0.020
#> GSM564680 3 0.0865 0.6863 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM564682 3 0.7391 -0.1269 0.004 0.316 0.372 0.000 0.124 0.184
#> GSM564683 3 0.0146 0.6846 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM564684 4 0.2480 0.6280 0.000 0.000 0.000 0.872 0.024 0.104
#> GSM564685 3 0.1049 0.6876 0.000 0.000 0.960 0.000 0.032 0.008
#> GSM564686 4 0.1921 0.6571 0.000 0.000 0.000 0.916 0.032 0.052
#> GSM564687 2 0.5910 -0.2322 0.000 0.448 0.000 0.000 0.220 0.332
#> GSM564688 2 0.6794 -0.2265 0.000 0.460 0.076 0.000 0.288 0.176
#> GSM564689 2 0.0725 0.6708 0.000 0.976 0.000 0.000 0.012 0.012
#> GSM564690 2 0.0405 0.6704 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564691 2 0.4914 0.4760 0.000 0.728 0.080 0.000 0.080 0.112
#> GSM564692 6 0.5176 0.1252 0.000 0.144 0.000 0.000 0.248 0.608
#> GSM564694 6 0.6085 0.2766 0.000 0.016 0.004 0.232 0.212 0.536
#> GSM564695 6 0.7773 -0.2286 0.000 0.244 0.008 0.160 0.264 0.324
#> GSM564696 3 0.1462 0.6829 0.000 0.000 0.936 0.000 0.056 0.008
#> GSM564697 2 0.0820 0.6703 0.000 0.972 0.000 0.000 0.012 0.016
#> GSM564698 3 0.5586 0.2653 0.000 0.000 0.532 0.000 0.292 0.176
#> GSM564700 4 0.2179 0.6501 0.000 0.000 0.000 0.900 0.036 0.064
#> GSM564701 5 0.6815 0.4450 0.000 0.156 0.084 0.000 0.448 0.312
#> GSM564702 6 0.7347 -0.0317 0.000 0.148 0.000 0.168 0.340 0.344
#> GSM564703 1 0.2361 0.8215 0.896 0.000 0.064 0.000 0.032 0.008
#> GSM564704 1 0.3706 0.8059 0.824 0.020 0.000 0.052 0.092 0.012
#> GSM564705 1 0.2476 0.8167 0.888 0.072 0.000 0.000 0.032 0.008
#> GSM564706 3 0.6041 -0.0365 0.420 0.004 0.436 0.012 0.124 0.004
#> GSM564707 1 0.2457 0.8236 0.896 0.036 0.004 0.000 0.056 0.008
#> GSM564708 1 0.4928 0.6172 0.668 0.000 0.232 0.008 0.088 0.004
#> GSM564709 1 0.5679 0.7088 0.672 0.092 0.000 0.132 0.096 0.008
#> GSM564710 1 0.1480 0.8234 0.940 0.000 0.000 0.000 0.040 0.020
#> GSM564711 1 0.5409 0.6598 0.656 0.000 0.192 0.020 0.124 0.008
#> GSM564712 1 0.2883 0.8109 0.844 0.012 0.000 0.000 0.132 0.012
#> GSM564713 1 0.5162 0.7001 0.696 0.000 0.060 0.052 0.184 0.008
#> GSM564714 3 0.6043 0.4915 0.200 0.008 0.600 0.016 0.164 0.012
#> GSM564715 1 0.1297 0.8201 0.948 0.000 0.000 0.000 0.040 0.012
#> GSM564716 1 0.2849 0.8106 0.864 0.000 0.000 0.044 0.084 0.008
#> GSM564717 1 0.4219 0.7210 0.720 0.224 0.000 0.000 0.048 0.008
#> GSM564718 1 0.5095 0.7166 0.700 0.000 0.096 0.036 0.164 0.004
#> GSM564719 2 0.3930 0.4136 0.236 0.728 0.000 0.000 0.032 0.004
#> GSM564720 1 0.3936 0.7922 0.780 0.088 0.000 0.000 0.124 0.008
#> GSM564721 1 0.2420 0.8206 0.888 0.076 0.000 0.000 0.032 0.004
#> GSM564722 4 0.7007 0.2047 0.356 0.060 0.000 0.404 0.168 0.012
#> GSM564723 1 0.3375 0.7932 0.816 0.136 0.000 0.000 0.040 0.008
#> GSM564724 3 0.6187 -0.0642 0.416 0.000 0.432 0.048 0.104 0.000
#> GSM564725 1 0.3399 0.7934 0.832 0.000 0.000 0.088 0.064 0.016
#> GSM564726 4 0.2699 0.6872 0.008 0.000 0.000 0.856 0.124 0.012
#> GSM564727 4 0.4516 0.2440 0.420 0.000 0.000 0.552 0.020 0.008
#> GSM564728 4 0.1769 0.6985 0.012 0.000 0.000 0.924 0.060 0.004
#> GSM564729 4 0.3282 0.6704 0.108 0.000 0.000 0.836 0.036 0.020
#> GSM564730 1 0.3909 0.7870 0.772 0.020 0.000 0.000 0.172 0.036
#> GSM564731 1 0.4128 0.7642 0.712 0.008 0.004 0.004 0.256 0.016
#> GSM564732 1 0.2773 0.8035 0.868 0.000 0.000 0.064 0.064 0.004
#> GSM564733 1 0.6358 0.3593 0.520 0.000 0.300 0.056 0.120 0.004
#> GSM564734 1 0.2910 0.8211 0.868 0.068 0.000 0.020 0.044 0.000
#> GSM564735 3 0.7495 -0.1183 0.080 0.000 0.356 0.332 0.212 0.020
#> GSM564736 4 0.7847 0.1904 0.288 0.000 0.200 0.296 0.208 0.008
#> GSM564737 1 0.1148 0.8199 0.960 0.020 0.000 0.000 0.016 0.004
#> GSM564738 4 0.6124 0.1658 0.000 0.000 0.368 0.444 0.172 0.016
#> GSM564739 1 0.1346 0.8231 0.952 0.000 0.016 0.000 0.024 0.008
#> GSM564740 4 0.2790 0.6755 0.000 0.000 0.000 0.844 0.132 0.024
#> GSM564741 4 0.6063 0.5238 0.048 0.000 0.192 0.596 0.160 0.004
#> GSM564742 3 0.5616 0.4239 0.304 0.004 0.560 0.000 0.124 0.008
#> GSM564743 1 0.5079 0.7207 0.672 0.028 0.000 0.000 0.212 0.088
#> GSM564744 1 0.4130 0.7758 0.756 0.036 0.000 0.000 0.180 0.028
#> GSM564745 1 0.3073 0.7891 0.788 0.000 0.000 0.000 0.204 0.008
#> GSM564746 1 0.2164 0.8215 0.900 0.000 0.000 0.000 0.032 0.068
#> GSM564747 1 0.1398 0.8199 0.940 0.008 0.000 0.000 0.052 0.000
#> GSM564748 3 0.5772 0.0790 0.424 0.000 0.448 0.008 0.116 0.004
#> GSM564749 1 0.4545 0.5262 0.616 0.344 0.000 0.000 0.032 0.008
#> GSM564750 4 0.4447 0.6580 0.072 0.000 0.008 0.756 0.144 0.020
#> GSM564751 1 0.2451 0.8203 0.888 0.000 0.068 0.000 0.040 0.004
#> GSM564752 4 0.3025 0.6835 0.020 0.000 0.000 0.844 0.120 0.016
#> GSM564753 3 0.5100 0.2719 0.392 0.000 0.524 0.000 0.084 0.000
#> GSM564754 1 0.1845 0.8181 0.916 0.000 0.000 0.004 0.072 0.008
#> GSM564755 4 0.1075 0.6946 0.000 0.000 0.000 0.952 0.048 0.000
#> GSM564756 1 0.5799 0.6585 0.588 0.064 0.000 0.052 0.288 0.008
#> GSM564757 4 0.1448 0.6905 0.012 0.000 0.000 0.948 0.024 0.016
#> GSM564758 4 0.6805 0.4336 0.208 0.008 0.000 0.512 0.200 0.072
#> GSM564759 3 0.6002 0.4267 0.280 0.000 0.536 0.004 0.164 0.016
#> GSM564760 1 0.4755 0.6105 0.664 0.000 0.000 0.244 0.088 0.004
#> GSM564761 1 0.2604 0.8224 0.872 0.004 0.000 0.000 0.096 0.028
#> GSM564762 1 0.4690 0.7208 0.716 0.000 0.004 0.044 0.200 0.036
#> GSM564681 6 0.6550 0.2597 0.000 0.040 0.000 0.236 0.256 0.468
#> GSM564693 5 0.7408 0.3304 0.000 0.124 0.004 0.196 0.384 0.292
#> GSM564646 4 0.2046 0.6544 0.000 0.000 0.000 0.908 0.032 0.060
#> GSM564699 4 0.2325 0.6641 0.000 0.000 0.000 0.892 0.060 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 genotype/variation(p) disease.state(p) k
#> CV:NMF 154 9.25e-01 4.76e-01 2
#> CV:NMF 131 8.68e-01 7.58e-05 3
#> CV:NMF 141 4.20e-01 2.63e-01 4
#> CV:NMF 115 3.30e-05 2.46e-01 5
#> CV:NMF 96 2.92e-07 2.50e-01 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.780 0.752 0.876 0.2283 0.905 0.809
#> 4 4 0.608 0.519 0.778 0.1236 0.892 0.741
#> 5 5 0.612 0.690 0.804 0.0916 0.880 0.649
#> 6 6 0.654 0.627 0.779 0.0432 0.988 0.950
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
#> GSM564615 1 0.0000 1.000 1.000 0.000
#> GSM564616 2 0.0000 1.000 0.000 1.000
#> GSM564617 2 0.0000 1.000 0.000 1.000
#> GSM564618 2 0.0000 1.000 0.000 1.000
#> GSM564619 1 0.0000 1.000 1.000 0.000
#> GSM564620 1 0.0000 1.000 1.000 0.000
#> GSM564621 1 0.0000 1.000 1.000 0.000
#> GSM564622 2 0.0000 1.000 0.000 1.000
#> GSM564623 2 0.0376 0.996 0.004 0.996
#> GSM564624 2 0.0000 1.000 0.000 1.000
#> GSM564625 1 0.0000 1.000 1.000 0.000
#> GSM564626 1 0.0000 1.000 1.000 0.000
#> GSM564627 1 0.0000 1.000 1.000 0.000
#> GSM564628 2 0.0000 1.000 0.000 1.000
#> GSM564629 1 0.0000 1.000 1.000 0.000
#> GSM564630 2 0.0000 1.000 0.000 1.000
#> GSM564609 2 0.0000 1.000 0.000 1.000
#> GSM564610 1 0.0000 1.000 1.000 0.000
#> GSM564611 1 0.0000 1.000 1.000 0.000
#> GSM564612 2 0.0000 1.000 0.000 1.000
#> GSM564613 2 0.0000 1.000 0.000 1.000
#> GSM564614 1 0.0000 1.000 1.000 0.000
#> GSM564631 2 0.0000 1.000 0.000 1.000
#> GSM564632 2 0.0000 1.000 0.000 1.000
#> GSM564633 2 0.0000 1.000 0.000 1.000
#> GSM564634 2 0.0000 1.000 0.000 1.000
#> GSM564635 2 0.0000 1.000 0.000 1.000
#> GSM564636 2 0.0000 1.000 0.000 1.000
#> GSM564637 2 0.0000 1.000 0.000 1.000
#> GSM564638 2 0.0000 1.000 0.000 1.000
#> GSM564639 2 0.0000 1.000 0.000 1.000
#> GSM564640 2 0.0000 1.000 0.000 1.000
#> GSM564641 2 0.0000 1.000 0.000 1.000
#> GSM564642 2 0.0000 1.000 0.000 1.000
#> GSM564643 2 0.0000 1.000 0.000 1.000
#> GSM564644 2 0.0000 1.000 0.000 1.000
#> GSM564645 2 0.0000 1.000 0.000 1.000
#> GSM564647 2 0.0000 1.000 0.000 1.000
#> GSM564648 2 0.0000 1.000 0.000 1.000
#> GSM564649 2 0.0000 1.000 0.000 1.000
#> GSM564650 2 0.0000 1.000 0.000 1.000
#> GSM564651 2 0.0000 1.000 0.000 1.000
#> GSM564652 2 0.0000 1.000 0.000 1.000
#> GSM564653 2 0.0000 1.000 0.000 1.000
#> GSM564654 2 0.0000 1.000 0.000 1.000
#> GSM564655 2 0.0000 1.000 0.000 1.000
#> GSM564656 2 0.0000 1.000 0.000 1.000
#> GSM564657 2 0.0000 1.000 0.000 1.000
#> GSM564658 2 0.0000 1.000 0.000 1.000
#> GSM564659 2 0.0000 1.000 0.000 1.000
#> GSM564660 2 0.0000 1.000 0.000 1.000
#> GSM564661 2 0.0000 1.000 0.000 1.000
#> GSM564662 2 0.0000 1.000 0.000 1.000
#> GSM564663 2 0.0000 1.000 0.000 1.000
#> GSM564664 2 0.0000 1.000 0.000 1.000
#> GSM564665 2 0.0000 1.000 0.000 1.000
#> GSM564666 2 0.0000 1.000 0.000 1.000
#> GSM564667 2 0.0000 1.000 0.000 1.000
#> GSM564668 2 0.0000 1.000 0.000 1.000
#> GSM564669 2 0.0000 1.000 0.000 1.000
#> GSM564670 2 0.0000 1.000 0.000 1.000
#> GSM564671 2 0.0376 0.996 0.004 0.996
#> GSM564672 2 0.0000 1.000 0.000 1.000
#> GSM564673 2 0.0000 1.000 0.000 1.000
#> GSM564674 2 0.0000 1.000 0.000 1.000
#> GSM564675 2 0.0938 0.988 0.012 0.988
#> GSM564676 2 0.0000 1.000 0.000 1.000
#> GSM564677 2 0.0000 1.000 0.000 1.000
#> GSM564678 2 0.0000 1.000 0.000 1.000
#> GSM564679 2 0.0000 1.000 0.000 1.000
#> GSM564680 2 0.0000 1.000 0.000 1.000
#> GSM564682 2 0.0000 1.000 0.000 1.000
#> GSM564683 2 0.0000 1.000 0.000 1.000
#> GSM564684 2 0.0376 0.996 0.004 0.996
#> GSM564685 2 0.0000 1.000 0.000 1.000
#> GSM564686 2 0.0376 0.996 0.004 0.996
#> GSM564687 2 0.0000 1.000 0.000 1.000
#> GSM564688 2 0.0000 1.000 0.000 1.000
#> GSM564689 2 0.0000 1.000 0.000 1.000
#> GSM564690 2 0.0000 1.000 0.000 1.000
#> GSM564691 2 0.0000 1.000 0.000 1.000
#> GSM564692 2 0.0000 1.000 0.000 1.000
#> GSM564694 2 0.0376 0.996 0.004 0.996
#> GSM564695 2 0.0000 1.000 0.000 1.000
#> GSM564696 2 0.0000 1.000 0.000 1.000
#> GSM564697 2 0.0000 1.000 0.000 1.000
#> GSM564698 2 0.0000 1.000 0.000 1.000
#> GSM564700 2 0.0376 0.996 0.004 0.996
#> GSM564701 2 0.0000 1.000 0.000 1.000
#> GSM564702 2 0.0000 1.000 0.000 1.000
#> GSM564703 1 0.0000 1.000 1.000 0.000
#> GSM564704 1 0.0000 1.000 1.000 0.000
#> GSM564705 1 0.0000 1.000 1.000 0.000
#> GSM564706 1 0.0000 1.000 1.000 0.000
#> GSM564707 1 0.0000 1.000 1.000 0.000
#> GSM564708 1 0.0000 1.000 1.000 0.000
#> GSM564709 1 0.0000 1.000 1.000 0.000
#> GSM564710 1 0.0000 1.000 1.000 0.000
#> GSM564711 1 0.0000 1.000 1.000 0.000
#> GSM564712 1 0.0000 1.000 1.000 0.000
#> GSM564713 1 0.0000 1.000 1.000 0.000
#> GSM564714 1 0.0000 1.000 1.000 0.000
#> GSM564715 1 0.0000 1.000 1.000 0.000
#> GSM564716 1 0.0000 1.000 1.000 0.000
#> GSM564717 1 0.0000 1.000 1.000 0.000
#> GSM564718 1 0.0000 1.000 1.000 0.000
#> GSM564719 1 0.0000 1.000 1.000 0.000
#> GSM564720 1 0.0000 1.000 1.000 0.000
#> GSM564721 1 0.0000 1.000 1.000 0.000
#> GSM564722 1 0.0000 1.000 1.000 0.000
#> GSM564723 1 0.0000 1.000 1.000 0.000
#> GSM564724 1 0.0000 1.000 1.000 0.000
#> GSM564725 1 0.0000 1.000 1.000 0.000
#> GSM564726 1 0.0000 1.000 1.000 0.000
#> GSM564727 1 0.0000 1.000 1.000 0.000
#> GSM564728 1 0.0000 1.000 1.000 0.000
#> GSM564729 1 0.0000 1.000 1.000 0.000
#> GSM564730 1 0.0000 1.000 1.000 0.000
#> GSM564731 1 0.0000 1.000 1.000 0.000
#> GSM564732 1 0.0000 1.000 1.000 0.000
#> GSM564733 1 0.0000 1.000 1.000 0.000
#> GSM564734 1 0.0000 1.000 1.000 0.000
#> GSM564735 1 0.0000 1.000 1.000 0.000
#> GSM564736 1 0.0000 1.000 1.000 0.000
#> GSM564737 1 0.0000 1.000 1.000 0.000
#> GSM564738 1 0.0000 1.000 1.000 0.000
#> GSM564739 1 0.0000 1.000 1.000 0.000
#> GSM564740 1 0.0000 1.000 1.000 0.000
#> GSM564741 1 0.0000 1.000 1.000 0.000
#> GSM564742 1 0.0000 1.000 1.000 0.000
#> GSM564743 1 0.0000 1.000 1.000 0.000
#> GSM564744 1 0.0000 1.000 1.000 0.000
#> GSM564745 1 0.0000 1.000 1.000 0.000
#> GSM564746 1 0.0000 1.000 1.000 0.000
#> GSM564747 1 0.0000 1.000 1.000 0.000
#> GSM564748 1 0.0000 1.000 1.000 0.000
#> GSM564749 1 0.0000 1.000 1.000 0.000
#> GSM564750 1 0.0000 1.000 1.000 0.000
#> GSM564751 1 0.0000 1.000 1.000 0.000
#> GSM564752 1 0.0000 1.000 1.000 0.000
#> GSM564753 1 0.0000 1.000 1.000 0.000
#> GSM564754 1 0.0000 1.000 1.000 0.000
#> GSM564755 1 0.0000 1.000 1.000 0.000
#> GSM564756 1 0.0000 1.000 1.000 0.000
#> GSM564757 1 0.0000 1.000 1.000 0.000
#> GSM564758 1 0.0000 1.000 1.000 0.000
#> GSM564759 1 0.0000 1.000 1.000 0.000
#> GSM564760 1 0.0000 1.000 1.000 0.000
#> GSM564761 1 0.0000 1.000 1.000 0.000
#> GSM564762 1 0.0000 1.000 1.000 0.000
#> GSM564681 2 0.0000 1.000 0.000 1.000
#> GSM564693 2 0.0000 1.000 0.000 1.000
#> GSM564646 2 0.0376 0.996 0.004 0.996
#> GSM564699 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564616 3 0.6252 0.0837 0.000 0.444 0.556
#> GSM564617 2 0.5098 0.7428 0.000 0.752 0.248
#> GSM564618 3 0.5810 0.4511 0.000 0.336 0.664
#> GSM564619 1 0.1289 0.9751 0.968 0.032 0.000
#> GSM564620 1 0.0424 0.9794 0.992 0.008 0.000
#> GSM564621 1 0.1031 0.9790 0.976 0.024 0.000
#> GSM564622 3 0.4974 0.6031 0.000 0.236 0.764
#> GSM564623 3 0.4887 0.6231 0.000 0.228 0.772
#> GSM564624 2 0.5016 0.7472 0.000 0.760 0.240
#> GSM564625 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564626 1 0.1529 0.9728 0.960 0.040 0.000
#> GSM564627 1 0.1163 0.9764 0.972 0.028 0.000
#> GSM564628 2 0.6295 0.3749 0.000 0.528 0.472
#> GSM564629 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564630 2 0.5327 0.7268 0.000 0.728 0.272
#> GSM564609 3 0.1643 0.7409 0.000 0.044 0.956
#> GSM564610 1 0.1643 0.9732 0.956 0.044 0.000
#> GSM564611 1 0.2537 0.9570 0.920 0.080 0.000
#> GSM564612 3 0.6286 -0.2029 0.000 0.464 0.536
#> GSM564613 3 0.6308 -0.3023 0.000 0.492 0.508
#> GSM564614 1 0.0592 0.9793 0.988 0.012 0.000
#> GSM564631 3 0.1643 0.7382 0.000 0.044 0.956
#> GSM564632 3 0.2537 0.7342 0.000 0.080 0.920
#> GSM564633 3 0.0592 0.7362 0.000 0.012 0.988
#> GSM564634 3 0.4452 0.6384 0.000 0.192 0.808
#> GSM564635 3 0.0424 0.7365 0.000 0.008 0.992
#> GSM564636 3 0.3340 0.7126 0.000 0.120 0.880
#> GSM564637 3 0.1643 0.7418 0.000 0.044 0.956
#> GSM564638 3 0.3192 0.7181 0.000 0.112 0.888
#> GSM564639 3 0.0424 0.7368 0.000 0.008 0.992
#> GSM564640 3 0.6079 0.1849 0.000 0.388 0.612
#> GSM564641 3 0.4931 0.5788 0.000 0.232 0.768
#> GSM564642 3 0.5926 0.3014 0.000 0.356 0.644
#> GSM564643 3 0.1529 0.7393 0.000 0.040 0.960
#> GSM564644 2 0.6235 0.4406 0.000 0.564 0.436
#> GSM564645 3 0.1289 0.7392 0.000 0.032 0.968
#> GSM564647 3 0.4796 0.6312 0.000 0.220 0.780
#> GSM564648 3 0.4654 0.6421 0.000 0.208 0.792
#> GSM564649 3 0.1643 0.7385 0.000 0.044 0.956
#> GSM564650 2 0.6026 0.6225 0.000 0.624 0.376
#> GSM564651 3 0.5397 0.5393 0.000 0.280 0.720
#> GSM564652 3 0.5431 0.5391 0.000 0.284 0.716
#> GSM564653 3 0.5810 0.4622 0.000 0.336 0.664
#> GSM564654 3 0.0892 0.7394 0.000 0.020 0.980
#> GSM564655 3 0.1964 0.7429 0.000 0.056 0.944
#> GSM564656 3 0.0592 0.7362 0.000 0.012 0.988
#> GSM564657 3 0.2066 0.7373 0.000 0.060 0.940
#> GSM564658 2 0.4555 0.7568 0.000 0.800 0.200
#> GSM564659 3 0.5968 0.2481 0.000 0.364 0.636
#> GSM564660 3 0.6309 -0.3022 0.000 0.496 0.504
#> GSM564661 3 0.5591 0.5033 0.000 0.304 0.696
#> GSM564662 3 0.1753 0.7365 0.000 0.048 0.952
#> GSM564663 2 0.6267 0.4089 0.000 0.548 0.452
#> GSM564664 2 0.6307 0.2670 0.000 0.512 0.488
#> GSM564665 3 0.3038 0.7305 0.000 0.104 0.896
#> GSM564666 3 0.6062 0.1709 0.000 0.384 0.616
#> GSM564667 3 0.3192 0.7160 0.000 0.112 0.888
#> GSM564668 3 0.0892 0.7358 0.000 0.020 0.980
#> GSM564669 3 0.0237 0.7363 0.000 0.004 0.996
#> GSM564670 2 0.6244 0.4529 0.000 0.560 0.440
#> GSM564671 3 0.1289 0.7351 0.000 0.032 0.968
#> GSM564672 3 0.1643 0.7379 0.000 0.044 0.956
#> GSM564673 3 0.4654 0.6373 0.000 0.208 0.792
#> GSM564674 3 0.6267 -0.0559 0.000 0.452 0.548
#> GSM564675 3 0.3965 0.7015 0.008 0.132 0.860
#> GSM564676 2 0.4121 0.7530 0.000 0.832 0.168
#> GSM564677 3 0.5529 0.5145 0.000 0.296 0.704
#> GSM564678 2 0.3816 0.7425 0.000 0.852 0.148
#> GSM564679 2 0.4002 0.7482 0.000 0.840 0.160
#> GSM564680 3 0.0424 0.7368 0.000 0.008 0.992
#> GSM564682 3 0.6286 -0.1723 0.000 0.464 0.536
#> GSM564683 3 0.1964 0.7351 0.000 0.056 0.944
#> GSM564684 3 0.1529 0.7395 0.000 0.040 0.960
#> GSM564685 3 0.1529 0.7396 0.000 0.040 0.960
#> GSM564686 3 0.1289 0.7408 0.000 0.032 0.968
#> GSM564687 3 0.3619 0.7094 0.000 0.136 0.864
#> GSM564688 3 0.5431 0.5329 0.000 0.284 0.716
#> GSM564689 2 0.3879 0.7429 0.000 0.848 0.152
#> GSM564690 2 0.3879 0.7429 0.000 0.848 0.152
#> GSM564691 2 0.6095 0.5978 0.000 0.608 0.392
#> GSM564692 3 0.5859 0.4178 0.000 0.344 0.656
#> GSM564694 3 0.2448 0.7419 0.000 0.076 0.924
#> GSM564695 3 0.5905 0.2646 0.000 0.352 0.648
#> GSM564696 3 0.2537 0.7322 0.000 0.080 0.920
#> GSM564697 2 0.4504 0.7540 0.000 0.804 0.196
#> GSM564698 3 0.0592 0.7368 0.000 0.012 0.988
#> GSM564700 3 0.1289 0.7372 0.000 0.032 0.968
#> GSM564701 3 0.6280 -0.0390 0.000 0.460 0.540
#> GSM564702 3 0.5529 0.5158 0.000 0.296 0.704
#> GSM564703 1 0.0424 0.9795 0.992 0.008 0.000
#> GSM564704 1 0.1163 0.9775 0.972 0.028 0.000
#> GSM564705 1 0.2711 0.9518 0.912 0.088 0.000
#> GSM564706 1 0.1289 0.9769 0.968 0.032 0.000
#> GSM564707 1 0.2537 0.9567 0.920 0.080 0.000
#> GSM564708 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564709 1 0.2261 0.9644 0.932 0.068 0.000
#> GSM564710 1 0.2711 0.9518 0.912 0.088 0.000
#> GSM564711 1 0.1031 0.9772 0.976 0.024 0.000
#> GSM564712 1 0.2796 0.9496 0.908 0.092 0.000
#> GSM564713 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564714 1 0.1529 0.9737 0.960 0.040 0.000
#> GSM564715 1 0.1163 0.9775 0.972 0.028 0.000
#> GSM564716 1 0.1031 0.9779 0.976 0.024 0.000
#> GSM564717 1 0.1753 0.9718 0.952 0.048 0.000
#> GSM564718 1 0.0747 0.9790 0.984 0.016 0.000
#> GSM564719 1 0.1529 0.9737 0.960 0.040 0.000
#> GSM564720 1 0.2448 0.9598 0.924 0.076 0.000
#> GSM564721 1 0.2356 0.9606 0.928 0.072 0.000
#> GSM564722 1 0.1529 0.9737 0.960 0.040 0.000
#> GSM564723 1 0.2537 0.9575 0.920 0.080 0.000
#> GSM564724 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564725 1 0.0892 0.9783 0.980 0.020 0.000
#> GSM564726 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564727 1 0.0747 0.9789 0.984 0.016 0.000
#> GSM564728 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564729 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564730 1 0.2066 0.9670 0.940 0.060 0.000
#> GSM564731 1 0.0592 0.9792 0.988 0.012 0.000
#> GSM564732 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564733 1 0.0237 0.9789 0.996 0.004 0.000
#> GSM564734 1 0.0237 0.9789 0.996 0.004 0.000
#> GSM564735 1 0.0237 0.9789 0.996 0.004 0.000
#> GSM564736 1 0.0237 0.9789 0.996 0.004 0.000
#> GSM564737 1 0.2796 0.9496 0.908 0.092 0.000
#> GSM564738 1 0.0592 0.9796 0.988 0.012 0.000
#> GSM564739 1 0.0000 0.9790 1.000 0.000 0.000
#> GSM564740 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564741 1 0.0592 0.9796 0.988 0.012 0.000
#> GSM564742 1 0.1031 0.9772 0.976 0.024 0.000
#> GSM564743 1 0.2625 0.9551 0.916 0.084 0.000
#> GSM564744 1 0.2537 0.9570 0.920 0.080 0.000
#> GSM564745 1 0.1529 0.9759 0.960 0.040 0.000
#> GSM564746 1 0.1163 0.9764 0.972 0.028 0.000
#> GSM564747 1 0.0892 0.9787 0.980 0.020 0.000
#> GSM564748 1 0.1031 0.9784 0.976 0.024 0.000
#> GSM564749 1 0.1964 0.9683 0.944 0.056 0.000
#> GSM564750 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564751 1 0.0000 0.9790 1.000 0.000 0.000
#> GSM564752 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564753 1 0.0237 0.9794 0.996 0.004 0.000
#> GSM564754 1 0.0892 0.9787 0.980 0.020 0.000
#> GSM564755 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564756 1 0.0892 0.9788 0.980 0.020 0.000
#> GSM564757 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564758 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564759 1 0.0237 0.9791 0.996 0.004 0.000
#> GSM564760 1 0.0237 0.9789 0.996 0.004 0.000
#> GSM564761 1 0.2356 0.9613 0.928 0.072 0.000
#> GSM564762 1 0.0424 0.9787 0.992 0.008 0.000
#> GSM564681 3 0.6192 0.1786 0.000 0.420 0.580
#> GSM564693 3 0.5591 0.4994 0.000 0.304 0.696
#> GSM564646 3 0.1411 0.7394 0.000 0.036 0.964
#> GSM564699 3 0.1529 0.7423 0.000 0.040 0.960
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564616 2 0.7634 0.3212 0.000 0.464 0.300 0.236
#> GSM564617 2 0.4989 0.6217 0.000 0.764 0.164 0.072
#> GSM564618 3 0.7866 -0.1414 0.000 0.336 0.384 0.280
#> GSM564619 1 0.5080 -0.6835 0.576 0.004 0.000 0.420
#> GSM564620 1 0.1022 0.7810 0.968 0.000 0.000 0.032
#> GSM564621 1 0.1661 0.7696 0.944 0.004 0.000 0.052
#> GSM564622 3 0.7634 0.1008 0.000 0.236 0.464 0.300
#> GSM564623 3 0.5288 0.5113 0.004 0.196 0.740 0.060
#> GSM564624 2 0.4706 0.6271 0.000 0.788 0.140 0.072
#> GSM564625 1 0.0817 0.7861 0.976 0.000 0.000 0.024
#> GSM564626 1 0.5143 -0.8016 0.540 0.004 0.000 0.456
#> GSM564627 1 0.3583 0.5222 0.816 0.004 0.000 0.180
#> GSM564628 2 0.6993 0.4752 0.000 0.532 0.336 0.132
#> GSM564629 1 0.0336 0.7848 0.992 0.000 0.000 0.008
#> GSM564630 2 0.5272 0.6193 0.000 0.744 0.172 0.084
#> GSM564609 3 0.2840 0.6788 0.000 0.044 0.900 0.056
#> GSM564610 1 0.3486 0.5158 0.812 0.000 0.000 0.188
#> GSM564611 4 0.4999 0.9019 0.492 0.000 0.000 0.508
#> GSM564612 2 0.5938 0.2608 0.000 0.484 0.480 0.036
#> GSM564613 2 0.6140 0.3205 0.000 0.500 0.452 0.048
#> GSM564614 1 0.0336 0.7867 0.992 0.000 0.000 0.008
#> GSM564631 3 0.1584 0.6833 0.000 0.036 0.952 0.012
#> GSM564632 3 0.4292 0.6312 0.000 0.080 0.820 0.100
#> GSM564633 3 0.1109 0.6900 0.000 0.004 0.968 0.028
#> GSM564634 3 0.4019 0.5665 0.000 0.196 0.792 0.012
#> GSM564635 3 0.1004 0.6901 0.000 0.004 0.972 0.024
#> GSM564636 3 0.3047 0.6493 0.000 0.116 0.872 0.012
#> GSM564637 3 0.2021 0.6911 0.000 0.056 0.932 0.012
#> GSM564638 3 0.2859 0.6567 0.000 0.112 0.880 0.008
#> GSM564639 3 0.1042 0.6914 0.000 0.008 0.972 0.020
#> GSM564640 3 0.6387 -0.2004 0.000 0.444 0.492 0.064
#> GSM564641 3 0.4546 0.4623 0.000 0.256 0.732 0.012
#> GSM564642 3 0.6101 0.0376 0.000 0.388 0.560 0.052
#> GSM564643 3 0.3550 0.6574 0.000 0.044 0.860 0.096
#> GSM564644 2 0.6052 0.5185 0.000 0.616 0.320 0.064
#> GSM564645 3 0.1118 0.6864 0.000 0.036 0.964 0.000
#> GSM564647 3 0.4671 0.5353 0.000 0.220 0.752 0.028
#> GSM564648 3 0.6944 0.2991 0.000 0.216 0.588 0.196
#> GSM564649 3 0.1706 0.6848 0.000 0.036 0.948 0.016
#> GSM564650 2 0.5673 0.5691 0.000 0.660 0.288 0.052
#> GSM564651 3 0.7837 -0.0137 0.000 0.292 0.408 0.300
#> GSM564652 3 0.7836 -0.0022 0.000 0.288 0.408 0.304
#> GSM564653 2 0.7896 0.0941 0.000 0.356 0.352 0.292
#> GSM564654 3 0.1520 0.6939 0.000 0.020 0.956 0.024
#> GSM564655 3 0.2335 0.6898 0.000 0.060 0.920 0.020
#> GSM564656 3 0.1109 0.6900 0.000 0.004 0.968 0.028
#> GSM564657 3 0.2021 0.6808 0.000 0.056 0.932 0.012
#> GSM564658 2 0.3247 0.6284 0.000 0.880 0.060 0.060
#> GSM564659 3 0.5984 0.0839 0.000 0.372 0.580 0.048
#> GSM564660 2 0.6204 0.3189 0.000 0.500 0.448 0.052
#> GSM564661 3 0.7877 -0.0587 0.000 0.312 0.388 0.300
#> GSM564662 3 0.1677 0.6810 0.000 0.040 0.948 0.012
#> GSM564663 2 0.6007 0.5054 0.000 0.604 0.340 0.056
#> GSM564664 2 0.6355 0.4592 0.000 0.576 0.348 0.076
#> GSM564665 3 0.3708 0.6358 0.000 0.148 0.832 0.020
#> GSM564666 3 0.6020 0.0164 0.000 0.384 0.568 0.048
#> GSM564667 3 0.3047 0.6500 0.000 0.116 0.872 0.012
#> GSM564668 3 0.1975 0.6887 0.000 0.016 0.936 0.048
#> GSM564669 3 0.1042 0.6916 0.000 0.008 0.972 0.020
#> GSM564670 2 0.6135 0.4515 0.000 0.568 0.376 0.056
#> GSM564671 3 0.2674 0.6841 0.004 0.020 0.908 0.068
#> GSM564672 3 0.1635 0.6841 0.000 0.044 0.948 0.008
#> GSM564673 3 0.7220 0.2430 0.000 0.196 0.544 0.260
#> GSM564674 2 0.6064 0.2869 0.000 0.512 0.444 0.044
#> GSM564675 3 0.4071 0.6297 0.012 0.112 0.840 0.036
#> GSM564676 2 0.3004 0.6253 0.000 0.892 0.060 0.048
#> GSM564677 3 0.7877 -0.0537 0.000 0.308 0.388 0.304
#> GSM564678 2 0.2408 0.6169 0.000 0.920 0.036 0.044
#> GSM564679 2 0.2385 0.6130 0.000 0.920 0.028 0.052
#> GSM564680 3 0.1042 0.6914 0.000 0.008 0.972 0.020
#> GSM564682 2 0.5493 0.3138 0.000 0.528 0.456 0.016
#> GSM564683 3 0.1854 0.6783 0.000 0.048 0.940 0.012
#> GSM564684 3 0.2680 0.6891 0.004 0.036 0.912 0.048
#> GSM564685 3 0.1833 0.6900 0.000 0.032 0.944 0.024
#> GSM564686 3 0.1575 0.6919 0.004 0.028 0.956 0.012
#> GSM564687 3 0.5375 0.5546 0.000 0.140 0.744 0.116
#> GSM564688 3 0.7845 -0.0185 0.000 0.292 0.404 0.304
#> GSM564689 2 0.2670 0.6147 0.000 0.908 0.040 0.052
#> GSM564690 2 0.2670 0.6147 0.000 0.908 0.040 0.052
#> GSM564691 2 0.5546 0.5758 0.000 0.664 0.292 0.044
#> GSM564692 3 0.7823 -0.1577 0.000 0.372 0.372 0.256
#> GSM564694 3 0.3134 0.6792 0.004 0.088 0.884 0.024
#> GSM564695 3 0.5742 0.1199 0.000 0.368 0.596 0.036
#> GSM564696 3 0.2329 0.6759 0.000 0.072 0.916 0.012
#> GSM564697 2 0.3435 0.6275 0.000 0.864 0.100 0.036
#> GSM564698 3 0.1042 0.6902 0.000 0.008 0.972 0.020
#> GSM564700 3 0.2731 0.6858 0.004 0.028 0.908 0.060
#> GSM564701 2 0.6783 0.3628 0.000 0.512 0.388 0.100
#> GSM564702 3 0.7854 -0.0386 0.000 0.304 0.400 0.296
#> GSM564703 1 0.2888 0.6776 0.872 0.004 0.000 0.124
#> GSM564704 1 0.2921 0.6748 0.860 0.000 0.000 0.140
#> GSM564705 4 0.4967 0.9174 0.452 0.000 0.000 0.548
#> GSM564706 1 0.1792 0.7641 0.932 0.000 0.000 0.068
#> GSM564707 4 0.4998 0.9018 0.488 0.000 0.000 0.512
#> GSM564708 1 0.0779 0.7871 0.980 0.004 0.000 0.016
#> GSM564709 1 0.5050 -0.5994 0.588 0.004 0.000 0.408
#> GSM564710 4 0.4961 0.9133 0.448 0.000 0.000 0.552
#> GSM564711 1 0.1867 0.7550 0.928 0.000 0.000 0.072
#> GSM564712 4 0.4967 0.9185 0.452 0.000 0.000 0.548
#> GSM564713 1 0.0524 0.7863 0.988 0.004 0.000 0.008
#> GSM564714 1 0.2589 0.7116 0.884 0.000 0.000 0.116
#> GSM564715 1 0.3172 0.6222 0.840 0.000 0.000 0.160
#> GSM564716 1 0.1661 0.7637 0.944 0.004 0.000 0.052
#> GSM564717 1 0.3400 0.6045 0.820 0.000 0.000 0.180
#> GSM564718 1 0.1302 0.7767 0.956 0.000 0.000 0.044
#> GSM564719 1 0.2868 0.6922 0.864 0.000 0.000 0.136
#> GSM564720 1 0.4999 -0.8783 0.508 0.000 0.000 0.492
#> GSM564721 1 0.5163 -0.8637 0.516 0.004 0.000 0.480
#> GSM564722 1 0.2589 0.7116 0.884 0.000 0.000 0.116
#> GSM564723 4 0.5000 0.8925 0.500 0.000 0.000 0.500
#> GSM564724 1 0.0657 0.7861 0.984 0.004 0.000 0.012
#> GSM564725 1 0.4837 -0.3972 0.648 0.004 0.000 0.348
#> GSM564726 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564727 1 0.4720 -0.2753 0.672 0.004 0.000 0.324
#> GSM564728 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564729 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564730 1 0.4776 -0.4796 0.624 0.000 0.000 0.376
#> GSM564731 1 0.1004 0.7851 0.972 0.004 0.000 0.024
#> GSM564732 1 0.0524 0.7850 0.988 0.004 0.000 0.008
#> GSM564733 1 0.0524 0.7856 0.988 0.004 0.000 0.008
#> GSM564734 1 0.0779 0.7863 0.980 0.004 0.000 0.016
#> GSM564735 1 0.0524 0.7861 0.988 0.004 0.000 0.008
#> GSM564736 1 0.0188 0.7860 0.996 0.000 0.000 0.004
#> GSM564737 4 0.4967 0.9185 0.452 0.000 0.000 0.548
#> GSM564738 1 0.1305 0.7777 0.960 0.004 0.000 0.036
#> GSM564739 1 0.2773 0.6812 0.880 0.004 0.000 0.116
#> GSM564740 1 0.0592 0.7847 0.984 0.000 0.000 0.016
#> GSM564741 1 0.1398 0.7785 0.956 0.004 0.000 0.040
#> GSM564742 1 0.1716 0.7631 0.936 0.000 0.000 0.064
#> GSM564743 4 0.5000 0.8330 0.500 0.000 0.000 0.500
#> GSM564744 4 0.4994 0.9144 0.480 0.000 0.000 0.520
#> GSM564745 1 0.3311 0.5588 0.828 0.000 0.000 0.172
#> GSM564746 1 0.3400 0.5249 0.820 0.000 0.000 0.180
#> GSM564747 1 0.2266 0.7529 0.912 0.004 0.000 0.084
#> GSM564748 1 0.2530 0.7243 0.896 0.004 0.000 0.100
#> GSM564749 1 0.4948 -0.7329 0.560 0.000 0.000 0.440
#> GSM564750 1 0.0188 0.7853 0.996 0.000 0.000 0.004
#> GSM564751 1 0.1661 0.7669 0.944 0.004 0.000 0.052
#> GSM564752 1 0.0188 0.7853 0.996 0.000 0.000 0.004
#> GSM564753 1 0.1004 0.7849 0.972 0.004 0.000 0.024
#> GSM564754 1 0.4535 -0.0192 0.704 0.004 0.000 0.292
#> GSM564755 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564756 1 0.2149 0.7347 0.912 0.000 0.000 0.088
#> GSM564757 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564758 1 0.0336 0.7851 0.992 0.000 0.000 0.008
#> GSM564759 1 0.0469 0.7859 0.988 0.000 0.000 0.012
#> GSM564760 1 0.0657 0.7874 0.984 0.004 0.000 0.012
#> GSM564761 4 0.5000 0.9004 0.496 0.000 0.000 0.504
#> GSM564762 1 0.0817 0.7828 0.976 0.000 0.000 0.024
#> GSM564681 2 0.7694 0.2867 0.000 0.448 0.308 0.244
#> GSM564693 3 0.7847 -0.0882 0.000 0.328 0.396 0.276
#> GSM564646 3 0.2585 0.6887 0.004 0.032 0.916 0.048
#> GSM564699 3 0.1584 0.6907 0.000 0.036 0.952 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0404 0.88744 0.012 0.000 0.000 0.988 0.000
#> GSM564616 5 0.5206 0.59974 0.004 0.216 0.096 0.000 0.684
#> GSM564617 2 0.6105 0.51962 0.020 0.608 0.120 0.000 0.252
#> GSM564618 5 0.4700 0.69146 0.004 0.132 0.116 0.000 0.748
#> GSM564619 1 0.3935 0.82674 0.760 0.012 0.000 0.220 0.008
#> GSM564620 4 0.1914 0.87723 0.056 0.008 0.000 0.928 0.008
#> GSM564621 4 0.2457 0.86614 0.076 0.016 0.000 0.900 0.008
#> GSM564622 5 0.3731 0.73758 0.000 0.040 0.160 0.000 0.800
#> GSM564623 3 0.5298 0.59474 0.012 0.140 0.716 0.004 0.128
#> GSM564624 2 0.5867 0.52189 0.020 0.632 0.100 0.000 0.248
#> GSM564625 4 0.1280 0.88788 0.024 0.008 0.000 0.960 0.008
#> GSM564626 1 0.3437 0.85044 0.808 0.012 0.000 0.176 0.004
#> GSM564627 4 0.4530 0.31747 0.376 0.008 0.000 0.612 0.004
#> GSM564628 2 0.6871 0.28032 0.004 0.388 0.256 0.000 0.352
#> GSM564629 4 0.0451 0.88936 0.008 0.004 0.000 0.988 0.000
#> GSM564630 2 0.6226 0.50143 0.020 0.584 0.120 0.000 0.276
#> GSM564609 3 0.3456 0.73016 0.000 0.016 0.800 0.000 0.184
#> GSM564610 4 0.4446 0.26074 0.400 0.008 0.000 0.592 0.000
#> GSM564611 1 0.2873 0.86241 0.856 0.016 0.000 0.128 0.000
#> GSM564612 3 0.6211 -0.24927 0.004 0.432 0.444 0.000 0.120
#> GSM564613 2 0.6521 0.24744 0.008 0.424 0.420 0.000 0.148
#> GSM564614 4 0.0566 0.88902 0.012 0.000 0.000 0.984 0.004
#> GSM564631 3 0.1386 0.78581 0.000 0.032 0.952 0.000 0.016
#> GSM564632 3 0.4603 0.57635 0.000 0.032 0.668 0.000 0.300
#> GSM564633 3 0.1571 0.78571 0.000 0.004 0.936 0.000 0.060
#> GSM564634 3 0.4068 0.69253 0.004 0.144 0.792 0.000 0.060
#> GSM564635 3 0.1502 0.78632 0.000 0.004 0.940 0.000 0.056
#> GSM564636 3 0.3033 0.74971 0.000 0.084 0.864 0.000 0.052
#> GSM564637 3 0.2446 0.79168 0.000 0.044 0.900 0.000 0.056
#> GSM564638 3 0.3019 0.75384 0.000 0.088 0.864 0.000 0.048
#> GSM564639 3 0.1557 0.78888 0.000 0.008 0.940 0.000 0.052
#> GSM564640 3 0.6796 -0.29355 0.000 0.316 0.380 0.000 0.304
#> GSM564641 3 0.4465 0.60237 0.000 0.204 0.736 0.000 0.060
#> GSM564642 3 0.6666 -0.00273 0.004 0.288 0.476 0.000 0.232
#> GSM564643 3 0.3690 0.68748 0.000 0.012 0.764 0.000 0.224
#> GSM564644 2 0.6665 0.36174 0.004 0.480 0.244 0.000 0.272
#> GSM564645 3 0.1281 0.78820 0.000 0.032 0.956 0.000 0.012
#> GSM564647 3 0.4674 0.63772 0.004 0.148 0.748 0.000 0.100
#> GSM564648 5 0.4608 0.46739 0.000 0.024 0.336 0.000 0.640
#> GSM564649 3 0.1485 0.78858 0.000 0.032 0.948 0.000 0.020
#> GSM564650 2 0.5404 0.54176 0.000 0.636 0.264 0.000 0.100
#> GSM564651 5 0.2361 0.79358 0.000 0.012 0.096 0.000 0.892
#> GSM564652 5 0.2304 0.79203 0.000 0.008 0.100 0.000 0.892
#> GSM564653 5 0.3479 0.76622 0.000 0.084 0.080 0.000 0.836
#> GSM564654 3 0.2110 0.79170 0.000 0.016 0.912 0.000 0.072
#> GSM564655 3 0.2504 0.79049 0.000 0.040 0.896 0.000 0.064
#> GSM564656 3 0.1571 0.78571 0.000 0.004 0.936 0.000 0.060
#> GSM564657 3 0.1522 0.78300 0.000 0.044 0.944 0.000 0.012
#> GSM564658 2 0.4245 0.52064 0.008 0.744 0.024 0.000 0.224
#> GSM564659 3 0.6100 0.19783 0.004 0.304 0.556 0.000 0.136
#> GSM564660 2 0.6594 0.27445 0.008 0.428 0.404 0.000 0.160
#> GSM564661 5 0.2889 0.79060 0.000 0.044 0.084 0.000 0.872
#> GSM564662 3 0.1364 0.78336 0.000 0.036 0.952 0.000 0.012
#> GSM564663 2 0.6642 0.45017 0.004 0.480 0.292 0.000 0.224
#> GSM564664 2 0.6830 0.20617 0.004 0.396 0.240 0.000 0.360
#> GSM564665 3 0.3806 0.73223 0.000 0.104 0.812 0.000 0.084
#> GSM564666 3 0.6159 0.10991 0.004 0.332 0.532 0.000 0.132
#> GSM564667 3 0.2628 0.76192 0.000 0.088 0.884 0.000 0.028
#> GSM564668 3 0.2389 0.77637 0.000 0.004 0.880 0.000 0.116
#> GSM564669 3 0.1764 0.78761 0.000 0.008 0.928 0.000 0.064
#> GSM564670 2 0.6729 0.43729 0.008 0.456 0.340 0.000 0.196
#> GSM564671 3 0.2921 0.76137 0.000 0.004 0.844 0.004 0.148
#> GSM564672 3 0.1568 0.78705 0.000 0.036 0.944 0.000 0.020
#> GSM564673 5 0.3766 0.62120 0.000 0.004 0.268 0.000 0.728
#> GSM564674 3 0.6742 -0.33042 0.004 0.388 0.396 0.000 0.212
#> GSM564675 3 0.3975 0.72851 0.008 0.076 0.828 0.012 0.076
#> GSM564676 2 0.4074 0.54620 0.012 0.780 0.028 0.000 0.180
#> GSM564677 5 0.2676 0.78934 0.000 0.036 0.080 0.000 0.884
#> GSM564678 2 0.3328 0.53087 0.008 0.812 0.004 0.000 0.176
#> GSM564679 2 0.3427 0.51317 0.012 0.796 0.000 0.000 0.192
#> GSM564680 3 0.1557 0.78888 0.000 0.008 0.940 0.000 0.052
#> GSM564682 2 0.5966 0.25028 0.000 0.460 0.432 0.000 0.108
#> GSM564683 3 0.1549 0.78298 0.000 0.040 0.944 0.000 0.016
#> GSM564684 3 0.3001 0.76475 0.000 0.008 0.844 0.004 0.144
#> GSM564685 3 0.1493 0.79202 0.000 0.024 0.948 0.000 0.028
#> GSM564686 3 0.2102 0.79088 0.000 0.012 0.916 0.004 0.068
#> GSM564687 3 0.5129 0.46419 0.000 0.056 0.616 0.000 0.328
#> GSM564688 5 0.2351 0.79239 0.000 0.016 0.088 0.000 0.896
#> GSM564689 2 0.2976 0.53377 0.012 0.852 0.004 0.000 0.132
#> GSM564690 2 0.3022 0.53206 0.012 0.848 0.004 0.000 0.136
#> GSM564691 2 0.5673 0.55686 0.000 0.616 0.252 0.000 0.132
#> GSM564692 5 0.3962 0.75725 0.000 0.112 0.088 0.000 0.800
#> GSM564694 3 0.3400 0.76980 0.000 0.040 0.840 0.004 0.116
#> GSM564695 3 0.5805 0.28220 0.004 0.308 0.584 0.000 0.104
#> GSM564696 3 0.2054 0.78077 0.000 0.052 0.920 0.000 0.028
#> GSM564697 2 0.4288 0.56712 0.008 0.784 0.072 0.000 0.136
#> GSM564698 3 0.1282 0.78905 0.000 0.004 0.952 0.000 0.044
#> GSM564700 3 0.2964 0.75850 0.000 0.004 0.840 0.004 0.152
#> GSM564701 5 0.6796 -0.22813 0.000 0.336 0.292 0.000 0.372
#> GSM564702 5 0.2983 0.79300 0.000 0.040 0.096 0.000 0.864
#> GSM564703 4 0.3616 0.67834 0.224 0.004 0.000 0.768 0.004
#> GSM564704 4 0.4220 0.72993 0.200 0.032 0.000 0.760 0.008
#> GSM564705 1 0.1768 0.83651 0.924 0.004 0.000 0.072 0.000
#> GSM564706 4 0.2234 0.86554 0.044 0.036 0.000 0.916 0.004
#> GSM564707 1 0.2612 0.85851 0.868 0.008 0.000 0.124 0.000
#> GSM564708 4 0.1412 0.88727 0.036 0.008 0.000 0.952 0.004
#> GSM564709 1 0.4337 0.75065 0.696 0.016 0.000 0.284 0.004
#> GSM564710 1 0.1704 0.83270 0.928 0.004 0.000 0.068 0.000
#> GSM564711 4 0.2584 0.85688 0.052 0.040 0.000 0.900 0.008
#> GSM564712 1 0.1830 0.83342 0.924 0.008 0.000 0.068 0.000
#> GSM564713 4 0.1026 0.88895 0.024 0.004 0.000 0.968 0.004
#> GSM564714 4 0.3720 0.81200 0.096 0.048 0.000 0.836 0.020
#> GSM564715 4 0.4597 0.54783 0.300 0.024 0.000 0.672 0.004
#> GSM564716 4 0.2349 0.86007 0.084 0.012 0.000 0.900 0.004
#> GSM564717 4 0.4914 0.57416 0.280 0.040 0.000 0.672 0.008
#> GSM564718 4 0.1300 0.88163 0.028 0.016 0.000 0.956 0.000
#> GSM564719 4 0.4523 0.75602 0.160 0.052 0.000 0.768 0.020
#> GSM564720 1 0.3292 0.85946 0.836 0.016 0.000 0.140 0.008
#> GSM564721 1 0.2930 0.85858 0.832 0.004 0.000 0.164 0.000
#> GSM564722 4 0.3720 0.81200 0.096 0.048 0.000 0.836 0.020
#> GSM564723 1 0.3201 0.86288 0.844 0.016 0.000 0.132 0.008
#> GSM564724 4 0.0932 0.88832 0.020 0.004 0.000 0.972 0.004
#> GSM564725 1 0.4403 0.72434 0.668 0.012 0.000 0.316 0.004
#> GSM564726 4 0.0404 0.88744 0.012 0.000 0.000 0.988 0.000
#> GSM564727 1 0.4581 0.65093 0.624 0.012 0.000 0.360 0.004
#> GSM564728 4 0.0566 0.88725 0.012 0.000 0.000 0.984 0.004
#> GSM564729 4 0.0566 0.88725 0.012 0.000 0.000 0.984 0.004
#> GSM564730 1 0.4225 0.63648 0.632 0.004 0.000 0.364 0.000
#> GSM564731 4 0.0912 0.88655 0.016 0.012 0.000 0.972 0.000
#> GSM564732 4 0.0613 0.88842 0.004 0.008 0.000 0.984 0.004
#> GSM564733 4 0.0992 0.88893 0.024 0.008 0.000 0.968 0.000
#> GSM564734 4 0.0968 0.88956 0.012 0.012 0.000 0.972 0.004
#> GSM564735 4 0.0960 0.88847 0.016 0.008 0.000 0.972 0.004
#> GSM564736 4 0.0609 0.88854 0.020 0.000 0.000 0.980 0.000
#> GSM564737 1 0.1830 0.83342 0.924 0.008 0.000 0.068 0.000
#> GSM564738 4 0.1547 0.88166 0.032 0.016 0.000 0.948 0.004
#> GSM564739 4 0.3616 0.68161 0.224 0.004 0.000 0.768 0.004
#> GSM564740 4 0.0510 0.88685 0.016 0.000 0.000 0.984 0.000
#> GSM564741 4 0.1862 0.88197 0.048 0.016 0.000 0.932 0.004
#> GSM564742 4 0.2584 0.86117 0.052 0.040 0.000 0.900 0.008
#> GSM564743 1 0.3731 0.83602 0.800 0.016 0.000 0.172 0.012
#> GSM564744 1 0.2625 0.85809 0.876 0.016 0.000 0.108 0.000
#> GSM564745 4 0.3944 0.61207 0.272 0.004 0.000 0.720 0.004
#> GSM564746 4 0.4414 0.32080 0.376 0.004 0.000 0.616 0.004
#> GSM564747 4 0.2982 0.83930 0.116 0.020 0.000 0.860 0.004
#> GSM564748 4 0.3264 0.79569 0.164 0.016 0.000 0.820 0.000
#> GSM564749 1 0.3795 0.83858 0.780 0.028 0.000 0.192 0.000
#> GSM564750 4 0.0290 0.88759 0.008 0.000 0.000 0.992 0.000
#> GSM564751 4 0.2352 0.85586 0.092 0.008 0.000 0.896 0.004
#> GSM564752 4 0.0290 0.88759 0.008 0.000 0.000 0.992 0.000
#> GSM564753 4 0.1522 0.88403 0.044 0.012 0.000 0.944 0.000
#> GSM564754 1 0.4590 0.48673 0.568 0.012 0.000 0.420 0.000
#> GSM564755 4 0.0404 0.88744 0.012 0.000 0.000 0.988 0.000
#> GSM564756 4 0.3098 0.80102 0.148 0.016 0.000 0.836 0.000
#> GSM564757 4 0.0404 0.88744 0.012 0.000 0.000 0.988 0.000
#> GSM564758 4 0.0404 0.88744 0.012 0.000 0.000 0.988 0.000
#> GSM564759 4 0.0566 0.88863 0.012 0.004 0.000 0.984 0.000
#> GSM564760 4 0.1202 0.88817 0.032 0.004 0.000 0.960 0.004
#> GSM564761 1 0.2548 0.85840 0.876 0.004 0.000 0.116 0.004
#> GSM564762 4 0.0566 0.88895 0.000 0.012 0.000 0.984 0.004
#> GSM564681 5 0.4851 0.64287 0.000 0.196 0.092 0.000 0.712
#> GSM564693 5 0.3754 0.76846 0.000 0.084 0.100 0.000 0.816
#> GSM564646 3 0.2877 0.76468 0.000 0.004 0.848 0.004 0.144
#> GSM564699 3 0.2067 0.79307 0.000 0.032 0.920 0.000 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0260 0.8407 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM564616 5 0.5114 0.5837 0.000 0.200 0.032 0.000 0.676 0.092
#> GSM564617 6 0.6314 0.3575 0.000 0.404 0.088 0.000 0.072 0.436
#> GSM564618 5 0.4843 0.6597 0.000 0.100 0.060 0.000 0.732 0.108
#> GSM564619 1 0.3312 0.8008 0.792 0.000 0.000 0.180 0.000 0.028
#> GSM564620 4 0.2605 0.8185 0.028 0.000 0.000 0.864 0.000 0.108
#> GSM564621 4 0.3044 0.8061 0.048 0.000 0.000 0.836 0.000 0.116
#> GSM564622 5 0.3628 0.7193 0.000 0.040 0.084 0.000 0.824 0.052
#> GSM564623 3 0.4601 0.4483 0.000 0.032 0.680 0.004 0.020 0.264
#> GSM564624 2 0.6192 -0.3709 0.000 0.452 0.064 0.000 0.084 0.400
#> GSM564625 4 0.1320 0.8436 0.016 0.000 0.000 0.948 0.000 0.036
#> GSM564626 1 0.2771 0.8261 0.852 0.000 0.000 0.116 0.000 0.032
#> GSM564627 4 0.5673 0.1147 0.372 0.000 0.000 0.468 0.000 0.160
#> GSM564628 6 0.7621 0.3872 0.000 0.236 0.196 0.000 0.232 0.336
#> GSM564629 4 0.1152 0.8470 0.004 0.000 0.000 0.952 0.000 0.044
#> GSM564630 6 0.6447 0.3676 0.000 0.380 0.080 0.000 0.096 0.444
#> GSM564609 3 0.4028 0.6628 0.000 0.012 0.756 0.000 0.184 0.048
#> GSM564610 4 0.5675 0.0262 0.400 0.000 0.000 0.444 0.000 0.156
#> GSM564611 1 0.2527 0.8347 0.884 0.000 0.000 0.048 0.004 0.064
#> GSM564612 3 0.6672 -0.4493 0.000 0.292 0.388 0.000 0.032 0.288
#> GSM564613 6 0.6432 0.5252 0.000 0.184 0.356 0.000 0.032 0.428
#> GSM564614 4 0.0520 0.8424 0.008 0.000 0.000 0.984 0.000 0.008
#> GSM564631 3 0.1908 0.7485 0.000 0.020 0.924 0.000 0.012 0.044
#> GSM564632 3 0.4988 0.4837 0.000 0.016 0.628 0.000 0.292 0.064
#> GSM564633 3 0.1542 0.7524 0.000 0.004 0.936 0.000 0.052 0.008
#> GSM564634 3 0.4311 0.6237 0.000 0.108 0.756 0.000 0.016 0.120
#> GSM564635 3 0.1429 0.7529 0.000 0.004 0.940 0.000 0.052 0.004
#> GSM564636 3 0.3239 0.6796 0.000 0.024 0.816 0.000 0.008 0.152
#> GSM564637 3 0.2777 0.7552 0.000 0.036 0.880 0.000 0.036 0.048
#> GSM564638 3 0.3196 0.6843 0.000 0.020 0.816 0.000 0.008 0.156
#> GSM564639 3 0.1523 0.7537 0.000 0.008 0.940 0.000 0.044 0.008
#> GSM564640 2 0.7160 0.0169 0.000 0.324 0.312 0.000 0.288 0.076
#> GSM564641 3 0.4872 0.5201 0.000 0.156 0.692 0.000 0.012 0.140
#> GSM564642 3 0.7350 -0.1322 0.000 0.208 0.412 0.000 0.224 0.156
#> GSM564643 3 0.4073 0.6215 0.000 0.016 0.732 0.000 0.224 0.028
#> GSM564644 2 0.7028 0.2911 0.000 0.472 0.196 0.000 0.208 0.124
#> GSM564645 3 0.1922 0.7524 0.000 0.024 0.924 0.000 0.012 0.040
#> GSM564647 3 0.4925 0.5441 0.000 0.088 0.700 0.000 0.032 0.180
#> GSM564648 5 0.4576 0.4408 0.000 0.016 0.264 0.000 0.676 0.044
#> GSM564649 3 0.2151 0.7515 0.000 0.024 0.912 0.000 0.016 0.048
#> GSM564650 2 0.5388 0.2189 0.000 0.636 0.228 0.000 0.028 0.108
#> GSM564651 5 0.1088 0.7826 0.000 0.024 0.016 0.000 0.960 0.000
#> GSM564652 5 0.1148 0.7790 0.000 0.016 0.020 0.000 0.960 0.004
#> GSM564653 5 0.2019 0.7627 0.000 0.088 0.012 0.000 0.900 0.000
#> GSM564654 3 0.2196 0.7571 0.000 0.016 0.908 0.000 0.056 0.020
#> GSM564655 3 0.2968 0.7511 0.000 0.032 0.868 0.000 0.044 0.056
#> GSM564656 3 0.1542 0.7524 0.000 0.004 0.936 0.000 0.052 0.008
#> GSM564657 3 0.2074 0.7447 0.000 0.036 0.912 0.000 0.004 0.048
#> GSM564658 2 0.4656 0.4362 0.000 0.720 0.016 0.000 0.112 0.152
#> GSM564659 3 0.6551 -0.1595 0.000 0.176 0.488 0.000 0.056 0.280
#> GSM564660 6 0.6438 0.5527 0.000 0.192 0.332 0.000 0.032 0.444
#> GSM564661 5 0.1462 0.7771 0.000 0.056 0.008 0.000 0.936 0.000
#> GSM564662 3 0.1957 0.7443 0.000 0.024 0.920 0.000 0.008 0.048
#> GSM564663 2 0.7399 -0.0535 0.000 0.384 0.248 0.000 0.140 0.228
#> GSM564664 2 0.7203 0.2255 0.000 0.364 0.176 0.000 0.344 0.116
#> GSM564665 3 0.4375 0.6890 0.000 0.092 0.772 0.000 0.060 0.076
#> GSM564666 3 0.6137 -0.3337 0.000 0.140 0.464 0.000 0.028 0.368
#> GSM564667 3 0.3090 0.7145 0.000 0.056 0.848 0.000 0.008 0.088
#> GSM564668 3 0.2611 0.7387 0.000 0.008 0.864 0.000 0.116 0.012
#> GSM564669 3 0.1757 0.7523 0.000 0.012 0.928 0.000 0.052 0.008
#> GSM564670 6 0.6596 0.5708 0.000 0.212 0.268 0.000 0.048 0.472
#> GSM564671 3 0.3090 0.7184 0.000 0.000 0.828 0.004 0.140 0.028
#> GSM564672 3 0.2202 0.7490 0.000 0.028 0.908 0.000 0.012 0.052
#> GSM564673 5 0.3200 0.6170 0.000 0.000 0.196 0.000 0.788 0.016
#> GSM564674 3 0.7397 -0.3478 0.000 0.320 0.340 0.000 0.132 0.208
#> GSM564675 3 0.3477 0.6515 0.000 0.008 0.800 0.012 0.012 0.168
#> GSM564676 2 0.2953 0.5120 0.000 0.864 0.020 0.000 0.076 0.040
#> GSM564677 5 0.1429 0.7761 0.000 0.052 0.004 0.000 0.940 0.004
#> GSM564678 2 0.2322 0.5070 0.000 0.896 0.004 0.000 0.064 0.036
#> GSM564679 2 0.2937 0.4923 0.000 0.848 0.000 0.000 0.096 0.056
#> GSM564680 3 0.1523 0.7537 0.000 0.008 0.940 0.000 0.044 0.008
#> GSM564682 2 0.6408 -0.1004 0.000 0.420 0.372 0.000 0.032 0.176
#> GSM564683 3 0.2101 0.7436 0.000 0.028 0.912 0.000 0.008 0.052
#> GSM564684 3 0.3393 0.7176 0.000 0.012 0.824 0.004 0.128 0.032
#> GSM564685 3 0.1922 0.7588 0.000 0.024 0.924 0.000 0.012 0.040
#> GSM564686 3 0.2256 0.7531 0.000 0.008 0.908 0.004 0.048 0.032
#> GSM564687 3 0.5778 0.3489 0.000 0.052 0.564 0.000 0.308 0.076
#> GSM564688 5 0.0993 0.7798 0.000 0.024 0.012 0.000 0.964 0.000
#> GSM564689 2 0.1196 0.5002 0.000 0.952 0.000 0.000 0.040 0.008
#> GSM564690 2 0.1082 0.5000 0.000 0.956 0.000 0.000 0.040 0.004
#> GSM564691 2 0.5952 0.1368 0.000 0.576 0.204 0.000 0.032 0.188
#> GSM564692 5 0.3259 0.7474 0.000 0.104 0.012 0.000 0.836 0.048
#> GSM564694 3 0.3761 0.7218 0.000 0.032 0.820 0.004 0.072 0.072
#> GSM564695 3 0.6141 0.0167 0.000 0.180 0.524 0.000 0.028 0.268
#> GSM564696 3 0.2331 0.7413 0.000 0.032 0.888 0.000 0.000 0.080
#> GSM564697 2 0.3301 0.4799 0.000 0.848 0.056 0.000 0.040 0.056
#> GSM564698 3 0.1152 0.7552 0.000 0.004 0.952 0.000 0.044 0.000
#> GSM564700 3 0.3441 0.7094 0.000 0.012 0.816 0.004 0.140 0.028
#> GSM564701 5 0.7647 -0.2739 0.000 0.268 0.228 0.000 0.312 0.192
#> GSM564702 5 0.1932 0.7815 0.000 0.040 0.016 0.000 0.924 0.020
#> GSM564703 4 0.4223 0.6483 0.236 0.000 0.000 0.704 0.000 0.060
#> GSM564704 4 0.5066 0.6408 0.176 0.000 0.000 0.636 0.000 0.188
#> GSM564705 1 0.1116 0.8146 0.960 0.000 0.000 0.008 0.004 0.028
#> GSM564706 4 0.3189 0.8021 0.020 0.000 0.000 0.796 0.000 0.184
#> GSM564707 1 0.1921 0.8345 0.916 0.000 0.000 0.052 0.000 0.032
#> GSM564708 4 0.1865 0.8448 0.040 0.000 0.000 0.920 0.000 0.040
#> GSM564709 1 0.4149 0.7380 0.720 0.000 0.000 0.216 0.000 0.064
#> GSM564710 1 0.1194 0.8134 0.956 0.000 0.000 0.008 0.004 0.032
#> GSM564711 4 0.3645 0.7681 0.024 0.000 0.000 0.740 0.000 0.236
#> GSM564712 1 0.0717 0.8147 0.976 0.000 0.000 0.008 0.000 0.016
#> GSM564713 4 0.1257 0.8449 0.020 0.000 0.000 0.952 0.000 0.028
#> GSM564714 4 0.4406 0.6687 0.040 0.000 0.000 0.624 0.000 0.336
#> GSM564715 4 0.5364 0.4740 0.300 0.000 0.000 0.560 0.000 0.140
#> GSM564716 4 0.3150 0.8045 0.064 0.000 0.000 0.832 0.000 0.104
#> GSM564717 4 0.5947 0.3306 0.240 0.000 0.000 0.448 0.000 0.312
#> GSM564718 4 0.2653 0.8250 0.012 0.000 0.000 0.844 0.000 0.144
#> GSM564719 4 0.5198 0.5438 0.096 0.000 0.000 0.524 0.000 0.380
#> GSM564720 1 0.2794 0.8306 0.860 0.000 0.000 0.060 0.000 0.080
#> GSM564721 1 0.2622 0.8375 0.868 0.000 0.000 0.104 0.004 0.024
#> GSM564722 4 0.4406 0.6687 0.040 0.000 0.000 0.624 0.000 0.336
#> GSM564723 1 0.2625 0.8342 0.872 0.000 0.000 0.056 0.000 0.072
#> GSM564724 4 0.0806 0.8445 0.008 0.000 0.000 0.972 0.000 0.020
#> GSM564725 1 0.3956 0.7048 0.704 0.000 0.000 0.264 0.000 0.032
#> GSM564726 4 0.0146 0.8408 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM564727 1 0.4134 0.6181 0.656 0.000 0.000 0.316 0.000 0.028
#> GSM564728 4 0.0363 0.8402 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM564729 4 0.0458 0.8419 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM564730 1 0.4408 0.6319 0.656 0.000 0.000 0.292 0.000 0.052
#> GSM564731 4 0.2357 0.8352 0.012 0.000 0.000 0.872 0.000 0.116
#> GSM564732 4 0.0858 0.8448 0.004 0.000 0.000 0.968 0.000 0.028
#> GSM564733 4 0.0806 0.8435 0.008 0.000 0.000 0.972 0.000 0.020
#> GSM564734 4 0.1480 0.8460 0.020 0.000 0.000 0.940 0.000 0.040
#> GSM564735 4 0.1049 0.8456 0.008 0.000 0.000 0.960 0.000 0.032
#> GSM564736 4 0.0725 0.8429 0.012 0.000 0.000 0.976 0.000 0.012
#> GSM564737 1 0.0717 0.8147 0.976 0.000 0.000 0.008 0.000 0.016
#> GSM564738 4 0.2361 0.8378 0.028 0.000 0.000 0.884 0.000 0.088
#> GSM564739 4 0.4215 0.6481 0.244 0.000 0.000 0.700 0.000 0.056
#> GSM564740 4 0.1802 0.8436 0.012 0.000 0.000 0.916 0.000 0.072
#> GSM564741 4 0.2376 0.8399 0.044 0.000 0.000 0.888 0.000 0.068
#> GSM564742 4 0.3455 0.7959 0.036 0.000 0.000 0.784 0.000 0.180
#> GSM564743 1 0.3563 0.8107 0.800 0.000 0.000 0.092 0.000 0.108
#> GSM564744 1 0.2201 0.8326 0.904 0.000 0.000 0.036 0.004 0.056
#> GSM564745 4 0.4151 0.5835 0.276 0.000 0.000 0.684 0.000 0.040
#> GSM564746 4 0.5648 0.1186 0.372 0.000 0.000 0.472 0.000 0.156
#> GSM564747 4 0.4095 0.7769 0.100 0.000 0.000 0.748 0.000 0.152
#> GSM564748 4 0.4209 0.7532 0.160 0.000 0.000 0.736 0.000 0.104
#> GSM564749 1 0.3514 0.8221 0.804 0.000 0.000 0.088 0.000 0.108
#> GSM564750 4 0.0603 0.8432 0.004 0.000 0.000 0.980 0.000 0.016
#> GSM564751 4 0.3520 0.8041 0.096 0.000 0.000 0.804 0.000 0.100
#> GSM564752 4 0.1010 0.8455 0.004 0.000 0.000 0.960 0.000 0.036
#> GSM564753 4 0.2908 0.8303 0.048 0.000 0.000 0.848 0.000 0.104
#> GSM564754 1 0.4538 0.5071 0.612 0.000 0.000 0.340 0.000 0.048
#> GSM564755 4 0.0363 0.8412 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM564756 4 0.4085 0.7481 0.156 0.000 0.000 0.748 0.000 0.096
#> GSM564757 4 0.0260 0.8407 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM564758 4 0.0260 0.8407 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM564759 4 0.1524 0.8447 0.008 0.000 0.000 0.932 0.000 0.060
#> GSM564760 4 0.1492 0.8442 0.036 0.000 0.000 0.940 0.000 0.024
#> GSM564761 1 0.1594 0.8342 0.932 0.000 0.000 0.052 0.000 0.016
#> GSM564762 4 0.1265 0.8482 0.008 0.000 0.000 0.948 0.000 0.044
#> GSM564681 5 0.4694 0.6436 0.000 0.160 0.028 0.000 0.724 0.088
#> GSM564693 5 0.2858 0.7582 0.000 0.092 0.028 0.000 0.864 0.016
#> GSM564646 3 0.3360 0.7161 0.000 0.012 0.824 0.004 0.132 0.028
#> GSM564699 3 0.2322 0.7572 0.000 0.024 0.904 0.000 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 genotype/variation(p) disease.state(p) k
#> MAD:hclust 154 0.925 0.476 2
#> MAD:hclust 132 0.202 0.487 3
#> MAD:hclust 114 0.244 0.688 4
#> MAD:hclust 132 0.411 0.146 5
#> MAD:hclust 123 0.073 0.428 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 154 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 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.696 0.667 0.773 0.2497 0.880 0.760
#> 4 4 0.604 0.726 0.760 0.1249 0.826 0.568
#> 5 5 0.574 0.746 0.759 0.0754 0.931 0.742
#> 6 6 0.677 0.601 0.707 0.0572 0.970 0.875
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
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564616 2 0.6267 0.9188 0.000 0.548 0.452
#> GSM564617 2 0.6286 0.9417 0.000 0.536 0.464
#> GSM564618 3 0.6308 -0.8370 0.000 0.492 0.508
#> GSM564619 1 0.2625 0.8009 0.916 0.084 0.000
#> GSM564620 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564621 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564622 3 0.5905 -0.3388 0.000 0.352 0.648
#> GSM564623 3 0.2165 0.6909 0.000 0.064 0.936
#> GSM564624 2 0.6274 0.9461 0.000 0.544 0.456
#> GSM564625 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564626 1 0.0424 0.7827 0.992 0.008 0.000
#> GSM564627 1 0.3482 0.8119 0.872 0.128 0.000
#> GSM564628 2 0.6305 0.8898 0.000 0.516 0.484
#> GSM564629 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564630 2 0.6286 0.9417 0.000 0.536 0.464
#> GSM564609 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564610 1 0.0424 0.7833 0.992 0.008 0.000
#> GSM564611 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564612 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564613 3 0.5621 -0.0917 0.000 0.308 0.692
#> GSM564614 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564631 3 0.0237 0.7349 0.000 0.004 0.996
#> GSM564632 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564633 3 0.0747 0.7332 0.000 0.016 0.984
#> GSM564634 3 0.2066 0.6886 0.000 0.060 0.940
#> GSM564635 3 0.0747 0.7332 0.000 0.016 0.984
#> GSM564636 3 0.0592 0.7319 0.000 0.012 0.988
#> GSM564637 3 0.0237 0.7349 0.000 0.004 0.996
#> GSM564638 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564639 3 0.0237 0.7353 0.000 0.004 0.996
#> GSM564640 2 0.6274 0.9310 0.000 0.544 0.456
#> GSM564641 3 0.1289 0.7160 0.000 0.032 0.968
#> GSM564642 3 0.3551 0.5738 0.000 0.132 0.868
#> GSM564643 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564644 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564645 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564647 3 0.1860 0.6939 0.000 0.052 0.948
#> GSM564648 3 0.6140 -0.5540 0.000 0.404 0.596
#> GSM564649 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564650 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564651 3 0.6215 -0.6329 0.000 0.428 0.572
#> GSM564652 3 0.6215 -0.6329 0.000 0.428 0.572
#> GSM564653 3 0.6309 -0.8483 0.000 0.500 0.500
#> GSM564654 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564655 3 0.0747 0.7341 0.000 0.016 0.984
#> GSM564656 3 0.0592 0.7343 0.000 0.012 0.988
#> GSM564657 3 0.0747 0.7297 0.000 0.016 0.984
#> GSM564658 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564659 3 0.3686 0.5632 0.000 0.140 0.860
#> GSM564660 3 0.6302 -0.8219 0.000 0.480 0.520
#> GSM564661 2 0.6308 0.8574 0.000 0.508 0.492
#> GSM564662 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564663 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564664 3 0.6309 -0.8684 0.000 0.496 0.504
#> GSM564665 3 0.3412 0.5778 0.000 0.124 0.876
#> GSM564666 3 0.2066 0.6897 0.000 0.060 0.940
#> GSM564667 3 0.0747 0.7297 0.000 0.016 0.984
#> GSM564668 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564669 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564670 3 0.6280 -0.7591 0.000 0.460 0.540
#> GSM564671 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564672 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564673 3 0.5397 0.0693 0.000 0.280 0.720
#> GSM564674 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564675 3 0.0892 0.7337 0.000 0.020 0.980
#> GSM564676 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564677 3 0.6309 -0.8483 0.000 0.500 0.500
#> GSM564678 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564679 2 0.6274 0.9461 0.000 0.544 0.456
#> GSM564680 3 0.0237 0.7353 0.000 0.004 0.996
#> GSM564682 3 0.6291 -0.7897 0.000 0.468 0.532
#> GSM564683 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564684 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564685 3 0.0424 0.7339 0.000 0.008 0.992
#> GSM564686 3 0.0592 0.7352 0.000 0.012 0.988
#> GSM564687 3 0.4062 0.5037 0.000 0.164 0.836
#> GSM564688 2 0.6309 0.8390 0.000 0.500 0.500
#> GSM564689 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564690 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564691 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564692 3 0.6309 -0.8483 0.000 0.500 0.500
#> GSM564694 3 0.0747 0.7343 0.000 0.016 0.984
#> GSM564695 3 0.4121 0.4997 0.000 0.168 0.832
#> GSM564696 3 0.0747 0.7297 0.000 0.016 0.984
#> GSM564697 2 0.6286 0.9506 0.000 0.536 0.464
#> GSM564698 3 0.0892 0.7330 0.000 0.020 0.980
#> GSM564700 3 0.1031 0.7313 0.000 0.024 0.976
#> GSM564701 2 0.6309 0.8463 0.000 0.504 0.496
#> GSM564702 2 0.6309 0.8390 0.000 0.500 0.500
#> GSM564703 1 0.3752 0.8244 0.856 0.144 0.000
#> GSM564704 1 0.5560 0.8482 0.700 0.300 0.000
#> GSM564705 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564706 1 0.6168 0.8596 0.588 0.412 0.000
#> GSM564707 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564708 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564709 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564710 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564711 1 0.6215 0.8599 0.572 0.428 0.000
#> GSM564712 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564713 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564714 1 0.6026 0.8574 0.624 0.376 0.000
#> GSM564715 1 0.0424 0.7842 0.992 0.008 0.000
#> GSM564716 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564717 1 0.0747 0.7873 0.984 0.016 0.000
#> GSM564718 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564719 1 0.2711 0.8110 0.912 0.088 0.000
#> GSM564720 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564721 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564722 1 0.6045 0.8580 0.620 0.380 0.000
#> GSM564723 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564724 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564725 1 0.3879 0.8175 0.848 0.152 0.000
#> GSM564726 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564727 1 0.4399 0.8264 0.812 0.188 0.000
#> GSM564728 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564729 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564730 1 0.1643 0.7970 0.956 0.044 0.000
#> GSM564731 1 0.6235 0.8599 0.564 0.436 0.000
#> GSM564732 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564733 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564734 1 0.6225 0.8600 0.568 0.432 0.000
#> GSM564735 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564736 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564737 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564738 1 0.6225 0.8599 0.568 0.432 0.000
#> GSM564739 1 0.3941 0.8270 0.844 0.156 0.000
#> GSM564740 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564741 1 0.6126 0.8592 0.600 0.400 0.000
#> GSM564742 1 0.5835 0.8531 0.660 0.340 0.000
#> GSM564743 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564744 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564745 1 0.6045 0.8580 0.620 0.380 0.000
#> GSM564746 1 0.3340 0.8104 0.880 0.120 0.000
#> GSM564747 1 0.5621 0.8502 0.692 0.308 0.000
#> GSM564748 1 0.1860 0.7997 0.948 0.052 0.000
#> GSM564749 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564750 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564751 1 0.4291 0.8317 0.820 0.180 0.000
#> GSM564752 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564753 1 0.6204 0.8600 0.576 0.424 0.000
#> GSM564754 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564755 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564756 1 0.5948 0.8557 0.640 0.360 0.000
#> GSM564757 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564758 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564759 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564760 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564761 1 0.0000 0.7809 1.000 0.000 0.000
#> GSM564762 1 0.6244 0.8598 0.560 0.440 0.000
#> GSM564681 2 0.6274 0.9144 0.000 0.544 0.456
#> GSM564693 3 0.6309 -0.8483 0.000 0.500 0.500
#> GSM564646 3 0.1163 0.7299 0.000 0.028 0.972
#> GSM564699 3 0.0237 0.7357 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564616 2 0.7534 0.7550 0.000 0.492 0.268 0.240
#> GSM564617 2 0.5387 0.7933 0.000 0.696 0.256 0.048
#> GSM564618 2 0.7676 0.7203 0.000 0.452 0.308 0.240
#> GSM564619 1 0.3547 0.6843 0.840 0.016 0.000 0.144
#> GSM564620 4 0.4988 0.8839 0.288 0.020 0.000 0.692
#> GSM564621 4 0.4988 0.8839 0.288 0.020 0.000 0.692
#> GSM564622 3 0.7700 -0.4358 0.000 0.304 0.448 0.248
#> GSM564623 3 0.3833 0.7991 0.000 0.080 0.848 0.072
#> GSM564624 2 0.5052 0.8047 0.000 0.720 0.244 0.036
#> GSM564625 4 0.4509 0.8913 0.288 0.004 0.000 0.708
#> GSM564626 1 0.1256 0.7766 0.964 0.008 0.000 0.028
#> GSM564627 1 0.5585 0.5696 0.712 0.084 0.000 0.204
#> GSM564628 2 0.7493 0.7315 0.000 0.488 0.304 0.208
#> GSM564629 4 0.5522 0.8716 0.288 0.044 0.000 0.668
#> GSM564630 2 0.5055 0.8022 0.000 0.712 0.256 0.032
#> GSM564609 3 0.2111 0.8247 0.000 0.024 0.932 0.044
#> GSM564610 1 0.2334 0.7609 0.908 0.088 0.000 0.004
#> GSM564611 1 0.1109 0.7804 0.968 0.028 0.000 0.004
#> GSM564612 2 0.4283 0.8096 0.000 0.740 0.256 0.004
#> GSM564613 3 0.6082 -0.3501 0.000 0.476 0.480 0.044
#> GSM564614 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564631 3 0.0895 0.8431 0.000 0.020 0.976 0.004
#> GSM564632 3 0.3659 0.7704 0.000 0.024 0.840 0.136
#> GSM564633 3 0.0592 0.8378 0.000 0.016 0.984 0.000
#> GSM564634 3 0.3523 0.7740 0.000 0.112 0.856 0.032
#> GSM564635 3 0.0336 0.8403 0.000 0.008 0.992 0.000
#> GSM564636 3 0.1929 0.8415 0.000 0.024 0.940 0.036
#> GSM564637 3 0.2124 0.8418 0.000 0.028 0.932 0.040
#> GSM564638 3 0.1151 0.8422 0.000 0.024 0.968 0.008
#> GSM564639 3 0.0895 0.8430 0.000 0.020 0.976 0.004
#> GSM564640 2 0.5953 0.8036 0.000 0.656 0.268 0.076
#> GSM564641 3 0.1635 0.8336 0.000 0.044 0.948 0.008
#> GSM564642 3 0.4906 0.6371 0.000 0.140 0.776 0.084
#> GSM564643 3 0.3497 0.7705 0.000 0.024 0.852 0.124
#> GSM564644 2 0.4283 0.8121 0.000 0.740 0.256 0.004
#> GSM564645 3 0.1004 0.8420 0.000 0.024 0.972 0.004
#> GSM564647 3 0.3051 0.7970 0.000 0.088 0.884 0.028
#> GSM564648 3 0.7800 -0.6119 0.000 0.372 0.380 0.248
#> GSM564649 3 0.1109 0.8413 0.000 0.028 0.968 0.004
#> GSM564650 2 0.4103 0.8110 0.000 0.744 0.256 0.000
#> GSM564651 2 0.7658 0.6404 0.000 0.416 0.372 0.212
#> GSM564652 2 0.7732 0.6006 0.000 0.388 0.384 0.228
#> GSM564653 2 0.7464 0.7492 0.000 0.496 0.296 0.208
#> GSM564654 3 0.2111 0.8176 0.000 0.024 0.932 0.044
#> GSM564655 3 0.1388 0.8416 0.000 0.012 0.960 0.028
#> GSM564656 3 0.0336 0.8403 0.000 0.008 0.992 0.000
#> GSM564657 3 0.1398 0.8356 0.000 0.040 0.956 0.004
#> GSM564658 2 0.4072 0.8122 0.000 0.748 0.252 0.000
#> GSM564659 3 0.4462 0.7261 0.000 0.132 0.804 0.064
#> GSM564660 2 0.6071 0.7142 0.000 0.612 0.324 0.064
#> GSM564661 2 0.7493 0.7447 0.000 0.488 0.304 0.208
#> GSM564662 3 0.1109 0.8413 0.000 0.028 0.968 0.004
#> GSM564663 2 0.4103 0.8110 0.000 0.744 0.256 0.000
#> GSM564664 2 0.6757 0.7849 0.000 0.572 0.308 0.120
#> GSM564665 3 0.3672 0.6906 0.000 0.164 0.824 0.012
#> GSM564666 3 0.3621 0.7999 0.000 0.072 0.860 0.068
#> GSM564667 3 0.1398 0.8356 0.000 0.040 0.956 0.004
#> GSM564668 3 0.2021 0.8266 0.000 0.024 0.936 0.040
#> GSM564669 3 0.0336 0.8403 0.000 0.008 0.992 0.000
#> GSM564670 2 0.6074 0.7029 0.000 0.600 0.340 0.060
#> GSM564671 3 0.3659 0.7712 0.000 0.024 0.840 0.136
#> GSM564672 3 0.1109 0.8413 0.000 0.028 0.968 0.004
#> GSM564673 3 0.7458 -0.2373 0.000 0.252 0.508 0.240
#> GSM564674 2 0.4868 0.8023 0.000 0.720 0.256 0.024
#> GSM564675 3 0.2546 0.8373 0.000 0.028 0.912 0.060
#> GSM564676 2 0.4283 0.8121 0.000 0.740 0.256 0.004
#> GSM564677 2 0.7506 0.7407 0.000 0.484 0.308 0.208
#> GSM564678 2 0.4283 0.8121 0.000 0.740 0.256 0.004
#> GSM564679 2 0.4220 0.8130 0.000 0.748 0.248 0.004
#> GSM564680 3 0.0895 0.8430 0.000 0.020 0.976 0.004
#> GSM564682 2 0.5213 0.7195 0.000 0.652 0.328 0.020
#> GSM564683 3 0.1109 0.8413 0.000 0.028 0.968 0.004
#> GSM564684 3 0.3080 0.8064 0.000 0.024 0.880 0.096
#> GSM564685 3 0.0817 0.8428 0.000 0.024 0.976 0.000
#> GSM564686 3 0.2179 0.8311 0.000 0.012 0.924 0.064
#> GSM564687 3 0.5396 0.5837 0.000 0.156 0.740 0.104
#> GSM564688 2 0.7506 0.7407 0.000 0.484 0.308 0.208
#> GSM564689 2 0.4283 0.8121 0.000 0.740 0.256 0.004
#> GSM564690 2 0.4283 0.8121 0.000 0.740 0.256 0.004
#> GSM564691 2 0.4283 0.8096 0.000 0.740 0.256 0.004
#> GSM564692 2 0.7530 0.7391 0.000 0.480 0.308 0.212
#> GSM564694 3 0.2255 0.8296 0.000 0.012 0.920 0.068
#> GSM564695 3 0.4635 0.6059 0.000 0.216 0.756 0.028
#> GSM564696 3 0.2124 0.8348 0.000 0.040 0.932 0.028
#> GSM564697 2 0.4661 0.8041 0.000 0.728 0.256 0.016
#> GSM564698 3 0.0376 0.8420 0.000 0.004 0.992 0.004
#> GSM564700 3 0.3813 0.7621 0.000 0.024 0.828 0.148
#> GSM564701 2 0.7412 0.7538 0.000 0.504 0.296 0.200
#> GSM564702 2 0.7530 0.7391 0.000 0.480 0.308 0.212
#> GSM564703 1 0.6167 0.3919 0.664 0.116 0.000 0.220
#> GSM564704 1 0.7526 -0.3201 0.468 0.200 0.000 0.332
#> GSM564705 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564706 4 0.7374 0.7389 0.308 0.188 0.000 0.504
#> GSM564707 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564708 4 0.6307 0.8551 0.288 0.092 0.000 0.620
#> GSM564709 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564710 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564711 4 0.7330 0.7524 0.304 0.184 0.000 0.512
#> GSM564712 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564713 4 0.4509 0.8937 0.288 0.004 0.000 0.708
#> GSM564714 4 0.7684 0.5786 0.360 0.220 0.000 0.420
#> GSM564715 1 0.2011 0.7647 0.920 0.080 0.000 0.000
#> GSM564716 4 0.4957 0.8774 0.300 0.016 0.000 0.684
#> GSM564717 1 0.4136 0.6916 0.788 0.196 0.000 0.016
#> GSM564718 4 0.6835 0.8195 0.288 0.136 0.000 0.576
#> GSM564719 1 0.5816 0.5657 0.688 0.224 0.000 0.088
#> GSM564720 1 0.1109 0.7804 0.968 0.028 0.000 0.004
#> GSM564721 1 0.0524 0.7848 0.988 0.004 0.000 0.008
#> GSM564722 4 0.7702 0.5733 0.360 0.224 0.000 0.416
#> GSM564723 1 0.0000 0.7853 1.000 0.000 0.000 0.000
#> GSM564724 4 0.5599 0.8803 0.288 0.048 0.000 0.664
#> GSM564725 1 0.4391 0.5179 0.740 0.008 0.000 0.252
#> GSM564726 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564727 1 0.4831 0.4484 0.704 0.016 0.000 0.280
#> GSM564728 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564729 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564730 1 0.2222 0.7632 0.924 0.016 0.000 0.060
#> GSM564731 4 0.6681 0.8356 0.292 0.120 0.000 0.588
#> GSM564732 4 0.4509 0.8935 0.288 0.004 0.000 0.708
#> GSM564733 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564734 4 0.5557 0.8729 0.308 0.040 0.000 0.652
#> GSM564735 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564736 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564737 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564738 4 0.7031 0.7962 0.296 0.152 0.000 0.552
#> GSM564739 1 0.6104 0.3716 0.664 0.104 0.000 0.232
#> GSM564740 4 0.6307 0.8484 0.288 0.092 0.000 0.620
#> GSM564741 4 0.7066 0.7857 0.304 0.152 0.000 0.544
#> GSM564742 1 0.7679 -0.4674 0.408 0.216 0.000 0.376
#> GSM564743 1 0.2266 0.7614 0.912 0.084 0.000 0.004
#> GSM564744 1 0.0000 0.7853 1.000 0.000 0.000 0.000
#> GSM564745 4 0.5161 0.7575 0.400 0.008 0.000 0.592
#> GSM564746 1 0.5536 0.6020 0.724 0.096 0.000 0.180
#> GSM564747 1 0.7576 -0.3525 0.452 0.204 0.000 0.344
#> GSM564748 1 0.4982 0.6242 0.772 0.136 0.000 0.092
#> GSM564749 1 0.1209 0.7806 0.964 0.032 0.000 0.004
#> GSM564750 4 0.4509 0.8937 0.288 0.004 0.000 0.708
#> GSM564751 1 0.6773 0.0567 0.584 0.132 0.000 0.284
#> GSM564752 4 0.5088 0.8871 0.288 0.024 0.000 0.688
#> GSM564753 4 0.7011 0.7965 0.300 0.148 0.000 0.552
#> GSM564754 1 0.0188 0.7859 0.996 0.000 0.000 0.004
#> GSM564755 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564756 4 0.6396 0.7538 0.380 0.072 0.000 0.548
#> GSM564757 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564758 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564759 4 0.6994 0.8069 0.288 0.152 0.000 0.560
#> GSM564760 4 0.4331 0.8935 0.288 0.000 0.000 0.712
#> GSM564761 1 0.0376 0.7854 0.992 0.004 0.000 0.004
#> GSM564762 4 0.5442 0.8847 0.288 0.040 0.000 0.672
#> GSM564681 2 0.7363 0.7626 0.000 0.520 0.272 0.208
#> GSM564693 2 0.7493 0.7447 0.000 0.488 0.304 0.208
#> GSM564646 3 0.3910 0.7559 0.000 0.024 0.820 0.156
#> GSM564699 3 0.1888 0.8423 0.000 0.016 0.940 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564616 5 0.3589 0.7996 0.004 0.040 0.132 0.000 0.824
#> GSM564617 2 0.6594 0.8241 0.024 0.556 0.164 0.000 0.256
#> GSM564618 5 0.4411 0.7920 0.032 0.036 0.152 0.000 0.780
#> GSM564619 1 0.5568 0.7137 0.616 0.016 0.000 0.308 0.060
#> GSM564620 4 0.1845 0.7932 0.000 0.016 0.000 0.928 0.056
#> GSM564621 4 0.1914 0.7890 0.000 0.016 0.000 0.924 0.060
#> GSM564622 5 0.4834 0.7212 0.016 0.044 0.224 0.000 0.716
#> GSM564623 3 0.5189 0.7756 0.144 0.104 0.728 0.000 0.024
#> GSM564624 2 0.6804 0.8233 0.028 0.516 0.160 0.000 0.296
#> GSM564625 4 0.0324 0.8118 0.000 0.004 0.000 0.992 0.004
#> GSM564626 1 0.4514 0.8106 0.756 0.016 0.000 0.184 0.044
#> GSM564627 1 0.7223 0.5441 0.436 0.084 0.000 0.384 0.096
#> GSM564628 5 0.7480 0.3940 0.144 0.152 0.172 0.000 0.532
#> GSM564629 4 0.2157 0.7935 0.004 0.040 0.000 0.920 0.036
#> GSM564630 2 0.6715 0.8404 0.024 0.528 0.164 0.000 0.284
#> GSM564609 3 0.4264 0.8112 0.084 0.032 0.808 0.000 0.076
#> GSM564610 1 0.6721 0.7686 0.612 0.100 0.000 0.176 0.112
#> GSM564611 1 0.4638 0.8183 0.764 0.032 0.000 0.160 0.044
#> GSM564612 2 0.6393 0.8617 0.004 0.524 0.180 0.000 0.292
#> GSM564613 2 0.6903 0.4247 0.040 0.440 0.400 0.000 0.120
#> GSM564614 4 0.0162 0.8144 0.000 0.000 0.000 0.996 0.004
#> GSM564631 3 0.0324 0.8409 0.000 0.004 0.992 0.000 0.004
#> GSM564632 3 0.6466 0.6851 0.136 0.068 0.632 0.000 0.164
#> GSM564633 3 0.1710 0.8344 0.004 0.016 0.940 0.000 0.040
#> GSM564634 3 0.4925 0.7032 0.072 0.180 0.732 0.000 0.016
#> GSM564635 3 0.1525 0.8354 0.004 0.012 0.948 0.000 0.036
#> GSM564636 3 0.2952 0.8355 0.088 0.036 0.872 0.000 0.004
#> GSM564637 3 0.3765 0.8257 0.124 0.048 0.820 0.000 0.008
#> GSM564638 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564639 3 0.0566 0.8400 0.000 0.004 0.984 0.000 0.012
#> GSM564640 5 0.7203 -0.3883 0.048 0.336 0.156 0.000 0.460
#> GSM564641 3 0.1116 0.8384 0.004 0.028 0.964 0.000 0.004
#> GSM564642 3 0.6151 0.5708 0.088 0.040 0.616 0.000 0.256
#> GSM564643 3 0.6055 0.6816 0.132 0.036 0.652 0.000 0.180
#> GSM564644 2 0.6572 0.8624 0.012 0.508 0.164 0.000 0.316
#> GSM564645 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564647 3 0.3968 0.7995 0.072 0.100 0.816 0.000 0.012
#> GSM564648 5 0.3953 0.7893 0.008 0.024 0.188 0.000 0.780
#> GSM564649 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564650 2 0.6295 0.8683 0.004 0.536 0.164 0.000 0.296
#> GSM564651 5 0.3597 0.8019 0.008 0.012 0.180 0.000 0.800
#> GSM564652 5 0.3988 0.7845 0.008 0.024 0.192 0.000 0.776
#> GSM564653 5 0.3039 0.8254 0.000 0.012 0.152 0.000 0.836
#> GSM564654 3 0.2407 0.8141 0.004 0.012 0.896 0.000 0.088
#> GSM564655 3 0.3895 0.8295 0.108 0.044 0.824 0.000 0.024
#> GSM564656 3 0.1461 0.8386 0.004 0.016 0.952 0.000 0.028
#> GSM564657 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564658 2 0.6461 0.8657 0.008 0.516 0.164 0.000 0.312
#> GSM564659 3 0.3941 0.7726 0.036 0.036 0.824 0.000 0.104
#> GSM564660 2 0.7635 0.5901 0.072 0.456 0.244 0.000 0.228
#> GSM564661 5 0.2886 0.8250 0.000 0.008 0.148 0.000 0.844
#> GSM564662 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564663 2 0.6191 0.8675 0.000 0.528 0.164 0.000 0.308
#> GSM564664 5 0.6295 0.0869 0.008 0.256 0.172 0.000 0.564
#> GSM564665 3 0.2952 0.7733 0.008 0.020 0.868 0.000 0.104
#> GSM564666 3 0.4911 0.7797 0.144 0.104 0.740 0.000 0.012
#> GSM564667 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564668 3 0.3525 0.8140 0.040 0.028 0.852 0.000 0.080
#> GSM564669 3 0.1605 0.8343 0.004 0.012 0.944 0.000 0.040
#> GSM564670 2 0.7389 0.6128 0.048 0.460 0.276 0.000 0.216
#> GSM564671 3 0.6356 0.6941 0.140 0.060 0.640 0.000 0.160
#> GSM564672 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564673 5 0.4676 0.6629 0.008 0.032 0.264 0.000 0.696
#> GSM564674 2 0.6829 0.8327 0.032 0.528 0.164 0.000 0.276
#> GSM564675 3 0.4303 0.8106 0.132 0.076 0.784 0.000 0.008
#> GSM564676 2 0.6461 0.8657 0.008 0.516 0.164 0.000 0.312
#> GSM564677 5 0.2929 0.8274 0.000 0.008 0.152 0.000 0.840
#> GSM564678 2 0.6461 0.8657 0.008 0.516 0.164 0.000 0.312
#> GSM564679 2 0.6461 0.8657 0.008 0.516 0.164 0.000 0.312
#> GSM564680 3 0.0566 0.8400 0.000 0.004 0.984 0.000 0.012
#> GSM564682 2 0.6180 0.7904 0.000 0.556 0.220 0.000 0.224
#> GSM564683 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564684 3 0.5999 0.7469 0.140 0.068 0.680 0.000 0.112
#> GSM564685 3 0.0162 0.8407 0.000 0.004 0.996 0.000 0.000
#> GSM564686 3 0.4449 0.8113 0.140 0.060 0.780 0.000 0.020
#> GSM564687 3 0.6584 0.6538 0.132 0.088 0.628 0.000 0.152
#> GSM564688 5 0.2929 0.8264 0.000 0.008 0.152 0.000 0.840
#> GSM564689 2 0.6461 0.8657 0.008 0.516 0.164 0.000 0.312
#> GSM564690 2 0.6461 0.8657 0.008 0.516 0.164 0.000 0.312
#> GSM564691 2 0.6206 0.8683 0.000 0.528 0.168 0.000 0.304
#> GSM564692 5 0.2929 0.8274 0.000 0.008 0.152 0.000 0.840
#> GSM564694 3 0.4848 0.8029 0.148 0.064 0.756 0.000 0.032
#> GSM564695 3 0.5183 0.7271 0.076 0.112 0.748 0.000 0.064
#> GSM564696 3 0.2770 0.8333 0.076 0.044 0.880 0.000 0.000
#> GSM564697 2 0.6345 0.8620 0.008 0.548 0.164 0.000 0.280
#> GSM564698 3 0.1168 0.8380 0.000 0.008 0.960 0.000 0.032
#> GSM564700 3 0.6751 0.6440 0.144 0.072 0.600 0.000 0.184
#> GSM564701 5 0.3723 0.7990 0.000 0.044 0.152 0.000 0.804
#> GSM564702 5 0.2929 0.8274 0.000 0.008 0.152 0.000 0.840
#> GSM564703 1 0.7029 0.3961 0.436 0.204 0.000 0.340 0.020
#> GSM564704 4 0.6519 0.4358 0.152 0.328 0.000 0.508 0.012
#> GSM564705 1 0.3606 0.8253 0.808 0.004 0.000 0.164 0.024
#> GSM564706 4 0.4733 0.6422 0.028 0.348 0.000 0.624 0.000
#> GSM564707 1 0.3359 0.8250 0.816 0.000 0.000 0.164 0.020
#> GSM564708 4 0.2929 0.7843 0.000 0.152 0.000 0.840 0.008
#> GSM564709 1 0.3653 0.8268 0.808 0.012 0.000 0.164 0.016
#> GSM564710 1 0.3516 0.8255 0.812 0.004 0.000 0.164 0.020
#> GSM564711 4 0.4451 0.6636 0.016 0.340 0.000 0.644 0.000
#> GSM564712 1 0.3163 0.8245 0.824 0.000 0.000 0.164 0.012
#> GSM564713 4 0.0566 0.8166 0.000 0.012 0.000 0.984 0.004
#> GSM564714 4 0.6033 0.5540 0.060 0.348 0.000 0.560 0.032
#> GSM564715 1 0.5562 0.7784 0.692 0.120 0.000 0.164 0.024
#> GSM564716 4 0.2312 0.7811 0.012 0.016 0.000 0.912 0.060
#> GSM564717 1 0.7396 0.6323 0.472 0.296 0.000 0.168 0.064
#> GSM564718 4 0.3707 0.7200 0.000 0.284 0.000 0.716 0.000
#> GSM564719 1 0.7680 0.4235 0.364 0.328 0.000 0.256 0.052
#> GSM564720 1 0.4859 0.8163 0.752 0.040 0.000 0.160 0.048
#> GSM564721 1 0.3645 0.8235 0.804 0.004 0.000 0.168 0.024
#> GSM564722 4 0.6145 0.5467 0.064 0.344 0.000 0.556 0.036
#> GSM564723 1 0.3651 0.8246 0.808 0.004 0.000 0.160 0.028
#> GSM564724 4 0.1768 0.8112 0.000 0.072 0.000 0.924 0.004
#> GSM564725 1 0.5722 0.5912 0.536 0.016 0.000 0.396 0.052
#> GSM564726 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564727 1 0.5941 0.4834 0.472 0.016 0.000 0.448 0.064
#> GSM564728 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564729 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564730 1 0.4629 0.7852 0.704 0.000 0.000 0.244 0.052
#> GSM564731 4 0.3612 0.7483 0.000 0.228 0.000 0.764 0.008
#> GSM564732 4 0.0771 0.8161 0.000 0.020 0.000 0.976 0.004
#> GSM564733 4 0.0162 0.8143 0.000 0.000 0.000 0.996 0.004
#> GSM564734 4 0.2526 0.8033 0.012 0.080 0.000 0.896 0.012
#> GSM564735 4 0.0771 0.8172 0.000 0.020 0.000 0.976 0.004
#> GSM564736 4 0.0162 0.8150 0.000 0.004 0.000 0.996 0.000
#> GSM564737 1 0.3163 0.8245 0.824 0.000 0.000 0.164 0.012
#> GSM564738 4 0.3928 0.7102 0.004 0.296 0.000 0.700 0.000
#> GSM564739 1 0.6936 0.3939 0.440 0.200 0.000 0.344 0.016
#> GSM564740 4 0.3067 0.7774 0.004 0.140 0.000 0.844 0.012
#> GSM564741 4 0.4491 0.7013 0.024 0.280 0.000 0.692 0.004
#> GSM564742 4 0.6260 0.4425 0.120 0.372 0.000 0.500 0.008
#> GSM564743 1 0.6104 0.7829 0.668 0.100 0.000 0.160 0.072
#> GSM564744 1 0.3651 0.8246 0.808 0.004 0.000 0.160 0.028
#> GSM564745 4 0.3273 0.7302 0.112 0.004 0.000 0.848 0.036
#> GSM564746 1 0.7547 0.5821 0.436 0.108 0.000 0.344 0.112
#> GSM564747 4 0.6140 0.4437 0.140 0.356 0.000 0.504 0.000
#> GSM564748 1 0.6602 0.5549 0.516 0.224 0.000 0.252 0.008
#> GSM564749 1 0.4708 0.8186 0.760 0.032 0.000 0.160 0.048
#> GSM564750 4 0.0404 0.8164 0.000 0.012 0.000 0.988 0.000
#> GSM564751 4 0.7071 -0.1143 0.344 0.232 0.000 0.408 0.016
#> GSM564752 4 0.1638 0.8107 0.004 0.064 0.000 0.932 0.000
#> GSM564753 4 0.4230 0.7111 0.008 0.280 0.000 0.704 0.008
#> GSM564754 1 0.3443 0.8244 0.816 0.008 0.000 0.164 0.012
#> GSM564755 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564756 4 0.4662 0.7267 0.080 0.132 0.000 0.768 0.020
#> GSM564757 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564758 4 0.0000 0.8144 0.000 0.000 0.000 1.000 0.000
#> GSM564759 4 0.3607 0.7412 0.004 0.244 0.000 0.752 0.000
#> GSM564760 4 0.0290 0.8141 0.000 0.000 0.000 0.992 0.008
#> GSM564761 1 0.3053 0.8243 0.828 0.000 0.000 0.164 0.008
#> GSM564762 4 0.1894 0.8098 0.000 0.072 0.000 0.920 0.008
#> GSM564681 5 0.3078 0.8001 0.004 0.016 0.132 0.000 0.848
#> GSM564693 5 0.2886 0.8250 0.000 0.008 0.148 0.000 0.844
#> GSM564646 3 0.6815 0.6327 0.148 0.072 0.592 0.000 0.188
#> GSM564699 3 0.3971 0.8200 0.136 0.052 0.804 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.3961 0.7080 0.000 0.440 0.000 0.556 0.000 0.004
#> GSM564616 5 0.3198 0.7866 0.020 0.020 0.056 0.000 0.864 0.040
#> GSM564617 2 0.8016 0.4069 0.052 0.392 0.120 0.000 0.192 0.244
#> GSM564618 5 0.4181 0.7526 0.024 0.028 0.092 0.000 0.800 0.056
#> GSM564619 1 0.6049 0.6608 0.624 0.132 0.000 0.152 0.004 0.088
#> GSM564620 4 0.5597 0.6815 0.000 0.360 0.000 0.524 0.016 0.100
#> GSM564621 4 0.5359 0.6796 0.000 0.376 0.000 0.520 0.004 0.100
#> GSM564622 5 0.3726 0.7457 0.012 0.012 0.148 0.000 0.800 0.028
#> GSM564623 3 0.4104 0.4402 0.052 0.036 0.800 0.000 0.012 0.100
#> GSM564624 2 0.7958 0.4650 0.052 0.384 0.096 0.000 0.232 0.236
#> GSM564625 4 0.4093 0.7080 0.000 0.440 0.000 0.552 0.004 0.004
#> GSM564626 1 0.3787 0.8097 0.804 0.020 0.000 0.104 0.000 0.072
#> GSM564627 1 0.7617 0.4212 0.364 0.172 0.000 0.200 0.004 0.260
#> GSM564628 5 0.7310 -0.0684 0.056 0.052 0.344 0.000 0.420 0.128
#> GSM564629 4 0.5430 0.6846 0.000 0.408 0.000 0.492 0.008 0.092
#> GSM564630 2 0.7946 0.4743 0.052 0.388 0.096 0.000 0.224 0.240
#> GSM564609 3 0.3663 0.6541 0.012 0.004 0.808 0.000 0.048 0.128
#> GSM564610 1 0.5941 0.7087 0.564 0.052 0.000 0.100 0.000 0.284
#> GSM564611 1 0.4017 0.8169 0.796 0.016 0.000 0.092 0.008 0.088
#> GSM564612 2 0.7150 0.6961 0.008 0.428 0.072 0.000 0.252 0.240
#> GSM564613 6 0.8112 0.0000 0.048 0.296 0.248 0.000 0.108 0.300
#> GSM564614 4 0.4057 0.7080 0.000 0.436 0.000 0.556 0.000 0.008
#> GSM564631 3 0.3923 0.6856 0.000 0.000 0.620 0.000 0.008 0.372
#> GSM564632 3 0.3268 0.4931 0.000 0.000 0.812 0.000 0.144 0.044
#> GSM564633 3 0.4543 0.6890 0.012 0.000 0.624 0.000 0.028 0.336
#> GSM564634 3 0.5065 0.4854 0.024 0.076 0.696 0.000 0.012 0.192
#> GSM564635 3 0.4569 0.6884 0.012 0.000 0.616 0.000 0.028 0.344
#> GSM564636 3 0.2982 0.6553 0.012 0.004 0.820 0.000 0.000 0.164
#> GSM564637 3 0.1409 0.6146 0.008 0.012 0.948 0.000 0.000 0.032
#> GSM564638 3 0.3923 0.6877 0.000 0.000 0.620 0.000 0.008 0.372
#> GSM564639 3 0.4341 0.6861 0.012 0.004 0.620 0.000 0.008 0.356
#> GSM564640 5 0.7333 -0.3872 0.016 0.264 0.148 0.000 0.452 0.120
#> GSM564641 3 0.4284 0.6691 0.004 0.004 0.608 0.000 0.012 0.372
#> GSM564642 3 0.4945 0.3309 0.016 0.004 0.640 0.000 0.288 0.052
#> GSM564643 3 0.3855 0.5270 0.020 0.004 0.796 0.000 0.136 0.044
#> GSM564644 2 0.7232 0.7473 0.020 0.444 0.076 0.000 0.288 0.172
#> GSM564645 3 0.3923 0.6856 0.000 0.000 0.620 0.000 0.008 0.372
#> GSM564647 3 0.4987 0.5538 0.028 0.060 0.700 0.000 0.012 0.200
#> GSM564648 5 0.2588 0.7929 0.000 0.004 0.124 0.000 0.860 0.012
#> GSM564649 3 0.4058 0.6852 0.004 0.000 0.616 0.000 0.008 0.372
#> GSM564650 2 0.6720 0.7655 0.000 0.492 0.080 0.000 0.252 0.176
#> GSM564651 5 0.1700 0.8240 0.004 0.000 0.080 0.000 0.916 0.000
#> GSM564652 5 0.1910 0.8095 0.000 0.000 0.108 0.000 0.892 0.000
#> GSM564653 5 0.1719 0.8283 0.000 0.000 0.060 0.000 0.924 0.016
#> GSM564654 3 0.5444 0.6680 0.016 0.004 0.572 0.000 0.080 0.328
#> GSM564655 3 0.2009 0.6420 0.004 0.000 0.904 0.000 0.008 0.084
#> GSM564656 3 0.4472 0.6897 0.012 0.000 0.628 0.000 0.024 0.336
#> GSM564657 3 0.4079 0.6830 0.004 0.000 0.608 0.000 0.008 0.380
#> GSM564658 2 0.6837 0.7697 0.004 0.468 0.072 0.000 0.288 0.168
#> GSM564659 3 0.6567 0.5226 0.048 0.044 0.520 0.000 0.072 0.316
#> GSM564660 3 0.8458 -0.8245 0.060 0.252 0.288 0.000 0.176 0.224
#> GSM564661 5 0.1411 0.8305 0.000 0.000 0.060 0.000 0.936 0.004
#> GSM564662 3 0.3923 0.6856 0.000 0.000 0.620 0.000 0.008 0.372
#> GSM564663 2 0.7036 0.7512 0.008 0.456 0.072 0.000 0.260 0.204
#> GSM564664 5 0.6379 0.0067 0.016 0.244 0.068 0.000 0.572 0.100
#> GSM564665 3 0.5954 0.5618 0.004 0.020 0.532 0.000 0.132 0.312
#> GSM564666 3 0.4020 0.4565 0.040 0.032 0.800 0.000 0.012 0.116
#> GSM564667 3 0.4090 0.6814 0.004 0.000 0.604 0.000 0.008 0.384
#> GSM564668 3 0.4875 0.6624 0.020 0.008 0.692 0.000 0.060 0.220
#> GSM564669 3 0.4734 0.6887 0.016 0.004 0.624 0.000 0.028 0.328
#> GSM564670 2 0.8488 -0.4757 0.064 0.296 0.200 0.000 0.192 0.248
#> GSM564671 3 0.2680 0.5238 0.004 0.000 0.856 0.000 0.124 0.016
#> GSM564672 3 0.3923 0.6856 0.000 0.000 0.620 0.000 0.008 0.372
#> GSM564673 5 0.2597 0.7280 0.000 0.000 0.176 0.000 0.824 0.000
#> GSM564674 2 0.7883 0.5501 0.024 0.372 0.144 0.000 0.232 0.228
#> GSM564675 3 0.1155 0.5867 0.004 0.004 0.956 0.000 0.000 0.036
#> GSM564676 2 0.6912 0.7622 0.008 0.468 0.072 0.000 0.288 0.164
#> GSM564677 5 0.1267 0.8309 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM564678 2 0.6790 0.7665 0.004 0.476 0.072 0.000 0.288 0.160
#> GSM564679 2 0.6782 0.7614 0.004 0.472 0.068 0.000 0.292 0.164
#> GSM564680 3 0.4341 0.6861 0.012 0.004 0.620 0.000 0.008 0.356
#> GSM564682 2 0.7459 0.4920 0.012 0.420 0.116 0.000 0.200 0.252
#> GSM564683 3 0.3934 0.6851 0.000 0.000 0.616 0.000 0.008 0.376
#> GSM564684 3 0.2635 0.5409 0.004 0.004 0.880 0.000 0.076 0.036
#> GSM564685 3 0.3905 0.6909 0.004 0.000 0.636 0.000 0.004 0.356
#> GSM564686 3 0.1003 0.5907 0.004 0.004 0.964 0.000 0.000 0.028
#> GSM564687 3 0.4881 0.3317 0.024 0.012 0.720 0.000 0.168 0.076
#> GSM564688 5 0.1411 0.8305 0.000 0.000 0.060 0.000 0.936 0.004
#> GSM564689 2 0.6754 0.7712 0.004 0.488 0.072 0.000 0.276 0.160
#> GSM564690 2 0.6790 0.7665 0.004 0.476 0.072 0.000 0.288 0.160
#> GSM564691 2 0.6711 0.7666 0.000 0.484 0.072 0.000 0.260 0.184
#> GSM564692 5 0.1625 0.8295 0.000 0.000 0.060 0.000 0.928 0.012
#> GSM564694 3 0.1655 0.5806 0.004 0.004 0.936 0.000 0.012 0.044
#> GSM564695 3 0.6148 0.3057 0.032 0.096 0.620 0.000 0.048 0.204
#> GSM564696 3 0.3074 0.6598 0.004 0.004 0.792 0.000 0.000 0.200
#> GSM564697 2 0.6937 0.7409 0.004 0.492 0.100 0.000 0.232 0.172
#> GSM564698 3 0.4457 0.6907 0.012 0.000 0.632 0.000 0.024 0.332
#> GSM564700 3 0.3516 0.4674 0.004 0.008 0.812 0.000 0.136 0.040
#> GSM564701 5 0.2503 0.8134 0.008 0.012 0.060 0.000 0.896 0.024
#> GSM564702 5 0.1524 0.8303 0.000 0.000 0.060 0.000 0.932 0.008
#> GSM564703 4 0.5126 -0.2089 0.408 0.004 0.000 0.532 0.016 0.040
#> GSM564704 4 0.4212 0.4014 0.132 0.008 0.000 0.772 0.012 0.076
#> GSM564705 1 0.2648 0.8312 0.876 0.004 0.000 0.092 0.008 0.020
#> GSM564706 4 0.2265 0.5408 0.008 0.008 0.000 0.896 0.004 0.084
#> GSM564707 1 0.2326 0.8302 0.888 0.000 0.000 0.092 0.012 0.008
#> GSM564708 4 0.3565 0.6637 0.000 0.156 0.000 0.796 0.008 0.040
#> GSM564709 1 0.2113 0.8306 0.896 0.000 0.000 0.092 0.004 0.008
#> GSM564710 1 0.2648 0.8312 0.876 0.004 0.000 0.092 0.008 0.020
#> GSM564711 4 0.2213 0.5499 0.008 0.008 0.000 0.904 0.008 0.072
#> GSM564712 1 0.2113 0.8301 0.896 0.000 0.000 0.092 0.004 0.008
#> GSM564713 4 0.4102 0.7130 0.000 0.356 0.000 0.628 0.004 0.012
#> GSM564714 4 0.4087 0.4606 0.040 0.020 0.000 0.776 0.008 0.156
#> GSM564715 1 0.4510 0.7350 0.704 0.000 0.000 0.228 0.020 0.048
#> GSM564716 4 0.5731 0.6702 0.008 0.360 0.000 0.516 0.008 0.108
#> GSM564717 1 0.6723 0.4579 0.400 0.020 0.000 0.332 0.012 0.236
#> GSM564718 4 0.0665 0.6053 0.000 0.008 0.000 0.980 0.004 0.008
#> GSM564719 4 0.6541 -0.2111 0.272 0.020 0.000 0.476 0.012 0.220
#> GSM564720 1 0.4244 0.8110 0.780 0.020 0.000 0.092 0.008 0.100
#> GSM564721 1 0.2760 0.8277 0.872 0.008 0.000 0.092 0.008 0.020
#> GSM564722 4 0.4278 0.4519 0.032 0.020 0.000 0.744 0.008 0.196
#> GSM564723 1 0.3093 0.8294 0.852 0.004 0.000 0.092 0.008 0.044
#> GSM564724 4 0.4096 0.7076 0.000 0.304 0.000 0.672 0.008 0.016
#> GSM564725 1 0.6647 0.4839 0.528 0.172 0.000 0.220 0.004 0.076
#> GSM564726 4 0.3961 0.7080 0.000 0.440 0.000 0.556 0.000 0.004
#> GSM564727 1 0.7076 0.3613 0.460 0.196 0.000 0.244 0.004 0.096
#> GSM564728 4 0.3961 0.7080 0.000 0.440 0.000 0.556 0.000 0.004
#> GSM564729 4 0.3961 0.7080 0.000 0.440 0.000 0.556 0.000 0.004
#> GSM564730 1 0.4776 0.7648 0.740 0.052 0.000 0.144 0.008 0.056
#> GSM564731 4 0.1957 0.6363 0.000 0.072 0.000 0.912 0.008 0.008
#> GSM564732 4 0.4253 0.7120 0.000 0.412 0.000 0.572 0.008 0.008
#> GSM564733 4 0.3828 0.7086 0.000 0.440 0.000 0.560 0.000 0.000
#> GSM564734 4 0.4663 0.7100 0.012 0.340 0.000 0.620 0.016 0.012
#> GSM564735 4 0.3797 0.7127 0.000 0.420 0.000 0.580 0.000 0.000
#> GSM564736 4 0.3833 0.7080 0.000 0.444 0.000 0.556 0.000 0.000
#> GSM564737 1 0.2113 0.8301 0.896 0.000 0.000 0.092 0.004 0.008
#> GSM564738 4 0.0837 0.5949 0.004 0.004 0.000 0.972 0.000 0.020
#> GSM564739 4 0.5025 -0.1680 0.396 0.004 0.000 0.548 0.012 0.040
#> GSM564740 4 0.4882 0.6583 0.000 0.236 0.000 0.656 0.004 0.104
#> GSM564741 4 0.1893 0.5782 0.024 0.004 0.000 0.928 0.008 0.036
#> GSM564742 4 0.4323 0.4051 0.084 0.020 0.000 0.772 0.008 0.116
#> GSM564743 1 0.5619 0.7332 0.632 0.020 0.000 0.104 0.016 0.228
#> GSM564744 1 0.2956 0.8301 0.860 0.004 0.000 0.092 0.008 0.036
#> GSM564745 4 0.6592 0.6245 0.124 0.336 0.000 0.480 0.016 0.044
#> GSM564746 1 0.7410 0.4661 0.372 0.144 0.000 0.204 0.000 0.280
#> GSM564747 4 0.4157 0.4163 0.104 0.016 0.000 0.784 0.008 0.088
#> GSM564748 4 0.4744 -0.3056 0.440 0.000 0.000 0.520 0.008 0.032
#> GSM564749 1 0.4017 0.8162 0.796 0.016 0.000 0.092 0.008 0.088
#> GSM564750 4 0.4109 0.7149 0.000 0.392 0.000 0.596 0.004 0.008
#> GSM564751 4 0.4452 0.1373 0.288 0.000 0.000 0.664 0.008 0.040
#> GSM564752 4 0.3867 0.7082 0.000 0.328 0.000 0.660 0.000 0.012
#> GSM564753 4 0.1268 0.5935 0.004 0.000 0.000 0.952 0.008 0.036
#> GSM564754 1 0.2376 0.8286 0.884 0.000 0.000 0.096 0.008 0.012
#> GSM564755 4 0.3961 0.7080 0.000 0.440 0.000 0.556 0.000 0.004
#> GSM564756 4 0.5653 0.6537 0.072 0.220 0.000 0.648 0.024 0.036
#> GSM564757 4 0.4165 0.7110 0.000 0.420 0.000 0.568 0.008 0.004
#> GSM564758 4 0.4165 0.7110 0.000 0.420 0.000 0.568 0.008 0.004
#> GSM564759 4 0.2789 0.6226 0.000 0.088 0.000 0.864 0.004 0.044
#> GSM564760 4 0.3828 0.7086 0.000 0.440 0.000 0.560 0.000 0.000
#> GSM564761 1 0.1970 0.8297 0.900 0.000 0.000 0.092 0.000 0.008
#> GSM564762 4 0.4076 0.7127 0.000 0.348 0.000 0.636 0.012 0.004
#> GSM564681 5 0.1578 0.8122 0.000 0.004 0.048 0.000 0.936 0.012
#> GSM564693 5 0.1699 0.8302 0.004 0.004 0.060 0.000 0.928 0.004
#> GSM564646 3 0.3761 0.4391 0.004 0.008 0.792 0.000 0.148 0.048
#> GSM564699 3 0.0922 0.6054 0.004 0.004 0.968 0.000 0.000 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n genotype/variation(p) disease.state(p) k
#> MAD:kmeans 154 0.925 0.476 2
#> MAD:kmeans 138 0.167 0.161 3
#> MAD:kmeans 143 0.329 0.549 4
#> MAD:kmeans 142 0.451 0.201 5
#> MAD:kmeans 120 0.193 0.208 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 154 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 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.898 0.907 0.947 0.2873 0.854 0.709
#> 4 4 0.714 0.763 0.877 0.1562 0.884 0.679
#> 5 5 0.653 0.566 0.770 0.0630 0.929 0.736
#> 6 6 0.640 0.572 0.718 0.0387 0.927 0.691
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
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564616 2 0.0592 0.907 0.000 0.988 0.012
#> GSM564617 2 0.0424 0.908 0.000 0.992 0.008
#> GSM564618 2 0.2066 0.898 0.000 0.940 0.060
#> GSM564619 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564620 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564621 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564622 2 0.4504 0.789 0.000 0.804 0.196
#> GSM564623 3 0.5760 0.610 0.000 0.328 0.672
#> GSM564624 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564625 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564626 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564627 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564628 2 0.1289 0.907 0.000 0.968 0.032
#> GSM564629 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564630 2 0.0424 0.908 0.000 0.992 0.008
#> GSM564609 3 0.1289 0.903 0.000 0.032 0.968
#> GSM564610 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564611 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564612 2 0.0424 0.908 0.000 0.992 0.008
#> GSM564613 2 0.4235 0.769 0.000 0.824 0.176
#> GSM564614 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564631 3 0.1031 0.905 0.000 0.024 0.976
#> GSM564632 3 0.4235 0.796 0.000 0.176 0.824
#> GSM564633 3 0.0892 0.903 0.000 0.020 0.980
#> GSM564634 3 0.6291 0.227 0.000 0.468 0.532
#> GSM564635 3 0.0892 0.905 0.000 0.020 0.980
#> GSM564636 3 0.2165 0.895 0.000 0.064 0.936
#> GSM564637 3 0.4178 0.818 0.000 0.172 0.828
#> GSM564638 3 0.1289 0.902 0.000 0.032 0.968
#> GSM564639 3 0.0747 0.904 0.000 0.016 0.984
#> GSM564640 2 0.1031 0.908 0.000 0.976 0.024
#> GSM564641 3 0.4291 0.816 0.000 0.180 0.820
#> GSM564642 2 0.5397 0.667 0.000 0.720 0.280
#> GSM564643 3 0.1753 0.898 0.000 0.048 0.952
#> GSM564644 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564645 3 0.1031 0.905 0.000 0.024 0.976
#> GSM564647 3 0.6026 0.488 0.000 0.376 0.624
#> GSM564648 2 0.4002 0.827 0.000 0.840 0.160
#> GSM564649 3 0.1031 0.905 0.000 0.024 0.976
#> GSM564650 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564651 2 0.4002 0.823 0.000 0.840 0.160
#> GSM564652 2 0.3816 0.839 0.000 0.852 0.148
#> GSM564653 2 0.1643 0.904 0.000 0.956 0.044
#> GSM564654 3 0.0892 0.903 0.000 0.020 0.980
#> GSM564655 3 0.2261 0.890 0.000 0.068 0.932
#> GSM564656 3 0.0747 0.904 0.000 0.016 0.984
#> GSM564657 3 0.2537 0.887 0.000 0.080 0.920
#> GSM564658 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564659 2 0.5948 0.448 0.000 0.640 0.360
#> GSM564660 2 0.0424 0.908 0.000 0.992 0.008
#> GSM564661 2 0.1163 0.907 0.000 0.972 0.028
#> GSM564662 3 0.1031 0.905 0.000 0.024 0.976
#> GSM564663 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564664 2 0.1860 0.904 0.000 0.948 0.052
#> GSM564665 3 0.6045 0.428 0.000 0.380 0.620
#> GSM564666 3 0.4654 0.785 0.000 0.208 0.792
#> GSM564667 3 0.4178 0.821 0.000 0.172 0.828
#> GSM564668 3 0.0892 0.903 0.000 0.020 0.980
#> GSM564669 3 0.0892 0.903 0.000 0.020 0.980
#> GSM564670 2 0.1529 0.900 0.000 0.960 0.040
#> GSM564671 3 0.1643 0.900 0.000 0.044 0.956
#> GSM564672 3 0.1031 0.905 0.000 0.024 0.976
#> GSM564673 2 0.5948 0.501 0.000 0.640 0.360
#> GSM564674 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564675 3 0.1964 0.900 0.000 0.056 0.944
#> GSM564676 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564677 2 0.2066 0.897 0.000 0.940 0.060
#> GSM564678 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564679 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564680 3 0.0747 0.904 0.000 0.016 0.984
#> GSM564682 2 0.2796 0.859 0.000 0.908 0.092
#> GSM564683 3 0.1163 0.904 0.000 0.028 0.972
#> GSM564684 3 0.3340 0.855 0.000 0.120 0.880
#> GSM564685 3 0.0892 0.904 0.000 0.020 0.980
#> GSM564686 3 0.1289 0.904 0.000 0.032 0.968
#> GSM564687 2 0.5650 0.579 0.000 0.688 0.312
#> GSM564688 2 0.2625 0.883 0.000 0.916 0.084
#> GSM564689 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564690 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564691 2 0.0424 0.908 0.000 0.992 0.008
#> GSM564692 2 0.1753 0.902 0.000 0.952 0.048
#> GSM564694 3 0.4974 0.741 0.000 0.236 0.764
#> GSM564695 2 0.5138 0.654 0.000 0.748 0.252
#> GSM564696 3 0.2356 0.886 0.000 0.072 0.928
#> GSM564697 2 0.0237 0.908 0.000 0.996 0.004
#> GSM564698 3 0.0747 0.904 0.000 0.016 0.984
#> GSM564700 3 0.5733 0.553 0.000 0.324 0.676
#> GSM564701 2 0.1753 0.903 0.000 0.952 0.048
#> GSM564702 2 0.2261 0.893 0.000 0.932 0.068
#> GSM564703 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564704 1 0.0424 0.994 0.992 0.000 0.008
#> GSM564705 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564706 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564707 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564708 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564709 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564710 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564711 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564712 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564713 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564714 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564715 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564716 1 0.0237 0.995 0.996 0.000 0.004
#> GSM564717 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564718 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564719 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564720 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564721 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564722 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564723 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564724 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564725 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564726 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564727 1 0.0424 0.994 0.992 0.000 0.008
#> GSM564728 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564729 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564730 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564731 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564732 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564733 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564734 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564735 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564736 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564737 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564738 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564739 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564740 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564741 1 0.0237 0.995 0.996 0.000 0.004
#> GSM564742 1 0.0237 0.995 0.996 0.000 0.004
#> GSM564743 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564744 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564745 1 0.0424 0.994 0.992 0.000 0.008
#> GSM564746 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564747 1 0.0237 0.995 0.996 0.000 0.004
#> GSM564748 1 0.0424 0.994 0.992 0.000 0.008
#> GSM564749 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564750 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564751 1 0.0592 0.994 0.988 0.000 0.012
#> GSM564752 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564753 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564754 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564755 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564756 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564757 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564758 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564759 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564760 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564761 1 0.0747 0.993 0.984 0.000 0.016
#> GSM564762 1 0.0000 0.995 1.000 0.000 0.000
#> GSM564681 2 0.0892 0.907 0.000 0.980 0.020
#> GSM564693 2 0.1643 0.903 0.000 0.956 0.044
#> GSM564646 2 0.6274 0.171 0.000 0.544 0.456
#> GSM564699 3 0.0892 0.904 0.000 0.020 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564616 2 0.1888 0.8585 0.016 0.940 0.044 0.000
#> GSM564617 2 0.0672 0.8583 0.008 0.984 0.008 0.000
#> GSM564618 2 0.3279 0.8394 0.032 0.872 0.096 0.000
#> GSM564619 1 0.4164 0.7084 0.736 0.000 0.000 0.264
#> GSM564620 4 0.2149 0.8259 0.088 0.000 0.000 0.912
#> GSM564621 4 0.2814 0.7817 0.132 0.000 0.000 0.868
#> GSM564622 2 0.4993 0.6918 0.028 0.712 0.260 0.000
#> GSM564623 3 0.5544 0.5474 0.020 0.332 0.640 0.008
#> GSM564624 2 0.0376 0.8585 0.004 0.992 0.004 0.000
#> GSM564625 4 0.0188 0.8710 0.004 0.000 0.000 0.996
#> GSM564626 1 0.2530 0.8345 0.888 0.000 0.000 0.112
#> GSM564627 1 0.4907 0.4223 0.580 0.000 0.000 0.420
#> GSM564628 2 0.2596 0.8520 0.024 0.908 0.068 0.000
#> GSM564629 4 0.1118 0.8594 0.036 0.000 0.000 0.964
#> GSM564630 2 0.0779 0.8593 0.004 0.980 0.016 0.000
#> GSM564609 3 0.1411 0.8795 0.020 0.020 0.960 0.000
#> GSM564610 1 0.2921 0.8234 0.860 0.000 0.000 0.140
#> GSM564611 1 0.1211 0.8563 0.960 0.000 0.000 0.040
#> GSM564612 2 0.1209 0.8534 0.004 0.964 0.032 0.000
#> GSM564613 2 0.4019 0.7170 0.012 0.792 0.196 0.000
#> GSM564614 4 0.0188 0.8710 0.004 0.000 0.000 0.996
#> GSM564631 3 0.0817 0.8871 0.000 0.024 0.976 0.000
#> GSM564632 3 0.4289 0.7529 0.032 0.172 0.796 0.000
#> GSM564633 3 0.0188 0.8858 0.000 0.004 0.996 0.000
#> GSM564634 2 0.5257 0.1236 0.008 0.548 0.444 0.000
#> GSM564635 3 0.0524 0.8868 0.004 0.008 0.988 0.000
#> GSM564636 3 0.2773 0.8564 0.004 0.116 0.880 0.000
#> GSM564637 3 0.4284 0.7502 0.012 0.224 0.764 0.000
#> GSM564638 3 0.1398 0.8821 0.004 0.040 0.956 0.000
#> GSM564639 3 0.0469 0.8871 0.000 0.012 0.988 0.000
#> GSM564640 2 0.1256 0.8602 0.008 0.964 0.028 0.000
#> GSM564641 3 0.4511 0.6890 0.008 0.268 0.724 0.000
#> GSM564642 2 0.4697 0.6235 0.008 0.696 0.296 0.000
#> GSM564643 3 0.2670 0.8590 0.040 0.052 0.908 0.000
#> GSM564644 2 0.0188 0.8592 0.000 0.996 0.004 0.000
#> GSM564645 3 0.0707 0.8875 0.000 0.020 0.980 0.000
#> GSM564647 2 0.5296 -0.0650 0.008 0.500 0.492 0.000
#> GSM564648 2 0.4799 0.7333 0.032 0.744 0.224 0.000
#> GSM564649 3 0.1302 0.8849 0.000 0.044 0.956 0.000
#> GSM564650 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564651 2 0.4574 0.7389 0.024 0.756 0.220 0.000
#> GSM564652 2 0.4446 0.7600 0.028 0.776 0.196 0.000
#> GSM564653 2 0.2596 0.8497 0.024 0.908 0.068 0.000
#> GSM564654 3 0.0469 0.8845 0.012 0.000 0.988 0.000
#> GSM564655 3 0.3351 0.8178 0.008 0.148 0.844 0.000
#> GSM564656 3 0.0336 0.8866 0.000 0.008 0.992 0.000
#> GSM564657 3 0.2704 0.8486 0.000 0.124 0.876 0.000
#> GSM564658 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564659 2 0.5444 0.3189 0.016 0.560 0.424 0.000
#> GSM564660 2 0.1584 0.8538 0.012 0.952 0.036 0.000
#> GSM564661 2 0.2282 0.8539 0.024 0.924 0.052 0.000
#> GSM564662 3 0.0817 0.8869 0.000 0.024 0.976 0.000
#> GSM564663 2 0.0188 0.8590 0.000 0.996 0.004 0.000
#> GSM564664 2 0.1398 0.8605 0.004 0.956 0.040 0.000
#> GSM564665 3 0.5040 0.4214 0.008 0.364 0.628 0.000
#> GSM564666 3 0.4776 0.7230 0.024 0.244 0.732 0.000
#> GSM564667 3 0.3688 0.7703 0.000 0.208 0.792 0.000
#> GSM564668 3 0.0592 0.8842 0.016 0.000 0.984 0.000
#> GSM564669 3 0.0657 0.8852 0.012 0.004 0.984 0.000
#> GSM564670 2 0.2198 0.8368 0.008 0.920 0.072 0.000
#> GSM564671 3 0.2409 0.8672 0.032 0.040 0.924 0.004
#> GSM564672 3 0.0817 0.8869 0.000 0.024 0.976 0.000
#> GSM564673 2 0.5671 0.4146 0.028 0.572 0.400 0.000
#> GSM564674 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564675 3 0.3302 0.8622 0.016 0.064 0.888 0.032
#> GSM564676 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564677 2 0.2915 0.8441 0.028 0.892 0.080 0.000
#> GSM564678 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564679 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564680 3 0.0336 0.8868 0.000 0.008 0.992 0.000
#> GSM564682 2 0.2944 0.7860 0.004 0.868 0.128 0.000
#> GSM564683 3 0.0817 0.8869 0.000 0.024 0.976 0.000
#> GSM564684 3 0.4379 0.7525 0.036 0.172 0.792 0.000
#> GSM564685 3 0.0592 0.8870 0.000 0.016 0.984 0.000
#> GSM564686 3 0.1388 0.8872 0.012 0.028 0.960 0.000
#> GSM564687 2 0.5204 0.4522 0.012 0.612 0.376 0.000
#> GSM564688 2 0.2973 0.8397 0.020 0.884 0.096 0.000
#> GSM564689 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564690 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564691 2 0.0188 0.8585 0.004 0.996 0.000 0.000
#> GSM564692 2 0.2413 0.8522 0.020 0.916 0.064 0.000
#> GSM564694 3 0.5182 0.5872 0.028 0.288 0.684 0.000
#> GSM564695 2 0.4663 0.6250 0.012 0.716 0.272 0.000
#> GSM564696 3 0.2011 0.8683 0.000 0.080 0.920 0.000
#> GSM564697 2 0.0336 0.8583 0.008 0.992 0.000 0.000
#> GSM564698 3 0.0188 0.8849 0.004 0.000 0.996 0.000
#> GSM564700 3 0.5535 0.4924 0.040 0.304 0.656 0.000
#> GSM564701 2 0.2413 0.8558 0.020 0.916 0.064 0.000
#> GSM564702 2 0.2882 0.8437 0.024 0.892 0.084 0.000
#> GSM564703 1 0.4916 0.3740 0.576 0.000 0.000 0.424
#> GSM564704 4 0.5000 -0.0572 0.500 0.000 0.000 0.500
#> GSM564705 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564706 4 0.2530 0.8161 0.112 0.000 0.000 0.888
#> GSM564707 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564708 4 0.1302 0.8612 0.044 0.000 0.000 0.956
#> GSM564709 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564710 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564711 4 0.1867 0.8437 0.072 0.000 0.000 0.928
#> GSM564712 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564713 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564714 4 0.3942 0.6742 0.236 0.000 0.000 0.764
#> GSM564715 1 0.3172 0.8077 0.840 0.000 0.000 0.160
#> GSM564716 4 0.3123 0.7638 0.156 0.000 0.000 0.844
#> GSM564717 1 0.3172 0.8034 0.840 0.000 0.000 0.160
#> GSM564718 4 0.0707 0.8676 0.020 0.000 0.000 0.980
#> GSM564719 1 0.4040 0.7067 0.752 0.000 0.000 0.248
#> GSM564720 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564721 1 0.1867 0.8539 0.928 0.000 0.000 0.072
#> GSM564722 4 0.4697 0.4477 0.356 0.000 0.000 0.644
#> GSM564723 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564724 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564725 1 0.4972 0.3238 0.544 0.000 0.000 0.456
#> GSM564726 4 0.0188 0.8710 0.004 0.000 0.000 0.996
#> GSM564727 4 0.4967 -0.0139 0.452 0.000 0.000 0.548
#> GSM564728 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564729 4 0.0188 0.8710 0.004 0.000 0.000 0.996
#> GSM564730 1 0.4193 0.7064 0.732 0.000 0.000 0.268
#> GSM564731 4 0.1211 0.8626 0.040 0.000 0.000 0.960
#> GSM564732 4 0.0336 0.8710 0.008 0.000 0.000 0.992
#> GSM564733 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564734 4 0.2469 0.8239 0.108 0.000 0.000 0.892
#> GSM564735 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564736 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564737 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564738 4 0.1118 0.8620 0.036 0.000 0.000 0.964
#> GSM564739 1 0.4898 0.3728 0.584 0.000 0.000 0.416
#> GSM564740 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564741 4 0.3311 0.7557 0.172 0.000 0.000 0.828
#> GSM564742 4 0.4790 0.3760 0.380 0.000 0.000 0.620
#> GSM564743 1 0.1389 0.8583 0.952 0.000 0.000 0.048
#> GSM564744 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564745 4 0.3726 0.7102 0.212 0.000 0.000 0.788
#> GSM564746 1 0.4697 0.5792 0.644 0.000 0.000 0.356
#> GSM564747 4 0.4972 0.1349 0.456 0.000 0.000 0.544
#> GSM564748 1 0.4222 0.6709 0.728 0.000 0.000 0.272
#> GSM564749 1 0.1389 0.8583 0.952 0.000 0.000 0.048
#> GSM564750 4 0.0188 0.8710 0.004 0.000 0.000 0.996
#> GSM564751 4 0.4999 -0.0527 0.492 0.000 0.000 0.508
#> GSM564752 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564753 4 0.2149 0.8329 0.088 0.000 0.000 0.912
#> GSM564754 1 0.1716 0.8557 0.936 0.000 0.000 0.064
#> GSM564755 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564756 4 0.4431 0.5630 0.304 0.000 0.000 0.696
#> GSM564757 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564758 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564759 4 0.0000 0.8713 0.000 0.000 0.000 1.000
#> GSM564760 4 0.0188 0.8710 0.004 0.000 0.000 0.996
#> GSM564761 1 0.1302 0.8593 0.956 0.000 0.000 0.044
#> GSM564762 4 0.0336 0.8706 0.008 0.000 0.000 0.992
#> GSM564681 2 0.1975 0.8564 0.016 0.936 0.048 0.000
#> GSM564693 2 0.2489 0.8513 0.020 0.912 0.068 0.000
#> GSM564646 2 0.6009 0.1576 0.040 0.492 0.468 0.000
#> GSM564699 3 0.1151 0.8884 0.008 0.024 0.968 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0609 0.85981 0.000 0.000 0.000 0.980 0.020
#> GSM564616 2 0.4505 0.34226 0.000 0.604 0.012 0.000 0.384
#> GSM564617 2 0.2672 0.59585 0.004 0.872 0.008 0.000 0.116
#> GSM564618 5 0.5407 0.03821 0.004 0.424 0.048 0.000 0.524
#> GSM564619 1 0.4042 0.69909 0.756 0.000 0.000 0.212 0.032
#> GSM564620 4 0.3215 0.79799 0.092 0.000 0.000 0.852 0.056
#> GSM564621 4 0.4256 0.68097 0.192 0.000 0.004 0.760 0.044
#> GSM564622 5 0.5579 0.44277 0.000 0.264 0.116 0.000 0.620
#> GSM564623 5 0.7059 0.11130 0.004 0.308 0.256 0.008 0.424
#> GSM564624 2 0.2672 0.60812 0.004 0.872 0.008 0.000 0.116
#> GSM564625 4 0.0963 0.86142 0.000 0.000 0.000 0.964 0.036
#> GSM564626 1 0.2172 0.78933 0.908 0.000 0.000 0.076 0.016
#> GSM564627 1 0.5002 0.52044 0.612 0.000 0.000 0.344 0.044
#> GSM564628 2 0.4827 0.17839 0.000 0.504 0.020 0.000 0.476
#> GSM564629 4 0.2300 0.84166 0.052 0.000 0.000 0.908 0.040
#> GSM564630 2 0.2517 0.61134 0.004 0.884 0.008 0.000 0.104
#> GSM564609 3 0.4744 0.02914 0.000 0.016 0.508 0.000 0.476
#> GSM564610 1 0.3019 0.78681 0.864 0.000 0.000 0.088 0.048
#> GSM564611 1 0.0566 0.80345 0.984 0.000 0.000 0.004 0.012
#> GSM564612 2 0.3484 0.56525 0.004 0.824 0.144 0.000 0.028
#> GSM564613 2 0.5585 0.37022 0.004 0.652 0.208 0.000 0.136
#> GSM564614 4 0.0703 0.86164 0.000 0.000 0.000 0.976 0.024
#> GSM564631 3 0.1117 0.73472 0.000 0.016 0.964 0.000 0.020
#> GSM564632 5 0.5304 0.50438 0.000 0.080 0.292 0.000 0.628
#> GSM564633 3 0.2806 0.68264 0.000 0.004 0.844 0.000 0.152
#> GSM564634 2 0.5878 0.17560 0.000 0.548 0.336 0.000 0.116
#> GSM564635 3 0.2286 0.70283 0.000 0.004 0.888 0.000 0.108
#> GSM564636 3 0.4137 0.67065 0.004 0.076 0.792 0.000 0.128
#> GSM564637 3 0.6366 0.24914 0.000 0.204 0.512 0.000 0.284
#> GSM564638 3 0.1885 0.73582 0.004 0.020 0.932 0.000 0.044
#> GSM564639 3 0.1043 0.73504 0.000 0.000 0.960 0.000 0.040
#> GSM564640 2 0.3970 0.53880 0.000 0.752 0.024 0.000 0.224
#> GSM564641 3 0.4054 0.55836 0.000 0.248 0.732 0.000 0.020
#> GSM564642 2 0.6601 -0.00866 0.000 0.460 0.248 0.000 0.292
#> GSM564643 5 0.4862 0.34470 0.000 0.032 0.364 0.000 0.604
#> GSM564644 2 0.2144 0.62264 0.000 0.912 0.020 0.000 0.068
#> GSM564645 3 0.1195 0.73439 0.000 0.028 0.960 0.000 0.012
#> GSM564647 3 0.5983 0.25056 0.000 0.380 0.504 0.000 0.116
#> GSM564648 5 0.5569 0.26498 0.000 0.364 0.080 0.000 0.556
#> GSM564649 3 0.2067 0.73397 0.000 0.032 0.920 0.000 0.048
#> GSM564650 2 0.0880 0.62710 0.000 0.968 0.000 0.000 0.032
#> GSM564651 5 0.5535 0.20507 0.000 0.392 0.072 0.000 0.536
#> GSM564652 5 0.5422 0.30804 0.000 0.348 0.072 0.000 0.580
#> GSM564653 2 0.4767 0.23172 0.000 0.560 0.020 0.000 0.420
#> GSM564654 3 0.4183 0.40855 0.000 0.008 0.668 0.000 0.324
#> GSM564655 3 0.5793 0.36180 0.000 0.124 0.584 0.000 0.292
#> GSM564656 3 0.1638 0.72939 0.000 0.004 0.932 0.000 0.064
#> GSM564657 3 0.2411 0.70350 0.000 0.108 0.884 0.000 0.008
#> GSM564658 2 0.0290 0.62742 0.000 0.992 0.000 0.000 0.008
#> GSM564659 2 0.6922 -0.19495 0.004 0.360 0.356 0.000 0.280
#> GSM564660 2 0.4756 0.49549 0.004 0.704 0.052 0.000 0.240
#> GSM564661 2 0.4760 0.23907 0.000 0.564 0.020 0.000 0.416
#> GSM564662 3 0.1106 0.73477 0.000 0.024 0.964 0.000 0.012
#> GSM564663 2 0.1828 0.62313 0.004 0.936 0.028 0.000 0.032
#> GSM564664 2 0.4765 0.47676 0.000 0.704 0.068 0.000 0.228
#> GSM564665 3 0.6273 0.09567 0.000 0.316 0.512 0.000 0.172
#> GSM564666 3 0.6942 0.04218 0.004 0.308 0.368 0.000 0.320
#> GSM564667 3 0.3326 0.66589 0.000 0.152 0.824 0.000 0.024
#> GSM564668 3 0.4367 0.26477 0.000 0.004 0.580 0.000 0.416
#> GSM564669 3 0.3563 0.62372 0.000 0.012 0.780 0.000 0.208
#> GSM564670 2 0.4788 0.52348 0.004 0.740 0.120 0.000 0.136
#> GSM564671 5 0.4716 0.44235 0.000 0.036 0.308 0.000 0.656
#> GSM564672 3 0.0898 0.73332 0.000 0.020 0.972 0.000 0.008
#> GSM564673 5 0.5775 0.48398 0.000 0.244 0.148 0.000 0.608
#> GSM564674 2 0.2654 0.62161 0.000 0.884 0.032 0.000 0.084
#> GSM564675 5 0.6951 -0.17403 0.000 0.112 0.400 0.048 0.440
#> GSM564676 2 0.0566 0.62683 0.000 0.984 0.004 0.000 0.012
#> GSM564677 2 0.4961 0.14405 0.000 0.524 0.028 0.000 0.448
#> GSM564678 2 0.0451 0.62644 0.000 0.988 0.004 0.000 0.008
#> GSM564679 2 0.0794 0.62632 0.000 0.972 0.000 0.000 0.028
#> GSM564680 3 0.1251 0.73278 0.000 0.008 0.956 0.000 0.036
#> GSM564682 2 0.4550 0.45711 0.004 0.744 0.188 0.000 0.064
#> GSM564683 3 0.0912 0.73441 0.000 0.016 0.972 0.000 0.012
#> GSM564684 5 0.4948 0.46149 0.000 0.068 0.256 0.000 0.676
#> GSM564685 3 0.1557 0.73683 0.000 0.008 0.940 0.000 0.052
#> GSM564686 3 0.5111 0.24727 0.000 0.036 0.500 0.000 0.464
#> GSM564687 2 0.6532 -0.12095 0.000 0.420 0.196 0.000 0.384
#> GSM564688 2 0.5236 0.04317 0.000 0.492 0.044 0.000 0.464
#> GSM564689 2 0.0566 0.62607 0.000 0.984 0.004 0.000 0.012
#> GSM564690 2 0.0671 0.62716 0.000 0.980 0.004 0.000 0.016
#> GSM564691 2 0.1661 0.61969 0.000 0.940 0.036 0.000 0.024
#> GSM564692 2 0.4787 0.20971 0.000 0.548 0.020 0.000 0.432
#> GSM564694 5 0.6325 0.31256 0.000 0.180 0.316 0.000 0.504
#> GSM564695 2 0.6295 0.24994 0.004 0.552 0.256 0.000 0.188
#> GSM564696 3 0.3732 0.69183 0.000 0.056 0.820 0.004 0.120
#> GSM564697 2 0.1430 0.61812 0.000 0.944 0.004 0.000 0.052
#> GSM564698 3 0.1732 0.72328 0.000 0.000 0.920 0.000 0.080
#> GSM564700 5 0.4519 0.57499 0.000 0.100 0.148 0.000 0.752
#> GSM564701 2 0.5470 0.30897 0.000 0.588 0.080 0.000 0.332
#> GSM564702 2 0.5109 0.09508 0.000 0.504 0.036 0.000 0.460
#> GSM564703 1 0.5149 0.29770 0.540 0.000 0.004 0.424 0.032
#> GSM564704 1 0.6121 0.27627 0.504 0.000 0.004 0.376 0.116
#> GSM564705 1 0.0451 0.80311 0.988 0.000 0.000 0.008 0.004
#> GSM564706 4 0.4720 0.74275 0.136 0.000 0.008 0.752 0.104
#> GSM564707 1 0.0693 0.80419 0.980 0.000 0.000 0.008 0.012
#> GSM564708 4 0.3078 0.83482 0.064 0.000 0.008 0.872 0.056
#> GSM564709 1 0.0992 0.80495 0.968 0.000 0.000 0.008 0.024
#> GSM564710 1 0.0798 0.80366 0.976 0.000 0.000 0.008 0.016
#> GSM564711 4 0.3928 0.79632 0.084 0.000 0.008 0.816 0.092
#> GSM564712 1 0.0579 0.80263 0.984 0.000 0.000 0.008 0.008
#> GSM564713 4 0.0671 0.86363 0.000 0.000 0.004 0.980 0.016
#> GSM564714 4 0.5623 0.59361 0.220 0.000 0.008 0.652 0.120
#> GSM564715 1 0.3339 0.76882 0.840 0.000 0.000 0.112 0.048
#> GSM564716 4 0.3914 0.72230 0.164 0.000 0.000 0.788 0.048
#> GSM564717 1 0.4063 0.75245 0.800 0.000 0.004 0.112 0.084
#> GSM564718 4 0.2284 0.84873 0.028 0.000 0.004 0.912 0.056
#> GSM564719 1 0.5537 0.61803 0.660 0.000 0.008 0.220 0.112
#> GSM564720 1 0.1041 0.80284 0.964 0.000 0.000 0.004 0.032
#> GSM564721 1 0.1943 0.79821 0.924 0.000 0.000 0.056 0.020
#> GSM564722 4 0.6111 0.30273 0.340 0.000 0.004 0.532 0.124
#> GSM564723 1 0.0865 0.80397 0.972 0.000 0.000 0.004 0.024
#> GSM564724 4 0.0898 0.86373 0.000 0.000 0.008 0.972 0.020
#> GSM564725 1 0.4937 0.33011 0.544 0.000 0.000 0.428 0.028
#> GSM564726 4 0.0771 0.86361 0.004 0.000 0.000 0.976 0.020
#> GSM564727 4 0.5385 -0.04293 0.432 0.000 0.000 0.512 0.056
#> GSM564728 4 0.0510 0.86151 0.000 0.000 0.000 0.984 0.016
#> GSM564729 4 0.0609 0.85981 0.000 0.000 0.000 0.980 0.020
#> GSM564730 1 0.4709 0.68213 0.716 0.000 0.004 0.224 0.056
#> GSM564731 4 0.2494 0.85153 0.032 0.000 0.008 0.904 0.056
#> GSM564732 4 0.1399 0.86253 0.020 0.000 0.000 0.952 0.028
#> GSM564733 4 0.0671 0.86360 0.000 0.000 0.004 0.980 0.016
#> GSM564734 4 0.3195 0.82053 0.100 0.000 0.004 0.856 0.040
#> GSM564735 4 0.0404 0.86252 0.000 0.000 0.000 0.988 0.012
#> GSM564736 4 0.0566 0.86332 0.000 0.000 0.004 0.984 0.012
#> GSM564737 1 0.0693 0.80340 0.980 0.000 0.000 0.008 0.012
#> GSM564738 4 0.3053 0.83286 0.044 0.000 0.008 0.872 0.076
#> GSM564739 1 0.5155 0.28531 0.536 0.000 0.004 0.428 0.032
#> GSM564740 4 0.1697 0.86003 0.008 0.000 0.000 0.932 0.060
#> GSM564741 4 0.4820 0.69090 0.180 0.000 0.004 0.728 0.088
#> GSM564742 4 0.6262 0.23215 0.356 0.000 0.008 0.512 0.124
#> GSM564743 1 0.1628 0.80009 0.936 0.000 0.000 0.008 0.056
#> GSM564744 1 0.0566 0.80270 0.984 0.000 0.000 0.004 0.012
#> GSM564745 4 0.4805 0.48033 0.312 0.000 0.000 0.648 0.040
#> GSM564746 1 0.5124 0.63623 0.668 0.000 0.004 0.260 0.068
#> GSM564747 1 0.6036 0.09204 0.460 0.000 0.004 0.436 0.100
#> GSM564748 1 0.5170 0.59063 0.660 0.000 0.004 0.268 0.068
#> GSM564749 1 0.0771 0.80377 0.976 0.000 0.000 0.004 0.020
#> GSM564750 4 0.0833 0.86408 0.004 0.000 0.004 0.976 0.016
#> GSM564751 1 0.5788 0.15624 0.480 0.000 0.004 0.440 0.076
#> GSM564752 4 0.1202 0.86302 0.004 0.000 0.004 0.960 0.032
#> GSM564753 4 0.3924 0.79593 0.096 0.000 0.008 0.816 0.080
#> GSM564754 1 0.2110 0.79561 0.912 0.000 0.000 0.072 0.016
#> GSM564755 4 0.0404 0.86221 0.000 0.000 0.000 0.988 0.012
#> GSM564756 4 0.5364 0.37438 0.352 0.000 0.004 0.588 0.056
#> GSM564757 4 0.0794 0.86373 0.000 0.000 0.000 0.972 0.028
#> GSM564758 4 0.0510 0.86342 0.000 0.000 0.000 0.984 0.016
#> GSM564759 4 0.2050 0.85752 0.008 0.000 0.008 0.920 0.064
#> GSM564760 4 0.0404 0.86117 0.000 0.000 0.000 0.988 0.012
#> GSM564761 1 0.0693 0.80365 0.980 0.000 0.000 0.008 0.012
#> GSM564762 4 0.1569 0.86196 0.008 0.000 0.004 0.944 0.044
#> GSM564681 2 0.4276 0.33995 0.000 0.616 0.004 0.000 0.380
#> GSM564693 2 0.4948 0.18688 0.000 0.536 0.028 0.000 0.436
#> GSM564646 5 0.5243 0.52573 0.000 0.188 0.132 0.000 0.680
#> GSM564699 3 0.4734 0.53866 0.000 0.036 0.652 0.000 0.312
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0632 0.8162 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM564616 5 0.4573 0.4488 0.000 0.372 0.000 0.000 0.584 0.044
#> GSM564617 2 0.2744 0.7118 0.000 0.864 0.000 0.000 0.072 0.064
#> GSM564618 5 0.4198 0.5678 0.000 0.232 0.000 0.000 0.708 0.060
#> GSM564619 1 0.4722 0.5719 0.656 0.000 0.000 0.264 0.004 0.076
#> GSM564620 4 0.3411 0.7710 0.060 0.000 0.000 0.816 0.004 0.120
#> GSM564621 4 0.3930 0.7133 0.116 0.000 0.000 0.776 0.004 0.104
#> GSM564622 5 0.4146 0.6117 0.000 0.124 0.056 0.000 0.780 0.040
#> GSM564623 6 0.7653 -0.0667 0.000 0.292 0.124 0.008 0.260 0.316
#> GSM564624 2 0.3691 0.6684 0.000 0.788 0.004 0.000 0.148 0.060
#> GSM564625 4 0.1152 0.8206 0.004 0.000 0.000 0.952 0.000 0.044
#> GSM564626 1 0.2821 0.6706 0.860 0.000 0.000 0.096 0.004 0.040
#> GSM564627 1 0.5978 0.3376 0.452 0.000 0.000 0.364 0.008 0.176
#> GSM564628 5 0.5642 0.2974 0.000 0.352 0.000 0.000 0.488 0.160
#> GSM564629 4 0.2963 0.8005 0.016 0.000 0.000 0.828 0.004 0.152
#> GSM564630 2 0.3802 0.6889 0.000 0.792 0.008 0.000 0.116 0.084
#> GSM564609 3 0.5512 0.2400 0.000 0.008 0.472 0.000 0.420 0.100
#> GSM564610 1 0.4627 0.6390 0.696 0.000 0.000 0.104 0.004 0.196
#> GSM564611 1 0.1075 0.6920 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM564612 2 0.4286 0.6580 0.000 0.764 0.144 0.000 0.048 0.044
#> GSM564613 2 0.5311 0.5867 0.000 0.684 0.132 0.000 0.056 0.128
#> GSM564614 4 0.1411 0.8232 0.000 0.000 0.000 0.936 0.004 0.060
#> GSM564631 3 0.1036 0.7666 0.000 0.004 0.964 0.000 0.024 0.008
#> GSM564632 5 0.5113 0.4889 0.000 0.032 0.176 0.000 0.684 0.108
#> GSM564633 3 0.2901 0.7433 0.000 0.000 0.840 0.000 0.128 0.032
#> GSM564634 2 0.6600 0.4063 0.000 0.536 0.208 0.000 0.104 0.152
#> GSM564635 3 0.2301 0.7610 0.000 0.000 0.884 0.000 0.096 0.020
#> GSM564636 3 0.6458 0.5237 0.000 0.132 0.568 0.000 0.140 0.160
#> GSM564637 3 0.7247 0.2783 0.000 0.168 0.428 0.000 0.248 0.156
#> GSM564638 3 0.2159 0.7628 0.000 0.012 0.904 0.000 0.012 0.072
#> GSM564639 3 0.0909 0.7654 0.000 0.000 0.968 0.000 0.020 0.012
#> GSM564640 2 0.5004 0.0305 0.000 0.548 0.028 0.000 0.396 0.028
#> GSM564641 3 0.4374 0.5492 0.000 0.272 0.680 0.000 0.008 0.040
#> GSM564642 5 0.7020 0.4041 0.000 0.268 0.188 0.000 0.444 0.100
#> GSM564643 5 0.5158 0.3603 0.000 0.008 0.232 0.000 0.636 0.124
#> GSM564644 2 0.3816 0.6102 0.000 0.784 0.024 0.000 0.160 0.032
#> GSM564645 3 0.1053 0.7662 0.000 0.004 0.964 0.000 0.012 0.020
#> GSM564647 2 0.6606 0.1386 0.000 0.440 0.348 0.000 0.060 0.152
#> GSM564648 5 0.3662 0.6159 0.000 0.172 0.044 0.000 0.780 0.004
#> GSM564649 3 0.2570 0.7643 0.000 0.036 0.892 0.000 0.040 0.032
#> GSM564650 2 0.1857 0.7296 0.000 0.924 0.004 0.000 0.028 0.044
#> GSM564651 5 0.4340 0.6051 0.000 0.208 0.064 0.000 0.720 0.008
#> GSM564652 5 0.3755 0.6120 0.000 0.192 0.028 0.000 0.768 0.012
#> GSM564653 5 0.3774 0.5441 0.000 0.328 0.000 0.000 0.664 0.008
#> GSM564654 3 0.4094 0.5104 0.000 0.000 0.652 0.000 0.324 0.024
#> GSM564655 5 0.7084 0.0115 0.000 0.132 0.348 0.000 0.388 0.132
#> GSM564656 3 0.2190 0.7607 0.000 0.000 0.900 0.000 0.060 0.040
#> GSM564657 3 0.3005 0.7237 0.000 0.108 0.848 0.000 0.008 0.036
#> GSM564658 2 0.1088 0.7242 0.000 0.960 0.000 0.000 0.024 0.016
#> GSM564659 2 0.7229 0.0239 0.000 0.340 0.336 0.000 0.228 0.096
#> GSM564660 2 0.5313 0.5903 0.000 0.668 0.036 0.000 0.168 0.128
#> GSM564661 5 0.4174 0.5208 0.000 0.352 0.004 0.000 0.628 0.016
#> GSM564662 3 0.0862 0.7639 0.000 0.008 0.972 0.000 0.016 0.004
#> GSM564663 2 0.1944 0.7315 0.000 0.924 0.016 0.000 0.024 0.036
#> GSM564664 2 0.5527 0.0693 0.000 0.560 0.044 0.000 0.340 0.056
#> GSM564665 3 0.6803 0.1634 0.000 0.240 0.456 0.000 0.240 0.064
#> GSM564666 6 0.7589 -0.0527 0.000 0.304 0.172 0.000 0.220 0.304
#> GSM564667 3 0.4413 0.6191 0.000 0.208 0.720 0.000 0.016 0.056
#> GSM564668 3 0.4797 0.2733 0.000 0.000 0.504 0.000 0.444 0.052
#> GSM564669 3 0.3835 0.6887 0.000 0.000 0.756 0.000 0.188 0.056
#> GSM564670 2 0.5216 0.6202 0.000 0.696 0.072 0.000 0.144 0.088
#> GSM564671 5 0.5163 0.4266 0.000 0.004 0.120 0.008 0.652 0.216
#> GSM564672 3 0.1546 0.7640 0.000 0.016 0.944 0.000 0.020 0.020
#> GSM564673 5 0.4174 0.6096 0.000 0.104 0.064 0.000 0.784 0.048
#> GSM564674 2 0.3852 0.6816 0.000 0.796 0.020 0.000 0.120 0.064
#> GSM564675 6 0.8122 -0.0250 0.000 0.108 0.204 0.060 0.272 0.356
#> GSM564676 2 0.1644 0.7203 0.000 0.932 0.000 0.000 0.040 0.028
#> GSM564677 5 0.3809 0.5616 0.000 0.304 0.004 0.000 0.684 0.008
#> GSM564678 2 0.1225 0.7200 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM564679 2 0.1838 0.7133 0.000 0.916 0.000 0.000 0.068 0.016
#> GSM564680 3 0.1151 0.7655 0.000 0.000 0.956 0.000 0.032 0.012
#> GSM564682 2 0.3835 0.6653 0.000 0.796 0.116 0.000 0.016 0.072
#> GSM564683 3 0.0951 0.7599 0.000 0.008 0.968 0.000 0.004 0.020
#> GSM564684 5 0.5548 0.3431 0.000 0.020 0.108 0.000 0.580 0.292
#> GSM564685 3 0.2583 0.7615 0.000 0.008 0.884 0.000 0.052 0.056
#> GSM564686 5 0.6833 -0.0297 0.000 0.036 0.264 0.004 0.384 0.312
#> GSM564687 5 0.7127 0.2861 0.000 0.276 0.208 0.000 0.416 0.100
#> GSM564688 5 0.3835 0.5702 0.000 0.300 0.016 0.000 0.684 0.000
#> GSM564689 2 0.0858 0.7240 0.000 0.968 0.000 0.000 0.028 0.004
#> GSM564690 2 0.1434 0.7156 0.000 0.940 0.000 0.000 0.048 0.012
#> GSM564691 2 0.1546 0.7290 0.000 0.944 0.020 0.000 0.016 0.020
#> GSM564692 5 0.4008 0.5524 0.000 0.308 0.004 0.000 0.672 0.016
#> GSM564694 5 0.7326 0.2176 0.000 0.144 0.220 0.000 0.412 0.224
#> GSM564695 2 0.6932 0.3283 0.000 0.484 0.212 0.000 0.192 0.112
#> GSM564696 3 0.5576 0.6037 0.000 0.100 0.656 0.000 0.072 0.172
#> GSM564697 2 0.1708 0.7301 0.000 0.932 0.004 0.000 0.024 0.040
#> GSM564698 3 0.2801 0.7559 0.000 0.000 0.860 0.000 0.072 0.068
#> GSM564700 5 0.4238 0.4651 0.000 0.016 0.036 0.000 0.720 0.228
#> GSM564701 5 0.5946 0.3185 0.000 0.408 0.068 0.000 0.468 0.056
#> GSM564702 5 0.3935 0.5706 0.000 0.288 0.008 0.000 0.692 0.012
#> GSM564703 1 0.5863 0.3578 0.492 0.000 0.000 0.336 0.008 0.164
#> GSM564704 1 0.6080 0.2552 0.396 0.000 0.000 0.288 0.000 0.316
#> GSM564705 1 0.0748 0.6917 0.976 0.000 0.000 0.004 0.004 0.016
#> GSM564706 4 0.5157 0.5918 0.088 0.000 0.000 0.568 0.004 0.340
#> GSM564707 1 0.1588 0.6956 0.924 0.000 0.000 0.000 0.004 0.072
#> GSM564708 4 0.3756 0.7910 0.052 0.000 0.000 0.784 0.008 0.156
#> GSM564709 1 0.1524 0.6978 0.932 0.000 0.000 0.008 0.000 0.060
#> GSM564710 1 0.0603 0.6921 0.980 0.000 0.000 0.004 0.000 0.016
#> GSM564711 4 0.4511 0.6530 0.048 0.000 0.000 0.620 0.000 0.332
#> GSM564712 1 0.0405 0.6905 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM564713 4 0.2118 0.8282 0.000 0.000 0.000 0.888 0.008 0.104
#> GSM564714 4 0.5604 0.3690 0.144 0.000 0.000 0.452 0.000 0.404
#> GSM564715 1 0.4676 0.6396 0.700 0.000 0.000 0.104 0.008 0.188
#> GSM564716 4 0.4380 0.7157 0.136 0.000 0.000 0.744 0.012 0.108
#> GSM564717 1 0.5049 0.5814 0.616 0.000 0.000 0.084 0.008 0.292
#> GSM564718 4 0.3564 0.7804 0.024 0.000 0.000 0.768 0.004 0.204
#> GSM564719 1 0.5742 0.3668 0.432 0.000 0.000 0.168 0.000 0.400
#> GSM564720 1 0.2100 0.6876 0.884 0.000 0.000 0.000 0.004 0.112
#> GSM564721 1 0.3039 0.6822 0.848 0.000 0.000 0.088 0.004 0.060
#> GSM564722 6 0.5930 -0.3597 0.212 0.000 0.000 0.384 0.000 0.404
#> GSM564723 1 0.1082 0.6941 0.956 0.000 0.000 0.004 0.000 0.040
#> GSM564724 4 0.2196 0.8244 0.004 0.000 0.000 0.884 0.004 0.108
#> GSM564725 1 0.5018 0.3826 0.536 0.000 0.000 0.396 0.004 0.064
#> GSM564726 4 0.1082 0.8267 0.000 0.000 0.000 0.956 0.004 0.040
#> GSM564727 1 0.5457 0.2156 0.448 0.000 0.000 0.444 0.004 0.104
#> GSM564728 4 0.0508 0.8197 0.000 0.000 0.000 0.984 0.004 0.012
#> GSM564729 4 0.0713 0.8166 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM564730 1 0.4948 0.5810 0.648 0.000 0.000 0.252 0.008 0.092
#> GSM564731 4 0.4020 0.7713 0.044 0.000 0.000 0.744 0.008 0.204
#> GSM564732 4 0.2126 0.8288 0.020 0.000 0.000 0.904 0.004 0.072
#> GSM564733 4 0.1285 0.8256 0.004 0.000 0.000 0.944 0.000 0.052
#> GSM564734 4 0.4183 0.7410 0.128 0.000 0.000 0.752 0.004 0.116
#> GSM564735 4 0.1471 0.8298 0.004 0.000 0.000 0.932 0.000 0.064
#> GSM564736 4 0.0777 0.8261 0.000 0.000 0.000 0.972 0.004 0.024
#> GSM564737 1 0.0858 0.6924 0.968 0.000 0.000 0.004 0.000 0.028
#> GSM564738 4 0.3641 0.7523 0.020 0.000 0.000 0.732 0.000 0.248
#> GSM564739 1 0.5754 0.4096 0.512 0.000 0.000 0.312 0.004 0.172
#> GSM564740 4 0.2964 0.7921 0.004 0.000 0.000 0.792 0.000 0.204
#> GSM564741 4 0.5510 0.5400 0.156 0.000 0.000 0.568 0.004 0.272
#> GSM564742 6 0.6086 -0.2491 0.284 0.000 0.000 0.328 0.000 0.388
#> GSM564743 1 0.2882 0.6720 0.812 0.000 0.000 0.008 0.000 0.180
#> GSM564744 1 0.0692 0.6923 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM564745 4 0.4921 0.5758 0.216 0.000 0.000 0.660 0.004 0.120
#> GSM564746 1 0.6003 0.4565 0.476 0.000 0.000 0.268 0.004 0.252
#> GSM564747 1 0.6103 0.1639 0.380 0.000 0.000 0.320 0.000 0.300
#> GSM564748 1 0.5299 0.5481 0.612 0.000 0.000 0.156 0.004 0.228
#> GSM564749 1 0.1910 0.6891 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM564750 4 0.1429 0.8290 0.004 0.000 0.000 0.940 0.004 0.052
#> GSM564751 1 0.5939 0.1691 0.412 0.000 0.000 0.372 0.000 0.216
#> GSM564752 4 0.2504 0.8200 0.004 0.000 0.000 0.856 0.004 0.136
#> GSM564753 4 0.4936 0.6612 0.064 0.000 0.000 0.624 0.012 0.300
#> GSM564754 1 0.2822 0.6887 0.864 0.000 0.000 0.056 0.004 0.076
#> GSM564755 4 0.1141 0.8257 0.000 0.000 0.000 0.948 0.000 0.052
#> GSM564756 4 0.5630 0.4645 0.240 0.000 0.000 0.560 0.004 0.196
#> GSM564757 4 0.1204 0.8298 0.000 0.000 0.000 0.944 0.000 0.056
#> GSM564758 4 0.1349 0.8297 0.004 0.000 0.000 0.940 0.000 0.056
#> GSM564759 4 0.3410 0.7813 0.008 0.000 0.000 0.768 0.008 0.216
#> GSM564760 4 0.1531 0.8265 0.004 0.000 0.000 0.928 0.000 0.068
#> GSM564761 1 0.0603 0.6899 0.980 0.000 0.000 0.004 0.000 0.016
#> GSM564762 4 0.2772 0.8118 0.004 0.000 0.000 0.816 0.000 0.180
#> GSM564681 5 0.4571 0.3777 0.000 0.432 0.004 0.000 0.536 0.028
#> GSM564693 5 0.4778 0.5039 0.000 0.360 0.008 0.000 0.588 0.044
#> GSM564646 5 0.4814 0.4515 0.000 0.052 0.032 0.000 0.684 0.232
#> GSM564699 3 0.6537 0.3630 0.000 0.036 0.456 0.000 0.228 0.280
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 genotype/variation(p) disease.state(p) k
#> MAD:skmeans 154 0.925 4.76e-01 2
#> MAD:skmeans 149 0.367 2.17e-01 3
#> MAD:skmeans 136 0.430 5.60e-01 4
#> MAD:skmeans 102 0.574 1.05e-05 5
#> MAD:skmeans 111 0.410 1.07e-01 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 154 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 0.945 0.942 0.976 0.5025 0.497 0.497
#> 3 3 0.666 0.744 0.851 0.2626 0.849 0.700
#> 4 4 0.556 0.624 0.787 0.1488 0.902 0.735
#> 5 5 0.621 0.609 0.786 0.0739 0.887 0.633
#> 6 6 0.654 0.582 0.742 0.0397 0.944 0.761
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
#> GSM564615 1 0.0000 0.969 1.000 0.000
#> GSM564616 2 0.0000 0.981 0.000 1.000
#> GSM564617 2 0.1184 0.970 0.016 0.984
#> GSM564618 2 0.0000 0.981 0.000 1.000
#> GSM564619 1 0.0376 0.966 0.996 0.004
#> GSM564620 1 0.0000 0.969 1.000 0.000
#> GSM564621 1 0.0000 0.969 1.000 0.000
#> GSM564622 2 0.0000 0.981 0.000 1.000
#> GSM564623 1 0.9248 0.493 0.660 0.340
#> GSM564624 2 0.0000 0.981 0.000 1.000
#> GSM564625 1 0.0000 0.969 1.000 0.000
#> GSM564626 1 0.0376 0.966 0.996 0.004
#> GSM564627 1 0.0000 0.969 1.000 0.000
#> GSM564628 2 0.0000 0.981 0.000 1.000
#> GSM564629 1 0.0000 0.969 1.000 0.000
#> GSM564630 2 0.0672 0.976 0.008 0.992
#> GSM564609 2 0.0000 0.981 0.000 1.000
#> GSM564610 1 0.0000 0.969 1.000 0.000
#> GSM564611 1 0.1843 0.946 0.972 0.028
#> GSM564612 2 0.0000 0.981 0.000 1.000
#> GSM564613 2 0.0672 0.976 0.008 0.992
#> GSM564614 1 0.0000 0.969 1.000 0.000
#> GSM564631 2 0.0000 0.981 0.000 1.000
#> GSM564632 2 0.2778 0.941 0.048 0.952
#> GSM564633 2 0.0000 0.981 0.000 1.000
#> GSM564634 2 0.0376 0.979 0.004 0.996
#> GSM564635 2 0.0000 0.981 0.000 1.000
#> GSM564636 2 0.0000 0.981 0.000 1.000
#> GSM564637 2 0.0000 0.981 0.000 1.000
#> GSM564638 2 0.0000 0.981 0.000 1.000
#> GSM564639 2 0.0000 0.981 0.000 1.000
#> GSM564640 2 0.0000 0.981 0.000 1.000
#> GSM564641 2 0.0000 0.981 0.000 1.000
#> GSM564642 2 0.0000 0.981 0.000 1.000
#> GSM564643 2 0.0000 0.981 0.000 1.000
#> GSM564644 2 0.0000 0.981 0.000 1.000
#> GSM564645 2 0.0000 0.981 0.000 1.000
#> GSM564647 2 0.0000 0.981 0.000 1.000
#> GSM564648 2 0.0000 0.981 0.000 1.000
#> GSM564649 2 0.0000 0.981 0.000 1.000
#> GSM564650 2 0.0000 0.981 0.000 1.000
#> GSM564651 2 0.0000 0.981 0.000 1.000
#> GSM564652 2 0.0000 0.981 0.000 1.000
#> GSM564653 2 0.0000 0.981 0.000 1.000
#> GSM564654 2 0.0000 0.981 0.000 1.000
#> GSM564655 2 0.1184 0.970 0.016 0.984
#> GSM564656 2 0.3584 0.922 0.068 0.932
#> GSM564657 2 0.0000 0.981 0.000 1.000
#> GSM564658 2 0.0000 0.981 0.000 1.000
#> GSM564659 2 0.0000 0.981 0.000 1.000
#> GSM564660 2 0.0672 0.976 0.008 0.992
#> GSM564661 2 0.0000 0.981 0.000 1.000
#> GSM564662 2 0.0000 0.981 0.000 1.000
#> GSM564663 2 0.0000 0.981 0.000 1.000
#> GSM564664 2 0.0000 0.981 0.000 1.000
#> GSM564665 2 0.0000 0.981 0.000 1.000
#> GSM564666 2 0.5408 0.861 0.124 0.876
#> GSM564667 2 0.0376 0.979 0.004 0.996
#> GSM564668 2 0.1633 0.963 0.024 0.976
#> GSM564669 2 0.0672 0.976 0.008 0.992
#> GSM564670 2 0.0000 0.981 0.000 1.000
#> GSM564671 1 0.9427 0.453 0.640 0.360
#> GSM564672 2 0.0376 0.979 0.004 0.996
#> GSM564673 2 0.0000 0.981 0.000 1.000
#> GSM564674 2 0.0000 0.981 0.000 1.000
#> GSM564675 2 0.6887 0.780 0.184 0.816
#> GSM564676 2 0.0000 0.981 0.000 1.000
#> GSM564677 2 0.0000 0.981 0.000 1.000
#> GSM564678 2 0.0000 0.981 0.000 1.000
#> GSM564679 2 0.0000 0.981 0.000 1.000
#> GSM564680 2 0.0376 0.979 0.004 0.996
#> GSM564682 2 0.0000 0.981 0.000 1.000
#> GSM564683 2 0.5842 0.836 0.140 0.860
#> GSM564684 2 0.5408 0.859 0.124 0.876
#> GSM564685 2 0.0376 0.979 0.004 0.996
#> GSM564686 1 0.9922 0.209 0.552 0.448
#> GSM564687 2 0.0000 0.981 0.000 1.000
#> GSM564688 2 0.0000 0.981 0.000 1.000
#> GSM564689 2 0.0000 0.981 0.000 1.000
#> GSM564690 2 0.0000 0.981 0.000 1.000
#> GSM564691 2 0.0000 0.981 0.000 1.000
#> GSM564692 2 0.0000 0.981 0.000 1.000
#> GSM564694 2 0.0000 0.981 0.000 1.000
#> GSM564695 2 0.0938 0.973 0.012 0.988
#> GSM564696 2 0.5059 0.875 0.112 0.888
#> GSM564697 2 0.0000 0.981 0.000 1.000
#> GSM564698 2 0.0000 0.981 0.000 1.000
#> GSM564700 1 0.9963 0.152 0.536 0.464
#> GSM564701 2 0.0000 0.981 0.000 1.000
#> GSM564702 2 0.0000 0.981 0.000 1.000
#> GSM564703 1 0.0000 0.969 1.000 0.000
#> GSM564704 1 0.0000 0.969 1.000 0.000
#> GSM564705 1 0.4815 0.868 0.896 0.104
#> GSM564706 1 0.0376 0.966 0.996 0.004
#> GSM564707 1 0.0000 0.969 1.000 0.000
#> GSM564708 1 0.0376 0.966 0.996 0.004
#> GSM564709 1 0.0000 0.969 1.000 0.000
#> GSM564710 1 0.9580 0.400 0.620 0.380
#> GSM564711 1 0.0000 0.969 1.000 0.000
#> GSM564712 1 0.0000 0.969 1.000 0.000
#> GSM564713 1 0.0000 0.969 1.000 0.000
#> GSM564714 1 0.0000 0.969 1.000 0.000
#> GSM564715 1 0.0000 0.969 1.000 0.000
#> GSM564716 1 0.0000 0.969 1.000 0.000
#> GSM564717 1 0.0000 0.969 1.000 0.000
#> GSM564718 1 0.0000 0.969 1.000 0.000
#> GSM564719 1 0.0000 0.969 1.000 0.000
#> GSM564720 1 0.0000 0.969 1.000 0.000
#> GSM564721 1 0.2236 0.939 0.964 0.036
#> GSM564722 1 0.0000 0.969 1.000 0.000
#> GSM564723 1 0.0672 0.963 0.992 0.008
#> GSM564724 1 0.0000 0.969 1.000 0.000
#> GSM564725 1 0.0000 0.969 1.000 0.000
#> GSM564726 1 0.0000 0.969 1.000 0.000
#> GSM564727 1 0.0000 0.969 1.000 0.000
#> GSM564728 1 0.0000 0.969 1.000 0.000
#> GSM564729 1 0.0000 0.969 1.000 0.000
#> GSM564730 1 0.1184 0.957 0.984 0.016
#> GSM564731 1 0.0000 0.969 1.000 0.000
#> GSM564732 1 0.0000 0.969 1.000 0.000
#> GSM564733 1 0.0000 0.969 1.000 0.000
#> GSM564734 1 0.0000 0.969 1.000 0.000
#> GSM564735 1 0.0000 0.969 1.000 0.000
#> GSM564736 1 0.0000 0.969 1.000 0.000
#> GSM564737 1 0.0000 0.969 1.000 0.000
#> GSM564738 1 0.0000 0.969 1.000 0.000
#> GSM564739 1 0.0000 0.969 1.000 0.000
#> GSM564740 1 0.0000 0.969 1.000 0.000
#> GSM564741 1 0.0000 0.969 1.000 0.000
#> GSM564742 1 0.0000 0.969 1.000 0.000
#> GSM564743 1 0.0000 0.969 1.000 0.000
#> GSM564744 1 0.0938 0.960 0.988 0.012
#> GSM564745 1 0.0000 0.969 1.000 0.000
#> GSM564746 1 0.0000 0.969 1.000 0.000
#> GSM564747 1 0.0000 0.969 1.000 0.000
#> GSM564748 1 0.0000 0.969 1.000 0.000
#> GSM564749 1 0.0000 0.969 1.000 0.000
#> GSM564750 1 0.0000 0.969 1.000 0.000
#> GSM564751 1 0.0000 0.969 1.000 0.000
#> GSM564752 1 0.0000 0.969 1.000 0.000
#> GSM564753 1 0.0000 0.969 1.000 0.000
#> GSM564754 1 0.0000 0.969 1.000 0.000
#> GSM564755 1 0.0000 0.969 1.000 0.000
#> GSM564756 1 0.0000 0.969 1.000 0.000
#> GSM564757 1 0.0000 0.969 1.000 0.000
#> GSM564758 1 0.0000 0.969 1.000 0.000
#> GSM564759 1 0.0000 0.969 1.000 0.000
#> GSM564760 1 0.0000 0.969 1.000 0.000
#> GSM564761 1 0.2603 0.932 0.956 0.044
#> GSM564762 1 0.0000 0.969 1.000 0.000
#> GSM564681 2 0.0000 0.981 0.000 1.000
#> GSM564693 2 0.0000 0.981 0.000 1.000
#> GSM564646 2 0.4161 0.905 0.084 0.916
#> GSM564699 2 0.9850 0.228 0.428 0.572
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.1163 0.9428 0.972 0.000 0.028
#> GSM564616 2 0.0237 0.7327 0.000 0.996 0.004
#> GSM564617 2 0.0983 0.7369 0.004 0.980 0.016
#> GSM564618 2 0.3038 0.7202 0.000 0.896 0.104
#> GSM564619 1 0.1267 0.9427 0.972 0.004 0.024
#> GSM564620 1 0.0983 0.9427 0.980 0.004 0.016
#> GSM564621 1 0.0892 0.9443 0.980 0.000 0.020
#> GSM564622 2 0.4504 0.6690 0.000 0.804 0.196
#> GSM564623 1 0.8607 0.3908 0.592 0.152 0.256
#> GSM564624 2 0.0000 0.7322 0.000 1.000 0.000
#> GSM564625 1 0.1031 0.9431 0.976 0.000 0.024
#> GSM564626 1 0.1289 0.9454 0.968 0.000 0.032
#> GSM564627 1 0.1529 0.9465 0.960 0.000 0.040
#> GSM564628 2 0.3941 0.6888 0.000 0.844 0.156
#> GSM564629 1 0.1129 0.9437 0.976 0.004 0.020
#> GSM564630 2 0.2261 0.7360 0.000 0.932 0.068
#> GSM564609 3 0.6095 0.5227 0.000 0.392 0.608
#> GSM564610 1 0.2537 0.9440 0.920 0.000 0.080
#> GSM564611 1 0.5165 0.8887 0.832 0.072 0.096
#> GSM564612 2 0.6309 -0.0183 0.000 0.504 0.496
#> GSM564613 3 0.7156 0.5011 0.028 0.400 0.572
#> GSM564614 1 0.1163 0.9429 0.972 0.000 0.028
#> GSM564631 3 0.3412 0.8010 0.000 0.124 0.876
#> GSM564632 2 0.5335 0.6059 0.008 0.760 0.232
#> GSM564633 3 0.3267 0.8015 0.000 0.116 0.884
#> GSM564634 3 0.5706 0.6360 0.000 0.320 0.680
#> GSM564635 3 0.3267 0.8015 0.000 0.116 0.884
#> GSM564636 3 0.5560 0.6727 0.000 0.300 0.700
#> GSM564637 3 0.5254 0.7231 0.000 0.264 0.736
#> GSM564638 3 0.3340 0.8026 0.000 0.120 0.880
#> GSM564639 3 0.3412 0.8022 0.000 0.124 0.876
#> GSM564640 2 0.6252 -0.0948 0.000 0.556 0.444
#> GSM564641 3 0.3412 0.8008 0.000 0.124 0.876
#> GSM564642 2 0.5905 0.3210 0.000 0.648 0.352
#> GSM564643 3 0.6169 0.5626 0.004 0.360 0.636
#> GSM564644 2 0.5465 0.5026 0.000 0.712 0.288
#> GSM564645 3 0.3412 0.8020 0.000 0.124 0.876
#> GSM564647 3 0.5431 0.6906 0.000 0.284 0.716
#> GSM564648 2 0.1860 0.7372 0.000 0.948 0.052
#> GSM564649 3 0.3551 0.8023 0.000 0.132 0.868
#> GSM564650 2 0.2878 0.7212 0.000 0.904 0.096
#> GSM564651 3 0.6308 0.1669 0.000 0.492 0.508
#> GSM564652 2 0.1529 0.7374 0.000 0.960 0.040
#> GSM564653 2 0.0892 0.7379 0.000 0.980 0.020
#> GSM564654 3 0.3619 0.8008 0.000 0.136 0.864
#> GSM564655 3 0.6255 0.6418 0.012 0.320 0.668
#> GSM564656 3 0.3116 0.7974 0.000 0.108 0.892
#> GSM564657 3 0.3340 0.8009 0.000 0.120 0.880
#> GSM564658 2 0.1964 0.7321 0.000 0.944 0.056
#> GSM564659 3 0.5529 0.6550 0.000 0.296 0.704
#> GSM564660 2 0.2796 0.7303 0.000 0.908 0.092
#> GSM564661 2 0.1031 0.7375 0.000 0.976 0.024
#> GSM564662 3 0.3267 0.8015 0.000 0.116 0.884
#> GSM564663 2 0.1860 0.7330 0.000 0.948 0.052
#> GSM564664 2 0.6307 -0.1573 0.000 0.512 0.488
#> GSM564665 3 0.6168 0.4162 0.000 0.412 0.588
#> GSM564666 2 0.9953 -0.0570 0.300 0.380 0.320
#> GSM564667 3 0.4062 0.7907 0.000 0.164 0.836
#> GSM564668 3 0.5858 0.7342 0.020 0.240 0.740
#> GSM564669 3 0.3267 0.8015 0.000 0.116 0.884
#> GSM564670 2 0.6260 -0.0798 0.000 0.552 0.448
#> GSM564671 1 0.8958 0.2858 0.552 0.280 0.168
#> GSM564672 3 0.3340 0.8009 0.000 0.120 0.880
#> GSM564673 2 0.5529 0.4587 0.000 0.704 0.296
#> GSM564674 2 0.6235 -0.0142 0.000 0.564 0.436
#> GSM564675 3 0.9744 0.2301 0.256 0.300 0.444
#> GSM564676 2 0.1964 0.7388 0.000 0.944 0.056
#> GSM564677 2 0.0592 0.7348 0.000 0.988 0.012
#> GSM564678 2 0.1860 0.7326 0.000 0.948 0.052
#> GSM564679 2 0.0592 0.7356 0.000 0.988 0.012
#> GSM564680 3 0.3340 0.8009 0.000 0.120 0.880
#> GSM564682 3 0.4974 0.7492 0.000 0.236 0.764
#> GSM564683 3 0.3610 0.7859 0.016 0.096 0.888
#> GSM564684 2 0.9269 0.1653 0.308 0.508 0.184
#> GSM564685 3 0.3412 0.8025 0.000 0.124 0.876
#> GSM564686 1 0.9561 -0.0090 0.468 0.216 0.316
#> GSM564687 2 0.6154 0.1318 0.000 0.592 0.408
#> GSM564688 2 0.4346 0.6557 0.000 0.816 0.184
#> GSM564689 2 0.5016 0.5783 0.000 0.760 0.240
#> GSM564690 2 0.1031 0.7396 0.000 0.976 0.024
#> GSM564691 3 0.6302 0.2341 0.000 0.480 0.520
#> GSM564692 2 0.0592 0.7353 0.000 0.988 0.012
#> GSM564694 3 0.6126 0.5097 0.000 0.400 0.600
#> GSM564695 2 0.5363 0.5278 0.000 0.724 0.276
#> GSM564696 3 0.5798 0.7388 0.040 0.184 0.776
#> GSM564697 2 0.4605 0.6262 0.000 0.796 0.204
#> GSM564698 3 0.3340 0.8025 0.000 0.120 0.880
#> GSM564700 2 0.5677 0.5644 0.160 0.792 0.048
#> GSM564701 2 0.5859 0.3754 0.000 0.656 0.344
#> GSM564702 2 0.3038 0.7153 0.000 0.896 0.104
#> GSM564703 1 0.2165 0.9442 0.936 0.000 0.064
#> GSM564704 1 0.2066 0.9432 0.940 0.000 0.060
#> GSM564705 1 0.5831 0.8515 0.796 0.076 0.128
#> GSM564706 1 0.2711 0.9317 0.912 0.000 0.088
#> GSM564707 1 0.2796 0.9370 0.908 0.000 0.092
#> GSM564708 1 0.2537 0.9346 0.920 0.000 0.080
#> GSM564709 1 0.2945 0.9365 0.908 0.004 0.088
#> GSM564710 2 0.8797 0.3371 0.276 0.568 0.156
#> GSM564711 1 0.2261 0.9412 0.932 0.000 0.068
#> GSM564712 1 0.5585 0.8656 0.812 0.092 0.096
#> GSM564713 1 0.0747 0.9432 0.984 0.000 0.016
#> GSM564714 1 0.2711 0.9358 0.912 0.000 0.088
#> GSM564715 1 0.1643 0.9480 0.956 0.000 0.044
#> GSM564716 1 0.1163 0.9444 0.972 0.000 0.028
#> GSM564717 1 0.2537 0.9396 0.920 0.000 0.080
#> GSM564718 1 0.1031 0.9480 0.976 0.000 0.024
#> GSM564719 1 0.2945 0.9351 0.908 0.004 0.088
#> GSM564720 1 0.2625 0.9409 0.916 0.000 0.084
#> GSM564721 1 0.3045 0.9396 0.916 0.020 0.064
#> GSM564722 1 0.2537 0.9389 0.920 0.000 0.080
#> GSM564723 1 0.2945 0.9349 0.908 0.004 0.088
#> GSM564724 1 0.1411 0.9476 0.964 0.000 0.036
#> GSM564725 1 0.1031 0.9431 0.976 0.000 0.024
#> GSM564726 1 0.0747 0.9432 0.984 0.000 0.016
#> GSM564727 1 0.1031 0.9431 0.976 0.000 0.024
#> GSM564728 1 0.0592 0.9435 0.988 0.000 0.012
#> GSM564729 1 0.0892 0.9434 0.980 0.000 0.020
#> GSM564730 1 0.3267 0.9254 0.884 0.000 0.116
#> GSM564731 1 0.0747 0.9456 0.984 0.000 0.016
#> GSM564732 1 0.1643 0.9476 0.956 0.000 0.044
#> GSM564733 1 0.1031 0.9479 0.976 0.000 0.024
#> GSM564734 1 0.1289 0.9450 0.968 0.000 0.032
#> GSM564735 1 0.1289 0.9474 0.968 0.000 0.032
#> GSM564736 1 0.1163 0.9470 0.972 0.000 0.028
#> GSM564737 1 0.3910 0.9266 0.876 0.020 0.104
#> GSM564738 1 0.2261 0.9412 0.932 0.000 0.068
#> GSM564739 1 0.1529 0.9482 0.960 0.000 0.040
#> GSM564740 1 0.0747 0.9459 0.984 0.000 0.016
#> GSM564741 1 0.1964 0.9433 0.944 0.000 0.056
#> GSM564742 1 0.2796 0.9352 0.908 0.000 0.092
#> GSM564743 1 0.2878 0.9351 0.904 0.000 0.096
#> GSM564744 1 0.4357 0.9105 0.868 0.052 0.080
#> GSM564745 1 0.0747 0.9438 0.984 0.000 0.016
#> GSM564746 1 0.1860 0.9438 0.948 0.000 0.052
#> GSM564747 1 0.2356 0.9413 0.928 0.000 0.072
#> GSM564748 1 0.2066 0.9437 0.940 0.000 0.060
#> GSM564749 1 0.2878 0.9373 0.904 0.000 0.096
#> GSM564750 1 0.0892 0.9425 0.980 0.000 0.020
#> GSM564751 1 0.2261 0.9427 0.932 0.000 0.068
#> GSM564752 1 0.0747 0.9432 0.984 0.000 0.016
#> GSM564753 1 0.2448 0.9403 0.924 0.000 0.076
#> GSM564754 1 0.2261 0.9438 0.932 0.000 0.068
#> GSM564755 1 0.0747 0.9432 0.984 0.000 0.016
#> GSM564756 1 0.1163 0.9469 0.972 0.000 0.028
#> GSM564757 1 0.0592 0.9442 0.988 0.000 0.012
#> GSM564758 1 0.1163 0.9462 0.972 0.000 0.028
#> GSM564759 1 0.1643 0.9466 0.956 0.000 0.044
#> GSM564760 1 0.1289 0.9477 0.968 0.000 0.032
#> GSM564761 1 0.3678 0.9308 0.892 0.028 0.080
#> GSM564762 1 0.1411 0.9474 0.964 0.000 0.036
#> GSM564681 2 0.1529 0.7384 0.000 0.960 0.040
#> GSM564693 2 0.4062 0.6512 0.000 0.836 0.164
#> GSM564646 2 0.2846 0.7340 0.020 0.924 0.056
#> GSM564699 3 0.9998 0.0461 0.336 0.324 0.340
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.1474 0.7416 0.948 0.000 0.000 0.052
#> GSM564616 2 0.1406 0.7248 0.000 0.960 0.016 0.024
#> GSM564617 2 0.3015 0.7263 0.000 0.884 0.024 0.092
#> GSM564618 2 0.2081 0.7220 0.000 0.916 0.084 0.000
#> GSM564619 1 0.1940 0.7306 0.924 0.000 0.000 0.076
#> GSM564620 1 0.1389 0.7429 0.952 0.000 0.000 0.048
#> GSM564621 1 0.1716 0.7435 0.936 0.000 0.000 0.064
#> GSM564622 2 0.4372 0.6048 0.000 0.728 0.268 0.004
#> GSM564623 1 0.7076 0.3155 0.612 0.140 0.232 0.016
#> GSM564624 2 0.2124 0.7183 0.000 0.924 0.008 0.068
#> GSM564625 1 0.1474 0.7352 0.948 0.000 0.000 0.052
#> GSM564626 1 0.2760 0.7214 0.872 0.000 0.000 0.128
#> GSM564627 1 0.3907 0.5800 0.768 0.000 0.000 0.232
#> GSM564628 2 0.3862 0.6739 0.000 0.824 0.152 0.024
#> GSM564629 1 0.1389 0.7424 0.952 0.000 0.000 0.048
#> GSM564630 2 0.3903 0.7238 0.000 0.844 0.076 0.080
#> GSM564609 3 0.5699 0.3788 0.000 0.380 0.588 0.032
#> GSM564610 4 0.4382 0.7599 0.296 0.000 0.000 0.704
#> GSM564611 4 0.1716 0.7889 0.064 0.000 0.000 0.936
#> GSM564612 3 0.4920 0.2693 0.000 0.368 0.628 0.004
#> GSM564613 3 0.7409 0.3488 0.020 0.304 0.552 0.124
#> GSM564614 1 0.1637 0.7404 0.940 0.000 0.000 0.060
#> GSM564631 3 0.0779 0.7655 0.000 0.016 0.980 0.004
#> GSM564632 2 0.5046 0.5993 0.004 0.732 0.232 0.032
#> GSM564633 3 0.0592 0.7666 0.000 0.016 0.984 0.000
#> GSM564634 3 0.4964 0.5968 0.000 0.244 0.724 0.032
#> GSM564635 3 0.0592 0.7675 0.000 0.016 0.984 0.000
#> GSM564636 3 0.4964 0.6085 0.000 0.244 0.724 0.032
#> GSM564637 3 0.5055 0.5893 0.000 0.256 0.712 0.032
#> GSM564638 3 0.0469 0.7671 0.000 0.012 0.988 0.000
#> GSM564639 3 0.1389 0.7657 0.000 0.048 0.952 0.000
#> GSM564640 2 0.5980 0.1753 0.000 0.560 0.396 0.044
#> GSM564641 3 0.0469 0.7632 0.000 0.012 0.988 0.000
#> GSM564642 2 0.5254 0.4907 0.000 0.672 0.300 0.028
#> GSM564643 3 0.5875 0.3399 0.008 0.396 0.572 0.024
#> GSM564644 2 0.6411 0.4939 0.000 0.600 0.308 0.092
#> GSM564645 3 0.0707 0.7681 0.000 0.020 0.980 0.000
#> GSM564647 3 0.4059 0.6631 0.000 0.200 0.788 0.012
#> GSM564648 2 0.2021 0.7191 0.000 0.936 0.040 0.024
#> GSM564649 3 0.1545 0.7683 0.000 0.040 0.952 0.008
#> GSM564650 2 0.4100 0.6923 0.000 0.816 0.148 0.036
#> GSM564651 3 0.4994 0.0763 0.000 0.480 0.520 0.000
#> GSM564652 2 0.0921 0.7243 0.000 0.972 0.028 0.000
#> GSM564653 2 0.2032 0.7282 0.000 0.936 0.028 0.036
#> GSM564654 3 0.1716 0.7630 0.000 0.064 0.936 0.000
#> GSM564655 3 0.5708 0.5398 0.012 0.288 0.668 0.032
#> GSM564656 3 0.0469 0.7661 0.000 0.012 0.988 0.000
#> GSM564657 3 0.0469 0.7618 0.000 0.012 0.988 0.000
#> GSM564658 2 0.3903 0.7043 0.000 0.844 0.076 0.080
#> GSM564659 3 0.3942 0.6137 0.000 0.236 0.764 0.000
#> GSM564660 2 0.2909 0.7152 0.000 0.888 0.092 0.020
#> GSM564661 2 0.2871 0.7189 0.000 0.896 0.032 0.072
#> GSM564662 3 0.0188 0.7642 0.000 0.004 0.996 0.000
#> GSM564663 2 0.3972 0.7021 0.000 0.840 0.080 0.080
#> GSM564664 3 0.6775 0.1080 0.000 0.384 0.516 0.100
#> GSM564665 3 0.4713 0.3830 0.000 0.360 0.640 0.000
#> GSM564666 2 0.8525 0.1384 0.236 0.436 0.292 0.036
#> GSM564667 3 0.2149 0.7528 0.000 0.088 0.912 0.000
#> GSM564668 3 0.4584 0.6819 0.016 0.196 0.776 0.012
#> GSM564669 3 0.0817 0.7678 0.000 0.024 0.976 0.000
#> GSM564670 2 0.5155 0.0129 0.000 0.528 0.468 0.004
#> GSM564671 1 0.8453 0.1397 0.484 0.304 0.148 0.064
#> GSM564672 3 0.0469 0.7618 0.000 0.012 0.988 0.000
#> GSM564673 2 0.5036 0.4990 0.000 0.696 0.280 0.024
#> GSM564674 2 0.6425 0.1361 0.000 0.508 0.424 0.068
#> GSM564675 3 0.8475 0.1239 0.188 0.352 0.420 0.040
#> GSM564676 2 0.5559 0.6458 0.000 0.696 0.064 0.240
#> GSM564677 2 0.1182 0.7235 0.000 0.968 0.016 0.016
#> GSM564678 2 0.5055 0.6215 0.000 0.712 0.032 0.256
#> GSM564679 2 0.2882 0.7199 0.000 0.892 0.024 0.084
#> GSM564680 3 0.0336 0.7628 0.000 0.008 0.992 0.000
#> GSM564682 3 0.4411 0.7007 0.000 0.108 0.812 0.080
#> GSM564683 3 0.0376 0.7630 0.004 0.004 0.992 0.000
#> GSM564684 2 0.7884 0.3376 0.232 0.548 0.188 0.032
#> GSM564685 3 0.1042 0.7680 0.000 0.020 0.972 0.008
#> GSM564686 1 0.8621 -0.1664 0.368 0.260 0.340 0.032
#> GSM564687 2 0.5428 0.3091 0.000 0.600 0.380 0.020
#> GSM564688 2 0.4220 0.5963 0.000 0.748 0.248 0.004
#> GSM564689 2 0.7289 0.4634 0.000 0.528 0.280 0.192
#> GSM564690 2 0.5971 0.4735 0.000 0.532 0.040 0.428
#> GSM564691 3 0.6477 0.2164 0.000 0.368 0.552 0.080
#> GSM564692 2 0.0188 0.7184 0.000 0.996 0.004 0.000
#> GSM564694 3 0.5847 0.3176 0.000 0.404 0.560 0.036
#> GSM564695 2 0.4406 0.5042 0.000 0.700 0.300 0.000
#> GSM564696 3 0.4126 0.7203 0.020 0.120 0.836 0.024
#> GSM564697 2 0.6083 0.6102 0.000 0.672 0.216 0.112
#> GSM564698 3 0.1302 0.7637 0.000 0.044 0.956 0.000
#> GSM564700 2 0.4914 0.6360 0.116 0.804 0.048 0.032
#> GSM564701 2 0.5467 0.4018 0.000 0.612 0.364 0.024
#> GSM564702 2 0.2647 0.7074 0.000 0.880 0.120 0.000
#> GSM564703 1 0.4477 0.6584 0.688 0.000 0.000 0.312
#> GSM564704 1 0.4661 0.6014 0.652 0.000 0.000 0.348
#> GSM564705 4 0.2466 0.8240 0.096 0.000 0.004 0.900
#> GSM564706 1 0.5364 0.5897 0.652 0.000 0.028 0.320
#> GSM564707 4 0.3311 0.8212 0.172 0.000 0.000 0.828
#> GSM564708 1 0.4049 0.7181 0.780 0.000 0.008 0.212
#> GSM564709 1 0.5165 0.2301 0.512 0.004 0.000 0.484
#> GSM564710 4 0.5176 0.7683 0.100 0.108 0.012 0.780
#> GSM564711 1 0.4730 0.5713 0.636 0.000 0.000 0.364
#> GSM564712 4 0.3208 0.8420 0.148 0.004 0.000 0.848
#> GSM564713 1 0.0817 0.7455 0.976 0.000 0.000 0.024
#> GSM564714 1 0.4999 0.2516 0.508 0.000 0.000 0.492
#> GSM564715 1 0.4193 0.6650 0.732 0.000 0.000 0.268
#> GSM564716 1 0.2011 0.7325 0.920 0.000 0.000 0.080
#> GSM564717 4 0.4304 0.7598 0.284 0.000 0.000 0.716
#> GSM564718 1 0.2921 0.7482 0.860 0.000 0.000 0.140
#> GSM564719 4 0.3486 0.8197 0.188 0.000 0.000 0.812
#> GSM564720 4 0.3975 0.8061 0.240 0.000 0.000 0.760
#> GSM564721 1 0.4761 0.5508 0.628 0.000 0.000 0.372
#> GSM564722 1 0.4989 0.2658 0.528 0.000 0.000 0.472
#> GSM564723 4 0.3801 0.8322 0.220 0.000 0.000 0.780
#> GSM564724 1 0.3444 0.7288 0.816 0.000 0.000 0.184
#> GSM564725 1 0.2081 0.7295 0.916 0.000 0.000 0.084
#> GSM564726 1 0.0817 0.7488 0.976 0.000 0.000 0.024
#> GSM564727 1 0.2081 0.7336 0.916 0.000 0.000 0.084
#> GSM564728 1 0.0469 0.7480 0.988 0.000 0.000 0.012
#> GSM564729 1 0.1389 0.7438 0.952 0.000 0.000 0.048
#> GSM564730 1 0.5189 0.4541 0.616 0.000 0.012 0.372
#> GSM564731 1 0.1867 0.7561 0.928 0.000 0.000 0.072
#> GSM564732 1 0.3873 0.7168 0.772 0.000 0.000 0.228
#> GSM564733 1 0.3172 0.7474 0.840 0.000 0.000 0.160
#> GSM564734 1 0.2408 0.7555 0.896 0.000 0.000 0.104
#> GSM564735 1 0.3356 0.7323 0.824 0.000 0.000 0.176
#> GSM564736 1 0.2081 0.7586 0.916 0.000 0.000 0.084
#> GSM564737 4 0.2921 0.8432 0.140 0.000 0.000 0.860
#> GSM564738 1 0.4585 0.6080 0.668 0.000 0.000 0.332
#> GSM564739 1 0.3610 0.7264 0.800 0.000 0.000 0.200
#> GSM564740 1 0.2469 0.7524 0.892 0.000 0.000 0.108
#> GSM564741 1 0.4331 0.6555 0.712 0.000 0.000 0.288
#> GSM564742 1 0.4999 0.2226 0.508 0.000 0.000 0.492
#> GSM564743 4 0.3123 0.8356 0.156 0.000 0.000 0.844
#> GSM564744 4 0.4164 0.7808 0.264 0.000 0.000 0.736
#> GSM564745 1 0.0592 0.7485 0.984 0.000 0.000 0.016
#> GSM564746 4 0.4989 0.4422 0.472 0.000 0.000 0.528
#> GSM564747 1 0.4605 0.6121 0.664 0.000 0.000 0.336
#> GSM564748 1 0.4477 0.6528 0.688 0.000 0.000 0.312
#> GSM564749 4 0.2973 0.8384 0.144 0.000 0.000 0.856
#> GSM564750 1 0.1807 0.7562 0.940 0.000 0.008 0.052
#> GSM564751 1 0.4431 0.6514 0.696 0.000 0.000 0.304
#> GSM564752 1 0.1118 0.7483 0.964 0.000 0.000 0.036
#> GSM564753 1 0.4883 0.6511 0.696 0.000 0.016 0.288
#> GSM564754 1 0.4585 0.6353 0.668 0.000 0.000 0.332
#> GSM564755 1 0.0707 0.7475 0.980 0.000 0.000 0.020
#> GSM564756 1 0.2760 0.7412 0.872 0.000 0.000 0.128
#> GSM564757 1 0.0817 0.7474 0.976 0.000 0.000 0.024
#> GSM564758 1 0.1716 0.7569 0.936 0.000 0.000 0.064
#> GSM564759 1 0.3764 0.7157 0.784 0.000 0.000 0.216
#> GSM564760 1 0.3306 0.7476 0.840 0.004 0.000 0.156
#> GSM564761 4 0.4164 0.7476 0.264 0.000 0.000 0.736
#> GSM564762 1 0.3688 0.7186 0.792 0.000 0.000 0.208
#> GSM564681 2 0.1209 0.7250 0.000 0.964 0.032 0.004
#> GSM564693 2 0.3024 0.6606 0.000 0.852 0.148 0.000
#> GSM564646 2 0.2466 0.7200 0.000 0.916 0.056 0.028
#> GSM564699 2 0.8731 -0.0108 0.212 0.372 0.368 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.1341 0.76663 0.056 0.000 0.000 0.944 0.000
#> GSM564616 2 0.4310 0.48941 0.000 0.604 0.392 0.000 0.004
#> GSM564617 3 0.4304 0.03148 0.000 0.484 0.516 0.000 0.000
#> GSM564618 2 0.5322 0.45498 0.000 0.552 0.392 0.000 0.056
#> GSM564619 4 0.2179 0.74269 0.100 0.004 0.000 0.896 0.000
#> GSM564620 4 0.1662 0.76616 0.056 0.004 0.004 0.936 0.000
#> GSM564621 4 0.1671 0.76602 0.076 0.000 0.000 0.924 0.000
#> GSM564622 3 0.6674 -0.07867 0.000 0.304 0.436 0.000 0.260
#> GSM564623 4 0.6718 0.08324 0.004 0.004 0.352 0.456 0.184
#> GSM564624 2 0.3109 0.60617 0.000 0.800 0.200 0.000 0.000
#> GSM564625 4 0.1341 0.75852 0.056 0.000 0.000 0.944 0.000
#> GSM564626 4 0.2890 0.72901 0.160 0.004 0.000 0.836 0.000
#> GSM564627 4 0.4220 0.47413 0.300 0.008 0.004 0.688 0.000
#> GSM564628 3 0.1830 0.61795 0.000 0.068 0.924 0.000 0.008
#> GSM564629 4 0.1557 0.76475 0.052 0.008 0.000 0.940 0.000
#> GSM564630 2 0.4028 0.55185 0.000 0.768 0.192 0.000 0.040
#> GSM564609 3 0.3550 0.59778 0.000 0.004 0.760 0.000 0.236
#> GSM564610 1 0.3525 0.70358 0.800 0.008 0.008 0.184 0.000
#> GSM564611 1 0.3115 0.70141 0.852 0.112 0.000 0.036 0.000
#> GSM564612 5 0.4686 0.25099 0.000 0.384 0.020 0.000 0.596
#> GSM564613 5 0.7653 -0.01417 0.048 0.196 0.320 0.008 0.428
#> GSM564614 4 0.1478 0.76094 0.064 0.000 0.000 0.936 0.000
#> GSM564631 5 0.1197 0.83721 0.000 0.000 0.048 0.000 0.952
#> GSM564632 3 0.2625 0.65832 0.000 0.016 0.876 0.000 0.108
#> GSM564633 5 0.0510 0.84847 0.000 0.000 0.016 0.000 0.984
#> GSM564634 5 0.4612 0.63091 0.000 0.056 0.232 0.000 0.712
#> GSM564635 5 0.0162 0.84829 0.000 0.000 0.004 0.000 0.996
#> GSM564636 5 0.4283 0.00794 0.000 0.000 0.456 0.000 0.544
#> GSM564637 3 0.3715 0.60943 0.000 0.004 0.736 0.000 0.260
#> GSM564638 5 0.0290 0.84866 0.000 0.000 0.008 0.000 0.992
#> GSM564639 5 0.0880 0.84173 0.000 0.000 0.032 0.000 0.968
#> GSM564640 3 0.2790 0.66217 0.000 0.052 0.880 0.000 0.068
#> GSM564641 5 0.0451 0.84838 0.000 0.004 0.008 0.000 0.988
#> GSM564642 3 0.5480 0.45997 0.000 0.168 0.656 0.000 0.176
#> GSM564643 3 0.3196 0.64669 0.000 0.004 0.804 0.000 0.192
#> GSM564644 2 0.5335 0.41339 0.000 0.668 0.200 0.000 0.132
#> GSM564645 5 0.0290 0.84861 0.000 0.000 0.008 0.000 0.992
#> GSM564647 5 0.2929 0.76106 0.000 0.008 0.152 0.000 0.840
#> GSM564648 3 0.2233 0.58944 0.000 0.104 0.892 0.000 0.004
#> GSM564649 5 0.1740 0.83070 0.000 0.012 0.056 0.000 0.932
#> GSM564650 3 0.4062 0.55733 0.000 0.196 0.764 0.000 0.040
#> GSM564651 3 0.6555 0.11448 0.000 0.200 0.400 0.000 0.400
#> GSM564652 2 0.4674 0.43713 0.000 0.568 0.416 0.000 0.016
#> GSM564653 2 0.3266 0.61193 0.000 0.796 0.200 0.000 0.004
#> GSM564654 5 0.1197 0.83733 0.000 0.000 0.048 0.000 0.952
#> GSM564655 3 0.4650 0.12031 0.000 0.012 0.520 0.000 0.468
#> GSM564656 5 0.0290 0.84873 0.000 0.000 0.008 0.000 0.992
#> GSM564657 5 0.0162 0.84793 0.000 0.000 0.004 0.000 0.996
#> GSM564658 2 0.0963 0.60811 0.000 0.964 0.036 0.000 0.000
#> GSM564659 5 0.3569 0.75373 0.000 0.104 0.068 0.000 0.828
#> GSM564660 3 0.3487 0.47138 0.000 0.212 0.780 0.000 0.008
#> GSM564661 2 0.2124 0.62016 0.000 0.900 0.096 0.000 0.004
#> GSM564662 5 0.0000 0.84745 0.000 0.000 0.000 0.000 1.000
#> GSM564663 2 0.1270 0.61287 0.000 0.948 0.052 0.000 0.000
#> GSM564664 2 0.6523 0.11460 0.000 0.484 0.268 0.000 0.248
#> GSM564665 5 0.5584 0.31151 0.000 0.324 0.092 0.000 0.584
#> GSM564666 3 0.1996 0.65021 0.004 0.008 0.932 0.040 0.016
#> GSM564667 5 0.1571 0.82994 0.000 0.004 0.060 0.000 0.936
#> GSM564668 5 0.3559 0.72530 0.008 0.000 0.176 0.012 0.804
#> GSM564669 5 0.0404 0.84699 0.000 0.000 0.012 0.000 0.988
#> GSM564670 3 0.6012 0.25654 0.000 0.116 0.484 0.000 0.400
#> GSM564671 3 0.4355 0.59595 0.012 0.008 0.796 0.124 0.060
#> GSM564672 5 0.0000 0.84745 0.000 0.000 0.000 0.000 1.000
#> GSM564673 3 0.2416 0.66785 0.000 0.012 0.888 0.000 0.100
#> GSM564674 3 0.4832 0.59636 0.000 0.104 0.720 0.000 0.176
#> GSM564675 3 0.3701 0.64382 0.004 0.008 0.808 0.016 0.164
#> GSM564676 2 0.5297 0.44583 0.072 0.684 0.228 0.000 0.016
#> GSM564677 2 0.3857 0.54963 0.000 0.688 0.312 0.000 0.000
#> GSM564678 2 0.1851 0.57402 0.088 0.912 0.000 0.000 0.000
#> GSM564679 2 0.1121 0.61040 0.000 0.956 0.044 0.000 0.000
#> GSM564680 5 0.0000 0.84745 0.000 0.000 0.000 0.000 1.000
#> GSM564682 5 0.5519 0.39062 0.000 0.332 0.084 0.000 0.584
#> GSM564683 5 0.0000 0.84745 0.000 0.000 0.000 0.000 1.000
#> GSM564684 3 0.1597 0.65412 0.000 0.008 0.948 0.024 0.020
#> GSM564685 5 0.1043 0.83592 0.000 0.000 0.040 0.000 0.960
#> GSM564686 3 0.3547 0.64952 0.004 0.000 0.836 0.060 0.100
#> GSM564687 3 0.5229 0.47939 0.000 0.064 0.612 0.000 0.324
#> GSM564688 2 0.5215 0.50493 0.000 0.664 0.096 0.000 0.240
#> GSM564689 3 0.6372 0.15209 0.008 0.408 0.456 0.000 0.128
#> GSM564690 2 0.6142 0.30454 0.232 0.580 0.184 0.000 0.004
#> GSM564691 2 0.5213 0.34485 0.000 0.616 0.064 0.000 0.320
#> GSM564692 2 0.4249 0.43853 0.000 0.568 0.432 0.000 0.000
#> GSM564694 3 0.2074 0.67034 0.000 0.000 0.896 0.000 0.104
#> GSM564695 2 0.6049 0.44859 0.000 0.564 0.272 0.000 0.164
#> GSM564696 5 0.3854 0.69457 0.008 0.004 0.180 0.016 0.792
#> GSM564697 3 0.5285 0.28971 0.000 0.356 0.584 0.000 0.060
#> GSM564698 5 0.0963 0.83976 0.000 0.000 0.036 0.000 0.964
#> GSM564700 3 0.3585 0.58880 0.004 0.112 0.840 0.032 0.012
#> GSM564701 2 0.6410 0.33477 0.000 0.488 0.192 0.000 0.320
#> GSM564702 2 0.5671 0.47736 0.000 0.568 0.336 0.000 0.096
#> GSM564703 4 0.4474 0.65504 0.332 0.012 0.004 0.652 0.000
#> GSM564704 4 0.5240 0.55032 0.368 0.032 0.012 0.588 0.000
#> GSM564705 1 0.1082 0.76450 0.964 0.008 0.000 0.028 0.000
#> GSM564706 4 0.5311 0.55810 0.340 0.032 0.008 0.612 0.008
#> GSM564707 1 0.1864 0.74913 0.924 0.004 0.004 0.068 0.000
#> GSM564708 4 0.4277 0.72740 0.208 0.024 0.008 0.756 0.004
#> GSM564709 1 0.4819 0.09802 0.576 0.012 0.008 0.404 0.000
#> GSM564710 1 0.2872 0.73021 0.884 0.072 0.004 0.036 0.004
#> GSM564711 4 0.5262 0.52656 0.376 0.032 0.012 0.580 0.000
#> GSM564712 1 0.1124 0.76501 0.960 0.004 0.000 0.036 0.000
#> GSM564713 4 0.1278 0.77413 0.016 0.020 0.004 0.960 0.000
#> GSM564714 1 0.5283 0.09454 0.572 0.032 0.012 0.384 0.000
#> GSM564715 4 0.4791 0.58530 0.316 0.024 0.008 0.652 0.000
#> GSM564716 4 0.2304 0.74898 0.100 0.000 0.008 0.892 0.000
#> GSM564717 1 0.3718 0.70306 0.784 0.016 0.004 0.196 0.000
#> GSM564718 4 0.3474 0.76413 0.132 0.028 0.008 0.832 0.000
#> GSM564719 1 0.2177 0.74692 0.908 0.008 0.004 0.080 0.000
#> GSM564720 1 0.2911 0.73508 0.852 0.008 0.004 0.136 0.000
#> GSM564721 4 0.4219 0.51171 0.416 0.000 0.000 0.584 0.000
#> GSM564722 1 0.5311 0.10902 0.560 0.032 0.012 0.396 0.000
#> GSM564723 1 0.2411 0.75749 0.884 0.008 0.000 0.108 0.000
#> GSM564724 4 0.3831 0.73743 0.188 0.024 0.004 0.784 0.000
#> GSM564725 4 0.1908 0.74900 0.092 0.000 0.000 0.908 0.000
#> GSM564726 4 0.1018 0.77687 0.016 0.016 0.000 0.968 0.000
#> GSM564727 4 0.2249 0.74996 0.096 0.008 0.000 0.896 0.000
#> GSM564728 4 0.0671 0.77464 0.016 0.000 0.004 0.980 0.000
#> GSM564729 4 0.1341 0.76696 0.056 0.000 0.000 0.944 0.000
#> GSM564730 4 0.5033 0.22492 0.444 0.008 0.004 0.532 0.012
#> GSM564731 4 0.2741 0.77545 0.064 0.032 0.012 0.892 0.000
#> GSM564732 4 0.3607 0.72864 0.244 0.000 0.004 0.752 0.000
#> GSM564733 4 0.3441 0.76733 0.148 0.024 0.004 0.824 0.000
#> GSM564734 4 0.2452 0.78284 0.084 0.016 0.004 0.896 0.000
#> GSM564735 4 0.3888 0.73889 0.176 0.032 0.004 0.788 0.000
#> GSM564736 4 0.1792 0.78269 0.084 0.000 0.000 0.916 0.000
#> GSM564737 1 0.1041 0.76470 0.964 0.004 0.000 0.032 0.000
#> GSM564738 4 0.5190 0.55956 0.352 0.032 0.012 0.604 0.000
#> GSM564739 4 0.3883 0.72984 0.216 0.016 0.004 0.764 0.000
#> GSM564740 4 0.3573 0.76627 0.124 0.032 0.012 0.832 0.000
#> GSM564741 4 0.4986 0.62416 0.308 0.028 0.008 0.652 0.004
#> GSM564742 1 0.5355 -0.04386 0.536 0.032 0.012 0.420 0.000
#> GSM564743 1 0.1202 0.76362 0.960 0.004 0.004 0.032 0.000
#> GSM564744 1 0.2806 0.72353 0.844 0.004 0.000 0.152 0.000
#> GSM564745 4 0.0880 0.77356 0.032 0.000 0.000 0.968 0.000
#> GSM564746 1 0.4970 0.51141 0.624 0.028 0.008 0.340 0.000
#> GSM564747 4 0.4866 0.58174 0.352 0.016 0.012 0.620 0.000
#> GSM564748 4 0.4838 0.62614 0.336 0.028 0.004 0.632 0.000
#> GSM564749 1 0.0955 0.76332 0.968 0.004 0.000 0.028 0.000
#> GSM564750 4 0.2017 0.78183 0.060 0.004 0.004 0.924 0.008
#> GSM564751 4 0.4557 0.63314 0.324 0.012 0.008 0.656 0.000
#> GSM564752 4 0.1369 0.77501 0.028 0.008 0.008 0.956 0.000
#> GSM564753 4 0.4830 0.64131 0.300 0.028 0.004 0.664 0.004
#> GSM564754 4 0.4060 0.62313 0.360 0.000 0.000 0.640 0.000
#> GSM564755 4 0.0609 0.77408 0.020 0.000 0.000 0.980 0.000
#> GSM564756 4 0.3491 0.76256 0.124 0.028 0.012 0.836 0.000
#> GSM564757 4 0.0609 0.77401 0.020 0.000 0.000 0.980 0.000
#> GSM564758 4 0.1831 0.78159 0.076 0.000 0.004 0.920 0.000
#> GSM564759 4 0.4066 0.72647 0.196 0.032 0.004 0.768 0.000
#> GSM564760 4 0.2886 0.76975 0.148 0.000 0.008 0.844 0.000
#> GSM564761 1 0.3196 0.65931 0.804 0.004 0.000 0.192 0.000
#> GSM564762 4 0.3675 0.73088 0.216 0.008 0.004 0.772 0.000
#> GSM564681 2 0.4726 0.45498 0.000 0.580 0.400 0.000 0.020
#> GSM564693 2 0.5103 0.41382 0.000 0.556 0.404 0.000 0.040
#> GSM564646 3 0.1768 0.62295 0.000 0.072 0.924 0.000 0.004
#> GSM564699 3 0.2669 0.67324 0.000 0.000 0.876 0.020 0.104
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0146 0.74397 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564616 5 0.2912 0.70497 0.000 0.000 0.000 0.000 0.784 0.216
#> GSM564617 2 0.5621 -0.03537 0.000 0.460 0.000 0.000 0.148 0.392
#> GSM564618 5 0.3017 0.72511 0.000 0.000 0.020 0.000 0.816 0.164
#> GSM564619 4 0.3276 0.71696 0.132 0.052 0.000 0.816 0.000 0.000
#> GSM564620 4 0.1334 0.75141 0.020 0.032 0.000 0.948 0.000 0.000
#> GSM564621 4 0.1245 0.75436 0.032 0.016 0.000 0.952 0.000 0.000
#> GSM564622 5 0.5860 0.35097 0.000 0.020 0.124 0.000 0.504 0.352
#> GSM564623 4 0.6020 0.09023 0.000 0.012 0.168 0.456 0.000 0.364
#> GSM564624 5 0.3563 0.69109 0.000 0.108 0.000 0.000 0.800 0.092
#> GSM564625 4 0.0405 0.74434 0.008 0.004 0.000 0.988 0.000 0.000
#> GSM564626 4 0.3404 0.67998 0.224 0.016 0.000 0.760 0.000 0.000
#> GSM564627 4 0.5058 0.45049 0.292 0.108 0.000 0.600 0.000 0.000
#> GSM564628 6 0.2069 0.72991 0.000 0.020 0.004 0.000 0.068 0.908
#> GSM564629 4 0.1657 0.75312 0.016 0.056 0.000 0.928 0.000 0.000
#> GSM564630 5 0.5879 0.20566 0.000 0.380 0.028 0.000 0.488 0.104
#> GSM564609 6 0.3053 0.70472 0.000 0.020 0.168 0.000 0.000 0.812
#> GSM564610 1 0.4243 0.71023 0.736 0.132 0.000 0.132 0.000 0.000
#> GSM564611 1 0.3144 0.66773 0.808 0.172 0.000 0.004 0.016 0.000
#> GSM564612 3 0.3860 0.06758 0.000 0.000 0.528 0.000 0.472 0.000
#> GSM564613 3 0.7404 0.00453 0.016 0.348 0.356 0.008 0.052 0.220
#> GSM564614 4 0.0790 0.74633 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564631 3 0.1141 0.86071 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM564632 6 0.2554 0.75617 0.000 0.020 0.088 0.000 0.012 0.880
#> GSM564633 3 0.0405 0.87545 0.000 0.004 0.988 0.000 0.000 0.008
#> GSM564634 3 0.4856 0.60898 0.000 0.080 0.676 0.000 0.016 0.228
#> GSM564635 3 0.0146 0.87548 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564636 6 0.3854 0.23066 0.000 0.000 0.464 0.000 0.000 0.536
#> GSM564637 6 0.2805 0.71806 0.000 0.000 0.184 0.000 0.004 0.812
#> GSM564638 3 0.0146 0.87552 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM564639 3 0.0713 0.86869 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM564640 6 0.2078 0.76381 0.000 0.032 0.040 0.000 0.012 0.916
#> GSM564641 3 0.0520 0.87490 0.000 0.000 0.984 0.000 0.008 0.008
#> GSM564642 6 0.4832 0.52715 0.000 0.004 0.116 0.000 0.208 0.672
#> GSM564643 6 0.2553 0.74400 0.000 0.008 0.144 0.000 0.000 0.848
#> GSM564644 2 0.6239 0.13166 0.000 0.532 0.056 0.000 0.284 0.128
#> GSM564645 3 0.0146 0.87526 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM564647 3 0.2942 0.78198 0.000 0.032 0.836 0.000 0.000 0.132
#> GSM564648 6 0.2830 0.67247 0.000 0.020 0.000 0.000 0.144 0.836
#> GSM564649 3 0.1563 0.85120 0.000 0.000 0.932 0.000 0.012 0.056
#> GSM564650 6 0.4286 0.63049 0.000 0.052 0.028 0.000 0.168 0.752
#> GSM564651 5 0.6359 0.11195 0.000 0.012 0.264 0.000 0.380 0.344
#> GSM564652 5 0.2859 0.72604 0.000 0.000 0.016 0.000 0.828 0.156
#> GSM564653 5 0.3055 0.64141 0.000 0.096 0.000 0.000 0.840 0.064
#> GSM564654 3 0.0790 0.86898 0.000 0.000 0.968 0.000 0.000 0.032
#> GSM564655 6 0.4183 0.05126 0.000 0.000 0.480 0.000 0.012 0.508
#> GSM564656 3 0.0547 0.87337 0.000 0.000 0.980 0.000 0.000 0.020
#> GSM564657 3 0.0146 0.87503 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM564658 2 0.3993 -0.08789 0.000 0.520 0.000 0.000 0.476 0.004
#> GSM564659 3 0.3099 0.79185 0.000 0.012 0.848 0.000 0.096 0.044
#> GSM564660 6 0.3349 0.56399 0.000 0.000 0.008 0.000 0.244 0.748
#> GSM564661 5 0.1053 0.68455 0.000 0.012 0.004 0.000 0.964 0.020
#> GSM564662 3 0.0000 0.87478 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564663 5 0.3076 0.48447 0.000 0.240 0.000 0.000 0.760 0.000
#> GSM564664 2 0.6901 0.15771 0.000 0.496 0.136 0.000 0.216 0.152
#> GSM564665 3 0.4851 0.16306 0.000 0.000 0.536 0.000 0.404 0.060
#> GSM564666 6 0.0405 0.75389 0.000 0.008 0.004 0.000 0.000 0.988
#> GSM564667 3 0.1245 0.86231 0.000 0.016 0.952 0.000 0.000 0.032
#> GSM564668 3 0.2656 0.79756 0.000 0.012 0.860 0.008 0.000 0.120
#> GSM564669 3 0.0363 0.87425 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM564670 6 0.5863 0.15520 0.000 0.016 0.420 0.000 0.124 0.440
#> GSM564671 6 0.3009 0.72965 0.004 0.000 0.052 0.084 0.004 0.856
#> GSM564672 3 0.0000 0.87478 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564673 6 0.2463 0.75815 0.000 0.020 0.068 0.000 0.020 0.892
#> GSM564674 6 0.4387 0.68202 0.000 0.104 0.132 0.000 0.016 0.748
#> GSM564675 6 0.3195 0.72480 0.000 0.036 0.116 0.012 0.000 0.836
#> GSM564676 2 0.6252 0.13165 0.036 0.544 0.012 0.000 0.284 0.124
#> GSM564677 5 0.2048 0.73181 0.000 0.000 0.000 0.000 0.880 0.120
#> GSM564678 2 0.4617 -0.04332 0.024 0.524 0.000 0.000 0.444 0.008
#> GSM564679 5 0.4026 0.26376 0.000 0.376 0.000 0.000 0.612 0.012
#> GSM564680 3 0.0000 0.87478 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564682 2 0.6824 -0.03720 0.000 0.396 0.380 0.000 0.128 0.096
#> GSM564683 3 0.0000 0.87478 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564684 6 0.0508 0.75014 0.000 0.012 0.000 0.004 0.000 0.984
#> GSM564685 3 0.1204 0.85395 0.000 0.000 0.944 0.000 0.000 0.056
#> GSM564686 6 0.2134 0.75391 0.000 0.000 0.052 0.044 0.000 0.904
#> GSM564687 6 0.5161 0.50830 0.000 0.020 0.316 0.000 0.064 0.600
#> GSM564688 5 0.1858 0.68802 0.000 0.000 0.092 0.000 0.904 0.004
#> GSM564689 2 0.6502 0.19021 0.000 0.524 0.068 0.000 0.172 0.236
#> GSM564690 2 0.6431 0.15583 0.168 0.536 0.004 0.000 0.240 0.052
#> GSM564691 2 0.6030 0.10921 0.000 0.528 0.148 0.000 0.296 0.028
#> GSM564692 5 0.3014 0.71556 0.000 0.012 0.000 0.000 0.804 0.184
#> GSM564694 6 0.1141 0.76206 0.000 0.000 0.052 0.000 0.000 0.948
#> GSM564695 5 0.4587 0.62879 0.000 0.000 0.108 0.000 0.688 0.204
#> GSM564696 3 0.4358 0.61557 0.004 0.048 0.716 0.008 0.000 0.224
#> GSM564697 2 0.6248 0.07779 0.000 0.480 0.040 0.000 0.136 0.344
#> GSM564698 3 0.0632 0.87018 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM564700 6 0.2361 0.71517 0.000 0.004 0.000 0.012 0.104 0.880
#> GSM564701 5 0.5680 0.47116 0.000 0.028 0.280 0.000 0.580 0.112
#> GSM564702 5 0.3014 0.72828 0.000 0.000 0.036 0.000 0.832 0.132
#> GSM564703 4 0.5528 0.59455 0.252 0.192 0.000 0.556 0.000 0.000
#> GSM564704 2 0.5800 -0.48103 0.180 0.424 0.000 0.396 0.000 0.000
#> GSM564705 1 0.1204 0.77656 0.944 0.056 0.000 0.000 0.000 0.000
#> GSM564706 4 0.6000 0.45684 0.200 0.368 0.004 0.428 0.000 0.000
#> GSM564707 1 0.3175 0.74454 0.808 0.164 0.000 0.028 0.000 0.000
#> GSM564708 4 0.5086 0.65862 0.104 0.276 0.004 0.616 0.000 0.000
#> GSM564709 1 0.5943 -0.15312 0.404 0.216 0.000 0.380 0.000 0.000
#> GSM564710 1 0.2285 0.75502 0.900 0.028 0.000 0.008 0.064 0.000
#> GSM564711 2 0.5832 -0.46635 0.188 0.428 0.000 0.384 0.000 0.000
#> GSM564712 1 0.1701 0.77493 0.920 0.072 0.000 0.008 0.000 0.000
#> GSM564713 4 0.2333 0.75878 0.024 0.092 0.000 0.884 0.000 0.000
#> GSM564714 2 0.5957 -0.23096 0.344 0.428 0.000 0.228 0.000 0.000
#> GSM564715 4 0.5830 0.50119 0.220 0.296 0.000 0.484 0.000 0.000
#> GSM564716 4 0.3509 0.73193 0.112 0.084 0.000 0.804 0.000 0.000
#> GSM564717 1 0.4797 0.68230 0.664 0.212 0.000 0.124 0.000 0.000
#> GSM564718 4 0.4524 0.66419 0.052 0.320 0.000 0.628 0.000 0.000
#> GSM564719 1 0.3789 0.69854 0.716 0.260 0.000 0.024 0.000 0.000
#> GSM564720 1 0.1908 0.77148 0.916 0.028 0.000 0.056 0.000 0.000
#> GSM564721 4 0.4945 0.52067 0.328 0.084 0.000 0.588 0.000 0.000
#> GSM564722 2 0.6029 -0.25022 0.356 0.396 0.000 0.248 0.000 0.000
#> GSM564723 1 0.2506 0.77987 0.880 0.052 0.000 0.068 0.000 0.000
#> GSM564724 4 0.4449 0.70057 0.088 0.216 0.000 0.696 0.000 0.000
#> GSM564725 4 0.2260 0.72157 0.140 0.000 0.000 0.860 0.000 0.000
#> GSM564726 4 0.1007 0.75448 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM564727 4 0.3088 0.72567 0.120 0.048 0.000 0.832 0.000 0.000
#> GSM564728 4 0.0363 0.74747 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM564729 4 0.0622 0.74799 0.012 0.008 0.000 0.980 0.000 0.000
#> GSM564730 4 0.5503 0.29121 0.400 0.076 0.020 0.504 0.000 0.000
#> GSM564731 4 0.4029 0.69682 0.028 0.292 0.000 0.680 0.000 0.000
#> GSM564732 4 0.3947 0.71993 0.136 0.100 0.000 0.764 0.000 0.000
#> GSM564733 4 0.4376 0.71709 0.084 0.212 0.000 0.704 0.000 0.000
#> GSM564734 4 0.3412 0.75330 0.064 0.128 0.000 0.808 0.000 0.000
#> GSM564735 4 0.4692 0.67405 0.080 0.276 0.000 0.644 0.000 0.000
#> GSM564736 4 0.1649 0.75712 0.032 0.036 0.000 0.932 0.000 0.000
#> GSM564737 1 0.0865 0.77713 0.964 0.036 0.000 0.000 0.000 0.000
#> GSM564738 2 0.5670 -0.47708 0.156 0.452 0.000 0.392 0.000 0.000
#> GSM564739 4 0.4624 0.70469 0.120 0.192 0.000 0.688 0.000 0.000
#> GSM564740 4 0.4396 0.65668 0.036 0.352 0.000 0.612 0.000 0.000
#> GSM564741 4 0.5668 0.56852 0.152 0.328 0.004 0.516 0.000 0.000
#> GSM564742 2 0.5961 -0.26185 0.312 0.444 0.000 0.244 0.000 0.000
#> GSM564743 1 0.3253 0.75183 0.788 0.192 0.000 0.020 0.000 0.000
#> GSM564744 1 0.2361 0.75329 0.884 0.028 0.000 0.088 0.000 0.000
#> GSM564745 4 0.0717 0.75154 0.016 0.008 0.000 0.976 0.000 0.000
#> GSM564746 1 0.5731 0.53900 0.516 0.260 0.000 0.224 0.000 0.000
#> GSM564747 4 0.5774 0.48906 0.176 0.384 0.000 0.440 0.000 0.000
#> GSM564748 4 0.5716 0.56818 0.192 0.304 0.000 0.504 0.000 0.000
#> GSM564749 1 0.2489 0.77629 0.860 0.128 0.000 0.012 0.000 0.000
#> GSM564750 4 0.2203 0.75962 0.016 0.084 0.004 0.896 0.000 0.000
#> GSM564751 4 0.5191 0.64054 0.172 0.212 0.000 0.616 0.000 0.000
#> GSM564752 4 0.2362 0.74446 0.004 0.136 0.000 0.860 0.000 0.000
#> GSM564753 4 0.5635 0.58040 0.152 0.316 0.004 0.528 0.000 0.000
#> GSM564754 4 0.4549 0.64376 0.232 0.088 0.000 0.680 0.000 0.000
#> GSM564755 4 0.0993 0.75359 0.012 0.024 0.000 0.964 0.000 0.000
#> GSM564756 4 0.4446 0.64481 0.040 0.348 0.000 0.612 0.000 0.000
#> GSM564757 4 0.1010 0.75660 0.004 0.036 0.000 0.960 0.000 0.000
#> GSM564758 4 0.1983 0.75638 0.020 0.072 0.000 0.908 0.000 0.000
#> GSM564759 4 0.4892 0.66010 0.100 0.272 0.000 0.628 0.000 0.000
#> GSM564760 4 0.3382 0.75026 0.124 0.048 0.000 0.820 0.000 0.008
#> GSM564761 1 0.2696 0.71218 0.856 0.028 0.000 0.116 0.000 0.000
#> GSM564762 4 0.4174 0.72438 0.084 0.184 0.000 0.732 0.000 0.000
#> GSM564681 5 0.2768 0.72529 0.000 0.000 0.012 0.000 0.832 0.156
#> GSM564693 5 0.3766 0.58650 0.000 0.000 0.012 0.000 0.684 0.304
#> GSM564646 6 0.1584 0.74227 0.000 0.008 0.000 0.000 0.064 0.928
#> GSM564699 6 0.1500 0.76645 0.000 0.000 0.052 0.012 0.000 0.936
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 genotype/variation(p) disease.state(p) k
#> MAD:pam 148 0.87248 0.4321 2
#> MAD:pam 134 0.05835 0.1342 3
#> MAD:pam 122 0.00435 0.3617 4
#> MAD:pam 118 0.10371 0.2254 5
#> MAD:pam 119 0.05389 0.0616 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.797 0.891 0.908 0.2674 0.856 0.711
#> 4 4 0.676 0.809 0.824 0.0881 0.934 0.820
#> 5 5 0.667 0.807 0.848 0.1230 0.880 0.626
#> 6 6 0.682 0.686 0.757 0.0325 0.949 0.790
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
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564616 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564617 2 0.5178 0.8329 0.000 0.744 0.256
#> GSM564618 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564619 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564620 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564621 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564622 2 0.2066 0.8417 0.000 0.940 0.060
#> GSM564623 3 0.1163 0.8603 0.000 0.028 0.972
#> GSM564624 2 0.4750 0.8461 0.000 0.784 0.216
#> GSM564625 1 0.0237 0.9801 0.996 0.004 0.000
#> GSM564626 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564627 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564628 2 0.4974 0.7896 0.000 0.764 0.236
#> GSM564629 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564630 2 0.6235 0.6137 0.000 0.564 0.436
#> GSM564609 3 0.4291 0.8477 0.000 0.180 0.820
#> GSM564610 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564611 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564612 2 0.5178 0.8268 0.000 0.744 0.256
#> GSM564613 3 0.1529 0.8591 0.000 0.040 0.960
#> GSM564614 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564631 3 0.4062 0.8266 0.000 0.164 0.836
#> GSM564632 3 0.4887 0.8147 0.000 0.228 0.772
#> GSM564633 3 0.3752 0.8623 0.000 0.144 0.856
#> GSM564634 3 0.0747 0.8589 0.000 0.016 0.984
#> GSM564635 3 0.4750 0.8177 0.000 0.216 0.784
#> GSM564636 3 0.4002 0.8317 0.000 0.160 0.840
#> GSM564637 3 0.4235 0.8162 0.000 0.176 0.824
#> GSM564638 3 0.3816 0.8386 0.000 0.148 0.852
#> GSM564639 3 0.0000 0.8564 0.000 0.000 1.000
#> GSM564640 2 0.5138 0.8025 0.000 0.748 0.252
#> GSM564641 3 0.2625 0.8651 0.000 0.084 0.916
#> GSM564642 3 0.4555 0.8348 0.000 0.200 0.800
#> GSM564643 3 0.2711 0.8554 0.000 0.088 0.912
#> GSM564644 2 0.4931 0.8410 0.000 0.768 0.232
#> GSM564645 3 0.3619 0.8462 0.000 0.136 0.864
#> GSM564647 3 0.1753 0.8691 0.000 0.048 0.952
#> GSM564648 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564649 3 0.4178 0.8200 0.000 0.172 0.828
#> GSM564650 2 0.4974 0.8404 0.000 0.764 0.236
#> GSM564651 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564652 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564653 2 0.2066 0.8406 0.000 0.940 0.060
#> GSM564654 3 0.4974 0.8067 0.000 0.236 0.764
#> GSM564655 3 0.2165 0.8599 0.000 0.064 0.936
#> GSM564656 3 0.2959 0.8688 0.000 0.100 0.900
#> GSM564657 3 0.4002 0.8312 0.000 0.160 0.840
#> GSM564658 2 0.5016 0.8408 0.000 0.760 0.240
#> GSM564659 3 0.4931 0.7691 0.000 0.232 0.768
#> GSM564660 3 0.5650 0.4752 0.000 0.312 0.688
#> GSM564661 2 0.1964 0.8418 0.000 0.944 0.056
#> GSM564662 3 0.1163 0.8647 0.000 0.028 0.972
#> GSM564663 2 0.4974 0.8404 0.000 0.764 0.236
#> GSM564664 2 0.4291 0.8464 0.000 0.820 0.180
#> GSM564665 3 0.5138 0.7043 0.000 0.252 0.748
#> GSM564666 3 0.0592 0.8584 0.000 0.012 0.988
#> GSM564667 3 0.4002 0.8307 0.000 0.160 0.840
#> GSM564668 3 0.2537 0.8525 0.000 0.080 0.920
#> GSM564669 3 0.2448 0.8531 0.000 0.076 0.924
#> GSM564670 2 0.5497 0.7912 0.000 0.708 0.292
#> GSM564671 3 0.2448 0.8531 0.000 0.076 0.924
#> GSM564672 3 0.4062 0.8266 0.000 0.164 0.836
#> GSM564673 2 0.4605 0.8092 0.000 0.796 0.204
#> GSM564674 2 0.5497 0.7924 0.000 0.708 0.292
#> GSM564675 3 0.2165 0.8576 0.000 0.064 0.936
#> GSM564676 2 0.5760 0.7781 0.000 0.672 0.328
#> GSM564677 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564678 2 0.4931 0.8410 0.000 0.768 0.232
#> GSM564679 2 0.4931 0.8410 0.000 0.768 0.232
#> GSM564680 3 0.3752 0.8416 0.000 0.144 0.856
#> GSM564682 3 0.4796 0.6232 0.000 0.220 0.780
#> GSM564683 3 0.0237 0.8576 0.000 0.004 0.996
#> GSM564684 3 0.2448 0.8531 0.000 0.076 0.924
#> GSM564685 3 0.1860 0.8695 0.000 0.052 0.948
#> GSM564686 3 0.2261 0.8564 0.000 0.068 0.932
#> GSM564687 2 0.6308 -0.0586 0.000 0.508 0.492
#> GSM564688 2 0.2066 0.8406 0.000 0.940 0.060
#> GSM564689 2 0.5058 0.8394 0.000 0.756 0.244
#> GSM564690 2 0.6079 0.7050 0.000 0.612 0.388
#> GSM564691 2 0.4974 0.8404 0.000 0.764 0.236
#> GSM564692 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564694 3 0.2448 0.8531 0.000 0.076 0.924
#> GSM564695 3 0.2448 0.8425 0.000 0.076 0.924
#> GSM564696 3 0.0237 0.8576 0.000 0.004 0.996
#> GSM564697 2 0.5706 0.7868 0.000 0.680 0.320
#> GSM564698 3 0.3482 0.8672 0.000 0.128 0.872
#> GSM564700 3 0.2448 0.8531 0.000 0.076 0.924
#> GSM564701 2 0.3412 0.8511 0.000 0.876 0.124
#> GSM564702 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564703 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564704 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564705 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564706 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564707 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564708 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564709 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564710 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564711 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564712 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564713 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564714 1 0.1163 0.9798 0.972 0.028 0.000
#> GSM564715 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564716 1 0.1643 0.9786 0.956 0.044 0.000
#> GSM564717 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564718 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564719 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564720 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564721 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564722 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564723 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564724 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564725 1 0.1753 0.9782 0.952 0.048 0.000
#> GSM564726 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564727 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564728 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564729 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564730 1 0.1643 0.9786 0.956 0.044 0.000
#> GSM564731 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564732 1 0.0237 0.9801 0.996 0.004 0.000
#> GSM564733 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564734 1 0.0237 0.9801 0.996 0.004 0.000
#> GSM564735 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564736 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564737 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564738 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564739 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564740 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564741 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564742 1 0.1163 0.9798 0.972 0.028 0.000
#> GSM564743 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564744 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564745 1 0.0592 0.9801 0.988 0.012 0.000
#> GSM564746 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564747 1 0.1163 0.9798 0.972 0.028 0.000
#> GSM564748 1 0.0892 0.9804 0.980 0.020 0.000
#> GSM564749 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564750 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564751 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564752 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564753 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564754 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564755 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564756 1 0.1289 0.9797 0.968 0.032 0.000
#> GSM564757 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564758 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564759 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564760 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564761 1 0.1860 0.9777 0.948 0.052 0.000
#> GSM564762 1 0.0000 0.9799 1.000 0.000 0.000
#> GSM564681 2 0.1964 0.8403 0.000 0.944 0.056
#> GSM564693 2 0.3267 0.8500 0.000 0.884 0.116
#> GSM564646 3 0.4121 0.8502 0.000 0.168 0.832
#> GSM564699 3 0.0237 0.8576 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.1902 0.8793 0.932 0.064 0.000 0.004
#> GSM564616 4 0.0469 0.8234 0.000 0.000 0.012 0.988
#> GSM564617 2 0.6320 0.8283 0.000 0.660 0.160 0.180
#> GSM564618 4 0.0336 0.8223 0.000 0.000 0.008 0.992
#> GSM564619 1 0.3801 0.8874 0.780 0.220 0.000 0.000
#> GSM564620 1 0.3688 0.8916 0.792 0.208 0.000 0.000
#> GSM564621 1 0.3801 0.8874 0.780 0.220 0.000 0.000
#> GSM564622 4 0.2805 0.7920 0.000 0.012 0.100 0.888
#> GSM564623 3 0.1452 0.8175 0.000 0.008 0.956 0.036
#> GSM564624 2 0.6773 0.6328 0.000 0.544 0.108 0.348
#> GSM564625 1 0.1978 0.8814 0.928 0.068 0.000 0.004
#> GSM564626 1 0.3942 0.8809 0.764 0.236 0.000 0.000
#> GSM564627 1 0.3873 0.8842 0.772 0.228 0.000 0.000
#> GSM564628 4 0.4514 0.7151 0.000 0.064 0.136 0.800
#> GSM564629 1 0.1743 0.8875 0.940 0.056 0.000 0.004
#> GSM564630 2 0.6549 0.7591 0.000 0.612 0.268 0.120
#> GSM564609 3 0.3525 0.8260 0.000 0.040 0.860 0.100
#> GSM564610 1 0.3873 0.8808 0.772 0.228 0.000 0.000
#> GSM564611 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564612 2 0.6240 0.8303 0.000 0.664 0.136 0.200
#> GSM564613 2 0.5368 0.7584 0.000 0.636 0.340 0.024
#> GSM564614 1 0.1902 0.8793 0.932 0.064 0.000 0.004
#> GSM564631 3 0.4898 0.7523 0.000 0.116 0.780 0.104
#> GSM564632 3 0.4088 0.8016 0.000 0.040 0.820 0.140
#> GSM564633 3 0.3056 0.8292 0.000 0.040 0.888 0.072
#> GSM564634 2 0.5284 0.7252 0.000 0.616 0.368 0.016
#> GSM564635 3 0.3716 0.8076 0.000 0.052 0.852 0.096
#> GSM564636 3 0.5968 0.5645 0.000 0.236 0.672 0.092
#> GSM564637 3 0.6063 0.6105 0.000 0.196 0.680 0.124
#> GSM564638 3 0.4104 0.7983 0.000 0.080 0.832 0.088
#> GSM564639 3 0.1297 0.8312 0.000 0.016 0.964 0.020
#> GSM564640 4 0.7466 -0.2905 0.000 0.388 0.176 0.436
#> GSM564641 3 0.6290 0.1573 0.000 0.364 0.568 0.068
#> GSM564642 3 0.7480 0.1032 0.000 0.276 0.500 0.224
#> GSM564643 3 0.2399 0.8327 0.000 0.032 0.920 0.048
#> GSM564644 2 0.6240 0.8285 0.000 0.664 0.136 0.200
#> GSM564645 3 0.4817 0.7549 0.000 0.128 0.784 0.088
#> GSM564647 2 0.5865 0.7294 0.000 0.612 0.340 0.048
#> GSM564648 4 0.1545 0.8255 0.000 0.008 0.040 0.952
#> GSM564649 3 0.5911 0.6313 0.000 0.196 0.692 0.112
#> GSM564650 2 0.6096 0.8354 0.000 0.680 0.136 0.184
#> GSM564651 4 0.1452 0.8260 0.000 0.008 0.036 0.956
#> GSM564652 4 0.1489 0.8251 0.000 0.004 0.044 0.952
#> GSM564653 4 0.0336 0.8196 0.000 0.000 0.008 0.992
#> GSM564654 3 0.3464 0.8091 0.000 0.032 0.860 0.108
#> GSM564655 3 0.1406 0.8251 0.000 0.024 0.960 0.016
#> GSM564656 3 0.3081 0.8275 0.000 0.048 0.888 0.064
#> GSM564657 2 0.6714 0.5720 0.000 0.540 0.360 0.100
#> GSM564658 2 0.6163 0.8448 0.000 0.676 0.160 0.164
#> GSM564659 3 0.6951 0.4096 0.000 0.140 0.556 0.304
#> GSM564660 2 0.6245 0.8124 0.000 0.648 0.244 0.108
#> GSM564661 4 0.3205 0.7833 0.000 0.024 0.104 0.872
#> GSM564662 3 0.1929 0.8322 0.000 0.036 0.940 0.024
#> GSM564663 2 0.6170 0.8335 0.000 0.672 0.136 0.192
#> GSM564664 4 0.6418 0.4144 0.000 0.216 0.140 0.644
#> GSM564665 3 0.6941 0.4343 0.000 0.220 0.588 0.192
#> GSM564666 3 0.1256 0.8247 0.000 0.008 0.964 0.028
#> GSM564667 2 0.6867 0.3985 0.000 0.484 0.412 0.104
#> GSM564668 3 0.2002 0.8318 0.000 0.020 0.936 0.044
#> GSM564669 3 0.1635 0.8280 0.000 0.008 0.948 0.044
#> GSM564670 2 0.6295 0.8148 0.000 0.656 0.132 0.212
#> GSM564671 3 0.1452 0.8175 0.000 0.008 0.956 0.036
#> GSM564672 3 0.5272 0.7230 0.000 0.136 0.752 0.112
#> GSM564673 4 0.4289 0.7125 0.000 0.032 0.172 0.796
#> GSM564674 2 0.6162 0.8455 0.000 0.676 0.168 0.156
#> GSM564675 3 0.1452 0.8175 0.000 0.008 0.956 0.036
#> GSM564676 2 0.5944 0.8416 0.000 0.684 0.212 0.104
#> GSM564677 4 0.0188 0.8202 0.000 0.000 0.004 0.996
#> GSM564678 2 0.6303 0.8335 0.000 0.660 0.148 0.192
#> GSM564679 2 0.6193 0.8396 0.000 0.672 0.148 0.180
#> GSM564680 3 0.3266 0.8201 0.000 0.040 0.876 0.084
#> GSM564682 2 0.5475 0.7918 0.000 0.656 0.308 0.036
#> GSM564683 3 0.0804 0.8209 0.000 0.008 0.980 0.012
#> GSM564684 3 0.1452 0.8175 0.000 0.008 0.956 0.036
#> GSM564685 3 0.2589 0.8324 0.000 0.044 0.912 0.044
#> GSM564686 3 0.1452 0.8215 0.000 0.008 0.956 0.036
#> GSM564687 4 0.7578 -0.0431 0.000 0.284 0.236 0.480
#> GSM564688 4 0.0188 0.8202 0.000 0.000 0.004 0.996
#> GSM564689 2 0.5994 0.8448 0.000 0.692 0.156 0.152
#> GSM564690 2 0.6056 0.8210 0.000 0.660 0.248 0.092
#> GSM564691 2 0.6205 0.8319 0.000 0.668 0.136 0.196
#> GSM564692 4 0.0188 0.8202 0.000 0.000 0.004 0.996
#> GSM564694 3 0.1677 0.8296 0.000 0.012 0.948 0.040
#> GSM564695 2 0.5417 0.8070 0.000 0.676 0.284 0.040
#> GSM564696 3 0.0804 0.8209 0.000 0.008 0.980 0.012
#> GSM564697 2 0.5982 0.8463 0.000 0.684 0.204 0.112
#> GSM564698 3 0.2751 0.8303 0.000 0.040 0.904 0.056
#> GSM564700 3 0.1635 0.8213 0.000 0.008 0.948 0.044
#> GSM564701 4 0.3803 0.7519 0.000 0.032 0.132 0.836
#> GSM564702 4 0.0188 0.8202 0.000 0.000 0.004 0.996
#> GSM564703 1 0.1118 0.9056 0.964 0.036 0.000 0.000
#> GSM564704 1 0.3356 0.8945 0.824 0.176 0.000 0.000
#> GSM564705 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564706 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564707 1 0.3837 0.8803 0.776 0.224 0.000 0.000
#> GSM564708 1 0.0188 0.9003 0.996 0.004 0.000 0.000
#> GSM564709 1 0.3688 0.8871 0.792 0.208 0.000 0.000
#> GSM564710 1 0.3873 0.8794 0.772 0.228 0.000 0.000
#> GSM564711 1 0.0592 0.9034 0.984 0.016 0.000 0.000
#> GSM564712 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564713 1 0.1902 0.8793 0.932 0.064 0.000 0.004
#> GSM564714 1 0.1716 0.9060 0.936 0.064 0.000 0.000
#> GSM564715 1 0.3528 0.8913 0.808 0.192 0.000 0.000
#> GSM564716 1 0.3688 0.8916 0.792 0.208 0.000 0.000
#> GSM564717 1 0.3726 0.8854 0.788 0.212 0.000 0.000
#> GSM564718 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564719 1 0.3649 0.8884 0.796 0.204 0.000 0.000
#> GSM564720 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564721 1 0.3873 0.8830 0.772 0.228 0.000 0.000
#> GSM564722 1 0.2921 0.9000 0.860 0.140 0.000 0.000
#> GSM564723 1 0.3837 0.8806 0.776 0.224 0.000 0.000
#> GSM564724 1 0.1022 0.8946 0.968 0.032 0.000 0.000
#> GSM564725 1 0.3610 0.8939 0.800 0.200 0.000 0.000
#> GSM564726 1 0.1902 0.8793 0.932 0.064 0.000 0.004
#> GSM564727 1 0.3873 0.8842 0.772 0.228 0.000 0.000
#> GSM564728 1 0.1489 0.8896 0.952 0.044 0.000 0.004
#> GSM564729 1 0.1743 0.8840 0.940 0.056 0.000 0.004
#> GSM564730 1 0.3444 0.8957 0.816 0.184 0.000 0.000
#> GSM564731 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564732 1 0.0469 0.8992 0.988 0.012 0.000 0.000
#> GSM564733 1 0.1661 0.8856 0.944 0.052 0.000 0.004
#> GSM564734 1 0.0469 0.9028 0.988 0.012 0.000 0.000
#> GSM564735 1 0.1109 0.8956 0.968 0.028 0.000 0.004
#> GSM564736 1 0.1743 0.8834 0.940 0.056 0.000 0.004
#> GSM564737 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564738 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564739 1 0.0188 0.9014 0.996 0.004 0.000 0.000
#> GSM564740 1 0.0817 0.8967 0.976 0.024 0.000 0.000
#> GSM564741 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564742 1 0.1716 0.9060 0.936 0.064 0.000 0.000
#> GSM564743 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564744 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564745 1 0.2530 0.9048 0.888 0.112 0.000 0.000
#> GSM564746 1 0.3837 0.8858 0.776 0.224 0.000 0.000
#> GSM564747 1 0.1792 0.9060 0.932 0.068 0.000 0.000
#> GSM564748 1 0.1867 0.9064 0.928 0.072 0.000 0.000
#> GSM564749 1 0.3837 0.8803 0.776 0.224 0.000 0.000
#> GSM564750 1 0.1118 0.8932 0.964 0.036 0.000 0.000
#> GSM564751 1 0.0336 0.9021 0.992 0.008 0.000 0.000
#> GSM564752 1 0.0707 0.8976 0.980 0.020 0.000 0.000
#> GSM564753 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564754 1 0.3400 0.8938 0.820 0.180 0.000 0.000
#> GSM564755 1 0.1902 0.8793 0.932 0.064 0.000 0.004
#> GSM564756 1 0.2081 0.9061 0.916 0.084 0.000 0.000
#> GSM564757 1 0.0921 0.8956 0.972 0.028 0.000 0.000
#> GSM564758 1 0.0592 0.8993 0.984 0.016 0.000 0.000
#> GSM564759 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564760 1 0.1302 0.8928 0.956 0.044 0.000 0.000
#> GSM564761 1 0.3873 0.8787 0.772 0.228 0.000 0.000
#> GSM564762 1 0.0000 0.9005 1.000 0.000 0.000 0.000
#> GSM564681 4 0.0188 0.8202 0.000 0.000 0.004 0.996
#> GSM564693 4 0.3427 0.7775 0.000 0.028 0.112 0.860
#> GSM564646 3 0.3587 0.8254 0.000 0.040 0.856 0.104
#> GSM564699 3 0.0927 0.8267 0.000 0.008 0.976 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.2326 0.8922 0.044 0.020 0.000 0.916 0.020
#> GSM564616 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564617 2 0.4334 0.8192 0.000 0.764 0.080 0.000 0.156
#> GSM564618 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564619 1 0.2377 0.8922 0.872 0.000 0.000 0.128 0.000
#> GSM564620 1 0.2690 0.8777 0.844 0.000 0.000 0.156 0.000
#> GSM564621 1 0.2424 0.8939 0.868 0.000 0.000 0.132 0.000
#> GSM564622 5 0.3159 0.8086 0.000 0.056 0.088 0.000 0.856
#> GSM564623 3 0.3277 0.8385 0.008 0.148 0.832 0.000 0.012
#> GSM564624 2 0.5128 0.7285 0.000 0.656 0.076 0.000 0.268
#> GSM564625 4 0.2805 0.8929 0.072 0.020 0.000 0.888 0.020
#> GSM564626 1 0.1851 0.8995 0.912 0.000 0.000 0.088 0.000
#> GSM564627 1 0.2179 0.8993 0.888 0.000 0.000 0.112 0.000
#> GSM564628 5 0.4889 0.6494 0.000 0.136 0.144 0.000 0.720
#> GSM564629 4 0.4365 0.7369 0.212 0.020 0.000 0.748 0.020
#> GSM564630 2 0.3765 0.7756 0.004 0.820 0.112 0.000 0.064
#> GSM564609 3 0.1981 0.8581 0.000 0.048 0.924 0.000 0.028
#> GSM564610 1 0.1851 0.9024 0.912 0.000 0.000 0.088 0.000
#> GSM564611 1 0.0963 0.9009 0.964 0.000 0.000 0.036 0.000
#> GSM564612 2 0.4680 0.8294 0.000 0.740 0.128 0.000 0.132
#> GSM564613 2 0.3550 0.7330 0.004 0.760 0.236 0.000 0.000
#> GSM564614 4 0.2170 0.8872 0.036 0.020 0.000 0.924 0.020
#> GSM564631 3 0.2653 0.8219 0.000 0.096 0.880 0.000 0.024
#> GSM564632 3 0.2983 0.8348 0.000 0.056 0.868 0.000 0.076
#> GSM564633 3 0.1018 0.8547 0.000 0.016 0.968 0.000 0.016
#> GSM564634 2 0.3635 0.7090 0.004 0.748 0.248 0.000 0.000
#> GSM564635 3 0.1648 0.8471 0.000 0.040 0.940 0.000 0.020
#> GSM564636 3 0.2616 0.8230 0.000 0.100 0.880 0.000 0.020
#> GSM564637 3 0.3409 0.7839 0.000 0.144 0.824 0.000 0.032
#> GSM564638 3 0.2172 0.8350 0.000 0.076 0.908 0.000 0.016
#> GSM564639 3 0.1205 0.8553 0.004 0.040 0.956 0.000 0.000
#> GSM564640 2 0.6173 0.3175 0.000 0.468 0.136 0.000 0.396
#> GSM564641 3 0.3635 0.6495 0.000 0.248 0.748 0.000 0.004
#> GSM564642 3 0.6736 -0.0381 0.000 0.312 0.412 0.000 0.276
#> GSM564643 3 0.2172 0.8562 0.000 0.076 0.908 0.000 0.016
#> GSM564644 2 0.4587 0.8242 0.000 0.744 0.096 0.000 0.160
#> GSM564645 3 0.2130 0.8371 0.000 0.080 0.908 0.000 0.012
#> GSM564647 2 0.4147 0.6726 0.000 0.676 0.316 0.000 0.008
#> GSM564648 5 0.1364 0.8591 0.000 0.036 0.012 0.000 0.952
#> GSM564649 3 0.3165 0.8003 0.000 0.116 0.848 0.000 0.036
#> GSM564650 2 0.4559 0.8283 0.000 0.748 0.100 0.000 0.152
#> GSM564651 5 0.1281 0.8596 0.000 0.032 0.012 0.000 0.956
#> GSM564652 5 0.1281 0.8595 0.000 0.032 0.012 0.000 0.956
#> GSM564653 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564654 3 0.1997 0.8488 0.000 0.036 0.924 0.000 0.040
#> GSM564655 3 0.2536 0.8474 0.004 0.128 0.868 0.000 0.000
#> GSM564656 3 0.0798 0.8528 0.000 0.008 0.976 0.000 0.016
#> GSM564657 3 0.4666 0.1528 0.000 0.412 0.572 0.000 0.016
#> GSM564658 2 0.3339 0.8074 0.000 0.836 0.040 0.000 0.124
#> GSM564659 3 0.5493 0.5557 0.000 0.112 0.632 0.000 0.256
#> GSM564660 2 0.4584 0.7740 0.000 0.716 0.228 0.000 0.056
#> GSM564661 5 0.3176 0.8148 0.000 0.080 0.064 0.000 0.856
#> GSM564662 3 0.1952 0.8601 0.004 0.084 0.912 0.000 0.000
#> GSM564663 2 0.4535 0.8305 0.000 0.752 0.108 0.000 0.140
#> GSM564664 5 0.5757 0.2431 0.000 0.336 0.104 0.000 0.560
#> GSM564665 3 0.5027 0.6518 0.000 0.188 0.700 0.000 0.112
#> GSM564666 3 0.2964 0.8423 0.004 0.152 0.840 0.000 0.004
#> GSM564667 3 0.4651 0.3011 0.000 0.372 0.608 0.000 0.020
#> GSM564668 3 0.2351 0.8533 0.000 0.088 0.896 0.000 0.016
#> GSM564669 3 0.2568 0.8507 0.004 0.092 0.888 0.000 0.016
#> GSM564670 2 0.5032 0.8070 0.000 0.704 0.128 0.000 0.168
#> GSM564671 3 0.3201 0.8348 0.008 0.132 0.844 0.000 0.016
#> GSM564672 3 0.2769 0.8220 0.000 0.092 0.876 0.000 0.032
#> GSM564673 5 0.4199 0.7069 0.000 0.056 0.180 0.000 0.764
#> GSM564674 2 0.4855 0.8099 0.000 0.720 0.168 0.000 0.112
#> GSM564675 3 0.3099 0.8353 0.008 0.132 0.848 0.000 0.012
#> GSM564676 2 0.3003 0.8180 0.000 0.864 0.044 0.000 0.092
#> GSM564677 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564678 2 0.3413 0.8080 0.000 0.832 0.044 0.000 0.124
#> GSM564679 2 0.3339 0.8074 0.000 0.836 0.040 0.000 0.124
#> GSM564680 3 0.1211 0.8485 0.000 0.024 0.960 0.000 0.016
#> GSM564682 2 0.3300 0.7731 0.004 0.792 0.204 0.000 0.000
#> GSM564683 3 0.2488 0.8433 0.004 0.124 0.872 0.000 0.000
#> GSM564684 3 0.2886 0.8439 0.004 0.116 0.864 0.000 0.016
#> GSM564685 3 0.0912 0.8534 0.000 0.016 0.972 0.000 0.012
#> GSM564686 3 0.2575 0.8490 0.004 0.100 0.884 0.000 0.012
#> GSM564687 5 0.6417 0.2160 0.000 0.264 0.228 0.000 0.508
#> GSM564688 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564689 2 0.3255 0.8237 0.000 0.848 0.052 0.000 0.100
#> GSM564690 2 0.2694 0.8146 0.000 0.884 0.040 0.000 0.076
#> GSM564691 2 0.4509 0.8271 0.000 0.752 0.096 0.000 0.152
#> GSM564692 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564694 3 0.2464 0.8564 0.004 0.092 0.892 0.000 0.012
#> GSM564695 2 0.3395 0.7566 0.000 0.764 0.236 0.000 0.000
#> GSM564696 3 0.2536 0.8414 0.004 0.128 0.868 0.000 0.000
#> GSM564697 2 0.3758 0.8387 0.000 0.816 0.088 0.000 0.096
#> GSM564698 3 0.0912 0.8531 0.000 0.016 0.972 0.000 0.012
#> GSM564700 3 0.2784 0.8474 0.004 0.108 0.872 0.000 0.016
#> GSM564701 5 0.3471 0.7918 0.000 0.072 0.092 0.000 0.836
#> GSM564702 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564703 4 0.3438 0.8545 0.172 0.020 0.000 0.808 0.000
#> GSM564704 1 0.3183 0.8456 0.828 0.016 0.000 0.156 0.000
#> GSM564705 1 0.0880 0.8994 0.968 0.000 0.000 0.032 0.000
#> GSM564706 4 0.2632 0.8936 0.072 0.040 0.000 0.888 0.000
#> GSM564707 1 0.1270 0.9046 0.948 0.000 0.000 0.052 0.000
#> GSM564708 4 0.2144 0.8887 0.068 0.020 0.000 0.912 0.000
#> GSM564709 1 0.1851 0.9035 0.912 0.000 0.000 0.088 0.000
#> GSM564710 1 0.1197 0.8952 0.952 0.000 0.000 0.048 0.000
#> GSM564711 4 0.2927 0.8913 0.092 0.040 0.000 0.868 0.000
#> GSM564712 1 0.0794 0.8974 0.972 0.000 0.000 0.028 0.000
#> GSM564713 4 0.1820 0.8854 0.020 0.020 0.000 0.940 0.020
#> GSM564714 4 0.3691 0.8407 0.156 0.040 0.000 0.804 0.000
#> GSM564715 1 0.2818 0.8748 0.856 0.012 0.000 0.132 0.000
#> GSM564716 1 0.3003 0.8419 0.812 0.000 0.000 0.188 0.000
#> GSM564717 1 0.2513 0.8866 0.876 0.008 0.000 0.116 0.000
#> GSM564718 4 0.2595 0.8971 0.080 0.032 0.000 0.888 0.000
#> GSM564719 1 0.2361 0.8944 0.892 0.012 0.000 0.096 0.000
#> GSM564720 1 0.0963 0.9009 0.964 0.000 0.000 0.036 0.000
#> GSM564721 1 0.1732 0.9040 0.920 0.000 0.000 0.080 0.000
#> GSM564722 1 0.4779 0.4852 0.628 0.032 0.000 0.340 0.000
#> GSM564723 1 0.1043 0.9020 0.960 0.000 0.000 0.040 0.000
#> GSM564724 4 0.1195 0.8958 0.028 0.000 0.000 0.960 0.012
#> GSM564725 1 0.3086 0.8530 0.816 0.000 0.000 0.180 0.004
#> GSM564726 4 0.1820 0.8800 0.020 0.020 0.000 0.940 0.020
#> GSM564727 1 0.2127 0.8946 0.892 0.000 0.000 0.108 0.000
#> GSM564728 4 0.2400 0.8942 0.048 0.020 0.000 0.912 0.020
#> GSM564729 4 0.2472 0.8969 0.052 0.020 0.000 0.908 0.020
#> GSM564730 1 0.4171 0.3396 0.604 0.000 0.000 0.396 0.000
#> GSM564731 4 0.2616 0.8964 0.076 0.036 0.000 0.888 0.000
#> GSM564732 4 0.2518 0.9013 0.080 0.008 0.000 0.896 0.016
#> GSM564733 4 0.2416 0.8945 0.060 0.016 0.000 0.908 0.016
#> GSM564734 4 0.3559 0.8424 0.176 0.012 0.000 0.804 0.008
#> GSM564735 4 0.2100 0.9002 0.048 0.012 0.000 0.924 0.016
#> GSM564736 4 0.1815 0.8889 0.024 0.016 0.000 0.940 0.020
#> GSM564737 1 0.0880 0.8994 0.968 0.000 0.000 0.032 0.000
#> GSM564738 4 0.2632 0.8962 0.072 0.040 0.000 0.888 0.000
#> GSM564739 4 0.2864 0.8932 0.112 0.024 0.000 0.864 0.000
#> GSM564740 4 0.1774 0.9035 0.052 0.016 0.000 0.932 0.000
#> GSM564741 4 0.2570 0.8972 0.084 0.028 0.000 0.888 0.000
#> GSM564742 4 0.3608 0.8476 0.148 0.040 0.000 0.812 0.000
#> GSM564743 1 0.0963 0.9009 0.964 0.000 0.000 0.036 0.000
#> GSM564744 1 0.0703 0.8949 0.976 0.000 0.000 0.024 0.000
#> GSM564745 4 0.3766 0.7239 0.268 0.000 0.000 0.728 0.004
#> GSM564746 1 0.2377 0.8947 0.872 0.000 0.000 0.128 0.000
#> GSM564747 4 0.5028 0.1902 0.444 0.032 0.000 0.524 0.000
#> GSM564748 4 0.4620 0.5890 0.320 0.028 0.000 0.652 0.000
#> GSM564749 1 0.0963 0.9009 0.964 0.000 0.000 0.036 0.000
#> GSM564750 4 0.1041 0.9007 0.032 0.004 0.000 0.964 0.000
#> GSM564751 4 0.3283 0.8701 0.140 0.028 0.000 0.832 0.000
#> GSM564752 4 0.1469 0.9017 0.036 0.016 0.000 0.948 0.000
#> GSM564753 4 0.2504 0.8936 0.064 0.040 0.000 0.896 0.000
#> GSM564754 1 0.2411 0.8955 0.884 0.008 0.000 0.108 0.000
#> GSM564755 4 0.2002 0.8853 0.028 0.020 0.000 0.932 0.020
#> GSM564756 1 0.4557 0.3615 0.584 0.012 0.000 0.404 0.000
#> GSM564757 4 0.1883 0.9031 0.048 0.008 0.000 0.932 0.012
#> GSM564758 4 0.1197 0.8890 0.048 0.000 0.000 0.952 0.000
#> GSM564759 4 0.2632 0.8936 0.072 0.040 0.000 0.888 0.000
#> GSM564760 4 0.2270 0.8947 0.076 0.000 0.000 0.904 0.020
#> GSM564761 1 0.0880 0.8986 0.968 0.000 0.000 0.032 0.000
#> GSM564762 4 0.3096 0.8926 0.108 0.024 0.000 0.860 0.008
#> GSM564681 5 0.0609 0.8592 0.000 0.020 0.000 0.000 0.980
#> GSM564693 5 0.3130 0.8063 0.000 0.048 0.096 0.000 0.856
#> GSM564646 3 0.2790 0.8508 0.000 0.068 0.880 0.000 0.052
#> GSM564699 3 0.2179 0.8537 0.004 0.100 0.896 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.1713 0.8175 0.000 0.044 0.000 0.928 0.000 NA
#> GSM564616 5 0.0405 0.8320 0.000 0.004 0.008 0.000 0.988 NA
#> GSM564617 2 0.4537 0.7226 0.000 0.748 0.108 0.000 0.112 NA
#> GSM564618 5 0.0767 0.8324 0.000 0.008 0.012 0.000 0.976 NA
#> GSM564619 1 0.2689 0.8695 0.876 0.004 0.000 0.080 0.004 NA
#> GSM564620 1 0.4762 0.4993 0.604 0.004 0.000 0.344 0.004 NA
#> GSM564621 1 0.3733 0.8217 0.784 0.004 0.000 0.164 0.004 NA
#> GSM564622 5 0.2677 0.7590 0.000 0.016 0.084 0.000 0.876 NA
#> GSM564623 3 0.3103 0.6265 0.000 0.036 0.848 0.000 0.016 NA
#> GSM564624 2 0.4758 0.2806 0.000 0.500 0.032 0.000 0.460 NA
#> GSM564625 4 0.2009 0.8103 0.004 0.040 0.000 0.916 0.000 NA
#> GSM564626 1 0.1810 0.8765 0.932 0.008 0.000 0.036 0.004 NA
#> GSM564627 1 0.3237 0.8591 0.836 0.004 0.000 0.108 0.004 NA
#> GSM564628 5 0.6774 -0.0825 0.000 0.124 0.348 0.000 0.432 NA
#> GSM564629 4 0.3893 0.7636 0.124 0.044 0.000 0.796 0.000 NA
#> GSM564630 2 0.5728 0.5763 0.000 0.604 0.248 0.000 0.048 NA
#> GSM564609 3 0.2420 0.6852 0.000 0.004 0.888 0.000 0.032 NA
#> GSM564610 1 0.2344 0.8789 0.896 0.004 0.000 0.048 0.000 NA
#> GSM564611 1 0.1391 0.8738 0.944 0.016 0.000 0.000 0.000 NA
#> GSM564612 2 0.7028 0.5325 0.000 0.464 0.156 0.000 0.132 NA
#> GSM564613 3 0.5264 0.2398 0.000 0.304 0.588 0.000 0.008 NA
#> GSM564614 4 0.1863 0.8178 0.000 0.044 0.000 0.920 0.000 NA
#> GSM564631 3 0.4955 0.5794 0.000 0.056 0.520 0.000 0.004 NA
#> GSM564632 3 0.4461 0.6573 0.000 0.020 0.736 0.000 0.076 NA
#> GSM564633 3 0.2482 0.6855 0.000 0.004 0.848 0.000 0.000 NA
#> GSM564634 3 0.4633 0.4570 0.000 0.188 0.704 0.000 0.008 NA
#> GSM564635 3 0.4521 0.6068 0.000 0.028 0.568 0.000 0.004 NA
#> GSM564636 3 0.4703 0.5932 0.000 0.048 0.544 0.000 0.000 NA
#> GSM564637 3 0.5271 0.5749 0.000 0.068 0.512 0.000 0.012 NA
#> GSM564638 3 0.4603 0.5942 0.000 0.040 0.544 0.000 0.000 NA
#> GSM564639 3 0.2112 0.6898 0.000 0.016 0.896 0.000 0.000 NA
#> GSM564640 2 0.6656 0.3503 0.000 0.412 0.360 0.000 0.176 NA
#> GSM564641 3 0.4968 0.6112 0.000 0.068 0.576 0.000 0.004 NA
#> GSM564642 3 0.5105 0.5629 0.000 0.124 0.708 0.000 0.064 NA
#> GSM564643 3 0.2022 0.6790 0.000 0.008 0.916 0.000 0.024 NA
#> GSM564644 2 0.4616 0.6669 0.000 0.684 0.084 0.000 0.228 NA
#> GSM564645 3 0.4709 0.5920 0.000 0.048 0.540 0.000 0.000 NA
#> GSM564647 3 0.4780 0.5209 0.000 0.180 0.692 0.000 0.008 NA
#> GSM564648 5 0.1086 0.8297 0.000 0.012 0.012 0.000 0.964 NA
#> GSM564649 3 0.5052 0.5741 0.000 0.064 0.512 0.000 0.004 NA
#> GSM564650 2 0.5583 0.7108 0.000 0.660 0.108 0.000 0.156 NA
#> GSM564651 5 0.1059 0.8303 0.000 0.016 0.016 0.000 0.964 NA
#> GSM564652 5 0.0984 0.8312 0.000 0.012 0.012 0.000 0.968 NA
#> GSM564653 5 0.0520 0.8302 0.000 0.008 0.008 0.000 0.984 NA
#> GSM564654 3 0.4887 0.6083 0.000 0.020 0.572 0.000 0.032 NA
#> GSM564655 3 0.2182 0.6592 0.000 0.020 0.904 0.000 0.008 NA
#> GSM564656 3 0.2473 0.6852 0.000 0.008 0.856 0.000 0.000 NA
#> GSM564657 3 0.5137 0.5657 0.000 0.072 0.508 0.000 0.004 NA
#> GSM564658 2 0.2670 0.6928 0.000 0.872 0.040 0.000 0.084 NA
#> GSM564659 3 0.6465 0.4640 0.000 0.048 0.452 0.000 0.152 NA
#> GSM564660 3 0.6295 -0.1145 0.000 0.376 0.464 0.000 0.076 NA
#> GSM564661 5 0.3258 0.7302 0.000 0.092 0.064 0.000 0.836 NA
#> GSM564662 3 0.4180 0.6294 0.000 0.024 0.628 0.000 0.000 NA
#> GSM564663 2 0.5248 0.6853 0.000 0.672 0.084 0.000 0.196 NA
#> GSM564664 2 0.6152 0.5217 0.000 0.496 0.156 0.000 0.320 NA
#> GSM564665 3 0.6378 0.4883 0.000 0.088 0.476 0.000 0.084 NA
#> GSM564666 3 0.2778 0.6416 0.000 0.032 0.872 0.000 0.016 NA
#> GSM564667 3 0.5288 0.5659 0.000 0.088 0.504 0.000 0.004 NA
#> GSM564668 3 0.1952 0.6732 0.000 0.012 0.920 0.000 0.016 NA
#> GSM564669 3 0.1820 0.6760 0.000 0.016 0.928 0.000 0.012 NA
#> GSM564670 2 0.6662 0.4446 0.000 0.452 0.328 0.000 0.152 NA
#> GSM564671 3 0.2933 0.6245 0.000 0.032 0.860 0.000 0.016 NA
#> GSM564672 3 0.5004 0.5768 0.000 0.060 0.516 0.000 0.004 NA
#> GSM564673 5 0.5430 0.1693 0.000 0.024 0.368 0.000 0.540 NA
#> GSM564674 2 0.6013 0.4391 0.000 0.508 0.356 0.000 0.068 NA
#> GSM564675 3 0.3005 0.6262 0.000 0.036 0.856 0.000 0.016 NA
#> GSM564676 2 0.2685 0.6916 0.000 0.872 0.044 0.000 0.080 NA
#> GSM564677 5 0.0405 0.8317 0.000 0.004 0.008 0.000 0.988 NA
#> GSM564678 2 0.2579 0.6945 0.000 0.872 0.040 0.000 0.088 NA
#> GSM564679 2 0.2579 0.6898 0.000 0.876 0.032 0.000 0.088 NA
#> GSM564680 3 0.4254 0.6080 0.000 0.020 0.576 0.000 0.000 NA
#> GSM564682 2 0.5337 0.2220 0.000 0.472 0.448 0.000 0.016 NA
#> GSM564683 3 0.2222 0.6490 0.000 0.012 0.896 0.000 0.008 NA
#> GSM564684 3 0.2508 0.6391 0.000 0.016 0.884 0.000 0.016 NA
#> GSM564685 3 0.2623 0.6865 0.000 0.016 0.852 0.000 0.000 NA
#> GSM564686 3 0.2317 0.6522 0.000 0.020 0.900 0.000 0.016 NA
#> GSM564687 3 0.7295 0.1528 0.000 0.172 0.424 0.000 0.236 NA
#> GSM564688 5 0.0520 0.8302 0.000 0.008 0.008 0.000 0.984 NA
#> GSM564689 2 0.2956 0.7132 0.000 0.848 0.064 0.000 0.088 NA
#> GSM564690 2 0.3951 0.7197 0.000 0.796 0.100 0.000 0.076 NA
#> GSM564691 2 0.5960 0.6718 0.000 0.616 0.084 0.000 0.184 NA
#> GSM564692 5 0.0405 0.8317 0.000 0.004 0.008 0.000 0.988 NA
#> GSM564694 3 0.1887 0.6723 0.000 0.012 0.924 0.000 0.016 NA
#> GSM564695 3 0.5386 0.2234 0.000 0.312 0.580 0.000 0.016 NA
#> GSM564696 3 0.2262 0.6482 0.000 0.016 0.896 0.000 0.008 NA
#> GSM564697 2 0.4730 0.7237 0.000 0.728 0.148 0.000 0.088 NA
#> GSM564698 3 0.2431 0.6879 0.000 0.008 0.860 0.000 0.000 NA
#> GSM564700 3 0.2317 0.6496 0.000 0.020 0.900 0.000 0.016 NA
#> GSM564701 5 0.4866 0.5147 0.000 0.140 0.136 0.000 0.704 NA
#> GSM564702 5 0.0405 0.8317 0.000 0.004 0.008 0.000 0.988 NA
#> GSM564703 4 0.3843 0.7898 0.104 0.004 0.000 0.784 0.000 NA
#> GSM564704 1 0.4613 0.6908 0.696 0.004 0.000 0.200 0.000 NA
#> GSM564705 1 0.0951 0.8757 0.968 0.020 0.000 0.004 0.000 NA
#> GSM564706 4 0.4681 0.7217 0.020 0.016 0.000 0.604 0.004 NA
#> GSM564707 1 0.0653 0.8810 0.980 0.004 0.000 0.012 0.000 NA
#> GSM564708 4 0.3075 0.8213 0.016 0.012 0.000 0.840 0.004 NA
#> GSM564709 1 0.2066 0.8737 0.908 0.000 0.000 0.052 0.000 NA
#> GSM564710 1 0.2649 0.8600 0.880 0.012 0.000 0.036 0.000 NA
#> GSM564711 4 0.4691 0.7306 0.024 0.016 0.000 0.620 0.004 NA
#> GSM564712 1 0.1262 0.8763 0.956 0.020 0.000 0.008 0.000 NA
#> GSM564713 4 0.1934 0.8111 0.000 0.040 0.000 0.916 0.000 NA
#> GSM564714 4 0.5205 0.7041 0.056 0.016 0.000 0.580 0.004 NA
#> GSM564715 1 0.3835 0.7987 0.772 0.004 0.000 0.164 0.000 NA
#> GSM564716 4 0.5064 -0.0514 0.460 0.004 0.000 0.480 0.004 NA
#> GSM564717 1 0.3649 0.8151 0.800 0.004 0.000 0.112 0.000 NA
#> GSM564718 4 0.3700 0.8030 0.020 0.016 0.000 0.784 0.004 NA
#> GSM564719 1 0.3996 0.7998 0.776 0.008 0.000 0.104 0.000 NA
#> GSM564720 1 0.1785 0.8764 0.928 0.016 0.000 0.008 0.000 NA
#> GSM564721 1 0.2507 0.8711 0.884 0.004 0.000 0.072 0.000 NA
#> GSM564722 4 0.6177 0.4249 0.296 0.012 0.000 0.464 0.000 NA
#> GSM564723 1 0.1251 0.8784 0.956 0.012 0.000 0.008 0.000 NA
#> GSM564724 4 0.1333 0.8260 0.000 0.008 0.000 0.944 0.000 NA
#> GSM564725 1 0.3791 0.7957 0.760 0.008 0.000 0.200 0.000 NA
#> GSM564726 4 0.1865 0.8194 0.000 0.040 0.000 0.920 0.000 NA
#> GSM564727 1 0.3751 0.8233 0.788 0.004 0.000 0.152 0.004 NA
#> GSM564728 4 0.1713 0.8126 0.000 0.044 0.000 0.928 0.000 NA
#> GSM564729 4 0.1863 0.8182 0.000 0.044 0.000 0.920 0.000 NA
#> GSM564730 1 0.4841 0.6385 0.648 0.004 0.000 0.260 0.000 NA
#> GSM564731 4 0.3542 0.8086 0.020 0.012 0.000 0.796 0.004 NA
#> GSM564732 4 0.2133 0.8186 0.020 0.016 0.000 0.912 0.000 NA
#> GSM564733 4 0.2122 0.8098 0.008 0.040 0.000 0.912 0.000 NA
#> GSM564734 4 0.3750 0.7826 0.104 0.004 0.000 0.800 0.004 NA
#> GSM564735 4 0.1856 0.8230 0.000 0.032 0.000 0.920 0.000 NA
#> GSM564736 4 0.1794 0.8185 0.000 0.040 0.000 0.924 0.000 NA
#> GSM564737 1 0.0951 0.8757 0.968 0.020 0.000 0.004 0.000 NA
#> GSM564738 4 0.4458 0.7560 0.020 0.016 0.000 0.664 0.004 NA
#> GSM564739 4 0.3596 0.8108 0.036 0.016 0.000 0.812 0.004 NA
#> GSM564740 4 0.3779 0.7566 0.008 0.008 0.000 0.708 0.000 NA
#> GSM564741 4 0.3078 0.8163 0.020 0.016 0.000 0.848 0.004 NA
#> GSM564742 4 0.5245 0.7032 0.060 0.016 0.000 0.580 0.004 NA
#> GSM564743 1 0.1692 0.8769 0.932 0.012 0.000 0.008 0.000 NA
#> GSM564744 1 0.0717 0.8741 0.976 0.016 0.000 0.000 0.000 NA
#> GSM564745 4 0.4730 0.5990 0.232 0.004 0.000 0.680 0.004 NA
#> GSM564746 1 0.3517 0.8457 0.812 0.004 0.000 0.128 0.004 NA
#> GSM564747 4 0.5549 0.6743 0.200 0.016 0.000 0.624 0.004 NA
#> GSM564748 4 0.4322 0.7711 0.116 0.004 0.000 0.748 0.004 NA
#> GSM564749 1 0.1785 0.8764 0.928 0.016 0.000 0.008 0.000 NA
#> GSM564750 4 0.1493 0.8270 0.004 0.004 0.000 0.936 0.000 NA
#> GSM564751 4 0.3641 0.8088 0.052 0.012 0.000 0.812 0.004 NA
#> GSM564752 4 0.3650 0.7566 0.000 0.012 0.000 0.708 0.000 NA
#> GSM564753 4 0.4656 0.7270 0.020 0.016 0.000 0.612 0.004 NA
#> GSM564754 1 0.2586 0.8690 0.880 0.008 0.000 0.080 0.000 NA
#> GSM564755 4 0.2070 0.8080 0.000 0.044 0.000 0.908 0.000 NA
#> GSM564756 4 0.5397 0.4828 0.304 0.008 0.000 0.584 0.004 NA
#> GSM564757 4 0.1232 0.8229 0.004 0.024 0.000 0.956 0.000 NA
#> GSM564758 4 0.1806 0.8253 0.000 0.004 0.000 0.908 0.000 NA
#> GSM564759 4 0.4669 0.7241 0.020 0.016 0.000 0.608 0.004 NA
#> GSM564760 4 0.2283 0.8058 0.020 0.020 0.000 0.904 0.000 NA
#> GSM564761 1 0.1851 0.8732 0.928 0.012 0.000 0.024 0.000 NA
#> GSM564762 4 0.2823 0.8222 0.028 0.020 0.000 0.876 0.004 NA
#> GSM564681 5 0.0405 0.8317 0.000 0.004 0.008 0.000 0.988 NA
#> GSM564693 5 0.3554 0.6977 0.000 0.028 0.112 0.000 0.820 NA
#> GSM564646 3 0.2944 0.6687 0.000 0.004 0.856 0.000 0.068 NA
#> GSM564699 3 0.2001 0.6604 0.000 0.012 0.912 0.000 0.008 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 genotype/variation(p) disease.state(p) k
#> MAD:mclust 154 0.925 0.4759 2
#> MAD:mclust 152 0.168 0.1854 3
#> MAD:mclust 146 0.327 0.0374 4
#> MAD:mclust 144 0.270 0.1799 5
#> MAD:mclust 136 0.481 0.1648 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 154 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 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.774 0.856 0.935 0.2713 0.806 0.634
#> 4 4 0.607 0.622 0.777 0.1373 0.807 0.535
#> 5 5 0.617 0.669 0.810 0.0815 0.841 0.516
#> 6 6 0.613 0.560 0.758 0.0413 0.899 0.607
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564616 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564617 2 0.3619 0.828 0.000 0.864 0.136
#> GSM564618 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564619 3 0.4931 0.714 0.232 0.000 0.768
#> GSM564620 1 0.2878 0.846 0.904 0.000 0.096
#> GSM564621 1 0.4842 0.684 0.776 0.000 0.224
#> GSM564622 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564623 2 0.2796 0.882 0.092 0.908 0.000
#> GSM564624 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564625 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564626 3 0.2878 0.834 0.096 0.000 0.904
#> GSM564627 3 0.3941 0.791 0.156 0.000 0.844
#> GSM564628 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564629 1 0.4291 0.748 0.820 0.000 0.180
#> GSM564630 3 0.4702 0.698 0.000 0.212 0.788
#> GSM564609 2 0.1643 0.927 0.044 0.956 0.000
#> GSM564610 3 0.3116 0.826 0.108 0.000 0.892
#> GSM564611 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564612 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564613 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564614 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564631 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564632 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564633 2 0.4654 0.745 0.208 0.792 0.000
#> GSM564634 2 0.3340 0.851 0.000 0.880 0.120
#> GSM564635 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564636 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564637 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564638 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564639 2 0.3116 0.867 0.108 0.892 0.000
#> GSM564640 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564641 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564642 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564643 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564644 2 0.3116 0.865 0.000 0.892 0.108
#> GSM564645 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564647 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564648 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564649 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564650 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564651 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564652 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564653 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564654 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564655 2 0.0424 0.954 0.008 0.992 0.000
#> GSM564656 2 0.5968 0.438 0.364 0.636 0.000
#> GSM564657 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564658 3 0.5560 0.546 0.000 0.300 0.700
#> GSM564659 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564660 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564661 2 0.1643 0.928 0.000 0.956 0.044
#> GSM564662 2 0.0237 0.957 0.004 0.996 0.000
#> GSM564663 2 0.0424 0.954 0.000 0.992 0.008
#> GSM564664 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564665 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564666 2 0.1860 0.921 0.052 0.948 0.000
#> GSM564667 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564668 2 0.1753 0.924 0.048 0.952 0.000
#> GSM564669 2 0.0592 0.952 0.012 0.988 0.000
#> GSM564670 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564671 1 0.4654 0.671 0.792 0.208 0.000
#> GSM564672 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564673 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564674 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564675 1 0.5327 0.576 0.728 0.272 0.000
#> GSM564676 3 0.5431 0.579 0.000 0.284 0.716
#> GSM564677 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564678 3 0.2796 0.808 0.000 0.092 0.908
#> GSM564679 3 0.3038 0.799 0.000 0.104 0.896
#> GSM564680 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564682 2 0.4654 0.735 0.000 0.792 0.208
#> GSM564683 1 0.3941 0.740 0.844 0.156 0.000
#> GSM564684 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564685 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564686 2 0.4750 0.733 0.216 0.784 0.000
#> GSM564687 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564688 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564689 2 0.6299 0.104 0.000 0.524 0.476
#> GSM564690 3 0.0237 0.865 0.000 0.004 0.996
#> GSM564691 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564692 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564694 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564695 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564696 1 0.6724 0.252 0.568 0.420 0.012
#> GSM564697 2 0.1964 0.918 0.000 0.944 0.056
#> GSM564698 2 0.2448 0.898 0.076 0.924 0.000
#> GSM564700 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564701 2 0.0424 0.954 0.000 0.992 0.008
#> GSM564702 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564703 1 0.1411 0.892 0.964 0.000 0.036
#> GSM564704 3 0.6305 0.013 0.484 0.000 0.516
#> GSM564705 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564706 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564707 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564708 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564709 3 0.0424 0.865 0.008 0.000 0.992
#> GSM564710 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564711 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564712 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564713 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564714 1 0.3686 0.816 0.860 0.000 0.140
#> GSM564715 3 0.5363 0.636 0.276 0.000 0.724
#> GSM564716 1 0.1031 0.899 0.976 0.000 0.024
#> GSM564717 3 0.1753 0.855 0.048 0.000 0.952
#> GSM564718 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564719 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564720 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564721 3 0.1860 0.854 0.052 0.000 0.948
#> GSM564722 3 0.4842 0.718 0.224 0.000 0.776
#> GSM564723 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564724 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564725 1 0.6192 0.191 0.580 0.000 0.420
#> GSM564726 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564727 3 0.6180 0.360 0.416 0.000 0.584
#> GSM564728 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564729 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564730 3 0.5948 0.492 0.360 0.000 0.640
#> GSM564731 1 0.0237 0.909 0.996 0.000 0.004
#> GSM564732 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564733 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564734 1 0.2066 0.881 0.940 0.000 0.060
#> GSM564735 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564736 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564737 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564738 1 0.0237 0.909 0.996 0.000 0.004
#> GSM564739 1 0.2261 0.877 0.932 0.000 0.068
#> GSM564740 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564741 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564742 1 0.2448 0.872 0.924 0.000 0.076
#> GSM564743 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564744 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564745 1 0.3619 0.817 0.864 0.000 0.136
#> GSM564746 3 0.4062 0.785 0.164 0.000 0.836
#> GSM564747 1 0.4750 0.729 0.784 0.000 0.216
#> GSM564748 1 0.5948 0.472 0.640 0.000 0.360
#> GSM564749 3 0.0000 0.867 0.000 0.000 1.000
#> GSM564750 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564751 1 0.3340 0.833 0.880 0.000 0.120
#> GSM564752 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564753 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564754 3 0.3116 0.825 0.108 0.000 0.892
#> GSM564755 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564756 1 0.5291 0.649 0.732 0.000 0.268
#> GSM564757 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564758 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564759 1 0.0000 0.910 1.000 0.000 0.000
#> GSM564760 1 0.0237 0.909 0.996 0.000 0.004
#> GSM564761 3 0.0237 0.866 0.004 0.000 0.996
#> GSM564762 1 0.0237 0.909 0.996 0.000 0.004
#> GSM564681 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564693 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564646 2 0.0000 0.959 0.000 1.000 0.000
#> GSM564699 2 0.5560 0.582 0.300 0.700 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.0188 0.6831 0.996 0.000 0.004 0.000
#> GSM564616 2 0.2586 0.8269 0.004 0.900 0.092 0.004
#> GSM564617 4 0.6213 0.5196 0.008 0.280 0.068 0.644
#> GSM564618 2 0.1356 0.8711 0.008 0.960 0.032 0.000
#> GSM564619 1 0.5237 0.5685 0.628 0.000 0.356 0.016
#> GSM564620 1 0.3401 0.6956 0.840 0.000 0.152 0.008
#> GSM564621 1 0.5057 0.5860 0.648 0.000 0.340 0.012
#> GSM564622 2 0.1716 0.8579 0.000 0.936 0.064 0.000
#> GSM564623 2 0.3812 0.7659 0.140 0.832 0.028 0.000
#> GSM564624 2 0.1356 0.8748 0.008 0.960 0.032 0.000
#> GSM564625 1 0.2589 0.7006 0.884 0.000 0.116 0.000
#> GSM564626 1 0.5452 0.5557 0.616 0.000 0.360 0.024
#> GSM564627 4 0.7677 0.1556 0.372 0.000 0.216 0.412
#> GSM564628 2 0.1284 0.8735 0.024 0.964 0.012 0.000
#> GSM564629 1 0.3570 0.6677 0.860 0.000 0.048 0.092
#> GSM564630 4 0.6892 0.6631 0.072 0.124 0.116 0.688
#> GSM564609 2 0.1042 0.8763 0.008 0.972 0.020 0.000
#> GSM564610 4 0.4834 0.6693 0.120 0.000 0.096 0.784
#> GSM564611 4 0.1302 0.7219 0.000 0.000 0.044 0.956
#> GSM564612 2 0.0817 0.8735 0.000 0.976 0.024 0.000
#> GSM564613 4 0.7777 0.1025 0.000 0.316 0.260 0.424
#> GSM564614 1 0.0817 0.6840 0.976 0.000 0.024 0.000
#> GSM564631 2 0.4999 -0.0502 0.000 0.508 0.492 0.000
#> GSM564632 2 0.0336 0.8744 0.000 0.992 0.008 0.000
#> GSM564633 2 0.6274 0.4735 0.152 0.664 0.184 0.000
#> GSM564634 3 0.7622 0.3319 0.000 0.280 0.472 0.248
#> GSM564635 2 0.3249 0.7852 0.008 0.852 0.140 0.000
#> GSM564636 2 0.3219 0.7687 0.000 0.836 0.164 0.000
#> GSM564637 2 0.1118 0.8708 0.000 0.964 0.036 0.000
#> GSM564638 3 0.5807 0.4649 0.044 0.344 0.612 0.000
#> GSM564639 3 0.7250 0.5989 0.220 0.236 0.544 0.000
#> GSM564640 2 0.0188 0.8755 0.000 0.996 0.004 0.000
#> GSM564641 3 0.4925 0.2663 0.000 0.428 0.572 0.000
#> GSM564642 2 0.0469 0.8758 0.000 0.988 0.012 0.000
#> GSM564643 2 0.1297 0.8739 0.020 0.964 0.016 0.000
#> GSM564644 2 0.3542 0.8017 0.000 0.852 0.028 0.120
#> GSM564645 2 0.4585 0.4786 0.000 0.668 0.332 0.000
#> GSM564647 2 0.2868 0.8052 0.000 0.864 0.136 0.000
#> GSM564648 2 0.0921 0.8737 0.000 0.972 0.028 0.000
#> GSM564649 2 0.2973 0.7940 0.000 0.856 0.144 0.000
#> GSM564650 2 0.0707 0.8738 0.000 0.980 0.020 0.000
#> GSM564651 2 0.1792 0.8544 0.000 0.932 0.068 0.000
#> GSM564652 2 0.3870 0.6946 0.004 0.788 0.208 0.000
#> GSM564653 2 0.1256 0.8732 0.000 0.964 0.028 0.008
#> GSM564654 2 0.0817 0.8756 0.000 0.976 0.024 0.000
#> GSM564655 2 0.4372 0.6050 0.004 0.728 0.268 0.000
#> GSM564656 3 0.7702 0.5407 0.260 0.288 0.452 0.000
#> GSM564657 3 0.5168 0.0628 0.000 0.492 0.504 0.004
#> GSM564658 4 0.3377 0.6633 0.000 0.140 0.012 0.848
#> GSM564659 2 0.0817 0.8748 0.000 0.976 0.024 0.000
#> GSM564660 2 0.0707 0.8738 0.000 0.980 0.020 0.000
#> GSM564661 2 0.3577 0.7556 0.000 0.832 0.156 0.012
#> GSM564662 3 0.5582 0.3491 0.024 0.400 0.576 0.000
#> GSM564663 2 0.2413 0.8549 0.000 0.916 0.020 0.064
#> GSM564664 2 0.0804 0.8776 0.000 0.980 0.008 0.012
#> GSM564665 2 0.1118 0.8708 0.000 0.964 0.036 0.000
#> GSM564666 2 0.7200 -0.2141 0.056 0.456 0.452 0.036
#> GSM564667 3 0.4989 0.1298 0.000 0.472 0.528 0.000
#> GSM564668 2 0.1833 0.8703 0.024 0.944 0.032 0.000
#> GSM564669 2 0.1209 0.8763 0.004 0.964 0.032 0.000
#> GSM564670 2 0.1388 0.8741 0.000 0.960 0.028 0.012
#> GSM564671 1 0.5137 0.4212 0.716 0.244 0.040 0.000
#> GSM564672 2 0.2081 0.8487 0.000 0.916 0.084 0.000
#> GSM564673 2 0.1022 0.8754 0.000 0.968 0.032 0.000
#> GSM564674 2 0.0592 0.8744 0.000 0.984 0.016 0.000
#> GSM564675 2 0.7519 -0.2299 0.392 0.424 0.184 0.000
#> GSM564676 4 0.0804 0.7103 0.000 0.008 0.012 0.980
#> GSM564677 2 0.2011 0.8448 0.000 0.920 0.080 0.000
#> GSM564678 4 0.1042 0.7122 0.000 0.020 0.008 0.972
#> GSM564679 4 0.3710 0.6315 0.000 0.192 0.004 0.804
#> GSM564680 2 0.2921 0.8005 0.000 0.860 0.140 0.000
#> GSM564682 4 0.6873 0.3039 0.000 0.160 0.252 0.588
#> GSM564683 3 0.4889 0.6178 0.360 0.004 0.636 0.000
#> GSM564684 2 0.0376 0.8768 0.004 0.992 0.004 0.000
#> GSM564685 3 0.6356 0.5117 0.084 0.320 0.596 0.000
#> GSM564686 2 0.7450 -0.0409 0.280 0.504 0.216 0.000
#> GSM564687 2 0.0336 0.8754 0.000 0.992 0.008 0.000
#> GSM564688 2 0.1211 0.8701 0.000 0.960 0.040 0.000
#> GSM564689 4 0.1510 0.7063 0.000 0.028 0.016 0.956
#> GSM564690 4 0.0376 0.7121 0.000 0.004 0.004 0.992
#> GSM564691 2 0.2596 0.8446 0.000 0.908 0.024 0.068
#> GSM564692 2 0.0469 0.8752 0.000 0.988 0.012 0.000
#> GSM564694 2 0.0817 0.8745 0.000 0.976 0.024 0.000
#> GSM564695 2 0.0921 0.8730 0.000 0.972 0.028 0.000
#> GSM564696 3 0.6510 0.6272 0.276 0.028 0.640 0.056
#> GSM564697 4 0.5237 0.4048 0.000 0.356 0.016 0.628
#> GSM564698 2 0.6753 0.3298 0.164 0.608 0.228 0.000
#> GSM564700 2 0.0779 0.8768 0.016 0.980 0.004 0.000
#> GSM564701 2 0.2048 0.8558 0.000 0.928 0.064 0.008
#> GSM564702 2 0.0921 0.8732 0.000 0.972 0.028 0.000
#> GSM564703 1 0.5158 0.3902 0.524 0.000 0.472 0.004
#> GSM564704 1 0.6425 0.4679 0.604 0.000 0.096 0.300
#> GSM564705 4 0.5898 0.5939 0.048 0.000 0.348 0.604
#> GSM564706 3 0.4843 0.6026 0.396 0.000 0.604 0.000
#> GSM564707 4 0.7245 0.4483 0.164 0.000 0.324 0.512
#> GSM564708 1 0.4008 0.3695 0.756 0.000 0.244 0.000
#> GSM564709 4 0.7913 0.0525 0.316 0.000 0.324 0.360
#> GSM564710 4 0.6887 0.5277 0.116 0.000 0.356 0.528
#> GSM564711 3 0.4843 0.6026 0.396 0.000 0.604 0.000
#> GSM564712 4 0.7119 0.4681 0.140 0.000 0.352 0.508
#> GSM564713 1 0.1211 0.6614 0.960 0.000 0.040 0.000
#> GSM564714 3 0.6302 0.3412 0.068 0.000 0.564 0.368
#> GSM564715 1 0.6729 0.5311 0.588 0.000 0.284 0.128
#> GSM564716 1 0.4283 0.6515 0.740 0.000 0.256 0.004
#> GSM564717 4 0.0927 0.7169 0.016 0.000 0.008 0.976
#> GSM564718 3 0.4907 0.5787 0.420 0.000 0.580 0.000
#> GSM564719 4 0.1792 0.6852 0.000 0.000 0.068 0.932
#> GSM564720 4 0.2593 0.7186 0.016 0.000 0.080 0.904
#> GSM564721 1 0.6249 0.5122 0.580 0.000 0.352 0.068
#> GSM564722 4 0.4964 0.4741 0.032 0.000 0.244 0.724
#> GSM564723 4 0.5272 0.6357 0.032 0.000 0.288 0.680
#> GSM564724 1 0.2011 0.6301 0.920 0.000 0.080 0.000
#> GSM564725 1 0.4889 0.5769 0.636 0.000 0.360 0.004
#> GSM564726 1 0.1389 0.6542 0.952 0.000 0.048 0.000
#> GSM564727 1 0.5403 0.5666 0.628 0.000 0.348 0.024
#> GSM564728 1 0.1389 0.6538 0.952 0.000 0.048 0.000
#> GSM564729 1 0.2149 0.7010 0.912 0.000 0.088 0.000
#> GSM564730 1 0.5127 0.5725 0.632 0.000 0.356 0.012
#> GSM564731 3 0.4933 0.5600 0.432 0.000 0.568 0.000
#> GSM564732 1 0.1211 0.6648 0.960 0.000 0.040 0.000
#> GSM564733 1 0.1637 0.6946 0.940 0.000 0.060 0.000
#> GSM564734 1 0.2699 0.6987 0.904 0.000 0.068 0.028
#> GSM564735 1 0.4008 0.3296 0.756 0.000 0.244 0.000
#> GSM564736 1 0.1716 0.6422 0.936 0.000 0.064 0.000
#> GSM564737 4 0.6023 0.5875 0.056 0.000 0.344 0.600
#> GSM564738 3 0.4843 0.6026 0.396 0.000 0.604 0.000
#> GSM564739 1 0.5992 0.5308 0.516 0.000 0.444 0.040
#> GSM564740 1 0.5004 -0.1912 0.604 0.000 0.392 0.004
#> GSM564741 3 0.4907 0.5762 0.420 0.000 0.580 0.000
#> GSM564742 3 0.6715 0.5844 0.252 0.000 0.604 0.144
#> GSM564743 4 0.1118 0.7212 0.000 0.000 0.036 0.964
#> GSM564744 4 0.5898 0.5939 0.048 0.000 0.348 0.604
#> GSM564745 1 0.4855 0.5830 0.644 0.000 0.352 0.004
#> GSM564746 4 0.5265 0.6362 0.160 0.000 0.092 0.748
#> GSM564747 3 0.6520 0.5636 0.364 0.000 0.552 0.084
#> GSM564748 3 0.6678 0.3425 0.240 0.000 0.612 0.148
#> GSM564749 4 0.0817 0.7195 0.000 0.000 0.024 0.976
#> GSM564750 1 0.3764 0.4263 0.784 0.000 0.216 0.000
#> GSM564751 1 0.5444 0.1519 0.560 0.000 0.424 0.016
#> GSM564752 3 0.4972 0.5254 0.456 0.000 0.544 0.000
#> GSM564753 3 0.4843 0.6026 0.396 0.000 0.604 0.000
#> GSM564754 1 0.7369 0.4370 0.512 0.000 0.292 0.196
#> GSM564755 1 0.1302 0.6788 0.956 0.000 0.044 0.000
#> GSM564756 1 0.5848 0.6257 0.684 0.000 0.228 0.088
#> GSM564757 1 0.1716 0.6436 0.936 0.000 0.064 0.000
#> GSM564758 1 0.2345 0.6080 0.900 0.000 0.100 0.000
#> GSM564759 3 0.4843 0.6026 0.396 0.000 0.604 0.000
#> GSM564760 1 0.1118 0.6933 0.964 0.000 0.036 0.000
#> GSM564761 1 0.7943 0.1606 0.392 0.004 0.360 0.244
#> GSM564762 1 0.1792 0.6403 0.932 0.000 0.068 0.000
#> GSM564681 2 0.0188 0.8748 0.000 0.996 0.004 0.000
#> GSM564693 2 0.0469 0.8758 0.000 0.988 0.012 0.000
#> GSM564646 2 0.0188 0.8754 0.000 0.996 0.004 0.000
#> GSM564699 3 0.7337 0.5960 0.272 0.204 0.524 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0162 0.8373 0.004 0.000 0.000 0.996 0.000
#> GSM564616 5 0.2172 0.7912 0.016 0.076 0.000 0.000 0.908
#> GSM564617 2 0.3873 0.7097 0.024 0.808 0.012 0.004 0.152
#> GSM564618 5 0.2251 0.8008 0.024 0.052 0.008 0.000 0.916
#> GSM564619 1 0.4029 0.5792 0.680 0.004 0.000 0.316 0.000
#> GSM564620 4 0.1082 0.8349 0.008 0.028 0.000 0.964 0.000
#> GSM564621 4 0.1251 0.8275 0.036 0.008 0.000 0.956 0.000
#> GSM564622 5 0.1347 0.8004 0.008 0.008 0.020 0.004 0.960
#> GSM564623 4 0.6926 0.3206 0.004 0.260 0.024 0.524 0.188
#> GSM564624 5 0.3511 0.7447 0.004 0.184 0.012 0.000 0.800
#> GSM564625 4 0.0404 0.8370 0.012 0.000 0.000 0.988 0.000
#> GSM564626 1 0.2771 0.7424 0.860 0.012 0.000 0.128 0.000
#> GSM564627 4 0.6333 0.2167 0.176 0.328 0.000 0.496 0.000
#> GSM564628 5 0.3246 0.7756 0.000 0.120 0.008 0.024 0.848
#> GSM564629 4 0.1732 0.8204 0.000 0.080 0.000 0.920 0.000
#> GSM564630 2 0.4557 0.7126 0.104 0.772 0.000 0.012 0.112
#> GSM564609 5 0.1774 0.7952 0.000 0.000 0.052 0.016 0.932
#> GSM564610 2 0.4421 0.6514 0.184 0.748 0.000 0.068 0.000
#> GSM564611 1 0.4430 0.2343 0.628 0.360 0.012 0.000 0.000
#> GSM564612 5 0.3631 0.7758 0.000 0.104 0.072 0.000 0.824
#> GSM564613 2 0.3704 0.7173 0.000 0.820 0.088 0.000 0.092
#> GSM564614 4 0.0290 0.8371 0.008 0.000 0.000 0.992 0.000
#> GSM564631 3 0.2439 0.7598 0.000 0.004 0.876 0.000 0.120
#> GSM564632 5 0.0609 0.7981 0.000 0.000 0.020 0.000 0.980
#> GSM564633 3 0.3906 0.5835 0.000 0.000 0.704 0.004 0.292
#> GSM564634 5 0.7517 0.0736 0.036 0.280 0.336 0.000 0.348
#> GSM564635 5 0.4287 0.1845 0.000 0.000 0.460 0.000 0.540
#> GSM564636 5 0.3526 0.7933 0.000 0.072 0.096 0.000 0.832
#> GSM564637 5 0.2719 0.8020 0.000 0.048 0.068 0.000 0.884
#> GSM564638 3 0.2678 0.7595 0.000 0.016 0.880 0.004 0.100
#> GSM564639 3 0.2354 0.7755 0.000 0.008 0.904 0.012 0.076
#> GSM564640 5 0.1282 0.7982 0.000 0.044 0.004 0.000 0.952
#> GSM564641 3 0.3366 0.7034 0.000 0.032 0.828 0.000 0.140
#> GSM564642 5 0.1731 0.7982 0.008 0.012 0.040 0.000 0.940
#> GSM564643 5 0.3327 0.7255 0.000 0.000 0.028 0.144 0.828
#> GSM564644 5 0.4637 0.7642 0.064 0.088 0.060 0.000 0.788
#> GSM564645 3 0.2230 0.7635 0.000 0.000 0.884 0.000 0.116
#> GSM564647 5 0.4752 0.7378 0.000 0.092 0.184 0.000 0.724
#> GSM564648 5 0.0609 0.7966 0.000 0.000 0.020 0.000 0.980
#> GSM564649 5 0.4350 0.3829 0.000 0.004 0.408 0.000 0.588
#> GSM564650 5 0.3612 0.7033 0.000 0.228 0.008 0.000 0.764
#> GSM564651 5 0.3897 0.6731 0.204 0.000 0.028 0.000 0.768
#> GSM564652 5 0.3883 0.6609 0.216 0.000 0.016 0.004 0.764
#> GSM564653 5 0.4084 0.4847 0.328 0.000 0.004 0.000 0.668
#> GSM564654 5 0.1965 0.7845 0.000 0.000 0.096 0.000 0.904
#> GSM564655 3 0.4684 0.1242 0.008 0.004 0.536 0.000 0.452
#> GSM564656 3 0.2818 0.7582 0.000 0.000 0.856 0.012 0.132
#> GSM564657 3 0.1831 0.7690 0.000 0.004 0.920 0.000 0.076
#> GSM564658 2 0.5361 0.6783 0.100 0.696 0.016 0.000 0.188
#> GSM564659 5 0.3090 0.7901 0.000 0.104 0.040 0.000 0.856
#> GSM564660 5 0.3403 0.7594 0.000 0.160 0.012 0.008 0.820
#> GSM564661 5 0.4350 0.2979 0.408 0.004 0.000 0.000 0.588
#> GSM564662 3 0.1544 0.7757 0.000 0.000 0.932 0.000 0.068
#> GSM564663 5 0.4042 0.7148 0.000 0.212 0.032 0.000 0.756
#> GSM564664 5 0.2568 0.7851 0.064 0.012 0.024 0.000 0.900
#> GSM564665 5 0.1768 0.7930 0.000 0.004 0.072 0.000 0.924
#> GSM564666 5 0.6206 0.4412 0.000 0.344 0.080 0.028 0.548
#> GSM564667 3 0.1357 0.7745 0.000 0.004 0.948 0.000 0.048
#> GSM564668 5 0.2580 0.7898 0.016 0.000 0.064 0.020 0.900
#> GSM564669 5 0.3790 0.6224 0.000 0.004 0.272 0.000 0.724
#> GSM564670 5 0.4082 0.7635 0.008 0.140 0.056 0.000 0.796
#> GSM564671 4 0.4155 0.6445 0.004 0.000 0.024 0.744 0.228
#> GSM564672 5 0.4415 0.4387 0.000 0.008 0.388 0.000 0.604
#> GSM564673 5 0.1492 0.7962 0.008 0.000 0.040 0.004 0.948
#> GSM564674 5 0.2464 0.7841 0.000 0.096 0.016 0.000 0.888
#> GSM564675 4 0.4945 0.6815 0.000 0.092 0.048 0.764 0.096
#> GSM564676 2 0.3463 0.7356 0.124 0.836 0.008 0.000 0.032
#> GSM564677 5 0.0671 0.7992 0.016 0.004 0.000 0.000 0.980
#> GSM564678 2 0.3620 0.7461 0.112 0.832 0.008 0.000 0.048
#> GSM564679 2 0.5268 0.6603 0.112 0.668 0.000 0.000 0.220
#> GSM564680 3 0.3561 0.6230 0.000 0.000 0.740 0.000 0.260
#> GSM564682 2 0.4291 0.7191 0.028 0.796 0.128 0.000 0.048
#> GSM564683 3 0.0451 0.7676 0.000 0.000 0.988 0.008 0.004
#> GSM564684 4 0.4995 0.3521 0.000 0.028 0.004 0.584 0.384
#> GSM564685 3 0.1410 0.7768 0.000 0.000 0.940 0.000 0.060
#> GSM564686 5 0.5982 0.4337 0.000 0.060 0.032 0.332 0.576
#> GSM564687 5 0.2305 0.7839 0.000 0.092 0.012 0.000 0.896
#> GSM564688 5 0.1579 0.7963 0.032 0.000 0.024 0.000 0.944
#> GSM564689 2 0.2300 0.7494 0.024 0.904 0.000 0.000 0.072
#> GSM564690 2 0.3693 0.7282 0.156 0.808 0.004 0.000 0.032
#> GSM564691 5 0.5411 0.3664 0.004 0.392 0.052 0.000 0.552
#> GSM564692 5 0.0613 0.7983 0.008 0.004 0.004 0.000 0.984
#> GSM564694 5 0.2822 0.7971 0.000 0.064 0.036 0.012 0.888
#> GSM564695 5 0.3401 0.7841 0.000 0.096 0.064 0.000 0.840
#> GSM564696 3 0.0510 0.7637 0.000 0.016 0.984 0.000 0.000
#> GSM564697 2 0.4839 0.4974 0.004 0.668 0.040 0.000 0.288
#> GSM564698 3 0.3496 0.7075 0.000 0.000 0.788 0.012 0.200
#> GSM564700 4 0.4738 0.2973 0.000 0.012 0.004 0.564 0.420
#> GSM564701 1 0.4648 0.0718 0.524 0.000 0.012 0.000 0.464
#> GSM564702 5 0.1864 0.7864 0.068 0.004 0.004 0.000 0.924
#> GSM564703 1 0.6063 0.3554 0.540 0.000 0.316 0.144 0.000
#> GSM564704 4 0.6860 0.0456 0.380 0.152 0.024 0.444 0.000
#> GSM564705 1 0.0671 0.7613 0.980 0.016 0.000 0.004 0.000
#> GSM564706 3 0.2824 0.7427 0.000 0.020 0.864 0.116 0.000
#> GSM564707 1 0.2172 0.7544 0.916 0.060 0.004 0.020 0.000
#> GSM564708 3 0.3817 0.6616 0.004 0.004 0.740 0.252 0.000
#> GSM564709 1 0.1981 0.7619 0.924 0.048 0.000 0.028 0.000
#> GSM564710 1 0.1300 0.7672 0.956 0.016 0.000 0.028 0.000
#> GSM564711 3 0.4290 0.5590 0.000 0.016 0.680 0.304 0.000
#> GSM564712 1 0.0290 0.7614 0.992 0.008 0.000 0.000 0.000
#> GSM564713 4 0.1267 0.8371 0.012 0.004 0.024 0.960 0.000
#> GSM564714 2 0.5097 0.0188 0.012 0.496 0.476 0.016 0.000
#> GSM564715 1 0.3355 0.7244 0.804 0.012 0.000 0.184 0.000
#> GSM564716 4 0.1670 0.8204 0.052 0.012 0.000 0.936 0.000
#> GSM564717 2 0.4208 0.6926 0.156 0.788 0.032 0.024 0.000
#> GSM564718 4 0.4313 0.3831 0.000 0.008 0.356 0.636 0.000
#> GSM564719 2 0.3678 0.6956 0.140 0.816 0.040 0.004 0.000
#> GSM564720 2 0.4560 0.1611 0.484 0.508 0.008 0.000 0.000
#> GSM564721 1 0.2230 0.7570 0.884 0.000 0.000 0.116 0.000
#> GSM564722 2 0.3986 0.6963 0.068 0.828 0.036 0.068 0.000
#> GSM564723 1 0.1043 0.7501 0.960 0.040 0.000 0.000 0.000
#> GSM564724 4 0.2462 0.7860 0.008 0.000 0.112 0.880 0.000
#> GSM564725 1 0.4047 0.5760 0.676 0.004 0.000 0.320 0.000
#> GSM564726 4 0.0451 0.8379 0.000 0.004 0.008 0.988 0.000
#> GSM564727 4 0.3355 0.7060 0.184 0.012 0.000 0.804 0.000
#> GSM564728 4 0.0324 0.8381 0.000 0.004 0.004 0.992 0.000
#> GSM564729 4 0.0510 0.8361 0.016 0.000 0.000 0.984 0.000
#> GSM564730 1 0.3990 0.6025 0.688 0.004 0.000 0.308 0.000
#> GSM564731 3 0.4516 0.3064 0.004 0.004 0.576 0.416 0.000
#> GSM564732 4 0.0290 0.8374 0.008 0.000 0.000 0.992 0.000
#> GSM564733 4 0.0963 0.8302 0.036 0.000 0.000 0.964 0.000
#> GSM564734 4 0.1369 0.8341 0.028 0.008 0.008 0.956 0.000
#> GSM564735 4 0.1830 0.8209 0.000 0.008 0.068 0.924 0.000
#> GSM564736 4 0.1329 0.8357 0.008 0.004 0.032 0.956 0.000
#> GSM564737 1 0.0404 0.7599 0.988 0.012 0.000 0.000 0.000
#> GSM564738 3 0.3821 0.6897 0.000 0.020 0.764 0.216 0.000
#> GSM564739 1 0.4166 0.6888 0.788 0.008 0.148 0.056 0.000
#> GSM564740 4 0.2771 0.7827 0.000 0.128 0.012 0.860 0.000
#> GSM564741 3 0.2646 0.7446 0.004 0.004 0.868 0.124 0.000
#> GSM564742 3 0.3154 0.7340 0.008 0.088 0.864 0.040 0.000
#> GSM564743 2 0.3048 0.6916 0.176 0.820 0.004 0.000 0.000
#> GSM564744 1 0.0771 0.7609 0.976 0.020 0.000 0.004 0.000
#> GSM564745 4 0.3123 0.7061 0.184 0.004 0.000 0.812 0.000
#> GSM564746 2 0.5038 0.6096 0.164 0.704 0.000 0.132 0.000
#> GSM564747 3 0.4187 0.7267 0.040 0.048 0.812 0.100 0.000
#> GSM564748 3 0.5539 0.3710 0.348 0.048 0.588 0.016 0.000
#> GSM564749 1 0.4306 0.3274 0.660 0.328 0.012 0.000 0.000
#> GSM564750 4 0.3816 0.5133 0.000 0.000 0.304 0.696 0.000
#> GSM564751 3 0.5990 0.3682 0.324 0.028 0.580 0.068 0.000
#> GSM564752 4 0.2723 0.7758 0.000 0.012 0.124 0.864 0.000
#> GSM564753 3 0.2548 0.7467 0.004 0.004 0.876 0.116 0.000
#> GSM564754 1 0.3142 0.7553 0.856 0.032 0.004 0.108 0.000
#> GSM564755 4 0.0324 0.8382 0.000 0.004 0.004 0.992 0.000
#> GSM564756 4 0.3219 0.7389 0.136 0.020 0.004 0.840 0.000
#> GSM564757 4 0.0912 0.8366 0.000 0.012 0.016 0.972 0.000
#> GSM564758 4 0.1243 0.8356 0.004 0.008 0.028 0.960 0.000
#> GSM564759 3 0.3675 0.7114 0.000 0.024 0.788 0.188 0.000
#> GSM564760 4 0.0162 0.8373 0.004 0.000 0.000 0.996 0.000
#> GSM564761 1 0.1168 0.7690 0.960 0.008 0.000 0.032 0.000
#> GSM564762 4 0.0854 0.8376 0.004 0.008 0.012 0.976 0.000
#> GSM564681 5 0.1202 0.7990 0.004 0.032 0.004 0.000 0.960
#> GSM564693 5 0.0613 0.7991 0.004 0.008 0.004 0.000 0.984
#> GSM564646 5 0.5420 0.2454 0.000 0.052 0.004 0.396 0.548
#> GSM564699 5 0.5600 0.5171 0.000 0.028 0.300 0.048 0.624
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0260 0.7949 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM564616 5 0.3219 0.6661 0.012 0.192 0.000 0.000 0.792 0.004
#> GSM564617 2 0.1723 0.4901 0.004 0.932 0.004 0.000 0.048 0.012
#> GSM564618 5 0.3352 0.6686 0.016 0.180 0.004 0.000 0.796 0.004
#> GSM564619 1 0.4347 0.6521 0.756 0.036 0.000 0.152 0.000 0.056
#> GSM564620 4 0.1149 0.7964 0.008 0.008 0.000 0.960 0.000 0.024
#> GSM564621 4 0.3189 0.7552 0.100 0.020 0.000 0.844 0.000 0.036
#> GSM564622 5 0.4174 0.6306 0.192 0.016 0.020 0.004 0.756 0.012
#> GSM564623 2 0.6248 0.3674 0.004 0.604 0.016 0.208 0.120 0.048
#> GSM564624 2 0.4564 0.1720 0.004 0.540 0.004 0.000 0.432 0.020
#> GSM564625 4 0.1401 0.7964 0.020 0.004 0.000 0.948 0.000 0.028
#> GSM564626 1 0.1605 0.7931 0.940 0.016 0.000 0.032 0.000 0.012
#> GSM564627 2 0.5646 0.1832 0.132 0.632 0.000 0.192 0.000 0.044
#> GSM564628 5 0.3705 0.6367 0.004 0.220 0.004 0.004 0.756 0.012
#> GSM564629 4 0.2145 0.7815 0.000 0.072 0.000 0.900 0.000 0.028
#> GSM564630 2 0.2109 0.4632 0.028 0.920 0.004 0.000 0.024 0.024
#> GSM564609 5 0.2303 0.7261 0.000 0.000 0.052 0.024 0.904 0.020
#> GSM564610 2 0.4523 0.2736 0.084 0.756 0.000 0.048 0.000 0.112
#> GSM564611 1 0.5087 0.1839 0.508 0.080 0.000 0.000 0.000 0.412
#> GSM564612 5 0.5044 0.5743 0.000 0.224 0.128 0.000 0.644 0.004
#> GSM564613 2 0.2183 0.4829 0.000 0.912 0.028 0.000 0.040 0.020
#> GSM564614 4 0.0405 0.7956 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM564631 3 0.2738 0.6516 0.000 0.000 0.820 0.000 0.176 0.004
#> GSM564632 5 0.0912 0.7255 0.000 0.004 0.008 0.004 0.972 0.012
#> GSM564633 3 0.4303 0.5136 0.008 0.000 0.652 0.000 0.316 0.024
#> GSM564634 6 0.5392 0.3059 0.000 0.148 0.060 0.000 0.116 0.676
#> GSM564635 5 0.3938 0.4980 0.004 0.000 0.312 0.000 0.672 0.012
#> GSM564636 5 0.5258 0.6767 0.004 0.120 0.112 0.004 0.708 0.052
#> GSM564637 5 0.2865 0.7177 0.000 0.064 0.012 0.000 0.868 0.056
#> GSM564638 3 0.2145 0.6801 0.000 0.028 0.900 0.000 0.072 0.000
#> GSM564639 3 0.2058 0.6818 0.000 0.012 0.908 0.000 0.072 0.008
#> GSM564640 5 0.3236 0.7019 0.000 0.036 0.004 0.000 0.820 0.140
#> GSM564641 3 0.4897 0.4871 0.000 0.040 0.684 0.000 0.224 0.052
#> GSM564642 5 0.4594 0.5583 0.020 0.016 0.004 0.004 0.660 0.296
#> GSM564643 5 0.3813 0.6030 0.000 0.004 0.024 0.180 0.776 0.016
#> GSM564644 5 0.5457 0.5848 0.016 0.096 0.016 0.000 0.644 0.228
#> GSM564645 3 0.1714 0.6868 0.000 0.000 0.908 0.000 0.092 0.000
#> GSM564647 5 0.6402 0.4782 0.000 0.188 0.248 0.000 0.516 0.048
#> GSM564648 5 0.0665 0.7226 0.008 0.000 0.008 0.000 0.980 0.004
#> GSM564649 5 0.4723 0.5323 0.000 0.004 0.296 0.000 0.636 0.064
#> GSM564650 5 0.4581 0.5633 0.004 0.256 0.000 0.000 0.672 0.068
#> GSM564651 5 0.3093 0.6664 0.164 0.000 0.008 0.000 0.816 0.012
#> GSM564652 5 0.3037 0.6639 0.160 0.000 0.004 0.000 0.820 0.016
#> GSM564653 5 0.3566 0.6602 0.156 0.000 0.000 0.000 0.788 0.056
#> GSM564654 5 0.3813 0.6147 0.000 0.008 0.236 0.000 0.736 0.020
#> GSM564655 5 0.5149 0.5773 0.004 0.004 0.108 0.004 0.652 0.228
#> GSM564656 3 0.2859 0.6605 0.000 0.000 0.828 0.000 0.156 0.016
#> GSM564657 3 0.3150 0.6551 0.000 0.016 0.844 0.000 0.104 0.036
#> GSM564658 2 0.4614 0.4744 0.052 0.720 0.000 0.000 0.192 0.036
#> GSM564659 5 0.4758 0.6274 0.000 0.172 0.124 0.000 0.696 0.008
#> GSM564660 2 0.5267 0.2158 0.004 0.528 0.000 0.024 0.404 0.040
#> GSM564661 5 0.3841 0.3731 0.380 0.000 0.000 0.000 0.616 0.004
#> GSM564662 3 0.0937 0.6810 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM564663 5 0.4094 0.5995 0.000 0.252 0.004 0.000 0.708 0.036
#> GSM564664 5 0.5037 0.5255 0.056 0.008 0.012 0.000 0.632 0.292
#> GSM564665 5 0.2420 0.7214 0.000 0.004 0.076 0.000 0.888 0.032
#> GSM564666 2 0.6250 0.4601 0.004 0.620 0.076 0.032 0.208 0.060
#> GSM564667 3 0.1196 0.6810 0.000 0.000 0.952 0.000 0.040 0.008
#> GSM564668 5 0.5004 0.6138 0.056 0.008 0.180 0.008 0.716 0.032
#> GSM564669 3 0.4513 0.3162 0.000 0.004 0.572 0.000 0.396 0.028
#> GSM564670 2 0.5544 0.3808 0.000 0.584 0.100 0.000 0.292 0.024
#> GSM564671 4 0.4789 0.5674 0.020 0.000 0.004 0.676 0.252 0.048
#> GSM564672 3 0.3861 0.4789 0.000 0.008 0.672 0.000 0.316 0.004
#> GSM564673 5 0.1596 0.7250 0.020 0.000 0.012 0.004 0.944 0.020
#> GSM564674 5 0.3835 0.6379 0.000 0.188 0.000 0.000 0.756 0.056
#> GSM564675 4 0.4006 0.7215 0.004 0.052 0.008 0.816 0.056 0.064
#> GSM564676 6 0.5677 0.1788 0.012 0.316 0.000 0.000 0.132 0.540
#> GSM564677 5 0.1390 0.7253 0.032 0.016 0.000 0.000 0.948 0.004
#> GSM564678 2 0.5090 0.3883 0.028 0.680 0.000 0.000 0.104 0.188
#> GSM564679 2 0.5900 0.2312 0.032 0.492 0.000 0.000 0.376 0.100
#> GSM564680 3 0.2593 0.6652 0.000 0.008 0.844 0.000 0.148 0.000
#> GSM564682 2 0.5927 0.3371 0.000 0.616 0.088 0.000 0.100 0.196
#> GSM564683 3 0.0146 0.6680 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM564684 4 0.5316 0.3782 0.004 0.020 0.000 0.564 0.356 0.056
#> GSM564685 3 0.1858 0.6851 0.000 0.000 0.912 0.000 0.076 0.012
#> GSM564686 4 0.5815 0.2006 0.004 0.032 0.000 0.500 0.388 0.076
#> GSM564687 5 0.3637 0.6714 0.000 0.164 0.008 0.000 0.788 0.040
#> GSM564688 5 0.1802 0.7161 0.072 0.000 0.000 0.000 0.916 0.012
#> GSM564689 6 0.5736 -0.0287 0.004 0.412 0.000 0.000 0.144 0.440
#> GSM564690 2 0.5695 -0.0559 0.028 0.476 0.000 0.000 0.080 0.416
#> GSM564691 2 0.4890 0.4506 0.000 0.652 0.052 0.000 0.272 0.024
#> GSM564692 5 0.0653 0.7221 0.000 0.012 0.004 0.000 0.980 0.004
#> GSM564694 5 0.4077 0.6963 0.004 0.092 0.004 0.048 0.804 0.048
#> GSM564695 5 0.4870 0.6397 0.000 0.168 0.120 0.000 0.696 0.016
#> GSM564696 3 0.3790 0.5282 0.000 0.016 0.716 0.000 0.004 0.264
#> GSM564697 5 0.5608 0.0664 0.004 0.424 0.008 0.000 0.468 0.096
#> GSM564698 3 0.2482 0.6704 0.000 0.004 0.848 0.000 0.148 0.000
#> GSM564700 4 0.4495 0.3907 0.004 0.004 0.000 0.580 0.392 0.020
#> GSM564701 1 0.4701 0.1674 0.556 0.008 0.024 0.000 0.408 0.004
#> GSM564702 5 0.3088 0.6643 0.172 0.020 0.000 0.000 0.808 0.000
#> GSM564703 1 0.5208 0.2979 0.556 0.000 0.372 0.040 0.000 0.032
#> GSM564704 6 0.4940 0.4189 0.108 0.004 0.008 0.200 0.000 0.680
#> GSM564705 1 0.1225 0.7982 0.952 0.012 0.000 0.000 0.000 0.036
#> GSM564706 3 0.5433 0.4023 0.000 0.008 0.596 0.144 0.000 0.252
#> GSM564707 1 0.2838 0.7181 0.808 0.000 0.000 0.004 0.000 0.188
#> GSM564708 3 0.3998 0.5214 0.004 0.000 0.724 0.236 0.000 0.036
#> GSM564709 1 0.2169 0.7814 0.900 0.012 0.000 0.008 0.000 0.080
#> GSM564710 1 0.1138 0.7980 0.960 0.024 0.000 0.004 0.000 0.012
#> GSM564711 4 0.6122 0.2358 0.000 0.016 0.300 0.488 0.000 0.196
#> GSM564712 1 0.0777 0.8001 0.972 0.000 0.000 0.004 0.000 0.024
#> GSM564713 4 0.3123 0.7800 0.040 0.004 0.040 0.864 0.000 0.052
#> GSM564714 6 0.6463 0.3370 0.000 0.240 0.280 0.028 0.000 0.452
#> GSM564715 1 0.3658 0.7416 0.800 0.004 0.000 0.092 0.000 0.104
#> GSM564716 4 0.3412 0.7578 0.100 0.024 0.004 0.836 0.000 0.036
#> GSM564717 6 0.5606 0.2456 0.040 0.396 0.008 0.040 0.000 0.516
#> GSM564718 4 0.3914 0.6916 0.000 0.000 0.104 0.768 0.000 0.128
#> GSM564719 6 0.3780 0.4335 0.008 0.236 0.012 0.004 0.000 0.740
#> GSM564720 6 0.5888 0.3423 0.268 0.256 0.000 0.000 0.000 0.476
#> GSM564721 1 0.1074 0.8020 0.960 0.000 0.000 0.028 0.000 0.012
#> GSM564722 2 0.4417 0.2094 0.008 0.712 0.004 0.052 0.000 0.224
#> GSM564723 1 0.2512 0.7757 0.880 0.060 0.000 0.000 0.000 0.060
#> GSM564724 4 0.2249 0.7893 0.004 0.000 0.032 0.900 0.000 0.064
#> GSM564725 1 0.3105 0.7346 0.844 0.012 0.000 0.108 0.000 0.036
#> GSM564726 4 0.1096 0.7958 0.004 0.008 0.004 0.964 0.000 0.020
#> GSM564727 4 0.5505 0.2670 0.388 0.032 0.000 0.520 0.000 0.060
#> GSM564728 4 0.0692 0.7952 0.000 0.004 0.000 0.976 0.000 0.020
#> GSM564729 4 0.1718 0.7945 0.016 0.000 0.008 0.932 0.000 0.044
#> GSM564730 1 0.3043 0.7227 0.836 0.008 0.000 0.132 0.000 0.024
#> GSM564731 4 0.5495 0.3792 0.000 0.000 0.288 0.548 0.000 0.164
#> GSM564732 4 0.1387 0.7904 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM564733 4 0.3255 0.7556 0.100 0.004 0.008 0.840 0.000 0.048
#> GSM564734 4 0.3200 0.7084 0.016 0.000 0.000 0.788 0.000 0.196
#> GSM564735 4 0.2981 0.7653 0.008 0.000 0.100 0.852 0.000 0.040
#> GSM564736 4 0.1555 0.7966 0.008 0.000 0.012 0.940 0.000 0.040
#> GSM564737 1 0.1074 0.7989 0.960 0.012 0.000 0.000 0.000 0.028
#> GSM564738 3 0.4795 0.4984 0.000 0.016 0.692 0.204 0.000 0.088
#> GSM564739 1 0.4214 0.6544 0.756 0.004 0.176 0.020 0.000 0.044
#> GSM564740 4 0.3354 0.7457 0.004 0.100 0.012 0.836 0.000 0.048
#> GSM564741 3 0.3209 0.6095 0.000 0.008 0.840 0.064 0.000 0.088
#> GSM564742 3 0.3820 0.5631 0.000 0.008 0.756 0.032 0.000 0.204
#> GSM564743 2 0.3841 0.1909 0.028 0.716 0.000 0.000 0.000 0.256
#> GSM564744 1 0.1088 0.7988 0.960 0.024 0.000 0.000 0.000 0.016
#> GSM564745 4 0.3665 0.6255 0.252 0.000 0.000 0.728 0.000 0.020
#> GSM564746 2 0.4541 0.2798 0.084 0.756 0.000 0.052 0.000 0.108
#> GSM564747 6 0.5751 0.0480 0.016 0.008 0.380 0.088 0.000 0.508
#> GSM564748 3 0.6158 -0.0341 0.368 0.000 0.380 0.004 0.000 0.248
#> GSM564749 6 0.4419 0.3040 0.304 0.040 0.004 0.000 0.000 0.652
#> GSM564750 3 0.4830 0.0829 0.000 0.004 0.496 0.456 0.000 0.044
#> GSM564751 3 0.6585 0.1375 0.332 0.004 0.444 0.036 0.000 0.184
#> GSM564752 4 0.2613 0.7782 0.000 0.016 0.068 0.884 0.000 0.032
#> GSM564753 3 0.2994 0.6135 0.000 0.004 0.852 0.064 0.000 0.080
#> GSM564754 1 0.3450 0.7148 0.780 0.000 0.000 0.032 0.000 0.188
#> GSM564755 4 0.1375 0.7958 0.008 0.008 0.004 0.952 0.000 0.028
#> GSM564756 4 0.3916 0.6820 0.064 0.000 0.000 0.752 0.000 0.184
#> GSM564757 4 0.1010 0.7935 0.004 0.000 0.000 0.960 0.000 0.036
#> GSM564758 4 0.0858 0.7940 0.004 0.000 0.000 0.968 0.000 0.028
#> GSM564759 3 0.6388 0.1764 0.000 0.020 0.444 0.268 0.000 0.268
#> GSM564760 4 0.0865 0.7974 0.000 0.000 0.000 0.964 0.000 0.036
#> GSM564761 1 0.0508 0.8006 0.984 0.000 0.000 0.012 0.000 0.004
#> GSM564762 4 0.3052 0.6919 0.000 0.000 0.004 0.780 0.000 0.216
#> GSM564681 5 0.2165 0.7082 0.008 0.108 0.000 0.000 0.884 0.000
#> GSM564693 5 0.1053 0.7242 0.004 0.020 0.000 0.000 0.964 0.012
#> GSM564646 4 0.5629 0.2377 0.004 0.060 0.000 0.520 0.384 0.032
#> GSM564699 5 0.6067 0.4933 0.004 0.028 0.016 0.132 0.608 0.212
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 genotype/variation(p) disease.state(p) k
#> MAD:NMF 154 0.925 0.476 2
#> MAD:NMF 146 0.876 0.825 3
#> MAD:NMF 121 0.236 0.598 4
#> MAD:NMF 127 0.315 0.878 5
#> MAD:NMF 101 0.489 0.740 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 154 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 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.772 0.703 0.829 0.2700 0.856 0.711
#> 4 4 0.854 0.795 0.884 0.0811 0.908 0.757
#> 5 5 0.739 0.683 0.820 0.0833 0.925 0.757
#> 6 6 0.703 0.639 0.784 0.0406 0.979 0.912
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
#> GSM564615 1 0.0000 1.000 1.000 0.000
#> GSM564616 2 0.0000 1.000 0.000 1.000
#> GSM564617 2 0.0000 1.000 0.000 1.000
#> GSM564618 2 0.0000 1.000 0.000 1.000
#> GSM564619 1 0.0000 1.000 1.000 0.000
#> GSM564620 1 0.0000 1.000 1.000 0.000
#> GSM564621 1 0.0000 1.000 1.000 0.000
#> GSM564622 2 0.0000 1.000 0.000 1.000
#> GSM564623 2 0.0938 0.988 0.012 0.988
#> GSM564624 2 0.0000 1.000 0.000 1.000
#> GSM564625 1 0.0000 1.000 1.000 0.000
#> GSM564626 1 0.0000 1.000 1.000 0.000
#> GSM564627 1 0.0000 1.000 1.000 0.000
#> GSM564628 2 0.0000 1.000 0.000 1.000
#> GSM564629 1 0.0000 1.000 1.000 0.000
#> GSM564630 2 0.1414 0.980 0.020 0.980
#> GSM564609 2 0.0000 1.000 0.000 1.000
#> GSM564610 1 0.0000 1.000 1.000 0.000
#> GSM564611 1 0.0000 1.000 1.000 0.000
#> GSM564612 2 0.0000 1.000 0.000 1.000
#> GSM564613 2 0.0000 1.000 0.000 1.000
#> GSM564614 1 0.0000 1.000 1.000 0.000
#> GSM564631 2 0.0000 1.000 0.000 1.000
#> GSM564632 2 0.0000 1.000 0.000 1.000
#> GSM564633 2 0.0000 1.000 0.000 1.000
#> GSM564634 2 0.0000 1.000 0.000 1.000
#> GSM564635 2 0.0000 1.000 0.000 1.000
#> GSM564636 2 0.0000 1.000 0.000 1.000
#> GSM564637 2 0.0000 1.000 0.000 1.000
#> GSM564638 2 0.0000 1.000 0.000 1.000
#> GSM564639 2 0.0000 1.000 0.000 1.000
#> GSM564640 2 0.0000 1.000 0.000 1.000
#> GSM564641 2 0.0000 1.000 0.000 1.000
#> GSM564642 2 0.0000 1.000 0.000 1.000
#> GSM564643 2 0.0000 1.000 0.000 1.000
#> GSM564644 2 0.0000 1.000 0.000 1.000
#> GSM564645 2 0.0000 1.000 0.000 1.000
#> GSM564647 2 0.0000 1.000 0.000 1.000
#> GSM564648 2 0.0000 1.000 0.000 1.000
#> GSM564649 2 0.0000 1.000 0.000 1.000
#> GSM564650 2 0.0000 1.000 0.000 1.000
#> GSM564651 2 0.0000 1.000 0.000 1.000
#> GSM564652 2 0.0000 1.000 0.000 1.000
#> GSM564653 2 0.0000 1.000 0.000 1.000
#> GSM564654 2 0.0000 1.000 0.000 1.000
#> GSM564655 2 0.0000 1.000 0.000 1.000
#> GSM564656 2 0.0000 1.000 0.000 1.000
#> GSM564657 2 0.0000 1.000 0.000 1.000
#> GSM564658 2 0.0000 1.000 0.000 1.000
#> GSM564659 2 0.0000 1.000 0.000 1.000
#> GSM564660 2 0.0000 1.000 0.000 1.000
#> GSM564661 2 0.0000 1.000 0.000 1.000
#> GSM564662 2 0.0000 1.000 0.000 1.000
#> GSM564663 2 0.0000 1.000 0.000 1.000
#> GSM564664 2 0.0000 1.000 0.000 1.000
#> GSM564665 2 0.0000 1.000 0.000 1.000
#> GSM564666 2 0.0000 1.000 0.000 1.000
#> GSM564667 2 0.0000 1.000 0.000 1.000
#> GSM564668 2 0.0000 1.000 0.000 1.000
#> GSM564669 2 0.0000 1.000 0.000 1.000
#> GSM564670 2 0.0000 1.000 0.000 1.000
#> GSM564671 2 0.0000 1.000 0.000 1.000
#> GSM564672 2 0.0000 1.000 0.000 1.000
#> GSM564673 2 0.0000 1.000 0.000 1.000
#> GSM564674 2 0.0000 1.000 0.000 1.000
#> GSM564675 2 0.0000 1.000 0.000 1.000
#> GSM564676 2 0.0000 1.000 0.000 1.000
#> GSM564677 2 0.0000 1.000 0.000 1.000
#> GSM564678 2 0.0000 1.000 0.000 1.000
#> GSM564679 2 0.0000 1.000 0.000 1.000
#> GSM564680 2 0.0000 1.000 0.000 1.000
#> GSM564682 2 0.0000 1.000 0.000 1.000
#> GSM564683 2 0.0000 1.000 0.000 1.000
#> GSM564684 2 0.0000 1.000 0.000 1.000
#> GSM564685 2 0.0000 1.000 0.000 1.000
#> GSM564686 2 0.0000 1.000 0.000 1.000
#> GSM564687 2 0.0000 1.000 0.000 1.000
#> GSM564688 2 0.0000 1.000 0.000 1.000
#> GSM564689 2 0.0000 1.000 0.000 1.000
#> GSM564690 2 0.0000 1.000 0.000 1.000
#> GSM564691 2 0.0000 1.000 0.000 1.000
#> GSM564692 2 0.0000 1.000 0.000 1.000
#> GSM564694 2 0.0000 1.000 0.000 1.000
#> GSM564695 2 0.0000 1.000 0.000 1.000
#> GSM564696 2 0.0000 1.000 0.000 1.000
#> GSM564697 2 0.0000 1.000 0.000 1.000
#> GSM564698 2 0.0000 1.000 0.000 1.000
#> GSM564700 2 0.0000 1.000 0.000 1.000
#> GSM564701 2 0.0000 1.000 0.000 1.000
#> GSM564702 2 0.0000 1.000 0.000 1.000
#> GSM564703 1 0.0000 1.000 1.000 0.000
#> GSM564704 1 0.0000 1.000 1.000 0.000
#> GSM564705 1 0.0000 1.000 1.000 0.000
#> GSM564706 1 0.0000 1.000 1.000 0.000
#> GSM564707 1 0.0000 1.000 1.000 0.000
#> GSM564708 1 0.0000 1.000 1.000 0.000
#> GSM564709 1 0.0000 1.000 1.000 0.000
#> GSM564710 1 0.0000 1.000 1.000 0.000
#> GSM564711 1 0.0000 1.000 1.000 0.000
#> GSM564712 1 0.0000 1.000 1.000 0.000
#> GSM564713 1 0.0000 1.000 1.000 0.000
#> GSM564714 1 0.0000 1.000 1.000 0.000
#> GSM564715 1 0.0000 1.000 1.000 0.000
#> GSM564716 1 0.0000 1.000 1.000 0.000
#> GSM564717 1 0.0000 1.000 1.000 0.000
#> GSM564718 1 0.0000 1.000 1.000 0.000
#> GSM564719 1 0.0000 1.000 1.000 0.000
#> GSM564720 1 0.0000 1.000 1.000 0.000
#> GSM564721 1 0.0000 1.000 1.000 0.000
#> GSM564722 1 0.0000 1.000 1.000 0.000
#> GSM564723 1 0.0000 1.000 1.000 0.000
#> GSM564724 1 0.0000 1.000 1.000 0.000
#> GSM564725 1 0.0000 1.000 1.000 0.000
#> GSM564726 1 0.0000 1.000 1.000 0.000
#> GSM564727 1 0.0000 1.000 1.000 0.000
#> GSM564728 1 0.0000 1.000 1.000 0.000
#> GSM564729 1 0.0000 1.000 1.000 0.000
#> GSM564730 1 0.0000 1.000 1.000 0.000
#> GSM564731 1 0.0000 1.000 1.000 0.000
#> GSM564732 1 0.0000 1.000 1.000 0.000
#> GSM564733 1 0.0000 1.000 1.000 0.000
#> GSM564734 1 0.0000 1.000 1.000 0.000
#> GSM564735 1 0.0000 1.000 1.000 0.000
#> GSM564736 1 0.0000 1.000 1.000 0.000
#> GSM564737 1 0.0000 1.000 1.000 0.000
#> GSM564738 1 0.0000 1.000 1.000 0.000
#> GSM564739 1 0.0000 1.000 1.000 0.000
#> GSM564740 1 0.0000 1.000 1.000 0.000
#> GSM564741 1 0.0000 1.000 1.000 0.000
#> GSM564742 1 0.0000 1.000 1.000 0.000
#> GSM564743 1 0.0000 1.000 1.000 0.000
#> GSM564744 1 0.0000 1.000 1.000 0.000
#> GSM564745 1 0.0000 1.000 1.000 0.000
#> GSM564746 1 0.0000 1.000 1.000 0.000
#> GSM564747 1 0.0000 1.000 1.000 0.000
#> GSM564748 1 0.0000 1.000 1.000 0.000
#> GSM564749 1 0.0000 1.000 1.000 0.000
#> GSM564750 1 0.0000 1.000 1.000 0.000
#> GSM564751 1 0.0000 1.000 1.000 0.000
#> GSM564752 1 0.0000 1.000 1.000 0.000
#> GSM564753 1 0.0000 1.000 1.000 0.000
#> GSM564754 1 0.0000 1.000 1.000 0.000
#> GSM564755 1 0.0000 1.000 1.000 0.000
#> GSM564756 1 0.0000 1.000 1.000 0.000
#> GSM564757 1 0.0000 1.000 1.000 0.000
#> GSM564758 1 0.0000 1.000 1.000 0.000
#> GSM564759 1 0.0000 1.000 1.000 0.000
#> GSM564760 1 0.0000 1.000 1.000 0.000
#> GSM564761 1 0.0000 1.000 1.000 0.000
#> GSM564762 1 0.0000 1.000 1.000 0.000
#> GSM564681 2 0.0000 1.000 0.000 1.000
#> GSM564693 2 0.0000 1.000 0.000 1.000
#> GSM564646 2 0.0000 1.000 0.000 1.000
#> GSM564699 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564616 2 0.6180 0.53570 0.000 0.584 0.416
#> GSM564617 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564618 2 0.6126 0.54550 0.000 0.600 0.400
#> GSM564619 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564620 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564621 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564622 2 0.6168 0.49415 0.000 0.588 0.412
#> GSM564623 3 0.6600 0.36992 0.012 0.384 0.604
#> GSM564624 3 0.1411 0.59301 0.000 0.036 0.964
#> GSM564625 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564626 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564627 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564628 3 0.5591 0.24750 0.000 0.304 0.696
#> GSM564629 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564630 3 0.3234 0.59008 0.020 0.072 0.908
#> GSM564609 2 0.4121 0.37653 0.000 0.832 0.168
#> GSM564610 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564611 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564612 3 0.1753 0.62158 0.000 0.048 0.952
#> GSM564613 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564614 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564631 3 0.6299 0.40201 0.000 0.476 0.524
#> GSM564632 2 0.5785 0.55039 0.000 0.668 0.332
#> GSM564633 2 0.4121 0.37653 0.000 0.832 0.168
#> GSM564634 3 0.4346 0.58406 0.000 0.184 0.816
#> GSM564635 2 0.4121 0.37653 0.000 0.832 0.168
#> GSM564636 3 0.6299 0.40411 0.000 0.476 0.524
#> GSM564637 3 0.5988 0.44966 0.000 0.368 0.632
#> GSM564638 3 0.6302 0.39803 0.000 0.480 0.520
#> GSM564639 2 0.6286 -0.32615 0.000 0.536 0.464
#> GSM564640 3 0.5363 0.34743 0.000 0.276 0.724
#> GSM564641 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564642 3 0.5859 0.47574 0.000 0.344 0.656
#> GSM564643 2 0.5785 0.55039 0.000 0.668 0.332
#> GSM564644 3 0.5465 0.50072 0.000 0.288 0.712
#> GSM564645 2 0.6286 -0.33045 0.000 0.536 0.464
#> GSM564647 3 0.5327 0.52915 0.000 0.272 0.728
#> GSM564648 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564649 3 0.6302 0.40011 0.000 0.480 0.520
#> GSM564650 3 0.0237 0.62080 0.000 0.004 0.996
#> GSM564651 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564652 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564653 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564654 2 0.5785 0.55039 0.000 0.668 0.332
#> GSM564655 2 0.4974 0.38909 0.000 0.764 0.236
#> GSM564656 2 0.4121 0.37653 0.000 0.832 0.168
#> GSM564657 3 0.6299 0.40201 0.000 0.476 0.524
#> GSM564658 3 0.3192 0.53479 0.000 0.112 0.888
#> GSM564659 3 0.6295 0.29244 0.000 0.472 0.528
#> GSM564660 3 0.0237 0.62080 0.000 0.004 0.996
#> GSM564661 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564662 3 0.6299 0.40201 0.000 0.476 0.524
#> GSM564663 3 0.1289 0.59703 0.000 0.032 0.968
#> GSM564664 3 0.5785 0.45517 0.000 0.332 0.668
#> GSM564665 3 0.5988 0.44966 0.000 0.368 0.632
#> GSM564666 3 0.6305 0.27014 0.000 0.484 0.516
#> GSM564667 3 0.6299 0.40201 0.000 0.476 0.524
#> GSM564668 2 0.3340 0.40502 0.000 0.880 0.120
#> GSM564669 2 0.3340 0.40502 0.000 0.880 0.120
#> GSM564670 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564671 2 0.5650 0.45268 0.000 0.688 0.312
#> GSM564672 3 0.6302 0.40011 0.000 0.480 0.520
#> GSM564673 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564674 3 0.2448 0.59926 0.000 0.076 0.924
#> GSM564675 3 0.6291 0.30030 0.000 0.468 0.532
#> GSM564676 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564677 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564678 3 0.1289 0.59703 0.000 0.032 0.968
#> GSM564679 3 0.3192 0.53479 0.000 0.112 0.888
#> GSM564680 2 0.6286 -0.32615 0.000 0.536 0.464
#> GSM564682 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564683 3 0.6302 0.39803 0.000 0.480 0.520
#> GSM564684 2 0.5650 0.45268 0.000 0.688 0.312
#> GSM564685 3 0.6299 0.40261 0.000 0.476 0.524
#> GSM564686 2 0.5760 0.25143 0.000 0.672 0.328
#> GSM564687 3 0.5650 0.46970 0.000 0.312 0.688
#> GSM564688 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564689 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564690 3 0.0237 0.62080 0.000 0.004 0.996
#> GSM564691 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564692 2 0.6111 0.55096 0.000 0.604 0.396
#> GSM564694 3 0.6260 0.22703 0.000 0.448 0.552
#> GSM564695 2 0.6260 -0.00166 0.000 0.552 0.448
#> GSM564696 3 0.6079 0.45247 0.000 0.388 0.612
#> GSM564697 3 0.0000 0.62364 0.000 0.000 1.000
#> GSM564698 2 0.4974 0.25691 0.000 0.764 0.236
#> GSM564700 2 0.5650 0.45268 0.000 0.688 0.312
#> GSM564701 2 0.6126 0.54550 0.000 0.600 0.400
#> GSM564702 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564703 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564704 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564705 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564706 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564707 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564708 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564709 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564710 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564711 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564712 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564713 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564714 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564715 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564716 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564717 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564718 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564719 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564720 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564721 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564722 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564723 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564724 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564725 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564726 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564727 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564728 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564729 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564730 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564731 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564732 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564733 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564734 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564735 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564736 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564737 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564738 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564739 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564740 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564741 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564742 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564743 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564744 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564745 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564746 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564747 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564748 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564749 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564750 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564751 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564752 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564753 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564754 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564755 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564756 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564757 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564758 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564759 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564760 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564761 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564762 1 0.0000 1.00000 1.000 0.000 0.000
#> GSM564681 2 0.6126 0.54550 0.000 0.600 0.400
#> GSM564693 2 0.6095 0.55259 0.000 0.608 0.392
#> GSM564646 2 0.5650 0.45268 0.000 0.688 0.312
#> GSM564699 2 0.5785 0.24998 0.000 0.668 0.332
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564616 4 0.1118 0.8252 0.000 0.036 0.000 0.964
#> GSM564617 2 0.1888 0.7961 0.000 0.940 0.044 0.016
#> GSM564618 4 0.0592 0.8392 0.000 0.016 0.000 0.984
#> GSM564619 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564620 1 0.0336 0.9920 0.992 0.000 0.008 0.000
#> GSM564621 1 0.0469 0.9906 0.988 0.000 0.012 0.000
#> GSM564622 4 0.4877 0.6023 0.000 0.044 0.204 0.752
#> GSM564623 2 0.7879 -0.0644 0.012 0.464 0.332 0.192
#> GSM564624 2 0.2546 0.7936 0.000 0.912 0.028 0.060
#> GSM564625 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564626 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564627 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564628 2 0.5671 0.4473 0.000 0.572 0.028 0.400
#> GSM564629 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564630 2 0.4090 0.7043 0.012 0.844 0.096 0.048
#> GSM564609 3 0.4679 0.4904 0.000 0.000 0.648 0.352
#> GSM564610 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564611 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564612 2 0.4792 0.6053 0.000 0.680 0.312 0.008
#> GSM564613 2 0.3280 0.7728 0.000 0.860 0.124 0.016
#> GSM564614 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564631 3 0.1211 0.6925 0.000 0.040 0.960 0.000
#> GSM564632 4 0.3123 0.7319 0.000 0.000 0.156 0.844
#> GSM564633 3 0.4679 0.4904 0.000 0.000 0.648 0.352
#> GSM564634 2 0.5127 0.3810 0.000 0.632 0.356 0.012
#> GSM564635 3 0.4679 0.4904 0.000 0.000 0.648 0.352
#> GSM564636 3 0.1302 0.6904 0.000 0.044 0.956 0.000
#> GSM564637 3 0.7279 0.1253 0.000 0.408 0.444 0.148
#> GSM564638 3 0.0817 0.6950 0.000 0.024 0.976 0.000
#> GSM564639 3 0.2363 0.7038 0.000 0.024 0.920 0.056
#> GSM564640 2 0.5839 0.5359 0.000 0.604 0.044 0.352
#> GSM564641 2 0.3636 0.7419 0.000 0.820 0.172 0.008
#> GSM564642 2 0.7436 0.3837 0.000 0.512 0.252 0.236
#> GSM564643 4 0.3123 0.7319 0.000 0.000 0.156 0.844
#> GSM564644 2 0.6769 0.5576 0.000 0.608 0.172 0.220
#> GSM564645 3 0.2644 0.7056 0.000 0.032 0.908 0.060
#> GSM564647 3 0.4677 0.3586 0.000 0.316 0.680 0.004
#> GSM564648 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564649 3 0.1118 0.6936 0.000 0.036 0.964 0.000
#> GSM564650 2 0.2111 0.7975 0.000 0.932 0.044 0.024
#> GSM564651 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564652 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564653 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564654 4 0.3123 0.7319 0.000 0.000 0.156 0.844
#> GSM564655 3 0.5070 0.3741 0.000 0.004 0.580 0.416
#> GSM564656 3 0.4679 0.4904 0.000 0.000 0.648 0.352
#> GSM564657 3 0.1211 0.6925 0.000 0.040 0.960 0.000
#> GSM564658 2 0.3355 0.7512 0.000 0.836 0.004 0.160
#> GSM564659 3 0.6324 0.6143 0.000 0.168 0.660 0.172
#> GSM564660 2 0.2111 0.7975 0.000 0.932 0.044 0.024
#> GSM564661 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564662 3 0.1211 0.6925 0.000 0.040 0.960 0.000
#> GSM564663 2 0.2466 0.7941 0.000 0.916 0.028 0.056
#> GSM564664 2 0.7182 0.4655 0.000 0.552 0.200 0.248
#> GSM564665 3 0.7279 0.1253 0.000 0.408 0.444 0.148
#> GSM564666 3 0.7380 0.4765 0.000 0.288 0.512 0.200
#> GSM564667 3 0.1211 0.6925 0.000 0.040 0.960 0.000
#> GSM564668 3 0.5959 0.3695 0.000 0.044 0.568 0.388
#> GSM564669 3 0.5959 0.3695 0.000 0.044 0.568 0.388
#> GSM564670 2 0.3280 0.7728 0.000 0.860 0.124 0.016
#> GSM564671 4 0.6482 0.2212 0.000 0.084 0.352 0.564
#> GSM564672 3 0.1118 0.6936 0.000 0.036 0.964 0.000
#> GSM564673 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564674 2 0.3312 0.7874 0.000 0.876 0.052 0.072
#> GSM564675 3 0.7401 0.4590 0.000 0.300 0.504 0.196
#> GSM564676 2 0.1888 0.7961 0.000 0.940 0.044 0.016
#> GSM564677 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564678 2 0.2466 0.7941 0.000 0.916 0.028 0.056
#> GSM564679 2 0.3355 0.7512 0.000 0.836 0.004 0.160
#> GSM564680 3 0.2363 0.7038 0.000 0.024 0.920 0.056
#> GSM564682 2 0.3636 0.7419 0.000 0.820 0.172 0.008
#> GSM564683 3 0.0817 0.6950 0.000 0.024 0.976 0.000
#> GSM564684 4 0.6482 0.2212 0.000 0.084 0.352 0.564
#> GSM564685 3 0.1022 0.6952 0.000 0.032 0.968 0.000
#> GSM564686 3 0.7345 0.4244 0.000 0.172 0.492 0.336
#> GSM564687 2 0.6976 0.5157 0.000 0.580 0.180 0.240
#> GSM564688 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564689 2 0.1888 0.7961 0.000 0.940 0.044 0.016
#> GSM564690 2 0.2111 0.7969 0.000 0.932 0.044 0.024
#> GSM564691 2 0.3636 0.7419 0.000 0.820 0.172 0.008
#> GSM564692 4 0.0336 0.8440 0.000 0.008 0.000 0.992
#> GSM564694 3 0.7694 0.3573 0.000 0.308 0.448 0.244
#> GSM564695 3 0.7164 0.4797 0.000 0.156 0.524 0.320
#> GSM564696 3 0.3610 0.5921 0.000 0.200 0.800 0.000
#> GSM564697 2 0.1888 0.7961 0.000 0.940 0.044 0.016
#> GSM564698 3 0.4304 0.5659 0.000 0.000 0.716 0.284
#> GSM564700 4 0.6482 0.2212 0.000 0.084 0.352 0.564
#> GSM564701 4 0.0592 0.8392 0.000 0.016 0.000 0.984
#> GSM564702 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564703 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564704 1 0.0336 0.9920 0.992 0.000 0.008 0.000
#> GSM564705 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564706 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564707 1 0.0376 0.9926 0.992 0.004 0.004 0.000
#> GSM564708 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564709 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564710 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564711 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564712 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564713 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564714 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564715 1 0.0336 0.9920 0.992 0.000 0.008 0.000
#> GSM564716 1 0.0336 0.9920 0.992 0.000 0.008 0.000
#> GSM564717 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564718 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564719 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564720 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564721 1 0.0672 0.9898 0.984 0.008 0.008 0.000
#> GSM564722 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564723 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564724 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564725 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564726 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564727 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564728 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564729 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564730 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564731 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564732 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564733 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564734 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564735 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564736 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564737 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564738 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564739 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564740 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564741 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564742 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564743 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564744 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564745 1 0.0657 0.9896 0.984 0.004 0.012 0.000
#> GSM564746 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564747 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564748 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564749 1 0.0336 0.9922 0.992 0.008 0.000 0.000
#> GSM564750 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564751 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564752 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564753 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564754 1 0.0524 0.9913 0.988 0.004 0.008 0.000
#> GSM564755 1 0.0000 0.9934 1.000 0.000 0.000 0.000
#> GSM564756 1 0.0336 0.9920 0.992 0.000 0.008 0.000
#> GSM564757 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564758 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564759 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564760 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564761 1 0.0804 0.9881 0.980 0.008 0.012 0.000
#> GSM564762 1 0.0188 0.9933 0.996 0.000 0.004 0.000
#> GSM564681 4 0.0592 0.8392 0.000 0.016 0.000 0.984
#> GSM564693 4 0.0000 0.8471 0.000 0.000 0.000 1.000
#> GSM564646 4 0.6482 0.2212 0.000 0.084 0.352 0.564
#> GSM564699 3 0.7146 0.4505 0.000 0.148 0.516 0.336
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564616 5 0.0963 0.8265 0.000 0.036 0.000 0.000 0.964
#> GSM564617 2 0.0404 0.8047 0.012 0.988 0.000 0.000 0.000
#> GSM564618 5 0.0510 0.8405 0.000 0.016 0.000 0.000 0.984
#> GSM564619 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564620 4 0.3966 -0.0677 0.336 0.000 0.000 0.664 0.000
#> GSM564621 4 0.4114 -0.2799 0.376 0.000 0.000 0.624 0.000
#> GSM564622 5 0.4864 0.6274 0.164 0.000 0.116 0.000 0.720
#> GSM564623 1 0.8304 -0.5636 0.368 0.256 0.228 0.000 0.148
#> GSM564624 2 0.1725 0.8032 0.020 0.936 0.000 0.000 0.044
#> GSM564625 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564626 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564627 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564628 2 0.4856 0.4561 0.000 0.584 0.028 0.000 0.388
#> GSM564629 4 0.0510 0.8836 0.016 0.000 0.000 0.984 0.000
#> GSM564630 2 0.4588 0.5817 0.380 0.604 0.000 0.000 0.016
#> GSM564609 3 0.5037 0.4703 0.048 0.000 0.616 0.000 0.336
#> GSM564610 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564611 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564612 2 0.4161 0.6136 0.016 0.704 0.280 0.000 0.000
#> GSM564613 2 0.2293 0.7824 0.016 0.900 0.084 0.000 0.000
#> GSM564614 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564631 3 0.0898 0.6912 0.008 0.020 0.972 0.000 0.000
#> GSM564632 5 0.3413 0.7346 0.044 0.000 0.124 0.000 0.832
#> GSM564633 3 0.5037 0.4703 0.048 0.000 0.616 0.000 0.336
#> GSM564634 2 0.6067 0.3975 0.164 0.560 0.276 0.000 0.000
#> GSM564635 3 0.5037 0.4703 0.048 0.000 0.616 0.000 0.336
#> GSM564636 3 0.0880 0.6892 0.000 0.032 0.968 0.000 0.000
#> GSM564637 3 0.6583 0.1828 0.012 0.404 0.440 0.000 0.144
#> GSM564638 3 0.0290 0.6928 0.008 0.000 0.992 0.000 0.000
#> GSM564639 3 0.1701 0.6911 0.016 0.000 0.936 0.000 0.048
#> GSM564640 2 0.4987 0.5417 0.000 0.616 0.044 0.000 0.340
#> GSM564641 2 0.3016 0.7547 0.020 0.848 0.132 0.000 0.000
#> GSM564642 2 0.6685 0.3581 0.012 0.516 0.240 0.000 0.232
#> GSM564643 5 0.3413 0.7346 0.044 0.000 0.124 0.000 0.832
#> GSM564644 2 0.6075 0.5439 0.012 0.612 0.160 0.000 0.216
#> GSM564645 3 0.2506 0.6917 0.036 0.008 0.904 0.000 0.052
#> GSM564647 3 0.3895 0.3659 0.000 0.320 0.680 0.000 0.000
#> GSM564648 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564649 3 0.0703 0.6921 0.000 0.024 0.976 0.000 0.000
#> GSM564650 2 0.0451 0.8062 0.004 0.988 0.000 0.000 0.008
#> GSM564651 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564652 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564653 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564654 5 0.3413 0.7346 0.044 0.000 0.124 0.000 0.832
#> GSM564655 3 0.5423 0.3690 0.064 0.000 0.548 0.000 0.388
#> GSM564656 3 0.5037 0.4703 0.048 0.000 0.616 0.000 0.336
#> GSM564657 3 0.0898 0.6912 0.008 0.020 0.972 0.000 0.000
#> GSM564658 2 0.3151 0.7664 0.020 0.836 0.000 0.000 0.144
#> GSM564659 3 0.6112 0.6069 0.036 0.152 0.648 0.000 0.164
#> GSM564660 2 0.0451 0.8062 0.004 0.988 0.000 0.000 0.008
#> GSM564661 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564662 3 0.0898 0.6912 0.008 0.020 0.972 0.000 0.000
#> GSM564663 2 0.1648 0.8038 0.020 0.940 0.000 0.000 0.040
#> GSM564664 2 0.6540 0.4427 0.016 0.552 0.188 0.000 0.244
#> GSM564665 3 0.6583 0.1828 0.012 0.404 0.440 0.000 0.144
#> GSM564666 3 0.8093 0.4513 0.248 0.168 0.424 0.000 0.160
#> GSM564667 3 0.0898 0.6912 0.008 0.020 0.972 0.000 0.000
#> GSM564668 3 0.6353 0.3231 0.172 0.000 0.480 0.000 0.348
#> GSM564669 3 0.6353 0.3231 0.172 0.000 0.480 0.000 0.348
#> GSM564670 2 0.2293 0.7824 0.016 0.900 0.084 0.000 0.000
#> GSM564671 5 0.6326 0.2739 0.208 0.000 0.268 0.000 0.524
#> GSM564672 3 0.0703 0.6921 0.000 0.024 0.976 0.000 0.000
#> GSM564673 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564674 2 0.2321 0.7953 0.008 0.912 0.024 0.000 0.056
#> GSM564675 3 0.8134 0.4374 0.248 0.180 0.416 0.000 0.156
#> GSM564676 2 0.0162 0.8048 0.004 0.996 0.000 0.000 0.000
#> GSM564677 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564678 2 0.1648 0.8038 0.020 0.940 0.000 0.000 0.040
#> GSM564679 2 0.3151 0.7664 0.020 0.836 0.000 0.000 0.144
#> GSM564680 3 0.1800 0.6909 0.020 0.000 0.932 0.000 0.048
#> GSM564682 2 0.3016 0.7547 0.020 0.848 0.132 0.000 0.000
#> GSM564683 3 0.0290 0.6928 0.008 0.000 0.992 0.000 0.000
#> GSM564684 5 0.6326 0.2739 0.208 0.000 0.268 0.000 0.524
#> GSM564685 3 0.0912 0.6916 0.012 0.016 0.972 0.000 0.000
#> GSM564686 3 0.7777 0.3801 0.160 0.104 0.436 0.000 0.300
#> GSM564687 2 0.6261 0.5005 0.012 0.584 0.168 0.000 0.236
#> GSM564688 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564689 2 0.0162 0.8047 0.004 0.996 0.000 0.000 0.000
#> GSM564690 2 0.0451 0.8059 0.004 0.988 0.000 0.000 0.008
#> GSM564691 2 0.3016 0.7547 0.020 0.848 0.132 0.000 0.000
#> GSM564692 5 0.0290 0.8455 0.000 0.008 0.000 0.000 0.992
#> GSM564694 3 0.7033 0.3774 0.016 0.304 0.440 0.000 0.240
#> GSM564695 3 0.6867 0.4559 0.032 0.152 0.504 0.000 0.312
#> GSM564696 3 0.4704 0.5528 0.152 0.112 0.736 0.000 0.000
#> GSM564697 2 0.0404 0.8047 0.012 0.988 0.000 0.000 0.000
#> GSM564698 3 0.4268 0.5605 0.024 0.000 0.708 0.000 0.268
#> GSM564700 5 0.6326 0.2739 0.208 0.000 0.268 0.000 0.524
#> GSM564701 5 0.0510 0.8405 0.000 0.016 0.000 0.000 0.984
#> GSM564702 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564703 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564704 4 0.3983 -0.0858 0.340 0.000 0.000 0.660 0.000
#> GSM564705 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564706 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564707 4 0.4171 -0.3721 0.396 0.000 0.000 0.604 0.000
#> GSM564708 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564709 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564710 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564711 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564712 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564713 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564714 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564715 4 0.3949 -0.0453 0.332 0.000 0.000 0.668 0.000
#> GSM564716 4 0.3966 -0.0677 0.336 0.000 0.000 0.664 0.000
#> GSM564717 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564718 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564719 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564720 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564721 1 0.4242 0.8738 0.572 0.000 0.000 0.428 0.000
#> GSM564722 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564723 1 0.4302 0.7757 0.520 0.000 0.000 0.480 0.000
#> GSM564724 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564725 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564726 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564727 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564728 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564729 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564730 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564731 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564732 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564733 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564734 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564735 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564736 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564737 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564738 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564739 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564740 4 0.0510 0.8836 0.016 0.000 0.000 0.984 0.000
#> GSM564741 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564742 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564743 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564744 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564745 1 0.4306 0.7339 0.508 0.000 0.000 0.492 0.000
#> GSM564746 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564747 4 0.0404 0.8852 0.012 0.000 0.000 0.988 0.000
#> GSM564748 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564749 4 0.0880 0.8736 0.032 0.000 0.000 0.968 0.000
#> GSM564750 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564751 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564752 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564753 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564754 4 0.4307 -0.7196 0.496 0.000 0.000 0.504 0.000
#> GSM564755 4 0.0880 0.8735 0.032 0.000 0.000 0.968 0.000
#> GSM564756 4 0.3949 -0.0409 0.332 0.000 0.000 0.668 0.000
#> GSM564757 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564758 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564759 4 0.0000 0.8878 0.000 0.000 0.000 1.000 0.000
#> GSM564760 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564761 1 0.4235 0.8802 0.576 0.000 0.000 0.424 0.000
#> GSM564762 4 0.0162 0.8888 0.004 0.000 0.000 0.996 0.000
#> GSM564681 5 0.0510 0.8405 0.000 0.016 0.000 0.000 0.984
#> GSM564693 5 0.0000 0.8486 0.000 0.000 0.000 0.000 1.000
#> GSM564646 5 0.6326 0.2739 0.208 0.000 0.268 0.000 0.524
#> GSM564699 3 0.7483 0.4247 0.120 0.104 0.476 0.000 0.300
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0790 0.8358 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564616 5 0.1452 0.8609 0.012 0.020 0.000 0.000 0.948 0.020
#> GSM564617 2 0.1434 0.7640 0.020 0.948 0.008 0.000 0.000 0.024
#> GSM564618 5 0.0603 0.8883 0.004 0.000 0.000 0.000 0.980 0.016
#> GSM564619 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564620 4 0.3647 0.1045 0.360 0.000 0.000 0.640 0.000 0.000
#> GSM564621 4 0.3756 -0.0802 0.400 0.000 0.000 0.600 0.000 0.000
#> GSM564622 5 0.3769 0.2946 0.000 0.000 0.004 0.000 0.640 0.356
#> GSM564623 1 0.8514 -0.5680 0.264 0.212 0.212 0.000 0.060 0.252
#> GSM564624 2 0.3522 0.7393 0.032 0.824 0.004 0.000 0.024 0.116
#> GSM564625 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564626 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564627 4 0.2994 0.7373 0.004 0.000 0.000 0.788 0.000 0.208
#> GSM564628 2 0.5266 0.4337 0.012 0.560 0.020 0.000 0.372 0.036
#> GSM564629 4 0.2632 0.7706 0.004 0.000 0.000 0.832 0.000 0.164
#> GSM564630 2 0.5984 0.4303 0.284 0.444 0.000 0.000 0.000 0.272
#> GSM564609 3 0.5638 -0.1370 0.004 0.000 0.504 0.000 0.140 0.352
#> GSM564610 4 0.3454 0.7169 0.024 0.000 0.000 0.768 0.000 0.208
#> GSM564611 4 0.3290 0.7257 0.016 0.000 0.000 0.776 0.000 0.208
#> GSM564612 2 0.4504 0.5614 0.004 0.652 0.296 0.000 0.000 0.048
#> GSM564613 2 0.3356 0.7309 0.020 0.836 0.092 0.000 0.000 0.052
#> GSM564614 4 0.0790 0.8358 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564631 3 0.0363 0.6108 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM564632 5 0.4011 0.5246 0.000 0.000 0.056 0.000 0.732 0.212
#> GSM564633 3 0.5638 -0.1370 0.004 0.000 0.504 0.000 0.140 0.352
#> GSM564634 2 0.6386 0.3411 0.156 0.520 0.268 0.000 0.000 0.056
#> GSM564635 3 0.5638 -0.1370 0.004 0.000 0.504 0.000 0.140 0.352
#> GSM564636 3 0.0909 0.6103 0.000 0.020 0.968 0.000 0.000 0.012
#> GSM564637 3 0.6696 0.1785 0.004 0.372 0.432 0.000 0.080 0.112
#> GSM564638 3 0.0405 0.6054 0.008 0.000 0.988 0.000 0.000 0.004
#> GSM564639 3 0.1728 0.5853 0.008 0.000 0.924 0.000 0.004 0.064
#> GSM564640 2 0.5402 0.5204 0.012 0.592 0.036 0.000 0.324 0.036
#> GSM564641 2 0.3645 0.7030 0.008 0.796 0.144 0.000 0.000 0.052
#> GSM564642 2 0.7076 0.3346 0.012 0.492 0.224 0.000 0.176 0.096
#> GSM564643 5 0.4011 0.5246 0.000 0.000 0.056 0.000 0.732 0.212
#> GSM564644 2 0.6487 0.5240 0.016 0.588 0.144 0.000 0.172 0.080
#> GSM564645 3 0.1845 0.5863 0.000 0.008 0.916 0.000 0.004 0.072
#> GSM564647 3 0.4079 0.4065 0.000 0.288 0.680 0.000 0.000 0.032
#> GSM564648 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564649 3 0.0725 0.6111 0.000 0.012 0.976 0.000 0.000 0.012
#> GSM564650 2 0.1026 0.7661 0.008 0.968 0.004 0.000 0.008 0.012
#> GSM564651 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564652 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564653 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564654 5 0.4011 0.5246 0.000 0.000 0.056 0.000 0.732 0.212
#> GSM564655 3 0.6044 -0.3346 0.004 0.000 0.440 0.000 0.220 0.336
#> GSM564656 3 0.5638 -0.1370 0.004 0.000 0.504 0.000 0.140 0.352
#> GSM564657 3 0.0363 0.6108 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM564658 2 0.4744 0.7009 0.032 0.728 0.000 0.000 0.124 0.116
#> GSM564659 3 0.5685 0.4304 0.000 0.136 0.656 0.000 0.104 0.104
#> GSM564660 2 0.1026 0.7661 0.008 0.968 0.004 0.000 0.008 0.012
#> GSM564661 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564662 3 0.0363 0.6108 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM564663 2 0.3441 0.7402 0.032 0.828 0.004 0.000 0.020 0.116
#> GSM564664 2 0.6872 0.4333 0.016 0.536 0.172 0.000 0.188 0.088
#> GSM564665 3 0.6696 0.1785 0.004 0.372 0.432 0.000 0.080 0.112
#> GSM564666 3 0.8249 0.0738 0.188 0.148 0.388 0.000 0.072 0.204
#> GSM564667 3 0.0363 0.6108 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM564668 6 0.5480 0.5198 0.000 0.000 0.328 0.000 0.144 0.528
#> GSM564669 6 0.5480 0.5198 0.000 0.000 0.328 0.000 0.144 0.528
#> GSM564670 2 0.3356 0.7309 0.020 0.836 0.092 0.000 0.000 0.052
#> GSM564671 6 0.6102 0.7420 0.032 0.000 0.128 0.000 0.360 0.480
#> GSM564672 3 0.0725 0.6111 0.000 0.012 0.976 0.000 0.000 0.012
#> GSM564673 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564674 2 0.3506 0.7524 0.024 0.844 0.024 0.000 0.032 0.076
#> GSM564675 3 0.8190 0.0920 0.188 0.160 0.380 0.000 0.056 0.216
#> GSM564676 2 0.0779 0.7655 0.008 0.976 0.008 0.000 0.000 0.008
#> GSM564677 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564678 2 0.3441 0.7402 0.032 0.828 0.004 0.000 0.020 0.116
#> GSM564679 2 0.4744 0.7009 0.032 0.728 0.000 0.000 0.124 0.116
#> GSM564680 3 0.1787 0.5835 0.008 0.000 0.920 0.000 0.004 0.068
#> GSM564682 2 0.3645 0.7030 0.008 0.796 0.144 0.000 0.000 0.052
#> GSM564683 3 0.0405 0.6054 0.008 0.000 0.988 0.000 0.000 0.004
#> GSM564684 6 0.6102 0.7420 0.032 0.000 0.128 0.000 0.360 0.480
#> GSM564685 3 0.1262 0.6080 0.020 0.008 0.956 0.000 0.000 0.016
#> GSM564686 6 0.7498 0.2971 0.036 0.092 0.336 0.000 0.136 0.400
#> GSM564687 2 0.6727 0.4855 0.016 0.560 0.152 0.000 0.180 0.092
#> GSM564688 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564689 2 0.0551 0.7650 0.008 0.984 0.004 0.000 0.000 0.004
#> GSM564690 2 0.0696 0.7661 0.004 0.980 0.004 0.000 0.008 0.004
#> GSM564691 2 0.3645 0.7030 0.008 0.796 0.144 0.000 0.000 0.052
#> GSM564692 5 0.0767 0.8849 0.012 0.008 0.000 0.000 0.976 0.004
#> GSM564694 3 0.7134 0.1719 0.000 0.280 0.428 0.000 0.168 0.124
#> GSM564695 3 0.6899 0.1278 0.000 0.132 0.492 0.000 0.228 0.148
#> GSM564696 3 0.4980 0.4733 0.148 0.088 0.712 0.000 0.000 0.052
#> GSM564697 2 0.1065 0.7648 0.020 0.964 0.008 0.000 0.000 0.008
#> GSM564698 3 0.5012 0.2412 0.008 0.000 0.644 0.000 0.100 0.248
#> GSM564700 6 0.6102 0.7420 0.032 0.000 0.128 0.000 0.360 0.480
#> GSM564701 5 0.0748 0.8859 0.004 0.004 0.000 0.000 0.976 0.016
#> GSM564702 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564703 4 0.0363 0.8431 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM564704 4 0.3659 0.0886 0.364 0.000 0.000 0.636 0.000 0.000
#> GSM564705 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564706 4 0.1267 0.8296 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM564707 4 0.4804 -0.4524 0.456 0.000 0.000 0.492 0.000 0.052
#> GSM564708 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564709 1 0.3499 0.8897 0.680 0.000 0.000 0.320 0.000 0.000
#> GSM564710 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564711 4 0.1267 0.8296 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM564712 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564713 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564714 4 0.1387 0.8267 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM564715 4 0.3647 0.1054 0.360 0.000 0.000 0.640 0.000 0.000
#> GSM564716 4 0.3647 0.1045 0.360 0.000 0.000 0.640 0.000 0.000
#> GSM564717 4 0.2994 0.7373 0.004 0.000 0.000 0.788 0.000 0.208
#> GSM564718 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564719 4 0.2994 0.7373 0.004 0.000 0.000 0.788 0.000 0.208
#> GSM564720 4 0.3290 0.7257 0.016 0.000 0.000 0.776 0.000 0.208
#> GSM564721 1 0.3499 0.8892 0.680 0.000 0.000 0.320 0.000 0.000
#> GSM564722 4 0.2994 0.7373 0.004 0.000 0.000 0.788 0.000 0.208
#> GSM564723 1 0.4357 0.8315 0.624 0.000 0.000 0.340 0.000 0.036
#> GSM564724 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564725 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564726 4 0.0790 0.8358 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564727 1 0.3499 0.8897 0.680 0.000 0.000 0.320 0.000 0.000
#> GSM564728 4 0.0790 0.8358 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564729 4 0.0790 0.8358 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564730 1 0.3499 0.8897 0.680 0.000 0.000 0.320 0.000 0.000
#> GSM564731 4 0.0000 0.8453 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564732 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564733 4 0.0865 0.8335 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM564734 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564735 4 0.0000 0.8453 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564736 4 0.0000 0.8453 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564737 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564738 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564739 4 0.0363 0.8431 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM564740 4 0.2595 0.7731 0.004 0.000 0.000 0.836 0.000 0.160
#> GSM564741 4 0.0363 0.8431 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM564742 4 0.1387 0.8267 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM564743 4 0.2994 0.7373 0.004 0.000 0.000 0.788 0.000 0.208
#> GSM564744 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564745 1 0.3804 0.6935 0.576 0.000 0.000 0.424 0.000 0.000
#> GSM564746 4 0.2994 0.7373 0.004 0.000 0.000 0.788 0.000 0.208
#> GSM564747 4 0.2558 0.7755 0.004 0.000 0.000 0.840 0.000 0.156
#> GSM564748 4 0.0363 0.8431 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM564749 4 0.3290 0.7257 0.016 0.000 0.000 0.776 0.000 0.208
#> GSM564750 4 0.0000 0.8453 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564751 4 0.0000 0.8453 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564752 4 0.0000 0.8453 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564753 4 0.1204 0.8312 0.000 0.000 0.000 0.944 0.000 0.056
#> GSM564754 1 0.3797 0.7133 0.580 0.000 0.000 0.420 0.000 0.000
#> GSM564755 4 0.0790 0.8358 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM564756 4 0.3563 0.2090 0.336 0.000 0.000 0.664 0.000 0.000
#> GSM564757 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564758 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564759 4 0.1387 0.8267 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM564760 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564761 1 0.3482 0.8921 0.684 0.000 0.000 0.316 0.000 0.000
#> GSM564762 4 0.0146 0.8454 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM564681 5 0.0603 0.8883 0.004 0.000 0.000 0.000 0.980 0.016
#> GSM564693 5 0.0000 0.8989 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564646 6 0.6102 0.7420 0.032 0.000 0.128 0.000 0.360 0.480
#> GSM564699 3 0.7498 -0.2509 0.036 0.092 0.400 0.000 0.136 0.336
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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 genotype/variation(p) disease.state(p) k
#> ATC:hclust 154 0.925 0.47591 2
#> ATC:hclust 112 0.749 0.03100 3
#> ATC:hclust 129 0.472 0.06751 4
#> ATC:hclust 121 0.531 0.20643 5
#> ATC:hclust 122 0.740 0.00823 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 154 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 0.996 0.996 0.5007 0.500 0.500
#> 3 3 0.657 0.735 0.765 0.2480 0.886 0.772
#> 4 4 0.606 0.601 0.642 0.1205 0.865 0.658
#> 5 5 0.586 0.778 0.750 0.0849 0.889 0.618
#> 6 6 0.638 0.779 0.779 0.0588 0.959 0.802
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
#> GSM564615 1 0.0000 0.996 1.000 0.000
#> GSM564616 2 0.0000 0.996 0.000 1.000
#> GSM564617 2 0.0000 0.996 0.000 1.000
#> GSM564618 2 0.0000 0.996 0.000 1.000
#> GSM564619 1 0.0672 0.996 0.992 0.008
#> GSM564620 1 0.0672 0.996 0.992 0.008
#> GSM564621 1 0.0672 0.996 0.992 0.008
#> GSM564622 2 0.0000 0.996 0.000 1.000
#> GSM564623 2 0.0000 0.996 0.000 1.000
#> GSM564624 2 0.0000 0.996 0.000 1.000
#> GSM564625 1 0.0376 0.996 0.996 0.004
#> GSM564626 1 0.0672 0.996 0.992 0.008
#> GSM564627 1 0.0672 0.996 0.992 0.008
#> GSM564628 2 0.0000 0.996 0.000 1.000
#> GSM564629 1 0.0672 0.996 0.992 0.008
#> GSM564630 2 0.0000 0.996 0.000 1.000
#> GSM564609 2 0.0672 0.996 0.008 0.992
#> GSM564610 1 0.0672 0.996 0.992 0.008
#> GSM564611 1 0.0672 0.996 0.992 0.008
#> GSM564612 2 0.0672 0.996 0.008 0.992
#> GSM564613 2 0.0672 0.996 0.008 0.992
#> GSM564614 1 0.0000 0.996 1.000 0.000
#> GSM564631 2 0.0672 0.996 0.008 0.992
#> GSM564632 2 0.0000 0.996 0.000 1.000
#> GSM564633 2 0.0672 0.996 0.008 0.992
#> GSM564634 2 0.0672 0.996 0.008 0.992
#> GSM564635 2 0.0672 0.996 0.008 0.992
#> GSM564636 2 0.0672 0.996 0.008 0.992
#> GSM564637 2 0.0672 0.996 0.008 0.992
#> GSM564638 2 0.0672 0.996 0.008 0.992
#> GSM564639 2 0.0672 0.996 0.008 0.992
#> GSM564640 2 0.0000 0.996 0.000 1.000
#> GSM564641 2 0.0672 0.996 0.008 0.992
#> GSM564642 2 0.0000 0.996 0.000 1.000
#> GSM564643 2 0.0000 0.996 0.000 1.000
#> GSM564644 2 0.0000 0.996 0.000 1.000
#> GSM564645 2 0.0672 0.996 0.008 0.992
#> GSM564647 2 0.0672 0.996 0.008 0.992
#> GSM564648 2 0.0000 0.996 0.000 1.000
#> GSM564649 2 0.0672 0.996 0.008 0.992
#> GSM564650 2 0.0000 0.996 0.000 1.000
#> GSM564651 2 0.0000 0.996 0.000 1.000
#> GSM564652 2 0.0000 0.996 0.000 1.000
#> GSM564653 2 0.0000 0.996 0.000 1.000
#> GSM564654 2 0.0672 0.996 0.008 0.992
#> GSM564655 2 0.0672 0.996 0.008 0.992
#> GSM564656 2 0.0672 0.996 0.008 0.992
#> GSM564657 2 0.0672 0.996 0.008 0.992
#> GSM564658 2 0.0000 0.996 0.000 1.000
#> GSM564659 2 0.0672 0.996 0.008 0.992
#> GSM564660 2 0.0000 0.996 0.000 1.000
#> GSM564661 2 0.0000 0.996 0.000 1.000
#> GSM564662 2 0.0672 0.996 0.008 0.992
#> GSM564663 2 0.0000 0.996 0.000 1.000
#> GSM564664 2 0.0000 0.996 0.000 1.000
#> GSM564665 2 0.0672 0.996 0.008 0.992
#> GSM564666 2 0.0672 0.996 0.008 0.992
#> GSM564667 2 0.0672 0.996 0.008 0.992
#> GSM564668 2 0.0672 0.996 0.008 0.992
#> GSM564669 2 0.0672 0.996 0.008 0.992
#> GSM564670 2 0.0376 0.996 0.004 0.996
#> GSM564671 2 0.0000 0.996 0.000 1.000
#> GSM564672 2 0.0672 0.996 0.008 0.992
#> GSM564673 2 0.0000 0.996 0.000 1.000
#> GSM564674 2 0.0000 0.996 0.000 1.000
#> GSM564675 2 0.0376 0.996 0.004 0.996
#> GSM564676 2 0.0000 0.996 0.000 1.000
#> GSM564677 2 0.0000 0.996 0.000 1.000
#> GSM564678 2 0.0000 0.996 0.000 1.000
#> GSM564679 2 0.0000 0.996 0.000 1.000
#> GSM564680 2 0.0672 0.996 0.008 0.992
#> GSM564682 2 0.0672 0.996 0.008 0.992
#> GSM564683 2 0.0672 0.996 0.008 0.992
#> GSM564684 2 0.0000 0.996 0.000 1.000
#> GSM564685 2 0.0672 0.996 0.008 0.992
#> GSM564686 2 0.0672 0.996 0.008 0.992
#> GSM564687 2 0.0000 0.996 0.000 1.000
#> GSM564688 2 0.0000 0.996 0.000 1.000
#> GSM564689 2 0.0000 0.996 0.000 1.000
#> GSM564690 2 0.0000 0.996 0.000 1.000
#> GSM564691 2 0.0672 0.996 0.008 0.992
#> GSM564692 2 0.0000 0.996 0.000 1.000
#> GSM564694 2 0.0000 0.996 0.000 1.000
#> GSM564695 2 0.0000 0.996 0.000 1.000
#> GSM564696 2 0.0672 0.996 0.008 0.992
#> GSM564697 2 0.0000 0.996 0.000 1.000
#> GSM564698 2 0.0672 0.996 0.008 0.992
#> GSM564700 2 0.0000 0.996 0.000 1.000
#> GSM564701 2 0.0000 0.996 0.000 1.000
#> GSM564702 2 0.0000 0.996 0.000 1.000
#> GSM564703 1 0.0000 0.996 1.000 0.000
#> GSM564704 1 0.0672 0.996 0.992 0.008
#> GSM564705 1 0.0672 0.996 0.992 0.008
#> GSM564706 1 0.0000 0.996 1.000 0.000
#> GSM564707 1 0.0672 0.996 0.992 0.008
#> GSM564708 1 0.0000 0.996 1.000 0.000
#> GSM564709 1 0.0672 0.996 0.992 0.008
#> GSM564710 1 0.0672 0.996 0.992 0.008
#> GSM564711 1 0.0000 0.996 1.000 0.000
#> GSM564712 1 0.0672 0.996 0.992 0.008
#> GSM564713 1 0.0000 0.996 1.000 0.000
#> GSM564714 1 0.0000 0.996 1.000 0.000
#> GSM564715 1 0.0672 0.996 0.992 0.008
#> GSM564716 1 0.0672 0.996 0.992 0.008
#> GSM564717 1 0.0672 0.996 0.992 0.008
#> GSM564718 1 0.0000 0.996 1.000 0.000
#> GSM564719 1 0.0672 0.996 0.992 0.008
#> GSM564720 1 0.0672 0.996 0.992 0.008
#> GSM564721 1 0.0672 0.996 0.992 0.008
#> GSM564722 1 0.0000 0.996 1.000 0.000
#> GSM564723 1 0.0672 0.996 0.992 0.008
#> GSM564724 1 0.0000 0.996 1.000 0.000
#> GSM564725 1 0.0672 0.996 0.992 0.008
#> GSM564726 1 0.0000 0.996 1.000 0.000
#> GSM564727 1 0.0672 0.996 0.992 0.008
#> GSM564728 1 0.0000 0.996 1.000 0.000
#> GSM564729 1 0.0376 0.996 0.996 0.004
#> GSM564730 1 0.0672 0.996 0.992 0.008
#> GSM564731 1 0.0000 0.996 1.000 0.000
#> GSM564732 1 0.0000 0.996 1.000 0.000
#> GSM564733 1 0.0000 0.996 1.000 0.000
#> GSM564734 1 0.0376 0.996 0.996 0.004
#> GSM564735 1 0.0000 0.996 1.000 0.000
#> GSM564736 1 0.0000 0.996 1.000 0.000
#> GSM564737 1 0.0672 0.996 0.992 0.008
#> GSM564738 1 0.0000 0.996 1.000 0.000
#> GSM564739 1 0.0000 0.996 1.000 0.000
#> GSM564740 1 0.0000 0.996 1.000 0.000
#> GSM564741 1 0.0000 0.996 1.000 0.000
#> GSM564742 1 0.0000 0.996 1.000 0.000
#> GSM564743 1 0.0672 0.996 0.992 0.008
#> GSM564744 1 0.0672 0.996 0.992 0.008
#> GSM564745 1 0.0672 0.996 0.992 0.008
#> GSM564746 1 0.0672 0.996 0.992 0.008
#> GSM564747 1 0.0000 0.996 1.000 0.000
#> GSM564748 1 0.0000 0.996 1.000 0.000
#> GSM564749 1 0.0672 0.996 0.992 0.008
#> GSM564750 1 0.0000 0.996 1.000 0.000
#> GSM564751 1 0.0000 0.996 1.000 0.000
#> GSM564752 1 0.0000 0.996 1.000 0.000
#> GSM564753 1 0.0000 0.996 1.000 0.000
#> GSM564754 1 0.0672 0.996 0.992 0.008
#> GSM564755 1 0.0000 0.996 1.000 0.000
#> GSM564756 1 0.0672 0.996 0.992 0.008
#> GSM564757 1 0.0000 0.996 1.000 0.000
#> GSM564758 1 0.0000 0.996 1.000 0.000
#> GSM564759 1 0.0000 0.996 1.000 0.000
#> GSM564760 1 0.0376 0.996 0.996 0.004
#> GSM564761 1 0.0672 0.996 0.992 0.008
#> GSM564762 1 0.0000 0.996 1.000 0.000
#> GSM564681 2 0.0000 0.996 0.000 1.000
#> GSM564693 2 0.0000 0.996 0.000 1.000
#> GSM564646 2 0.0000 0.996 0.000 1.000
#> GSM564699 2 0.0672 0.996 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564616 2 0.6140 0.970 0.000 0.596 0.404
#> GSM564617 3 0.4346 0.616 0.000 0.184 0.816
#> GSM564618 2 0.6154 0.974 0.000 0.592 0.408
#> GSM564619 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564620 1 0.2356 0.852 0.928 0.072 0.000
#> GSM564621 1 0.0237 0.837 0.996 0.004 0.000
#> GSM564622 2 0.6192 0.955 0.000 0.580 0.420
#> GSM564623 3 0.3267 0.665 0.000 0.116 0.884
#> GSM564624 3 0.4887 0.558 0.000 0.228 0.772
#> GSM564625 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564626 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564627 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564628 3 0.5785 0.223 0.000 0.332 0.668
#> GSM564629 1 0.5988 0.857 0.632 0.368 0.000
#> GSM564630 3 0.5016 0.532 0.000 0.240 0.760
#> GSM564609 3 0.5058 0.184 0.000 0.244 0.756
#> GSM564610 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564611 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564612 3 0.1411 0.708 0.000 0.036 0.964
#> GSM564613 3 0.0237 0.714 0.000 0.004 0.996
#> GSM564614 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564631 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564632 2 0.6168 0.967 0.000 0.588 0.412
#> GSM564633 3 0.5988 -0.328 0.000 0.368 0.632
#> GSM564634 3 0.0237 0.714 0.000 0.004 0.996
#> GSM564635 3 0.4452 0.367 0.000 0.192 0.808
#> GSM564636 3 0.0424 0.714 0.000 0.008 0.992
#> GSM564637 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564638 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564639 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564640 3 0.5216 0.485 0.000 0.260 0.740
#> GSM564641 3 0.0892 0.713 0.000 0.020 0.980
#> GSM564642 3 0.4605 0.595 0.000 0.204 0.796
#> GSM564643 2 0.6192 0.955 0.000 0.580 0.420
#> GSM564644 3 0.4887 0.558 0.000 0.228 0.772
#> GSM564645 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564647 3 0.0747 0.714 0.000 0.016 0.984
#> GSM564648 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564649 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564650 3 0.4702 0.585 0.000 0.212 0.788
#> GSM564651 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564652 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564653 2 0.6154 0.974 0.000 0.592 0.408
#> GSM564654 3 0.6286 -0.632 0.000 0.464 0.536
#> GSM564655 3 0.5835 -0.235 0.000 0.340 0.660
#> GSM564656 3 0.5058 0.184 0.000 0.244 0.756
#> GSM564657 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564658 3 0.5678 0.293 0.000 0.316 0.684
#> GSM564659 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564660 3 0.4702 0.585 0.000 0.212 0.788
#> GSM564661 2 0.6140 0.970 0.000 0.596 0.404
#> GSM564662 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564663 3 0.4887 0.558 0.000 0.228 0.772
#> GSM564664 3 0.5621 0.316 0.000 0.308 0.692
#> GSM564665 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564666 3 0.0000 0.715 0.000 0.000 1.000
#> GSM564667 3 0.0424 0.714 0.000 0.008 0.992
#> GSM564668 3 0.6140 -0.444 0.000 0.404 0.596
#> GSM564669 3 0.5882 -0.263 0.000 0.348 0.652
#> GSM564670 3 0.0892 0.713 0.000 0.020 0.980
#> GSM564671 2 0.6235 0.923 0.000 0.564 0.436
#> GSM564672 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564673 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564674 3 0.4887 0.558 0.000 0.228 0.772
#> GSM564675 3 0.3267 0.665 0.000 0.116 0.884
#> GSM564676 3 0.4750 0.579 0.000 0.216 0.784
#> GSM564677 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564678 3 0.4887 0.558 0.000 0.228 0.772
#> GSM564679 3 0.5733 0.260 0.000 0.324 0.676
#> GSM564680 3 0.0424 0.712 0.000 0.008 0.992
#> GSM564682 3 0.0892 0.713 0.000 0.020 0.980
#> GSM564683 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564684 2 0.6260 0.905 0.000 0.552 0.448
#> GSM564685 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564686 3 0.3340 0.665 0.000 0.120 0.880
#> GSM564687 3 0.4842 0.566 0.000 0.224 0.776
#> GSM564688 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564689 3 0.4654 0.590 0.000 0.208 0.792
#> GSM564690 3 0.4887 0.558 0.000 0.228 0.772
#> GSM564691 3 0.1411 0.708 0.000 0.036 0.964
#> GSM564692 2 0.6140 0.970 0.000 0.596 0.404
#> GSM564694 3 0.4399 0.607 0.000 0.188 0.812
#> GSM564695 3 0.4654 0.591 0.000 0.208 0.792
#> GSM564696 3 0.0237 0.715 0.000 0.004 0.996
#> GSM564697 3 0.4346 0.616 0.000 0.184 0.816
#> GSM564698 3 0.4974 0.214 0.000 0.236 0.764
#> GSM564700 2 0.6180 0.961 0.000 0.584 0.416
#> GSM564701 2 0.6154 0.974 0.000 0.592 0.408
#> GSM564702 2 0.6140 0.976 0.000 0.596 0.404
#> GSM564703 1 0.5529 0.870 0.704 0.296 0.000
#> GSM564704 1 0.0592 0.839 0.988 0.012 0.000
#> GSM564705 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564706 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564707 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564708 1 0.5560 0.868 0.700 0.300 0.000
#> GSM564709 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564710 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564711 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564712 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564713 1 0.5621 0.868 0.692 0.308 0.000
#> GSM564714 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564715 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564716 1 0.0592 0.839 0.988 0.012 0.000
#> GSM564717 1 0.3340 0.826 0.880 0.120 0.000
#> GSM564718 1 0.5926 0.862 0.644 0.356 0.000
#> GSM564719 1 0.4931 0.851 0.768 0.232 0.000
#> GSM564720 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564721 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564722 1 0.5465 0.858 0.712 0.288 0.000
#> GSM564723 1 0.1860 0.826 0.948 0.052 0.000
#> GSM564724 1 0.5560 0.868 0.700 0.300 0.000
#> GSM564725 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564726 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564727 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564728 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564729 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564730 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564731 1 0.6026 0.858 0.624 0.376 0.000
#> GSM564732 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564733 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564734 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564735 1 0.5926 0.862 0.644 0.356 0.000
#> GSM564736 1 0.5621 0.868 0.692 0.308 0.000
#> GSM564737 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564738 1 0.5835 0.865 0.660 0.340 0.000
#> GSM564739 1 0.5431 0.869 0.716 0.284 0.000
#> GSM564740 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564741 1 0.5621 0.868 0.692 0.308 0.000
#> GSM564742 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564743 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564744 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564745 1 0.0747 0.840 0.984 0.016 0.000
#> GSM564746 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564747 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564748 1 0.5591 0.869 0.696 0.304 0.000
#> GSM564749 1 0.3038 0.820 0.896 0.104 0.000
#> GSM564750 1 0.5621 0.868 0.692 0.308 0.000
#> GSM564751 1 0.6026 0.858 0.624 0.376 0.000
#> GSM564752 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564753 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564754 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564755 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564756 1 0.4887 0.870 0.772 0.228 0.000
#> GSM564757 1 0.5926 0.862 0.644 0.356 0.000
#> GSM564758 1 0.5926 0.862 0.644 0.356 0.000
#> GSM564759 1 0.6126 0.852 0.600 0.400 0.000
#> GSM564760 1 0.5529 0.868 0.704 0.296 0.000
#> GSM564761 1 0.0000 0.836 1.000 0.000 0.000
#> GSM564762 1 0.5926 0.862 0.644 0.356 0.000
#> GSM564681 2 0.6140 0.970 0.000 0.596 0.404
#> GSM564693 2 0.6140 0.970 0.000 0.596 0.404
#> GSM564646 2 0.6235 0.921 0.000 0.564 0.436
#> GSM564699 3 0.0237 0.715 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.0376 0.7669 0.004 0.004 0.000 0.992
#> GSM564616 2 0.7776 0.8296 0.340 0.412 0.248 0.000
#> GSM564617 3 0.4977 0.5290 0.000 0.460 0.540 0.000
#> GSM564618 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564619 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564620 4 0.6451 -0.6239 0.404 0.072 0.000 0.524
#> GSM564621 1 0.5602 0.7975 0.508 0.020 0.000 0.472
#> GSM564622 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564623 3 0.4713 0.5718 0.000 0.360 0.640 0.000
#> GSM564624 3 0.4994 0.5043 0.000 0.480 0.520 0.000
#> GSM564625 4 0.0657 0.7680 0.004 0.012 0.000 0.984
#> GSM564626 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564627 1 0.7576 0.5705 0.452 0.204 0.000 0.344
#> GSM564628 2 0.4941 -0.3629 0.000 0.564 0.436 0.000
#> GSM564629 4 0.6897 0.3517 0.180 0.228 0.000 0.592
#> GSM564630 3 0.5000 0.4764 0.000 0.496 0.504 0.000
#> GSM564609 3 0.3400 0.3240 0.000 0.180 0.820 0.000
#> GSM564610 1 0.7478 0.5888 0.468 0.188 0.000 0.344
#> GSM564611 1 0.7451 0.5919 0.472 0.184 0.000 0.344
#> GSM564612 3 0.4679 0.5938 0.000 0.352 0.648 0.000
#> GSM564613 3 0.4697 0.5929 0.000 0.356 0.644 0.000
#> GSM564614 4 0.0524 0.7662 0.004 0.008 0.000 0.988
#> GSM564631 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564632 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564633 3 0.4567 0.0622 0.008 0.276 0.716 0.000
#> GSM564634 3 0.4643 0.5959 0.000 0.344 0.656 0.000
#> GSM564635 3 0.2973 0.3955 0.000 0.144 0.856 0.000
#> GSM564636 3 0.0592 0.6015 0.000 0.016 0.984 0.000
#> GSM564637 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564638 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564639 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564640 2 0.4996 -0.4666 0.000 0.516 0.484 0.000
#> GSM564641 3 0.2469 0.6021 0.000 0.108 0.892 0.000
#> GSM564642 3 0.4981 0.5254 0.000 0.464 0.536 0.000
#> GSM564643 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564644 3 0.4994 0.5043 0.000 0.480 0.520 0.000
#> GSM564645 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564647 3 0.4661 0.5948 0.000 0.348 0.652 0.000
#> GSM564648 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564649 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564650 3 0.4981 0.5254 0.000 0.464 0.536 0.000
#> GSM564651 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564652 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564653 2 0.7803 0.8346 0.340 0.404 0.256 0.000
#> GSM564654 2 0.7922 0.7447 0.340 0.340 0.320 0.000
#> GSM564655 3 0.4630 0.1213 0.016 0.252 0.732 0.000
#> GSM564656 3 0.3400 0.3240 0.000 0.180 0.820 0.000
#> GSM564657 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564658 2 0.4961 -0.3876 0.000 0.552 0.448 0.000
#> GSM564659 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564660 3 0.4981 0.5254 0.000 0.464 0.536 0.000
#> GSM564661 2 0.7776 0.8296 0.340 0.412 0.248 0.000
#> GSM564662 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564663 3 0.4994 0.5043 0.000 0.480 0.520 0.000
#> GSM564664 3 0.4989 0.4434 0.000 0.472 0.528 0.000
#> GSM564665 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564666 3 0.4331 0.6004 0.000 0.288 0.712 0.000
#> GSM564667 3 0.0336 0.6010 0.000 0.008 0.992 0.000
#> GSM564668 3 0.7834 -0.6283 0.284 0.308 0.408 0.000
#> GSM564669 3 0.4134 0.1265 0.000 0.260 0.740 0.000
#> GSM564670 3 0.4697 0.5929 0.000 0.356 0.644 0.000
#> GSM564671 2 0.7858 0.8085 0.316 0.396 0.288 0.000
#> GSM564672 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564673 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564674 3 0.4994 0.5043 0.000 0.480 0.520 0.000
#> GSM564675 3 0.4713 0.5718 0.000 0.360 0.640 0.000
#> GSM564676 3 0.4989 0.5154 0.000 0.472 0.528 0.000
#> GSM564677 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564678 3 0.4994 0.5043 0.000 0.480 0.520 0.000
#> GSM564679 2 0.4955 -0.3791 0.000 0.556 0.444 0.000
#> GSM564680 3 0.0188 0.5966 0.000 0.004 0.996 0.000
#> GSM564682 3 0.4697 0.5929 0.000 0.356 0.644 0.000
#> GSM564683 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564684 2 0.7871 0.7530 0.284 0.384 0.332 0.000
#> GSM564685 3 0.0000 0.6005 0.000 0.000 1.000 0.000
#> GSM564686 3 0.3356 0.5791 0.000 0.176 0.824 0.000
#> GSM564687 3 0.4981 0.5108 0.000 0.464 0.536 0.000
#> GSM564688 2 0.7803 0.8346 0.340 0.404 0.256 0.000
#> GSM564689 3 0.4981 0.5254 0.000 0.464 0.536 0.000
#> GSM564690 3 0.4994 0.5043 0.000 0.480 0.520 0.000
#> GSM564691 3 0.4713 0.5911 0.000 0.360 0.640 0.000
#> GSM564692 2 0.7790 0.8322 0.340 0.408 0.252 0.000
#> GSM564694 3 0.4955 0.5328 0.000 0.444 0.556 0.000
#> GSM564695 3 0.4981 0.5254 0.000 0.464 0.536 0.000
#> GSM564696 3 0.0188 0.6013 0.000 0.004 0.996 0.000
#> GSM564697 3 0.4977 0.5290 0.000 0.460 0.540 0.000
#> GSM564698 3 0.3266 0.3481 0.000 0.168 0.832 0.000
#> GSM564700 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564701 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564702 2 0.7816 0.8361 0.340 0.400 0.260 0.000
#> GSM564703 4 0.1297 0.7573 0.020 0.016 0.000 0.964
#> GSM564704 1 0.5607 0.7766 0.492 0.020 0.000 0.488
#> GSM564705 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564706 4 0.5217 0.6332 0.108 0.136 0.000 0.756
#> GSM564707 1 0.5378 0.8281 0.540 0.012 0.000 0.448
#> GSM564708 4 0.0469 0.7690 0.000 0.012 0.000 0.988
#> GSM564709 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564710 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564711 4 0.5452 0.6098 0.108 0.156 0.000 0.736
#> GSM564712 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564713 4 0.0469 0.7690 0.000 0.012 0.000 0.988
#> GSM564714 4 0.6127 0.5081 0.108 0.228 0.000 0.664
#> GSM564715 1 0.5483 0.8265 0.536 0.016 0.000 0.448
#> GSM564716 1 0.5607 0.7766 0.492 0.020 0.000 0.488
#> GSM564717 1 0.7710 0.4976 0.408 0.224 0.000 0.368
#> GSM564718 4 0.2670 0.7568 0.072 0.024 0.000 0.904
#> GSM564719 4 0.7572 -0.1713 0.300 0.224 0.000 0.476
#> GSM564720 1 0.7451 0.5919 0.472 0.184 0.000 0.344
#> GSM564721 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564722 4 0.7369 0.0708 0.248 0.228 0.000 0.524
#> GSM564723 1 0.4866 0.7992 0.596 0.000 0.000 0.404
#> GSM564724 4 0.0524 0.7662 0.004 0.008 0.000 0.988
#> GSM564725 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564726 4 0.0524 0.7662 0.004 0.008 0.000 0.988
#> GSM564727 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564728 4 0.0524 0.7662 0.004 0.008 0.000 0.988
#> GSM564729 4 0.0524 0.7662 0.004 0.008 0.000 0.988
#> GSM564730 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564731 4 0.3279 0.7392 0.096 0.032 0.000 0.872
#> GSM564732 4 0.0657 0.7680 0.004 0.012 0.000 0.984
#> GSM564733 4 0.0779 0.7667 0.004 0.016 0.000 0.980
#> GSM564734 4 0.0657 0.7680 0.004 0.012 0.000 0.984
#> GSM564735 4 0.2256 0.7633 0.056 0.020 0.000 0.924
#> GSM564736 4 0.0469 0.7696 0.000 0.012 0.000 0.988
#> GSM564737 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564738 4 0.2256 0.7636 0.056 0.020 0.000 0.924
#> GSM564739 4 0.1406 0.7513 0.024 0.016 0.000 0.960
#> GSM564740 4 0.6127 0.5081 0.108 0.228 0.000 0.664
#> GSM564741 4 0.0707 0.7697 0.000 0.020 0.000 0.980
#> GSM564742 4 0.6187 0.5101 0.108 0.236 0.000 0.656
#> GSM564743 1 0.7553 0.5747 0.456 0.200 0.000 0.344
#> GSM564744 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564745 4 0.5296 -0.7798 0.496 0.008 0.000 0.496
#> GSM564746 1 0.7687 0.5328 0.428 0.224 0.000 0.348
#> GSM564747 4 0.6187 0.5101 0.108 0.236 0.000 0.656
#> GSM564748 4 0.0779 0.7689 0.004 0.016 0.000 0.980
#> GSM564749 1 0.7553 0.5756 0.456 0.200 0.000 0.344
#> GSM564750 4 0.0469 0.7690 0.000 0.012 0.000 0.988
#> GSM564751 4 0.3308 0.7397 0.092 0.036 0.000 0.872
#> GSM564752 4 0.3734 0.7184 0.108 0.044 0.000 0.848
#> GSM564753 4 0.3978 0.7149 0.108 0.056 0.000 0.836
#> GSM564754 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564755 4 0.0524 0.7662 0.004 0.008 0.000 0.988
#> GSM564756 4 0.2611 0.6190 0.096 0.008 0.000 0.896
#> GSM564757 4 0.2670 0.7552 0.072 0.024 0.000 0.904
#> GSM564758 4 0.2773 0.7538 0.072 0.028 0.000 0.900
#> GSM564759 4 0.5314 0.6337 0.108 0.144 0.000 0.748
#> GSM564760 4 0.0657 0.7680 0.004 0.012 0.000 0.984
#> GSM564761 1 0.4961 0.8326 0.552 0.000 0.000 0.448
#> GSM564762 4 0.2871 0.7532 0.072 0.032 0.000 0.896
#> GSM564681 2 0.7776 0.8296 0.340 0.412 0.248 0.000
#> GSM564693 2 0.7761 0.8252 0.340 0.416 0.244 0.000
#> GSM564646 2 0.7521 0.6217 0.220 0.488 0.292 0.000
#> GSM564699 3 0.0000 0.6005 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.468 0.8359 0.300 0.028 0.004 0.668 0.000
#> GSM564616 5 0.455 0.9297 0.000 0.100 0.100 0.020 0.780
#> GSM564617 2 0.381 0.8661 0.000 0.772 0.204 0.024 0.000
#> GSM564618 5 0.436 0.9330 0.000 0.100 0.100 0.012 0.788
#> GSM564619 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564620 1 0.512 0.6438 0.772 0.048 0.020 0.096 0.064
#> GSM564621 1 0.240 0.7696 0.916 0.040 0.004 0.016 0.024
#> GSM564622 5 0.478 0.9188 0.000 0.088 0.104 0.036 0.772
#> GSM564623 2 0.619 0.6452 0.000 0.552 0.308 0.132 0.008
#> GSM564624 2 0.373 0.8671 0.000 0.792 0.184 0.016 0.008
#> GSM564625 4 0.499 0.8370 0.304 0.044 0.004 0.648 0.000
#> GSM564626 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564627 1 0.777 0.5711 0.568 0.076 0.088 0.084 0.184
#> GSM564628 2 0.441 0.8064 0.000 0.788 0.120 0.020 0.072
#> GSM564629 4 0.901 0.3670 0.256 0.100 0.088 0.400 0.156
#> GSM564630 2 0.451 0.8555 0.000 0.760 0.176 0.048 0.016
#> GSM564609 3 0.392 0.7687 0.000 0.000 0.804 0.092 0.104
#> GSM564610 1 0.771 0.5800 0.576 0.080 0.088 0.080 0.176
#> GSM564611 1 0.761 0.5805 0.584 0.068 0.088 0.084 0.176
#> GSM564612 2 0.499 0.7068 0.000 0.600 0.360 0.040 0.000
#> GSM564613 2 0.478 0.7770 0.000 0.664 0.292 0.044 0.000
#> GSM564614 4 0.475 0.8349 0.300 0.024 0.004 0.668 0.004
#> GSM564631 3 0.196 0.8584 0.000 0.096 0.904 0.000 0.000
#> GSM564632 5 0.454 0.9255 0.000 0.092 0.100 0.024 0.784
#> GSM564633 3 0.476 0.6137 0.000 0.000 0.712 0.076 0.212
#> GSM564634 2 0.516 0.7531 0.000 0.628 0.308 0.064 0.000
#> GSM564635 3 0.320 0.8112 0.000 0.024 0.868 0.028 0.080
#> GSM564636 3 0.247 0.8471 0.000 0.104 0.884 0.012 0.000
#> GSM564637 3 0.435 0.8140 0.000 0.096 0.784 0.112 0.008
#> GSM564638 3 0.191 0.8591 0.000 0.092 0.908 0.000 0.000
#> GSM564639 3 0.279 0.8556 0.000 0.092 0.880 0.020 0.008
#> GSM564640 2 0.396 0.8538 0.000 0.796 0.160 0.012 0.032
#> GSM564641 3 0.458 0.5016 0.000 0.268 0.692 0.040 0.000
#> GSM564642 2 0.424 0.8634 0.000 0.752 0.200 0.048 0.000
#> GSM564643 5 0.453 0.9232 0.000 0.088 0.104 0.024 0.784
#> GSM564644 2 0.352 0.8686 0.000 0.800 0.184 0.008 0.008
#> GSM564645 3 0.196 0.8584 0.000 0.096 0.904 0.000 0.000
#> GSM564647 2 0.499 0.7038 0.000 0.600 0.360 0.040 0.000
#> GSM564648 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564649 3 0.225 0.8560 0.000 0.096 0.896 0.008 0.000
#> GSM564650 2 0.386 0.8674 0.000 0.772 0.200 0.028 0.000
#> GSM564651 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564652 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564653 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564654 5 0.467 0.8360 0.000 0.028 0.164 0.048 0.760
#> GSM564655 3 0.570 0.5962 0.000 0.008 0.652 0.152 0.188
#> GSM564656 3 0.375 0.7723 0.000 0.000 0.816 0.080 0.104
#> GSM564657 3 0.212 0.8572 0.000 0.096 0.900 0.004 0.000
#> GSM564658 2 0.434 0.8257 0.000 0.788 0.136 0.020 0.056
#> GSM564659 3 0.196 0.8584 0.000 0.096 0.904 0.000 0.000
#> GSM564660 2 0.390 0.8686 0.000 0.776 0.196 0.024 0.004
#> GSM564661 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564662 3 0.196 0.8584 0.000 0.096 0.904 0.000 0.000
#> GSM564663 2 0.373 0.8671 0.000 0.792 0.184 0.016 0.008
#> GSM564664 2 0.575 0.7566 0.000 0.652 0.244 0.068 0.036
#> GSM564665 3 0.274 0.8556 0.000 0.096 0.876 0.028 0.000
#> GSM564666 2 0.627 0.5374 0.000 0.496 0.364 0.136 0.004
#> GSM564667 3 0.225 0.8559 0.000 0.096 0.896 0.008 0.000
#> GSM564668 5 0.595 0.6028 0.000 0.008 0.268 0.124 0.600
#> GSM564669 3 0.495 0.6563 0.000 0.000 0.712 0.120 0.168
#> GSM564670 2 0.471 0.7818 0.000 0.668 0.292 0.040 0.000
#> GSM564671 5 0.620 0.8265 0.000 0.080 0.144 0.112 0.664
#> GSM564672 3 0.196 0.8584 0.000 0.096 0.904 0.000 0.000
#> GSM564673 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564674 2 0.352 0.8684 0.000 0.800 0.184 0.008 0.008
#> GSM564675 2 0.619 0.6452 0.000 0.552 0.308 0.132 0.008
#> GSM564676 2 0.312 0.8700 0.000 0.812 0.184 0.000 0.004
#> GSM564677 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564678 2 0.373 0.8686 0.000 0.792 0.184 0.016 0.008
#> GSM564679 2 0.441 0.8064 0.000 0.788 0.120 0.020 0.072
#> GSM564680 3 0.302 0.8528 0.000 0.088 0.872 0.028 0.012
#> GSM564682 2 0.482 0.7596 0.000 0.644 0.316 0.040 0.000
#> GSM564683 3 0.191 0.8591 0.000 0.092 0.908 0.000 0.000
#> GSM564684 5 0.684 0.7351 0.000 0.084 0.196 0.128 0.592
#> GSM564685 3 0.207 0.8592 0.000 0.092 0.904 0.004 0.000
#> GSM564686 3 0.627 0.3644 0.000 0.292 0.552 0.148 0.008
#> GSM564687 2 0.436 0.8566 0.000 0.748 0.208 0.036 0.008
#> GSM564688 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564689 2 0.374 0.8680 0.000 0.780 0.196 0.024 0.000
#> GSM564690 2 0.337 0.8690 0.000 0.808 0.180 0.004 0.008
#> GSM564691 2 0.470 0.7765 0.000 0.660 0.304 0.036 0.000
#> GSM564692 5 0.425 0.9341 0.000 0.100 0.100 0.008 0.792
#> GSM564694 2 0.465 0.8445 0.000 0.720 0.212 0.068 0.000
#> GSM564695 2 0.443 0.8679 0.000 0.748 0.196 0.052 0.004
#> GSM564696 3 0.412 0.7953 0.000 0.108 0.788 0.104 0.000
#> GSM564697 2 0.363 0.8679 0.000 0.780 0.204 0.016 0.000
#> GSM564698 3 0.412 0.7631 0.000 0.000 0.788 0.112 0.100
#> GSM564700 5 0.519 0.9058 0.000 0.088 0.104 0.060 0.748
#> GSM564701 5 0.455 0.9297 0.000 0.100 0.100 0.020 0.780
#> GSM564702 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564703 4 0.530 0.8391 0.300 0.048 0.008 0.640 0.004
#> GSM564704 1 0.300 0.7460 0.888 0.040 0.004 0.044 0.024
#> GSM564705 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.761 0.6419 0.216 0.084 0.056 0.564 0.080
#> GSM564707 1 0.136 0.7846 0.956 0.028 0.004 0.000 0.012
#> GSM564708 4 0.476 0.8396 0.296 0.020 0.004 0.672 0.008
#> GSM564709 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564710 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.800 0.6097 0.216 0.100 0.064 0.532 0.088
#> GSM564712 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.476 0.8396 0.296 0.020 0.004 0.672 0.008
#> GSM564714 4 0.873 0.4934 0.216 0.124 0.084 0.460 0.116
#> GSM564715 1 0.191 0.7807 0.932 0.032 0.004 0.000 0.032
#> GSM564716 1 0.300 0.7460 0.888 0.040 0.004 0.044 0.024
#> GSM564717 1 0.824 0.5191 0.524 0.084 0.088 0.120 0.184
#> GSM564718 4 0.503 0.8319 0.252 0.036 0.004 0.692 0.016
#> GSM564719 1 0.898 0.1860 0.396 0.088 0.084 0.252 0.180
#> GSM564720 1 0.761 0.5805 0.584 0.068 0.088 0.084 0.176
#> GSM564721 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564722 1 0.909 -0.0495 0.340 0.092 0.084 0.308 0.176
#> GSM564723 1 0.205 0.7655 0.920 0.000 0.000 0.052 0.028
#> GSM564724 4 0.473 0.8370 0.296 0.024 0.004 0.672 0.004
#> GSM564725 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564726 4 0.475 0.8349 0.300 0.024 0.004 0.668 0.004
#> GSM564727 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564728 4 0.475 0.8349 0.300 0.024 0.004 0.668 0.004
#> GSM564729 4 0.491 0.8327 0.300 0.032 0.004 0.660 0.004
#> GSM564730 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564731 4 0.509 0.8178 0.240 0.064 0.004 0.688 0.004
#> GSM564732 4 0.499 0.8370 0.304 0.044 0.004 0.648 0.000
#> GSM564733 4 0.504 0.8358 0.304 0.040 0.008 0.648 0.000
#> GSM564734 4 0.499 0.8370 0.304 0.044 0.004 0.648 0.000
#> GSM564735 4 0.514 0.8282 0.256 0.060 0.004 0.676 0.004
#> GSM564736 4 0.490 0.8393 0.300 0.040 0.004 0.656 0.000
#> GSM564737 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.498 0.8379 0.268 0.028 0.004 0.684 0.016
#> GSM564739 4 0.465 0.8394 0.304 0.020 0.008 0.668 0.000
#> GSM564740 4 0.877 0.4548 0.216 0.092 0.084 0.448 0.160
#> GSM564741 4 0.510 0.8407 0.300 0.036 0.008 0.652 0.004
#> GSM564742 4 0.880 0.4948 0.216 0.128 0.088 0.452 0.116
#> GSM564743 1 0.777 0.5711 0.568 0.076 0.088 0.084 0.184
#> GSM564744 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564745 1 0.233 0.7409 0.912 0.020 0.004 0.060 0.004
#> GSM564746 1 0.777 0.5711 0.568 0.076 0.088 0.084 0.184
#> GSM564747 4 0.879 0.4991 0.220 0.128 0.088 0.452 0.112
#> GSM564748 4 0.530 0.8391 0.300 0.048 0.008 0.640 0.004
#> GSM564749 1 0.766 0.5769 0.580 0.072 0.088 0.084 0.176
#> GSM564750 4 0.476 0.8396 0.296 0.020 0.004 0.672 0.008
#> GSM564751 4 0.512 0.8193 0.244 0.064 0.004 0.684 0.004
#> GSM564752 4 0.483 0.8007 0.216 0.060 0.000 0.716 0.008
#> GSM564753 4 0.504 0.7971 0.216 0.072 0.004 0.704 0.004
#> GSM564754 1 0.029 0.7912 0.992 0.008 0.000 0.000 0.000
#> GSM564755 4 0.475 0.8349 0.300 0.024 0.004 0.668 0.004
#> GSM564756 4 0.556 0.6877 0.396 0.044 0.004 0.548 0.008
#> GSM564757 4 0.433 0.8387 0.252 0.032 0.000 0.716 0.000
#> GSM564758 4 0.417 0.8386 0.252 0.024 0.000 0.724 0.000
#> GSM564759 4 0.762 0.6612 0.216 0.108 0.060 0.560 0.056
#> GSM564760 4 0.499 0.8370 0.304 0.044 0.004 0.648 0.000
#> GSM564761 1 0.000 0.7931 1.000 0.000 0.000 0.000 0.000
#> GSM564762 4 0.479 0.8352 0.256 0.048 0.004 0.692 0.000
#> GSM564681 5 0.455 0.9297 0.000 0.100 0.100 0.020 0.780
#> GSM564693 5 0.397 0.9364 0.000 0.100 0.100 0.000 0.800
#> GSM564646 5 0.740 0.5526 0.000 0.296 0.112 0.104 0.488
#> GSM564699 3 0.486 0.7846 0.000 0.096 0.736 0.160 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.3711 0.8355 0.156 0.008 0.012 0.800 0.008 0.016
#> GSM564616 5 0.2084 0.9012 0.000 0.044 0.000 0.016 0.916 0.024
#> GSM564617 2 0.3157 0.8505 0.000 0.860 0.024 0.020 0.080 0.016
#> GSM564618 5 0.1577 0.9109 0.000 0.036 0.000 0.008 0.940 0.016
#> GSM564619 1 0.0146 0.9231 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM564620 1 0.5979 0.4965 0.656 0.020 0.032 0.092 0.020 0.180
#> GSM564621 1 0.3542 0.8395 0.856 0.016 0.028 0.036 0.020 0.044
#> GSM564622 5 0.1857 0.9053 0.000 0.028 0.004 0.000 0.924 0.044
#> GSM564623 2 0.6499 0.6580 0.000 0.616 0.104 0.056 0.064 0.160
#> GSM564624 2 0.3422 0.8488 0.000 0.832 0.000 0.032 0.100 0.036
#> GSM564625 4 0.4722 0.8348 0.156 0.016 0.052 0.748 0.012 0.016
#> GSM564626 1 0.0291 0.9231 0.992 0.004 0.000 0.004 0.000 0.000
#> GSM564627 6 0.4234 0.6879 0.324 0.000 0.000 0.032 0.000 0.644
#> GSM564628 2 0.3395 0.8383 0.000 0.820 0.000 0.020 0.132 0.028
#> GSM564629 6 0.6009 0.6117 0.132 0.004 0.036 0.228 0.004 0.596
#> GSM564630 2 0.4378 0.8403 0.000 0.776 0.004 0.060 0.104 0.056
#> GSM564609 3 0.6561 0.7082 0.000 0.076 0.604 0.040 0.116 0.164
#> GSM564610 6 0.5183 0.6484 0.340 0.012 0.020 0.028 0.004 0.596
#> GSM564611 6 0.4389 0.6463 0.372 0.000 0.000 0.032 0.000 0.596
#> GSM564612 2 0.4774 0.6176 0.000 0.684 0.240 0.008 0.012 0.056
#> GSM564613 2 0.3809 0.7625 0.000 0.808 0.104 0.044 0.000 0.044
#> GSM564614 4 0.2921 0.8361 0.156 0.008 0.000 0.828 0.000 0.008
#> GSM564631 3 0.2838 0.8320 0.000 0.188 0.808 0.000 0.004 0.000
#> GSM564632 5 0.2316 0.8929 0.000 0.028 0.004 0.004 0.900 0.064
#> GSM564633 3 0.6093 0.6172 0.000 0.028 0.608 0.024 0.192 0.148
#> GSM564634 2 0.4526 0.7349 0.000 0.756 0.116 0.052 0.000 0.076
#> GSM564635 3 0.5180 0.7761 0.000 0.096 0.728 0.020 0.096 0.060
#> GSM564636 3 0.3485 0.8111 0.000 0.204 0.772 0.004 0.000 0.020
#> GSM564637 3 0.5737 0.7662 0.000 0.196 0.612 0.024 0.004 0.164
#> GSM564638 3 0.2805 0.8327 0.000 0.184 0.812 0.000 0.004 0.000
#> GSM564639 3 0.3900 0.8285 0.000 0.184 0.764 0.004 0.004 0.044
#> GSM564640 2 0.3191 0.8436 0.000 0.832 0.000 0.016 0.128 0.024
#> GSM564641 3 0.4787 0.4767 0.000 0.388 0.564 0.008 0.000 0.040
#> GSM564642 2 0.4101 0.8446 0.000 0.796 0.008 0.028 0.096 0.072
#> GSM564643 5 0.2369 0.8930 0.000 0.028 0.004 0.008 0.900 0.060
#> GSM564644 2 0.2786 0.8550 0.000 0.864 0.000 0.012 0.100 0.024
#> GSM564645 3 0.2805 0.8327 0.000 0.184 0.812 0.000 0.004 0.000
#> GSM564647 2 0.4266 0.6024 0.000 0.700 0.252 0.008 0.000 0.040
#> GSM564648 5 0.0790 0.9157 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564649 3 0.3229 0.8284 0.000 0.188 0.796 0.004 0.004 0.008
#> GSM564650 2 0.2499 0.8562 0.000 0.880 0.004 0.004 0.096 0.016
#> GSM564651 5 0.0790 0.9157 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564652 5 0.0790 0.9157 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564653 5 0.0865 0.9159 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM564654 5 0.4344 0.7887 0.000 0.028 0.064 0.024 0.788 0.096
#> GSM564655 3 0.6823 0.5631 0.000 0.036 0.524 0.036 0.184 0.220
#> GSM564656 3 0.6095 0.7216 0.000 0.072 0.644 0.024 0.120 0.140
#> GSM564657 3 0.2838 0.8320 0.000 0.188 0.808 0.000 0.004 0.000
#> GSM564658 2 0.3757 0.8327 0.000 0.804 0.000 0.032 0.124 0.040
#> GSM564659 3 0.2979 0.8307 0.000 0.188 0.804 0.000 0.004 0.004
#> GSM564660 2 0.2586 0.8561 0.000 0.876 0.004 0.004 0.096 0.020
#> GSM564661 5 0.0937 0.9146 0.000 0.040 0.000 0.000 0.960 0.000
#> GSM564662 3 0.2838 0.8320 0.000 0.188 0.808 0.000 0.004 0.000
#> GSM564663 2 0.3422 0.8488 0.000 0.832 0.000 0.032 0.100 0.036
#> GSM564664 2 0.5590 0.7549 0.000 0.684 0.040 0.028 0.148 0.100
#> GSM564665 3 0.4102 0.8271 0.000 0.188 0.752 0.008 0.004 0.048
#> GSM564666 2 0.6040 0.5459 0.000 0.608 0.164 0.056 0.004 0.168
#> GSM564667 3 0.3281 0.8186 0.000 0.200 0.784 0.004 0.000 0.012
#> GSM564668 5 0.6479 0.5118 0.000 0.028 0.160 0.036 0.568 0.208
#> GSM564669 3 0.6475 0.6159 0.000 0.032 0.576 0.036 0.160 0.196
#> GSM564670 2 0.3265 0.7698 0.000 0.836 0.108 0.016 0.000 0.040
#> GSM564671 5 0.4707 0.7755 0.000 0.028 0.048 0.020 0.740 0.164
#> GSM564672 3 0.2838 0.8320 0.000 0.188 0.808 0.000 0.004 0.000
#> GSM564673 5 0.0790 0.9157 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM564674 2 0.2796 0.8554 0.000 0.864 0.000 0.020 0.100 0.016
#> GSM564675 2 0.6499 0.6580 0.000 0.616 0.104 0.056 0.064 0.160
#> GSM564676 2 0.2407 0.8574 0.000 0.884 0.004 0.004 0.096 0.012
#> GSM564677 5 0.0865 0.9159 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM564678 2 0.3198 0.8517 0.000 0.844 0.000 0.024 0.100 0.032
#> GSM564679 2 0.3869 0.8288 0.000 0.796 0.000 0.036 0.128 0.040
#> GSM564680 3 0.4393 0.8236 0.000 0.180 0.744 0.016 0.008 0.052
#> GSM564682 2 0.3789 0.7246 0.000 0.784 0.160 0.016 0.000 0.040
#> GSM564683 3 0.2805 0.8327 0.000 0.184 0.812 0.000 0.004 0.000
#> GSM564684 5 0.5404 0.7116 0.000 0.032 0.072 0.024 0.680 0.192
#> GSM564685 3 0.3154 0.8331 0.000 0.184 0.800 0.000 0.004 0.012
#> GSM564686 3 0.7560 0.3076 0.000 0.328 0.344 0.028 0.068 0.232
#> GSM564687 2 0.4119 0.8444 0.000 0.800 0.020 0.024 0.100 0.056
#> GSM564688 5 0.0865 0.9159 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM564689 2 0.2586 0.8565 0.000 0.876 0.004 0.004 0.096 0.020
#> GSM564690 2 0.2325 0.8569 0.000 0.884 0.000 0.008 0.100 0.008
#> GSM564691 2 0.3741 0.7423 0.000 0.796 0.148 0.008 0.008 0.040
#> GSM564692 5 0.1196 0.9134 0.000 0.040 0.000 0.000 0.952 0.008
#> GSM564694 2 0.4679 0.8206 0.000 0.756 0.020 0.024 0.088 0.112
#> GSM564695 2 0.4919 0.8353 0.000 0.740 0.020 0.032 0.096 0.112
#> GSM564696 3 0.5571 0.7535 0.000 0.204 0.648 0.048 0.004 0.096
#> GSM564697 2 0.2886 0.8543 0.000 0.872 0.024 0.012 0.080 0.012
#> GSM564698 3 0.6286 0.7345 0.000 0.088 0.624 0.024 0.104 0.160
#> GSM564700 5 0.3050 0.8699 0.000 0.028 0.004 0.016 0.856 0.096
#> GSM564701 5 0.2103 0.9020 0.000 0.040 0.000 0.020 0.916 0.024
#> GSM564702 5 0.1010 0.9157 0.000 0.036 0.000 0.000 0.960 0.004
#> GSM564703 4 0.5597 0.8154 0.148 0.044 0.112 0.680 0.004 0.012
#> GSM564704 1 0.3830 0.8224 0.840 0.020 0.028 0.044 0.020 0.048
#> GSM564705 1 0.0291 0.9214 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM564706 4 0.7859 0.0187 0.100 0.072 0.108 0.416 0.004 0.300
#> GSM564707 1 0.1659 0.8930 0.940 0.000 0.020 0.004 0.008 0.028
#> GSM564708 4 0.4476 0.8334 0.152 0.028 0.040 0.760 0.000 0.020
#> GSM564709 1 0.0436 0.9224 0.988 0.004 0.004 0.004 0.000 0.000
#> GSM564710 1 0.0291 0.9214 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM564711 6 0.7902 0.1973 0.100 0.064 0.104 0.360 0.008 0.364
#> GSM564712 1 0.0436 0.9224 0.988 0.004 0.004 0.004 0.000 0.000
#> GSM564713 4 0.4440 0.8332 0.148 0.028 0.040 0.764 0.000 0.020
#> GSM564714 6 0.7743 0.4504 0.100 0.060 0.104 0.276 0.008 0.452
#> GSM564715 1 0.3148 0.8495 0.872 0.016 0.028 0.008 0.020 0.056
#> GSM564716 1 0.3895 0.8179 0.836 0.020 0.028 0.048 0.020 0.048
#> GSM564717 6 0.4463 0.6961 0.300 0.000 0.000 0.044 0.004 0.652
#> GSM564718 4 0.5154 0.8005 0.124 0.040 0.048 0.740 0.008 0.040
#> GSM564719 6 0.5204 0.7163 0.224 0.004 0.004 0.116 0.004 0.648
#> GSM564720 6 0.4389 0.6463 0.372 0.000 0.000 0.032 0.000 0.596
#> GSM564721 1 0.0146 0.9231 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM564722 6 0.5504 0.7138 0.184 0.004 0.008 0.156 0.008 0.640
#> GSM564723 1 0.2876 0.7194 0.836 0.004 0.004 0.008 0.000 0.148
#> GSM564724 4 0.2982 0.8367 0.152 0.008 0.000 0.828 0.000 0.012
#> GSM564725 1 0.0146 0.9231 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM564726 4 0.2883 0.8375 0.152 0.008 0.000 0.832 0.000 0.008
#> GSM564727 1 0.0146 0.9231 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM564728 4 0.3020 0.8353 0.156 0.008 0.000 0.824 0.000 0.012
#> GSM564729 4 0.3496 0.8288 0.164 0.008 0.008 0.804 0.008 0.008
#> GSM564730 1 0.0146 0.9220 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM564731 4 0.6219 0.7638 0.108 0.048 0.132 0.656 0.008 0.048
#> GSM564732 4 0.4687 0.8359 0.152 0.016 0.052 0.752 0.012 0.016
#> GSM564733 4 0.4215 0.8377 0.156 0.004 0.044 0.772 0.012 0.012
#> GSM564734 4 0.4722 0.8348 0.156 0.016 0.052 0.748 0.012 0.016
#> GSM564735 4 0.5946 0.7932 0.124 0.048 0.128 0.668 0.008 0.024
#> GSM564736 4 0.4554 0.8388 0.148 0.016 0.052 0.760 0.008 0.016
#> GSM564737 1 0.0291 0.9214 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM564738 4 0.5185 0.8063 0.132 0.040 0.052 0.732 0.004 0.040
#> GSM564739 4 0.3944 0.8427 0.156 0.012 0.040 0.784 0.004 0.004
#> GSM564740 6 0.5699 0.5825 0.100 0.008 0.012 0.272 0.008 0.600
#> GSM564741 4 0.5300 0.8279 0.148 0.036 0.096 0.704 0.004 0.012
#> GSM564742 6 0.7698 0.4674 0.100 0.064 0.116 0.248 0.004 0.468
#> GSM564743 6 0.4249 0.6845 0.328 0.000 0.000 0.032 0.000 0.640
#> GSM564744 1 0.0291 0.9214 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM564745 1 0.2917 0.8464 0.880 0.004 0.016 0.060 0.012 0.028
#> GSM564746 6 0.4219 0.6899 0.320 0.000 0.000 0.032 0.000 0.648
#> GSM564747 6 0.7729 0.4641 0.100 0.064 0.120 0.248 0.004 0.464
#> GSM564748 4 0.5597 0.8154 0.148 0.044 0.112 0.680 0.004 0.012
#> GSM564749 6 0.4344 0.6636 0.356 0.000 0.000 0.032 0.000 0.612
#> GSM564750 4 0.4493 0.8337 0.148 0.032 0.044 0.760 0.000 0.016
#> GSM564751 4 0.6360 0.7301 0.112 0.072 0.140 0.636 0.004 0.036
#> GSM564752 4 0.6304 0.6980 0.096 0.068 0.108 0.656 0.004 0.068
#> GSM564753 4 0.6585 0.6884 0.100 0.072 0.128 0.628 0.004 0.068
#> GSM564754 1 0.0436 0.9208 0.988 0.000 0.004 0.004 0.004 0.000
#> GSM564755 4 0.2921 0.8361 0.156 0.008 0.000 0.828 0.000 0.008
#> GSM564756 4 0.5759 0.6122 0.264 0.024 0.032 0.628 0.020 0.032
#> GSM564757 4 0.3547 0.8368 0.124 0.004 0.020 0.824 0.008 0.020
#> GSM564758 4 0.3967 0.8366 0.124 0.016 0.032 0.804 0.004 0.020
#> GSM564759 4 0.8106 0.1844 0.100 0.072 0.144 0.416 0.008 0.260
#> GSM564760 4 0.4722 0.8348 0.156 0.016 0.052 0.748 0.012 0.016
#> GSM564761 1 0.0291 0.9231 0.992 0.004 0.000 0.004 0.000 0.000
#> GSM564762 4 0.4544 0.8266 0.128 0.016 0.052 0.772 0.012 0.020
#> GSM564681 5 0.2252 0.8972 0.000 0.044 0.000 0.020 0.908 0.028
#> GSM564693 5 0.0937 0.9146 0.000 0.040 0.000 0.000 0.960 0.000
#> GSM564646 5 0.6298 0.4485 0.000 0.252 0.008 0.032 0.540 0.168
#> GSM564699 3 0.6191 0.7227 0.000 0.192 0.544 0.028 0.004 0.232
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 genotype/variation(p) disease.state(p) k
#> ATC:kmeans 154 0.925 0.4759 2
#> ATC:kmeans 140 0.850 0.0185 3
#> ATC:kmeans 134 0.576 0.1615 4
#> ATC:kmeans 146 0.395 0.2123 5
#> ATC:kmeans 144 0.243 0.4205 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.875 0.849 0.917 0.2806 0.860 0.720
#> 4 4 0.743 0.832 0.823 0.1138 0.917 0.779
#> 5 5 0.906 0.929 0.945 0.1149 0.888 0.638
#> 6 6 0.912 0.890 0.920 0.0397 0.954 0.778
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 5
There is also optional best \(k\) = 2 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564616 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564617 3 0.5785 0.6971 0.000 0.332 0.668
#> GSM564618 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564619 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564620 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564621 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564622 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564623 3 0.5621 0.7055 0.000 0.308 0.692
#> GSM564624 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564625 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564626 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564627 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564628 3 0.6062 0.6314 0.000 0.384 0.616
#> GSM564629 1 0.0424 0.9948 0.992 0.008 0.000
#> GSM564630 3 0.5948 0.6738 0.000 0.360 0.640
#> GSM564609 2 0.5968 0.5806 0.000 0.636 0.364
#> GSM564610 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564611 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564612 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564613 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564614 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564631 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564632 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564633 2 0.5859 0.5991 0.000 0.656 0.344
#> GSM564634 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564635 2 0.5968 0.5806 0.000 0.636 0.364
#> GSM564636 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564637 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564638 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564639 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564640 3 0.5926 0.6720 0.000 0.356 0.644
#> GSM564641 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564642 3 0.5785 0.6971 0.000 0.332 0.668
#> GSM564643 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564644 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564645 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564647 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564648 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564649 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564650 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564651 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564652 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564653 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564654 2 0.5785 0.6090 0.000 0.668 0.332
#> GSM564655 2 0.5882 0.5954 0.000 0.652 0.348
#> GSM564656 2 0.5968 0.5806 0.000 0.636 0.364
#> GSM564657 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564658 3 0.5905 0.6766 0.000 0.352 0.648
#> GSM564659 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564660 3 0.5785 0.6971 0.000 0.332 0.668
#> GSM564661 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564662 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564663 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564664 2 0.3752 0.6741 0.000 0.856 0.144
#> GSM564665 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564666 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564667 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564668 2 0.5835 0.6027 0.000 0.660 0.340
#> GSM564669 2 0.5882 0.5954 0.000 0.652 0.348
#> GSM564670 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564671 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564672 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564673 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564674 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564675 3 0.5621 0.7055 0.000 0.308 0.692
#> GSM564676 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564677 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564678 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564679 3 0.5948 0.6668 0.000 0.360 0.640
#> GSM564680 3 0.6095 -0.0457 0.000 0.392 0.608
#> GSM564682 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564683 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564684 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564685 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564686 2 0.6299 -0.2949 0.000 0.524 0.476
#> GSM564687 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564688 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564689 3 0.5785 0.6971 0.000 0.332 0.668
#> GSM564690 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564691 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564692 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564694 3 0.5810 0.6946 0.000 0.336 0.664
#> GSM564695 3 0.5678 0.7034 0.000 0.316 0.684
#> GSM564696 3 0.0000 0.7749 0.000 0.000 1.000
#> GSM564697 3 0.5785 0.6971 0.000 0.332 0.668
#> GSM564698 2 0.5968 0.5806 0.000 0.636 0.364
#> GSM564700 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564701 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564702 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564703 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564704 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564705 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564706 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564707 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564708 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564709 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564710 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564711 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564712 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564713 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564714 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564715 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564716 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564717 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564718 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564719 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564720 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564721 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564722 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564723 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564724 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564725 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564726 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564727 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564728 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564729 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564730 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564731 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564732 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564733 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564734 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564735 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564736 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564737 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564738 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564739 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564740 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564741 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564742 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564743 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564744 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564745 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564746 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564747 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564748 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564749 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564750 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564751 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564752 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564753 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564754 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564755 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564756 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564757 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564758 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564759 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564760 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564761 1 0.0592 0.9946 0.988 0.012 0.000
#> GSM564762 1 0.0000 0.9951 1.000 0.000 0.000
#> GSM564681 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564693 2 0.0592 0.8420 0.000 0.988 0.012
#> GSM564646 2 0.0892 0.8344 0.000 0.980 0.020
#> GSM564699 3 0.0000 0.7749 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564616 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564617 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564618 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564619 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564620 1 0.4605 0.8103 0.664 0.000 0.000 0.336
#> GSM564621 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564622 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564623 2 0.1022 0.8748 0.000 0.968 0.032 0.000
#> GSM564624 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564625 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564626 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564627 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564628 2 0.0592 0.8813 0.000 0.984 0.016 0.000
#> GSM564629 1 0.4431 0.8130 0.696 0.000 0.000 0.304
#> GSM564630 2 0.0469 0.8851 0.000 0.988 0.012 0.000
#> GSM564609 3 0.0469 0.7487 0.000 0.012 0.988 0.000
#> GSM564610 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564611 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564612 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564613 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564614 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564631 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564632 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564633 3 0.2814 0.5475 0.000 0.000 0.868 0.132
#> GSM564634 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564635 3 0.0469 0.7487 0.000 0.012 0.988 0.000
#> GSM564636 3 0.3726 0.8645 0.000 0.212 0.788 0.000
#> GSM564637 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564638 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564639 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564640 2 0.0469 0.8851 0.000 0.988 0.012 0.000
#> GSM564641 2 0.4977 -0.0929 0.000 0.540 0.460 0.000
#> GSM564642 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564643 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564644 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564645 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564647 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564648 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564649 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564650 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564651 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564652 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564653 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564654 4 0.4905 0.7392 0.000 0.004 0.364 0.632
#> GSM564655 3 0.3486 0.4291 0.000 0.000 0.812 0.188
#> GSM564656 3 0.0469 0.7487 0.000 0.012 0.988 0.000
#> GSM564657 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564658 2 0.0469 0.8851 0.000 0.988 0.012 0.000
#> GSM564659 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564660 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564661 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564662 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564663 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564664 2 0.7037 -0.1319 0.000 0.564 0.168 0.268
#> GSM564665 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564666 2 0.3837 0.6571 0.000 0.776 0.224 0.000
#> GSM564667 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564668 4 0.4746 0.7319 0.000 0.000 0.368 0.632
#> GSM564669 3 0.0000 0.7327 0.000 0.000 1.000 0.000
#> GSM564670 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564671 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564672 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564673 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564674 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564675 2 0.0817 0.8808 0.000 0.976 0.024 0.000
#> GSM564676 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564677 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564678 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564679 2 0.0469 0.8851 0.000 0.988 0.012 0.000
#> GSM564680 3 0.1940 0.8033 0.000 0.076 0.924 0.000
#> GSM564682 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564683 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564684 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564685 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564686 3 0.4826 0.6166 0.000 0.264 0.716 0.020
#> GSM564687 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564688 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564689 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564690 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564691 2 0.3172 0.7475 0.000 0.840 0.160 0.000
#> GSM564692 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564694 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564695 2 0.0592 0.8848 0.000 0.984 0.016 0.000
#> GSM564696 3 0.3688 0.8690 0.000 0.208 0.792 0.000
#> GSM564697 2 0.0000 0.8936 0.000 1.000 0.000 0.000
#> GSM564698 3 0.0469 0.7487 0.000 0.012 0.988 0.000
#> GSM564700 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564701 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564702 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564703 1 0.0707 0.8277 0.980 0.000 0.000 0.020
#> GSM564704 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564705 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564706 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564707 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564708 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564709 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564710 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564711 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564712 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564713 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564714 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564715 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564716 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564717 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564718 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564719 1 0.4605 0.8103 0.664 0.000 0.000 0.336
#> GSM564720 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564721 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564722 1 0.4605 0.8103 0.664 0.000 0.000 0.336
#> GSM564723 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564724 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564725 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564726 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564727 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564728 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564729 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564730 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564731 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564732 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564733 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564734 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564735 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564736 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564737 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564738 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564739 1 0.0707 0.8277 0.980 0.000 0.000 0.020
#> GSM564740 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564741 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564742 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564743 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564744 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564745 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564746 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564747 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564748 1 0.0469 0.8286 0.988 0.000 0.000 0.012
#> GSM564749 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564750 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564751 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564752 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564753 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564754 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564755 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564756 1 0.4477 0.8124 0.688 0.000 0.000 0.312
#> GSM564757 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564758 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564759 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564760 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564761 1 0.4746 0.8071 0.632 0.000 0.000 0.368
#> GSM564762 1 0.0000 0.8297 1.000 0.000 0.000 0.000
#> GSM564681 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564693 4 0.6563 0.9792 0.000 0.160 0.208 0.632
#> GSM564646 4 0.6678 0.9665 0.000 0.172 0.208 0.620
#> GSM564699 3 0.3688 0.8690 0.000 0.208 0.792 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564616 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564617 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564618 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564619 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564620 1 0.3074 0.780 0.804 0.000 0.000 0.196 0.000
#> GSM564621 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564622 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564623 2 0.2171 0.944 0.032 0.924 0.028 0.000 0.016
#> GSM564624 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564625 4 0.1671 0.943 0.076 0.000 0.000 0.924 0.000
#> GSM564626 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564627 1 0.3166 0.888 0.860 0.016 0.020 0.104 0.000
#> GSM564628 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564629 1 0.5408 0.375 0.516 0.020 0.024 0.440 0.000
#> GSM564630 2 0.1485 0.961 0.032 0.948 0.000 0.000 0.020
#> GSM564609 3 0.0703 0.945 0.000 0.000 0.976 0.000 0.024
#> GSM564610 1 0.3166 0.888 0.860 0.016 0.020 0.104 0.000
#> GSM564611 1 0.3166 0.888 0.860 0.016 0.020 0.104 0.000
#> GSM564612 2 0.0609 0.964 0.000 0.980 0.020 0.000 0.000
#> GSM564613 2 0.1216 0.958 0.020 0.960 0.020 0.000 0.000
#> GSM564614 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564631 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564632 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564633 3 0.2690 0.817 0.000 0.000 0.844 0.000 0.156
#> GSM564634 2 0.1493 0.951 0.028 0.948 0.024 0.000 0.000
#> GSM564635 3 0.0703 0.945 0.000 0.000 0.976 0.000 0.024
#> GSM564636 3 0.0794 0.955 0.000 0.028 0.972 0.000 0.000
#> GSM564637 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564638 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564639 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564640 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564641 3 0.4182 0.373 0.000 0.400 0.600 0.000 0.000
#> GSM564642 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564643 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564644 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564645 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564647 2 0.0609 0.964 0.000 0.980 0.020 0.000 0.000
#> GSM564648 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564649 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564650 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564651 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564652 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564653 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564654 5 0.0609 0.973 0.000 0.000 0.020 0.000 0.980
#> GSM564655 3 0.3636 0.640 0.000 0.000 0.728 0.000 0.272
#> GSM564656 3 0.0703 0.945 0.000 0.000 0.976 0.000 0.024
#> GSM564657 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564658 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564659 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564660 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564661 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564662 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564663 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564664 2 0.4030 0.489 0.000 0.648 0.000 0.000 0.352
#> GSM564665 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564666 2 0.2654 0.890 0.032 0.884 0.084 0.000 0.000
#> GSM564667 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564668 5 0.0703 0.970 0.000 0.000 0.024 0.000 0.976
#> GSM564669 3 0.0794 0.942 0.000 0.000 0.972 0.000 0.028
#> GSM564670 2 0.0609 0.964 0.000 0.980 0.020 0.000 0.000
#> GSM564671 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564672 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564673 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564674 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564675 2 0.1989 0.951 0.032 0.932 0.020 0.000 0.016
#> GSM564676 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564677 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564678 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564679 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564680 3 0.0807 0.952 0.000 0.012 0.976 0.000 0.012
#> GSM564682 2 0.0609 0.964 0.000 0.980 0.020 0.000 0.000
#> GSM564683 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564684 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564685 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
#> GSM564686 3 0.1981 0.914 0.000 0.016 0.920 0.000 0.064
#> GSM564687 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564688 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564689 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564690 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564691 2 0.0609 0.964 0.000 0.980 0.020 0.000 0.000
#> GSM564692 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564694 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564695 2 0.0703 0.973 0.000 0.976 0.000 0.000 0.024
#> GSM564696 3 0.1579 0.942 0.032 0.024 0.944 0.000 0.000
#> GSM564697 2 0.0609 0.974 0.000 0.980 0.000 0.000 0.020
#> GSM564698 3 0.0703 0.945 0.000 0.000 0.976 0.000 0.024
#> GSM564700 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564701 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564702 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564703 4 0.3003 0.826 0.188 0.000 0.000 0.812 0.000
#> GSM564704 1 0.1043 0.924 0.960 0.000 0.000 0.040 0.000
#> GSM564705 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564706 4 0.1310 0.919 0.000 0.020 0.024 0.956 0.000
#> GSM564707 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564708 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564709 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564710 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564711 4 0.1310 0.919 0.000 0.020 0.024 0.956 0.000
#> GSM564712 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564713 4 0.1544 0.944 0.068 0.000 0.000 0.932 0.000
#> GSM564714 4 0.1310 0.919 0.000 0.020 0.024 0.956 0.000
#> GSM564715 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564716 1 0.1043 0.924 0.960 0.000 0.000 0.040 0.000
#> GSM564717 1 0.3405 0.883 0.848 0.020 0.024 0.108 0.000
#> GSM564718 4 0.0000 0.937 0.000 0.000 0.000 1.000 0.000
#> GSM564719 1 0.4902 0.715 0.676 0.020 0.024 0.280 0.000
#> GSM564720 1 0.3166 0.888 0.860 0.016 0.020 0.104 0.000
#> GSM564721 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564722 1 0.5005 0.685 0.656 0.020 0.024 0.300 0.000
#> GSM564723 1 0.1270 0.921 0.948 0.000 0.000 0.052 0.000
#> GSM564724 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564725 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564726 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564727 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564728 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564729 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564730 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564731 4 0.0162 0.937 0.004 0.000 0.000 0.996 0.000
#> GSM564732 4 0.1671 0.943 0.076 0.000 0.000 0.924 0.000
#> GSM564733 4 0.1671 0.943 0.076 0.000 0.000 0.924 0.000
#> GSM564734 4 0.1671 0.943 0.076 0.000 0.000 0.924 0.000
#> GSM564735 4 0.0162 0.937 0.004 0.000 0.000 0.996 0.000
#> GSM564736 4 0.1608 0.944 0.072 0.000 0.000 0.928 0.000
#> GSM564737 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564738 4 0.0794 0.942 0.028 0.000 0.000 0.972 0.000
#> GSM564739 4 0.3039 0.823 0.192 0.000 0.000 0.808 0.000
#> GSM564740 4 0.1310 0.919 0.000 0.020 0.024 0.956 0.000
#> GSM564741 4 0.1608 0.944 0.072 0.000 0.000 0.928 0.000
#> GSM564742 4 0.1310 0.919 0.000 0.020 0.024 0.956 0.000
#> GSM564743 1 0.3257 0.886 0.856 0.016 0.024 0.104 0.000
#> GSM564744 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564745 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564746 1 0.3353 0.884 0.852 0.020 0.024 0.104 0.000
#> GSM564747 4 0.1471 0.919 0.004 0.020 0.024 0.952 0.000
#> GSM564748 4 0.2377 0.899 0.128 0.000 0.000 0.872 0.000
#> GSM564749 1 0.3166 0.888 0.860 0.016 0.020 0.104 0.000
#> GSM564750 4 0.1544 0.944 0.068 0.000 0.000 0.932 0.000
#> GSM564751 4 0.0162 0.937 0.004 0.000 0.000 0.996 0.000
#> GSM564752 4 0.0324 0.935 0.000 0.004 0.004 0.992 0.000
#> GSM564753 4 0.0324 0.935 0.000 0.004 0.004 0.992 0.000
#> GSM564754 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564755 4 0.1608 0.943 0.072 0.000 0.000 0.928 0.000
#> GSM564756 1 0.3857 0.594 0.688 0.000 0.000 0.312 0.000
#> GSM564757 4 0.0000 0.937 0.000 0.000 0.000 1.000 0.000
#> GSM564758 4 0.0000 0.937 0.000 0.000 0.000 1.000 0.000
#> GSM564759 4 0.1310 0.919 0.000 0.020 0.024 0.956 0.000
#> GSM564760 4 0.1671 0.943 0.076 0.000 0.000 0.924 0.000
#> GSM564761 1 0.0880 0.927 0.968 0.000 0.000 0.032 0.000
#> GSM564762 4 0.0162 0.937 0.004 0.000 0.000 0.996 0.000
#> GSM564681 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564693 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> GSM564646 5 0.0703 0.973 0.000 0.024 0.000 0.000 0.976
#> GSM564699 3 0.0703 0.958 0.000 0.024 0.976 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0405 0.956 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM564616 5 0.0363 0.981 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM564617 2 0.0508 0.949 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM564618 5 0.0260 0.983 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM564619 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564620 1 0.4079 0.576 0.744 0.000 0.000 0.172 0.000 0.084
#> GSM564621 1 0.0405 0.939 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM564622 5 0.0632 0.975 0.000 0.000 0.000 0.000 0.976 0.024
#> GSM564623 2 0.3345 0.817 0.000 0.776 0.020 0.000 0.000 0.204
#> GSM564624 2 0.0405 0.949 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564625 4 0.0458 0.955 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM564626 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564627 6 0.3578 0.638 0.340 0.000 0.000 0.000 0.000 0.660
#> GSM564628 2 0.1003 0.944 0.000 0.964 0.000 0.000 0.016 0.020
#> GSM564629 6 0.4666 0.743 0.108 0.000 0.000 0.216 0.000 0.676
#> GSM564630 2 0.1806 0.914 0.000 0.908 0.000 0.000 0.004 0.088
#> GSM564609 3 0.1219 0.927 0.000 0.000 0.948 0.000 0.004 0.048
#> GSM564610 6 0.3756 0.548 0.400 0.000 0.000 0.000 0.000 0.600
#> GSM564611 6 0.3706 0.590 0.380 0.000 0.000 0.000 0.000 0.620
#> GSM564612 2 0.1245 0.937 0.000 0.952 0.032 0.000 0.000 0.016
#> GSM564613 2 0.1349 0.933 0.000 0.940 0.004 0.000 0.000 0.056
#> GSM564614 4 0.0508 0.956 0.004 0.000 0.000 0.984 0.000 0.012
#> GSM564631 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564632 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564633 3 0.2506 0.884 0.000 0.000 0.880 0.000 0.068 0.052
#> GSM564634 2 0.2877 0.855 0.000 0.820 0.012 0.000 0.000 0.168
#> GSM564635 3 0.1082 0.929 0.000 0.000 0.956 0.000 0.004 0.040
#> GSM564636 3 0.0603 0.932 0.000 0.016 0.980 0.000 0.000 0.004
#> GSM564637 3 0.0935 0.934 0.000 0.004 0.964 0.000 0.000 0.032
#> GSM564638 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564639 3 0.0508 0.937 0.000 0.004 0.984 0.000 0.000 0.012
#> GSM564640 2 0.1003 0.944 0.000 0.964 0.000 0.000 0.016 0.020
#> GSM564641 3 0.4110 0.390 0.000 0.376 0.608 0.000 0.000 0.016
#> GSM564642 2 0.1036 0.945 0.000 0.964 0.004 0.000 0.008 0.024
#> GSM564643 5 0.0363 0.979 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM564644 2 0.0717 0.948 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM564645 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564647 2 0.1320 0.934 0.000 0.948 0.036 0.000 0.000 0.016
#> GSM564648 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564649 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564650 2 0.0405 0.949 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564651 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564652 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564653 5 0.0146 0.984 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564654 5 0.0937 0.963 0.000 0.000 0.000 0.000 0.960 0.040
#> GSM564655 3 0.4552 0.558 0.000 0.000 0.648 0.000 0.288 0.064
#> GSM564656 3 0.1285 0.926 0.000 0.000 0.944 0.000 0.004 0.052
#> GSM564657 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564658 2 0.0508 0.949 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM564659 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564660 2 0.0405 0.949 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564661 5 0.0146 0.984 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564662 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564663 2 0.0405 0.949 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564664 2 0.3789 0.644 0.000 0.716 0.000 0.000 0.260 0.024
#> GSM564665 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564666 2 0.3923 0.799 0.000 0.748 0.060 0.000 0.000 0.192
#> GSM564667 3 0.0622 0.933 0.000 0.012 0.980 0.000 0.000 0.008
#> GSM564668 5 0.1643 0.937 0.000 0.000 0.008 0.000 0.924 0.068
#> GSM564669 3 0.1643 0.918 0.000 0.000 0.924 0.000 0.008 0.068
#> GSM564670 2 0.0603 0.946 0.000 0.980 0.004 0.000 0.000 0.016
#> GSM564671 5 0.0632 0.976 0.000 0.000 0.000 0.000 0.976 0.024
#> GSM564672 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564673 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564674 2 0.0405 0.949 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM564675 2 0.3424 0.817 0.000 0.772 0.024 0.000 0.000 0.204
#> GSM564676 2 0.0291 0.949 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM564677 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564678 2 0.0146 0.949 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM564679 2 0.0820 0.946 0.000 0.972 0.000 0.000 0.016 0.012
#> GSM564680 3 0.0935 0.933 0.000 0.004 0.964 0.000 0.000 0.032
#> GSM564682 2 0.1168 0.939 0.000 0.956 0.028 0.000 0.000 0.016
#> GSM564683 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564684 5 0.1327 0.949 0.000 0.000 0.000 0.000 0.936 0.064
#> GSM564685 3 0.0146 0.938 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564686 3 0.3382 0.846 0.000 0.004 0.820 0.000 0.064 0.112
#> GSM564687 2 0.1138 0.944 0.000 0.960 0.004 0.000 0.012 0.024
#> GSM564688 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564689 2 0.0291 0.949 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM564690 2 0.0291 0.949 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM564691 2 0.1088 0.940 0.000 0.960 0.024 0.000 0.000 0.016
#> GSM564692 5 0.0363 0.981 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM564694 2 0.1088 0.943 0.000 0.960 0.000 0.000 0.016 0.024
#> GSM564695 2 0.1321 0.942 0.000 0.952 0.024 0.000 0.004 0.020
#> GSM564696 3 0.2912 0.825 0.000 0.012 0.816 0.000 0.000 0.172
#> GSM564697 2 0.0291 0.949 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM564698 3 0.1285 0.926 0.000 0.000 0.944 0.000 0.004 0.052
#> GSM564700 5 0.0000 0.984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564701 5 0.0260 0.983 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM564702 5 0.0146 0.984 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564703 4 0.3013 0.768 0.152 0.004 0.004 0.828 0.000 0.012
#> GSM564704 1 0.1334 0.903 0.948 0.000 0.000 0.032 0.000 0.020
#> GSM564705 1 0.0146 0.944 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564706 6 0.3820 0.627 0.000 0.004 0.004 0.332 0.000 0.660
#> GSM564707 1 0.0146 0.944 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564708 4 0.0632 0.955 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM564709 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564710 1 0.0146 0.944 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564711 6 0.3721 0.661 0.000 0.004 0.004 0.308 0.000 0.684
#> GSM564712 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.0713 0.954 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM564714 6 0.3665 0.672 0.000 0.004 0.004 0.296 0.000 0.696
#> GSM564715 1 0.0363 0.941 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM564716 1 0.1088 0.913 0.960 0.000 0.000 0.024 0.000 0.016
#> GSM564717 6 0.3729 0.668 0.296 0.000 0.000 0.012 0.000 0.692
#> GSM564718 4 0.1007 0.947 0.000 0.000 0.000 0.956 0.000 0.044
#> GSM564719 6 0.4595 0.738 0.136 0.000 0.000 0.168 0.000 0.696
#> GSM564720 6 0.3706 0.590 0.380 0.000 0.000 0.000 0.000 0.620
#> GSM564721 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564722 6 0.4506 0.742 0.120 0.000 0.000 0.176 0.000 0.704
#> GSM564723 1 0.0547 0.931 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM564724 4 0.0363 0.956 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM564725 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564726 4 0.0363 0.956 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM564727 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564728 4 0.0508 0.956 0.004 0.000 0.000 0.984 0.000 0.012
#> GSM564729 4 0.0909 0.949 0.020 0.000 0.000 0.968 0.000 0.012
#> GSM564730 1 0.0146 0.944 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564731 4 0.1338 0.943 0.008 0.004 0.004 0.952 0.000 0.032
#> GSM564732 4 0.0458 0.955 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM564733 4 0.0458 0.955 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM564734 4 0.0458 0.955 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM564735 4 0.1007 0.952 0.008 0.004 0.004 0.968 0.000 0.016
#> GSM564736 4 0.0260 0.956 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM564737 1 0.0146 0.944 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564738 4 0.0790 0.952 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM564739 4 0.2340 0.782 0.148 0.000 0.000 0.852 0.000 0.000
#> GSM564740 6 0.3371 0.678 0.000 0.000 0.000 0.292 0.000 0.708
#> GSM564741 4 0.0622 0.955 0.008 0.000 0.000 0.980 0.000 0.012
#> GSM564742 6 0.3858 0.670 0.004 0.004 0.004 0.308 0.000 0.680
#> GSM564743 6 0.3563 0.641 0.336 0.000 0.000 0.000 0.000 0.664
#> GSM564744 1 0.0146 0.944 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564745 1 0.0508 0.935 0.984 0.000 0.000 0.012 0.000 0.004
#> GSM564746 6 0.3482 0.655 0.316 0.000 0.000 0.000 0.000 0.684
#> GSM564747 6 0.3946 0.671 0.008 0.004 0.004 0.304 0.000 0.680
#> GSM564748 4 0.2305 0.866 0.088 0.004 0.004 0.892 0.000 0.012
#> GSM564749 6 0.3607 0.631 0.348 0.000 0.000 0.000 0.000 0.652
#> GSM564750 4 0.0632 0.955 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM564751 4 0.1440 0.941 0.012 0.004 0.004 0.948 0.000 0.032
#> GSM564752 4 0.1897 0.907 0.000 0.004 0.004 0.908 0.000 0.084
#> GSM564753 4 0.1732 0.907 0.000 0.004 0.004 0.920 0.000 0.072
#> GSM564754 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564755 4 0.0508 0.956 0.004 0.000 0.000 0.984 0.000 0.012
#> GSM564756 1 0.4282 0.212 0.560 0.000 0.000 0.420 0.000 0.020
#> GSM564757 4 0.0405 0.957 0.004 0.000 0.000 0.988 0.000 0.008
#> GSM564758 4 0.0603 0.956 0.000 0.000 0.004 0.980 0.000 0.016
#> GSM564759 6 0.4100 0.566 0.004 0.004 0.004 0.376 0.000 0.612
#> GSM564760 4 0.0458 0.955 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM564761 1 0.0000 0.945 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564762 4 0.0725 0.955 0.012 0.000 0.000 0.976 0.000 0.012
#> GSM564681 5 0.0260 0.983 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM564693 5 0.0146 0.984 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564646 5 0.2512 0.893 0.000 0.060 0.000 0.000 0.880 0.060
#> GSM564699 3 0.2402 0.877 0.000 0.004 0.856 0.000 0.000 0.140
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 genotype/variation(p) disease.state(p) k
#> ATC:skmeans 154 0.925 0.4759 2
#> ATC:skmeans 152 0.996 0.0942 3
#> ATC:skmeans 151 0.460 0.0471 4
#> ATC:skmeans 151 0.388 0.1786 5
#> ATC:skmeans 152 0.383 0.3876 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 154 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 1.000 0.999 0.999 0.5008 0.500 0.500
#> 3 3 0.653 0.636 0.744 0.2329 0.901 0.801
#> 4 4 0.699 0.768 0.867 0.1824 0.796 0.536
#> 5 5 0.806 0.856 0.916 0.0900 0.900 0.654
#> 6 6 0.813 0.799 0.893 0.0467 0.943 0.734
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
#> GSM564615 1 0.000 1.000 1.000 0.000
#> GSM564616 2 0.000 0.999 0.000 1.000
#> GSM564617 2 0.000 0.999 0.000 1.000
#> GSM564618 2 0.000 0.999 0.000 1.000
#> GSM564619 1 0.000 1.000 1.000 0.000
#> GSM564620 1 0.000 1.000 1.000 0.000
#> GSM564621 1 0.000 1.000 1.000 0.000
#> GSM564622 2 0.000 0.999 0.000 1.000
#> GSM564623 2 0.456 0.894 0.096 0.904
#> GSM564624 2 0.000 0.999 0.000 1.000
#> GSM564625 1 0.000 1.000 1.000 0.000
#> GSM564626 1 0.000 1.000 1.000 0.000
#> GSM564627 1 0.000 1.000 1.000 0.000
#> GSM564628 2 0.000 0.999 0.000 1.000
#> GSM564629 1 0.000 1.000 1.000 0.000
#> GSM564630 2 0.000 0.999 0.000 1.000
#> GSM564609 2 0.000 0.999 0.000 1.000
#> GSM564610 1 0.000 1.000 1.000 0.000
#> GSM564611 1 0.000 1.000 1.000 0.000
#> GSM564612 2 0.000 0.999 0.000 1.000
#> GSM564613 2 0.000 0.999 0.000 1.000
#> GSM564614 1 0.000 1.000 1.000 0.000
#> GSM564631 2 0.000 0.999 0.000 1.000
#> GSM564632 2 0.000 0.999 0.000 1.000
#> GSM564633 2 0.000 0.999 0.000 1.000
#> GSM564634 2 0.000 0.999 0.000 1.000
#> GSM564635 2 0.000 0.999 0.000 1.000
#> GSM564636 2 0.000 0.999 0.000 1.000
#> GSM564637 2 0.000 0.999 0.000 1.000
#> GSM564638 2 0.000 0.999 0.000 1.000
#> GSM564639 2 0.000 0.999 0.000 1.000
#> GSM564640 2 0.000 0.999 0.000 1.000
#> GSM564641 2 0.000 0.999 0.000 1.000
#> GSM564642 2 0.000 0.999 0.000 1.000
#> GSM564643 2 0.000 0.999 0.000 1.000
#> GSM564644 2 0.000 0.999 0.000 1.000
#> GSM564645 2 0.000 0.999 0.000 1.000
#> GSM564647 2 0.000 0.999 0.000 1.000
#> GSM564648 2 0.000 0.999 0.000 1.000
#> GSM564649 2 0.000 0.999 0.000 1.000
#> GSM564650 2 0.000 0.999 0.000 1.000
#> GSM564651 2 0.000 0.999 0.000 1.000
#> GSM564652 2 0.000 0.999 0.000 1.000
#> GSM564653 2 0.000 0.999 0.000 1.000
#> GSM564654 2 0.000 0.999 0.000 1.000
#> GSM564655 2 0.000 0.999 0.000 1.000
#> GSM564656 2 0.000 0.999 0.000 1.000
#> GSM564657 2 0.000 0.999 0.000 1.000
#> GSM564658 2 0.000 0.999 0.000 1.000
#> GSM564659 2 0.000 0.999 0.000 1.000
#> GSM564660 2 0.000 0.999 0.000 1.000
#> GSM564661 2 0.000 0.999 0.000 1.000
#> GSM564662 2 0.000 0.999 0.000 1.000
#> GSM564663 2 0.000 0.999 0.000 1.000
#> GSM564664 2 0.000 0.999 0.000 1.000
#> GSM564665 2 0.000 0.999 0.000 1.000
#> GSM564666 2 0.000 0.999 0.000 1.000
#> GSM564667 2 0.000 0.999 0.000 1.000
#> GSM564668 2 0.000 0.999 0.000 1.000
#> GSM564669 2 0.000 0.999 0.000 1.000
#> GSM564670 2 0.000 0.999 0.000 1.000
#> GSM564671 2 0.000 0.999 0.000 1.000
#> GSM564672 2 0.000 0.999 0.000 1.000
#> GSM564673 2 0.000 0.999 0.000 1.000
#> GSM564674 2 0.000 0.999 0.000 1.000
#> GSM564675 2 0.000 0.999 0.000 1.000
#> GSM564676 2 0.000 0.999 0.000 1.000
#> GSM564677 2 0.000 0.999 0.000 1.000
#> GSM564678 2 0.000 0.999 0.000 1.000
#> GSM564679 2 0.000 0.999 0.000 1.000
#> GSM564680 2 0.000 0.999 0.000 1.000
#> GSM564682 2 0.000 0.999 0.000 1.000
#> GSM564683 2 0.000 0.999 0.000 1.000
#> GSM564684 2 0.000 0.999 0.000 1.000
#> GSM564685 2 0.000 0.999 0.000 1.000
#> GSM564686 2 0.000 0.999 0.000 1.000
#> GSM564687 2 0.000 0.999 0.000 1.000
#> GSM564688 2 0.000 0.999 0.000 1.000
#> GSM564689 2 0.000 0.999 0.000 1.000
#> GSM564690 2 0.000 0.999 0.000 1.000
#> GSM564691 2 0.000 0.999 0.000 1.000
#> GSM564692 2 0.000 0.999 0.000 1.000
#> GSM564694 2 0.000 0.999 0.000 1.000
#> GSM564695 2 0.000 0.999 0.000 1.000
#> GSM564696 2 0.000 0.999 0.000 1.000
#> GSM564697 2 0.000 0.999 0.000 1.000
#> GSM564698 2 0.000 0.999 0.000 1.000
#> GSM564700 2 0.000 0.999 0.000 1.000
#> GSM564701 2 0.000 0.999 0.000 1.000
#> GSM564702 2 0.000 0.999 0.000 1.000
#> GSM564703 1 0.000 1.000 1.000 0.000
#> GSM564704 1 0.000 1.000 1.000 0.000
#> GSM564705 1 0.000 1.000 1.000 0.000
#> GSM564706 1 0.000 1.000 1.000 0.000
#> GSM564707 1 0.000 1.000 1.000 0.000
#> GSM564708 1 0.000 1.000 1.000 0.000
#> GSM564709 1 0.000 1.000 1.000 0.000
#> GSM564710 1 0.000 1.000 1.000 0.000
#> GSM564711 1 0.000 1.000 1.000 0.000
#> GSM564712 1 0.000 1.000 1.000 0.000
#> GSM564713 1 0.000 1.000 1.000 0.000
#> GSM564714 1 0.000 1.000 1.000 0.000
#> GSM564715 1 0.000 1.000 1.000 0.000
#> GSM564716 1 0.000 1.000 1.000 0.000
#> GSM564717 1 0.000 1.000 1.000 0.000
#> GSM564718 1 0.000 1.000 1.000 0.000
#> GSM564719 1 0.000 1.000 1.000 0.000
#> GSM564720 1 0.000 1.000 1.000 0.000
#> GSM564721 1 0.000 1.000 1.000 0.000
#> GSM564722 1 0.000 1.000 1.000 0.000
#> GSM564723 1 0.000 1.000 1.000 0.000
#> GSM564724 1 0.000 1.000 1.000 0.000
#> GSM564725 1 0.000 1.000 1.000 0.000
#> GSM564726 1 0.000 1.000 1.000 0.000
#> GSM564727 1 0.000 1.000 1.000 0.000
#> GSM564728 1 0.000 1.000 1.000 0.000
#> GSM564729 1 0.000 1.000 1.000 0.000
#> GSM564730 1 0.000 1.000 1.000 0.000
#> GSM564731 1 0.000 1.000 1.000 0.000
#> GSM564732 1 0.000 1.000 1.000 0.000
#> GSM564733 1 0.000 1.000 1.000 0.000
#> GSM564734 1 0.000 1.000 1.000 0.000
#> GSM564735 1 0.000 1.000 1.000 0.000
#> GSM564736 1 0.000 1.000 1.000 0.000
#> GSM564737 1 0.000 1.000 1.000 0.000
#> GSM564738 1 0.000 1.000 1.000 0.000
#> GSM564739 1 0.000 1.000 1.000 0.000
#> GSM564740 1 0.000 1.000 1.000 0.000
#> GSM564741 1 0.000 1.000 1.000 0.000
#> GSM564742 1 0.000 1.000 1.000 0.000
#> GSM564743 1 0.000 1.000 1.000 0.000
#> GSM564744 1 0.000 1.000 1.000 0.000
#> GSM564745 1 0.000 1.000 1.000 0.000
#> GSM564746 1 0.000 1.000 1.000 0.000
#> GSM564747 1 0.000 1.000 1.000 0.000
#> GSM564748 1 0.000 1.000 1.000 0.000
#> GSM564749 1 0.000 1.000 1.000 0.000
#> GSM564750 1 0.000 1.000 1.000 0.000
#> GSM564751 1 0.000 1.000 1.000 0.000
#> GSM564752 1 0.000 1.000 1.000 0.000
#> GSM564753 1 0.000 1.000 1.000 0.000
#> GSM564754 1 0.000 1.000 1.000 0.000
#> GSM564755 1 0.000 1.000 1.000 0.000
#> GSM564756 1 0.000 1.000 1.000 0.000
#> GSM564757 1 0.000 1.000 1.000 0.000
#> GSM564758 1 0.000 1.000 1.000 0.000
#> GSM564759 1 0.000 1.000 1.000 0.000
#> GSM564760 1 0.000 1.000 1.000 0.000
#> GSM564761 1 0.000 1.000 1.000 0.000
#> GSM564762 1 0.000 1.000 1.000 0.000
#> GSM564681 2 0.000 0.999 0.000 1.000
#> GSM564693 2 0.000 0.999 0.000 1.000
#> GSM564646 2 0.000 0.999 0.000 1.000
#> GSM564699 2 0.000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 3 0.6299 0.9305 0.476 0.000 0.524
#> GSM564616 2 0.3879 0.8092 0.000 0.848 0.152
#> GSM564617 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564618 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564619 1 0.1964 0.5944 0.944 0.000 0.056
#> GSM564620 1 0.6215 -0.6853 0.572 0.000 0.428
#> GSM564621 1 0.0592 0.6047 0.988 0.000 0.012
#> GSM564622 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564623 2 0.3947 0.7866 0.076 0.884 0.040
#> GSM564624 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564625 1 0.5835 -0.2225 0.660 0.000 0.340
#> GSM564626 1 0.1964 0.5944 0.944 0.000 0.056
#> GSM564627 1 0.0892 0.5997 0.980 0.000 0.020
#> GSM564628 2 0.3879 0.8092 0.000 0.848 0.152
#> GSM564629 1 0.5016 0.1581 0.760 0.000 0.240
#> GSM564630 2 0.3112 0.8056 0.096 0.900 0.004
#> GSM564609 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564610 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564611 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564612 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564613 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564614 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564631 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564632 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564633 2 0.6267 0.7653 0.000 0.548 0.452
#> GSM564634 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564635 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564636 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564637 2 0.4062 0.8376 0.000 0.836 0.164
#> GSM564638 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564639 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564640 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564641 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564642 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564643 2 0.6225 0.7731 0.000 0.568 0.432
#> GSM564644 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564645 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564647 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564648 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564649 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564650 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564651 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564652 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564653 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564654 2 0.6267 0.7653 0.000 0.548 0.452
#> GSM564655 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564656 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564657 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564658 2 0.1529 0.8460 0.000 0.960 0.040
#> GSM564659 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564660 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564661 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564662 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564663 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564664 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564665 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564666 2 0.5497 0.8127 0.000 0.708 0.292
#> GSM564667 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564668 2 0.5968 0.7697 0.000 0.636 0.364
#> GSM564669 2 0.5835 0.7832 0.000 0.660 0.340
#> GSM564670 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564671 2 0.0592 0.8510 0.000 0.988 0.012
#> GSM564672 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564673 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564674 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564675 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564676 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564677 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564678 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564679 2 0.3879 0.8092 0.000 0.848 0.152
#> GSM564680 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564682 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564683 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564684 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564685 2 0.5529 0.8110 0.000 0.704 0.296
#> GSM564686 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564687 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564688 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564689 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564690 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564691 2 0.2537 0.8491 0.000 0.920 0.080
#> GSM564692 2 0.3879 0.8092 0.000 0.848 0.152
#> GSM564694 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564695 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564696 2 0.5560 0.8101 0.000 0.700 0.300
#> GSM564697 2 0.0237 0.8520 0.000 0.996 0.004
#> GSM564698 2 0.5560 0.8104 0.000 0.700 0.300
#> GSM564700 2 0.1289 0.8473 0.000 0.968 0.032
#> GSM564701 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564702 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564703 3 0.6299 0.9468 0.476 0.000 0.524
#> GSM564704 1 0.6204 -0.6521 0.576 0.000 0.424
#> GSM564705 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564706 1 0.6305 -0.7894 0.516 0.000 0.484
#> GSM564707 1 0.2796 0.5716 0.908 0.000 0.092
#> GSM564708 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564709 1 0.2066 0.5923 0.940 0.000 0.060
#> GSM564710 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564711 1 0.6305 -0.7894 0.516 0.000 0.484
#> GSM564712 1 0.1031 0.6050 0.976 0.000 0.024
#> GSM564713 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564714 1 0.6299 -0.7672 0.524 0.000 0.476
#> GSM564715 1 0.0892 0.6025 0.980 0.000 0.020
#> GSM564716 1 0.6225 -0.7198 0.568 0.000 0.432
#> GSM564717 1 0.3686 0.4711 0.860 0.000 0.140
#> GSM564718 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564719 1 0.6286 -0.7438 0.536 0.000 0.464
#> GSM564720 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564721 1 0.2356 0.5854 0.928 0.000 0.072
#> GSM564722 1 0.6295 -0.7608 0.528 0.000 0.472
#> GSM564723 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564724 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564725 1 0.2261 0.5874 0.932 0.000 0.068
#> GSM564726 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564727 1 0.1964 0.5944 0.944 0.000 0.056
#> GSM564728 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564729 1 0.6309 -0.8714 0.504 0.000 0.496
#> GSM564730 1 0.1964 0.5944 0.944 0.000 0.056
#> GSM564731 3 0.6305 0.9349 0.484 0.000 0.516
#> GSM564732 3 0.6309 0.9072 0.496 0.000 0.504
#> GSM564733 1 0.6008 -0.3718 0.628 0.000 0.372
#> GSM564734 1 0.6215 -0.6501 0.572 0.000 0.428
#> GSM564735 3 0.6302 0.9413 0.480 0.000 0.520
#> GSM564736 3 0.6299 0.9468 0.476 0.000 0.524
#> GSM564737 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564738 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564739 3 0.6309 0.9072 0.496 0.000 0.504
#> GSM564740 1 0.6299 -0.7672 0.524 0.000 0.476
#> GSM564741 3 0.6299 0.9468 0.476 0.000 0.524
#> GSM564742 1 0.6295 -0.7608 0.528 0.000 0.472
#> GSM564743 1 0.0892 0.5997 0.980 0.000 0.020
#> GSM564744 1 0.0000 0.6047 1.000 0.000 0.000
#> GSM564745 1 0.3116 0.5625 0.892 0.000 0.108
#> GSM564746 1 0.0892 0.5997 0.980 0.000 0.020
#> GSM564747 1 0.6267 -0.7497 0.548 0.000 0.452
#> GSM564748 3 0.6299 0.9468 0.476 0.000 0.524
#> GSM564749 1 0.0892 0.5997 0.980 0.000 0.020
#> GSM564750 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564751 3 0.6302 0.9420 0.480 0.000 0.520
#> GSM564752 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564753 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564754 1 0.2261 0.5883 0.932 0.000 0.068
#> GSM564755 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564756 3 0.6309 0.8706 0.500 0.000 0.500
#> GSM564757 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564758 3 0.6274 0.9606 0.456 0.000 0.544
#> GSM564759 1 0.6302 -0.7835 0.520 0.000 0.480
#> GSM564760 1 0.5397 0.0992 0.720 0.000 0.280
#> GSM564761 1 0.2261 0.5874 0.932 0.000 0.068
#> GSM564762 3 0.6307 0.9307 0.488 0.000 0.512
#> GSM564681 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564693 2 0.3941 0.8079 0.000 0.844 0.156
#> GSM564646 2 0.0000 0.8523 0.000 1.000 0.000
#> GSM564699 2 0.5529 0.8110 0.000 0.704 0.296
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 4 0.1867 0.8431 0.072 0.000 0.000 0.928
#> GSM564616 2 0.4356 0.8922 0.000 0.708 0.292 0.000
#> GSM564617 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564618 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564619 1 0.2647 0.8620 0.880 0.000 0.000 0.120
#> GSM564620 4 0.4500 0.6571 0.316 0.000 0.000 0.684
#> GSM564621 1 0.2281 0.8562 0.904 0.000 0.000 0.096
#> GSM564622 2 0.4776 0.8814 0.016 0.712 0.272 0.000
#> GSM564623 3 0.4755 0.5547 0.040 0.000 0.760 0.200
#> GSM564624 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564625 4 0.4564 0.5165 0.328 0.000 0.000 0.672
#> GSM564626 1 0.2760 0.8595 0.872 0.000 0.000 0.128
#> GSM564627 1 0.2589 0.8353 0.884 0.000 0.000 0.116
#> GSM564628 2 0.4406 0.8874 0.000 0.700 0.300 0.000
#> GSM564629 4 0.4996 0.2325 0.484 0.000 0.000 0.516
#> GSM564630 3 0.3074 0.5979 0.152 0.000 0.848 0.000
#> GSM564609 3 0.4697 0.7909 0.008 0.296 0.696 0.000
#> GSM564610 1 0.1389 0.8669 0.952 0.000 0.000 0.048
#> GSM564611 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564612 3 0.4988 0.7924 0.020 0.288 0.692 0.000
#> GSM564613 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564614 4 0.0592 0.8667 0.016 0.000 0.000 0.984
#> GSM564631 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564632 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564633 2 0.5161 -0.3882 0.008 0.592 0.400 0.000
#> GSM564634 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564635 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564636 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564637 3 0.3450 0.7774 0.008 0.156 0.836 0.000
#> GSM564638 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564639 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564640 3 0.2530 0.5786 0.000 0.112 0.888 0.000
#> GSM564641 3 0.4988 0.7924 0.020 0.288 0.692 0.000
#> GSM564642 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564643 2 0.0895 0.6129 0.000 0.976 0.020 0.004
#> GSM564644 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564645 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564647 3 0.4509 0.7944 0.004 0.288 0.708 0.000
#> GSM564648 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564649 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564650 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564651 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564652 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564653 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564654 2 0.0000 0.5848 0.000 1.000 0.000 0.000
#> GSM564655 3 0.4431 0.7899 0.000 0.304 0.696 0.000
#> GSM564656 3 0.4697 0.7909 0.008 0.296 0.696 0.000
#> GSM564657 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564658 3 0.5256 -0.3557 0.012 0.392 0.596 0.000
#> GSM564659 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564660 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564661 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564662 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564663 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564664 3 0.0592 0.7257 0.000 0.016 0.984 0.000
#> GSM564665 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564666 3 0.5308 0.7876 0.012 0.256 0.708 0.024
#> GSM564667 3 0.4988 0.7924 0.020 0.288 0.692 0.000
#> GSM564668 2 0.5496 0.3964 0.000 0.724 0.188 0.088
#> GSM564669 3 0.6009 0.7525 0.008 0.292 0.648 0.052
#> GSM564670 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564671 2 0.6413 0.6279 0.000 0.516 0.416 0.068
#> GSM564672 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564673 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564674 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564675 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564676 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564677 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564678 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564679 2 0.4406 0.8874 0.000 0.700 0.300 0.000
#> GSM564680 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564682 3 0.4770 0.7929 0.012 0.288 0.700 0.000
#> GSM564683 3 0.4988 0.7924 0.020 0.288 0.692 0.000
#> GSM564684 3 0.2868 0.5406 0.000 0.136 0.864 0.000
#> GSM564685 3 0.4647 0.7939 0.008 0.288 0.704 0.000
#> GSM564686 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564687 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564688 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564689 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564690 3 0.0336 0.7345 0.008 0.000 0.992 0.000
#> GSM564691 3 0.2402 0.7584 0.012 0.076 0.912 0.000
#> GSM564692 2 0.4356 0.8922 0.000 0.708 0.292 0.000
#> GSM564694 3 0.0000 0.7353 0.000 0.000 1.000 0.000
#> GSM564695 3 0.4770 0.7929 0.012 0.288 0.700 0.000
#> GSM564696 3 0.4770 0.7936 0.012 0.288 0.700 0.000
#> GSM564697 3 0.0469 0.7341 0.012 0.000 0.988 0.000
#> GSM564698 3 0.4988 0.7883 0.008 0.292 0.692 0.008
#> GSM564700 2 0.4967 0.6890 0.000 0.548 0.452 0.000
#> GSM564701 2 0.4356 0.8924 0.000 0.708 0.292 0.000
#> GSM564702 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564703 4 0.0817 0.8660 0.024 0.000 0.000 0.976
#> GSM564704 4 0.4277 0.6699 0.280 0.000 0.000 0.720
#> GSM564705 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564706 4 0.2408 0.8212 0.104 0.000 0.000 0.896
#> GSM564707 1 0.4134 0.7256 0.740 0.000 0.000 0.260
#> GSM564708 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564709 1 0.2973 0.8495 0.856 0.000 0.000 0.144
#> GSM564710 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564711 4 0.2408 0.8212 0.104 0.000 0.000 0.896
#> GSM564712 1 0.1940 0.8738 0.924 0.000 0.000 0.076
#> GSM564713 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564714 4 0.2921 0.7998 0.140 0.000 0.000 0.860
#> GSM564715 1 0.3444 0.7644 0.816 0.000 0.000 0.184
#> GSM564716 4 0.4304 0.7077 0.284 0.000 0.000 0.716
#> GSM564717 1 0.4804 0.3966 0.616 0.000 0.000 0.384
#> GSM564718 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564719 4 0.3400 0.7746 0.180 0.000 0.000 0.820
#> GSM564720 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564721 1 0.3266 0.8342 0.832 0.000 0.000 0.168
#> GSM564722 4 0.3219 0.7871 0.164 0.000 0.000 0.836
#> GSM564723 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564724 4 0.0592 0.8667 0.016 0.000 0.000 0.984
#> GSM564725 1 0.3311 0.8321 0.828 0.000 0.000 0.172
#> GSM564726 4 0.0592 0.8667 0.016 0.000 0.000 0.984
#> GSM564727 1 0.2647 0.8620 0.880 0.000 0.000 0.120
#> GSM564728 4 0.0592 0.8667 0.016 0.000 0.000 0.984
#> GSM564729 4 0.3172 0.7771 0.160 0.000 0.000 0.840
#> GSM564730 1 0.2647 0.8620 0.880 0.000 0.000 0.120
#> GSM564731 4 0.1474 0.8613 0.052 0.000 0.000 0.948
#> GSM564732 4 0.2281 0.8358 0.096 0.000 0.000 0.904
#> GSM564733 4 0.4522 0.5246 0.320 0.000 0.000 0.680
#> GSM564734 4 0.4356 0.6220 0.292 0.000 0.000 0.708
#> GSM564735 4 0.1557 0.8622 0.056 0.000 0.000 0.944
#> GSM564736 4 0.1118 0.8650 0.036 0.000 0.000 0.964
#> GSM564737 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564738 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564739 4 0.2281 0.8358 0.096 0.000 0.000 0.904
#> GSM564740 4 0.2921 0.8003 0.140 0.000 0.000 0.860
#> GSM564741 4 0.1211 0.8641 0.040 0.000 0.000 0.960
#> GSM564742 4 0.3172 0.7898 0.160 0.000 0.000 0.840
#> GSM564743 1 0.2216 0.8423 0.908 0.000 0.000 0.092
#> GSM564744 1 0.0707 0.8724 0.980 0.000 0.000 0.020
#> GSM564745 1 0.4697 0.6011 0.644 0.000 0.000 0.356
#> GSM564746 1 0.3024 0.8128 0.852 0.000 0.000 0.148
#> GSM564747 4 0.3486 0.7807 0.188 0.000 0.000 0.812
#> GSM564748 4 0.0817 0.8660 0.024 0.000 0.000 0.976
#> GSM564749 1 0.1716 0.8599 0.936 0.000 0.000 0.064
#> GSM564750 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564751 4 0.1118 0.8648 0.036 0.000 0.000 0.964
#> GSM564752 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564753 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> GSM564754 1 0.3528 0.8204 0.808 0.000 0.000 0.192
#> GSM564755 4 0.0592 0.8667 0.016 0.000 0.000 0.984
#> GSM564756 4 0.3266 0.7683 0.168 0.000 0.000 0.832
#> GSM564757 4 0.0817 0.8676 0.024 0.000 0.000 0.976
#> GSM564758 4 0.0592 0.8667 0.016 0.000 0.000 0.984
#> GSM564759 4 0.2760 0.8138 0.128 0.000 0.000 0.872
#> GSM564760 4 0.4989 0.0629 0.472 0.000 0.000 0.528
#> GSM564761 1 0.3219 0.8373 0.836 0.000 0.000 0.164
#> GSM564762 4 0.2011 0.8556 0.080 0.000 0.000 0.920
#> GSM564681 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564693 2 0.4331 0.8943 0.000 0.712 0.288 0.000
#> GSM564646 3 0.2469 0.5899 0.000 0.108 0.892 0.000
#> GSM564699 3 0.4647 0.7939 0.008 0.288 0.704 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.1732 0.862 0.080 0.000 0.000 0.920 0.000
#> GSM564616 5 0.0162 0.930 0.000 0.000 0.004 0.000 0.996
#> GSM564617 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564618 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564619 1 0.1341 0.902 0.944 0.000 0.000 0.056 0.000
#> GSM564620 4 0.3636 0.725 0.272 0.000 0.000 0.728 0.000
#> GSM564621 1 0.1792 0.880 0.916 0.000 0.000 0.084 0.000
#> GSM564622 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564623 2 0.0404 0.908 0.000 0.988 0.000 0.012 0.000
#> GSM564624 2 0.0290 0.912 0.000 0.992 0.008 0.000 0.000
#> GSM564625 4 0.3949 0.562 0.332 0.000 0.000 0.668 0.000
#> GSM564626 1 0.1478 0.900 0.936 0.000 0.000 0.064 0.000
#> GSM564627 1 0.2127 0.857 0.892 0.000 0.000 0.108 0.000
#> GSM564628 2 0.4009 0.594 0.000 0.684 0.004 0.000 0.312
#> GSM564629 4 0.4242 0.396 0.428 0.000 0.000 0.572 0.000
#> GSM564630 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564609 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564610 1 0.0880 0.897 0.968 0.000 0.000 0.032 0.000
#> GSM564611 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564612 3 0.2732 0.815 0.000 0.160 0.840 0.000 0.000
#> GSM564613 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564614 4 0.0510 0.889 0.016 0.000 0.000 0.984 0.000
#> GSM564631 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564632 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564633 3 0.3143 0.737 0.000 0.000 0.796 0.000 0.204
#> GSM564634 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564635 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564636 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564637 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564638 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564639 3 0.0162 0.942 0.000 0.004 0.996 0.000 0.000
#> GSM564640 2 0.3495 0.825 0.000 0.812 0.160 0.000 0.028
#> GSM564641 3 0.2690 0.818 0.000 0.156 0.844 0.000 0.000
#> GSM564642 2 0.3109 0.808 0.000 0.800 0.200 0.000 0.000
#> GSM564643 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564644 2 0.2732 0.836 0.000 0.840 0.160 0.000 0.000
#> GSM564645 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564647 3 0.2377 0.823 0.000 0.128 0.872 0.000 0.000
#> GSM564648 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564649 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564650 2 0.0162 0.912 0.000 0.996 0.004 0.000 0.000
#> GSM564651 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564652 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564653 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564654 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564655 5 0.3395 0.731 0.000 0.000 0.236 0.000 0.764
#> GSM564656 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564657 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564658 2 0.0162 0.912 0.000 0.996 0.004 0.000 0.000
#> GSM564659 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564660 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564661 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564662 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564663 2 0.0162 0.912 0.000 0.996 0.004 0.000 0.000
#> GSM564664 5 0.4297 0.759 0.000 0.072 0.164 0.000 0.764
#> GSM564665 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564666 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564667 3 0.2690 0.818 0.000 0.156 0.844 0.000 0.000
#> GSM564668 5 0.2929 0.796 0.000 0.000 0.180 0.000 0.820
#> GSM564669 3 0.0162 0.942 0.000 0.000 0.996 0.000 0.004
#> GSM564670 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564671 5 0.2690 0.819 0.000 0.000 0.156 0.000 0.844
#> GSM564672 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564673 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564674 2 0.0290 0.912 0.000 0.992 0.008 0.000 0.000
#> GSM564675 2 0.2690 0.837 0.000 0.844 0.156 0.000 0.000
#> GSM564676 2 0.2690 0.837 0.000 0.844 0.156 0.000 0.000
#> GSM564677 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564678 2 0.0162 0.912 0.000 0.996 0.004 0.000 0.000
#> GSM564679 2 0.3048 0.789 0.000 0.820 0.004 0.000 0.176
#> GSM564680 3 0.0000 0.944 0.000 0.000 1.000 0.000 0.000
#> GSM564682 2 0.0162 0.911 0.000 0.996 0.004 0.000 0.000
#> GSM564683 3 0.2732 0.817 0.000 0.160 0.840 0.000 0.000
#> GSM564684 5 0.2690 0.819 0.000 0.000 0.156 0.000 0.844
#> GSM564685 3 0.0162 0.942 0.000 0.004 0.996 0.000 0.000
#> GSM564686 2 0.3109 0.805 0.000 0.800 0.200 0.000 0.000
#> GSM564687 2 0.2966 0.821 0.000 0.816 0.184 0.000 0.000
#> GSM564688 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564689 2 0.0162 0.912 0.000 0.996 0.004 0.000 0.000
#> GSM564690 2 0.2074 0.871 0.000 0.896 0.104 0.000 0.000
#> GSM564691 2 0.0162 0.912 0.000 0.996 0.004 0.000 0.000
#> GSM564692 5 0.0162 0.930 0.000 0.000 0.004 0.000 0.996
#> GSM564694 2 0.3074 0.812 0.000 0.804 0.196 0.000 0.000
#> GSM564695 2 0.3684 0.592 0.000 0.720 0.280 0.000 0.000
#> GSM564696 3 0.3274 0.707 0.000 0.220 0.780 0.000 0.000
#> GSM564697 2 0.0000 0.912 0.000 1.000 0.000 0.000 0.000
#> GSM564698 3 0.0162 0.942 0.000 0.000 0.996 0.000 0.004
#> GSM564700 5 0.2561 0.830 0.000 0.000 0.144 0.000 0.856
#> GSM564701 5 0.0162 0.930 0.000 0.000 0.004 0.000 0.996
#> GSM564702 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564703 4 0.0609 0.889 0.020 0.000 0.000 0.980 0.000
#> GSM564704 4 0.3636 0.695 0.272 0.000 0.000 0.728 0.000
#> GSM564705 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.1410 0.869 0.060 0.000 0.000 0.940 0.000
#> GSM564707 1 0.3210 0.759 0.788 0.000 0.000 0.212 0.000
#> GSM564708 4 0.0000 0.887 0.000 0.000 0.000 1.000 0.000
#> GSM564709 1 0.1544 0.898 0.932 0.000 0.000 0.068 0.000
#> GSM564710 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.1410 0.869 0.060 0.000 0.000 0.940 0.000
#> GSM564712 1 0.0703 0.906 0.976 0.000 0.000 0.024 0.000
#> GSM564713 4 0.0000 0.887 0.000 0.000 0.000 1.000 0.000
#> GSM564714 4 0.1732 0.862 0.080 0.000 0.000 0.920 0.000
#> GSM564715 1 0.2929 0.773 0.820 0.000 0.000 0.180 0.000
#> GSM564716 4 0.3534 0.751 0.256 0.000 0.000 0.744 0.000
#> GSM564717 1 0.4126 0.418 0.620 0.000 0.000 0.380 0.000
#> GSM564718 4 0.0000 0.887 0.000 0.000 0.000 1.000 0.000
#> GSM564719 4 0.2377 0.838 0.128 0.000 0.000 0.872 0.000
#> GSM564720 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564721 1 0.1851 0.889 0.912 0.000 0.000 0.088 0.000
#> GSM564722 4 0.2179 0.848 0.112 0.000 0.000 0.888 0.000
#> GSM564723 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564724 4 0.0510 0.889 0.016 0.000 0.000 0.984 0.000
#> GSM564725 1 0.1851 0.889 0.912 0.000 0.000 0.088 0.000
#> GSM564726 4 0.0609 0.889 0.020 0.000 0.000 0.980 0.000
#> GSM564727 1 0.1341 0.902 0.944 0.000 0.000 0.056 0.000
#> GSM564728 4 0.0510 0.889 0.016 0.000 0.000 0.984 0.000
#> GSM564729 4 0.2813 0.798 0.168 0.000 0.000 0.832 0.000
#> GSM564730 1 0.1341 0.902 0.944 0.000 0.000 0.056 0.000
#> GSM564731 4 0.1197 0.885 0.048 0.000 0.000 0.952 0.000
#> GSM564732 4 0.2020 0.857 0.100 0.000 0.000 0.900 0.000
#> GSM564733 4 0.3949 0.552 0.332 0.000 0.000 0.668 0.000
#> GSM564734 4 0.3816 0.646 0.304 0.000 0.000 0.696 0.000
#> GSM564735 4 0.1270 0.886 0.052 0.000 0.000 0.948 0.000
#> GSM564736 4 0.0880 0.889 0.032 0.000 0.000 0.968 0.000
#> GSM564737 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.0000 0.887 0.000 0.000 0.000 1.000 0.000
#> GSM564739 4 0.2020 0.857 0.100 0.000 0.000 0.900 0.000
#> GSM564740 4 0.1792 0.861 0.084 0.000 0.000 0.916 0.000
#> GSM564741 4 0.0963 0.888 0.036 0.000 0.000 0.964 0.000
#> GSM564742 4 0.2127 0.853 0.108 0.000 0.000 0.892 0.000
#> GSM564743 1 0.1732 0.867 0.920 0.000 0.000 0.080 0.000
#> GSM564744 1 0.0000 0.904 1.000 0.000 0.000 0.000 0.000
#> GSM564745 1 0.3752 0.669 0.708 0.000 0.000 0.292 0.000
#> GSM564746 1 0.2516 0.833 0.860 0.000 0.000 0.140 0.000
#> GSM564747 4 0.2424 0.845 0.132 0.000 0.000 0.868 0.000
#> GSM564748 4 0.0609 0.889 0.020 0.000 0.000 0.980 0.000
#> GSM564749 1 0.1197 0.888 0.952 0.000 0.000 0.048 0.000
#> GSM564750 4 0.0162 0.888 0.004 0.000 0.000 0.996 0.000
#> GSM564751 4 0.0880 0.888 0.032 0.000 0.000 0.968 0.000
#> GSM564752 4 0.0000 0.887 0.000 0.000 0.000 1.000 0.000
#> GSM564753 4 0.0000 0.887 0.000 0.000 0.000 1.000 0.000
#> GSM564754 1 0.2179 0.878 0.888 0.000 0.000 0.112 0.000
#> GSM564755 4 0.0609 0.889 0.020 0.000 0.000 0.980 0.000
#> GSM564756 4 0.2966 0.783 0.184 0.000 0.000 0.816 0.000
#> GSM564757 4 0.0703 0.891 0.024 0.000 0.000 0.976 0.000
#> GSM564758 4 0.0510 0.889 0.016 0.000 0.000 0.984 0.000
#> GSM564759 4 0.1851 0.862 0.088 0.000 0.000 0.912 0.000
#> GSM564760 4 0.4302 0.147 0.480 0.000 0.000 0.520 0.000
#> GSM564761 1 0.1732 0.893 0.920 0.000 0.000 0.080 0.000
#> GSM564762 4 0.1792 0.878 0.084 0.000 0.000 0.916 0.000
#> GSM564681 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564693 5 0.0000 0.932 0.000 0.000 0.000 0.000 1.000
#> GSM564646 5 0.6281 0.188 0.000 0.352 0.160 0.000 0.488
#> GSM564699 3 0.1732 0.884 0.000 0.080 0.920 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0146 0.7911 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM564616 5 0.0146 0.9258 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM564617 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564618 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564619 1 0.0146 0.8372 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM564620 6 0.3989 0.6217 0.236 0.000 0.000 0.044 0.000 0.720
#> GSM564621 1 0.2384 0.7935 0.888 0.000 0.000 0.064 0.000 0.048
#> GSM564622 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564623 2 0.0458 0.9046 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM564624 2 0.0260 0.9114 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM564625 4 0.0508 0.7848 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM564626 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564627 1 0.4473 0.2095 0.488 0.000 0.000 0.028 0.000 0.484
#> GSM564628 2 0.3601 0.5936 0.000 0.684 0.004 0.000 0.312 0.000
#> GSM564629 6 0.3244 0.6126 0.000 0.000 0.000 0.268 0.000 0.732
#> GSM564630 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564609 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564610 1 0.2793 0.7527 0.800 0.000 0.000 0.000 0.000 0.200
#> GSM564611 1 0.2762 0.7531 0.804 0.000 0.000 0.000 0.000 0.196
#> GSM564612 3 0.2340 0.8275 0.000 0.148 0.852 0.000 0.000 0.000
#> GSM564613 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564614 4 0.3151 0.7549 0.000 0.000 0.000 0.748 0.000 0.252
#> GSM564631 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564632 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564633 3 0.2823 0.7399 0.000 0.000 0.796 0.000 0.204 0.000
#> GSM564634 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564635 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564636 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564637 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564638 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564639 3 0.0146 0.9416 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564640 2 0.3027 0.8356 0.000 0.824 0.148 0.000 0.028 0.000
#> GSM564641 3 0.2300 0.8302 0.000 0.144 0.856 0.000 0.000 0.000
#> GSM564642 2 0.2793 0.8084 0.000 0.800 0.200 0.000 0.000 0.000
#> GSM564643 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564644 2 0.2340 0.8458 0.000 0.852 0.148 0.000 0.000 0.000
#> GSM564645 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564647 3 0.2178 0.8218 0.000 0.132 0.868 0.000 0.000 0.000
#> GSM564648 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564649 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564650 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564651 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564652 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564653 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564654 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564655 5 0.3050 0.7298 0.000 0.000 0.236 0.000 0.764 0.000
#> GSM564656 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564657 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564658 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564659 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564660 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564661 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564662 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564663 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564664 5 0.3857 0.7627 0.000 0.080 0.152 0.000 0.768 0.000
#> GSM564665 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564666 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564667 3 0.2300 0.8302 0.000 0.144 0.856 0.000 0.000 0.000
#> GSM564668 5 0.2597 0.8009 0.000 0.000 0.176 0.000 0.824 0.000
#> GSM564669 3 0.0146 0.9413 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM564670 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564671 5 0.2300 0.8283 0.000 0.000 0.144 0.000 0.856 0.000
#> GSM564672 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564673 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564674 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564675 2 0.2300 0.8467 0.000 0.856 0.144 0.000 0.000 0.000
#> GSM564676 2 0.2300 0.8467 0.000 0.856 0.144 0.000 0.000 0.000
#> GSM564677 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564678 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564679 2 0.2738 0.7909 0.000 0.820 0.004 0.000 0.176 0.000
#> GSM564680 3 0.0000 0.9437 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564682 2 0.0146 0.9106 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564683 3 0.2340 0.8286 0.000 0.148 0.852 0.000 0.000 0.000
#> GSM564684 5 0.2300 0.8283 0.000 0.000 0.144 0.000 0.856 0.000
#> GSM564685 3 0.0146 0.9416 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564686 2 0.2793 0.8055 0.000 0.800 0.200 0.000 0.000 0.000
#> GSM564687 2 0.2631 0.8254 0.000 0.820 0.180 0.000 0.000 0.000
#> GSM564688 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564689 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564690 2 0.1714 0.8793 0.000 0.908 0.092 0.000 0.000 0.000
#> GSM564691 2 0.0146 0.9117 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM564692 5 0.0146 0.9258 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM564694 2 0.2730 0.8164 0.000 0.808 0.192 0.000 0.000 0.000
#> GSM564695 2 0.3351 0.5841 0.000 0.712 0.288 0.000 0.000 0.000
#> GSM564696 3 0.2996 0.6976 0.000 0.228 0.772 0.000 0.000 0.000
#> GSM564697 2 0.0000 0.9113 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564698 3 0.0146 0.9413 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM564700 5 0.2178 0.8381 0.000 0.000 0.132 0.000 0.868 0.000
#> GSM564701 5 0.0146 0.9262 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM564702 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564703 4 0.4039 0.7564 0.060 0.000 0.000 0.732 0.000 0.208
#> GSM564704 1 0.3679 0.6265 0.760 0.000 0.000 0.040 0.000 0.200
#> GSM564705 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564706 6 0.1141 0.8155 0.000 0.000 0.000 0.052 0.000 0.948
#> GSM564707 1 0.4328 0.6699 0.720 0.000 0.000 0.180 0.000 0.100
#> GSM564708 4 0.3244 0.7403 0.000 0.000 0.000 0.732 0.000 0.268
#> GSM564709 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564710 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564711 6 0.1556 0.8027 0.000 0.000 0.000 0.080 0.000 0.920
#> GSM564712 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.3244 0.7403 0.000 0.000 0.000 0.732 0.000 0.268
#> GSM564714 6 0.1007 0.8171 0.000 0.000 0.000 0.044 0.000 0.956
#> GSM564715 1 0.2480 0.7609 0.872 0.000 0.000 0.024 0.000 0.104
#> GSM564716 1 0.5990 -0.2172 0.400 0.000 0.000 0.368 0.000 0.232
#> GSM564717 6 0.1471 0.7756 0.064 0.000 0.000 0.004 0.000 0.932
#> GSM564718 6 0.2793 0.6862 0.000 0.000 0.000 0.200 0.000 0.800
#> GSM564719 6 0.0000 0.8133 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM564720 1 0.2793 0.7505 0.800 0.000 0.000 0.000 0.000 0.200
#> GSM564721 1 0.3851 0.0441 0.540 0.000 0.000 0.460 0.000 0.000
#> GSM564722 6 0.0000 0.8133 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM564723 1 0.2730 0.7557 0.808 0.000 0.000 0.000 0.000 0.192
#> GSM564724 4 0.3175 0.7518 0.000 0.000 0.000 0.744 0.000 0.256
#> GSM564725 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564726 4 0.2527 0.7987 0.000 0.000 0.000 0.832 0.000 0.168
#> GSM564727 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564728 4 0.3076 0.7635 0.000 0.000 0.000 0.760 0.000 0.240
#> GSM564729 4 0.3172 0.7984 0.036 0.000 0.000 0.816 0.000 0.148
#> GSM564730 1 0.0865 0.8302 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM564731 4 0.3864 -0.3050 0.000 0.000 0.000 0.520 0.000 0.480
#> GSM564732 4 0.0000 0.7890 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564733 4 0.0632 0.7779 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM564734 4 0.1176 0.7695 0.020 0.000 0.000 0.956 0.000 0.024
#> GSM564735 4 0.0790 0.7939 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM564736 4 0.0713 0.7964 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM564737 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564738 6 0.3862 -0.1703 0.000 0.000 0.000 0.476 0.000 0.524
#> GSM564739 4 0.2357 0.8078 0.012 0.000 0.000 0.872 0.000 0.116
#> GSM564740 6 0.1007 0.8173 0.000 0.000 0.000 0.044 0.000 0.956
#> GSM564741 4 0.2743 0.8003 0.008 0.000 0.000 0.828 0.000 0.164
#> GSM564742 6 0.0547 0.8180 0.000 0.000 0.000 0.020 0.000 0.980
#> GSM564743 1 0.2793 0.7505 0.800 0.000 0.000 0.000 0.000 0.200
#> GSM564744 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564745 4 0.5127 0.4105 0.348 0.000 0.000 0.556 0.000 0.096
#> GSM564746 6 0.2219 0.6875 0.136 0.000 0.000 0.000 0.000 0.864
#> GSM564747 6 0.1812 0.7904 0.008 0.000 0.000 0.080 0.000 0.912
#> GSM564748 4 0.2980 0.7952 0.012 0.000 0.000 0.808 0.000 0.180
#> GSM564749 1 0.4144 0.7085 0.728 0.000 0.000 0.072 0.000 0.200
#> GSM564750 4 0.3244 0.7403 0.000 0.000 0.000 0.732 0.000 0.268
#> GSM564751 4 0.3615 0.6843 0.008 0.000 0.000 0.700 0.000 0.292
#> GSM564752 6 0.2793 0.6862 0.000 0.000 0.000 0.200 0.000 0.800
#> GSM564753 6 0.2730 0.6943 0.000 0.000 0.000 0.192 0.000 0.808
#> GSM564754 1 0.1588 0.8097 0.924 0.000 0.000 0.072 0.000 0.004
#> GSM564755 4 0.2340 0.8050 0.000 0.000 0.000 0.852 0.000 0.148
#> GSM564756 4 0.4924 0.6462 0.144 0.000 0.000 0.652 0.000 0.204
#> GSM564757 4 0.0363 0.7877 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM564758 4 0.1556 0.7983 0.000 0.000 0.000 0.920 0.000 0.080
#> GSM564759 6 0.2664 0.7355 0.000 0.000 0.000 0.184 0.000 0.816
#> GSM564760 4 0.0865 0.7718 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM564761 1 0.0000 0.8380 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564762 4 0.0937 0.7686 0.000 0.000 0.000 0.960 0.000 0.040
#> GSM564681 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564693 5 0.0000 0.9281 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564646 5 0.5571 0.1950 0.000 0.356 0.148 0.000 0.496 0.000
#> GSM564699 3 0.1501 0.8900 0.000 0.076 0.924 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 genotype/variation(p) disease.state(p) k
#> ATC:pam 154 0.925 0.476 2
#> ATC:pam 135 0.568 0.958 3
#> ATC:pam 148 0.482 0.288 4
#> ATC:pam 150 0.357 0.259 5
#> ATC:pam 147 0.571 0.500 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.747 0.884 0.863 0.2089 0.894 0.787
#> 4 4 0.617 0.670 0.781 0.1236 0.860 0.676
#> 5 5 0.727 0.727 0.807 0.1076 0.861 0.603
#> 6 6 0.888 0.883 0.923 0.0554 0.941 0.764
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
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564616 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564617 2 0.0424 0.939 0.000 0.992 0.008
#> GSM564618 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564619 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564620 1 0.4291 0.694 0.820 0.000 0.180
#> GSM564621 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564622 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564623 2 0.4654 0.860 0.000 0.792 0.208
#> GSM564624 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564625 3 0.6307 0.488 0.488 0.000 0.512
#> GSM564626 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564627 1 0.2959 0.825 0.900 0.000 0.100
#> GSM564628 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564629 1 0.5216 0.589 0.740 0.000 0.260
#> GSM564630 2 0.4654 0.860 0.000 0.792 0.208
#> GSM564609 2 0.3686 0.892 0.000 0.860 0.140
#> GSM564610 1 0.2959 0.825 0.900 0.000 0.100
#> GSM564611 1 0.2959 0.825 0.900 0.000 0.100
#> GSM564612 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564613 2 0.4555 0.865 0.000 0.800 0.200
#> GSM564614 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564631 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564632 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564633 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564634 2 0.4654 0.860 0.000 0.792 0.208
#> GSM564635 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564636 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564637 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564638 2 0.0747 0.937 0.000 0.984 0.016
#> GSM564639 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564640 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564641 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564642 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564643 2 0.2959 0.909 0.000 0.900 0.100
#> GSM564644 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564645 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564647 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564648 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564649 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564650 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564651 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564652 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564653 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564654 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564655 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564656 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564657 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564658 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564659 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564660 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564661 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564662 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564663 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564664 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564665 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564666 2 0.4654 0.860 0.000 0.792 0.208
#> GSM564667 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564668 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564669 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564670 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564671 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564672 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564673 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564674 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564675 2 0.4654 0.860 0.000 0.792 0.208
#> GSM564676 2 0.0424 0.939 0.000 0.992 0.008
#> GSM564677 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564678 2 0.0424 0.939 0.000 0.992 0.008
#> GSM564679 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564680 2 0.2711 0.914 0.000 0.912 0.088
#> GSM564682 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564683 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564684 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564685 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564686 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564687 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564688 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564689 2 0.0424 0.939 0.000 0.992 0.008
#> GSM564690 2 0.0424 0.939 0.000 0.992 0.008
#> GSM564691 2 0.0237 0.940 0.000 0.996 0.004
#> GSM564692 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564694 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564695 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564696 2 0.4654 0.860 0.000 0.792 0.208
#> GSM564697 2 0.0424 0.939 0.000 0.992 0.008
#> GSM564698 2 0.4555 0.863 0.000 0.800 0.200
#> GSM564700 2 0.4504 0.866 0.000 0.804 0.196
#> GSM564701 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564702 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564703 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564704 1 0.1753 0.844 0.952 0.000 0.048
#> GSM564705 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564706 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564707 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564708 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564709 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564710 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564711 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564712 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564713 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564714 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564715 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564716 1 0.1411 0.851 0.964 0.000 0.036
#> GSM564717 1 0.3482 0.797 0.872 0.000 0.128
#> GSM564718 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564719 1 0.5529 0.498 0.704 0.000 0.296
#> GSM564720 1 0.2959 0.825 0.900 0.000 0.100
#> GSM564721 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564722 1 0.5733 0.410 0.676 0.000 0.324
#> GSM564723 1 0.2537 0.836 0.920 0.000 0.080
#> GSM564724 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564725 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564726 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564727 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564728 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564729 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564730 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564731 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564732 3 0.6244 0.646 0.440 0.000 0.560
#> GSM564733 3 0.6235 0.658 0.436 0.000 0.564
#> GSM564734 1 0.4605 0.653 0.796 0.000 0.204
#> GSM564735 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564736 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564737 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564738 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564739 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564740 1 0.6045 0.171 0.620 0.000 0.380
#> GSM564741 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564742 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564743 1 0.2959 0.825 0.900 0.000 0.100
#> GSM564744 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564745 1 0.0747 0.859 0.984 0.000 0.016
#> GSM564746 1 0.3038 0.822 0.896 0.000 0.104
#> GSM564747 1 0.5733 0.410 0.676 0.000 0.324
#> GSM564748 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564749 1 0.2959 0.825 0.900 0.000 0.100
#> GSM564750 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564751 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564752 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564753 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564754 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564755 3 0.5431 0.960 0.284 0.000 0.716
#> GSM564756 1 0.4605 0.653 0.796 0.000 0.204
#> GSM564757 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564758 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564759 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564760 1 0.5098 0.587 0.752 0.000 0.248
#> GSM564761 1 0.0000 0.862 1.000 0.000 0.000
#> GSM564762 3 0.5363 0.971 0.276 0.000 0.724
#> GSM564681 2 0.2261 0.916 0.000 0.932 0.068
#> GSM564693 2 0.2165 0.918 0.000 0.936 0.064
#> GSM564646 2 0.0000 0.940 0.000 1.000 0.000
#> GSM564699 2 0.4555 0.863 0.000 0.800 0.200
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564616 2 0.4697 0.92557 0.000 0.644 0.356 0.000
#> GSM564617 3 0.5170 0.58095 0.000 0.228 0.724 0.048
#> GSM564618 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564619 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564620 4 0.4941 0.62851 0.436 0.000 0.000 0.564
#> GSM564621 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564622 3 0.5307 0.60240 0.000 0.076 0.736 0.188
#> GSM564623 3 0.4776 0.62192 0.000 0.060 0.776 0.164
#> GSM564624 3 0.5848 0.47141 0.000 0.336 0.616 0.048
#> GSM564625 1 0.3486 0.61429 0.812 0.000 0.000 0.188
#> GSM564626 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564627 1 0.7273 -0.23446 0.452 0.148 0.000 0.400
#> GSM564628 3 0.5733 0.47299 0.000 0.312 0.640 0.048
#> GSM564629 1 0.6873 0.20915 0.580 0.148 0.000 0.272
#> GSM564630 3 0.4907 0.62363 0.000 0.060 0.764 0.176
#> GSM564609 3 0.2021 0.71530 0.000 0.024 0.936 0.040
#> GSM564610 1 0.7273 -0.23446 0.452 0.148 0.000 0.400
#> GSM564611 1 0.7273 -0.23446 0.452 0.148 0.000 0.400
#> GSM564612 3 0.4800 0.60084 0.000 0.196 0.760 0.044
#> GSM564613 3 0.4286 0.65603 0.000 0.052 0.812 0.136
#> GSM564614 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564631 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564632 3 0.1520 0.71264 0.000 0.020 0.956 0.024
#> GSM564633 3 0.1022 0.71823 0.000 0.000 0.968 0.032
#> GSM564634 3 0.4776 0.63181 0.000 0.060 0.776 0.164
#> GSM564635 3 0.1833 0.72244 0.000 0.024 0.944 0.032
#> GSM564636 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564637 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564638 3 0.2214 0.71795 0.000 0.044 0.928 0.028
#> GSM564639 3 0.5035 0.61188 0.000 0.056 0.748 0.196
#> GSM564640 3 0.5830 0.47409 0.000 0.332 0.620 0.048
#> GSM564641 3 0.1890 0.71688 0.000 0.056 0.936 0.008
#> GSM564642 3 0.2111 0.71136 0.000 0.044 0.932 0.024
#> GSM564643 3 0.1936 0.71302 0.000 0.028 0.940 0.032
#> GSM564644 3 0.5830 0.47409 0.000 0.332 0.620 0.048
#> GSM564645 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564647 3 0.2021 0.71639 0.000 0.056 0.932 0.012
#> GSM564648 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564649 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564650 3 0.5646 0.52022 0.000 0.296 0.656 0.048
#> GSM564651 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564652 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564653 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564654 3 0.1022 0.71823 0.000 0.000 0.968 0.032
#> GSM564655 3 0.5035 0.61188 0.000 0.056 0.748 0.196
#> GSM564656 3 0.4881 0.61961 0.000 0.048 0.756 0.196
#> GSM564657 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564658 3 0.5773 0.47093 0.000 0.320 0.632 0.048
#> GSM564659 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564660 3 0.5235 0.57206 0.000 0.236 0.716 0.048
#> GSM564661 2 0.4817 0.96683 0.000 0.612 0.388 0.000
#> GSM564662 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564663 3 0.5830 0.47706 0.000 0.332 0.620 0.048
#> GSM564664 3 0.1767 0.70550 0.000 0.044 0.944 0.012
#> GSM564665 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564666 3 0.4776 0.62192 0.000 0.060 0.776 0.164
#> GSM564667 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564668 3 0.5180 0.60866 0.000 0.064 0.740 0.196
#> GSM564669 3 0.5035 0.61188 0.000 0.056 0.748 0.196
#> GSM564670 3 0.4050 0.65052 0.000 0.144 0.820 0.036
#> GSM564671 3 0.5307 0.60240 0.000 0.076 0.736 0.188
#> GSM564672 3 0.1151 0.72329 0.000 0.024 0.968 0.008
#> GSM564673 3 0.2654 0.65131 0.000 0.108 0.888 0.004
#> GSM564674 3 0.5267 0.56946 0.000 0.240 0.712 0.048
#> GSM564675 3 0.4776 0.62192 0.000 0.060 0.776 0.164
#> GSM564676 3 0.5235 0.57206 0.000 0.236 0.716 0.048
#> GSM564677 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564678 3 0.5848 0.47141 0.000 0.336 0.616 0.048
#> GSM564679 3 0.5733 0.46995 0.000 0.312 0.640 0.048
#> GSM564680 3 0.1820 0.71897 0.000 0.020 0.944 0.036
#> GSM564682 3 0.3479 0.66369 0.000 0.148 0.840 0.012
#> GSM564683 3 0.4789 0.61884 0.000 0.056 0.772 0.172
#> GSM564684 3 0.5307 0.60240 0.000 0.076 0.736 0.188
#> GSM564685 3 0.4789 0.61884 0.000 0.056 0.772 0.172
#> GSM564686 3 0.4956 0.61496 0.000 0.056 0.756 0.188
#> GSM564687 3 0.3198 0.68643 0.000 0.080 0.880 0.040
#> GSM564688 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564689 3 0.5203 0.57663 0.000 0.232 0.720 0.048
#> GSM564690 3 0.4957 0.58976 0.000 0.204 0.748 0.048
#> GSM564691 3 0.4877 0.59543 0.000 0.204 0.752 0.044
#> GSM564692 2 0.4994 0.76621 0.000 0.520 0.480 0.000
#> GSM564694 3 0.0188 0.72142 0.000 0.000 0.996 0.004
#> GSM564695 3 0.0000 0.72075 0.000 0.000 1.000 0.000
#> GSM564696 3 0.4864 0.61912 0.000 0.060 0.768 0.172
#> GSM564697 3 0.5102 0.58744 0.000 0.220 0.732 0.048
#> GSM564698 3 0.5035 0.61188 0.000 0.056 0.748 0.196
#> GSM564700 3 0.3398 0.69521 0.000 0.068 0.872 0.060
#> GSM564701 3 0.3024 0.59505 0.000 0.148 0.852 0.000
#> GSM564702 2 0.4817 0.97272 0.000 0.612 0.388 0.000
#> GSM564703 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564704 4 0.4164 0.91425 0.264 0.000 0.000 0.736
#> GSM564705 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564706 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564707 4 0.4406 0.86549 0.300 0.000 0.000 0.700
#> GSM564708 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564709 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564710 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564711 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564712 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564713 1 0.0188 0.79806 0.996 0.000 0.000 0.004
#> GSM564714 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564715 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564716 4 0.4898 0.67261 0.416 0.000 0.000 0.584
#> GSM564717 1 0.7191 -0.07127 0.500 0.148 0.000 0.352
#> GSM564718 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564719 1 0.6115 0.43885 0.680 0.148 0.000 0.172
#> GSM564720 1 0.7273 -0.23446 0.452 0.148 0.000 0.400
#> GSM564721 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564722 1 0.5950 0.48659 0.696 0.148 0.000 0.156
#> GSM564723 4 0.6773 0.72934 0.276 0.136 0.000 0.588
#> GSM564724 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564725 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564726 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564727 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564728 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564729 1 0.0336 0.79566 0.992 0.000 0.000 0.008
#> GSM564730 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564731 1 0.1022 0.78635 0.968 0.000 0.000 0.032
#> GSM564732 1 0.1940 0.75272 0.924 0.000 0.000 0.076
#> GSM564733 1 0.3444 0.61315 0.816 0.000 0.000 0.184
#> GSM564734 4 0.4961 0.57841 0.448 0.000 0.000 0.552
#> GSM564735 1 0.1022 0.78635 0.968 0.000 0.000 0.032
#> GSM564736 1 0.1022 0.78635 0.968 0.000 0.000 0.032
#> GSM564737 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564738 1 0.0336 0.79688 0.992 0.000 0.000 0.008
#> GSM564739 1 0.0188 0.79797 0.996 0.000 0.000 0.004
#> GSM564740 1 0.2610 0.72266 0.900 0.012 0.000 0.088
#> GSM564741 1 0.1022 0.78635 0.968 0.000 0.000 0.032
#> GSM564742 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564743 1 0.7273 -0.23446 0.452 0.148 0.000 0.400
#> GSM564744 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564745 4 0.4134 0.91961 0.260 0.000 0.000 0.740
#> GSM564746 1 0.7254 -0.18228 0.468 0.148 0.000 0.384
#> GSM564747 1 0.3808 0.62281 0.812 0.012 0.000 0.176
#> GSM564748 1 0.1022 0.78635 0.968 0.000 0.000 0.032
#> GSM564749 1 0.7273 -0.23446 0.452 0.148 0.000 0.400
#> GSM564750 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564751 1 0.0817 0.79039 0.976 0.000 0.000 0.024
#> GSM564752 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564753 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564754 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564755 1 0.0336 0.79702 0.992 0.000 0.000 0.008
#> GSM564756 4 0.4925 0.63259 0.428 0.000 0.000 0.572
#> GSM564757 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564758 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564759 1 0.0000 0.79843 1.000 0.000 0.000 0.000
#> GSM564760 1 0.4967 -0.28353 0.548 0.000 0.000 0.452
#> GSM564761 4 0.4040 0.92984 0.248 0.000 0.000 0.752
#> GSM564762 1 0.0188 0.79806 0.996 0.000 0.000 0.004
#> GSM564681 2 0.4761 0.95260 0.000 0.628 0.372 0.000
#> GSM564693 3 0.4679 -0.00656 0.000 0.352 0.648 0.000
#> GSM564646 3 0.1256 0.71862 0.000 0.008 0.964 0.028
#> GSM564699 3 0.4916 0.61616 0.000 0.056 0.760 0.184
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564616 5 0.2411 0.9503 0.000 0.108 0.008 0.000 0.884
#> GSM564617 2 0.3612 0.8320 0.000 0.732 0.268 0.000 0.000
#> GSM564618 5 0.2127 0.9588 0.000 0.108 0.000 0.000 0.892
#> GSM564619 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564620 1 0.3109 0.7179 0.800 0.000 0.000 0.200 0.000
#> GSM564621 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564622 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564623 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564624 2 0.4240 0.8571 0.000 0.736 0.228 0.000 0.036
#> GSM564625 4 0.4171 0.3303 0.396 0.000 0.000 0.604 0.000
#> GSM564626 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564627 1 0.4453 0.7737 0.724 0.228 0.000 0.048 0.000
#> GSM564628 2 0.4313 0.8577 0.000 0.732 0.228 0.000 0.040
#> GSM564629 4 0.6460 -0.1391 0.404 0.180 0.000 0.416 0.000
#> GSM564630 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564609 3 0.5303 0.6445 0.000 0.232 0.660 0.000 0.108
#> GSM564610 1 0.4384 0.7754 0.728 0.228 0.000 0.044 0.000
#> GSM564611 1 0.4453 0.7737 0.724 0.228 0.000 0.048 0.000
#> GSM564612 2 0.5719 0.4320 0.000 0.552 0.352 0.000 0.096
#> GSM564613 3 0.2929 0.6473 0.000 0.180 0.820 0.000 0.000
#> GSM564614 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564631 3 0.5579 0.6211 0.000 0.264 0.620 0.000 0.116
#> GSM564632 3 0.4599 0.4981 0.000 0.356 0.624 0.000 0.020
#> GSM564633 3 0.5513 0.6312 0.000 0.252 0.632 0.000 0.116
#> GSM564634 3 0.2648 0.6470 0.000 0.152 0.848 0.000 0.000
#> GSM564635 3 0.5513 0.6312 0.000 0.252 0.632 0.000 0.116
#> GSM564636 3 0.5748 0.5589 0.000 0.300 0.584 0.000 0.116
#> GSM564637 3 0.5579 0.6211 0.000 0.264 0.620 0.000 0.116
#> GSM564638 3 0.5358 0.6377 0.000 0.248 0.648 0.000 0.104
#> GSM564639 3 0.2361 0.6337 0.000 0.012 0.892 0.000 0.096
#> GSM564640 2 0.4313 0.8577 0.000 0.732 0.228 0.000 0.040
#> GSM564641 3 0.5962 0.1479 0.000 0.424 0.468 0.000 0.108
#> GSM564642 2 0.4787 0.3327 0.000 0.548 0.432 0.000 0.020
#> GSM564643 3 0.3912 0.6346 0.000 0.228 0.752 0.000 0.020
#> GSM564644 2 0.4313 0.8577 0.000 0.732 0.228 0.000 0.040
#> GSM564645 3 0.5579 0.6211 0.000 0.264 0.620 0.000 0.116
#> GSM564647 3 0.5854 0.1144 0.000 0.436 0.468 0.000 0.096
#> GSM564648 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564649 3 0.5640 0.6042 0.000 0.276 0.608 0.000 0.116
#> GSM564650 2 0.4000 0.8592 0.000 0.748 0.228 0.000 0.024
#> GSM564651 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564652 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564653 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564654 3 0.5466 0.6365 0.000 0.244 0.640 0.000 0.116
#> GSM564655 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564656 3 0.4164 0.6575 0.000 0.120 0.784 0.000 0.096
#> GSM564657 3 0.5579 0.6211 0.000 0.264 0.620 0.000 0.116
#> GSM564658 2 0.4240 0.8571 0.000 0.736 0.228 0.000 0.036
#> GSM564659 3 0.5620 0.6105 0.000 0.272 0.612 0.000 0.116
#> GSM564660 2 0.3491 0.8538 0.000 0.768 0.228 0.000 0.004
#> GSM564661 5 0.2179 0.9555 0.000 0.112 0.000 0.000 0.888
#> GSM564662 3 0.5579 0.6211 0.000 0.264 0.620 0.000 0.116
#> GSM564663 2 0.4240 0.8571 0.000 0.736 0.228 0.000 0.036
#> GSM564664 3 0.4613 0.4910 0.000 0.360 0.620 0.000 0.020
#> GSM564665 3 0.5600 0.6162 0.000 0.268 0.616 0.000 0.116
#> GSM564666 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564667 3 0.5600 0.6162 0.000 0.268 0.616 0.000 0.116
#> GSM564668 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564669 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564670 2 0.4437 0.2090 0.000 0.532 0.464 0.000 0.004
#> GSM564671 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564672 3 0.5579 0.6211 0.000 0.264 0.620 0.000 0.116
#> GSM564673 3 0.4132 0.5866 0.000 0.260 0.720 0.000 0.020
#> GSM564674 2 0.3912 0.8588 0.000 0.752 0.228 0.000 0.020
#> GSM564675 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564676 2 0.3579 0.8513 0.000 0.756 0.240 0.000 0.004
#> GSM564677 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564678 2 0.4240 0.8571 0.000 0.736 0.228 0.000 0.036
#> GSM564679 2 0.4313 0.8577 0.000 0.732 0.228 0.000 0.040
#> GSM564680 3 0.5303 0.6444 0.000 0.232 0.660 0.000 0.108
#> GSM564682 3 0.5816 0.1015 0.000 0.440 0.468 0.000 0.092
#> GSM564683 3 0.2653 0.6380 0.000 0.024 0.880 0.000 0.096
#> GSM564684 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564685 3 0.4020 0.6563 0.000 0.108 0.796 0.000 0.096
#> GSM564686 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564687 2 0.4498 0.8009 0.000 0.688 0.280 0.000 0.032
#> GSM564688 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564689 2 0.3612 0.8320 0.000 0.732 0.268 0.000 0.000
#> GSM564690 2 0.3612 0.8320 0.000 0.732 0.268 0.000 0.000
#> GSM564691 2 0.3861 0.8129 0.000 0.712 0.284 0.000 0.004
#> GSM564692 5 0.4847 0.5792 0.000 0.240 0.068 0.000 0.692
#> GSM564694 3 0.4613 0.4910 0.000 0.360 0.620 0.000 0.020
#> GSM564695 3 0.4787 0.2556 0.000 0.432 0.548 0.000 0.020
#> GSM564696 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564697 2 0.3612 0.8320 0.000 0.732 0.268 0.000 0.000
#> GSM564698 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
#> GSM564700 3 0.1251 0.6283 0.000 0.036 0.956 0.000 0.008
#> GSM564701 3 0.5584 0.3843 0.000 0.324 0.584 0.000 0.092
#> GSM564702 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564703 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564704 1 0.0794 0.8693 0.972 0.000 0.000 0.028 0.000
#> GSM564705 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564707 1 0.0880 0.8681 0.968 0.000 0.000 0.032 0.000
#> GSM564708 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564709 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564710 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564712 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564714 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564715 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564716 1 0.3074 0.7212 0.804 0.000 0.000 0.196 0.000
#> GSM564717 1 0.6261 0.4337 0.524 0.180 0.000 0.296 0.000
#> GSM564718 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564719 4 0.6386 0.0704 0.340 0.180 0.000 0.480 0.000
#> GSM564720 1 0.4453 0.7737 0.724 0.228 0.000 0.048 0.000
#> GSM564721 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564722 4 0.6416 0.0289 0.356 0.180 0.000 0.464 0.000
#> GSM564723 1 0.3650 0.8061 0.796 0.176 0.000 0.028 0.000
#> GSM564724 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564725 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564726 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564727 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564728 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564729 4 0.0703 0.8895 0.024 0.000 0.000 0.976 0.000
#> GSM564730 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564731 4 0.0609 0.8940 0.020 0.000 0.000 0.980 0.000
#> GSM564732 4 0.3586 0.6146 0.264 0.000 0.000 0.736 0.000
#> GSM564733 4 0.3774 0.5540 0.296 0.000 0.000 0.704 0.000
#> GSM564734 1 0.2074 0.8310 0.896 0.000 0.000 0.104 0.000
#> GSM564735 4 0.0794 0.8895 0.028 0.000 0.000 0.972 0.000
#> GSM564736 4 0.0703 0.8919 0.024 0.000 0.000 0.976 0.000
#> GSM564737 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564739 4 0.0162 0.9003 0.004 0.000 0.000 0.996 0.000
#> GSM564740 4 0.3366 0.6516 0.232 0.000 0.000 0.768 0.000
#> GSM564741 4 0.0703 0.8919 0.024 0.000 0.000 0.976 0.000
#> GSM564742 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564743 1 0.4453 0.7737 0.724 0.228 0.000 0.048 0.000
#> GSM564744 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564745 1 0.0510 0.8716 0.984 0.000 0.000 0.016 0.000
#> GSM564746 1 0.5500 0.6951 0.648 0.212 0.000 0.140 0.000
#> GSM564747 4 0.3561 0.6185 0.260 0.000 0.000 0.740 0.000
#> GSM564748 4 0.0703 0.8919 0.024 0.000 0.000 0.976 0.000
#> GSM564749 1 0.4453 0.7737 0.724 0.228 0.000 0.048 0.000
#> GSM564750 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564751 4 0.0510 0.8959 0.016 0.000 0.000 0.984 0.000
#> GSM564752 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564753 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564754 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564755 4 0.0290 0.8990 0.008 0.000 0.000 0.992 0.000
#> GSM564756 1 0.3274 0.6951 0.780 0.000 0.000 0.220 0.000
#> GSM564757 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564758 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564759 4 0.0000 0.9012 0.000 0.000 0.000 1.000 0.000
#> GSM564760 1 0.4201 0.2906 0.592 0.000 0.000 0.408 0.000
#> GSM564761 1 0.0000 0.8759 1.000 0.000 0.000 0.000 0.000
#> GSM564762 4 0.0162 0.9003 0.004 0.000 0.000 0.996 0.000
#> GSM564681 5 0.1965 0.9659 0.000 0.096 0.000 0.000 0.904
#> GSM564693 2 0.6519 0.5094 0.000 0.456 0.204 0.000 0.340
#> GSM564646 3 0.4626 0.4825 0.000 0.364 0.616 0.000 0.020
#> GSM564699 3 0.0000 0.6190 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0146 0.9556 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM564616 5 0.0260 0.9151 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM564617 2 0.1556 0.8888 0.000 0.920 0.080 0.000 0.000 0.000
#> GSM564618 5 0.0260 0.9151 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM564619 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564620 1 0.2738 0.7334 0.820 0.000 0.000 0.176 0.000 0.004
#> GSM564621 1 0.0146 0.9396 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564622 3 0.2135 0.8913 0.000 0.000 0.872 0.000 0.000 0.128
#> GSM564623 3 0.2300 0.8859 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM564624 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564625 4 0.1713 0.9290 0.028 0.000 0.000 0.928 0.000 0.044
#> GSM564626 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564627 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564628 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564629 6 0.3370 0.7925 0.048 0.000 0.000 0.148 0.000 0.804
#> GSM564630 3 0.2178 0.8906 0.000 0.000 0.868 0.000 0.000 0.132
#> GSM564609 3 0.0405 0.9060 0.000 0.008 0.988 0.000 0.000 0.004
#> GSM564610 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564611 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564612 3 0.3860 0.2343 0.000 0.472 0.528 0.000 0.000 0.000
#> GSM564613 3 0.2278 0.8913 0.000 0.004 0.868 0.000 0.000 0.128
#> GSM564614 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564631 3 0.0972 0.9029 0.000 0.028 0.964 0.000 0.000 0.008
#> GSM564632 3 0.0777 0.9040 0.000 0.024 0.972 0.000 0.000 0.004
#> GSM564633 3 0.0603 0.9050 0.000 0.016 0.980 0.000 0.000 0.004
#> GSM564634 3 0.2178 0.8906 0.000 0.000 0.868 0.000 0.000 0.132
#> GSM564635 3 0.0777 0.9040 0.000 0.024 0.972 0.000 0.000 0.004
#> GSM564636 3 0.1196 0.9000 0.000 0.040 0.952 0.000 0.000 0.008
#> GSM564637 3 0.0891 0.9032 0.000 0.024 0.968 0.000 0.000 0.008
#> GSM564638 3 0.1584 0.9028 0.000 0.008 0.928 0.000 0.000 0.064
#> GSM564639 3 0.2260 0.8872 0.000 0.000 0.860 0.000 0.000 0.140
#> GSM564640 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564641 3 0.2048 0.8562 0.000 0.120 0.880 0.000 0.000 0.000
#> GSM564642 3 0.2362 0.8304 0.000 0.136 0.860 0.000 0.000 0.004
#> GSM564643 3 0.0260 0.9056 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM564644 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564645 3 0.0891 0.9032 0.000 0.024 0.968 0.000 0.000 0.008
#> GSM564647 3 0.2697 0.7744 0.000 0.188 0.812 0.000 0.000 0.000
#> GSM564648 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564649 3 0.1049 0.9024 0.000 0.032 0.960 0.000 0.000 0.008
#> GSM564650 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564651 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564652 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564653 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564654 3 0.0508 0.9052 0.000 0.012 0.984 0.000 0.000 0.004
#> GSM564655 3 0.1957 0.8932 0.000 0.000 0.888 0.000 0.000 0.112
#> GSM564656 3 0.1556 0.8974 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM564657 3 0.0972 0.9029 0.000 0.028 0.964 0.000 0.000 0.008
#> GSM564658 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564659 3 0.1049 0.9024 0.000 0.032 0.960 0.000 0.000 0.008
#> GSM564660 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564661 5 0.0260 0.9149 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM564662 3 0.0891 0.9032 0.000 0.024 0.968 0.000 0.000 0.008
#> GSM564663 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564664 3 0.0777 0.9040 0.000 0.024 0.972 0.000 0.000 0.004
#> GSM564665 3 0.0972 0.9029 0.000 0.028 0.964 0.000 0.000 0.008
#> GSM564666 3 0.2300 0.8859 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM564667 3 0.1049 0.9024 0.000 0.032 0.960 0.000 0.000 0.008
#> GSM564668 3 0.2300 0.8860 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM564669 3 0.2300 0.8860 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM564670 2 0.3737 0.3623 0.000 0.608 0.392 0.000 0.000 0.000
#> GSM564671 3 0.2260 0.8872 0.000 0.000 0.860 0.000 0.000 0.140
#> GSM564672 3 0.1049 0.9024 0.000 0.032 0.960 0.000 0.000 0.008
#> GSM564673 3 0.0260 0.9056 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM564674 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564675 3 0.2300 0.8859 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM564676 2 0.1204 0.9037 0.000 0.944 0.056 0.000 0.000 0.000
#> GSM564677 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564678 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564679 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564680 3 0.1398 0.9040 0.000 0.008 0.940 0.000 0.000 0.052
#> GSM564682 3 0.3828 0.2109 0.000 0.440 0.560 0.000 0.000 0.000
#> GSM564683 3 0.2260 0.8872 0.000 0.000 0.860 0.000 0.000 0.140
#> GSM564684 3 0.2219 0.8888 0.000 0.000 0.864 0.000 0.000 0.136
#> GSM564685 3 0.2260 0.8872 0.000 0.000 0.860 0.000 0.000 0.140
#> GSM564686 3 0.2135 0.8907 0.000 0.000 0.872 0.000 0.000 0.128
#> GSM564687 2 0.3240 0.6905 0.000 0.752 0.244 0.000 0.000 0.004
#> GSM564688 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564689 2 0.1387 0.8972 0.000 0.932 0.068 0.000 0.000 0.000
#> GSM564690 2 0.1501 0.8919 0.000 0.924 0.076 0.000 0.000 0.000
#> GSM564691 2 0.0000 0.9269 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564692 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564694 3 0.0858 0.9038 0.000 0.028 0.968 0.000 0.000 0.004
#> GSM564695 3 0.1285 0.8951 0.000 0.052 0.944 0.000 0.000 0.004
#> GSM564696 3 0.2300 0.8859 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM564697 2 0.1556 0.8888 0.000 0.920 0.080 0.000 0.000 0.000
#> GSM564698 3 0.2260 0.8872 0.000 0.000 0.860 0.000 0.000 0.140
#> GSM564700 3 0.0146 0.9056 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564701 5 0.4407 0.1247 0.000 0.024 0.480 0.000 0.496 0.000
#> GSM564702 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564703 4 0.0260 0.9553 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM564704 1 0.1779 0.8633 0.920 0.000 0.000 0.064 0.000 0.016
#> GSM564705 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564706 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564707 1 0.0692 0.9233 0.976 0.000 0.000 0.020 0.000 0.004
#> GSM564708 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564709 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564710 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564711 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564712 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564713 4 0.0547 0.9527 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM564714 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564715 1 0.0146 0.9396 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564716 1 0.2772 0.7298 0.816 0.000 0.000 0.180 0.000 0.004
#> GSM564717 6 0.3014 0.9142 0.184 0.000 0.000 0.012 0.000 0.804
#> GSM564718 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564719 6 0.2980 0.7461 0.008 0.000 0.000 0.192 0.000 0.800
#> GSM564720 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564721 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564722 6 0.3122 0.7620 0.020 0.000 0.000 0.176 0.000 0.804
#> GSM564723 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564724 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564725 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564726 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564727 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564728 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564729 4 0.0146 0.9556 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM564730 1 0.0146 0.9396 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564731 4 0.1245 0.9420 0.016 0.000 0.000 0.952 0.000 0.032
#> GSM564732 4 0.1461 0.9364 0.016 0.000 0.000 0.940 0.000 0.044
#> GSM564733 4 0.1934 0.9160 0.040 0.000 0.000 0.916 0.000 0.044
#> GSM564734 4 0.4472 0.0553 0.476 0.000 0.000 0.496 0.000 0.028
#> GSM564735 4 0.1549 0.9338 0.020 0.000 0.000 0.936 0.000 0.044
#> GSM564736 4 0.1461 0.9364 0.016 0.000 0.000 0.940 0.000 0.044
#> GSM564737 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564738 4 0.0632 0.9515 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM564739 4 0.0508 0.9539 0.004 0.000 0.000 0.984 0.000 0.012
#> GSM564740 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564741 4 0.1461 0.9364 0.016 0.000 0.000 0.940 0.000 0.044
#> GSM564742 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564743 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564744 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564745 1 0.0858 0.9160 0.968 0.000 0.000 0.028 0.000 0.004
#> GSM564746 6 0.2948 0.9160 0.188 0.000 0.000 0.008 0.000 0.804
#> GSM564747 4 0.2006 0.8876 0.080 0.000 0.000 0.904 0.000 0.016
#> GSM564748 4 0.1461 0.9364 0.016 0.000 0.000 0.940 0.000 0.044
#> GSM564749 6 0.2762 0.9192 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM564750 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564751 4 0.1003 0.9463 0.016 0.000 0.000 0.964 0.000 0.020
#> GSM564752 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564753 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564754 1 0.0146 0.9396 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM564755 4 0.0858 0.9491 0.004 0.000 0.000 0.968 0.000 0.028
#> GSM564756 1 0.4014 0.5873 0.716 0.000 0.000 0.240 0.000 0.044
#> GSM564757 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564758 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564759 4 0.0000 0.9562 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM564760 4 0.4107 0.6307 0.256 0.000 0.000 0.700 0.000 0.044
#> GSM564761 1 0.0000 0.9414 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM564762 4 0.0291 0.9550 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM564681 5 0.0000 0.9198 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM564693 5 0.3940 0.6765 0.000 0.096 0.140 0.000 0.764 0.000
#> GSM564646 3 0.0713 0.9047 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM564699 3 0.2260 0.8872 0.000 0.000 0.860 0.000 0.000 0.140
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 genotype/variation(p) disease.state(p) k
#> ATC:mclust 154 0.925 0.4759 2
#> ATC:mclust 149 0.347 0.9164 3
#> ATC:mclust 133 0.528 0.1888 4
#> ATC:mclust 136 0.566 0.0888 5
#> ATC:mclust 149 0.212 0.1857 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 154 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5007 0.500 0.500
#> 3 3 0.778 0.841 0.907 0.2429 0.857 0.719
#> 4 4 0.699 0.685 0.856 0.1486 0.870 0.672
#> 5 5 0.651 0.479 0.735 0.0829 0.842 0.538
#> 6 6 0.799 0.760 0.826 0.0549 0.876 0.547
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
#> GSM564615 1 0 1 1 0
#> GSM564616 2 0 1 0 1
#> GSM564617 2 0 1 0 1
#> GSM564618 2 0 1 0 1
#> GSM564619 1 0 1 1 0
#> GSM564620 1 0 1 1 0
#> GSM564621 1 0 1 1 0
#> GSM564622 2 0 1 0 1
#> GSM564623 2 0 1 0 1
#> GSM564624 2 0 1 0 1
#> GSM564625 1 0 1 1 0
#> GSM564626 1 0 1 1 0
#> GSM564627 1 0 1 1 0
#> GSM564628 2 0 1 0 1
#> GSM564629 1 0 1 1 0
#> GSM564630 2 0 1 0 1
#> GSM564609 2 0 1 0 1
#> GSM564610 1 0 1 1 0
#> GSM564611 1 0 1 1 0
#> GSM564612 2 0 1 0 1
#> GSM564613 2 0 1 0 1
#> GSM564614 1 0 1 1 0
#> GSM564631 2 0 1 0 1
#> GSM564632 2 0 1 0 1
#> GSM564633 2 0 1 0 1
#> GSM564634 2 0 1 0 1
#> GSM564635 2 0 1 0 1
#> GSM564636 2 0 1 0 1
#> GSM564637 2 0 1 0 1
#> GSM564638 2 0 1 0 1
#> GSM564639 2 0 1 0 1
#> GSM564640 2 0 1 0 1
#> GSM564641 2 0 1 0 1
#> GSM564642 2 0 1 0 1
#> GSM564643 2 0 1 0 1
#> GSM564644 2 0 1 0 1
#> GSM564645 2 0 1 0 1
#> GSM564647 2 0 1 0 1
#> GSM564648 2 0 1 0 1
#> GSM564649 2 0 1 0 1
#> GSM564650 2 0 1 0 1
#> GSM564651 2 0 1 0 1
#> GSM564652 2 0 1 0 1
#> GSM564653 2 0 1 0 1
#> GSM564654 2 0 1 0 1
#> GSM564655 2 0 1 0 1
#> GSM564656 2 0 1 0 1
#> GSM564657 2 0 1 0 1
#> GSM564658 2 0 1 0 1
#> GSM564659 2 0 1 0 1
#> GSM564660 2 0 1 0 1
#> GSM564661 2 0 1 0 1
#> GSM564662 2 0 1 0 1
#> GSM564663 2 0 1 0 1
#> GSM564664 2 0 1 0 1
#> GSM564665 2 0 1 0 1
#> GSM564666 2 0 1 0 1
#> GSM564667 2 0 1 0 1
#> GSM564668 2 0 1 0 1
#> GSM564669 2 0 1 0 1
#> GSM564670 2 0 1 0 1
#> GSM564671 2 0 1 0 1
#> GSM564672 2 0 1 0 1
#> GSM564673 2 0 1 0 1
#> GSM564674 2 0 1 0 1
#> GSM564675 2 0 1 0 1
#> GSM564676 2 0 1 0 1
#> GSM564677 2 0 1 0 1
#> GSM564678 2 0 1 0 1
#> GSM564679 2 0 1 0 1
#> GSM564680 2 0 1 0 1
#> GSM564682 2 0 1 0 1
#> GSM564683 2 0 1 0 1
#> GSM564684 2 0 1 0 1
#> GSM564685 2 0 1 0 1
#> GSM564686 2 0 1 0 1
#> GSM564687 2 0 1 0 1
#> GSM564688 2 0 1 0 1
#> GSM564689 2 0 1 0 1
#> GSM564690 2 0 1 0 1
#> GSM564691 2 0 1 0 1
#> GSM564692 2 0 1 0 1
#> GSM564694 2 0 1 0 1
#> GSM564695 2 0 1 0 1
#> GSM564696 2 0 1 0 1
#> GSM564697 2 0 1 0 1
#> GSM564698 2 0 1 0 1
#> GSM564700 2 0 1 0 1
#> GSM564701 2 0 1 0 1
#> GSM564702 2 0 1 0 1
#> GSM564703 1 0 1 1 0
#> GSM564704 1 0 1 1 0
#> GSM564705 1 0 1 1 0
#> GSM564706 1 0 1 1 0
#> GSM564707 1 0 1 1 0
#> GSM564708 1 0 1 1 0
#> GSM564709 1 0 1 1 0
#> GSM564710 1 0 1 1 0
#> GSM564711 1 0 1 1 0
#> GSM564712 1 0 1 1 0
#> GSM564713 1 0 1 1 0
#> GSM564714 1 0 1 1 0
#> GSM564715 1 0 1 1 0
#> GSM564716 1 0 1 1 0
#> GSM564717 1 0 1 1 0
#> GSM564718 1 0 1 1 0
#> GSM564719 1 0 1 1 0
#> GSM564720 1 0 1 1 0
#> GSM564721 1 0 1 1 0
#> GSM564722 1 0 1 1 0
#> GSM564723 1 0 1 1 0
#> GSM564724 1 0 1 1 0
#> GSM564725 1 0 1 1 0
#> GSM564726 1 0 1 1 0
#> GSM564727 1 0 1 1 0
#> GSM564728 1 0 1 1 0
#> GSM564729 1 0 1 1 0
#> GSM564730 1 0 1 1 0
#> GSM564731 1 0 1 1 0
#> GSM564732 1 0 1 1 0
#> GSM564733 1 0 1 1 0
#> GSM564734 1 0 1 1 0
#> GSM564735 1 0 1 1 0
#> GSM564736 1 0 1 1 0
#> GSM564737 1 0 1 1 0
#> GSM564738 1 0 1 1 0
#> GSM564739 1 0 1 1 0
#> GSM564740 1 0 1 1 0
#> GSM564741 1 0 1 1 0
#> GSM564742 1 0 1 1 0
#> GSM564743 1 0 1 1 0
#> GSM564744 1 0 1 1 0
#> GSM564745 1 0 1 1 0
#> GSM564746 1 0 1 1 0
#> GSM564747 1 0 1 1 0
#> GSM564748 1 0 1 1 0
#> GSM564749 1 0 1 1 0
#> GSM564750 1 0 1 1 0
#> GSM564751 1 0 1 1 0
#> GSM564752 1 0 1 1 0
#> GSM564753 1 0 1 1 0
#> GSM564754 1 0 1 1 0
#> GSM564755 1 0 1 1 0
#> GSM564756 1 0 1 1 0
#> GSM564757 1 0 1 1 0
#> GSM564758 1 0 1 1 0
#> GSM564759 1 0 1 1 0
#> GSM564760 1 0 1 1 0
#> GSM564761 1 0 1 1 0
#> GSM564762 1 0 1 1 0
#> GSM564681 2 0 1 0 1
#> GSM564693 2 0 1 0 1
#> GSM564646 2 0 1 0 1
#> GSM564699 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM564615 1 0.0000 0.969492 1.000 0.000 0.000
#> GSM564616 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564617 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564618 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564619 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564620 1 0.0424 0.969144 0.992 0.000 0.008
#> GSM564621 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564622 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564623 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564624 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564625 1 0.0000 0.969492 1.000 0.000 0.000
#> GSM564626 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564627 1 0.0000 0.969492 1.000 0.000 0.000
#> GSM564628 2 0.0237 0.871218 0.000 0.996 0.004
#> GSM564629 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564630 2 0.4399 0.846574 0.000 0.812 0.188
#> GSM564609 2 0.0424 0.870918 0.000 0.992 0.008
#> GSM564610 1 0.0424 0.969144 0.992 0.000 0.008
#> GSM564611 1 0.0424 0.969141 0.992 0.000 0.008
#> GSM564612 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564613 3 0.5560 0.448872 0.000 0.300 0.700
#> GSM564614 1 0.0237 0.969025 0.996 0.000 0.004
#> GSM564631 3 0.3482 0.703576 0.000 0.128 0.872
#> GSM564632 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564633 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564634 3 0.5560 0.449852 0.000 0.300 0.700
#> GSM564635 2 0.0892 0.865902 0.000 0.980 0.020
#> GSM564636 2 0.5560 0.710376 0.000 0.700 0.300
#> GSM564637 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564638 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564639 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564640 2 0.2165 0.867664 0.000 0.936 0.064
#> GSM564641 3 0.4002 0.678181 0.000 0.160 0.840
#> GSM564642 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564643 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564644 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564645 3 0.6079 0.249074 0.000 0.388 0.612
#> GSM564647 2 0.4842 0.815122 0.000 0.776 0.224
#> GSM564648 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564649 2 0.6215 0.422007 0.000 0.572 0.428
#> GSM564650 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564651 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564652 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564653 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564654 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564655 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564656 2 0.4504 0.637183 0.000 0.804 0.196
#> GSM564657 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564658 2 0.1289 0.870325 0.000 0.968 0.032
#> GSM564659 2 0.4291 0.849057 0.000 0.820 0.180
#> GSM564660 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564661 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564662 3 0.4842 0.608301 0.000 0.224 0.776
#> GSM564663 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564664 2 0.0237 0.871218 0.000 0.996 0.004
#> GSM564665 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564666 2 0.4555 0.839589 0.000 0.800 0.200
#> GSM564667 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564668 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564669 2 0.2165 0.817134 0.000 0.936 0.064
#> GSM564670 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564671 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564672 3 0.4062 0.677121 0.000 0.164 0.836
#> GSM564673 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564674 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564675 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564676 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564677 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564678 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564679 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564680 3 0.5678 0.538965 0.000 0.316 0.684
#> GSM564682 3 0.5785 0.387613 0.000 0.332 0.668
#> GSM564683 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564684 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564685 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564686 2 0.3619 0.857909 0.000 0.864 0.136
#> GSM564687 2 0.4346 0.847866 0.000 0.816 0.184
#> GSM564688 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564689 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564690 2 0.4452 0.845250 0.000 0.808 0.192
#> GSM564691 2 0.5926 0.605015 0.000 0.644 0.356
#> GSM564692 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564694 2 0.4399 0.846773 0.000 0.812 0.188
#> GSM564695 2 0.3941 0.854516 0.000 0.844 0.156
#> GSM564696 3 0.1031 0.745452 0.000 0.024 0.976
#> GSM564697 2 0.4504 0.842860 0.000 0.804 0.196
#> GSM564698 3 0.6180 0.466809 0.000 0.416 0.584
#> GSM564700 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564701 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564702 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564703 1 0.0892 0.959913 0.980 0.000 0.020
#> GSM564704 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564705 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564706 3 0.5529 0.538442 0.296 0.000 0.704
#> GSM564707 1 0.0000 0.969492 1.000 0.000 0.000
#> GSM564708 1 0.1529 0.945671 0.960 0.000 0.040
#> GSM564709 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564710 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564711 1 0.6299 0.000143 0.524 0.000 0.476
#> GSM564712 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564713 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564714 3 0.4702 0.632077 0.212 0.000 0.788
#> GSM564715 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564716 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564717 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564718 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564719 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564720 1 0.0237 0.969538 0.996 0.000 0.004
#> GSM564721 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564722 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564723 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564724 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564725 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564726 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564727 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564728 1 0.0424 0.969558 0.992 0.000 0.008
#> GSM564729 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564730 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564731 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564732 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564733 1 0.0237 0.969515 0.996 0.000 0.004
#> GSM564734 1 0.0000 0.969492 1.000 0.000 0.000
#> GSM564735 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564736 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564737 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564738 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564739 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564740 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564741 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564742 3 0.6026 0.400028 0.376 0.000 0.624
#> GSM564743 1 0.0424 0.969141 0.992 0.000 0.008
#> GSM564744 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564745 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564746 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564747 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564748 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564749 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564750 1 0.6062 0.317247 0.616 0.000 0.384
#> GSM564751 1 0.5529 0.534482 0.704 0.000 0.296
#> GSM564752 3 0.5706 0.503240 0.320 0.000 0.680
#> GSM564753 3 0.4796 0.625040 0.220 0.000 0.780
#> GSM564754 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564755 1 0.0237 0.969515 0.996 0.000 0.004
#> GSM564756 1 0.0237 0.969515 0.996 0.000 0.004
#> GSM564757 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564758 3 0.6111 0.354382 0.396 0.000 0.604
#> GSM564759 3 0.4702 0.632077 0.212 0.000 0.788
#> GSM564760 1 0.0000 0.969492 1.000 0.000 0.000
#> GSM564761 1 0.0747 0.967577 0.984 0.000 0.016
#> GSM564762 1 0.0424 0.968342 0.992 0.000 0.008
#> GSM564681 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564693 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564646 2 0.0000 0.870905 0.000 1.000 0.000
#> GSM564699 2 0.4605 0.836164 0.000 0.796 0.204
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM564615 1 0.2408 0.8947 0.896 0.000 0.104 0.000
#> GSM564616 2 0.0188 0.8375 0.000 0.996 0.004 0.000
#> GSM564617 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564618 2 0.0188 0.8375 0.000 0.996 0.004 0.000
#> GSM564619 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564620 1 0.0817 0.9003 0.976 0.000 0.024 0.000
#> GSM564621 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564622 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564623 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564624 4 0.4252 0.5349 0.000 0.252 0.004 0.744
#> GSM564625 1 0.2281 0.8966 0.904 0.000 0.096 0.000
#> GSM564626 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564627 1 0.2466 0.8941 0.916 0.000 0.056 0.028
#> GSM564628 2 0.4991 0.3333 0.000 0.608 0.004 0.388
#> GSM564629 4 0.6655 -0.2377 0.440 0.000 0.084 0.476
#> GSM564630 4 0.0895 0.7014 0.000 0.020 0.004 0.976
#> GSM564609 2 0.2530 0.7826 0.000 0.888 0.112 0.000
#> GSM564610 1 0.0188 0.8981 0.996 0.000 0.000 0.004
#> GSM564611 1 0.2408 0.8334 0.896 0.000 0.000 0.104
#> GSM564612 4 0.5167 -0.0572 0.000 0.488 0.004 0.508
#> GSM564613 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564614 1 0.2408 0.8947 0.896 0.000 0.104 0.000
#> GSM564631 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564632 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564633 2 0.0469 0.8343 0.000 0.988 0.012 0.000
#> GSM564634 4 0.0188 0.6997 0.000 0.000 0.004 0.996
#> GSM564635 2 0.5112 0.3672 0.000 0.608 0.384 0.008
#> GSM564636 4 0.7595 0.0708 0.000 0.372 0.200 0.428
#> GSM564637 2 0.4673 0.7160 0.000 0.792 0.132 0.076
#> GSM564638 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564639 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564640 2 0.5080 0.2590 0.000 0.576 0.004 0.420
#> GSM564641 4 0.6521 0.0268 0.000 0.076 0.412 0.512
#> GSM564642 2 0.4992 0.1096 0.000 0.524 0.000 0.476
#> GSM564643 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564644 4 0.5000 -0.0799 0.000 0.500 0.000 0.500
#> GSM564645 3 0.3674 0.7070 0.000 0.044 0.852 0.104
#> GSM564647 4 0.5147 0.0379 0.000 0.460 0.004 0.536
#> GSM564648 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564649 3 0.7904 -0.0784 0.000 0.308 0.368 0.324
#> GSM564650 4 0.0707 0.7022 0.000 0.020 0.000 0.980
#> GSM564651 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564652 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564653 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564654 2 0.0188 0.8366 0.000 0.996 0.004 0.000
#> GSM564655 2 0.1637 0.8121 0.000 0.940 0.060 0.000
#> GSM564656 3 0.4564 0.4395 0.000 0.328 0.672 0.000
#> GSM564657 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564658 2 0.5119 0.2026 0.000 0.556 0.004 0.440
#> GSM564659 2 0.4931 0.7001 0.000 0.776 0.132 0.092
#> GSM564660 4 0.2647 0.6715 0.000 0.120 0.000 0.880
#> GSM564661 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564662 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564663 4 0.3626 0.6212 0.000 0.184 0.004 0.812
#> GSM564664 2 0.2011 0.7911 0.000 0.920 0.000 0.080
#> GSM564665 2 0.5217 0.6804 0.000 0.756 0.136 0.108
#> GSM564666 4 0.4313 0.5150 0.000 0.260 0.004 0.736
#> GSM564667 3 0.4585 0.4616 0.000 0.000 0.668 0.332
#> GSM564668 2 0.0469 0.8340 0.000 0.988 0.012 0.000
#> GSM564669 2 0.4941 0.1713 0.000 0.564 0.436 0.000
#> GSM564670 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564671 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564672 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564673 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564674 4 0.5168 -0.0708 0.000 0.496 0.004 0.500
#> GSM564675 4 0.2814 0.6642 0.000 0.132 0.000 0.868
#> GSM564676 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564677 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564678 4 0.0657 0.7020 0.000 0.012 0.004 0.984
#> GSM564679 2 0.5039 0.2950 0.000 0.592 0.004 0.404
#> GSM564680 3 0.3149 0.6948 0.000 0.088 0.880 0.032
#> GSM564682 4 0.0188 0.6997 0.000 0.000 0.004 0.996
#> GSM564683 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564684 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564685 3 0.2469 0.7241 0.000 0.000 0.892 0.108
#> GSM564686 2 0.3697 0.7678 0.000 0.852 0.100 0.048
#> GSM564687 2 0.4992 0.1096 0.000 0.524 0.000 0.476
#> GSM564688 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564689 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564690 4 0.0779 0.7013 0.000 0.016 0.004 0.980
#> GSM564691 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564692 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564694 2 0.4977 0.1608 0.000 0.540 0.000 0.460
#> GSM564695 2 0.4283 0.6043 0.000 0.740 0.004 0.256
#> GSM564696 3 0.4804 0.3769 0.000 0.000 0.616 0.384
#> GSM564697 4 0.0188 0.7027 0.000 0.004 0.000 0.996
#> GSM564698 3 0.2704 0.6733 0.000 0.124 0.876 0.000
#> GSM564700 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564701 2 0.0188 0.8375 0.000 0.996 0.004 0.000
#> GSM564702 2 0.0000 0.8383 0.000 1.000 0.000 0.000
#> GSM564703 1 0.4188 0.7040 0.752 0.000 0.244 0.004
#> GSM564704 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564705 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564706 3 0.3710 0.6108 0.192 0.000 0.804 0.004
#> GSM564707 1 0.0469 0.8997 0.988 0.000 0.012 0.000
#> GSM564708 1 0.4948 0.3724 0.560 0.000 0.440 0.000
#> GSM564709 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564710 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564711 1 0.4991 0.4996 0.608 0.000 0.388 0.004
#> GSM564712 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564713 1 0.2530 0.8953 0.896 0.000 0.100 0.004
#> GSM564714 4 0.7440 -0.1180 0.172 0.000 0.388 0.440
#> GSM564715 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564716 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564717 1 0.6222 0.3720 0.532 0.000 0.056 0.412
#> GSM564718 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564719 4 0.6425 -0.1495 0.424 0.000 0.068 0.508
#> GSM564720 1 0.1940 0.8572 0.924 0.000 0.000 0.076
#> GSM564721 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564722 1 0.5873 0.6482 0.668 0.000 0.076 0.256
#> GSM564723 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564724 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564725 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564726 1 0.2408 0.8947 0.896 0.000 0.104 0.000
#> GSM564727 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564728 1 0.2408 0.8947 0.896 0.000 0.104 0.000
#> GSM564729 1 0.0469 0.8986 0.988 0.000 0.012 0.000
#> GSM564730 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564731 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564732 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564733 1 0.2216 0.8973 0.908 0.000 0.092 0.000
#> GSM564734 1 0.1940 0.8989 0.924 0.000 0.076 0.000
#> GSM564735 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564736 1 0.2831 0.8855 0.876 0.000 0.120 0.004
#> GSM564737 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564738 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564739 1 0.1389 0.8980 0.952 0.000 0.048 0.000
#> GSM564740 1 0.3015 0.8905 0.884 0.000 0.092 0.024
#> GSM564741 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564742 3 0.5229 0.0264 0.428 0.000 0.564 0.008
#> GSM564743 1 0.4277 0.6193 0.720 0.000 0.000 0.280
#> GSM564744 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564745 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564746 1 0.6136 0.4943 0.584 0.000 0.060 0.356
#> GSM564747 1 0.2530 0.8951 0.896 0.000 0.100 0.004
#> GSM564748 1 0.2530 0.8953 0.896 0.000 0.100 0.004
#> GSM564749 1 0.3858 0.8410 0.844 0.000 0.056 0.100
#> GSM564750 3 0.4188 0.5371 0.244 0.000 0.752 0.004
#> GSM564751 1 0.5167 0.2212 0.508 0.000 0.488 0.004
#> GSM564752 3 0.3257 0.6390 0.152 0.000 0.844 0.004
#> GSM564753 3 0.3105 0.6458 0.140 0.000 0.856 0.004
#> GSM564754 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564755 1 0.2408 0.8947 0.896 0.000 0.104 0.000
#> GSM564756 1 0.1389 0.9001 0.952 0.000 0.048 0.000
#> GSM564757 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564758 3 0.4761 0.3451 0.332 0.000 0.664 0.004
#> GSM564759 3 0.3105 0.6458 0.140 0.000 0.856 0.004
#> GSM564760 1 0.2216 0.8973 0.908 0.000 0.092 0.000
#> GSM564761 1 0.0000 0.8982 1.000 0.000 0.000 0.000
#> GSM564762 1 0.2593 0.8938 0.892 0.000 0.104 0.004
#> GSM564681 2 0.0188 0.8375 0.000 0.996 0.004 0.000
#> GSM564693 2 0.0188 0.8375 0.000 0.996 0.004 0.000
#> GSM564646 2 0.0592 0.8321 0.000 0.984 0.000 0.016
#> GSM564699 2 0.6295 0.5771 0.000 0.660 0.196 0.144
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM564615 4 0.0290 0.4625 0.008 0.000 0.000 0.992 0.000
#> GSM564616 5 0.1965 0.8105 0.096 0.000 0.000 0.000 0.904
#> GSM564617 2 0.1043 0.6852 0.040 0.960 0.000 0.000 0.000
#> GSM564618 5 0.2127 0.8034 0.108 0.000 0.000 0.000 0.892
#> GSM564619 4 0.4367 0.4346 0.416 0.000 0.004 0.580 0.000
#> GSM564620 1 0.4138 0.4008 0.616 0.000 0.000 0.384 0.000
#> GSM564621 4 0.4359 0.4351 0.412 0.000 0.004 0.584 0.000
#> GSM564622 5 0.1270 0.8261 0.052 0.000 0.000 0.000 0.948
#> GSM564623 2 0.0451 0.7090 0.000 0.988 0.004 0.000 0.008
#> GSM564624 2 0.3607 0.7145 0.000 0.752 0.004 0.000 0.244
#> GSM564625 4 0.0880 0.4623 0.032 0.000 0.000 0.968 0.000
#> GSM564626 4 0.4375 0.4325 0.420 0.000 0.004 0.576 0.000
#> GSM564627 1 0.4290 0.6238 0.680 0.016 0.000 0.304 0.000
#> GSM564628 2 0.4108 0.6711 0.000 0.684 0.008 0.000 0.308
#> GSM564629 1 0.6572 0.5756 0.452 0.220 0.000 0.328 0.000
#> GSM564630 2 0.5673 0.5589 0.216 0.628 0.000 0.000 0.156
#> GSM564609 5 0.5313 0.1356 0.000 0.056 0.388 0.000 0.556
#> GSM564610 1 0.3003 0.5654 0.812 0.000 0.000 0.188 0.000
#> GSM564611 1 0.3343 0.6086 0.812 0.016 0.000 0.172 0.000
#> GSM564612 2 0.4465 0.6497 0.000 0.736 0.204 0.000 0.060
#> GSM564613 2 0.1197 0.6796 0.048 0.952 0.000 0.000 0.000
#> GSM564614 4 0.0404 0.4641 0.012 0.000 0.000 0.988 0.000
#> GSM564631 3 0.1914 0.6930 0.000 0.060 0.924 0.000 0.016
#> GSM564632 5 0.1408 0.8257 0.000 0.044 0.008 0.000 0.948
#> GSM564633 5 0.4735 0.4255 0.000 0.044 0.284 0.000 0.672
#> GSM564634 2 0.1117 0.7010 0.016 0.964 0.020 0.000 0.000
#> GSM564635 3 0.5296 0.0552 0.000 0.048 0.480 0.000 0.472
#> GSM564636 2 0.5338 0.3327 0.000 0.544 0.400 0.000 0.056
#> GSM564637 2 0.6633 0.0430 0.000 0.396 0.384 0.000 0.220
#> GSM564638 3 0.0451 0.7024 0.004 0.008 0.988 0.000 0.000
#> GSM564639 3 0.0451 0.6997 0.008 0.004 0.988 0.000 0.000
#> GSM564640 2 0.4067 0.6798 0.000 0.692 0.008 0.000 0.300
#> GSM564641 2 0.4640 0.6009 0.000 0.696 0.256 0.000 0.048
#> GSM564642 2 0.3980 0.6963 0.000 0.708 0.008 0.000 0.284
#> GSM564643 5 0.0854 0.8433 0.008 0.012 0.004 0.000 0.976
#> GSM564644 2 0.3835 0.7087 0.000 0.732 0.008 0.000 0.260
#> GSM564645 3 0.1547 0.7105 0.004 0.016 0.948 0.000 0.032
#> GSM564647 2 0.4434 0.6470 0.000 0.736 0.208 0.000 0.056
#> GSM564648 5 0.0693 0.8412 0.000 0.012 0.008 0.000 0.980
#> GSM564649 2 0.5557 0.1510 0.000 0.472 0.460 0.000 0.068
#> GSM564650 2 0.1251 0.7185 0.000 0.956 0.008 0.000 0.036
#> GSM564651 5 0.0613 0.8435 0.008 0.004 0.004 0.000 0.984
#> GSM564652 5 0.0703 0.8391 0.024 0.000 0.000 0.000 0.976
#> GSM564653 5 0.0290 0.8425 0.008 0.000 0.000 0.000 0.992
#> GSM564654 5 0.1461 0.8356 0.028 0.000 0.016 0.004 0.952
#> GSM564655 5 0.4547 0.5087 0.000 0.044 0.252 0.000 0.704
#> GSM564656 3 0.4736 0.2650 0.000 0.020 0.576 0.000 0.404
#> GSM564657 3 0.3010 0.5842 0.004 0.172 0.824 0.000 0.000
#> GSM564658 2 0.5404 0.6511 0.100 0.636 0.000 0.000 0.264
#> GSM564659 3 0.6428 0.1886 0.000 0.176 0.440 0.000 0.384
#> GSM564660 2 0.3013 0.7327 0.000 0.832 0.008 0.000 0.160
#> GSM564661 5 0.0290 0.8425 0.008 0.000 0.000 0.000 0.992
#> GSM564662 3 0.1568 0.7046 0.000 0.036 0.944 0.000 0.020
#> GSM564663 2 0.3643 0.7218 0.008 0.776 0.004 0.000 0.212
#> GSM564664 5 0.4415 0.0539 0.000 0.388 0.008 0.000 0.604
#> GSM564665 3 0.6719 -0.0701 0.000 0.372 0.380 0.000 0.248
#> GSM564666 2 0.4400 0.6450 0.000 0.736 0.212 0.000 0.052
#> GSM564667 2 0.4658 0.1666 0.000 0.504 0.484 0.000 0.012
#> GSM564668 5 0.2409 0.8068 0.028 0.000 0.056 0.008 0.908
#> GSM564669 5 0.5071 0.0262 0.000 0.012 0.440 0.016 0.532
#> GSM564670 2 0.2116 0.7060 0.004 0.912 0.076 0.000 0.008
#> GSM564671 5 0.0693 0.8412 0.000 0.012 0.008 0.000 0.980
#> GSM564672 3 0.4360 0.4057 0.000 0.284 0.692 0.000 0.024
#> GSM564673 5 0.0609 0.8400 0.020 0.000 0.000 0.000 0.980
#> GSM564674 2 0.3835 0.7087 0.000 0.732 0.008 0.000 0.260
#> GSM564675 2 0.1764 0.7260 0.000 0.928 0.008 0.000 0.064
#> GSM564676 2 0.0880 0.6899 0.032 0.968 0.000 0.000 0.000
#> GSM564677 5 0.0794 0.8386 0.028 0.000 0.000 0.000 0.972
#> GSM564678 2 0.1043 0.6852 0.040 0.960 0.000 0.000 0.000
#> GSM564679 2 0.5026 0.6553 0.064 0.656 0.000 0.000 0.280
#> GSM564680 3 0.1270 0.7081 0.000 0.000 0.948 0.000 0.052
#> GSM564682 2 0.3243 0.6594 0.004 0.812 0.180 0.000 0.004
#> GSM564683 3 0.0451 0.6997 0.008 0.004 0.988 0.000 0.000
#> GSM564684 5 0.1484 0.8229 0.000 0.048 0.008 0.000 0.944
#> GSM564685 3 0.0865 0.7022 0.004 0.024 0.972 0.000 0.000
#> GSM564686 5 0.6183 -0.2471 0.000 0.408 0.136 0.000 0.456
#> GSM564687 2 0.3957 0.6974 0.000 0.712 0.008 0.000 0.280
#> GSM564688 5 0.0727 0.8428 0.004 0.012 0.004 0.000 0.980
#> GSM564689 2 0.1408 0.6856 0.044 0.948 0.000 0.000 0.008
#> GSM564690 2 0.3622 0.7150 0.048 0.816 0.000 0.000 0.136
#> GSM564691 2 0.2170 0.7003 0.004 0.904 0.088 0.000 0.004
#> GSM564692 5 0.1408 0.8257 0.000 0.044 0.008 0.000 0.948
#> GSM564694 2 0.3980 0.6951 0.000 0.708 0.008 0.000 0.284
#> GSM564695 2 0.5918 0.4436 0.020 0.524 0.060 0.000 0.396
#> GSM564696 3 0.4552 -0.1231 0.008 0.468 0.524 0.000 0.000
#> GSM564697 2 0.0963 0.6877 0.036 0.964 0.000 0.000 0.000
#> GSM564698 3 0.1704 0.7032 0.004 0.000 0.928 0.000 0.068
#> GSM564700 5 0.1168 0.8329 0.000 0.032 0.008 0.000 0.960
#> GSM564701 5 0.2179 0.8007 0.112 0.000 0.000 0.000 0.888
#> GSM564702 5 0.1121 0.8346 0.044 0.000 0.000 0.000 0.956
#> GSM564703 4 0.4761 0.3761 0.124 0.000 0.144 0.732 0.000
#> GSM564704 4 0.4321 0.4362 0.396 0.000 0.004 0.600 0.000
#> GSM564705 4 0.4375 0.4325 0.420 0.000 0.004 0.576 0.000
#> GSM564706 4 0.6189 -0.3809 0.384 0.000 0.140 0.476 0.000
#> GSM564707 4 0.4225 0.4314 0.364 0.000 0.004 0.632 0.000
#> GSM564708 4 0.4879 0.4022 0.156 0.000 0.124 0.720 0.000
#> GSM564709 4 0.4367 0.4346 0.416 0.000 0.004 0.580 0.000
#> GSM564710 4 0.4443 0.3728 0.472 0.000 0.004 0.524 0.000
#> GSM564711 4 0.4917 -0.3774 0.416 0.000 0.028 0.556 0.000
#> GSM564712 4 0.4390 0.4267 0.428 0.000 0.004 0.568 0.000
#> GSM564713 4 0.0290 0.4621 0.008 0.000 0.000 0.992 0.000
#> GSM564714 1 0.6155 0.4235 0.460 0.040 0.048 0.452 0.000
#> GSM564715 4 0.4367 0.4291 0.416 0.000 0.004 0.580 0.000
#> GSM564716 4 0.4367 0.4346 0.416 0.000 0.004 0.580 0.000
#> GSM564717 1 0.6054 0.6385 0.560 0.160 0.000 0.280 0.000
#> GSM564718 4 0.4165 -0.1970 0.320 0.000 0.008 0.672 0.000
#> GSM564719 1 0.6612 0.5734 0.452 0.240 0.000 0.308 0.000
#> GSM564720 1 0.3242 0.5978 0.816 0.012 0.000 0.172 0.000
#> GSM564721 4 0.4367 0.4346 0.416 0.000 0.004 0.580 0.000
#> GSM564722 1 0.6130 0.5296 0.448 0.128 0.000 0.424 0.000
#> GSM564723 1 0.3398 0.4561 0.780 0.000 0.004 0.216 0.000
#> GSM564724 4 0.0000 0.4574 0.000 0.000 0.000 1.000 0.000
#> GSM564725 4 0.4367 0.4346 0.416 0.000 0.004 0.580 0.000
#> GSM564726 4 0.0290 0.4625 0.008 0.000 0.000 0.992 0.000
#> GSM564727 4 0.4341 0.4359 0.404 0.000 0.004 0.592 0.000
#> GSM564728 4 0.0290 0.4625 0.008 0.000 0.000 0.992 0.000
#> GSM564729 4 0.4288 0.4413 0.384 0.000 0.004 0.612 0.000
#> GSM564730 4 0.4367 0.4346 0.416 0.000 0.004 0.580 0.000
#> GSM564731 4 0.3966 -0.2111 0.336 0.000 0.000 0.664 0.000
#> GSM564732 4 0.0880 0.4321 0.032 0.000 0.000 0.968 0.000
#> GSM564733 4 0.1043 0.4679 0.040 0.000 0.000 0.960 0.000
#> GSM564734 4 0.2690 0.4359 0.156 0.000 0.000 0.844 0.000
#> GSM564735 4 0.1469 0.4161 0.036 0.000 0.016 0.948 0.000
#> GSM564736 4 0.0566 0.4457 0.012 0.000 0.004 0.984 0.000
#> GSM564737 4 0.4375 0.4325 0.420 0.000 0.004 0.576 0.000
#> GSM564738 4 0.1211 0.4253 0.024 0.000 0.016 0.960 0.000
#> GSM564739 4 0.3143 0.4614 0.204 0.000 0.000 0.796 0.000
#> GSM564740 4 0.4367 -0.3617 0.416 0.004 0.000 0.580 0.000
#> GSM564741 4 0.0000 0.4574 0.000 0.000 0.000 1.000 0.000
#> GSM564742 4 0.5607 -0.3928 0.408 0.004 0.064 0.524 0.000
#> GSM564743 1 0.3772 0.6235 0.792 0.036 0.000 0.172 0.000
#> GSM564744 4 0.4375 0.4317 0.420 0.000 0.004 0.576 0.000
#> GSM564745 4 0.4383 0.4298 0.424 0.000 0.004 0.572 0.000
#> GSM564746 1 0.6114 0.6287 0.536 0.152 0.000 0.312 0.000
#> GSM564747 4 0.4219 -0.3555 0.416 0.000 0.000 0.584 0.000
#> GSM564748 4 0.1018 0.4604 0.016 0.000 0.016 0.968 0.000
#> GSM564749 1 0.4445 0.6398 0.676 0.024 0.000 0.300 0.000
#> GSM564750 4 0.4126 0.0329 0.000 0.000 0.380 0.620 0.000
#> GSM564751 4 0.4862 -0.0150 0.032 0.000 0.364 0.604 0.000
#> GSM564752 4 0.6203 -0.1828 0.140 0.000 0.396 0.464 0.000
#> GSM564753 3 0.5396 -0.1758 0.056 0.000 0.500 0.444 0.000
#> GSM564754 4 0.4331 0.4357 0.400 0.000 0.004 0.596 0.000
#> GSM564755 4 0.0510 0.4652 0.016 0.000 0.000 0.984 0.000
#> GSM564756 4 0.3707 0.4256 0.284 0.000 0.000 0.716 0.000
#> GSM564757 4 0.3480 -0.0119 0.248 0.000 0.000 0.752 0.000
#> GSM564758 4 0.5659 -0.1071 0.100 0.000 0.320 0.580 0.000
#> GSM564759 4 0.6377 -0.3803 0.380 0.000 0.168 0.452 0.000
#> GSM564760 4 0.1121 0.4665 0.044 0.000 0.000 0.956 0.000
#> GSM564761 4 0.4843 0.4122 0.428 0.000 0.004 0.552 0.016
#> GSM564762 4 0.3752 -0.1147 0.292 0.000 0.000 0.708 0.000
#> GSM564681 5 0.2127 0.8030 0.108 0.000 0.000 0.000 0.892
#> GSM564693 5 0.0693 0.8412 0.000 0.012 0.008 0.000 0.980
#> GSM564646 5 0.2753 0.7298 0.000 0.136 0.008 0.000 0.856
#> GSM564699 2 0.6299 0.1081 0.000 0.432 0.416 0.000 0.152
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM564615 4 0.0713 0.805 0.028 0.000 0.000 0.972 0.000 0.000
#> GSM564616 5 0.2565 0.813 0.072 0.012 0.000 0.004 0.888 0.024
#> GSM564617 2 0.0146 0.894 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM564618 5 0.2471 0.812 0.052 0.000 0.000 0.004 0.888 0.056
#> GSM564619 1 0.2912 0.949 0.784 0.000 0.000 0.216 0.000 0.000
#> GSM564620 6 0.5294 0.282 0.356 0.000 0.000 0.112 0.000 0.532
#> GSM564621 1 0.3175 0.917 0.744 0.000 0.000 0.256 0.000 0.000
#> GSM564622 5 0.0665 0.850 0.008 0.000 0.004 0.000 0.980 0.008
#> GSM564623 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564624 2 0.1116 0.889 0.008 0.960 0.000 0.000 0.004 0.028
#> GSM564625 4 0.1391 0.801 0.040 0.000 0.000 0.944 0.000 0.016
#> GSM564626 1 0.2823 0.951 0.796 0.000 0.000 0.204 0.000 0.000
#> GSM564627 6 0.2506 0.853 0.068 0.000 0.000 0.052 0.000 0.880
#> GSM564628 2 0.1226 0.885 0.004 0.952 0.000 0.000 0.040 0.004
#> GSM564629 6 0.2110 0.859 0.012 0.004 0.000 0.084 0.000 0.900
#> GSM564630 2 0.3794 0.777 0.144 0.788 0.000 0.004 0.004 0.060
#> GSM564609 5 0.3221 0.646 0.000 0.000 0.264 0.000 0.736 0.000
#> GSM564610 6 0.3570 0.741 0.144 0.000 0.000 0.064 0.000 0.792
#> GSM564611 6 0.2302 0.817 0.120 0.000 0.000 0.008 0.000 0.872
#> GSM564612 2 0.2301 0.876 0.008 0.912 0.028 0.004 0.008 0.040
#> GSM564613 2 0.0146 0.894 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM564614 4 0.0260 0.812 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM564631 3 0.0603 0.842 0.000 0.016 0.980 0.000 0.000 0.004
#> GSM564632 5 0.0582 0.853 0.004 0.004 0.004 0.000 0.984 0.004
#> GSM564633 5 0.2278 0.785 0.000 0.000 0.128 0.000 0.868 0.004
#> GSM564634 2 0.0146 0.894 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM564635 5 0.3737 0.458 0.000 0.000 0.392 0.000 0.608 0.000
#> GSM564636 2 0.3534 0.627 0.000 0.716 0.276 0.000 0.008 0.000
#> GSM564637 5 0.5885 0.259 0.000 0.248 0.276 0.000 0.476 0.000
#> GSM564638 3 0.0291 0.842 0.000 0.004 0.992 0.000 0.000 0.004
#> GSM564639 3 0.0000 0.842 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM564640 2 0.1296 0.882 0.004 0.948 0.000 0.000 0.044 0.004
#> GSM564641 2 0.1349 0.872 0.000 0.940 0.056 0.000 0.004 0.000
#> GSM564642 2 0.0748 0.893 0.000 0.976 0.004 0.000 0.016 0.004
#> GSM564643 5 0.0291 0.852 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM564644 2 0.0458 0.893 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM564645 3 0.0405 0.842 0.000 0.004 0.988 0.000 0.008 0.000
#> GSM564647 2 0.0891 0.888 0.000 0.968 0.024 0.000 0.008 0.000
#> GSM564648 5 0.0582 0.853 0.004 0.004 0.004 0.000 0.984 0.004
#> GSM564649 2 0.3881 0.425 0.000 0.600 0.396 0.000 0.004 0.000
#> GSM564650 2 0.0291 0.894 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM564651 5 0.0291 0.853 0.000 0.004 0.000 0.000 0.992 0.004
#> GSM564652 5 0.0146 0.852 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM564653 5 0.0291 0.853 0.004 0.004 0.000 0.000 0.992 0.000
#> GSM564654 5 0.0820 0.850 0.000 0.000 0.012 0.000 0.972 0.016
#> GSM564655 5 0.1501 0.824 0.000 0.000 0.076 0.000 0.924 0.000
#> GSM564656 5 0.3774 0.427 0.000 0.000 0.408 0.000 0.592 0.000
#> GSM564657 3 0.2320 0.770 0.000 0.132 0.864 0.000 0.000 0.004
#> GSM564658 2 0.4771 0.719 0.196 0.712 0.000 0.004 0.032 0.056
#> GSM564659 5 0.5031 0.340 0.000 0.064 0.404 0.000 0.528 0.004
#> GSM564660 2 0.0260 0.894 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM564661 5 0.0862 0.851 0.004 0.008 0.000 0.000 0.972 0.016
#> GSM564662 3 0.0146 0.843 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM564663 2 0.1440 0.883 0.004 0.944 0.000 0.004 0.004 0.044
#> GSM564664 5 0.4199 0.111 0.000 0.444 0.008 0.000 0.544 0.004
#> GSM564665 5 0.5636 0.347 0.000 0.180 0.300 0.000 0.520 0.000
#> GSM564666 2 0.1152 0.882 0.000 0.952 0.044 0.000 0.000 0.004
#> GSM564667 2 0.3756 0.418 0.000 0.600 0.400 0.000 0.000 0.000
#> GSM564668 5 0.1442 0.838 0.000 0.000 0.040 0.012 0.944 0.004
#> GSM564669 5 0.3741 0.570 0.000 0.000 0.320 0.008 0.672 0.000
#> GSM564670 2 0.0508 0.894 0.000 0.984 0.004 0.000 0.000 0.012
#> GSM564671 5 0.0436 0.853 0.000 0.004 0.004 0.004 0.988 0.000
#> GSM564672 3 0.2527 0.727 0.000 0.168 0.832 0.000 0.000 0.000
#> GSM564673 5 0.0146 0.853 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM564674 2 0.0820 0.893 0.000 0.972 0.000 0.000 0.012 0.016
#> GSM564675 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564676 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564677 5 0.0520 0.852 0.008 0.000 0.000 0.000 0.984 0.008
#> GSM564678 2 0.1049 0.888 0.008 0.960 0.000 0.000 0.000 0.032
#> GSM564679 2 0.4810 0.717 0.192 0.712 0.000 0.004 0.036 0.056
#> GSM564680 3 0.1714 0.787 0.000 0.000 0.908 0.000 0.092 0.000
#> GSM564682 2 0.0520 0.894 0.000 0.984 0.008 0.000 0.000 0.008
#> GSM564683 3 0.0291 0.840 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM564684 5 0.0767 0.852 0.000 0.012 0.008 0.000 0.976 0.004
#> GSM564685 3 0.1007 0.833 0.000 0.044 0.956 0.000 0.000 0.000
#> GSM564686 2 0.4822 0.511 0.000 0.628 0.072 0.000 0.296 0.004
#> GSM564687 2 0.0692 0.892 0.000 0.976 0.004 0.000 0.020 0.000
#> GSM564688 5 0.0436 0.853 0.004 0.004 0.000 0.000 0.988 0.004
#> GSM564689 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM564690 2 0.0653 0.893 0.004 0.980 0.000 0.000 0.012 0.004
#> GSM564691 2 0.0146 0.894 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM564692 5 0.0653 0.851 0.004 0.012 0.000 0.000 0.980 0.004
#> GSM564694 2 0.0603 0.893 0.000 0.980 0.000 0.000 0.016 0.004
#> GSM564695 2 0.5089 0.725 0.100 0.724 0.004 0.004 0.116 0.052
#> GSM564696 2 0.3221 0.648 0.000 0.736 0.264 0.000 0.000 0.000
#> GSM564697 2 0.0146 0.894 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM564698 3 0.2135 0.749 0.000 0.000 0.872 0.000 0.128 0.000
#> GSM564700 5 0.0291 0.853 0.004 0.004 0.000 0.000 0.992 0.000
#> GSM564701 5 0.3911 0.721 0.160 0.000 0.000 0.004 0.768 0.068
#> GSM564702 5 0.0806 0.850 0.008 0.000 0.000 0.000 0.972 0.020
#> GSM564703 3 0.6123 -0.110 0.184 0.000 0.408 0.396 0.000 0.012
#> GSM564704 1 0.3802 0.825 0.676 0.000 0.000 0.312 0.000 0.012
#> GSM564705 1 0.2730 0.946 0.808 0.000 0.000 0.192 0.000 0.000
#> GSM564706 6 0.3046 0.803 0.000 0.000 0.012 0.188 0.000 0.800
#> GSM564707 1 0.3956 0.891 0.712 0.000 0.000 0.252 0.000 0.036
#> GSM564708 4 0.5757 0.117 0.148 0.000 0.404 0.444 0.000 0.004
#> GSM564709 1 0.2823 0.951 0.796 0.000 0.000 0.204 0.000 0.000
#> GSM564710 1 0.2389 0.871 0.864 0.000 0.000 0.128 0.000 0.008
#> GSM564711 6 0.2520 0.834 0.000 0.000 0.004 0.152 0.000 0.844
#> GSM564712 1 0.2823 0.951 0.796 0.000 0.000 0.204 0.000 0.000
#> GSM564713 4 0.0891 0.806 0.024 0.000 0.000 0.968 0.000 0.008
#> GSM564714 6 0.2257 0.848 0.000 0.000 0.008 0.116 0.000 0.876
#> GSM564715 1 0.3588 0.918 0.776 0.000 0.000 0.180 0.000 0.044
#> GSM564716 1 0.2912 0.949 0.784 0.000 0.000 0.216 0.000 0.000
#> GSM564717 6 0.2513 0.852 0.060 0.008 0.000 0.044 0.000 0.888
#> GSM564718 4 0.3756 0.144 0.000 0.000 0.000 0.600 0.000 0.400
#> GSM564719 6 0.2339 0.857 0.020 0.012 0.000 0.072 0.000 0.896
#> GSM564720 6 0.2402 0.819 0.120 0.000 0.000 0.012 0.000 0.868
#> GSM564721 1 0.2941 0.948 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM564722 6 0.1951 0.861 0.016 0.000 0.000 0.076 0.000 0.908
#> GSM564723 1 0.3871 0.764 0.768 0.000 0.000 0.084 0.000 0.148
#> GSM564724 4 0.0146 0.812 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM564725 1 0.3076 0.934 0.760 0.000 0.000 0.240 0.000 0.000
#> GSM564726 4 0.0260 0.812 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM564727 1 0.3217 0.943 0.768 0.000 0.000 0.224 0.000 0.008
#> GSM564728 4 0.0458 0.810 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM564729 4 0.2793 0.584 0.200 0.000 0.000 0.800 0.000 0.000
#> GSM564730 1 0.2793 0.950 0.800 0.000 0.000 0.200 0.000 0.000
#> GSM564731 6 0.3828 0.359 0.000 0.000 0.000 0.440 0.000 0.560
#> GSM564732 4 0.0777 0.811 0.004 0.000 0.000 0.972 0.000 0.024
#> GSM564733 4 0.1007 0.796 0.044 0.000 0.000 0.956 0.000 0.000
#> GSM564734 4 0.1528 0.795 0.048 0.000 0.000 0.936 0.000 0.016
#> GSM564735 4 0.1610 0.770 0.000 0.000 0.000 0.916 0.000 0.084
#> GSM564736 4 0.0547 0.810 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM564737 1 0.2762 0.948 0.804 0.000 0.000 0.196 0.000 0.000
#> GSM564738 4 0.1141 0.801 0.000 0.000 0.000 0.948 0.000 0.052
#> GSM564739 4 0.2996 0.536 0.228 0.000 0.000 0.772 0.000 0.000
#> GSM564740 6 0.1714 0.857 0.000 0.000 0.000 0.092 0.000 0.908
#> GSM564741 4 0.0458 0.811 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM564742 6 0.2613 0.839 0.000 0.000 0.012 0.140 0.000 0.848
#> GSM564743 6 0.2163 0.833 0.092 0.000 0.000 0.016 0.000 0.892
#> GSM564744 1 0.2902 0.946 0.800 0.000 0.000 0.196 0.000 0.004
#> GSM564745 1 0.2883 0.951 0.788 0.000 0.000 0.212 0.000 0.000
#> GSM564746 6 0.2250 0.859 0.040 0.000 0.000 0.064 0.000 0.896
#> GSM564747 6 0.2416 0.837 0.000 0.000 0.000 0.156 0.000 0.844
#> GSM564748 4 0.2519 0.769 0.044 0.000 0.048 0.892 0.000 0.016
#> GSM564749 6 0.2325 0.854 0.060 0.000 0.000 0.048 0.000 0.892
#> GSM564750 4 0.1168 0.804 0.000 0.000 0.028 0.956 0.000 0.016
#> GSM564751 4 0.5592 0.193 0.004 0.000 0.376 0.492 0.000 0.128
#> GSM564752 6 0.5335 0.560 0.000 0.000 0.148 0.276 0.000 0.576
#> GSM564753 3 0.5546 0.216 0.000 0.000 0.552 0.256 0.000 0.192
#> GSM564754 4 0.4076 -0.264 0.452 0.000 0.000 0.540 0.000 0.008
#> GSM564755 4 0.0363 0.811 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM564756 4 0.3245 0.524 0.228 0.000 0.000 0.764 0.000 0.008
#> GSM564757 4 0.2854 0.591 0.000 0.000 0.000 0.792 0.000 0.208
#> GSM564758 4 0.3348 0.583 0.000 0.000 0.016 0.768 0.000 0.216
#> GSM564759 6 0.3230 0.779 0.000 0.000 0.012 0.212 0.000 0.776
#> GSM564760 4 0.1196 0.799 0.040 0.000 0.000 0.952 0.000 0.008
#> GSM564761 1 0.2883 0.950 0.788 0.000 0.000 0.212 0.000 0.000
#> GSM564762 4 0.3563 0.343 0.000 0.000 0.000 0.664 0.000 0.336
#> GSM564681 5 0.4051 0.722 0.160 0.004 0.000 0.004 0.764 0.068
#> GSM564693 5 0.0767 0.852 0.004 0.008 0.000 0.000 0.976 0.012
#> GSM564646 5 0.1555 0.824 0.004 0.060 0.000 0.000 0.932 0.004
#> GSM564699 2 0.5486 0.223 0.000 0.496 0.372 0.000 0.132 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 genotype/variation(p) disease.state(p) k
#> ATC:NMF 154 0.925 0.476 2
#> ATC:NMF 144 0.462 0.758 3
#> ATC:NMF 125 0.473 0.524 4
#> ATC:NMF 77 0.217 0.690 5
#> ATC:NMF 136 0.848 0.692 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