Date: 2019-12-25 23:09:05 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 16450 rows and 111 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)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 16450 111
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, 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 | ||
|---|---|---|---|---|---|---|
| SD:skmeans | 2 | 1.000 | 0.972 | 0.990 | ** | |
| CV:NMF | 2 | 1.000 | 0.970 | 0.986 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.965 | 0.987 | ** | |
| ATC:pam | 2 | 1.000 | 0.967 | 0.986 | ** | |
| ATC:mclust | 2 | 1.000 | 0.967 | 0.986 | ** | |
| SD:NMF | 3 | 0.952 | 0.906 | 0.964 | ** | |
| CV:skmeans | 2 | 0.945 | 0.982 | 0.990 | * | |
| MAD:NMF | 3 | 0.945 | 0.928 | 0.970 | * | |
| ATC:hclust | 2 | 0.944 | 0.956 | 0.976 | * | |
| ATC:skmeans | 3 | 0.905 | 0.897 | 0.948 | * | 2 |
| SD:mclust | 4 | 0.896 | 0.877 | 0.940 | ||
| CV:mclust | 4 | 0.832 | 0.849 | 0.914 | ||
| MAD:pam | 2 | 0.822 | 0.919 | 0.958 | ||
| SD:pam | 2 | 0.815 | 0.886 | 0.951 | ||
| CV:pam | 4 | 0.769 | 0.831 | 0.935 | ||
| ATC:kmeans | 4 | 0.718 | 0.796 | 0.887 | ||
| MAD:mclust | 4 | 0.675 | 0.778 | 0.903 | ||
| SD:hclust | 4 | 0.577 | 0.800 | 0.839 | ||
| CV:hclust | 3 | 0.530 | 0.900 | 0.926 | ||
| SD:kmeans | 2 | 0.499 | 0.908 | 0.925 | ||
| MAD:kmeans | 2 | 0.455 | 0.876 | 0.915 | ||
| CV:kmeans | 2 | 0.372 | 0.829 | 0.845 | ||
| MAD:hclust | 3 | 0.364 | 0.719 | 0.852 | ||
| ATC:NMF | 2 | 0.357 | 0.808 | 0.858 |
**: 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 0.889 0.912 0.965 0.487 0.510 0.510
#> CV:NMF 2 1.000 0.970 0.986 0.498 0.499 0.499
#> MAD:NMF 2 0.890 0.928 0.971 0.487 0.517 0.517
#> ATC:NMF 2 0.357 0.808 0.858 0.491 0.496 0.496
#> SD:skmeans 2 1.000 0.972 0.990 0.502 0.499 0.499
#> CV:skmeans 2 0.945 0.982 0.990 0.502 0.499 0.499
#> MAD:skmeans 2 1.000 0.965 0.987 0.503 0.499 0.499
#> ATC:skmeans 2 1.000 0.986 0.995 0.488 0.510 0.510
#> SD:mclust 2 0.514 0.761 0.860 0.414 0.629 0.629
#> CV:mclust 2 0.344 0.771 0.830 0.387 0.517 0.517
#> MAD:mclust 2 0.478 0.778 0.884 0.391 0.638 0.638
#> ATC:mclust 2 1.000 0.967 0.986 0.127 0.897 0.897
#> SD:kmeans 2 0.499 0.908 0.925 0.454 0.499 0.499
#> CV:kmeans 2 0.372 0.829 0.845 0.425 0.500 0.500
#> MAD:kmeans 2 0.455 0.876 0.915 0.451 0.500 0.500
#> ATC:kmeans 2 0.889 0.911 0.957 0.405 0.558 0.558
#> SD:pam 2 0.815 0.886 0.951 0.500 0.500 0.500
#> CV:pam 2 0.618 0.830 0.924 0.483 0.510 0.510
#> MAD:pam 2 0.822 0.919 0.958 0.498 0.497 0.497
#> ATC:pam 2 1.000 0.967 0.986 0.138 0.865 0.865
#> SD:hclust 2 0.632 0.859 0.928 0.202 0.897 0.897
#> CV:hclust 2 0.514 0.888 0.914 0.183 0.897 0.897
#> MAD:hclust 2 0.646 0.792 0.894 0.237 0.897 0.897
#> ATC:hclust 2 0.944 0.956 0.976 0.318 0.702 0.702
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.952 0.906 0.964 0.148 0.877 0.772
#> CV:NMF 3 0.768 0.859 0.914 0.185 0.938 0.876
#> MAD:NMF 3 0.945 0.928 0.970 0.161 0.887 0.788
#> ATC:NMF 3 0.397 0.592 0.790 0.302 0.651 0.407
#> SD:skmeans 3 0.782 0.378 0.704 0.273 0.701 0.480
#> CV:skmeans 3 0.801 0.913 0.946 0.281 0.863 0.725
#> MAD:skmeans 3 0.759 0.732 0.891 0.278 0.769 0.569
#> ATC:skmeans 3 0.905 0.897 0.948 0.259 0.846 0.707
#> SD:mclust 3 0.531 0.685 0.844 0.456 0.591 0.443
#> CV:mclust 3 0.690 0.773 0.885 0.528 0.702 0.511
#> MAD:mclust 3 0.565 0.681 0.866 0.496 0.611 0.466
#> ATC:mclust 3 0.357 0.689 0.840 2.898 0.583 0.535
#> SD:kmeans 3 0.611 0.623 0.778 0.344 0.930 0.860
#> CV:kmeans 3 0.478 0.792 0.811 0.353 0.882 0.768
#> MAD:kmeans 3 0.539 0.650 0.789 0.352 0.907 0.821
#> ATC:kmeans 3 0.686 0.763 0.878 0.429 0.637 0.444
#> SD:pam 3 0.629 0.816 0.841 0.278 0.785 0.593
#> CV:pam 3 0.659 0.823 0.921 0.117 0.944 0.891
#> MAD:pam 3 0.825 0.868 0.944 0.315 0.771 0.571
#> ATC:pam 3 0.867 0.904 0.963 2.859 0.576 0.515
#> SD:hclust 3 0.358 0.600 0.753 1.285 0.691 0.656
#> CV:hclust 3 0.530 0.900 0.926 1.855 0.553 0.502
#> MAD:hclust 3 0.364 0.719 0.852 1.049 0.552 0.500
#> ATC:hclust 3 0.901 0.935 0.955 0.123 0.986 0.980
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.593 0.629 0.826 0.2562 0.714 0.441
#> CV:NMF 4 0.528 0.456 0.718 0.1898 0.695 0.414
#> MAD:NMF 4 0.679 0.703 0.866 0.2467 0.757 0.501
#> ATC:NMF 4 0.477 0.600 0.773 0.1166 0.781 0.465
#> SD:skmeans 4 0.828 0.824 0.906 0.1445 0.773 0.481
#> CV:skmeans 4 0.848 0.885 0.931 0.1455 0.895 0.708
#> MAD:skmeans 4 0.820 0.830 0.909 0.1409 0.910 0.750
#> ATC:skmeans 4 0.764 0.766 0.881 0.1155 0.934 0.830
#> SD:mclust 4 0.896 0.877 0.940 0.1083 0.780 0.552
#> CV:mclust 4 0.832 0.849 0.914 0.1286 0.936 0.844
#> MAD:mclust 4 0.675 0.778 0.903 0.1293 0.760 0.517
#> ATC:mclust 4 0.460 0.769 0.810 0.2941 0.848 0.687
#> SD:kmeans 4 0.562 0.695 0.736 0.1308 0.771 0.534
#> CV:kmeans 4 0.595 0.713 0.744 0.1687 1.000 1.000
#> MAD:kmeans 4 0.527 0.612 0.688 0.1473 0.801 0.581
#> ATC:kmeans 4 0.718 0.796 0.887 0.1418 0.878 0.709
#> SD:pam 4 0.641 0.761 0.837 0.0658 0.960 0.886
#> CV:pam 4 0.769 0.831 0.935 0.1507 0.918 0.826
#> MAD:pam 4 0.740 0.818 0.876 0.0656 0.928 0.805
#> ATC:pam 4 0.853 0.859 0.944 0.2227 0.812 0.628
#> SD:hclust 4 0.577 0.800 0.839 0.3812 0.727 0.547
#> CV:hclust 4 0.589 0.872 0.875 0.1544 0.904 0.787
#> MAD:hclust 4 0.475 0.650 0.800 0.3266 0.652 0.392
#> ATC:hclust 4 0.514 0.815 0.862 0.5987 0.700 0.564
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.759 0.757 0.872 0.1193 0.814 0.466
#> CV:NMF 5 0.723 0.639 0.770 0.0989 0.800 0.456
#> MAD:NMF 5 0.601 0.673 0.807 0.1099 0.852 0.550
#> ATC:NMF 5 0.458 0.517 0.705 0.0522 0.885 0.639
#> SD:skmeans 5 0.781 0.781 0.809 0.0677 0.916 0.716
#> CV:skmeans 5 0.840 0.849 0.867 0.0628 0.935 0.756
#> MAD:skmeans 5 0.767 0.652 0.763 0.0654 0.928 0.756
#> ATC:skmeans 5 0.784 0.836 0.907 0.0766 0.906 0.720
#> SD:mclust 5 0.688 0.722 0.828 0.0905 0.864 0.639
#> CV:mclust 5 0.788 0.781 0.859 0.0695 1.000 1.000
#> MAD:mclust 5 0.618 0.599 0.808 0.1054 0.848 0.570
#> ATC:mclust 5 0.343 0.551 0.681 0.0764 0.922 0.777
#> SD:kmeans 5 0.621 0.731 0.741 0.0816 0.885 0.660
#> CV:kmeans 5 0.616 0.664 0.742 0.0854 0.857 0.642
#> MAD:kmeans 5 0.562 0.551 0.662 0.0782 0.845 0.544
#> ATC:kmeans 5 0.694 0.783 0.834 0.1085 0.780 0.445
#> SD:pam 5 0.841 0.874 0.938 0.1293 0.834 0.528
#> CV:pam 5 0.800 0.807 0.923 0.1352 0.889 0.719
#> MAD:pam 5 0.841 0.882 0.941 0.1027 0.859 0.589
#> ATC:pam 5 0.868 0.834 0.939 0.1137 0.909 0.752
#> SD:hclust 5 0.652 0.784 0.871 0.0749 0.972 0.919
#> CV:hclust 5 0.599 0.774 0.878 0.0944 0.986 0.960
#> MAD:hclust 5 0.628 0.643 0.793 0.0976 0.884 0.710
#> ATC:hclust 5 0.720 0.809 0.915 0.1180 0.976 0.938
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.766 0.694 0.814 0.0479 0.917 0.648
#> CV:NMF 6 0.844 0.860 0.905 0.0566 0.913 0.655
#> MAD:NMF 6 0.717 0.732 0.810 0.0509 0.935 0.716
#> ATC:NMF 6 0.553 0.548 0.729 0.0447 0.843 0.488
#> SD:skmeans 6 0.780 0.768 0.841 0.0527 0.949 0.765
#> CV:skmeans 6 0.822 0.835 0.857 0.0432 0.971 0.861
#> MAD:skmeans 6 0.774 0.662 0.763 0.0528 0.946 0.775
#> ATC:skmeans 6 0.804 0.753 0.873 0.0398 0.991 0.963
#> SD:mclust 6 0.821 0.791 0.892 0.0875 0.884 0.619
#> CV:mclust 6 0.716 0.683 0.801 0.0781 0.866 0.621
#> MAD:mclust 6 0.689 0.668 0.846 0.0834 0.883 0.575
#> ATC:mclust 6 0.549 0.518 0.704 0.0894 0.805 0.439
#> SD:kmeans 6 0.630 0.646 0.725 0.0586 0.960 0.834
#> CV:kmeans 6 0.642 0.732 0.766 0.0547 0.943 0.786
#> MAD:kmeans 6 0.678 0.656 0.743 0.0556 0.878 0.545
#> ATC:kmeans 6 0.716 0.615 0.800 0.0701 0.972 0.888
#> SD:pam 6 0.853 0.780 0.891 0.0424 0.947 0.776
#> CV:pam 6 0.722 0.580 0.829 0.0651 0.963 0.876
#> MAD:pam 6 0.869 0.786 0.882 0.0402 0.928 0.708
#> ATC:pam 6 0.753 0.674 0.820 0.0708 0.874 0.586
#> SD:hclust 6 0.715 0.771 0.826 0.0586 0.988 0.963
#> CV:hclust 6 0.696 0.820 0.886 0.0701 0.954 0.865
#> MAD:hclust 6 0.689 0.618 0.800 0.0578 0.942 0.828
#> ATC:hclust 6 0.727 0.824 0.921 0.0194 0.996 0.989
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)
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 16450 rows and 111 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 0.632 0.859 0.928 0.2023 0.897 0.897
#> 3 3 0.358 0.600 0.753 1.2849 0.691 0.656
#> 4 4 0.577 0.800 0.839 0.3812 0.727 0.547
#> 5 5 0.652 0.784 0.871 0.0749 0.972 0.919
#> 6 6 0.715 0.771 0.826 0.0586 0.988 0.963
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
#> SRR191639 2 0.844 0.697 0.272 0.728
#> SRR191640 2 0.443 0.859 0.092 0.908
#> SRR191641 2 0.469 0.853 0.100 0.900
#> SRR191642 2 0.443 0.859 0.092 0.908
#> SRR191643 2 0.000 0.917 0.000 1.000
#> SRR191644 2 0.000 0.917 0.000 1.000
#> SRR191645 2 0.000 0.917 0.000 1.000
#> SRR191646 2 0.000 0.917 0.000 1.000
#> SRR191647 2 0.000 0.917 0.000 1.000
#> SRR191648 2 0.000 0.917 0.000 1.000
#> SRR191649 2 0.000 0.917 0.000 1.000
#> SRR191650 2 0.788 0.723 0.236 0.764
#> SRR191651 2 0.788 0.723 0.236 0.764
#> SRR191652 2 0.921 0.620 0.336 0.664
#> SRR191653 2 0.000 0.917 0.000 1.000
#> SRR191654 2 0.000 0.917 0.000 1.000
#> SRR191655 2 0.000 0.917 0.000 1.000
#> SRR191656 2 0.921 0.620 0.336 0.664
#> SRR191657 2 0.921 0.620 0.336 0.664
#> SRR191658 2 0.921 0.620 0.336 0.664
#> SRR191659 2 0.921 0.620 0.336 0.664
#> SRR191660 2 0.921 0.620 0.336 0.664
#> SRR191661 2 0.921 0.620 0.336 0.664
#> SRR191662 2 0.921 0.620 0.336 0.664
#> SRR191663 2 0.921 0.620 0.336 0.664
#> SRR191664 2 0.921 0.620 0.336 0.664
#> SRR191665 2 0.921 0.620 0.336 0.664
#> SRR191666 2 0.921 0.620 0.336 0.664
#> SRR191667 2 0.921 0.620 0.336 0.664
#> SRR191668 2 0.921 0.620 0.336 0.664
#> SRR191669 2 0.921 0.620 0.336 0.664
#> SRR191670 2 0.921 0.620 0.336 0.664
#> SRR191671 2 0.921 0.620 0.336 0.664
#> SRR191672 2 0.921 0.620 0.336 0.664
#> SRR191673 2 0.921 0.620 0.336 0.664
#> SRR191674 2 0.000 0.917 0.000 1.000
#> SRR191675 2 0.000 0.917 0.000 1.000
#> SRR191677 2 0.000 0.917 0.000 1.000
#> SRR191678 2 0.000 0.917 0.000 1.000
#> SRR191679 2 0.000 0.917 0.000 1.000
#> SRR191680 2 0.000 0.917 0.000 1.000
#> SRR191681 2 0.000 0.917 0.000 1.000
#> SRR191682 2 0.000 0.917 0.000 1.000
#> SRR191683 2 0.000 0.917 0.000 1.000
#> SRR191684 2 0.000 0.917 0.000 1.000
#> SRR191685 2 0.000 0.917 0.000 1.000
#> SRR191686 2 0.000 0.917 0.000 1.000
#> SRR191687 2 0.000 0.917 0.000 1.000
#> SRR191688 2 0.000 0.917 0.000 1.000
#> SRR191689 2 0.000 0.917 0.000 1.000
#> SRR191690 2 0.000 0.917 0.000 1.000
#> SRR191691 2 0.000 0.917 0.000 1.000
#> SRR191692 2 0.000 0.917 0.000 1.000
#> SRR191693 2 0.000 0.917 0.000 1.000
#> SRR191694 2 0.000 0.917 0.000 1.000
#> SRR191695 2 0.000 0.917 0.000 1.000
#> SRR191696 2 0.000 0.917 0.000 1.000
#> SRR191697 2 0.000 0.917 0.000 1.000
#> SRR191698 2 0.000 0.917 0.000 1.000
#> SRR191699 2 0.000 0.917 0.000 1.000
#> SRR191700 2 0.000 0.917 0.000 1.000
#> SRR191701 2 0.000 0.917 0.000 1.000
#> SRR191702 2 0.000 0.917 0.000 1.000
#> SRR191703 2 0.000 0.917 0.000 1.000
#> SRR191704 2 0.000 0.917 0.000 1.000
#> SRR191705 2 0.000 0.917 0.000 1.000
#> SRR191706 2 0.000 0.917 0.000 1.000
#> SRR191707 2 0.000 0.917 0.000 1.000
#> SRR191708 2 0.000 0.917 0.000 1.000
#> SRR191709 2 0.000 0.917 0.000 1.000
#> SRR191710 2 0.000 0.917 0.000 1.000
#> SRR191711 2 0.000 0.917 0.000 1.000
#> SRR191712 2 0.000 0.917 0.000 1.000
#> SRR191713 2 0.000 0.917 0.000 1.000
#> SRR191714 2 0.000 0.917 0.000 1.000
#> SRR191715 2 0.000 0.917 0.000 1.000
#> SRR191716 2 0.000 0.917 0.000 1.000
#> SRR191717 2 0.000 0.917 0.000 1.000
#> SRR191718 2 0.000 0.917 0.000 1.000
#> SRR537099 2 0.443 0.859 0.092 0.908
#> SRR537100 2 0.443 0.859 0.092 0.908
#> SRR537101 2 0.469 0.853 0.100 0.900
#> SRR537102 2 0.443 0.859 0.092 0.908
#> SRR537104 2 0.000 0.917 0.000 1.000
#> SRR537105 2 0.000 0.917 0.000 1.000
#> SRR537106 2 0.000 0.917 0.000 1.000
#> SRR537107 2 0.000 0.917 0.000 1.000
#> SRR537108 2 0.000 0.917 0.000 1.000
#> SRR537109 2 0.000 0.917 0.000 1.000
#> SRR537110 2 0.000 0.917 0.000 1.000
#> SRR537111 2 0.788 0.723 0.236 0.764
#> SRR537113 2 0.000 0.917 0.000 1.000
#> SRR537114 2 0.000 0.917 0.000 1.000
#> SRR537115 2 0.000 0.917 0.000 1.000
#> SRR537116 2 0.000 0.917 0.000 1.000
#> SRR537117 2 0.000 0.917 0.000 1.000
#> SRR537118 2 0.000 0.917 0.000 1.000
#> SRR537119 2 0.000 0.917 0.000 1.000
#> SRR537120 2 0.000 0.917 0.000 1.000
#> SRR537121 2 0.000 0.917 0.000 1.000
#> SRR537122 2 0.000 0.917 0.000 1.000
#> SRR537123 2 0.000 0.917 0.000 1.000
#> SRR537124 2 0.000 0.917 0.000 1.000
#> SRR537125 2 0.000 0.917 0.000 1.000
#> SRR537126 2 0.000 0.917 0.000 1.000
#> SRR537127 1 0.000 1.000 1.000 0.000
#> SRR537128 1 0.000 1.000 1.000 0.000
#> SRR537129 1 0.000 1.000 1.000 0.000
#> SRR537130 1 0.000 1.000 1.000 0.000
#> SRR537131 1 0.000 1.000 1.000 0.000
#> SRR537132 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.6215 0.577 0.572 0.428 0
#> SRR191640 1 0.5016 0.629 0.760 0.240 0
#> SRR191641 1 0.5098 0.627 0.752 0.248 0
#> SRR191642 1 0.5016 0.629 0.760 0.240 0
#> SRR191643 1 0.3816 0.636 0.852 0.148 0
#> SRR191644 1 0.3816 0.636 0.852 0.148 0
#> SRR191645 1 0.3752 0.637 0.856 0.144 0
#> SRR191646 1 0.3752 0.637 0.856 0.144 0
#> SRR191647 1 0.3752 0.637 0.856 0.144 0
#> SRR191648 1 0.3752 0.637 0.856 0.144 0
#> SRR191649 1 0.3752 0.637 0.856 0.144 0
#> SRR191650 1 0.6111 0.579 0.604 0.396 0
#> SRR191651 1 0.6111 0.579 0.604 0.396 0
#> SRR191652 1 0.6309 0.526 0.504 0.496 0
#> SRR191653 1 0.3879 0.635 0.848 0.152 0
#> SRR191654 1 0.3879 0.635 0.848 0.152 0
#> SRR191655 1 0.3879 0.635 0.848 0.152 0
#> SRR191656 1 0.6309 0.526 0.504 0.496 0
#> SRR191657 1 0.6309 0.526 0.504 0.496 0
#> SRR191658 1 0.6309 0.526 0.504 0.496 0
#> SRR191659 1 0.6309 0.526 0.504 0.496 0
#> SRR191660 1 0.6309 0.526 0.504 0.496 0
#> SRR191661 1 0.6309 0.526 0.504 0.496 0
#> SRR191662 1 0.6309 0.526 0.504 0.496 0
#> SRR191663 1 0.6309 0.526 0.504 0.496 0
#> SRR191664 1 0.6309 0.526 0.504 0.496 0
#> SRR191665 1 0.6309 0.526 0.504 0.496 0
#> SRR191666 1 0.6309 0.526 0.504 0.496 0
#> SRR191667 1 0.6309 0.526 0.504 0.496 0
#> SRR191668 1 0.6309 0.526 0.504 0.496 0
#> SRR191669 1 0.6309 0.526 0.504 0.496 0
#> SRR191670 1 0.6309 0.526 0.504 0.496 0
#> SRR191671 1 0.6309 0.526 0.504 0.496 0
#> SRR191672 1 0.6309 0.526 0.504 0.496 0
#> SRR191673 1 0.6309 0.526 0.504 0.496 0
#> SRR191674 2 0.6309 1.000 0.496 0.504 0
#> SRR191675 2 0.6309 1.000 0.496 0.504 0
#> SRR191677 2 0.6309 1.000 0.496 0.504 0
#> SRR191678 2 0.6309 1.000 0.496 0.504 0
#> SRR191679 2 0.6309 1.000 0.496 0.504 0
#> SRR191680 2 0.6309 1.000 0.496 0.504 0
#> SRR191681 2 0.6309 1.000 0.496 0.504 0
#> SRR191682 2 0.6309 1.000 0.496 0.504 0
#> SRR191683 2 0.6309 1.000 0.496 0.504 0
#> SRR191684 2 0.6309 1.000 0.496 0.504 0
#> SRR191685 2 0.6309 1.000 0.496 0.504 0
#> SRR191686 2 0.6309 1.000 0.496 0.504 0
#> SRR191687 2 0.6309 1.000 0.496 0.504 0
#> SRR191688 1 0.3340 0.337 0.880 0.120 0
#> SRR191689 1 0.3116 0.369 0.892 0.108 0
#> SRR191690 1 0.3116 0.369 0.892 0.108 0
#> SRR191691 1 0.2959 0.387 0.900 0.100 0
#> SRR191692 1 0.6267 -0.892 0.548 0.452 0
#> SRR191693 1 0.6267 -0.892 0.548 0.452 0
#> SRR191694 1 0.6267 -0.892 0.548 0.452 0
#> SRR191695 1 0.3340 0.337 0.880 0.120 0
#> SRR191696 1 0.3340 0.337 0.880 0.120 0
#> SRR191697 1 0.2959 0.387 0.900 0.100 0
#> SRR191698 1 0.2959 0.387 0.900 0.100 0
#> SRR191699 1 0.3116 0.369 0.892 0.108 0
#> SRR191700 1 0.2959 0.387 0.900 0.100 0
#> SRR191701 1 0.2959 0.387 0.900 0.100 0
#> SRR191702 2 0.6309 1.000 0.496 0.504 0
#> SRR191703 2 0.6309 1.000 0.496 0.504 0
#> SRR191704 2 0.6309 1.000 0.496 0.504 0
#> SRR191705 2 0.6309 1.000 0.496 0.504 0
#> SRR191706 2 0.6309 1.000 0.496 0.504 0
#> SRR191707 1 0.5733 -0.538 0.676 0.324 0
#> SRR191708 2 0.6309 1.000 0.496 0.504 0
#> SRR191709 2 0.6309 1.000 0.496 0.504 0
#> SRR191710 2 0.6309 1.000 0.496 0.504 0
#> SRR191711 1 0.2959 0.386 0.900 0.100 0
#> SRR191712 1 0.2959 0.386 0.900 0.100 0
#> SRR191713 2 0.6309 1.000 0.496 0.504 0
#> SRR191714 2 0.6309 1.000 0.496 0.504 0
#> SRR191715 1 0.3038 0.377 0.896 0.104 0
#> SRR191716 1 0.3340 0.337 0.880 0.120 0
#> SRR191717 1 0.3340 0.337 0.880 0.120 0
#> SRR191718 1 0.3340 0.337 0.880 0.120 0
#> SRR537099 1 0.5016 0.629 0.760 0.240 0
#> SRR537100 1 0.5016 0.629 0.760 0.240 0
#> SRR537101 1 0.5098 0.627 0.752 0.248 0
#> SRR537102 1 0.5016 0.629 0.760 0.240 0
#> SRR537104 1 0.3816 0.636 0.852 0.148 0
#> SRR537105 1 0.3752 0.637 0.856 0.144 0
#> SRR537106 1 0.3752 0.637 0.856 0.144 0
#> SRR537107 1 0.3752 0.637 0.856 0.144 0
#> SRR537108 1 0.3752 0.637 0.856 0.144 0
#> SRR537109 1 0.3340 0.337 0.880 0.120 0
#> SRR537110 1 0.3192 0.432 0.888 0.112 0
#> SRR537111 1 0.6111 0.579 0.604 0.396 0
#> SRR537113 1 0.3340 0.627 0.880 0.120 0
#> SRR537114 1 0.3340 0.627 0.880 0.120 0
#> SRR537115 1 0.3340 0.627 0.880 0.120 0
#> SRR537116 1 0.3340 0.337 0.880 0.120 0
#> SRR537117 1 0.0237 0.535 0.996 0.004 0
#> SRR537118 1 0.0237 0.535 0.996 0.004 0
#> SRR537119 1 0.0237 0.535 0.996 0.004 0
#> SRR537120 1 0.0237 0.535 0.996 0.004 0
#> SRR537121 1 0.0237 0.535 0.996 0.004 0
#> SRR537122 1 0.0237 0.535 0.996 0.004 0
#> SRR537123 1 0.0237 0.535 0.996 0.004 0
#> SRR537124 1 0.0237 0.535 0.996 0.004 0
#> SRR537125 1 0.0237 0.535 0.996 0.004 0
#> SRR537126 1 0.0237 0.535 0.996 0.004 0
#> SRR537127 3 0.0000 1.000 0.000 0.000 1
#> SRR537128 3 0.0000 1.000 0.000 0.000 1
#> SRR537129 3 0.0000 1.000 0.000 0.000 1
#> SRR537130 3 0.0000 1.000 0.000 0.000 1
#> SRR537131 3 0.0000 1.000 0.000 0.000 1
#> SRR537132 3 0.0000 1.000 0.000 0.000 1
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.2921 0.817 0.860 0.000 0 0.140
#> SRR191640 4 0.4040 0.707 0.248 0.000 0 0.752
#> SRR191641 4 0.4103 0.696 0.256 0.000 0 0.744
#> SRR191642 4 0.4040 0.707 0.248 0.000 0 0.752
#> SRR191643 4 0.3257 0.810 0.152 0.004 0 0.844
#> SRR191644 4 0.3257 0.810 0.152 0.004 0 0.844
#> SRR191645 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR191646 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR191647 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR191648 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR191649 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR191650 1 0.5088 0.271 0.572 0.004 0 0.424
#> SRR191651 1 0.5088 0.271 0.572 0.004 0 0.424
#> SRR191652 1 0.1716 0.906 0.936 0.000 0 0.064
#> SRR191653 4 0.3695 0.807 0.156 0.016 0 0.828
#> SRR191654 4 0.3695 0.807 0.156 0.016 0 0.828
#> SRR191655 4 0.3695 0.807 0.156 0.016 0 0.828
#> SRR191656 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191657 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191658 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191659 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191660 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191661 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191662 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191663 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191664 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191665 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191666 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191667 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191668 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191669 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191670 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191671 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191672 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191673 1 0.1557 0.914 0.944 0.000 0 0.056
#> SRR191674 2 0.4595 0.763 0.044 0.780 0 0.176
#> SRR191675 2 0.4595 0.763 0.044 0.780 0 0.176
#> SRR191677 2 0.5090 0.729 0.044 0.728 0 0.228
#> SRR191678 2 0.5090 0.729 0.044 0.728 0 0.228
#> SRR191679 2 0.4417 0.763 0.044 0.796 0 0.160
#> SRR191680 2 0.4595 0.763 0.044 0.780 0 0.176
#> SRR191681 2 0.5090 0.729 0.044 0.728 0 0.228
#> SRR191682 2 0.1488 0.816 0.012 0.956 0 0.032
#> SRR191683 2 0.1488 0.816 0.012 0.956 0 0.032
#> SRR191684 2 0.1488 0.816 0.012 0.956 0 0.032
#> SRR191685 2 0.1488 0.816 0.012 0.956 0 0.032
#> SRR191686 2 0.1488 0.816 0.012 0.956 0 0.032
#> SRR191687 2 0.1488 0.816 0.012 0.956 0 0.032
#> SRR191688 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR191689 4 0.2469 0.801 0.000 0.108 0 0.892
#> SRR191690 4 0.2469 0.801 0.000 0.108 0 0.892
#> SRR191691 4 0.2760 0.790 0.000 0.128 0 0.872
#> SRR191692 2 0.5853 0.332 0.032 0.508 0 0.460
#> SRR191693 2 0.5853 0.332 0.032 0.508 0 0.460
#> SRR191694 2 0.5853 0.332 0.032 0.508 0 0.460
#> SRR191695 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR191696 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR191697 4 0.2760 0.790 0.000 0.128 0 0.872
#> SRR191698 4 0.2760 0.790 0.000 0.128 0 0.872
#> SRR191699 4 0.2469 0.801 0.000 0.108 0 0.892
#> SRR191700 4 0.2760 0.790 0.000 0.128 0 0.872
#> SRR191701 4 0.2760 0.790 0.000 0.128 0 0.872
#> SRR191702 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191703 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191704 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191705 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191706 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191707 2 0.4072 0.628 0.000 0.748 0 0.252
#> SRR191708 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191709 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191710 2 0.1118 0.822 0.000 0.964 0 0.036
#> SRR191711 4 0.2345 0.805 0.000 0.100 0 0.900
#> SRR191712 4 0.2345 0.805 0.000 0.100 0 0.900
#> SRR191713 2 0.1388 0.813 0.012 0.960 0 0.028
#> SRR191714 2 0.1388 0.813 0.012 0.960 0 0.028
#> SRR191715 4 0.2408 0.803 0.000 0.104 0 0.896
#> SRR191716 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR191717 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR191718 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR537099 4 0.4360 0.704 0.248 0.008 0 0.744
#> SRR537100 4 0.4360 0.704 0.248 0.008 0 0.744
#> SRR537101 4 0.4103 0.696 0.256 0.000 0 0.744
#> SRR537102 4 0.4040 0.707 0.248 0.000 0 0.752
#> SRR537104 4 0.3529 0.808 0.152 0.012 0 0.836
#> SRR537105 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR537106 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR537107 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR537108 4 0.3074 0.809 0.152 0.000 0 0.848
#> SRR537109 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR537110 4 0.3099 0.813 0.020 0.104 0 0.876
#> SRR537111 1 0.5088 0.271 0.572 0.004 0 0.424
#> SRR537113 4 0.2760 0.820 0.128 0.000 0 0.872
#> SRR537114 4 0.2760 0.820 0.128 0.000 0 0.872
#> SRR537115 4 0.2760 0.820 0.128 0.000 0 0.872
#> SRR537116 4 0.2647 0.791 0.000 0.120 0 0.880
#> SRR537117 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537118 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537119 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537120 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537121 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537122 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537123 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537124 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537125 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537126 4 0.0524 0.841 0.008 0.004 0 0.988
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.2230 0.769 0.884 0.000 0 0.116 0.000
#> SRR191640 4 0.2280 0.732 0.120 0.000 0 0.880 0.000
#> SRR191641 4 0.2424 0.718 0.132 0.000 0 0.868 0.000
#> SRR191642 4 0.2280 0.732 0.120 0.000 0 0.880 0.000
#> SRR191643 4 0.0955 0.812 0.028 0.004 0 0.968 0.000
#> SRR191644 4 0.0955 0.812 0.028 0.004 0 0.968 0.000
#> SRR191645 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR191646 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR191647 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR191648 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR191649 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR191650 1 0.4310 0.347 0.604 0.004 0 0.392 0.000
#> SRR191651 1 0.4310 0.347 0.604 0.004 0 0.392 0.000
#> SRR191652 1 0.0609 0.887 0.980 0.000 0 0.020 0.000
#> SRR191653 4 0.1630 0.808 0.036 0.016 0 0.944 0.004
#> SRR191654 4 0.1630 0.808 0.036 0.016 0 0.944 0.004
#> SRR191655 4 0.1630 0.808 0.036 0.016 0 0.944 0.004
#> SRR191656 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191657 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191658 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191659 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191660 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191661 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191662 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191663 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191664 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191665 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191666 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191667 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191668 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191669 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191670 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191671 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191672 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191673 1 0.0000 0.905 1.000 0.000 0 0.000 0.000
#> SRR191674 5 0.0963 0.721 0.000 0.000 0 0.036 0.964
#> SRR191675 5 0.0963 0.721 0.000 0.000 0 0.036 0.964
#> SRR191677 5 0.1851 0.738 0.000 0.000 0 0.088 0.912
#> SRR191678 5 0.1851 0.738 0.000 0.000 0 0.088 0.912
#> SRR191679 5 0.0609 0.694 0.000 0.000 0 0.020 0.980
#> SRR191680 5 0.0963 0.721 0.000 0.000 0 0.036 0.964
#> SRR191681 5 0.1851 0.738 0.000 0.000 0 0.088 0.912
#> SRR191682 2 0.4161 0.695 0.000 0.704 0 0.016 0.280
#> SRR191683 2 0.4161 0.695 0.000 0.704 0 0.016 0.280
#> SRR191684 2 0.4161 0.695 0.000 0.704 0 0.016 0.280
#> SRR191685 2 0.4161 0.695 0.000 0.704 0 0.016 0.280
#> SRR191686 2 0.4161 0.695 0.000 0.704 0 0.016 0.280
#> SRR191687 2 0.4161 0.695 0.000 0.704 0 0.016 0.280
#> SRR191688 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR191689 4 0.4134 0.773 0.000 0.044 0 0.760 0.196
#> SRR191690 4 0.4134 0.773 0.000 0.044 0 0.760 0.196
#> SRR191691 4 0.4429 0.764 0.000 0.064 0 0.744 0.192
#> SRR191692 5 0.4655 0.532 0.000 0.028 0 0.328 0.644
#> SRR191693 5 0.4655 0.532 0.000 0.028 0 0.328 0.644
#> SRR191694 5 0.4655 0.532 0.000 0.028 0 0.328 0.644
#> SRR191695 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR191696 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR191697 4 0.4429 0.764 0.000 0.064 0 0.744 0.192
#> SRR191698 4 0.4429 0.764 0.000 0.064 0 0.744 0.192
#> SRR191699 4 0.4134 0.773 0.000 0.044 0 0.760 0.196
#> SRR191700 4 0.4429 0.764 0.000 0.064 0 0.744 0.192
#> SRR191701 4 0.4429 0.764 0.000 0.064 0 0.744 0.192
#> SRR191702 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191703 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191704 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191705 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191706 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191707 2 0.3942 0.453 0.000 0.748 0 0.232 0.020
#> SRR191708 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191709 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191710 2 0.2561 0.758 0.000 0.856 0 0.000 0.144
#> SRR191711 4 0.4028 0.777 0.000 0.040 0 0.768 0.192
#> SRR191712 4 0.4028 0.777 0.000 0.040 0 0.768 0.192
#> SRR191713 2 0.3630 0.723 0.000 0.780 0 0.016 0.204
#> SRR191714 2 0.3630 0.723 0.000 0.780 0 0.016 0.204
#> SRR191715 4 0.4062 0.775 0.000 0.040 0 0.764 0.196
#> SRR191716 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR191717 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR191718 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR537099 4 0.2597 0.728 0.120 0.004 0 0.872 0.004
#> SRR537100 4 0.2597 0.728 0.120 0.004 0 0.872 0.004
#> SRR537101 4 0.2377 0.722 0.128 0.000 0 0.872 0.000
#> SRR537102 4 0.2280 0.732 0.120 0.000 0 0.880 0.000
#> SRR537104 4 0.1153 0.811 0.024 0.008 0 0.964 0.004
#> SRR537105 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR537106 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR537107 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR537108 4 0.0703 0.813 0.024 0.000 0 0.976 0.000
#> SRR537109 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR537110 4 0.4711 0.786 0.020 0.048 0 0.744 0.188
#> SRR537111 1 0.4310 0.347 0.604 0.004 0 0.392 0.000
#> SRR537113 4 0.0000 0.817 0.000 0.000 0 1.000 0.000
#> SRR537114 4 0.0000 0.817 0.000 0.000 0 1.000 0.000
#> SRR537115 4 0.0000 0.817 0.000 0.000 0 1.000 0.000
#> SRR537116 4 0.4150 0.760 0.000 0.036 0 0.748 0.216
#> SRR537117 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537118 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537119 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537120 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537121 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537122 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537123 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537124 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537125 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537126 4 0.2329 0.822 0.000 0.000 0 0.876 0.124
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.3366 0.760 0.832 0.016 0 0.100 0.000 0.052
#> SRR191640 4 0.5074 0.685 0.108 0.296 0 0.596 0.000 0.000
#> SRR191641 4 0.5169 0.676 0.120 0.292 0 0.588 0.000 0.000
#> SRR191642 4 0.5074 0.685 0.108 0.296 0 0.596 0.000 0.000
#> SRR191643 4 0.3935 0.758 0.016 0.292 0 0.688 0.004 0.000
#> SRR191644 4 0.3935 0.758 0.016 0.292 0 0.688 0.004 0.000
#> SRR191645 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR191646 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR191647 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR191648 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR191649 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR191650 1 0.4333 0.304 0.596 0.020 0 0.380 0.004 0.000
#> SRR191651 1 0.4333 0.304 0.596 0.020 0 0.380 0.004 0.000
#> SRR191652 1 0.0622 0.871 0.980 0.012 0 0.008 0.000 0.000
#> SRR191653 4 0.4397 0.754 0.024 0.296 0 0.664 0.016 0.000
#> SRR191654 4 0.4397 0.754 0.024 0.296 0 0.664 0.016 0.000
#> SRR191655 4 0.4397 0.754 0.024 0.296 0 0.664 0.016 0.000
#> SRR191656 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191657 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191662 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191665 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191666 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191667 1 0.0000 0.884 1.000 0.000 0 0.000 0.000 0.000
#> SRR191668 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191669 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191670 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191671 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191672 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191673 1 0.1141 0.876 0.948 0.000 0 0.000 0.000 0.052
#> SRR191674 6 0.1930 0.711 0.000 0.000 0 0.036 0.048 0.916
#> SRR191675 6 0.1930 0.711 0.000 0.000 0 0.036 0.048 0.916
#> SRR191677 6 0.1556 0.713 0.000 0.000 0 0.080 0.000 0.920
#> SRR191678 6 0.1556 0.713 0.000 0.000 0 0.080 0.000 0.920
#> SRR191679 6 0.1434 0.678 0.000 0.000 0 0.012 0.048 0.940
#> SRR191680 6 0.1930 0.711 0.000 0.000 0 0.036 0.048 0.916
#> SRR191681 6 0.1556 0.713 0.000 0.000 0 0.080 0.000 0.920
#> SRR191682 5 0.0000 0.915 0.000 0.000 0 0.000 1.000 0.000
#> SRR191683 5 0.0000 0.915 0.000 0.000 0 0.000 1.000 0.000
#> SRR191684 5 0.0000 0.915 0.000 0.000 0 0.000 1.000 0.000
#> SRR191685 5 0.0000 0.915 0.000 0.000 0 0.000 1.000 0.000
#> SRR191686 5 0.0000 0.915 0.000 0.000 0 0.000 1.000 0.000
#> SRR191687 5 0.0000 0.915 0.000 0.000 0 0.000 1.000 0.000
#> SRR191688 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR191689 4 0.2418 0.694 0.000 0.016 0 0.884 0.092 0.008
#> SRR191690 4 0.2418 0.694 0.000 0.016 0 0.884 0.092 0.008
#> SRR191691 4 0.2494 0.685 0.000 0.016 0 0.864 0.120 0.000
#> SRR191692 6 0.5689 0.490 0.000 0.012 0 0.408 0.112 0.468
#> SRR191693 6 0.5689 0.490 0.000 0.012 0 0.408 0.112 0.468
#> SRR191694 6 0.5689 0.490 0.000 0.012 0 0.408 0.112 0.468
#> SRR191695 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR191696 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR191697 4 0.2494 0.685 0.000 0.016 0 0.864 0.120 0.000
#> SRR191698 4 0.2494 0.685 0.000 0.016 0 0.864 0.120 0.000
#> SRR191699 4 0.2418 0.694 0.000 0.016 0 0.884 0.092 0.008
#> SRR191700 4 0.2494 0.685 0.000 0.016 0 0.864 0.120 0.000
#> SRR191701 4 0.2494 0.685 0.000 0.016 0 0.864 0.120 0.000
#> SRR191702 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191703 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191704 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191705 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191706 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191707 2 0.5881 0.330 0.000 0.472 0 0.232 0.296 0.000
#> SRR191708 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191709 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191710 2 0.3634 0.914 0.000 0.696 0 0.000 0.296 0.008
#> SRR191711 4 0.3808 0.746 0.000 0.112 0 0.792 0.088 0.008
#> SRR191712 4 0.3808 0.746 0.000 0.112 0 0.792 0.088 0.008
#> SRR191713 5 0.2597 0.677 0.000 0.176 0 0.000 0.824 0.000
#> SRR191714 5 0.2597 0.677 0.000 0.176 0 0.000 0.824 0.000
#> SRR191715 4 0.3906 0.744 0.000 0.112 0 0.788 0.088 0.012
#> SRR191716 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR191717 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR191718 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR537099 4 0.5311 0.682 0.108 0.296 0 0.588 0.008 0.000
#> SRR537100 4 0.5311 0.682 0.108 0.296 0 0.588 0.008 0.000
#> SRR537101 4 0.5148 0.677 0.116 0.296 0 0.588 0.000 0.000
#> SRR537102 4 0.5074 0.685 0.108 0.296 0 0.596 0.000 0.000
#> SRR537104 4 0.4067 0.755 0.012 0.296 0 0.680 0.012 0.000
#> SRR537105 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR537106 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR537107 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR537108 4 0.3729 0.757 0.012 0.296 0 0.692 0.000 0.000
#> SRR537109 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR537110 4 0.4253 0.753 0.012 0.132 0 0.756 0.100 0.000
#> SRR537111 1 0.4333 0.304 0.596 0.020 0 0.380 0.004 0.000
#> SRR537113 4 0.3330 0.762 0.000 0.284 0 0.716 0.000 0.000
#> SRR537114 4 0.3330 0.762 0.000 0.284 0 0.716 0.000 0.000
#> SRR537115 4 0.3330 0.762 0.000 0.284 0 0.716 0.000 0.000
#> SRR537116 4 0.4259 0.735 0.000 0.112 0 0.772 0.084 0.032
#> SRR537117 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537118 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537119 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537120 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537121 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537122 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537123 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537124 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537125 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537126 4 0.0000 0.749 0.000 0.000 0 1.000 0.000 0.000
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.499 0.908 0.925 0.4544 0.499 0.499
#> 3 3 0.611 0.623 0.778 0.3442 0.930 0.860
#> 4 4 0.562 0.695 0.736 0.1308 0.771 0.534
#> 5 5 0.621 0.731 0.741 0.0816 0.885 0.660
#> 6 6 0.630 0.646 0.725 0.0586 0.960 0.834
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR191639 1 0.4690 0.905 0.900 0.100
#> SRR191640 1 0.4690 0.905 0.900 0.100
#> SRR191641 1 0.4690 0.905 0.900 0.100
#> SRR191642 1 0.8909 0.739 0.692 0.308
#> SRR191643 1 0.9393 0.674 0.644 0.356
#> SRR191644 1 0.9044 0.724 0.680 0.320
#> SRR191645 1 0.4690 0.905 0.900 0.100
#> SRR191646 1 0.4690 0.905 0.900 0.100
#> SRR191647 1 0.4690 0.905 0.900 0.100
#> SRR191648 1 0.4690 0.905 0.900 0.100
#> SRR191649 1 0.4690 0.905 0.900 0.100
#> SRR191650 1 0.4690 0.905 0.900 0.100
#> SRR191651 1 0.4690 0.905 0.900 0.100
#> SRR191652 1 0.4690 0.905 0.900 0.100
#> SRR191653 1 0.7376 0.833 0.792 0.208
#> SRR191654 1 0.9358 0.680 0.648 0.352
#> SRR191655 1 0.7528 0.832 0.784 0.216
#> SRR191656 1 0.4690 0.905 0.900 0.100
#> SRR191657 1 0.4690 0.905 0.900 0.100
#> SRR191658 1 0.4690 0.905 0.900 0.100
#> SRR191659 1 0.4690 0.905 0.900 0.100
#> SRR191660 1 0.4690 0.905 0.900 0.100
#> SRR191661 1 0.4690 0.905 0.900 0.100
#> SRR191662 1 0.4690 0.905 0.900 0.100
#> SRR191663 1 0.4690 0.905 0.900 0.100
#> SRR191664 1 0.4690 0.905 0.900 0.100
#> SRR191665 1 0.4690 0.905 0.900 0.100
#> SRR191666 1 0.1843 0.860 0.972 0.028
#> SRR191667 1 0.1843 0.860 0.972 0.028
#> SRR191668 1 0.4690 0.905 0.900 0.100
#> SRR191669 1 0.4690 0.905 0.900 0.100
#> SRR191670 1 0.4690 0.905 0.900 0.100
#> SRR191671 1 0.4690 0.905 0.900 0.100
#> SRR191672 1 0.4690 0.905 0.900 0.100
#> SRR191673 1 0.4690 0.905 0.900 0.100
#> SRR191674 2 0.0000 0.986 0.000 1.000
#> SRR191675 2 0.0000 0.986 0.000 1.000
#> SRR191677 2 0.0000 0.986 0.000 1.000
#> SRR191678 2 0.0000 0.986 0.000 1.000
#> SRR191679 2 0.0000 0.986 0.000 1.000
#> SRR191680 2 0.0000 0.986 0.000 1.000
#> SRR191681 2 0.0000 0.986 0.000 1.000
#> SRR191682 2 0.0000 0.986 0.000 1.000
#> SRR191683 2 0.0000 0.986 0.000 1.000
#> SRR191684 2 0.0000 0.986 0.000 1.000
#> SRR191685 2 0.0000 0.986 0.000 1.000
#> SRR191686 2 0.0000 0.986 0.000 1.000
#> SRR191687 2 0.0000 0.986 0.000 1.000
#> SRR191688 2 0.0000 0.986 0.000 1.000
#> SRR191689 2 0.0000 0.986 0.000 1.000
#> SRR191690 2 0.0000 0.986 0.000 1.000
#> SRR191691 2 0.0000 0.986 0.000 1.000
#> SRR191692 2 0.0000 0.986 0.000 1.000
#> SRR191693 2 0.0000 0.986 0.000 1.000
#> SRR191694 2 0.0000 0.986 0.000 1.000
#> SRR191695 2 0.0000 0.986 0.000 1.000
#> SRR191696 2 0.0000 0.986 0.000 1.000
#> SRR191697 2 0.0000 0.986 0.000 1.000
#> SRR191698 2 0.0000 0.986 0.000 1.000
#> SRR191699 2 0.0000 0.986 0.000 1.000
#> SRR191700 2 0.0000 0.986 0.000 1.000
#> SRR191701 2 0.0000 0.986 0.000 1.000
#> SRR191702 2 0.0000 0.986 0.000 1.000
#> SRR191703 2 0.0000 0.986 0.000 1.000
#> SRR191704 2 0.0000 0.986 0.000 1.000
#> SRR191705 2 0.0000 0.986 0.000 1.000
#> SRR191706 2 0.0000 0.986 0.000 1.000
#> SRR191707 2 0.0000 0.986 0.000 1.000
#> SRR191708 2 0.0000 0.986 0.000 1.000
#> SRR191709 2 0.0000 0.986 0.000 1.000
#> SRR191710 2 0.0000 0.986 0.000 1.000
#> SRR191711 2 0.0000 0.986 0.000 1.000
#> SRR191712 2 0.0000 0.986 0.000 1.000
#> SRR191713 2 0.0000 0.986 0.000 1.000
#> SRR191714 2 0.0000 0.986 0.000 1.000
#> SRR191715 2 0.0000 0.986 0.000 1.000
#> SRR191716 2 0.0000 0.986 0.000 1.000
#> SRR191717 2 0.0000 0.986 0.000 1.000
#> SRR191718 2 0.0000 0.986 0.000 1.000
#> SRR537099 1 0.9427 0.668 0.640 0.360
#> SRR537100 1 0.7528 0.832 0.784 0.216
#> SRR537101 1 0.4690 0.905 0.900 0.100
#> SRR537102 1 0.9491 0.654 0.632 0.368
#> SRR537104 1 0.9963 0.433 0.536 0.464
#> SRR537105 1 0.7883 0.814 0.764 0.236
#> SRR537106 1 0.9491 0.654 0.632 0.368
#> SRR537107 1 0.9491 0.654 0.632 0.368
#> SRR537108 1 0.9491 0.654 0.632 0.368
#> SRR537109 2 0.0000 0.986 0.000 1.000
#> SRR537110 2 0.0000 0.986 0.000 1.000
#> SRR537111 1 0.9209 0.703 0.664 0.336
#> SRR537113 2 0.8608 0.471 0.284 0.716
#> SRR537114 2 0.8608 0.471 0.284 0.716
#> SRR537115 2 0.0000 0.986 0.000 1.000
#> SRR537116 2 0.0000 0.986 0.000 1.000
#> SRR537117 2 0.0000 0.986 0.000 1.000
#> SRR537118 2 0.0672 0.980 0.008 0.992
#> SRR537119 2 0.0672 0.980 0.008 0.992
#> SRR537120 2 0.0672 0.980 0.008 0.992
#> SRR537121 2 0.0672 0.980 0.008 0.992
#> SRR537122 2 0.0672 0.980 0.008 0.992
#> SRR537123 2 0.0672 0.980 0.008 0.992
#> SRR537124 2 0.0672 0.980 0.008 0.992
#> SRR537125 2 0.0672 0.980 0.008 0.992
#> SRR537126 2 0.0672 0.980 0.008 0.992
#> SRR537127 1 0.0000 0.840 1.000 0.000
#> SRR537128 1 0.0000 0.840 1.000 0.000
#> SRR537129 1 0.0000 0.840 1.000 0.000
#> SRR537130 1 0.0000 0.840 1.000 0.000
#> SRR537131 1 0.0000 0.840 1.000 0.000
#> SRR537132 1 0.0000 0.840 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.1753 0.709 0.952 0.000 0.048
#> SRR191640 1 0.6180 0.631 0.584 0.000 0.416
#> SRR191641 1 0.6062 0.650 0.616 0.000 0.384
#> SRR191642 1 0.7430 0.591 0.540 0.036 0.424
#> SRR191643 1 0.8039 0.550 0.508 0.064 0.428
#> SRR191644 1 0.7801 0.568 0.520 0.052 0.428
#> SRR191645 1 0.6111 0.642 0.604 0.000 0.396
#> SRR191646 1 0.6111 0.642 0.604 0.000 0.396
#> SRR191647 1 0.6140 0.639 0.596 0.000 0.404
#> SRR191648 1 0.6140 0.639 0.596 0.000 0.404
#> SRR191649 1 0.6140 0.639 0.596 0.000 0.404
#> SRR191650 1 0.5905 0.647 0.648 0.000 0.352
#> SRR191651 1 0.0592 0.710 0.988 0.000 0.012
#> SRR191652 1 0.2711 0.709 0.912 0.000 0.088
#> SRR191653 1 0.7755 0.548 0.492 0.048 0.460
#> SRR191654 1 0.8141 0.512 0.472 0.068 0.460
#> SRR191655 1 0.7337 0.593 0.540 0.032 0.428
#> SRR191656 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191660 1 0.0237 0.709 0.996 0.000 0.004
#> SRR191661 1 0.0592 0.709 0.988 0.000 0.012
#> SRR191662 1 0.0424 0.709 0.992 0.000 0.008
#> SRR191663 1 0.0592 0.709 0.988 0.000 0.012
#> SRR191664 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191665 1 0.0237 0.709 0.996 0.000 0.004
#> SRR191666 1 0.1964 0.708 0.944 0.000 0.056
#> SRR191667 1 0.1964 0.708 0.944 0.000 0.056
#> SRR191668 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.708 1.000 0.000 0.000
#> SRR191674 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191675 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191677 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191678 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191679 2 0.3879 0.730 0.000 0.848 0.152
#> SRR191680 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191681 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191682 2 0.3267 0.780 0.000 0.884 0.116
#> SRR191683 2 0.3267 0.780 0.000 0.884 0.116
#> SRR191684 2 0.3340 0.780 0.000 0.880 0.120
#> SRR191685 2 0.3340 0.780 0.000 0.880 0.120
#> SRR191686 2 0.3192 0.779 0.000 0.888 0.112
#> SRR191687 2 0.3340 0.780 0.000 0.880 0.120
#> SRR191688 2 0.0592 0.812 0.000 0.988 0.012
#> SRR191689 2 0.1964 0.799 0.000 0.944 0.056
#> SRR191690 2 0.0747 0.811 0.000 0.984 0.016
#> SRR191691 2 0.1860 0.806 0.000 0.948 0.052
#> SRR191692 2 0.3941 0.726 0.000 0.844 0.156
#> SRR191693 2 0.4291 0.705 0.000 0.820 0.180
#> SRR191694 2 0.3340 0.761 0.000 0.880 0.120
#> SRR191695 2 0.0424 0.813 0.000 0.992 0.008
#> SRR191696 2 0.0424 0.813 0.000 0.992 0.008
#> SRR191697 2 0.1163 0.811 0.000 0.972 0.028
#> SRR191698 2 0.1860 0.806 0.000 0.948 0.052
#> SRR191699 2 0.1411 0.810 0.000 0.964 0.036
#> SRR191700 2 0.3941 0.663 0.000 0.844 0.156
#> SRR191701 2 0.1753 0.807 0.000 0.952 0.048
#> SRR191702 2 0.1643 0.803 0.000 0.956 0.044
#> SRR191703 2 0.1643 0.803 0.000 0.956 0.044
#> SRR191704 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191705 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191706 2 0.1753 0.804 0.000 0.952 0.048
#> SRR191707 2 0.1529 0.808 0.000 0.960 0.040
#> SRR191708 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191709 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191710 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191711 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191712 2 0.1860 0.804 0.000 0.948 0.052
#> SRR191713 2 0.1964 0.803 0.000 0.944 0.056
#> SRR191714 2 0.1964 0.803 0.000 0.944 0.056
#> SRR191715 2 0.1529 0.804 0.000 0.960 0.040
#> SRR191716 2 0.0747 0.811 0.000 0.984 0.016
#> SRR191717 2 0.0747 0.811 0.000 0.984 0.016
#> SRR191718 2 0.0237 0.813 0.000 0.996 0.004
#> SRR537099 1 0.8119 0.538 0.500 0.068 0.432
#> SRR537100 1 0.7446 0.586 0.532 0.036 0.432
#> SRR537101 1 0.6062 0.650 0.616 0.000 0.384
#> SRR537102 1 0.8119 0.538 0.500 0.068 0.432
#> SRR537104 3 0.9515 -0.250 0.388 0.188 0.424
#> SRR537105 1 0.7735 0.561 0.512 0.048 0.440
#> SRR537106 1 0.8450 0.491 0.484 0.088 0.428
#> SRR537107 1 0.8450 0.491 0.484 0.088 0.428
#> SRR537108 1 0.8450 0.491 0.484 0.088 0.428
#> SRR537109 2 0.1860 0.790 0.000 0.948 0.052
#> SRR537110 2 0.3619 0.688 0.000 0.864 0.136
#> SRR537111 1 0.7552 0.594 0.596 0.052 0.352
#> SRR537113 3 0.9192 0.542 0.176 0.308 0.516
#> SRR537114 3 0.9151 0.520 0.180 0.292 0.528
#> SRR537115 3 0.6664 0.514 0.008 0.464 0.528
#> SRR537116 2 0.1163 0.810 0.000 0.972 0.028
#> SRR537117 2 0.6204 -0.177 0.000 0.576 0.424
#> SRR537118 2 0.6302 -0.405 0.000 0.520 0.480
#> SRR537119 2 0.6302 -0.405 0.000 0.520 0.480
#> SRR537120 2 0.6280 -0.319 0.000 0.540 0.460
#> SRR537121 3 0.6286 0.529 0.000 0.464 0.536
#> SRR537122 3 0.6286 0.529 0.000 0.464 0.536
#> SRR537123 3 0.6286 0.529 0.000 0.464 0.536
#> SRR537124 2 0.6291 -0.354 0.000 0.532 0.468
#> SRR537125 3 0.6309 0.402 0.000 0.500 0.500
#> SRR537126 2 0.6309 -0.486 0.000 0.500 0.500
#> SRR537127 1 0.6235 0.491 0.564 0.000 0.436
#> SRR537128 1 0.6235 0.491 0.564 0.000 0.436
#> SRR537129 1 0.6235 0.491 0.564 0.000 0.436
#> SRR537130 1 0.6235 0.491 0.564 0.000 0.436
#> SRR537131 1 0.6235 0.491 0.564 0.000 0.436
#> SRR537132 1 0.6235 0.491 0.564 0.000 0.436
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.1576 0.9140 0.948 0.000 0.004 0.048
#> SRR191640 4 0.4776 0.5235 0.376 0.000 0.000 0.624
#> SRR191641 4 0.4830 0.5033 0.392 0.000 0.000 0.608
#> SRR191642 4 0.5175 0.5509 0.328 0.012 0.004 0.656
#> SRR191643 4 0.5134 0.5525 0.320 0.012 0.004 0.664
#> SRR191644 4 0.5154 0.5513 0.324 0.012 0.004 0.660
#> SRR191645 4 0.4830 0.5033 0.392 0.000 0.000 0.608
#> SRR191646 4 0.4830 0.5033 0.392 0.000 0.000 0.608
#> SRR191647 4 0.4804 0.5164 0.384 0.000 0.000 0.616
#> SRR191648 4 0.4804 0.5164 0.384 0.000 0.000 0.616
#> SRR191649 4 0.4804 0.5164 0.384 0.000 0.000 0.616
#> SRR191650 4 0.4985 0.3748 0.468 0.000 0.000 0.532
#> SRR191651 1 0.1743 0.9038 0.940 0.000 0.004 0.056
#> SRR191652 1 0.2345 0.8314 0.900 0.000 0.000 0.100
#> SRR191653 4 0.4923 0.5511 0.304 0.008 0.004 0.684
#> SRR191654 4 0.4923 0.5511 0.304 0.008 0.004 0.684
#> SRR191655 4 0.5134 0.5516 0.320 0.012 0.004 0.664
#> SRR191656 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191657 1 0.0592 0.9452 0.984 0.000 0.016 0.000
#> SRR191658 1 0.0592 0.9452 0.984 0.000 0.016 0.000
#> SRR191659 1 0.0592 0.9452 0.984 0.000 0.016 0.000
#> SRR191660 1 0.0927 0.9454 0.976 0.000 0.016 0.008
#> SRR191661 1 0.1182 0.9410 0.968 0.000 0.016 0.016
#> SRR191662 1 0.0779 0.9456 0.980 0.000 0.016 0.004
#> SRR191663 1 0.1059 0.9434 0.972 0.000 0.016 0.012
#> SRR191664 1 0.0592 0.9452 0.984 0.000 0.016 0.000
#> SRR191665 1 0.0779 0.9458 0.980 0.000 0.004 0.016
#> SRR191666 1 0.2542 0.8337 0.904 0.000 0.012 0.084
#> SRR191667 1 0.2542 0.8337 0.904 0.000 0.012 0.084
#> SRR191668 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191669 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191670 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191671 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191672 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191673 1 0.0524 0.9486 0.988 0.000 0.004 0.008
#> SRR191674 2 0.6792 0.6715 0.000 0.588 0.272 0.140
#> SRR191675 2 0.6792 0.6715 0.000 0.588 0.272 0.140
#> SRR191677 2 0.6833 0.6692 0.000 0.584 0.272 0.144
#> SRR191678 2 0.6950 0.6618 0.000 0.572 0.272 0.156
#> SRR191679 2 0.6770 0.6751 0.000 0.592 0.268 0.140
#> SRR191680 2 0.6792 0.6715 0.000 0.588 0.272 0.140
#> SRR191681 2 0.6833 0.6692 0.000 0.584 0.272 0.144
#> SRR191682 2 0.5731 0.7776 0.000 0.712 0.172 0.116
#> SRR191683 2 0.5731 0.7776 0.000 0.712 0.172 0.116
#> SRR191684 2 0.5690 0.7805 0.000 0.716 0.168 0.116
#> SRR191685 2 0.5783 0.7771 0.000 0.708 0.172 0.120
#> SRR191686 2 0.5731 0.7776 0.000 0.712 0.172 0.116
#> SRR191687 2 0.5783 0.7771 0.000 0.708 0.172 0.120
#> SRR191688 2 0.1936 0.8316 0.000 0.940 0.028 0.032
#> SRR191689 2 0.3716 0.8205 0.000 0.852 0.096 0.052
#> SRR191690 2 0.1913 0.8319 0.000 0.940 0.020 0.040
#> SRR191691 2 0.4424 0.8088 0.000 0.812 0.088 0.100
#> SRR191692 2 0.6833 0.6692 0.000 0.584 0.272 0.144
#> SRR191693 2 0.7254 0.6405 0.000 0.524 0.300 0.176
#> SRR191694 2 0.6488 0.7082 0.000 0.628 0.244 0.128
#> SRR191695 2 0.2131 0.8312 0.000 0.932 0.032 0.036
#> SRR191696 2 0.2131 0.8312 0.000 0.932 0.032 0.036
#> SRR191697 2 0.3903 0.8234 0.000 0.844 0.080 0.076
#> SRR191698 2 0.4535 0.8065 0.000 0.804 0.084 0.112
#> SRR191699 2 0.3903 0.8168 0.000 0.844 0.080 0.076
#> SRR191700 2 0.5728 0.7233 0.000 0.708 0.104 0.188
#> SRR191701 2 0.4297 0.8109 0.000 0.820 0.084 0.096
#> SRR191702 2 0.2179 0.8240 0.000 0.924 0.064 0.012
#> SRR191703 2 0.2179 0.8240 0.000 0.924 0.064 0.012
#> SRR191704 2 0.2376 0.8230 0.000 0.916 0.068 0.016
#> SRR191705 2 0.2376 0.8230 0.000 0.916 0.068 0.016
#> SRR191706 2 0.2255 0.8248 0.000 0.920 0.068 0.012
#> SRR191707 2 0.2623 0.8234 0.000 0.908 0.064 0.028
#> SRR191708 2 0.2376 0.8230 0.000 0.916 0.068 0.016
#> SRR191709 2 0.2376 0.8230 0.000 0.916 0.068 0.016
#> SRR191710 2 0.2376 0.8230 0.000 0.916 0.068 0.016
#> SRR191711 2 0.1610 0.8295 0.000 0.952 0.032 0.016
#> SRR191712 2 0.1610 0.8295 0.000 0.952 0.032 0.016
#> SRR191713 2 0.2699 0.8202 0.000 0.904 0.068 0.028
#> SRR191714 2 0.2699 0.8202 0.000 0.904 0.068 0.028
#> SRR191715 2 0.1798 0.8286 0.000 0.944 0.040 0.016
#> SRR191716 2 0.2224 0.8305 0.000 0.928 0.032 0.040
#> SRR191717 2 0.1929 0.8319 0.000 0.940 0.024 0.036
#> SRR191718 2 0.2131 0.8312 0.000 0.932 0.032 0.036
#> SRR537099 4 0.5045 0.5515 0.304 0.012 0.004 0.680
#> SRR537100 4 0.5068 0.5522 0.308 0.012 0.004 0.676
#> SRR537101 4 0.4830 0.5033 0.392 0.000 0.000 0.608
#> SRR537102 4 0.5239 0.5530 0.300 0.020 0.004 0.676
#> SRR537104 4 0.5968 0.5125 0.240 0.056 0.016 0.688
#> SRR537105 4 0.5405 0.5515 0.312 0.024 0.004 0.660
#> SRR537106 4 0.5799 0.5443 0.292 0.048 0.004 0.656
#> SRR537107 4 0.5799 0.5443 0.292 0.048 0.004 0.656
#> SRR537108 4 0.5799 0.5443 0.292 0.048 0.004 0.656
#> SRR537109 2 0.2773 0.8129 0.000 0.900 0.028 0.072
#> SRR537110 2 0.4719 0.6999 0.000 0.772 0.048 0.180
#> SRR537111 4 0.5687 0.3812 0.456 0.012 0.008 0.524
#> SRR537113 4 0.6524 0.3853 0.092 0.116 0.076 0.716
#> SRR537114 4 0.6417 0.3885 0.092 0.108 0.076 0.724
#> SRR537115 4 0.5575 0.3183 0.004 0.156 0.104 0.736
#> SRR537116 2 0.1929 0.8290 0.000 0.940 0.036 0.024
#> SRR537117 4 0.7479 -0.0373 0.000 0.300 0.208 0.492
#> SRR537118 4 0.7301 0.1424 0.000 0.236 0.228 0.536
#> SRR537119 4 0.7301 0.1424 0.000 0.236 0.228 0.536
#> SRR537120 4 0.7369 0.1156 0.000 0.248 0.228 0.524
#> SRR537121 4 0.7058 0.2197 0.000 0.200 0.228 0.572
#> SRR537122 4 0.7058 0.2197 0.000 0.200 0.228 0.572
#> SRR537123 4 0.7058 0.2197 0.000 0.200 0.228 0.572
#> SRR537124 4 0.7369 0.1156 0.000 0.248 0.228 0.524
#> SRR537125 4 0.7301 0.1424 0.000 0.236 0.228 0.536
#> SRR537126 4 0.7301 0.1424 0.000 0.236 0.228 0.536
#> SRR537127 3 0.7345 0.9945 0.336 0.000 0.492 0.172
#> SRR537128 3 0.7250 0.9955 0.336 0.000 0.504 0.160
#> SRR537129 3 0.7345 0.9945 0.336 0.000 0.492 0.172
#> SRR537130 3 0.7314 0.9953 0.336 0.000 0.496 0.168
#> SRR537131 3 0.7250 0.9955 0.336 0.000 0.504 0.160
#> SRR537132 3 0.7250 0.9955 0.336 0.000 0.504 0.160
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.4737 0.847 0.600 0.000 0.004 0.380 0.016
#> SRR191640 4 0.0880 0.862 0.032 0.000 0.000 0.968 0.000
#> SRR191641 4 0.1341 0.844 0.056 0.000 0.000 0.944 0.000
#> SRR191642 4 0.0727 0.880 0.004 0.012 0.000 0.980 0.004
#> SRR191643 4 0.0566 0.880 0.000 0.012 0.000 0.984 0.004
#> SRR191644 4 0.0960 0.879 0.004 0.016 0.000 0.972 0.008
#> SRR191645 4 0.1410 0.850 0.060 0.000 0.000 0.940 0.000
#> SRR191646 4 0.1410 0.850 0.060 0.000 0.000 0.940 0.000
#> SRR191647 4 0.1197 0.852 0.048 0.000 0.000 0.952 0.000
#> SRR191648 4 0.1197 0.852 0.048 0.000 0.000 0.952 0.000
#> SRR191649 4 0.1270 0.850 0.052 0.000 0.000 0.948 0.000
#> SRR191650 4 0.2463 0.789 0.100 0.000 0.004 0.888 0.008
#> SRR191651 1 0.4726 0.894 0.644 0.000 0.004 0.328 0.024
#> SRR191652 1 0.4101 0.914 0.664 0.000 0.000 0.332 0.004
#> SRR191653 4 0.1679 0.872 0.012 0.016 0.004 0.948 0.020
#> SRR191654 4 0.1679 0.872 0.012 0.016 0.004 0.948 0.020
#> SRR191655 4 0.0671 0.880 0.000 0.016 0.000 0.980 0.004
#> SRR191656 1 0.4410 0.934 0.700 0.000 0.008 0.276 0.016
#> SRR191657 1 0.4229 0.927 0.704 0.000 0.000 0.276 0.020
#> SRR191658 1 0.4229 0.927 0.704 0.000 0.000 0.276 0.020
#> SRR191659 1 0.4229 0.927 0.704 0.000 0.000 0.276 0.020
#> SRR191660 1 0.4275 0.927 0.696 0.000 0.000 0.284 0.020
#> SRR191661 1 0.4437 0.913 0.664 0.000 0.000 0.316 0.020
#> SRR191662 1 0.4400 0.918 0.672 0.000 0.000 0.308 0.020
#> SRR191663 1 0.4419 0.916 0.668 0.000 0.000 0.312 0.020
#> SRR191664 1 0.4229 0.927 0.704 0.000 0.000 0.276 0.020
#> SRR191665 1 0.4455 0.933 0.692 0.000 0.008 0.284 0.016
#> SRR191666 1 0.4260 0.921 0.680 0.000 0.004 0.308 0.008
#> SRR191667 1 0.4260 0.921 0.680 0.000 0.004 0.308 0.008
#> SRR191668 1 0.4387 0.933 0.704 0.000 0.008 0.272 0.016
#> SRR191669 1 0.4387 0.933 0.704 0.000 0.008 0.272 0.016
#> SRR191670 1 0.4387 0.933 0.704 0.000 0.008 0.272 0.016
#> SRR191671 1 0.4387 0.933 0.704 0.000 0.008 0.272 0.016
#> SRR191672 1 0.4478 0.932 0.700 0.000 0.008 0.272 0.020
#> SRR191673 1 0.4478 0.932 0.700 0.000 0.008 0.272 0.020
#> SRR191674 5 0.7608 0.420 0.128 0.348 0.100 0.000 0.424
#> SRR191675 5 0.7608 0.420 0.128 0.348 0.100 0.000 0.424
#> SRR191677 5 0.7603 0.425 0.128 0.344 0.100 0.000 0.428
#> SRR191678 5 0.7585 0.427 0.128 0.332 0.100 0.000 0.440
#> SRR191679 5 0.7642 0.408 0.132 0.352 0.100 0.000 0.416
#> SRR191680 5 0.7608 0.420 0.128 0.348 0.100 0.000 0.424
#> SRR191681 5 0.7603 0.425 0.128 0.344 0.100 0.000 0.428
#> SRR191682 2 0.7988 0.419 0.112 0.468 0.176 0.008 0.236
#> SRR191683 2 0.7988 0.419 0.112 0.468 0.176 0.008 0.236
#> SRR191684 2 0.8012 0.417 0.112 0.464 0.180 0.008 0.236
#> SRR191685 2 0.8012 0.417 0.112 0.464 0.180 0.008 0.236
#> SRR191686 2 0.7897 0.413 0.112 0.468 0.176 0.004 0.240
#> SRR191687 2 0.8012 0.417 0.112 0.464 0.180 0.008 0.236
#> SRR191688 2 0.2806 0.727 0.012 0.900 0.016 0.028 0.044
#> SRR191689 2 0.6178 0.580 0.080 0.676 0.100 0.004 0.140
#> SRR191690 2 0.2891 0.731 0.012 0.896 0.016 0.032 0.044
#> SRR191691 2 0.6224 0.627 0.040 0.664 0.112 0.012 0.172
#> SRR191692 5 0.7603 0.425 0.128 0.344 0.100 0.000 0.428
#> SRR191693 5 0.7929 0.377 0.156 0.280 0.132 0.000 0.432
#> SRR191694 2 0.7874 -0.287 0.148 0.388 0.116 0.000 0.348
#> SRR191695 2 0.3030 0.723 0.012 0.888 0.020 0.024 0.056
#> SRR191696 2 0.3030 0.723 0.012 0.888 0.020 0.024 0.056
#> SRR191697 2 0.5832 0.650 0.048 0.716 0.084 0.020 0.132
#> SRR191698 2 0.6242 0.620 0.036 0.660 0.100 0.016 0.188
#> SRR191699 2 0.5650 0.680 0.056 0.724 0.100 0.008 0.112
#> SRR191700 2 0.6770 0.572 0.036 0.616 0.100 0.032 0.216
#> SRR191701 2 0.5945 0.645 0.036 0.688 0.100 0.012 0.164
#> SRR191702 2 0.3195 0.735 0.040 0.880 0.052 0.012 0.016
#> SRR191703 2 0.3195 0.735 0.040 0.880 0.052 0.012 0.016
#> SRR191704 2 0.3772 0.738 0.040 0.848 0.076 0.012 0.024
#> SRR191705 2 0.3772 0.738 0.040 0.848 0.076 0.012 0.024
#> SRR191706 2 0.3344 0.734 0.048 0.872 0.052 0.012 0.016
#> SRR191707 2 0.3283 0.746 0.012 0.876 0.056 0.020 0.036
#> SRR191708 2 0.3772 0.738 0.040 0.848 0.076 0.012 0.024
#> SRR191709 2 0.3772 0.738 0.040 0.848 0.076 0.012 0.024
#> SRR191710 2 0.3772 0.738 0.040 0.848 0.076 0.012 0.024
#> SRR191711 2 0.1772 0.751 0.016 0.944 0.024 0.012 0.004
#> SRR191712 2 0.1772 0.751 0.016 0.944 0.024 0.012 0.004
#> SRR191713 2 0.3550 0.744 0.032 0.860 0.068 0.008 0.032
#> SRR191714 2 0.3550 0.744 0.032 0.860 0.068 0.008 0.032
#> SRR191715 2 0.2307 0.735 0.016 0.924 0.024 0.012 0.024
#> SRR191716 2 0.2952 0.725 0.012 0.892 0.016 0.028 0.052
#> SRR191717 2 0.2891 0.728 0.012 0.896 0.016 0.032 0.044
#> SRR191718 2 0.2933 0.724 0.012 0.892 0.016 0.024 0.056
#> SRR537099 4 0.0833 0.880 0.000 0.016 0.004 0.976 0.004
#> SRR537100 4 0.0833 0.880 0.000 0.016 0.004 0.976 0.004
#> SRR537101 4 0.1341 0.844 0.056 0.000 0.000 0.944 0.000
#> SRR537102 4 0.1243 0.876 0.000 0.028 0.004 0.960 0.008
#> SRR537104 4 0.2347 0.852 0.016 0.040 0.012 0.920 0.012
#> SRR537105 4 0.2029 0.865 0.008 0.028 0.004 0.932 0.028
#> SRR537106 4 0.2029 0.865 0.008 0.028 0.004 0.932 0.028
#> SRR537107 4 0.2029 0.865 0.008 0.028 0.004 0.932 0.028
#> SRR537108 4 0.2029 0.865 0.008 0.028 0.004 0.932 0.028
#> SRR537109 2 0.2952 0.725 0.012 0.892 0.016 0.052 0.028
#> SRR537110 2 0.4658 0.659 0.008 0.784 0.044 0.128 0.036
#> SRR537111 4 0.3400 0.775 0.104 0.012 0.004 0.852 0.028
#> SRR537113 4 0.5112 0.564 0.008 0.048 0.004 0.676 0.264
#> SRR537114 4 0.5135 0.557 0.008 0.048 0.004 0.672 0.268
#> SRR537115 4 0.5955 0.225 0.012 0.068 0.004 0.540 0.376
#> SRR537116 2 0.2312 0.737 0.012 0.924 0.020 0.024 0.020
#> SRR537117 5 0.4197 0.606 0.000 0.076 0.000 0.148 0.776
#> SRR537118 5 0.4396 0.598 0.000 0.056 0.012 0.160 0.772
#> SRR537119 5 0.4396 0.598 0.000 0.056 0.012 0.160 0.772
#> SRR537120 5 0.4214 0.605 0.000 0.064 0.004 0.152 0.780
#> SRR537121 5 0.4561 0.583 0.004 0.044 0.016 0.172 0.764
#> SRR537122 5 0.4561 0.583 0.004 0.044 0.016 0.172 0.764
#> SRR537123 5 0.4561 0.583 0.004 0.044 0.016 0.172 0.764
#> SRR537124 5 0.4173 0.604 0.000 0.064 0.004 0.148 0.784
#> SRR537125 5 0.4396 0.598 0.000 0.056 0.012 0.160 0.772
#> SRR537126 5 0.4396 0.598 0.000 0.056 0.012 0.160 0.772
#> SRR537127 3 0.5346 0.995 0.212 0.000 0.688 0.084 0.016
#> SRR537128 3 0.5550 0.995 0.216 0.000 0.676 0.084 0.024
#> SRR537129 3 0.5346 0.995 0.212 0.000 0.688 0.084 0.016
#> SRR537130 3 0.5375 0.994 0.216 0.000 0.684 0.084 0.016
#> SRR537131 3 0.5578 0.994 0.220 0.000 0.672 0.084 0.024
#> SRR537132 3 0.5550 0.995 0.216 0.000 0.676 0.084 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.3240 0.7786 0.752 0.000 0.004 0.244 0.000 0.000
#> SRR191640 4 0.0713 0.8707 0.028 0.000 0.000 0.972 0.000 0.000
#> SRR191641 4 0.0790 0.8687 0.032 0.000 0.000 0.968 0.000 0.000
#> SRR191642 4 0.0291 0.8773 0.004 0.004 0.000 0.992 0.000 0.000
#> SRR191643 4 0.0436 0.8774 0.004 0.000 0.004 0.988 0.004 0.000
#> SRR191644 4 0.0291 0.8772 0.004 0.000 0.004 0.992 0.000 0.000
#> SRR191645 4 0.2613 0.8574 0.048 0.000 0.012 0.892 0.008 0.040
#> SRR191646 4 0.2613 0.8574 0.048 0.000 0.012 0.892 0.008 0.040
#> SRR191647 4 0.2547 0.8597 0.044 0.000 0.012 0.896 0.008 0.040
#> SRR191648 4 0.2547 0.8597 0.044 0.000 0.012 0.896 0.008 0.040
#> SRR191649 4 0.2547 0.8597 0.044 0.000 0.012 0.896 0.008 0.040
#> SRR191650 4 0.3144 0.7365 0.172 0.000 0.016 0.808 0.004 0.000
#> SRR191651 1 0.2946 0.8495 0.812 0.000 0.012 0.176 0.000 0.000
#> SRR191652 1 0.4189 0.8396 0.744 0.000 0.012 0.204 0.012 0.028
#> SRR191653 4 0.0405 0.8759 0.000 0.000 0.004 0.988 0.008 0.000
#> SRR191654 4 0.0405 0.8759 0.000 0.000 0.004 0.988 0.008 0.000
#> SRR191655 4 0.0291 0.8769 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR191656 1 0.2445 0.8822 0.868 0.000 0.004 0.120 0.008 0.000
#> SRR191657 1 0.4408 0.8617 0.780 0.000 0.016 0.100 0.032 0.072
#> SRR191658 1 0.4408 0.8617 0.780 0.000 0.016 0.100 0.032 0.072
#> SRR191659 1 0.4408 0.8617 0.780 0.000 0.016 0.100 0.032 0.072
#> SRR191660 1 0.4541 0.8627 0.768 0.000 0.016 0.112 0.032 0.072
#> SRR191661 1 0.5012 0.8462 0.724 0.000 0.020 0.152 0.032 0.072
#> SRR191662 1 0.4904 0.8532 0.736 0.000 0.020 0.140 0.032 0.072
#> SRR191663 1 0.4929 0.8490 0.728 0.000 0.016 0.152 0.032 0.072
#> SRR191664 1 0.4408 0.8617 0.780 0.000 0.016 0.100 0.032 0.072
#> SRR191665 1 0.2531 0.8814 0.860 0.000 0.004 0.128 0.008 0.000
#> SRR191666 1 0.4265 0.8500 0.756 0.000 0.008 0.168 0.012 0.056
#> SRR191667 1 0.4265 0.8500 0.756 0.000 0.008 0.168 0.012 0.056
#> SRR191668 1 0.2587 0.8826 0.864 0.000 0.004 0.120 0.008 0.004
#> SRR191669 1 0.2587 0.8826 0.864 0.000 0.004 0.120 0.008 0.004
#> SRR191670 1 0.2698 0.8829 0.860 0.000 0.008 0.120 0.008 0.004
#> SRR191671 1 0.2698 0.8829 0.860 0.000 0.008 0.120 0.008 0.004
#> SRR191672 1 0.2445 0.8828 0.868 0.000 0.000 0.120 0.008 0.004
#> SRR191673 1 0.2445 0.8828 0.868 0.000 0.000 0.120 0.008 0.004
#> SRR191674 5 0.7894 0.1627 0.020 0.260 0.132 0.000 0.312 0.276
#> SRR191675 5 0.7894 0.1627 0.020 0.260 0.132 0.000 0.312 0.276
#> SRR191677 5 0.7890 0.1649 0.020 0.256 0.132 0.000 0.316 0.276
#> SRR191678 5 0.7883 0.1613 0.020 0.248 0.132 0.000 0.320 0.280
#> SRR191679 5 0.7934 0.1621 0.024 0.260 0.128 0.000 0.312 0.276
#> SRR191680 5 0.7934 0.1621 0.024 0.260 0.128 0.000 0.312 0.276
#> SRR191681 5 0.7890 0.1649 0.020 0.256 0.132 0.000 0.316 0.276
#> SRR191682 6 0.5354 0.7081 0.000 0.260 0.000 0.000 0.160 0.580
#> SRR191683 6 0.5354 0.7081 0.000 0.260 0.000 0.000 0.160 0.580
#> SRR191684 6 0.5336 0.7067 0.000 0.256 0.000 0.000 0.160 0.584
#> SRR191685 6 0.5336 0.7067 0.000 0.256 0.000 0.000 0.160 0.584
#> SRR191686 6 0.5354 0.7081 0.000 0.260 0.000 0.000 0.160 0.580
#> SRR191687 6 0.5336 0.7067 0.000 0.256 0.000 0.000 0.160 0.584
#> SRR191688 2 0.3576 0.5889 0.008 0.840 0.016 0.008 0.056 0.072
#> SRR191689 2 0.5635 0.0586 0.004 0.560 0.020 0.000 0.092 0.324
#> SRR191690 2 0.3626 0.5924 0.008 0.840 0.012 0.020 0.044 0.076
#> SRR191691 2 0.6304 0.0782 0.020 0.488 0.016 0.004 0.112 0.360
#> SRR191692 5 0.7883 0.1582 0.020 0.248 0.132 0.000 0.320 0.280
#> SRR191693 6 0.7612 -0.1409 0.016 0.184 0.120 0.000 0.316 0.364
#> SRR191694 6 0.7799 -0.1992 0.016 0.272 0.124 0.000 0.272 0.316
#> SRR191695 2 0.3893 0.5767 0.012 0.820 0.016 0.008 0.060 0.084
#> SRR191696 2 0.3893 0.5767 0.012 0.820 0.016 0.008 0.060 0.084
#> SRR191697 2 0.6449 0.1824 0.024 0.540 0.020 0.008 0.124 0.284
#> SRR191698 2 0.6582 0.1140 0.024 0.484 0.016 0.008 0.132 0.336
#> SRR191699 2 0.5509 0.1844 0.004 0.556 0.016 0.004 0.068 0.352
#> SRR191700 2 0.6840 0.0973 0.024 0.468 0.016 0.016 0.148 0.328
#> SRR191701 2 0.6454 0.1173 0.024 0.492 0.016 0.004 0.128 0.336
#> SRR191702 2 0.4756 0.6095 0.032 0.752 0.072 0.004 0.012 0.128
#> SRR191703 2 0.4756 0.6095 0.032 0.752 0.072 0.004 0.012 0.128
#> SRR191704 2 0.5196 0.5966 0.032 0.704 0.076 0.004 0.012 0.172
#> SRR191705 2 0.5196 0.5966 0.032 0.704 0.076 0.004 0.012 0.172
#> SRR191706 2 0.4944 0.6027 0.032 0.732 0.072 0.004 0.012 0.148
#> SRR191707 2 0.3894 0.6269 0.016 0.796 0.032 0.004 0.008 0.144
#> SRR191708 2 0.5146 0.6001 0.032 0.708 0.072 0.004 0.012 0.172
#> SRR191709 2 0.5164 0.5992 0.032 0.708 0.076 0.004 0.012 0.168
#> SRR191710 2 0.5114 0.6012 0.032 0.712 0.072 0.004 0.012 0.168
#> SRR191711 2 0.2878 0.6450 0.012 0.876 0.036 0.004 0.004 0.068
#> SRR191712 2 0.2820 0.6463 0.012 0.880 0.036 0.004 0.004 0.064
#> SRR191713 2 0.4935 0.5911 0.028 0.712 0.072 0.004 0.004 0.180
#> SRR191714 2 0.4935 0.5911 0.028 0.712 0.072 0.004 0.004 0.180
#> SRR191715 2 0.1918 0.6368 0.004 0.932 0.020 0.004 0.016 0.024
#> SRR191716 2 0.3580 0.5898 0.008 0.840 0.016 0.008 0.060 0.068
#> SRR191717 2 0.3372 0.5927 0.008 0.852 0.012 0.008 0.056 0.064
#> SRR191718 2 0.3788 0.5838 0.012 0.828 0.016 0.008 0.060 0.076
#> SRR537099 4 0.0436 0.8769 0.000 0.004 0.004 0.988 0.004 0.000
#> SRR537100 4 0.0436 0.8769 0.000 0.004 0.004 0.988 0.004 0.000
#> SRR537101 4 0.0790 0.8687 0.032 0.000 0.000 0.968 0.000 0.000
#> SRR537102 4 0.0993 0.8659 0.000 0.024 0.000 0.964 0.012 0.000
#> SRR537104 4 0.1067 0.8662 0.000 0.024 0.004 0.964 0.004 0.004
#> SRR537105 4 0.3043 0.8520 0.008 0.028 0.020 0.880 0.020 0.044
#> SRR537106 4 0.3043 0.8520 0.008 0.028 0.020 0.880 0.020 0.044
#> SRR537107 4 0.3043 0.8520 0.008 0.028 0.020 0.880 0.020 0.044
#> SRR537108 4 0.3043 0.8520 0.008 0.028 0.020 0.880 0.020 0.044
#> SRR537109 2 0.2618 0.6198 0.000 0.896 0.012 0.036 0.020 0.036
#> SRR537110 2 0.4019 0.5848 0.008 0.804 0.008 0.096 0.012 0.072
#> SRR537111 4 0.3928 0.5967 0.264 0.004 0.016 0.712 0.004 0.000
#> SRR537113 4 0.5189 0.5143 0.004 0.016 0.016 0.612 0.320 0.032
#> SRR537114 4 0.5189 0.5143 0.004 0.016 0.016 0.612 0.320 0.032
#> SRR537115 4 0.5417 0.3118 0.000 0.024 0.016 0.524 0.404 0.032
#> SRR537116 2 0.1242 0.6374 0.000 0.960 0.008 0.008 0.012 0.012
#> SRR537117 5 0.2272 0.5371 0.000 0.040 0.000 0.056 0.900 0.004
#> SRR537118 5 0.2600 0.5469 0.000 0.036 0.000 0.084 0.876 0.004
#> SRR537119 5 0.2600 0.5469 0.000 0.036 0.000 0.084 0.876 0.004
#> SRR537120 5 0.2322 0.5422 0.000 0.036 0.000 0.064 0.896 0.004
#> SRR537121 5 0.2918 0.5418 0.004 0.032 0.004 0.104 0.856 0.000
#> SRR537122 5 0.2918 0.5418 0.004 0.032 0.004 0.104 0.856 0.000
#> SRR537123 5 0.2918 0.5418 0.004 0.032 0.004 0.104 0.856 0.000
#> SRR537124 5 0.2221 0.5460 0.000 0.032 0.000 0.072 0.896 0.000
#> SRR537125 5 0.2487 0.5475 0.000 0.032 0.000 0.092 0.876 0.000
#> SRR537126 5 0.2487 0.5475 0.000 0.032 0.000 0.092 0.876 0.000
#> SRR537127 3 0.4800 0.9919 0.192 0.000 0.696 0.100 0.008 0.004
#> SRR537128 3 0.5088 0.9924 0.192 0.000 0.684 0.100 0.016 0.008
#> SRR537129 3 0.4800 0.9919 0.192 0.000 0.696 0.100 0.008 0.004
#> SRR537130 3 0.4757 0.9918 0.192 0.000 0.696 0.100 0.012 0.000
#> SRR537131 3 0.5088 0.9924 0.192 0.000 0.684 0.100 0.016 0.008
#> SRR537132 3 0.5088 0.9924 0.192 0.000 0.684 0.100 0.016 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.990 0.5025 0.499 0.499
#> 3 3 0.782 0.378 0.704 0.2725 0.701 0.480
#> 4 4 0.828 0.824 0.906 0.1445 0.773 0.481
#> 5 5 0.781 0.781 0.809 0.0677 0.916 0.716
#> 6 6 0.780 0.768 0.841 0.0527 0.949 0.765
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
#> SRR191639 1 0.000 0.996 1.000 0.000
#> SRR191640 1 0.000 0.996 1.000 0.000
#> SRR191641 1 0.000 0.996 1.000 0.000
#> SRR191642 1 0.000 0.996 1.000 0.000
#> SRR191643 1 0.000 0.996 1.000 0.000
#> SRR191644 1 0.000 0.996 1.000 0.000
#> SRR191645 1 0.000 0.996 1.000 0.000
#> SRR191646 1 0.000 0.996 1.000 0.000
#> SRR191647 1 0.000 0.996 1.000 0.000
#> SRR191648 1 0.000 0.996 1.000 0.000
#> SRR191649 1 0.000 0.996 1.000 0.000
#> SRR191650 1 0.000 0.996 1.000 0.000
#> SRR191651 1 0.000 0.996 1.000 0.000
#> SRR191652 1 0.000 0.996 1.000 0.000
#> SRR191653 1 0.000 0.996 1.000 0.000
#> SRR191654 1 0.000 0.996 1.000 0.000
#> SRR191655 1 0.000 0.996 1.000 0.000
#> SRR191656 1 0.000 0.996 1.000 0.000
#> SRR191657 1 0.000 0.996 1.000 0.000
#> SRR191658 1 0.000 0.996 1.000 0.000
#> SRR191659 1 0.000 0.996 1.000 0.000
#> SRR191660 1 0.000 0.996 1.000 0.000
#> SRR191661 1 0.000 0.996 1.000 0.000
#> SRR191662 1 0.000 0.996 1.000 0.000
#> SRR191663 1 0.000 0.996 1.000 0.000
#> SRR191664 1 0.000 0.996 1.000 0.000
#> SRR191665 1 0.000 0.996 1.000 0.000
#> SRR191666 1 0.000 0.996 1.000 0.000
#> SRR191667 1 0.000 0.996 1.000 0.000
#> SRR191668 1 0.000 0.996 1.000 0.000
#> SRR191669 1 0.000 0.996 1.000 0.000
#> SRR191670 1 0.000 0.996 1.000 0.000
#> SRR191671 1 0.000 0.996 1.000 0.000
#> SRR191672 1 0.000 0.996 1.000 0.000
#> SRR191673 1 0.000 0.996 1.000 0.000
#> SRR191674 2 0.000 0.983 0.000 1.000
#> SRR191675 2 0.000 0.983 0.000 1.000
#> SRR191677 2 0.000 0.983 0.000 1.000
#> SRR191678 2 0.000 0.983 0.000 1.000
#> SRR191679 2 0.000 0.983 0.000 1.000
#> SRR191680 2 0.000 0.983 0.000 1.000
#> SRR191681 2 0.000 0.983 0.000 1.000
#> SRR191682 2 0.000 0.983 0.000 1.000
#> SRR191683 2 0.000 0.983 0.000 1.000
#> SRR191684 2 0.000 0.983 0.000 1.000
#> SRR191685 2 0.000 0.983 0.000 1.000
#> SRR191686 2 0.000 0.983 0.000 1.000
#> SRR191687 2 0.000 0.983 0.000 1.000
#> SRR191688 2 0.000 0.983 0.000 1.000
#> SRR191689 2 0.000 0.983 0.000 1.000
#> SRR191690 2 0.000 0.983 0.000 1.000
#> SRR191691 2 0.000 0.983 0.000 1.000
#> SRR191692 2 0.000 0.983 0.000 1.000
#> SRR191693 2 0.000 0.983 0.000 1.000
#> SRR191694 2 0.000 0.983 0.000 1.000
#> SRR191695 2 0.000 0.983 0.000 1.000
#> SRR191696 2 0.000 0.983 0.000 1.000
#> SRR191697 2 0.000 0.983 0.000 1.000
#> SRR191698 2 0.000 0.983 0.000 1.000
#> SRR191699 2 0.000 0.983 0.000 1.000
#> SRR191700 2 0.000 0.983 0.000 1.000
#> SRR191701 2 0.000 0.983 0.000 1.000
#> SRR191702 2 0.000 0.983 0.000 1.000
#> SRR191703 2 0.000 0.983 0.000 1.000
#> SRR191704 2 0.000 0.983 0.000 1.000
#> SRR191705 2 0.000 0.983 0.000 1.000
#> SRR191706 2 0.000 0.983 0.000 1.000
#> SRR191707 2 0.000 0.983 0.000 1.000
#> SRR191708 2 0.000 0.983 0.000 1.000
#> SRR191709 2 0.000 0.983 0.000 1.000
#> SRR191710 2 0.000 0.983 0.000 1.000
#> SRR191711 2 0.000 0.983 0.000 1.000
#> SRR191712 2 0.000 0.983 0.000 1.000
#> SRR191713 2 0.000 0.983 0.000 1.000
#> SRR191714 2 0.000 0.983 0.000 1.000
#> SRR191715 2 0.000 0.983 0.000 1.000
#> SRR191716 2 0.000 0.983 0.000 1.000
#> SRR191717 2 0.000 0.983 0.000 1.000
#> SRR191718 2 0.000 0.983 0.000 1.000
#> SRR537099 1 0.000 0.996 1.000 0.000
#> SRR537100 1 0.000 0.996 1.000 0.000
#> SRR537101 1 0.000 0.996 1.000 0.000
#> SRR537102 1 0.000 0.996 1.000 0.000
#> SRR537104 1 0.722 0.742 0.800 0.200
#> SRR537105 1 0.000 0.996 1.000 0.000
#> SRR537106 1 0.000 0.996 1.000 0.000
#> SRR537107 1 0.000 0.996 1.000 0.000
#> SRR537108 1 0.000 0.996 1.000 0.000
#> SRR537109 2 0.000 0.983 0.000 1.000
#> SRR537110 2 0.000 0.983 0.000 1.000
#> SRR537111 1 0.000 0.996 1.000 0.000
#> SRR537113 2 0.994 0.169 0.456 0.544
#> SRR537114 2 0.994 0.169 0.456 0.544
#> SRR537115 2 0.278 0.936 0.048 0.952
#> SRR537116 2 0.000 0.983 0.000 1.000
#> SRR537117 2 0.000 0.983 0.000 1.000
#> SRR537118 2 0.000 0.983 0.000 1.000
#> SRR537119 2 0.000 0.983 0.000 1.000
#> SRR537120 2 0.000 0.983 0.000 1.000
#> SRR537121 2 0.000 0.983 0.000 1.000
#> SRR537122 2 0.000 0.983 0.000 1.000
#> SRR537123 2 0.000 0.983 0.000 1.000
#> SRR537124 2 0.000 0.983 0.000 1.000
#> SRR537125 2 0.000 0.983 0.000 1.000
#> SRR537126 2 0.000 0.983 0.000 1.000
#> SRR537127 1 0.000 0.996 1.000 0.000
#> SRR537128 1 0.000 0.996 1.000 0.000
#> SRR537129 1 0.000 0.996 1.000 0.000
#> SRR537130 1 0.000 0.996 1.000 0.000
#> SRR537131 1 0.000 0.996 1.000 0.000
#> SRR537132 1 0.000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191640 1 0.7295 0.5630 0.488 0.484 0.028
#> SRR191641 1 0.6678 0.5847 0.512 0.480 0.008
#> SRR191642 2 0.9152 -0.1577 0.152 0.484 0.364
#> SRR191643 2 0.9152 -0.1577 0.152 0.484 0.364
#> SRR191644 1 0.6680 0.5815 0.508 0.484 0.008
#> SRR191645 1 0.6518 0.5838 0.512 0.484 0.004
#> SRR191646 1 0.6518 0.5838 0.512 0.484 0.004
#> SRR191647 1 0.7295 0.5630 0.488 0.484 0.028
#> SRR191648 1 0.7295 0.5630 0.488 0.484 0.028
#> SRR191649 1 0.6954 0.5744 0.500 0.484 0.016
#> SRR191650 1 0.3038 0.8296 0.896 0.104 0.000
#> SRR191651 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191652 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191653 2 0.8975 -0.1399 0.132 0.484 0.384
#> SRR191654 2 0.8936 -0.1353 0.128 0.484 0.388
#> SRR191655 2 0.9050 -0.1456 0.140 0.484 0.376
#> SRR191656 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191661 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191662 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191663 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191666 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191667 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191668 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.8799 1.000 0.000 0.000
#> SRR191674 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191675 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191677 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191678 3 0.6244 -0.0754 0.000 0.440 0.560
#> SRR191679 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191680 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191681 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191682 2 0.6308 0.2433 0.000 0.508 0.492
#> SRR191683 2 0.6308 0.2433 0.000 0.508 0.492
#> SRR191684 2 0.6308 0.2433 0.000 0.508 0.492
#> SRR191685 2 0.6308 0.2433 0.000 0.508 0.492
#> SRR191686 2 0.6308 0.2433 0.000 0.508 0.492
#> SRR191687 2 0.6308 0.2433 0.000 0.508 0.492
#> SRR191688 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191689 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191690 2 0.6302 0.2529 0.000 0.520 0.480
#> SRR191691 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191692 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191693 3 0.6252 -0.0806 0.000 0.444 0.556
#> SRR191694 3 0.6267 -0.1083 0.000 0.452 0.548
#> SRR191695 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191696 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191697 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191698 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191699 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191700 3 0.5621 0.1606 0.000 0.308 0.692
#> SRR191701 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191702 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191703 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191704 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191705 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191706 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191707 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191708 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191709 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191710 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191711 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191712 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191713 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191714 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191715 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191716 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191717 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR191718 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR537099 2 0.8975 -0.1378 0.132 0.484 0.384
#> SRR537100 2 0.9014 -0.1425 0.136 0.484 0.380
#> SRR537101 1 0.7295 0.5630 0.488 0.484 0.028
#> SRR537102 2 0.8391 -0.0898 0.084 0.484 0.432
#> SRR537104 2 0.8434 -0.0893 0.088 0.496 0.416
#> SRR537105 2 0.8507 -0.0944 0.092 0.484 0.424
#> SRR537106 2 0.8507 -0.0944 0.092 0.484 0.424
#> SRR537107 2 0.8507 -0.0944 0.092 0.484 0.424
#> SRR537108 2 0.8507 -0.0944 0.092 0.484 0.424
#> SRR537109 2 0.5926 0.0421 0.000 0.644 0.356
#> SRR537110 2 0.6095 0.0825 0.000 0.608 0.392
#> SRR537111 1 0.1753 0.8593 0.952 0.048 0.000
#> SRR537113 3 0.6307 0.0816 0.000 0.488 0.512
#> SRR537114 3 0.6305 0.0775 0.000 0.484 0.516
#> SRR537115 3 0.6244 0.1152 0.000 0.440 0.560
#> SRR537116 2 0.6305 0.2606 0.000 0.516 0.484
#> SRR537117 3 0.0237 0.4507 0.000 0.004 0.996
#> SRR537118 3 0.0000 0.4527 0.000 0.000 1.000
#> SRR537119 3 0.0000 0.4527 0.000 0.000 1.000
#> SRR537120 3 0.0000 0.4527 0.000 0.000 1.000
#> SRR537121 3 0.1643 0.4311 0.000 0.044 0.956
#> SRR537122 3 0.1643 0.4311 0.000 0.044 0.956
#> SRR537123 3 0.1643 0.4311 0.000 0.044 0.956
#> SRR537124 3 0.0000 0.4527 0.000 0.000 1.000
#> SRR537125 3 0.0237 0.4520 0.000 0.004 0.996
#> SRR537126 3 0.0237 0.4520 0.000 0.004 0.996
#> SRR537127 1 0.0892 0.8725 0.980 0.000 0.020
#> SRR537128 1 0.0892 0.8725 0.980 0.000 0.020
#> SRR537129 1 0.0892 0.8725 0.980 0.000 0.020
#> SRR537130 1 0.0892 0.8725 0.980 0.000 0.020
#> SRR537131 1 0.0892 0.8725 0.980 0.000 0.020
#> SRR537132 1 0.0892 0.8725 0.980 0.000 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191640 4 0.0469 0.9855 0.012 0.000 0.000 0.988
#> SRR191641 4 0.0592 0.9815 0.016 0.000 0.000 0.984
#> SRR191642 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR191643 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR191644 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR191645 4 0.1118 0.9695 0.036 0.000 0.000 0.964
#> SRR191646 4 0.1118 0.9695 0.036 0.000 0.000 0.964
#> SRR191647 4 0.0592 0.9842 0.016 0.000 0.000 0.984
#> SRR191648 4 0.0592 0.9842 0.016 0.000 0.000 0.984
#> SRR191649 4 0.0707 0.9820 0.020 0.000 0.000 0.980
#> SRR191650 1 0.2704 0.8528 0.876 0.000 0.000 0.124
#> SRR191651 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191652 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191653 4 0.0524 0.9816 0.004 0.000 0.008 0.988
#> SRR191654 4 0.0524 0.9816 0.004 0.000 0.008 0.988
#> SRR191655 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR191656 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191662 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191666 1 0.1209 0.9521 0.964 0.000 0.004 0.032
#> SRR191667 1 0.1209 0.9521 0.964 0.000 0.004 0.032
#> SRR191668 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR191674 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191675 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191677 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191678 2 0.5263 0.4666 0.000 0.544 0.448 0.008
#> SRR191679 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191680 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191681 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191682 2 0.4722 0.6601 0.000 0.692 0.300 0.008
#> SRR191683 2 0.4722 0.6601 0.000 0.692 0.300 0.008
#> SRR191684 2 0.4722 0.6601 0.000 0.692 0.300 0.008
#> SRR191685 2 0.4722 0.6601 0.000 0.692 0.300 0.008
#> SRR191686 2 0.4722 0.6601 0.000 0.692 0.300 0.008
#> SRR191687 2 0.4722 0.6601 0.000 0.692 0.300 0.008
#> SRR191688 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191689 2 0.3725 0.7347 0.000 0.812 0.180 0.008
#> SRR191690 2 0.0336 0.8084 0.000 0.992 0.008 0.000
#> SRR191691 2 0.1867 0.7893 0.000 0.928 0.072 0.000
#> SRR191692 2 0.5250 0.4772 0.000 0.552 0.440 0.008
#> SRR191693 2 0.5285 0.4372 0.000 0.524 0.468 0.008
#> SRR191694 2 0.5212 0.5071 0.000 0.572 0.420 0.008
#> SRR191695 2 0.0336 0.8084 0.000 0.992 0.008 0.000
#> SRR191696 2 0.0336 0.8084 0.000 0.992 0.008 0.000
#> SRR191697 2 0.1389 0.8007 0.000 0.952 0.048 0.000
#> SRR191698 2 0.4072 0.6112 0.000 0.748 0.252 0.000
#> SRR191699 2 0.1109 0.8047 0.000 0.968 0.028 0.004
#> SRR191700 2 0.4855 0.3207 0.000 0.600 0.400 0.000
#> SRR191701 2 0.1716 0.7929 0.000 0.936 0.064 0.000
#> SRR191702 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191704 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191705 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191706 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191707 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191708 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191709 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191710 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191711 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191712 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191713 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191714 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191715 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191716 2 0.0336 0.8084 0.000 0.992 0.008 0.000
#> SRR191717 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR191718 2 0.0336 0.8084 0.000 0.992 0.008 0.000
#> SRR537099 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR537100 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR537101 4 0.0469 0.9843 0.012 0.000 0.000 0.988
#> SRR537102 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR537104 4 0.0336 0.9859 0.008 0.000 0.000 0.992
#> SRR537105 4 0.1388 0.9680 0.012 0.000 0.028 0.960
#> SRR537106 4 0.1388 0.9680 0.012 0.000 0.028 0.960
#> SRR537107 4 0.1388 0.9680 0.012 0.000 0.028 0.960
#> SRR537108 4 0.1388 0.9680 0.012 0.000 0.028 0.960
#> SRR537109 2 0.2081 0.7573 0.000 0.916 0.000 0.084
#> SRR537110 2 0.4331 0.4927 0.000 0.712 0.000 0.288
#> SRR537111 1 0.0817 0.9530 0.976 0.000 0.000 0.024
#> SRR537113 3 0.5508 0.0614 0.000 0.016 0.508 0.476
#> SRR537114 3 0.4999 0.0195 0.000 0.000 0.508 0.492
#> SRR537115 3 0.3606 0.7704 0.000 0.024 0.844 0.132
#> SRR537116 2 0.0000 0.8105 0.000 1.000 0.000 0.000
#> SRR537117 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537118 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537119 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537120 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537121 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537122 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537123 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537124 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537125 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537126 3 0.0188 0.8879 0.000 0.004 0.996 0.000
#> SRR537127 1 0.3216 0.9058 0.880 0.000 0.044 0.076
#> SRR537128 1 0.3216 0.9058 0.880 0.000 0.044 0.076
#> SRR537129 1 0.3216 0.9058 0.880 0.000 0.044 0.076
#> SRR537130 1 0.3216 0.9058 0.880 0.000 0.044 0.076
#> SRR537131 1 0.3216 0.9058 0.880 0.000 0.044 0.076
#> SRR537132 1 0.3216 0.9058 0.880 0.000 0.044 0.076
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191640 4 0.0162 0.9320 0.004 0.000 0.000 0.996 0.000
#> SRR191641 4 0.2127 0.8823 0.000 0.000 0.108 0.892 0.000
#> SRR191642 4 0.0000 0.9318 0.000 0.000 0.000 1.000 0.000
#> SRR191643 4 0.0510 0.9296 0.000 0.000 0.016 0.984 0.000
#> SRR191644 4 0.2561 0.8542 0.000 0.000 0.144 0.856 0.000
#> SRR191645 4 0.1493 0.9239 0.028 0.000 0.024 0.948 0.000
#> SRR191646 4 0.1493 0.9239 0.028 0.000 0.024 0.948 0.000
#> SRR191647 4 0.1211 0.9296 0.016 0.000 0.024 0.960 0.000
#> SRR191648 4 0.1211 0.9296 0.016 0.000 0.024 0.960 0.000
#> SRR191649 4 0.1211 0.9296 0.016 0.000 0.024 0.960 0.000
#> SRR191650 1 0.1608 0.8177 0.928 0.000 0.000 0.072 0.000
#> SRR191651 1 0.0162 0.8666 0.996 0.000 0.004 0.000 0.000
#> SRR191652 1 0.1768 0.8455 0.924 0.000 0.072 0.004 0.000
#> SRR191653 4 0.4088 0.6130 0.000 0.000 0.368 0.632 0.000
#> SRR191654 4 0.3661 0.7145 0.000 0.000 0.276 0.724 0.000
#> SRR191655 4 0.0404 0.9305 0.000 0.000 0.012 0.988 0.000
#> SRR191656 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191657 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191658 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191659 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191660 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191661 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191662 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191663 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191664 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR191665 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191666 1 0.4909 0.6632 0.588 0.000 0.380 0.032 0.000
#> SRR191667 1 0.4909 0.6632 0.588 0.000 0.380 0.032 0.000
#> SRR191668 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.8666 1.000 0.000 0.000 0.000 0.000
#> SRR191674 3 0.6693 0.7884 0.000 0.320 0.424 0.000 0.256
#> SRR191675 3 0.6693 0.7884 0.000 0.320 0.424 0.000 0.256
#> SRR191677 3 0.6693 0.7884 0.000 0.320 0.424 0.000 0.256
#> SRR191678 3 0.6694 0.7660 0.000 0.292 0.432 0.000 0.276
#> SRR191679 3 0.6693 0.7884 0.000 0.320 0.424 0.000 0.256
#> SRR191680 3 0.6693 0.7884 0.000 0.320 0.424 0.000 0.256
#> SRR191681 3 0.6698 0.7872 0.000 0.316 0.424 0.000 0.260
#> SRR191682 3 0.5962 0.6426 0.000 0.424 0.468 0.000 0.108
#> SRR191683 3 0.5962 0.6426 0.000 0.424 0.468 0.000 0.108
#> SRR191684 3 0.5959 0.6412 0.000 0.420 0.472 0.000 0.108
#> SRR191685 3 0.5962 0.6426 0.000 0.424 0.468 0.000 0.108
#> SRR191686 3 0.5966 0.6397 0.000 0.432 0.460 0.000 0.108
#> SRR191687 3 0.5962 0.6426 0.000 0.424 0.468 0.000 0.108
#> SRR191688 2 0.2179 0.7624 0.000 0.888 0.112 0.000 0.000
#> SRR191689 3 0.5403 0.5585 0.000 0.456 0.488 0.000 0.056
#> SRR191690 2 0.2233 0.7698 0.000 0.892 0.104 0.000 0.004
#> SRR191691 2 0.4526 0.3925 0.000 0.672 0.300 0.000 0.028
#> SRR191692 3 0.6691 0.7874 0.000 0.312 0.428 0.000 0.260
#> SRR191693 3 0.6507 0.7658 0.000 0.268 0.488 0.000 0.244
#> SRR191694 3 0.6610 0.7790 0.000 0.340 0.436 0.000 0.224
#> SRR191695 2 0.2536 0.7526 0.000 0.868 0.128 0.000 0.004
#> SRR191696 2 0.2536 0.7526 0.000 0.868 0.128 0.000 0.004
#> SRR191697 2 0.4620 0.4381 0.000 0.652 0.320 0.000 0.028
#> SRR191698 2 0.5754 0.3333 0.000 0.604 0.260 0.000 0.136
#> SRR191699 2 0.4457 0.0923 0.000 0.620 0.368 0.000 0.012
#> SRR191700 2 0.6292 0.2395 0.000 0.532 0.208 0.000 0.260
#> SRR191701 2 0.4276 0.4845 0.000 0.716 0.256 0.000 0.028
#> SRR191702 2 0.0794 0.7947 0.000 0.972 0.028 0.000 0.000
#> SRR191703 2 0.0794 0.7947 0.000 0.972 0.028 0.000 0.000
#> SRR191704 2 0.1121 0.7896 0.000 0.956 0.044 0.000 0.000
#> SRR191705 2 0.1121 0.7896 0.000 0.956 0.044 0.000 0.000
#> SRR191706 2 0.0794 0.7947 0.000 0.972 0.028 0.000 0.000
#> SRR191707 2 0.1671 0.7748 0.000 0.924 0.076 0.000 0.000
#> SRR191708 2 0.1121 0.7896 0.000 0.956 0.044 0.000 0.000
#> SRR191709 2 0.1121 0.7896 0.000 0.956 0.044 0.000 0.000
#> SRR191710 2 0.1121 0.7896 0.000 0.956 0.044 0.000 0.000
#> SRR191711 2 0.0290 0.7973 0.000 0.992 0.008 0.000 0.000
#> SRR191712 2 0.0290 0.7973 0.000 0.992 0.008 0.000 0.000
#> SRR191713 2 0.1270 0.7835 0.000 0.948 0.052 0.000 0.000
#> SRR191714 2 0.1197 0.7858 0.000 0.952 0.048 0.000 0.000
#> SRR191715 2 0.1732 0.7699 0.000 0.920 0.080 0.000 0.000
#> SRR191716 2 0.2338 0.7610 0.000 0.884 0.112 0.000 0.004
#> SRR191717 2 0.2179 0.7624 0.000 0.888 0.112 0.000 0.000
#> SRR191718 2 0.2389 0.7596 0.000 0.880 0.116 0.000 0.004
#> SRR537099 4 0.0510 0.9296 0.000 0.000 0.016 0.984 0.000
#> SRR537100 4 0.0510 0.9296 0.000 0.000 0.016 0.984 0.000
#> SRR537101 4 0.1732 0.9007 0.000 0.000 0.080 0.920 0.000
#> SRR537102 4 0.0000 0.9318 0.000 0.000 0.000 1.000 0.000
#> SRR537104 4 0.0912 0.9273 0.000 0.012 0.016 0.972 0.000
#> SRR537105 4 0.1518 0.9245 0.012 0.000 0.020 0.952 0.016
#> SRR537106 4 0.1777 0.9207 0.012 0.004 0.020 0.944 0.020
#> SRR537107 4 0.1777 0.9207 0.012 0.004 0.020 0.944 0.020
#> SRR537108 4 0.1777 0.9207 0.012 0.004 0.020 0.944 0.020
#> SRR537109 2 0.3800 0.6643 0.000 0.812 0.108 0.080 0.000
#> SRR537110 2 0.3622 0.6323 0.000 0.820 0.056 0.124 0.000
#> SRR537111 1 0.1074 0.8562 0.968 0.004 0.016 0.012 0.000
#> SRR537113 5 0.4468 0.6038 0.000 0.004 0.024 0.276 0.696
#> SRR537114 5 0.4275 0.5922 0.000 0.000 0.020 0.284 0.696
#> SRR537115 5 0.2824 0.8369 0.000 0.008 0.024 0.088 0.880
#> SRR537116 2 0.1732 0.7700 0.000 0.920 0.080 0.000 0.000
#> SRR537117 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537118 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537119 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537120 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537121 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537122 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537123 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537124 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537125 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537126 5 0.0000 0.9216 0.000 0.000 0.000 0.000 1.000
#> SRR537127 1 0.6068 0.6031 0.504 0.000 0.408 0.064 0.024
#> SRR537128 1 0.6068 0.6031 0.504 0.000 0.408 0.064 0.024
#> SRR537129 1 0.6068 0.6031 0.504 0.000 0.408 0.064 0.024
#> SRR537130 1 0.6068 0.6031 0.504 0.000 0.408 0.064 0.024
#> SRR537131 1 0.6068 0.6031 0.504 0.000 0.408 0.064 0.024
#> SRR537132 1 0.6068 0.6031 0.504 0.000 0.408 0.064 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191640 4 0.0000 0.909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR191641 4 0.2562 0.767 0.000 0.000 0.172 0.828 0.000 0.000
#> SRR191642 4 0.0000 0.909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR191643 4 0.0000 0.909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR191644 4 0.3175 0.630 0.000 0.000 0.256 0.744 0.000 0.000
#> SRR191645 4 0.2533 0.909 0.004 0.000 0.052 0.892 0.008 0.044
#> SRR191646 4 0.2533 0.909 0.004 0.000 0.052 0.892 0.008 0.044
#> SRR191647 4 0.2533 0.909 0.004 0.000 0.052 0.892 0.008 0.044
#> SRR191648 4 0.2533 0.909 0.004 0.000 0.052 0.892 0.008 0.044
#> SRR191649 4 0.2533 0.909 0.004 0.000 0.052 0.892 0.008 0.044
#> SRR191650 1 0.1049 0.931 0.960 0.000 0.008 0.032 0.000 0.000
#> SRR191651 1 0.0405 0.965 0.988 0.000 0.008 0.000 0.000 0.004
#> SRR191652 1 0.1765 0.894 0.904 0.000 0.096 0.000 0.000 0.000
#> SRR191653 3 0.3531 0.464 0.000 0.000 0.672 0.328 0.000 0.000
#> SRR191654 3 0.3847 0.165 0.000 0.000 0.544 0.456 0.000 0.000
#> SRR191655 4 0.0363 0.905 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR191656 1 0.0146 0.967 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191657 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191658 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191659 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191660 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191661 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191662 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191663 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191664 1 0.1010 0.962 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR191665 1 0.0146 0.967 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191666 3 0.3360 0.817 0.264 0.000 0.732 0.004 0.000 0.000
#> SRR191667 3 0.3360 0.817 0.264 0.000 0.732 0.004 0.000 0.000
#> SRR191668 1 0.0291 0.967 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR191669 1 0.0291 0.967 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR191670 1 0.0146 0.967 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191671 1 0.0146 0.967 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191672 1 0.0291 0.967 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR191673 1 0.0291 0.967 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR191674 6 0.4261 0.690 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191675 6 0.4261 0.690 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191677 6 0.4261 0.690 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191678 6 0.4261 0.685 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191679 6 0.4261 0.690 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191680 6 0.4261 0.690 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191681 6 0.4261 0.690 0.000 0.112 0.000 0.000 0.156 0.732
#> SRR191682 6 0.5036 0.583 0.000 0.220 0.080 0.000 0.028 0.672
#> SRR191683 6 0.5036 0.583 0.000 0.220 0.080 0.000 0.028 0.672
#> SRR191684 6 0.5036 0.583 0.000 0.220 0.080 0.000 0.028 0.672
#> SRR191685 6 0.5036 0.583 0.000 0.220 0.080 0.000 0.028 0.672
#> SRR191686 6 0.5011 0.585 0.000 0.216 0.080 0.000 0.028 0.676
#> SRR191687 6 0.5036 0.583 0.000 0.220 0.080 0.000 0.028 0.672
#> SRR191688 2 0.4317 0.631 0.000 0.688 0.060 0.000 0.000 0.252
#> SRR191689 6 0.3010 0.636 0.000 0.148 0.020 0.000 0.004 0.828
#> SRR191690 2 0.4317 0.631 0.000 0.688 0.060 0.000 0.000 0.252
#> SRR191691 2 0.6001 0.225 0.000 0.532 0.132 0.000 0.032 0.304
#> SRR191692 6 0.4067 0.692 0.000 0.104 0.000 0.000 0.144 0.752
#> SRR191693 6 0.3823 0.677 0.000 0.044 0.032 0.000 0.124 0.800
#> SRR191694 6 0.3787 0.685 0.000 0.120 0.000 0.000 0.100 0.780
#> SRR191695 2 0.4495 0.606 0.000 0.660 0.064 0.000 0.000 0.276
#> SRR191696 2 0.4495 0.606 0.000 0.660 0.064 0.000 0.000 0.276
#> SRR191697 6 0.5815 -0.274 0.000 0.424 0.128 0.000 0.012 0.436
#> SRR191698 2 0.6508 0.243 0.000 0.512 0.124 0.000 0.084 0.280
#> SRR191699 6 0.5326 0.209 0.000 0.404 0.092 0.000 0.004 0.500
#> SRR191700 2 0.7101 0.245 0.000 0.464 0.140 0.000 0.188 0.208
#> SRR191701 2 0.5831 0.292 0.000 0.556 0.124 0.000 0.028 0.292
#> SRR191702 2 0.2361 0.725 0.000 0.884 0.028 0.000 0.000 0.088
#> SRR191703 2 0.2361 0.725 0.000 0.884 0.028 0.000 0.000 0.088
#> SRR191704 2 0.1984 0.717 0.000 0.912 0.032 0.000 0.000 0.056
#> SRR191705 2 0.1984 0.717 0.000 0.912 0.032 0.000 0.000 0.056
#> SRR191706 2 0.2537 0.723 0.000 0.872 0.032 0.000 0.000 0.096
#> SRR191707 2 0.2499 0.692 0.000 0.880 0.048 0.000 0.000 0.072
#> SRR191708 2 0.1921 0.719 0.000 0.916 0.032 0.000 0.000 0.052
#> SRR191709 2 0.1984 0.717 0.000 0.912 0.032 0.000 0.000 0.056
#> SRR191710 2 0.1984 0.717 0.000 0.912 0.032 0.000 0.000 0.056
#> SRR191711 2 0.1549 0.731 0.000 0.936 0.020 0.000 0.000 0.044
#> SRR191712 2 0.1616 0.731 0.000 0.932 0.020 0.000 0.000 0.048
#> SRR191713 2 0.1895 0.717 0.000 0.912 0.016 0.000 0.000 0.072
#> SRR191714 2 0.1895 0.717 0.000 0.912 0.016 0.000 0.000 0.072
#> SRR191715 2 0.3301 0.679 0.000 0.788 0.024 0.000 0.000 0.188
#> SRR191716 2 0.4271 0.634 0.000 0.696 0.060 0.000 0.000 0.244
#> SRR191717 2 0.4215 0.636 0.000 0.700 0.056 0.000 0.000 0.244
#> SRR191718 2 0.4435 0.619 0.000 0.672 0.064 0.000 0.000 0.264
#> SRR537099 4 0.0865 0.895 0.000 0.000 0.036 0.964 0.000 0.000
#> SRR537100 4 0.1007 0.891 0.000 0.000 0.044 0.956 0.000 0.000
#> SRR537101 4 0.2178 0.815 0.000 0.000 0.132 0.868 0.000 0.000
#> SRR537102 4 0.0000 0.909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR537104 4 0.0692 0.903 0.000 0.000 0.020 0.976 0.000 0.004
#> SRR537105 4 0.2390 0.910 0.000 0.000 0.052 0.896 0.008 0.044
#> SRR537106 4 0.2390 0.910 0.000 0.000 0.052 0.896 0.008 0.044
#> SRR537107 4 0.2390 0.910 0.000 0.000 0.052 0.896 0.008 0.044
#> SRR537108 4 0.2390 0.910 0.000 0.000 0.052 0.896 0.008 0.044
#> SRR537109 2 0.5093 0.594 0.000 0.648 0.056 0.036 0.000 0.260
#> SRR537110 2 0.2893 0.684 0.000 0.872 0.028 0.044 0.000 0.056
#> SRR537111 1 0.1080 0.942 0.960 0.000 0.032 0.004 0.000 0.004
#> SRR537113 5 0.4478 0.724 0.000 0.000 0.044 0.152 0.748 0.056
#> SRR537114 5 0.4367 0.717 0.000 0.000 0.044 0.160 0.752 0.044
#> SRR537115 5 0.2706 0.861 0.000 0.000 0.044 0.016 0.880 0.060
#> SRR537116 2 0.3279 0.686 0.000 0.796 0.028 0.000 0.000 0.176
#> SRR537117 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537118 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537119 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537120 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537121 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537122 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537123 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537124 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537125 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537126 5 0.0260 0.941 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR537127 3 0.3479 0.867 0.212 0.000 0.768 0.008 0.012 0.000
#> SRR537128 3 0.3479 0.867 0.212 0.000 0.768 0.008 0.012 0.000
#> SRR537129 3 0.3479 0.867 0.212 0.000 0.768 0.008 0.012 0.000
#> SRR537130 3 0.3479 0.867 0.212 0.000 0.768 0.008 0.012 0.000
#> SRR537131 3 0.3479 0.867 0.212 0.000 0.768 0.008 0.012 0.000
#> SRR537132 3 0.3479 0.867 0.212 0.000 0.768 0.008 0.012 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)
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 16450 rows and 111 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 0.815 0.886 0.951 0.5004 0.500 0.500
#> 3 3 0.629 0.816 0.841 0.2783 0.785 0.593
#> 4 4 0.641 0.761 0.837 0.0658 0.960 0.886
#> 5 5 0.841 0.874 0.938 0.1293 0.834 0.528
#> 6 6 0.853 0.780 0.891 0.0424 0.947 0.776
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
#> SRR191639 1 0.0000 0.932 1.000 0.000
#> SRR191640 1 0.0376 0.931 0.996 0.004
#> SRR191641 1 0.0376 0.931 0.996 0.004
#> SRR191642 1 0.0376 0.931 0.996 0.004
#> SRR191643 1 0.1414 0.923 0.980 0.020
#> SRR191644 1 0.0376 0.931 0.996 0.004
#> SRR191645 1 0.0000 0.932 1.000 0.000
#> SRR191646 1 0.0000 0.932 1.000 0.000
#> SRR191647 1 0.0376 0.931 0.996 0.004
#> SRR191648 1 0.0376 0.931 0.996 0.004
#> SRR191649 1 0.0376 0.931 0.996 0.004
#> SRR191650 1 0.0376 0.931 0.996 0.004
#> SRR191651 1 0.0000 0.932 1.000 0.000
#> SRR191652 1 0.0000 0.932 1.000 0.000
#> SRR191653 1 0.0376 0.931 0.996 0.004
#> SRR191654 1 0.1633 0.921 0.976 0.024
#> SRR191655 1 0.0376 0.931 0.996 0.004
#> SRR191656 1 0.0000 0.932 1.000 0.000
#> SRR191657 1 0.0000 0.932 1.000 0.000
#> SRR191658 1 0.0000 0.932 1.000 0.000
#> SRR191659 1 0.0000 0.932 1.000 0.000
#> SRR191660 1 0.0000 0.932 1.000 0.000
#> SRR191661 1 0.0000 0.932 1.000 0.000
#> SRR191662 1 0.0000 0.932 1.000 0.000
#> SRR191663 1 0.0000 0.932 1.000 0.000
#> SRR191664 1 0.0000 0.932 1.000 0.000
#> SRR191665 1 0.0000 0.932 1.000 0.000
#> SRR191666 1 0.0000 0.932 1.000 0.000
#> SRR191667 1 0.0000 0.932 1.000 0.000
#> SRR191668 1 0.0000 0.932 1.000 0.000
#> SRR191669 1 0.0000 0.932 1.000 0.000
#> SRR191670 1 0.0000 0.932 1.000 0.000
#> SRR191671 1 0.0000 0.932 1.000 0.000
#> SRR191672 1 0.0000 0.932 1.000 0.000
#> SRR191673 1 0.0000 0.932 1.000 0.000
#> SRR191674 2 0.0000 0.965 0.000 1.000
#> SRR191675 2 0.0000 0.965 0.000 1.000
#> SRR191677 2 0.0000 0.965 0.000 1.000
#> SRR191678 2 0.0000 0.965 0.000 1.000
#> SRR191679 2 0.0000 0.965 0.000 1.000
#> SRR191680 2 0.0000 0.965 0.000 1.000
#> SRR191681 2 0.0000 0.965 0.000 1.000
#> SRR191682 2 0.0000 0.965 0.000 1.000
#> SRR191683 2 0.0000 0.965 0.000 1.000
#> SRR191684 2 0.8144 0.650 0.252 0.748
#> SRR191685 2 0.0376 0.962 0.004 0.996
#> SRR191686 2 0.0000 0.965 0.000 1.000
#> SRR191687 2 0.0000 0.965 0.000 1.000
#> SRR191688 2 0.9323 0.434 0.348 0.652
#> SRR191689 2 0.0000 0.965 0.000 1.000
#> SRR191690 1 0.7299 0.749 0.796 0.204
#> SRR191691 2 0.3274 0.913 0.060 0.940
#> SRR191692 2 0.0000 0.965 0.000 1.000
#> SRR191693 2 0.0000 0.965 0.000 1.000
#> SRR191694 2 0.0000 0.965 0.000 1.000
#> SRR191695 2 0.0000 0.965 0.000 1.000
#> SRR191696 2 0.0000 0.965 0.000 1.000
#> SRR191697 2 0.0000 0.965 0.000 1.000
#> SRR191698 2 0.0000 0.965 0.000 1.000
#> SRR191699 2 0.0000 0.965 0.000 1.000
#> SRR191700 2 0.4690 0.873 0.100 0.900
#> SRR191701 2 0.0000 0.965 0.000 1.000
#> SRR191702 2 0.0000 0.965 0.000 1.000
#> SRR191703 2 0.0000 0.965 0.000 1.000
#> SRR191704 2 0.0000 0.965 0.000 1.000
#> SRR191705 2 0.0000 0.965 0.000 1.000
#> SRR191706 2 0.0000 0.965 0.000 1.000
#> SRR191707 2 0.9286 0.444 0.344 0.656
#> SRR191708 1 0.8608 0.636 0.716 0.284
#> SRR191709 2 0.0000 0.965 0.000 1.000
#> SRR191710 1 0.9427 0.496 0.640 0.360
#> SRR191711 2 0.0376 0.962 0.004 0.996
#> SRR191712 2 0.6343 0.794 0.160 0.840
#> SRR191713 1 0.9850 0.329 0.572 0.428
#> SRR191714 1 0.9635 0.434 0.612 0.388
#> SRR191715 2 0.6247 0.801 0.156 0.844
#> SRR191716 1 0.9922 0.232 0.552 0.448
#> SRR191717 2 0.1184 0.954 0.016 0.984
#> SRR191718 2 0.0000 0.965 0.000 1.000
#> SRR537099 1 0.0376 0.931 0.996 0.004
#> SRR537100 1 0.0376 0.931 0.996 0.004
#> SRR537101 1 0.0376 0.931 0.996 0.004
#> SRR537102 1 0.4562 0.863 0.904 0.096
#> SRR537104 1 0.0672 0.930 0.992 0.008
#> SRR537105 1 0.3431 0.892 0.936 0.064
#> SRR537106 1 0.3584 0.889 0.932 0.068
#> SRR537107 1 0.3584 0.889 0.932 0.068
#> SRR537108 1 0.3431 0.892 0.936 0.064
#> SRR537109 1 0.8207 0.683 0.744 0.256
#> SRR537110 1 0.6887 0.773 0.816 0.184
#> SRR537111 1 0.0376 0.931 0.996 0.004
#> SRR537113 1 0.9850 0.283 0.572 0.428
#> SRR537114 1 0.9909 0.236 0.556 0.444
#> SRR537115 2 0.3584 0.905 0.068 0.932
#> SRR537116 2 0.0000 0.965 0.000 1.000
#> SRR537117 2 0.0000 0.965 0.000 1.000
#> SRR537118 2 0.0000 0.965 0.000 1.000
#> SRR537119 2 0.0000 0.965 0.000 1.000
#> SRR537120 2 0.0000 0.965 0.000 1.000
#> SRR537121 2 0.1184 0.953 0.016 0.984
#> SRR537122 2 0.2043 0.941 0.032 0.968
#> SRR537123 2 0.0672 0.959 0.008 0.992
#> SRR537124 2 0.0000 0.965 0.000 1.000
#> SRR537125 2 0.0000 0.965 0.000 1.000
#> SRR537126 2 0.0000 0.965 0.000 1.000
#> SRR537127 1 0.0672 0.929 0.992 0.008
#> SRR537128 1 0.0000 0.932 1.000 0.000
#> SRR537129 1 0.1414 0.922 0.980 0.020
#> SRR537130 1 0.0000 0.932 1.000 0.000
#> SRR537131 1 0.0000 0.932 1.000 0.000
#> SRR537132 1 0.0000 0.932 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.4796 0.748 0.780 0.000 0.220
#> SRR191640 1 0.4235 0.764 0.824 0.000 0.176
#> SRR191641 1 0.4235 0.764 0.824 0.000 0.176
#> SRR191642 1 0.4235 0.764 0.824 0.000 0.176
#> SRR191643 1 0.4409 0.766 0.824 0.004 0.172
#> SRR191644 1 0.4235 0.764 0.824 0.000 0.176
#> SRR191645 1 0.4974 0.736 0.764 0.000 0.236
#> SRR191646 1 0.4974 0.736 0.764 0.000 0.236
#> SRR191647 1 0.4796 0.748 0.780 0.000 0.220
#> SRR191648 1 0.4796 0.748 0.780 0.000 0.220
#> SRR191649 1 0.4796 0.748 0.780 0.000 0.220
#> SRR191650 1 0.4750 0.750 0.784 0.000 0.216
#> SRR191651 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191652 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191653 1 0.4235 0.764 0.824 0.000 0.176
#> SRR191654 1 0.4295 0.767 0.864 0.032 0.104
#> SRR191655 1 0.4235 0.764 0.824 0.000 0.176
#> SRR191656 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191657 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191658 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191659 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191660 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191661 3 0.3752 0.933 0.144 0.000 0.856
#> SRR191662 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191663 3 0.3619 0.942 0.136 0.000 0.864
#> SRR191664 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191665 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191666 3 0.3412 0.947 0.124 0.000 0.876
#> SRR191667 3 0.3038 0.934 0.104 0.000 0.896
#> SRR191668 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191669 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191670 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191671 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191672 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191673 3 0.3482 0.950 0.128 0.000 0.872
#> SRR191674 2 0.0747 0.906 0.016 0.984 0.000
#> SRR191675 2 0.0747 0.906 0.016 0.984 0.000
#> SRR191677 2 0.0747 0.906 0.016 0.984 0.000
#> SRR191678 2 0.0892 0.906 0.020 0.980 0.000
#> SRR191679 2 0.1411 0.911 0.036 0.964 0.000
#> SRR191680 2 0.0237 0.909 0.004 0.996 0.000
#> SRR191681 2 0.3038 0.874 0.104 0.896 0.000
#> SRR191682 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191683 2 0.1411 0.911 0.036 0.964 0.000
#> SRR191684 2 0.4443 0.874 0.084 0.864 0.052
#> SRR191685 2 0.2774 0.907 0.072 0.920 0.008
#> SRR191686 2 0.1289 0.911 0.032 0.968 0.000
#> SRR191687 2 0.2066 0.908 0.060 0.940 0.000
#> SRR191688 1 0.4702 0.686 0.788 0.212 0.000
#> SRR191689 2 0.1031 0.911 0.024 0.976 0.000
#> SRR191690 1 0.4002 0.717 0.840 0.160 0.000
#> SRR191691 2 0.3293 0.895 0.088 0.900 0.012
#> SRR191692 2 0.1411 0.901 0.036 0.964 0.000
#> SRR191693 2 0.2959 0.876 0.100 0.900 0.000
#> SRR191694 2 0.0747 0.910 0.016 0.984 0.000
#> SRR191695 2 0.2625 0.903 0.084 0.916 0.000
#> SRR191696 2 0.2537 0.904 0.080 0.920 0.000
#> SRR191697 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191698 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191699 2 0.2625 0.902 0.084 0.916 0.000
#> SRR191700 2 0.2878 0.898 0.096 0.904 0.000
#> SRR191701 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191702 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191703 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191704 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191705 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191706 2 0.2356 0.906 0.072 0.928 0.000
#> SRR191707 1 0.4974 0.661 0.764 0.236 0.000
#> SRR191708 1 0.7292 0.122 0.500 0.472 0.028
#> SRR191709 2 0.3752 0.844 0.144 0.856 0.000
#> SRR191710 1 0.7203 0.309 0.556 0.416 0.028
#> SRR191711 1 0.6215 0.297 0.572 0.428 0.000
#> SRR191712 1 0.5988 0.444 0.632 0.368 0.000
#> SRR191713 1 0.9350 0.430 0.488 0.328 0.184
#> SRR191714 1 0.6630 0.565 0.672 0.300 0.028
#> SRR191715 2 0.7145 0.113 0.440 0.536 0.024
#> SRR191716 1 0.3879 0.722 0.848 0.152 0.000
#> SRR191717 1 0.5706 0.536 0.680 0.320 0.000
#> SRR191718 2 0.2356 0.906 0.072 0.928 0.000
#> SRR537099 1 0.4235 0.764 0.824 0.000 0.176
#> SRR537100 1 0.4235 0.764 0.824 0.000 0.176
#> SRR537101 1 0.4235 0.764 0.824 0.000 0.176
#> SRR537102 1 0.3921 0.749 0.884 0.080 0.036
#> SRR537104 1 0.4209 0.769 0.860 0.020 0.120
#> SRR537105 1 0.5356 0.762 0.784 0.020 0.196
#> SRR537106 1 0.5508 0.763 0.784 0.028 0.188
#> SRR537107 1 0.5455 0.765 0.788 0.028 0.184
#> SRR537108 1 0.5356 0.762 0.784 0.020 0.196
#> SRR537109 1 0.3425 0.736 0.884 0.112 0.004
#> SRR537110 1 0.3532 0.736 0.884 0.108 0.008
#> SRR537111 3 0.3482 0.950 0.128 0.000 0.872
#> SRR537113 1 0.3454 0.690 0.888 0.104 0.008
#> SRR537114 1 0.4033 0.673 0.856 0.136 0.008
#> SRR537115 2 0.4654 0.776 0.208 0.792 0.000
#> SRR537116 2 0.4605 0.765 0.204 0.796 0.000
#> SRR537117 2 0.3038 0.874 0.104 0.896 0.000
#> SRR537118 2 0.3038 0.874 0.104 0.896 0.000
#> SRR537119 2 0.3038 0.874 0.104 0.896 0.000
#> SRR537120 2 0.3038 0.874 0.104 0.896 0.000
#> SRR537121 2 0.3192 0.869 0.112 0.888 0.000
#> SRR537122 2 0.3619 0.853 0.136 0.864 0.000
#> SRR537123 2 0.3116 0.872 0.108 0.892 0.000
#> SRR537124 2 0.3038 0.874 0.104 0.896 0.000
#> SRR537125 2 0.3116 0.872 0.108 0.892 0.000
#> SRR537126 2 0.3116 0.872 0.108 0.892 0.000
#> SRR537127 3 0.1860 0.833 0.052 0.000 0.948
#> SRR537128 3 0.1643 0.840 0.044 0.000 0.956
#> SRR537129 3 0.2261 0.817 0.068 0.000 0.932
#> SRR537130 3 0.1643 0.840 0.044 0.000 0.956
#> SRR537131 3 0.1643 0.840 0.044 0.000 0.956
#> SRR537132 3 0.1643 0.840 0.044 0.000 0.956
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 4 0.4072 0.7472 0.252 0.000 0.000 0.748
#> SRR191640 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR191641 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR191642 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR191643 4 0.4542 0.7514 0.228 0.020 0.000 0.752
#> SRR191644 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR191645 1 0.4948 -0.1534 0.560 0.000 0.000 0.440
#> SRR191646 1 0.4948 -0.1534 0.560 0.000 0.000 0.440
#> SRR191647 4 0.4072 0.7472 0.252 0.000 0.000 0.748
#> SRR191648 4 0.4072 0.7472 0.252 0.000 0.000 0.748
#> SRR191649 4 0.4072 0.7472 0.252 0.000 0.000 0.748
#> SRR191650 4 0.4072 0.7472 0.252 0.000 0.000 0.748
#> SRR191651 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191652 1 0.0469 0.9034 0.988 0.000 0.000 0.012
#> SRR191653 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR191654 4 0.4829 0.7411 0.156 0.068 0.000 0.776
#> SRR191655 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR191656 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191660 1 0.0188 0.9092 0.996 0.000 0.000 0.004
#> SRR191661 1 0.4477 0.3745 0.688 0.000 0.000 0.312
#> SRR191662 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191663 1 0.2647 0.7675 0.880 0.000 0.000 0.120
#> SRR191664 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191666 1 0.0188 0.9077 0.996 0.000 0.004 0.000
#> SRR191667 1 0.0921 0.8792 0.972 0.000 0.028 0.000
#> SRR191668 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> SRR191674 2 0.1474 0.8552 0.000 0.948 0.000 0.052
#> SRR191675 2 0.1474 0.8552 0.000 0.948 0.000 0.052
#> SRR191677 2 0.1474 0.8552 0.000 0.948 0.000 0.052
#> SRR191678 2 0.1637 0.8531 0.000 0.940 0.000 0.060
#> SRR191679 2 0.0592 0.8622 0.000 0.984 0.000 0.016
#> SRR191680 2 0.0921 0.8601 0.000 0.972 0.000 0.028
#> SRR191681 2 0.3266 0.8052 0.000 0.832 0.000 0.168
#> SRR191682 2 0.1824 0.8569 0.000 0.936 0.004 0.060
#> SRR191683 2 0.0469 0.8622 0.000 0.988 0.000 0.012
#> SRR191684 2 0.6405 0.4497 0.332 0.592 0.004 0.072
#> SRR191685 2 0.2365 0.8565 0.012 0.920 0.004 0.064
#> SRR191686 2 0.0469 0.8624 0.000 0.988 0.000 0.012
#> SRR191687 2 0.1389 0.8608 0.000 0.952 0.000 0.048
#> SRR191688 4 0.3569 0.6644 0.000 0.196 0.000 0.804
#> SRR191689 2 0.0000 0.8620 0.000 1.000 0.000 0.000
#> SRR191690 4 0.4267 0.6862 0.024 0.188 0.000 0.788
#> SRR191691 2 0.5607 0.6381 0.208 0.716 0.004 0.072
#> SRR191692 2 0.2011 0.8460 0.000 0.920 0.000 0.080
#> SRR191693 2 0.3219 0.8074 0.000 0.836 0.000 0.164
#> SRR191694 2 0.0817 0.8606 0.000 0.976 0.000 0.024
#> SRR191695 2 0.2125 0.8535 0.000 0.920 0.004 0.076
#> SRR191696 2 0.2125 0.8535 0.000 0.920 0.004 0.076
#> SRR191697 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191698 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191699 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191700 2 0.2197 0.8521 0.000 0.916 0.004 0.080
#> SRR191701 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191702 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191703 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191704 2 0.2238 0.8529 0.004 0.920 0.004 0.072
#> SRR191705 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191706 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR191707 4 0.3831 0.6563 0.000 0.204 0.004 0.792
#> SRR191708 4 0.7912 0.2741 0.304 0.260 0.004 0.432
#> SRR191709 2 0.3157 0.7952 0.000 0.852 0.004 0.144
#> SRR191710 4 0.7730 0.3233 0.304 0.220 0.004 0.472
#> SRR191711 4 0.5070 0.3430 0.000 0.416 0.004 0.580
#> SRR191712 4 0.4819 0.5036 0.000 0.344 0.004 0.652
#> SRR191713 4 0.7862 0.2881 0.324 0.236 0.004 0.436
#> SRR191714 4 0.7707 0.3293 0.304 0.216 0.004 0.476
#> SRR191715 2 0.7892 0.0197 0.252 0.436 0.004 0.308
#> SRR191716 4 0.4379 0.6986 0.036 0.172 0.000 0.792
#> SRR191717 4 0.4699 0.5449 0.000 0.320 0.004 0.676
#> SRR191718 2 0.2053 0.8544 0.000 0.924 0.004 0.072
#> SRR537099 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR537100 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR537101 4 0.4040 0.7492 0.248 0.000 0.000 0.752
#> SRR537102 4 0.4724 0.7169 0.076 0.136 0.000 0.788
#> SRR537104 4 0.4638 0.7459 0.180 0.044 0.000 0.776
#> SRR537105 4 0.4328 0.7508 0.244 0.008 0.000 0.748
#> SRR537106 4 0.4328 0.7508 0.244 0.008 0.000 0.748
#> SRR537107 4 0.4328 0.7508 0.244 0.008 0.000 0.748
#> SRR537108 4 0.4220 0.7495 0.248 0.004 0.000 0.748
#> SRR537109 4 0.4578 0.7060 0.052 0.160 0.000 0.788
#> SRR537110 4 0.5868 0.6258 0.116 0.168 0.004 0.712
#> SRR537111 1 0.0336 0.9064 0.992 0.000 0.000 0.008
#> SRR537113 4 0.3243 0.6212 0.036 0.088 0.000 0.876
#> SRR537114 4 0.3895 0.5834 0.036 0.132 0.000 0.832
#> SRR537115 2 0.3649 0.7786 0.000 0.796 0.000 0.204
#> SRR537116 2 0.3791 0.7265 0.000 0.796 0.004 0.200
#> SRR537117 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537118 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537119 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537120 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537121 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537122 2 0.3486 0.7932 0.000 0.812 0.000 0.188
#> SRR537123 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537124 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537125 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537126 2 0.3356 0.8009 0.000 0.824 0.000 0.176
#> SRR537127 3 0.0188 1.0000 0.004 0.000 0.996 0.000
#> SRR537128 3 0.0188 1.0000 0.004 0.000 0.996 0.000
#> SRR537129 3 0.0188 1.0000 0.004 0.000 0.996 0.000
#> SRR537130 3 0.0188 1.0000 0.004 0.000 0.996 0.000
#> SRR537131 3 0.0188 1.0000 0.004 0.000 0.996 0.000
#> SRR537132 3 0.0188 1.0000 0.004 0.000 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.1121 0.922 0.044 0.000 0 0.956 0.000
#> SRR191640 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191641 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191642 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191643 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191644 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191645 4 0.2329 0.864 0.124 0.000 0 0.876 0.000
#> SRR191646 4 0.2329 0.864 0.124 0.000 0 0.876 0.000
#> SRR191647 4 0.1121 0.922 0.044 0.000 0 0.956 0.000
#> SRR191648 4 0.1121 0.922 0.044 0.000 0 0.956 0.000
#> SRR191649 4 0.1544 0.911 0.068 0.000 0 0.932 0.000
#> SRR191650 4 0.1121 0.922 0.044 0.000 0 0.956 0.000
#> SRR191651 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191652 1 0.2127 0.873 0.892 0.000 0 0.108 0.000
#> SRR191653 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191654 4 0.0566 0.924 0.012 0.004 0 0.984 0.000
#> SRR191655 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR191656 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191657 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191658 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191659 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191660 1 0.1410 0.922 0.940 0.000 0 0.060 0.000
#> SRR191661 1 0.2561 0.829 0.856 0.000 0 0.144 0.000
#> SRR191662 1 0.0162 0.967 0.996 0.000 0 0.004 0.000
#> SRR191663 1 0.2020 0.883 0.900 0.000 0 0.100 0.000
#> SRR191664 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191665 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191666 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191667 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191668 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191669 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191670 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191671 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191672 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191673 1 0.0000 0.970 1.000 0.000 0 0.000 0.000
#> SRR191674 5 0.2230 0.865 0.000 0.116 0 0.000 0.884
#> SRR191675 5 0.2230 0.865 0.000 0.116 0 0.000 0.884
#> SRR191677 5 0.2230 0.865 0.000 0.116 0 0.000 0.884
#> SRR191678 5 0.2127 0.868 0.000 0.108 0 0.000 0.892
#> SRR191679 5 0.3305 0.780 0.000 0.224 0 0.000 0.776
#> SRR191680 5 0.2773 0.839 0.000 0.164 0 0.000 0.836
#> SRR191681 5 0.0703 0.870 0.000 0.024 0 0.000 0.976
#> SRR191682 5 0.3816 0.662 0.000 0.304 0 0.000 0.696
#> SRR191683 5 0.3074 0.811 0.000 0.196 0 0.000 0.804
#> SRR191684 2 0.0162 0.920 0.004 0.996 0 0.000 0.000
#> SRR191685 2 0.0703 0.905 0.000 0.976 0 0.000 0.024
#> SRR191686 5 0.2813 0.834 0.000 0.168 0 0.000 0.832
#> SRR191687 2 0.3586 0.573 0.000 0.736 0 0.000 0.264
#> SRR191688 4 0.3707 0.620 0.000 0.284 0 0.716 0.000
#> SRR191689 5 0.2891 0.828 0.000 0.176 0 0.000 0.824
#> SRR191690 4 0.3336 0.700 0.000 0.228 0 0.772 0.000
#> SRR191691 2 0.0162 0.920 0.000 0.996 0 0.000 0.004
#> SRR191692 5 0.1908 0.870 0.000 0.092 0 0.000 0.908
#> SRR191693 5 0.0794 0.871 0.000 0.028 0 0.000 0.972
#> SRR191694 5 0.2471 0.855 0.000 0.136 0 0.000 0.864
#> SRR191695 2 0.4359 0.144 0.000 0.584 0 0.004 0.412
#> SRR191696 2 0.4359 0.146 0.000 0.584 0 0.004 0.412
#> SRR191697 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191698 2 0.0162 0.921 0.000 0.996 0 0.000 0.004
#> SRR191699 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191700 2 0.0404 0.914 0.000 0.988 0 0.000 0.012
#> SRR191701 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191702 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191703 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191704 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191705 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191706 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191707 2 0.0162 0.920 0.000 0.996 0 0.004 0.000
#> SRR191708 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191709 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191710 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191711 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191712 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191713 2 0.1043 0.888 0.040 0.960 0 0.000 0.000
#> SRR191714 2 0.0609 0.908 0.020 0.980 0 0.000 0.000
#> SRR191715 2 0.0000 0.922 0.000 1.000 0 0.000 0.000
#> SRR191716 4 0.3074 0.740 0.000 0.196 0 0.804 0.000
#> SRR191717 2 0.4341 0.253 0.000 0.592 0 0.404 0.004
#> SRR191718 5 0.4294 0.263 0.000 0.468 0 0.000 0.532
#> SRR537099 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR537100 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR537101 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR537102 4 0.0510 0.915 0.000 0.016 0 0.984 0.000
#> SRR537104 4 0.0510 0.926 0.016 0.000 0 0.984 0.000
#> SRR537105 4 0.1478 0.913 0.064 0.000 0 0.936 0.000
#> SRR537106 4 0.1544 0.910 0.068 0.000 0 0.932 0.000
#> SRR537107 4 0.1197 0.920 0.048 0.000 0 0.952 0.000
#> SRR537108 4 0.1121 0.922 0.044 0.000 0 0.956 0.000
#> SRR537109 4 0.2561 0.802 0.000 0.144 0 0.856 0.000
#> SRR537110 2 0.0703 0.899 0.000 0.976 0 0.024 0.000
#> SRR537111 1 0.1478 0.915 0.936 0.000 0 0.064 0.000
#> SRR537113 4 0.2732 0.781 0.000 0.000 0 0.840 0.160
#> SRR537114 4 0.2929 0.758 0.000 0.000 0 0.820 0.180
#> SRR537115 5 0.1331 0.860 0.000 0.008 0 0.040 0.952
#> SRR537116 2 0.0162 0.920 0.000 0.996 0 0.004 0.000
#> SRR537117 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537118 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537119 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537120 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537121 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537122 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537123 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537124 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537125 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537126 5 0.0510 0.866 0.000 0.000 0 0.016 0.984
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 4 0.0547 0.920 0.020 0.000 0 0.980 0.000 0.000
#> SRR191640 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191641 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191642 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191643 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191644 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191645 4 0.1863 0.860 0.104 0.000 0 0.896 0.000 0.000
#> SRR191646 4 0.1863 0.860 0.104 0.000 0 0.896 0.000 0.000
#> SRR191647 4 0.0458 0.921 0.016 0.000 0 0.984 0.000 0.000
#> SRR191648 4 0.0458 0.921 0.016 0.000 0 0.984 0.000 0.000
#> SRR191649 4 0.1141 0.904 0.052 0.000 0 0.948 0.000 0.000
#> SRR191650 4 0.0458 0.921 0.016 0.000 0 0.984 0.000 0.000
#> SRR191651 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191652 1 0.2003 0.862 0.884 0.000 0 0.116 0.000 0.000
#> SRR191653 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191654 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191655 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR191656 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191660 1 0.1267 0.920 0.940 0.000 0 0.060 0.000 0.000
#> SRR191661 1 0.2378 0.818 0.848 0.000 0 0.152 0.000 0.000
#> SRR191662 1 0.0146 0.965 0.996 0.000 0 0.004 0.000 0.000
#> SRR191663 1 0.1765 0.886 0.904 0.000 0 0.096 0.000 0.000
#> SRR191664 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191666 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191667 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191668 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.968 1.000 0.000 0 0.000 0.000 0.000
#> SRR191674 6 0.4264 0.374 0.000 0.016 0 0.000 0.484 0.500
#> SRR191675 6 0.4264 0.374 0.000 0.016 0 0.000 0.484 0.500
#> SRR191677 6 0.4264 0.374 0.000 0.016 0 0.000 0.484 0.500
#> SRR191678 6 0.4315 0.362 0.000 0.012 0 0.004 0.488 0.496
#> SRR191679 6 0.4800 0.379 0.000 0.052 0 0.000 0.448 0.500
#> SRR191680 6 0.4264 0.374 0.000 0.016 0 0.000 0.484 0.500
#> SRR191681 6 0.4264 0.374 0.000 0.016 0 0.000 0.484 0.500
#> SRR191682 6 0.4926 0.198 0.000 0.240 0 0.000 0.120 0.640
#> SRR191683 6 0.1327 0.364 0.000 0.064 0 0.000 0.000 0.936
#> SRR191684 6 0.3817 -0.181 0.000 0.432 0 0.000 0.000 0.568
#> SRR191685 6 0.3817 -0.181 0.000 0.432 0 0.000 0.000 0.568
#> SRR191686 6 0.0363 0.374 0.000 0.012 0 0.000 0.000 0.988
#> SRR191687 6 0.3944 -0.174 0.000 0.428 0 0.000 0.004 0.568
#> SRR191688 4 0.3428 0.569 0.000 0.304 0 0.696 0.000 0.000
#> SRR191689 6 0.3907 0.405 0.000 0.004 0 0.000 0.408 0.588
#> SRR191690 4 0.3101 0.676 0.000 0.244 0 0.756 0.000 0.000
#> SRR191691 2 0.1444 0.841 0.000 0.928 0 0.000 0.000 0.072
#> SRR191692 6 0.4264 0.374 0.000 0.016 0 0.000 0.484 0.500
#> SRR191693 6 0.2883 0.430 0.000 0.000 0 0.000 0.212 0.788
#> SRR191694 6 0.3482 0.420 0.000 0.000 0 0.000 0.316 0.684
#> SRR191695 2 0.2912 0.677 0.000 0.784 0 0.000 0.216 0.000
#> SRR191696 2 0.2941 0.671 0.000 0.780 0 0.000 0.220 0.000
#> SRR191697 2 0.1204 0.850 0.000 0.944 0 0.000 0.056 0.000
#> SRR191698 2 0.1584 0.847 0.000 0.928 0 0.000 0.064 0.008
#> SRR191699 2 0.3659 0.511 0.000 0.636 0 0.000 0.000 0.364
#> SRR191700 2 0.1444 0.845 0.000 0.928 0 0.000 0.072 0.000
#> SRR191701 2 0.1204 0.850 0.000 0.944 0 0.000 0.056 0.000
#> SRR191702 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191703 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191704 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191705 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191706 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191707 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191708 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191709 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191710 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191711 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR191712 2 0.0547 0.863 0.000 0.980 0 0.020 0.000 0.000
#> SRR191713 2 0.4488 0.331 0.032 0.548 0 0.000 0.000 0.420
#> SRR191714 2 0.2094 0.810 0.020 0.900 0 0.000 0.000 0.080
#> SRR191715 2 0.2823 0.674 0.000 0.796 0 0.000 0.000 0.204
#> SRR191716 4 0.2941 0.708 0.000 0.220 0 0.780 0.000 0.000
#> SRR191717 2 0.3899 0.272 0.000 0.592 0 0.404 0.004 0.000
#> SRR191718 2 0.3482 0.496 0.000 0.684 0 0.000 0.316 0.000
#> SRR537099 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR537100 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR537101 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR537102 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR537104 4 0.0000 0.924 0.000 0.000 0 1.000 0.000 0.000
#> SRR537105 4 0.1075 0.906 0.048 0.000 0 0.952 0.000 0.000
#> SRR537106 4 0.1141 0.903 0.052 0.000 0 0.948 0.000 0.000
#> SRR537107 4 0.0632 0.919 0.024 0.000 0 0.976 0.000 0.000
#> SRR537108 4 0.0458 0.921 0.016 0.000 0 0.984 0.000 0.000
#> SRR537109 4 0.1501 0.871 0.000 0.076 0 0.924 0.000 0.000
#> SRR537110 2 0.0146 0.870 0.000 0.996 0 0.004 0.000 0.000
#> SRR537111 1 0.1700 0.894 0.916 0.004 0 0.080 0.000 0.000
#> SRR537113 4 0.3528 0.617 0.000 0.004 0 0.700 0.296 0.000
#> SRR537114 4 0.3409 0.614 0.000 0.000 0 0.700 0.300 0.000
#> SRR537115 5 0.3775 0.500 0.000 0.016 0 0.228 0.744 0.012
#> SRR537116 2 0.0000 0.872 0.000 1.000 0 0.000 0.000 0.000
#> SRR537117 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537118 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537119 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537120 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537121 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537122 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537123 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537124 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537125 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537126 5 0.0000 0.953 0.000 0.000 0 0.000 1.000 0.000
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.514 0.761 0.860 0.4140 0.629 0.629
#> 3 3 0.531 0.685 0.844 0.4558 0.591 0.443
#> 4 4 0.896 0.877 0.940 0.1083 0.780 0.552
#> 5 5 0.688 0.722 0.828 0.0905 0.864 0.639
#> 6 6 0.821 0.791 0.892 0.0875 0.884 0.619
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
#> SRR191639 1 0.3274 0.899 0.940 0.060
#> SRR191640 2 0.9710 0.617 0.400 0.600
#> SRR191641 2 0.9710 0.617 0.400 0.600
#> SRR191642 2 0.9710 0.617 0.400 0.600
#> SRR191643 2 0.9710 0.617 0.400 0.600
#> SRR191644 2 0.9710 0.617 0.400 0.600
#> SRR191645 2 0.9710 0.617 0.400 0.600
#> SRR191646 2 0.9710 0.617 0.400 0.600
#> SRR191647 2 0.9710 0.617 0.400 0.600
#> SRR191648 2 0.9710 0.617 0.400 0.600
#> SRR191649 2 0.9710 0.617 0.400 0.600
#> SRR191650 2 0.9710 0.617 0.400 0.600
#> SRR191651 1 0.0376 0.967 0.996 0.004
#> SRR191652 1 0.6343 0.722 0.840 0.160
#> SRR191653 2 0.9710 0.617 0.400 0.600
#> SRR191654 2 0.9710 0.617 0.400 0.600
#> SRR191655 2 0.9710 0.617 0.400 0.600
#> SRR191656 1 0.0376 0.967 0.996 0.004
#> SRR191657 1 0.0376 0.967 0.996 0.004
#> SRR191658 1 0.0376 0.967 0.996 0.004
#> SRR191659 1 0.0376 0.967 0.996 0.004
#> SRR191660 1 0.0376 0.967 0.996 0.004
#> SRR191661 1 0.9427 0.125 0.640 0.360
#> SRR191662 1 0.0376 0.967 0.996 0.004
#> SRR191663 1 0.0672 0.964 0.992 0.008
#> SRR191664 1 0.0376 0.967 0.996 0.004
#> SRR191665 1 0.0376 0.967 0.996 0.004
#> SRR191666 1 0.0000 0.966 1.000 0.000
#> SRR191667 1 0.0000 0.966 1.000 0.000
#> SRR191668 1 0.0376 0.967 0.996 0.004
#> SRR191669 1 0.0376 0.967 0.996 0.004
#> SRR191670 1 0.0376 0.967 0.996 0.004
#> SRR191671 1 0.0376 0.967 0.996 0.004
#> SRR191672 1 0.0376 0.967 0.996 0.004
#> SRR191673 1 0.0376 0.967 0.996 0.004
#> SRR191674 2 0.0000 0.778 0.000 1.000
#> SRR191675 2 0.0000 0.778 0.000 1.000
#> SRR191677 2 0.0000 0.778 0.000 1.000
#> SRR191678 2 0.0000 0.778 0.000 1.000
#> SRR191679 2 0.0000 0.778 0.000 1.000
#> SRR191680 2 0.0000 0.778 0.000 1.000
#> SRR191681 2 0.0000 0.778 0.000 1.000
#> SRR191682 2 0.0000 0.778 0.000 1.000
#> SRR191683 2 0.0000 0.778 0.000 1.000
#> SRR191684 2 0.0000 0.778 0.000 1.000
#> SRR191685 2 0.0000 0.778 0.000 1.000
#> SRR191686 2 0.0000 0.778 0.000 1.000
#> SRR191687 2 0.0000 0.778 0.000 1.000
#> SRR191688 2 0.0000 0.778 0.000 1.000
#> SRR191689 2 0.0000 0.778 0.000 1.000
#> SRR191690 2 0.0000 0.778 0.000 1.000
#> SRR191691 2 0.0000 0.778 0.000 1.000
#> SRR191692 2 0.0000 0.778 0.000 1.000
#> SRR191693 2 0.0000 0.778 0.000 1.000
#> SRR191694 2 0.0000 0.778 0.000 1.000
#> SRR191695 2 0.0000 0.778 0.000 1.000
#> SRR191696 2 0.0000 0.778 0.000 1.000
#> SRR191697 2 0.0000 0.778 0.000 1.000
#> SRR191698 2 0.0000 0.778 0.000 1.000
#> SRR191699 2 0.0000 0.778 0.000 1.000
#> SRR191700 2 0.0000 0.778 0.000 1.000
#> SRR191701 2 0.0000 0.778 0.000 1.000
#> SRR191702 2 0.0000 0.778 0.000 1.000
#> SRR191703 2 0.0000 0.778 0.000 1.000
#> SRR191704 2 0.0000 0.778 0.000 1.000
#> SRR191705 2 0.0000 0.778 0.000 1.000
#> SRR191706 2 0.0000 0.778 0.000 1.000
#> SRR191707 2 0.0000 0.778 0.000 1.000
#> SRR191708 2 0.0000 0.778 0.000 1.000
#> SRR191709 2 0.0000 0.778 0.000 1.000
#> SRR191710 2 0.0000 0.778 0.000 1.000
#> SRR191711 2 0.0000 0.778 0.000 1.000
#> SRR191712 2 0.0000 0.778 0.000 1.000
#> SRR191713 2 0.0000 0.778 0.000 1.000
#> SRR191714 2 0.0000 0.778 0.000 1.000
#> SRR191715 2 0.0000 0.778 0.000 1.000
#> SRR191716 2 0.0000 0.778 0.000 1.000
#> SRR191717 2 0.0000 0.778 0.000 1.000
#> SRR191718 2 0.0000 0.778 0.000 1.000
#> SRR537099 2 0.9710 0.617 0.400 0.600
#> SRR537100 2 0.9710 0.617 0.400 0.600
#> SRR537101 2 0.9710 0.617 0.400 0.600
#> SRR537102 2 0.9710 0.617 0.400 0.600
#> SRR537104 2 0.9710 0.617 0.400 0.600
#> SRR537105 2 0.9710 0.617 0.400 0.600
#> SRR537106 2 0.9710 0.617 0.400 0.600
#> SRR537107 2 0.9710 0.617 0.400 0.600
#> SRR537108 2 0.9710 0.617 0.400 0.600
#> SRR537109 2 0.8016 0.696 0.244 0.756
#> SRR537110 2 0.7139 0.714 0.196 0.804
#> SRR537111 2 0.9686 0.618 0.396 0.604
#> SRR537113 2 0.9710 0.617 0.400 0.600
#> SRR537114 2 0.9710 0.617 0.400 0.600
#> SRR537115 2 0.9491 0.643 0.368 0.632
#> SRR537116 2 0.0000 0.778 0.000 1.000
#> SRR537117 2 0.9491 0.643 0.368 0.632
#> SRR537118 2 0.9491 0.643 0.368 0.632
#> SRR537119 2 0.9491 0.643 0.368 0.632
#> SRR537120 2 0.9491 0.643 0.368 0.632
#> SRR537121 2 0.9491 0.643 0.368 0.632
#> SRR537122 2 0.9491 0.643 0.368 0.632
#> SRR537123 2 0.9491 0.643 0.368 0.632
#> SRR537124 2 0.9491 0.643 0.368 0.632
#> SRR537125 2 0.9491 0.643 0.368 0.632
#> SRR537126 2 0.9491 0.643 0.368 0.632
#> SRR537127 1 0.0000 0.966 1.000 0.000
#> SRR537128 1 0.0000 0.966 1.000 0.000
#> SRR537129 1 0.0000 0.966 1.000 0.000
#> SRR537130 1 0.0000 0.966 1.000 0.000
#> SRR537131 1 0.0000 0.966 1.000 0.000
#> SRR537132 1 0.0000 0.966 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0592 0.642 0.988 0.000 0.012
#> SRR191640 1 0.7717 0.505 0.680 0.148 0.172
#> SRR191641 1 0.3482 0.615 0.872 0.000 0.128
#> SRR191642 1 0.8972 0.392 0.564 0.236 0.200
#> SRR191643 1 0.8322 0.418 0.604 0.276 0.120
#> SRR191644 1 0.3253 0.639 0.912 0.052 0.036
#> SRR191645 1 0.0829 0.643 0.984 0.004 0.012
#> SRR191646 1 0.0983 0.643 0.980 0.004 0.016
#> SRR191647 1 0.4679 0.581 0.832 0.148 0.020
#> SRR191648 1 0.4937 0.579 0.824 0.148 0.028
#> SRR191649 1 0.5412 0.585 0.796 0.032 0.172
#> SRR191650 1 0.0424 0.642 0.992 0.000 0.008
#> SRR191651 1 0.2796 0.627 0.908 0.000 0.092
#> SRR191652 1 0.2165 0.635 0.936 0.000 0.064
#> SRR191653 1 0.7729 0.206 0.516 0.048 0.436
#> SRR191654 1 0.7806 0.348 0.584 0.064 0.352
#> SRR191655 1 0.5061 0.564 0.784 0.008 0.208
#> SRR191656 1 0.4654 0.519 0.792 0.000 0.208
#> SRR191657 1 0.2625 0.631 0.916 0.000 0.084
#> SRR191658 1 0.2711 0.629 0.912 0.000 0.088
#> SRR191659 1 0.2711 0.629 0.912 0.000 0.088
#> SRR191660 1 0.2711 0.629 0.912 0.000 0.088
#> SRR191661 1 0.2625 0.631 0.916 0.000 0.084
#> SRR191662 1 0.2711 0.629 0.912 0.000 0.088
#> SRR191663 1 0.2711 0.631 0.912 0.000 0.088
#> SRR191664 1 0.2448 0.633 0.924 0.000 0.076
#> SRR191665 1 0.2959 0.622 0.900 0.000 0.100
#> SRR191666 3 0.5098 0.522 0.248 0.000 0.752
#> SRR191667 3 0.5098 0.522 0.248 0.000 0.752
#> SRR191668 1 0.4654 0.519 0.792 0.000 0.208
#> SRR191669 1 0.4654 0.519 0.792 0.000 0.208
#> SRR191670 1 0.2959 0.622 0.900 0.000 0.100
#> SRR191671 1 0.2959 0.622 0.900 0.000 0.100
#> SRR191672 1 0.4750 0.512 0.784 0.000 0.216
#> SRR191673 1 0.4750 0.512 0.784 0.000 0.216
#> SRR191674 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191678 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191679 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191682 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191690 2 0.3482 0.794 0.128 0.872 0.000
#> SRR191691 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191692 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191693 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191695 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191700 2 0.6126 0.220 0.400 0.600 0.000
#> SRR191701 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191709 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191713 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191716 2 0.3038 0.826 0.104 0.896 0.000
#> SRR191717 2 0.0000 0.946 0.000 1.000 0.000
#> SRR191718 2 0.0000 0.946 0.000 1.000 0.000
#> SRR537099 1 0.6848 0.565 0.736 0.100 0.164
#> SRR537100 1 0.5158 0.547 0.764 0.004 0.232
#> SRR537101 1 0.3038 0.623 0.896 0.000 0.104
#> SRR537102 1 0.8925 0.394 0.564 0.256 0.180
#> SRR537104 2 0.9633 -0.181 0.352 0.436 0.212
#> SRR537105 1 0.8972 0.392 0.564 0.236 0.200
#> SRR537106 1 0.9081 0.382 0.552 0.236 0.212
#> SRR537107 1 0.9081 0.382 0.552 0.236 0.212
#> SRR537108 1 0.9081 0.382 0.552 0.236 0.212
#> SRR537109 2 0.0000 0.946 0.000 1.000 0.000
#> SRR537110 2 0.4062 0.740 0.164 0.836 0.000
#> SRR537111 1 0.0424 0.642 0.992 0.000 0.008
#> SRR537113 2 0.8966 0.205 0.268 0.556 0.176
#> SRR537114 1 0.9696 0.175 0.396 0.388 0.216
#> SRR537115 2 0.8748 0.295 0.172 0.584 0.244
#> SRR537116 2 0.0000 0.946 0.000 1.000 0.000
#> SRR537117 1 0.9666 0.134 0.412 0.376 0.212
#> SRR537118 1 0.8610 0.295 0.548 0.116 0.336
#> SRR537119 1 0.8610 0.295 0.548 0.116 0.336
#> SRR537120 1 0.9001 0.299 0.548 0.172 0.280
#> SRR537121 1 0.8610 0.295 0.548 0.116 0.336
#> SRR537122 1 0.8592 0.302 0.552 0.116 0.332
#> SRR537123 1 0.8719 0.297 0.548 0.128 0.324
#> SRR537124 1 0.9083 0.303 0.548 0.196 0.256
#> SRR537125 1 0.8610 0.295 0.548 0.116 0.336
#> SRR537126 1 0.8610 0.295 0.548 0.116 0.336
#> SRR537127 3 0.0000 0.887 0.000 0.000 1.000
#> SRR537128 3 0.0000 0.887 0.000 0.000 1.000
#> SRR537129 3 0.0000 0.887 0.000 0.000 1.000
#> SRR537130 3 0.0000 0.887 0.000 0.000 1.000
#> SRR537131 3 0.0000 0.887 0.000 0.000 1.000
#> SRR537132 3 0.0000 0.887 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.2281 0.8713 0.904 0.000 0 0.096
#> SRR191640 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR191641 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR191642 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR191643 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR191644 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR191645 4 0.2081 0.8606 0.084 0.000 0 0.916
#> SRR191646 4 0.2814 0.8218 0.132 0.000 0 0.868
#> SRR191647 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR191648 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR191649 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR191650 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR191651 1 0.2081 0.8888 0.916 0.000 0 0.084
#> SRR191652 4 0.4804 0.4413 0.384 0.000 0 0.616
#> SRR191653 4 0.1635 0.8759 0.044 0.008 0 0.948
#> SRR191654 4 0.1635 0.8759 0.044 0.008 0 0.948
#> SRR191655 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR191656 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191657 1 0.0336 0.9719 0.992 0.000 0 0.008
#> SRR191658 1 0.0336 0.9719 0.992 0.000 0 0.008
#> SRR191659 1 0.0336 0.9719 0.992 0.000 0 0.008
#> SRR191660 1 0.0336 0.9719 0.992 0.000 0 0.008
#> SRR191661 1 0.0707 0.9608 0.980 0.000 0 0.020
#> SRR191662 1 0.0336 0.9719 0.992 0.000 0 0.008
#> SRR191663 1 0.0336 0.9719 0.992 0.000 0 0.008
#> SRR191664 1 0.2011 0.8939 0.920 0.000 0 0.080
#> SRR191665 1 0.0188 0.9714 0.996 0.000 0 0.004
#> SRR191666 4 0.4804 0.4413 0.384 0.000 0 0.616
#> SRR191667 4 0.4804 0.4413 0.384 0.000 0 0.616
#> SRR191668 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191669 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191670 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191671 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191672 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191673 1 0.0000 0.9703 1.000 0.000 0 0.000
#> SRR191674 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191675 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191677 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191678 2 0.2149 0.8886 0.000 0.912 0 0.088
#> SRR191679 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191680 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191681 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191682 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191683 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191684 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191685 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191686 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191687 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191688 2 0.3907 0.6899 0.000 0.768 0 0.232
#> SRR191689 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191690 4 0.4955 0.2462 0.000 0.444 0 0.556
#> SRR191691 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191692 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191693 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191694 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191695 2 0.1474 0.9272 0.000 0.948 0 0.052
#> SRR191696 2 0.0921 0.9499 0.000 0.972 0 0.028
#> SRR191697 2 0.0921 0.9499 0.000 0.972 0 0.028
#> SRR191698 2 0.3975 0.6773 0.000 0.760 0 0.240
#> SRR191699 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191700 4 0.4898 0.3032 0.000 0.416 0 0.584
#> SRR191701 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191702 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191703 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191704 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191705 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191706 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191707 2 0.1940 0.9021 0.000 0.924 0 0.076
#> SRR191708 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191709 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191710 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191711 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191712 2 0.0921 0.9499 0.000 0.972 0 0.028
#> SRR191713 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191714 2 0.0000 0.9690 0.000 1.000 0 0.000
#> SRR191715 2 0.0336 0.9639 0.000 0.992 0 0.008
#> SRR191716 4 0.4925 0.2934 0.000 0.428 0 0.572
#> SRR191717 2 0.3123 0.8002 0.000 0.844 0 0.156
#> SRR191718 2 0.0921 0.9499 0.000 0.972 0 0.028
#> SRR537099 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537100 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537101 4 0.1716 0.8716 0.064 0.000 0 0.936
#> SRR537102 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537104 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537105 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537106 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537107 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537108 4 0.2002 0.8776 0.044 0.020 0 0.936
#> SRR537109 4 0.5000 0.0753 0.000 0.496 0 0.504
#> SRR537110 4 0.4888 0.3369 0.000 0.412 0 0.588
#> SRR537111 4 0.3975 0.6921 0.240 0.000 0 0.760
#> SRR537113 4 0.2111 0.8760 0.044 0.024 0 0.932
#> SRR537114 4 0.1913 0.8771 0.040 0.020 0 0.940
#> SRR537115 4 0.1792 0.8278 0.000 0.068 0 0.932
#> SRR537116 2 0.0921 0.9499 0.000 0.972 0 0.028
#> SRR537117 4 0.0707 0.8613 0.000 0.020 0 0.980
#> SRR537118 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537119 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537120 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537121 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537122 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537123 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537124 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537125 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537126 4 0.0000 0.8589 0.000 0.000 0 1.000
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 5 0.6576 -0.696 0.340 0.000 0 0.216 0.444
#> SRR191640 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR191641 4 0.2230 0.729 0.000 0.000 0 0.884 0.116
#> SRR191642 4 0.0162 0.769 0.000 0.004 0 0.996 0.000
#> SRR191643 4 0.3461 0.671 0.000 0.224 0 0.772 0.004
#> SRR191644 4 0.3461 0.671 0.000 0.224 0 0.772 0.004
#> SRR191645 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR191646 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR191647 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR191648 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR191649 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR191650 4 0.3707 0.657 0.004 0.008 0 0.768 0.220
#> SRR191651 1 0.5689 0.889 0.480 0.000 0 0.080 0.440
#> SRR191652 4 0.5382 0.495 0.104 0.000 0 0.644 0.252
#> SRR191653 4 0.3461 0.671 0.000 0.224 0 0.772 0.004
#> SRR191654 4 0.3461 0.671 0.000 0.224 0 0.772 0.004
#> SRR191655 4 0.2377 0.735 0.000 0.128 0 0.872 0.000
#> SRR191656 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191657 1 0.4415 0.980 0.552 0.000 0 0.004 0.444
#> SRR191658 1 0.4268 0.982 0.556 0.000 0 0.000 0.444
#> SRR191659 1 0.4415 0.980 0.552 0.000 0 0.004 0.444
#> SRR191660 1 0.4415 0.980 0.552 0.000 0 0.004 0.444
#> SRR191661 5 0.6217 -0.809 0.416 0.000 0 0.140 0.444
#> SRR191662 1 0.4415 0.980 0.552 0.000 0 0.004 0.444
#> SRR191663 1 0.4627 0.974 0.544 0.000 0 0.012 0.444
#> SRR191664 1 0.5283 0.933 0.508 0.000 0 0.048 0.444
#> SRR191665 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191666 4 0.5595 0.467 0.124 0.000 0 0.624 0.252
#> SRR191667 4 0.5595 0.467 0.124 0.000 0 0.624 0.252
#> SRR191668 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191669 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191670 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191671 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191672 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191673 1 0.4262 0.983 0.560 0.000 0 0.000 0.440
#> SRR191674 2 0.4182 0.804 0.400 0.600 0 0.000 0.000
#> SRR191675 2 0.4182 0.804 0.400 0.600 0 0.000 0.000
#> SRR191677 2 0.3966 0.798 0.336 0.664 0 0.000 0.000
#> SRR191678 2 0.0771 0.583 0.000 0.976 0 0.020 0.004
#> SRR191679 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191680 2 0.4150 0.802 0.388 0.612 0 0.000 0.000
#> SRR191681 2 0.3932 0.797 0.328 0.672 0 0.000 0.000
#> SRR191682 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191683 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191684 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191685 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191686 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191687 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191688 2 0.0566 0.583 0.000 0.984 0 0.012 0.004
#> SRR191689 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191690 2 0.3491 0.380 0.000 0.768 0 0.228 0.004
#> SRR191691 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191692 2 0.4150 0.803 0.388 0.612 0 0.000 0.000
#> SRR191693 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191694 2 0.4256 0.805 0.436 0.564 0 0.000 0.000
#> SRR191695 2 0.0404 0.588 0.000 0.988 0 0.012 0.000
#> SRR191696 2 0.0798 0.610 0.016 0.976 0 0.008 0.000
#> SRR191697 2 0.2074 0.695 0.104 0.896 0 0.000 0.000
#> SRR191698 2 0.4335 0.517 0.072 0.772 0 0.152 0.004
#> SRR191699 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191700 2 0.3607 0.358 0.000 0.752 0 0.244 0.004
#> SRR191701 2 0.3949 0.797 0.332 0.668 0 0.000 0.000
#> SRR191702 2 0.4161 0.803 0.392 0.608 0 0.000 0.000
#> SRR191703 2 0.4171 0.803 0.396 0.604 0 0.000 0.000
#> SRR191704 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191705 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191706 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191707 2 0.0451 0.588 0.000 0.988 0 0.008 0.004
#> SRR191708 2 0.3932 0.785 0.328 0.672 0 0.000 0.000
#> SRR191709 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191710 2 0.3949 0.786 0.332 0.668 0 0.000 0.000
#> SRR191711 2 0.3730 0.781 0.288 0.712 0 0.000 0.000
#> SRR191712 2 0.3160 0.740 0.188 0.808 0 0.004 0.000
#> SRR191713 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191714 2 0.4262 0.806 0.440 0.560 0 0.000 0.000
#> SRR191715 2 0.3534 0.772 0.256 0.744 0 0.000 0.000
#> SRR191716 2 0.3160 0.435 0.000 0.808 0 0.188 0.004
#> SRR191717 2 0.0566 0.583 0.000 0.984 0 0.012 0.004
#> SRR191718 2 0.2338 0.698 0.112 0.884 0 0.004 0.000
#> SRR537099 4 0.3461 0.671 0.000 0.224 0 0.772 0.004
#> SRR537100 4 0.3461 0.671 0.000 0.224 0 0.772 0.004
#> SRR537101 4 0.0290 0.767 0.000 0.000 0 0.992 0.008
#> SRR537102 4 0.1357 0.770 0.000 0.048 0 0.948 0.004
#> SRR537104 4 0.3579 0.662 0.000 0.240 0 0.756 0.004
#> SRR537105 4 0.0162 0.769 0.000 0.004 0 0.996 0.000
#> SRR537106 4 0.1124 0.771 0.000 0.036 0 0.960 0.004
#> SRR537107 4 0.1357 0.770 0.000 0.048 0 0.948 0.004
#> SRR537108 4 0.1952 0.759 0.000 0.084 0 0.912 0.004
#> SRR537109 2 0.1124 0.563 0.000 0.960 0 0.036 0.004
#> SRR537110 2 0.3741 0.314 0.000 0.732 0 0.264 0.004
#> SRR537111 4 0.3790 0.624 0.004 0.004 0 0.744 0.248
#> SRR537113 4 0.5425 0.459 0.000 0.320 0 0.600 0.080
#> SRR537114 4 0.4905 0.564 0.000 0.224 0 0.696 0.080
#> SRR537115 2 0.6363 -0.474 0.000 0.504 0 0.304 0.192
#> SRR537116 2 0.0290 0.592 0.000 0.992 0 0.008 0.000
#> SRR537117 5 0.6603 0.503 0.000 0.392 0 0.212 0.396
#> SRR537118 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537119 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537120 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537121 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537122 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537123 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537124 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537125 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537126 5 0.6203 0.727 0.000 0.224 0 0.224 0.552
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.3923 0.2626 0.580 0.004 0.000 0.416 0.000 0.000
#> SRR191640 4 0.0000 0.8981 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR191641 4 0.0260 0.8966 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR191642 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR191643 4 0.0363 0.8961 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR191644 4 0.0547 0.8928 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR191645 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR191646 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR191647 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR191648 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR191649 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR191650 4 0.0363 0.8952 0.012 0.000 0.000 0.988 0.000 0.000
#> SRR191651 1 0.2838 0.7038 0.808 0.004 0.000 0.188 0.000 0.000
#> SRR191652 4 0.1349 0.8669 0.056 0.004 0.000 0.940 0.000 0.000
#> SRR191653 4 0.0363 0.8961 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR191654 4 0.0363 0.8961 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR191655 4 0.0260 0.8971 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR191656 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191660 1 0.0458 0.9099 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR191661 4 0.3737 0.3317 0.392 0.000 0.000 0.608 0.000 0.000
#> SRR191662 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191663 1 0.2996 0.6510 0.772 0.000 0.000 0.228 0.000 0.000
#> SRR191664 1 0.0291 0.9185 0.992 0.004 0.000 0.004 0.000 0.000
#> SRR191665 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191666 4 0.2011 0.8506 0.064 0.004 0.020 0.912 0.000 0.000
#> SRR191667 4 0.2322 0.8401 0.064 0.004 0.036 0.896 0.000 0.000
#> SRR191668 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.9235 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191674 6 0.2730 0.7249 0.000 0.192 0.000 0.000 0.000 0.808
#> SRR191675 6 0.2664 0.7323 0.000 0.184 0.000 0.000 0.000 0.816
#> SRR191677 6 0.3717 0.3707 0.000 0.384 0.000 0.000 0.000 0.616
#> SRR191678 2 0.3126 0.6537 0.000 0.752 0.000 0.000 0.000 0.248
#> SRR191679 6 0.0632 0.8337 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR191680 6 0.3266 0.6195 0.000 0.272 0.000 0.000 0.000 0.728
#> SRR191681 6 0.3804 0.2519 0.000 0.424 0.000 0.000 0.000 0.576
#> SRR191682 6 0.1765 0.8395 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR191683 6 0.1765 0.8395 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR191684 6 0.1007 0.8428 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR191685 6 0.0363 0.8311 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR191686 6 0.0547 0.8372 0.000 0.020 0.000 0.000 0.000 0.980
#> SRR191687 6 0.0363 0.8311 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR191688 2 0.1444 0.7873 0.000 0.928 0.000 0.000 0.000 0.072
#> SRR191689 6 0.0363 0.8311 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR191690 2 0.3307 0.7234 0.000 0.820 0.000 0.108 0.000 0.072
#> SRR191691 6 0.1610 0.8426 0.000 0.084 0.000 0.000 0.000 0.916
#> SRR191692 6 0.2664 0.7282 0.000 0.184 0.000 0.000 0.000 0.816
#> SRR191693 6 0.0458 0.8289 0.000 0.016 0.000 0.000 0.000 0.984
#> SRR191694 6 0.0458 0.8289 0.000 0.016 0.000 0.000 0.000 0.984
#> SRR191695 2 0.1444 0.7873 0.000 0.928 0.000 0.000 0.000 0.072
#> SRR191696 2 0.1444 0.7873 0.000 0.928 0.000 0.000 0.000 0.072
#> SRR191697 2 0.2730 0.7463 0.000 0.808 0.000 0.000 0.000 0.192
#> SRR191698 6 0.3684 0.3656 0.000 0.372 0.000 0.000 0.000 0.628
#> SRR191699 6 0.0937 0.8424 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR191700 4 0.6711 -0.3107 0.000 0.304 0.000 0.356 0.032 0.308
#> SRR191701 6 0.2562 0.7849 0.000 0.172 0.000 0.000 0.000 0.828
#> SRR191702 6 0.3198 0.7084 0.000 0.260 0.000 0.000 0.000 0.740
#> SRR191703 6 0.3076 0.7344 0.000 0.240 0.000 0.000 0.000 0.760
#> SRR191704 6 0.1765 0.8395 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR191705 6 0.1765 0.8395 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR191706 6 0.1765 0.8395 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR191707 2 0.2631 0.7584 0.000 0.820 0.000 0.000 0.000 0.180
#> SRR191708 6 0.2092 0.8224 0.000 0.124 0.000 0.000 0.000 0.876
#> SRR191709 6 0.1663 0.8411 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR191710 6 0.1863 0.8361 0.000 0.104 0.000 0.000 0.000 0.896
#> SRR191711 6 0.3371 0.6683 0.000 0.292 0.000 0.000 0.000 0.708
#> SRR191712 2 0.3531 0.5222 0.000 0.672 0.000 0.000 0.000 0.328
#> SRR191713 6 0.1556 0.8420 0.000 0.080 0.000 0.000 0.000 0.920
#> SRR191714 6 0.1556 0.8420 0.000 0.080 0.000 0.000 0.000 0.920
#> SRR191715 2 0.3843 -0.0582 0.000 0.548 0.000 0.000 0.000 0.452
#> SRR191716 2 0.2744 0.7587 0.000 0.864 0.000 0.064 0.000 0.072
#> SRR191717 2 0.1444 0.7873 0.000 0.928 0.000 0.000 0.000 0.072
#> SRR191718 2 0.2664 0.7578 0.000 0.816 0.000 0.000 0.000 0.184
#> SRR537099 4 0.1267 0.8664 0.000 0.060 0.000 0.940 0.000 0.000
#> SRR537100 4 0.0260 0.8971 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR537101 4 0.0000 0.8981 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR537102 4 0.0000 0.8981 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR537104 4 0.2912 0.6963 0.000 0.216 0.000 0.784 0.000 0.000
#> SRR537105 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR537106 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR537107 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR537108 4 0.0146 0.8984 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR537109 2 0.2340 0.7446 0.000 0.852 0.000 0.000 0.000 0.148
#> SRR537110 4 0.6032 -0.1250 0.000 0.288 0.000 0.424 0.000 0.288
#> SRR537111 4 0.1663 0.8431 0.088 0.000 0.000 0.912 0.000 0.000
#> SRR537113 4 0.4795 0.2691 0.000 0.400 0.000 0.544 0.056 0.000
#> SRR537114 4 0.2009 0.8487 0.000 0.024 0.000 0.908 0.068 0.000
#> SRR537115 2 0.5303 0.3966 0.000 0.600 0.000 0.204 0.196 0.000
#> SRR537116 2 0.2697 0.7550 0.000 0.812 0.000 0.000 0.000 0.188
#> SRR537117 2 0.3872 0.2847 0.000 0.604 0.000 0.004 0.392 0.000
#> SRR537118 5 0.0000 0.9902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537119 5 0.0000 0.9902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537120 5 0.0000 0.9902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537121 5 0.0260 0.9877 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR537122 5 0.0260 0.9877 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR537123 5 0.0806 0.9637 0.000 0.008 0.000 0.020 0.972 0.000
#> SRR537124 5 0.0260 0.9877 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR537125 5 0.0000 0.9902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537126 5 0.0000 0.9902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 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 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 0.889 0.912 0.965 0.4869 0.510 0.510
#> 3 3 0.952 0.906 0.964 0.1475 0.877 0.772
#> 4 4 0.593 0.629 0.826 0.2562 0.714 0.441
#> 5 5 0.759 0.757 0.872 0.1193 0.814 0.466
#> 6 6 0.766 0.694 0.814 0.0479 0.917 0.648
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
#> SRR191639 1 0.0000 0.9539 1.000 0.000
#> SRR191640 1 0.0000 0.9539 1.000 0.000
#> SRR191641 1 0.0000 0.9539 1.000 0.000
#> SRR191642 1 0.6973 0.7655 0.812 0.188
#> SRR191643 1 0.9635 0.3921 0.612 0.388
#> SRR191644 1 0.6438 0.7948 0.836 0.164
#> SRR191645 1 0.0000 0.9539 1.000 0.000
#> SRR191646 1 0.0000 0.9539 1.000 0.000
#> SRR191647 1 0.0000 0.9539 1.000 0.000
#> SRR191648 1 0.0000 0.9539 1.000 0.000
#> SRR191649 1 0.0000 0.9539 1.000 0.000
#> SRR191650 1 0.0000 0.9539 1.000 0.000
#> SRR191651 1 0.0000 0.9539 1.000 0.000
#> SRR191652 1 0.0000 0.9539 1.000 0.000
#> SRR191653 1 0.0000 0.9539 1.000 0.000
#> SRR191654 1 0.8207 0.6681 0.744 0.256
#> SRR191655 1 0.0000 0.9539 1.000 0.000
#> SRR191656 1 0.0000 0.9539 1.000 0.000
#> SRR191657 1 0.0000 0.9539 1.000 0.000
#> SRR191658 1 0.0000 0.9539 1.000 0.000
#> SRR191659 1 0.0000 0.9539 1.000 0.000
#> SRR191660 1 0.0000 0.9539 1.000 0.000
#> SRR191661 1 0.0000 0.9539 1.000 0.000
#> SRR191662 1 0.0000 0.9539 1.000 0.000
#> SRR191663 1 0.0000 0.9539 1.000 0.000
#> SRR191664 1 0.0000 0.9539 1.000 0.000
#> SRR191665 1 0.0000 0.9539 1.000 0.000
#> SRR191666 1 0.0000 0.9539 1.000 0.000
#> SRR191667 1 0.0000 0.9539 1.000 0.000
#> SRR191668 1 0.0000 0.9539 1.000 0.000
#> SRR191669 1 0.0000 0.9539 1.000 0.000
#> SRR191670 1 0.0000 0.9539 1.000 0.000
#> SRR191671 1 0.0000 0.9539 1.000 0.000
#> SRR191672 1 0.0000 0.9539 1.000 0.000
#> SRR191673 1 0.0000 0.9539 1.000 0.000
#> SRR191674 2 0.0000 0.9674 0.000 1.000
#> SRR191675 2 0.0000 0.9674 0.000 1.000
#> SRR191677 2 0.0000 0.9674 0.000 1.000
#> SRR191678 2 0.0000 0.9674 0.000 1.000
#> SRR191679 2 0.0000 0.9674 0.000 1.000
#> SRR191680 2 0.0000 0.9674 0.000 1.000
#> SRR191681 2 0.0000 0.9674 0.000 1.000
#> SRR191682 2 0.0000 0.9674 0.000 1.000
#> SRR191683 2 0.0000 0.9674 0.000 1.000
#> SRR191684 2 0.0000 0.9674 0.000 1.000
#> SRR191685 2 0.0000 0.9674 0.000 1.000
#> SRR191686 2 0.0000 0.9674 0.000 1.000
#> SRR191687 2 0.0000 0.9674 0.000 1.000
#> SRR191688 2 0.0000 0.9674 0.000 1.000
#> SRR191689 2 0.0000 0.9674 0.000 1.000
#> SRR191690 2 0.0000 0.9674 0.000 1.000
#> SRR191691 2 0.0000 0.9674 0.000 1.000
#> SRR191692 2 0.0000 0.9674 0.000 1.000
#> SRR191693 2 0.0000 0.9674 0.000 1.000
#> SRR191694 2 0.0000 0.9674 0.000 1.000
#> SRR191695 2 0.0000 0.9674 0.000 1.000
#> SRR191696 2 0.0000 0.9674 0.000 1.000
#> SRR191697 2 0.0000 0.9674 0.000 1.000
#> SRR191698 2 0.0000 0.9674 0.000 1.000
#> SRR191699 2 0.0000 0.9674 0.000 1.000
#> SRR191700 2 0.0000 0.9674 0.000 1.000
#> SRR191701 2 0.0000 0.9674 0.000 1.000
#> SRR191702 2 0.0000 0.9674 0.000 1.000
#> SRR191703 2 0.0000 0.9674 0.000 1.000
#> SRR191704 2 0.0000 0.9674 0.000 1.000
#> SRR191705 2 0.0000 0.9674 0.000 1.000
#> SRR191706 2 0.0000 0.9674 0.000 1.000
#> SRR191707 2 0.0000 0.9674 0.000 1.000
#> SRR191708 2 0.0000 0.9674 0.000 1.000
#> SRR191709 2 0.0000 0.9674 0.000 1.000
#> SRR191710 2 0.0000 0.9674 0.000 1.000
#> SRR191711 2 0.0000 0.9674 0.000 1.000
#> SRR191712 2 0.0000 0.9674 0.000 1.000
#> SRR191713 2 0.0000 0.9674 0.000 1.000
#> SRR191714 2 0.0000 0.9674 0.000 1.000
#> SRR191715 2 0.0000 0.9674 0.000 1.000
#> SRR191716 2 0.0000 0.9674 0.000 1.000
#> SRR191717 2 0.0000 0.9674 0.000 1.000
#> SRR191718 2 0.0000 0.9674 0.000 1.000
#> SRR537099 1 0.9661 0.3815 0.608 0.392
#> SRR537100 1 0.0000 0.9539 1.000 0.000
#> SRR537101 1 0.0000 0.9539 1.000 0.000
#> SRR537102 2 0.8861 0.5409 0.304 0.696
#> SRR537104 2 0.9833 0.2323 0.424 0.576
#> SRR537105 1 0.8443 0.6418 0.728 0.272
#> SRR537106 2 0.9963 0.0929 0.464 0.536
#> SRR537107 2 0.9358 0.4337 0.352 0.648
#> SRR537108 2 0.9323 0.4433 0.348 0.652
#> SRR537109 2 0.0000 0.9674 0.000 1.000
#> SRR537110 2 0.0000 0.9674 0.000 1.000
#> SRR537111 1 0.8661 0.6128 0.712 0.288
#> SRR537113 2 0.1843 0.9405 0.028 0.972
#> SRR537114 2 0.0376 0.9638 0.004 0.996
#> SRR537115 2 0.0000 0.9674 0.000 1.000
#> SRR537116 2 0.0000 0.9674 0.000 1.000
#> SRR537117 2 0.0000 0.9674 0.000 1.000
#> SRR537118 2 0.0000 0.9674 0.000 1.000
#> SRR537119 2 0.0000 0.9674 0.000 1.000
#> SRR537120 2 0.0000 0.9674 0.000 1.000
#> SRR537121 2 0.0000 0.9674 0.000 1.000
#> SRR537122 2 0.0000 0.9674 0.000 1.000
#> SRR537123 2 0.0000 0.9674 0.000 1.000
#> SRR537124 2 0.0000 0.9674 0.000 1.000
#> SRR537125 2 0.0000 0.9674 0.000 1.000
#> SRR537126 2 0.0000 0.9674 0.000 1.000
#> SRR537127 1 0.0000 0.9539 1.000 0.000
#> SRR537128 1 0.0000 0.9539 1.000 0.000
#> SRR537129 1 0.0000 0.9539 1.000 0.000
#> SRR537130 1 0.0000 0.9539 1.000 0.000
#> SRR537131 1 0.0000 0.9539 1.000 0.000
#> SRR537132 1 0.0000 0.9539 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191640 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191641 1 0.4555 0.722 0.800 0.000 0.200
#> SRR191642 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191643 1 0.6799 0.144 0.532 0.456 0.012
#> SRR191644 3 0.9386 0.401 0.204 0.296 0.500
#> SRR191645 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191646 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191647 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191650 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191651 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191652 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191653 3 0.0000 0.884 0.000 0.000 1.000
#> SRR191654 3 0.1753 0.845 0.000 0.048 0.952
#> SRR191655 1 0.5588 0.579 0.720 0.004 0.276
#> SRR191656 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191661 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191662 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191663 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191666 3 0.6244 0.132 0.440 0.000 0.560
#> SRR191667 1 0.6225 0.221 0.568 0.000 0.432
#> SRR191668 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.935 1.000 0.000 0.000
#> SRR191674 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191678 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191679 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191682 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191690 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191691 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191692 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191693 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191695 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191700 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191701 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191709 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191713 2 0.0424 0.973 0.008 0.992 0.000
#> SRR191714 2 0.0237 0.977 0.004 0.996 0.000
#> SRR191715 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191716 2 0.0424 0.973 0.008 0.992 0.000
#> SRR191717 2 0.0000 0.981 0.000 1.000 0.000
#> SRR191718 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537099 2 0.7807 0.321 0.336 0.596 0.068
#> SRR537100 1 0.7918 0.483 0.660 0.136 0.204
#> SRR537101 1 0.2878 0.850 0.904 0.000 0.096
#> SRR537102 2 0.5810 0.455 0.336 0.664 0.000
#> SRR537104 2 0.6728 0.638 0.080 0.736 0.184
#> SRR537105 1 0.0000 0.935 1.000 0.000 0.000
#> SRR537106 1 0.0000 0.935 1.000 0.000 0.000
#> SRR537107 1 0.1411 0.893 0.964 0.036 0.000
#> SRR537108 1 0.1860 0.872 0.948 0.052 0.000
#> SRR537109 2 0.0592 0.968 0.012 0.988 0.000
#> SRR537110 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537111 1 0.1289 0.910 0.968 0.000 0.032
#> SRR537113 2 0.0237 0.977 0.000 0.996 0.004
#> SRR537114 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537115 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537116 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537117 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537118 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537119 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537120 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537121 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537122 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537123 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537124 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537125 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537126 2 0.0000 0.981 0.000 1.000 0.000
#> SRR537127 3 0.0000 0.884 0.000 0.000 1.000
#> SRR537128 3 0.0000 0.884 0.000 0.000 1.000
#> SRR537129 3 0.0000 0.884 0.000 0.000 1.000
#> SRR537130 3 0.0000 0.884 0.000 0.000 1.000
#> SRR537131 3 0.0000 0.884 0.000 0.000 1.000
#> SRR537132 3 0.0000 0.884 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0188 0.95322 0.996 0.000 0.004 0.000
#> SRR191640 4 0.4543 0.44182 0.324 0.000 0.000 0.676
#> SRR191641 3 0.4589 0.69750 0.048 0.000 0.784 0.168
#> SRR191642 4 0.3123 0.61657 0.156 0.000 0.000 0.844
#> SRR191643 4 0.3496 0.63569 0.072 0.004 0.052 0.872
#> SRR191644 3 0.4985 0.23371 0.000 0.000 0.532 0.468
#> SRR191645 4 0.5268 0.05586 0.452 0.000 0.008 0.540
#> SRR191646 4 0.5273 0.04828 0.456 0.000 0.008 0.536
#> SRR191647 4 0.4454 0.38029 0.308 0.000 0.000 0.692
#> SRR191648 4 0.4406 0.38630 0.300 0.000 0.000 0.700
#> SRR191649 4 0.5606 -0.02773 0.480 0.000 0.020 0.500
#> SRR191650 1 0.0524 0.94979 0.988 0.000 0.008 0.004
#> SRR191651 1 0.0000 0.95384 1.000 0.000 0.000 0.000
#> SRR191652 1 0.0188 0.95360 0.996 0.000 0.000 0.004
#> SRR191653 3 0.4277 0.66875 0.000 0.000 0.720 0.280
#> SRR191654 4 0.3873 0.42332 0.000 0.000 0.228 0.772
#> SRR191655 4 0.2399 0.62714 0.032 0.000 0.048 0.920
#> SRR191656 1 0.0000 0.95384 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0188 0.95360 0.996 0.000 0.000 0.004
#> SRR191658 1 0.0000 0.95384 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0376 0.95281 0.992 0.000 0.004 0.004
#> SRR191660 1 0.0188 0.95360 0.996 0.000 0.000 0.004
#> SRR191661 1 0.0188 0.95360 0.996 0.000 0.000 0.004
#> SRR191662 1 0.0524 0.95108 0.988 0.000 0.008 0.004
#> SRR191663 1 0.0188 0.95360 0.996 0.000 0.000 0.004
#> SRR191664 1 0.0188 0.95360 0.996 0.000 0.000 0.004
#> SRR191665 1 0.0000 0.95384 1.000 0.000 0.000 0.000
#> SRR191666 1 0.4661 0.51607 0.652 0.000 0.348 0.000
#> SRR191667 1 0.4477 0.58673 0.688 0.000 0.312 0.000
#> SRR191668 1 0.0336 0.95112 0.992 0.000 0.008 0.000
#> SRR191669 1 0.0336 0.95112 0.992 0.000 0.008 0.000
#> SRR191670 1 0.0000 0.95384 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.95384 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0188 0.95322 0.996 0.000 0.004 0.000
#> SRR191673 1 0.0188 0.95322 0.996 0.000 0.004 0.000
#> SRR191674 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191675 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191677 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191678 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191679 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191680 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191681 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191682 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191683 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191684 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191685 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191686 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191687 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191688 4 0.4608 0.61750 0.004 0.304 0.000 0.692
#> SRR191689 2 0.0469 0.75546 0.000 0.988 0.000 0.012
#> SRR191690 4 0.4391 0.65598 0.008 0.252 0.000 0.740
#> SRR191691 2 0.4967 0.00949 0.000 0.548 0.000 0.452
#> SRR191692 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191693 2 0.0188 0.76199 0.000 0.996 0.000 0.004
#> SRR191694 2 0.0000 0.76290 0.000 1.000 0.000 0.000
#> SRR191695 4 0.5000 0.21826 0.000 0.496 0.000 0.504
#> SRR191696 2 0.4994 -0.17944 0.000 0.520 0.000 0.480
#> SRR191697 2 0.4877 0.10309 0.000 0.592 0.000 0.408
#> SRR191698 4 0.4994 0.21975 0.000 0.480 0.000 0.520
#> SRR191699 2 0.4981 -0.10778 0.000 0.536 0.000 0.464
#> SRR191700 4 0.6182 0.58579 0.000 0.276 0.088 0.636
#> SRR191701 2 0.4941 0.00791 0.000 0.564 0.000 0.436
#> SRR191702 4 0.4817 0.49343 0.000 0.388 0.000 0.612
#> SRR191703 4 0.4948 0.37930 0.000 0.440 0.000 0.560
#> SRR191704 2 0.4933 -0.00184 0.000 0.568 0.000 0.432
#> SRR191705 2 0.4888 0.07312 0.000 0.588 0.000 0.412
#> SRR191706 2 0.3873 0.50428 0.000 0.772 0.000 0.228
#> SRR191707 4 0.3873 0.66628 0.000 0.228 0.000 0.772
#> SRR191708 4 0.4331 0.63283 0.000 0.288 0.000 0.712
#> SRR191709 4 0.3942 0.66450 0.000 0.236 0.000 0.764
#> SRR191710 4 0.4454 0.61536 0.000 0.308 0.000 0.692
#> SRR191711 4 0.4304 0.63601 0.000 0.284 0.000 0.716
#> SRR191712 4 0.4522 0.60079 0.000 0.320 0.000 0.680
#> SRR191713 4 0.4722 0.62009 0.008 0.300 0.000 0.692
#> SRR191714 4 0.4973 0.55795 0.008 0.348 0.000 0.644
#> SRR191715 4 0.4907 0.42801 0.000 0.420 0.000 0.580
#> SRR191716 4 0.4391 0.65598 0.008 0.252 0.000 0.740
#> SRR191717 4 0.4837 0.56017 0.004 0.348 0.000 0.648
#> SRR191718 2 0.4916 0.02820 0.000 0.576 0.000 0.424
#> SRR537099 4 0.3056 0.62205 0.040 0.000 0.072 0.888
#> SRR537100 4 0.3088 0.63164 0.052 0.000 0.060 0.888
#> SRR537101 4 0.7489 0.03639 0.184 0.000 0.364 0.452
#> SRR537102 4 0.1913 0.65886 0.040 0.020 0.000 0.940
#> SRR537104 4 0.1545 0.63934 0.008 0.000 0.040 0.952
#> SRR537105 4 0.0707 0.63645 0.020 0.000 0.000 0.980
#> SRR537106 4 0.0921 0.63071 0.028 0.000 0.000 0.972
#> SRR537107 4 0.0707 0.63445 0.020 0.000 0.000 0.980
#> SRR537108 4 0.0707 0.63445 0.020 0.000 0.000 0.980
#> SRR537109 4 0.2480 0.67803 0.008 0.088 0.000 0.904
#> SRR537110 4 0.1970 0.67093 0.008 0.060 0.000 0.932
#> SRR537111 1 0.5272 0.69162 0.752 0.000 0.112 0.136
#> SRR537113 2 0.7206 0.21027 0.000 0.460 0.140 0.400
#> SRR537114 2 0.6735 0.33332 0.000 0.516 0.096 0.388
#> SRR537115 2 0.3808 0.68617 0.004 0.808 0.004 0.184
#> SRR537116 4 0.3172 0.68329 0.000 0.160 0.000 0.840
#> SRR537117 2 0.3024 0.71645 0.000 0.852 0.000 0.148
#> SRR537118 2 0.3311 0.70131 0.000 0.828 0.000 0.172
#> SRR537119 2 0.3810 0.68503 0.000 0.804 0.008 0.188
#> SRR537120 2 0.3024 0.71645 0.000 0.852 0.000 0.148
#> SRR537121 2 0.3486 0.68724 0.000 0.812 0.000 0.188
#> SRR537122 2 0.4387 0.65065 0.000 0.776 0.024 0.200
#> SRR537123 2 0.3400 0.69470 0.000 0.820 0.000 0.180
#> SRR537124 2 0.2814 0.72337 0.000 0.868 0.000 0.132
#> SRR537125 2 0.3494 0.69866 0.000 0.824 0.004 0.172
#> SRR537126 2 0.3311 0.70131 0.000 0.828 0.000 0.172
#> SRR537127 3 0.0000 0.86559 0.000 0.000 1.000 0.000
#> SRR537128 3 0.0000 0.86559 0.000 0.000 1.000 0.000
#> SRR537129 3 0.0000 0.86559 0.000 0.000 1.000 0.000
#> SRR537130 3 0.0000 0.86559 0.000 0.000 1.000 0.000
#> SRR537131 3 0.0000 0.86559 0.000 0.000 1.000 0.000
#> SRR537132 3 0.0000 0.86559 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
#> SRR191639 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191640 4 0.6282 0.4475 0.248 0.216 0.000 0.536 0.000
#> SRR191641 3 0.0290 0.8935 0.000 0.000 0.992 0.008 0.000
#> SRR191642 4 0.4812 0.4273 0.028 0.372 0.000 0.600 0.000
#> SRR191643 4 0.6287 0.4598 0.004 0.260 0.184 0.552 0.000
#> SRR191644 3 0.4540 0.3372 0.000 0.020 0.640 0.340 0.000
#> SRR191645 4 0.1800 0.7014 0.048 0.020 0.000 0.932 0.000
#> SRR191646 4 0.2079 0.6965 0.064 0.020 0.000 0.916 0.000
#> SRR191647 4 0.1740 0.7123 0.012 0.056 0.000 0.932 0.000
#> SRR191648 4 0.1670 0.7122 0.012 0.052 0.000 0.936 0.000
#> SRR191649 4 0.3099 0.6636 0.124 0.028 0.000 0.848 0.000
#> SRR191650 1 0.2605 0.8035 0.852 0.000 0.000 0.148 0.000
#> SRR191651 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191652 1 0.0290 0.9599 0.992 0.000 0.000 0.008 0.000
#> SRR191653 4 0.4415 0.2152 0.000 0.000 0.444 0.552 0.004
#> SRR191654 4 0.4938 0.4474 0.000 0.036 0.324 0.636 0.004
#> SRR191655 4 0.4277 0.6590 0.000 0.100 0.112 0.784 0.004
#> SRR191656 1 0.0324 0.9614 0.992 0.000 0.000 0.004 0.004
#> SRR191657 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191658 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191659 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191660 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191661 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191662 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191663 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191664 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191665 1 0.0162 0.9624 0.996 0.000 0.000 0.004 0.000
#> SRR191666 1 0.3796 0.6123 0.700 0.000 0.300 0.000 0.000
#> SRR191667 1 0.3561 0.6769 0.740 0.000 0.260 0.000 0.000
#> SRR191668 1 0.0162 0.9603 0.996 0.000 0.000 0.000 0.004
#> SRR191669 1 0.0162 0.9603 0.996 0.000 0.000 0.000 0.004
#> SRR191670 1 0.0162 0.9603 0.996 0.000 0.000 0.000 0.004
#> SRR191671 1 0.0162 0.9603 0.996 0.000 0.000 0.000 0.004
#> SRR191672 1 0.0162 0.9603 0.996 0.000 0.000 0.000 0.004
#> SRR191673 1 0.0162 0.9603 0.996 0.000 0.000 0.000 0.004
#> SRR191674 5 0.1608 0.8445 0.000 0.072 0.000 0.000 0.928
#> SRR191675 5 0.1608 0.8445 0.000 0.072 0.000 0.000 0.928
#> SRR191677 5 0.1608 0.8445 0.000 0.072 0.000 0.000 0.928
#> SRR191678 5 0.1671 0.8434 0.000 0.076 0.000 0.000 0.924
#> SRR191679 5 0.2329 0.8131 0.000 0.124 0.000 0.000 0.876
#> SRR191680 5 0.1965 0.8331 0.000 0.096 0.000 0.000 0.904
#> SRR191681 5 0.1608 0.8445 0.000 0.072 0.000 0.000 0.928
#> SRR191682 5 0.2660 0.8098 0.000 0.128 0.000 0.008 0.864
#> SRR191683 5 0.2249 0.8332 0.000 0.096 0.000 0.008 0.896
#> SRR191684 5 0.2464 0.8273 0.000 0.096 0.000 0.016 0.888
#> SRR191685 5 0.2144 0.8385 0.000 0.068 0.000 0.020 0.912
#> SRR191686 5 0.1764 0.8417 0.000 0.064 0.000 0.008 0.928
#> SRR191687 5 0.1943 0.8391 0.000 0.056 0.000 0.020 0.924
#> SRR191688 2 0.0794 0.8882 0.000 0.972 0.000 0.028 0.000
#> SRR191689 5 0.4242 0.2764 0.000 0.428 0.000 0.000 0.572
#> SRR191690 2 0.1197 0.8761 0.000 0.952 0.000 0.048 0.000
#> SRR191691 2 0.4509 0.6725 0.000 0.716 0.000 0.048 0.236
#> SRR191692 5 0.1478 0.8452 0.000 0.064 0.000 0.000 0.936
#> SRR191693 5 0.0963 0.8412 0.000 0.036 0.000 0.000 0.964
#> SRR191694 5 0.1608 0.8445 0.000 0.072 0.000 0.000 0.928
#> SRR191695 2 0.0963 0.8855 0.000 0.964 0.000 0.000 0.036
#> SRR191696 2 0.1168 0.8893 0.000 0.960 0.000 0.008 0.032
#> SRR191697 2 0.2951 0.8362 0.000 0.860 0.000 0.028 0.112
#> SRR191698 2 0.3752 0.7837 0.000 0.804 0.000 0.048 0.148
#> SRR191699 2 0.1697 0.8756 0.000 0.932 0.000 0.008 0.060
#> SRR191700 2 0.3237 0.8339 0.000 0.864 0.012 0.048 0.076
#> SRR191701 2 0.3002 0.8309 0.000 0.856 0.000 0.028 0.116
#> SRR191702 2 0.1106 0.8906 0.000 0.964 0.000 0.012 0.024
#> SRR191703 2 0.1195 0.8895 0.000 0.960 0.000 0.012 0.028
#> SRR191704 2 0.1357 0.8807 0.000 0.948 0.000 0.004 0.048
#> SRR191705 2 0.1357 0.8807 0.000 0.948 0.000 0.004 0.048
#> SRR191706 2 0.3143 0.7235 0.000 0.796 0.000 0.000 0.204
#> SRR191707 2 0.1205 0.8848 0.000 0.956 0.000 0.040 0.004
#> SRR191708 2 0.0671 0.8925 0.000 0.980 0.000 0.016 0.004
#> SRR191709 2 0.0912 0.8927 0.000 0.972 0.000 0.016 0.012
#> SRR191710 2 0.0451 0.8928 0.000 0.988 0.000 0.008 0.004
#> SRR191711 2 0.0880 0.8859 0.000 0.968 0.000 0.032 0.000
#> SRR191712 2 0.0703 0.8889 0.000 0.976 0.000 0.024 0.000
#> SRR191713 2 0.0609 0.8912 0.000 0.980 0.000 0.020 0.000
#> SRR191714 2 0.0510 0.8912 0.000 0.984 0.000 0.016 0.000
#> SRR191715 2 0.1117 0.8927 0.000 0.964 0.000 0.020 0.016
#> SRR191716 2 0.1341 0.8709 0.000 0.944 0.000 0.056 0.000
#> SRR191717 2 0.1121 0.8791 0.000 0.956 0.000 0.044 0.000
#> SRR191718 2 0.1408 0.8787 0.000 0.948 0.000 0.008 0.044
#> SRR537099 4 0.5363 0.5501 0.000 0.100 0.232 0.664 0.004
#> SRR537100 4 0.5533 0.5216 0.000 0.104 0.252 0.640 0.004
#> SRR537101 3 0.4157 0.5405 0.000 0.020 0.716 0.264 0.000
#> SRR537102 4 0.4074 0.4567 0.000 0.364 0.000 0.636 0.000
#> SRR537104 4 0.4599 0.6453 0.000 0.156 0.088 0.752 0.004
#> SRR537105 4 0.1478 0.7115 0.000 0.064 0.000 0.936 0.000
#> SRR537106 4 0.1043 0.7093 0.000 0.040 0.000 0.960 0.000
#> SRR537107 4 0.1282 0.7092 0.000 0.044 0.000 0.952 0.004
#> SRR537108 4 0.1121 0.7097 0.000 0.044 0.000 0.956 0.000
#> SRR537109 2 0.4291 0.0118 0.000 0.536 0.000 0.464 0.000
#> SRR537110 2 0.4291 0.0197 0.000 0.536 0.000 0.464 0.000
#> SRR537111 4 0.3516 0.5917 0.164 0.000 0.004 0.812 0.020
#> SRR537113 4 0.2230 0.6273 0.000 0.000 0.000 0.884 0.116
#> SRR537114 4 0.2583 0.6144 0.000 0.004 0.000 0.864 0.132
#> SRR537115 4 0.4291 -0.1468 0.000 0.000 0.000 0.536 0.464
#> SRR537116 2 0.2127 0.8257 0.000 0.892 0.000 0.108 0.000
#> SRR537117 5 0.2806 0.7742 0.000 0.004 0.000 0.152 0.844
#> SRR537118 5 0.3885 0.6764 0.000 0.008 0.000 0.268 0.724
#> SRR537119 5 0.4478 0.5492 0.000 0.008 0.004 0.360 0.628
#> SRR537120 5 0.3171 0.7571 0.000 0.008 0.000 0.176 0.816
#> SRR537121 5 0.4088 0.5277 0.000 0.000 0.000 0.368 0.632
#> SRR537122 4 0.4452 -0.2582 0.000 0.000 0.004 0.500 0.496
#> SRR537123 5 0.4101 0.5230 0.000 0.000 0.000 0.372 0.628
#> SRR537124 5 0.1732 0.8062 0.000 0.000 0.000 0.080 0.920
#> SRR537125 5 0.3109 0.7365 0.000 0.000 0.000 0.200 0.800
#> SRR537126 5 0.3143 0.7330 0.000 0.000 0.000 0.204 0.796
#> SRR537127 3 0.0000 0.8987 0.000 0.000 1.000 0.000 0.000
#> SRR537128 3 0.0000 0.8987 0.000 0.000 1.000 0.000 0.000
#> SRR537129 3 0.0000 0.8987 0.000 0.000 1.000 0.000 0.000
#> SRR537130 3 0.0000 0.8987 0.000 0.000 1.000 0.000 0.000
#> SRR537131 3 0.0000 0.8987 0.000 0.000 1.000 0.000 0.000
#> SRR537132 3 0.0000 0.8987 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
#> SRR191639 1 0.0291 0.948 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR191640 4 0.5464 0.638 0.136 0.188 0.000 0.644 0.000 0.032
#> SRR191641 3 0.0790 0.868 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR191642 4 0.4650 0.681 0.028 0.224 0.004 0.704 0.000 0.040
#> SRR191643 4 0.5323 0.631 0.000 0.204 0.152 0.632 0.000 0.012
#> SRR191644 3 0.3847 0.333 0.000 0.008 0.644 0.348 0.000 0.000
#> SRR191645 4 0.0748 0.742 0.004 0.004 0.000 0.976 0.016 0.000
#> SRR191646 4 0.0748 0.742 0.004 0.004 0.000 0.976 0.016 0.000
#> SRR191647 4 0.1643 0.762 0.000 0.068 0.000 0.924 0.008 0.000
#> SRR191648 4 0.1524 0.761 0.000 0.060 0.000 0.932 0.008 0.000
#> SRR191649 4 0.2164 0.758 0.028 0.056 0.000 0.908 0.008 0.000
#> SRR191650 1 0.4334 0.356 0.608 0.000 0.000 0.368 0.012 0.012
#> SRR191651 1 0.1036 0.947 0.964 0.000 0.000 0.008 0.004 0.024
#> SRR191652 1 0.0665 0.949 0.980 0.000 0.000 0.008 0.004 0.008
#> SRR191653 4 0.4072 0.237 0.000 0.000 0.448 0.544 0.008 0.000
#> SRR191654 4 0.4395 0.587 0.000 0.044 0.264 0.684 0.008 0.000
#> SRR191655 4 0.3505 0.744 0.000 0.132 0.036 0.816 0.004 0.012
#> SRR191656 1 0.0922 0.948 0.968 0.000 0.000 0.004 0.004 0.024
#> SRR191657 1 0.0291 0.948 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR191658 1 0.0146 0.948 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191659 1 0.0291 0.948 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR191660 1 0.0405 0.947 0.988 0.000 0.000 0.008 0.000 0.004
#> SRR191661 1 0.0405 0.947 0.988 0.000 0.000 0.008 0.000 0.004
#> SRR191662 1 0.0508 0.945 0.984 0.000 0.000 0.012 0.000 0.004
#> SRR191663 1 0.0405 0.947 0.988 0.000 0.000 0.008 0.000 0.004
#> SRR191664 1 0.0291 0.948 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR191665 1 0.1036 0.947 0.964 0.000 0.000 0.008 0.004 0.024
#> SRR191666 1 0.2006 0.867 0.892 0.000 0.104 0.000 0.000 0.004
#> SRR191667 1 0.2562 0.791 0.828 0.000 0.172 0.000 0.000 0.000
#> SRR191668 1 0.0922 0.948 0.968 0.000 0.000 0.004 0.004 0.024
#> SRR191669 1 0.0922 0.948 0.968 0.000 0.000 0.004 0.004 0.024
#> SRR191670 1 0.0922 0.948 0.968 0.000 0.000 0.004 0.004 0.024
#> SRR191671 1 0.0922 0.948 0.968 0.000 0.000 0.004 0.004 0.024
#> SRR191672 1 0.1036 0.947 0.964 0.000 0.000 0.008 0.004 0.024
#> SRR191673 1 0.1036 0.947 0.964 0.000 0.000 0.008 0.004 0.024
#> SRR191674 5 0.4040 0.589 0.000 0.032 0.000 0.000 0.688 0.280
#> SRR191675 5 0.4040 0.589 0.000 0.032 0.000 0.000 0.688 0.280
#> SRR191677 5 0.4172 0.588 0.000 0.040 0.000 0.000 0.680 0.280
#> SRR191678 5 0.4234 0.587 0.000 0.044 0.000 0.000 0.676 0.280
#> SRR191679 5 0.5120 0.517 0.000 0.120 0.000 0.000 0.600 0.280
#> SRR191680 5 0.4515 0.572 0.000 0.064 0.000 0.000 0.656 0.280
#> SRR191681 5 0.4172 0.588 0.000 0.040 0.000 0.000 0.680 0.280
#> SRR191682 6 0.2250 0.522 0.000 0.020 0.000 0.000 0.092 0.888
#> SRR191683 6 0.2945 0.450 0.000 0.020 0.000 0.000 0.156 0.824
#> SRR191684 6 0.2039 0.535 0.000 0.020 0.000 0.000 0.076 0.904
#> SRR191685 6 0.2301 0.524 0.000 0.020 0.000 0.000 0.096 0.884
#> SRR191686 6 0.3088 0.419 0.000 0.020 0.000 0.000 0.172 0.808
#> SRR191687 6 0.2581 0.503 0.000 0.020 0.000 0.000 0.120 0.860
#> SRR191688 2 0.2263 0.879 0.000 0.896 0.000 0.048 0.000 0.056
#> SRR191689 5 0.5906 0.345 0.000 0.236 0.000 0.000 0.464 0.300
#> SRR191690 2 0.2066 0.880 0.000 0.908 0.000 0.052 0.000 0.040
#> SRR191691 6 0.4212 0.605 0.000 0.264 0.000 0.000 0.048 0.688
#> SRR191692 5 0.4152 0.575 0.000 0.032 0.000 0.000 0.664 0.304
#> SRR191693 5 0.3684 0.556 0.000 0.004 0.000 0.000 0.664 0.332
#> SRR191694 5 0.4040 0.589 0.000 0.032 0.000 0.000 0.688 0.280
#> SRR191695 2 0.2926 0.863 0.000 0.852 0.000 0.012 0.024 0.112
#> SRR191696 2 0.2890 0.870 0.000 0.860 0.000 0.012 0.032 0.096
#> SRR191697 6 0.3802 0.561 0.000 0.312 0.000 0.000 0.012 0.676
#> SRR191698 6 0.4009 0.583 0.000 0.288 0.000 0.000 0.028 0.684
#> SRR191699 6 0.3954 0.454 0.000 0.372 0.000 0.004 0.004 0.620
#> SRR191700 6 0.4078 0.566 0.000 0.300 0.000 0.008 0.016 0.676
#> SRR191701 6 0.3766 0.569 0.000 0.304 0.000 0.000 0.012 0.684
#> SRR191702 2 0.1245 0.897 0.000 0.952 0.000 0.000 0.032 0.016
#> SRR191703 2 0.1549 0.889 0.000 0.936 0.000 0.000 0.044 0.020
#> SRR191704 2 0.1498 0.891 0.000 0.940 0.000 0.000 0.032 0.028
#> SRR191705 2 0.1713 0.884 0.000 0.928 0.000 0.000 0.044 0.028
#> SRR191706 2 0.3041 0.787 0.000 0.832 0.000 0.000 0.128 0.040
#> SRR191707 2 0.3721 0.612 0.000 0.728 0.000 0.016 0.004 0.252
#> SRR191708 2 0.1226 0.898 0.000 0.952 0.000 0.004 0.004 0.040
#> SRR191709 2 0.0922 0.902 0.000 0.968 0.000 0.004 0.004 0.024
#> SRR191710 2 0.1036 0.902 0.000 0.964 0.000 0.008 0.004 0.024
#> SRR191711 2 0.0717 0.905 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR191712 2 0.0603 0.905 0.000 0.980 0.000 0.016 0.000 0.004
#> SRR191713 2 0.0717 0.904 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR191714 2 0.0820 0.904 0.000 0.972 0.000 0.016 0.000 0.012
#> SRR191715 2 0.2570 0.885 0.000 0.892 0.000 0.032 0.036 0.040
#> SRR191716 2 0.1984 0.880 0.000 0.912 0.000 0.056 0.000 0.032
#> SRR191717 2 0.1421 0.898 0.000 0.944 0.000 0.028 0.000 0.028
#> SRR191718 2 0.3284 0.751 0.000 0.784 0.000 0.000 0.020 0.196
#> SRR537099 4 0.4927 0.699 0.000 0.100 0.128 0.728 0.008 0.036
#> SRR537100 4 0.4846 0.681 0.000 0.092 0.156 0.716 0.000 0.036
#> SRR537101 3 0.4475 0.399 0.000 0.032 0.636 0.324 0.000 0.008
#> SRR537102 4 0.4011 0.696 0.000 0.212 0.000 0.732 0.000 0.056
#> SRR537104 4 0.3939 0.740 0.000 0.124 0.036 0.800 0.008 0.032
#> SRR537105 4 0.1951 0.762 0.000 0.076 0.000 0.908 0.016 0.000
#> SRR537106 4 0.1225 0.741 0.000 0.012 0.000 0.952 0.036 0.000
#> SRR537107 4 0.1082 0.733 0.000 0.004 0.000 0.956 0.040 0.000
#> SRR537108 4 0.1082 0.733 0.000 0.004 0.000 0.956 0.040 0.000
#> SRR537109 4 0.4269 0.610 0.000 0.316 0.000 0.648 0.000 0.036
#> SRR537110 4 0.4646 0.275 0.000 0.460 0.000 0.500 0.000 0.040
#> SRR537111 4 0.3559 0.649 0.056 0.000 0.000 0.820 0.104 0.020
#> SRR537113 4 0.3515 0.450 0.000 0.000 0.000 0.676 0.324 0.000
#> SRR537114 4 0.3578 0.428 0.000 0.000 0.000 0.660 0.340 0.000
#> SRR537115 5 0.4105 0.345 0.000 0.000 0.000 0.348 0.632 0.020
#> SRR537116 2 0.2384 0.852 0.000 0.884 0.000 0.084 0.000 0.032
#> SRR537117 5 0.3920 0.443 0.000 0.000 0.000 0.120 0.768 0.112
#> SRR537118 6 0.5054 0.200 0.000 0.000 0.000 0.076 0.420 0.504
#> SRR537119 6 0.5443 0.184 0.000 0.000 0.000 0.124 0.384 0.492
#> SRR537120 5 0.4808 -0.227 0.000 0.000 0.000 0.052 0.476 0.472
#> SRR537121 5 0.4796 0.386 0.000 0.000 0.000 0.224 0.660 0.116
#> SRR537122 5 0.4986 0.340 0.000 0.000 0.000 0.304 0.600 0.096
#> SRR537123 5 0.4479 0.405 0.000 0.000 0.000 0.236 0.684 0.080
#> SRR537124 5 0.3464 0.465 0.000 0.000 0.000 0.108 0.808 0.084
#> SRR537125 5 0.4764 0.314 0.000 0.000 0.000 0.108 0.660 0.232
#> SRR537126 5 0.4796 0.323 0.000 0.000 0.000 0.116 0.660 0.224
#> SRR537127 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537128 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537129 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537130 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537131 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537132 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 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 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 0.514 0.888 0.914 0.1831 0.897 0.897
#> 3 3 0.530 0.900 0.926 1.8547 0.553 0.502
#> 4 4 0.589 0.872 0.875 0.1544 0.904 0.787
#> 5 5 0.599 0.774 0.878 0.0944 0.986 0.960
#> 6 6 0.696 0.820 0.886 0.0701 0.954 0.865
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
#> SRR191639 2 0.000 0.859 0.000 1.000
#> SRR191640 2 0.000 0.859 0.000 1.000
#> SRR191641 2 0.000 0.859 0.000 1.000
#> SRR191642 2 0.000 0.859 0.000 1.000
#> SRR191643 2 0.000 0.859 0.000 1.000
#> SRR191644 2 0.000 0.859 0.000 1.000
#> SRR191645 2 0.000 0.859 0.000 1.000
#> SRR191646 2 0.000 0.859 0.000 1.000
#> SRR191647 2 0.000 0.859 0.000 1.000
#> SRR191648 2 0.000 0.859 0.000 1.000
#> SRR191649 2 0.000 0.859 0.000 1.000
#> SRR191650 2 0.000 0.859 0.000 1.000
#> SRR191651 2 0.000 0.859 0.000 1.000
#> SRR191652 2 0.000 0.859 0.000 1.000
#> SRR191653 2 0.000 0.859 0.000 1.000
#> SRR191654 2 0.000 0.859 0.000 1.000
#> SRR191655 2 0.000 0.859 0.000 1.000
#> SRR191656 2 0.000 0.859 0.000 1.000
#> SRR191657 2 0.000 0.859 0.000 1.000
#> SRR191658 2 0.000 0.859 0.000 1.000
#> SRR191659 2 0.000 0.859 0.000 1.000
#> SRR191660 2 0.000 0.859 0.000 1.000
#> SRR191661 2 0.000 0.859 0.000 1.000
#> SRR191662 2 0.000 0.859 0.000 1.000
#> SRR191663 2 0.000 0.859 0.000 1.000
#> SRR191664 2 0.000 0.859 0.000 1.000
#> SRR191665 2 0.000 0.859 0.000 1.000
#> SRR191666 2 0.000 0.859 0.000 1.000
#> SRR191667 2 0.000 0.859 0.000 1.000
#> SRR191668 2 0.000 0.859 0.000 1.000
#> SRR191669 2 0.000 0.859 0.000 1.000
#> SRR191670 2 0.000 0.859 0.000 1.000
#> SRR191671 2 0.000 0.859 0.000 1.000
#> SRR191672 2 0.000 0.859 0.000 1.000
#> SRR191673 2 0.000 0.859 0.000 1.000
#> SRR191674 2 0.730 0.895 0.204 0.796
#> SRR191675 2 0.730 0.895 0.204 0.796
#> SRR191677 2 0.730 0.895 0.204 0.796
#> SRR191678 2 0.730 0.895 0.204 0.796
#> SRR191679 2 0.730 0.895 0.204 0.796
#> SRR191680 2 0.730 0.895 0.204 0.796
#> SRR191681 2 0.730 0.895 0.204 0.796
#> SRR191682 2 0.714 0.899 0.196 0.804
#> SRR191683 2 0.714 0.899 0.196 0.804
#> SRR191684 2 0.714 0.899 0.196 0.804
#> SRR191685 2 0.714 0.899 0.196 0.804
#> SRR191686 2 0.714 0.899 0.196 0.804
#> SRR191687 2 0.714 0.899 0.196 0.804
#> SRR191688 2 0.714 0.899 0.196 0.804
#> SRR191689 2 0.714 0.899 0.196 0.804
#> SRR191690 2 0.714 0.899 0.196 0.804
#> SRR191691 2 0.714 0.899 0.196 0.804
#> SRR191692 2 0.730 0.895 0.204 0.796
#> SRR191693 2 0.730 0.895 0.204 0.796
#> SRR191694 2 0.730 0.895 0.204 0.796
#> SRR191695 2 0.714 0.899 0.196 0.804
#> SRR191696 2 0.714 0.899 0.196 0.804
#> SRR191697 2 0.714 0.899 0.196 0.804
#> SRR191698 2 0.714 0.899 0.196 0.804
#> SRR191699 2 0.714 0.899 0.196 0.804
#> SRR191700 2 0.714 0.899 0.196 0.804
#> SRR191701 2 0.714 0.899 0.196 0.804
#> SRR191702 2 0.714 0.899 0.196 0.804
#> SRR191703 2 0.714 0.899 0.196 0.804
#> SRR191704 2 0.714 0.899 0.196 0.804
#> SRR191705 2 0.714 0.899 0.196 0.804
#> SRR191706 2 0.714 0.899 0.196 0.804
#> SRR191707 2 0.714 0.899 0.196 0.804
#> SRR191708 2 0.714 0.899 0.196 0.804
#> SRR191709 2 0.714 0.899 0.196 0.804
#> SRR191710 2 0.714 0.899 0.196 0.804
#> SRR191711 2 0.714 0.899 0.196 0.804
#> SRR191712 2 0.714 0.899 0.196 0.804
#> SRR191713 2 0.714 0.899 0.196 0.804
#> SRR191714 2 0.714 0.899 0.196 0.804
#> SRR191715 2 0.714 0.899 0.196 0.804
#> SRR191716 2 0.714 0.899 0.196 0.804
#> SRR191717 2 0.714 0.899 0.196 0.804
#> SRR191718 2 0.714 0.899 0.196 0.804
#> SRR537099 2 0.000 0.859 0.000 1.000
#> SRR537100 2 0.000 0.859 0.000 1.000
#> SRR537101 2 0.000 0.859 0.000 1.000
#> SRR537102 2 0.000 0.859 0.000 1.000
#> SRR537104 2 0.000 0.859 0.000 1.000
#> SRR537105 2 0.000 0.859 0.000 1.000
#> SRR537106 2 0.000 0.859 0.000 1.000
#> SRR537107 2 0.000 0.859 0.000 1.000
#> SRR537108 2 0.000 0.859 0.000 1.000
#> SRR537109 2 0.714 0.899 0.196 0.804
#> SRR537110 2 0.714 0.899 0.196 0.804
#> SRR537111 2 0.000 0.859 0.000 1.000
#> SRR537113 2 0.722 0.897 0.200 0.800
#> SRR537114 2 0.722 0.897 0.200 0.800
#> SRR537115 2 0.722 0.897 0.200 0.800
#> SRR537116 2 0.714 0.899 0.196 0.804
#> SRR537117 2 0.697 0.899 0.188 0.812
#> SRR537118 2 0.697 0.899 0.188 0.812
#> SRR537119 2 0.697 0.899 0.188 0.812
#> SRR537120 2 0.697 0.899 0.188 0.812
#> SRR537121 2 0.722 0.897 0.200 0.800
#> SRR537122 2 0.722 0.897 0.200 0.800
#> SRR537123 2 0.722 0.897 0.200 0.800
#> SRR537124 2 0.722 0.897 0.200 0.800
#> SRR537125 2 0.722 0.897 0.200 0.800
#> SRR537126 2 0.722 0.897 0.200 0.800
#> SRR537127 1 0.730 1.000 0.796 0.204
#> SRR537128 1 0.730 1.000 0.796 0.204
#> SRR537129 1 0.730 1.000 0.796 0.204
#> SRR537130 1 0.730 1.000 0.796 0.204
#> SRR537131 1 0.730 1.000 0.796 0.204
#> SRR537132 1 0.730 1.000 0.796 0.204
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191640 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191641 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191642 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191643 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191644 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191645 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191646 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191647 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191650 1 0.0237 0.901 0.996 0.004 0.000
#> SRR191651 1 0.0237 0.901 0.996 0.004 0.000
#> SRR191652 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191653 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191654 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191655 1 0.3482 0.835 0.872 0.128 0.000
#> SRR191656 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191661 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191662 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191663 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191666 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191667 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191668 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.902 1.000 0.000 0.000
#> SRR191674 2 0.0892 0.902 0.020 0.980 0.000
#> SRR191675 2 0.0892 0.902 0.020 0.980 0.000
#> SRR191677 2 0.0892 0.902 0.020 0.980 0.000
#> SRR191678 2 0.0892 0.902 0.020 0.980 0.000
#> SRR191679 2 0.0000 0.883 0.000 1.000 0.000
#> SRR191680 2 0.0892 0.902 0.020 0.980 0.000
#> SRR191681 2 0.0892 0.902 0.020 0.980 0.000
#> SRR191682 2 0.2796 0.954 0.092 0.908 0.000
#> SRR191683 2 0.2796 0.954 0.092 0.908 0.000
#> SRR191684 2 0.0424 0.891 0.008 0.992 0.000
#> SRR191685 2 0.2796 0.954 0.092 0.908 0.000
#> SRR191686 2 0.2796 0.954 0.092 0.908 0.000
#> SRR191687 2 0.2796 0.954 0.092 0.908 0.000
#> SRR191688 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191689 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191690 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191691 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191692 2 0.2878 0.953 0.096 0.904 0.000
#> SRR191693 2 0.2878 0.953 0.096 0.904 0.000
#> SRR191694 2 0.2878 0.953 0.096 0.904 0.000
#> SRR191695 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191696 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191697 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191698 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191699 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191700 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191701 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191702 2 0.2625 0.951 0.084 0.916 0.000
#> SRR191703 2 0.2625 0.951 0.084 0.916 0.000
#> SRR191704 2 0.0424 0.891 0.008 0.992 0.000
#> SRR191705 2 0.1163 0.911 0.028 0.972 0.000
#> SRR191706 2 0.2625 0.951 0.084 0.916 0.000
#> SRR191707 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191708 2 0.2625 0.951 0.084 0.916 0.000
#> SRR191709 2 0.2625 0.951 0.084 0.916 0.000
#> SRR191710 2 0.2625 0.951 0.084 0.916 0.000
#> SRR191711 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191712 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191713 2 0.0424 0.891 0.008 0.992 0.000
#> SRR191714 2 0.0424 0.891 0.008 0.992 0.000
#> SRR191715 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191716 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191717 2 0.3192 0.954 0.112 0.888 0.000
#> SRR191718 2 0.3192 0.954 0.112 0.888 0.000
#> SRR537099 1 0.3482 0.835 0.872 0.128 0.000
#> SRR537100 1 0.3482 0.835 0.872 0.128 0.000
#> SRR537101 1 0.0000 0.902 1.000 0.000 0.000
#> SRR537102 1 0.3482 0.835 0.872 0.128 0.000
#> SRR537104 1 0.3482 0.835 0.872 0.128 0.000
#> SRR537105 1 0.0000 0.902 1.000 0.000 0.000
#> SRR537106 1 0.0000 0.902 1.000 0.000 0.000
#> SRR537107 1 0.0000 0.902 1.000 0.000 0.000
#> SRR537108 1 0.0000 0.902 1.000 0.000 0.000
#> SRR537109 2 0.3482 0.938 0.128 0.872 0.000
#> SRR537110 2 0.3879 0.909 0.152 0.848 0.000
#> SRR537111 1 0.0237 0.901 0.996 0.004 0.000
#> SRR537113 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537114 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537115 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537116 2 0.3879 0.909 0.152 0.848 0.000
#> SRR537117 1 0.4605 0.765 0.796 0.204 0.000
#> SRR537118 1 0.4605 0.765 0.796 0.204 0.000
#> SRR537119 1 0.4605 0.765 0.796 0.204 0.000
#> SRR537120 1 0.4605 0.765 0.796 0.204 0.000
#> SRR537121 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537122 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537123 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537124 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537125 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537126 1 0.5348 0.781 0.796 0.176 0.028
#> SRR537127 3 0.1163 1.000 0.028 0.000 0.972
#> SRR537128 3 0.1163 1.000 0.028 0.000 0.972
#> SRR537129 3 0.1163 1.000 0.028 0.000 0.972
#> SRR537130 3 0.1163 1.000 0.028 0.000 0.972
#> SRR537131 3 0.1163 1.000 0.028 0.000 0.972
#> SRR537132 3 0.1163 1.000 0.028 0.000 0.972
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0188 0.842 0.996 0.000 0 0.004
#> SRR191640 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191641 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191642 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191643 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191644 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191645 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR191646 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR191647 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR191648 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR191649 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR191650 1 0.0376 0.843 0.992 0.004 0 0.004
#> SRR191651 1 0.0376 0.843 0.992 0.004 0 0.004
#> SRR191652 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191653 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191654 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191655 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR191656 1 0.2408 0.767 0.896 0.000 0 0.104
#> SRR191657 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191658 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191659 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191660 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191661 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191662 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191663 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191664 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191665 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191666 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191667 1 0.0000 0.842 1.000 0.000 0 0.000
#> SRR191668 1 0.2408 0.767 0.896 0.000 0 0.104
#> SRR191669 1 0.2408 0.767 0.896 0.000 0 0.104
#> SRR191670 1 0.2408 0.767 0.896 0.000 0 0.104
#> SRR191671 1 0.2408 0.767 0.896 0.000 0 0.104
#> SRR191672 1 0.4431 0.526 0.696 0.000 0 0.304
#> SRR191673 1 0.4431 0.526 0.696 0.000 0 0.304
#> SRR191674 2 0.0895 0.904 0.004 0.976 0 0.020
#> SRR191675 2 0.0895 0.904 0.004 0.976 0 0.020
#> SRR191677 2 0.0895 0.904 0.004 0.976 0 0.020
#> SRR191678 2 0.0895 0.904 0.004 0.976 0 0.020
#> SRR191679 2 0.1118 0.891 0.000 0.964 0 0.036
#> SRR191680 2 0.0895 0.904 0.004 0.976 0 0.020
#> SRR191681 2 0.0895 0.904 0.004 0.976 0 0.020
#> SRR191682 2 0.2048 0.954 0.064 0.928 0 0.008
#> SRR191683 2 0.2048 0.954 0.064 0.928 0 0.008
#> SRR191684 2 0.1452 0.899 0.008 0.956 0 0.036
#> SRR191685 2 0.2048 0.954 0.064 0.928 0 0.008
#> SRR191686 2 0.2048 0.954 0.064 0.928 0 0.008
#> SRR191687 2 0.2048 0.954 0.064 0.928 0 0.008
#> SRR191688 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191689 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191690 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191691 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191692 2 0.1637 0.954 0.060 0.940 0 0.000
#> SRR191693 2 0.1637 0.954 0.060 0.940 0 0.000
#> SRR191694 2 0.1637 0.954 0.060 0.940 0 0.000
#> SRR191695 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191696 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191697 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191698 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191699 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191700 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191701 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191702 2 0.2300 0.952 0.064 0.920 0 0.016
#> SRR191703 2 0.2300 0.952 0.064 0.920 0 0.016
#> SRR191704 2 0.1452 0.899 0.008 0.956 0 0.036
#> SRR191705 2 0.1624 0.916 0.020 0.952 0 0.028
#> SRR191706 2 0.2300 0.952 0.064 0.920 0 0.016
#> SRR191707 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191708 2 0.2300 0.952 0.064 0.920 0 0.016
#> SRR191709 2 0.2300 0.952 0.064 0.920 0 0.016
#> SRR191710 2 0.2300 0.952 0.064 0.920 0 0.016
#> SRR191711 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191712 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191713 2 0.1452 0.899 0.008 0.956 0 0.036
#> SRR191714 2 0.1452 0.899 0.008 0.956 0 0.036
#> SRR191715 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191716 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191717 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR191718 2 0.2124 0.955 0.068 0.924 0 0.008
#> SRR537099 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR537100 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR537101 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR537102 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR537104 1 0.4370 0.749 0.800 0.156 0 0.044
#> SRR537105 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR537106 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR537107 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR537108 1 0.2466 0.820 0.916 0.028 0 0.056
#> SRR537109 2 0.2563 0.940 0.072 0.908 0 0.020
#> SRR537110 2 0.3082 0.910 0.084 0.884 0 0.032
#> SRR537111 1 0.0376 0.843 0.992 0.004 0 0.004
#> SRR537113 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537114 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537115 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537116 2 0.3082 0.910 0.084 0.884 0 0.032
#> SRR537117 4 0.7599 0.759 0.316 0.220 0 0.464
#> SRR537118 4 0.7599 0.759 0.316 0.220 0 0.464
#> SRR537119 4 0.7599 0.759 0.316 0.220 0 0.464
#> SRR537120 4 0.7599 0.759 0.316 0.220 0 0.464
#> SRR537121 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537122 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537123 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537124 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537125 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537126 4 0.5453 0.894 0.304 0.036 0 0.660
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.1205 0.716 0.000 0.040 0 0.956 0.004
#> SRR191640 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191641 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191642 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191643 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191644 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191645 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR191646 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR191647 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR191648 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR191649 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR191650 4 0.1357 0.719 0.000 0.048 0 0.948 0.004
#> SRR191651 4 0.1357 0.719 0.000 0.048 0 0.948 0.004
#> SRR191652 4 0.1043 0.712 0.000 0.040 0 0.960 0.000
#> SRR191653 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191654 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191655 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR191656 4 0.2732 0.321 0.160 0.000 0 0.840 0.000
#> SRR191657 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191658 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191659 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191660 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191661 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191662 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191663 4 0.1331 0.709 0.008 0.040 0 0.952 0.000
#> SRR191664 4 0.1043 0.712 0.000 0.040 0 0.960 0.000
#> SRR191665 4 0.1648 0.694 0.020 0.040 0 0.940 0.000
#> SRR191666 4 0.1043 0.712 0.000 0.040 0 0.960 0.000
#> SRR191667 4 0.1043 0.712 0.000 0.040 0 0.960 0.000
#> SRR191668 4 0.2813 0.295 0.168 0.000 0 0.832 0.000
#> SRR191669 4 0.2813 0.295 0.168 0.000 0 0.832 0.000
#> SRR191670 4 0.2813 0.295 0.168 0.000 0 0.832 0.000
#> SRR191671 4 0.2813 0.295 0.168 0.000 0 0.832 0.000
#> SRR191672 1 0.5059 1.000 0.548 0.000 0 0.416 0.036
#> SRR191673 1 0.5059 1.000 0.548 0.000 0 0.416 0.036
#> SRR191674 2 0.4268 0.499 0.444 0.556 0 0.000 0.000
#> SRR191675 2 0.4268 0.499 0.444 0.556 0 0.000 0.000
#> SRR191677 2 0.4268 0.499 0.444 0.556 0 0.000 0.000
#> SRR191678 2 0.4268 0.499 0.444 0.556 0 0.000 0.000
#> SRR191679 2 0.4803 0.490 0.444 0.536 0 0.000 0.020
#> SRR191680 2 0.4268 0.499 0.444 0.556 0 0.000 0.000
#> SRR191681 2 0.4268 0.499 0.444 0.556 0 0.000 0.000
#> SRR191682 2 0.0404 0.899 0.000 0.988 0 0.000 0.012
#> SRR191683 2 0.0404 0.899 0.000 0.988 0 0.000 0.012
#> SRR191684 2 0.2270 0.843 0.076 0.904 0 0.000 0.020
#> SRR191685 2 0.0404 0.899 0.000 0.988 0 0.000 0.012
#> SRR191686 2 0.0404 0.899 0.000 0.988 0 0.000 0.012
#> SRR191687 2 0.0404 0.899 0.000 0.988 0 0.000 0.012
#> SRR191688 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191689 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191690 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191691 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191692 2 0.1043 0.889 0.040 0.960 0 0.000 0.000
#> SRR191693 2 0.1043 0.889 0.040 0.960 0 0.000 0.000
#> SRR191694 2 0.1043 0.889 0.040 0.960 0 0.000 0.000
#> SRR191695 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191696 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191697 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191698 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191699 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191700 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191701 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191702 2 0.0609 0.897 0.000 0.980 0 0.000 0.020
#> SRR191703 2 0.0609 0.897 0.000 0.980 0 0.000 0.020
#> SRR191704 2 0.2270 0.843 0.076 0.904 0 0.000 0.020
#> SRR191705 2 0.1943 0.860 0.056 0.924 0 0.000 0.020
#> SRR191706 2 0.0609 0.897 0.000 0.980 0 0.000 0.020
#> SRR191707 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191708 2 0.0609 0.897 0.000 0.980 0 0.000 0.020
#> SRR191709 2 0.0609 0.897 0.000 0.980 0 0.000 0.020
#> SRR191710 2 0.0609 0.897 0.000 0.980 0 0.000 0.020
#> SRR191711 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191712 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191713 2 0.2270 0.843 0.076 0.904 0 0.000 0.020
#> SRR191714 2 0.2270 0.843 0.076 0.904 0 0.000 0.020
#> SRR191715 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191716 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191717 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR191718 2 0.0290 0.902 0.000 0.992 0 0.000 0.008
#> SRR537099 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR537100 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR537101 4 0.3962 0.714 0.000 0.088 0 0.800 0.112
#> SRR537102 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR537104 4 0.4558 0.658 0.000 0.216 0 0.724 0.060
#> SRR537105 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR537106 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR537107 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR537108 4 0.4016 0.714 0.000 0.092 0 0.796 0.112
#> SRR537109 2 0.0703 0.889 0.000 0.976 0 0.000 0.024
#> SRR537110 2 0.1197 0.864 0.000 0.952 0 0.000 0.048
#> SRR537111 4 0.1357 0.719 0.000 0.048 0 0.948 0.004
#> SRR537113 5 0.1908 0.860 0.000 0.092 0 0.000 0.908
#> SRR537114 5 0.1908 0.860 0.000 0.092 0 0.000 0.908
#> SRR537115 5 0.1908 0.860 0.000 0.092 0 0.000 0.908
#> SRR537116 2 0.1197 0.864 0.000 0.952 0 0.000 0.048
#> SRR537117 5 0.3635 0.763 0.000 0.248 0 0.004 0.748
#> SRR537118 5 0.3635 0.763 0.000 0.248 0 0.004 0.748
#> SRR537119 5 0.3635 0.763 0.000 0.248 0 0.004 0.748
#> SRR537120 5 0.3635 0.763 0.000 0.248 0 0.004 0.748
#> SRR537121 5 0.1341 0.870 0.000 0.056 0 0.000 0.944
#> SRR537122 5 0.1341 0.870 0.000 0.056 0 0.000 0.944
#> SRR537123 5 0.1341 0.870 0.000 0.056 0 0.000 0.944
#> SRR537124 5 0.1341 0.870 0.000 0.056 0 0.000 0.944
#> SRR537125 5 0.1341 0.870 0.000 0.056 0 0.000 0.944
#> SRR537126 5 0.1341 0.870 0.000 0.056 0 0.000 0.944
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 4 0.1663 0.692 0.088 0.000 0 0.912 0.000 0.000
#> SRR191640 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR191641 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR191642 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR191643 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR191644 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR191645 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR191646 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR191647 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR191648 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR191649 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR191650 4 0.1036 0.719 0.024 0.008 0 0.964 0.004 0.000
#> SRR191651 4 0.1036 0.719 0.024 0.008 0 0.964 0.004 0.000
#> SRR191652 4 0.0632 0.712 0.024 0.000 0 0.976 0.000 0.000
#> SRR191653 4 0.3891 0.699 0.004 0.164 0 0.768 0.064 0.000
#> SRR191654 4 0.3891 0.699 0.004 0.164 0 0.768 0.064 0.000
#> SRR191655 4 0.3891 0.699 0.004 0.164 0 0.768 0.064 0.000
#> SRR191656 4 0.3672 0.277 0.368 0.000 0 0.632 0.000 0.000
#> SRR191657 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191658 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191659 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191660 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191661 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191662 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191663 4 0.2823 0.617 0.204 0.000 0 0.796 0.000 0.000
#> SRR191664 4 0.2092 0.673 0.124 0.000 0 0.876 0.000 0.000
#> SRR191665 4 0.2340 0.656 0.148 0.000 0 0.852 0.000 0.000
#> SRR191666 4 0.0632 0.712 0.024 0.000 0 0.976 0.000 0.000
#> SRR191667 4 0.0632 0.712 0.024 0.000 0 0.976 0.000 0.000
#> SRR191668 4 0.3563 0.233 0.336 0.000 0 0.664 0.000 0.000
#> SRR191669 4 0.3563 0.233 0.336 0.000 0 0.664 0.000 0.000
#> SRR191670 4 0.3578 0.221 0.340 0.000 0 0.660 0.000 0.000
#> SRR191671 4 0.3578 0.221 0.340 0.000 0 0.660 0.000 0.000
#> SRR191672 1 0.2597 1.000 0.824 0.000 0 0.176 0.000 0.000
#> SRR191673 1 0.2597 1.000 0.824 0.000 0 0.176 0.000 0.000
#> SRR191674 6 0.0547 0.994 0.000 0.020 0 0.000 0.000 0.980
#> SRR191675 6 0.0547 0.994 0.000 0.020 0 0.000 0.000 0.980
#> SRR191677 6 0.0547 0.994 0.000 0.020 0 0.000 0.000 0.980
#> SRR191678 6 0.0547 0.994 0.000 0.020 0 0.000 0.000 0.980
#> SRR191679 6 0.0000 0.961 0.000 0.000 0 0.000 0.000 1.000
#> SRR191680 6 0.0547 0.994 0.000 0.020 0 0.000 0.000 0.980
#> SRR191681 6 0.0547 0.994 0.000 0.020 0 0.000 0.000 0.980
#> SRR191682 2 0.1297 0.964 0.000 0.948 0 0.040 0.000 0.012
#> SRR191683 2 0.1297 0.964 0.000 0.948 0 0.040 0.000 0.012
#> SRR191684 2 0.1408 0.891 0.000 0.944 0 0.000 0.036 0.020
#> SRR191685 2 0.1297 0.964 0.000 0.948 0 0.040 0.000 0.012
#> SRR191686 2 0.1297 0.964 0.000 0.948 0 0.040 0.000 0.012
#> SRR191687 2 0.1297 0.964 0.000 0.948 0 0.040 0.000 0.012
#> SRR191688 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191689 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191690 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191691 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191692 2 0.2579 0.902 0.000 0.872 0 0.040 0.000 0.088
#> SRR191693 2 0.2579 0.902 0.000 0.872 0 0.040 0.000 0.088
#> SRR191694 2 0.2579 0.902 0.000 0.872 0 0.040 0.000 0.088
#> SRR191695 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191696 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191697 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191698 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191699 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191700 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191701 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191702 2 0.1480 0.961 0.000 0.940 0 0.040 0.000 0.020
#> SRR191703 2 0.1480 0.961 0.000 0.940 0 0.040 0.000 0.020
#> SRR191704 2 0.1408 0.891 0.000 0.944 0 0.000 0.036 0.020
#> SRR191705 2 0.1434 0.912 0.000 0.948 0 0.008 0.024 0.020
#> SRR191706 2 0.1480 0.961 0.000 0.940 0 0.040 0.000 0.020
#> SRR191707 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191708 2 0.1480 0.961 0.000 0.940 0 0.040 0.000 0.020
#> SRR191709 2 0.1480 0.961 0.000 0.940 0 0.040 0.000 0.020
#> SRR191710 2 0.1480 0.961 0.000 0.940 0 0.040 0.000 0.020
#> SRR191711 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191712 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191713 2 0.1408 0.891 0.000 0.944 0 0.000 0.036 0.020
#> SRR191714 2 0.1408 0.891 0.000 0.944 0 0.000 0.036 0.020
#> SRR191715 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191716 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191717 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR191718 2 0.1152 0.967 0.000 0.952 0 0.044 0.004 0.000
#> SRR537099 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR537100 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR537101 4 0.2843 0.733 0.000 0.036 0 0.848 0.116 0.000
#> SRR537102 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR537104 4 0.3752 0.698 0.000 0.164 0 0.772 0.064 0.000
#> SRR537105 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR537106 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR537107 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR537108 4 0.2912 0.734 0.000 0.040 0 0.844 0.116 0.000
#> SRR537109 2 0.1633 0.953 0.000 0.932 0 0.044 0.024 0.000
#> SRR537110 2 0.2134 0.924 0.000 0.904 0 0.044 0.052 0.000
#> SRR537111 4 0.1036 0.719 0.024 0.008 0 0.964 0.004 0.000
#> SRR537113 5 0.1723 0.842 0.000 0.036 0 0.036 0.928 0.000
#> SRR537114 5 0.1723 0.842 0.000 0.036 0 0.036 0.928 0.000
#> SRR537115 5 0.1723 0.842 0.000 0.036 0 0.036 0.928 0.000
#> SRR537116 2 0.2134 0.924 0.000 0.904 0 0.044 0.052 0.000
#> SRR537117 5 0.3271 0.741 0.000 0.232 0 0.008 0.760 0.000
#> SRR537118 5 0.3271 0.741 0.000 0.232 0 0.008 0.760 0.000
#> SRR537119 5 0.3271 0.741 0.000 0.232 0 0.008 0.760 0.000
#> SRR537120 5 0.3271 0.741 0.000 0.232 0 0.008 0.760 0.000
#> SRR537121 5 0.1007 0.858 0.000 0.044 0 0.000 0.956 0.000
#> SRR537122 5 0.1007 0.858 0.000 0.044 0 0.000 0.956 0.000
#> SRR537123 5 0.1007 0.858 0.000 0.044 0 0.000 0.956 0.000
#> SRR537124 5 0.1007 0.858 0.000 0.044 0 0.000 0.956 0.000
#> SRR537125 5 0.1007 0.858 0.000 0.044 0 0.000 0.956 0.000
#> SRR537126 5 0.1007 0.858 0.000 0.044 0 0.000 0.956 0.000
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 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)
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 16450 rows and 111 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 0.372 0.829 0.845 0.4252 0.500 0.500
#> 3 3 0.478 0.792 0.811 0.3527 0.882 0.768
#> 4 4 0.595 0.713 0.744 0.1687 1.000 1.000
#> 5 5 0.616 0.664 0.742 0.0854 0.857 0.642
#> 6 6 0.642 0.732 0.766 0.0547 0.943 0.786
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
#> SRR191639 1 0.6531 0.930 0.832 0.168
#> SRR191640 1 0.7453 0.929 0.788 0.212
#> SRR191641 1 0.7453 0.929 0.788 0.212
#> SRR191642 1 0.7453 0.929 0.788 0.212
#> SRR191643 1 0.7674 0.921 0.776 0.224
#> SRR191644 1 0.8443 0.862 0.728 0.272
#> SRR191645 1 0.7376 0.930 0.792 0.208
#> SRR191646 1 0.7376 0.930 0.792 0.208
#> SRR191647 1 0.7376 0.930 0.792 0.208
#> SRR191648 1 0.7376 0.930 0.792 0.208
#> SRR191649 1 0.7376 0.930 0.792 0.208
#> SRR191650 1 0.7056 0.934 0.808 0.192
#> SRR191651 1 0.6973 0.934 0.812 0.188
#> SRR191652 1 0.6887 0.934 0.816 0.184
#> SRR191653 1 0.8327 0.873 0.736 0.264
#> SRR191654 1 0.8386 0.867 0.732 0.268
#> SRR191655 1 0.7602 0.924 0.780 0.220
#> SRR191656 1 0.7056 0.912 0.808 0.192
#> SRR191657 1 0.6531 0.930 0.832 0.168
#> SRR191658 1 0.6438 0.928 0.836 0.164
#> SRR191659 1 0.6531 0.930 0.832 0.168
#> SRR191660 1 0.6531 0.930 0.832 0.168
#> SRR191661 1 0.6531 0.930 0.832 0.168
#> SRR191662 1 0.6531 0.930 0.832 0.168
#> SRR191663 1 0.6531 0.930 0.832 0.168
#> SRR191664 1 0.6531 0.930 0.832 0.168
#> SRR191665 1 0.6531 0.930 0.832 0.168
#> SRR191666 1 0.6343 0.929 0.840 0.160
#> SRR191667 1 0.6343 0.929 0.840 0.160
#> SRR191668 1 0.7056 0.912 0.808 0.192
#> SRR191669 1 0.7056 0.912 0.808 0.192
#> SRR191670 1 0.6438 0.928 0.836 0.164
#> SRR191671 1 0.6438 0.928 0.836 0.164
#> SRR191672 1 0.7056 0.912 0.808 0.192
#> SRR191673 1 0.7056 0.912 0.808 0.192
#> SRR191674 2 0.0000 0.829 0.000 1.000
#> SRR191675 2 0.0000 0.829 0.000 1.000
#> SRR191677 2 0.0000 0.829 0.000 1.000
#> SRR191678 2 0.0000 0.829 0.000 1.000
#> SRR191679 2 0.2778 0.852 0.048 0.952
#> SRR191680 2 0.0000 0.829 0.000 1.000
#> SRR191681 2 0.0000 0.829 0.000 1.000
#> SRR191682 2 0.2043 0.845 0.032 0.968
#> SRR191683 2 0.2043 0.845 0.032 0.968
#> SRR191684 2 0.4022 0.861 0.080 0.920
#> SRR191685 2 0.3431 0.857 0.064 0.936
#> SRR191686 2 0.2043 0.845 0.032 0.968
#> SRR191687 2 0.2778 0.852 0.048 0.952
#> SRR191688 2 0.4562 0.865 0.096 0.904
#> SRR191689 2 0.3733 0.859 0.072 0.928
#> SRR191690 2 0.4562 0.865 0.096 0.904
#> SRR191691 2 0.4562 0.865 0.096 0.904
#> SRR191692 2 0.0000 0.829 0.000 1.000
#> SRR191693 2 0.0000 0.829 0.000 1.000
#> SRR191694 2 0.0938 0.835 0.012 0.988
#> SRR191695 2 0.4562 0.865 0.096 0.904
#> SRR191696 2 0.4562 0.865 0.096 0.904
#> SRR191697 2 0.4562 0.865 0.096 0.904
#> SRR191698 2 0.4562 0.865 0.096 0.904
#> SRR191699 2 0.4562 0.865 0.096 0.904
#> SRR191700 2 0.4562 0.865 0.096 0.904
#> SRR191701 2 0.4562 0.865 0.096 0.904
#> SRR191702 2 0.4562 0.865 0.096 0.904
#> SRR191703 2 0.4562 0.865 0.096 0.904
#> SRR191704 2 0.4562 0.865 0.096 0.904
#> SRR191705 2 0.4562 0.865 0.096 0.904
#> SRR191706 2 0.4562 0.865 0.096 0.904
#> SRR191707 2 0.4562 0.865 0.096 0.904
#> SRR191708 2 0.4562 0.865 0.096 0.904
#> SRR191709 2 0.4562 0.865 0.096 0.904
#> SRR191710 2 0.4562 0.865 0.096 0.904
#> SRR191711 2 0.4562 0.865 0.096 0.904
#> SRR191712 2 0.4562 0.865 0.096 0.904
#> SRR191713 2 0.4562 0.865 0.096 0.904
#> SRR191714 2 0.4562 0.865 0.096 0.904
#> SRR191715 2 0.4562 0.865 0.096 0.904
#> SRR191716 2 0.4562 0.865 0.096 0.904
#> SRR191717 2 0.4562 0.865 0.096 0.904
#> SRR191718 2 0.4562 0.865 0.096 0.904
#> SRR537099 1 0.7745 0.917 0.772 0.228
#> SRR537100 1 0.7674 0.921 0.776 0.224
#> SRR537101 1 0.7453 0.929 0.788 0.212
#> SRR537102 1 0.7745 0.917 0.772 0.228
#> SRR537104 2 0.9998 -0.200 0.492 0.508
#> SRR537105 1 0.7453 0.928 0.788 0.212
#> SRR537106 1 0.7602 0.923 0.780 0.220
#> SRR537107 1 0.7602 0.923 0.780 0.220
#> SRR537108 1 0.7602 0.923 0.780 0.220
#> SRR537109 2 0.4562 0.865 0.096 0.904
#> SRR537110 2 0.4562 0.865 0.096 0.904
#> SRR537111 1 0.7528 0.926 0.784 0.216
#> SRR537113 2 0.9286 0.446 0.344 0.656
#> SRR537114 2 0.9286 0.446 0.344 0.656
#> SRR537115 2 0.9248 0.456 0.340 0.660
#> SRR537116 2 0.4562 0.865 0.096 0.904
#> SRR537117 2 0.9044 0.541 0.320 0.680
#> SRR537118 2 0.8555 0.606 0.280 0.720
#> SRR537119 2 0.8499 0.609 0.276 0.724
#> SRR537120 2 0.6973 0.713 0.188 0.812
#> SRR537121 2 0.9580 0.430 0.380 0.620
#> SRR537122 2 0.9580 0.430 0.380 0.620
#> SRR537123 2 0.9580 0.430 0.380 0.620
#> SRR537124 2 0.9522 0.449 0.372 0.628
#> SRR537125 2 0.9522 0.449 0.372 0.628
#> SRR537126 2 0.9522 0.449 0.372 0.628
#> SRR537127 1 0.4431 0.833 0.908 0.092
#> SRR537128 1 0.4431 0.833 0.908 0.092
#> SRR537129 1 0.4431 0.833 0.908 0.092
#> SRR537130 1 0.4431 0.833 0.908 0.092
#> SRR537131 1 0.4431 0.833 0.908 0.092
#> SRR537132 1 0.4431 0.833 0.908 0.092
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.6388 0.750 0.752 0.064 0.184
#> SRR191640 1 0.3112 0.775 0.900 0.096 0.004
#> SRR191641 1 0.3846 0.767 0.876 0.108 0.016
#> SRR191642 1 0.3846 0.767 0.876 0.108 0.016
#> SRR191643 1 0.4209 0.756 0.856 0.128 0.016
#> SRR191644 1 0.5843 0.584 0.732 0.252 0.016
#> SRR191645 1 0.3043 0.771 0.908 0.084 0.008
#> SRR191646 1 0.3043 0.771 0.908 0.084 0.008
#> SRR191647 1 0.3207 0.769 0.904 0.084 0.012
#> SRR191648 1 0.3207 0.769 0.904 0.084 0.012
#> SRR191649 1 0.3043 0.771 0.908 0.084 0.008
#> SRR191650 1 0.3528 0.776 0.892 0.092 0.016
#> SRR191651 1 0.4253 0.779 0.872 0.080 0.048
#> SRR191652 1 0.4658 0.775 0.856 0.068 0.076
#> SRR191653 1 0.5803 0.584 0.736 0.248 0.016
#> SRR191654 1 0.5803 0.584 0.736 0.248 0.016
#> SRR191655 1 0.4068 0.762 0.864 0.120 0.016
#> SRR191656 1 0.6986 0.702 0.688 0.056 0.256
#> SRR191657 1 0.6673 0.742 0.732 0.068 0.200
#> SRR191658 1 0.6585 0.741 0.736 0.064 0.200
#> SRR191659 1 0.6673 0.742 0.732 0.068 0.200
#> SRR191660 1 0.6673 0.742 0.732 0.068 0.200
#> SRR191661 1 0.6673 0.742 0.732 0.068 0.200
#> SRR191662 1 0.6673 0.742 0.732 0.068 0.200
#> SRR191663 1 0.6673 0.742 0.732 0.068 0.200
#> SRR191664 1 0.6585 0.741 0.736 0.064 0.200
#> SRR191665 1 0.6229 0.747 0.764 0.064 0.172
#> SRR191666 1 0.4556 0.774 0.860 0.060 0.080
#> SRR191667 1 0.4652 0.774 0.856 0.064 0.080
#> SRR191668 1 0.6913 0.709 0.696 0.056 0.248
#> SRR191669 1 0.6913 0.709 0.696 0.056 0.248
#> SRR191670 1 0.6847 0.722 0.708 0.060 0.232
#> SRR191671 1 0.6847 0.722 0.708 0.060 0.232
#> SRR191672 1 0.6986 0.702 0.688 0.056 0.256
#> SRR191673 1 0.6986 0.702 0.688 0.056 0.256
#> SRR191674 2 0.5443 0.617 0.004 0.736 0.260
#> SRR191675 2 0.5443 0.617 0.004 0.736 0.260
#> SRR191677 2 0.5285 0.646 0.004 0.752 0.244
#> SRR191678 2 0.5443 0.617 0.004 0.736 0.260
#> SRR191679 2 0.3030 0.852 0.004 0.904 0.092
#> SRR191680 2 0.4521 0.746 0.004 0.816 0.180
#> SRR191681 2 0.5443 0.617 0.004 0.736 0.260
#> SRR191682 2 0.1647 0.903 0.004 0.960 0.036
#> SRR191683 2 0.1647 0.903 0.004 0.960 0.036
#> SRR191684 2 0.0983 0.914 0.004 0.980 0.016
#> SRR191685 2 0.1267 0.910 0.004 0.972 0.024
#> SRR191686 2 0.1647 0.903 0.004 0.960 0.036
#> SRR191687 2 0.1267 0.910 0.004 0.972 0.024
#> SRR191688 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191689 2 0.0475 0.920 0.004 0.992 0.004
#> SRR191690 2 0.0829 0.922 0.012 0.984 0.004
#> SRR191691 2 0.1315 0.913 0.008 0.972 0.020
#> SRR191692 2 0.5365 0.632 0.004 0.744 0.252
#> SRR191693 2 0.5618 0.608 0.008 0.732 0.260
#> SRR191694 2 0.2496 0.869 0.004 0.928 0.068
#> SRR191695 2 0.1015 0.918 0.008 0.980 0.012
#> SRR191696 2 0.1015 0.918 0.008 0.980 0.012
#> SRR191697 2 0.1315 0.913 0.008 0.972 0.020
#> SRR191698 2 0.1315 0.913 0.008 0.972 0.020
#> SRR191699 2 0.0661 0.922 0.008 0.988 0.004
#> SRR191700 2 0.1315 0.913 0.008 0.972 0.020
#> SRR191701 2 0.1315 0.913 0.008 0.972 0.020
#> SRR191702 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191703 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191704 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191705 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191706 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191707 2 0.1315 0.913 0.008 0.972 0.020
#> SRR191708 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191709 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191710 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191711 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191712 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191713 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191714 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191715 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191716 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191717 2 0.0592 0.923 0.012 0.988 0.000
#> SRR191718 2 0.1015 0.918 0.008 0.980 0.012
#> SRR537099 1 0.4209 0.756 0.856 0.128 0.016
#> SRR537100 1 0.4068 0.762 0.864 0.120 0.016
#> SRR537101 1 0.3769 0.767 0.880 0.104 0.016
#> SRR537102 1 0.4209 0.756 0.856 0.128 0.016
#> SRR537104 1 0.6510 0.319 0.624 0.364 0.012
#> SRR537105 1 0.4094 0.755 0.872 0.100 0.028
#> SRR537106 1 0.4249 0.750 0.864 0.108 0.028
#> SRR537107 1 0.4249 0.750 0.864 0.108 0.028
#> SRR537108 1 0.4249 0.750 0.864 0.108 0.028
#> SRR537109 2 0.0592 0.923 0.012 0.988 0.000
#> SRR537110 2 0.0592 0.923 0.012 0.988 0.000
#> SRR537111 1 0.3995 0.771 0.868 0.116 0.016
#> SRR537113 3 0.9849 0.855 0.332 0.260 0.408
#> SRR537114 3 0.9830 0.844 0.340 0.252 0.408
#> SRR537115 3 0.9806 0.875 0.328 0.252 0.420
#> SRR537116 2 0.0592 0.923 0.012 0.988 0.000
#> SRR537117 3 0.9664 0.901 0.244 0.296 0.460
#> SRR537118 3 0.9585 0.855 0.212 0.332 0.456
#> SRR537119 3 0.9596 0.850 0.212 0.336 0.452
#> SRR537120 3 0.9475 0.797 0.188 0.360 0.452
#> SRR537121 3 0.9604 0.924 0.268 0.256 0.476
#> SRR537122 3 0.9604 0.924 0.268 0.256 0.476
#> SRR537123 3 0.9604 0.924 0.268 0.256 0.476
#> SRR537124 3 0.9604 0.924 0.268 0.256 0.476
#> SRR537125 3 0.9604 0.924 0.268 0.256 0.476
#> SRR537126 3 0.9604 0.924 0.268 0.256 0.476
#> SRR537127 1 0.7600 0.453 0.600 0.056 0.344
#> SRR537128 1 0.7559 0.453 0.608 0.056 0.336
#> SRR537129 1 0.7600 0.453 0.600 0.056 0.344
#> SRR537130 1 0.7600 0.453 0.600 0.056 0.344
#> SRR537131 1 0.7559 0.453 0.608 0.056 0.336
#> SRR537132 1 0.7559 0.453 0.608 0.056 0.336
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.5272 0.639 0.608 0.008 NA 0.004
#> SRR191640 1 0.1920 0.664 0.944 0.028 NA 0.004
#> SRR191641 1 0.2594 0.651 0.920 0.032 NA 0.012
#> SRR191642 1 0.2742 0.652 0.912 0.040 NA 0.008
#> SRR191643 1 0.2870 0.647 0.908 0.044 NA 0.012
#> SRR191644 1 0.4914 0.513 0.772 0.180 NA 0.012
#> SRR191645 1 0.2797 0.649 0.912 0.016 NA 0.044
#> SRR191646 1 0.2797 0.649 0.912 0.016 NA 0.044
#> SRR191647 1 0.2552 0.647 0.920 0.012 NA 0.048
#> SRR191648 1 0.2552 0.647 0.920 0.012 NA 0.048
#> SRR191649 1 0.2400 0.651 0.924 0.004 NA 0.044
#> SRR191650 1 0.2957 0.669 0.900 0.016 NA 0.016
#> SRR191651 1 0.4000 0.673 0.828 0.012 NA 0.016
#> SRR191652 1 0.4377 0.671 0.788 0.008 NA 0.016
#> SRR191653 1 0.4987 0.510 0.772 0.176 NA 0.016
#> SRR191654 1 0.4987 0.510 0.772 0.176 NA 0.016
#> SRR191655 1 0.2781 0.649 0.912 0.040 NA 0.012
#> SRR191656 1 0.6232 0.568 0.480 0.008 NA 0.036
#> SRR191657 1 0.5614 0.624 0.568 0.008 NA 0.012
#> SRR191658 1 0.5623 0.622 0.564 0.008 NA 0.012
#> SRR191659 1 0.5614 0.624 0.568 0.008 NA 0.012
#> SRR191660 1 0.5614 0.624 0.568 0.008 NA 0.012
#> SRR191661 1 0.5605 0.625 0.572 0.008 NA 0.012
#> SRR191662 1 0.5605 0.625 0.572 0.008 NA 0.012
#> SRR191663 1 0.5605 0.625 0.572 0.008 NA 0.012
#> SRR191664 1 0.5614 0.624 0.568 0.008 NA 0.012
#> SRR191665 1 0.5112 0.631 0.608 0.008 NA 0.000
#> SRR191666 1 0.4475 0.668 0.748 0.004 NA 0.008
#> SRR191667 1 0.4475 0.668 0.748 0.004 NA 0.008
#> SRR191668 1 0.5990 0.577 0.492 0.008 NA 0.024
#> SRR191669 1 0.5990 0.577 0.492 0.008 NA 0.024
#> SRR191670 1 0.5899 0.583 0.500 0.008 NA 0.020
#> SRR191671 1 0.5899 0.583 0.500 0.008 NA 0.020
#> SRR191672 1 0.6232 0.568 0.480 0.008 NA 0.036
#> SRR191673 1 0.6232 0.568 0.480 0.008 NA 0.036
#> SRR191674 2 0.6685 0.449 0.000 0.568 NA 0.324
#> SRR191675 2 0.6685 0.449 0.000 0.568 NA 0.324
#> SRR191677 2 0.6473 0.529 0.000 0.612 NA 0.280
#> SRR191678 2 0.6685 0.449 0.000 0.568 NA 0.324
#> SRR191679 2 0.4608 0.765 0.000 0.800 NA 0.096
#> SRR191680 2 0.5857 0.654 0.000 0.696 NA 0.196
#> SRR191681 2 0.6685 0.449 0.000 0.568 NA 0.324
#> SRR191682 2 0.3439 0.827 0.000 0.868 NA 0.048
#> SRR191683 2 0.3439 0.827 0.000 0.868 NA 0.048
#> SRR191684 2 0.3107 0.836 0.000 0.884 NA 0.036
#> SRR191685 2 0.3107 0.836 0.000 0.884 NA 0.036
#> SRR191686 2 0.3439 0.827 0.000 0.868 NA 0.048
#> SRR191687 2 0.3176 0.834 0.000 0.880 NA 0.036
#> SRR191688 2 0.0707 0.884 0.020 0.980 NA 0.000
#> SRR191689 2 0.0524 0.882 0.004 0.988 NA 0.000
#> SRR191690 2 0.1042 0.884 0.020 0.972 NA 0.000
#> SRR191691 2 0.1998 0.876 0.020 0.944 NA 0.020
#> SRR191692 2 0.6669 0.453 0.000 0.572 NA 0.320
#> SRR191693 2 0.6634 0.461 0.000 0.580 NA 0.312
#> SRR191694 2 0.3705 0.817 0.004 0.860 NA 0.052
#> SRR191695 2 0.1509 0.882 0.020 0.960 NA 0.012
#> SRR191696 2 0.1509 0.882 0.020 0.960 NA 0.012
#> SRR191697 2 0.1998 0.876 0.020 0.944 NA 0.020
#> SRR191698 2 0.1998 0.876 0.020 0.944 NA 0.020
#> SRR191699 2 0.0804 0.884 0.012 0.980 NA 0.000
#> SRR191700 2 0.1998 0.876 0.020 0.944 NA 0.020
#> SRR191701 2 0.1998 0.876 0.020 0.944 NA 0.020
#> SRR191702 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191703 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191704 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191705 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191706 2 0.1516 0.883 0.016 0.960 NA 0.008
#> SRR191707 2 0.2324 0.873 0.020 0.932 NA 0.028
#> SRR191708 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191709 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191710 2 0.1631 0.883 0.020 0.956 NA 0.008
#> SRR191711 2 0.0895 0.884 0.020 0.976 NA 0.004
#> SRR191712 2 0.0895 0.884 0.020 0.976 NA 0.004
#> SRR191713 2 0.1362 0.884 0.020 0.964 NA 0.004
#> SRR191714 2 0.1362 0.884 0.020 0.964 NA 0.004
#> SRR191715 2 0.0895 0.884 0.020 0.976 NA 0.004
#> SRR191716 2 0.0707 0.884 0.020 0.980 NA 0.000
#> SRR191717 2 0.0707 0.884 0.020 0.980 NA 0.000
#> SRR191718 2 0.1509 0.882 0.020 0.960 NA 0.012
#> SRR537099 1 0.3167 0.644 0.896 0.048 NA 0.016
#> SRR537100 1 0.3081 0.646 0.900 0.044 NA 0.016
#> SRR537101 1 0.2686 0.652 0.916 0.032 NA 0.012
#> SRR537102 1 0.3167 0.644 0.896 0.048 NA 0.016
#> SRR537104 1 0.5627 0.384 0.684 0.268 NA 0.008
#> SRR537105 1 0.3606 0.621 0.872 0.020 NA 0.080
#> SRR537106 1 0.3606 0.621 0.872 0.020 NA 0.080
#> SRR537107 1 0.3606 0.621 0.872 0.020 NA 0.080
#> SRR537108 1 0.3606 0.621 0.872 0.020 NA 0.080
#> SRR537109 2 0.0707 0.884 0.020 0.980 NA 0.000
#> SRR537110 2 0.1082 0.884 0.020 0.972 NA 0.004
#> SRR537111 1 0.3423 0.666 0.884 0.024 NA 0.028
#> SRR537113 4 0.6770 0.842 0.252 0.088 NA 0.636
#> SRR537114 4 0.6739 0.838 0.256 0.084 NA 0.636
#> SRR537115 4 0.6385 0.881 0.224 0.076 NA 0.676
#> SRR537116 2 0.0895 0.884 0.020 0.976 NA 0.004
#> SRR537117 4 0.6384 0.901 0.148 0.128 NA 0.700
#> SRR537118 4 0.6382 0.888 0.140 0.144 NA 0.696
#> SRR537119 4 0.6515 0.877 0.140 0.156 NA 0.684
#> SRR537120 4 0.6515 0.877 0.140 0.156 NA 0.684
#> SRR537121 4 0.5477 0.924 0.156 0.080 NA 0.752
#> SRR537122 4 0.5477 0.924 0.156 0.080 NA 0.752
#> SRR537123 4 0.5477 0.924 0.156 0.080 NA 0.752
#> SRR537124 4 0.5370 0.923 0.152 0.084 NA 0.756
#> SRR537125 4 0.5352 0.924 0.156 0.080 NA 0.756
#> SRR537126 4 0.5352 0.924 0.156 0.080 NA 0.756
#> SRR537127 1 0.7793 0.310 0.448 0.020 NA 0.140
#> SRR537128 1 0.7763 0.310 0.448 0.020 NA 0.136
#> SRR537129 1 0.7793 0.310 0.448 0.020 NA 0.140
#> SRR537130 1 0.7763 0.310 0.448 0.020 NA 0.136
#> SRR537131 1 0.7763 0.310 0.448 0.020 NA 0.136
#> SRR537132 1 0.7763 0.310 0.448 0.020 NA 0.136
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.4595 -0.6478 0.488 0.004 0.004 0.504 0.000
#> SRR191640 4 0.1932 0.6243 0.020 0.032 0.004 0.936 0.008
#> SRR191641 4 0.1686 0.6303 0.004 0.036 0.004 0.944 0.012
#> SRR191642 4 0.1766 0.6315 0.004 0.040 0.004 0.940 0.012
#> SRR191643 4 0.2061 0.6337 0.004 0.056 0.004 0.924 0.012
#> SRR191644 4 0.3039 0.6001 0.004 0.124 0.004 0.856 0.012
#> SRR191645 4 0.4684 0.5947 0.080 0.016 0.056 0.800 0.048
#> SRR191646 4 0.4684 0.5947 0.080 0.016 0.056 0.800 0.048
#> SRR191647 4 0.4684 0.5947 0.080 0.016 0.056 0.800 0.048
#> SRR191648 4 0.4684 0.5947 0.080 0.016 0.056 0.800 0.048
#> SRR191649 4 0.4587 0.5868 0.088 0.008 0.056 0.800 0.048
#> SRR191650 4 0.3865 0.4863 0.160 0.012 0.004 0.804 0.020
#> SRR191651 4 0.4307 0.3085 0.236 0.008 0.004 0.736 0.016
#> SRR191652 4 0.4821 0.0694 0.308 0.008 0.004 0.660 0.020
#> SRR191653 4 0.3141 0.5833 0.000 0.152 0.000 0.832 0.016
#> SRR191654 4 0.3141 0.5833 0.000 0.152 0.000 0.832 0.016
#> SRR191655 4 0.1701 0.6370 0.000 0.048 0.000 0.936 0.016
#> SRR191656 1 0.5184 0.8444 0.656 0.004 0.032 0.292 0.016
#> SRR191657 1 0.5337 0.8630 0.580 0.004 0.028 0.376 0.012
#> SRR191658 1 0.5303 0.8646 0.592 0.004 0.028 0.364 0.012
#> SRR191659 1 0.5337 0.8630 0.580 0.004 0.028 0.376 0.012
#> SRR191660 1 0.5337 0.8630 0.580 0.004 0.028 0.376 0.012
#> SRR191661 1 0.5348 0.8603 0.576 0.004 0.028 0.380 0.012
#> SRR191662 1 0.5348 0.8603 0.576 0.004 0.028 0.380 0.012
#> SRR191663 1 0.5348 0.8603 0.576 0.004 0.028 0.380 0.012
#> SRR191664 1 0.5337 0.8630 0.580 0.004 0.028 0.376 0.012
#> SRR191665 1 0.4434 0.7297 0.536 0.004 0.000 0.460 0.000
#> SRR191666 4 0.4757 -0.1480 0.364 0.008 0.008 0.616 0.004
#> SRR191667 4 0.4757 -0.1480 0.364 0.008 0.008 0.616 0.004
#> SRR191668 1 0.5224 0.8462 0.648 0.004 0.032 0.300 0.016
#> SRR191669 1 0.5224 0.8462 0.648 0.004 0.032 0.300 0.016
#> SRR191670 1 0.5147 0.8492 0.648 0.004 0.032 0.304 0.012
#> SRR191671 1 0.5147 0.8492 0.648 0.004 0.032 0.304 0.012
#> SRR191672 1 0.5474 0.8380 0.620 0.004 0.036 0.320 0.020
#> SRR191673 1 0.5474 0.8380 0.620 0.004 0.036 0.320 0.020
#> SRR191674 3 0.6706 0.9645 0.000 0.324 0.452 0.004 0.220
#> SRR191675 3 0.6706 0.9645 0.000 0.324 0.452 0.004 0.220
#> SRR191677 3 0.6628 0.9363 0.000 0.344 0.456 0.004 0.196
#> SRR191678 3 0.6706 0.9645 0.000 0.324 0.452 0.004 0.220
#> SRR191679 2 0.5023 -0.4535 0.004 0.520 0.456 0.004 0.016
#> SRR191680 3 0.6173 0.7628 0.000 0.420 0.460 0.004 0.116
#> SRR191681 3 0.6706 0.9645 0.000 0.324 0.452 0.004 0.220
#> SRR191682 2 0.5657 0.2187 0.040 0.604 0.328 0.004 0.024
#> SRR191683 2 0.5657 0.2187 0.040 0.604 0.328 0.004 0.024
#> SRR191684 2 0.5626 0.2379 0.040 0.612 0.320 0.004 0.024
#> SRR191685 2 0.5657 0.2187 0.040 0.604 0.328 0.004 0.024
#> SRR191686 2 0.5657 0.2187 0.040 0.604 0.328 0.004 0.024
#> SRR191687 2 0.5657 0.2187 0.040 0.604 0.328 0.004 0.024
#> SRR191688 2 0.1699 0.8073 0.008 0.944 0.036 0.008 0.004
#> SRR191689 2 0.1168 0.8088 0.000 0.960 0.032 0.008 0.000
#> SRR191690 2 0.1779 0.8075 0.008 0.940 0.040 0.008 0.004
#> SRR191691 2 0.2775 0.7683 0.036 0.884 0.076 0.000 0.004
#> SRR191692 3 0.6706 0.9645 0.000 0.324 0.452 0.004 0.220
#> SRR191693 3 0.6706 0.9645 0.000 0.324 0.452 0.004 0.220
#> SRR191694 2 0.4675 0.0710 0.000 0.640 0.336 0.004 0.020
#> SRR191695 2 0.2444 0.7903 0.028 0.908 0.056 0.004 0.004
#> SRR191696 2 0.2444 0.7903 0.028 0.908 0.056 0.004 0.004
#> SRR191697 2 0.2913 0.7652 0.040 0.876 0.080 0.000 0.004
#> SRR191698 2 0.2913 0.7652 0.040 0.876 0.080 0.000 0.004
#> SRR191699 2 0.0992 0.8113 0.000 0.968 0.024 0.008 0.000
#> SRR191700 2 0.2913 0.7652 0.040 0.876 0.080 0.000 0.004
#> SRR191701 2 0.2913 0.7652 0.040 0.876 0.080 0.000 0.004
#> SRR191702 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191703 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191704 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191705 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191706 2 0.1884 0.8035 0.024 0.940 0.020 0.008 0.008
#> SRR191707 2 0.2913 0.7651 0.040 0.876 0.080 0.000 0.004
#> SRR191708 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191709 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191710 2 0.1785 0.8052 0.024 0.944 0.016 0.008 0.008
#> SRR191711 2 0.0902 0.8115 0.004 0.976 0.008 0.008 0.004
#> SRR191712 2 0.0902 0.8115 0.004 0.976 0.008 0.008 0.004
#> SRR191713 2 0.1488 0.8089 0.016 0.956 0.012 0.008 0.008
#> SRR191714 2 0.1362 0.8100 0.016 0.960 0.012 0.008 0.004
#> SRR191715 2 0.0902 0.8115 0.004 0.976 0.008 0.008 0.004
#> SRR191716 2 0.1892 0.8052 0.012 0.936 0.040 0.008 0.004
#> SRR191717 2 0.1812 0.8063 0.012 0.940 0.036 0.008 0.004
#> SRR191718 2 0.2444 0.7903 0.028 0.908 0.056 0.004 0.004
#> SRR537099 4 0.2061 0.6337 0.004 0.056 0.004 0.924 0.012
#> SRR537100 4 0.2061 0.6337 0.004 0.056 0.004 0.924 0.012
#> SRR537101 4 0.1686 0.6303 0.004 0.036 0.004 0.944 0.012
#> SRR537102 4 0.2312 0.6337 0.008 0.056 0.008 0.916 0.012
#> SRR537104 4 0.3896 0.5288 0.004 0.196 0.008 0.780 0.012
#> SRR537105 4 0.5361 0.5862 0.072 0.024 0.056 0.760 0.088
#> SRR537106 4 0.5361 0.5862 0.072 0.024 0.056 0.760 0.088
#> SRR537107 4 0.5361 0.5862 0.072 0.024 0.056 0.760 0.088
#> SRR537108 4 0.5361 0.5862 0.072 0.024 0.056 0.760 0.088
#> SRR537109 2 0.1243 0.8117 0.000 0.960 0.028 0.008 0.004
#> SRR537110 2 0.0775 0.8123 0.004 0.980 0.004 0.008 0.004
#> SRR537111 4 0.4108 0.5211 0.140 0.020 0.004 0.804 0.032
#> SRR537113 5 0.5071 0.8154 0.016 0.024 0.028 0.208 0.724
#> SRR537114 5 0.5071 0.8154 0.016 0.024 0.028 0.208 0.724
#> SRR537115 5 0.4874 0.8382 0.016 0.024 0.028 0.184 0.748
#> SRR537116 2 0.0740 0.8113 0.000 0.980 0.008 0.008 0.004
#> SRR537117 5 0.3765 0.8962 0.000 0.040 0.040 0.080 0.840
#> SRR537118 5 0.4046 0.8940 0.008 0.040 0.040 0.080 0.832
#> SRR537119 5 0.4046 0.8940 0.008 0.040 0.040 0.080 0.832
#> SRR537120 5 0.4046 0.8940 0.008 0.040 0.040 0.080 0.832
#> SRR537121 5 0.2707 0.9146 0.000 0.024 0.008 0.080 0.888
#> SRR537122 5 0.2707 0.9146 0.000 0.024 0.008 0.080 0.888
#> SRR537123 5 0.2707 0.9146 0.000 0.024 0.008 0.080 0.888
#> SRR537124 5 0.2930 0.9118 0.008 0.024 0.008 0.076 0.884
#> SRR537125 5 0.2930 0.9118 0.008 0.024 0.008 0.076 0.884
#> SRR537126 5 0.2930 0.9118 0.008 0.024 0.008 0.076 0.884
#> SRR537127 4 0.8219 0.2969 0.248 0.012 0.192 0.436 0.112
#> SRR537128 4 0.8195 0.2969 0.244 0.012 0.204 0.436 0.104
#> SRR537129 4 0.8225 0.2968 0.244 0.012 0.196 0.436 0.112
#> SRR537130 4 0.8219 0.2969 0.248 0.012 0.192 0.436 0.112
#> SRR537131 4 0.8195 0.2969 0.244 0.012 0.204 0.436 0.104
#> SRR537132 4 0.8195 0.2969 0.244 0.012 0.204 0.436 0.104
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.4127 0.4889 0.508 0.000 0.004 0.484 0.004 0.000
#> SRR191640 4 0.1456 0.7919 0.020 0.012 0.004 0.952 0.008 0.004
#> SRR191641 4 0.1036 0.7904 0.000 0.024 0.000 0.964 0.008 0.004
#> SRR191642 4 0.1241 0.7928 0.004 0.020 0.004 0.960 0.008 0.004
#> SRR191643 4 0.1116 0.7888 0.000 0.028 0.000 0.960 0.008 0.004
#> SRR191644 4 0.1863 0.7600 0.000 0.056 0.004 0.924 0.008 0.008
#> SRR191645 4 0.4637 0.7652 0.052 0.008 0.036 0.780 0.028 0.096
#> SRR191646 4 0.4637 0.7652 0.052 0.008 0.036 0.780 0.028 0.096
#> SRR191647 4 0.4637 0.7652 0.052 0.008 0.036 0.780 0.028 0.096
#> SRR191648 4 0.4637 0.7652 0.052 0.008 0.036 0.780 0.028 0.096
#> SRR191649 4 0.4637 0.7652 0.052 0.008 0.036 0.780 0.028 0.096
#> SRR191650 4 0.4513 0.5817 0.204 0.004 0.016 0.732 0.020 0.024
#> SRR191651 4 0.4836 0.2985 0.300 0.000 0.016 0.644 0.016 0.024
#> SRR191652 4 0.5102 -0.0981 0.384 0.000 0.016 0.560 0.016 0.024
#> SRR191653 4 0.2097 0.7509 0.000 0.064 0.008 0.912 0.008 0.008
#> SRR191654 4 0.2097 0.7509 0.000 0.064 0.008 0.912 0.008 0.008
#> SRR191655 4 0.1109 0.7915 0.000 0.016 0.004 0.964 0.012 0.004
#> SRR191656 1 0.3867 0.7193 0.784 0.000 0.000 0.152 0.020 0.044
#> SRR191657 1 0.5503 0.7853 0.596 0.000 0.112 0.272 0.000 0.020
#> SRR191658 1 0.5485 0.7841 0.600 0.000 0.112 0.268 0.000 0.020
#> SRR191659 1 0.5503 0.7853 0.596 0.000 0.112 0.272 0.000 0.020
#> SRR191660 1 0.5503 0.7853 0.596 0.000 0.112 0.272 0.000 0.020
#> SRR191661 1 0.5503 0.7853 0.596 0.000 0.112 0.272 0.000 0.020
#> SRR191662 1 0.5503 0.7853 0.596 0.000 0.112 0.272 0.000 0.020
#> SRR191663 1 0.5503 0.7853 0.596 0.000 0.112 0.272 0.000 0.020
#> SRR191664 1 0.5485 0.7848 0.600 0.000 0.112 0.268 0.000 0.020
#> SRR191665 1 0.4181 0.6606 0.616 0.000 0.004 0.368 0.004 0.008
#> SRR191666 1 0.5298 0.4837 0.476 0.000 0.044 0.456 0.004 0.020
#> SRR191667 1 0.5298 0.4837 0.476 0.000 0.044 0.456 0.004 0.020
#> SRR191668 1 0.3764 0.7278 0.784 0.000 0.000 0.160 0.012 0.044
#> SRR191669 1 0.3764 0.7278 0.784 0.000 0.000 0.160 0.012 0.044
#> SRR191670 1 0.3667 0.7288 0.788 0.000 0.000 0.160 0.008 0.044
#> SRR191671 1 0.3667 0.7288 0.788 0.000 0.000 0.160 0.008 0.044
#> SRR191672 1 0.4483 0.7073 0.748 0.000 0.008 0.160 0.020 0.064
#> SRR191673 1 0.4483 0.7073 0.748 0.000 0.008 0.160 0.020 0.064
#> SRR191674 6 0.5231 0.9376 0.000 0.216 0.004 0.000 0.156 0.624
#> SRR191675 6 0.5231 0.9376 0.000 0.216 0.004 0.000 0.156 0.624
#> SRR191677 6 0.5012 0.9243 0.000 0.236 0.000 0.000 0.132 0.632
#> SRR191678 6 0.5096 0.9374 0.000 0.216 0.000 0.000 0.156 0.628
#> SRR191679 6 0.3795 0.6926 0.000 0.364 0.004 0.000 0.000 0.632
#> SRR191680 6 0.4587 0.8377 0.000 0.296 0.000 0.000 0.064 0.640
#> SRR191681 6 0.5096 0.9374 0.000 0.216 0.000 0.000 0.156 0.628
#> SRR191682 2 0.6443 0.1602 0.048 0.528 0.080 0.008 0.016 0.320
#> SRR191683 2 0.6443 0.1602 0.048 0.528 0.080 0.008 0.016 0.320
#> SRR191684 2 0.6410 0.1950 0.044 0.544 0.088 0.008 0.016 0.300
#> SRR191685 2 0.6430 0.1602 0.044 0.528 0.084 0.008 0.016 0.320
#> SRR191686 2 0.6443 0.1602 0.048 0.528 0.080 0.008 0.016 0.320
#> SRR191687 2 0.6430 0.1602 0.044 0.528 0.084 0.008 0.016 0.320
#> SRR191688 2 0.2308 0.7723 0.008 0.904 0.028 0.004 0.000 0.056
#> SRR191689 2 0.2154 0.7748 0.004 0.908 0.020 0.000 0.004 0.064
#> SRR191690 2 0.2705 0.7697 0.008 0.884 0.032 0.004 0.004 0.068
#> SRR191691 2 0.4299 0.7153 0.028 0.788 0.104 0.000 0.020 0.060
#> SRR191692 6 0.5427 0.9356 0.000 0.216 0.012 0.000 0.156 0.616
#> SRR191693 6 0.5473 0.9319 0.000 0.224 0.012 0.000 0.156 0.608
#> SRR191694 2 0.5115 -0.3698 0.000 0.480 0.020 0.000 0.040 0.460
#> SRR191695 2 0.3413 0.7434 0.024 0.836 0.068 0.000 0.000 0.072
#> SRR191696 2 0.3413 0.7434 0.024 0.836 0.068 0.000 0.000 0.072
#> SRR191697 2 0.4517 0.7075 0.028 0.772 0.104 0.000 0.020 0.076
#> SRR191698 2 0.4517 0.7075 0.028 0.772 0.104 0.000 0.020 0.076
#> SRR191699 2 0.2094 0.7759 0.004 0.912 0.020 0.000 0.004 0.060
#> SRR191700 2 0.4517 0.7075 0.028 0.772 0.104 0.000 0.020 0.076
#> SRR191701 2 0.4517 0.7075 0.028 0.772 0.104 0.000 0.020 0.076
#> SRR191702 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191703 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191704 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191705 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191706 2 0.2274 0.7630 0.028 0.908 0.028 0.000 0.000 0.036
#> SRR191707 2 0.4133 0.7197 0.036 0.796 0.108 0.000 0.012 0.048
#> SRR191708 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191709 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191710 2 0.2345 0.7640 0.028 0.908 0.028 0.004 0.000 0.032
#> SRR191711 2 0.1007 0.7797 0.004 0.968 0.008 0.004 0.000 0.016
#> SRR191712 2 0.1007 0.7797 0.004 0.968 0.008 0.004 0.000 0.016
#> SRR191713 2 0.1490 0.7756 0.008 0.948 0.024 0.004 0.000 0.016
#> SRR191714 2 0.1490 0.7756 0.008 0.948 0.024 0.004 0.000 0.016
#> SRR191715 2 0.1007 0.7797 0.004 0.968 0.008 0.004 0.000 0.016
#> SRR191716 2 0.2369 0.7717 0.008 0.900 0.028 0.004 0.000 0.060
#> SRR191717 2 0.2308 0.7723 0.008 0.904 0.028 0.004 0.000 0.056
#> SRR191718 2 0.3356 0.7453 0.024 0.840 0.064 0.000 0.000 0.072
#> SRR537099 4 0.1405 0.7892 0.004 0.028 0.004 0.952 0.008 0.004
#> SRR537100 4 0.1405 0.7892 0.004 0.028 0.004 0.952 0.008 0.004
#> SRR537101 4 0.1241 0.7899 0.004 0.020 0.004 0.960 0.008 0.004
#> SRR537102 4 0.1709 0.7877 0.004 0.032 0.008 0.940 0.008 0.008
#> SRR537104 4 0.2623 0.7185 0.004 0.092 0.008 0.880 0.008 0.008
#> SRR537105 4 0.5331 0.7443 0.056 0.016 0.040 0.740 0.048 0.100
#> SRR537106 4 0.5331 0.7443 0.056 0.016 0.040 0.740 0.048 0.100
#> SRR537107 4 0.5331 0.7443 0.056 0.016 0.040 0.740 0.048 0.100
#> SRR537108 4 0.5331 0.7443 0.056 0.016 0.040 0.740 0.048 0.100
#> SRR537109 2 0.1921 0.7780 0.004 0.924 0.024 0.004 0.000 0.044
#> SRR537110 2 0.0798 0.7800 0.004 0.976 0.012 0.004 0.000 0.004
#> SRR537111 4 0.4425 0.6030 0.192 0.004 0.016 0.744 0.020 0.024
#> SRR537113 5 0.4221 0.7933 0.004 0.012 0.020 0.152 0.776 0.036
#> SRR537114 5 0.4221 0.7933 0.004 0.012 0.020 0.152 0.776 0.036
#> SRR537115 5 0.3535 0.8628 0.004 0.012 0.020 0.088 0.840 0.036
#> SRR537116 2 0.0767 0.7797 0.000 0.976 0.008 0.004 0.000 0.012
#> SRR537117 5 0.3427 0.8918 0.004 0.024 0.020 0.052 0.856 0.044
#> SRR537118 5 0.3567 0.8913 0.004 0.028 0.020 0.056 0.848 0.044
#> SRR537119 5 0.3567 0.8913 0.004 0.028 0.020 0.056 0.848 0.044
#> SRR537120 5 0.3567 0.8913 0.004 0.028 0.020 0.056 0.848 0.044
#> SRR537121 5 0.1979 0.9124 0.008 0.008 0.004 0.036 0.928 0.016
#> SRR537122 5 0.1979 0.9124 0.008 0.008 0.004 0.036 0.928 0.016
#> SRR537123 5 0.1979 0.9124 0.008 0.008 0.004 0.036 0.928 0.016
#> SRR537124 5 0.1307 0.9094 0.000 0.008 0.000 0.032 0.952 0.008
#> SRR537125 5 0.1382 0.9097 0.000 0.008 0.000 0.036 0.948 0.008
#> SRR537126 5 0.1382 0.9097 0.000 0.008 0.000 0.036 0.948 0.008
#> SRR537127 3 0.5961 0.9829 0.084 0.008 0.592 0.276 0.024 0.016
#> SRR537128 3 0.5403 0.9819 0.068 0.008 0.628 0.272 0.020 0.004
#> SRR537129 3 0.5961 0.9829 0.084 0.008 0.592 0.276 0.024 0.016
#> SRR537130 3 0.5961 0.9829 0.084 0.008 0.592 0.276 0.024 0.016
#> SRR537131 3 0.5352 0.9825 0.064 0.008 0.632 0.272 0.020 0.004
#> SRR537132 3 0.5403 0.9819 0.068 0.008 0.628 0.272 0.020 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.945 0.982 0.990 0.5018 0.499 0.499
#> 3 3 0.801 0.913 0.946 0.2812 0.863 0.725
#> 4 4 0.848 0.885 0.931 0.1455 0.895 0.708
#> 5 5 0.840 0.849 0.867 0.0628 0.935 0.756
#> 6 6 0.822 0.835 0.857 0.0432 0.971 0.861
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
#> SRR191639 1 0.000 1.000 1.000 0.000
#> SRR191640 1 0.000 1.000 1.000 0.000
#> SRR191641 1 0.000 1.000 1.000 0.000
#> SRR191642 1 0.000 1.000 1.000 0.000
#> SRR191643 1 0.000 1.000 1.000 0.000
#> SRR191644 1 0.000 1.000 1.000 0.000
#> SRR191645 1 0.000 1.000 1.000 0.000
#> SRR191646 1 0.000 1.000 1.000 0.000
#> SRR191647 1 0.000 1.000 1.000 0.000
#> SRR191648 1 0.000 1.000 1.000 0.000
#> SRR191649 1 0.000 1.000 1.000 0.000
#> SRR191650 1 0.000 1.000 1.000 0.000
#> SRR191651 1 0.000 1.000 1.000 0.000
#> SRR191652 1 0.000 1.000 1.000 0.000
#> SRR191653 1 0.000 1.000 1.000 0.000
#> SRR191654 1 0.000 1.000 1.000 0.000
#> SRR191655 1 0.000 1.000 1.000 0.000
#> SRR191656 1 0.000 1.000 1.000 0.000
#> SRR191657 1 0.000 1.000 1.000 0.000
#> SRR191658 1 0.000 1.000 1.000 0.000
#> SRR191659 1 0.000 1.000 1.000 0.000
#> SRR191660 1 0.000 1.000 1.000 0.000
#> SRR191661 1 0.000 1.000 1.000 0.000
#> SRR191662 1 0.000 1.000 1.000 0.000
#> SRR191663 1 0.000 1.000 1.000 0.000
#> SRR191664 1 0.000 1.000 1.000 0.000
#> SRR191665 1 0.000 1.000 1.000 0.000
#> SRR191666 1 0.000 1.000 1.000 0.000
#> SRR191667 1 0.000 1.000 1.000 0.000
#> SRR191668 1 0.000 1.000 1.000 0.000
#> SRR191669 1 0.000 1.000 1.000 0.000
#> SRR191670 1 0.000 1.000 1.000 0.000
#> SRR191671 1 0.000 1.000 1.000 0.000
#> SRR191672 1 0.000 1.000 1.000 0.000
#> SRR191673 1 0.000 1.000 1.000 0.000
#> SRR191674 2 0.000 0.982 0.000 1.000
#> SRR191675 2 0.000 0.982 0.000 1.000
#> SRR191677 2 0.000 0.982 0.000 1.000
#> SRR191678 2 0.000 0.982 0.000 1.000
#> SRR191679 2 0.000 0.982 0.000 1.000
#> SRR191680 2 0.000 0.982 0.000 1.000
#> SRR191681 2 0.000 0.982 0.000 1.000
#> SRR191682 2 0.000 0.982 0.000 1.000
#> SRR191683 2 0.000 0.982 0.000 1.000
#> SRR191684 2 0.000 0.982 0.000 1.000
#> SRR191685 2 0.000 0.982 0.000 1.000
#> SRR191686 2 0.000 0.982 0.000 1.000
#> SRR191687 2 0.000 0.982 0.000 1.000
#> SRR191688 2 0.000 0.982 0.000 1.000
#> SRR191689 2 0.000 0.982 0.000 1.000
#> SRR191690 2 0.000 0.982 0.000 1.000
#> SRR191691 2 0.000 0.982 0.000 1.000
#> SRR191692 2 0.000 0.982 0.000 1.000
#> SRR191693 2 0.000 0.982 0.000 1.000
#> SRR191694 2 0.000 0.982 0.000 1.000
#> SRR191695 2 0.000 0.982 0.000 1.000
#> SRR191696 2 0.000 0.982 0.000 1.000
#> SRR191697 2 0.000 0.982 0.000 1.000
#> SRR191698 2 0.000 0.982 0.000 1.000
#> SRR191699 2 0.000 0.982 0.000 1.000
#> SRR191700 2 0.000 0.982 0.000 1.000
#> SRR191701 2 0.000 0.982 0.000 1.000
#> SRR191702 2 0.000 0.982 0.000 1.000
#> SRR191703 2 0.000 0.982 0.000 1.000
#> SRR191704 2 0.000 0.982 0.000 1.000
#> SRR191705 2 0.000 0.982 0.000 1.000
#> SRR191706 2 0.000 0.982 0.000 1.000
#> SRR191707 2 0.000 0.982 0.000 1.000
#> SRR191708 2 0.000 0.982 0.000 1.000
#> SRR191709 2 0.000 0.982 0.000 1.000
#> SRR191710 2 0.000 0.982 0.000 1.000
#> SRR191711 2 0.000 0.982 0.000 1.000
#> SRR191712 2 0.000 0.982 0.000 1.000
#> SRR191713 2 0.000 0.982 0.000 1.000
#> SRR191714 2 0.000 0.982 0.000 1.000
#> SRR191715 2 0.000 0.982 0.000 1.000
#> SRR191716 2 0.000 0.982 0.000 1.000
#> SRR191717 2 0.000 0.982 0.000 1.000
#> SRR191718 2 0.000 0.982 0.000 1.000
#> SRR537099 1 0.000 1.000 1.000 0.000
#> SRR537100 1 0.000 1.000 1.000 0.000
#> SRR537101 1 0.000 1.000 1.000 0.000
#> SRR537102 1 0.000 1.000 1.000 0.000
#> SRR537104 1 0.000 1.000 1.000 0.000
#> SRR537105 1 0.000 1.000 1.000 0.000
#> SRR537106 1 0.000 1.000 1.000 0.000
#> SRR537107 1 0.000 1.000 1.000 0.000
#> SRR537108 1 0.000 1.000 1.000 0.000
#> SRR537109 2 0.000 0.982 0.000 1.000
#> SRR537110 2 0.000 0.982 0.000 1.000
#> SRR537111 1 0.000 1.000 1.000 0.000
#> SRR537113 2 0.529 0.881 0.120 0.880
#> SRR537114 2 0.653 0.822 0.168 0.832
#> SRR537115 2 0.518 0.885 0.116 0.884
#> SRR537116 2 0.000 0.982 0.000 1.000
#> SRR537117 2 0.000 0.982 0.000 1.000
#> SRR537118 2 0.000 0.982 0.000 1.000
#> SRR537119 2 0.000 0.982 0.000 1.000
#> SRR537120 2 0.000 0.982 0.000 1.000
#> SRR537121 2 0.529 0.881 0.120 0.880
#> SRR537122 2 0.529 0.881 0.120 0.880
#> SRR537123 2 0.529 0.881 0.120 0.880
#> SRR537124 2 0.416 0.914 0.084 0.916
#> SRR537125 2 0.518 0.885 0.116 0.884
#> SRR537126 2 0.518 0.885 0.116 0.884
#> SRR537127 1 0.000 1.000 1.000 0.000
#> SRR537128 1 0.000 1.000 1.000 0.000
#> SRR537129 1 0.000 1.000 1.000 0.000
#> SRR537130 1 0.000 1.000 1.000 0.000
#> SRR537131 1 0.000 1.000 1.000 0.000
#> SRR537132 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191640 1 0.0747 0.934 0.984 0.000 0.016
#> SRR191641 1 0.0747 0.934 0.984 0.000 0.016
#> SRR191642 1 0.0892 0.934 0.980 0.000 0.020
#> SRR191643 1 0.0892 0.934 0.980 0.000 0.020
#> SRR191644 1 0.1491 0.930 0.968 0.016 0.016
#> SRR191645 1 0.2878 0.904 0.904 0.000 0.096
#> SRR191646 1 0.2878 0.904 0.904 0.000 0.096
#> SRR191647 1 0.2878 0.904 0.904 0.000 0.096
#> SRR191648 1 0.2878 0.904 0.904 0.000 0.096
#> SRR191649 1 0.2878 0.904 0.904 0.000 0.096
#> SRR191650 1 0.1031 0.933 0.976 0.000 0.024
#> SRR191651 1 0.1031 0.933 0.976 0.000 0.024
#> SRR191652 1 0.1031 0.933 0.976 0.000 0.024
#> SRR191653 1 0.2663 0.921 0.932 0.024 0.044
#> SRR191654 1 0.2663 0.921 0.932 0.024 0.044
#> SRR191655 1 0.0892 0.934 0.980 0.000 0.020
#> SRR191656 1 0.4654 0.765 0.792 0.000 0.208
#> SRR191657 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191658 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191659 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191660 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191661 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191662 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191663 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191664 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191665 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191666 1 0.0592 0.934 0.988 0.000 0.012
#> SRR191667 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191668 1 0.3816 0.836 0.852 0.000 0.148
#> SRR191669 1 0.3816 0.836 0.852 0.000 0.148
#> SRR191670 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191671 1 0.0237 0.934 0.996 0.000 0.004
#> SRR191672 1 0.4605 0.770 0.796 0.000 0.204
#> SRR191673 1 0.4605 0.770 0.796 0.000 0.204
#> SRR191674 3 0.4796 0.793 0.000 0.220 0.780
#> SRR191675 3 0.4796 0.793 0.000 0.220 0.780
#> SRR191677 3 0.5948 0.579 0.000 0.360 0.640
#> SRR191678 3 0.4796 0.793 0.000 0.220 0.780
#> SRR191679 2 0.2261 0.918 0.000 0.932 0.068
#> SRR191680 3 0.6126 0.492 0.000 0.400 0.600
#> SRR191681 3 0.4796 0.793 0.000 0.220 0.780
#> SRR191682 2 0.1411 0.961 0.000 0.964 0.036
#> SRR191683 2 0.1411 0.961 0.000 0.964 0.036
#> SRR191684 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191685 2 0.1163 0.968 0.000 0.972 0.028
#> SRR191686 2 0.1411 0.961 0.000 0.964 0.036
#> SRR191687 2 0.1163 0.968 0.000 0.972 0.028
#> SRR191688 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191690 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191691 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191692 3 0.4796 0.793 0.000 0.220 0.780
#> SRR191693 3 0.4796 0.793 0.000 0.220 0.780
#> SRR191694 3 0.5254 0.743 0.000 0.264 0.736
#> SRR191695 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191700 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191701 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191709 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191713 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191716 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191717 2 0.0000 0.993 0.000 1.000 0.000
#> SRR191718 2 0.0000 0.993 0.000 1.000 0.000
#> SRR537099 1 0.0892 0.934 0.980 0.000 0.020
#> SRR537100 1 0.0892 0.934 0.980 0.000 0.020
#> SRR537101 1 0.0892 0.934 0.980 0.000 0.020
#> SRR537102 1 0.0892 0.934 0.980 0.000 0.020
#> SRR537104 1 0.6717 0.469 0.628 0.352 0.020
#> SRR537105 1 0.3412 0.886 0.876 0.000 0.124
#> SRR537106 1 0.3412 0.886 0.876 0.000 0.124
#> SRR537107 1 0.3412 0.886 0.876 0.000 0.124
#> SRR537108 1 0.3412 0.886 0.876 0.000 0.124
#> SRR537109 2 0.0000 0.993 0.000 1.000 0.000
#> SRR537110 2 0.0000 0.993 0.000 1.000 0.000
#> SRR537111 1 0.1529 0.930 0.960 0.000 0.040
#> SRR537113 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537114 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537115 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537116 2 0.0000 0.993 0.000 1.000 0.000
#> SRR537117 3 0.0237 0.879 0.000 0.004 0.996
#> SRR537118 3 0.0237 0.879 0.000 0.004 0.996
#> SRR537119 3 0.0237 0.879 0.000 0.004 0.996
#> SRR537120 3 0.0237 0.879 0.000 0.004 0.996
#> SRR537121 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537122 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537123 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537124 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537125 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537126 3 0.0000 0.879 0.000 0.000 1.000
#> SRR537127 1 0.3038 0.899 0.896 0.000 0.104
#> SRR537128 1 0.3038 0.899 0.896 0.000 0.104
#> SRR537129 1 0.3038 0.899 0.896 0.000 0.104
#> SRR537130 1 0.3038 0.899 0.896 0.000 0.104
#> SRR537131 1 0.3038 0.899 0.896 0.000 0.104
#> SRR537132 1 0.3038 0.899 0.896 0.000 0.104
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191640 4 0.1022 0.787 0.032 0.000 0.000 0.968
#> SRR191641 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191642 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191643 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191644 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191645 4 0.5955 0.590 0.328 0.000 0.056 0.616
#> SRR191646 4 0.5955 0.590 0.328 0.000 0.056 0.616
#> SRR191647 4 0.5936 0.596 0.324 0.000 0.056 0.620
#> SRR191648 4 0.5936 0.596 0.324 0.000 0.056 0.620
#> SRR191649 4 0.5955 0.590 0.328 0.000 0.056 0.616
#> SRR191650 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191651 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191652 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191653 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191654 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191655 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR191656 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191662 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191666 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR191667 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR191668 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR191674 3 0.3810 0.839 0.000 0.188 0.804 0.008
#> SRR191675 3 0.3810 0.839 0.000 0.188 0.804 0.008
#> SRR191677 3 0.3810 0.839 0.000 0.188 0.804 0.008
#> SRR191678 3 0.3810 0.839 0.000 0.188 0.804 0.008
#> SRR191679 2 0.2342 0.901 0.000 0.912 0.080 0.008
#> SRR191680 3 0.3972 0.822 0.000 0.204 0.788 0.008
#> SRR191681 3 0.3810 0.839 0.000 0.188 0.804 0.008
#> SRR191682 2 0.2412 0.901 0.000 0.908 0.084 0.008
#> SRR191683 2 0.2412 0.901 0.000 0.908 0.084 0.008
#> SRR191684 2 0.0336 0.979 0.000 0.992 0.000 0.008
#> SRR191685 2 0.1890 0.930 0.000 0.936 0.056 0.008
#> SRR191686 2 0.2412 0.901 0.000 0.908 0.084 0.008
#> SRR191687 2 0.2342 0.906 0.000 0.912 0.080 0.008
#> SRR191688 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191689 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191690 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191691 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191692 3 0.3810 0.839 0.000 0.188 0.804 0.008
#> SRR191693 3 0.3591 0.845 0.000 0.168 0.824 0.008
#> SRR191694 3 0.4452 0.744 0.000 0.260 0.732 0.008
#> SRR191695 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191696 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191697 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191698 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191699 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191700 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191701 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191702 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191704 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191705 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191706 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191707 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191708 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191709 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191710 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191711 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191712 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191713 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191714 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191715 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191716 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191717 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR191718 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR537099 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR537100 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR537101 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR537102 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR537104 4 0.0336 0.793 0.008 0.000 0.000 0.992
#> SRR537105 4 0.6141 0.600 0.312 0.000 0.072 0.616
#> SRR537106 4 0.6141 0.600 0.312 0.000 0.072 0.616
#> SRR537107 4 0.6141 0.600 0.312 0.000 0.072 0.616
#> SRR537108 4 0.6141 0.600 0.312 0.000 0.072 0.616
#> SRR537109 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR537110 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR537111 1 0.0336 0.988 0.992 0.000 0.000 0.008
#> SRR537113 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537114 3 0.0188 0.885 0.000 0.000 0.996 0.004
#> SRR537115 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537116 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR537117 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537118 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537119 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537120 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537121 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537122 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537123 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537124 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537125 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537126 3 0.0000 0.888 0.000 0.000 1.000 0.000
#> SRR537127 4 0.5198 0.621 0.252 0.000 0.040 0.708
#> SRR537128 4 0.5198 0.621 0.252 0.000 0.040 0.708
#> SRR537129 4 0.5198 0.621 0.252 0.000 0.040 0.708
#> SRR537130 4 0.5198 0.621 0.252 0.000 0.040 0.708
#> SRR537131 4 0.5198 0.621 0.252 0.000 0.040 0.708
#> SRR537132 4 0.5198 0.621 0.252 0.000 0.040 0.708
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191640 4 0.0771 0.745 0.000 0.000 0.004 0.976 0.020
#> SRR191641 4 0.0566 0.747 0.000 0.000 0.004 0.984 0.012
#> SRR191642 4 0.0671 0.746 0.000 0.000 0.004 0.980 0.016
#> SRR191643 4 0.0290 0.747 0.000 0.000 0.000 0.992 0.008
#> SRR191644 4 0.2361 0.721 0.000 0.000 0.012 0.892 0.096
#> SRR191645 4 0.6950 0.593 0.192 0.000 0.044 0.544 0.220
#> SRR191646 4 0.6950 0.593 0.192 0.000 0.044 0.544 0.220
#> SRR191647 4 0.6869 0.605 0.180 0.000 0.044 0.556 0.220
#> SRR191648 4 0.6869 0.605 0.180 0.000 0.044 0.556 0.220
#> SRR191649 4 0.6950 0.593 0.192 0.000 0.044 0.544 0.220
#> SRR191650 1 0.1280 0.961 0.960 0.000 0.008 0.008 0.024
#> SRR191651 1 0.1059 0.966 0.968 0.000 0.008 0.004 0.020
#> SRR191652 1 0.0960 0.968 0.972 0.000 0.008 0.004 0.016
#> SRR191653 4 0.2017 0.728 0.000 0.000 0.008 0.912 0.080
#> SRR191654 4 0.2017 0.728 0.000 0.000 0.008 0.912 0.080
#> SRR191655 4 0.0404 0.747 0.000 0.000 0.000 0.988 0.012
#> SRR191656 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191662 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191665 1 0.0162 0.980 0.996 0.000 0.000 0.000 0.004
#> SRR191666 1 0.2753 0.876 0.876 0.000 0.012 0.008 0.104
#> SRR191667 1 0.2864 0.871 0.872 0.000 0.012 0.012 0.104
#> SRR191668 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.982 1.000 0.000 0.000 0.000 0.000
#> SRR191674 3 0.2278 0.691 0.000 0.060 0.908 0.000 0.032
#> SRR191675 3 0.2278 0.691 0.000 0.060 0.908 0.000 0.032
#> SRR191677 3 0.2278 0.691 0.000 0.060 0.908 0.000 0.032
#> SRR191678 3 0.2278 0.691 0.000 0.060 0.908 0.000 0.032
#> SRR191679 3 0.4151 0.663 0.000 0.344 0.652 0.000 0.004
#> SRR191680 3 0.2824 0.707 0.000 0.096 0.872 0.000 0.032
#> SRR191681 3 0.2278 0.691 0.000 0.060 0.908 0.000 0.032
#> SRR191682 3 0.3983 0.685 0.000 0.340 0.660 0.000 0.000
#> SRR191683 3 0.3983 0.685 0.000 0.340 0.660 0.000 0.000
#> SRR191684 3 0.4161 0.589 0.000 0.392 0.608 0.000 0.000
#> SRR191685 3 0.4060 0.654 0.000 0.360 0.640 0.000 0.000
#> SRR191686 3 0.3983 0.685 0.000 0.340 0.660 0.000 0.000
#> SRR191687 3 0.3999 0.680 0.000 0.344 0.656 0.000 0.000
#> SRR191688 2 0.0566 0.977 0.000 0.984 0.012 0.000 0.004
#> SRR191689 2 0.2286 0.861 0.000 0.888 0.108 0.000 0.004
#> SRR191690 2 0.0566 0.977 0.000 0.984 0.012 0.000 0.004
#> SRR191691 2 0.0794 0.972 0.000 0.972 0.028 0.000 0.000
#> SRR191692 3 0.2278 0.691 0.000 0.060 0.908 0.000 0.032
#> SRR191693 3 0.2300 0.673 0.000 0.052 0.908 0.000 0.040
#> SRR191694 3 0.2233 0.705 0.000 0.080 0.904 0.000 0.016
#> SRR191695 2 0.1124 0.966 0.000 0.960 0.036 0.000 0.004
#> SRR191696 2 0.1124 0.966 0.000 0.960 0.036 0.000 0.004
#> SRR191697 2 0.0794 0.972 0.000 0.972 0.028 0.000 0.000
#> SRR191698 2 0.0794 0.972 0.000 0.972 0.028 0.000 0.000
#> SRR191699 2 0.0451 0.979 0.000 0.988 0.008 0.000 0.004
#> SRR191700 2 0.0794 0.972 0.000 0.972 0.028 0.000 0.000
#> SRR191701 2 0.0794 0.972 0.000 0.972 0.028 0.000 0.000
#> SRR191702 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191703 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191704 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191705 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191706 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191707 2 0.0510 0.977 0.000 0.984 0.016 0.000 0.000
#> SRR191708 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191709 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191710 2 0.0510 0.976 0.000 0.984 0.016 0.000 0.000
#> SRR191711 2 0.0162 0.979 0.000 0.996 0.000 0.000 0.004
#> SRR191712 2 0.0162 0.979 0.000 0.996 0.000 0.000 0.004
#> SRR191713 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> SRR191714 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> SRR191715 2 0.0162 0.979 0.000 0.996 0.000 0.000 0.004
#> SRR191716 2 0.0451 0.977 0.000 0.988 0.008 0.000 0.004
#> SRR191717 2 0.0451 0.978 0.000 0.988 0.008 0.000 0.004
#> SRR191718 2 0.0671 0.978 0.000 0.980 0.016 0.000 0.004
#> SRR537099 4 0.0290 0.748 0.000 0.000 0.000 0.992 0.008
#> SRR537100 4 0.0290 0.748 0.000 0.000 0.000 0.992 0.008
#> SRR537101 4 0.0162 0.748 0.000 0.000 0.000 0.996 0.004
#> SRR537102 4 0.0671 0.746 0.000 0.000 0.004 0.980 0.016
#> SRR537104 4 0.0162 0.747 0.000 0.000 0.000 0.996 0.004
#> SRR537105 4 0.6805 0.587 0.136 0.000 0.044 0.544 0.276
#> SRR537106 4 0.6805 0.587 0.136 0.000 0.044 0.544 0.276
#> SRR537107 4 0.6805 0.587 0.136 0.000 0.044 0.544 0.276
#> SRR537108 4 0.6805 0.587 0.136 0.000 0.044 0.544 0.276
#> SRR537109 2 0.0162 0.979 0.000 0.996 0.000 0.000 0.004
#> SRR537110 2 0.0162 0.979 0.000 0.996 0.000 0.000 0.004
#> SRR537111 1 0.1492 0.952 0.948 0.000 0.008 0.004 0.040
#> SRR537113 5 0.3684 0.955 0.000 0.000 0.280 0.000 0.720
#> SRR537114 5 0.3766 0.942 0.000 0.000 0.268 0.004 0.728
#> SRR537115 5 0.3774 0.968 0.000 0.000 0.296 0.000 0.704
#> SRR537116 2 0.0162 0.979 0.000 0.996 0.000 0.000 0.004
#> SRR537117 5 0.3837 0.973 0.000 0.000 0.308 0.000 0.692
#> SRR537118 5 0.3837 0.973 0.000 0.000 0.308 0.000 0.692
#> SRR537119 5 0.3837 0.973 0.000 0.000 0.308 0.000 0.692
#> SRR537120 5 0.3837 0.973 0.000 0.000 0.308 0.000 0.692
#> SRR537121 5 0.3837 0.977 0.000 0.000 0.308 0.000 0.692
#> SRR537122 5 0.3837 0.977 0.000 0.000 0.308 0.000 0.692
#> SRR537123 5 0.3837 0.977 0.000 0.000 0.308 0.000 0.692
#> SRR537124 5 0.3857 0.975 0.000 0.000 0.312 0.000 0.688
#> SRR537125 5 0.3857 0.975 0.000 0.000 0.312 0.000 0.688
#> SRR537126 5 0.3857 0.975 0.000 0.000 0.312 0.000 0.688
#> SRR537127 4 0.6101 0.553 0.120 0.000 0.016 0.596 0.268
#> SRR537128 4 0.6101 0.553 0.120 0.000 0.016 0.596 0.268
#> SRR537129 4 0.6101 0.553 0.120 0.000 0.016 0.596 0.268
#> SRR537130 4 0.6101 0.553 0.120 0.000 0.016 0.596 0.268
#> SRR537131 4 0.6101 0.553 0.120 0.000 0.016 0.596 0.268
#> SRR537132 4 0.6101 0.553 0.120 0.000 0.016 0.596 0.268
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.0547 0.924 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR191640 4 0.4097 -0.518 0.008 0.000 0.492 0.500 0.000 0.000
#> SRR191641 3 0.3672 0.629 0.000 0.000 0.632 0.368 0.000 0.000
#> SRR191642 3 0.3860 0.525 0.000 0.000 0.528 0.472 0.000 0.000
#> SRR191643 3 0.3756 0.614 0.000 0.000 0.600 0.400 0.000 0.000
#> SRR191644 3 0.3349 0.640 0.000 0.000 0.748 0.244 0.000 0.008
#> SRR191645 4 0.2030 0.897 0.064 0.000 0.000 0.908 0.028 0.000
#> SRR191646 4 0.2030 0.897 0.064 0.000 0.000 0.908 0.028 0.000
#> SRR191647 4 0.2030 0.897 0.064 0.000 0.000 0.908 0.028 0.000
#> SRR191648 4 0.2030 0.897 0.064 0.000 0.000 0.908 0.028 0.000
#> SRR191649 4 0.2030 0.897 0.064 0.000 0.000 0.908 0.028 0.000
#> SRR191650 1 0.3387 0.849 0.852 0.000 0.032 0.048 0.056 0.012
#> SRR191651 1 0.3179 0.858 0.864 0.000 0.028 0.040 0.056 0.012
#> SRR191652 1 0.2931 0.866 0.876 0.000 0.024 0.036 0.056 0.008
#> SRR191653 3 0.3575 0.644 0.000 0.000 0.708 0.284 0.000 0.008
#> SRR191654 3 0.3575 0.644 0.000 0.000 0.708 0.284 0.000 0.008
#> SRR191655 3 0.3975 0.617 0.000 0.000 0.600 0.392 0.000 0.008
#> SRR191656 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191657 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191658 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191659 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191660 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191661 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191662 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191663 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191664 1 0.1464 0.922 0.944 0.000 0.036 0.016 0.000 0.004
#> SRR191665 1 0.0891 0.919 0.968 0.000 0.008 0.024 0.000 0.000
#> SRR191666 1 0.4800 0.643 0.652 0.000 0.272 0.004 0.004 0.068
#> SRR191667 1 0.4800 0.643 0.652 0.000 0.272 0.004 0.004 0.068
#> SRR191668 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191669 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191670 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191671 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191672 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191673 1 0.0260 0.924 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191674 6 0.2896 0.869 0.000 0.016 0.000 0.000 0.160 0.824
#> SRR191675 6 0.2896 0.869 0.000 0.016 0.000 0.000 0.160 0.824
#> SRR191677 6 0.2896 0.869 0.000 0.016 0.000 0.000 0.160 0.824
#> SRR191678 6 0.2896 0.869 0.000 0.016 0.000 0.000 0.160 0.824
#> SRR191679 6 0.2118 0.848 0.000 0.104 0.000 0.000 0.008 0.888
#> SRR191680 6 0.2748 0.874 0.000 0.024 0.000 0.000 0.128 0.848
#> SRR191681 6 0.2896 0.869 0.000 0.016 0.000 0.000 0.160 0.824
#> SRR191682 6 0.2940 0.848 0.000 0.108 0.012 0.020 0.004 0.856
#> SRR191683 6 0.2940 0.848 0.000 0.108 0.012 0.020 0.004 0.856
#> SRR191684 6 0.2976 0.835 0.000 0.124 0.012 0.020 0.000 0.844
#> SRR191685 6 0.2940 0.848 0.000 0.108 0.012 0.020 0.004 0.856
#> SRR191686 6 0.2940 0.848 0.000 0.108 0.012 0.020 0.004 0.856
#> SRR191687 6 0.2940 0.848 0.000 0.108 0.012 0.020 0.004 0.856
#> SRR191688 2 0.1313 0.913 0.000 0.952 0.028 0.004 0.000 0.016
#> SRR191689 2 0.2738 0.806 0.000 0.820 0.000 0.004 0.000 0.176
#> SRR191690 2 0.0951 0.916 0.000 0.968 0.020 0.004 0.000 0.008
#> SRR191691 2 0.1957 0.912 0.000 0.920 0.048 0.024 0.000 0.008
#> SRR191692 6 0.2896 0.869 0.000 0.016 0.000 0.000 0.160 0.824
#> SRR191693 6 0.2841 0.863 0.000 0.012 0.000 0.000 0.164 0.824
#> SRR191694 6 0.2709 0.872 0.000 0.020 0.000 0.000 0.132 0.848
#> SRR191695 2 0.1693 0.907 0.000 0.936 0.032 0.012 0.000 0.020
#> SRR191696 2 0.1693 0.907 0.000 0.936 0.032 0.012 0.000 0.020
#> SRR191697 2 0.2058 0.911 0.000 0.916 0.048 0.024 0.000 0.012
#> SRR191698 2 0.1957 0.912 0.000 0.920 0.048 0.024 0.000 0.008
#> SRR191699 2 0.1010 0.920 0.000 0.960 0.000 0.004 0.000 0.036
#> SRR191700 2 0.1957 0.910 0.000 0.920 0.048 0.024 0.000 0.008
#> SRR191701 2 0.1957 0.912 0.000 0.920 0.048 0.024 0.000 0.008
#> SRR191702 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191703 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191704 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191705 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191706 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191707 2 0.2000 0.912 0.000 0.916 0.048 0.032 0.000 0.004
#> SRR191708 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191709 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191710 2 0.3470 0.893 0.000 0.836 0.052 0.040 0.000 0.072
#> SRR191711 2 0.1908 0.917 0.000 0.916 0.028 0.000 0.000 0.056
#> SRR191712 2 0.1845 0.917 0.000 0.920 0.028 0.000 0.000 0.052
#> SRR191713 2 0.2101 0.917 0.000 0.912 0.028 0.008 0.000 0.052
#> SRR191714 2 0.2101 0.917 0.000 0.912 0.028 0.008 0.000 0.052
#> SRR191715 2 0.1845 0.917 0.000 0.920 0.028 0.000 0.000 0.052
#> SRR191716 2 0.1296 0.912 0.000 0.952 0.032 0.004 0.000 0.012
#> SRR191717 2 0.1296 0.912 0.000 0.952 0.032 0.004 0.000 0.012
#> SRR191718 2 0.1605 0.908 0.000 0.940 0.032 0.012 0.000 0.016
#> SRR537099 3 0.3828 0.581 0.000 0.000 0.560 0.440 0.000 0.000
#> SRR537100 3 0.3828 0.581 0.000 0.000 0.560 0.440 0.000 0.000
#> SRR537101 3 0.3828 0.581 0.000 0.000 0.560 0.440 0.000 0.000
#> SRR537102 3 0.3860 0.525 0.000 0.000 0.528 0.472 0.000 0.000
#> SRR537104 3 0.3955 0.583 0.000 0.000 0.560 0.436 0.000 0.004
#> SRR537105 4 0.2070 0.894 0.048 0.000 0.000 0.908 0.044 0.000
#> SRR537106 4 0.2070 0.894 0.048 0.000 0.000 0.908 0.044 0.000
#> SRR537107 4 0.2070 0.894 0.048 0.000 0.000 0.908 0.044 0.000
#> SRR537108 4 0.2070 0.894 0.048 0.000 0.000 0.908 0.044 0.000
#> SRR537109 2 0.1036 0.916 0.000 0.964 0.024 0.004 0.000 0.008
#> SRR537110 2 0.1845 0.917 0.000 0.920 0.028 0.000 0.000 0.052
#> SRR537111 1 0.3940 0.824 0.816 0.000 0.040 0.060 0.072 0.012
#> SRR537113 5 0.0922 0.959 0.000 0.000 0.004 0.024 0.968 0.004
#> SRR537114 5 0.1003 0.956 0.000 0.000 0.004 0.028 0.964 0.004
#> SRR537115 5 0.0748 0.962 0.000 0.000 0.004 0.016 0.976 0.004
#> SRR537116 2 0.1845 0.917 0.000 0.920 0.028 0.000 0.000 0.052
#> SRR537117 5 0.1327 0.950 0.000 0.000 0.000 0.064 0.936 0.000
#> SRR537118 5 0.1327 0.950 0.000 0.000 0.000 0.064 0.936 0.000
#> SRR537119 5 0.1327 0.950 0.000 0.000 0.000 0.064 0.936 0.000
#> SRR537120 5 0.1327 0.950 0.000 0.000 0.000 0.064 0.936 0.000
#> SRR537121 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR537122 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR537123 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR537124 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR537125 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR537126 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR537127 3 0.5077 0.560 0.060 0.000 0.748 0.048 0.080 0.064
#> SRR537128 3 0.5077 0.560 0.060 0.000 0.748 0.048 0.080 0.064
#> SRR537129 3 0.5077 0.560 0.060 0.000 0.748 0.048 0.080 0.064
#> SRR537130 3 0.5077 0.560 0.060 0.000 0.748 0.048 0.080 0.064
#> SRR537131 3 0.5077 0.560 0.060 0.000 0.748 0.048 0.080 0.064
#> SRR537132 3 0.5077 0.560 0.060 0.000 0.748 0.048 0.080 0.064
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 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 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 0.618 0.830 0.924 0.4826 0.510 0.510
#> 3 3 0.659 0.823 0.921 0.1173 0.944 0.891
#> 4 4 0.769 0.831 0.935 0.1507 0.918 0.826
#> 5 5 0.800 0.807 0.923 0.1352 0.889 0.719
#> 6 6 0.722 0.580 0.829 0.0651 0.963 0.876
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
#> SRR191639 1 0.0000 0.87940 1.000 0.000
#> SRR191640 1 0.1633 0.87102 0.976 0.024
#> SRR191641 1 0.7219 0.75863 0.800 0.200
#> SRR191642 1 0.9286 0.47265 0.656 0.344
#> SRR191643 2 0.4161 0.86313 0.084 0.916
#> SRR191644 2 0.0376 0.93069 0.004 0.996
#> SRR191645 1 0.0000 0.87940 1.000 0.000
#> SRR191646 1 0.0000 0.87940 1.000 0.000
#> SRR191647 1 0.0000 0.87940 1.000 0.000
#> SRR191648 1 0.0000 0.87940 1.000 0.000
#> SRR191649 1 0.0000 0.87940 1.000 0.000
#> SRR191650 1 0.4690 0.81868 0.900 0.100
#> SRR191651 1 0.0938 0.87627 0.988 0.012
#> SRR191652 1 0.0000 0.87940 1.000 0.000
#> SRR191653 2 0.0376 0.93069 0.004 0.996
#> SRR191654 2 0.0376 0.93069 0.004 0.996
#> SRR191655 1 0.9710 0.32866 0.600 0.400
#> SRR191656 1 0.0000 0.87940 1.000 0.000
#> SRR191657 1 0.0000 0.87940 1.000 0.000
#> SRR191658 1 0.0000 0.87940 1.000 0.000
#> SRR191659 1 0.0000 0.87940 1.000 0.000
#> SRR191660 1 0.0000 0.87940 1.000 0.000
#> SRR191661 1 0.3274 0.84857 0.940 0.060
#> SRR191662 1 0.6048 0.77264 0.852 0.148
#> SRR191663 1 0.0000 0.87940 1.000 0.000
#> SRR191664 1 0.0000 0.87940 1.000 0.000
#> SRR191665 1 0.0000 0.87940 1.000 0.000
#> SRR191666 1 0.0000 0.87940 1.000 0.000
#> SRR191667 1 0.0000 0.87940 1.000 0.000
#> SRR191668 1 0.0000 0.87940 1.000 0.000
#> SRR191669 1 0.0000 0.87940 1.000 0.000
#> SRR191670 1 0.0000 0.87940 1.000 0.000
#> SRR191671 1 0.0000 0.87940 1.000 0.000
#> SRR191672 1 0.0376 0.87825 0.996 0.004
#> SRR191673 1 0.0000 0.87940 1.000 0.000
#> SRR191674 2 0.6247 0.76986 0.156 0.844
#> SRR191675 2 0.1843 0.91170 0.028 0.972
#> SRR191677 2 0.0000 0.93316 0.000 1.000
#> SRR191678 2 0.0000 0.93316 0.000 1.000
#> SRR191679 2 0.0000 0.93316 0.000 1.000
#> SRR191680 2 0.0000 0.93316 0.000 1.000
#> SRR191681 2 0.0000 0.93316 0.000 1.000
#> SRR191682 2 0.0000 0.93316 0.000 1.000
#> SRR191683 2 0.0000 0.93316 0.000 1.000
#> SRR191684 2 0.0000 0.93316 0.000 1.000
#> SRR191685 2 0.0000 0.93316 0.000 1.000
#> SRR191686 2 0.0000 0.93316 0.000 1.000
#> SRR191687 2 0.0000 0.93316 0.000 1.000
#> SRR191688 2 0.0000 0.93316 0.000 1.000
#> SRR191689 2 0.0000 0.93316 0.000 1.000
#> SRR191690 2 0.0000 0.93316 0.000 1.000
#> SRR191691 2 0.0000 0.93316 0.000 1.000
#> SRR191692 2 0.0000 0.93316 0.000 1.000
#> SRR191693 2 0.8386 0.58397 0.268 0.732
#> SRR191694 2 0.0000 0.93316 0.000 1.000
#> SRR191695 2 0.0000 0.93316 0.000 1.000
#> SRR191696 2 0.0000 0.93316 0.000 1.000
#> SRR191697 2 0.0000 0.93316 0.000 1.000
#> SRR191698 2 0.0000 0.93316 0.000 1.000
#> SRR191699 2 0.0000 0.93316 0.000 1.000
#> SRR191700 2 0.0000 0.93316 0.000 1.000
#> SRR191701 2 0.0000 0.93316 0.000 1.000
#> SRR191702 2 0.0000 0.93316 0.000 1.000
#> SRR191703 2 0.0000 0.93316 0.000 1.000
#> SRR191704 2 0.0000 0.93316 0.000 1.000
#> SRR191705 2 0.0000 0.93316 0.000 1.000
#> SRR191706 2 0.0000 0.93316 0.000 1.000
#> SRR191707 2 0.0000 0.93316 0.000 1.000
#> SRR191708 2 0.4815 0.83535 0.104 0.896
#> SRR191709 2 0.0000 0.93316 0.000 1.000
#> SRR191710 2 0.7950 0.63730 0.240 0.760
#> SRR191711 2 0.0000 0.93316 0.000 1.000
#> SRR191712 2 0.0000 0.93316 0.000 1.000
#> SRR191713 2 0.0000 0.93316 0.000 1.000
#> SRR191714 2 0.0000 0.93316 0.000 1.000
#> SRR191715 2 0.0000 0.93316 0.000 1.000
#> SRR191716 2 0.0000 0.93316 0.000 1.000
#> SRR191717 2 0.0000 0.93316 0.000 1.000
#> SRR191718 2 0.0000 0.93316 0.000 1.000
#> SRR537099 2 0.6048 0.79102 0.148 0.852
#> SRR537100 2 0.8909 0.53835 0.308 0.692
#> SRR537101 1 0.0000 0.87940 1.000 0.000
#> SRR537102 2 0.4298 0.85738 0.088 0.912
#> SRR537104 2 0.0938 0.92532 0.012 0.988
#> SRR537105 1 0.2043 0.86745 0.968 0.032
#> SRR537106 2 0.9635 0.36133 0.388 0.612
#> SRR537107 2 0.9608 0.37223 0.384 0.616
#> SRR537108 2 0.9129 0.49791 0.328 0.672
#> SRR537109 2 0.0000 0.93316 0.000 1.000
#> SRR537110 2 0.0000 0.93316 0.000 1.000
#> SRR537111 1 0.2778 0.86224 0.952 0.048
#> SRR537113 2 0.8443 0.58745 0.272 0.728
#> SRR537114 1 0.8267 0.69346 0.740 0.260
#> SRR537115 1 0.9922 0.30833 0.552 0.448
#> SRR537116 2 0.0000 0.93316 0.000 1.000
#> SRR537117 1 0.8016 0.72127 0.756 0.244
#> SRR537118 2 0.0376 0.93071 0.004 0.996
#> SRR537119 2 0.0376 0.93071 0.004 0.996
#> SRR537120 2 0.0000 0.93316 0.000 1.000
#> SRR537121 1 0.9323 0.56055 0.652 0.348
#> SRR537122 2 0.1843 0.91388 0.028 0.972
#> SRR537123 1 0.8443 0.68535 0.728 0.272
#> SRR537124 1 0.8763 0.65334 0.704 0.296
#> SRR537125 2 0.9954 0.00176 0.460 0.540
#> SRR537126 2 0.9170 0.43778 0.332 0.668
#> SRR537127 1 0.8443 0.68591 0.728 0.272
#> SRR537128 1 0.8144 0.71050 0.748 0.252
#> SRR537129 1 0.8443 0.68576 0.728 0.272
#> SRR537130 1 0.8144 0.71050 0.748 0.252
#> SRR537131 1 0.8144 0.71050 0.748 0.252
#> SRR537132 1 0.8144 0.71050 0.748 0.252
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191640 1 0.0424 0.8752 0.992 0.008 0.000
#> SRR191641 1 0.4555 0.6486 0.800 0.200 0.000
#> SRR191642 1 0.5810 0.4218 0.664 0.336 0.000
#> SRR191643 2 0.1529 0.8811 0.040 0.960 0.000
#> SRR191644 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191645 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191646 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191647 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191650 1 0.3412 0.7561 0.876 0.124 0.000
#> SRR191651 1 0.0592 0.8720 0.988 0.012 0.000
#> SRR191652 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191653 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191654 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191655 1 0.6126 0.2854 0.600 0.400 0.000
#> SRR191656 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191661 1 0.2537 0.8079 0.920 0.080 0.000
#> SRR191662 1 0.4178 0.6883 0.828 0.172 0.000
#> SRR191663 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191666 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191667 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191668 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR191674 2 0.4235 0.6947 0.176 0.824 0.000
#> SRR191675 2 0.1163 0.8907 0.028 0.972 0.000
#> SRR191677 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191678 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191679 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191681 2 0.0237 0.9128 0.000 0.996 0.004
#> SRR191682 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191690 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191691 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191692 2 0.0237 0.9126 0.000 0.996 0.004
#> SRR191693 2 0.6326 0.4578 0.292 0.688 0.020
#> SRR191694 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191695 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191700 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191701 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191708 2 0.3267 0.7850 0.116 0.884 0.000
#> SRR191709 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191710 2 0.5216 0.5482 0.260 0.740 0.000
#> SRR191711 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191713 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191716 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191717 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR191718 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR537099 2 0.3267 0.7954 0.116 0.884 0.000
#> SRR537100 2 0.5291 0.5706 0.268 0.732 0.000
#> SRR537101 1 0.0000 0.8795 1.000 0.000 0.000
#> SRR537102 2 0.1964 0.8649 0.056 0.944 0.000
#> SRR537104 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR537105 1 0.0424 0.8752 0.992 0.008 0.000
#> SRR537106 2 0.5968 0.3970 0.364 0.636 0.000
#> SRR537107 2 0.5926 0.4134 0.356 0.644 0.000
#> SRR537108 2 0.5560 0.5146 0.300 0.700 0.000
#> SRR537109 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR537110 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR537111 1 0.1163 0.8621 0.972 0.028 0.000
#> SRR537113 2 0.8992 0.2555 0.272 0.552 0.176
#> SRR537114 1 0.7927 0.5759 0.664 0.160 0.176
#> SRR537115 1 0.8760 0.4320 0.584 0.240 0.176
#> SRR537116 2 0.0000 0.9156 0.000 1.000 0.000
#> SRR537117 1 0.6848 0.6258 0.736 0.164 0.100
#> SRR537118 2 0.3551 0.7949 0.000 0.868 0.132
#> SRR537119 2 0.1753 0.8768 0.000 0.952 0.048
#> SRR537120 2 0.1753 0.8768 0.000 0.952 0.048
#> SRR537121 1 0.7717 0.5921 0.668 0.112 0.220
#> SRR537122 2 0.5772 0.6498 0.024 0.756 0.220
#> SRR537123 1 0.6722 0.6540 0.720 0.060 0.220
#> SRR537124 1 0.6722 0.6540 0.720 0.060 0.220
#> SRR537125 1 0.9543 0.2229 0.476 0.304 0.220
#> SRR537126 2 0.9676 -0.0177 0.348 0.432 0.220
#> SRR537127 3 0.4589 0.9991 0.008 0.172 0.820
#> SRR537128 3 0.4589 0.9991 0.008 0.172 0.820
#> SRR537129 3 0.4409 0.9955 0.004 0.172 0.824
#> SRR537130 3 0.4589 0.9991 0.008 0.172 0.820
#> SRR537131 3 0.4589 0.9991 0.008 0.172 0.820
#> SRR537132 3 0.4589 0.9991 0.008 0.172 0.820
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191640 1 0.1022 0.894 0.968 0.032 0.000 0.000
#> SRR191641 1 0.3569 0.668 0.804 0.196 0.000 0.000
#> SRR191642 1 0.4624 0.475 0.660 0.340 0.000 0.000
#> SRR191643 2 0.2081 0.838 0.084 0.916 0.000 0.000
#> SRR191644 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191645 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191646 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191647 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191648 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191649 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191650 1 0.2216 0.830 0.908 0.092 0.000 0.000
#> SRR191651 1 0.0469 0.909 0.988 0.012 0.000 0.000
#> SRR191652 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191653 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191654 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191655 1 0.4855 0.332 0.600 0.400 0.000 0.000
#> SRR191656 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191661 1 0.1637 0.866 0.940 0.060 0.000 0.000
#> SRR191662 1 0.2868 0.774 0.864 0.136 0.000 0.000
#> SRR191663 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191666 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191667 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191668 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR191674 2 0.3894 0.757 0.140 0.832 0.004 0.024
#> SRR191675 2 0.1004 0.890 0.024 0.972 0.004 0.000
#> SRR191677 2 0.0188 0.906 0.000 0.996 0.004 0.000
#> SRR191678 2 0.2593 0.819 0.000 0.892 0.004 0.104
#> SRR191679 2 0.0188 0.906 0.000 0.996 0.004 0.000
#> SRR191680 2 0.0188 0.906 0.000 0.996 0.004 0.000
#> SRR191681 2 0.4677 0.496 0.000 0.680 0.004 0.316
#> SRR191682 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191683 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191684 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191685 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191686 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191687 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191688 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191689 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191690 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191691 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191692 2 0.4655 0.501 0.000 0.684 0.004 0.312
#> SRR191693 2 0.5988 0.218 0.036 0.568 0.004 0.392
#> SRR191694 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191695 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191696 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191697 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191698 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191699 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191700 2 0.3074 0.767 0.000 0.848 0.000 0.152
#> SRR191701 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191702 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191704 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191705 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191706 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191707 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191708 2 0.2345 0.823 0.100 0.900 0.000 0.000
#> SRR191709 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191710 2 0.3873 0.663 0.228 0.772 0.000 0.000
#> SRR191711 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191712 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191713 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191714 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191715 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191716 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191717 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR191718 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR537099 2 0.3649 0.699 0.204 0.796 0.000 0.000
#> SRR537100 2 0.4661 0.460 0.348 0.652 0.000 0.000
#> SRR537101 1 0.0000 0.917 1.000 0.000 0.000 0.000
#> SRR537102 2 0.2704 0.796 0.124 0.876 0.000 0.000
#> SRR537104 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR537105 1 0.1211 0.887 0.960 0.040 0.000 0.000
#> SRR537106 2 0.4916 0.261 0.424 0.576 0.000 0.000
#> SRR537107 2 0.4907 0.273 0.420 0.580 0.000 0.000
#> SRR537108 2 0.4746 0.414 0.368 0.632 0.000 0.000
#> SRR537109 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR537110 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR537111 1 0.1474 0.877 0.948 0.052 0.000 0.000
#> SRR537113 2 0.7764 -0.054 0.252 0.424 0.000 0.324
#> SRR537114 1 0.6071 0.429 0.612 0.064 0.000 0.324
#> SRR537115 1 0.6991 0.287 0.540 0.136 0.000 0.324
#> SRR537116 2 0.0000 0.909 0.000 1.000 0.000 0.000
#> SRR537117 4 0.2867 0.806 0.012 0.104 0.000 0.884
#> SRR537118 4 0.2216 0.820 0.000 0.092 0.000 0.908
#> SRR537119 4 0.3569 0.695 0.000 0.196 0.000 0.804
#> SRR537120 4 0.3610 0.689 0.000 0.200 0.000 0.800
#> SRR537121 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR537122 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR537123 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR537124 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR537125 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR537126 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR537127 3 0.0188 1.000 0.000 0.004 0.996 0.000
#> SRR537128 3 0.0188 1.000 0.000 0.004 0.996 0.000
#> SRR537129 3 0.0188 1.000 0.000 0.004 0.996 0.000
#> SRR537130 3 0.0188 1.000 0.000 0.004 0.996 0.000
#> SRR537131 3 0.0188 1.000 0.000 0.004 0.996 0.000
#> SRR537132 3 0.0188 1.000 0.000 0.004 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.0290 0.9253 0.992 0.000 0 0.008 0.000
#> SRR191640 1 0.1877 0.8745 0.924 0.064 0 0.012 0.000
#> SRR191641 1 0.3355 0.6926 0.804 0.184 0 0.012 0.000
#> SRR191642 1 0.4430 0.4153 0.628 0.360 0 0.012 0.000
#> SRR191643 2 0.1830 0.8335 0.068 0.924 0 0.008 0.000
#> SRR191644 2 0.0290 0.8944 0.000 0.992 0 0.008 0.000
#> SRR191645 1 0.0162 0.9276 0.996 0.000 0 0.004 0.000
#> SRR191646 1 0.0162 0.9276 0.996 0.000 0 0.004 0.000
#> SRR191647 1 0.0162 0.9276 0.996 0.000 0 0.004 0.000
#> SRR191648 1 0.0162 0.9276 0.996 0.000 0 0.004 0.000
#> SRR191649 1 0.0162 0.9276 0.996 0.000 0 0.004 0.000
#> SRR191650 1 0.1478 0.8782 0.936 0.064 0 0.000 0.000
#> SRR191651 1 0.0404 0.9210 0.988 0.012 0 0.000 0.000
#> SRR191652 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191653 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191654 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191655 1 0.4537 0.3266 0.592 0.396 0 0.012 0.000
#> SRR191656 1 0.0290 0.9250 0.992 0.000 0 0.008 0.000
#> SRR191657 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191658 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191659 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191660 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191661 1 0.1043 0.9004 0.960 0.040 0 0.000 0.000
#> SRR191662 1 0.2020 0.8377 0.900 0.100 0 0.000 0.000
#> SRR191663 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191664 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191665 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191666 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191667 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191668 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191669 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191670 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191671 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191672 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191673 1 0.0000 0.9286 1.000 0.000 0 0.000 0.000
#> SRR191674 4 0.0451 0.6958 0.000 0.008 0 0.988 0.004
#> SRR191675 4 0.0404 0.6983 0.000 0.012 0 0.988 0.000
#> SRR191677 4 0.0794 0.7020 0.000 0.028 0 0.972 0.000
#> SRR191678 4 0.0912 0.6970 0.000 0.016 0 0.972 0.012
#> SRR191679 4 0.3143 0.5832 0.000 0.204 0 0.796 0.000
#> SRR191680 4 0.0794 0.7020 0.000 0.028 0 0.972 0.000
#> SRR191681 4 0.0451 0.6958 0.000 0.008 0 0.988 0.004
#> SRR191682 4 0.4182 0.5247 0.000 0.400 0 0.600 0.000
#> SRR191683 4 0.4060 0.5783 0.000 0.360 0 0.640 0.000
#> SRR191684 2 0.0510 0.8882 0.000 0.984 0 0.016 0.000
#> SRR191685 2 0.0510 0.8882 0.000 0.984 0 0.016 0.000
#> SRR191686 4 0.3966 0.5966 0.000 0.336 0 0.664 0.000
#> SRR191687 2 0.0609 0.8858 0.000 0.980 0 0.020 0.000
#> SRR191688 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191689 4 0.4273 0.4163 0.000 0.448 0 0.552 0.000
#> SRR191690 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191691 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191692 4 0.0451 0.6958 0.000 0.008 0 0.988 0.004
#> SRR191693 4 0.1502 0.7016 0.000 0.056 0 0.940 0.004
#> SRR191694 4 0.3752 0.6220 0.000 0.292 0 0.708 0.000
#> SRR191695 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191696 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191697 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191698 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191699 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191700 2 0.2377 0.7716 0.000 0.872 0 0.000 0.128
#> SRR191701 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191702 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191703 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191704 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191705 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191706 2 0.4101 0.1688 0.000 0.628 0 0.372 0.000
#> SRR191707 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191708 2 0.1732 0.8241 0.080 0.920 0 0.000 0.000
#> SRR191709 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191710 2 0.3039 0.6756 0.192 0.808 0 0.000 0.000
#> SRR191711 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191712 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191713 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191714 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191715 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191716 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191717 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR191718 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR537099 2 0.3093 0.7172 0.168 0.824 0 0.008 0.000
#> SRR537100 2 0.4025 0.5453 0.292 0.700 0 0.008 0.000
#> SRR537101 1 0.0404 0.9244 0.988 0.000 0 0.012 0.000
#> SRR537102 2 0.2753 0.7570 0.136 0.856 0 0.008 0.000
#> SRR537104 2 0.0290 0.8944 0.000 0.992 0 0.008 0.000
#> SRR537105 1 0.1768 0.8697 0.924 0.072 0 0.004 0.000
#> SRR537106 2 0.4310 0.3499 0.392 0.604 0 0.004 0.000
#> SRR537107 2 0.4299 0.3606 0.388 0.608 0 0.004 0.000
#> SRR537108 2 0.4084 0.4956 0.328 0.668 0 0.004 0.000
#> SRR537109 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR537110 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR537111 1 0.2011 0.8545 0.908 0.088 0 0.004 0.000
#> SRR537113 2 0.6605 -0.0295 0.220 0.432 0 0.000 0.348
#> SRR537114 1 0.5671 0.3301 0.568 0.080 0 0.004 0.348
#> SRR537115 4 0.7404 0.1203 0.168 0.056 0 0.428 0.348
#> SRR537116 2 0.0000 0.8996 0.000 1.000 0 0.000 0.000
#> SRR537117 5 0.2361 0.8237 0.012 0.096 0 0.000 0.892
#> SRR537118 5 0.1851 0.8342 0.000 0.088 0 0.000 0.912
#> SRR537119 5 0.3074 0.7056 0.000 0.196 0 0.000 0.804
#> SRR537120 5 0.3109 0.6992 0.000 0.200 0 0.000 0.800
#> SRR537121 5 0.0000 0.8757 0.000 0.000 0 0.000 1.000
#> SRR537122 5 0.0000 0.8757 0.000 0.000 0 0.000 1.000
#> SRR537123 5 0.0000 0.8757 0.000 0.000 0 0.000 1.000
#> SRR537124 5 0.0000 0.8757 0.000 0.000 0 0.000 1.000
#> SRR537125 5 0.0000 0.8757 0.000 0.000 0 0.000 1.000
#> SRR537126 5 0.0000 0.8757 0.000 0.000 0 0.000 1.000
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.3592 0.54901 0.656 0.000 0.344 0.000 0.000 0.000
#> SRR191640 1 0.5319 0.40108 0.504 0.000 0.388 0.108 0.000 0.000
#> SRR191641 1 0.5319 0.40108 0.504 0.000 0.388 0.108 0.000 0.000
#> SRR191642 3 0.7251 0.00320 0.280 0.224 0.388 0.108 0.000 0.000
#> SRR191643 2 0.3862 0.48018 0.004 0.608 0.388 0.000 0.000 0.000
#> SRR191644 2 0.3684 0.50413 0.000 0.628 0.372 0.000 0.000 0.000
#> SRR191645 1 0.3337 0.79770 0.824 0.000 0.064 0.108 0.000 0.004
#> SRR191646 1 0.3337 0.79770 0.824 0.000 0.064 0.108 0.000 0.004
#> SRR191647 1 0.3337 0.79770 0.824 0.000 0.064 0.108 0.000 0.004
#> SRR191648 1 0.3337 0.79770 0.824 0.000 0.064 0.108 0.000 0.004
#> SRR191649 1 0.3279 0.80014 0.828 0.000 0.060 0.108 0.000 0.004
#> SRR191650 1 0.0508 0.87138 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR191651 1 0.0458 0.86866 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR191652 1 0.0291 0.87652 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR191653 2 0.0865 0.79802 0.000 0.964 0.036 0.000 0.000 0.000
#> SRR191654 2 0.1556 0.77703 0.000 0.920 0.080 0.000 0.000 0.000
#> SRR191655 3 0.7270 -0.00538 0.284 0.228 0.380 0.108 0.000 0.000
#> SRR191656 1 0.0260 0.87547 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR191657 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191661 1 0.0458 0.86823 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR191662 1 0.0790 0.85357 0.968 0.032 0.000 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191665 1 0.0146 0.87693 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191666 1 0.0146 0.87639 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR191667 1 0.0146 0.87693 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR191668 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.87722 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191672 1 0.0146 0.87639 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR191673 1 0.0146 0.87639 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR191674 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191675 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191677 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191678 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191679 4 0.5585 0.00000 0.000 0.148 0.000 0.488 0.000 0.364
#> SRR191680 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191681 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191682 6 0.3499 0.18408 0.000 0.320 0.000 0.000 0.000 0.680
#> SRR191683 6 0.3482 0.18467 0.000 0.316 0.000 0.000 0.000 0.684
#> SRR191684 2 0.3866 -0.01344 0.000 0.516 0.000 0.000 0.000 0.484
#> SRR191685 2 0.3866 -0.01344 0.000 0.516 0.000 0.000 0.000 0.484
#> SRR191686 6 0.3428 0.18010 0.000 0.304 0.000 0.000 0.000 0.696
#> SRR191687 6 0.3869 -0.04445 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR191688 2 0.0713 0.79897 0.000 0.972 0.028 0.000 0.000 0.000
#> SRR191689 2 0.5327 0.17245 0.000 0.596 0.000 0.196 0.000 0.208
#> SRR191690 2 0.1411 0.78550 0.004 0.936 0.060 0.000 0.000 0.000
#> SRR191691 2 0.0458 0.80192 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR191692 6 0.3997 -0.35282 0.000 0.004 0.000 0.488 0.000 0.508
#> SRR191693 6 0.4091 -0.29809 0.000 0.056 0.000 0.224 0.000 0.720
#> SRR191694 6 0.5931 -0.00868 0.000 0.360 0.000 0.216 0.000 0.424
#> SRR191695 2 0.1152 0.78775 0.000 0.952 0.004 0.000 0.000 0.044
#> SRR191696 2 0.1007 0.78822 0.000 0.956 0.000 0.000 0.000 0.044
#> SRR191697 2 0.0458 0.80192 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR191698 2 0.0458 0.80192 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR191699 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR191700 2 0.2859 0.68402 0.000 0.828 0.000 0.016 0.156 0.000
#> SRR191701 2 0.0458 0.80192 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR191702 2 0.0146 0.80424 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR191703 2 0.0146 0.80424 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR191704 2 0.0146 0.80424 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR191705 2 0.0146 0.80424 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR191706 2 0.4023 0.52790 0.000 0.756 0.000 0.100 0.000 0.144
#> SRR191707 2 0.0603 0.80082 0.000 0.980 0.000 0.016 0.000 0.004
#> SRR191708 2 0.1218 0.78692 0.028 0.956 0.000 0.012 0.000 0.004
#> SRR191709 2 0.0146 0.80424 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR191710 2 0.2006 0.71881 0.104 0.892 0.000 0.000 0.000 0.004
#> SRR191711 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR191712 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR191713 2 0.0146 0.80424 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR191714 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR191715 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR191716 2 0.1141 0.78993 0.000 0.948 0.052 0.000 0.000 0.000
#> SRR191717 2 0.1196 0.79216 0.008 0.952 0.040 0.000 0.000 0.000
#> SRR191718 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR537099 2 0.3862 0.48018 0.004 0.608 0.388 0.000 0.000 0.000
#> SRR537100 2 0.3965 0.47522 0.008 0.604 0.388 0.000 0.000 0.000
#> SRR537101 1 0.5319 0.40108 0.504 0.000 0.388 0.108 0.000 0.000
#> SRR537102 2 0.3862 0.48085 0.000 0.608 0.388 0.004 0.000 0.000
#> SRR537104 2 0.3672 0.50527 0.000 0.632 0.368 0.000 0.000 0.000
#> SRR537105 1 0.3337 0.79770 0.824 0.000 0.064 0.108 0.000 0.004
#> SRR537106 2 0.6277 0.12681 0.384 0.460 0.044 0.108 0.000 0.004
#> SRR537107 2 0.6268 0.15096 0.376 0.468 0.044 0.108 0.000 0.004
#> SRR537108 2 0.6168 0.26355 0.324 0.520 0.044 0.108 0.000 0.004
#> SRR537109 2 0.0146 0.80441 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR537110 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR537111 1 0.2822 0.81426 0.856 0.032 0.004 0.108 0.000 0.000
#> SRR537113 2 0.6672 0.15833 0.164 0.468 0.024 0.000 0.320 0.024
#> SRR537114 1 0.7124 0.30620 0.464 0.044 0.068 0.104 0.320 0.000
#> SRR537115 5 0.8548 -0.12865 0.256 0.072 0.004 0.156 0.320 0.192
#> SRR537116 2 0.0000 0.80428 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR537117 5 0.2162 0.77813 0.012 0.088 0.000 0.000 0.896 0.004
#> SRR537118 5 0.1501 0.79180 0.000 0.076 0.000 0.000 0.924 0.000
#> SRR537119 5 0.2762 0.65459 0.000 0.196 0.000 0.000 0.804 0.000
#> SRR537120 5 0.2793 0.64823 0.000 0.200 0.000 0.000 0.800 0.000
#> SRR537121 5 0.0000 0.82628 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537122 5 0.0000 0.82628 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537123 5 0.0000 0.82628 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537124 5 0.0000 0.82628 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537125 5 0.0000 0.82628 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537126 5 0.0000 0.82628 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537127 3 0.3727 0.67352 0.000 0.000 0.612 0.388 0.000 0.000
#> SRR537128 3 0.3727 0.67352 0.000 0.000 0.612 0.388 0.000 0.000
#> SRR537129 3 0.3727 0.67352 0.000 0.000 0.612 0.388 0.000 0.000
#> SRR537130 3 0.3727 0.67352 0.000 0.000 0.612 0.388 0.000 0.000
#> SRR537131 3 0.3727 0.67352 0.000 0.000 0.612 0.388 0.000 0.000
#> SRR537132 3 0.3727 0.67352 0.000 0.000 0.612 0.388 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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.344 0.771 0.830 0.3871 0.517 0.517
#> 3 3 0.690 0.773 0.885 0.5278 0.702 0.511
#> 4 4 0.832 0.849 0.914 0.1286 0.936 0.844
#> 5 5 0.788 0.781 0.859 0.0695 1.000 1.000
#> 6 6 0.716 0.683 0.801 0.0781 0.866 0.621
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
#> SRR191639 1 0.9460 0.841 0.636 0.364
#> SRR191640 1 0.9522 0.836 0.628 0.372
#> SRR191641 1 0.9775 0.781 0.588 0.412
#> SRR191642 1 0.9732 0.801 0.596 0.404
#> SRR191643 2 0.9635 -0.110 0.388 0.612
#> SRR191644 2 0.8661 0.371 0.288 0.712
#> SRR191645 1 0.9491 0.839 0.632 0.368
#> SRR191646 1 0.9491 0.839 0.632 0.368
#> SRR191647 1 0.9460 0.841 0.636 0.364
#> SRR191648 1 0.9460 0.841 0.636 0.364
#> SRR191649 1 0.9491 0.839 0.632 0.368
#> SRR191650 1 0.9732 0.801 0.596 0.404
#> SRR191651 1 0.9732 0.801 0.596 0.404
#> SRR191652 1 0.9427 0.841 0.640 0.360
#> SRR191653 2 0.9129 0.210 0.328 0.672
#> SRR191654 2 0.7219 0.622 0.200 0.800
#> SRR191655 1 0.9732 0.801 0.596 0.404
#> SRR191656 1 0.7453 0.763 0.788 0.212
#> SRR191657 1 0.9491 0.839 0.632 0.368
#> SRR191658 1 0.9393 0.841 0.644 0.356
#> SRR191659 1 0.9460 0.841 0.636 0.364
#> SRR191660 1 0.9393 0.841 0.644 0.356
#> SRR191661 1 0.9491 0.839 0.632 0.368
#> SRR191662 1 0.9608 0.825 0.616 0.384
#> SRR191663 1 0.9460 0.841 0.636 0.364
#> SRR191664 1 0.9393 0.841 0.644 0.356
#> SRR191665 1 0.9393 0.841 0.644 0.356
#> SRR191666 1 0.9209 0.833 0.664 0.336
#> SRR191667 1 0.9129 0.830 0.672 0.328
#> SRR191668 1 0.7528 0.765 0.784 0.216
#> SRR191669 1 0.7453 0.763 0.788 0.212
#> SRR191670 1 0.7602 0.767 0.780 0.220
#> SRR191671 1 0.7602 0.767 0.780 0.220
#> SRR191672 1 0.7453 0.763 0.788 0.212
#> SRR191673 1 0.7453 0.763 0.788 0.212
#> SRR191674 2 0.0938 0.876 0.012 0.988
#> SRR191675 2 0.0938 0.876 0.012 0.988
#> SRR191677 2 0.0938 0.876 0.012 0.988
#> SRR191678 2 0.0938 0.876 0.012 0.988
#> SRR191679 2 0.0938 0.876 0.012 0.988
#> SRR191680 2 0.0938 0.876 0.012 0.988
#> SRR191681 2 0.0938 0.876 0.012 0.988
#> SRR191682 2 0.0000 0.880 0.000 1.000
#> SRR191683 2 0.0000 0.880 0.000 1.000
#> SRR191684 2 0.0000 0.880 0.000 1.000
#> SRR191685 2 0.0000 0.880 0.000 1.000
#> SRR191686 2 0.0938 0.876 0.012 0.988
#> SRR191687 2 0.0000 0.880 0.000 1.000
#> SRR191688 2 0.0000 0.880 0.000 1.000
#> SRR191689 2 0.0000 0.880 0.000 1.000
#> SRR191690 2 0.0000 0.880 0.000 1.000
#> SRR191691 2 0.0000 0.880 0.000 1.000
#> SRR191692 2 0.0938 0.876 0.012 0.988
#> SRR191693 2 0.0938 0.876 0.012 0.988
#> SRR191694 2 0.0938 0.876 0.012 0.988
#> SRR191695 2 0.0000 0.880 0.000 1.000
#> SRR191696 2 0.0000 0.880 0.000 1.000
#> SRR191697 2 0.0000 0.880 0.000 1.000
#> SRR191698 2 0.0000 0.880 0.000 1.000
#> SRR191699 2 0.0000 0.880 0.000 1.000
#> SRR191700 2 0.0000 0.880 0.000 1.000
#> SRR191701 2 0.0000 0.880 0.000 1.000
#> SRR191702 2 0.0000 0.880 0.000 1.000
#> SRR191703 2 0.0000 0.880 0.000 1.000
#> SRR191704 2 0.0000 0.880 0.000 1.000
#> SRR191705 2 0.0000 0.880 0.000 1.000
#> SRR191706 2 0.0000 0.880 0.000 1.000
#> SRR191707 2 0.0000 0.880 0.000 1.000
#> SRR191708 2 0.0000 0.880 0.000 1.000
#> SRR191709 2 0.0000 0.880 0.000 1.000
#> SRR191710 2 0.0000 0.880 0.000 1.000
#> SRR191711 2 0.0000 0.880 0.000 1.000
#> SRR191712 2 0.0000 0.880 0.000 1.000
#> SRR191713 2 0.0000 0.880 0.000 1.000
#> SRR191714 2 0.0000 0.880 0.000 1.000
#> SRR191715 2 0.0000 0.880 0.000 1.000
#> SRR191716 2 0.0000 0.880 0.000 1.000
#> SRR191717 2 0.0000 0.880 0.000 1.000
#> SRR191718 2 0.0000 0.880 0.000 1.000
#> SRR537099 2 0.9866 -0.321 0.432 0.568
#> SRR537100 1 0.9754 0.794 0.592 0.408
#> SRR537101 1 0.9286 0.837 0.656 0.344
#> SRR537102 2 0.9323 0.114 0.348 0.652
#> SRR537104 2 0.6438 0.693 0.164 0.836
#> SRR537105 1 0.9522 0.836 0.628 0.372
#> SRR537106 1 0.9732 0.801 0.596 0.404
#> SRR537107 1 0.9732 0.801 0.596 0.404
#> SRR537108 1 0.9732 0.801 0.596 0.404
#> SRR537109 2 0.0000 0.880 0.000 1.000
#> SRR537110 2 0.0938 0.876 0.012 0.988
#> SRR537111 1 0.9732 0.801 0.596 0.404
#> SRR537113 2 0.5946 0.723 0.144 0.856
#> SRR537114 2 0.5946 0.723 0.144 0.856
#> SRR537115 2 0.5946 0.723 0.144 0.856
#> SRR537116 2 0.0000 0.880 0.000 1.000
#> SRR537117 2 0.9170 0.568 0.332 0.668
#> SRR537118 2 0.8955 0.592 0.312 0.688
#> SRR537119 2 0.6531 0.718 0.168 0.832
#> SRR537120 2 0.6531 0.718 0.168 0.832
#> SRR537121 2 0.9209 0.562 0.336 0.664
#> SRR537122 2 0.8955 0.592 0.312 0.688
#> SRR537123 2 0.9209 0.562 0.336 0.664
#> SRR537124 2 0.9209 0.562 0.336 0.664
#> SRR537125 2 0.9209 0.562 0.336 0.664
#> SRR537126 2 0.9209 0.562 0.336 0.664
#> SRR537127 1 0.0000 0.596 1.000 0.000
#> SRR537128 1 0.0000 0.596 1.000 0.000
#> SRR537129 1 0.0000 0.596 1.000 0.000
#> SRR537130 1 0.0000 0.596 1.000 0.000
#> SRR537131 1 0.0000 0.596 1.000 0.000
#> SRR537132 1 0.0000 0.596 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.3038 0.789 0.896 0.000 0.104
#> SRR191640 1 0.2860 0.794 0.912 0.004 0.084
#> SRR191641 1 0.0237 0.785 0.996 0.004 0.000
#> SRR191642 1 0.0237 0.785 0.996 0.004 0.000
#> SRR191643 1 0.1529 0.771 0.960 0.040 0.000
#> SRR191644 1 0.2261 0.748 0.932 0.068 0.000
#> SRR191645 1 0.0747 0.786 0.984 0.000 0.016
#> SRR191646 1 0.0747 0.786 0.984 0.000 0.016
#> SRR191647 1 0.3272 0.790 0.892 0.004 0.104
#> SRR191648 1 0.3272 0.790 0.892 0.004 0.104
#> SRR191649 1 0.0747 0.786 0.984 0.000 0.016
#> SRR191650 1 0.0892 0.789 0.980 0.000 0.020
#> SRR191651 1 0.0424 0.787 0.992 0.000 0.008
#> SRR191652 1 0.1860 0.791 0.948 0.000 0.052
#> SRR191653 1 0.2356 0.744 0.928 0.072 0.000
#> SRR191654 1 0.2356 0.744 0.928 0.072 0.000
#> SRR191655 1 0.0592 0.784 0.988 0.012 0.000
#> SRR191656 1 0.5497 0.657 0.708 0.000 0.292
#> SRR191657 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191658 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191659 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191660 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191661 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191662 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191663 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191664 1 0.3941 0.780 0.844 0.000 0.156
#> SRR191665 1 0.3752 0.783 0.856 0.000 0.144
#> SRR191666 1 0.2356 0.794 0.928 0.000 0.072
#> SRR191667 1 0.3038 0.789 0.896 0.000 0.104
#> SRR191668 1 0.5497 0.657 0.708 0.000 0.292
#> SRR191669 1 0.5497 0.657 0.708 0.000 0.292
#> SRR191670 1 0.4002 0.778 0.840 0.000 0.160
#> SRR191671 1 0.4002 0.778 0.840 0.000 0.160
#> SRR191672 1 0.5291 0.669 0.732 0.000 0.268
#> SRR191673 1 0.5291 0.669 0.732 0.000 0.268
#> SRR191674 2 0.0892 0.971 0.000 0.980 0.020
#> SRR191675 2 0.1031 0.967 0.000 0.976 0.024
#> SRR191677 2 0.1163 0.964 0.000 0.972 0.028
#> SRR191678 2 0.1289 0.960 0.000 0.968 0.032
#> SRR191679 2 0.0237 0.981 0.000 0.996 0.004
#> SRR191680 2 0.1163 0.964 0.000 0.972 0.028
#> SRR191681 2 0.1289 0.960 0.000 0.968 0.032
#> SRR191682 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191686 2 0.0237 0.981 0.000 0.996 0.004
#> SRR191687 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191690 2 0.3551 0.790 0.132 0.868 0.000
#> SRR191691 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191692 2 0.0892 0.971 0.000 0.980 0.020
#> SRR191693 2 0.2772 0.864 0.080 0.916 0.004
#> SRR191694 2 0.0237 0.981 0.000 0.996 0.004
#> SRR191695 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191700 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191701 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191709 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191713 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.984 0.000 1.000 0.000
#> SRR191716 2 0.3482 0.797 0.128 0.872 0.000
#> SRR191717 2 0.0237 0.980 0.004 0.996 0.000
#> SRR191718 2 0.0000 0.984 0.000 1.000 0.000
#> SRR537099 1 0.2261 0.748 0.932 0.068 0.000
#> SRR537100 1 0.0892 0.782 0.980 0.020 0.000
#> SRR537101 1 0.1399 0.794 0.968 0.004 0.028
#> SRR537102 1 0.1964 0.759 0.944 0.056 0.000
#> SRR537104 1 0.3375 0.712 0.892 0.100 0.008
#> SRR537105 1 0.0475 0.787 0.992 0.004 0.004
#> SRR537106 1 0.0237 0.785 0.996 0.004 0.000
#> SRR537107 1 0.0237 0.785 0.996 0.004 0.000
#> SRR537108 1 0.0237 0.785 0.996 0.004 0.000
#> SRR537109 2 0.0000 0.984 0.000 1.000 0.000
#> SRR537110 2 0.0237 0.981 0.000 0.996 0.004
#> SRR537111 1 0.3532 0.789 0.884 0.008 0.108
#> SRR537113 1 0.9823 -0.228 0.412 0.252 0.336
#> SRR537114 1 0.9823 -0.228 0.412 0.252 0.336
#> SRR537115 1 0.9823 -0.228 0.412 0.252 0.336
#> SRR537116 2 0.0000 0.984 0.000 1.000 0.000
#> SRR537117 1 0.9874 -0.226 0.412 0.284 0.304
#> SRR537118 1 0.9849 -0.204 0.420 0.280 0.300
#> SRR537119 1 0.9849 -0.204 0.420 0.280 0.300
#> SRR537120 1 0.9863 -0.214 0.416 0.284 0.300
#> SRR537121 3 0.9563 0.541 0.284 0.236 0.480
#> SRR537122 3 0.9636 0.526 0.284 0.248 0.468
#> SRR537123 3 0.9563 0.541 0.284 0.236 0.480
#> SRR537124 3 0.9563 0.541 0.284 0.236 0.480
#> SRR537125 3 0.9563 0.541 0.284 0.236 0.480
#> SRR537126 3 0.9563 0.541 0.284 0.236 0.480
#> SRR537127 3 0.1860 0.669 0.052 0.000 0.948
#> SRR537128 3 0.1860 0.669 0.052 0.000 0.948
#> SRR537129 3 0.1860 0.669 0.052 0.000 0.948
#> SRR537130 3 0.1860 0.669 0.052 0.000 0.948
#> SRR537131 3 0.1860 0.669 0.052 0.000 0.948
#> SRR537132 3 0.1860 0.669 0.052 0.000 0.948
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0524 0.910 0.988 0.004 0.008 0.000
#> SRR191640 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> SRR191641 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR191642 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR191643 1 0.0779 0.907 0.980 0.004 0.000 0.016
#> SRR191644 1 0.1406 0.895 0.960 0.024 0.000 0.016
#> SRR191645 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> SRR191646 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> SRR191647 1 0.1576 0.896 0.948 0.004 0.000 0.048
#> SRR191648 1 0.1576 0.896 0.948 0.004 0.000 0.048
#> SRR191649 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR191650 1 0.0524 0.909 0.988 0.004 0.000 0.008
#> SRR191651 1 0.0376 0.909 0.992 0.004 0.000 0.004
#> SRR191652 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR191653 1 0.1406 0.895 0.960 0.024 0.000 0.016
#> SRR191654 1 0.1406 0.895 0.960 0.024 0.000 0.016
#> SRR191655 1 0.0779 0.907 0.980 0.004 0.000 0.016
#> SRR191656 1 0.3937 0.855 0.800 0.000 0.188 0.012
#> SRR191657 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191658 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191659 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191660 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191661 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191662 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191663 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191664 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191665 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191666 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR191667 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR191668 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191669 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191670 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191671 1 0.3810 0.858 0.804 0.000 0.188 0.008
#> SRR191672 1 0.3937 0.855 0.800 0.000 0.188 0.012
#> SRR191673 1 0.3937 0.855 0.800 0.000 0.188 0.012
#> SRR191674 2 0.4925 0.335 0.000 0.572 0.000 0.428
#> SRR191675 2 0.4925 0.335 0.000 0.572 0.000 0.428
#> SRR191677 2 0.4941 0.315 0.000 0.564 0.000 0.436
#> SRR191678 2 0.4941 0.315 0.000 0.564 0.000 0.436
#> SRR191679 2 0.1637 0.875 0.000 0.940 0.000 0.060
#> SRR191680 2 0.4406 0.587 0.000 0.700 0.000 0.300
#> SRR191681 2 0.4941 0.315 0.000 0.564 0.000 0.436
#> SRR191682 2 0.0469 0.913 0.000 0.988 0.000 0.012
#> SRR191683 2 0.0592 0.910 0.000 0.984 0.000 0.016
#> SRR191684 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191685 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191686 2 0.1022 0.900 0.000 0.968 0.000 0.032
#> SRR191687 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191688 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191689 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191690 2 0.0592 0.905 0.000 0.984 0.000 0.016
#> SRR191691 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191692 2 0.4454 0.575 0.000 0.692 0.000 0.308
#> SRR191693 2 0.4961 0.283 0.000 0.552 0.000 0.448
#> SRR191694 2 0.1118 0.897 0.000 0.964 0.000 0.036
#> SRR191695 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191696 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191697 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191698 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191699 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191700 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191701 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191702 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191704 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191705 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191706 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191707 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191708 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191709 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191710 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191711 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191712 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191713 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR191714 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191715 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR191716 2 0.0592 0.905 0.000 0.984 0.000 0.016
#> SRR191717 2 0.0188 0.914 0.000 0.996 0.000 0.004
#> SRR191718 2 0.0188 0.916 0.000 0.996 0.000 0.004
#> SRR537099 1 0.1406 0.895 0.960 0.024 0.000 0.016
#> SRR537100 1 0.0779 0.907 0.980 0.004 0.000 0.016
#> SRR537101 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR537102 1 0.0927 0.905 0.976 0.008 0.000 0.016
#> SRR537104 1 0.4399 0.602 0.760 0.224 0.000 0.016
#> SRR537105 1 0.0188 0.910 0.996 0.004 0.000 0.000
#> SRR537106 1 0.0779 0.907 0.980 0.004 0.000 0.016
#> SRR537107 1 0.0779 0.907 0.980 0.004 0.000 0.016
#> SRR537108 1 0.0779 0.907 0.980 0.004 0.000 0.016
#> SRR537109 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR537110 2 0.1118 0.895 0.000 0.964 0.000 0.036
#> SRR537111 1 0.1004 0.906 0.972 0.004 0.000 0.024
#> SRR537113 4 0.6573 0.456 0.164 0.184 0.004 0.648
#> SRR537114 4 0.5794 0.352 0.320 0.040 0.004 0.636
#> SRR537115 4 0.4419 0.585 0.028 0.176 0.004 0.792
#> SRR537116 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> SRR537117 4 0.0469 0.844 0.000 0.000 0.012 0.988
#> SRR537118 4 0.0592 0.847 0.000 0.000 0.016 0.984
#> SRR537119 4 0.0592 0.847 0.000 0.000 0.016 0.984
#> SRR537120 4 0.0592 0.847 0.000 0.000 0.016 0.984
#> SRR537121 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR537122 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR537123 4 0.0188 0.848 0.000 0.000 0.004 0.996
#> SRR537124 4 0.0336 0.843 0.000 0.000 0.008 0.992
#> SRR537125 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR537126 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR537127 3 0.3486 1.000 0.000 0.000 0.812 0.188
#> SRR537128 3 0.3486 1.000 0.000 0.000 0.812 0.188
#> SRR537129 3 0.3486 1.000 0.000 0.000 0.812 0.188
#> SRR537130 3 0.3486 1.000 0.000 0.000 0.812 0.188
#> SRR537131 3 0.3486 1.000 0.000 0.000 0.812 0.188
#> SRR537132 3 0.3486 1.000 0.000 0.000 0.812 0.188
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.2377 0.790 NA 0.000 0 0.872 0.000
#> SRR191640 4 0.0290 0.787 NA 0.000 0 0.992 0.000
#> SRR191641 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191642 4 0.2929 0.776 NA 0.000 0 0.820 0.000
#> SRR191643 4 0.3508 0.745 NA 0.000 0 0.748 0.000
#> SRR191644 4 0.3783 0.741 NA 0.000 0 0.740 0.008
#> SRR191645 4 0.0290 0.787 NA 0.000 0 0.992 0.000
#> SRR191646 4 0.0290 0.787 NA 0.000 0 0.992 0.000
#> SRR191647 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191648 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191649 4 0.2127 0.786 NA 0.000 0 0.892 0.000
#> SRR191650 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191651 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191652 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191653 4 0.3783 0.741 NA 0.000 0 0.740 0.008
#> SRR191654 4 0.3783 0.741 NA 0.000 0 0.740 0.008
#> SRR191655 4 0.3480 0.748 NA 0.000 0 0.752 0.000
#> SRR191656 4 0.4235 0.630 NA 0.000 0 0.656 0.008
#> SRR191657 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191658 4 0.3366 0.720 NA 0.000 0 0.768 0.000
#> SRR191659 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191660 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191661 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191662 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191663 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191664 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191665 4 0.3336 0.722 NA 0.000 0 0.772 0.000
#> SRR191666 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191667 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR191668 4 0.3999 0.631 NA 0.000 0 0.656 0.000
#> SRR191669 4 0.3999 0.631 NA 0.000 0 0.656 0.000
#> SRR191670 4 0.3395 0.717 NA 0.000 0 0.764 0.000
#> SRR191671 4 0.3395 0.717 NA 0.000 0 0.764 0.000
#> SRR191672 4 0.4268 0.623 NA 0.000 0 0.648 0.008
#> SRR191673 4 0.4252 0.626 NA 0.000 0 0.652 0.008
#> SRR191674 2 0.6150 0.408 NA 0.464 0 0.000 0.132
#> SRR191675 2 0.6150 0.408 NA 0.464 0 0.000 0.132
#> SRR191677 2 0.6236 0.392 NA 0.456 0 0.000 0.144
#> SRR191678 2 0.6239 0.386 NA 0.452 0 0.000 0.144
#> SRR191679 2 0.1478 0.842 NA 0.936 0 0.000 0.064
#> SRR191680 2 0.6171 0.433 NA 0.488 0 0.000 0.140
#> SRR191681 2 0.6239 0.386 NA 0.452 0 0.000 0.144
#> SRR191682 2 0.3177 0.753 NA 0.792 0 0.000 0.000
#> SRR191683 2 0.3210 0.750 NA 0.788 0 0.000 0.000
#> SRR191684 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191685 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191686 2 0.3480 0.722 NA 0.752 0 0.000 0.000
#> SRR191687 2 0.0510 0.874 NA 0.984 0 0.000 0.000
#> SRR191688 2 0.0290 0.874 NA 0.992 0 0.008 0.000
#> SRR191689 2 0.0290 0.877 NA 0.992 0 0.000 0.000
#> SRR191690 2 0.0703 0.862 NA 0.976 0 0.024 0.000
#> SRR191691 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191692 2 0.5742 0.477 NA 0.508 0 0.000 0.088
#> SRR191693 2 0.6519 0.284 NA 0.408 0 0.000 0.192
#> SRR191694 2 0.4114 0.601 NA 0.624 0 0.000 0.000
#> SRR191695 2 0.0290 0.877 NA 0.992 0 0.000 0.000
#> SRR191696 2 0.0290 0.877 NA 0.992 0 0.000 0.000
#> SRR191697 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191698 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191699 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191700 2 0.1270 0.857 NA 0.948 0 0.000 0.000
#> SRR191701 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191702 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191703 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191704 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191705 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191706 2 0.0290 0.877 NA 0.992 0 0.000 0.000
#> SRR191707 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191708 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191709 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191710 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191711 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191712 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191713 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191714 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191715 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR191716 2 0.0703 0.862 NA 0.976 0 0.024 0.000
#> SRR191717 2 0.0798 0.869 NA 0.976 0 0.016 0.000
#> SRR191718 2 0.0290 0.877 NA 0.992 0 0.000 0.000
#> SRR537099 4 0.3662 0.744 NA 0.000 0 0.744 0.004
#> SRR537100 4 0.3508 0.745 NA 0.000 0 0.748 0.000
#> SRR537101 4 0.3039 0.774 NA 0.000 0 0.808 0.000
#> SRR537102 4 0.3480 0.748 NA 0.000 0 0.752 0.000
#> SRR537104 4 0.6408 0.424 NA 0.264 0 0.532 0.004
#> SRR537105 4 0.0290 0.787 NA 0.000 0 0.992 0.000
#> SRR537106 4 0.0290 0.788 NA 0.000 0 0.992 0.000
#> SRR537107 4 0.0162 0.788 NA 0.000 0 0.996 0.000
#> SRR537108 4 0.0000 0.788 NA 0.000 0 1.000 0.000
#> SRR537109 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR537110 2 0.1668 0.846 NA 0.940 0 0.032 0.028
#> SRR537111 4 0.0000 0.788 NA 0.000 0 1.000 0.000
#> SRR537113 5 0.5839 0.582 NA 0.168 0 0.108 0.680
#> SRR537114 5 0.4339 0.483 NA 0.000 0 0.296 0.684
#> SRR537115 5 0.2270 0.842 NA 0.020 0 0.000 0.904
#> SRR537116 2 0.0000 0.879 NA 1.000 0 0.000 0.000
#> SRR537117 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537118 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537119 5 0.0794 0.897 NA 0.000 0 0.000 0.972
#> SRR537120 5 0.0794 0.897 NA 0.000 0 0.000 0.972
#> SRR537121 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537122 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537123 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537124 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537125 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537126 5 0.0000 0.910 NA 0.000 0 0.000 1.000
#> SRR537127 3 0.0000 1.000 NA 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.000 NA 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.000 NA 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.000 NA 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.000 NA 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.000 NA 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 4 0.1745 0.634 0.068 0.000 0 0.920 0.000 0.012
#> SRR191640 4 0.3432 0.405 0.216 0.000 0 0.764 0.000 0.020
#> SRR191641 4 0.2712 0.638 0.088 0.000 0 0.864 0.000 0.048
#> SRR191642 4 0.1003 0.660 0.016 0.000 0 0.964 0.000 0.020
#> SRR191643 4 0.3958 0.578 0.128 0.000 0 0.764 0.000 0.108
#> SRR191644 4 0.5481 0.507 0.128 0.088 0 0.676 0.000 0.108
#> SRR191645 4 0.3431 0.349 0.228 0.000 0 0.756 0.000 0.016
#> SRR191646 4 0.3457 0.341 0.232 0.000 0 0.752 0.000 0.016
#> SRR191647 4 0.1010 0.656 0.036 0.000 0 0.960 0.000 0.004
#> SRR191648 4 0.1010 0.656 0.036 0.000 0 0.960 0.000 0.004
#> SRR191649 4 0.2118 0.590 0.104 0.000 0 0.888 0.000 0.008
#> SRR191650 4 0.0146 0.661 0.000 0.000 0 0.996 0.000 0.004
#> SRR191651 4 0.0291 0.661 0.004 0.000 0 0.992 0.000 0.004
#> SRR191652 4 0.1010 0.656 0.036 0.000 0 0.960 0.000 0.004
#> SRR191653 4 0.5526 0.503 0.128 0.092 0 0.672 0.000 0.108
#> SRR191654 4 0.5613 0.494 0.128 0.100 0 0.664 0.000 0.108
#> SRR191655 4 0.3595 0.596 0.120 0.000 0 0.796 0.000 0.084
#> SRR191656 1 0.3394 0.595 0.776 0.000 0 0.200 0.000 0.024
#> SRR191657 1 0.3823 0.752 0.564 0.000 0 0.436 0.000 0.000
#> SRR191658 1 0.3828 0.750 0.560 0.000 0 0.440 0.000 0.000
#> SRR191659 1 0.3833 0.745 0.556 0.000 0 0.444 0.000 0.000
#> SRR191660 1 0.3828 0.750 0.560 0.000 0 0.440 0.000 0.000
#> SRR191661 1 0.3838 0.741 0.552 0.000 0 0.448 0.000 0.000
#> SRR191662 1 0.3851 0.726 0.540 0.000 0 0.460 0.000 0.000
#> SRR191663 1 0.3828 0.750 0.560 0.000 0 0.440 0.000 0.000
#> SRR191664 1 0.3833 0.745 0.556 0.000 0 0.444 0.000 0.000
#> SRR191665 4 0.4184 -0.665 0.488 0.000 0 0.500 0.000 0.012
#> SRR191666 4 0.0865 0.656 0.036 0.000 0 0.964 0.000 0.000
#> SRR191667 4 0.1007 0.651 0.044 0.000 0 0.956 0.000 0.000
#> SRR191668 1 0.3403 0.621 0.768 0.000 0 0.212 0.000 0.020
#> SRR191669 1 0.3403 0.621 0.768 0.000 0 0.212 0.000 0.020
#> SRR191670 1 0.3810 0.752 0.572 0.000 0 0.428 0.000 0.000
#> SRR191671 1 0.3810 0.752 0.572 0.000 0 0.428 0.000 0.000
#> SRR191672 1 0.3645 0.578 0.740 0.000 0 0.236 0.000 0.024
#> SRR191673 1 0.3619 0.582 0.744 0.000 0 0.232 0.000 0.024
#> SRR191674 6 0.2740 0.828 0.000 0.076 0 0.000 0.060 0.864
#> SRR191675 6 0.2740 0.828 0.000 0.076 0 0.000 0.060 0.864
#> SRR191677 6 0.2997 0.826 0.000 0.096 0 0.000 0.060 0.844
#> SRR191678 6 0.2740 0.828 0.000 0.076 0 0.000 0.060 0.864
#> SRR191679 2 0.3244 0.651 0.000 0.732 0 0.000 0.000 0.268
#> SRR191680 6 0.4443 0.616 0.000 0.276 0 0.000 0.060 0.664
#> SRR191681 6 0.2740 0.828 0.000 0.076 0 0.000 0.060 0.864
#> SRR191682 2 0.3866 0.213 0.000 0.516 0 0.000 0.000 0.484
#> SRR191683 2 0.3866 0.217 0.000 0.516 0 0.000 0.000 0.484
#> SRR191684 2 0.1556 0.831 0.000 0.920 0 0.000 0.000 0.080
#> SRR191685 2 0.2823 0.786 0.000 0.796 0 0.000 0.000 0.204
#> SRR191686 6 0.3717 0.317 0.000 0.384 0 0.000 0.000 0.616
#> SRR191687 2 0.3390 0.709 0.000 0.704 0 0.000 0.000 0.296
#> SRR191688 2 0.0508 0.812 0.004 0.984 0 0.000 0.000 0.012
#> SRR191689 2 0.3351 0.719 0.000 0.712 0 0.000 0.000 0.288
#> SRR191690 2 0.2203 0.756 0.004 0.896 0 0.084 0.000 0.016
#> SRR191691 2 0.2092 0.820 0.000 0.876 0 0.000 0.000 0.124
#> SRR191692 6 0.2697 0.828 0.000 0.092 0 0.000 0.044 0.864
#> SRR191693 6 0.2799 0.824 0.000 0.076 0 0.000 0.064 0.860
#> SRR191694 6 0.3547 0.577 0.000 0.300 0 0.000 0.004 0.696
#> SRR191695 2 0.3240 0.647 0.004 0.752 0 0.000 0.000 0.244
#> SRR191696 2 0.2703 0.758 0.004 0.824 0 0.000 0.000 0.172
#> SRR191697 2 0.1141 0.817 0.000 0.948 0 0.000 0.000 0.052
#> SRR191698 2 0.2631 0.810 0.000 0.820 0 0.000 0.000 0.180
#> SRR191699 2 0.2092 0.820 0.000 0.876 0 0.000 0.000 0.124
#> SRR191700 2 0.3446 0.658 0.000 0.692 0 0.000 0.000 0.308
#> SRR191701 2 0.1387 0.828 0.000 0.932 0 0.000 0.000 0.068
#> SRR191702 2 0.0000 0.810 0.000 1.000 0 0.000 0.000 0.000
#> SRR191703 2 0.0000 0.810 0.000 1.000 0 0.000 0.000 0.000
#> SRR191704 2 0.0713 0.822 0.000 0.972 0 0.000 0.000 0.028
#> SRR191705 2 0.2260 0.820 0.000 0.860 0 0.000 0.000 0.140
#> SRR191706 2 0.2969 0.782 0.000 0.776 0 0.000 0.000 0.224
#> SRR191707 2 0.0000 0.810 0.000 1.000 0 0.000 0.000 0.000
#> SRR191708 2 0.2912 0.787 0.000 0.784 0 0.000 0.000 0.216
#> SRR191709 2 0.0865 0.824 0.000 0.964 0 0.000 0.000 0.036
#> SRR191710 2 0.2912 0.787 0.000 0.784 0 0.000 0.000 0.216
#> SRR191711 2 0.1610 0.829 0.000 0.916 0 0.000 0.000 0.084
#> SRR191712 2 0.2135 0.826 0.000 0.872 0 0.000 0.000 0.128
#> SRR191713 2 0.2762 0.790 0.000 0.804 0 0.000 0.000 0.196
#> SRR191714 2 0.2854 0.785 0.000 0.792 0 0.000 0.000 0.208
#> SRR191715 2 0.0146 0.808 0.004 0.996 0 0.000 0.000 0.000
#> SRR191716 2 0.2265 0.760 0.004 0.896 0 0.076 0.000 0.024
#> SRR191717 2 0.3301 0.657 0.004 0.772 0 0.008 0.000 0.216
#> SRR191718 2 0.1765 0.799 0.000 0.904 0 0.000 0.000 0.096
#> SRR537099 4 0.4475 0.566 0.128 0.020 0 0.744 0.000 0.108
#> SRR537100 4 0.3873 0.584 0.124 0.000 0 0.772 0.000 0.104
#> SRR537101 4 0.1320 0.656 0.036 0.000 0 0.948 0.000 0.016
#> SRR537102 4 0.3686 0.592 0.124 0.000 0 0.788 0.000 0.088
#> SRR537104 4 0.5898 0.313 0.080 0.300 0 0.560 0.000 0.060
#> SRR537105 4 0.3374 0.375 0.208 0.000 0 0.772 0.000 0.020
#> SRR537106 4 0.3253 0.407 0.192 0.000 0 0.788 0.000 0.020
#> SRR537107 4 0.3284 0.393 0.196 0.000 0 0.784 0.000 0.020
#> SRR537108 4 0.3284 0.393 0.196 0.000 0 0.784 0.000 0.020
#> SRR537109 2 0.0405 0.811 0.004 0.988 0 0.000 0.000 0.008
#> SRR537110 2 0.2473 0.818 0.000 0.856 0 0.008 0.000 0.136
#> SRR537111 4 0.2946 0.450 0.176 0.000 0 0.812 0.000 0.012
#> SRR537113 5 0.4559 0.649 0.004 0.012 0 0.020 0.620 0.344
#> SRR537114 5 0.4901 0.659 0.004 0.004 0 0.060 0.612 0.320
#> SRR537115 5 0.3820 0.683 0.000 0.004 0 0.004 0.660 0.332
#> SRR537116 2 0.0260 0.814 0.000 0.992 0 0.000 0.000 0.008
#> SRR537117 5 0.1910 0.759 0.000 0.000 0 0.000 0.892 0.108
#> SRR537118 5 0.3634 0.678 0.000 0.000 0 0.000 0.644 0.356
#> SRR537119 5 0.3774 0.629 0.000 0.000 0 0.000 0.592 0.408
#> SRR537120 5 0.3774 0.629 0.000 0.000 0 0.000 0.592 0.408
#> SRR537121 5 0.0000 0.765 0.000 0.000 0 0.000 1.000 0.000
#> SRR537122 5 0.1610 0.778 0.000 0.000 0 0.000 0.916 0.084
#> SRR537123 5 0.0000 0.765 0.000 0.000 0 0.000 1.000 0.000
#> SRR537124 5 0.0000 0.765 0.000 0.000 0 0.000 1.000 0.000
#> SRR537125 5 0.0000 0.765 0.000 0.000 0 0.000 1.000 0.000
#> SRR537126 5 0.0000 0.765 0.000 0.000 0 0.000 1.000 0.000
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 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)
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 16450 rows and 111 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 0.970 0.986 0.4981 0.499 0.499
#> 3 3 0.768 0.859 0.914 0.1849 0.938 0.876
#> 4 4 0.528 0.456 0.718 0.1898 0.695 0.414
#> 5 5 0.723 0.639 0.770 0.0989 0.800 0.456
#> 6 6 0.844 0.860 0.905 0.0566 0.913 0.655
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
#> SRR191639 1 0.0000 0.969 1.000 0.000
#> SRR191640 1 0.0000 0.969 1.000 0.000
#> SRR191641 1 0.0000 0.969 1.000 0.000
#> SRR191642 1 0.0000 0.969 1.000 0.000
#> SRR191643 1 0.0000 0.969 1.000 0.000
#> SRR191644 1 0.0000 0.969 1.000 0.000
#> SRR191645 1 0.0000 0.969 1.000 0.000
#> SRR191646 1 0.0000 0.969 1.000 0.000
#> SRR191647 1 0.0000 0.969 1.000 0.000
#> SRR191648 1 0.0000 0.969 1.000 0.000
#> SRR191649 1 0.0000 0.969 1.000 0.000
#> SRR191650 1 0.0000 0.969 1.000 0.000
#> SRR191651 1 0.0000 0.969 1.000 0.000
#> SRR191652 1 0.0000 0.969 1.000 0.000
#> SRR191653 1 0.0000 0.969 1.000 0.000
#> SRR191654 1 0.0000 0.969 1.000 0.000
#> SRR191655 1 0.0000 0.969 1.000 0.000
#> SRR191656 1 0.6712 0.804 0.824 0.176
#> SRR191657 1 0.0000 0.969 1.000 0.000
#> SRR191658 1 0.0000 0.969 1.000 0.000
#> SRR191659 1 0.0000 0.969 1.000 0.000
#> SRR191660 1 0.0000 0.969 1.000 0.000
#> SRR191661 1 0.0000 0.969 1.000 0.000
#> SRR191662 1 0.0000 0.969 1.000 0.000
#> SRR191663 1 0.0000 0.969 1.000 0.000
#> SRR191664 1 0.0000 0.969 1.000 0.000
#> SRR191665 1 0.0000 0.969 1.000 0.000
#> SRR191666 1 0.0000 0.969 1.000 0.000
#> SRR191667 1 0.0000 0.969 1.000 0.000
#> SRR191668 1 0.0000 0.969 1.000 0.000
#> SRR191669 1 0.0000 0.969 1.000 0.000
#> SRR191670 1 0.0000 0.969 1.000 0.000
#> SRR191671 1 0.0000 0.969 1.000 0.000
#> SRR191672 1 0.3114 0.927 0.944 0.056
#> SRR191673 1 0.5946 0.842 0.856 0.144
#> SRR191674 2 0.0000 1.000 0.000 1.000
#> SRR191675 2 0.0000 1.000 0.000 1.000
#> SRR191677 2 0.0000 1.000 0.000 1.000
#> SRR191678 2 0.0000 1.000 0.000 1.000
#> SRR191679 2 0.0000 1.000 0.000 1.000
#> SRR191680 2 0.0000 1.000 0.000 1.000
#> SRR191681 2 0.0000 1.000 0.000 1.000
#> SRR191682 2 0.0000 1.000 0.000 1.000
#> SRR191683 2 0.0000 1.000 0.000 1.000
#> SRR191684 2 0.0000 1.000 0.000 1.000
#> SRR191685 2 0.0000 1.000 0.000 1.000
#> SRR191686 2 0.0000 1.000 0.000 1.000
#> SRR191687 2 0.0000 1.000 0.000 1.000
#> SRR191688 2 0.0000 1.000 0.000 1.000
#> SRR191689 2 0.0000 1.000 0.000 1.000
#> SRR191690 2 0.0000 1.000 0.000 1.000
#> SRR191691 2 0.0000 1.000 0.000 1.000
#> SRR191692 2 0.0000 1.000 0.000 1.000
#> SRR191693 2 0.0000 1.000 0.000 1.000
#> SRR191694 2 0.0000 1.000 0.000 1.000
#> SRR191695 2 0.0000 1.000 0.000 1.000
#> SRR191696 2 0.0000 1.000 0.000 1.000
#> SRR191697 2 0.0000 1.000 0.000 1.000
#> SRR191698 2 0.0000 1.000 0.000 1.000
#> SRR191699 2 0.0000 1.000 0.000 1.000
#> SRR191700 2 0.0000 1.000 0.000 1.000
#> SRR191701 2 0.0000 1.000 0.000 1.000
#> SRR191702 2 0.0000 1.000 0.000 1.000
#> SRR191703 2 0.0000 1.000 0.000 1.000
#> SRR191704 2 0.0000 1.000 0.000 1.000
#> SRR191705 2 0.0000 1.000 0.000 1.000
#> SRR191706 2 0.0000 1.000 0.000 1.000
#> SRR191707 2 0.0000 1.000 0.000 1.000
#> SRR191708 2 0.0000 1.000 0.000 1.000
#> SRR191709 2 0.0000 1.000 0.000 1.000
#> SRR191710 2 0.0000 1.000 0.000 1.000
#> SRR191711 2 0.0000 1.000 0.000 1.000
#> SRR191712 2 0.0000 1.000 0.000 1.000
#> SRR191713 2 0.0000 1.000 0.000 1.000
#> SRR191714 2 0.0000 1.000 0.000 1.000
#> SRR191715 2 0.0000 1.000 0.000 1.000
#> SRR191716 2 0.0000 1.000 0.000 1.000
#> SRR191717 2 0.0000 1.000 0.000 1.000
#> SRR191718 2 0.0000 1.000 0.000 1.000
#> SRR537099 1 0.0000 0.969 1.000 0.000
#> SRR537100 1 0.0000 0.969 1.000 0.000
#> SRR537101 1 0.0000 0.969 1.000 0.000
#> SRR537102 1 0.9944 0.223 0.544 0.456
#> SRR537104 1 0.6247 0.829 0.844 0.156
#> SRR537105 1 0.1633 0.953 0.976 0.024
#> SRR537106 1 0.4022 0.906 0.920 0.080
#> SRR537107 1 0.8443 0.659 0.728 0.272
#> SRR537108 1 0.6712 0.804 0.824 0.176
#> SRR537109 2 0.0000 1.000 0.000 1.000
#> SRR537110 2 0.0000 1.000 0.000 1.000
#> SRR537111 1 0.1414 0.955 0.980 0.020
#> SRR537113 2 0.0376 0.996 0.004 0.996
#> SRR537114 2 0.0000 1.000 0.000 1.000
#> SRR537115 2 0.0000 1.000 0.000 1.000
#> SRR537116 2 0.0000 1.000 0.000 1.000
#> SRR537117 2 0.0000 1.000 0.000 1.000
#> SRR537118 2 0.0000 1.000 0.000 1.000
#> SRR537119 2 0.0000 1.000 0.000 1.000
#> SRR537120 2 0.0000 1.000 0.000 1.000
#> SRR537121 2 0.0000 1.000 0.000 1.000
#> SRR537122 2 0.0000 1.000 0.000 1.000
#> SRR537123 2 0.0000 1.000 0.000 1.000
#> SRR537124 2 0.0000 1.000 0.000 1.000
#> SRR537125 2 0.0000 1.000 0.000 1.000
#> SRR537126 2 0.0000 1.000 0.000 1.000
#> SRR537127 1 0.0000 0.969 1.000 0.000
#> SRR537128 1 0.0000 0.969 1.000 0.000
#> SRR537129 1 0.0000 0.969 1.000 0.000
#> SRR537130 1 0.0000 0.969 1.000 0.000
#> SRR537131 1 0.0000 0.969 1.000 0.000
#> SRR537132 1 0.0000 0.969 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.839 1.000 0.000 0.000
#> SRR191640 1 0.0592 0.837 0.988 0.000 0.012
#> SRR191641 1 0.6045 0.476 0.620 0.000 0.380
#> SRR191642 1 0.1163 0.837 0.972 0.000 0.028
#> SRR191643 1 0.6168 0.406 0.588 0.000 0.412
#> SRR191644 3 0.2796 0.992 0.092 0.000 0.908
#> SRR191645 1 0.4555 0.797 0.800 0.000 0.200
#> SRR191646 1 0.4555 0.797 0.800 0.000 0.200
#> SRR191647 1 0.4702 0.791 0.788 0.000 0.212
#> SRR191648 1 0.4702 0.791 0.788 0.000 0.212
#> SRR191649 1 0.4291 0.804 0.820 0.000 0.180
#> SRR191650 1 0.4702 0.791 0.788 0.000 0.212
#> SRR191651 1 0.3619 0.819 0.864 0.000 0.136
#> SRR191652 1 0.2356 0.825 0.928 0.000 0.072
#> SRR191653 3 0.2165 0.962 0.064 0.000 0.936
#> SRR191654 3 0.2796 0.971 0.092 0.000 0.908
#> SRR191655 1 0.4178 0.788 0.828 0.000 0.172
#> SRR191656 1 0.0424 0.838 0.992 0.000 0.008
#> SRR191657 1 0.0000 0.839 1.000 0.000 0.000
#> SRR191658 1 0.0237 0.838 0.996 0.000 0.004
#> SRR191659 1 0.0237 0.838 0.996 0.000 0.004
#> SRR191660 1 0.0000 0.839 1.000 0.000 0.000
#> SRR191661 1 0.0000 0.839 1.000 0.000 0.000
#> SRR191662 1 0.0237 0.838 0.996 0.000 0.004
#> SRR191663 1 0.0000 0.839 1.000 0.000 0.000
#> SRR191664 1 0.0237 0.838 0.996 0.000 0.004
#> SRR191665 1 0.0000 0.839 1.000 0.000 0.000
#> SRR191666 1 0.5497 0.510 0.708 0.000 0.292
#> SRR191667 1 0.4931 0.641 0.768 0.000 0.232
#> SRR191668 1 0.0424 0.838 0.992 0.000 0.008
#> SRR191669 1 0.0424 0.838 0.992 0.000 0.008
#> SRR191670 1 0.0237 0.838 0.996 0.000 0.004
#> SRR191671 1 0.0237 0.838 0.996 0.000 0.004
#> SRR191672 1 0.0747 0.838 0.984 0.000 0.016
#> SRR191673 1 0.0592 0.838 0.988 0.000 0.012
#> SRR191674 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191678 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191679 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191682 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191688 2 0.1182 0.941 0.012 0.976 0.012
#> SRR191689 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191690 2 0.1182 0.941 0.012 0.976 0.012
#> SRR191691 2 0.0424 0.949 0.000 0.992 0.008
#> SRR191692 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191693 2 0.0237 0.949 0.000 0.996 0.004
#> SRR191694 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191695 2 0.0424 0.950 0.000 0.992 0.008
#> SRR191696 2 0.0424 0.950 0.000 0.992 0.008
#> SRR191697 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191698 2 0.0424 0.949 0.000 0.992 0.008
#> SRR191699 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191700 2 0.0424 0.950 0.000 0.992 0.008
#> SRR191701 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191702 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191703 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191704 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191705 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191707 2 0.0592 0.948 0.000 0.988 0.012
#> SRR191708 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191709 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191710 2 0.0000 0.951 0.000 1.000 0.000
#> SRR191711 2 0.0424 0.950 0.000 0.992 0.008
#> SRR191712 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191713 2 0.0661 0.948 0.004 0.988 0.008
#> SRR191714 2 0.0424 0.950 0.000 0.992 0.008
#> SRR191715 2 0.0237 0.950 0.000 0.996 0.004
#> SRR191716 2 0.1620 0.932 0.024 0.964 0.012
#> SRR191717 2 0.0592 0.948 0.000 0.988 0.012
#> SRR191718 2 0.0000 0.951 0.000 1.000 0.000
#> SRR537099 1 0.7363 0.440 0.588 0.040 0.372
#> SRR537100 1 0.5968 0.523 0.636 0.000 0.364
#> SRR537101 1 0.3816 0.800 0.852 0.000 0.148
#> SRR537102 1 0.8444 0.441 0.612 0.236 0.152
#> SRR537104 1 0.8996 0.347 0.560 0.244 0.196
#> SRR537105 1 0.4750 0.789 0.784 0.000 0.216
#> SRR537106 1 0.4750 0.789 0.784 0.000 0.216
#> SRR537107 1 0.4750 0.789 0.784 0.000 0.216
#> SRR537108 1 0.4750 0.789 0.784 0.000 0.216
#> SRR537109 2 0.2297 0.925 0.020 0.944 0.036
#> SRR537110 2 0.1337 0.940 0.012 0.972 0.016
#> SRR537111 1 0.4842 0.781 0.776 0.000 0.224
#> SRR537113 2 0.5497 0.669 0.000 0.708 0.292
#> SRR537114 2 0.5560 0.657 0.000 0.700 0.300
#> SRR537115 2 0.4654 0.780 0.000 0.792 0.208
#> SRR537116 2 0.0424 0.950 0.000 0.992 0.008
#> SRR537117 2 0.2537 0.902 0.000 0.920 0.080
#> SRR537118 2 0.4452 0.803 0.000 0.808 0.192
#> SRR537119 2 0.4654 0.784 0.000 0.792 0.208
#> SRR537120 2 0.2711 0.900 0.000 0.912 0.088
#> SRR537121 2 0.4702 0.775 0.000 0.788 0.212
#> SRR537122 2 0.5465 0.675 0.000 0.712 0.288
#> SRR537123 2 0.4702 0.775 0.000 0.788 0.212
#> SRR537124 2 0.2537 0.902 0.000 0.920 0.080
#> SRR537125 2 0.4178 0.821 0.000 0.828 0.172
#> SRR537126 2 0.4504 0.795 0.000 0.804 0.196
#> SRR537127 3 0.2796 0.992 0.092 0.000 0.908
#> SRR537128 3 0.2796 0.992 0.092 0.000 0.908
#> SRR537129 3 0.2796 0.992 0.092 0.000 0.908
#> SRR537130 3 0.2796 0.992 0.092 0.000 0.908
#> SRR537131 3 0.2796 0.992 0.092 0.000 0.908
#> SRR537132 3 0.2796 0.992 0.092 0.000 0.908
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0000 0.9002 1.000 0.000 0.000 0.000
#> SRR191640 2 0.5738 0.0233 0.432 0.540 0.028 0.000
#> SRR191641 3 0.6079 0.4991 0.052 0.380 0.568 0.000
#> SRR191642 2 0.6617 0.0982 0.280 0.600 0.120 0.000
#> SRR191643 2 0.6748 -0.1510 0.112 0.560 0.328 0.000
#> SRR191644 3 0.3099 0.8097 0.020 0.104 0.876 0.000
#> SRR191645 2 0.7718 -0.0700 0.408 0.452 0.028 0.112
#> SRR191646 2 0.7715 -0.0596 0.404 0.456 0.028 0.112
#> SRR191647 2 0.7887 0.0679 0.344 0.496 0.036 0.124
#> SRR191648 2 0.7944 0.0703 0.336 0.496 0.036 0.132
#> SRR191649 2 0.7645 -0.1010 0.424 0.444 0.028 0.104
#> SRR191650 1 0.4882 0.7352 0.776 0.004 0.056 0.164
#> SRR191651 1 0.3634 0.8204 0.856 0.000 0.048 0.096
#> SRR191652 1 0.3205 0.8260 0.872 0.000 0.024 0.104
#> SRR191653 3 0.2140 0.8334 0.008 0.052 0.932 0.008
#> SRR191654 3 0.5510 0.3640 0.016 0.480 0.504 0.000
#> SRR191655 2 0.7078 0.0798 0.276 0.580 0.136 0.008
#> SRR191656 1 0.3024 0.8153 0.852 0.000 0.000 0.148
#> SRR191657 1 0.0188 0.9006 0.996 0.000 0.004 0.000
#> SRR191658 1 0.0188 0.9006 0.996 0.000 0.004 0.000
#> SRR191659 1 0.0188 0.9006 0.996 0.000 0.004 0.000
#> SRR191660 1 0.0000 0.9002 1.000 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.9002 1.000 0.000 0.000 0.000
#> SRR191662 1 0.0188 0.9006 0.996 0.000 0.004 0.000
#> SRR191663 1 0.0000 0.9002 1.000 0.000 0.000 0.000
#> SRR191664 1 0.0188 0.9006 0.996 0.000 0.004 0.000
#> SRR191665 1 0.0000 0.9002 1.000 0.000 0.000 0.000
#> SRR191666 1 0.2408 0.8412 0.896 0.000 0.104 0.000
#> SRR191667 1 0.2868 0.8178 0.864 0.000 0.136 0.000
#> SRR191668 1 0.2593 0.8514 0.892 0.000 0.004 0.104
#> SRR191669 1 0.2999 0.8296 0.864 0.000 0.004 0.132
#> SRR191670 1 0.0524 0.9000 0.988 0.000 0.004 0.008
#> SRR191671 1 0.0524 0.9000 0.988 0.000 0.004 0.008
#> SRR191672 1 0.3074 0.8144 0.848 0.000 0.000 0.152
#> SRR191673 1 0.3074 0.8144 0.848 0.000 0.000 0.152
#> SRR191674 4 0.4214 0.7058 0.000 0.204 0.016 0.780
#> SRR191675 4 0.4214 0.7058 0.000 0.204 0.016 0.780
#> SRR191677 4 0.5237 0.6234 0.000 0.356 0.016 0.628
#> SRR191678 4 0.5253 0.6186 0.000 0.360 0.016 0.624
#> SRR191679 4 0.5253 0.6186 0.000 0.360 0.016 0.624
#> SRR191680 4 0.5253 0.6186 0.000 0.360 0.016 0.624
#> SRR191681 4 0.5047 0.6619 0.000 0.316 0.016 0.668
#> SRR191682 4 0.4535 0.6993 0.000 0.240 0.016 0.744
#> SRR191683 4 0.4535 0.6993 0.000 0.240 0.016 0.744
#> SRR191684 4 0.5220 0.6302 0.000 0.352 0.016 0.632
#> SRR191685 4 0.5090 0.6566 0.000 0.324 0.016 0.660
#> SRR191686 4 0.4567 0.6987 0.000 0.244 0.016 0.740
#> SRR191687 4 0.4661 0.6940 0.000 0.256 0.016 0.728
#> SRR191688 2 0.1743 0.4302 0.004 0.940 0.000 0.056
#> SRR191689 4 0.5151 0.4254 0.000 0.464 0.004 0.532
#> SRR191690 2 0.1661 0.4318 0.004 0.944 0.000 0.052
#> SRR191691 2 0.4888 -0.1138 0.000 0.588 0.000 0.412
#> SRR191692 4 0.5090 0.6556 0.000 0.324 0.016 0.660
#> SRR191693 4 0.4175 0.7051 0.000 0.200 0.016 0.784
#> SRR191694 4 0.4214 0.7049 0.000 0.204 0.016 0.780
#> SRR191695 2 0.4898 -0.1232 0.000 0.584 0.000 0.416
#> SRR191696 2 0.4866 -0.0960 0.000 0.596 0.000 0.404
#> SRR191697 2 0.4916 -0.1438 0.000 0.576 0.000 0.424
#> SRR191698 2 0.4898 -0.1236 0.000 0.584 0.000 0.416
#> SRR191699 2 0.4941 -0.1744 0.000 0.564 0.000 0.436
#> SRR191700 2 0.4877 -0.1045 0.000 0.592 0.000 0.408
#> SRR191701 2 0.4925 -0.1538 0.000 0.572 0.000 0.428
#> SRR191702 2 0.4955 -0.1991 0.000 0.556 0.000 0.444
#> SRR191703 2 0.4967 -0.2191 0.000 0.548 0.000 0.452
#> SRR191704 2 0.4985 -0.2660 0.000 0.532 0.000 0.468
#> SRR191705 2 0.4989 -0.2775 0.000 0.528 0.000 0.472
#> SRR191706 4 0.4843 0.5736 0.000 0.396 0.000 0.604
#> SRR191707 2 0.3074 0.3575 0.000 0.848 0.000 0.152
#> SRR191708 2 0.4989 -0.2773 0.000 0.528 0.000 0.472
#> SRR191709 2 0.4961 -0.2108 0.000 0.552 0.000 0.448
#> SRR191710 4 0.4967 0.4593 0.000 0.452 0.000 0.548
#> SRR191711 2 0.2973 0.3669 0.000 0.856 0.000 0.144
#> SRR191712 2 0.3688 0.2870 0.000 0.792 0.000 0.208
#> SRR191713 2 0.3052 0.3760 0.004 0.860 0.000 0.136
#> SRR191714 2 0.4936 -0.0259 0.004 0.624 0.000 0.372
#> SRR191715 2 0.3569 0.3055 0.000 0.804 0.000 0.196
#> SRR191716 2 0.1661 0.4318 0.004 0.944 0.000 0.052
#> SRR191717 2 0.3105 0.3733 0.004 0.856 0.000 0.140
#> SRR191718 2 0.4967 -0.2208 0.000 0.548 0.000 0.452
#> SRR537099 2 0.5067 0.1605 0.048 0.736 0.216 0.000
#> SRR537100 2 0.5564 0.1321 0.076 0.708 0.216 0.000
#> SRR537101 2 0.7015 -0.0651 0.168 0.568 0.264 0.000
#> SRR537102 2 0.3335 0.3274 0.016 0.856 0.128 0.000
#> SRR537104 2 0.4919 0.1915 0.048 0.752 0.200 0.000
#> SRR537105 2 0.7543 0.1655 0.184 0.588 0.028 0.200
#> SRR537106 2 0.7609 0.1593 0.192 0.580 0.028 0.200
#> SRR537107 2 0.7543 0.1655 0.184 0.588 0.028 0.200
#> SRR537108 2 0.7609 0.1593 0.192 0.580 0.028 0.200
#> SRR537109 2 0.1369 0.4424 0.004 0.964 0.016 0.016
#> SRR537110 2 0.0779 0.4462 0.004 0.980 0.016 0.000
#> SRR537111 1 0.5947 0.5919 0.628 0.000 0.060 0.312
#> SRR537113 4 0.1557 0.6210 0.000 0.000 0.056 0.944
#> SRR537114 4 0.4562 0.4601 0.000 0.152 0.056 0.792
#> SRR537115 4 0.1557 0.6210 0.000 0.000 0.056 0.944
#> SRR537116 2 0.1637 0.4266 0.000 0.940 0.000 0.060
#> SRR537117 4 0.0000 0.6478 0.000 0.000 0.000 1.000
#> SRR537118 4 0.2831 0.6497 0.000 0.120 0.004 0.876
#> SRR537119 4 0.3710 0.6008 0.000 0.192 0.004 0.804
#> SRR537120 4 0.3208 0.6365 0.000 0.148 0.004 0.848
#> SRR537121 4 0.1557 0.6210 0.000 0.000 0.056 0.944
#> SRR537122 4 0.1743 0.6228 0.000 0.004 0.056 0.940
#> SRR537123 4 0.1557 0.6210 0.000 0.000 0.056 0.944
#> SRR537124 4 0.0707 0.6400 0.000 0.000 0.020 0.980
#> SRR537125 4 0.1474 0.6239 0.000 0.000 0.052 0.948
#> SRR537126 4 0.1474 0.6239 0.000 0.000 0.052 0.948
#> SRR537127 3 0.1118 0.8571 0.036 0.000 0.964 0.000
#> SRR537128 3 0.1118 0.8571 0.036 0.000 0.964 0.000
#> SRR537129 3 0.1118 0.8571 0.036 0.000 0.964 0.000
#> SRR537130 3 0.1118 0.8571 0.036 0.000 0.964 0.000
#> SRR537131 3 0.1118 0.8571 0.036 0.000 0.964 0.000
#> SRR537132 3 0.1118 0.8571 0.036 0.000 0.964 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191640 4 0.5404 0.65603 0.184 0.152 0.000 0.664 0.000
#> SRR191641 3 0.3628 0.64810 0.012 0.000 0.772 0.216 0.000
#> SRR191642 4 0.5807 0.69825 0.072 0.144 0.088 0.696 0.000
#> SRR191643 4 0.5965 0.49272 0.044 0.048 0.320 0.588 0.000
#> SRR191644 3 0.1195 0.87381 0.012 0.000 0.960 0.028 0.000
#> SRR191645 4 0.4618 0.75549 0.068 0.000 0.000 0.724 0.208
#> SRR191646 4 0.4618 0.75549 0.068 0.000 0.000 0.724 0.208
#> SRR191647 4 0.4497 0.75760 0.060 0.000 0.000 0.732 0.208
#> SRR191648 4 0.4528 0.75499 0.060 0.000 0.000 0.728 0.212
#> SRR191649 4 0.4718 0.75086 0.092 0.000 0.000 0.728 0.180
#> SRR191650 4 0.6763 0.36415 0.276 0.000 0.000 0.392 0.332
#> SRR191651 1 0.2411 0.86383 0.884 0.000 0.000 0.008 0.108
#> SRR191652 1 0.2929 0.81337 0.840 0.000 0.000 0.008 0.152
#> SRR191653 3 0.4538 -0.00254 0.008 0.000 0.540 0.452 0.000
#> SRR191654 4 0.4449 0.45575 0.004 0.008 0.352 0.636 0.000
#> SRR191655 4 0.5694 0.71395 0.052 0.104 0.108 0.724 0.012
#> SRR191656 1 0.1851 0.90395 0.912 0.000 0.000 0.000 0.088
#> SRR191657 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191658 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191659 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191660 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191661 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191662 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191663 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191664 1 0.0290 0.94683 0.992 0.000 0.000 0.008 0.000
#> SRR191665 1 0.0609 0.94085 0.980 0.000 0.000 0.020 0.000
#> SRR191666 1 0.1544 0.91066 0.932 0.000 0.068 0.000 0.000
#> SRR191667 1 0.2930 0.81097 0.832 0.000 0.164 0.004 0.000
#> SRR191668 1 0.1341 0.92162 0.944 0.000 0.000 0.000 0.056
#> SRR191669 1 0.1410 0.92001 0.940 0.000 0.000 0.000 0.060
#> SRR191670 1 0.0162 0.94458 0.996 0.000 0.000 0.000 0.004
#> SRR191671 1 0.0162 0.94458 0.996 0.000 0.000 0.000 0.004
#> SRR191672 1 0.1792 0.90682 0.916 0.000 0.000 0.000 0.084
#> SRR191673 1 0.1965 0.89745 0.904 0.000 0.000 0.000 0.096
#> SRR191674 5 0.7086 0.19499 0.000 0.292 0.016 0.264 0.428
#> SRR191675 5 0.7066 0.21307 0.000 0.284 0.016 0.264 0.436
#> SRR191677 2 0.6854 0.27124 0.000 0.492 0.016 0.268 0.224
#> SRR191678 2 0.6771 0.30239 0.000 0.508 0.016 0.268 0.208
#> SRR191679 2 0.6484 0.37670 0.000 0.552 0.016 0.268 0.164
#> SRR191680 2 0.6814 0.28737 0.000 0.500 0.016 0.268 0.216
#> SRR191681 2 0.7162 -0.00139 0.000 0.384 0.016 0.268 0.332
#> SRR191682 2 0.7300 -0.00622 0.004 0.380 0.016 0.268 0.332
#> SRR191683 2 0.7310 -0.06878 0.004 0.360 0.016 0.268 0.352
#> SRR191684 2 0.7032 0.17363 0.000 0.448 0.016 0.268 0.268
#> SRR191685 2 0.7148 0.03773 0.000 0.396 0.016 0.268 0.320
#> SRR191686 5 0.7309 0.03582 0.004 0.348 0.016 0.268 0.364
#> SRR191687 5 0.7173 0.02746 0.000 0.352 0.016 0.268 0.364
#> SRR191688 2 0.0794 0.75972 0.000 0.972 0.000 0.028 0.000
#> SRR191689 2 0.3596 0.64503 0.000 0.776 0.012 0.212 0.000
#> SRR191690 2 0.1043 0.75172 0.000 0.960 0.000 0.040 0.000
#> SRR191691 2 0.0671 0.76655 0.000 0.980 0.000 0.004 0.016
#> SRR191692 2 0.7067 0.14523 0.000 0.436 0.016 0.268 0.280
#> SRR191693 5 0.6951 0.24162 0.000 0.280 0.016 0.236 0.468
#> SRR191694 5 0.7077 0.24280 0.004 0.280 0.016 0.232 0.468
#> SRR191695 2 0.0510 0.76739 0.000 0.984 0.000 0.000 0.016
#> SRR191696 2 0.0671 0.76655 0.000 0.980 0.000 0.004 0.016
#> SRR191697 2 0.0510 0.76739 0.000 0.984 0.000 0.000 0.016
#> SRR191698 2 0.0510 0.76739 0.000 0.984 0.000 0.000 0.016
#> SRR191699 2 0.0000 0.76810 0.000 1.000 0.000 0.000 0.000
#> SRR191700 2 0.0671 0.76655 0.000 0.980 0.000 0.004 0.016
#> SRR191701 2 0.0510 0.76739 0.000 0.984 0.000 0.000 0.016
#> SRR191702 2 0.1469 0.76313 0.000 0.948 0.000 0.036 0.016
#> SRR191703 2 0.1701 0.75955 0.000 0.936 0.000 0.048 0.016
#> SRR191704 2 0.2519 0.73687 0.000 0.884 0.000 0.100 0.016
#> SRR191705 2 0.2293 0.74469 0.000 0.900 0.000 0.084 0.016
#> SRR191706 2 0.4096 0.66698 0.000 0.784 0.000 0.144 0.072
#> SRR191707 2 0.0671 0.76655 0.000 0.980 0.000 0.004 0.016
#> SRR191708 2 0.1725 0.76078 0.000 0.936 0.000 0.044 0.020
#> SRR191709 2 0.1981 0.75351 0.000 0.920 0.000 0.064 0.016
#> SRR191710 2 0.2879 0.72857 0.000 0.868 0.000 0.100 0.032
#> SRR191711 2 0.0404 0.76656 0.000 0.988 0.000 0.012 0.000
#> SRR191712 2 0.0510 0.76540 0.000 0.984 0.000 0.016 0.000
#> SRR191713 2 0.0794 0.76510 0.000 0.972 0.000 0.028 0.000
#> SRR191714 2 0.0609 0.76530 0.000 0.980 0.000 0.020 0.000
#> SRR191715 2 0.0404 0.76756 0.000 0.988 0.000 0.012 0.000
#> SRR191716 2 0.2516 0.64129 0.000 0.860 0.000 0.140 0.000
#> SRR191717 2 0.1205 0.75049 0.000 0.956 0.000 0.040 0.004
#> SRR191718 2 0.0510 0.76739 0.000 0.984 0.000 0.000 0.016
#> SRR537099 4 0.5261 0.67282 0.012 0.200 0.092 0.696 0.000
#> SRR537100 4 0.5453 0.66065 0.012 0.200 0.108 0.680 0.000
#> SRR537101 4 0.5982 0.57766 0.032 0.084 0.260 0.624 0.000
#> SRR537102 4 0.4066 0.57672 0.000 0.324 0.004 0.672 0.000
#> SRR537104 4 0.5178 0.67814 0.016 0.204 0.076 0.704 0.000
#> SRR537105 4 0.4765 0.76032 0.040 0.020 0.000 0.728 0.212
#> SRR537106 4 0.4744 0.76012 0.044 0.016 0.000 0.728 0.212
#> SRR537107 4 0.4779 0.75910 0.036 0.024 0.000 0.728 0.212
#> SRR537108 4 0.4765 0.76032 0.040 0.020 0.000 0.728 0.212
#> SRR537109 2 0.4235 -0.04064 0.000 0.576 0.000 0.424 0.000
#> SRR537110 2 0.4268 -0.12298 0.000 0.556 0.000 0.444 0.000
#> SRR537111 5 0.4794 -0.03103 0.032 0.000 0.000 0.344 0.624
#> SRR537113 5 0.3074 0.41763 0.000 0.000 0.000 0.196 0.804
#> SRR537114 5 0.4835 -0.12570 0.000 0.028 0.000 0.380 0.592
#> SRR537115 5 0.1043 0.63942 0.000 0.000 0.000 0.040 0.960
#> SRR537116 2 0.0703 0.76196 0.000 0.976 0.000 0.024 0.000
#> SRR537117 5 0.0000 0.65823 0.000 0.000 0.000 0.000 1.000
#> SRR537118 5 0.1800 0.63902 0.000 0.048 0.000 0.020 0.932
#> SRR537119 5 0.2795 0.60253 0.000 0.056 0.000 0.064 0.880
#> SRR537120 5 0.2370 0.62318 0.000 0.056 0.000 0.040 0.904
#> SRR537121 5 0.0162 0.65766 0.000 0.000 0.000 0.004 0.996
#> SRR537122 5 0.0794 0.64862 0.000 0.000 0.000 0.028 0.972
#> SRR537123 5 0.0000 0.65823 0.000 0.000 0.000 0.000 1.000
#> SRR537124 5 0.0162 0.65771 0.000 0.000 0.000 0.004 0.996
#> SRR537125 5 0.0290 0.65734 0.000 0.000 0.000 0.008 0.992
#> SRR537126 5 0.0404 0.65627 0.000 0.000 0.000 0.012 0.988
#> SRR537127 3 0.0510 0.89180 0.016 0.000 0.984 0.000 0.000
#> SRR537128 3 0.0510 0.89180 0.016 0.000 0.984 0.000 0.000
#> SRR537129 3 0.0510 0.89180 0.016 0.000 0.984 0.000 0.000
#> SRR537130 3 0.0510 0.89180 0.016 0.000 0.984 0.000 0.000
#> SRR537131 3 0.0510 0.89180 0.016 0.000 0.984 0.000 0.000
#> SRR537132 3 0.0510 0.89180 0.016 0.000 0.984 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.0551 0.9183 0.984 0.000 0.004 0.008 0.000 0.004
#> SRR191640 4 0.4719 0.7219 0.160 0.036 0.044 0.744 0.004 0.012
#> SRR191641 3 0.4268 -0.0139 0.000 0.000 0.556 0.428 0.004 0.012
#> SRR191642 4 0.4015 0.7818 0.028 0.008 0.176 0.772 0.004 0.012
#> SRR191643 4 0.3826 0.7663 0.012 0.004 0.208 0.760 0.004 0.012
#> SRR191644 3 0.3154 0.6679 0.000 0.000 0.800 0.184 0.004 0.012
#> SRR191645 4 0.0508 0.8253 0.012 0.000 0.000 0.984 0.004 0.000
#> SRR191646 4 0.0508 0.8253 0.012 0.000 0.000 0.984 0.004 0.000
#> SRR191647 4 0.0508 0.8261 0.004 0.000 0.000 0.984 0.012 0.000
#> SRR191648 4 0.0508 0.8261 0.004 0.000 0.000 0.984 0.012 0.000
#> SRR191649 4 0.0692 0.8222 0.020 0.000 0.000 0.976 0.004 0.000
#> SRR191650 4 0.5842 0.3116 0.228 0.000 0.000 0.484 0.288 0.000
#> SRR191651 1 0.2834 0.8345 0.848 0.000 0.000 0.008 0.128 0.016
#> SRR191652 1 0.3109 0.7891 0.812 0.000 0.000 0.004 0.168 0.016
#> SRR191653 4 0.3767 0.6952 0.000 0.000 0.276 0.708 0.004 0.012
#> SRR191654 4 0.3507 0.7618 0.000 0.000 0.216 0.764 0.008 0.012
#> SRR191655 4 0.2412 0.8204 0.000 0.004 0.080 0.892 0.012 0.012
#> SRR191656 1 0.3425 0.8259 0.800 0.000 0.000 0.008 0.164 0.028
#> SRR191657 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191658 1 0.0000 0.9197 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191659 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191660 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191661 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191662 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191663 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191664 1 0.0260 0.9208 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR191665 1 0.1116 0.9172 0.960 0.000 0.000 0.008 0.004 0.028
#> SRR191666 1 0.2003 0.8599 0.884 0.000 0.116 0.000 0.000 0.000
#> SRR191667 1 0.3190 0.7408 0.772 0.000 0.220 0.008 0.000 0.000
#> SRR191668 1 0.1970 0.9027 0.920 0.000 0.000 0.008 0.044 0.028
#> SRR191669 1 0.2164 0.8977 0.908 0.000 0.000 0.008 0.056 0.028
#> SRR191670 1 0.1116 0.9145 0.960 0.000 0.000 0.008 0.004 0.028
#> SRR191671 1 0.1003 0.9154 0.964 0.000 0.000 0.004 0.004 0.028
#> SRR191672 1 0.3460 0.8224 0.796 0.000 0.000 0.008 0.168 0.028
#> SRR191673 1 0.3748 0.7850 0.760 0.000 0.000 0.008 0.204 0.028
#> SRR191674 6 0.2448 0.9016 0.000 0.064 0.000 0.000 0.052 0.884
#> SRR191675 6 0.2511 0.8994 0.000 0.064 0.000 0.000 0.056 0.880
#> SRR191677 6 0.1806 0.9156 0.000 0.088 0.000 0.000 0.004 0.908
#> SRR191678 6 0.1806 0.9156 0.000 0.088 0.000 0.000 0.004 0.908
#> SRR191679 6 0.1858 0.9136 0.000 0.092 0.000 0.000 0.004 0.904
#> SRR191680 6 0.1806 0.9156 0.000 0.088 0.000 0.000 0.004 0.908
#> SRR191681 6 0.1918 0.9162 0.000 0.088 0.000 0.000 0.008 0.904
#> SRR191682 6 0.1858 0.9094 0.000 0.092 0.000 0.000 0.004 0.904
#> SRR191683 6 0.1858 0.9094 0.000 0.092 0.000 0.000 0.004 0.904
#> SRR191684 6 0.1908 0.9063 0.000 0.096 0.000 0.000 0.004 0.900
#> SRR191685 6 0.1858 0.9094 0.000 0.092 0.000 0.000 0.004 0.904
#> SRR191686 6 0.1858 0.9094 0.000 0.092 0.000 0.000 0.004 0.904
#> SRR191687 6 0.1858 0.9094 0.000 0.092 0.000 0.000 0.004 0.904
#> SRR191688 2 0.0520 0.9465 0.000 0.984 0.000 0.008 0.000 0.008
#> SRR191689 2 0.2912 0.7170 0.000 0.784 0.000 0.000 0.000 0.216
#> SRR191690 2 0.0622 0.9479 0.000 0.980 0.000 0.008 0.000 0.012
#> SRR191691 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191692 6 0.1866 0.9163 0.000 0.084 0.000 0.000 0.008 0.908
#> SRR191693 6 0.4233 0.6814 0.000 0.048 0.000 0.000 0.268 0.684
#> SRR191694 6 0.4548 0.6079 0.000 0.056 0.000 0.000 0.312 0.632
#> SRR191695 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191696 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191697 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191698 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191699 2 0.0458 0.9475 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR191700 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191701 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191702 2 0.1753 0.9269 0.000 0.912 0.000 0.000 0.004 0.084
#> SRR191703 2 0.1753 0.9269 0.000 0.912 0.000 0.000 0.004 0.084
#> SRR191704 2 0.2402 0.8831 0.000 0.856 0.000 0.000 0.004 0.140
#> SRR191705 2 0.1858 0.9226 0.000 0.904 0.000 0.000 0.004 0.092
#> SRR191706 2 0.1858 0.9226 0.000 0.904 0.000 0.000 0.004 0.092
#> SRR191707 2 0.0363 0.9485 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR191708 2 0.1471 0.9324 0.000 0.932 0.000 0.000 0.004 0.064
#> SRR191709 2 0.1858 0.9226 0.000 0.904 0.000 0.000 0.004 0.092
#> SRR191710 2 0.1858 0.9226 0.000 0.904 0.000 0.000 0.004 0.092
#> SRR191711 2 0.0858 0.9463 0.000 0.968 0.000 0.004 0.000 0.028
#> SRR191712 2 0.0858 0.9463 0.000 0.968 0.000 0.004 0.000 0.028
#> SRR191713 2 0.1918 0.9086 0.000 0.904 0.000 0.008 0.000 0.088
#> SRR191714 2 0.0458 0.9470 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR191715 2 0.0777 0.9472 0.000 0.972 0.000 0.004 0.000 0.024
#> SRR191716 2 0.0717 0.9431 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR191717 2 0.0260 0.9486 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR191718 2 0.0260 0.9489 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR537099 4 0.3839 0.7828 0.000 0.032 0.176 0.776 0.004 0.012
#> SRR537100 4 0.3839 0.7828 0.000 0.032 0.176 0.776 0.004 0.012
#> SRR537101 4 0.3780 0.7431 0.000 0.008 0.236 0.740 0.004 0.012
#> SRR537102 4 0.3686 0.6815 0.000 0.196 0.016 0.772 0.004 0.012
#> SRR537104 4 0.3943 0.7902 0.000 0.036 0.156 0.784 0.012 0.012
#> SRR537105 4 0.0363 0.8255 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR537106 4 0.0458 0.8246 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR537107 4 0.0458 0.8246 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR537108 4 0.0458 0.8246 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR537109 4 0.2442 0.7079 0.000 0.144 0.000 0.852 0.000 0.004
#> SRR537110 2 0.3273 0.7139 0.000 0.776 0.000 0.212 0.004 0.008
#> SRR537111 5 0.2859 0.8149 0.000 0.000 0.000 0.156 0.828 0.016
#> SRR537113 5 0.2019 0.8938 0.000 0.000 0.000 0.088 0.900 0.012
#> SRR537114 5 0.3323 0.7040 0.000 0.000 0.000 0.240 0.752 0.008
#> SRR537115 5 0.0993 0.9252 0.000 0.000 0.000 0.024 0.964 0.012
#> SRR537116 2 0.0858 0.9427 0.000 0.968 0.000 0.004 0.000 0.028
#> SRR537117 5 0.0806 0.9180 0.000 0.000 0.000 0.008 0.972 0.020
#> SRR537118 5 0.1633 0.9201 0.000 0.000 0.000 0.044 0.932 0.024
#> SRR537119 5 0.3148 0.8702 0.000 0.024 0.000 0.116 0.840 0.020
#> SRR537120 5 0.2959 0.8845 0.000 0.048 0.000 0.056 0.868 0.028
#> SRR537121 5 0.0891 0.9222 0.000 0.000 0.000 0.008 0.968 0.024
#> SRR537122 5 0.1341 0.9252 0.000 0.000 0.000 0.028 0.948 0.024
#> SRR537123 5 0.0458 0.9174 0.000 0.000 0.000 0.000 0.984 0.016
#> SRR537124 5 0.0458 0.9174 0.000 0.000 0.000 0.000 0.984 0.016
#> SRR537125 5 0.1003 0.9250 0.000 0.000 0.000 0.016 0.964 0.020
#> SRR537126 5 0.1003 0.9250 0.000 0.000 0.000 0.016 0.964 0.020
#> SRR537127 3 0.0146 0.8857 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR537128 3 0.0146 0.8857 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR537129 3 0.0146 0.8857 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR537130 3 0.0146 0.8857 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR537131 3 0.0146 0.8857 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR537132 3 0.0146 0.8857 0.000 0.000 0.996 0.000 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.646 0.792 0.894 0.2368 0.897 0.897
#> 3 3 0.364 0.719 0.852 1.0486 0.552 0.500
#> 4 4 0.475 0.650 0.800 0.3266 0.652 0.392
#> 5 5 0.628 0.643 0.793 0.0976 0.884 0.710
#> 6 6 0.689 0.618 0.800 0.0578 0.942 0.828
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
#> SRR191639 2 0.9933 0.412 0.452 0.548
#> SRR191640 2 0.3431 0.865 0.064 0.936
#> SRR191641 2 0.9209 0.597 0.336 0.664
#> SRR191642 2 0.3431 0.865 0.064 0.936
#> SRR191643 2 0.4431 0.852 0.092 0.908
#> SRR191644 2 0.4431 0.852 0.092 0.908
#> SRR191645 2 0.3733 0.863 0.072 0.928
#> SRR191646 2 0.3733 0.863 0.072 0.928
#> SRR191647 2 0.3733 0.863 0.072 0.928
#> SRR191648 2 0.3733 0.863 0.072 0.928
#> SRR191649 2 0.3733 0.863 0.072 0.928
#> SRR191650 2 0.4939 0.841 0.108 0.892
#> SRR191651 2 0.4939 0.841 0.108 0.892
#> SRR191652 2 0.9922 0.420 0.448 0.552
#> SRR191653 2 0.3733 0.862 0.072 0.928
#> SRR191654 2 0.3733 0.862 0.072 0.928
#> SRR191655 2 0.3733 0.862 0.072 0.928
#> SRR191656 2 0.9933 0.412 0.452 0.548
#> SRR191657 2 0.9933 0.412 0.452 0.548
#> SRR191658 2 0.9933 0.412 0.452 0.548
#> SRR191659 2 0.9933 0.412 0.452 0.548
#> SRR191660 2 0.9933 0.412 0.452 0.548
#> SRR191661 2 0.9933 0.412 0.452 0.548
#> SRR191662 2 0.9933 0.412 0.452 0.548
#> SRR191663 2 0.9933 0.412 0.452 0.548
#> SRR191664 2 0.9933 0.412 0.452 0.548
#> SRR191665 2 0.9933 0.412 0.452 0.548
#> SRR191666 2 0.9933 0.412 0.452 0.548
#> SRR191667 2 0.9933 0.412 0.452 0.548
#> SRR191668 2 0.9933 0.412 0.452 0.548
#> SRR191669 2 0.9933 0.412 0.452 0.548
#> SRR191670 2 0.9933 0.412 0.452 0.548
#> SRR191671 2 0.9933 0.412 0.452 0.548
#> SRR191672 2 0.9933 0.412 0.452 0.548
#> SRR191673 2 0.9933 0.412 0.452 0.548
#> SRR191674 2 0.0000 0.878 0.000 1.000
#> SRR191675 2 0.0000 0.878 0.000 1.000
#> SRR191677 2 0.0000 0.878 0.000 1.000
#> SRR191678 2 0.0000 0.878 0.000 1.000
#> SRR191679 2 0.0000 0.878 0.000 1.000
#> SRR191680 2 0.0000 0.878 0.000 1.000
#> SRR191681 2 0.0000 0.878 0.000 1.000
#> SRR191682 2 0.0000 0.878 0.000 1.000
#> SRR191683 2 0.0000 0.878 0.000 1.000
#> SRR191684 2 0.0000 0.878 0.000 1.000
#> SRR191685 2 0.0000 0.878 0.000 1.000
#> SRR191686 2 0.0000 0.878 0.000 1.000
#> SRR191687 2 0.0000 0.878 0.000 1.000
#> SRR191688 2 0.0000 0.878 0.000 1.000
#> SRR191689 2 0.0000 0.878 0.000 1.000
#> SRR191690 2 0.0000 0.878 0.000 1.000
#> SRR191691 2 0.1633 0.876 0.024 0.976
#> SRR191692 2 0.0000 0.878 0.000 1.000
#> SRR191693 2 0.0000 0.878 0.000 1.000
#> SRR191694 2 0.0000 0.878 0.000 1.000
#> SRR191695 2 0.0000 0.878 0.000 1.000
#> SRR191696 2 0.0000 0.878 0.000 1.000
#> SRR191697 2 0.1633 0.876 0.024 0.976
#> SRR191698 2 0.1633 0.876 0.024 0.976
#> SRR191699 2 0.0000 0.878 0.000 1.000
#> SRR191700 2 0.1633 0.876 0.024 0.976
#> SRR191701 2 0.1633 0.876 0.024 0.976
#> SRR191702 2 0.0000 0.878 0.000 1.000
#> SRR191703 2 0.0000 0.878 0.000 1.000
#> SRR191704 2 0.0000 0.878 0.000 1.000
#> SRR191705 2 0.0000 0.878 0.000 1.000
#> SRR191706 2 0.0000 0.878 0.000 1.000
#> SRR191707 2 0.0000 0.878 0.000 1.000
#> SRR191708 2 0.0000 0.878 0.000 1.000
#> SRR191709 2 0.0000 0.878 0.000 1.000
#> SRR191710 2 0.0000 0.878 0.000 1.000
#> SRR191711 2 0.1184 0.877 0.016 0.984
#> SRR191712 2 0.1184 0.877 0.016 0.984
#> SRR191713 2 0.0000 0.878 0.000 1.000
#> SRR191714 2 0.0000 0.878 0.000 1.000
#> SRR191715 2 0.0000 0.878 0.000 1.000
#> SRR191716 2 0.0000 0.878 0.000 1.000
#> SRR191717 2 0.0000 0.878 0.000 1.000
#> SRR191718 2 0.0000 0.878 0.000 1.000
#> SRR537099 2 0.3733 0.862 0.072 0.928
#> SRR537100 2 0.3733 0.862 0.072 0.928
#> SRR537101 2 0.9209 0.597 0.336 0.664
#> SRR537102 2 0.3431 0.865 0.064 0.936
#> SRR537104 2 0.3733 0.862 0.072 0.928
#> SRR537105 2 0.3431 0.865 0.064 0.936
#> SRR537106 2 0.3431 0.865 0.064 0.936
#> SRR537107 2 0.3431 0.865 0.064 0.936
#> SRR537108 2 0.3431 0.865 0.064 0.936
#> SRR537109 2 0.0000 0.878 0.000 1.000
#> SRR537110 2 0.3274 0.866 0.060 0.940
#> SRR537111 2 0.4939 0.841 0.108 0.892
#> SRR537113 2 0.1843 0.875 0.028 0.972
#> SRR537114 2 0.1843 0.875 0.028 0.972
#> SRR537115 2 0.1843 0.875 0.028 0.972
#> SRR537116 2 0.0000 0.878 0.000 1.000
#> SRR537117 2 0.0376 0.879 0.004 0.996
#> SRR537118 2 0.0376 0.879 0.004 0.996
#> SRR537119 2 0.0376 0.879 0.004 0.996
#> SRR537120 2 0.0376 0.879 0.004 0.996
#> SRR537121 2 0.0376 0.879 0.004 0.996
#> SRR537122 2 0.0376 0.879 0.004 0.996
#> SRR537123 2 0.0376 0.879 0.004 0.996
#> SRR537124 2 0.0376 0.879 0.004 0.996
#> SRR537125 2 0.0376 0.879 0.004 0.996
#> SRR537126 2 0.0376 0.879 0.004 0.996
#> SRR537127 1 0.0000 1.000 1.000 0.000
#> SRR537128 1 0.0000 1.000 1.000 0.000
#> SRR537129 1 0.0000 1.000 1.000 0.000
#> SRR537130 1 0.0000 1.000 1.000 0.000
#> SRR537131 1 0.0000 1.000 1.000 0.000
#> SRR537132 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.645 1.000 0.000 0
#> SRR191640 1 0.6079 0.584 0.612 0.388 0
#> SRR191641 1 0.3267 0.681 0.884 0.116 0
#> SRR191642 1 0.6079 0.584 0.612 0.388 0
#> SRR191643 1 0.5948 0.611 0.640 0.360 0
#> SRR191644 1 0.5948 0.611 0.640 0.360 0
#> SRR191645 1 0.6045 0.594 0.620 0.380 0
#> SRR191646 1 0.6045 0.594 0.620 0.380 0
#> SRR191647 1 0.6045 0.594 0.620 0.380 0
#> SRR191648 1 0.6045 0.594 0.620 0.380 0
#> SRR191649 1 0.6045 0.594 0.620 0.380 0
#> SRR191650 1 0.5882 0.618 0.652 0.348 0
#> SRR191651 1 0.5882 0.618 0.652 0.348 0
#> SRR191652 1 0.0237 0.646 0.996 0.004 0
#> SRR191653 1 0.6168 0.551 0.588 0.412 0
#> SRR191654 1 0.6168 0.551 0.588 0.412 0
#> SRR191655 1 0.6168 0.551 0.588 0.412 0
#> SRR191656 1 0.0000 0.645 1.000 0.000 0
#> SRR191657 1 0.0000 0.645 1.000 0.000 0
#> SRR191658 1 0.0000 0.645 1.000 0.000 0
#> SRR191659 1 0.0000 0.645 1.000 0.000 0
#> SRR191660 1 0.0000 0.645 1.000 0.000 0
#> SRR191661 1 0.0000 0.645 1.000 0.000 0
#> SRR191662 1 0.0000 0.645 1.000 0.000 0
#> SRR191663 1 0.0000 0.645 1.000 0.000 0
#> SRR191664 1 0.0000 0.645 1.000 0.000 0
#> SRR191665 1 0.0000 0.645 1.000 0.000 0
#> SRR191666 1 0.0000 0.645 1.000 0.000 0
#> SRR191667 1 0.0000 0.645 1.000 0.000 0
#> SRR191668 1 0.0000 0.645 1.000 0.000 0
#> SRR191669 1 0.0000 0.645 1.000 0.000 0
#> SRR191670 1 0.0000 0.645 1.000 0.000 0
#> SRR191671 1 0.0000 0.645 1.000 0.000 0
#> SRR191672 1 0.0000 0.645 1.000 0.000 0
#> SRR191673 1 0.0000 0.645 1.000 0.000 0
#> SRR191674 2 0.0000 0.850 0.000 1.000 0
#> SRR191675 2 0.0000 0.850 0.000 1.000 0
#> SRR191677 2 0.0000 0.850 0.000 1.000 0
#> SRR191678 2 0.0000 0.850 0.000 1.000 0
#> SRR191679 2 0.0000 0.850 0.000 1.000 0
#> SRR191680 2 0.0000 0.850 0.000 1.000 0
#> SRR191681 2 0.0000 0.850 0.000 1.000 0
#> SRR191682 2 0.0000 0.850 0.000 1.000 0
#> SRR191683 2 0.0000 0.850 0.000 1.000 0
#> SRR191684 2 0.0000 0.850 0.000 1.000 0
#> SRR191685 2 0.0000 0.850 0.000 1.000 0
#> SRR191686 2 0.0000 0.850 0.000 1.000 0
#> SRR191687 2 0.0000 0.850 0.000 1.000 0
#> SRR191688 2 0.4235 0.778 0.176 0.824 0
#> SRR191689 2 0.3340 0.818 0.120 0.880 0
#> SRR191690 2 0.3340 0.818 0.120 0.880 0
#> SRR191691 2 0.3192 0.803 0.112 0.888 0
#> SRR191692 2 0.0000 0.850 0.000 1.000 0
#> SRR191693 2 0.0000 0.850 0.000 1.000 0
#> SRR191694 2 0.0000 0.850 0.000 1.000 0
#> SRR191695 2 0.4235 0.778 0.176 0.824 0
#> SRR191696 2 0.4235 0.778 0.176 0.824 0
#> SRR191697 2 0.3192 0.803 0.112 0.888 0
#> SRR191698 2 0.3192 0.803 0.112 0.888 0
#> SRR191699 2 0.3340 0.818 0.120 0.880 0
#> SRR191700 2 0.3192 0.803 0.112 0.888 0
#> SRR191701 2 0.3192 0.803 0.112 0.888 0
#> SRR191702 2 0.0000 0.850 0.000 1.000 0
#> SRR191703 2 0.0000 0.850 0.000 1.000 0
#> SRR191704 2 0.0000 0.850 0.000 1.000 0
#> SRR191705 2 0.0000 0.850 0.000 1.000 0
#> SRR191706 2 0.0000 0.850 0.000 1.000 0
#> SRR191707 2 0.0237 0.850 0.004 0.996 0
#> SRR191708 2 0.0000 0.850 0.000 1.000 0
#> SRR191709 2 0.0000 0.850 0.000 1.000 0
#> SRR191710 2 0.0000 0.850 0.000 1.000 0
#> SRR191711 2 0.4702 0.741 0.212 0.788 0
#> SRR191712 2 0.4702 0.741 0.212 0.788 0
#> SRR191713 2 0.0000 0.850 0.000 1.000 0
#> SRR191714 2 0.0000 0.850 0.000 1.000 0
#> SRR191715 2 0.4399 0.767 0.188 0.812 0
#> SRR191716 2 0.4235 0.778 0.176 0.824 0
#> SRR191717 2 0.4235 0.778 0.176 0.824 0
#> SRR191718 2 0.4235 0.778 0.176 0.824 0
#> SRR537099 1 0.6154 0.555 0.592 0.408 0
#> SRR537100 1 0.6154 0.555 0.592 0.408 0
#> SRR537101 1 0.3267 0.681 0.884 0.116 0
#> SRR537102 1 0.6079 0.584 0.612 0.388 0
#> SRR537104 1 0.6168 0.551 0.588 0.412 0
#> SRR537105 1 0.6079 0.584 0.612 0.388 0
#> SRR537106 1 0.6079 0.584 0.612 0.388 0
#> SRR537107 1 0.6079 0.584 0.612 0.388 0
#> SRR537108 1 0.6079 0.584 0.612 0.388 0
#> SRR537109 2 0.5058 0.692 0.244 0.756 0
#> SRR537110 2 0.5621 0.523 0.308 0.692 0
#> SRR537111 1 0.5882 0.618 0.652 0.348 0
#> SRR537113 1 0.6302 0.356 0.520 0.480 0
#> SRR537114 1 0.6302 0.356 0.520 0.480 0
#> SRR537115 1 0.6302 0.356 0.520 0.480 0
#> SRR537116 2 0.4504 0.759 0.196 0.804 0
#> SRR537117 2 0.5138 0.673 0.252 0.748 0
#> SRR537118 2 0.5138 0.673 0.252 0.748 0
#> SRR537119 2 0.5138 0.673 0.252 0.748 0
#> SRR537120 2 0.5138 0.673 0.252 0.748 0
#> SRR537121 2 0.5138 0.673 0.252 0.748 0
#> SRR537122 2 0.5138 0.673 0.252 0.748 0
#> SRR537123 2 0.5138 0.673 0.252 0.748 0
#> SRR537124 2 0.5138 0.673 0.252 0.748 0
#> SRR537125 2 0.5138 0.673 0.252 0.748 0
#> SRR537126 2 0.5138 0.673 0.252 0.748 0
#> SRR537127 3 0.0000 1.000 0.000 0.000 1
#> SRR537128 3 0.0000 1.000 0.000 0.000 1
#> SRR537129 3 0.0000 1.000 0.000 0.000 1
#> SRR537130 3 0.0000 1.000 0.000 0.000 1
#> SRR537131 3 0.0000 1.000 0.000 0.000 1
#> SRR537132 3 0.0000 1.000 0.000 0.000 1
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0921 0.8395 0.972 0.000 0 0.028
#> SRR191640 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR191641 1 0.4925 0.2529 0.572 0.000 0 0.428
#> SRR191642 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR191643 4 0.4477 0.5059 0.312 0.000 0 0.688
#> SRR191644 4 0.4477 0.5059 0.312 0.000 0 0.688
#> SRR191645 4 0.4193 0.5538 0.268 0.000 0 0.732
#> SRR191646 4 0.4193 0.5538 0.268 0.000 0 0.732
#> SRR191647 4 0.4193 0.5538 0.268 0.000 0 0.732
#> SRR191648 4 0.4193 0.5538 0.268 0.000 0 0.732
#> SRR191649 4 0.4193 0.5538 0.268 0.000 0 0.732
#> SRR191650 1 0.5147 0.0262 0.536 0.004 0 0.460
#> SRR191651 1 0.5147 0.0262 0.536 0.004 0 0.460
#> SRR191652 1 0.0469 0.8552 0.988 0.000 0 0.012
#> SRR191653 4 0.4868 0.5752 0.256 0.024 0 0.720
#> SRR191654 4 0.4868 0.5752 0.256 0.024 0 0.720
#> SRR191655 4 0.4868 0.5752 0.256 0.024 0 0.720
#> SRR191656 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191657 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191658 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191659 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191660 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191661 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191662 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191663 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191664 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191665 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191666 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191667 1 0.0188 0.8605 0.996 0.000 0 0.004
#> SRR191668 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191669 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191670 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191671 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191672 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191673 1 0.0000 0.8592 1.000 0.000 0 0.000
#> SRR191674 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191675 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191677 4 0.4855 0.1394 0.000 0.400 0 0.600
#> SRR191678 4 0.4855 0.1394 0.000 0.400 0 0.600
#> SRR191679 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191680 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191681 4 0.4855 0.1394 0.000 0.400 0 0.600
#> SRR191682 2 0.2704 0.8119 0.000 0.876 0 0.124
#> SRR191683 2 0.2704 0.8119 0.000 0.876 0 0.124
#> SRR191684 2 0.2408 0.8066 0.000 0.896 0 0.104
#> SRR191685 2 0.2469 0.8084 0.000 0.892 0 0.108
#> SRR191686 2 0.2704 0.8119 0.000 0.876 0 0.124
#> SRR191687 2 0.2469 0.8084 0.000 0.892 0 0.108
#> SRR191688 4 0.3942 0.5135 0.000 0.236 0 0.764
#> SRR191689 4 0.4193 0.4804 0.000 0.268 0 0.732
#> SRR191690 4 0.4193 0.4804 0.000 0.268 0 0.732
#> SRR191691 4 0.5632 0.3714 0.036 0.340 0 0.624
#> SRR191692 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191693 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191694 2 0.4103 0.7734 0.000 0.744 0 0.256
#> SRR191695 4 0.3942 0.5135 0.000 0.236 0 0.764
#> SRR191696 4 0.3942 0.5135 0.000 0.236 0 0.764
#> SRR191697 4 0.5558 0.3977 0.036 0.324 0 0.640
#> SRR191698 4 0.5632 0.3714 0.036 0.340 0 0.624
#> SRR191699 4 0.4193 0.4804 0.000 0.268 0 0.732
#> SRR191700 4 0.5558 0.3977 0.036 0.324 0 0.640
#> SRR191701 4 0.5558 0.3977 0.036 0.324 0 0.640
#> SRR191702 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191703 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191704 2 0.3400 0.7604 0.000 0.820 0 0.180
#> SRR191705 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191706 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191707 2 0.3444 0.7608 0.000 0.816 0 0.184
#> SRR191708 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191709 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191710 2 0.3400 0.7664 0.000 0.820 0 0.180
#> SRR191711 4 0.4323 0.5735 0.020 0.204 0 0.776
#> SRR191712 4 0.4323 0.5735 0.020 0.204 0 0.776
#> SRR191713 2 0.1118 0.7724 0.000 0.964 0 0.036
#> SRR191714 2 0.1118 0.7724 0.000 0.964 0 0.036
#> SRR191715 4 0.3837 0.5362 0.000 0.224 0 0.776
#> SRR191716 4 0.3942 0.5135 0.000 0.236 0 0.764
#> SRR191717 4 0.3942 0.5135 0.000 0.236 0 0.764
#> SRR191718 4 0.3942 0.5135 0.000 0.236 0 0.764
#> SRR537099 4 0.4767 0.5759 0.256 0.020 0 0.724
#> SRR537100 4 0.4767 0.5759 0.256 0.020 0 0.724
#> SRR537101 1 0.4925 0.2529 0.572 0.000 0 0.428
#> SRR537102 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR537104 4 0.4868 0.5752 0.256 0.024 0 0.720
#> SRR537105 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR537106 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR537107 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR537108 4 0.4072 0.5690 0.252 0.000 0 0.748
#> SRR537109 4 0.3024 0.5923 0.000 0.148 0 0.852
#> SRR537110 4 0.4436 0.6039 0.052 0.148 0 0.800
#> SRR537111 1 0.5147 0.0262 0.536 0.004 0 0.460
#> SRR537113 4 0.5690 0.6201 0.216 0.084 0 0.700
#> SRR537114 4 0.5690 0.6201 0.216 0.084 0 0.700
#> SRR537115 4 0.5690 0.6201 0.216 0.084 0 0.700
#> SRR537116 4 0.3837 0.5446 0.000 0.224 0 0.776
#> SRR537117 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537118 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537119 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537120 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537121 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537122 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537123 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537124 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537125 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537126 4 0.3390 0.6259 0.016 0.132 0 0.852
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.0963 0.9523 0.964 0.000 0 0.036 0.000
#> SRR191640 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR191641 4 0.4235 0.2029 0.424 0.000 0 0.576 0.000
#> SRR191642 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR191643 4 0.2561 0.6361 0.144 0.000 0 0.856 0.000
#> SRR191644 4 0.2561 0.6361 0.144 0.000 0 0.856 0.000
#> SRR191645 4 0.1792 0.6574 0.084 0.000 0 0.916 0.000
#> SRR191646 4 0.1792 0.6574 0.084 0.000 0 0.916 0.000
#> SRR191647 4 0.1792 0.6574 0.084 0.000 0 0.916 0.000
#> SRR191648 4 0.1792 0.6574 0.084 0.000 0 0.916 0.000
#> SRR191649 4 0.1792 0.6574 0.084 0.000 0 0.916 0.000
#> SRR191650 4 0.4446 0.2159 0.476 0.000 0 0.520 0.004
#> SRR191651 4 0.4446 0.2159 0.476 0.000 0 0.520 0.004
#> SRR191652 1 0.0510 0.9808 0.984 0.000 0 0.016 0.000
#> SRR191653 4 0.2450 0.6599 0.076 0.000 0 0.896 0.028
#> SRR191654 4 0.2450 0.6599 0.076 0.000 0 0.896 0.028
#> SRR191655 4 0.2450 0.6599 0.076 0.000 0 0.896 0.028
#> SRR191656 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191657 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191658 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191659 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191660 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191661 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191662 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191663 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191664 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191665 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191666 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191667 1 0.0162 0.9944 0.996 0.000 0 0.004 0.000
#> SRR191668 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191669 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191670 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191671 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191672 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191673 1 0.0000 0.9936 1.000 0.000 0 0.000 0.000
#> SRR191674 5 0.0324 0.6014 0.000 0.004 0 0.004 0.992
#> SRR191675 5 0.0324 0.6014 0.000 0.004 0 0.004 0.992
#> SRR191677 5 0.4030 -0.0946 0.000 0.000 0 0.352 0.648
#> SRR191678 5 0.4030 -0.0946 0.000 0.000 0 0.352 0.648
#> SRR191679 5 0.0324 0.6014 0.000 0.004 0 0.004 0.992
#> SRR191680 5 0.0324 0.6014 0.000 0.004 0 0.004 0.992
#> SRR191681 5 0.4030 -0.0946 0.000 0.000 0 0.352 0.648
#> SRR191682 5 0.5151 0.3065 0.000 0.396 0 0.044 0.560
#> SRR191683 5 0.5151 0.3065 0.000 0.396 0 0.044 0.560
#> SRR191684 5 0.5131 0.2580 0.000 0.420 0 0.040 0.540
#> SRR191685 5 0.5188 0.2688 0.000 0.416 0 0.044 0.540
#> SRR191686 5 0.5151 0.3065 0.000 0.396 0 0.044 0.560
#> SRR191687 5 0.5188 0.2688 0.000 0.416 0 0.044 0.540
#> SRR191688 4 0.4242 0.4634 0.000 0.000 0 0.572 0.428
#> SRR191689 4 0.5423 0.4471 0.000 0.064 0 0.548 0.388
#> SRR191690 4 0.5423 0.4471 0.000 0.064 0 0.548 0.388
#> SRR191691 4 0.6219 0.3461 0.000 0.212 0 0.548 0.240
#> SRR191692 5 0.1648 0.6075 0.000 0.020 0 0.040 0.940
#> SRR191693 5 0.1648 0.6075 0.000 0.020 0 0.040 0.940
#> SRR191694 5 0.1648 0.6075 0.000 0.020 0 0.040 0.940
#> SRR191695 4 0.4242 0.4634 0.000 0.000 0 0.572 0.428
#> SRR191696 4 0.4242 0.4634 0.000 0.000 0 0.572 0.428
#> SRR191697 4 0.6132 0.3762 0.000 0.212 0 0.564 0.224
#> SRR191698 4 0.6219 0.3461 0.000 0.212 0 0.548 0.240
#> SRR191699 4 0.5423 0.4471 0.000 0.064 0 0.548 0.388
#> SRR191700 4 0.6132 0.3762 0.000 0.212 0 0.564 0.224
#> SRR191701 4 0.6132 0.3762 0.000 0.212 0 0.564 0.224
#> SRR191702 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191703 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191704 2 0.1544 0.8779 0.000 0.932 0 0.000 0.068
#> SRR191705 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191706 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191707 2 0.1952 0.8821 0.000 0.912 0 0.004 0.084
#> SRR191708 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191709 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191710 2 0.1732 0.8919 0.000 0.920 0 0.000 0.080
#> SRR191711 4 0.4857 0.5373 0.000 0.040 0 0.636 0.324
#> SRR191712 4 0.4857 0.5373 0.000 0.040 0 0.636 0.324
#> SRR191713 2 0.4182 0.3113 0.000 0.600 0 0.000 0.400
#> SRR191714 2 0.4182 0.3113 0.000 0.600 0 0.000 0.400
#> SRR191715 4 0.5002 0.4970 0.000 0.040 0 0.596 0.364
#> SRR191716 4 0.4242 0.4634 0.000 0.000 0 0.572 0.428
#> SRR191717 4 0.4242 0.4634 0.000 0.000 0 0.572 0.428
#> SRR191718 4 0.4242 0.4634 0.000 0.000 0 0.572 0.428
#> SRR537099 4 0.2362 0.6610 0.076 0.000 0 0.900 0.024
#> SRR537100 4 0.2362 0.6610 0.076 0.000 0 0.900 0.024
#> SRR537101 4 0.4227 0.2097 0.420 0.000 0 0.580 0.000
#> SRR537102 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR537104 4 0.2450 0.6599 0.076 0.000 0 0.896 0.028
#> SRR537105 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR537106 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR537107 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR537108 4 0.1544 0.6608 0.068 0.000 0 0.932 0.000
#> SRR537109 4 0.4118 0.5543 0.000 0.004 0 0.660 0.336
#> SRR537110 4 0.4624 0.5777 0.000 0.112 0 0.744 0.144
#> SRR537111 4 0.4446 0.2159 0.476 0.000 0 0.520 0.004
#> SRR537113 4 0.3767 0.6522 0.068 0.000 0 0.812 0.120
#> SRR537114 4 0.3767 0.6522 0.068 0.000 0 0.812 0.120
#> SRR537115 4 0.3767 0.6522 0.068 0.000 0 0.812 0.120
#> SRR537116 4 0.5578 0.5055 0.000 0.112 0 0.616 0.272
#> SRR537117 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537118 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537119 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537120 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537121 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537122 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537123 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537124 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537125 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537126 4 0.3816 0.5911 0.000 0.000 0 0.696 0.304
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.1297 0.947 0.948 0.000 0 0.040 0.012 0.000
#> SRR191640 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR191641 4 0.3672 0.234 0.368 0.000 0 0.632 0.000 0.000
#> SRR191642 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR191643 4 0.1663 0.569 0.088 0.000 0 0.912 0.000 0.000
#> SRR191644 4 0.1663 0.569 0.088 0.000 0 0.912 0.000 0.000
#> SRR191645 4 0.0547 0.593 0.020 0.000 0 0.980 0.000 0.000
#> SRR191646 4 0.0547 0.593 0.020 0.000 0 0.980 0.000 0.000
#> SRR191647 4 0.0547 0.593 0.020 0.000 0 0.980 0.000 0.000
#> SRR191648 4 0.0547 0.593 0.020 0.000 0 0.980 0.000 0.000
#> SRR191649 4 0.0547 0.593 0.020 0.000 0 0.980 0.000 0.000
#> SRR191650 4 0.3993 0.148 0.476 0.000 0 0.520 0.004 0.000
#> SRR191651 4 0.3993 0.148 0.476 0.000 0 0.520 0.004 0.000
#> SRR191652 1 0.0458 0.974 0.984 0.000 0 0.016 0.000 0.000
#> SRR191653 4 0.1334 0.588 0.020 0.000 0 0.948 0.032 0.000
#> SRR191654 4 0.1334 0.588 0.020 0.000 0 0.948 0.032 0.000
#> SRR191655 4 0.1334 0.588 0.020 0.000 0 0.948 0.032 0.000
#> SRR191656 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191657 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191662 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191664 1 0.0146 0.990 0.996 0.000 0 0.000 0.004 0.000
#> SRR191665 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191666 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191667 1 0.0000 0.990 1.000 0.000 0 0.000 0.000 0.000
#> SRR191668 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191669 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191670 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191671 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191672 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191673 1 0.0363 0.989 0.988 0.000 0 0.000 0.012 0.000
#> SRR191674 6 0.0000 0.721 0.000 0.000 0 0.000 0.000 1.000
#> SRR191675 6 0.0000 0.721 0.000 0.000 0 0.000 0.000 1.000
#> SRR191677 6 0.4386 0.353 0.000 0.000 0 0.300 0.048 0.652
#> SRR191678 6 0.4386 0.353 0.000 0.000 0 0.300 0.048 0.652
#> SRR191679 6 0.0146 0.716 0.000 0.004 0 0.000 0.000 0.996
#> SRR191680 6 0.0000 0.721 0.000 0.000 0 0.000 0.000 1.000
#> SRR191681 6 0.4386 0.353 0.000 0.000 0 0.300 0.048 0.652
#> SRR191682 5 0.3826 0.388 0.000 0.124 0 0.004 0.784 0.088
#> SRR191683 5 0.3826 0.388 0.000 0.124 0 0.004 0.784 0.088
#> SRR191684 5 0.3689 0.373 0.000 0.136 0 0.004 0.792 0.068
#> SRR191685 5 0.3649 0.382 0.000 0.132 0 0.004 0.796 0.068
#> SRR191686 5 0.3826 0.388 0.000 0.124 0 0.004 0.784 0.088
#> SRR191687 5 0.3649 0.382 0.000 0.132 0 0.004 0.796 0.068
#> SRR191688 4 0.5814 0.313 0.000 0.000 0 0.448 0.188 0.364
#> SRR191689 4 0.6034 0.340 0.000 0.000 0 0.420 0.308 0.272
#> SRR191690 4 0.6034 0.340 0.000 0.000 0 0.420 0.308 0.272
#> SRR191691 5 0.4864 0.156 0.000 0.020 0 0.396 0.556 0.028
#> SRR191692 6 0.2048 0.708 0.000 0.000 0 0.000 0.120 0.880
#> SRR191693 6 0.2048 0.708 0.000 0.000 0 0.000 0.120 0.880
#> SRR191694 6 0.2048 0.708 0.000 0.000 0 0.000 0.120 0.880
#> SRR191695 4 0.5814 0.313 0.000 0.000 0 0.448 0.188 0.364
#> SRR191696 4 0.5814 0.313 0.000 0.000 0 0.448 0.188 0.364
#> SRR191697 5 0.4891 0.119 0.000 0.020 0 0.412 0.540 0.028
#> SRR191698 5 0.4864 0.156 0.000 0.020 0 0.396 0.556 0.028
#> SRR191699 4 0.6034 0.340 0.000 0.000 0 0.420 0.308 0.272
#> SRR191700 5 0.4891 0.119 0.000 0.020 0 0.412 0.540 0.028
#> SRR191701 5 0.4891 0.119 0.000 0.020 0 0.412 0.540 0.028
#> SRR191702 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191703 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191704 2 0.0146 0.880 0.000 0.996 0 0.000 0.000 0.004
#> SRR191705 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191706 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191707 2 0.1092 0.880 0.000 0.960 0 0.000 0.020 0.020
#> SRR191708 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191709 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191710 2 0.0547 0.895 0.000 0.980 0 0.000 0.000 0.020
#> SRR191711 4 0.5885 0.427 0.000 0.004 0 0.508 0.240 0.248
#> SRR191712 4 0.5885 0.427 0.000 0.004 0 0.508 0.240 0.248
#> SRR191713 2 0.4868 0.438 0.000 0.524 0 0.000 0.416 0.060
#> SRR191714 2 0.4868 0.438 0.000 0.524 0 0.000 0.416 0.060
#> SRR191715 4 0.6019 0.381 0.000 0.004 0 0.468 0.240 0.288
#> SRR191716 4 0.5814 0.313 0.000 0.000 0 0.448 0.188 0.364
#> SRR191717 4 0.5814 0.313 0.000 0.000 0 0.448 0.188 0.364
#> SRR191718 4 0.5814 0.313 0.000 0.000 0 0.448 0.188 0.364
#> SRR537099 4 0.1257 0.590 0.020 0.000 0 0.952 0.028 0.000
#> SRR537100 4 0.1257 0.590 0.020 0.000 0 0.952 0.028 0.000
#> SRR537101 4 0.3647 0.238 0.360 0.000 0 0.640 0.000 0.000
#> SRR537102 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR537104 4 0.1334 0.588 0.020 0.000 0 0.948 0.032 0.000
#> SRR537105 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR537106 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR537107 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR537108 4 0.0146 0.597 0.004 0.000 0 0.996 0.000 0.000
#> SRR537109 4 0.5624 0.444 0.000 0.000 0 0.536 0.200 0.264
#> SRR537110 4 0.4270 0.392 0.000 0.004 0 0.652 0.316 0.028
#> SRR537111 4 0.3993 0.148 0.476 0.000 0 0.520 0.004 0.000
#> SRR537113 4 0.2804 0.592 0.004 0.000 0 0.852 0.024 0.120
#> SRR537114 4 0.2804 0.592 0.004 0.000 0 0.852 0.024 0.120
#> SRR537115 4 0.2804 0.592 0.004 0.000 0 0.852 0.024 0.120
#> SRR537116 4 0.5849 0.386 0.000 0.004 0 0.484 0.332 0.180
#> SRR537117 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537118 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537119 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537120 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537121 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537122 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537123 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537124 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537125 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537126 4 0.5438 0.504 0.000 0.000 0 0.568 0.260 0.172
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.455 0.876 0.915 0.4509 0.500 0.500
#> 3 3 0.539 0.650 0.789 0.3517 0.907 0.821
#> 4 4 0.527 0.612 0.688 0.1473 0.801 0.581
#> 5 5 0.562 0.551 0.662 0.0782 0.845 0.544
#> 6 6 0.678 0.656 0.743 0.0556 0.878 0.545
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
#> SRR191639 1 0.5059 0.903 0.888 0.112
#> SRR191640 1 0.5059 0.903 0.888 0.112
#> SRR191641 1 0.5059 0.903 0.888 0.112
#> SRR191642 1 0.9129 0.678 0.672 0.328
#> SRR191643 1 0.9977 0.385 0.528 0.472
#> SRR191644 1 0.9087 0.684 0.676 0.324
#> SRR191645 1 0.5059 0.903 0.888 0.112
#> SRR191646 1 0.5059 0.903 0.888 0.112
#> SRR191647 1 0.5059 0.903 0.888 0.112
#> SRR191648 1 0.5059 0.903 0.888 0.112
#> SRR191649 1 0.5059 0.903 0.888 0.112
#> SRR191650 1 0.5059 0.903 0.888 0.112
#> SRR191651 1 0.4939 0.902 0.892 0.108
#> SRR191652 1 0.5059 0.903 0.888 0.112
#> SRR191653 1 0.5519 0.894 0.872 0.128
#> SRR191654 1 0.9248 0.666 0.660 0.340
#> SRR191655 1 0.5408 0.896 0.876 0.124
#> SRR191656 1 0.4939 0.902 0.892 0.108
#> SRR191657 1 0.4939 0.901 0.892 0.108
#> SRR191658 1 0.5059 0.903 0.888 0.112
#> SRR191659 1 0.4815 0.900 0.896 0.104
#> SRR191660 1 0.5059 0.903 0.888 0.112
#> SRR191661 1 0.5059 0.903 0.888 0.112
#> SRR191662 1 0.5059 0.903 0.888 0.112
#> SRR191663 1 0.5059 0.903 0.888 0.112
#> SRR191664 1 0.4939 0.901 0.892 0.108
#> SRR191665 1 0.5059 0.903 0.888 0.112
#> SRR191666 1 0.3114 0.862 0.944 0.056
#> SRR191667 1 0.3114 0.862 0.944 0.056
#> SRR191668 1 0.4939 0.902 0.892 0.108
#> SRR191669 1 0.4939 0.902 0.892 0.108
#> SRR191670 1 0.4939 0.902 0.892 0.108
#> SRR191671 1 0.4939 0.902 0.892 0.108
#> SRR191672 1 0.4939 0.902 0.892 0.108
#> SRR191673 1 0.4939 0.902 0.892 0.108
#> SRR191674 2 0.0672 0.970 0.008 0.992
#> SRR191675 2 0.0672 0.970 0.008 0.992
#> SRR191677 2 0.0672 0.970 0.008 0.992
#> SRR191678 2 0.0672 0.970 0.008 0.992
#> SRR191679 2 0.0672 0.970 0.008 0.992
#> SRR191680 2 0.0672 0.970 0.008 0.992
#> SRR191681 2 0.0672 0.970 0.008 0.992
#> SRR191682 2 0.0672 0.966 0.008 0.992
#> SRR191683 2 0.0672 0.966 0.008 0.992
#> SRR191684 2 0.0672 0.966 0.008 0.992
#> SRR191685 2 0.0672 0.966 0.008 0.992
#> SRR191686 2 0.0376 0.968 0.004 0.996
#> SRR191687 2 0.0672 0.966 0.008 0.992
#> SRR191688 2 0.0672 0.970 0.008 0.992
#> SRR191689 2 0.0672 0.969 0.008 0.992
#> SRR191690 2 0.0672 0.970 0.008 0.992
#> SRR191691 2 0.0672 0.966 0.008 0.992
#> SRR191692 2 0.0376 0.970 0.004 0.996
#> SRR191693 2 0.0376 0.968 0.004 0.996
#> SRR191694 2 0.0938 0.970 0.012 0.988
#> SRR191695 2 0.0672 0.970 0.008 0.992
#> SRR191696 2 0.0672 0.970 0.008 0.992
#> SRR191697 2 0.0672 0.970 0.008 0.992
#> SRR191698 2 0.0672 0.966 0.008 0.992
#> SRR191699 2 0.0672 0.966 0.008 0.992
#> SRR191700 2 0.0672 0.966 0.008 0.992
#> SRR191701 2 0.0376 0.968 0.004 0.996
#> SRR191702 2 0.0938 0.970 0.012 0.988
#> SRR191703 2 0.0938 0.970 0.012 0.988
#> SRR191704 2 0.0938 0.970 0.012 0.988
#> SRR191705 2 0.0938 0.970 0.012 0.988
#> SRR191706 2 0.0938 0.970 0.012 0.988
#> SRR191707 2 0.0672 0.970 0.008 0.992
#> SRR191708 2 0.0938 0.970 0.012 0.988
#> SRR191709 2 0.0938 0.970 0.012 0.988
#> SRR191710 2 0.0938 0.970 0.012 0.988
#> SRR191711 2 0.0672 0.970 0.008 0.992
#> SRR191712 2 0.0672 0.970 0.008 0.992
#> SRR191713 2 0.0938 0.970 0.012 0.988
#> SRR191714 2 0.0938 0.970 0.012 0.988
#> SRR191715 2 0.0672 0.970 0.008 0.992
#> SRR191716 2 0.0672 0.970 0.008 0.992
#> SRR191717 2 0.0672 0.970 0.008 0.992
#> SRR191718 2 0.0672 0.970 0.008 0.992
#> SRR537099 1 0.9977 0.385 0.528 0.472
#> SRR537100 1 0.5946 0.882 0.856 0.144
#> SRR537101 1 0.5059 0.903 0.888 0.112
#> SRR537102 1 0.9977 0.385 0.528 0.472
#> SRR537104 2 0.9993 -0.267 0.484 0.516
#> SRR537105 1 0.5408 0.896 0.876 0.124
#> SRR537106 1 0.9977 0.385 0.528 0.472
#> SRR537107 1 0.9977 0.385 0.528 0.472
#> SRR537108 1 0.9977 0.385 0.528 0.472
#> SRR537109 2 0.0672 0.970 0.008 0.992
#> SRR537110 2 0.0672 0.970 0.008 0.992
#> SRR537111 1 0.9087 0.684 0.676 0.324
#> SRR537113 2 0.9000 0.389 0.316 0.684
#> SRR537114 2 0.9000 0.389 0.316 0.684
#> SRR537115 2 0.0672 0.970 0.008 0.992
#> SRR537116 2 0.0672 0.970 0.008 0.992
#> SRR537117 2 0.0000 0.969 0.000 1.000
#> SRR537118 2 0.0376 0.967 0.004 0.996
#> SRR537119 2 0.0376 0.967 0.004 0.996
#> SRR537120 2 0.0376 0.967 0.004 0.996
#> SRR537121 2 0.0376 0.967 0.004 0.996
#> SRR537122 2 0.0376 0.967 0.004 0.996
#> SRR537123 2 0.0376 0.967 0.004 0.996
#> SRR537124 2 0.0376 0.967 0.004 0.996
#> SRR537125 2 0.0376 0.967 0.004 0.996
#> SRR537126 2 0.0376 0.967 0.004 0.996
#> SRR537127 1 0.0672 0.827 0.992 0.008
#> SRR537128 1 0.0672 0.827 0.992 0.008
#> SRR537129 1 0.0672 0.827 0.992 0.008
#> SRR537130 1 0.0672 0.827 0.992 0.008
#> SRR537131 1 0.0672 0.827 0.992 0.008
#> SRR537132 1 0.0672 0.827 0.992 0.008
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.5882 0.395 0.652 0.000 0.348
#> SRR191640 1 0.0892 0.534 0.980 0.000 0.020
#> SRR191641 1 0.2066 0.533 0.940 0.000 0.060
#> SRR191642 1 0.1753 0.525 0.952 0.048 0.000
#> SRR191643 1 0.2682 0.502 0.920 0.076 0.004
#> SRR191644 1 0.1643 0.525 0.956 0.044 0.000
#> SRR191645 1 0.2066 0.538 0.940 0.000 0.060
#> SRR191646 1 0.2066 0.538 0.940 0.000 0.060
#> SRR191647 1 0.1753 0.536 0.952 0.000 0.048
#> SRR191648 1 0.1753 0.536 0.952 0.000 0.048
#> SRR191649 1 0.1753 0.536 0.952 0.000 0.048
#> SRR191650 1 0.3192 0.535 0.888 0.000 0.112
#> SRR191651 1 0.6111 0.351 0.604 0.000 0.396
#> SRR191652 1 0.5968 0.336 0.636 0.000 0.364
#> SRR191653 1 0.2743 0.496 0.928 0.020 0.052
#> SRR191654 1 0.3375 0.474 0.908 0.048 0.044
#> SRR191655 1 0.0829 0.530 0.984 0.012 0.004
#> SRR191656 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191657 1 0.6140 0.320 0.596 0.000 0.404
#> SRR191658 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191659 1 0.6140 0.320 0.596 0.000 0.404
#> SRR191660 1 0.6111 0.333 0.604 0.000 0.396
#> SRR191661 1 0.5706 0.403 0.680 0.000 0.320
#> SRR191662 1 0.6026 0.355 0.624 0.000 0.376
#> SRR191663 1 0.5882 0.383 0.652 0.000 0.348
#> SRR191664 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191665 1 0.6168 0.332 0.588 0.000 0.412
#> SRR191666 1 0.5988 0.251 0.632 0.000 0.368
#> SRR191667 1 0.5988 0.251 0.632 0.000 0.368
#> SRR191668 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191669 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191670 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191671 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191672 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191673 1 0.6192 0.320 0.580 0.000 0.420
#> SRR191674 2 0.2878 0.855 0.000 0.904 0.096
#> SRR191675 2 0.2878 0.855 0.000 0.904 0.096
#> SRR191677 2 0.2878 0.855 0.000 0.904 0.096
#> SRR191678 2 0.4094 0.851 0.028 0.872 0.100
#> SRR191679 2 0.2711 0.857 0.000 0.912 0.088
#> SRR191680 2 0.2878 0.855 0.000 0.904 0.096
#> SRR191681 2 0.2878 0.855 0.000 0.904 0.096
#> SRR191682 2 0.3340 0.853 0.000 0.880 0.120
#> SRR191683 2 0.3340 0.853 0.000 0.880 0.120
#> SRR191684 2 0.3500 0.854 0.004 0.880 0.116
#> SRR191685 2 0.3573 0.853 0.004 0.876 0.120
#> SRR191686 2 0.3340 0.853 0.000 0.880 0.120
#> SRR191687 2 0.3573 0.853 0.004 0.876 0.120
#> SRR191688 2 0.3484 0.858 0.048 0.904 0.048
#> SRR191689 2 0.1411 0.864 0.000 0.964 0.036
#> SRR191690 2 0.3589 0.857 0.052 0.900 0.048
#> SRR191691 2 0.3030 0.855 0.004 0.904 0.092
#> SRR191692 2 0.3192 0.853 0.000 0.888 0.112
#> SRR191693 2 0.3619 0.845 0.000 0.864 0.136
#> SRR191694 2 0.2711 0.857 0.000 0.912 0.088
#> SRR191695 2 0.3369 0.860 0.040 0.908 0.052
#> SRR191696 2 0.3369 0.860 0.040 0.908 0.052
#> SRR191697 2 0.2414 0.865 0.020 0.940 0.040
#> SRR191698 2 0.4172 0.854 0.028 0.868 0.104
#> SRR191699 2 0.2682 0.860 0.004 0.920 0.076
#> SRR191700 2 0.6902 0.759 0.148 0.736 0.116
#> SRR191701 2 0.2796 0.858 0.000 0.908 0.092
#> SRR191702 2 0.3461 0.849 0.024 0.900 0.076
#> SRR191703 2 0.3461 0.849 0.024 0.900 0.076
#> SRR191704 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191705 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191706 2 0.3045 0.853 0.020 0.916 0.064
#> SRR191707 2 0.3993 0.856 0.052 0.884 0.064
#> SRR191708 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191709 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191710 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191711 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191712 2 0.3181 0.853 0.024 0.912 0.064
#> SRR191713 2 0.3461 0.850 0.024 0.900 0.076
#> SRR191714 2 0.3461 0.850 0.024 0.900 0.076
#> SRR191715 2 0.3550 0.850 0.024 0.896 0.080
#> SRR191716 2 0.3589 0.858 0.048 0.900 0.052
#> SRR191717 2 0.3484 0.858 0.048 0.904 0.048
#> SRR191718 2 0.2806 0.863 0.032 0.928 0.040
#> SRR537099 1 0.3415 0.482 0.900 0.080 0.020
#> SRR537100 1 0.2564 0.514 0.936 0.036 0.028
#> SRR537101 1 0.2066 0.533 0.940 0.000 0.060
#> SRR537102 1 0.3310 0.493 0.908 0.064 0.028
#> SRR537104 1 0.5874 0.269 0.760 0.208 0.032
#> SRR537105 1 0.1337 0.526 0.972 0.016 0.012
#> SRR537106 1 0.3207 0.490 0.904 0.084 0.012
#> SRR537107 1 0.3207 0.490 0.904 0.084 0.012
#> SRR537108 1 0.3207 0.490 0.904 0.084 0.012
#> SRR537109 2 0.3039 0.859 0.036 0.920 0.044
#> SRR537110 2 0.7924 0.626 0.304 0.612 0.084
#> SRR537111 1 0.5507 0.496 0.808 0.056 0.136
#> SRR537113 1 0.7279 0.106 0.588 0.376 0.036
#> SRR537114 1 0.7170 0.122 0.612 0.352 0.036
#> SRR537115 2 0.8173 0.622 0.300 0.600 0.100
#> SRR537116 2 0.3083 0.855 0.024 0.916 0.060
#> SRR537117 2 0.6313 0.803 0.084 0.768 0.148
#> SRR537118 2 0.8934 0.614 0.236 0.568 0.196
#> SRR537119 2 0.8934 0.614 0.236 0.568 0.196
#> SRR537120 2 0.8566 0.661 0.204 0.608 0.188
#> SRR537121 2 0.9055 0.592 0.252 0.552 0.196
#> SRR537122 2 0.9162 0.568 0.268 0.536 0.196
#> SRR537123 2 0.9055 0.592 0.252 0.552 0.196
#> SRR537124 2 0.8118 0.701 0.188 0.648 0.164
#> SRR537125 2 0.8965 0.609 0.240 0.564 0.196
#> SRR537126 2 0.8965 0.609 0.240 0.564 0.196
#> SRR537127 3 0.5810 1.000 0.336 0.000 0.664
#> SRR537128 3 0.5810 1.000 0.336 0.000 0.664
#> SRR537129 3 0.5810 1.000 0.336 0.000 0.664
#> SRR537130 3 0.5810 1.000 0.336 0.000 0.664
#> SRR537131 3 0.5810 1.000 0.336 0.000 0.664
#> SRR537132 3 0.5810 1.000 0.336 0.000 0.664
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.5576 0.5149 0.536 0.000 0.020 0.444
#> SRR191640 4 0.1389 0.7813 0.048 0.000 0.000 0.952
#> SRR191641 4 0.2081 0.7534 0.084 0.000 0.000 0.916
#> SRR191642 4 0.0712 0.8039 0.004 0.008 0.004 0.984
#> SRR191643 4 0.0844 0.8039 0.004 0.012 0.004 0.980
#> SRR191644 4 0.0992 0.8017 0.012 0.008 0.004 0.976
#> SRR191645 4 0.2973 0.6789 0.144 0.000 0.000 0.856
#> SRR191646 4 0.2973 0.6789 0.144 0.000 0.000 0.856
#> SRR191647 4 0.2149 0.7517 0.088 0.000 0.000 0.912
#> SRR191648 4 0.2149 0.7517 0.088 0.000 0.000 0.912
#> SRR191649 4 0.2216 0.7486 0.092 0.000 0.000 0.908
#> SRR191650 4 0.4072 0.4661 0.252 0.000 0.000 0.748
#> SRR191651 1 0.5403 0.7052 0.628 0.000 0.024 0.348
#> SRR191652 1 0.4522 0.7451 0.680 0.000 0.000 0.320
#> SRR191653 4 0.2057 0.7862 0.020 0.008 0.032 0.940
#> SRR191654 4 0.2074 0.7832 0.016 0.012 0.032 0.940
#> SRR191655 4 0.0672 0.8029 0.008 0.008 0.000 0.984
#> SRR191656 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191657 1 0.4382 0.7582 0.704 0.000 0.000 0.296
#> SRR191658 1 0.4795 0.7599 0.696 0.000 0.012 0.292
#> SRR191659 1 0.4382 0.7582 0.704 0.000 0.000 0.296
#> SRR191660 1 0.4406 0.7568 0.700 0.000 0.000 0.300
#> SRR191661 4 0.4999 -0.3880 0.492 0.000 0.000 0.508
#> SRR191662 1 0.4730 0.6930 0.636 0.000 0.000 0.364
#> SRR191663 1 0.4877 0.6164 0.592 0.000 0.000 0.408
#> SRR191664 1 0.4382 0.7582 0.704 0.000 0.000 0.296
#> SRR191665 1 0.5137 0.7593 0.680 0.000 0.024 0.296
#> SRR191666 1 0.5143 0.6967 0.628 0.000 0.012 0.360
#> SRR191667 1 0.5143 0.6967 0.628 0.000 0.012 0.360
#> SRR191668 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191669 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191670 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191671 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191672 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191673 1 0.5207 0.7610 0.680 0.000 0.028 0.292
#> SRR191674 2 0.5112 0.5136 0.012 0.668 0.316 0.004
#> SRR191675 2 0.5112 0.5136 0.012 0.668 0.316 0.004
#> SRR191677 2 0.5112 0.5136 0.012 0.668 0.316 0.004
#> SRR191678 2 0.6393 0.3475 0.012 0.572 0.368 0.048
#> SRR191679 2 0.5068 0.5242 0.012 0.676 0.308 0.004
#> SRR191680 2 0.5090 0.5187 0.012 0.672 0.312 0.004
#> SRR191681 2 0.5175 0.4992 0.012 0.656 0.328 0.004
#> SRR191682 2 0.5453 0.4757 0.020 0.592 0.388 0.000
#> SRR191683 2 0.5453 0.4757 0.020 0.592 0.388 0.000
#> SRR191684 2 0.5734 0.4764 0.020 0.592 0.380 0.008
#> SRR191685 2 0.5747 0.4712 0.020 0.588 0.384 0.008
#> SRR191686 2 0.5453 0.4757 0.020 0.592 0.388 0.000
#> SRR191687 2 0.5747 0.4712 0.020 0.588 0.384 0.008
#> SRR191688 2 0.4733 0.5993 0.008 0.800 0.128 0.064
#> SRR191689 2 0.4652 0.6148 0.020 0.756 0.220 0.004
#> SRR191690 2 0.4801 0.5916 0.008 0.800 0.108 0.084
#> SRR191691 2 0.5330 0.5224 0.008 0.648 0.332 0.012
#> SRR191692 2 0.5404 0.4243 0.012 0.600 0.384 0.004
#> SRR191693 2 0.5607 0.3303 0.020 0.492 0.488 0.000
#> SRR191694 2 0.5110 0.5442 0.016 0.684 0.296 0.004
#> SRR191695 2 0.5227 0.5631 0.008 0.756 0.176 0.060
#> SRR191696 2 0.5227 0.5631 0.008 0.756 0.176 0.060
#> SRR191697 2 0.5907 0.5122 0.012 0.672 0.268 0.048
#> SRR191698 2 0.7063 0.2091 0.012 0.504 0.396 0.088
#> SRR191699 2 0.5370 0.5348 0.012 0.660 0.316 0.012
#> SRR191700 2 0.7813 -0.2301 0.012 0.428 0.392 0.168
#> SRR191701 2 0.5285 0.5185 0.012 0.632 0.352 0.004
#> SRR191702 2 0.2074 0.6403 0.016 0.940 0.032 0.012
#> SRR191703 2 0.2074 0.6403 0.016 0.940 0.032 0.012
#> SRR191704 2 0.3285 0.6371 0.020 0.884 0.080 0.016
#> SRR191705 2 0.3285 0.6371 0.020 0.884 0.080 0.016
#> SRR191706 2 0.2275 0.6456 0.020 0.928 0.048 0.004
#> SRR191707 2 0.4073 0.6257 0.012 0.848 0.076 0.064
#> SRR191708 2 0.3285 0.6371 0.020 0.884 0.080 0.016
#> SRR191709 2 0.3285 0.6371 0.020 0.884 0.080 0.016
#> SRR191710 2 0.3285 0.6371 0.020 0.884 0.080 0.016
#> SRR191711 2 0.2074 0.6530 0.016 0.940 0.032 0.012
#> SRR191712 2 0.2074 0.6530 0.016 0.940 0.032 0.012
#> SRR191713 2 0.3782 0.6251 0.024 0.852 0.112 0.012
#> SRR191714 2 0.3782 0.6251 0.024 0.852 0.112 0.012
#> SRR191715 2 0.1262 0.6499 0.016 0.968 0.008 0.008
#> SRR191716 2 0.5193 0.5628 0.008 0.768 0.148 0.076
#> SRR191717 2 0.4733 0.5993 0.008 0.800 0.128 0.064
#> SRR191718 2 0.5077 0.5742 0.008 0.764 0.176 0.052
#> SRR537099 4 0.1543 0.7923 0.004 0.008 0.032 0.956
#> SRR537100 4 0.1082 0.8010 0.004 0.004 0.020 0.972
#> SRR537101 4 0.2081 0.7534 0.084 0.000 0.000 0.916
#> SRR537102 4 0.0927 0.8010 0.000 0.016 0.008 0.976
#> SRR537104 4 0.3164 0.7216 0.000 0.052 0.064 0.884
#> SRR537105 4 0.0779 0.8038 0.000 0.016 0.004 0.980
#> SRR537106 4 0.0895 0.8030 0.000 0.020 0.004 0.976
#> SRR537107 4 0.0895 0.8030 0.000 0.020 0.004 0.976
#> SRR537108 4 0.0895 0.8030 0.000 0.020 0.004 0.976
#> SRR537109 2 0.3777 0.6328 0.012 0.864 0.068 0.056
#> SRR537110 2 0.6941 -0.0866 0.012 0.492 0.076 0.420
#> SRR537111 4 0.6176 0.2763 0.284 0.036 0.028 0.652
#> SRR537113 4 0.5708 0.4475 0.004 0.212 0.076 0.708
#> SRR537114 4 0.5191 0.5503 0.004 0.120 0.108 0.768
#> SRR537115 4 0.7926 -0.4273 0.004 0.316 0.256 0.424
#> SRR537116 2 0.2170 0.6497 0.012 0.936 0.016 0.036
#> SRR537117 3 0.6763 0.6200 0.000 0.320 0.564 0.116
#> SRR537118 3 0.7386 0.9226 0.020 0.180 0.592 0.208
#> SRR537119 3 0.7386 0.9226 0.020 0.180 0.592 0.208
#> SRR537120 3 0.7309 0.8729 0.016 0.216 0.592 0.176
#> SRR537121 3 0.7359 0.9084 0.020 0.164 0.592 0.224
#> SRR537122 3 0.7350 0.9005 0.020 0.160 0.592 0.228
#> SRR537123 3 0.7359 0.9084 0.020 0.164 0.592 0.224
#> SRR537124 3 0.7091 0.8239 0.008 0.244 0.592 0.156
#> SRR537125 3 0.7386 0.9226 0.020 0.180 0.592 0.208
#> SRR537126 3 0.7386 0.9226 0.020 0.180 0.592 0.208
#> SRR537127 1 0.7079 0.3884 0.556 0.000 0.276 0.168
#> SRR537128 1 0.7037 0.3884 0.564 0.000 0.268 0.168
#> SRR537129 1 0.7079 0.3884 0.556 0.000 0.276 0.168
#> SRR537130 1 0.7079 0.3884 0.556 0.000 0.276 0.168
#> SRR537131 1 0.7037 0.3884 0.564 0.000 0.268 0.168
#> SRR537132 1 0.7037 0.3884 0.564 0.000 0.268 0.168
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.4592 0.78133 0.644 0.024 0.000 0.332 0.000
#> SRR191640 4 0.1357 0.83818 0.048 0.004 0.000 0.948 0.000
#> SRR191641 4 0.2351 0.80599 0.088 0.000 0.016 0.896 0.000
#> SRR191642 4 0.1074 0.84733 0.012 0.016 0.000 0.968 0.004
#> SRR191643 4 0.1074 0.84799 0.012 0.016 0.000 0.968 0.004
#> SRR191644 4 0.1588 0.84265 0.028 0.016 0.008 0.948 0.000
#> SRR191645 4 0.2573 0.78738 0.104 0.000 0.016 0.880 0.000
#> SRR191646 4 0.2573 0.78738 0.104 0.000 0.016 0.880 0.000
#> SRR191647 4 0.2293 0.80883 0.084 0.000 0.016 0.900 0.000
#> SRR191648 4 0.2293 0.80883 0.084 0.000 0.016 0.900 0.000
#> SRR191649 4 0.2293 0.80883 0.084 0.000 0.016 0.900 0.000
#> SRR191650 4 0.3814 0.46580 0.276 0.004 0.000 0.720 0.000
#> SRR191651 1 0.4880 0.85304 0.692 0.040 0.012 0.256 0.000
#> SRR191652 1 0.3934 0.88437 0.740 0.000 0.016 0.244 0.000
#> SRR191653 4 0.2047 0.83388 0.020 0.012 0.040 0.928 0.000
#> SRR191654 4 0.1885 0.83540 0.020 0.012 0.032 0.936 0.000
#> SRR191655 4 0.0566 0.84678 0.012 0.004 0.000 0.984 0.000
#> SRR191656 1 0.4054 0.89155 0.760 0.036 0.000 0.204 0.000
#> SRR191657 1 0.4389 0.87877 0.752 0.020 0.024 0.204 0.000
#> SRR191658 1 0.3933 0.88310 0.776 0.020 0.008 0.196 0.000
#> SRR191659 1 0.4389 0.87877 0.752 0.020 0.024 0.204 0.000
#> SRR191660 1 0.4483 0.87950 0.740 0.020 0.024 0.216 0.000
#> SRR191661 1 0.5301 0.67625 0.576 0.020 0.024 0.380 0.000
#> SRR191662 1 0.5078 0.83653 0.676 0.024 0.032 0.268 0.000
#> SRR191663 1 0.4879 0.83974 0.680 0.020 0.024 0.276 0.000
#> SRR191664 1 0.4389 0.87877 0.752 0.020 0.024 0.204 0.000
#> SRR191665 1 0.4150 0.89086 0.748 0.036 0.000 0.216 0.000
#> SRR191666 1 0.4351 0.84944 0.724 0.004 0.028 0.244 0.000
#> SRR191667 1 0.4351 0.84944 0.724 0.004 0.028 0.244 0.000
#> SRR191668 1 0.4054 0.89155 0.760 0.036 0.000 0.204 0.000
#> SRR191669 1 0.4054 0.89155 0.760 0.036 0.000 0.204 0.000
#> SRR191670 1 0.4087 0.89235 0.756 0.036 0.000 0.208 0.000
#> SRR191671 1 0.4087 0.89235 0.756 0.036 0.000 0.208 0.000
#> SRR191672 1 0.4054 0.89155 0.760 0.036 0.000 0.204 0.000
#> SRR191673 1 0.4054 0.89155 0.760 0.036 0.000 0.204 0.000
#> SRR191674 5 0.1410 0.34758 0.000 0.060 0.000 0.000 0.940
#> SRR191675 5 0.1410 0.34758 0.000 0.060 0.000 0.000 0.940
#> SRR191677 5 0.1341 0.34815 0.000 0.056 0.000 0.000 0.944
#> SRR191678 5 0.1518 0.37325 0.000 0.012 0.020 0.016 0.952
#> SRR191679 5 0.1478 0.34419 0.000 0.064 0.000 0.000 0.936
#> SRR191680 5 0.1410 0.34758 0.000 0.060 0.000 0.000 0.940
#> SRR191681 5 0.1197 0.35293 0.000 0.048 0.000 0.000 0.952
#> SRR191682 2 0.7062 0.35137 0.012 0.400 0.184 0.008 0.396
#> SRR191683 2 0.7062 0.35137 0.012 0.400 0.184 0.008 0.396
#> SRR191684 2 0.7348 0.39168 0.012 0.420 0.196 0.020 0.352
#> SRR191685 2 0.7359 0.37724 0.012 0.408 0.196 0.020 0.364
#> SRR191686 5 0.7062 -0.39020 0.012 0.396 0.184 0.008 0.400
#> SRR191687 2 0.7359 0.37724 0.012 0.408 0.196 0.020 0.364
#> SRR191688 5 0.6333 0.03610 0.008 0.332 0.048 0.048 0.564
#> SRR191689 5 0.4409 0.09180 0.008 0.180 0.052 0.000 0.760
#> SRR191690 5 0.6903 0.02102 0.012 0.312 0.052 0.084 0.540
#> SRR191691 2 0.7154 0.44879 0.012 0.500 0.196 0.020 0.272
#> SRR191692 5 0.0963 0.36090 0.000 0.036 0.000 0.000 0.964
#> SRR191693 5 0.5258 0.11621 0.012 0.200 0.080 0.004 0.704
#> SRR191694 5 0.2130 0.31632 0.000 0.080 0.012 0.000 0.908
#> SRR191695 5 0.6170 0.13809 0.008 0.256 0.064 0.044 0.628
#> SRR191696 5 0.6170 0.13809 0.008 0.256 0.064 0.044 0.628
#> SRR191697 5 0.6446 0.15034 0.012 0.192 0.132 0.032 0.632
#> SRR191698 2 0.7742 0.28918 0.012 0.452 0.248 0.048 0.240
#> SRR191699 2 0.7201 0.45820 0.016 0.500 0.184 0.020 0.280
#> SRR191700 2 0.8338 0.14205 0.012 0.388 0.272 0.100 0.228
#> SRR191701 2 0.7286 0.40147 0.012 0.448 0.216 0.016 0.308
#> SRR191702 2 0.5521 0.47940 0.032 0.600 0.016 0.008 0.344
#> SRR191703 2 0.5521 0.47940 0.032 0.600 0.016 0.008 0.344
#> SRR191704 2 0.4994 0.55550 0.032 0.680 0.008 0.008 0.272
#> SRR191705 2 0.4994 0.55550 0.032 0.680 0.008 0.008 0.272
#> SRR191706 2 0.5030 0.51292 0.032 0.624 0.008 0.000 0.336
#> SRR191707 2 0.5537 0.52367 0.016 0.652 0.028 0.024 0.280
#> SRR191708 2 0.4971 0.55536 0.032 0.684 0.008 0.008 0.268
#> SRR191709 2 0.4948 0.55540 0.032 0.688 0.008 0.008 0.264
#> SRR191710 2 0.4948 0.55540 0.032 0.688 0.008 0.008 0.264
#> SRR191711 2 0.5271 0.48289 0.008 0.616 0.020 0.016 0.340
#> SRR191712 2 0.5271 0.47960 0.008 0.616 0.020 0.016 0.340
#> SRR191713 2 0.5211 0.55946 0.008 0.696 0.040 0.020 0.236
#> SRR191714 2 0.5211 0.55946 0.008 0.696 0.040 0.020 0.236
#> SRR191715 5 0.5693 -0.28652 0.012 0.464 0.024 0.016 0.484
#> SRR191716 5 0.6400 0.10160 0.008 0.288 0.060 0.052 0.592
#> SRR191717 5 0.6333 0.03610 0.008 0.332 0.048 0.048 0.564
#> SRR191718 5 0.6146 0.12473 0.008 0.264 0.064 0.040 0.624
#> SRR537099 4 0.1256 0.84324 0.012 0.004 0.012 0.964 0.008
#> SRR537100 4 0.0854 0.84552 0.012 0.000 0.008 0.976 0.004
#> SRR537101 4 0.2351 0.80599 0.088 0.000 0.016 0.896 0.000
#> SRR537102 4 0.1200 0.84040 0.000 0.016 0.012 0.964 0.008
#> SRR537104 4 0.3126 0.78498 0.016 0.044 0.044 0.884 0.012
#> SRR537105 4 0.1490 0.84174 0.004 0.032 0.008 0.952 0.004
#> SRR537106 4 0.1652 0.84026 0.004 0.040 0.008 0.944 0.004
#> SRR537107 4 0.1573 0.84000 0.004 0.036 0.008 0.948 0.004
#> SRR537108 4 0.1573 0.84000 0.004 0.036 0.008 0.948 0.004
#> SRR537109 5 0.6491 -0.15322 0.008 0.408 0.040 0.056 0.488
#> SRR537110 2 0.7018 0.24861 0.016 0.496 0.048 0.360 0.080
#> SRR537111 4 0.6086 0.04065 0.340 0.072 0.020 0.564 0.004
#> SRR537113 4 0.5663 0.60163 0.004 0.076 0.060 0.712 0.148
#> SRR537114 4 0.5174 0.62957 0.000 0.052 0.076 0.744 0.128
#> SRR537115 4 0.6715 -0.00988 0.000 0.056 0.076 0.456 0.412
#> SRR537116 5 0.6127 -0.21803 0.012 0.444 0.032 0.032 0.480
#> SRR537117 5 0.7160 0.34972 0.000 0.104 0.296 0.088 0.512
#> SRR537118 5 0.7832 0.33959 0.000 0.104 0.356 0.156 0.384
#> SRR537119 5 0.7832 0.33959 0.000 0.104 0.356 0.156 0.384
#> SRR537120 5 0.7571 0.34587 0.000 0.104 0.340 0.120 0.436
#> SRR537121 5 0.7853 0.33779 0.000 0.104 0.356 0.160 0.380
#> SRR537122 5 0.7895 0.33197 0.000 0.104 0.356 0.168 0.372
#> SRR537123 5 0.7853 0.33779 0.000 0.104 0.356 0.160 0.380
#> SRR537124 5 0.7447 0.34879 0.000 0.104 0.328 0.108 0.460
#> SRR537125 5 0.7832 0.33959 0.000 0.104 0.356 0.156 0.384
#> SRR537126 5 0.7832 0.33959 0.000 0.104 0.356 0.156 0.384
#> SRR537127 3 0.5715 0.99498 0.388 0.000 0.524 0.088 0.000
#> SRR537128 3 0.6071 0.99498 0.388 0.012 0.512 0.088 0.000
#> SRR537129 3 0.5715 0.99498 0.388 0.000 0.524 0.088 0.000
#> SRR537130 3 0.5715 0.99498 0.388 0.000 0.524 0.088 0.000
#> SRR537131 3 0.6071 0.99498 0.388 0.012 0.512 0.088 0.000
#> SRR537132 3 0.6071 0.99498 0.388 0.012 0.512 0.088 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.4896 0.7965 0.704 0.000 0.016 0.200 0.016 0.064
#> SRR191640 4 0.0692 0.8828 0.020 0.000 0.004 0.976 0.000 0.000
#> SRR191641 4 0.0972 0.8802 0.028 0.000 0.008 0.964 0.000 0.000
#> SRR191642 4 0.0943 0.8841 0.012 0.004 0.004 0.972 0.004 0.004
#> SRR191643 4 0.0943 0.8843 0.012 0.004 0.004 0.972 0.004 0.004
#> SRR191644 4 0.1273 0.8833 0.012 0.004 0.008 0.960 0.004 0.012
#> SRR191645 4 0.2257 0.8535 0.060 0.000 0.016 0.904 0.000 0.020
#> SRR191646 4 0.2257 0.8535 0.060 0.000 0.016 0.904 0.000 0.020
#> SRR191647 4 0.1448 0.8797 0.024 0.000 0.012 0.948 0.000 0.016
#> SRR191648 4 0.1448 0.8797 0.024 0.000 0.012 0.948 0.000 0.016
#> SRR191649 4 0.1448 0.8797 0.024 0.000 0.012 0.948 0.000 0.016
#> SRR191650 4 0.4432 0.4455 0.304 0.000 0.012 0.660 0.008 0.016
#> SRR191651 1 0.4287 0.8648 0.784 0.000 0.020 0.096 0.016 0.084
#> SRR191652 1 0.3013 0.8524 0.828 0.000 0.012 0.152 0.004 0.004
#> SRR191653 4 0.1647 0.8667 0.000 0.004 0.008 0.940 0.016 0.032
#> SRR191654 4 0.1647 0.8667 0.000 0.004 0.008 0.940 0.016 0.032
#> SRR191655 4 0.1069 0.8810 0.004 0.004 0.008 0.968 0.008 0.008
#> SRR191656 1 0.3651 0.8741 0.828 0.000 0.012 0.068 0.016 0.076
#> SRR191657 1 0.2556 0.8659 0.884 0.000 0.012 0.076 0.000 0.028
#> SRR191658 1 0.2456 0.8668 0.888 0.000 0.008 0.076 0.000 0.028
#> SRR191659 1 0.2556 0.8659 0.884 0.000 0.012 0.076 0.000 0.028
#> SRR191660 1 0.2894 0.8641 0.864 0.000 0.020 0.088 0.000 0.028
#> SRR191661 1 0.4232 0.7297 0.716 0.000 0.020 0.236 0.000 0.028
#> SRR191662 1 0.3612 0.8322 0.800 0.000 0.016 0.148 0.000 0.036
#> SRR191663 1 0.3627 0.8296 0.796 0.000 0.020 0.156 0.000 0.028
#> SRR191664 1 0.2556 0.8659 0.884 0.000 0.012 0.076 0.000 0.028
#> SRR191665 1 0.3812 0.8745 0.816 0.000 0.016 0.080 0.012 0.076
#> SRR191666 1 0.3523 0.8215 0.812 0.000 0.008 0.144 0.016 0.020
#> SRR191667 1 0.3523 0.8215 0.812 0.000 0.008 0.144 0.016 0.020
#> SRR191668 1 0.3651 0.8741 0.828 0.000 0.012 0.068 0.016 0.076
#> SRR191669 1 0.3651 0.8741 0.828 0.000 0.012 0.068 0.016 0.076
#> SRR191670 1 0.3651 0.8741 0.828 0.000 0.012 0.068 0.016 0.076
#> SRR191671 1 0.3651 0.8741 0.828 0.000 0.012 0.068 0.016 0.076
#> SRR191672 1 0.3704 0.8732 0.824 0.000 0.012 0.068 0.016 0.080
#> SRR191673 1 0.3704 0.8732 0.824 0.000 0.012 0.068 0.016 0.080
#> SRR191674 6 0.5611 0.5782 0.000 0.228 0.000 0.000 0.228 0.544
#> SRR191675 6 0.5611 0.5782 0.000 0.228 0.000 0.000 0.228 0.544
#> SRR191677 6 0.5611 0.5768 0.000 0.224 0.000 0.000 0.232 0.544
#> SRR191678 6 0.5897 0.5021 0.000 0.184 0.004 0.004 0.292 0.516
#> SRR191679 6 0.5611 0.5767 0.000 0.232 0.000 0.000 0.224 0.544
#> SRR191680 6 0.5611 0.5782 0.000 0.228 0.000 0.000 0.228 0.544
#> SRR191681 6 0.5565 0.5673 0.000 0.208 0.000 0.000 0.240 0.552
#> SRR191682 6 0.7672 0.4123 0.016 0.284 0.100 0.004 0.216 0.380
#> SRR191683 6 0.7672 0.4123 0.016 0.284 0.100 0.004 0.216 0.380
#> SRR191684 6 0.7929 0.3894 0.016 0.272 0.104 0.016 0.216 0.376
#> SRR191685 6 0.7929 0.3894 0.016 0.272 0.104 0.016 0.216 0.376
#> SRR191686 6 0.7685 0.4145 0.016 0.284 0.100 0.004 0.220 0.376
#> SRR191687 6 0.7929 0.3894 0.016 0.272 0.104 0.016 0.216 0.376
#> SRR191688 2 0.6472 0.4482 0.004 0.524 0.072 0.008 0.084 0.308
#> SRR191689 6 0.5713 0.4019 0.000 0.332 0.024 0.000 0.104 0.540
#> SRR191690 2 0.7122 0.4508 0.004 0.504 0.072 0.052 0.084 0.284
#> SRR191691 2 0.8094 -0.2016 0.024 0.348 0.108 0.016 0.208 0.296
#> SRR191692 6 0.5608 0.5655 0.000 0.200 0.000 0.000 0.260 0.540
#> SRR191693 6 0.6607 0.4779 0.004 0.156 0.060 0.000 0.280 0.500
#> SRR191694 6 0.5528 0.5624 0.000 0.252 0.000 0.000 0.192 0.556
#> SRR191695 2 0.6823 0.4001 0.004 0.476 0.072 0.008 0.120 0.320
#> SRR191696 2 0.6823 0.4001 0.004 0.476 0.072 0.008 0.120 0.320
#> SRR191697 6 0.7663 -0.1328 0.020 0.332 0.092 0.004 0.196 0.356
#> SRR191698 5 0.8253 -0.1474 0.024 0.256 0.116 0.020 0.336 0.248
#> SRR191699 2 0.7939 -0.2007 0.020 0.364 0.104 0.016 0.184 0.312
#> SRR191700 5 0.8208 0.0869 0.020 0.220 0.100 0.052 0.424 0.184
#> SRR191701 2 0.8237 -0.1511 0.024 0.316 0.120 0.016 0.256 0.268
#> SRR191702 2 0.2118 0.5567 0.020 0.916 0.012 0.000 0.004 0.048
#> SRR191703 2 0.2118 0.5567 0.020 0.916 0.012 0.000 0.004 0.048
#> SRR191704 2 0.1533 0.5500 0.016 0.948 0.008 0.000 0.012 0.016
#> SRR191705 2 0.1533 0.5500 0.016 0.948 0.008 0.000 0.012 0.016
#> SRR191706 2 0.2007 0.5495 0.016 0.924 0.012 0.000 0.008 0.040
#> SRR191707 2 0.4049 0.5602 0.008 0.816 0.040 0.020 0.028 0.088
#> SRR191708 2 0.1533 0.5504 0.016 0.948 0.008 0.000 0.012 0.016
#> SRR191709 2 0.1705 0.5473 0.016 0.940 0.008 0.000 0.012 0.024
#> SRR191710 2 0.1533 0.5504 0.016 0.948 0.008 0.000 0.012 0.016
#> SRR191711 2 0.4360 0.5670 0.004 0.760 0.064 0.004 0.016 0.152
#> SRR191712 2 0.4443 0.5690 0.004 0.756 0.064 0.004 0.020 0.152
#> SRR191713 2 0.3959 0.5265 0.020 0.816 0.060 0.004 0.016 0.084
#> SRR191714 2 0.3959 0.5265 0.020 0.816 0.060 0.004 0.016 0.084
#> SRR191715 2 0.5124 0.5225 0.004 0.648 0.072 0.004 0.012 0.260
#> SRR191716 2 0.6741 0.4310 0.004 0.500 0.072 0.008 0.116 0.300
#> SRR191717 2 0.6472 0.4482 0.004 0.524 0.072 0.008 0.084 0.308
#> SRR191718 2 0.6773 0.4266 0.004 0.496 0.072 0.008 0.120 0.300
#> SRR537099 4 0.1495 0.8762 0.004 0.000 0.008 0.948 0.020 0.020
#> SRR537100 4 0.1312 0.8786 0.004 0.000 0.008 0.956 0.020 0.012
#> SRR537101 4 0.0972 0.8802 0.028 0.000 0.008 0.964 0.000 0.000
#> SRR537102 4 0.0912 0.8830 0.004 0.004 0.000 0.972 0.012 0.008
#> SRR537104 4 0.2220 0.8537 0.008 0.004 0.012 0.916 0.016 0.044
#> SRR537105 4 0.2159 0.8764 0.012 0.004 0.012 0.920 0.012 0.040
#> SRR537106 4 0.2466 0.8733 0.012 0.016 0.012 0.908 0.012 0.040
#> SRR537107 4 0.2466 0.8730 0.012 0.012 0.012 0.908 0.016 0.040
#> SRR537108 4 0.2466 0.8730 0.012 0.012 0.012 0.908 0.016 0.040
#> SRR537109 2 0.5993 0.4839 0.004 0.572 0.076 0.008 0.044 0.296
#> SRR537110 2 0.6853 0.1986 0.004 0.432 0.056 0.388 0.024 0.096
#> SRR537111 4 0.6279 -0.1199 0.400 0.004 0.028 0.464 0.016 0.088
#> SRR537113 4 0.5143 0.6915 0.004 0.072 0.020 0.740 0.072 0.092
#> SRR537114 4 0.4591 0.7312 0.004 0.024 0.020 0.772 0.096 0.084
#> SRR537115 4 0.7327 0.2926 0.008 0.128 0.020 0.516 0.180 0.148
#> SRR537116 2 0.5449 0.5124 0.004 0.612 0.076 0.004 0.020 0.284
#> SRR537117 5 0.2791 0.7531 0.000 0.024 0.004 0.028 0.880 0.064
#> SRR537118 5 0.2238 0.8304 0.000 0.016 0.004 0.076 0.900 0.004
#> SRR537119 5 0.2238 0.8304 0.000 0.016 0.004 0.076 0.900 0.004
#> SRR537120 5 0.2501 0.8072 0.000 0.016 0.004 0.056 0.896 0.028
#> SRR537121 5 0.1951 0.8293 0.000 0.016 0.000 0.076 0.908 0.000
#> SRR537122 5 0.1951 0.8293 0.000 0.016 0.000 0.076 0.908 0.000
#> SRR537123 5 0.1951 0.8293 0.000 0.016 0.000 0.076 0.908 0.000
#> SRR537124 5 0.2380 0.7828 0.000 0.016 0.000 0.036 0.900 0.048
#> SRR537125 5 0.2095 0.8305 0.000 0.016 0.000 0.076 0.904 0.004
#> SRR537126 5 0.2095 0.8305 0.000 0.016 0.000 0.076 0.904 0.004
#> SRR537127 3 0.4874 0.9926 0.148 0.000 0.732 0.072 0.036 0.012
#> SRR537128 3 0.4426 0.9926 0.144 0.000 0.752 0.072 0.032 0.000
#> SRR537129 3 0.4874 0.9926 0.148 0.000 0.732 0.072 0.036 0.012
#> SRR537130 3 0.4874 0.9926 0.148 0.000 0.732 0.072 0.036 0.012
#> SRR537131 3 0.4426 0.9926 0.144 0.000 0.752 0.072 0.032 0.000
#> SRR537132 3 0.4426 0.9926 0.144 0.000 0.752 0.072 0.032 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)
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 16450 rows and 111 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 0.965 0.987 0.5030 0.499 0.499
#> 3 3 0.759 0.732 0.891 0.2783 0.769 0.569
#> 4 4 0.820 0.830 0.909 0.1409 0.910 0.750
#> 5 5 0.767 0.652 0.763 0.0654 0.928 0.756
#> 6 6 0.774 0.662 0.763 0.0528 0.946 0.775
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
#> SRR191639 1 0.000 0.996 1.000 0.000
#> SRR191640 1 0.000 0.996 1.000 0.000
#> SRR191641 1 0.000 0.996 1.000 0.000
#> SRR191642 1 0.000 0.996 1.000 0.000
#> SRR191643 1 0.000 0.996 1.000 0.000
#> SRR191644 1 0.000 0.996 1.000 0.000
#> SRR191645 1 0.000 0.996 1.000 0.000
#> SRR191646 1 0.000 0.996 1.000 0.000
#> SRR191647 1 0.000 0.996 1.000 0.000
#> SRR191648 1 0.000 0.996 1.000 0.000
#> SRR191649 1 0.000 0.996 1.000 0.000
#> SRR191650 1 0.000 0.996 1.000 0.000
#> SRR191651 1 0.000 0.996 1.000 0.000
#> SRR191652 1 0.000 0.996 1.000 0.000
#> SRR191653 1 0.000 0.996 1.000 0.000
#> SRR191654 1 0.000 0.996 1.000 0.000
#> SRR191655 1 0.000 0.996 1.000 0.000
#> SRR191656 1 0.000 0.996 1.000 0.000
#> SRR191657 1 0.000 0.996 1.000 0.000
#> SRR191658 1 0.000 0.996 1.000 0.000
#> SRR191659 1 0.000 0.996 1.000 0.000
#> SRR191660 1 0.000 0.996 1.000 0.000
#> SRR191661 1 0.000 0.996 1.000 0.000
#> SRR191662 1 0.000 0.996 1.000 0.000
#> SRR191663 1 0.000 0.996 1.000 0.000
#> SRR191664 1 0.000 0.996 1.000 0.000
#> SRR191665 1 0.000 0.996 1.000 0.000
#> SRR191666 1 0.000 0.996 1.000 0.000
#> SRR191667 1 0.000 0.996 1.000 0.000
#> SRR191668 1 0.000 0.996 1.000 0.000
#> SRR191669 1 0.000 0.996 1.000 0.000
#> SRR191670 1 0.000 0.996 1.000 0.000
#> SRR191671 1 0.000 0.996 1.000 0.000
#> SRR191672 1 0.000 0.996 1.000 0.000
#> SRR191673 1 0.000 0.996 1.000 0.000
#> SRR191674 2 0.000 0.978 0.000 1.000
#> SRR191675 2 0.000 0.978 0.000 1.000
#> SRR191677 2 0.000 0.978 0.000 1.000
#> SRR191678 2 0.000 0.978 0.000 1.000
#> SRR191679 2 0.000 0.978 0.000 1.000
#> SRR191680 2 0.000 0.978 0.000 1.000
#> SRR191681 2 0.000 0.978 0.000 1.000
#> SRR191682 2 0.000 0.978 0.000 1.000
#> SRR191683 2 0.000 0.978 0.000 1.000
#> SRR191684 2 0.000 0.978 0.000 1.000
#> SRR191685 2 0.000 0.978 0.000 1.000
#> SRR191686 2 0.000 0.978 0.000 1.000
#> SRR191687 2 0.000 0.978 0.000 1.000
#> SRR191688 2 0.000 0.978 0.000 1.000
#> SRR191689 2 0.000 0.978 0.000 1.000
#> SRR191690 2 0.000 0.978 0.000 1.000
#> SRR191691 2 0.000 0.978 0.000 1.000
#> SRR191692 2 0.000 0.978 0.000 1.000
#> SRR191693 2 0.000 0.978 0.000 1.000
#> SRR191694 2 0.000 0.978 0.000 1.000
#> SRR191695 2 0.000 0.978 0.000 1.000
#> SRR191696 2 0.000 0.978 0.000 1.000
#> SRR191697 2 0.000 0.978 0.000 1.000
#> SRR191698 2 0.000 0.978 0.000 1.000
#> SRR191699 2 0.000 0.978 0.000 1.000
#> SRR191700 2 0.000 0.978 0.000 1.000
#> SRR191701 2 0.000 0.978 0.000 1.000
#> SRR191702 2 0.000 0.978 0.000 1.000
#> SRR191703 2 0.000 0.978 0.000 1.000
#> SRR191704 2 0.000 0.978 0.000 1.000
#> SRR191705 2 0.000 0.978 0.000 1.000
#> SRR191706 2 0.000 0.978 0.000 1.000
#> SRR191707 2 0.000 0.978 0.000 1.000
#> SRR191708 2 0.000 0.978 0.000 1.000
#> SRR191709 2 0.000 0.978 0.000 1.000
#> SRR191710 2 0.000 0.978 0.000 1.000
#> SRR191711 2 0.000 0.978 0.000 1.000
#> SRR191712 2 0.000 0.978 0.000 1.000
#> SRR191713 2 0.000 0.978 0.000 1.000
#> SRR191714 2 0.000 0.978 0.000 1.000
#> SRR191715 2 0.000 0.978 0.000 1.000
#> SRR191716 2 0.000 0.978 0.000 1.000
#> SRR191717 2 0.000 0.978 0.000 1.000
#> SRR191718 2 0.000 0.978 0.000 1.000
#> SRR537099 1 0.000 0.996 1.000 0.000
#> SRR537100 1 0.000 0.996 1.000 0.000
#> SRR537101 1 0.000 0.996 1.000 0.000
#> SRR537102 1 0.000 0.996 1.000 0.000
#> SRR537104 1 0.706 0.753 0.808 0.192
#> SRR537105 1 0.000 0.996 1.000 0.000
#> SRR537106 1 0.000 0.996 1.000 0.000
#> SRR537107 1 0.000 0.996 1.000 0.000
#> SRR537108 1 0.000 0.996 1.000 0.000
#> SRR537109 2 0.000 0.978 0.000 1.000
#> SRR537110 2 0.978 0.309 0.412 0.588
#> SRR537111 1 0.000 0.996 1.000 0.000
#> SRR537113 2 0.981 0.288 0.420 0.580
#> SRR537114 2 0.981 0.288 0.420 0.580
#> SRR537115 2 0.184 0.951 0.028 0.972
#> SRR537116 2 0.000 0.978 0.000 1.000
#> SRR537117 2 0.000 0.978 0.000 1.000
#> SRR537118 2 0.000 0.978 0.000 1.000
#> SRR537119 2 0.000 0.978 0.000 1.000
#> SRR537120 2 0.000 0.978 0.000 1.000
#> SRR537121 2 0.000 0.978 0.000 1.000
#> SRR537122 2 0.000 0.978 0.000 1.000
#> SRR537123 2 0.000 0.978 0.000 1.000
#> SRR537124 2 0.000 0.978 0.000 1.000
#> SRR537125 2 0.000 0.978 0.000 1.000
#> SRR537126 2 0.000 0.978 0.000 1.000
#> SRR537127 1 0.000 0.996 1.000 0.000
#> SRR537128 1 0.000 0.996 1.000 0.000
#> SRR537129 1 0.000 0.996 1.000 0.000
#> SRR537130 1 0.000 0.996 1.000 0.000
#> SRR537131 1 0.000 0.996 1.000 0.000
#> SRR537132 1 0.000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191640 3 0.6215 0.13032 0.428 0.000 0.572
#> SRR191641 3 0.6309 -0.07442 0.496 0.000 0.504
#> SRR191642 3 0.2796 0.63852 0.092 0.000 0.908
#> SRR191643 3 0.2796 0.63852 0.092 0.000 0.908
#> SRR191644 1 0.6302 0.07137 0.520 0.000 0.480
#> SRR191645 1 0.6302 0.07137 0.520 0.000 0.480
#> SRR191646 1 0.6302 0.07137 0.520 0.000 0.480
#> SRR191647 3 0.6252 0.08884 0.444 0.000 0.556
#> SRR191648 3 0.6252 0.08884 0.444 0.000 0.556
#> SRR191649 3 0.6309 -0.07442 0.496 0.000 0.504
#> SRR191650 1 0.1529 0.88498 0.960 0.000 0.040
#> SRR191651 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191652 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191653 3 0.0892 0.64181 0.020 0.000 0.980
#> SRR191654 3 0.0747 0.64195 0.016 0.000 0.984
#> SRR191655 3 0.2356 0.64346 0.072 0.000 0.928
#> SRR191656 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191661 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191662 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191663 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191666 1 0.1289 0.89813 0.968 0.000 0.032
#> SRR191667 1 0.1289 0.89813 0.968 0.000 0.032
#> SRR191668 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.91543 1.000 0.000 0.000
#> SRR191674 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191678 2 0.0592 0.94868 0.000 0.988 0.012
#> SRR191679 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191682 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191683 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191684 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191685 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191686 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191687 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191688 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191689 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191690 2 0.1964 0.90044 0.000 0.944 0.056
#> SRR191691 2 0.1529 0.91979 0.000 0.960 0.040
#> SRR191692 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191693 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191694 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191695 2 0.0424 0.95455 0.000 0.992 0.008
#> SRR191696 2 0.0424 0.95455 0.000 0.992 0.008
#> SRR191697 2 0.0237 0.95458 0.000 0.996 0.004
#> SRR191698 2 0.3619 0.80146 0.000 0.864 0.136
#> SRR191699 2 0.0000 0.95587 0.000 1.000 0.000
#> SRR191700 2 0.6062 0.29480 0.000 0.616 0.384
#> SRR191701 2 0.0237 0.95468 0.000 0.996 0.004
#> SRR191702 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191703 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191704 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191705 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191706 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191707 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191708 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191709 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191710 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191711 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191712 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191713 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191714 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191715 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191716 2 0.0592 0.95199 0.000 0.988 0.012
#> SRR191717 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR191718 2 0.0424 0.95455 0.000 0.992 0.008
#> SRR537099 3 0.1529 0.64826 0.040 0.000 0.960
#> SRR537100 3 0.1529 0.64826 0.040 0.000 0.960
#> SRR537101 3 0.6295 0.00319 0.472 0.000 0.528
#> SRR537102 3 0.1753 0.64889 0.048 0.000 0.952
#> SRR537104 3 0.1832 0.64948 0.036 0.008 0.956
#> SRR537105 3 0.2711 0.64076 0.088 0.000 0.912
#> SRR537106 3 0.2711 0.64076 0.088 0.000 0.912
#> SRR537107 3 0.2711 0.64076 0.088 0.000 0.912
#> SRR537108 3 0.2711 0.64076 0.088 0.000 0.912
#> SRR537109 2 0.1289 0.92739 0.000 0.968 0.032
#> SRR537110 3 0.5650 0.47701 0.000 0.312 0.688
#> SRR537111 1 0.0237 0.91227 0.996 0.004 0.000
#> SRR537113 3 0.4963 0.59395 0.008 0.200 0.792
#> SRR537114 3 0.2584 0.64627 0.008 0.064 0.928
#> SRR537115 3 0.5098 0.56506 0.000 0.248 0.752
#> SRR537116 2 0.0237 0.95599 0.000 0.996 0.004
#> SRR537117 2 0.6252 0.10088 0.000 0.556 0.444
#> SRR537118 3 0.6302 0.09543 0.000 0.480 0.520
#> SRR537119 3 0.6302 0.09543 0.000 0.480 0.520
#> SRR537120 3 0.6302 0.09543 0.000 0.480 0.520
#> SRR537121 3 0.6260 0.18320 0.000 0.448 0.552
#> SRR537122 3 0.6235 0.21088 0.000 0.436 0.564
#> SRR537123 3 0.6260 0.18320 0.000 0.448 0.552
#> SRR537124 2 0.6308 -0.04494 0.000 0.508 0.492
#> SRR537125 3 0.6302 0.09543 0.000 0.480 0.520
#> SRR537126 3 0.6302 0.09543 0.000 0.480 0.520
#> SRR537127 1 0.3116 0.84752 0.892 0.000 0.108
#> SRR537128 1 0.3116 0.84752 0.892 0.000 0.108
#> SRR537129 1 0.3116 0.84752 0.892 0.000 0.108
#> SRR537130 1 0.3116 0.84752 0.892 0.000 0.108
#> SRR537131 1 0.3116 0.84752 0.892 0.000 0.108
#> SRR537132 1 0.3116 0.84752 0.892 0.000 0.108
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191640 4 0.0188 0.93017 0.004 0.000 0.000 0.996
#> SRR191641 4 0.0817 0.91807 0.024 0.000 0.000 0.976
#> SRR191642 4 0.0188 0.93017 0.004 0.000 0.000 0.996
#> SRR191643 4 0.0188 0.93017 0.004 0.000 0.000 0.996
#> SRR191644 4 0.0469 0.92445 0.012 0.000 0.000 0.988
#> SRR191645 4 0.0592 0.92663 0.016 0.000 0.000 0.984
#> SRR191646 4 0.0592 0.92663 0.016 0.000 0.000 0.984
#> SRR191647 4 0.0188 0.93017 0.004 0.000 0.000 0.996
#> SRR191648 4 0.0188 0.93017 0.004 0.000 0.000 0.996
#> SRR191649 4 0.0592 0.92663 0.016 0.000 0.000 0.984
#> SRR191650 1 0.1302 0.94110 0.956 0.000 0.000 0.044
#> SRR191651 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191652 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191653 4 0.0000 0.92909 0.000 0.000 0.000 1.000
#> SRR191654 4 0.0000 0.92909 0.000 0.000 0.000 1.000
#> SRR191655 4 0.0000 0.92909 0.000 0.000 0.000 1.000
#> SRR191656 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191661 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191662 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191663 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191664 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191666 1 0.1302 0.95206 0.956 0.000 0.000 0.044
#> SRR191667 1 0.1302 0.95206 0.956 0.000 0.000 0.044
#> SRR191668 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR191674 2 0.4304 0.69263 0.000 0.716 0.284 0.000
#> SRR191675 2 0.4304 0.69263 0.000 0.716 0.284 0.000
#> SRR191677 2 0.4331 0.68977 0.000 0.712 0.288 0.000
#> SRR191678 2 0.4933 0.47636 0.000 0.568 0.432 0.000
#> SRR191679 2 0.4277 0.69496 0.000 0.720 0.280 0.000
#> SRR191680 2 0.4304 0.69263 0.000 0.716 0.284 0.000
#> SRR191681 2 0.4331 0.68977 0.000 0.712 0.288 0.000
#> SRR191682 2 0.4761 0.59181 0.000 0.628 0.372 0.000
#> SRR191683 2 0.4761 0.59181 0.000 0.628 0.372 0.000
#> SRR191684 2 0.4761 0.59181 0.000 0.628 0.372 0.000
#> SRR191685 2 0.4761 0.59181 0.000 0.628 0.372 0.000
#> SRR191686 2 0.4761 0.59181 0.000 0.628 0.372 0.000
#> SRR191687 2 0.4761 0.59181 0.000 0.628 0.372 0.000
#> SRR191688 2 0.0336 0.81026 0.000 0.992 0.008 0.000
#> SRR191689 2 0.3486 0.75355 0.000 0.812 0.188 0.000
#> SRR191690 2 0.1474 0.79005 0.000 0.948 0.052 0.000
#> SRR191691 2 0.4989 0.17018 0.000 0.528 0.472 0.000
#> SRR191692 2 0.4543 0.65989 0.000 0.676 0.324 0.000
#> SRR191693 2 0.4961 0.47464 0.000 0.552 0.448 0.000
#> SRR191694 2 0.4250 0.69851 0.000 0.724 0.276 0.000
#> SRR191695 2 0.1637 0.78856 0.000 0.940 0.060 0.000
#> SRR191696 2 0.1637 0.78856 0.000 0.940 0.060 0.000
#> SRR191697 2 0.3528 0.74819 0.000 0.808 0.192 0.000
#> SRR191698 3 0.4304 0.57582 0.000 0.284 0.716 0.000
#> SRR191699 2 0.3907 0.69427 0.000 0.768 0.232 0.000
#> SRR191700 3 0.4483 0.57915 0.000 0.284 0.712 0.004
#> SRR191701 2 0.4222 0.65265 0.000 0.728 0.272 0.000
#> SRR191702 2 0.0000 0.81102 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.81102 0.000 1.000 0.000 0.000
#> SRR191704 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191705 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191706 2 0.0000 0.81102 0.000 1.000 0.000 0.000
#> SRR191707 2 0.0592 0.80818 0.000 0.984 0.016 0.000
#> SRR191708 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191709 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191710 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191711 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191712 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191713 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191714 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR191715 2 0.0000 0.81102 0.000 1.000 0.000 0.000
#> SRR191716 2 0.1637 0.78856 0.000 0.940 0.060 0.000
#> SRR191717 2 0.0336 0.81026 0.000 0.992 0.008 0.000
#> SRR191718 2 0.1637 0.78856 0.000 0.940 0.060 0.000
#> SRR537099 4 0.0000 0.92909 0.000 0.000 0.000 1.000
#> SRR537100 4 0.0000 0.92909 0.000 0.000 0.000 1.000
#> SRR537101 4 0.0336 0.92914 0.008 0.000 0.000 0.992
#> SRR537102 4 0.0188 0.93017 0.004 0.000 0.000 0.996
#> SRR537104 4 0.0000 0.92909 0.000 0.000 0.000 1.000
#> SRR537105 4 0.0469 0.92788 0.012 0.000 0.000 0.988
#> SRR537106 4 0.0469 0.92788 0.012 0.000 0.000 0.988
#> SRR537107 4 0.0524 0.92820 0.008 0.000 0.004 0.988
#> SRR537108 4 0.0524 0.92820 0.008 0.000 0.004 0.988
#> SRR537109 2 0.0188 0.81078 0.000 0.996 0.004 0.000
#> SRR537110 4 0.4936 0.44698 0.000 0.372 0.004 0.624
#> SRR537111 1 0.0000 0.97460 1.000 0.000 0.000 0.000
#> SRR537113 4 0.5234 0.59127 0.004 0.256 0.032 0.708
#> SRR537114 4 0.4416 0.76513 0.004 0.052 0.132 0.812
#> SRR537115 4 0.7836 0.00199 0.000 0.328 0.272 0.400
#> SRR537116 2 0.0188 0.81115 0.000 0.996 0.004 0.000
#> SRR537117 3 0.0188 0.93215 0.000 0.004 0.996 0.000
#> SRR537118 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537119 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537120 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537121 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537122 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537123 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537124 3 0.0188 0.93215 0.000 0.004 0.996 0.000
#> SRR537125 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537126 3 0.0188 0.93515 0.000 0.000 0.996 0.004
#> SRR537127 1 0.2919 0.92076 0.896 0.000 0.060 0.044
#> SRR537128 1 0.2919 0.92076 0.896 0.000 0.060 0.044
#> SRR537129 1 0.2919 0.92076 0.896 0.000 0.060 0.044
#> SRR537130 1 0.2919 0.92076 0.896 0.000 0.060 0.044
#> SRR537131 1 0.2919 0.92076 0.896 0.000 0.060 0.044
#> SRR537132 1 0.2919 0.92076 0.896 0.000 0.060 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.4504 0.8586 0.564 0.000 0.000 0.008 0.428
#> SRR191640 4 0.0000 0.8993 0.000 0.000 0.000 1.000 0.000
#> SRR191641 4 0.1965 0.8629 0.096 0.000 0.000 0.904 0.000
#> SRR191642 4 0.0000 0.8993 0.000 0.000 0.000 1.000 0.000
#> SRR191643 4 0.0162 0.8994 0.000 0.000 0.000 0.996 0.004
#> SRR191644 4 0.3129 0.8162 0.156 0.000 0.004 0.832 0.008
#> SRR191645 4 0.0510 0.8989 0.016 0.000 0.000 0.984 0.000
#> SRR191646 4 0.0510 0.8989 0.016 0.000 0.000 0.984 0.000
#> SRR191647 4 0.0404 0.8994 0.012 0.000 0.000 0.988 0.000
#> SRR191648 4 0.0404 0.8994 0.012 0.000 0.000 0.988 0.000
#> SRR191649 4 0.0404 0.8994 0.012 0.000 0.000 0.988 0.000
#> SRR191650 1 0.4861 0.8483 0.548 0.000 0.000 0.024 0.428
#> SRR191651 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191652 1 0.4225 0.8461 0.632 0.000 0.000 0.004 0.364
#> SRR191653 4 0.4903 0.5467 0.400 0.000 0.016 0.576 0.008
#> SRR191654 4 0.4137 0.7178 0.248 0.000 0.012 0.732 0.008
#> SRR191655 4 0.0451 0.8992 0.008 0.000 0.000 0.988 0.004
#> SRR191656 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191657 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191658 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191659 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191660 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191661 1 0.4341 0.8617 0.592 0.000 0.000 0.004 0.404
#> SRR191662 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191663 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191664 1 0.4192 0.8623 0.596 0.000 0.000 0.000 0.404
#> SRR191665 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191666 1 0.0404 0.6373 0.988 0.000 0.000 0.012 0.000
#> SRR191667 1 0.0404 0.6373 0.988 0.000 0.000 0.012 0.000
#> SRR191668 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191669 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191670 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191671 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191672 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191673 1 0.4242 0.8616 0.572 0.000 0.000 0.000 0.428
#> SRR191674 2 0.5019 0.1289 0.000 0.568 0.396 0.000 0.036
#> SRR191675 2 0.5019 0.1289 0.000 0.568 0.396 0.000 0.036
#> SRR191677 2 0.5019 0.1289 0.000 0.568 0.396 0.000 0.036
#> SRR191678 2 0.5350 0.0255 0.000 0.488 0.460 0.000 0.052
#> SRR191679 2 0.5010 0.1363 0.000 0.572 0.392 0.000 0.036
#> SRR191680 2 0.5019 0.1289 0.000 0.568 0.396 0.000 0.036
#> SRR191681 2 0.5028 0.1297 0.000 0.564 0.400 0.000 0.036
#> SRR191682 3 0.3969 0.6780 0.000 0.304 0.692 0.000 0.004
#> SRR191683 3 0.3969 0.6780 0.000 0.304 0.692 0.000 0.004
#> SRR191684 3 0.3906 0.6838 0.000 0.292 0.704 0.000 0.004
#> SRR191685 3 0.3906 0.6838 0.000 0.292 0.704 0.000 0.004
#> SRR191686 3 0.3816 0.6757 0.000 0.304 0.696 0.000 0.000
#> SRR191687 3 0.3906 0.6838 0.000 0.292 0.704 0.000 0.004
#> SRR191688 2 0.1168 0.5786 0.000 0.960 0.032 0.000 0.008
#> SRR191689 2 0.4455 0.0788 0.000 0.588 0.404 0.000 0.008
#> SRR191690 2 0.2278 0.5529 0.000 0.916 0.044 0.032 0.008
#> SRR191691 3 0.4697 0.4990 0.008 0.360 0.620 0.000 0.012
#> SRR191692 2 0.5083 0.0317 0.000 0.532 0.432 0.000 0.036
#> SRR191693 3 0.4268 0.4788 0.000 0.344 0.648 0.000 0.008
#> SRR191694 2 0.4682 0.1001 0.000 0.564 0.420 0.000 0.016
#> SRR191695 2 0.1764 0.5690 0.000 0.928 0.064 0.000 0.008
#> SRR191696 2 0.1764 0.5690 0.000 0.928 0.064 0.000 0.008
#> SRR191697 2 0.4275 0.2504 0.000 0.696 0.284 0.000 0.020
#> SRR191698 3 0.5774 0.1991 0.000 0.232 0.612 0.000 0.156
#> SRR191699 3 0.4565 0.4880 0.000 0.408 0.580 0.000 0.012
#> SRR191700 3 0.6465 -0.2183 0.000 0.220 0.492 0.000 0.288
#> SRR191701 3 0.4653 0.4099 0.000 0.472 0.516 0.000 0.012
#> SRR191702 2 0.2189 0.5693 0.000 0.904 0.084 0.000 0.012
#> SRR191703 2 0.2189 0.5693 0.000 0.904 0.084 0.000 0.012
#> SRR191704 2 0.3596 0.4559 0.000 0.776 0.212 0.000 0.012
#> SRR191705 2 0.3596 0.4559 0.000 0.776 0.212 0.000 0.012
#> SRR191706 2 0.2522 0.5561 0.000 0.880 0.108 0.000 0.012
#> SRR191707 2 0.3628 0.4175 0.000 0.772 0.216 0.000 0.012
#> SRR191708 2 0.3563 0.4567 0.000 0.780 0.208 0.000 0.012
#> SRR191709 2 0.3596 0.4559 0.000 0.776 0.212 0.000 0.012
#> SRR191710 2 0.3596 0.4559 0.000 0.776 0.212 0.000 0.012
#> SRR191711 2 0.2361 0.5541 0.000 0.892 0.096 0.000 0.012
#> SRR191712 2 0.2361 0.5577 0.000 0.892 0.096 0.000 0.012
#> SRR191713 2 0.3720 0.4269 0.000 0.760 0.228 0.000 0.012
#> SRR191714 2 0.3720 0.4269 0.000 0.760 0.228 0.000 0.012
#> SRR191715 2 0.0693 0.5846 0.000 0.980 0.008 0.000 0.012
#> SRR191716 2 0.1913 0.5647 0.000 0.932 0.044 0.016 0.008
#> SRR191717 2 0.1168 0.5786 0.000 0.960 0.032 0.000 0.008
#> SRR191718 2 0.1557 0.5724 0.000 0.940 0.052 0.000 0.008
#> SRR537099 4 0.1041 0.8937 0.032 0.000 0.000 0.964 0.004
#> SRR537100 4 0.1124 0.8925 0.036 0.000 0.000 0.960 0.004
#> SRR537101 4 0.1671 0.8743 0.076 0.000 0.000 0.924 0.000
#> SRR537102 4 0.0162 0.8994 0.000 0.000 0.000 0.996 0.004
#> SRR537104 4 0.1565 0.8873 0.020 0.008 0.016 0.952 0.004
#> SRR537105 4 0.0162 0.8986 0.000 0.000 0.004 0.996 0.000
#> SRR537106 4 0.0324 0.8977 0.000 0.004 0.004 0.992 0.000
#> SRR537107 4 0.0324 0.8977 0.000 0.004 0.004 0.992 0.000
#> SRR537108 4 0.0324 0.8977 0.000 0.004 0.004 0.992 0.000
#> SRR537109 2 0.0324 0.5845 0.000 0.992 0.004 0.004 0.000
#> SRR537110 2 0.8418 -0.0250 0.132 0.424 0.192 0.236 0.016
#> SRR537111 1 0.4799 0.8547 0.556 0.004 0.004 0.008 0.428
#> SRR537113 4 0.5919 0.5797 0.000 0.212 0.052 0.660 0.076
#> SRR537114 4 0.5137 0.6978 0.000 0.116 0.048 0.748 0.088
#> SRR537115 4 0.8462 -0.1191 0.000 0.276 0.164 0.308 0.252
#> SRR537116 2 0.0566 0.5840 0.000 0.984 0.004 0.000 0.012
#> SRR537117 5 0.4291 0.9880 0.000 0.000 0.464 0.000 0.536
#> SRR537118 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537119 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537120 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537121 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537122 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537123 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537124 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537125 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537126 5 0.4283 0.9987 0.000 0.000 0.456 0.000 0.544
#> SRR537127 1 0.2131 0.5827 0.920 0.000 0.008 0.016 0.056
#> SRR537128 1 0.2131 0.5827 0.920 0.000 0.008 0.016 0.056
#> SRR537129 1 0.2131 0.5827 0.920 0.000 0.008 0.016 0.056
#> SRR537130 1 0.2131 0.5827 0.920 0.000 0.008 0.016 0.056
#> SRR537131 1 0.2131 0.5827 0.920 0.000 0.008 0.016 0.056
#> SRR537132 1 0.2131 0.5827 0.920 0.000 0.008 0.016 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191640 4 0.0363 0.9066 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR191641 4 0.2473 0.8157 0.008 0.000 0.136 0.856 0.000 0.000
#> SRR191642 4 0.0260 0.9075 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR191643 4 0.0520 0.9066 0.000 0.000 0.008 0.984 0.000 0.008
#> SRR191644 4 0.3808 0.5923 0.004 0.000 0.284 0.700 0.000 0.012
#> SRR191645 4 0.0653 0.9081 0.004 0.000 0.004 0.980 0.000 0.012
#> SRR191646 4 0.0653 0.9081 0.004 0.000 0.004 0.980 0.000 0.012
#> SRR191647 4 0.0622 0.9085 0.000 0.000 0.008 0.980 0.000 0.012
#> SRR191648 4 0.0622 0.9085 0.000 0.000 0.008 0.980 0.000 0.012
#> SRR191649 4 0.0622 0.9085 0.000 0.000 0.008 0.980 0.000 0.012
#> SRR191650 1 0.0551 0.9559 0.984 0.000 0.004 0.008 0.000 0.004
#> SRR191651 1 0.0260 0.9614 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR191652 1 0.2100 0.8830 0.884 0.000 0.112 0.004 0.000 0.000
#> SRR191653 3 0.3738 0.5171 0.000 0.000 0.704 0.280 0.000 0.016
#> SRR191654 3 0.4263 -0.0106 0.000 0.000 0.504 0.480 0.000 0.016
#> SRR191655 4 0.0806 0.9028 0.000 0.000 0.020 0.972 0.000 0.008
#> SRR191656 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191657 1 0.1141 0.9553 0.948 0.000 0.052 0.000 0.000 0.000
#> SRR191658 1 0.1141 0.9553 0.948 0.000 0.052 0.000 0.000 0.000
#> SRR191659 1 0.1141 0.9553 0.948 0.000 0.052 0.000 0.000 0.000
#> SRR191660 1 0.1141 0.9553 0.948 0.000 0.052 0.000 0.000 0.000
#> SRR191661 1 0.1204 0.9553 0.944 0.000 0.056 0.000 0.000 0.000
#> SRR191662 1 0.1204 0.9553 0.944 0.000 0.056 0.000 0.000 0.000
#> SRR191663 1 0.1204 0.9553 0.944 0.000 0.056 0.000 0.000 0.000
#> SRR191664 1 0.1141 0.9553 0.948 0.000 0.052 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191666 3 0.3056 0.8788 0.184 0.000 0.804 0.008 0.004 0.000
#> SRR191667 3 0.3056 0.8788 0.184 0.000 0.804 0.008 0.004 0.000
#> SRR191668 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.9657 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR191674 2 0.5894 0.1013 0.000 0.472 0.032 0.000 0.096 0.400
#> SRR191675 2 0.5894 0.1013 0.000 0.472 0.032 0.000 0.096 0.400
#> SRR191677 2 0.5987 0.0970 0.000 0.464 0.036 0.000 0.100 0.400
#> SRR191678 2 0.6274 0.1125 0.000 0.480 0.048 0.000 0.124 0.348
#> SRR191679 2 0.5916 0.1041 0.000 0.472 0.036 0.000 0.092 0.400
#> SRR191680 2 0.5952 0.1012 0.000 0.468 0.036 0.000 0.096 0.400
#> SRR191681 2 0.5987 0.0970 0.000 0.464 0.036 0.000 0.100 0.400
#> SRR191682 6 0.1794 0.7773 0.000 0.036 0.000 0.000 0.040 0.924
#> SRR191683 6 0.1794 0.7773 0.000 0.036 0.000 0.000 0.040 0.924
#> SRR191684 6 0.1788 0.7783 0.000 0.028 0.004 0.000 0.040 0.928
#> SRR191685 6 0.1788 0.7783 0.000 0.028 0.004 0.000 0.040 0.928
#> SRR191686 6 0.1934 0.7725 0.000 0.044 0.000 0.000 0.040 0.916
#> SRR191687 6 0.1788 0.7783 0.000 0.028 0.004 0.000 0.040 0.928
#> SRR191688 2 0.1579 0.4963 0.000 0.944 0.020 0.008 0.004 0.024
#> SRR191689 6 0.4494 0.0283 0.000 0.424 0.032 0.000 0.000 0.544
#> SRR191690 2 0.1862 0.4910 0.000 0.932 0.020 0.024 0.004 0.020
#> SRR191691 6 0.4551 0.6010 0.000 0.152 0.024 0.000 0.088 0.736
#> SRR191692 2 0.5990 0.0816 0.000 0.460 0.036 0.000 0.100 0.404
#> SRR191693 6 0.4553 0.5649 0.000 0.144 0.028 0.000 0.088 0.740
#> SRR191694 2 0.5465 0.0232 0.000 0.460 0.032 0.000 0.052 0.456
#> SRR191695 2 0.1736 0.4931 0.000 0.936 0.020 0.008 0.004 0.032
#> SRR191696 2 0.1736 0.4931 0.000 0.936 0.020 0.008 0.004 0.032
#> SRR191697 2 0.4816 0.2042 0.000 0.668 0.028 0.004 0.036 0.264
#> SRR191698 6 0.6143 0.3155 0.000 0.184 0.020 0.000 0.304 0.492
#> SRR191699 6 0.2295 0.7533 0.000 0.052 0.016 0.000 0.028 0.904
#> SRR191700 5 0.6242 0.0702 0.000 0.196 0.024 0.000 0.488 0.292
#> SRR191701 6 0.4912 0.5565 0.000 0.224 0.020 0.000 0.080 0.676
#> SRR191702 2 0.4871 0.4509 0.000 0.652 0.124 0.000 0.000 0.224
#> SRR191703 2 0.4871 0.4509 0.000 0.652 0.124 0.000 0.000 0.224
#> SRR191704 2 0.5475 0.2869 0.000 0.460 0.124 0.000 0.000 0.416
#> SRR191705 2 0.5472 0.2907 0.000 0.464 0.124 0.000 0.000 0.412
#> SRR191706 2 0.5103 0.4245 0.000 0.608 0.124 0.000 0.000 0.268
#> SRR191707 2 0.5456 0.3014 0.000 0.536 0.120 0.000 0.004 0.340
#> SRR191708 2 0.5439 0.2925 0.000 0.472 0.120 0.000 0.000 0.408
#> SRR191709 2 0.5445 0.2873 0.000 0.464 0.120 0.000 0.000 0.416
#> SRR191710 2 0.5445 0.2873 0.000 0.464 0.120 0.000 0.000 0.416
#> SRR191711 2 0.4556 0.4502 0.000 0.688 0.100 0.000 0.000 0.212
#> SRR191712 2 0.4490 0.4530 0.000 0.700 0.104 0.000 0.000 0.196
#> SRR191713 2 0.5357 0.2636 0.000 0.464 0.108 0.000 0.000 0.428
#> SRR191714 2 0.5357 0.2636 0.000 0.464 0.108 0.000 0.000 0.428
#> SRR191715 2 0.2910 0.5026 0.000 0.852 0.068 0.000 0.000 0.080
#> SRR191716 2 0.1774 0.4932 0.000 0.936 0.020 0.016 0.004 0.024
#> SRR191717 2 0.1579 0.4963 0.000 0.944 0.020 0.008 0.004 0.024
#> SRR191718 2 0.1579 0.4963 0.000 0.944 0.020 0.008 0.004 0.024
#> SRR537099 4 0.1867 0.8764 0.000 0.000 0.064 0.916 0.000 0.020
#> SRR537100 4 0.1701 0.8755 0.000 0.000 0.072 0.920 0.000 0.008
#> SRR537101 4 0.2092 0.8364 0.000 0.000 0.124 0.876 0.000 0.000
#> SRR537102 4 0.0260 0.9075 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR537104 4 0.2361 0.8496 0.000 0.000 0.028 0.884 0.000 0.088
#> SRR537105 4 0.0508 0.9065 0.000 0.000 0.004 0.984 0.000 0.012
#> SRR537106 4 0.0622 0.9051 0.000 0.000 0.008 0.980 0.000 0.012
#> SRR537107 4 0.0725 0.9033 0.000 0.000 0.012 0.976 0.000 0.012
#> SRR537108 4 0.0725 0.9033 0.000 0.000 0.012 0.976 0.000 0.012
#> SRR537109 2 0.2011 0.5014 0.000 0.912 0.020 0.004 0.000 0.064
#> SRR537110 2 0.7113 0.1877 0.000 0.416 0.160 0.120 0.000 0.304
#> SRR537111 1 0.0862 0.9456 0.972 0.008 0.016 0.000 0.000 0.004
#> SRR537113 4 0.6720 0.4164 0.004 0.256 0.032 0.544 0.120 0.044
#> SRR537114 4 0.5950 0.5693 0.004 0.172 0.032 0.640 0.132 0.020
#> SRR537115 5 0.7672 0.1541 0.004 0.304 0.032 0.184 0.396 0.080
#> SRR537116 2 0.2965 0.4990 0.000 0.848 0.072 0.000 0.000 0.080
#> SRR537117 5 0.0146 0.8844 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR537118 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537119 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537120 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537121 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537122 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537123 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537124 5 0.0000 0.8880 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR537125 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537126 5 0.0146 0.8907 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR537127 3 0.3133 0.8822 0.180 0.000 0.804 0.008 0.008 0.000
#> SRR537128 3 0.3133 0.8822 0.180 0.000 0.804 0.008 0.008 0.000
#> SRR537129 3 0.3133 0.8822 0.180 0.000 0.804 0.008 0.008 0.000
#> SRR537130 3 0.3133 0.8822 0.180 0.000 0.804 0.008 0.008 0.000
#> SRR537131 3 0.3133 0.8822 0.180 0.000 0.804 0.008 0.008 0.000
#> SRR537132 3 0.3133 0.8822 0.180 0.000 0.804 0.008 0.008 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)
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 16450 rows and 111 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 0.822 0.919 0.958 0.4981 0.497 0.497
#> 3 3 0.825 0.868 0.944 0.3154 0.771 0.571
#> 4 4 0.740 0.818 0.876 0.0656 0.928 0.805
#> 5 5 0.841 0.882 0.941 0.1027 0.859 0.589
#> 6 6 0.869 0.786 0.882 0.0402 0.928 0.708
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
#> SRR191639 1 0.1633 0.947 0.976 0.024
#> SRR191640 1 0.1633 0.947 0.976 0.024
#> SRR191641 1 0.1633 0.947 0.976 0.024
#> SRR191642 1 0.2948 0.938 0.948 0.052
#> SRR191643 1 0.2948 0.938 0.948 0.052
#> SRR191644 1 0.2948 0.938 0.948 0.052
#> SRR191645 1 0.1633 0.947 0.976 0.024
#> SRR191646 1 0.1633 0.947 0.976 0.024
#> SRR191647 1 0.1633 0.947 0.976 0.024
#> SRR191648 1 0.1633 0.947 0.976 0.024
#> SRR191649 1 0.1633 0.947 0.976 0.024
#> SRR191650 1 0.1633 0.947 0.976 0.024
#> SRR191651 1 0.0000 0.947 1.000 0.000
#> SRR191652 1 0.0000 0.947 1.000 0.000
#> SRR191653 1 0.2948 0.938 0.948 0.052
#> SRR191654 1 0.2948 0.938 0.948 0.052
#> SRR191655 1 0.2948 0.938 0.948 0.052
#> SRR191656 1 0.0000 0.947 1.000 0.000
#> SRR191657 1 0.0000 0.947 1.000 0.000
#> SRR191658 1 0.0000 0.947 1.000 0.000
#> SRR191659 1 0.0000 0.947 1.000 0.000
#> SRR191660 1 0.0000 0.947 1.000 0.000
#> SRR191661 1 0.0000 0.947 1.000 0.000
#> SRR191662 1 0.0000 0.947 1.000 0.000
#> SRR191663 1 0.0000 0.947 1.000 0.000
#> SRR191664 1 0.0000 0.947 1.000 0.000
#> SRR191665 1 0.0000 0.947 1.000 0.000
#> SRR191666 1 0.0000 0.947 1.000 0.000
#> SRR191667 1 0.0000 0.947 1.000 0.000
#> SRR191668 1 0.0000 0.947 1.000 0.000
#> SRR191669 1 0.0000 0.947 1.000 0.000
#> SRR191670 1 0.0000 0.947 1.000 0.000
#> SRR191671 1 0.0000 0.947 1.000 0.000
#> SRR191672 1 0.0000 0.947 1.000 0.000
#> SRR191673 1 0.0000 0.947 1.000 0.000
#> SRR191674 2 0.0000 0.965 0.000 1.000
#> SRR191675 2 0.0000 0.965 0.000 1.000
#> SRR191677 2 0.0000 0.965 0.000 1.000
#> SRR191678 2 0.0000 0.965 0.000 1.000
#> SRR191679 2 0.0000 0.965 0.000 1.000
#> SRR191680 2 0.0000 0.965 0.000 1.000
#> SRR191681 2 0.0000 0.965 0.000 1.000
#> SRR191682 2 0.0000 0.965 0.000 1.000
#> SRR191683 2 0.0000 0.965 0.000 1.000
#> SRR191684 2 0.5842 0.834 0.140 0.860
#> SRR191685 2 0.0000 0.965 0.000 1.000
#> SRR191686 2 0.0000 0.965 0.000 1.000
#> SRR191687 2 0.0000 0.965 0.000 1.000
#> SRR191688 2 0.5946 0.829 0.144 0.856
#> SRR191689 2 0.0000 0.965 0.000 1.000
#> SRR191690 1 0.6801 0.812 0.820 0.180
#> SRR191691 2 0.3584 0.918 0.068 0.932
#> SRR191692 2 0.0000 0.965 0.000 1.000
#> SRR191693 2 0.0000 0.965 0.000 1.000
#> SRR191694 2 0.0000 0.965 0.000 1.000
#> SRR191695 2 0.0000 0.965 0.000 1.000
#> SRR191696 2 0.0000 0.965 0.000 1.000
#> SRR191697 2 0.0000 0.965 0.000 1.000
#> SRR191698 2 0.0000 0.965 0.000 1.000
#> SRR191699 2 0.0000 0.965 0.000 1.000
#> SRR191700 2 0.6438 0.805 0.164 0.836
#> SRR191701 2 0.0000 0.965 0.000 1.000
#> SRR191702 2 0.0000 0.965 0.000 1.000
#> SRR191703 2 0.0000 0.965 0.000 1.000
#> SRR191704 2 0.0000 0.965 0.000 1.000
#> SRR191705 2 0.0000 0.965 0.000 1.000
#> SRR191706 2 0.0000 0.965 0.000 1.000
#> SRR191707 2 0.6247 0.815 0.156 0.844
#> SRR191708 1 0.8267 0.691 0.740 0.260
#> SRR191709 2 0.0000 0.965 0.000 1.000
#> SRR191710 1 0.9087 0.582 0.676 0.324
#> SRR191711 2 0.0376 0.962 0.004 0.996
#> SRR191712 2 0.2236 0.939 0.036 0.964
#> SRR191713 1 0.9954 0.233 0.540 0.460
#> SRR191714 2 0.9129 0.472 0.328 0.672
#> SRR191715 2 0.0000 0.965 0.000 1.000
#> SRR191716 2 0.8555 0.616 0.280 0.720
#> SRR191717 2 0.0000 0.965 0.000 1.000
#> SRR191718 2 0.0000 0.965 0.000 1.000
#> SRR537099 1 0.5059 0.890 0.888 0.112
#> SRR537100 1 0.1843 0.947 0.972 0.028
#> SRR537101 1 0.1633 0.947 0.976 0.024
#> SRR537102 1 0.2948 0.938 0.948 0.052
#> SRR537104 1 0.2948 0.938 0.948 0.052
#> SRR537105 1 0.2948 0.938 0.948 0.052
#> SRR537106 1 0.2948 0.938 0.948 0.052
#> SRR537107 1 0.2948 0.938 0.948 0.052
#> SRR537108 1 0.2948 0.938 0.948 0.052
#> SRR537109 1 0.6148 0.849 0.848 0.152
#> SRR537110 1 0.3431 0.931 0.936 0.064
#> SRR537111 1 0.2236 0.931 0.964 0.036
#> SRR537113 1 0.6973 0.806 0.812 0.188
#> SRR537114 1 0.6247 0.843 0.844 0.156
#> SRR537115 2 0.7745 0.694 0.228 0.772
#> SRR537116 2 0.0000 0.965 0.000 1.000
#> SRR537117 2 0.0000 0.965 0.000 1.000
#> SRR537118 2 0.0000 0.965 0.000 1.000
#> SRR537119 2 0.2423 0.936 0.040 0.960
#> SRR537120 2 0.0000 0.965 0.000 1.000
#> SRR537121 2 0.2423 0.936 0.040 0.960
#> SRR537122 2 0.2423 0.936 0.040 0.960
#> SRR537123 2 0.0000 0.965 0.000 1.000
#> SRR537124 2 0.0000 0.965 0.000 1.000
#> SRR537125 2 0.0000 0.965 0.000 1.000
#> SRR537126 2 0.0000 0.965 0.000 1.000
#> SRR537127 1 0.0672 0.946 0.992 0.008
#> SRR537128 1 0.0000 0.947 1.000 0.000
#> SRR537129 1 0.6973 0.769 0.812 0.188
#> SRR537130 1 0.0000 0.947 1.000 0.000
#> SRR537131 1 0.0000 0.947 1.000 0.000
#> SRR537132 1 0.0000 0.947 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191640 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191641 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191642 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191643 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191644 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191645 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191646 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191647 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191650 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191651 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191652 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191653 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191654 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191655 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR191656 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191657 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191658 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191659 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191660 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191661 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191662 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191663 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191664 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191665 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191666 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191667 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191668 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191669 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191670 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191671 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191672 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191673 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR191674 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR191675 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR191677 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR191678 2 0.3272 0.85839 0.104 0.892 0.004
#> SRR191679 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191681 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR191682 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191688 1 0.4555 0.72359 0.800 0.200 0.000
#> SRR191689 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191690 1 0.0424 0.93436 0.992 0.008 0.000
#> SRR191691 2 0.3412 0.81792 0.000 0.876 0.124
#> SRR191692 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR191693 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR191694 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191695 2 0.3551 0.83423 0.132 0.868 0.000
#> SRR191696 2 0.2878 0.86591 0.096 0.904 0.000
#> SRR191697 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191700 2 0.3412 0.84155 0.124 0.876 0.000
#> SRR191701 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191702 2 0.0237 0.91577 0.004 0.996 0.000
#> SRR191703 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.91671 0.000 1.000 0.000
#> SRR191707 1 0.4654 0.71257 0.792 0.208 0.000
#> SRR191708 1 0.6260 0.16032 0.552 0.448 0.000
#> SRR191709 2 0.4062 0.76929 0.164 0.836 0.000
#> SRR191710 2 0.6309 -0.07247 0.500 0.500 0.000
#> SRR191711 2 0.6299 0.02360 0.476 0.524 0.000
#> SRR191712 1 0.5678 0.51896 0.684 0.316 0.000
#> SRR191713 3 0.9892 0.06875 0.268 0.340 0.392
#> SRR191714 2 0.6676 0.00606 0.476 0.516 0.008
#> SRR191715 1 0.6309 0.04396 0.504 0.496 0.000
#> SRR191716 1 0.0592 0.93121 0.988 0.012 0.000
#> SRR191717 1 0.3816 0.79306 0.852 0.148 0.000
#> SRR191718 2 0.0237 0.91577 0.004 0.996 0.000
#> SRR537099 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537100 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537101 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537102 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537104 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537105 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537106 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537107 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537108 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537109 1 0.0237 0.93706 0.996 0.004 0.000
#> SRR537110 1 0.0747 0.92917 0.984 0.016 0.000
#> SRR537111 3 0.0592 0.97613 0.012 0.000 0.988
#> SRR537113 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537114 1 0.0000 0.93961 1.000 0.000 0.000
#> SRR537115 2 0.5760 0.54824 0.328 0.672 0.000
#> SRR537116 2 0.5291 0.65854 0.268 0.732 0.000
#> SRR537117 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR537118 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR537119 2 0.3349 0.85526 0.108 0.888 0.004
#> SRR537120 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR537121 2 0.3573 0.84464 0.120 0.876 0.004
#> SRR537122 2 0.4110 0.81371 0.152 0.844 0.004
#> SRR537123 2 0.2682 0.87811 0.076 0.920 0.004
#> SRR537124 2 0.0237 0.91608 0.000 0.996 0.004
#> SRR537125 2 0.2682 0.87811 0.076 0.920 0.004
#> SRR537126 2 0.2590 0.88058 0.072 0.924 0.004
#> SRR537127 3 0.0000 0.96967 0.000 0.000 1.000
#> SRR537128 3 0.0000 0.96967 0.000 0.000 1.000
#> SRR537129 3 0.0000 0.96967 0.000 0.000 1.000
#> SRR537130 3 0.0000 0.96967 0.000 0.000 1.000
#> SRR537131 3 0.0000 0.96967 0.000 0.000 1.000
#> SRR537132 3 0.0000 0.96967 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 4 0.0188 0.8624 0.004 0.000 0.000 0.996
#> SRR191640 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191641 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191642 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191643 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191644 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191645 4 0.1302 0.8336 0.044 0.000 0.000 0.956
#> SRR191646 4 0.1302 0.8336 0.044 0.000 0.000 0.956
#> SRR191647 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191648 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191649 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191650 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191651 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191652 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191653 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191654 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191655 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR191656 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191658 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191659 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191660 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191661 1 0.1302 0.9020 0.956 0.000 0.000 0.044
#> SRR191662 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191663 1 0.0592 0.9629 0.984 0.000 0.000 0.016
#> SRR191664 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191665 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191666 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191667 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191668 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR191674 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191675 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191677 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191678 2 0.2469 0.7980 0.000 0.892 0.000 0.108
#> SRR191679 2 0.1302 0.8466 0.000 0.956 0.044 0.000
#> SRR191680 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191681 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191682 2 0.1118 0.8465 0.000 0.964 0.036 0.000
#> SRR191683 2 0.0188 0.8448 0.000 0.996 0.004 0.000
#> SRR191684 2 0.4467 0.7971 0.040 0.788 0.172 0.000
#> SRR191685 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191686 2 0.1792 0.8452 0.000 0.932 0.068 0.000
#> SRR191687 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191688 4 0.4332 0.7065 0.000 0.160 0.040 0.800
#> SRR191689 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191690 4 0.0817 0.8525 0.000 0.000 0.024 0.976
#> SRR191691 2 0.6284 0.6721 0.164 0.664 0.172 0.000
#> SRR191692 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191693 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191694 2 0.0000 0.8443 0.000 1.000 0.000 0.000
#> SRR191695 2 0.5677 0.7670 0.000 0.720 0.140 0.140
#> SRR191696 2 0.5151 0.7974 0.000 0.760 0.140 0.100
#> SRR191697 2 0.2760 0.8332 0.000 0.872 0.128 0.000
#> SRR191698 2 0.4500 0.7971 0.000 0.684 0.316 0.000
#> SRR191699 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191700 2 0.4522 0.7953 0.000 0.680 0.320 0.000
#> SRR191701 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191702 2 0.3494 0.8198 0.000 0.824 0.172 0.004
#> SRR191703 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191704 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191705 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191706 2 0.3311 0.8200 0.000 0.828 0.172 0.000
#> SRR191707 4 0.6401 0.5619 0.000 0.176 0.172 0.652
#> SRR191708 4 0.8220 0.3352 0.044 0.276 0.172 0.508
#> SRR191709 2 0.6243 0.6606 0.000 0.668 0.172 0.160
#> SRR191710 4 0.8436 0.1808 0.044 0.348 0.172 0.436
#> SRR191711 4 0.7446 0.0901 0.000 0.396 0.172 0.432
#> SRR191712 4 0.6854 0.4666 0.000 0.232 0.172 0.596
#> SRR191713 2 0.9848 0.0365 0.256 0.312 0.172 0.260
#> SRR191714 4 0.8461 0.1162 0.044 0.368 0.172 0.416
#> SRR191715 4 0.8267 0.0648 0.032 0.388 0.172 0.408
#> SRR191716 4 0.1211 0.8427 0.000 0.000 0.040 0.960
#> SRR191717 4 0.4894 0.7003 0.000 0.120 0.100 0.780
#> SRR191718 2 0.2053 0.8455 0.000 0.924 0.072 0.004
#> SRR537099 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537100 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537101 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537102 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537104 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537105 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537106 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537107 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537108 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537109 4 0.0188 0.8630 0.000 0.000 0.004 0.996
#> SRR537110 4 0.4944 0.7089 0.036 0.016 0.172 0.776
#> SRR537111 1 0.0000 0.9935 1.000 0.000 0.000 0.000
#> SRR537113 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537114 4 0.0000 0.8647 0.000 0.000 0.000 1.000
#> SRR537115 2 0.4431 0.5308 0.000 0.696 0.000 0.304
#> SRR537116 2 0.7093 0.5234 0.000 0.556 0.172 0.272
#> SRR537117 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537118 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537119 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537120 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537121 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537122 2 0.3479 0.7899 0.000 0.840 0.148 0.012
#> SRR537123 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537124 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537125 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537126 2 0.3024 0.7981 0.000 0.852 0.148 0.000
#> SRR537127 3 0.4522 0.9986 0.320 0.000 0.680 0.000
#> SRR537128 3 0.4522 0.9986 0.320 0.000 0.680 0.000
#> SRR537129 3 0.4500 0.9930 0.316 0.000 0.684 0.000
#> SRR537130 3 0.4522 0.9986 0.320 0.000 0.680 0.000
#> SRR537131 3 0.4522 0.9986 0.320 0.000 0.680 0.000
#> SRR537132 3 0.4522 0.9986 0.320 0.000 0.680 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191640 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191641 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191642 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191643 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191644 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191645 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191646 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191647 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191648 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191649 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191650 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191651 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191652 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191653 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191654 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191655 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR191656 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191657 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191658 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191659 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191660 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191661 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191662 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191663 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191664 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191665 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191666 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191667 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191668 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191669 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191670 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191671 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191672 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191673 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR191674 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191675 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191677 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191678 5 0.3237 0.779 0 0.048 0 0.104 0.848
#> SRR191679 5 0.3983 0.661 0 0.340 0 0.000 0.660
#> SRR191680 5 0.2813 0.828 0 0.168 0 0.000 0.832
#> SRR191681 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191682 5 0.3774 0.719 0 0.296 0 0.000 0.704
#> SRR191683 5 0.3039 0.815 0 0.192 0 0.000 0.808
#> SRR191684 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191685 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191686 5 0.4101 0.610 0 0.372 0 0.000 0.628
#> SRR191687 2 0.0162 0.919 0 0.996 0 0.000 0.004
#> SRR191688 4 0.3109 0.732 0 0.200 0 0.800 0.000
#> SRR191689 5 0.2732 0.831 0 0.160 0 0.000 0.840
#> SRR191690 4 0.2179 0.846 0 0.112 0 0.888 0.000
#> SRR191691 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191692 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191693 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191694 5 0.2648 0.833 0 0.152 0 0.000 0.848
#> SRR191695 5 0.6121 0.213 0 0.408 0 0.128 0.464
#> SRR191696 5 0.5818 0.198 0 0.448 0 0.092 0.460
#> SRR191697 2 0.4304 -0.305 0 0.516 0 0.000 0.484
#> SRR191698 2 0.2471 0.807 0 0.864 0 0.000 0.136
#> SRR191699 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191700 2 0.2648 0.787 0 0.848 0 0.000 0.152
#> SRR191701 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191702 2 0.0162 0.920 0 0.996 0 0.004 0.000
#> SRR191703 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191704 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191705 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191706 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191707 2 0.2280 0.815 0 0.880 0 0.120 0.000
#> SRR191708 2 0.1732 0.859 0 0.920 0 0.080 0.000
#> SRR191709 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191710 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191711 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191712 2 0.2074 0.834 0 0.896 0 0.104 0.000
#> SRR191713 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191714 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191715 2 0.0000 0.922 0 1.000 0 0.000 0.000
#> SRR191716 4 0.2813 0.776 0 0.168 0 0.832 0.000
#> SRR191717 4 0.3816 0.557 0 0.304 0 0.696 0.000
#> SRR191718 5 0.4238 0.611 0 0.368 0 0.004 0.628
#> SRR537099 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537100 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537101 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537102 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537104 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537105 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537106 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537107 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537108 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537109 4 0.1043 0.926 0 0.040 0 0.960 0.000
#> SRR537110 2 0.2605 0.777 0 0.852 0 0.148 0.000
#> SRR537111 1 0.0000 1.000 1 0.000 0 0.000 0.000
#> SRR537113 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537114 4 0.0000 0.965 0 0.000 0 1.000 0.000
#> SRR537115 5 0.2813 0.716 0 0.000 0 0.168 0.832
#> SRR537116 2 0.2230 0.821 0 0.884 0 0.116 0.000
#> SRR537117 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537118 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537119 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537120 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537121 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537122 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537123 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537124 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537125 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537126 5 0.0000 0.807 0 0.000 0 0.000 1.000
#> SRR537127 3 0.0000 1.000 0 0.000 1 0.000 0.000
#> SRR537128 3 0.0000 1.000 0 0.000 1 0.000 0.000
#> SRR537129 3 0.0000 1.000 0 0.000 1 0.000 0.000
#> SRR537130 3 0.0000 1.000 0 0.000 1 0.000 0.000
#> SRR537131 3 0.0000 1.000 0 0.000 1 0.000 0.000
#> SRR537132 3 0.0000 1.000 0 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191640 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191641 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191642 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191643 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191644 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191645 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191646 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191647 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191648 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191649 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191650 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191651 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191652 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191653 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191654 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191655 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR191656 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191657 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191658 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191659 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191660 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191661 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191662 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191663 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191664 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191665 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191666 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191667 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191668 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191669 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191670 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191671 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191672 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191673 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR191674 6 0.0000 0.792 0 0.000 0 0.000 0.000 1.000
#> SRR191675 6 0.0000 0.792 0 0.000 0 0.000 0.000 1.000
#> SRR191677 6 0.0000 0.792 0 0.000 0 0.000 0.000 1.000
#> SRR191678 6 0.2823 0.412 0 0.000 0 0.204 0.000 0.796
#> SRR191679 6 0.0146 0.789 0 0.004 0 0.000 0.000 0.996
#> SRR191680 6 0.0000 0.792 0 0.000 0 0.000 0.000 1.000
#> SRR191681 6 0.0000 0.792 0 0.000 0 0.000 0.000 1.000
#> SRR191682 5 0.4509 -0.357 0 0.032 0 0.000 0.532 0.436
#> SRR191683 6 0.4066 0.411 0 0.012 0 0.000 0.392 0.596
#> SRR191684 5 0.6023 -0.234 0 0.364 0 0.000 0.392 0.244
#> SRR191685 5 0.6023 -0.234 0 0.364 0 0.000 0.392 0.244
#> SRR191686 6 0.5254 0.322 0 0.100 0 0.000 0.392 0.508
#> SRR191687 5 0.6021 -0.227 0 0.360 0 0.000 0.396 0.244
#> SRR191688 4 0.2793 0.727 0 0.200 0 0.800 0.000 0.000
#> SRR191689 6 0.0146 0.791 0 0.000 0 0.000 0.004 0.996
#> SRR191690 4 0.2003 0.832 0 0.116 0 0.884 0.000 0.000
#> SRR191691 2 0.2331 0.805 0 0.888 0 0.000 0.032 0.080
#> SRR191692 6 0.0000 0.792 0 0.000 0 0.000 0.000 1.000
#> SRR191693 6 0.3349 0.562 0 0.008 0 0.000 0.244 0.748
#> SRR191694 6 0.0363 0.786 0 0.000 0 0.000 0.012 0.988
#> SRR191695 2 0.5175 0.506 0 0.620 0 0.184 0.000 0.196
#> SRR191696 2 0.4228 0.639 0 0.716 0 0.072 0.000 0.212
#> SRR191697 2 0.3615 0.617 0 0.700 0 0.008 0.000 0.292
#> SRR191698 2 0.1858 0.807 0 0.904 0 0.000 0.092 0.004
#> SRR191699 2 0.5086 0.447 0 0.532 0 0.000 0.384 0.084
#> SRR191700 2 0.2118 0.796 0 0.888 0 0.000 0.104 0.008
#> SRR191701 2 0.1918 0.815 0 0.904 0 0.000 0.008 0.088
#> SRR191702 2 0.0260 0.835 0 0.992 0 0.000 0.000 0.008
#> SRR191703 2 0.0260 0.835 0 0.992 0 0.000 0.000 0.008
#> SRR191704 2 0.0000 0.835 0 1.000 0 0.000 0.000 0.000
#> SRR191705 2 0.0260 0.836 0 0.992 0 0.008 0.000 0.000
#> SRR191706 2 0.0865 0.826 0 0.964 0 0.000 0.000 0.036
#> SRR191707 2 0.0260 0.836 0 0.992 0 0.008 0.000 0.000
#> SRR191708 2 0.0260 0.836 0 0.992 0 0.008 0.000 0.000
#> SRR191709 2 0.0000 0.835 0 1.000 0 0.000 0.000 0.000
#> SRR191710 2 0.0260 0.836 0 0.992 0 0.008 0.000 0.000
#> SRR191711 2 0.1701 0.821 0 0.920 0 0.008 0.000 0.072
#> SRR191712 2 0.2009 0.806 0 0.908 0 0.068 0.000 0.024
#> SRR191713 2 0.5974 0.244 0 0.428 0 0.000 0.336 0.236
#> SRR191714 2 0.3403 0.702 0 0.768 0 0.000 0.020 0.212
#> SRR191715 2 0.3547 0.575 0 0.668 0 0.000 0.000 0.332
#> SRR191716 4 0.2631 0.754 0 0.180 0 0.820 0.000 0.000
#> SRR191717 4 0.3499 0.511 0 0.320 0 0.680 0.000 0.000
#> SRR191718 2 0.4051 0.341 0 0.560 0 0.008 0.000 0.432
#> SRR537099 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537100 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537101 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537102 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537104 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537105 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537106 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537107 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537108 4 0.0000 0.948 0 0.000 0 1.000 0.000 0.000
#> SRR537109 4 0.0937 0.913 0 0.040 0 0.960 0.000 0.000
#> SRR537110 2 0.0363 0.835 0 0.988 0 0.012 0.000 0.000
#> SRR537111 1 0.0000 1.000 1 0.000 0 0.000 0.000 0.000
#> SRR537113 4 0.0146 0.945 0 0.000 0 0.996 0.004 0.000
#> SRR537114 4 0.0260 0.943 0 0.000 0 0.992 0.008 0.000
#> SRR537115 4 0.4654 0.145 0 0.000 0 0.544 0.044 0.412
#> SRR537116 2 0.0260 0.836 0 0.992 0 0.008 0.000 0.000
#> SRR537117 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537118 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537119 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537120 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537121 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537122 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537123 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537124 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537125 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537126 5 0.3737 0.590 0 0.000 0 0.000 0.608 0.392
#> SRR537127 3 0.0000 1.000 0 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.000 0 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.000 0 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.000 0 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.000 0 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.000 0 0.000 1 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)
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 16450 rows and 111 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 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 0.478 0.778 0.884 0.3913 0.638 0.638
#> 3 3 0.565 0.681 0.866 0.4955 0.611 0.466
#> 4 4 0.675 0.778 0.903 0.1293 0.760 0.517
#> 5 5 0.618 0.599 0.808 0.1054 0.848 0.570
#> 6 6 0.689 0.668 0.846 0.0834 0.883 0.575
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
#> SRR191639 1 0.9491 0.282 0.632 0.368
#> SRR191640 2 0.9552 0.569 0.376 0.624
#> SRR191641 2 0.9552 0.569 0.376 0.624
#> SRR191642 2 0.9552 0.569 0.376 0.624
#> SRR191643 2 0.8386 0.704 0.268 0.732
#> SRR191644 2 0.8955 0.655 0.312 0.688
#> SRR191645 2 0.9686 0.529 0.396 0.604
#> SRR191646 2 0.9686 0.529 0.396 0.604
#> SRR191647 2 0.9552 0.569 0.376 0.624
#> SRR191648 2 0.9552 0.569 0.376 0.624
#> SRR191649 2 0.9552 0.569 0.376 0.624
#> SRR191650 2 0.9552 0.569 0.376 0.624
#> SRR191651 1 0.2423 0.919 0.960 0.040
#> SRR191652 1 0.8386 0.569 0.732 0.268
#> SRR191653 2 0.9552 0.569 0.376 0.624
#> SRR191654 2 0.7299 0.762 0.204 0.796
#> SRR191655 2 0.9552 0.569 0.376 0.624
#> SRR191656 1 0.2236 0.922 0.964 0.036
#> SRR191657 1 0.2236 0.922 0.964 0.036
#> SRR191658 1 0.2236 0.922 0.964 0.036
#> SRR191659 1 0.2236 0.922 0.964 0.036
#> SRR191660 1 0.2423 0.919 0.960 0.040
#> SRR191661 2 0.9710 0.520 0.400 0.600
#> SRR191662 1 0.7602 0.669 0.780 0.220
#> SRR191663 1 0.9963 -0.124 0.536 0.464
#> SRR191664 1 0.2236 0.922 0.964 0.036
#> SRR191665 1 0.2236 0.922 0.964 0.036
#> SRR191666 1 0.1633 0.917 0.976 0.024
#> SRR191667 1 0.1633 0.917 0.976 0.024
#> SRR191668 1 0.2236 0.922 0.964 0.036
#> SRR191669 1 0.2236 0.922 0.964 0.036
#> SRR191670 1 0.2236 0.922 0.964 0.036
#> SRR191671 1 0.2236 0.922 0.964 0.036
#> SRR191672 1 0.2236 0.922 0.964 0.036
#> SRR191673 1 0.2236 0.922 0.964 0.036
#> SRR191674 2 0.0000 0.842 0.000 1.000
#> SRR191675 2 0.0000 0.842 0.000 1.000
#> SRR191677 2 0.0000 0.842 0.000 1.000
#> SRR191678 2 0.0000 0.842 0.000 1.000
#> SRR191679 2 0.0000 0.842 0.000 1.000
#> SRR191680 2 0.0000 0.842 0.000 1.000
#> SRR191681 2 0.0000 0.842 0.000 1.000
#> SRR191682 2 0.0000 0.842 0.000 1.000
#> SRR191683 2 0.0000 0.842 0.000 1.000
#> SRR191684 2 0.0000 0.842 0.000 1.000
#> SRR191685 2 0.0000 0.842 0.000 1.000
#> SRR191686 2 0.0000 0.842 0.000 1.000
#> SRR191687 2 0.0000 0.842 0.000 1.000
#> SRR191688 2 0.0000 0.842 0.000 1.000
#> SRR191689 2 0.0000 0.842 0.000 1.000
#> SRR191690 2 0.0000 0.842 0.000 1.000
#> SRR191691 2 0.0000 0.842 0.000 1.000
#> SRR191692 2 0.0000 0.842 0.000 1.000
#> SRR191693 2 0.0000 0.842 0.000 1.000
#> SRR191694 2 0.0000 0.842 0.000 1.000
#> SRR191695 2 0.0000 0.842 0.000 1.000
#> SRR191696 2 0.0000 0.842 0.000 1.000
#> SRR191697 2 0.0000 0.842 0.000 1.000
#> SRR191698 2 0.0000 0.842 0.000 1.000
#> SRR191699 2 0.0000 0.842 0.000 1.000
#> SRR191700 2 0.0376 0.841 0.004 0.996
#> SRR191701 2 0.0000 0.842 0.000 1.000
#> SRR191702 2 0.0000 0.842 0.000 1.000
#> SRR191703 2 0.0000 0.842 0.000 1.000
#> SRR191704 2 0.0000 0.842 0.000 1.000
#> SRR191705 2 0.0000 0.842 0.000 1.000
#> SRR191706 2 0.0000 0.842 0.000 1.000
#> SRR191707 2 0.0000 0.842 0.000 1.000
#> SRR191708 2 0.0000 0.842 0.000 1.000
#> SRR191709 2 0.0000 0.842 0.000 1.000
#> SRR191710 2 0.0000 0.842 0.000 1.000
#> SRR191711 2 0.0000 0.842 0.000 1.000
#> SRR191712 2 0.0000 0.842 0.000 1.000
#> SRR191713 2 0.0000 0.842 0.000 1.000
#> SRR191714 2 0.0000 0.842 0.000 1.000
#> SRR191715 2 0.0000 0.842 0.000 1.000
#> SRR191716 2 0.0000 0.842 0.000 1.000
#> SRR191717 2 0.0000 0.842 0.000 1.000
#> SRR191718 2 0.0000 0.842 0.000 1.000
#> SRR537099 2 0.7299 0.762 0.204 0.796
#> SRR537100 2 0.9044 0.645 0.320 0.680
#> SRR537101 2 0.9552 0.569 0.376 0.624
#> SRR537102 2 0.9393 0.598 0.356 0.644
#> SRR537104 2 0.7299 0.762 0.204 0.796
#> SRR537105 2 0.9552 0.569 0.376 0.624
#> SRR537106 2 0.9552 0.569 0.376 0.624
#> SRR537107 2 0.9552 0.569 0.376 0.624
#> SRR537108 2 0.9552 0.569 0.376 0.624
#> SRR537109 2 0.0938 0.839 0.012 0.988
#> SRR537110 2 0.2948 0.828 0.052 0.948
#> SRR537111 2 0.9608 0.554 0.384 0.616
#> SRR537113 2 0.7299 0.762 0.204 0.796
#> SRR537114 2 0.7219 0.765 0.200 0.800
#> SRR537115 2 0.6438 0.789 0.164 0.836
#> SRR537116 2 0.0000 0.842 0.000 1.000
#> SRR537117 2 0.6438 0.789 0.164 0.836
#> SRR537118 2 0.6438 0.789 0.164 0.836
#> SRR537119 2 0.6438 0.789 0.164 0.836
#> SRR537120 2 0.6438 0.789 0.164 0.836
#> SRR537121 2 0.6438 0.789 0.164 0.836
#> SRR537122 2 0.6438 0.789 0.164 0.836
#> SRR537123 2 0.6438 0.789 0.164 0.836
#> SRR537124 2 0.6438 0.789 0.164 0.836
#> SRR537125 2 0.6438 0.789 0.164 0.836
#> SRR537126 2 0.6438 0.789 0.164 0.836
#> SRR537127 1 0.0000 0.903 1.000 0.000
#> SRR537128 1 0.0000 0.903 1.000 0.000
#> SRR537129 1 0.0000 0.903 1.000 0.000
#> SRR537130 1 0.0000 0.903 1.000 0.000
#> SRR537131 1 0.0000 0.903 1.000 0.000
#> SRR537132 1 0.0000 0.903 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0237 0.6887 0.996 0.004 0.000
#> SRR191640 1 0.4883 0.5603 0.788 0.208 0.004
#> SRR191641 1 0.0424 0.6902 0.992 0.008 0.000
#> SRR191642 1 0.6102 0.4548 0.672 0.320 0.008
#> SRR191643 1 0.6467 0.3838 0.604 0.388 0.008
#> SRR191644 1 0.3851 0.6162 0.860 0.136 0.004
#> SRR191645 1 0.0424 0.6902 0.992 0.008 0.000
#> SRR191646 1 0.0424 0.6902 0.992 0.008 0.000
#> SRR191647 1 0.1525 0.6851 0.964 0.032 0.004
#> SRR191648 1 0.1525 0.6851 0.964 0.032 0.004
#> SRR191649 1 0.0661 0.6895 0.988 0.008 0.004
#> SRR191650 1 0.0424 0.6902 0.992 0.008 0.000
#> SRR191651 1 0.3038 0.6442 0.896 0.000 0.104
#> SRR191652 1 0.4645 0.5614 0.816 0.008 0.176
#> SRR191653 2 0.9994 -0.2480 0.340 0.344 0.316
#> SRR191654 1 0.9980 0.0696 0.364 0.324 0.312
#> SRR191655 1 0.0661 0.6895 0.988 0.008 0.004
#> SRR191656 1 0.3482 0.6265 0.872 0.000 0.128
#> SRR191657 1 0.0000 0.6864 1.000 0.000 0.000
#> SRR191658 1 0.2711 0.6547 0.912 0.000 0.088
#> SRR191659 1 0.5016 0.5040 0.760 0.000 0.240
#> SRR191660 1 0.0000 0.6864 1.000 0.000 0.000
#> SRR191661 1 0.0424 0.6902 0.992 0.008 0.000
#> SRR191662 1 0.0237 0.6887 0.996 0.004 0.000
#> SRR191663 1 0.0424 0.6902 0.992 0.008 0.000
#> SRR191664 1 0.0892 0.6819 0.980 0.000 0.020
#> SRR191665 1 0.2711 0.6547 0.912 0.000 0.088
#> SRR191666 3 0.5763 0.5585 0.276 0.008 0.716
#> SRR191667 3 0.5763 0.5585 0.276 0.008 0.716
#> SRR191668 1 0.3482 0.6265 0.872 0.000 0.128
#> SRR191669 1 0.3482 0.6265 0.872 0.000 0.128
#> SRR191670 1 0.2711 0.6547 0.912 0.000 0.088
#> SRR191671 1 0.2711 0.6547 0.912 0.000 0.088
#> SRR191672 1 0.3482 0.6265 0.872 0.000 0.128
#> SRR191673 1 0.3482 0.6265 0.872 0.000 0.128
#> SRR191674 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191678 2 0.0424 0.9119 0.008 0.992 0.000
#> SRR191679 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191682 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191690 2 0.2261 0.8529 0.068 0.932 0.000
#> SRR191691 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191692 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191693 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191695 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191700 2 0.6111 0.2186 0.396 0.604 0.000
#> SRR191701 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191709 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191713 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191716 2 0.2261 0.8555 0.068 0.932 0.000
#> SRR191717 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR191718 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR537099 2 0.9343 0.0395 0.348 0.476 0.176
#> SRR537100 1 0.8487 0.2745 0.584 0.124 0.292
#> SRR537101 1 0.0661 0.6895 0.988 0.008 0.004
#> SRR537102 1 0.5929 0.4564 0.676 0.320 0.004
#> SRR537104 2 0.9392 0.1756 0.196 0.492 0.312
#> SRR537105 1 0.6255 0.4529 0.668 0.320 0.012
#> SRR537106 1 0.6769 0.4442 0.652 0.320 0.028
#> SRR537107 1 0.7948 0.4063 0.600 0.320 0.080
#> SRR537108 1 0.7948 0.4063 0.600 0.320 0.080
#> SRR537109 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR537110 2 0.4291 0.7220 0.180 0.820 0.000
#> SRR537111 1 0.0661 0.6899 0.988 0.008 0.004
#> SRR537113 2 0.8659 0.3734 0.228 0.596 0.176
#> SRR537114 2 0.9331 0.0525 0.344 0.480 0.176
#> SRR537115 2 0.6192 0.6661 0.060 0.764 0.176
#> SRR537116 2 0.0000 0.9191 0.000 1.000 0.000
#> SRR537117 2 0.6458 0.6533 0.072 0.752 0.176
#> SRR537118 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537119 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537120 1 0.9447 0.2011 0.464 0.348 0.188
#> SRR537121 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537122 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537123 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537124 1 0.9399 0.1886 0.452 0.372 0.176
#> SRR537125 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537126 1 0.9601 0.1671 0.464 0.224 0.312
#> SRR537127 3 0.0237 0.8939 0.004 0.000 0.996
#> SRR537128 3 0.0237 0.8939 0.004 0.000 0.996
#> SRR537129 3 0.0237 0.8939 0.004 0.000 0.996
#> SRR537130 3 0.0237 0.8939 0.004 0.000 0.996
#> SRR537131 3 0.0237 0.8939 0.004 0.000 0.996
#> SRR537132 3 0.0237 0.8939 0.004 0.000 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.4008 0.6900 0.756 0.000 0 0.244
#> SRR191640 4 0.2011 0.8237 0.080 0.000 0 0.920
#> SRR191641 4 0.2011 0.8236 0.080 0.000 0 0.920
#> SRR191642 4 0.2101 0.8495 0.012 0.060 0 0.928
#> SRR191643 4 0.2101 0.8495 0.012 0.060 0 0.928
#> SRR191644 4 0.2546 0.8463 0.028 0.060 0 0.912
#> SRR191645 4 0.4830 0.2791 0.392 0.000 0 0.608
#> SRR191646 4 0.4843 0.2665 0.396 0.000 0 0.604
#> SRR191647 4 0.2408 0.8086 0.104 0.000 0 0.896
#> SRR191648 4 0.2408 0.8086 0.104 0.000 0 0.896
#> SRR191649 4 0.2281 0.8141 0.096 0.000 0 0.904
#> SRR191650 4 0.3219 0.7488 0.164 0.000 0 0.836
#> SRR191651 1 0.1302 0.7961 0.956 0.000 0 0.044
#> SRR191652 1 0.5000 0.0737 0.500 0.000 0 0.500
#> SRR191653 4 0.0927 0.8435 0.016 0.008 0 0.976
#> SRR191654 4 0.0524 0.8434 0.004 0.008 0 0.988
#> SRR191655 4 0.2021 0.8502 0.012 0.056 0 0.932
#> SRR191656 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191657 1 0.2760 0.7641 0.872 0.000 0 0.128
#> SRR191658 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191659 1 0.0188 0.8025 0.996 0.000 0 0.004
#> SRR191660 1 0.4250 0.6288 0.724 0.000 0 0.276
#> SRR191661 1 0.4985 0.1710 0.532 0.000 0 0.468
#> SRR191662 1 0.3311 0.7394 0.828 0.000 0 0.172
#> SRR191663 1 0.4564 0.5436 0.672 0.000 0 0.328
#> SRR191664 1 0.0921 0.7992 0.972 0.000 0 0.028
#> SRR191665 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191666 4 0.4454 0.4273 0.308 0.000 0 0.692
#> SRR191667 4 0.4522 0.3956 0.320 0.000 0 0.680
#> SRR191668 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191669 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191670 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191671 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191672 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191673 1 0.0000 0.8024 1.000 0.000 0 0.000
#> SRR191674 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191675 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191677 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191678 2 0.4477 0.5850 0.000 0.688 0 0.312
#> SRR191679 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191680 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191681 2 0.1118 0.8831 0.000 0.964 0 0.036
#> SRR191682 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191683 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191684 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191685 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191686 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191687 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191688 2 0.4804 0.4172 0.000 0.616 0 0.384
#> SRR191689 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191690 4 0.5126 0.1630 0.004 0.444 0 0.552
#> SRR191691 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191692 2 0.0592 0.8929 0.000 0.984 0 0.016
#> SRR191693 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191694 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191695 2 0.3400 0.7797 0.000 0.820 0 0.180
#> SRR191696 2 0.3024 0.8109 0.000 0.852 0 0.148
#> SRR191697 2 0.2973 0.8138 0.000 0.856 0 0.144
#> SRR191698 2 0.4585 0.5408 0.000 0.668 0 0.332
#> SRR191699 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191700 4 0.4837 0.3845 0.004 0.348 0 0.648
#> SRR191701 2 0.1211 0.8809 0.000 0.960 0 0.040
#> SRR191702 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191703 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191704 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191705 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191706 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191707 2 0.4072 0.6864 0.000 0.748 0 0.252
#> SRR191708 2 0.2868 0.8193 0.000 0.864 0 0.136
#> SRR191709 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191710 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191711 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191712 2 0.3024 0.8111 0.000 0.852 0 0.148
#> SRR191713 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191714 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191715 2 0.0000 0.8999 0.000 1.000 0 0.000
#> SRR191716 4 0.5119 0.1775 0.004 0.440 0 0.556
#> SRR191717 2 0.4454 0.5883 0.000 0.692 0 0.308
#> SRR191718 2 0.2973 0.8138 0.000 0.856 0 0.144
#> SRR537099 4 0.1824 0.8484 0.004 0.060 0 0.936
#> SRR537100 4 0.1489 0.8505 0.004 0.044 0 0.952
#> SRR537101 4 0.1940 0.8257 0.076 0.000 0 0.924
#> SRR537102 4 0.2101 0.8495 0.012 0.060 0 0.928
#> SRR537104 4 0.1661 0.8502 0.004 0.052 0 0.944
#> SRR537105 4 0.2222 0.8491 0.016 0.060 0 0.924
#> SRR537106 4 0.2101 0.8495 0.012 0.060 0 0.928
#> SRR537107 4 0.2101 0.8495 0.012 0.060 0 0.928
#> SRR537108 4 0.2101 0.8495 0.012 0.060 0 0.928
#> SRR537109 2 0.4977 0.1721 0.000 0.540 0 0.460
#> SRR537110 4 0.4313 0.6476 0.004 0.260 0 0.736
#> SRR537111 1 0.3907 0.6979 0.768 0.000 0 0.232
#> SRR537113 4 0.2921 0.7865 0.000 0.140 0 0.860
#> SRR537114 4 0.1824 0.8484 0.004 0.060 0 0.936
#> SRR537115 4 0.4222 0.6356 0.000 0.272 0 0.728
#> SRR537116 2 0.3266 0.7923 0.000 0.832 0 0.168
#> SRR537117 4 0.3219 0.7265 0.000 0.164 0 0.836
#> SRR537118 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537119 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537120 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537121 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537122 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537123 4 0.0336 0.8423 0.000 0.008 0 0.992
#> SRR537124 4 0.0592 0.8437 0.000 0.016 0 0.984
#> SRR537125 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537126 4 0.0188 0.8408 0.000 0.004 0 0.996
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.4088 0.6194 0.632 0.000 0.000 0.368 0.000
#> SRR191640 4 0.0000 0.6679 0.000 0.000 0.000 1.000 0.000
#> SRR191641 4 0.2471 0.5724 0.136 0.000 0.000 0.864 0.000
#> SRR191642 4 0.2516 0.6892 0.000 0.000 0.000 0.860 0.140
#> SRR191643 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR191644 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR191645 4 0.2929 0.4662 0.180 0.000 0.000 0.820 0.000
#> SRR191646 4 0.2929 0.4629 0.180 0.000 0.000 0.820 0.000
#> SRR191647 4 0.0000 0.6679 0.000 0.000 0.000 1.000 0.000
#> SRR191648 4 0.0000 0.6679 0.000 0.000 0.000 1.000 0.000
#> SRR191649 4 0.0000 0.6679 0.000 0.000 0.000 1.000 0.000
#> SRR191650 4 0.4843 0.3970 0.292 0.000 0.000 0.660 0.048
#> SRR191651 1 0.0510 0.8188 0.984 0.000 0.000 0.016 0.000
#> SRR191652 1 0.4302 0.3834 0.520 0.000 0.000 0.480 0.000
#> SRR191653 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR191654 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR191655 4 0.3707 0.6013 0.000 0.000 0.000 0.716 0.284
#> SRR191656 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191657 1 0.2648 0.8199 0.848 0.000 0.000 0.152 0.000
#> SRR191658 1 0.2179 0.8236 0.888 0.000 0.000 0.112 0.000
#> SRR191659 1 0.2648 0.8199 0.848 0.000 0.000 0.152 0.000
#> SRR191660 1 0.3003 0.8077 0.812 0.000 0.000 0.188 0.000
#> SRR191661 1 0.4126 0.5883 0.620 0.000 0.000 0.380 0.000
#> SRR191662 1 0.2732 0.8189 0.840 0.000 0.000 0.160 0.000
#> SRR191663 1 0.3177 0.7975 0.792 0.000 0.000 0.208 0.000
#> SRR191664 1 0.2690 0.8199 0.844 0.000 0.000 0.156 0.000
#> SRR191665 1 0.0404 0.8184 0.988 0.000 0.000 0.012 0.000
#> SRR191666 4 0.4449 -0.3388 0.484 0.000 0.004 0.512 0.000
#> SRR191667 1 0.4450 0.3554 0.508 0.000 0.004 0.488 0.000
#> SRR191668 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191669 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191670 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191671 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191672 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191673 1 0.0162 0.8160 0.996 0.000 0.000 0.004 0.000
#> SRR191674 2 0.0162 0.8072 0.000 0.996 0.000 0.000 0.004
#> SRR191675 2 0.0162 0.8072 0.000 0.996 0.000 0.000 0.004
#> SRR191677 2 0.3508 0.6130 0.000 0.748 0.000 0.000 0.252
#> SRR191678 5 0.4455 0.3512 0.000 0.404 0.000 0.008 0.588
#> SRR191679 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191680 2 0.0162 0.8072 0.000 0.996 0.000 0.000 0.004
#> SRR191681 2 0.3480 0.6203 0.000 0.752 0.000 0.000 0.248
#> SRR191682 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191683 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191684 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191685 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191686 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191687 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191688 5 0.4192 0.3475 0.000 0.404 0.000 0.000 0.596
#> SRR191689 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191690 5 0.6392 0.3823 0.000 0.400 0.000 0.168 0.432
#> SRR191691 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191692 2 0.2648 0.7131 0.000 0.848 0.000 0.000 0.152
#> SRR191693 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191694 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191695 5 0.4192 0.3475 0.000 0.404 0.000 0.000 0.596
#> SRR191696 5 0.4201 0.3352 0.000 0.408 0.000 0.000 0.592
#> SRR191697 2 0.4302 0.0596 0.000 0.520 0.000 0.000 0.480
#> SRR191698 2 0.6334 -0.3624 0.000 0.452 0.000 0.160 0.388
#> SRR191699 2 0.1270 0.7744 0.000 0.948 0.000 0.000 0.052
#> SRR191700 2 0.6757 -0.4444 0.004 0.396 0.000 0.216 0.384
#> SRR191701 2 0.2813 0.6938 0.000 0.832 0.000 0.000 0.168
#> SRR191702 2 0.2471 0.7214 0.000 0.864 0.000 0.000 0.136
#> SRR191703 2 0.0162 0.8072 0.000 0.996 0.000 0.000 0.004
#> SRR191704 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191705 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191706 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191707 5 0.4192 0.3475 0.000 0.404 0.000 0.000 0.596
#> SRR191708 2 0.4108 0.4059 0.000 0.684 0.000 0.008 0.308
#> SRR191709 2 0.0000 0.8080 0.000 1.000 0.000 0.000 0.000
#> SRR191710 2 0.2891 0.6887 0.000 0.824 0.000 0.000 0.176
#> SRR191711 2 0.2891 0.6889 0.000 0.824 0.000 0.000 0.176
#> SRR191712 2 0.4262 0.1440 0.000 0.560 0.000 0.000 0.440
#> SRR191713 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191714 2 0.0162 0.8069 0.000 0.996 0.000 0.000 0.004
#> SRR191715 2 0.3210 0.6586 0.000 0.788 0.000 0.000 0.212
#> SRR191716 2 0.6729 -0.3223 0.000 0.396 0.000 0.256 0.348
#> SRR191717 5 0.4192 0.3475 0.000 0.404 0.000 0.000 0.596
#> SRR191718 2 0.4283 0.1607 0.000 0.544 0.000 0.000 0.456
#> SRR537099 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR537100 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR537101 4 0.0000 0.6679 0.000 0.000 0.000 1.000 0.000
#> SRR537102 4 0.2516 0.6892 0.000 0.000 0.000 0.860 0.140
#> SRR537104 4 0.4192 0.4972 0.000 0.000 0.000 0.596 0.404
#> SRR537105 4 0.2424 0.6884 0.000 0.000 0.000 0.868 0.132
#> SRR537106 4 0.2516 0.6892 0.000 0.000 0.000 0.860 0.140
#> SRR537107 4 0.2516 0.6892 0.000 0.000 0.000 0.860 0.140
#> SRR537108 4 0.2516 0.6892 0.000 0.000 0.000 0.860 0.140
#> SRR537109 5 0.4192 0.3475 0.000 0.404 0.000 0.000 0.596
#> SRR537110 5 0.6778 0.4288 0.000 0.340 0.000 0.280 0.380
#> SRR537111 1 0.5408 0.5079 0.652 0.000 0.000 0.228 0.120
#> SRR537113 4 0.4562 0.3402 0.000 0.008 0.000 0.500 0.492
#> SRR537114 4 0.4126 0.5293 0.000 0.000 0.000 0.620 0.380
#> SRR537115 5 0.3829 0.5241 0.000 0.196 0.000 0.028 0.776
#> SRR537116 5 0.4192 0.3475 0.000 0.404 0.000 0.000 0.596
#> SRR537117 5 0.5375 0.4740 0.000 0.156 0.000 0.176 0.668
#> SRR537118 5 0.3366 0.3846 0.004 0.000 0.000 0.212 0.784
#> SRR537119 5 0.3366 0.3846 0.004 0.000 0.000 0.212 0.784
#> SRR537120 5 0.3366 0.3846 0.004 0.000 0.000 0.212 0.784
#> SRR537121 5 0.3366 0.3846 0.004 0.000 0.000 0.212 0.784
#> SRR537122 5 0.3398 0.3794 0.004 0.000 0.000 0.216 0.780
#> SRR537123 5 0.3398 0.3794 0.004 0.000 0.000 0.216 0.780
#> SRR537124 5 0.3643 0.3885 0.004 0.008 0.000 0.212 0.776
#> SRR537125 5 0.3366 0.3846 0.004 0.000 0.000 0.212 0.784
#> SRR537126 5 0.3366 0.3846 0.004 0.000 0.000 0.212 0.784
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> SRR537132 3 0.0000 1.0000 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
#> SRR191639 4 0.3869 -0.23228 0.500 0.000 0 0.500 0.000 0.000
#> SRR191640 4 0.0000 0.76509 0.000 0.000 0 1.000 0.000 0.000
#> SRR191641 4 0.0260 0.76306 0.008 0.000 0 0.992 0.000 0.000
#> SRR191642 4 0.1444 0.77760 0.000 0.072 0 0.928 0.000 0.000
#> SRR191643 4 0.2883 0.71372 0.000 0.212 0 0.788 0.000 0.000
#> SRR191644 4 0.3738 0.71229 0.040 0.208 0 0.752 0.000 0.000
#> SRR191645 4 0.0146 0.76433 0.004 0.000 0 0.996 0.000 0.000
#> SRR191646 4 0.0146 0.76433 0.004 0.000 0 0.996 0.000 0.000
#> SRR191647 4 0.0000 0.76509 0.000 0.000 0 1.000 0.000 0.000
#> SRR191648 4 0.0000 0.76509 0.000 0.000 0 1.000 0.000 0.000
#> SRR191649 4 0.0000 0.76509 0.000 0.000 0 1.000 0.000 0.000
#> SRR191650 4 0.3261 0.73313 0.104 0.072 0 0.824 0.000 0.000
#> SRR191651 1 0.0260 0.79354 0.992 0.000 0 0.008 0.000 0.000
#> SRR191652 4 0.3727 0.10262 0.388 0.000 0 0.612 0.000 0.000
#> SRR191653 4 0.4371 0.67688 0.000 0.180 0 0.716 0.104 0.000
#> SRR191654 4 0.4371 0.67688 0.000 0.180 0 0.716 0.104 0.000
#> SRR191655 4 0.1765 0.77326 0.000 0.096 0 0.904 0.000 0.000
#> SRR191656 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191657 1 0.3221 0.70663 0.736 0.000 0 0.264 0.000 0.000
#> SRR191658 1 0.2003 0.78098 0.884 0.000 0 0.116 0.000 0.000
#> SRR191659 1 0.3101 0.72265 0.756 0.000 0 0.244 0.000 0.000
#> SRR191660 1 0.3774 0.49410 0.592 0.000 0 0.408 0.000 0.000
#> SRR191661 4 0.3351 0.40729 0.288 0.000 0 0.712 0.000 0.000
#> SRR191662 1 0.3330 0.67991 0.716 0.000 0 0.284 0.000 0.000
#> SRR191663 1 0.3823 0.43034 0.564 0.000 0 0.436 0.000 0.000
#> SRR191664 1 0.3151 0.71695 0.748 0.000 0 0.252 0.000 0.000
#> SRR191665 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191666 4 0.3727 0.10262 0.388 0.000 0 0.612 0.000 0.000
#> SRR191667 4 0.3727 0.10262 0.388 0.000 0 0.612 0.000 0.000
#> SRR191668 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191672 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191673 1 0.0000 0.79572 1.000 0.000 0 0.000 0.000 0.000
#> SRR191674 6 0.3371 0.66333 0.000 0.292 0 0.000 0.000 0.708
#> SRR191675 6 0.2969 0.74579 0.000 0.224 0 0.000 0.000 0.776
#> SRR191677 2 0.3828 -0.02862 0.000 0.560 0 0.000 0.000 0.440
#> SRR191678 2 0.0790 0.74870 0.000 0.968 0 0.000 0.000 0.032
#> SRR191679 6 0.2562 0.78909 0.000 0.172 0 0.000 0.000 0.828
#> SRR191680 6 0.3684 0.52383 0.000 0.372 0 0.000 0.000 0.628
#> SRR191681 2 0.3765 0.10743 0.000 0.596 0 0.000 0.000 0.404
#> SRR191682 6 0.0260 0.80397 0.000 0.008 0 0.000 0.000 0.992
#> SRR191683 6 0.0260 0.80397 0.000 0.008 0 0.000 0.000 0.992
#> SRR191684 6 0.0260 0.80476 0.000 0.008 0 0.000 0.000 0.992
#> SRR191685 6 0.1714 0.81247 0.000 0.092 0 0.000 0.000 0.908
#> SRR191686 6 0.2454 0.79640 0.000 0.160 0 0.000 0.000 0.840
#> SRR191687 6 0.1714 0.81247 0.000 0.092 0 0.000 0.000 0.908
#> SRR191688 2 0.0146 0.74850 0.000 0.996 0 0.000 0.000 0.004
#> SRR191689 6 0.2454 0.79614 0.000 0.160 0 0.000 0.000 0.840
#> SRR191690 2 0.2320 0.64622 0.000 0.864 0 0.132 0.000 0.004
#> SRR191691 6 0.0000 0.80160 0.000 0.000 0 0.000 0.000 1.000
#> SRR191692 6 0.3810 0.41599 0.000 0.428 0 0.000 0.000 0.572
#> SRR191693 6 0.2340 0.80089 0.000 0.148 0 0.000 0.000 0.852
#> SRR191694 6 0.2527 0.79164 0.000 0.168 0 0.000 0.000 0.832
#> SRR191695 2 0.0146 0.74850 0.000 0.996 0 0.000 0.000 0.004
#> SRR191696 2 0.0146 0.74850 0.000 0.996 0 0.000 0.000 0.004
#> SRR191697 2 0.0713 0.74946 0.000 0.972 0 0.000 0.000 0.028
#> SRR191698 6 0.4788 0.25389 0.000 0.396 0 0.000 0.056 0.548
#> SRR191699 6 0.2092 0.80899 0.000 0.124 0 0.000 0.000 0.876
#> SRR191700 2 0.5428 0.24677 0.000 0.556 0 0.072 0.348 0.024
#> SRR191701 6 0.2996 0.70949 0.000 0.228 0 0.000 0.000 0.772
#> SRR191702 6 0.3672 0.51648 0.000 0.368 0 0.000 0.000 0.632
#> SRR191703 6 0.3482 0.60164 0.000 0.316 0 0.000 0.000 0.684
#> SRR191704 6 0.0000 0.80160 0.000 0.000 0 0.000 0.000 1.000
#> SRR191705 6 0.0146 0.80190 0.000 0.004 0 0.000 0.000 0.996
#> SRR191706 6 0.0363 0.80377 0.000 0.012 0 0.000 0.000 0.988
#> SRR191707 2 0.0146 0.74850 0.000 0.996 0 0.000 0.000 0.004
#> SRR191708 6 0.3126 0.54106 0.000 0.248 0 0.000 0.000 0.752
#> SRR191709 6 0.1387 0.81356 0.000 0.068 0 0.000 0.000 0.932
#> SRR191710 6 0.2793 0.63000 0.000 0.200 0 0.000 0.000 0.800
#> SRR191711 2 0.3868 -0.22572 0.000 0.504 0 0.000 0.000 0.496
#> SRR191712 2 0.2491 0.64839 0.000 0.836 0 0.000 0.000 0.164
#> SRR191713 6 0.0146 0.79941 0.000 0.004 0 0.000 0.000 0.996
#> SRR191714 6 0.0146 0.79941 0.000 0.004 0 0.000 0.000 0.996
#> SRR191715 2 0.3747 0.12381 0.000 0.604 0 0.000 0.000 0.396
#> SRR191716 2 0.2320 0.64313 0.000 0.864 0 0.132 0.000 0.004
#> SRR191717 2 0.0146 0.74850 0.000 0.996 0 0.000 0.000 0.004
#> SRR191718 2 0.0713 0.74946 0.000 0.972 0 0.000 0.000 0.028
#> SRR537099 4 0.3630 0.70182 0.000 0.212 0 0.756 0.032 0.000
#> SRR537100 4 0.2631 0.73664 0.000 0.180 0 0.820 0.000 0.000
#> SRR537101 4 0.0000 0.76509 0.000 0.000 0 1.000 0.000 0.000
#> SRR537102 4 0.1444 0.77760 0.000 0.072 0 0.928 0.000 0.000
#> SRR537104 4 0.5109 0.49896 0.000 0.316 0 0.580 0.104 0.000
#> SRR537105 4 0.0363 0.76909 0.000 0.012 0 0.988 0.000 0.000
#> SRR537106 4 0.1444 0.77760 0.000 0.072 0 0.928 0.000 0.000
#> SRR537107 4 0.1444 0.77760 0.000 0.072 0 0.928 0.000 0.000
#> SRR537108 4 0.1444 0.77760 0.000 0.072 0 0.928 0.000 0.000
#> SRR537109 2 0.0146 0.74850 0.000 0.996 0 0.000 0.000 0.004
#> SRR537110 4 0.5782 0.17044 0.000 0.420 0 0.456 0.104 0.020
#> SRR537111 1 0.4978 0.18823 0.532 0.072 0 0.396 0.000 0.000
#> SRR537113 2 0.3765 -0.00358 0.000 0.596 0 0.404 0.000 0.000
#> SRR537114 4 0.3699 0.54657 0.000 0.336 0 0.660 0.004 0.000
#> SRR537115 2 0.3136 0.51371 0.000 0.796 0 0.016 0.188 0.000
#> SRR537116 2 0.0713 0.74910 0.000 0.972 0 0.000 0.000 0.028
#> SRR537117 2 0.3990 0.40222 0.000 0.688 0 0.000 0.284 0.028
#> SRR537118 5 0.0000 0.84469 0.000 0.000 0 0.000 1.000 0.000
#> SRR537119 5 0.0000 0.84469 0.000 0.000 0 0.000 1.000 0.000
#> SRR537120 5 0.0000 0.84469 0.000 0.000 0 0.000 1.000 0.000
#> SRR537121 5 0.2882 0.81114 0.000 0.180 0 0.008 0.812 0.000
#> SRR537122 5 0.2882 0.81114 0.000 0.180 0 0.008 0.812 0.000
#> SRR537123 5 0.3071 0.80534 0.000 0.180 0 0.016 0.804 0.000
#> SRR537124 5 0.2697 0.80458 0.000 0.188 0 0.000 0.812 0.000
#> SRR537125 5 0.0000 0.84469 0.000 0.000 0 0.000 1.000 0.000
#> SRR537126 5 0.0000 0.84469 0.000 0.000 0 0.000 1.000 0.000
#> SRR537127 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.00000 0.000 0.000 1 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)
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 16450 rows and 111 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 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 0.890 0.928 0.971 0.4867 0.517 0.517
#> 3 3 0.945 0.928 0.970 0.1608 0.887 0.788
#> 4 4 0.679 0.703 0.866 0.2467 0.757 0.501
#> 5 5 0.601 0.673 0.807 0.1099 0.852 0.550
#> 6 6 0.717 0.732 0.810 0.0509 0.935 0.716
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
#> SRR191639 1 0.0000 0.980 1.000 0.000
#> SRR191640 1 0.0000 0.980 1.000 0.000
#> SRR191641 1 0.0000 0.980 1.000 0.000
#> SRR191642 1 0.7745 0.688 0.772 0.228
#> SRR191643 2 0.9970 0.160 0.468 0.532
#> SRR191644 1 0.6712 0.772 0.824 0.176
#> SRR191645 1 0.0000 0.980 1.000 0.000
#> SRR191646 1 0.0000 0.980 1.000 0.000
#> SRR191647 1 0.0000 0.980 1.000 0.000
#> SRR191648 1 0.0000 0.980 1.000 0.000
#> SRR191649 1 0.0000 0.980 1.000 0.000
#> SRR191650 1 0.0000 0.980 1.000 0.000
#> SRR191651 1 0.0000 0.980 1.000 0.000
#> SRR191652 1 0.0000 0.980 1.000 0.000
#> SRR191653 1 0.0000 0.980 1.000 0.000
#> SRR191654 1 0.0938 0.969 0.988 0.012
#> SRR191655 1 0.0000 0.980 1.000 0.000
#> SRR191656 1 0.0000 0.980 1.000 0.000
#> SRR191657 1 0.0000 0.980 1.000 0.000
#> SRR191658 1 0.0000 0.980 1.000 0.000
#> SRR191659 1 0.0000 0.980 1.000 0.000
#> SRR191660 1 0.0000 0.980 1.000 0.000
#> SRR191661 1 0.0000 0.980 1.000 0.000
#> SRR191662 1 0.0000 0.980 1.000 0.000
#> SRR191663 1 0.0000 0.980 1.000 0.000
#> SRR191664 1 0.0000 0.980 1.000 0.000
#> SRR191665 1 0.0000 0.980 1.000 0.000
#> SRR191666 1 0.0000 0.980 1.000 0.000
#> SRR191667 1 0.0000 0.980 1.000 0.000
#> SRR191668 1 0.0000 0.980 1.000 0.000
#> SRR191669 1 0.0000 0.980 1.000 0.000
#> SRR191670 1 0.0000 0.980 1.000 0.000
#> SRR191671 1 0.0000 0.980 1.000 0.000
#> SRR191672 1 0.0000 0.980 1.000 0.000
#> SRR191673 1 0.0000 0.980 1.000 0.000
#> SRR191674 2 0.0000 0.961 0.000 1.000
#> SRR191675 2 0.0000 0.961 0.000 1.000
#> SRR191677 2 0.0000 0.961 0.000 1.000
#> SRR191678 2 0.0000 0.961 0.000 1.000
#> SRR191679 2 0.0000 0.961 0.000 1.000
#> SRR191680 2 0.0000 0.961 0.000 1.000
#> SRR191681 2 0.0000 0.961 0.000 1.000
#> SRR191682 2 0.0000 0.961 0.000 1.000
#> SRR191683 2 0.0000 0.961 0.000 1.000
#> SRR191684 2 0.0000 0.961 0.000 1.000
#> SRR191685 2 0.0000 0.961 0.000 1.000
#> SRR191686 2 0.0000 0.961 0.000 1.000
#> SRR191687 2 0.0000 0.961 0.000 1.000
#> SRR191688 2 0.0000 0.961 0.000 1.000
#> SRR191689 2 0.0000 0.961 0.000 1.000
#> SRR191690 2 0.0000 0.961 0.000 1.000
#> SRR191691 2 0.0000 0.961 0.000 1.000
#> SRR191692 2 0.0000 0.961 0.000 1.000
#> SRR191693 2 0.0000 0.961 0.000 1.000
#> SRR191694 2 0.0000 0.961 0.000 1.000
#> SRR191695 2 0.0000 0.961 0.000 1.000
#> SRR191696 2 0.0000 0.961 0.000 1.000
#> SRR191697 2 0.0000 0.961 0.000 1.000
#> SRR191698 2 0.0000 0.961 0.000 1.000
#> SRR191699 2 0.0000 0.961 0.000 1.000
#> SRR191700 2 0.0672 0.955 0.008 0.992
#> SRR191701 2 0.0000 0.961 0.000 1.000
#> SRR191702 2 0.0000 0.961 0.000 1.000
#> SRR191703 2 0.0000 0.961 0.000 1.000
#> SRR191704 2 0.0000 0.961 0.000 1.000
#> SRR191705 2 0.0000 0.961 0.000 1.000
#> SRR191706 2 0.0000 0.961 0.000 1.000
#> SRR191707 2 0.0000 0.961 0.000 1.000
#> SRR191708 2 0.0000 0.961 0.000 1.000
#> SRR191709 2 0.0000 0.961 0.000 1.000
#> SRR191710 2 0.0000 0.961 0.000 1.000
#> SRR191711 2 0.0000 0.961 0.000 1.000
#> SRR191712 2 0.0000 0.961 0.000 1.000
#> SRR191713 2 0.0000 0.961 0.000 1.000
#> SRR191714 2 0.0000 0.961 0.000 1.000
#> SRR191715 2 0.0000 0.961 0.000 1.000
#> SRR191716 2 0.0000 0.961 0.000 1.000
#> SRR191717 2 0.0000 0.961 0.000 1.000
#> SRR191718 2 0.0000 0.961 0.000 1.000
#> SRR537099 2 0.9795 0.323 0.416 0.584
#> SRR537100 1 0.0000 0.980 1.000 0.000
#> SRR537101 1 0.0000 0.980 1.000 0.000
#> SRR537102 2 0.6973 0.768 0.188 0.812
#> SRR537104 2 0.8327 0.655 0.264 0.736
#> SRR537105 1 0.0000 0.980 1.000 0.000
#> SRR537106 2 0.9661 0.388 0.392 0.608
#> SRR537107 2 0.8267 0.661 0.260 0.740
#> SRR537108 2 0.8267 0.661 0.260 0.740
#> SRR537109 2 0.0000 0.961 0.000 1.000
#> SRR537110 2 0.0000 0.961 0.000 1.000
#> SRR537111 1 0.9635 0.327 0.612 0.388
#> SRR537113 2 0.5737 0.830 0.136 0.864
#> SRR537114 2 0.3274 0.909 0.060 0.940
#> SRR537115 2 0.0000 0.961 0.000 1.000
#> SRR537116 2 0.0000 0.961 0.000 1.000
#> SRR537117 2 0.0000 0.961 0.000 1.000
#> SRR537118 2 0.0000 0.961 0.000 1.000
#> SRR537119 2 0.0000 0.961 0.000 1.000
#> SRR537120 2 0.0000 0.961 0.000 1.000
#> SRR537121 2 0.0000 0.961 0.000 1.000
#> SRR537122 2 0.0000 0.961 0.000 1.000
#> SRR537123 2 0.0000 0.961 0.000 1.000
#> SRR537124 2 0.0000 0.961 0.000 1.000
#> SRR537125 2 0.0000 0.961 0.000 1.000
#> SRR537126 2 0.0000 0.961 0.000 1.000
#> SRR537127 1 0.0000 0.980 1.000 0.000
#> SRR537128 1 0.0000 0.980 1.000 0.000
#> SRR537129 1 0.0000 0.980 1.000 0.000
#> SRR537130 1 0.0000 0.980 1.000 0.000
#> SRR537131 1 0.0000 0.980 1.000 0.000
#> SRR537132 1 0.0000 0.980 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191640 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191641 1 0.1643 0.939 0.956 0.000 0.044
#> SRR191642 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191643 2 0.7056 0.303 0.404 0.572 0.024
#> SRR191644 3 0.8957 0.331 0.132 0.376 0.492
#> SRR191645 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191646 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191647 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191650 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191651 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191652 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191653 3 0.0000 0.889 0.000 0.000 1.000
#> SRR191654 3 0.0000 0.889 0.000 0.000 1.000
#> SRR191655 3 0.6359 0.318 0.404 0.004 0.592
#> SRR191656 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191657 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191661 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191662 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191663 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191666 3 0.0892 0.879 0.020 0.000 0.980
#> SRR191667 3 0.1031 0.876 0.024 0.000 0.976
#> SRR191668 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.983 1.000 0.000 0.000
#> SRR191674 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191678 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191679 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191682 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191683 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191690 2 0.2066 0.915 0.060 0.940 0.000
#> SRR191691 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191692 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191693 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191695 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191696 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191697 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191698 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191699 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191700 2 0.3192 0.855 0.000 0.888 0.112
#> SRR191701 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191704 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191705 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191706 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191709 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191713 2 0.2537 0.892 0.080 0.920 0.000
#> SRR191714 2 0.2711 0.883 0.088 0.912 0.000
#> SRR191715 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191716 2 0.2711 0.884 0.088 0.912 0.000
#> SRR191717 2 0.0000 0.970 0.000 1.000 0.000
#> SRR191718 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537099 2 0.5402 0.739 0.028 0.792 0.180
#> SRR537100 3 0.8126 0.611 0.148 0.208 0.644
#> SRR537101 1 0.0747 0.968 0.984 0.000 0.016
#> SRR537102 2 0.5810 0.488 0.336 0.664 0.000
#> SRR537104 2 0.4293 0.793 0.004 0.832 0.164
#> SRR537105 1 0.0000 0.983 1.000 0.000 0.000
#> SRR537106 1 0.0000 0.983 1.000 0.000 0.000
#> SRR537107 1 0.3752 0.756 0.856 0.144 0.000
#> SRR537108 1 0.4235 0.698 0.824 0.176 0.000
#> SRR537109 2 0.0892 0.953 0.020 0.980 0.000
#> SRR537110 2 0.0747 0.957 0.016 0.984 0.000
#> SRR537111 1 0.0000 0.983 1.000 0.000 0.000
#> SRR537113 2 0.1620 0.943 0.012 0.964 0.024
#> SRR537114 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537115 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537116 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537117 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537118 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537119 2 0.0424 0.964 0.000 0.992 0.008
#> SRR537120 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537121 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537122 2 0.0424 0.964 0.000 0.992 0.008
#> SRR537123 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537124 2 0.0000 0.970 0.000 1.000 0.000
#> SRR537125 2 0.0424 0.964 0.000 0.992 0.008
#> SRR537126 2 0.0424 0.964 0.000 0.992 0.008
#> SRR537127 3 0.0000 0.889 0.000 0.000 1.000
#> SRR537128 3 0.0000 0.889 0.000 0.000 1.000
#> SRR537129 3 0.0000 0.889 0.000 0.000 1.000
#> SRR537130 3 0.0000 0.889 0.000 0.000 1.000
#> SRR537131 3 0.0000 0.889 0.000 0.000 1.000
#> SRR537132 3 0.0000 0.889 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191640 4 0.2011 0.7180 0.080 0.000 0.000 0.920
#> SRR191641 1 0.3757 0.7909 0.828 0.000 0.152 0.020
#> SRR191642 4 0.1940 0.7205 0.076 0.000 0.000 0.924
#> SRR191643 4 0.3113 0.7048 0.052 0.004 0.052 0.892
#> SRR191644 3 0.5799 0.2997 0.024 0.004 0.552 0.420
#> SRR191645 4 0.4888 0.2415 0.412 0.000 0.000 0.588
#> SRR191646 4 0.4977 0.0997 0.460 0.000 0.000 0.540
#> SRR191647 4 0.1940 0.7205 0.076 0.000 0.000 0.924
#> SRR191648 4 0.1867 0.7217 0.072 0.000 0.000 0.928
#> SRR191649 4 0.5000 -0.0465 0.496 0.000 0.000 0.504
#> SRR191650 1 0.0921 0.9429 0.972 0.000 0.000 0.028
#> SRR191651 1 0.0336 0.9573 0.992 0.000 0.000 0.008
#> SRR191652 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191653 3 0.5112 0.2955 0.004 0.000 0.560 0.436
#> SRR191654 4 0.3907 0.5761 0.004 0.008 0.180 0.808
#> SRR191655 4 0.1042 0.7367 0.020 0.000 0.008 0.972
#> SRR191656 1 0.0000 0.9577 1.000 0.000 0.000 0.000
#> SRR191657 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191658 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191659 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191660 1 0.0336 0.9573 0.992 0.000 0.000 0.008
#> SRR191661 1 0.1474 0.9197 0.948 0.000 0.000 0.052
#> SRR191662 1 0.3975 0.6645 0.760 0.000 0.000 0.240
#> SRR191663 1 0.0592 0.9522 0.984 0.000 0.000 0.016
#> SRR191664 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191665 1 0.0188 0.9588 0.996 0.000 0.000 0.004
#> SRR191666 3 0.3942 0.5831 0.236 0.000 0.764 0.000
#> SRR191667 3 0.4746 0.3501 0.368 0.000 0.632 0.000
#> SRR191668 1 0.0000 0.9577 1.000 0.000 0.000 0.000
#> SRR191669 1 0.0000 0.9577 1.000 0.000 0.000 0.000
#> SRR191670 1 0.0000 0.9577 1.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.9577 1.000 0.000 0.000 0.000
#> SRR191672 1 0.0188 0.9553 0.996 0.000 0.000 0.004
#> SRR191673 1 0.0188 0.9553 0.996 0.000 0.000 0.004
#> SRR191674 2 0.0336 0.8491 0.000 0.992 0.000 0.008
#> SRR191675 2 0.0469 0.8493 0.000 0.988 0.000 0.012
#> SRR191677 2 0.0336 0.8501 0.000 0.992 0.000 0.008
#> SRR191678 2 0.0336 0.8501 0.000 0.992 0.000 0.008
#> SRR191679 2 0.0000 0.8496 0.000 1.000 0.000 0.000
#> SRR191680 2 0.0188 0.8500 0.000 0.996 0.000 0.004
#> SRR191681 2 0.0336 0.8501 0.000 0.992 0.000 0.008
#> SRR191682 2 0.0469 0.8488 0.000 0.988 0.000 0.012
#> SRR191683 2 0.0336 0.8480 0.000 0.992 0.000 0.008
#> SRR191684 2 0.0817 0.8508 0.000 0.976 0.000 0.024
#> SRR191685 2 0.1022 0.8500 0.000 0.968 0.000 0.032
#> SRR191686 2 0.0188 0.8491 0.000 0.996 0.000 0.004
#> SRR191687 2 0.0707 0.8508 0.000 0.980 0.000 0.020
#> SRR191688 2 0.4948 0.2239 0.000 0.560 0.000 0.440
#> SRR191689 2 0.0336 0.8493 0.000 0.992 0.000 0.008
#> SRR191690 4 0.6083 0.3447 0.056 0.360 0.000 0.584
#> SRR191691 2 0.4916 0.3751 0.000 0.576 0.000 0.424
#> SRR191692 2 0.0188 0.8488 0.000 0.996 0.000 0.004
#> SRR191693 2 0.0469 0.8493 0.000 0.988 0.000 0.012
#> SRR191694 2 0.0336 0.8496 0.000 0.992 0.000 0.008
#> SRR191695 2 0.3400 0.7456 0.000 0.820 0.000 0.180
#> SRR191696 2 0.3024 0.7708 0.000 0.852 0.000 0.148
#> SRR191697 2 0.2081 0.8362 0.000 0.916 0.000 0.084
#> SRR191698 2 0.4843 0.4340 0.000 0.604 0.000 0.396
#> SRR191699 2 0.3975 0.6951 0.000 0.760 0.000 0.240
#> SRR191700 4 0.6740 0.4080 0.000 0.144 0.256 0.600
#> SRR191701 2 0.1716 0.8459 0.000 0.936 0.000 0.064
#> SRR191702 2 0.4933 0.2298 0.000 0.568 0.000 0.432
#> SRR191703 2 0.3873 0.6790 0.000 0.772 0.000 0.228
#> SRR191704 2 0.1637 0.8398 0.000 0.940 0.000 0.060
#> SRR191705 2 0.1211 0.8455 0.000 0.960 0.000 0.040
#> SRR191706 2 0.0469 0.8496 0.000 0.988 0.000 0.012
#> SRR191707 4 0.1716 0.7204 0.000 0.064 0.000 0.936
#> SRR191708 4 0.4522 0.4682 0.000 0.320 0.000 0.680
#> SRR191709 4 0.3801 0.6297 0.000 0.220 0.000 0.780
#> SRR191710 4 0.4804 0.3339 0.000 0.384 0.000 0.616
#> SRR191711 4 0.4877 0.2598 0.000 0.408 0.000 0.592
#> SRR191712 4 0.4985 0.0670 0.000 0.468 0.000 0.532
#> SRR191713 2 0.4720 0.5243 0.004 0.672 0.000 0.324
#> SRR191714 2 0.4741 0.5174 0.004 0.668 0.000 0.328
#> SRR191715 2 0.2408 0.8124 0.000 0.896 0.000 0.104
#> SRR191716 4 0.6324 0.3607 0.072 0.356 0.000 0.572
#> SRR191717 2 0.4955 0.2218 0.000 0.556 0.000 0.444
#> SRR191718 2 0.0817 0.8479 0.000 0.976 0.000 0.024
#> SRR537099 4 0.2360 0.7108 0.020 0.004 0.052 0.924
#> SRR537100 4 0.1484 0.7346 0.020 0.004 0.016 0.960
#> SRR537101 4 0.6003 0.2021 0.456 0.000 0.040 0.504
#> SRR537102 4 0.0657 0.7379 0.012 0.004 0.000 0.984
#> SRR537104 4 0.0992 0.7351 0.004 0.008 0.012 0.976
#> SRR537105 4 0.1118 0.7353 0.036 0.000 0.000 0.964
#> SRR537106 4 0.0921 0.7372 0.028 0.000 0.000 0.972
#> SRR537107 4 0.0895 0.7385 0.020 0.004 0.000 0.976
#> SRR537108 4 0.0895 0.7385 0.020 0.004 0.000 0.976
#> SRR537109 4 0.0779 0.7366 0.004 0.016 0.000 0.980
#> SRR537110 4 0.0592 0.7352 0.000 0.016 0.000 0.984
#> SRR537111 1 0.3311 0.7719 0.828 0.000 0.000 0.172
#> SRR537113 4 0.1584 0.7364 0.012 0.036 0.000 0.952
#> SRR537114 4 0.4663 0.5609 0.012 0.272 0.000 0.716
#> SRR537115 2 0.4206 0.7679 0.000 0.816 0.048 0.136
#> SRR537116 4 0.1389 0.7273 0.000 0.048 0.000 0.952
#> SRR537117 2 0.0707 0.8480 0.000 0.980 0.000 0.020
#> SRR537118 2 0.4153 0.7363 0.000 0.820 0.132 0.048
#> SRR537119 2 0.5720 0.4655 0.000 0.652 0.296 0.052
#> SRR537120 2 0.1302 0.8420 0.000 0.956 0.000 0.044
#> SRR537121 2 0.4039 0.7642 0.000 0.836 0.084 0.080
#> SRR537122 3 0.6672 0.1358 0.000 0.408 0.504 0.088
#> SRR537123 2 0.2385 0.8247 0.000 0.920 0.028 0.052
#> SRR537124 2 0.0592 0.8491 0.000 0.984 0.000 0.016
#> SRR537125 2 0.5344 0.4850 0.000 0.668 0.300 0.032
#> SRR537126 2 0.5021 0.5909 0.000 0.724 0.240 0.036
#> SRR537127 3 0.0000 0.7842 0.000 0.000 1.000 0.000
#> SRR537128 3 0.0000 0.7842 0.000 0.000 1.000 0.000
#> SRR537129 3 0.0000 0.7842 0.000 0.000 1.000 0.000
#> SRR537130 3 0.0000 0.7842 0.000 0.000 1.000 0.000
#> SRR537131 3 0.0000 0.7842 0.000 0.000 1.000 0.000
#> SRR537132 3 0.0000 0.7842 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
#> SRR191639 1 0.0510 0.912 0.984 0.000 0.000 0.016 0.000
#> SRR191640 4 0.3862 0.762 0.104 0.088 0.000 0.808 0.000
#> SRR191641 3 0.4708 0.241 0.436 0.000 0.548 0.016 0.000
#> SRR191642 4 0.3362 0.776 0.076 0.080 0.000 0.844 0.000
#> SRR191643 4 0.4442 0.768 0.060 0.088 0.052 0.800 0.000
#> SRR191644 3 0.4610 0.129 0.000 0.016 0.596 0.388 0.000
#> SRR191645 4 0.3231 0.702 0.196 0.004 0.000 0.800 0.000
#> SRR191646 4 0.3398 0.683 0.216 0.004 0.000 0.780 0.000
#> SRR191647 4 0.2171 0.802 0.064 0.024 0.000 0.912 0.000
#> SRR191648 4 0.1981 0.801 0.064 0.016 0.000 0.920 0.000
#> SRR191649 4 0.3990 0.548 0.308 0.004 0.000 0.688 0.000
#> SRR191650 1 0.2773 0.773 0.836 0.000 0.000 0.164 0.000
#> SRR191651 1 0.0963 0.907 0.964 0.000 0.000 0.036 0.000
#> SRR191652 1 0.0898 0.911 0.972 0.008 0.000 0.020 0.000
#> SRR191653 4 0.5021 0.223 0.000 0.008 0.416 0.556 0.020
#> SRR191654 4 0.4143 0.690 0.000 0.016 0.160 0.788 0.036
#> SRR191655 4 0.0807 0.804 0.000 0.012 0.000 0.976 0.012
#> SRR191656 1 0.0404 0.910 0.988 0.000 0.000 0.012 0.000
#> SRR191657 1 0.1310 0.904 0.956 0.024 0.000 0.020 0.000
#> SRR191658 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR191659 1 0.1310 0.904 0.956 0.024 0.000 0.020 0.000
#> SRR191660 1 0.1836 0.892 0.932 0.036 0.000 0.032 0.000
#> SRR191661 1 0.3182 0.806 0.844 0.032 0.000 0.124 0.000
#> SRR191662 1 0.4290 0.544 0.680 0.016 0.000 0.304 0.000
#> SRR191663 1 0.1915 0.889 0.928 0.032 0.000 0.040 0.000
#> SRR191664 1 0.0703 0.911 0.976 0.000 0.000 0.024 0.000
#> SRR191665 1 0.0510 0.912 0.984 0.000 0.000 0.016 0.000
#> SRR191666 3 0.4434 0.189 0.460 0.000 0.536 0.000 0.004
#> SRR191667 3 0.4305 0.104 0.488 0.000 0.512 0.000 0.000
#> SRR191668 1 0.0162 0.909 0.996 0.000 0.000 0.004 0.000
#> SRR191669 1 0.0162 0.909 0.996 0.000 0.000 0.004 0.000
#> SRR191670 1 0.0000 0.909 1.000 0.000 0.000 0.000 0.000
#> SRR191671 1 0.0000 0.909 1.000 0.000 0.000 0.000 0.000
#> SRR191672 1 0.0162 0.909 0.996 0.000 0.000 0.004 0.000
#> SRR191673 1 0.0162 0.909 0.996 0.000 0.000 0.004 0.000
#> SRR191674 5 0.4114 0.506 0.000 0.376 0.000 0.000 0.624
#> SRR191675 5 0.4114 0.506 0.000 0.376 0.000 0.000 0.624
#> SRR191677 5 0.4101 0.514 0.000 0.372 0.000 0.000 0.628
#> SRR191678 5 0.4030 0.537 0.000 0.352 0.000 0.000 0.648
#> SRR191679 2 0.4101 0.186 0.000 0.628 0.000 0.000 0.372
#> SRR191680 5 0.4304 0.272 0.000 0.484 0.000 0.000 0.516
#> SRR191681 5 0.4088 0.517 0.000 0.368 0.000 0.000 0.632
#> SRR191682 5 0.2852 0.675 0.000 0.172 0.000 0.000 0.828
#> SRR191683 5 0.3661 0.621 0.000 0.276 0.000 0.000 0.724
#> SRR191684 5 0.2351 0.693 0.000 0.088 0.000 0.016 0.896
#> SRR191685 5 0.1774 0.702 0.000 0.052 0.000 0.016 0.932
#> SRR191686 5 0.2377 0.691 0.000 0.128 0.000 0.000 0.872
#> SRR191687 5 0.1740 0.705 0.000 0.056 0.000 0.012 0.932
#> SRR191688 2 0.3612 0.771 0.000 0.800 0.000 0.172 0.028
#> SRR191689 5 0.4262 0.411 0.000 0.440 0.000 0.000 0.560
#> SRR191690 2 0.4608 0.657 0.036 0.700 0.000 0.260 0.004
#> SRR191691 5 0.5274 0.556 0.000 0.132 0.000 0.192 0.676
#> SRR191692 5 0.3983 0.556 0.000 0.340 0.000 0.000 0.660
#> SRR191693 5 0.3452 0.634 0.000 0.244 0.000 0.000 0.756
#> SRR191694 5 0.4171 0.475 0.000 0.396 0.000 0.000 0.604
#> SRR191695 2 0.5084 0.697 0.004 0.712 0.000 0.144 0.140
#> SRR191696 2 0.4911 0.674 0.004 0.728 0.000 0.120 0.148
#> SRR191697 5 0.3182 0.672 0.000 0.124 0.000 0.032 0.844
#> SRR191698 5 0.5271 0.560 0.000 0.152 0.000 0.168 0.680
#> SRR191699 5 0.4678 0.566 0.000 0.224 0.000 0.064 0.712
#> SRR191700 5 0.6668 0.426 0.000 0.184 0.036 0.204 0.576
#> SRR191701 5 0.3283 0.658 0.000 0.140 0.000 0.028 0.832
#> SRR191702 2 0.3289 0.777 0.000 0.844 0.000 0.108 0.048
#> SRR191703 2 0.2983 0.742 0.000 0.868 0.000 0.056 0.076
#> SRR191704 2 0.2519 0.707 0.000 0.884 0.000 0.016 0.100
#> SRR191705 2 0.3055 0.665 0.000 0.840 0.000 0.016 0.144
#> SRR191706 2 0.3086 0.593 0.000 0.816 0.000 0.004 0.180
#> SRR191707 2 0.6319 0.343 0.000 0.520 0.000 0.284 0.196
#> SRR191708 2 0.4369 0.708 0.000 0.740 0.000 0.208 0.052
#> SRR191709 2 0.4243 0.682 0.000 0.712 0.000 0.264 0.024
#> SRR191710 2 0.3847 0.765 0.000 0.784 0.000 0.180 0.036
#> SRR191711 2 0.3795 0.755 0.000 0.780 0.000 0.192 0.028
#> SRR191712 2 0.3492 0.745 0.000 0.796 0.000 0.188 0.016
#> SRR191713 2 0.2610 0.771 0.004 0.892 0.000 0.076 0.028
#> SRR191714 2 0.2952 0.776 0.004 0.872 0.000 0.088 0.036
#> SRR191715 2 0.2927 0.753 0.000 0.872 0.000 0.060 0.068
#> SRR191716 2 0.4348 0.596 0.016 0.668 0.000 0.316 0.000
#> SRR191717 2 0.4777 0.698 0.000 0.680 0.000 0.268 0.052
#> SRR191718 2 0.3491 0.600 0.004 0.768 0.000 0.000 0.228
#> SRR537099 4 0.2965 0.766 0.000 0.012 0.040 0.880 0.068
#> SRR537100 4 0.4169 0.719 0.000 0.024 0.060 0.808 0.108
#> SRR537101 4 0.7342 0.284 0.316 0.052 0.176 0.456 0.000
#> SRR537102 4 0.2144 0.794 0.000 0.068 0.000 0.912 0.020
#> SRR537104 4 0.1605 0.793 0.000 0.012 0.004 0.944 0.040
#> SRR537105 4 0.1341 0.802 0.000 0.056 0.000 0.944 0.000
#> SRR537106 4 0.0794 0.805 0.000 0.028 0.000 0.972 0.000
#> SRR537107 4 0.0865 0.805 0.000 0.024 0.000 0.972 0.004
#> SRR537108 4 0.0865 0.805 0.000 0.024 0.000 0.972 0.004
#> SRR537109 4 0.3333 0.676 0.000 0.208 0.000 0.788 0.004
#> SRR537110 4 0.2873 0.757 0.000 0.128 0.000 0.856 0.016
#> SRR537111 1 0.4390 0.284 0.568 0.004 0.000 0.428 0.000
#> SRR537113 4 0.2597 0.772 0.004 0.060 0.000 0.896 0.040
#> SRR537114 4 0.4316 0.651 0.008 0.128 0.000 0.784 0.080
#> SRR537115 5 0.6815 0.348 0.004 0.324 0.000 0.248 0.424
#> SRR537116 4 0.4416 0.359 0.000 0.356 0.000 0.632 0.012
#> SRR537117 5 0.3019 0.706 0.000 0.088 0.000 0.048 0.864
#> SRR537118 5 0.3238 0.680 0.000 0.000 0.028 0.136 0.836
#> SRR537119 5 0.3731 0.660 0.000 0.000 0.040 0.160 0.800
#> SRR537120 5 0.2189 0.702 0.000 0.012 0.000 0.084 0.904
#> SRR537121 5 0.3773 0.660 0.000 0.004 0.032 0.164 0.800
#> SRR537122 5 0.4392 0.618 0.000 0.004 0.048 0.200 0.748
#> SRR537123 5 0.3484 0.675 0.000 0.004 0.028 0.144 0.824
#> SRR537124 5 0.2972 0.706 0.004 0.084 0.000 0.040 0.872
#> SRR537125 5 0.3523 0.678 0.000 0.004 0.044 0.120 0.832
#> SRR537126 5 0.3523 0.678 0.000 0.004 0.044 0.120 0.832
#> SRR537127 3 0.0000 0.768 0.000 0.000 1.000 0.000 0.000
#> SRR537128 3 0.0000 0.768 0.000 0.000 1.000 0.000 0.000
#> SRR537129 3 0.0000 0.768 0.000 0.000 1.000 0.000 0.000
#> SRR537130 3 0.0000 0.768 0.000 0.000 1.000 0.000 0.000
#> SRR537131 3 0.0000 0.768 0.000 0.000 1.000 0.000 0.000
#> SRR537132 3 0.0000 0.768 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
#> SRR191639 1 0.1080 0.8899 0.960 0.004 0.000 0.032 0.004 0.000
#> SRR191640 4 0.4132 0.7614 0.028 0.144 0.000 0.780 0.040 0.008
#> SRR191641 3 0.3108 0.7871 0.076 0.004 0.844 0.076 0.000 0.000
#> SRR191642 4 0.3386 0.7793 0.020 0.124 0.000 0.824 0.032 0.000
#> SRR191643 4 0.3578 0.7921 0.016 0.072 0.032 0.844 0.032 0.004
#> SRR191644 3 0.4049 0.1342 0.000 0.004 0.580 0.412 0.000 0.004
#> SRR191645 4 0.1985 0.7816 0.064 0.004 0.000 0.916 0.008 0.008
#> SRR191646 4 0.2156 0.7811 0.068 0.008 0.000 0.908 0.008 0.008
#> SRR191647 4 0.2689 0.7934 0.016 0.112 0.000 0.864 0.004 0.004
#> SRR191648 4 0.2361 0.7970 0.012 0.104 0.000 0.880 0.000 0.004
#> SRR191649 4 0.2892 0.7443 0.136 0.020 0.000 0.840 0.000 0.004
#> SRR191650 1 0.3921 0.5540 0.676 0.004 0.000 0.308 0.000 0.012
#> SRR191651 1 0.1707 0.8807 0.928 0.004 0.000 0.056 0.000 0.012
#> SRR191652 1 0.1514 0.8879 0.948 0.016 0.000 0.016 0.004 0.016
#> SRR191653 4 0.4863 0.0384 0.000 0.008 0.476 0.484 0.024 0.008
#> SRR191654 4 0.4014 0.7466 0.000 0.016 0.096 0.808 0.048 0.032
#> SRR191655 4 0.1963 0.8007 0.000 0.044 0.004 0.924 0.012 0.016
#> SRR191656 1 0.1059 0.8860 0.964 0.004 0.000 0.016 0.000 0.016
#> SRR191657 1 0.2620 0.8739 0.892 0.048 0.000 0.024 0.004 0.032
#> SRR191658 1 0.1223 0.8890 0.960 0.008 0.000 0.016 0.004 0.012
#> SRR191659 1 0.2825 0.8702 0.880 0.056 0.000 0.028 0.004 0.032
#> SRR191660 1 0.3140 0.8627 0.864 0.060 0.000 0.028 0.008 0.040
#> SRR191661 1 0.4362 0.8172 0.784 0.072 0.000 0.096 0.016 0.032
#> SRR191662 1 0.5080 0.6953 0.684 0.068 0.000 0.212 0.008 0.028
#> SRR191663 1 0.3718 0.8477 0.828 0.068 0.000 0.060 0.008 0.036
#> SRR191664 1 0.1598 0.8876 0.940 0.008 0.000 0.040 0.004 0.008
#> SRR191665 1 0.0790 0.8895 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR191666 1 0.4203 0.4710 0.608 0.008 0.376 0.004 0.000 0.004
#> SRR191667 1 0.4121 0.4611 0.604 0.004 0.384 0.004 0.000 0.004
#> SRR191668 1 0.0291 0.8866 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR191669 1 0.0291 0.8866 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR191670 1 0.0291 0.8866 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR191671 1 0.0291 0.8866 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR191672 1 0.0405 0.8859 0.988 0.004 0.000 0.000 0.000 0.008
#> SRR191673 1 0.0405 0.8859 0.988 0.004 0.000 0.000 0.000 0.008
#> SRR191674 6 0.2282 0.7791 0.000 0.088 0.000 0.000 0.024 0.888
#> SRR191675 6 0.2282 0.7791 0.000 0.088 0.000 0.000 0.024 0.888
#> SRR191677 6 0.2412 0.7795 0.000 0.092 0.000 0.000 0.028 0.880
#> SRR191678 6 0.2724 0.7750 0.000 0.084 0.000 0.000 0.052 0.864
#> SRR191679 6 0.3323 0.5507 0.000 0.240 0.000 0.000 0.008 0.752
#> SRR191680 6 0.2357 0.7568 0.000 0.116 0.000 0.000 0.012 0.872
#> SRR191681 6 0.2412 0.7795 0.000 0.092 0.000 0.000 0.028 0.880
#> SRR191682 5 0.4175 0.7614 0.000 0.104 0.000 0.000 0.740 0.156
#> SRR191683 5 0.5244 0.4585 0.000 0.112 0.000 0.000 0.552 0.336
#> SRR191684 5 0.3227 0.8031 0.000 0.088 0.000 0.000 0.828 0.084
#> SRR191685 5 0.3328 0.8069 0.000 0.064 0.000 0.000 0.816 0.120
#> SRR191686 5 0.4327 0.6794 0.000 0.056 0.000 0.000 0.680 0.264
#> SRR191687 5 0.3435 0.8011 0.000 0.060 0.000 0.000 0.804 0.136
#> SRR191688 2 0.4632 0.7792 0.000 0.748 0.000 0.060 0.072 0.120
#> SRR191689 6 0.2868 0.7437 0.000 0.132 0.000 0.000 0.028 0.840
#> SRR191690 2 0.4517 0.7043 0.008 0.768 0.000 0.112 0.056 0.056
#> SRR191691 5 0.1700 0.8066 0.000 0.028 0.000 0.024 0.936 0.012
#> SRR191692 6 0.3062 0.7421 0.000 0.052 0.000 0.000 0.112 0.836
#> SRR191693 6 0.3420 0.5219 0.000 0.012 0.000 0.000 0.240 0.748
#> SRR191694 6 0.2333 0.7775 0.000 0.092 0.000 0.000 0.024 0.884
#> SRR191695 2 0.6104 0.7007 0.000 0.592 0.000 0.068 0.152 0.188
#> SRR191696 2 0.6321 0.5501 0.000 0.504 0.000 0.060 0.120 0.316
#> SRR191697 5 0.2123 0.8181 0.000 0.024 0.000 0.012 0.912 0.052
#> SRR191698 5 0.1498 0.8002 0.000 0.028 0.000 0.032 0.940 0.000
#> SRR191699 5 0.3516 0.7852 0.000 0.088 0.000 0.004 0.812 0.096
#> SRR191700 5 0.2332 0.7776 0.000 0.032 0.004 0.036 0.908 0.020
#> SRR191701 5 0.1867 0.8138 0.000 0.036 0.000 0.004 0.924 0.036
#> SRR191702 2 0.3309 0.7838 0.000 0.788 0.000 0.016 0.004 0.192
#> SRR191703 2 0.3584 0.7526 0.000 0.740 0.000 0.012 0.004 0.244
#> SRR191704 2 0.3424 0.7667 0.000 0.780 0.004 0.000 0.020 0.196
#> SRR191705 2 0.3342 0.7501 0.000 0.760 0.000 0.000 0.012 0.228
#> SRR191706 2 0.3748 0.6859 0.000 0.688 0.000 0.000 0.012 0.300
#> SRR191707 2 0.4959 0.5930 0.000 0.684 0.000 0.080 0.208 0.028
#> SRR191708 2 0.2445 0.7619 0.000 0.892 0.000 0.060 0.040 0.008
#> SRR191709 2 0.3623 0.7933 0.000 0.808 0.000 0.084 0.008 0.100
#> SRR191710 2 0.2979 0.8018 0.000 0.848 0.000 0.032 0.008 0.112
#> SRR191711 2 0.2870 0.8011 0.000 0.856 0.000 0.040 0.004 0.100
#> SRR191712 2 0.2282 0.7992 0.000 0.900 0.000 0.020 0.012 0.068
#> SRR191713 2 0.2532 0.7942 0.000 0.884 0.000 0.012 0.024 0.080
#> SRR191714 2 0.2911 0.7933 0.000 0.856 0.000 0.008 0.036 0.100
#> SRR191715 2 0.3571 0.7603 0.000 0.744 0.000 0.008 0.008 0.240
#> SRR191716 2 0.4556 0.6654 0.004 0.744 0.000 0.160 0.056 0.036
#> SRR191717 2 0.5780 0.5731 0.000 0.548 0.000 0.208 0.008 0.236
#> SRR191718 2 0.5022 0.6731 0.000 0.640 0.000 0.000 0.204 0.156
#> SRR537099 4 0.3404 0.7842 0.000 0.028 0.016 0.852 0.056 0.048
#> SRR537100 4 0.4232 0.7523 0.000 0.032 0.040 0.792 0.112 0.024
#> SRR537101 4 0.7227 0.2328 0.080 0.104 0.336 0.448 0.020 0.012
#> SRR537102 4 0.3352 0.7827 0.000 0.120 0.000 0.820 0.056 0.004
#> SRR537104 4 0.2036 0.7839 0.000 0.008 0.000 0.916 0.048 0.028
#> SRR537105 4 0.2454 0.7990 0.000 0.104 0.000 0.876 0.016 0.004
#> SRR537106 4 0.1599 0.7967 0.000 0.028 0.000 0.940 0.024 0.008
#> SRR537107 4 0.1518 0.7878 0.000 0.008 0.000 0.944 0.024 0.024
#> SRR537108 4 0.1599 0.7869 0.000 0.008 0.000 0.940 0.024 0.028
#> SRR537109 4 0.4254 0.6962 0.000 0.224 0.000 0.720 0.044 0.012
#> SRR537110 4 0.4461 0.7193 0.000 0.196 0.000 0.716 0.080 0.008
#> SRR537111 4 0.4706 0.5500 0.264 0.008 0.000 0.668 0.004 0.056
#> SRR537113 4 0.4126 0.6327 0.012 0.004 0.000 0.724 0.024 0.236
#> SRR537114 4 0.4394 0.5907 0.000 0.020 0.000 0.688 0.028 0.264
#> SRR537115 6 0.5293 0.3036 0.008 0.028 0.000 0.340 0.040 0.584
#> SRR537116 4 0.4746 0.3142 0.000 0.420 0.000 0.540 0.028 0.012
#> SRR537117 6 0.4837 -0.0258 0.000 0.000 0.000 0.056 0.432 0.512
#> SRR537118 5 0.2999 0.8192 0.000 0.000 0.000 0.048 0.840 0.112
#> SRR537119 5 0.3221 0.8136 0.000 0.000 0.000 0.076 0.828 0.096
#> SRR537120 5 0.3278 0.8071 0.000 0.000 0.000 0.040 0.808 0.152
#> SRR537121 5 0.3566 0.7995 0.000 0.000 0.000 0.104 0.800 0.096
#> SRR537122 5 0.3736 0.7610 0.000 0.000 0.000 0.156 0.776 0.068
#> SRR537123 5 0.4159 0.7630 0.000 0.000 0.000 0.116 0.744 0.140
#> SRR537124 6 0.4873 0.0203 0.000 0.000 0.000 0.060 0.420 0.520
#> SRR537125 5 0.3475 0.8030 0.000 0.000 0.000 0.060 0.800 0.140
#> SRR537126 5 0.3548 0.8014 0.000 0.000 0.000 0.068 0.796 0.136
#> SRR537127 3 0.0000 0.9038 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537128 3 0.0000 0.9038 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537129 3 0.0000 0.9038 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537130 3 0.0000 0.9038 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537131 3 0.0000 0.9038 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537132 3 0.0000 0.9038 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.944 0.956 0.976 0.3180 0.702 0.702
#> 3 3 0.901 0.935 0.955 0.1229 0.986 0.980
#> 4 4 0.514 0.815 0.862 0.5987 0.700 0.564
#> 5 5 0.720 0.809 0.915 0.1180 0.976 0.938
#> 6 6 0.727 0.824 0.921 0.0194 0.996 0.989
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
#> SRR191639 2 0.2948 0.937 0.052 0.948
#> SRR191640 2 0.0000 0.973 0.000 1.000
#> SRR191641 1 0.1414 0.993 0.980 0.020
#> SRR191642 2 0.0000 0.973 0.000 1.000
#> SRR191643 2 0.0000 0.973 0.000 1.000
#> SRR191644 2 0.0000 0.973 0.000 1.000
#> SRR191645 2 0.0672 0.969 0.008 0.992
#> SRR191646 2 0.0672 0.969 0.008 0.992
#> SRR191647 2 0.2948 0.937 0.052 0.948
#> SRR191648 2 0.2948 0.937 0.052 0.948
#> SRR191649 2 0.2948 0.937 0.052 0.948
#> SRR191650 2 0.0000 0.973 0.000 1.000
#> SRR191651 2 0.0000 0.973 0.000 1.000
#> SRR191652 1 0.1414 0.993 0.980 0.020
#> SRR191653 2 0.0000 0.973 0.000 1.000
#> SRR191654 2 0.0000 0.973 0.000 1.000
#> SRR191655 2 0.0000 0.973 0.000 1.000
#> SRR191656 2 0.6887 0.791 0.184 0.816
#> SRR191657 1 0.1414 0.993 0.980 0.020
#> SRR191658 1 0.1414 0.993 0.980 0.020
#> SRR191659 1 0.1633 0.989 0.976 0.024
#> SRR191660 1 0.1414 0.993 0.980 0.020
#> SRR191661 2 0.0672 0.969 0.008 0.992
#> SRR191662 2 0.0000 0.973 0.000 1.000
#> SRR191663 2 0.0672 0.969 0.008 0.992
#> SRR191664 2 0.3879 0.917 0.076 0.924
#> SRR191665 2 0.0938 0.967 0.012 0.988
#> SRR191666 1 0.1414 0.993 0.980 0.020
#> SRR191667 1 0.1414 0.993 0.980 0.020
#> SRR191668 2 0.7528 0.753 0.216 0.784
#> SRR191669 2 0.7528 0.753 0.216 0.784
#> SRR191670 1 0.1414 0.993 0.980 0.020
#> SRR191671 1 0.1414 0.993 0.980 0.020
#> SRR191672 1 0.1414 0.993 0.980 0.020
#> SRR191673 1 0.1414 0.993 0.980 0.020
#> SRR191674 2 0.0000 0.973 0.000 1.000
#> SRR191675 2 0.0000 0.973 0.000 1.000
#> SRR191677 2 0.0000 0.973 0.000 1.000
#> SRR191678 2 0.2603 0.944 0.044 0.956
#> SRR191679 2 0.0000 0.973 0.000 1.000
#> SRR191680 2 0.0000 0.973 0.000 1.000
#> SRR191681 2 0.0000 0.973 0.000 1.000
#> SRR191682 2 0.0000 0.973 0.000 1.000
#> SRR191683 2 0.0000 0.973 0.000 1.000
#> SRR191684 2 0.0000 0.973 0.000 1.000
#> SRR191685 2 0.0000 0.973 0.000 1.000
#> SRR191686 2 0.0000 0.973 0.000 1.000
#> SRR191687 2 0.0000 0.973 0.000 1.000
#> SRR191688 2 0.0000 0.973 0.000 1.000
#> SRR191689 2 0.0000 0.973 0.000 1.000
#> SRR191690 2 0.2603 0.944 0.044 0.956
#> SRR191691 2 0.0000 0.973 0.000 1.000
#> SRR191692 2 0.0000 0.973 0.000 1.000
#> SRR191693 2 0.0000 0.973 0.000 1.000
#> SRR191694 2 0.0000 0.973 0.000 1.000
#> SRR191695 2 0.0000 0.973 0.000 1.000
#> SRR191696 2 0.0000 0.973 0.000 1.000
#> SRR191697 2 0.0000 0.973 0.000 1.000
#> SRR191698 2 0.0000 0.973 0.000 1.000
#> SRR191699 2 0.0000 0.973 0.000 1.000
#> SRR191700 1 0.1414 0.993 0.980 0.020
#> SRR191701 2 0.0000 0.973 0.000 1.000
#> SRR191702 2 0.0000 0.973 0.000 1.000
#> SRR191703 2 0.0000 0.973 0.000 1.000
#> SRR191704 2 0.0000 0.973 0.000 1.000
#> SRR191705 2 0.0000 0.973 0.000 1.000
#> SRR191706 2 0.0000 0.973 0.000 1.000
#> SRR191707 2 0.0000 0.973 0.000 1.000
#> SRR191708 2 0.0000 0.973 0.000 1.000
#> SRR191709 2 0.0000 0.973 0.000 1.000
#> SRR191710 2 0.0000 0.973 0.000 1.000
#> SRR191711 2 0.0000 0.973 0.000 1.000
#> SRR191712 2 0.0000 0.973 0.000 1.000
#> SRR191713 2 0.0000 0.973 0.000 1.000
#> SRR191714 2 0.0000 0.973 0.000 1.000
#> SRR191715 2 0.0000 0.973 0.000 1.000
#> SRR191716 2 0.2603 0.944 0.044 0.956
#> SRR191717 2 0.0000 0.973 0.000 1.000
#> SRR191718 2 0.0000 0.973 0.000 1.000
#> SRR537099 2 0.0672 0.969 0.008 0.992
#> SRR537100 2 0.0672 0.969 0.008 0.992
#> SRR537101 1 0.1414 0.993 0.980 0.020
#> SRR537102 2 0.0000 0.973 0.000 1.000
#> SRR537104 2 0.0000 0.973 0.000 1.000
#> SRR537105 2 0.0000 0.973 0.000 1.000
#> SRR537106 2 0.0000 0.973 0.000 1.000
#> SRR537107 2 0.0000 0.973 0.000 1.000
#> SRR537108 2 0.0000 0.973 0.000 1.000
#> SRR537109 2 0.0000 0.973 0.000 1.000
#> SRR537110 2 0.0000 0.973 0.000 1.000
#> SRR537111 2 0.0000 0.973 0.000 1.000
#> SRR537113 2 0.0000 0.973 0.000 1.000
#> SRR537114 2 0.0000 0.973 0.000 1.000
#> SRR537115 2 0.0000 0.973 0.000 1.000
#> SRR537116 2 0.0000 0.973 0.000 1.000
#> SRR537117 2 0.8081 0.705 0.248 0.752
#> SRR537118 2 0.0672 0.969 0.008 0.992
#> SRR537119 2 0.8081 0.705 0.248 0.752
#> SRR537120 2 0.8081 0.705 0.248 0.752
#> SRR537121 2 0.0672 0.969 0.008 0.992
#> SRR537122 2 0.0672 0.969 0.008 0.992
#> SRR537123 2 0.8081 0.705 0.248 0.752
#> SRR537124 2 0.8081 0.705 0.248 0.752
#> SRR537125 2 0.0672 0.969 0.008 0.992
#> SRR537126 2 0.0672 0.969 0.008 0.992
#> SRR537127 1 0.0000 0.984 1.000 0.000
#> SRR537128 1 0.0000 0.984 1.000 0.000
#> SRR537129 1 0.0000 0.984 1.000 0.000
#> SRR537130 1 0.0000 0.984 1.000 0.000
#> SRR537131 1 0.0000 0.984 1.000 0.000
#> SRR537132 1 0.0000 0.984 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 2 0.2680 0.912 0.068 0.924 0.008
#> SRR191640 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191641 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191642 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191643 2 0.0000 0.949 0.000 1.000 0.000
#> SRR191644 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191645 2 0.1453 0.940 0.024 0.968 0.008
#> SRR191646 2 0.1453 0.940 0.024 0.968 0.008
#> SRR191647 2 0.2680 0.912 0.068 0.924 0.008
#> SRR191648 2 0.2680 0.912 0.068 0.924 0.008
#> SRR191649 2 0.2680 0.912 0.068 0.924 0.008
#> SRR191650 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191651 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191652 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191653 2 0.0000 0.949 0.000 1.000 0.000
#> SRR191654 2 0.0000 0.949 0.000 1.000 0.000
#> SRR191655 2 0.0000 0.949 0.000 1.000 0.000
#> SRR191656 2 0.4963 0.768 0.200 0.792 0.008
#> SRR191657 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191658 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191659 1 0.0424 0.992 0.992 0.008 0.000
#> SRR191660 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191661 2 0.1453 0.940 0.024 0.968 0.008
#> SRR191662 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191663 2 0.1453 0.940 0.024 0.968 0.008
#> SRR191664 2 0.3213 0.895 0.092 0.900 0.008
#> SRR191665 2 0.1585 0.938 0.028 0.964 0.008
#> SRR191666 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191667 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191668 2 0.5335 0.728 0.232 0.760 0.008
#> SRR191669 2 0.5335 0.728 0.232 0.760 0.008
#> SRR191670 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191671 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191672 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191673 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191674 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191675 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191677 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191678 2 0.2486 0.918 0.060 0.932 0.008
#> SRR191679 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191680 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191681 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191682 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191683 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191684 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191685 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191686 2 0.0747 0.949 0.000 0.984 0.016
#> SRR191687 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191688 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191689 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191690 2 0.2486 0.918 0.060 0.932 0.008
#> SRR191691 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191692 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191693 2 0.0747 0.949 0.000 0.984 0.016
#> SRR191694 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191695 2 0.0237 0.949 0.000 0.996 0.004
#> SRR191696 2 0.0237 0.949 0.000 0.996 0.004
#> SRR191697 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191698 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191699 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191700 1 0.0237 0.999 0.996 0.004 0.000
#> SRR191701 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191702 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191703 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191704 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191705 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191706 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191707 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191708 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191709 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191710 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191711 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191712 2 0.0424 0.948 0.000 0.992 0.008
#> SRR191713 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191714 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191715 2 0.1878 0.943 0.004 0.952 0.044
#> SRR191716 2 0.2486 0.918 0.060 0.932 0.008
#> SRR191717 2 0.0592 0.949 0.000 0.988 0.012
#> SRR191718 2 0.0424 0.948 0.000 0.992 0.008
#> SRR537099 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537100 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537101 1 0.0237 0.999 0.996 0.004 0.000
#> SRR537102 2 0.0000 0.949 0.000 1.000 0.000
#> SRR537104 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537105 2 0.0000 0.949 0.000 1.000 0.000
#> SRR537106 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537107 2 0.0000 0.949 0.000 1.000 0.000
#> SRR537108 2 0.0000 0.949 0.000 1.000 0.000
#> SRR537109 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537110 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537111 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537113 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537114 2 0.0424 0.948 0.000 0.992 0.008
#> SRR537115 2 0.0424 0.948 0.000 0.992 0.008
#> SRR537116 2 0.1878 0.943 0.004 0.952 0.044
#> SRR537117 2 0.5656 0.680 0.264 0.728 0.008
#> SRR537118 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537119 2 0.5656 0.680 0.264 0.728 0.008
#> SRR537120 2 0.5656 0.680 0.264 0.728 0.008
#> SRR537121 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537122 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537123 2 0.5656 0.680 0.264 0.728 0.008
#> SRR537124 2 0.5656 0.680 0.264 0.728 0.008
#> SRR537125 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537126 2 0.0848 0.947 0.008 0.984 0.008
#> SRR537127 3 0.1860 1.000 0.052 0.000 0.948
#> SRR537128 3 0.1860 1.000 0.052 0.000 0.948
#> SRR537129 3 0.1860 1.000 0.052 0.000 0.948
#> SRR537130 3 0.1860 1.000 0.052 0.000 0.948
#> SRR537131 3 0.1860 1.000 0.052 0.000 0.948
#> SRR537132 3 0.1860 1.000 0.052 0.000 0.948
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 4 0.1637 0.815 0.060 0.000 0 0.940
#> SRR191640 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191641 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191642 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191643 4 0.4985 -0.303 0.000 0.468 0 0.532
#> SRR191644 2 0.4331 0.915 0.000 0.712 0 0.288
#> SRR191645 4 0.1151 0.830 0.024 0.008 0 0.968
#> SRR191646 4 0.1151 0.830 0.024 0.008 0 0.968
#> SRR191647 4 0.1637 0.815 0.060 0.000 0 0.940
#> SRR191648 4 0.1637 0.815 0.060 0.000 0 0.940
#> SRR191649 4 0.1637 0.815 0.060 0.000 0 0.940
#> SRR191650 2 0.4331 0.915 0.000 0.712 0 0.288
#> SRR191651 2 0.4331 0.915 0.000 0.712 0 0.288
#> SRR191652 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191653 4 0.2704 0.752 0.000 0.124 0 0.876
#> SRR191654 4 0.2704 0.752 0.000 0.124 0 0.876
#> SRR191655 4 0.2704 0.752 0.000 0.124 0 0.876
#> SRR191656 4 0.3569 0.703 0.196 0.000 0 0.804
#> SRR191657 1 0.0188 0.994 0.996 0.000 0 0.004
#> SRR191658 1 0.0188 0.994 0.996 0.000 0 0.004
#> SRR191659 1 0.0336 0.988 0.992 0.000 0 0.008
#> SRR191660 1 0.0188 0.994 0.996 0.000 0 0.004
#> SRR191661 4 0.1151 0.830 0.024 0.008 0 0.968
#> SRR191662 2 0.4331 0.915 0.000 0.712 0 0.288
#> SRR191663 4 0.1151 0.830 0.024 0.008 0 0.968
#> SRR191664 4 0.2081 0.806 0.084 0.000 0 0.916
#> SRR191665 4 0.1004 0.828 0.024 0.004 0 0.972
#> SRR191666 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191667 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191668 4 0.3873 0.681 0.228 0.000 0 0.772
#> SRR191669 4 0.3873 0.681 0.228 0.000 0 0.772
#> SRR191670 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191671 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191672 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191673 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191674 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191675 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191677 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191678 4 0.1474 0.818 0.052 0.000 0 0.948
#> SRR191679 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191680 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191681 4 0.4164 0.518 0.000 0.264 0 0.736
#> SRR191682 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191683 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191684 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191685 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191686 4 0.4193 0.510 0.000 0.268 0 0.732
#> SRR191687 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191688 4 0.4888 -0.024 0.000 0.412 0 0.588
#> SRR191689 4 0.4134 0.525 0.000 0.260 0 0.740
#> SRR191690 4 0.1474 0.818 0.052 0.000 0 0.948
#> SRR191691 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191692 4 0.0592 0.831 0.000 0.016 0 0.984
#> SRR191693 4 0.4193 0.510 0.000 0.268 0 0.732
#> SRR191694 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191695 4 0.1389 0.813 0.000 0.048 0 0.952
#> SRR191696 4 0.1389 0.813 0.000 0.048 0 0.952
#> SRR191697 4 0.0469 0.832 0.000 0.012 0 0.988
#> SRR191698 4 0.0469 0.832 0.000 0.012 0 0.988
#> SRR191699 4 0.4164 0.518 0.000 0.264 0 0.736
#> SRR191700 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR191701 2 0.4998 0.428 0.000 0.512 0 0.488
#> SRR191702 2 0.4008 0.961 0.000 0.756 0 0.244
#> SRR191703 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191704 4 0.4898 0.446 0.000 0.416 0 0.584
#> SRR191705 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191706 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191707 4 0.4888 -0.024 0.000 0.412 0 0.588
#> SRR191708 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191709 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191710 4 0.4888 -0.024 0.000 0.412 0 0.588
#> SRR191711 2 0.4543 0.852 0.000 0.676 0 0.324
#> SRR191712 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR191713 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191714 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191715 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR191716 4 0.1474 0.818 0.052 0.000 0 0.948
#> SRR191717 4 0.4164 0.518 0.000 0.264 0 0.736
#> SRR191718 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR537099 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537100 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537101 1 0.0000 0.997 1.000 0.000 0 0.000
#> SRR537102 4 0.2704 0.752 0.000 0.124 0 0.876
#> SRR537104 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR537105 4 0.2704 0.752 0.000 0.124 0 0.876
#> SRR537106 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR537107 4 0.2868 0.740 0.000 0.136 0 0.864
#> SRR537108 4 0.2868 0.740 0.000 0.136 0 0.864
#> SRR537109 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR537110 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR537111 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR537113 2 0.4713 0.782 0.000 0.640 0 0.360
#> SRR537114 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR537115 4 0.0336 0.833 0.000 0.008 0 0.992
#> SRR537116 2 0.3975 0.964 0.000 0.760 0 0.240
#> SRR537117 4 0.4134 0.647 0.260 0.000 0 0.740
#> SRR537118 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537119 4 0.4134 0.647 0.260 0.000 0 0.740
#> SRR537120 4 0.4134 0.647 0.260 0.000 0 0.740
#> SRR537121 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537122 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537123 4 0.4134 0.647 0.260 0.000 0 0.740
#> SRR537124 4 0.4134 0.647 0.260 0.000 0 0.740
#> SRR537125 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537126 4 0.0000 0.833 0.000 0.000 0 1.000
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.0880 0.821 0.032 0.000 0 0.968 0.00
#> SRR191640 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191641 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191642 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191643 2 0.4227 0.153 0.000 0.580 0 0.420 0.00
#> SRR191644 2 0.1341 0.875 0.000 0.944 0 0.056 0.00
#> SRR191645 4 0.0404 0.837 0.000 0.012 0 0.988 0.00
#> SRR191646 4 0.0404 0.837 0.000 0.012 0 0.988 0.00
#> SRR191647 4 0.0880 0.821 0.032 0.000 0 0.968 0.00
#> SRR191648 4 0.0880 0.821 0.032 0.000 0 0.968 0.00
#> SRR191649 4 0.0880 0.821 0.032 0.000 0 0.968 0.00
#> SRR191650 2 0.1341 0.875 0.000 0.944 0 0.056 0.00
#> SRR191651 2 0.1341 0.875 0.000 0.944 0 0.056 0.00
#> SRR191652 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191653 4 0.2773 0.774 0.000 0.164 0 0.836 0.00
#> SRR191654 4 0.2773 0.774 0.000 0.164 0 0.836 0.00
#> SRR191655 4 0.2773 0.774 0.000 0.164 0 0.836 0.00
#> SRR191656 4 0.3523 0.699 0.044 0.004 0 0.832 0.12
#> SRR191657 1 0.0794 0.950 0.972 0.000 0 0.028 0.00
#> SRR191658 1 0.0794 0.950 0.972 0.000 0 0.028 0.00
#> SRR191659 1 0.0880 0.945 0.968 0.000 0 0.032 0.00
#> SRR191660 1 0.0794 0.950 0.972 0.000 0 0.028 0.00
#> SRR191661 4 0.0404 0.837 0.000 0.012 0 0.988 0.00
#> SRR191662 2 0.1341 0.875 0.000 0.944 0 0.056 0.00
#> SRR191663 4 0.0404 0.837 0.000 0.012 0 0.988 0.00
#> SRR191664 4 0.1571 0.810 0.060 0.004 0 0.936 0.00
#> SRR191665 4 0.0290 0.835 0.000 0.008 0 0.992 0.00
#> SRR191666 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191667 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191668 4 0.4044 0.674 0.076 0.004 0 0.800 0.12
#> SRR191669 4 0.4044 0.674 0.076 0.004 0 0.800 0.12
#> SRR191670 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191671 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191672 1 0.2597 0.846 0.872 0.004 0 0.004 0.12
#> SRR191673 1 0.2597 0.846 0.872 0.004 0 0.004 0.12
#> SRR191674 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191675 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191677 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191678 4 0.0703 0.824 0.024 0.000 0 0.976 0.00
#> SRR191679 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191680 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191681 4 0.3857 0.606 0.000 0.312 0 0.688 0.00
#> SRR191682 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191683 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191684 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191685 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191686 4 0.3876 0.601 0.000 0.316 0 0.684 0.00
#> SRR191687 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191688 4 0.4300 0.235 0.000 0.476 0 0.524 0.00
#> SRR191689 4 0.3837 0.610 0.000 0.308 0 0.692 0.00
#> SRR191690 4 0.0703 0.824 0.024 0.000 0 0.976 0.00
#> SRR191691 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191692 4 0.1121 0.843 0.000 0.044 0 0.956 0.00
#> SRR191693 4 0.3876 0.601 0.000 0.316 0 0.684 0.00
#> SRR191694 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191695 4 0.1732 0.827 0.000 0.080 0 0.920 0.00
#> SRR191696 4 0.1732 0.827 0.000 0.080 0 0.920 0.00
#> SRR191697 4 0.1043 0.844 0.000 0.040 0 0.960 0.00
#> SRR191698 4 0.1043 0.844 0.000 0.040 0 0.960 0.00
#> SRR191699 4 0.3857 0.606 0.000 0.312 0 0.688 0.00
#> SRR191700 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR191701 2 0.4150 0.250 0.000 0.612 0 0.388 0.00
#> SRR191702 2 0.0290 0.920 0.000 0.992 0 0.008 0.00
#> SRR191703 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191704 5 0.2280 0.000 0.000 0.000 0 0.120 0.88
#> SRR191705 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191706 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191707 4 0.4300 0.235 0.000 0.476 0 0.524 0.00
#> SRR191708 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191709 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191710 4 0.4300 0.235 0.000 0.476 0 0.524 0.00
#> SRR191711 2 0.3109 0.650 0.000 0.800 0 0.200 0.00
#> SRR191712 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR191713 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191714 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191715 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR191716 4 0.0703 0.824 0.024 0.000 0 0.976 0.00
#> SRR191717 4 0.3857 0.606 0.000 0.312 0 0.688 0.00
#> SRR191718 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR537099 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537100 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537101 1 0.0000 0.964 1.000 0.000 0 0.000 0.00
#> SRR537102 4 0.2773 0.774 0.000 0.164 0 0.836 0.00
#> SRR537104 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR537105 4 0.2773 0.774 0.000 0.164 0 0.836 0.00
#> SRR537106 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR537107 4 0.2891 0.765 0.000 0.176 0 0.824 0.00
#> SRR537108 4 0.2891 0.765 0.000 0.176 0 0.824 0.00
#> SRR537109 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR537110 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR537111 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR537113 2 0.3210 0.634 0.000 0.788 0 0.212 0.00
#> SRR537114 4 0.0963 0.845 0.000 0.036 0 0.964 0.00
#> SRR537115 4 0.1043 0.844 0.000 0.040 0 0.960 0.00
#> SRR537116 2 0.0162 0.923 0.000 0.996 0 0.004 0.00
#> SRR537117 4 0.3366 0.615 0.232 0.000 0 0.768 0.00
#> SRR537118 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537119 4 0.3366 0.615 0.232 0.000 0 0.768 0.00
#> SRR537120 4 0.3366 0.615 0.232 0.000 0 0.768 0.00
#> SRR537121 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537122 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537123 4 0.3366 0.615 0.232 0.000 0 0.768 0.00
#> SRR537124 4 0.3366 0.615 0.232 0.000 0 0.768 0.00
#> SRR537125 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537126 4 0.0794 0.844 0.000 0.028 0 0.972 0.00
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.00
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.00
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.00
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.00
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.00
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000 0.00
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 4 0.1411 0.848 0.004 0.000 0 0.936 0.060 0
#> SRR191640 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191641 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191642 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191643 2 0.3838 0.104 0.000 0.552 0 0.448 0.000 0
#> SRR191644 2 0.1327 0.862 0.000 0.936 0 0.064 0.000 0
#> SRR191645 4 0.0547 0.861 0.000 0.000 0 0.980 0.020 0
#> SRR191646 4 0.0547 0.861 0.000 0.000 0 0.980 0.020 0
#> SRR191647 4 0.1411 0.848 0.004 0.000 0 0.936 0.060 0
#> SRR191648 4 0.1411 0.848 0.004 0.000 0 0.936 0.060 0
#> SRR191649 4 0.1411 0.848 0.004 0.000 0 0.936 0.060 0
#> SRR191650 2 0.1327 0.862 0.000 0.936 0 0.064 0.000 0
#> SRR191651 2 0.1327 0.862 0.000 0.936 0 0.064 0.000 0
#> SRR191652 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191653 4 0.2178 0.806 0.000 0.132 0 0.868 0.000 0
#> SRR191654 4 0.2178 0.806 0.000 0.132 0 0.868 0.000 0
#> SRR191655 4 0.2178 0.806 0.000 0.132 0 0.868 0.000 0
#> SRR191656 4 0.2793 0.738 0.000 0.000 0 0.800 0.200 0
#> SRR191657 1 0.1524 0.932 0.932 0.000 0 0.008 0.060 0
#> SRR191658 1 0.1524 0.932 0.932 0.000 0 0.008 0.060 0
#> SRR191659 1 0.1625 0.926 0.928 0.000 0 0.012 0.060 0
#> SRR191660 1 0.1524 0.932 0.932 0.000 0 0.008 0.060 0
#> SRR191661 4 0.0547 0.861 0.000 0.000 0 0.980 0.020 0
#> SRR191662 2 0.1327 0.862 0.000 0.936 0 0.064 0.000 0
#> SRR191663 4 0.0547 0.861 0.000 0.000 0 0.980 0.020 0
#> SRR191664 4 0.1950 0.840 0.024 0.000 0 0.912 0.064 0
#> SRR191665 4 0.0632 0.860 0.000 0.000 0 0.976 0.024 0
#> SRR191666 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191667 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191668 4 0.3136 0.718 0.004 0.000 0 0.768 0.228 0
#> SRR191669 4 0.3136 0.718 0.004 0.000 0 0.768 0.228 0
#> SRR191670 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191671 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191672 5 0.1267 1.000 0.060 0.000 0 0.000 0.940 0
#> SRR191673 5 0.1267 1.000 0.060 0.000 0 0.000 0.940 0
#> SRR191674 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191675 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191677 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191678 4 0.1285 0.851 0.004 0.000 0 0.944 0.052 0
#> SRR191679 2 0.0520 0.906 0.000 0.984 0 0.008 0.008 0
#> SRR191680 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191681 4 0.3309 0.645 0.000 0.280 0 0.720 0.000 0
#> SRR191682 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191683 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191684 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191685 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191686 4 0.3330 0.640 0.000 0.284 0 0.716 0.000 0
#> SRR191687 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191688 4 0.3838 0.269 0.000 0.448 0 0.552 0.000 0
#> SRR191689 4 0.3288 0.650 0.000 0.276 0 0.724 0.000 0
#> SRR191690 4 0.1285 0.851 0.004 0.000 0 0.944 0.052 0
#> SRR191691 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191692 4 0.0363 0.864 0.000 0.012 0 0.988 0.000 0
#> SRR191693 4 0.3330 0.640 0.000 0.284 0 0.716 0.000 0
#> SRR191694 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191695 4 0.1075 0.851 0.000 0.048 0 0.952 0.000 0
#> SRR191696 4 0.1075 0.851 0.000 0.048 0 0.952 0.000 0
#> SRR191697 4 0.0260 0.865 0.000 0.008 0 0.992 0.000 0
#> SRR191698 4 0.0260 0.865 0.000 0.008 0 0.992 0.000 0
#> SRR191699 4 0.3309 0.645 0.000 0.280 0 0.720 0.000 0
#> SRR191700 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR191701 2 0.3782 0.212 0.000 0.588 0 0.412 0.000 0
#> SRR191702 2 0.0146 0.913 0.000 0.996 0 0.004 0.000 0
#> SRR191703 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191704 6 0.0000 0.000 0.000 0.000 0 0.000 0.000 1
#> SRR191705 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191706 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191707 4 0.3838 0.269 0.000 0.448 0 0.552 0.000 0
#> SRR191708 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191709 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191710 4 0.3838 0.269 0.000 0.448 0 0.552 0.000 0
#> SRR191711 2 0.2912 0.636 0.000 0.784 0 0.216 0.000 0
#> SRR191712 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR191713 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191714 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191715 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR191716 4 0.1285 0.851 0.004 0.000 0 0.944 0.052 0
#> SRR191717 4 0.3309 0.645 0.000 0.280 0 0.720 0.000 0
#> SRR191718 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR537099 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537100 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537101 1 0.0000 0.966 1.000 0.000 0 0.000 0.000 0
#> SRR537102 4 0.2178 0.806 0.000 0.132 0 0.868 0.000 0
#> SRR537104 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR537105 4 0.2178 0.806 0.000 0.132 0 0.868 0.000 0
#> SRR537106 2 0.0260 0.911 0.000 0.992 0 0.008 0.000 0
#> SRR537107 4 0.2300 0.798 0.000 0.144 0 0.856 0.000 0
#> SRR537108 4 0.2300 0.798 0.000 0.144 0 0.856 0.000 0
#> SRR537109 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR537110 2 0.0260 0.911 0.000 0.992 0 0.008 0.000 0
#> SRR537111 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR537113 2 0.2969 0.630 0.000 0.776 0 0.224 0.000 0
#> SRR537114 4 0.0146 0.865 0.000 0.004 0 0.996 0.000 0
#> SRR537115 4 0.0260 0.865 0.000 0.008 0 0.992 0.000 0
#> SRR537116 2 0.0000 0.915 0.000 1.000 0 0.000 0.000 0
#> SRR537117 4 0.4011 0.668 0.204 0.000 0 0.736 0.060 0
#> SRR537118 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537119 4 0.4011 0.668 0.204 0.000 0 0.736 0.060 0
#> SRR537120 4 0.4011 0.668 0.204 0.000 0 0.736 0.060 0
#> SRR537121 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537122 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537123 4 0.4011 0.668 0.204 0.000 0 0.736 0.060 0
#> SRR537124 4 0.4011 0.668 0.204 0.000 0 0.736 0.060 0
#> SRR537125 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537126 4 0.0405 0.865 0.000 0.004 0 0.988 0.008 0
#> SRR537127 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0
#> SRR537128 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0
#> SRR537129 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0
#> SRR537130 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0
#> SRR537131 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0
#> SRR537132 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0
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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.889 0.911 0.957 0.4053 0.558 0.558
#> 3 3 0.686 0.763 0.878 0.4285 0.637 0.444
#> 4 4 0.718 0.796 0.887 0.1418 0.878 0.709
#> 5 5 0.694 0.783 0.834 0.1085 0.780 0.445
#> 6 6 0.716 0.615 0.800 0.0701 0.972 0.888
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
#> SRR191639 1 0.946 0.550 0.636 0.364
#> SRR191640 2 0.000 0.999 0.000 1.000
#> SRR191641 1 0.000 0.860 1.000 0.000
#> SRR191642 2 0.000 0.999 0.000 1.000
#> SRR191643 2 0.000 0.999 0.000 1.000
#> SRR191644 2 0.000 0.999 0.000 1.000
#> SRR191645 2 0.000 0.999 0.000 1.000
#> SRR191646 2 0.000 0.999 0.000 1.000
#> SRR191647 1 0.000 0.860 1.000 0.000
#> SRR191648 1 0.689 0.751 0.816 0.184
#> SRR191649 1 0.985 0.443 0.572 0.428
#> SRR191650 2 0.000 0.999 0.000 1.000
#> SRR191651 2 0.000 0.999 0.000 1.000
#> SRR191652 1 0.000 0.860 1.000 0.000
#> SRR191653 2 0.000 0.999 0.000 1.000
#> SRR191654 2 0.000 0.999 0.000 1.000
#> SRR191655 2 0.000 0.999 0.000 1.000
#> SRR191656 2 0.000 0.999 0.000 1.000
#> SRR191657 1 0.000 0.860 1.000 0.000
#> SRR191658 1 0.000 0.860 1.000 0.000
#> SRR191659 1 0.000 0.860 1.000 0.000
#> SRR191660 1 0.000 0.860 1.000 0.000
#> SRR191661 2 0.000 0.999 0.000 1.000
#> SRR191662 2 0.000 0.999 0.000 1.000
#> SRR191663 2 0.000 0.999 0.000 1.000
#> SRR191664 1 0.988 0.426 0.564 0.436
#> SRR191665 2 0.000 0.999 0.000 1.000
#> SRR191666 1 0.000 0.860 1.000 0.000
#> SRR191667 1 0.000 0.860 1.000 0.000
#> SRR191668 1 0.000 0.860 1.000 0.000
#> SRR191669 1 0.753 0.725 0.784 0.216
#> SRR191670 1 0.000 0.860 1.000 0.000
#> SRR191671 1 0.000 0.860 1.000 0.000
#> SRR191672 1 0.000 0.860 1.000 0.000
#> SRR191673 1 0.000 0.860 1.000 0.000
#> SRR191674 2 0.000 0.999 0.000 1.000
#> SRR191675 2 0.000 0.999 0.000 1.000
#> SRR191677 2 0.000 0.999 0.000 1.000
#> SRR191678 2 0.430 0.882 0.088 0.912
#> SRR191679 2 0.000 0.999 0.000 1.000
#> SRR191680 2 0.000 0.999 0.000 1.000
#> SRR191681 2 0.000 0.999 0.000 1.000
#> SRR191682 2 0.000 0.999 0.000 1.000
#> SRR191683 2 0.000 0.999 0.000 1.000
#> SRR191684 2 0.000 0.999 0.000 1.000
#> SRR191685 2 0.000 0.999 0.000 1.000
#> SRR191686 2 0.000 0.999 0.000 1.000
#> SRR191687 2 0.000 0.999 0.000 1.000
#> SRR191688 2 0.000 0.999 0.000 1.000
#> SRR191689 2 0.000 0.999 0.000 1.000
#> SRR191690 1 0.985 0.443 0.572 0.428
#> SRR191691 2 0.000 0.999 0.000 1.000
#> SRR191692 2 0.000 0.999 0.000 1.000
#> SRR191693 2 0.000 0.999 0.000 1.000
#> SRR191694 2 0.000 0.999 0.000 1.000
#> SRR191695 2 0.000 0.999 0.000 1.000
#> SRR191696 2 0.000 0.999 0.000 1.000
#> SRR191697 2 0.000 0.999 0.000 1.000
#> SRR191698 2 0.000 0.999 0.000 1.000
#> SRR191699 2 0.000 0.999 0.000 1.000
#> SRR191700 1 0.000 0.860 1.000 0.000
#> SRR191701 2 0.000 0.999 0.000 1.000
#> SRR191702 2 0.000 0.999 0.000 1.000
#> SRR191703 2 0.000 0.999 0.000 1.000
#> SRR191704 2 0.000 0.999 0.000 1.000
#> SRR191705 2 0.000 0.999 0.000 1.000
#> SRR191706 2 0.000 0.999 0.000 1.000
#> SRR191707 2 0.000 0.999 0.000 1.000
#> SRR191708 2 0.000 0.999 0.000 1.000
#> SRR191709 2 0.000 0.999 0.000 1.000
#> SRR191710 2 0.000 0.999 0.000 1.000
#> SRR191711 2 0.000 0.999 0.000 1.000
#> SRR191712 2 0.000 0.999 0.000 1.000
#> SRR191713 2 0.000 0.999 0.000 1.000
#> SRR191714 2 0.000 0.999 0.000 1.000
#> SRR191715 2 0.000 0.999 0.000 1.000
#> SRR191716 2 0.000 0.999 0.000 1.000
#> SRR191717 2 0.000 0.999 0.000 1.000
#> SRR191718 2 0.000 0.999 0.000 1.000
#> SRR537099 2 0.000 0.999 0.000 1.000
#> SRR537100 1 0.795 0.702 0.760 0.240
#> SRR537101 1 0.000 0.860 1.000 0.000
#> SRR537102 2 0.000 0.999 0.000 1.000
#> SRR537104 2 0.000 0.999 0.000 1.000
#> SRR537105 2 0.000 0.999 0.000 1.000
#> SRR537106 2 0.000 0.999 0.000 1.000
#> SRR537107 2 0.000 0.999 0.000 1.000
#> SRR537108 2 0.000 0.999 0.000 1.000
#> SRR537109 2 0.000 0.999 0.000 1.000
#> SRR537110 2 0.000 0.999 0.000 1.000
#> SRR537111 2 0.000 0.999 0.000 1.000
#> SRR537113 2 0.000 0.999 0.000 1.000
#> SRR537114 2 0.000 0.999 0.000 1.000
#> SRR537115 2 0.000 0.999 0.000 1.000
#> SRR537116 2 0.000 0.999 0.000 1.000
#> SRR537117 1 1.000 0.297 0.512 0.488
#> SRR537118 2 0.000 0.999 0.000 1.000
#> SRR537119 1 1.000 0.297 0.512 0.488
#> SRR537120 1 0.988 0.426 0.564 0.436
#> SRR537121 2 0.000 0.999 0.000 1.000
#> SRR537122 2 0.000 0.999 0.000 1.000
#> SRR537123 1 0.184 0.848 0.972 0.028
#> SRR537124 1 0.000 0.860 1.000 0.000
#> SRR537125 1 1.000 0.297 0.512 0.488
#> SRR537126 1 1.000 0.297 0.512 0.488
#> SRR537127 1 0.000 0.860 1.000 0.000
#> SRR537128 1 0.000 0.860 1.000 0.000
#> SRR537129 1 0.000 0.860 1.000 0.000
#> SRR537130 1 0.000 0.860 1.000 0.000
#> SRR537131 1 0.000 0.860 1.000 0.000
#> SRR537132 1 0.000 0.860 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0424 0.7015 0.992 0.008 0.000
#> SRR191640 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR191641 1 0.5678 -0.0995 0.684 0.000 0.316
#> SRR191642 1 0.5733 0.6555 0.676 0.324 0.000
#> SRR191643 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191644 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191645 1 0.5810 0.6435 0.664 0.336 0.000
#> SRR191646 1 0.5733 0.6555 0.676 0.324 0.000
#> SRR191647 1 0.0237 0.6913 0.996 0.000 0.004
#> SRR191648 1 0.0237 0.6967 0.996 0.004 0.000
#> SRR191649 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR191650 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191651 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191652 3 0.6154 0.7740 0.408 0.000 0.592
#> SRR191653 1 0.6225 0.4755 0.568 0.432 0.000
#> SRR191654 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191655 1 0.6168 0.5191 0.588 0.412 0.000
#> SRR191656 1 0.4605 0.6948 0.796 0.204 0.000
#> SRR191657 1 0.6192 -0.4493 0.580 0.000 0.420
#> SRR191658 1 0.0592 0.6835 0.988 0.000 0.012
#> SRR191659 1 0.0237 0.6913 0.996 0.000 0.004
#> SRR191660 1 0.0592 0.6835 0.988 0.000 0.012
#> SRR191661 1 0.6192 0.5026 0.580 0.420 0.000
#> SRR191662 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191663 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR191664 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR191665 1 0.5733 0.6555 0.676 0.324 0.000
#> SRR191666 3 0.5706 0.8089 0.320 0.000 0.680
#> SRR191667 3 0.5706 0.8089 0.320 0.000 0.680
#> SRR191668 1 0.0237 0.6913 0.996 0.000 0.004
#> SRR191669 1 0.0424 0.7015 0.992 0.008 0.000
#> SRR191670 3 0.6154 0.7740 0.408 0.000 0.592
#> SRR191671 3 0.6154 0.7740 0.408 0.000 0.592
#> SRR191672 3 0.6235 0.7428 0.436 0.000 0.564
#> SRR191673 3 0.6235 0.7428 0.436 0.000 0.564
#> SRR191674 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191678 1 0.1860 0.7227 0.948 0.052 0.000
#> SRR191679 2 0.0592 0.9528 0.000 0.988 0.012
#> SRR191680 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191682 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR191683 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191684 2 0.0592 0.9528 0.000 0.988 0.012
#> SRR191685 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191690 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR191691 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191692 1 0.5835 0.6386 0.660 0.340 0.000
#> SRR191693 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191695 1 0.5835 0.6386 0.660 0.340 0.000
#> SRR191696 2 0.5785 0.3093 0.332 0.668 0.000
#> SRR191697 2 0.6286 -0.1987 0.464 0.536 0.000
#> SRR191698 1 0.5785 0.6477 0.668 0.332 0.000
#> SRR191699 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191700 3 0.6225 0.7482 0.432 0.000 0.568
#> SRR191701 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191704 2 0.8045 -0.1316 0.432 0.504 0.064
#> SRR191705 1 0.5810 0.6435 0.664 0.336 0.000
#> SRR191706 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191708 1 0.6126 0.5419 0.600 0.400 0.000
#> SRR191709 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191712 1 0.5905 0.6214 0.648 0.352 0.000
#> SRR191713 2 0.0592 0.9528 0.000 0.988 0.012
#> SRR191714 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191716 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR191717 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR191718 1 0.5810 0.6435 0.664 0.336 0.000
#> SRR537099 1 0.6168 0.5191 0.588 0.412 0.000
#> SRR537100 1 0.0424 0.7015 0.992 0.008 0.000
#> SRR537101 3 0.5706 0.8089 0.320 0.000 0.680
#> SRR537102 1 0.6225 0.4755 0.568 0.432 0.000
#> SRR537104 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537105 1 0.5835 0.6386 0.660 0.340 0.000
#> SRR537106 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537107 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537108 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537109 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537110 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537111 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537113 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537114 1 0.2625 0.7248 0.916 0.084 0.000
#> SRR537115 1 0.6180 0.5111 0.584 0.416 0.000
#> SRR537116 2 0.0000 0.9643 0.000 1.000 0.000
#> SRR537117 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR537118 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR537119 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR537120 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR537121 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR537122 1 0.2261 0.7272 0.932 0.068 0.000
#> SRR537123 1 0.0237 0.6967 0.996 0.004 0.000
#> SRR537124 1 0.0237 0.6913 0.996 0.000 0.004
#> SRR537125 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR537126 1 0.0747 0.7093 0.984 0.016 0.000
#> SRR537127 3 0.2165 0.7841 0.064 0.000 0.936
#> SRR537128 3 0.2165 0.7841 0.064 0.000 0.936
#> SRR537129 3 0.2165 0.7841 0.064 0.000 0.936
#> SRR537130 3 0.2165 0.7841 0.064 0.000 0.936
#> SRR537131 3 0.2165 0.7841 0.064 0.000 0.936
#> SRR537132 3 0.2165 0.7841 0.064 0.000 0.936
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 4 0.4431 0.595 0.304 0.000 0.000 0.696
#> SRR191640 4 0.1389 0.837 0.048 0.000 0.000 0.952
#> SRR191641 1 0.1867 0.849 0.928 0.000 0.000 0.072
#> SRR191642 4 0.1545 0.845 0.008 0.040 0.000 0.952
#> SRR191643 2 0.4776 0.565 0.000 0.624 0.000 0.376
#> SRR191644 2 0.0707 0.867 0.000 0.980 0.000 0.020
#> SRR191645 4 0.1635 0.845 0.008 0.044 0.000 0.948
#> SRR191646 4 0.1545 0.845 0.008 0.040 0.000 0.952
#> SRR191647 4 0.4431 0.595 0.304 0.000 0.000 0.696
#> SRR191648 4 0.4431 0.595 0.304 0.000 0.000 0.696
#> SRR191649 4 0.4431 0.595 0.304 0.000 0.000 0.696
#> SRR191650 2 0.3024 0.802 0.000 0.852 0.000 0.148
#> SRR191651 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191652 1 0.1209 0.844 0.964 0.000 0.004 0.032
#> SRR191653 4 0.2198 0.828 0.008 0.072 0.000 0.920
#> SRR191654 2 0.4843 0.524 0.000 0.604 0.000 0.396
#> SRR191655 4 0.2198 0.828 0.008 0.072 0.000 0.920
#> SRR191656 4 0.1510 0.844 0.016 0.028 0.000 0.956
#> SRR191657 1 0.1637 0.850 0.940 0.000 0.000 0.060
#> SRR191658 1 0.2530 0.835 0.888 0.000 0.000 0.112
#> SRR191659 1 0.2530 0.835 0.888 0.000 0.000 0.112
#> SRR191660 1 0.2530 0.835 0.888 0.000 0.000 0.112
#> SRR191661 4 0.2198 0.828 0.008 0.072 0.000 0.920
#> SRR191662 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191663 4 0.1389 0.837 0.048 0.000 0.000 0.952
#> SRR191664 4 0.4431 0.595 0.304 0.000 0.000 0.696
#> SRR191665 4 0.1635 0.845 0.008 0.044 0.000 0.948
#> SRR191666 1 0.1305 0.812 0.960 0.000 0.036 0.004
#> SRR191667 1 0.1661 0.797 0.944 0.000 0.052 0.004
#> SRR191668 1 0.2589 0.833 0.884 0.000 0.000 0.116
#> SRR191669 1 0.4761 0.444 0.628 0.000 0.000 0.372
#> SRR191670 1 0.1209 0.844 0.964 0.000 0.004 0.032
#> SRR191671 1 0.1209 0.844 0.964 0.000 0.004 0.032
#> SRR191672 1 0.1211 0.849 0.960 0.000 0.000 0.040
#> SRR191673 1 0.1211 0.849 0.960 0.000 0.000 0.040
#> SRR191674 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191675 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191677 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191678 4 0.3024 0.761 0.148 0.000 0.000 0.852
#> SRR191679 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191680 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191681 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191682 4 0.1211 0.837 0.040 0.000 0.000 0.960
#> SRR191683 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191684 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191685 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191686 2 0.3975 0.746 0.000 0.760 0.000 0.240
#> SRR191687 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191688 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191689 2 0.4855 0.516 0.000 0.600 0.000 0.400
#> SRR191690 4 0.4304 0.613 0.284 0.000 0.000 0.716
#> SRR191691 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191692 4 0.1302 0.845 0.000 0.044 0.000 0.956
#> SRR191693 2 0.2814 0.811 0.000 0.868 0.000 0.132
#> SRR191694 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191695 4 0.1302 0.845 0.000 0.044 0.000 0.956
#> SRR191696 4 0.3074 0.736 0.000 0.152 0.000 0.848
#> SRR191697 4 0.2281 0.805 0.000 0.096 0.000 0.904
#> SRR191698 4 0.1302 0.845 0.000 0.044 0.000 0.956
#> SRR191699 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191700 1 0.1211 0.847 0.960 0.000 0.000 0.040
#> SRR191701 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191702 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191704 4 0.3712 0.749 0.028 0.024 0.080 0.868
#> SRR191705 4 0.1302 0.845 0.000 0.044 0.000 0.956
#> SRR191706 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191707 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191708 4 0.1792 0.831 0.000 0.068 0.000 0.932
#> SRR191709 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191710 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191711 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191712 4 0.1302 0.845 0.000 0.044 0.000 0.956
#> SRR191713 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191714 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191715 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR191716 4 0.1211 0.837 0.040 0.000 0.000 0.960
#> SRR191717 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR191718 4 0.1302 0.845 0.000 0.044 0.000 0.956
#> SRR537099 4 0.1867 0.828 0.000 0.072 0.000 0.928
#> SRR537100 4 0.4382 0.597 0.296 0.000 0.000 0.704
#> SRR537101 1 0.1661 0.797 0.944 0.000 0.052 0.004
#> SRR537102 4 0.2198 0.828 0.008 0.072 0.000 0.920
#> SRR537104 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537105 4 0.1635 0.845 0.008 0.044 0.000 0.948
#> SRR537106 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537107 2 0.4972 0.374 0.000 0.544 0.000 0.456
#> SRR537108 2 0.4103 0.735 0.000 0.744 0.000 0.256
#> SRR537109 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537110 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537111 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537113 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537114 4 0.1211 0.837 0.040 0.000 0.000 0.960
#> SRR537115 4 0.1867 0.828 0.000 0.072 0.000 0.928
#> SRR537116 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> SRR537117 4 0.4382 0.597 0.296 0.000 0.000 0.704
#> SRR537118 4 0.1211 0.837 0.040 0.000 0.000 0.960
#> SRR537119 4 0.4382 0.597 0.296 0.000 0.000 0.704
#> SRR537120 4 0.4382 0.597 0.296 0.000 0.000 0.704
#> SRR537121 4 0.1211 0.837 0.040 0.000 0.000 0.960
#> SRR537122 4 0.1211 0.837 0.040 0.000 0.000 0.960
#> SRR537123 1 0.4898 0.322 0.584 0.000 0.000 0.416
#> SRR537124 1 0.4522 0.573 0.680 0.000 0.000 0.320
#> SRR537125 4 0.4382 0.597 0.296 0.000 0.000 0.704
#> SRR537126 4 0.4382 0.597 0.296 0.000 0.000 0.704
#> SRR537127 3 0.2011 1.000 0.080 0.000 0.920 0.000
#> SRR537128 3 0.2011 1.000 0.080 0.000 0.920 0.000
#> SRR537129 3 0.2011 1.000 0.080 0.000 0.920 0.000
#> SRR537130 3 0.2197 0.998 0.080 0.000 0.916 0.004
#> SRR537131 3 0.2011 1.000 0.080 0.000 0.920 0.000
#> SRR537132 3 0.2011 1.000 0.080 0.000 0.920 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 5 0.5013 0.8484 0.084 0.000 0.000 0.232 0.684
#> SRR191640 5 0.4446 0.5981 0.004 0.000 0.000 0.476 0.520
#> SRR191641 1 0.0771 0.9443 0.976 0.000 0.000 0.004 0.020
#> SRR191642 4 0.2230 0.6538 0.000 0.000 0.000 0.884 0.116
#> SRR191643 4 0.4767 0.6788 0.000 0.192 0.000 0.720 0.088
#> SRR191644 2 0.4736 0.0566 0.000 0.576 0.000 0.404 0.020
#> SRR191645 4 0.2230 0.6572 0.000 0.000 0.000 0.884 0.116
#> SRR191646 4 0.2230 0.6572 0.000 0.000 0.000 0.884 0.116
#> SRR191647 5 0.5112 0.8464 0.080 0.000 0.000 0.256 0.664
#> SRR191648 5 0.5112 0.8464 0.080 0.000 0.000 0.256 0.664
#> SRR191649 5 0.5181 0.8453 0.080 0.000 0.000 0.268 0.652
#> SRR191650 4 0.4798 0.3823 0.000 0.440 0.000 0.540 0.020
#> SRR191651 2 0.0693 0.9640 0.000 0.980 0.000 0.008 0.012
#> SRR191652 1 0.0609 0.9456 0.980 0.000 0.000 0.000 0.020
#> SRR191653 4 0.2563 0.6617 0.000 0.008 0.000 0.872 0.120
#> SRR191654 4 0.4832 0.6794 0.000 0.176 0.000 0.720 0.104
#> SRR191655 4 0.2230 0.6572 0.000 0.000 0.000 0.884 0.116
#> SRR191656 4 0.4348 -0.0397 0.016 0.000 0.000 0.668 0.316
#> SRR191657 1 0.0880 0.9427 0.968 0.000 0.000 0.000 0.032
#> SRR191658 1 0.1041 0.9420 0.964 0.000 0.000 0.004 0.032
#> SRR191659 1 0.1041 0.9420 0.964 0.000 0.000 0.004 0.032
#> SRR191660 1 0.1041 0.9420 0.964 0.000 0.000 0.004 0.032
#> SRR191661 4 0.2439 0.6606 0.000 0.004 0.000 0.876 0.120
#> SRR191662 2 0.1310 0.9450 0.000 0.956 0.000 0.024 0.020
#> SRR191663 5 0.4440 0.5990 0.004 0.000 0.000 0.468 0.528
#> SRR191664 5 0.5487 0.8248 0.100 0.000 0.000 0.280 0.620
#> SRR191665 4 0.2127 0.6651 0.000 0.000 0.000 0.892 0.108
#> SRR191666 1 0.0609 0.9456 0.980 0.000 0.000 0.000 0.020
#> SRR191667 1 0.0771 0.9448 0.976 0.000 0.004 0.000 0.020
#> SRR191668 1 0.4354 0.3448 0.624 0.000 0.000 0.008 0.368
#> SRR191669 5 0.5799 0.3317 0.416 0.000 0.000 0.092 0.492
#> SRR191670 1 0.0290 0.9377 0.992 0.000 0.000 0.000 0.008
#> SRR191671 1 0.0290 0.9377 0.992 0.000 0.000 0.000 0.008
#> SRR191672 1 0.1270 0.9162 0.948 0.000 0.000 0.000 0.052
#> SRR191673 1 0.1270 0.9162 0.948 0.000 0.000 0.000 0.052
#> SRR191674 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191675 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191677 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191678 5 0.5048 0.7994 0.040 0.000 0.000 0.380 0.580
#> SRR191679 2 0.0451 0.9619 0.000 0.988 0.004 0.000 0.008
#> SRR191680 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191681 4 0.4067 0.6159 0.000 0.300 0.000 0.692 0.008
#> SRR191682 5 0.4420 0.7342 0.004 0.000 0.000 0.448 0.548
#> SRR191683 4 0.4067 0.6159 0.000 0.300 0.000 0.692 0.008
#> SRR191684 2 0.0404 0.9636 0.000 0.988 0.000 0.000 0.012
#> SRR191685 2 0.0566 0.9653 0.000 0.984 0.000 0.004 0.012
#> SRR191686 4 0.4327 0.5474 0.000 0.360 0.000 0.632 0.008
#> SRR191687 2 0.0566 0.9653 0.000 0.984 0.000 0.004 0.012
#> SRR191688 4 0.4380 0.5394 0.000 0.376 0.000 0.616 0.008
#> SRR191689 4 0.3582 0.6731 0.000 0.224 0.000 0.768 0.008
#> SRR191690 5 0.5406 0.8097 0.068 0.000 0.000 0.360 0.572
#> SRR191691 2 0.0566 0.9653 0.000 0.984 0.000 0.004 0.012
#> SRR191692 4 0.0963 0.6752 0.000 0.000 0.000 0.964 0.036
#> SRR191693 4 0.4464 0.4516 0.000 0.408 0.000 0.584 0.008
#> SRR191694 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191695 4 0.0963 0.6752 0.000 0.000 0.000 0.964 0.036
#> SRR191696 4 0.1918 0.6910 0.000 0.036 0.000 0.928 0.036
#> SRR191697 4 0.1251 0.6830 0.000 0.008 0.000 0.956 0.036
#> SRR191698 4 0.0963 0.6752 0.000 0.000 0.000 0.964 0.036
#> SRR191699 4 0.4564 0.5342 0.000 0.372 0.000 0.612 0.016
#> SRR191700 1 0.0609 0.9456 0.980 0.000 0.000 0.000 0.020
#> SRR191701 2 0.1764 0.9064 0.000 0.928 0.000 0.064 0.008
#> SRR191702 2 0.0579 0.9646 0.000 0.984 0.000 0.008 0.008
#> SRR191703 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191704 4 0.3700 0.5954 0.000 0.008 0.000 0.752 0.240
#> SRR191705 4 0.0880 0.6768 0.000 0.000 0.000 0.968 0.032
#> SRR191706 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191707 4 0.4380 0.5388 0.000 0.376 0.000 0.616 0.008
#> SRR191708 4 0.0609 0.6827 0.000 0.000 0.000 0.980 0.020
#> SRR191709 2 0.0324 0.9668 0.000 0.992 0.000 0.004 0.004
#> SRR191710 4 0.4380 0.5394 0.000 0.376 0.000 0.616 0.008
#> SRR191711 2 0.0324 0.9669 0.000 0.992 0.000 0.004 0.004
#> SRR191712 4 0.0880 0.6768 0.000 0.000 0.000 0.968 0.032
#> SRR191713 2 0.0162 0.9654 0.000 0.996 0.000 0.000 0.004
#> SRR191714 2 0.0324 0.9669 0.000 0.992 0.000 0.004 0.004
#> SRR191715 2 0.0451 0.9664 0.000 0.988 0.000 0.004 0.008
#> SRR191716 5 0.4451 0.6734 0.004 0.000 0.000 0.492 0.504
#> SRR191717 4 0.4201 0.5904 0.000 0.328 0.000 0.664 0.008
#> SRR191718 4 0.0963 0.6752 0.000 0.000 0.000 0.964 0.036
#> SRR537099 4 0.2179 0.6576 0.000 0.000 0.000 0.888 0.112
#> SRR537100 5 0.5091 0.8508 0.084 0.000 0.000 0.244 0.672
#> SRR537101 1 0.0771 0.9448 0.976 0.000 0.004 0.000 0.020
#> SRR537102 4 0.2179 0.6576 0.000 0.000 0.000 0.888 0.112
#> SRR537104 2 0.0566 0.9653 0.000 0.984 0.000 0.004 0.012
#> SRR537105 4 0.2230 0.6572 0.000 0.000 0.000 0.884 0.116
#> SRR537106 2 0.0912 0.9590 0.000 0.972 0.000 0.012 0.016
#> SRR537107 4 0.4683 0.6822 0.000 0.176 0.000 0.732 0.092
#> SRR537108 4 0.4555 0.5668 0.000 0.344 0.000 0.636 0.020
#> SRR537109 2 0.0451 0.9659 0.000 0.988 0.000 0.004 0.008
#> SRR537110 2 0.0912 0.9590 0.000 0.972 0.000 0.012 0.016
#> SRR537111 2 0.0566 0.9653 0.000 0.984 0.000 0.004 0.012
#> SRR537113 2 0.0579 0.9657 0.000 0.984 0.000 0.008 0.008
#> SRR537114 4 0.2011 0.6108 0.004 0.000 0.000 0.908 0.088
#> SRR537115 4 0.0794 0.6793 0.000 0.000 0.000 0.972 0.028
#> SRR537116 2 0.0324 0.9668 0.000 0.992 0.000 0.004 0.004
#> SRR537117 5 0.5441 0.8106 0.080 0.000 0.000 0.324 0.596
#> SRR537118 5 0.4218 0.8017 0.008 0.000 0.000 0.332 0.660
#> SRR537119 5 0.5116 0.8507 0.084 0.000 0.000 0.248 0.668
#> SRR537120 5 0.5210 0.8480 0.084 0.000 0.000 0.264 0.652
#> SRR537121 5 0.4218 0.8017 0.008 0.000 0.000 0.332 0.660
#> SRR537122 5 0.4118 0.7901 0.004 0.000 0.000 0.336 0.660
#> SRR537123 5 0.5673 0.5752 0.292 0.000 0.000 0.112 0.596
#> SRR537124 5 0.5600 0.5127 0.316 0.000 0.000 0.096 0.588
#> SRR537125 5 0.5091 0.8508 0.084 0.000 0.000 0.244 0.672
#> SRR537126 5 0.5091 0.8508 0.084 0.000 0.000 0.244 0.672
#> SRR537127 3 0.0162 0.9982 0.004 0.000 0.996 0.000 0.000
#> SRR537128 3 0.0162 0.9982 0.004 0.000 0.996 0.000 0.000
#> SRR537129 3 0.0162 0.9982 0.004 0.000 0.996 0.000 0.000
#> SRR537130 3 0.0671 0.9911 0.004 0.000 0.980 0.000 0.016
#> SRR537131 3 0.0162 0.9982 0.004 0.000 0.996 0.000 0.000
#> SRR537132 3 0.0162 0.9982 0.004 0.000 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 5 0.1845 0.7339 0.000 0.000 0.000 0.028 0.920 0.052
#> SRR191640 5 0.5803 -0.1034 0.000 0.000 0.000 0.404 0.416 0.180
#> SRR191641 1 0.1296 0.9487 0.952 0.000 0.000 0.004 0.032 0.012
#> SRR191642 4 0.4474 0.3015 0.000 0.000 0.000 0.708 0.120 0.172
#> SRR191643 4 0.1794 0.4053 0.000 0.036 0.000 0.924 0.040 0.000
#> SRR191644 4 0.4277 0.1609 0.000 0.356 0.000 0.616 0.000 0.028
#> SRR191645 4 0.4253 0.3262 0.000 0.000 0.000 0.732 0.108 0.160
#> SRR191646 4 0.4328 0.3165 0.000 0.000 0.000 0.724 0.112 0.164
#> SRR191647 5 0.3033 0.7045 0.012 0.000 0.000 0.076 0.856 0.056
#> SRR191648 5 0.3033 0.7045 0.012 0.000 0.000 0.076 0.856 0.056
#> SRR191649 5 0.3670 0.6776 0.012 0.000 0.000 0.100 0.808 0.080
#> SRR191650 4 0.3745 0.2760 0.000 0.240 0.000 0.732 0.000 0.028
#> SRR191651 2 0.3377 0.7755 0.000 0.784 0.000 0.188 0.000 0.028
#> SRR191652 1 0.1116 0.9496 0.960 0.000 0.000 0.004 0.028 0.008
#> SRR191653 4 0.3159 0.3804 0.000 0.000 0.000 0.832 0.100 0.068
#> SRR191654 4 0.1938 0.4047 0.000 0.036 0.000 0.920 0.040 0.004
#> SRR191655 4 0.3832 0.3630 0.000 0.000 0.000 0.776 0.104 0.120
#> SRR191656 6 0.6366 0.3252 0.016 0.000 0.000 0.232 0.376 0.376
#> SRR191657 1 0.1552 0.9439 0.940 0.000 0.000 0.004 0.036 0.020
#> SRR191658 1 0.1793 0.9397 0.928 0.000 0.000 0.004 0.036 0.032
#> SRR191659 1 0.1938 0.9350 0.920 0.000 0.000 0.004 0.036 0.040
#> SRR191660 1 0.1793 0.9397 0.928 0.000 0.000 0.004 0.036 0.032
#> SRR191661 4 0.3745 0.3668 0.000 0.000 0.000 0.784 0.100 0.116
#> SRR191662 2 0.3929 0.6718 0.000 0.700 0.000 0.272 0.000 0.028
#> SRR191663 5 0.5871 -0.0945 0.000 0.000 0.000 0.396 0.408 0.196
#> SRR191664 5 0.5304 0.5018 0.032 0.000 0.000 0.216 0.652 0.100
#> SRR191665 4 0.4532 0.2958 0.000 0.000 0.000 0.696 0.108 0.196
#> SRR191666 1 0.1116 0.9496 0.960 0.000 0.000 0.004 0.028 0.008
#> SRR191667 1 0.1116 0.9496 0.960 0.000 0.000 0.004 0.028 0.008
#> SRR191668 5 0.5294 0.2251 0.356 0.000 0.000 0.000 0.532 0.112
#> SRR191669 5 0.4892 0.4737 0.248 0.000 0.000 0.000 0.640 0.112
#> SRR191670 1 0.1391 0.9319 0.944 0.000 0.000 0.000 0.016 0.040
#> SRR191671 1 0.1391 0.9319 0.944 0.000 0.000 0.000 0.016 0.040
#> SRR191672 1 0.3395 0.8313 0.808 0.000 0.000 0.000 0.060 0.132
#> SRR191673 1 0.3395 0.8313 0.808 0.000 0.000 0.000 0.060 0.132
#> SRR191674 2 0.1082 0.9268 0.000 0.956 0.000 0.004 0.000 0.040
#> SRR191675 2 0.1082 0.9268 0.000 0.956 0.000 0.004 0.000 0.040
#> SRR191677 2 0.0777 0.9301 0.000 0.972 0.000 0.004 0.000 0.024
#> SRR191678 5 0.3493 0.5994 0.000 0.000 0.000 0.056 0.796 0.148
#> SRR191679 2 0.2051 0.9090 0.008 0.916 0.000 0.040 0.000 0.036
#> SRR191680 2 0.1049 0.9268 0.000 0.960 0.000 0.008 0.000 0.032
#> SRR191681 4 0.5076 0.3191 0.000 0.132 0.000 0.620 0.000 0.248
#> SRR191682 5 0.4874 0.1818 0.000 0.000 0.000 0.084 0.608 0.308
#> SRR191683 4 0.5076 0.3191 0.000 0.132 0.000 0.620 0.000 0.248
#> SRR191684 2 0.0914 0.9270 0.000 0.968 0.000 0.016 0.000 0.016
#> SRR191685 2 0.0909 0.9262 0.000 0.968 0.000 0.012 0.000 0.020
#> SRR191686 4 0.5507 0.2905 0.000 0.208 0.000 0.564 0.000 0.228
#> SRR191687 2 0.0806 0.9276 0.000 0.972 0.000 0.008 0.000 0.020
#> SRR191688 4 0.4953 0.3649 0.000 0.172 0.000 0.652 0.000 0.176
#> SRR191689 4 0.4707 0.3306 0.000 0.096 0.000 0.660 0.000 0.244
#> SRR191690 5 0.3763 0.5905 0.000 0.000 0.000 0.060 0.768 0.172
#> SRR191691 2 0.0993 0.9249 0.000 0.964 0.000 0.012 0.000 0.024
#> SRR191692 4 0.5265 0.1668 0.000 0.000 0.000 0.500 0.100 0.400
#> SRR191693 4 0.5609 0.2654 0.000 0.236 0.000 0.544 0.000 0.220
#> SRR191694 2 0.1082 0.9268 0.000 0.956 0.000 0.004 0.000 0.040
#> SRR191695 4 0.5265 0.1668 0.000 0.000 0.000 0.500 0.100 0.400
#> SRR191696 4 0.5030 0.2048 0.000 0.000 0.000 0.588 0.096 0.316
#> SRR191697 4 0.5016 0.2107 0.000 0.000 0.000 0.592 0.096 0.312
#> SRR191698 4 0.5305 0.1572 0.000 0.000 0.000 0.492 0.104 0.404
#> SRR191699 4 0.4942 0.3583 0.000 0.192 0.000 0.652 0.000 0.156
#> SRR191700 1 0.1116 0.9496 0.960 0.000 0.000 0.004 0.028 0.008
#> SRR191701 2 0.4972 0.4810 0.000 0.628 0.000 0.256 0.000 0.116
#> SRR191702 2 0.1049 0.9268 0.000 0.960 0.000 0.008 0.000 0.032
#> SRR191703 2 0.0935 0.9287 0.000 0.964 0.000 0.004 0.000 0.032
#> SRR191704 6 0.3787 0.2044 0.000 0.008 0.000 0.260 0.012 0.720
#> SRR191705 4 0.5242 0.1572 0.000 0.000 0.000 0.492 0.096 0.412
#> SRR191706 2 0.1082 0.9268 0.000 0.956 0.000 0.004 0.000 0.040
#> SRR191707 4 0.4828 0.3683 0.000 0.176 0.000 0.668 0.000 0.156
#> SRR191708 4 0.5082 0.1841 0.000 0.000 0.000 0.512 0.080 0.408
#> SRR191709 2 0.0260 0.9310 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191710 4 0.5066 0.3492 0.000 0.188 0.000 0.636 0.000 0.176
#> SRR191711 2 0.0260 0.9313 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR191712 4 0.5242 0.1572 0.000 0.000 0.000 0.492 0.096 0.412
#> SRR191713 2 0.1003 0.9259 0.000 0.964 0.000 0.016 0.000 0.020
#> SRR191714 2 0.0713 0.9301 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR191715 2 0.0632 0.9304 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR191716 5 0.5461 -0.0614 0.000 0.000 0.000 0.140 0.528 0.332
#> SRR191717 4 0.5224 0.3267 0.000 0.164 0.000 0.608 0.000 0.228
#> SRR191718 4 0.5274 0.1559 0.000 0.000 0.000 0.492 0.100 0.408
#> SRR537099 4 0.3961 0.3643 0.000 0.000 0.000 0.764 0.112 0.124
#> SRR537100 5 0.0363 0.7450 0.000 0.000 0.000 0.012 0.988 0.000
#> SRR537101 1 0.1116 0.9496 0.960 0.000 0.000 0.004 0.028 0.008
#> SRR537102 4 0.3566 0.3752 0.000 0.000 0.000 0.800 0.104 0.096
#> SRR537104 2 0.1151 0.9259 0.000 0.956 0.000 0.012 0.000 0.032
#> SRR537105 4 0.3873 0.3606 0.000 0.000 0.000 0.772 0.104 0.124
#> SRR537106 2 0.1564 0.9114 0.000 0.936 0.000 0.040 0.000 0.024
#> SRR537107 4 0.1780 0.4049 0.000 0.028 0.000 0.924 0.048 0.000
#> SRR537108 4 0.2101 0.3907 0.000 0.100 0.000 0.892 0.004 0.004
#> SRR537109 2 0.0972 0.9273 0.000 0.964 0.000 0.008 0.000 0.028
#> SRR537110 2 0.1564 0.9114 0.000 0.936 0.000 0.040 0.000 0.024
#> SRR537111 2 0.1151 0.9259 0.000 0.956 0.000 0.012 0.000 0.032
#> SRR537113 2 0.2907 0.8064 0.000 0.828 0.000 0.152 0.000 0.020
#> SRR537114 4 0.5537 0.0297 0.000 0.000 0.000 0.520 0.152 0.328
#> SRR537115 4 0.5144 0.2070 0.000 0.000 0.000 0.536 0.092 0.372
#> SRR537116 2 0.0146 0.9312 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR537117 5 0.1564 0.7200 0.000 0.000 0.000 0.024 0.936 0.040
#> SRR537118 5 0.0865 0.7406 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR537119 5 0.0622 0.7446 0.000 0.000 0.000 0.012 0.980 0.008
#> SRR537120 5 0.1176 0.7339 0.000 0.000 0.000 0.024 0.956 0.020
#> SRR537121 5 0.0865 0.7406 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR537122 5 0.1075 0.7364 0.000 0.000 0.000 0.048 0.952 0.000
#> SRR537123 5 0.1528 0.7126 0.048 0.000 0.000 0.000 0.936 0.016
#> SRR537124 5 0.1780 0.7102 0.048 0.000 0.000 0.000 0.924 0.028
#> SRR537125 5 0.0458 0.7454 0.000 0.000 0.000 0.016 0.984 0.000
#> SRR537126 5 0.0458 0.7454 0.000 0.000 0.000 0.016 0.984 0.000
#> SRR537127 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537128 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537129 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537130 3 0.0291 0.9951 0.000 0.000 0.992 0.004 0.000 0.004
#> SRR537131 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR537132 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16450 rows and 111 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.986 0.995 0.4884 0.510 0.510
#> 3 3 0.905 0.897 0.948 0.2594 0.846 0.707
#> 4 4 0.764 0.766 0.881 0.1155 0.934 0.830
#> 5 5 0.784 0.836 0.907 0.0766 0.906 0.720
#> 6 6 0.804 0.753 0.873 0.0398 0.991 0.963
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR191639 1 0.000 0.987 1.000 0.000
#> SRR191640 1 0.000 0.987 1.000 0.000
#> SRR191641 1 0.000 0.987 1.000 0.000
#> SRR191642 2 0.000 1.000 0.000 1.000
#> SRR191643 2 0.000 1.000 0.000 1.000
#> SRR191644 2 0.000 1.000 0.000 1.000
#> SRR191645 2 0.000 1.000 0.000 1.000
#> SRR191646 2 0.000 1.000 0.000 1.000
#> SRR191647 1 0.000 0.987 1.000 0.000
#> SRR191648 1 0.000 0.987 1.000 0.000
#> SRR191649 1 0.000 0.987 1.000 0.000
#> SRR191650 2 0.000 1.000 0.000 1.000
#> SRR191651 2 0.000 1.000 0.000 1.000
#> SRR191652 1 0.000 0.987 1.000 0.000
#> SRR191653 2 0.000 1.000 0.000 1.000
#> SRR191654 2 0.000 1.000 0.000 1.000
#> SRR191655 2 0.000 1.000 0.000 1.000
#> SRR191656 1 0.994 0.164 0.544 0.456
#> SRR191657 1 0.000 0.987 1.000 0.000
#> SRR191658 1 0.000 0.987 1.000 0.000
#> SRR191659 1 0.000 0.987 1.000 0.000
#> SRR191660 1 0.000 0.987 1.000 0.000
#> SRR191661 2 0.000 1.000 0.000 1.000
#> SRR191662 2 0.000 1.000 0.000 1.000
#> SRR191663 1 0.000 0.987 1.000 0.000
#> SRR191664 1 0.000 0.987 1.000 0.000
#> SRR191665 2 0.000 1.000 0.000 1.000
#> SRR191666 1 0.000 0.987 1.000 0.000
#> SRR191667 1 0.000 0.987 1.000 0.000
#> SRR191668 1 0.000 0.987 1.000 0.000
#> SRR191669 1 0.000 0.987 1.000 0.000
#> SRR191670 1 0.000 0.987 1.000 0.000
#> SRR191671 1 0.000 0.987 1.000 0.000
#> SRR191672 1 0.000 0.987 1.000 0.000
#> SRR191673 1 0.000 0.987 1.000 0.000
#> SRR191674 2 0.000 1.000 0.000 1.000
#> SRR191675 2 0.000 1.000 0.000 1.000
#> SRR191677 2 0.000 1.000 0.000 1.000
#> SRR191678 1 0.000 0.987 1.000 0.000
#> SRR191679 2 0.000 1.000 0.000 1.000
#> SRR191680 2 0.000 1.000 0.000 1.000
#> SRR191681 2 0.000 1.000 0.000 1.000
#> SRR191682 1 0.000 0.987 1.000 0.000
#> SRR191683 2 0.000 1.000 0.000 1.000
#> SRR191684 2 0.000 1.000 0.000 1.000
#> SRR191685 2 0.000 1.000 0.000 1.000
#> SRR191686 2 0.000 1.000 0.000 1.000
#> SRR191687 2 0.000 1.000 0.000 1.000
#> SRR191688 2 0.000 1.000 0.000 1.000
#> SRR191689 2 0.000 1.000 0.000 1.000
#> SRR191690 1 0.000 0.987 1.000 0.000
#> SRR191691 2 0.000 1.000 0.000 1.000
#> SRR191692 2 0.000 1.000 0.000 1.000
#> SRR191693 2 0.000 1.000 0.000 1.000
#> SRR191694 2 0.000 1.000 0.000 1.000
#> SRR191695 2 0.000 1.000 0.000 1.000
#> SRR191696 2 0.000 1.000 0.000 1.000
#> SRR191697 2 0.000 1.000 0.000 1.000
#> SRR191698 2 0.000 1.000 0.000 1.000
#> SRR191699 2 0.000 1.000 0.000 1.000
#> SRR191700 1 0.000 0.987 1.000 0.000
#> SRR191701 2 0.000 1.000 0.000 1.000
#> SRR191702 2 0.000 1.000 0.000 1.000
#> SRR191703 2 0.000 1.000 0.000 1.000
#> SRR191704 2 0.000 1.000 0.000 1.000
#> SRR191705 2 0.000 1.000 0.000 1.000
#> SRR191706 2 0.000 1.000 0.000 1.000
#> SRR191707 2 0.000 1.000 0.000 1.000
#> SRR191708 2 0.000 1.000 0.000 1.000
#> SRR191709 2 0.000 1.000 0.000 1.000
#> SRR191710 2 0.000 1.000 0.000 1.000
#> SRR191711 2 0.000 1.000 0.000 1.000
#> SRR191712 2 0.000 1.000 0.000 1.000
#> SRR191713 2 0.000 1.000 0.000 1.000
#> SRR191714 2 0.000 1.000 0.000 1.000
#> SRR191715 2 0.000 1.000 0.000 1.000
#> SRR191716 1 0.000 0.987 1.000 0.000
#> SRR191717 2 0.000 1.000 0.000 1.000
#> SRR191718 2 0.000 1.000 0.000 1.000
#> SRR537099 2 0.000 1.000 0.000 1.000
#> SRR537100 1 0.000 0.987 1.000 0.000
#> SRR537101 1 0.000 0.987 1.000 0.000
#> SRR537102 2 0.000 1.000 0.000 1.000
#> SRR537104 2 0.000 1.000 0.000 1.000
#> SRR537105 2 0.000 1.000 0.000 1.000
#> SRR537106 2 0.000 1.000 0.000 1.000
#> SRR537107 2 0.000 1.000 0.000 1.000
#> SRR537108 2 0.000 1.000 0.000 1.000
#> SRR537109 2 0.000 1.000 0.000 1.000
#> SRR537110 2 0.000 1.000 0.000 1.000
#> SRR537111 2 0.000 1.000 0.000 1.000
#> SRR537113 2 0.000 1.000 0.000 1.000
#> SRR537114 1 0.563 0.842 0.868 0.132
#> SRR537115 2 0.000 1.000 0.000 1.000
#> SRR537116 2 0.000 1.000 0.000 1.000
#> SRR537117 1 0.000 0.987 1.000 0.000
#> SRR537118 1 0.000 0.987 1.000 0.000
#> SRR537119 1 0.000 0.987 1.000 0.000
#> SRR537120 1 0.000 0.987 1.000 0.000
#> SRR537121 1 0.000 0.987 1.000 0.000
#> SRR537122 1 0.000 0.987 1.000 0.000
#> SRR537123 1 0.000 0.987 1.000 0.000
#> SRR537124 1 0.000 0.987 1.000 0.000
#> SRR537125 1 0.000 0.987 1.000 0.000
#> SRR537126 1 0.000 0.987 1.000 0.000
#> SRR537127 1 0.000 0.987 1.000 0.000
#> SRR537128 1 0.000 0.987 1.000 0.000
#> SRR537129 1 0.000 0.987 1.000 0.000
#> SRR537130 1 0.000 0.987 1.000 0.000
#> SRR537131 1 0.000 0.987 1.000 0.000
#> SRR537132 1 0.000 0.987 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191640 3 0.2165 0.832 0.064 0.000 0.936
#> SRR191641 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191642 3 0.2066 0.878 0.000 0.060 0.940
#> SRR191643 3 0.5621 0.660 0.000 0.308 0.692
#> SRR191644 2 0.5397 0.570 0.000 0.720 0.280
#> SRR191645 3 0.2165 0.880 0.000 0.064 0.936
#> SRR191646 3 0.2165 0.880 0.000 0.064 0.936
#> SRR191647 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191650 2 0.5431 0.562 0.000 0.716 0.284
#> SRR191651 2 0.4399 0.725 0.000 0.812 0.188
#> SRR191652 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191653 3 0.2165 0.880 0.000 0.064 0.936
#> SRR191654 3 0.4796 0.773 0.000 0.220 0.780
#> SRR191655 3 0.2165 0.880 0.000 0.064 0.936
#> SRR191656 2 0.6490 0.520 0.256 0.708 0.036
#> SRR191657 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191658 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191659 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191660 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191661 3 0.2165 0.880 0.000 0.064 0.936
#> SRR191662 2 0.6235 0.115 0.000 0.564 0.436
#> SRR191663 3 0.2165 0.832 0.064 0.000 0.936
#> SRR191664 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191665 3 0.5244 0.735 0.004 0.240 0.756
#> SRR191666 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191667 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191668 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191670 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191671 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191672 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191673 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191674 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191678 1 0.0892 0.978 0.980 0.000 0.020
#> SRR191679 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191681 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191682 1 0.2165 0.955 0.936 0.000 0.064
#> SRR191683 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191684 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191686 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191687 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191688 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191689 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191690 1 0.0592 0.980 0.988 0.000 0.012
#> SRR191691 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191692 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191693 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191695 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191696 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191697 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191698 2 0.2066 0.879 0.000 0.940 0.060
#> SRR191699 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191700 1 0.0000 0.987 1.000 0.000 0.000
#> SRR191701 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191704 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191705 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191706 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191707 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191708 2 0.0592 0.927 0.000 0.988 0.012
#> SRR191709 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191710 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191712 2 0.0747 0.925 0.000 0.984 0.016
#> SRR191713 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191716 1 0.0747 0.978 0.984 0.000 0.016
#> SRR191717 2 0.0000 0.934 0.000 1.000 0.000
#> SRR191718 2 0.0747 0.925 0.000 0.984 0.016
#> SRR537099 2 0.6111 0.284 0.000 0.604 0.396
#> SRR537100 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537101 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537102 3 0.2165 0.880 0.000 0.064 0.936
#> SRR537104 2 0.4399 0.725 0.000 0.812 0.188
#> SRR537105 3 0.2165 0.880 0.000 0.064 0.936
#> SRR537106 2 0.5431 0.562 0.000 0.716 0.284
#> SRR537107 3 0.5621 0.659 0.000 0.308 0.692
#> SRR537108 3 0.5785 0.613 0.000 0.332 0.668
#> SRR537109 2 0.0000 0.934 0.000 1.000 0.000
#> SRR537110 2 0.5397 0.570 0.000 0.720 0.280
#> SRR537111 2 0.0000 0.934 0.000 1.000 0.000
#> SRR537113 2 0.0000 0.934 0.000 1.000 0.000
#> SRR537114 3 0.7298 0.615 0.220 0.088 0.692
#> SRR537115 2 0.0000 0.934 0.000 1.000 0.000
#> SRR537116 2 0.0000 0.934 0.000 1.000 0.000
#> SRR537117 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537118 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537119 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537120 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537121 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537122 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537123 1 0.1753 0.965 0.952 0.000 0.048
#> SRR537124 1 0.0747 0.981 0.984 0.000 0.016
#> SRR537125 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537126 1 0.1860 0.963 0.948 0.000 0.052
#> SRR537127 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537128 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537129 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537130 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537131 1 0.0000 0.987 1.000 0.000 0.000
#> SRR537132 1 0.0000 0.987 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191640 4 0.1940 0.7537 0.076 0.000 0.000 0.924
#> SRR191641 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191642 4 0.0188 0.7970 0.000 0.000 0.004 0.996
#> SRR191643 4 0.4382 0.6154 0.000 0.296 0.000 0.704
#> SRR191644 2 0.3074 0.7439 0.000 0.848 0.000 0.152
#> SRR191645 4 0.0188 0.8008 0.000 0.004 0.000 0.996
#> SRR191646 4 0.0188 0.8008 0.000 0.004 0.000 0.996
#> SRR191647 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191648 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191649 1 0.0188 0.9224 0.996 0.000 0.000 0.004
#> SRR191650 2 0.3123 0.7390 0.000 0.844 0.000 0.156
#> SRR191651 2 0.1940 0.8189 0.000 0.924 0.000 0.076
#> SRR191652 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191653 4 0.0707 0.8092 0.000 0.020 0.000 0.980
#> SRR191654 4 0.3311 0.7322 0.000 0.172 0.000 0.828
#> SRR191655 4 0.0707 0.8092 0.000 0.020 0.000 0.980
#> SRR191656 2 0.6454 0.3520 0.316 0.600 0.080 0.004
#> SRR191657 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191658 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191659 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191660 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191661 4 0.0707 0.8092 0.000 0.020 0.000 0.980
#> SRR191662 2 0.4605 0.4425 0.000 0.664 0.000 0.336
#> SRR191663 4 0.2081 0.7463 0.084 0.000 0.000 0.916
#> SRR191664 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191665 4 0.5085 0.4091 0.000 0.376 0.008 0.616
#> SRR191666 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191667 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191668 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191669 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191670 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191671 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191672 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191673 1 0.0524 0.9211 0.988 0.000 0.008 0.004
#> SRR191674 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191675 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191677 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191678 3 0.4955 0.3643 0.344 0.000 0.648 0.008
#> SRR191679 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191680 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191681 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191682 3 0.0336 0.4136 0.008 0.000 0.992 0.000
#> SRR191683 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191684 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191685 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191686 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191687 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191688 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191689 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191690 1 0.4792 0.3488 0.680 0.000 0.312 0.008
#> SRR191691 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191692 2 0.5217 0.5300 0.000 0.608 0.380 0.012
#> SRR191693 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191694 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191695 2 0.5313 0.5304 0.000 0.608 0.376 0.016
#> SRR191696 2 0.5055 0.5479 0.000 0.624 0.368 0.008
#> SRR191697 2 0.4889 0.5607 0.000 0.636 0.360 0.004
#> SRR191698 3 0.2888 0.2924 0.000 0.124 0.872 0.004
#> SRR191699 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191700 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR191701 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191702 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191703 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191704 2 0.5510 0.5199 0.000 0.600 0.376 0.024
#> SRR191705 2 0.5523 0.5149 0.000 0.596 0.380 0.024
#> SRR191706 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191707 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191708 2 0.5510 0.5204 0.000 0.600 0.376 0.024
#> SRR191709 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191710 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191711 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191712 2 0.5326 0.5252 0.000 0.604 0.380 0.016
#> SRR191713 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191714 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191715 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191716 1 0.5510 -0.0111 0.504 0.000 0.480 0.016
#> SRR191717 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR191718 2 0.5493 0.4219 0.000 0.528 0.456 0.016
#> SRR537099 2 0.5599 0.4891 0.000 0.672 0.052 0.276
#> SRR537100 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537101 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537102 4 0.0817 0.8084 0.000 0.024 0.000 0.976
#> SRR537104 2 0.1940 0.8189 0.000 0.924 0.000 0.076
#> SRR537105 4 0.0707 0.8092 0.000 0.020 0.000 0.980
#> SRR537106 2 0.3266 0.7278 0.000 0.832 0.000 0.168
#> SRR537107 4 0.4304 0.6304 0.000 0.284 0.000 0.716
#> SRR537108 4 0.4564 0.5484 0.000 0.328 0.000 0.672
#> SRR537109 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR537110 2 0.3024 0.7507 0.000 0.852 0.000 0.148
#> SRR537111 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR537113 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR537114 4 0.5971 0.3021 0.040 0.000 0.428 0.532
#> SRR537115 2 0.2521 0.8252 0.000 0.912 0.064 0.024
#> SRR537116 2 0.0000 0.8756 0.000 1.000 0.000 0.000
#> SRR537117 3 0.4843 0.7746 0.396 0.000 0.604 0.000
#> SRR537118 3 0.4817 0.7846 0.388 0.000 0.612 0.000
#> SRR537119 3 0.4817 0.7846 0.388 0.000 0.612 0.000
#> SRR537120 3 0.4817 0.7846 0.388 0.000 0.612 0.000
#> SRR537121 3 0.4817 0.7846 0.388 0.000 0.612 0.000
#> SRR537122 3 0.4978 0.7821 0.384 0.000 0.612 0.004
#> SRR537123 3 0.4989 0.6356 0.472 0.000 0.528 0.000
#> SRR537124 1 0.4994 -0.5640 0.520 0.000 0.480 0.000
#> SRR537125 3 0.4817 0.7846 0.388 0.000 0.612 0.000
#> SRR537126 3 0.4817 0.7846 0.388 0.000 0.612 0.000
#> SRR537127 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537128 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537129 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537130 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537131 1 0.0000 0.9230 1.000 0.000 0.000 0.000
#> SRR537132 1 0.0000 0.9230 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 1 0.0833 0.908 0.976 0.000 0.016 0.004 0.004
#> SRR191640 4 0.3145 0.706 0.060 0.000 0.064 0.868 0.008
#> SRR191641 1 0.2074 0.932 0.896 0.000 0.000 0.000 0.104
#> SRR191642 4 0.1662 0.745 0.000 0.004 0.056 0.936 0.004
#> SRR191643 4 0.4192 0.400 0.000 0.404 0.000 0.596 0.000
#> SRR191644 2 0.2286 0.848 0.000 0.888 0.004 0.108 0.000
#> SRR191645 4 0.1788 0.747 0.000 0.008 0.056 0.932 0.004
#> SRR191646 4 0.1822 0.744 0.004 0.004 0.056 0.932 0.004
#> SRR191647 1 0.2616 0.926 0.880 0.000 0.020 0.000 0.100
#> SRR191648 1 0.2616 0.926 0.880 0.000 0.020 0.000 0.100
#> SRR191649 1 0.3030 0.912 0.868 0.000 0.040 0.004 0.088
#> SRR191650 2 0.2389 0.839 0.000 0.880 0.004 0.116 0.000
#> SRR191651 2 0.1124 0.917 0.000 0.960 0.004 0.036 0.000
#> SRR191652 1 0.2074 0.932 0.896 0.000 0.000 0.000 0.104
#> SRR191653 4 0.0404 0.759 0.000 0.012 0.000 0.988 0.000
#> SRR191654 4 0.3109 0.640 0.000 0.200 0.000 0.800 0.000
#> SRR191655 4 0.0290 0.759 0.000 0.008 0.000 0.992 0.000
#> SRR191656 2 0.7216 0.080 0.376 0.456 0.056 0.008 0.104
#> SRR191657 1 0.0727 0.911 0.980 0.000 0.012 0.004 0.004
#> SRR191658 1 0.0932 0.906 0.972 0.000 0.020 0.004 0.004
#> SRR191659 1 0.0833 0.909 0.976 0.000 0.016 0.004 0.004
#> SRR191660 1 0.0613 0.912 0.984 0.000 0.008 0.004 0.004
#> SRR191661 4 0.0451 0.759 0.000 0.008 0.004 0.988 0.000
#> SRR191662 2 0.4029 0.486 0.000 0.680 0.004 0.316 0.000
#> SRR191663 4 0.3145 0.706 0.060 0.000 0.064 0.868 0.008
#> SRR191664 1 0.1369 0.901 0.956 0.000 0.028 0.008 0.008
#> SRR191665 4 0.7791 0.293 0.112 0.384 0.104 0.392 0.008
#> SRR191666 1 0.2074 0.932 0.896 0.000 0.000 0.000 0.104
#> SRR191667 1 0.2074 0.932 0.896 0.000 0.000 0.000 0.104
#> SRR191668 1 0.1686 0.902 0.944 0.000 0.028 0.008 0.020
#> SRR191669 1 0.1686 0.902 0.944 0.000 0.028 0.008 0.020
#> SRR191670 1 0.0833 0.917 0.976 0.000 0.004 0.004 0.016
#> SRR191671 1 0.0833 0.917 0.976 0.000 0.004 0.004 0.016
#> SRR191672 1 0.1869 0.904 0.936 0.000 0.028 0.008 0.028
#> SRR191673 1 0.1869 0.904 0.936 0.000 0.028 0.008 0.028
#> SRR191674 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191675 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191677 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191678 3 0.5422 0.512 0.212 0.000 0.656 0.000 0.132
#> SRR191679 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191680 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191681 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191682 5 0.3990 0.510 0.004 0.000 0.308 0.000 0.688
#> SRR191683 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191684 2 0.0162 0.945 0.000 0.996 0.004 0.000 0.000
#> SRR191685 2 0.0162 0.945 0.000 0.996 0.004 0.000 0.000
#> SRR191686 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191687 2 0.0162 0.945 0.000 0.996 0.004 0.000 0.000
#> SRR191688 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191689 2 0.0510 0.934 0.000 0.984 0.016 0.000 0.000
#> SRR191690 3 0.4256 0.245 0.436 0.000 0.564 0.000 0.000
#> SRR191691 2 0.0162 0.945 0.000 0.996 0.004 0.000 0.000
#> SRR191692 3 0.2127 0.803 0.000 0.108 0.892 0.000 0.000
#> SRR191693 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191694 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191695 3 0.2074 0.802 0.000 0.104 0.896 0.000 0.000
#> SRR191696 3 0.2813 0.780 0.000 0.168 0.832 0.000 0.000
#> SRR191697 3 0.3707 0.647 0.000 0.284 0.716 0.000 0.000
#> SRR191698 3 0.3810 0.676 0.000 0.040 0.792 0.000 0.168
#> SRR191699 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191700 1 0.2280 0.927 0.880 0.000 0.000 0.000 0.120
#> SRR191701 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191702 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191703 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191704 3 0.3353 0.759 0.000 0.196 0.796 0.008 0.000
#> SRR191705 3 0.2179 0.803 0.000 0.112 0.888 0.000 0.000
#> SRR191706 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191707 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191708 3 0.3177 0.749 0.000 0.208 0.792 0.000 0.000
#> SRR191709 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191710 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191711 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191712 3 0.2230 0.803 0.000 0.116 0.884 0.000 0.000
#> SRR191713 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191714 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191715 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191716 3 0.1792 0.708 0.084 0.000 0.916 0.000 0.000
#> SRR191717 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR191718 3 0.2020 0.800 0.000 0.100 0.900 0.000 0.000
#> SRR537099 2 0.5038 0.549 0.000 0.692 0.016 0.244 0.048
#> SRR537100 1 0.2329 0.925 0.876 0.000 0.000 0.000 0.124
#> SRR537101 1 0.2074 0.932 0.896 0.000 0.000 0.000 0.104
#> SRR537102 4 0.0510 0.759 0.000 0.016 0.000 0.984 0.000
#> SRR537104 2 0.1205 0.914 0.000 0.956 0.004 0.040 0.000
#> SRR537105 4 0.0290 0.759 0.000 0.008 0.000 0.992 0.000
#> SRR537106 2 0.2536 0.825 0.000 0.868 0.004 0.128 0.000
#> SRR537107 4 0.4030 0.508 0.000 0.352 0.000 0.648 0.000
#> SRR537108 4 0.4201 0.389 0.000 0.408 0.000 0.592 0.000
#> SRR537109 2 0.0324 0.942 0.000 0.992 0.004 0.004 0.000
#> SRR537110 2 0.2011 0.870 0.000 0.908 0.004 0.088 0.000
#> SRR537111 2 0.0671 0.934 0.000 0.980 0.004 0.016 0.000
#> SRR537113 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR537114 3 0.4553 0.433 0.016 0.000 0.652 0.328 0.004
#> SRR537115 2 0.2929 0.736 0.000 0.820 0.180 0.000 0.000
#> SRR537116 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR537117 5 0.0880 0.918 0.032 0.000 0.000 0.000 0.968
#> SRR537118 5 0.0404 0.928 0.012 0.000 0.000 0.000 0.988
#> SRR537119 5 0.0510 0.928 0.016 0.000 0.000 0.000 0.984
#> SRR537120 5 0.0609 0.927 0.020 0.000 0.000 0.000 0.980
#> SRR537121 5 0.0290 0.925 0.008 0.000 0.000 0.000 0.992
#> SRR537122 5 0.0404 0.928 0.012 0.000 0.000 0.000 0.988
#> SRR537123 5 0.1608 0.881 0.072 0.000 0.000 0.000 0.928
#> SRR537124 5 0.2929 0.749 0.180 0.000 0.000 0.000 0.820
#> SRR537125 5 0.0404 0.928 0.012 0.000 0.000 0.000 0.988
#> SRR537126 5 0.0404 0.928 0.012 0.000 0.000 0.000 0.988
#> SRR537127 1 0.2230 0.929 0.884 0.000 0.000 0.000 0.116
#> SRR537128 1 0.2230 0.929 0.884 0.000 0.000 0.000 0.116
#> SRR537129 1 0.2230 0.929 0.884 0.000 0.000 0.000 0.116
#> SRR537130 1 0.2230 0.929 0.884 0.000 0.000 0.000 0.116
#> SRR537131 1 0.2230 0.929 0.884 0.000 0.000 0.000 0.116
#> SRR537132 1 0.2230 0.929 0.884 0.000 0.000 0.000 0.116
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.2416 0.819 0.844 0.000 0.156 0.000 0.000 0.000
#> SRR191640 4 0.4687 0.486 0.044 0.012 0.276 0.664 0.004 0.000
#> SRR191641 1 0.0458 0.867 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR191642 4 0.2553 0.630 0.000 0.008 0.144 0.848 0.000 0.000
#> SRR191643 4 0.4084 0.195 0.000 0.000 0.012 0.588 0.000 0.400
#> SRR191644 6 0.2848 0.756 0.000 0.004 0.008 0.160 0.000 0.828
#> SRR191645 4 0.3357 0.591 0.000 0.008 0.224 0.764 0.004 0.000
#> SRR191646 4 0.3357 0.591 0.000 0.008 0.224 0.764 0.004 0.000
#> SRR191647 1 0.1956 0.837 0.908 0.004 0.080 0.000 0.008 0.000
#> SRR191648 1 0.1956 0.837 0.908 0.004 0.080 0.000 0.008 0.000
#> SRR191649 1 0.3538 0.691 0.764 0.012 0.216 0.004 0.004 0.000
#> SRR191650 6 0.2920 0.745 0.000 0.004 0.008 0.168 0.000 0.820
#> SRR191651 6 0.1477 0.876 0.000 0.004 0.008 0.048 0.000 0.940
#> SRR191652 1 0.0547 0.868 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR191653 4 0.0260 0.667 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR191654 4 0.2623 0.548 0.000 0.000 0.016 0.852 0.000 0.132
#> SRR191655 4 0.0458 0.668 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR191656 3 0.6193 0.565 0.128 0.020 0.596 0.000 0.040 0.216
#> SRR191657 1 0.2595 0.810 0.836 0.000 0.160 0.000 0.004 0.000
#> SRR191658 1 0.2838 0.795 0.808 0.000 0.188 0.000 0.004 0.000
#> SRR191659 1 0.2838 0.795 0.808 0.000 0.188 0.000 0.004 0.000
#> SRR191660 1 0.2595 0.810 0.836 0.000 0.160 0.000 0.004 0.000
#> SRR191661 4 0.1615 0.661 0.000 0.004 0.064 0.928 0.000 0.004
#> SRR191662 6 0.3925 0.454 0.000 0.004 0.008 0.332 0.000 0.656
#> SRR191663 4 0.4821 0.460 0.048 0.012 0.292 0.644 0.004 0.000
#> SRR191664 1 0.3405 0.714 0.724 0.000 0.272 0.000 0.004 0.000
#> SRR191665 3 0.5028 0.502 0.016 0.012 0.716 0.140 0.004 0.112
#> SRR191666 1 0.0547 0.868 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR191667 1 0.0547 0.868 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR191668 1 0.3592 0.577 0.656 0.000 0.344 0.000 0.000 0.000
#> SRR191669 1 0.3607 0.570 0.652 0.000 0.348 0.000 0.000 0.000
#> SRR191670 1 0.2006 0.833 0.892 0.000 0.104 0.000 0.004 0.000
#> SRR191671 1 0.2006 0.833 0.892 0.000 0.104 0.000 0.004 0.000
#> SRR191672 1 0.3563 0.585 0.664 0.000 0.336 0.000 0.000 0.000
#> SRR191673 1 0.3563 0.585 0.664 0.000 0.336 0.000 0.000 0.000
#> SRR191674 6 0.1226 0.899 0.000 0.004 0.040 0.000 0.004 0.952
#> SRR191675 6 0.1226 0.899 0.000 0.004 0.040 0.000 0.004 0.952
#> SRR191677 6 0.0260 0.912 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR191678 2 0.5274 0.491 0.176 0.664 0.028 0.000 0.132 0.000
#> SRR191679 6 0.0922 0.906 0.000 0.004 0.024 0.000 0.004 0.968
#> SRR191680 6 0.1003 0.904 0.000 0.004 0.028 0.000 0.004 0.964
#> SRR191681 6 0.1364 0.894 0.000 0.004 0.048 0.000 0.004 0.944
#> SRR191682 5 0.4382 0.546 0.000 0.264 0.060 0.000 0.676 0.000
#> SRR191683 6 0.1728 0.881 0.000 0.008 0.064 0.000 0.004 0.924
#> SRR191684 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191685 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191686 6 0.1555 0.887 0.000 0.004 0.060 0.000 0.004 0.932
#> SRR191687 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191688 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191689 6 0.2468 0.837 0.000 0.016 0.096 0.000 0.008 0.880
#> SRR191690 2 0.4254 0.236 0.404 0.576 0.020 0.000 0.000 0.000
#> SRR191691 6 0.0436 0.909 0.000 0.004 0.004 0.004 0.000 0.988
#> SRR191692 2 0.3413 0.685 0.000 0.824 0.112 0.000 0.012 0.052
#> SRR191693 6 0.1555 0.887 0.000 0.004 0.060 0.000 0.004 0.932
#> SRR191694 6 0.1226 0.899 0.000 0.004 0.040 0.000 0.004 0.952
#> SRR191695 2 0.3078 0.691 0.000 0.844 0.112 0.000 0.012 0.032
#> SRR191696 2 0.4263 0.620 0.000 0.756 0.124 0.000 0.012 0.108
#> SRR191697 2 0.5449 0.304 0.000 0.592 0.124 0.000 0.012 0.272
#> SRR191698 2 0.5211 0.546 0.000 0.652 0.100 0.000 0.224 0.024
#> SRR191699 6 0.0146 0.912 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR191700 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR191701 6 0.0146 0.913 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR191702 6 0.0922 0.906 0.000 0.004 0.024 0.000 0.004 0.968
#> SRR191703 6 0.0508 0.910 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR191704 2 0.3967 0.673 0.000 0.800 0.092 0.008 0.016 0.084
#> SRR191705 2 0.2637 0.698 0.000 0.876 0.088 0.000 0.012 0.024
#> SRR191706 6 0.1226 0.899 0.000 0.004 0.040 0.000 0.004 0.952
#> SRR191707 6 0.0146 0.912 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR191708 2 0.3771 0.659 0.000 0.800 0.088 0.000 0.012 0.100
#> SRR191709 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191710 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191711 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191712 2 0.2605 0.705 0.000 0.884 0.064 0.000 0.012 0.040
#> SRR191713 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191714 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191715 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR191716 2 0.1921 0.689 0.032 0.916 0.052 0.000 0.000 0.000
#> SRR191717 6 0.1429 0.892 0.000 0.004 0.052 0.000 0.004 0.940
#> SRR191718 2 0.1577 0.706 0.000 0.940 0.036 0.000 0.008 0.016
#> SRR537099 6 0.6102 0.109 0.000 0.008 0.056 0.356 0.068 0.512
#> SRR537100 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR537101 1 0.0547 0.868 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR537102 4 0.0260 0.668 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR537104 6 0.1728 0.863 0.000 0.004 0.008 0.064 0.000 0.924
#> SRR537105 4 0.0603 0.671 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR537106 6 0.3357 0.660 0.000 0.004 0.008 0.224 0.000 0.764
#> SRR537107 4 0.3584 0.326 0.000 0.000 0.004 0.688 0.000 0.308
#> SRR537108 4 0.3830 0.234 0.000 0.000 0.004 0.620 0.000 0.376
#> SRR537109 6 0.0436 0.909 0.000 0.004 0.004 0.004 0.000 0.988
#> SRR537110 6 0.2884 0.751 0.000 0.004 0.008 0.164 0.000 0.824
#> SRR537111 6 0.0665 0.905 0.000 0.004 0.008 0.008 0.000 0.980
#> SRR537113 6 0.0146 0.913 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR537114 2 0.5956 0.369 0.008 0.532 0.256 0.200 0.004 0.000
#> SRR537115 6 0.5433 0.367 0.000 0.164 0.200 0.004 0.008 0.624
#> SRR537116 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR537117 5 0.2164 0.887 0.068 0.000 0.032 0.000 0.900 0.000
#> SRR537118 5 0.0790 0.907 0.032 0.000 0.000 0.000 0.968 0.000
#> SRR537119 5 0.1480 0.904 0.040 0.000 0.020 0.000 0.940 0.000
#> SRR537120 5 0.1682 0.901 0.052 0.000 0.020 0.000 0.928 0.000
#> SRR537121 5 0.0790 0.907 0.032 0.000 0.000 0.000 0.968 0.000
#> SRR537122 5 0.0858 0.903 0.028 0.000 0.000 0.004 0.968 0.000
#> SRR537123 5 0.2531 0.827 0.132 0.000 0.012 0.000 0.856 0.000
#> SRR537124 5 0.3284 0.725 0.196 0.000 0.020 0.000 0.784 0.000
#> SRR537125 5 0.0790 0.907 0.032 0.000 0.000 0.000 0.968 0.000
#> SRR537126 5 0.0790 0.907 0.032 0.000 0.000 0.000 0.968 0.000
#> SRR537127 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR537128 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR537129 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR537130 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR537131 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR537132 1 0.0632 0.868 0.976 0.000 0.000 0.000 0.024 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)
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 16450 rows and 111 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 0.967 0.986 0.1380 0.865 0.865
#> 3 3 0.867 0.904 0.963 2.8588 0.576 0.515
#> 4 4 0.853 0.859 0.944 0.2227 0.812 0.628
#> 5 5 0.868 0.834 0.939 0.1137 0.909 0.752
#> 6 6 0.753 0.674 0.820 0.0708 0.874 0.586
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
#> SRR191639 2 0.0938 0.985 0.012 0.988
#> SRR191640 2 0.0000 0.990 0.000 1.000
#> SRR191641 2 0.1184 0.982 0.016 0.984
#> SRR191642 2 0.0000 0.990 0.000 1.000
#> SRR191643 2 0.0000 0.990 0.000 1.000
#> SRR191644 2 0.0000 0.990 0.000 1.000
#> SRR191645 2 0.0000 0.990 0.000 1.000
#> SRR191646 2 0.0000 0.990 0.000 1.000
#> SRR191647 2 0.0938 0.985 0.012 0.988
#> SRR191648 2 0.0938 0.985 0.012 0.988
#> SRR191649 2 0.0938 0.985 0.012 0.988
#> SRR191650 2 0.0000 0.990 0.000 1.000
#> SRR191651 2 0.0000 0.990 0.000 1.000
#> SRR191652 2 0.4815 0.881 0.104 0.896
#> SRR191653 2 0.0000 0.990 0.000 1.000
#> SRR191654 2 0.0000 0.990 0.000 1.000
#> SRR191655 2 0.0000 0.990 0.000 1.000
#> SRR191656 2 0.0672 0.987 0.008 0.992
#> SRR191657 2 0.1184 0.982 0.016 0.984
#> SRR191658 2 0.1184 0.982 0.016 0.984
#> SRR191659 2 0.1184 0.982 0.016 0.984
#> SRR191660 2 0.1184 0.982 0.016 0.984
#> SRR191661 2 0.0000 0.990 0.000 1.000
#> SRR191662 2 0.0000 0.990 0.000 1.000
#> SRR191663 2 0.0000 0.990 0.000 1.000
#> SRR191664 2 0.0938 0.985 0.012 0.988
#> SRR191665 2 0.0000 0.990 0.000 1.000
#> SRR191666 2 0.9686 0.265 0.396 0.604
#> SRR191667 1 0.9866 0.268 0.568 0.432
#> SRR191668 2 0.1184 0.982 0.016 0.984
#> SRR191669 2 0.0938 0.985 0.012 0.988
#> SRR191670 2 0.3114 0.941 0.056 0.944
#> SRR191671 2 0.1184 0.982 0.016 0.984
#> SRR191672 2 0.1184 0.982 0.016 0.984
#> SRR191673 2 0.1184 0.982 0.016 0.984
#> SRR191674 2 0.0000 0.990 0.000 1.000
#> SRR191675 2 0.0000 0.990 0.000 1.000
#> SRR191677 2 0.0000 0.990 0.000 1.000
#> SRR191678 2 0.0938 0.985 0.012 0.988
#> SRR191679 2 0.0000 0.990 0.000 1.000
#> SRR191680 2 0.0000 0.990 0.000 1.000
#> SRR191681 2 0.0000 0.990 0.000 1.000
#> SRR191682 2 0.0938 0.985 0.012 0.988
#> SRR191683 2 0.0000 0.990 0.000 1.000
#> SRR191684 2 0.0000 0.990 0.000 1.000
#> SRR191685 2 0.0000 0.990 0.000 1.000
#> SRR191686 2 0.0000 0.990 0.000 1.000
#> SRR191687 2 0.0000 0.990 0.000 1.000
#> SRR191688 2 0.0000 0.990 0.000 1.000
#> SRR191689 2 0.0000 0.990 0.000 1.000
#> SRR191690 2 0.0938 0.985 0.012 0.988
#> SRR191691 2 0.0000 0.990 0.000 1.000
#> SRR191692 2 0.0000 0.990 0.000 1.000
#> SRR191693 2 0.0000 0.990 0.000 1.000
#> SRR191694 2 0.0000 0.990 0.000 1.000
#> SRR191695 2 0.0000 0.990 0.000 1.000
#> SRR191696 2 0.0000 0.990 0.000 1.000
#> SRR191697 2 0.0000 0.990 0.000 1.000
#> SRR191698 2 0.0000 0.990 0.000 1.000
#> SRR191699 2 0.0000 0.990 0.000 1.000
#> SRR191700 2 0.1184 0.982 0.016 0.984
#> SRR191701 2 0.0000 0.990 0.000 1.000
#> SRR191702 2 0.0000 0.990 0.000 1.000
#> SRR191703 2 0.0000 0.990 0.000 1.000
#> SRR191704 2 0.0000 0.990 0.000 1.000
#> SRR191705 2 0.0000 0.990 0.000 1.000
#> SRR191706 2 0.0000 0.990 0.000 1.000
#> SRR191707 2 0.0000 0.990 0.000 1.000
#> SRR191708 2 0.0000 0.990 0.000 1.000
#> SRR191709 2 0.0000 0.990 0.000 1.000
#> SRR191710 2 0.0000 0.990 0.000 1.000
#> SRR191711 2 0.0000 0.990 0.000 1.000
#> SRR191712 2 0.0000 0.990 0.000 1.000
#> SRR191713 2 0.0000 0.990 0.000 1.000
#> SRR191714 2 0.0000 0.990 0.000 1.000
#> SRR191715 2 0.0000 0.990 0.000 1.000
#> SRR191716 2 0.0376 0.988 0.004 0.996
#> SRR191717 2 0.0000 0.990 0.000 1.000
#> SRR191718 2 0.0000 0.990 0.000 1.000
#> SRR537099 2 0.0000 0.990 0.000 1.000
#> SRR537100 2 0.0938 0.985 0.012 0.988
#> SRR537101 1 0.7219 0.737 0.800 0.200
#> SRR537102 2 0.0000 0.990 0.000 1.000
#> SRR537104 2 0.0000 0.990 0.000 1.000
#> SRR537105 2 0.0000 0.990 0.000 1.000
#> SRR537106 2 0.0000 0.990 0.000 1.000
#> SRR537107 2 0.0000 0.990 0.000 1.000
#> SRR537108 2 0.0000 0.990 0.000 1.000
#> SRR537109 2 0.0000 0.990 0.000 1.000
#> SRR537110 2 0.0000 0.990 0.000 1.000
#> SRR537111 2 0.0000 0.990 0.000 1.000
#> SRR537113 2 0.0000 0.990 0.000 1.000
#> SRR537114 2 0.0000 0.990 0.000 1.000
#> SRR537115 2 0.0000 0.990 0.000 1.000
#> SRR537116 2 0.0000 0.990 0.000 1.000
#> SRR537117 2 0.0938 0.985 0.012 0.988
#> SRR537118 2 0.0938 0.985 0.012 0.988
#> SRR537119 2 0.0938 0.985 0.012 0.988
#> SRR537120 2 0.0938 0.985 0.012 0.988
#> SRR537121 2 0.0938 0.985 0.012 0.988
#> SRR537122 2 0.0938 0.985 0.012 0.988
#> SRR537123 2 0.0938 0.985 0.012 0.988
#> SRR537124 2 0.1184 0.982 0.016 0.984
#> SRR537125 2 0.0938 0.985 0.012 0.988
#> SRR537126 2 0.0938 0.985 0.012 0.988
#> SRR537127 1 0.0000 0.907 1.000 0.000
#> SRR537128 1 0.0000 0.907 1.000 0.000
#> SRR537129 1 0.0000 0.907 1.000 0.000
#> SRR537130 1 0.0000 0.907 1.000 0.000
#> SRR537131 1 0.0000 0.907 1.000 0.000
#> SRR537132 1 0.0000 0.907 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191640 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191641 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191642 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191643 1 0.0237 0.9729 0.996 0.004 0.000
#> SRR191644 2 0.5431 0.5973 0.284 0.716 0.000
#> SRR191645 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191646 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191647 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191648 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191649 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191650 1 0.4062 0.7742 0.836 0.164 0.000
#> SRR191651 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191652 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191653 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191654 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191655 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191656 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191657 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191658 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191659 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191660 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191661 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191662 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191663 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191664 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191665 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191666 1 0.4504 0.7512 0.804 0.000 0.196
#> SRR191667 1 0.6299 0.0509 0.524 0.000 0.476
#> SRR191668 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191669 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191670 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191671 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191672 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191673 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191674 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191675 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191677 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191678 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191679 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191680 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191681 2 0.4842 0.6803 0.224 0.776 0.000
#> SRR191682 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191683 2 0.5591 0.5652 0.304 0.696 0.000
#> SRR191684 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191685 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191686 2 0.2711 0.8277 0.088 0.912 0.000
#> SRR191687 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191688 2 0.5098 0.6493 0.248 0.752 0.000
#> SRR191689 1 0.3482 0.8275 0.872 0.128 0.000
#> SRR191690 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191691 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191692 1 0.1411 0.9455 0.964 0.036 0.000
#> SRR191693 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191694 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191695 1 0.1411 0.9455 0.964 0.036 0.000
#> SRR191696 1 0.1411 0.9455 0.964 0.036 0.000
#> SRR191697 1 0.1411 0.9455 0.964 0.036 0.000
#> SRR191698 1 0.1289 0.9490 0.968 0.032 0.000
#> SRR191699 2 0.1289 0.8783 0.032 0.968 0.000
#> SRR191700 1 0.0747 0.9664 0.984 0.000 0.016
#> SRR191701 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191703 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191704 1 0.1411 0.9455 0.964 0.036 0.000
#> SRR191705 1 0.0237 0.9729 0.996 0.004 0.000
#> SRR191706 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191707 2 0.4702 0.6993 0.212 0.788 0.000
#> SRR191708 1 0.0237 0.9729 0.996 0.004 0.000
#> SRR191709 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191710 2 0.4504 0.7172 0.196 0.804 0.000
#> SRR191711 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191712 1 0.0237 0.9729 0.996 0.004 0.000
#> SRR191713 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191714 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191715 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR191716 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR191717 2 0.4504 0.7172 0.196 0.804 0.000
#> SRR191718 1 0.0237 0.9729 0.996 0.004 0.000
#> SRR537099 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537100 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537101 3 0.5216 0.6361 0.260 0.000 0.740
#> SRR537102 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537104 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537105 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537106 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537107 2 0.6305 0.1810 0.484 0.516 0.000
#> SRR537108 2 0.4750 0.6937 0.216 0.784 0.000
#> SRR537109 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537110 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537111 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537113 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537114 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537115 1 0.1289 0.9493 0.968 0.032 0.000
#> SRR537116 2 0.0000 0.9038 0.000 1.000 0.000
#> SRR537117 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537118 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537119 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537120 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537121 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537122 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537123 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537124 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537125 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537126 1 0.0000 0.9750 1.000 0.000 0.000
#> SRR537127 3 0.0000 0.9433 0.000 0.000 1.000
#> SRR537128 3 0.0000 0.9433 0.000 0.000 1.000
#> SRR537129 3 0.0000 0.9433 0.000 0.000 1.000
#> SRR537130 3 0.0000 0.9433 0.000 0.000 1.000
#> SRR537131 3 0.0000 0.9433 0.000 0.000 1.000
#> SRR537132 3 0.0000 0.9433 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 4 0.0592 0.909 0.016 0.000 0.00 0.984
#> SRR191640 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191641 1 0.1716 0.863 0.936 0.000 0.00 0.064
#> SRR191642 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191643 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191644 4 0.2149 0.839 0.000 0.088 0.00 0.912
#> SRR191645 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191646 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191647 4 0.0707 0.907 0.020 0.000 0.00 0.980
#> SRR191648 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191649 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191650 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191651 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191652 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191653 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191654 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191655 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191656 4 0.1474 0.877 0.052 0.000 0.00 0.948
#> SRR191657 1 0.0592 0.919 0.984 0.000 0.00 0.016
#> SRR191658 1 0.0592 0.919 0.984 0.000 0.00 0.016
#> SRR191659 1 0.4356 0.497 0.708 0.000 0.00 0.292
#> SRR191660 1 0.3074 0.729 0.848 0.000 0.00 0.152
#> SRR191661 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191662 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191663 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191664 4 0.4830 0.287 0.392 0.000 0.00 0.608
#> SRR191665 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191666 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191667 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191668 1 0.1211 0.897 0.960 0.000 0.00 0.040
#> SRR191669 4 0.4804 0.457 0.384 0.000 0.00 0.616
#> SRR191670 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191671 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191672 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191673 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191674 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191675 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191677 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191678 4 0.0188 0.915 0.004 0.000 0.00 0.996
#> SRR191679 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191680 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191681 4 0.3266 0.735 0.000 0.168 0.00 0.832
#> SRR191682 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191683 4 0.1389 0.878 0.000 0.048 0.00 0.952
#> SRR191684 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191685 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191686 2 0.2589 0.796 0.000 0.884 0.00 0.116
#> SRR191687 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191688 4 0.4804 0.327 0.000 0.384 0.00 0.616
#> SRR191689 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191690 4 0.0336 0.913 0.008 0.000 0.00 0.992
#> SRR191691 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191692 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191693 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191694 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191695 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191696 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191697 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191698 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191699 2 0.1940 0.847 0.000 0.924 0.00 0.076
#> SRR191700 1 0.0000 0.926 1.000 0.000 0.00 0.000
#> SRR191701 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191702 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191703 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191704 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191705 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191706 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191707 2 0.4790 0.409 0.000 0.620 0.00 0.380
#> SRR191708 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191709 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191710 2 0.4454 0.530 0.000 0.692 0.00 0.308
#> SRR191711 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191712 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191713 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191714 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191715 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR191716 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR191717 2 0.4454 0.530 0.000 0.692 0.00 0.308
#> SRR191718 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537099 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537100 4 0.0707 0.906 0.020 0.000 0.00 0.980
#> SRR537101 1 0.0895 0.912 0.976 0.000 0.02 0.004
#> SRR537102 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537104 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537105 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537106 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537107 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537108 2 0.4855 0.356 0.000 0.600 0.00 0.400
#> SRR537109 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537110 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537111 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537113 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537114 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537115 4 0.0000 0.917 0.000 0.000 0.00 1.000
#> SRR537116 2 0.0000 0.931 0.000 1.000 0.00 0.000
#> SRR537117 4 0.4643 0.539 0.344 0.000 0.00 0.656
#> SRR537118 4 0.0469 0.910 0.012 0.000 0.00 0.988
#> SRR537119 4 0.4585 0.558 0.332 0.000 0.00 0.668
#> SRR537120 4 0.4643 0.539 0.344 0.000 0.00 0.656
#> SRR537121 4 0.0469 0.910 0.012 0.000 0.00 0.988
#> SRR537122 4 0.0469 0.910 0.012 0.000 0.00 0.988
#> SRR537123 4 0.4643 0.539 0.344 0.000 0.00 0.656
#> SRR537124 4 0.4643 0.539 0.344 0.000 0.00 0.656
#> SRR537125 4 0.4643 0.539 0.344 0.000 0.00 0.656
#> SRR537126 4 0.4643 0.539 0.344 0.000 0.00 0.656
#> SRR537127 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR537128 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR537129 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR537130 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR537131 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR537132 3 0.0000 1.000 0.000 0.000 1.00 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 4 0.417 0.3537 0.000 0.000 0 0.604 0.396
#> SRR191640 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191641 1 0.247 0.7004 0.864 0.000 0 0.136 0.000
#> SRR191642 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191643 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191644 4 0.185 0.8324 0.000 0.088 0 0.912 0.000
#> SRR191645 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191646 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191647 4 0.430 0.0848 0.000 0.000 0 0.512 0.488
#> SRR191648 4 0.409 0.4189 0.000 0.000 0 0.632 0.368
#> SRR191649 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191650 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191651 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191652 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191653 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191654 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191655 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191656 4 0.275 0.7811 0.136 0.000 0 0.856 0.008
#> SRR191657 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191658 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191659 1 0.194 0.8473 0.920 0.000 0 0.012 0.068
#> SRR191660 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191661 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191662 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191663 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191664 4 0.423 0.2467 0.420 0.000 0 0.580 0.000
#> SRR191665 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191666 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191667 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191668 1 0.430 -0.0344 0.516 0.000 0 0.000 0.484
#> SRR191669 4 0.618 0.0227 0.136 0.000 0 0.464 0.400
#> SRR191670 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191671 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR191672 5 0.422 0.3199 0.416 0.000 0 0.000 0.584
#> SRR191673 5 0.386 0.5350 0.312 0.000 0 0.000 0.688
#> SRR191674 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191675 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191677 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191678 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191679 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191680 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191681 4 0.281 0.7272 0.000 0.168 0 0.832 0.000
#> SRR191682 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191683 4 0.120 0.8765 0.000 0.048 0 0.952 0.000
#> SRR191684 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191685 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191686 2 0.223 0.8076 0.000 0.884 0 0.116 0.000
#> SRR191687 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191688 4 0.414 0.3301 0.000 0.384 0 0.616 0.000
#> SRR191689 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191690 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191691 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191692 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191693 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191694 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191695 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191696 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191697 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191698 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191699 2 0.167 0.8560 0.000 0.924 0 0.076 0.000
#> SRR191700 5 0.397 0.4459 0.336 0.000 0 0.000 0.664
#> SRR191701 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191702 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191703 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191704 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191705 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191706 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191707 2 0.413 0.4218 0.000 0.620 0 0.380 0.000
#> SRR191708 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191709 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191710 2 0.384 0.5516 0.000 0.692 0 0.308 0.000
#> SRR191711 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191712 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191713 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191714 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191715 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR191716 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR191717 2 0.384 0.5516 0.000 0.692 0 0.308 0.000
#> SRR191718 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537099 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537100 5 0.407 0.3390 0.000 0.000 0 0.364 0.636
#> SRR537101 1 0.000 0.9148 1.000 0.000 0 0.000 0.000
#> SRR537102 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537104 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537105 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537106 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537107 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537108 2 0.418 0.3638 0.000 0.600 0 0.400 0.000
#> SRR537109 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537110 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537111 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537113 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537114 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537115 4 0.000 0.9205 0.000 0.000 0 1.000 0.000
#> SRR537116 2 0.000 0.9354 0.000 1.000 0 0.000 0.000
#> SRR537117 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537118 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537119 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537120 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537121 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537122 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537123 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537124 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537125 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537126 5 0.000 0.8581 0.000 0.000 0 0.000 1.000
#> SRR537127 3 0.000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537128 3 0.000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537129 3 0.000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537130 3 0.000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537131 3 0.000 1.0000 0.000 0.000 1 0.000 0.000
#> SRR537132 3 0.000 1.0000 0.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 4 0.3717 0.3635 0.000 0.000 0 0.616 0.384 0.000
#> SRR191640 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191641 1 0.5366 0.6126 0.568 0.284 0 0.148 0.000 0.000
#> SRR191642 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191643 4 0.1141 0.8140 0.000 0.052 0 0.948 0.000 0.000
#> SRR191644 2 0.4903 0.4739 0.000 0.568 0 0.360 0.000 0.072
#> SRR191645 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191646 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191647 4 0.3862 0.0737 0.000 0.000 0 0.524 0.476 0.000
#> SRR191648 4 0.3634 0.4305 0.000 0.000 0 0.644 0.356 0.000
#> SRR191649 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191650 2 0.3804 0.3754 0.000 0.576 0 0.424 0.000 0.000
#> SRR191651 2 0.3804 0.3314 0.000 0.576 0 0.000 0.000 0.424
#> SRR191652 1 0.3620 0.6591 0.648 0.352 0 0.000 0.000 0.000
#> SRR191653 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191654 4 0.3446 0.3994 0.000 0.308 0 0.692 0.000 0.000
#> SRR191655 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191656 4 0.3355 0.7231 0.072 0.076 0 0.836 0.016 0.000
#> SRR191657 1 0.0405 0.7258 0.988 0.004 0 0.008 0.000 0.000
#> SRR191658 1 0.1753 0.7184 0.912 0.004 0 0.084 0.000 0.000
#> SRR191659 1 0.2918 0.6990 0.856 0.004 0 0.088 0.052 0.000
#> SRR191660 1 0.1753 0.7184 0.912 0.004 0 0.084 0.000 0.000
#> SRR191661 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191662 2 0.3833 0.2870 0.000 0.556 0 0.000 0.000 0.444
#> SRR191663 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191664 4 0.3592 0.4279 0.344 0.000 0 0.656 0.000 0.000
#> SRR191665 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191666 1 0.3620 0.6591 0.648 0.352 0 0.000 0.000 0.000
#> SRR191667 1 0.3620 0.6591 0.648 0.352 0 0.000 0.000 0.000
#> SRR191668 1 0.5826 0.4326 0.588 0.068 0 0.076 0.268 0.000
#> SRR191669 4 0.6496 -0.0879 0.116 0.068 0 0.420 0.396 0.000
#> SRR191670 1 0.1444 0.7184 0.928 0.072 0 0.000 0.000 0.000
#> SRR191671 1 0.1387 0.7176 0.932 0.068 0 0.000 0.000 0.000
#> SRR191672 1 0.4148 0.5921 0.724 0.068 0 0.000 0.208 0.000
#> SRR191673 1 0.4799 0.3992 0.592 0.068 0 0.000 0.340 0.000
#> SRR191674 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191675 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191677 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191678 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191679 6 0.1007 0.8678 0.000 0.044 0 0.000 0.000 0.956
#> SRR191680 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191681 2 0.4184 0.3999 0.000 0.576 0 0.408 0.000 0.016
#> SRR191682 4 0.1663 0.7944 0.000 0.000 0 0.912 0.088 0.000
#> SRR191683 2 0.4543 0.4399 0.000 0.576 0 0.384 0.000 0.040
#> SRR191684 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191685 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191686 2 0.3930 0.3417 0.000 0.576 0 0.004 0.000 0.420
#> SRR191687 6 0.0363 0.8917 0.000 0.012 0 0.000 0.000 0.988
#> SRR191688 2 0.5335 0.5469 0.000 0.576 0 0.276 0.000 0.148
#> SRR191689 2 0.3804 0.3754 0.000 0.576 0 0.424 0.000 0.000
#> SRR191690 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191691 6 0.0865 0.8766 0.000 0.036 0 0.000 0.000 0.964
#> SRR191692 4 0.1814 0.7766 0.000 0.100 0 0.900 0.000 0.000
#> SRR191693 6 0.3847 -0.0368 0.000 0.456 0 0.000 0.000 0.544
#> SRR191694 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191695 4 0.2491 0.7091 0.000 0.164 0 0.836 0.000 0.000
#> SRR191696 2 0.3833 0.3381 0.000 0.556 0 0.444 0.000 0.000
#> SRR191697 2 0.3860 0.2762 0.000 0.528 0 0.472 0.000 0.000
#> SRR191698 4 0.3101 0.5344 0.000 0.244 0 0.756 0.000 0.000
#> SRR191699 2 0.4025 0.3508 0.000 0.576 0 0.008 0.000 0.416
#> SRR191700 2 0.6125 -0.6073 0.312 0.352 0 0.000 0.336 0.000
#> SRR191701 2 0.3804 0.3314 0.000 0.576 0 0.000 0.000 0.424
#> SRR191702 6 0.2454 0.7333 0.000 0.160 0 0.000 0.000 0.840
#> SRR191703 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191704 2 0.3860 0.2766 0.000 0.528 0 0.472 0.000 0.000
#> SRR191705 4 0.2969 0.5951 0.000 0.224 0 0.776 0.000 0.000
#> SRR191706 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191707 2 0.4379 0.3860 0.000 0.576 0 0.028 0.000 0.396
#> SRR191708 2 0.3868 0.2204 0.000 0.508 0 0.492 0.000 0.000
#> SRR191709 6 0.2378 0.7411 0.000 0.152 0 0.000 0.000 0.848
#> SRR191710 2 0.4319 0.3807 0.000 0.576 0 0.024 0.000 0.400
#> SRR191711 6 0.3515 0.4174 0.000 0.324 0 0.000 0.000 0.676
#> SRR191712 4 0.1610 0.7878 0.000 0.084 0 0.916 0.000 0.000
#> SRR191713 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191714 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191715 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR191716 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR191717 2 0.4319 0.3807 0.000 0.576 0 0.024 0.000 0.400
#> SRR191718 4 0.1327 0.8070 0.000 0.064 0 0.936 0.000 0.000
#> SRR537099 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR537100 5 0.3659 0.3854 0.000 0.000 0 0.364 0.636 0.000
#> SRR537101 1 0.3620 0.6591 0.648 0.352 0 0.000 0.000 0.000
#> SRR537102 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR537104 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR537105 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR537106 6 0.3050 0.6062 0.000 0.236 0 0.000 0.000 0.764
#> SRR537107 4 0.3126 0.5653 0.000 0.248 0 0.752 0.000 0.000
#> SRR537108 2 0.5870 0.4373 0.000 0.460 0 0.212 0.000 0.328
#> SRR537109 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR537110 6 0.3717 0.2428 0.000 0.384 0 0.000 0.000 0.616
#> SRR537111 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR537113 2 0.3864 0.1776 0.000 0.520 0 0.000 0.000 0.480
#> SRR537114 4 0.0000 0.8424 0.000 0.000 0 1.000 0.000 0.000
#> SRR537115 4 0.1663 0.7844 0.000 0.088 0 0.912 0.000 0.000
#> SRR537116 6 0.0000 0.8987 0.000 0.000 0 0.000 0.000 1.000
#> SRR537117 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537118 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537119 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537120 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537121 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537122 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537123 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537124 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537125 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537126 5 0.0000 0.9423 0.000 0.000 0 0.000 1.000 0.000
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 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)
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 16450 rows and 111 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 0.967 0.986 0.1269 0.897 0.897
#> 3 3 0.357 0.689 0.840 2.8980 0.583 0.535
#> 4 4 0.460 0.769 0.810 0.2941 0.848 0.687
#> 5 5 0.343 0.551 0.681 0.0764 0.922 0.777
#> 6 6 0.549 0.518 0.704 0.0894 0.805 0.439
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR191639 2 0.0000 0.985 0.000 1.000
#> SRR191640 2 0.0000 0.985 0.000 1.000
#> SRR191641 2 0.0376 0.983 0.004 0.996
#> SRR191642 2 0.0000 0.985 0.000 1.000
#> SRR191643 2 0.0000 0.985 0.000 1.000
#> SRR191644 2 0.0000 0.985 0.000 1.000
#> SRR191645 2 0.0000 0.985 0.000 1.000
#> SRR191646 2 0.0000 0.985 0.000 1.000
#> SRR191647 2 0.1633 0.969 0.024 0.976
#> SRR191648 2 0.1633 0.969 0.024 0.976
#> SRR191649 2 0.0000 0.985 0.000 1.000
#> SRR191650 2 0.0000 0.985 0.000 1.000
#> SRR191651 2 0.0000 0.985 0.000 1.000
#> SRR191652 2 0.0376 0.983 0.004 0.996
#> SRR191653 2 0.0376 0.983 0.004 0.996
#> SRR191654 2 0.0376 0.983 0.004 0.996
#> SRR191655 2 0.0000 0.985 0.000 1.000
#> SRR191656 2 0.2236 0.959 0.036 0.964
#> SRR191657 2 0.0376 0.983 0.004 0.996
#> SRR191658 2 0.0376 0.983 0.004 0.996
#> SRR191659 2 0.0376 0.983 0.004 0.996
#> SRR191660 2 0.0376 0.983 0.004 0.996
#> SRR191661 2 0.0000 0.985 0.000 1.000
#> SRR191662 2 0.0000 0.985 0.000 1.000
#> SRR191663 2 0.0000 0.985 0.000 1.000
#> SRR191664 2 0.0000 0.985 0.000 1.000
#> SRR191665 2 0.0000 0.985 0.000 1.000
#> SRR191666 2 0.0672 0.982 0.008 0.992
#> SRR191667 2 0.0672 0.982 0.008 0.992
#> SRR191668 2 0.0376 0.983 0.004 0.996
#> SRR191669 2 0.0376 0.983 0.004 0.996
#> SRR191670 2 0.0672 0.982 0.008 0.992
#> SRR191671 2 0.0672 0.982 0.008 0.992
#> SRR191672 2 0.9983 0.123 0.476 0.524
#> SRR191673 2 0.9983 0.123 0.476 0.524
#> SRR191674 2 0.0000 0.985 0.000 1.000
#> SRR191675 2 0.0000 0.985 0.000 1.000
#> SRR191677 2 0.0000 0.985 0.000 1.000
#> SRR191678 2 0.0000 0.985 0.000 1.000
#> SRR191679 2 0.0000 0.985 0.000 1.000
#> SRR191680 2 0.0000 0.985 0.000 1.000
#> SRR191681 2 0.0000 0.985 0.000 1.000
#> SRR191682 2 0.0376 0.983 0.004 0.996
#> SRR191683 2 0.0000 0.985 0.000 1.000
#> SRR191684 2 0.0000 0.985 0.000 1.000
#> SRR191685 2 0.0000 0.985 0.000 1.000
#> SRR191686 2 0.0000 0.985 0.000 1.000
#> SRR191687 2 0.0000 0.985 0.000 1.000
#> SRR191688 2 0.0000 0.985 0.000 1.000
#> SRR191689 2 0.0000 0.985 0.000 1.000
#> SRR191690 2 0.0000 0.985 0.000 1.000
#> SRR191691 2 0.0000 0.985 0.000 1.000
#> SRR191692 2 0.0000 0.985 0.000 1.000
#> SRR191693 2 0.0000 0.985 0.000 1.000
#> SRR191694 2 0.0000 0.985 0.000 1.000
#> SRR191695 2 0.0000 0.985 0.000 1.000
#> SRR191696 2 0.0000 0.985 0.000 1.000
#> SRR191697 2 0.0000 0.985 0.000 1.000
#> SRR191698 2 0.0000 0.985 0.000 1.000
#> SRR191699 2 0.0000 0.985 0.000 1.000
#> SRR191700 2 0.0672 0.982 0.008 0.992
#> SRR191701 2 0.0000 0.985 0.000 1.000
#> SRR191702 2 0.0000 0.985 0.000 1.000
#> SRR191703 2 0.0000 0.985 0.000 1.000
#> SRR191704 2 0.0000 0.985 0.000 1.000
#> SRR191705 2 0.0000 0.985 0.000 1.000
#> SRR191706 2 0.0000 0.985 0.000 1.000
#> SRR191707 2 0.0000 0.985 0.000 1.000
#> SRR191708 2 0.0000 0.985 0.000 1.000
#> SRR191709 2 0.0000 0.985 0.000 1.000
#> SRR191710 2 0.0000 0.985 0.000 1.000
#> SRR191711 2 0.0000 0.985 0.000 1.000
#> SRR191712 2 0.0000 0.985 0.000 1.000
#> SRR191713 2 0.0000 0.985 0.000 1.000
#> SRR191714 2 0.0000 0.985 0.000 1.000
#> SRR191715 2 0.0000 0.985 0.000 1.000
#> SRR191716 2 0.0000 0.985 0.000 1.000
#> SRR191717 2 0.0000 0.985 0.000 1.000
#> SRR191718 2 0.0000 0.985 0.000 1.000
#> SRR537099 2 0.2236 0.959 0.036 0.964
#> SRR537100 2 0.1184 0.976 0.016 0.984
#> SRR537101 2 0.0672 0.982 0.008 0.992
#> SRR537102 2 0.0376 0.983 0.004 0.996
#> SRR537104 2 0.0000 0.985 0.000 1.000
#> SRR537105 2 0.0938 0.978 0.012 0.988
#> SRR537106 2 0.0000 0.985 0.000 1.000
#> SRR537107 2 0.0000 0.985 0.000 1.000
#> SRR537108 2 0.0376 0.983 0.004 0.996
#> SRR537109 2 0.0000 0.985 0.000 1.000
#> SRR537110 2 0.0000 0.985 0.000 1.000
#> SRR537111 2 0.0000 0.985 0.000 1.000
#> SRR537113 2 0.0000 0.985 0.000 1.000
#> SRR537114 2 0.0000 0.985 0.000 1.000
#> SRR537115 2 0.0000 0.985 0.000 1.000
#> SRR537116 2 0.0000 0.985 0.000 1.000
#> SRR537117 2 0.2603 0.952 0.044 0.956
#> SRR537118 2 0.2948 0.945 0.052 0.948
#> SRR537119 2 0.0376 0.983 0.004 0.996
#> SRR537120 2 0.0376 0.983 0.004 0.996
#> SRR537121 2 0.2948 0.945 0.052 0.948
#> SRR537122 2 0.2423 0.956 0.040 0.960
#> SRR537123 2 0.2778 0.948 0.048 0.952
#> SRR537124 2 0.0376 0.983 0.004 0.996
#> SRR537125 2 0.2948 0.945 0.052 0.948
#> SRR537126 2 0.2948 0.945 0.052 0.948
#> SRR537127 1 0.0000 1.000 1.000 0.000
#> SRR537128 1 0.0000 1.000 1.000 0.000
#> SRR537129 1 0.0000 1.000 1.000 0.000
#> SRR537130 1 0.0000 1.000 1.000 0.000
#> SRR537131 1 0.0000 1.000 1.000 0.000
#> SRR537132 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 1 0.4974 0.7881 0.764 0.236 0.000
#> SRR191640 2 0.6062 0.2381 0.384 0.616 0.000
#> SRR191641 1 0.4861 0.7964 0.808 0.180 0.012
#> SRR191642 2 0.5926 0.3247 0.356 0.644 0.000
#> SRR191643 2 0.6252 0.2037 0.444 0.556 0.000
#> SRR191644 2 0.4796 0.6120 0.220 0.780 0.000
#> SRR191645 2 0.5905 0.3321 0.352 0.648 0.000
#> SRR191646 2 0.5926 0.3212 0.356 0.644 0.000
#> SRR191647 1 0.2878 0.7650 0.904 0.096 0.000
#> SRR191648 1 0.2878 0.7650 0.904 0.096 0.000
#> SRR191649 2 0.6267 0.0653 0.452 0.548 0.000
#> SRR191650 2 0.6235 0.2257 0.436 0.564 0.000
#> SRR191651 2 0.6045 0.3907 0.380 0.620 0.000
#> SRR191652 1 0.4897 0.7908 0.812 0.172 0.016
#> SRR191653 1 0.5058 0.6684 0.756 0.244 0.000
#> SRR191654 1 0.6192 0.2280 0.580 0.420 0.000
#> SRR191655 2 0.6045 0.2743 0.380 0.620 0.000
#> SRR191656 1 0.5098 0.7798 0.752 0.248 0.000
#> SRR191657 1 0.6062 0.7114 0.708 0.276 0.016
#> SRR191658 1 0.5797 0.7107 0.712 0.280 0.008
#> SRR191659 1 0.6228 0.5273 0.624 0.372 0.004
#> SRR191660 1 0.6600 0.4935 0.604 0.384 0.012
#> SRR191661 2 0.6062 0.2581 0.384 0.616 0.000
#> SRR191662 1 0.6308 -0.0449 0.508 0.492 0.000
#> SRR191663 2 0.6215 0.1402 0.428 0.572 0.000
#> SRR191664 2 0.6280 0.0628 0.460 0.540 0.000
#> SRR191665 2 0.4842 0.6286 0.224 0.776 0.000
#> SRR191666 1 0.4663 0.7924 0.828 0.156 0.016
#> SRR191667 1 0.4663 0.7924 0.828 0.156 0.016
#> SRR191668 1 0.4750 0.7741 0.784 0.216 0.000
#> SRR191669 1 0.4750 0.7741 0.784 0.216 0.000
#> SRR191670 1 0.5156 0.7716 0.776 0.216 0.008
#> SRR191671 1 0.5156 0.7716 0.776 0.216 0.008
#> SRR191672 1 0.6171 0.6555 0.776 0.080 0.144
#> SRR191673 1 0.6171 0.6555 0.776 0.080 0.144
#> SRR191674 2 0.0892 0.8173 0.020 0.980 0.000
#> SRR191675 2 0.0592 0.8220 0.012 0.988 0.000
#> SRR191677 2 0.0592 0.8209 0.012 0.988 0.000
#> SRR191678 2 0.1753 0.7894 0.048 0.952 0.000
#> SRR191679 2 0.2448 0.7813 0.076 0.924 0.000
#> SRR191680 2 0.0424 0.8232 0.008 0.992 0.000
#> SRR191681 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191682 1 0.5621 0.7397 0.692 0.308 0.000
#> SRR191683 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191684 2 0.1031 0.8150 0.024 0.976 0.000
#> SRR191685 2 0.2261 0.7775 0.068 0.932 0.000
#> SRR191686 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191687 2 0.1529 0.8070 0.040 0.960 0.000
#> SRR191688 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191689 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191690 2 0.4504 0.6599 0.196 0.804 0.000
#> SRR191691 2 0.0892 0.8171 0.020 0.980 0.000
#> SRR191692 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191693 2 0.0747 0.8199 0.016 0.984 0.000
#> SRR191694 2 0.0747 0.8199 0.016 0.984 0.000
#> SRR191695 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191696 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191697 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191698 2 0.5706 0.4097 0.320 0.680 0.000
#> SRR191699 2 0.0237 0.8230 0.004 0.996 0.000
#> SRR191700 1 0.4399 0.8003 0.812 0.188 0.000
#> SRR191701 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191702 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191703 2 0.0237 0.8230 0.004 0.996 0.000
#> SRR191704 2 0.0747 0.8186 0.016 0.984 0.000
#> SRR191705 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191706 2 0.0424 0.8232 0.008 0.992 0.000
#> SRR191707 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191708 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191709 2 0.0592 0.8207 0.012 0.988 0.000
#> SRR191710 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191711 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191712 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR191713 2 0.0892 0.8173 0.020 0.980 0.000
#> SRR191714 2 0.0747 0.8191 0.016 0.984 0.000
#> SRR191715 2 0.0592 0.8209 0.012 0.988 0.000
#> SRR191716 2 0.1529 0.7942 0.040 0.960 0.000
#> SRR191717 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR191718 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR537099 1 0.2959 0.7681 0.900 0.100 0.000
#> SRR537100 1 0.3551 0.7873 0.868 0.132 0.000
#> SRR537101 1 0.4663 0.7924 0.828 0.156 0.016
#> SRR537102 1 0.6252 0.1675 0.556 0.444 0.000
#> SRR537104 2 0.6111 0.3626 0.396 0.604 0.000
#> SRR537105 1 0.3816 0.7654 0.852 0.148 0.000
#> SRR537106 2 0.6008 0.4211 0.372 0.628 0.000
#> SRR537107 2 0.6295 0.1429 0.472 0.528 0.000
#> SRR537108 2 0.6295 0.1429 0.472 0.528 0.000
#> SRR537109 2 0.2066 0.7851 0.060 0.940 0.000
#> SRR537110 2 0.6280 0.1841 0.460 0.540 0.000
#> SRR537111 2 0.1643 0.7994 0.044 0.956 0.000
#> SRR537113 2 0.0000 0.8233 0.000 1.000 0.000
#> SRR537114 2 0.0424 0.8220 0.008 0.992 0.000
#> SRR537115 2 0.0237 0.8231 0.004 0.996 0.000
#> SRR537116 2 0.0592 0.8207 0.012 0.988 0.000
#> SRR537117 1 0.5178 0.7808 0.744 0.256 0.000
#> SRR537118 1 0.3112 0.7648 0.900 0.096 0.004
#> SRR537119 1 0.5178 0.7786 0.744 0.256 0.000
#> SRR537120 1 0.5178 0.7812 0.744 0.256 0.000
#> SRR537121 1 0.3112 0.7648 0.900 0.096 0.004
#> SRR537122 1 0.2878 0.7656 0.904 0.096 0.000
#> SRR537123 1 0.3349 0.7697 0.888 0.108 0.004
#> SRR537124 1 0.5216 0.7782 0.740 0.260 0.000
#> SRR537125 1 0.3112 0.7648 0.900 0.096 0.004
#> SRR537126 1 0.3112 0.7648 0.900 0.096 0.004
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.4488 0.8288 0.808 0.096 0.000 0.096
#> SRR191640 4 0.4204 0.8559 0.020 0.192 0.000 0.788
#> SRR191641 1 0.4837 0.8012 0.796 0.076 0.008 0.120
#> SRR191642 4 0.4158 0.8442 0.008 0.224 0.000 0.768
#> SRR191643 4 0.3933 0.8471 0.008 0.200 0.000 0.792
#> SRR191644 4 0.5292 0.3178 0.008 0.480 0.000 0.512
#> SRR191645 4 0.4098 0.8564 0.012 0.204 0.000 0.784
#> SRR191646 4 0.4098 0.8564 0.012 0.204 0.000 0.784
#> SRR191647 1 0.4244 0.7963 0.804 0.036 0.000 0.160
#> SRR191648 1 0.4285 0.7946 0.804 0.040 0.000 0.156
#> SRR191649 4 0.5076 0.8239 0.072 0.172 0.000 0.756
#> SRR191650 4 0.4049 0.8461 0.008 0.212 0.000 0.780
#> SRR191651 2 0.5731 -0.2013 0.028 0.544 0.000 0.428
#> SRR191652 1 0.5398 0.7924 0.772 0.076 0.024 0.128
#> SRR191653 1 0.6497 0.5636 0.596 0.100 0.000 0.304
#> SRR191654 4 0.6414 0.5135 0.240 0.124 0.000 0.636
#> SRR191655 4 0.3972 0.8581 0.008 0.204 0.000 0.788
#> SRR191656 1 0.4906 0.7883 0.776 0.140 0.000 0.084
#> SRR191657 1 0.6637 0.5737 0.616 0.144 0.000 0.240
#> SRR191658 1 0.6834 0.5324 0.600 0.176 0.000 0.224
#> SRR191659 1 0.7340 0.0727 0.436 0.156 0.000 0.408
#> SRR191660 1 0.7261 0.2232 0.480 0.152 0.000 0.368
#> SRR191661 4 0.3972 0.8606 0.008 0.204 0.000 0.788
#> SRR191662 4 0.3863 0.8035 0.028 0.144 0.000 0.828
#> SRR191663 4 0.4916 0.8422 0.056 0.184 0.000 0.760
#> SRR191664 4 0.5062 0.8359 0.064 0.184 0.000 0.752
#> SRR191665 2 0.4635 0.6839 0.028 0.756 0.000 0.216
#> SRR191666 1 0.3951 0.8162 0.860 0.048 0.024 0.068
#> SRR191667 1 0.4024 0.8155 0.856 0.048 0.024 0.072
#> SRR191668 1 0.4300 0.8085 0.820 0.092 0.000 0.088
#> SRR191669 1 0.4477 0.8016 0.808 0.108 0.000 0.084
#> SRR191670 1 0.3894 0.8110 0.844 0.088 0.000 0.068
#> SRR191671 1 0.3959 0.8105 0.840 0.092 0.000 0.068
#> SRR191672 1 0.3697 0.7783 0.868 0.012 0.052 0.068
#> SRR191673 1 0.3697 0.7783 0.868 0.012 0.052 0.068
#> SRR191674 2 0.2256 0.8421 0.056 0.924 0.000 0.020
#> SRR191675 2 0.2060 0.8433 0.052 0.932 0.000 0.016
#> SRR191677 2 0.1624 0.8504 0.020 0.952 0.000 0.028
#> SRR191678 2 0.3958 0.8004 0.032 0.824 0.000 0.144
#> SRR191679 2 0.3117 0.8426 0.028 0.880 0.000 0.092
#> SRR191680 2 0.0672 0.8572 0.008 0.984 0.000 0.008
#> SRR191681 2 0.2473 0.8548 0.012 0.908 0.000 0.080
#> SRR191682 1 0.5050 0.7945 0.764 0.152 0.000 0.084
#> SRR191683 2 0.3215 0.8511 0.032 0.876 0.000 0.092
#> SRR191684 2 0.1305 0.8502 0.004 0.960 0.000 0.036
#> SRR191685 2 0.1297 0.8545 0.016 0.964 0.000 0.020
#> SRR191686 2 0.3198 0.8516 0.040 0.880 0.000 0.080
#> SRR191687 2 0.0657 0.8588 0.004 0.984 0.000 0.012
#> SRR191688 2 0.2060 0.8599 0.016 0.932 0.000 0.052
#> SRR191689 2 0.2480 0.8512 0.008 0.904 0.000 0.088
#> SRR191690 2 0.4541 0.7749 0.060 0.796 0.000 0.144
#> SRR191691 2 0.1209 0.8485 0.004 0.964 0.000 0.032
#> SRR191692 2 0.3706 0.8374 0.040 0.848 0.000 0.112
#> SRR191693 2 0.3464 0.8460 0.056 0.868 0.000 0.076
#> SRR191694 2 0.2060 0.8433 0.052 0.932 0.000 0.016
#> SRR191695 2 0.2741 0.8493 0.012 0.892 0.000 0.096
#> SRR191696 2 0.2412 0.8529 0.008 0.908 0.000 0.084
#> SRR191697 2 0.3080 0.8509 0.024 0.880 0.000 0.096
#> SRR191698 2 0.6895 -0.1397 0.108 0.492 0.000 0.400
#> SRR191699 2 0.1867 0.8588 0.000 0.928 0.000 0.072
#> SRR191700 1 0.3015 0.8321 0.884 0.092 0.000 0.024
#> SRR191701 2 0.0779 0.8594 0.004 0.980 0.000 0.016
#> SRR191702 2 0.1004 0.8566 0.004 0.972 0.000 0.024
#> SRR191703 2 0.1388 0.8509 0.012 0.960 0.000 0.028
#> SRR191704 2 0.3547 0.8210 0.016 0.840 0.000 0.144
#> SRR191705 2 0.3377 0.8211 0.012 0.848 0.000 0.140
#> SRR191706 2 0.1706 0.8507 0.036 0.948 0.000 0.016
#> SRR191707 2 0.2737 0.8474 0.008 0.888 0.000 0.104
#> SRR191708 2 0.3351 0.8098 0.008 0.844 0.000 0.148
#> SRR191709 2 0.1356 0.8485 0.008 0.960 0.000 0.032
#> SRR191710 2 0.1867 0.8577 0.000 0.928 0.000 0.072
#> SRR191711 2 0.0921 0.8540 0.000 0.972 0.000 0.028
#> SRR191712 2 0.3047 0.8399 0.012 0.872 0.000 0.116
#> SRR191713 2 0.1109 0.8504 0.004 0.968 0.000 0.028
#> SRR191714 2 0.1209 0.8470 0.004 0.964 0.000 0.032
#> SRR191715 2 0.1936 0.8490 0.032 0.940 0.000 0.028
#> SRR191716 2 0.3763 0.8107 0.024 0.832 0.000 0.144
#> SRR191717 2 0.3037 0.8553 0.036 0.888 0.000 0.076
#> SRR191718 2 0.2799 0.8446 0.008 0.884 0.000 0.108
#> SRR537099 1 0.4285 0.8082 0.820 0.076 0.000 0.104
#> SRR537100 1 0.4104 0.8275 0.832 0.080 0.000 0.088
#> SRR537101 1 0.4094 0.8147 0.852 0.048 0.024 0.076
#> SRR537102 4 0.4534 0.7766 0.068 0.132 0.000 0.800
#> SRR537104 2 0.5508 -0.1505 0.020 0.572 0.000 0.408
#> SRR537105 1 0.4994 0.7663 0.744 0.048 0.000 0.208
#> SRR537106 2 0.5590 -0.2921 0.020 0.524 0.000 0.456
#> SRR537107 4 0.3852 0.8297 0.012 0.180 0.000 0.808
#> SRR537108 4 0.3958 0.8102 0.024 0.160 0.000 0.816
#> SRR537109 2 0.1610 0.8488 0.016 0.952 0.000 0.032
#> SRR537110 4 0.5597 0.4547 0.020 0.464 0.000 0.516
#> SRR537111 2 0.1584 0.8399 0.012 0.952 0.000 0.036
#> SRR537113 2 0.0817 0.8553 0.000 0.976 0.000 0.024
#> SRR537114 2 0.4253 0.7331 0.016 0.776 0.000 0.208
#> SRR537115 2 0.2805 0.8496 0.012 0.888 0.000 0.100
#> SRR537116 2 0.1209 0.8492 0.004 0.964 0.000 0.032
#> SRR537117 1 0.3266 0.8222 0.876 0.084 0.000 0.040
#> SRR537118 1 0.3919 0.7941 0.840 0.056 0.000 0.104
#> SRR537119 1 0.4374 0.8236 0.812 0.120 0.000 0.068
#> SRR537120 1 0.4410 0.8212 0.808 0.128 0.000 0.064
#> SRR537121 1 0.4102 0.7934 0.836 0.056 0.004 0.104
#> SRR537122 1 0.3919 0.7941 0.840 0.056 0.000 0.104
#> SRR537123 1 0.3504 0.8115 0.872 0.056 0.004 0.068
#> SRR537124 1 0.3307 0.8214 0.868 0.104 0.000 0.028
#> SRR537125 1 0.3919 0.7941 0.840 0.056 0.000 0.104
#> SRR537126 1 0.3919 0.7941 0.840 0.056 0.000 0.104
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> SRR537132 3 0.0000 1.0000 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
#> SRR191639 5 0.6358 0.1130 0.280 0.020 0.000 0.132 0.568
#> SRR191640 4 0.5984 0.5881 0.280 0.112 0.000 0.596 0.012
#> SRR191641 1 0.5354 0.4286 0.628 0.012 0.000 0.052 0.308
#> SRR191642 4 0.6255 0.6236 0.188 0.132 0.000 0.636 0.044
#> SRR191643 4 0.4651 0.6954 0.016 0.184 0.000 0.748 0.052
#> SRR191644 4 0.6706 0.6522 0.136 0.268 0.000 0.556 0.040
#> SRR191645 4 0.5958 0.6125 0.244 0.128 0.000 0.616 0.012
#> SRR191646 4 0.5907 0.5838 0.284 0.124 0.000 0.588 0.004
#> SRR191647 5 0.4756 0.4669 0.052 0.004 0.000 0.240 0.704
#> SRR191648 5 0.4690 0.4677 0.048 0.004 0.000 0.240 0.708
#> SRR191649 4 0.6544 0.4113 0.424 0.116 0.000 0.440 0.020
#> SRR191650 4 0.5458 0.6986 0.040 0.216 0.000 0.688 0.056
#> SRR191651 2 0.6953 -0.4108 0.080 0.428 0.000 0.420 0.072
#> SRR191652 1 0.5069 0.4312 0.648 0.012 0.000 0.036 0.304
#> SRR191653 4 0.6784 0.0750 0.032 0.120 0.000 0.436 0.412
#> SRR191654 4 0.6498 0.5132 0.032 0.144 0.000 0.584 0.240
#> SRR191655 4 0.5234 0.6768 0.096 0.128 0.000 0.736 0.040
#> SRR191656 1 0.7957 0.0558 0.380 0.148 0.000 0.128 0.344
#> SRR191657 1 0.5755 0.4174 0.700 0.072 0.000 0.084 0.144
#> SRR191658 1 0.6130 0.4229 0.672 0.088 0.000 0.100 0.140
#> SRR191659 1 0.6074 0.3321 0.680 0.096 0.000 0.112 0.112
#> SRR191660 1 0.5897 0.3863 0.692 0.076 0.000 0.104 0.128
#> SRR191661 4 0.5715 0.6703 0.124 0.136 0.000 0.696 0.044
#> SRR191662 4 0.5492 0.6798 0.024 0.208 0.000 0.684 0.084
#> SRR191663 4 0.6453 0.4746 0.376 0.120 0.000 0.488 0.016
#> SRR191664 4 0.6102 0.4229 0.420 0.108 0.000 0.468 0.004
#> SRR191665 2 0.5972 0.5613 0.140 0.560 0.000 0.300 0.000
#> SRR191666 1 0.4922 0.3265 0.552 0.004 0.000 0.020 0.424
#> SRR191667 1 0.4892 0.3467 0.568 0.004 0.000 0.020 0.408
#> SRR191668 1 0.7686 0.1907 0.440 0.080 0.000 0.188 0.292
#> SRR191669 1 0.7947 0.1800 0.424 0.108 0.000 0.200 0.268
#> SRR191670 1 0.6511 0.3224 0.588 0.064 0.000 0.084 0.264
#> SRR191671 1 0.6511 0.3224 0.588 0.064 0.000 0.084 0.264
#> SRR191672 5 0.7421 0.1781 0.208 0.052 0.024 0.164 0.552
#> SRR191673 5 0.7421 0.1781 0.208 0.052 0.024 0.164 0.552
#> SRR191674 2 0.4439 0.7012 0.140 0.784 0.000 0.040 0.036
#> SRR191675 2 0.4321 0.7035 0.136 0.792 0.000 0.036 0.036
#> SRR191677 2 0.3096 0.7112 0.084 0.868 0.000 0.040 0.008
#> SRR191678 2 0.5889 0.5946 0.124 0.592 0.000 0.280 0.004
#> SRR191679 2 0.3406 0.7236 0.020 0.856 0.000 0.084 0.040
#> SRR191680 2 0.2291 0.7380 0.072 0.908 0.000 0.012 0.008
#> SRR191681 2 0.3678 0.7252 0.040 0.816 0.000 0.140 0.004
#> SRR191682 5 0.8216 -0.1148 0.316 0.136 0.000 0.196 0.352
#> SRR191683 2 0.4350 0.7276 0.100 0.784 0.000 0.108 0.008
#> SRR191684 2 0.1740 0.6980 0.000 0.932 0.000 0.056 0.012
#> SRR191685 2 0.3732 0.7179 0.076 0.840 0.000 0.060 0.024
#> SRR191686 2 0.3268 0.7389 0.060 0.856 0.000 0.080 0.004
#> SRR191687 2 0.3563 0.7198 0.088 0.848 0.000 0.036 0.028
#> SRR191688 2 0.5115 0.6644 0.092 0.676 0.000 0.232 0.000
#> SRR191689 2 0.4251 0.6974 0.040 0.756 0.000 0.200 0.004
#> SRR191690 2 0.6271 0.5633 0.180 0.572 0.000 0.240 0.008
#> SRR191691 2 0.1557 0.6963 0.000 0.940 0.000 0.052 0.008
#> SRR191692 2 0.5316 0.6552 0.084 0.656 0.000 0.256 0.004
#> SRR191693 2 0.5184 0.6975 0.140 0.736 0.000 0.088 0.036
#> SRR191694 2 0.4286 0.6998 0.140 0.792 0.000 0.032 0.036
#> SRR191695 2 0.5124 0.6408 0.068 0.668 0.000 0.260 0.004
#> SRR191696 2 0.4756 0.6688 0.052 0.704 0.000 0.240 0.004
#> SRR191697 2 0.4538 0.6822 0.044 0.724 0.000 0.228 0.004
#> SRR191698 2 0.7270 0.3852 0.184 0.476 0.000 0.292 0.048
#> SRR191699 2 0.3930 0.7191 0.056 0.792 0.000 0.152 0.000
#> SRR191700 1 0.5745 0.2893 0.496 0.020 0.000 0.044 0.440
#> SRR191701 2 0.1662 0.7195 0.004 0.936 0.000 0.056 0.004
#> SRR191702 2 0.1377 0.7327 0.020 0.956 0.000 0.020 0.004
#> SRR191703 2 0.2674 0.7208 0.084 0.888 0.000 0.020 0.008
#> SRR191704 2 0.6255 0.5555 0.108 0.560 0.000 0.312 0.020
#> SRR191705 2 0.5737 0.5921 0.120 0.592 0.000 0.288 0.000
#> SRR191706 2 0.3218 0.7233 0.128 0.844 0.000 0.024 0.004
#> SRR191707 2 0.5339 0.6553 0.116 0.660 0.000 0.224 0.000
#> SRR191708 2 0.5957 0.5719 0.148 0.572 0.000 0.280 0.000
#> SRR191709 2 0.2778 0.7208 0.032 0.892 0.000 0.060 0.016
#> SRR191710 2 0.4210 0.7217 0.064 0.788 0.000 0.140 0.008
#> SRR191711 2 0.1717 0.7049 0.008 0.936 0.000 0.052 0.004
#> SRR191712 2 0.5737 0.5935 0.120 0.592 0.000 0.288 0.000
#> SRR191713 2 0.1557 0.6988 0.000 0.940 0.000 0.052 0.008
#> SRR191714 2 0.1597 0.6974 0.000 0.940 0.000 0.048 0.012
#> SRR191715 2 0.3282 0.7075 0.084 0.860 0.000 0.044 0.012
#> SRR191716 2 0.5776 0.5889 0.124 0.588 0.000 0.288 0.000
#> SRR191717 2 0.3209 0.7390 0.060 0.860 0.000 0.076 0.004
#> SRR191718 2 0.5828 0.5971 0.116 0.596 0.000 0.284 0.004
#> SRR537099 5 0.3375 0.5338 0.048 0.020 0.000 0.072 0.860
#> SRR537100 5 0.4444 0.3935 0.180 0.000 0.000 0.072 0.748
#> SRR537101 1 0.5078 0.3700 0.576 0.004 0.000 0.032 0.388
#> SRR537102 4 0.5457 0.6340 0.020 0.136 0.000 0.700 0.144
#> SRR537104 4 0.6457 0.5220 0.060 0.400 0.000 0.488 0.052
#> SRR537105 5 0.5930 0.3002 0.032 0.052 0.000 0.352 0.564
#> SRR537106 4 0.5759 0.5823 0.016 0.344 0.000 0.576 0.064
#> SRR537107 4 0.4480 0.6889 0.004 0.180 0.000 0.752 0.064
#> SRR537108 4 0.4571 0.6713 0.000 0.188 0.000 0.736 0.076
#> SRR537109 2 0.2395 0.7122 0.016 0.904 0.000 0.072 0.008
#> SRR537110 4 0.5746 0.5878 0.016 0.340 0.000 0.580 0.064
#> SRR537111 2 0.1788 0.6996 0.004 0.932 0.000 0.056 0.008
#> SRR537113 2 0.1857 0.7100 0.004 0.928 0.000 0.060 0.008
#> SRR537114 2 0.6420 0.5008 0.160 0.524 0.000 0.308 0.008
#> SRR537115 2 0.5191 0.6636 0.080 0.672 0.000 0.244 0.004
#> SRR537116 2 0.1740 0.6956 0.000 0.932 0.000 0.056 0.012
#> SRR537117 5 0.7680 -0.0465 0.344 0.096 0.000 0.144 0.416
#> SRR537118 5 0.1571 0.5667 0.000 0.004 0.000 0.060 0.936
#> SRR537119 5 0.6929 0.0577 0.260 0.044 0.000 0.160 0.536
#> SRR537120 5 0.7475 -0.1284 0.368 0.048 0.000 0.200 0.384
#> SRR537121 5 0.1571 0.5667 0.000 0.004 0.000 0.060 0.936
#> SRR537122 5 0.1857 0.5648 0.008 0.004 0.000 0.060 0.928
#> SRR537123 5 0.3056 0.5306 0.068 0.000 0.000 0.068 0.864
#> SRR537124 1 0.7561 0.0669 0.400 0.068 0.000 0.168 0.364
#> SRR537125 5 0.1571 0.5667 0.000 0.004 0.000 0.060 0.936
#> SRR537126 5 0.1571 0.5667 0.000 0.004 0.000 0.060 0.936
#> SRR537127 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000
#> SRR537128 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000
#> SRR537129 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000
#> SRR537130 3 0.0162 0.9948 0.004 0.000 0.996 0.000 0.000
#> SRR537131 3 0.0000 0.9990 0.000 0.000 1.000 0.000 0.000
#> SRR537132 3 0.0000 0.9990 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
#> SRR191639 5 0.6460 0.2659 0.108 0.348 0 0.032 0.488 0.024
#> SRR191640 2 0.6211 0.3864 0.276 0.460 0 0.252 0.000 0.012
#> SRR191641 1 0.3072 0.7381 0.836 0.000 0 0.036 0.124 0.004
#> SRR191642 2 0.6232 0.3818 0.208 0.480 0 0.292 0.000 0.020
#> SRR191643 4 0.5359 0.5928 0.008 0.052 0 0.536 0.016 0.388
#> SRR191644 6 0.5963 -0.3933 0.024 0.076 0 0.396 0.016 0.488
#> SRR191645 2 0.6334 0.3949 0.276 0.460 0 0.244 0.000 0.020
#> SRR191646 2 0.6334 0.3949 0.276 0.460 0 0.244 0.000 0.020
#> SRR191647 5 0.4905 0.5713 0.084 0.068 0 0.108 0.736 0.004
#> SRR191648 5 0.4905 0.5713 0.084 0.068 0 0.108 0.736 0.004
#> SRR191649 2 0.6370 0.3377 0.372 0.460 0 0.128 0.028 0.012
#> SRR191650 4 0.5622 0.5787 0.020 0.052 0 0.512 0.016 0.400
#> SRR191651 6 0.5133 -0.2767 0.020 0.024 0 0.404 0.012 0.540
#> SRR191652 1 0.3010 0.7356 0.836 0.000 0 0.028 0.132 0.004
#> SRR191653 5 0.7078 0.2562 0.052 0.052 0 0.212 0.524 0.160
#> SRR191654 6 0.7336 -0.4797 0.028 0.048 0 0.328 0.224 0.372
#> SRR191655 4 0.5621 -0.1268 0.024 0.412 0 0.504 0.020 0.040
#> SRR191656 5 0.8532 0.2479 0.144 0.220 0 0.252 0.300 0.084
#> SRR191657 1 0.3680 0.6980 0.824 0.032 0 0.088 0.052 0.004
#> SRR191658 1 0.3317 0.7271 0.852 0.052 0 0.024 0.064 0.008
#> SRR191659 1 0.4449 0.6571 0.776 0.080 0 0.088 0.048 0.008
#> SRR191660 1 0.3849 0.6924 0.812 0.036 0 0.096 0.052 0.004
#> SRR191661 4 0.7278 -0.0194 0.112 0.348 0 0.400 0.016 0.124
#> SRR191662 4 0.6090 0.5742 0.036 0.052 0 0.484 0.028 0.400
#> SRR191663 2 0.6327 0.3834 0.300 0.468 0 0.212 0.004 0.016
#> SRR191664 2 0.6009 0.3469 0.356 0.452 0 0.184 0.000 0.008
#> SRR191665 2 0.5750 0.5890 0.144 0.604 0 0.024 0.004 0.224
#> SRR191666 1 0.4135 0.5934 0.668 0.000 0 0.032 0.300 0.000
#> SRR191667 1 0.4117 0.5942 0.672 0.000 0 0.032 0.296 0.000
#> SRR191668 2 0.7349 0.0777 0.336 0.400 0 0.128 0.120 0.016
#> SRR191669 2 0.7216 0.1457 0.320 0.432 0 0.112 0.120 0.016
#> SRR191670 1 0.4235 0.6408 0.752 0.152 0 0.004 0.088 0.004
#> SRR191671 1 0.4235 0.6408 0.752 0.152 0 0.004 0.088 0.004
#> SRR191672 4 0.6404 -0.5182 0.088 0.068 0 0.420 0.420 0.004
#> SRR191673 5 0.6404 0.2314 0.088 0.068 0 0.420 0.420 0.004
#> SRR191674 6 0.3462 0.7256 0.000 0.100 0 0.004 0.080 0.816
#> SRR191675 6 0.3556 0.7268 0.004 0.096 0 0.004 0.080 0.816
#> SRR191677 6 0.0790 0.7752 0.000 0.032 0 0.000 0.000 0.968
#> SRR191678 2 0.1908 0.5785 0.000 0.900 0 0.000 0.004 0.096
#> SRR191679 6 0.2596 0.7621 0.024 0.060 0 0.008 0.016 0.892
#> SRR191680 6 0.1471 0.7726 0.000 0.064 0 0.004 0.000 0.932
#> SRR191681 6 0.3511 0.6138 0.000 0.216 0 0.024 0.000 0.760
#> SRR191682 2 0.6549 -0.0154 0.120 0.464 0 0.016 0.360 0.040
#> SRR191683 6 0.3032 0.7347 0.004 0.128 0 0.024 0.004 0.840
#> SRR191684 6 0.0909 0.7663 0.000 0.012 0 0.020 0.000 0.968
#> SRR191685 6 0.2116 0.7603 0.036 0.024 0 0.024 0.000 0.916
#> SRR191686 6 0.2760 0.7437 0.000 0.116 0 0.024 0.004 0.856
#> SRR191687 6 0.2016 0.7721 0.024 0.040 0 0.016 0.000 0.920
#> SRR191688 2 0.4903 0.4244 0.028 0.532 0 0.020 0.000 0.420
#> SRR191689 6 0.4527 -0.1727 0.000 0.456 0 0.024 0.004 0.516
#> SRR191690 2 0.2398 0.5732 0.016 0.888 0 0.004 0.004 0.088
#> SRR191691 6 0.0935 0.7612 0.000 0.004 0 0.032 0.000 0.964
#> SRR191692 2 0.4916 0.2274 0.004 0.484 0 0.028 0.012 0.472
#> SRR191693 6 0.3709 0.7190 0.000 0.112 0 0.020 0.060 0.808
#> SRR191694 6 0.3556 0.7268 0.004 0.096 0 0.004 0.080 0.816
#> SRR191695 2 0.3767 0.5658 0.000 0.708 0 0.012 0.004 0.276
#> SRR191696 2 0.4365 0.4869 0.000 0.636 0 0.024 0.008 0.332
#> SRR191697 6 0.4627 -0.1595 0.000 0.456 0 0.024 0.008 0.512
#> SRR191698 2 0.7409 0.5169 0.108 0.476 0 0.056 0.092 0.268
#> SRR191699 6 0.1821 0.7702 0.008 0.040 0 0.024 0.000 0.928
#> SRR191700 1 0.5344 0.4685 0.564 0.100 0 0.008 0.328 0.000
#> SRR191701 6 0.1176 0.7759 0.000 0.024 0 0.020 0.000 0.956
#> SRR191702 6 0.1082 0.7761 0.000 0.040 0 0.004 0.000 0.956
#> SRR191703 6 0.0865 0.7758 0.000 0.036 0 0.000 0.000 0.964
#> SRR191704 2 0.4374 0.5394 0.004 0.700 0 0.048 0.004 0.244
#> SRR191705 2 0.1910 0.5845 0.000 0.892 0 0.000 0.000 0.108
#> SRR191706 6 0.2981 0.7528 0.004 0.092 0 0.004 0.044 0.856
#> SRR191707 6 0.3120 0.6960 0.008 0.112 0 0.040 0.000 0.840
#> SRR191708 2 0.4899 0.4446 0.016 0.560 0 0.036 0.000 0.388
#> SRR191709 6 0.0665 0.7711 0.008 0.008 0 0.004 0.000 0.980
#> SRR191710 6 0.2030 0.7649 0.000 0.064 0 0.028 0.000 0.908
#> SRR191711 6 0.0291 0.7727 0.004 0.000 0 0.004 0.000 0.992
#> SRR191712 2 0.2340 0.5930 0.000 0.852 0 0.000 0.000 0.148
#> SRR191713 6 0.0146 0.7694 0.000 0.004 0 0.000 0.000 0.996
#> SRR191714 6 0.0622 0.7702 0.000 0.008 0 0.012 0.000 0.980
#> SRR191715 6 0.1010 0.7764 0.004 0.036 0 0.000 0.000 0.960
#> SRR191716 2 0.1908 0.5785 0.000 0.900 0 0.000 0.004 0.096
#> SRR191717 6 0.2573 0.7459 0.000 0.112 0 0.024 0.000 0.864
#> SRR191718 2 0.2544 0.5892 0.000 0.852 0 0.004 0.004 0.140
#> SRR537099 5 0.3204 0.6425 0.032 0.056 0 0.032 0.864 0.016
#> SRR537100 5 0.3430 0.6358 0.040 0.068 0 0.032 0.848 0.012
#> SRR537101 1 0.3929 0.6221 0.700 0.000 0 0.028 0.272 0.000
#> SRR537102 4 0.6611 0.5380 0.012 0.048 0 0.452 0.124 0.364
#> SRR537104 6 0.5044 -0.3883 0.028 0.004 0 0.428 0.020 0.520
#> SRR537105 5 0.5912 0.4827 0.052 0.068 0 0.172 0.660 0.048
#> SRR537106 6 0.4935 -0.5007 0.004 0.020 0 0.476 0.020 0.480
#> SRR537107 4 0.5226 0.5928 0.004 0.044 0 0.544 0.020 0.388
#> SRR537108 4 0.5168 0.5901 0.004 0.040 0 0.548 0.020 0.388
#> SRR537109 6 0.1708 0.7547 0.024 0.004 0 0.040 0.000 0.932
#> SRR537110 4 0.4782 0.4549 0.004 0.012 0 0.492 0.020 0.472
#> SRR537111 6 0.1010 0.7590 0.000 0.004 0 0.036 0.000 0.960
#> SRR537113 6 0.0806 0.7730 0.000 0.008 0 0.020 0.000 0.972
#> SRR537114 2 0.4620 0.5993 0.052 0.724 0 0.040 0.000 0.184
#> SRR537115 2 0.4655 0.3042 0.004 0.516 0 0.024 0.004 0.452
#> SRR537116 6 0.0291 0.7708 0.004 0.004 0 0.000 0.000 0.992
#> SRR537117 5 0.7842 0.3154 0.148 0.208 0 0.232 0.388 0.024
#> SRR537118 5 0.2015 0.6493 0.012 0.056 0 0.000 0.916 0.016
#> SRR537119 5 0.6271 0.0759 0.092 0.428 0 0.028 0.432 0.020
#> SRR537120 2 0.5984 -0.0200 0.132 0.492 0 0.004 0.356 0.016
#> SRR537121 5 0.1801 0.6507 0.000 0.056 0 0.004 0.924 0.016
#> SRR537122 5 0.2309 0.6494 0.016 0.052 0 0.012 0.908 0.012
#> SRR537123 5 0.2941 0.6360 0.008 0.072 0 0.044 0.868 0.008
#> SRR537124 5 0.7805 0.2575 0.172 0.320 0 0.152 0.336 0.020
#> SRR537125 5 0.1851 0.6502 0.004 0.056 0 0.004 0.924 0.012
#> SRR537126 5 0.1801 0.6507 0.000 0.056 0 0.004 0.924 0.016
#> SRR537127 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537128 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537129 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537130 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537131 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> SRR537132 3 0.0000 1.0000 0.000 0.000 1 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)
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 16450 rows and 111 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.357 0.808 0.858 0.4914 0.496 0.496
#> 3 3 0.397 0.592 0.790 0.3019 0.651 0.407
#> 4 4 0.477 0.600 0.773 0.1166 0.781 0.465
#> 5 5 0.458 0.517 0.705 0.0522 0.885 0.639
#> 6 6 0.553 0.548 0.729 0.0447 0.843 0.488
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
#> SRR191639 1 0.4022 0.858 0.920 0.080
#> SRR191640 1 0.0376 0.853 0.996 0.004
#> SRR191641 1 0.1414 0.859 0.980 0.020
#> SRR191642 1 0.0938 0.851 0.988 0.012
#> SRR191643 2 0.9608 0.660 0.384 0.616
#> SRR191644 2 0.9998 0.364 0.492 0.508
#> SRR191645 1 0.1184 0.848 0.984 0.016
#> SRR191646 1 0.1184 0.848 0.984 0.016
#> SRR191647 1 0.0000 0.855 1.000 0.000
#> SRR191648 1 0.0000 0.855 1.000 0.000
#> SRR191649 1 0.0000 0.855 1.000 0.000
#> SRR191650 1 0.4690 0.783 0.900 0.100
#> SRR191651 1 0.1633 0.851 0.976 0.024
#> SRR191652 1 0.1414 0.859 0.980 0.020
#> SRR191653 1 0.2423 0.836 0.960 0.040
#> SRR191654 1 0.7056 0.664 0.808 0.192
#> SRR191655 1 0.2423 0.836 0.960 0.040
#> SRR191656 1 0.7674 0.801 0.776 0.224
#> SRR191657 1 0.0000 0.855 1.000 0.000
#> SRR191658 1 0.2603 0.860 0.956 0.044
#> SRR191659 1 0.0000 0.855 1.000 0.000
#> SRR191660 1 0.0376 0.856 0.996 0.004
#> SRR191661 1 0.1633 0.845 0.976 0.024
#> SRR191662 1 0.2043 0.840 0.968 0.032
#> SRR191663 1 0.1633 0.844 0.976 0.024
#> SRR191664 1 0.0000 0.855 1.000 0.000
#> SRR191665 1 0.7056 0.823 0.808 0.192
#> SRR191666 1 0.1843 0.860 0.972 0.028
#> SRR191667 1 0.1843 0.860 0.972 0.028
#> SRR191668 1 0.7674 0.801 0.776 0.224
#> SRR191669 1 0.7674 0.801 0.776 0.224
#> SRR191670 1 0.7674 0.801 0.776 0.224
#> SRR191671 1 0.7674 0.801 0.776 0.224
#> SRR191672 1 0.7674 0.801 0.776 0.224
#> SRR191673 1 0.7674 0.801 0.776 0.224
#> SRR191674 2 0.2948 0.835 0.052 0.948
#> SRR191675 2 0.2423 0.839 0.040 0.960
#> SRR191677 2 0.1843 0.840 0.028 0.972
#> SRR191678 2 0.8081 0.598 0.248 0.752
#> SRR191679 2 0.6438 0.845 0.164 0.836
#> SRR191680 2 0.1843 0.840 0.028 0.972
#> SRR191681 2 0.2603 0.839 0.044 0.956
#> SRR191682 2 0.3274 0.831 0.060 0.940
#> SRR191683 2 0.2603 0.839 0.044 0.956
#> SRR191684 2 0.5629 0.845 0.132 0.868
#> SRR191685 2 0.2603 0.846 0.044 0.956
#> SRR191686 2 0.2778 0.837 0.048 0.952
#> SRR191687 2 0.1843 0.840 0.028 0.972
#> SRR191688 2 0.7815 0.824 0.232 0.768
#> SRR191689 2 0.2603 0.839 0.044 0.956
#> SRR191690 1 0.9248 0.408 0.660 0.340
#> SRR191691 2 0.7602 0.832 0.220 0.780
#> SRR191692 2 0.3114 0.833 0.056 0.944
#> SRR191693 2 0.3114 0.833 0.056 0.944
#> SRR191694 2 0.2603 0.839 0.044 0.956
#> SRR191695 2 0.2948 0.835 0.052 0.948
#> SRR191696 2 0.2948 0.835 0.052 0.948
#> SRR191697 2 0.2603 0.839 0.044 0.956
#> SRR191698 2 0.8207 0.708 0.256 0.744
#> SRR191699 2 0.7528 0.826 0.216 0.784
#> SRR191700 1 0.8813 0.729 0.700 0.300
#> SRR191701 2 0.5178 0.850 0.116 0.884
#> SRR191702 2 0.2778 0.848 0.048 0.952
#> SRR191703 2 0.2423 0.846 0.040 0.960
#> SRR191704 2 0.7883 0.821 0.236 0.764
#> SRR191705 2 0.7219 0.837 0.200 0.800
#> SRR191706 2 0.1843 0.840 0.028 0.972
#> SRR191707 2 0.7674 0.818 0.224 0.776
#> SRR191708 2 0.7815 0.825 0.232 0.768
#> SRR191709 2 0.7674 0.814 0.224 0.776
#> SRR191710 2 0.7528 0.835 0.216 0.784
#> SRR191711 2 0.7674 0.832 0.224 0.776
#> SRR191712 2 0.7376 0.839 0.208 0.792
#> SRR191713 2 0.6801 0.843 0.180 0.820
#> SRR191714 2 0.7056 0.844 0.192 0.808
#> SRR191715 2 0.1843 0.840 0.028 0.972
#> SRR191716 2 0.8555 0.731 0.280 0.720
#> SRR191717 2 0.2043 0.840 0.032 0.968
#> SRR191718 2 0.3584 0.841 0.068 0.932
#> SRR537099 1 0.5294 0.850 0.880 0.120
#> SRR537100 1 0.5629 0.848 0.868 0.132
#> SRR537101 1 0.1843 0.860 0.972 0.028
#> SRR537102 1 0.7950 0.576 0.760 0.240
#> SRR537104 2 0.8081 0.818 0.248 0.752
#> SRR537105 1 0.2603 0.834 0.956 0.044
#> SRR537106 2 0.7815 0.812 0.232 0.768
#> SRR537107 2 0.8081 0.818 0.248 0.752
#> SRR537108 2 0.8081 0.818 0.248 0.752
#> SRR537109 2 0.8081 0.818 0.248 0.752
#> SRR537110 2 0.7745 0.811 0.228 0.772
#> SRR537111 2 0.9833 0.572 0.424 0.576
#> SRR537113 2 0.8016 0.823 0.244 0.756
#> SRR537114 1 0.6438 0.721 0.836 0.164
#> SRR537115 2 0.5737 0.848 0.136 0.864
#> SRR537116 2 0.7815 0.826 0.232 0.768
#> SRR537117 1 0.9775 0.550 0.588 0.412
#> SRR537118 1 0.6801 0.833 0.820 0.180
#> SRR537119 1 0.7056 0.789 0.808 0.192
#> SRR537120 1 0.9393 0.646 0.644 0.356
#> SRR537121 1 0.7674 0.801 0.776 0.224
#> SRR537122 1 0.1414 0.859 0.980 0.020
#> SRR537123 1 0.7674 0.801 0.776 0.224
#> SRR537124 1 0.8386 0.769 0.732 0.268
#> SRR537125 1 0.4815 0.854 0.896 0.104
#> SRR537126 1 0.6438 0.837 0.836 0.164
#> SRR537127 1 0.6623 0.834 0.828 0.172
#> SRR537128 1 0.6531 0.836 0.832 0.168
#> SRR537129 1 0.6712 0.832 0.824 0.176
#> SRR537130 1 0.2043 0.860 0.968 0.032
#> SRR537131 1 0.6623 0.834 0.828 0.172
#> SRR537132 1 0.6623 0.834 0.828 0.172
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR191639 3 0.5650 0.42665 0.312 0.000 0.688
#> SRR191640 1 0.0661 0.83574 0.988 0.004 0.008
#> SRR191641 1 0.7825 0.48475 0.620 0.080 0.300
#> SRR191642 1 0.0747 0.83488 0.984 0.016 0.000
#> SRR191643 1 0.1289 0.82629 0.968 0.032 0.000
#> SRR191644 1 0.1163 0.82803 0.972 0.028 0.000
#> SRR191645 1 0.0661 0.83574 0.988 0.004 0.008
#> SRR191646 1 0.0829 0.83559 0.984 0.004 0.012
#> SRR191647 1 0.1289 0.83172 0.968 0.000 0.032
#> SRR191648 1 0.1031 0.83346 0.976 0.000 0.024
#> SRR191649 1 0.1989 0.82690 0.948 0.004 0.048
#> SRR191650 1 0.0424 0.83418 0.992 0.008 0.000
#> SRR191651 1 0.0000 0.83464 1.000 0.000 0.000
#> SRR191652 1 0.7909 0.11466 0.496 0.056 0.448
#> SRR191653 1 0.0424 0.83410 0.992 0.008 0.000
#> SRR191654 1 0.1163 0.82803 0.972 0.028 0.000
#> SRR191655 1 0.0592 0.83359 0.988 0.012 0.000
#> SRR191656 3 0.0237 0.66229 0.000 0.004 0.996
#> SRR191657 1 0.1860 0.82531 0.948 0.000 0.052
#> SRR191658 3 0.6260 0.09244 0.448 0.000 0.552
#> SRR191659 1 0.2537 0.81451 0.920 0.000 0.080
#> SRR191660 1 0.3784 0.77987 0.864 0.004 0.132
#> SRR191661 1 0.0000 0.83464 1.000 0.000 0.000
#> SRR191662 1 0.1031 0.82959 0.976 0.024 0.000
#> SRR191663 1 0.0424 0.83500 0.992 0.000 0.008
#> SRR191664 1 0.1860 0.82531 0.948 0.000 0.052
#> SRR191665 3 0.3619 0.63421 0.136 0.000 0.864
#> SRR191666 1 0.6448 0.48091 0.636 0.012 0.352
#> SRR191667 1 0.5860 0.67519 0.748 0.024 0.228
#> SRR191668 3 0.1031 0.66615 0.024 0.000 0.976
#> SRR191669 3 0.1031 0.66615 0.024 0.000 0.976
#> SRR191670 3 0.1163 0.66570 0.028 0.000 0.972
#> SRR191671 3 0.1163 0.66570 0.028 0.000 0.972
#> SRR191672 3 0.0424 0.66489 0.008 0.000 0.992
#> SRR191673 3 0.0424 0.66489 0.008 0.000 0.992
#> SRR191674 3 0.5810 0.17616 0.000 0.336 0.664
#> SRR191675 3 0.6274 -0.18370 0.000 0.456 0.544
#> SRR191677 2 0.4121 0.72426 0.000 0.832 0.168
#> SRR191678 3 0.5864 0.49805 0.008 0.288 0.704
#> SRR191679 2 0.3295 0.66278 0.008 0.896 0.096
#> SRR191680 2 0.4291 0.71908 0.000 0.820 0.180
#> SRR191681 2 0.5560 0.60706 0.000 0.700 0.300
#> SRR191682 3 0.4605 0.57711 0.000 0.204 0.796
#> SRR191683 2 0.5560 0.61453 0.000 0.700 0.300
#> SRR191684 2 0.1989 0.68255 0.004 0.948 0.048
#> SRR191685 2 0.4121 0.72426 0.000 0.832 0.168
#> SRR191686 2 0.6140 0.43754 0.000 0.596 0.404
#> SRR191687 2 0.4452 0.71334 0.000 0.808 0.192
#> SRR191688 2 0.5067 0.70989 0.116 0.832 0.052
#> SRR191689 3 0.6299 -0.00428 0.000 0.476 0.524
#> SRR191690 3 0.7889 0.50115 0.088 0.288 0.624
#> SRR191691 2 0.6191 0.70655 0.140 0.776 0.084
#> SRR191692 3 0.5058 0.54089 0.000 0.244 0.756
#> SRR191693 3 0.2356 0.63527 0.000 0.072 0.928
#> SRR191694 3 0.6026 0.07068 0.000 0.376 0.624
#> SRR191695 3 0.5465 0.48491 0.000 0.288 0.712
#> SRR191696 3 0.5835 0.39659 0.000 0.340 0.660
#> SRR191697 2 0.6252 0.27105 0.000 0.556 0.444
#> SRR191698 3 0.7245 0.33036 0.036 0.368 0.596
#> SRR191699 2 0.5060 0.71256 0.100 0.836 0.064
#> SRR191700 3 0.3530 0.66364 0.032 0.068 0.900
#> SRR191701 2 0.4172 0.72859 0.004 0.840 0.156
#> SRR191702 2 0.3941 0.72791 0.000 0.844 0.156
#> SRR191703 2 0.3816 0.72929 0.000 0.852 0.148
#> SRR191704 3 0.8141 0.28266 0.068 0.460 0.472
#> SRR191705 2 0.6416 0.07562 0.008 0.616 0.376
#> SRR191706 2 0.5968 0.54726 0.000 0.636 0.364
#> SRR191707 2 0.4291 0.65451 0.180 0.820 0.000
#> SRR191708 2 0.8333 0.10649 0.100 0.572 0.328
#> SRR191709 2 0.4291 0.65973 0.180 0.820 0.000
#> SRR191710 2 0.5442 0.73042 0.056 0.812 0.132
#> SRR191711 2 0.4676 0.73591 0.040 0.848 0.112
#> SRR191712 2 0.7453 0.12270 0.036 0.528 0.436
#> SRR191713 2 0.2050 0.68057 0.020 0.952 0.028
#> SRR191714 2 0.5331 0.71836 0.100 0.824 0.076
#> SRR191715 2 0.4654 0.70733 0.000 0.792 0.208
#> SRR191716 3 0.7425 0.41212 0.052 0.328 0.620
#> SRR191717 2 0.5465 0.63358 0.000 0.712 0.288
#> SRR191718 3 0.6111 0.28402 0.000 0.396 0.604
#> SRR537099 1 0.5465 0.62746 0.712 0.000 0.288
#> SRR537100 3 0.6661 0.21881 0.400 0.012 0.588
#> SRR537101 3 0.8185 0.07608 0.428 0.072 0.500
#> SRR537102 1 0.1163 0.82803 0.972 0.028 0.000
#> SRR537104 2 0.6302 0.16666 0.480 0.520 0.000
#> SRR537105 1 0.0592 0.83359 0.988 0.012 0.000
#> SRR537106 1 0.2066 0.80982 0.940 0.060 0.000
#> SRR537107 1 0.1860 0.81804 0.948 0.052 0.000
#> SRR537108 1 0.1964 0.81424 0.944 0.056 0.000
#> SRR537109 2 0.5291 0.59806 0.268 0.732 0.000
#> SRR537110 1 0.4702 0.65075 0.788 0.212 0.000
#> SRR537111 1 0.2772 0.79735 0.916 0.080 0.004
#> SRR537113 2 0.5591 0.55881 0.304 0.696 0.000
#> SRR537114 1 0.8487 0.25724 0.536 0.100 0.364
#> SRR537115 3 0.5728 0.48834 0.008 0.272 0.720
#> SRR537116 2 0.5538 0.70742 0.132 0.808 0.060
#> SRR537117 3 0.1289 0.65262 0.000 0.032 0.968
#> SRR537118 3 0.7794 0.26357 0.368 0.060 0.572
#> SRR537119 3 0.8005 0.53446 0.224 0.128 0.648
#> SRR537120 3 0.3805 0.65513 0.024 0.092 0.884
#> SRR537121 3 0.5178 0.47756 0.256 0.000 0.744
#> SRR537122 1 0.1585 0.83401 0.964 0.008 0.028
#> SRR537123 3 0.1411 0.66352 0.036 0.000 0.964
#> SRR537124 3 0.1163 0.66258 0.000 0.028 0.972
#> SRR537125 1 0.6912 0.48611 0.628 0.028 0.344
#> SRR537126 1 0.7075 0.12359 0.492 0.020 0.488
#> SRR537127 1 0.6095 0.43826 0.608 0.000 0.392
#> SRR537128 1 0.5560 0.60068 0.700 0.000 0.300
#> SRR537129 1 0.5678 0.57789 0.684 0.000 0.316
#> SRR537130 1 0.2711 0.81051 0.912 0.000 0.088
#> SRR537131 1 0.5810 0.54726 0.664 0.000 0.336
#> SRR537132 1 0.5431 0.61958 0.716 0.000 0.284
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR191639 1 0.5522 0.4167 0.668 0.044 0.000 0.288
#> SRR191640 4 0.3450 0.6671 0.000 0.156 0.008 0.836
#> SRR191641 2 0.5157 0.5528 0.000 0.688 0.028 0.284
#> SRR191642 4 0.5762 0.2616 0.000 0.352 0.040 0.608
#> SRR191643 4 0.1209 0.7613 0.000 0.004 0.032 0.964
#> SRR191644 4 0.1398 0.7599 0.000 0.004 0.040 0.956
#> SRR191645 4 0.4535 0.4660 0.000 0.292 0.004 0.704
#> SRR191646 4 0.5203 0.1248 0.000 0.416 0.008 0.576
#> SRR191647 4 0.0376 0.7605 0.004 0.004 0.000 0.992
#> SRR191648 4 0.0188 0.7608 0.000 0.004 0.000 0.996
#> SRR191649 2 0.5168 0.0997 0.000 0.500 0.004 0.496
#> SRR191650 4 0.0000 0.7600 0.000 0.000 0.000 1.000
#> SRR191651 4 0.0188 0.7598 0.004 0.000 0.000 0.996
#> SRR191652 2 0.6313 0.5504 0.028 0.648 0.044 0.280
#> SRR191653 4 0.0188 0.7601 0.000 0.000 0.004 0.996
#> SRR191654 4 0.0817 0.7620 0.000 0.000 0.024 0.976
#> SRR191655 4 0.1042 0.7623 0.000 0.008 0.020 0.972
#> SRR191656 1 0.0188 0.7793 0.996 0.004 0.000 0.000
#> SRR191657 2 0.5388 0.2212 0.012 0.532 0.000 0.456
#> SRR191658 2 0.7121 0.4122 0.160 0.540 0.000 0.300
#> SRR191659 2 0.5912 0.2348 0.036 0.524 0.000 0.440
#> SRR191660 2 0.5125 0.3957 0.008 0.604 0.000 0.388
#> SRR191661 4 0.0336 0.7612 0.000 0.008 0.000 0.992
#> SRR191662 4 0.0000 0.7600 0.000 0.000 0.000 1.000
#> SRR191663 4 0.1118 0.7590 0.000 0.036 0.000 0.964
#> SRR191664 4 0.4933 0.4691 0.016 0.296 0.000 0.688
#> SRR191665 1 0.6669 0.3282 0.572 0.320 0.000 0.108
#> SRR191666 4 0.6544 0.2791 0.060 0.352 0.012 0.576
#> SRR191667 4 0.6263 -0.0698 0.032 0.460 0.012 0.496
#> SRR191668 1 0.0895 0.7802 0.976 0.020 0.004 0.000
#> SRR191669 1 0.0779 0.7809 0.980 0.016 0.004 0.000
#> SRR191670 1 0.4452 0.5837 0.732 0.260 0.000 0.008
#> SRR191671 1 0.4699 0.4807 0.676 0.320 0.000 0.004
#> SRR191672 1 0.0336 0.7804 0.992 0.008 0.000 0.000
#> SRR191673 1 0.0336 0.7804 0.992 0.008 0.000 0.000
#> SRR191674 1 0.3052 0.7135 0.860 0.004 0.136 0.000
#> SRR191675 1 0.4661 0.4171 0.652 0.000 0.348 0.000
#> SRR191677 3 0.1474 0.8136 0.052 0.000 0.948 0.000
#> SRR191678 2 0.4095 0.7060 0.016 0.792 0.192 0.000
#> SRR191679 2 0.2839 0.5969 0.004 0.884 0.108 0.004
#> SRR191680 3 0.1661 0.8131 0.052 0.004 0.944 0.000
#> SRR191681 3 0.6039 0.1124 0.056 0.348 0.596 0.000
#> SRR191682 2 0.7299 0.5571 0.224 0.536 0.240 0.000
#> SRR191683 3 0.2867 0.7928 0.104 0.012 0.884 0.000
#> SRR191684 2 0.4655 0.2484 0.004 0.684 0.312 0.000
#> SRR191685 3 0.1576 0.8151 0.048 0.004 0.948 0.000
#> SRR191686 3 0.4248 0.6659 0.220 0.012 0.768 0.000
#> SRR191687 3 0.1824 0.8138 0.060 0.004 0.936 0.000
#> SRR191688 2 0.5345 0.5767 0.004 0.584 0.404 0.008
#> SRR191689 2 0.5615 0.6053 0.032 0.612 0.356 0.000
#> SRR191690 2 0.3668 0.7072 0.004 0.808 0.188 0.000
#> SRR191691 3 0.2739 0.7835 0.000 0.036 0.904 0.060
#> SRR191692 2 0.6943 0.5967 0.160 0.576 0.264 0.000
#> SRR191693 1 0.2654 0.7343 0.888 0.004 0.108 0.000
#> SRR191694 1 0.4679 0.4098 0.648 0.000 0.352 0.000
#> SRR191695 2 0.5687 0.6701 0.068 0.684 0.248 0.000
#> SRR191696 2 0.5851 0.6559 0.068 0.660 0.272 0.000
#> SRR191697 2 0.5775 0.5428 0.032 0.560 0.408 0.000
#> SRR191698 2 0.4978 0.6560 0.012 0.664 0.324 0.000
#> SRR191699 2 0.4978 0.6049 0.000 0.612 0.384 0.004
#> SRR191700 2 0.5091 0.7065 0.068 0.752 0.180 0.000
#> SRR191701 3 0.2918 0.7401 0.008 0.116 0.876 0.000
#> SRR191702 3 0.2737 0.7668 0.008 0.104 0.888 0.000
#> SRR191703 3 0.1182 0.8114 0.016 0.016 0.968 0.000
#> SRR191704 2 0.1082 0.6173 0.004 0.972 0.020 0.004
#> SRR191705 2 0.3052 0.6995 0.004 0.860 0.136 0.000
#> SRR191706 3 0.4661 0.3878 0.348 0.000 0.652 0.000
#> SRR191707 2 0.6079 0.5279 0.000 0.544 0.408 0.048
#> SRR191708 2 0.3626 0.7058 0.000 0.812 0.184 0.004
#> SRR191709 3 0.2565 0.7864 0.000 0.032 0.912 0.056
#> SRR191710 3 0.1930 0.7985 0.004 0.056 0.936 0.004
#> SRR191711 3 0.1847 0.7999 0.004 0.052 0.940 0.004
#> SRR191712 2 0.4632 0.6701 0.004 0.688 0.308 0.000
#> SRR191713 2 0.3375 0.6065 0.012 0.864 0.116 0.008
#> SRR191714 3 0.2553 0.7870 0.016 0.008 0.916 0.060
#> SRR191715 3 0.2973 0.7335 0.144 0.000 0.856 0.000
#> SRR191716 2 0.3870 0.7062 0.004 0.788 0.208 0.000
#> SRR191717 3 0.2867 0.7912 0.104 0.012 0.884 0.000
#> SRR191718 2 0.4472 0.7008 0.020 0.760 0.220 0.000
#> SRR537099 4 0.3711 0.6897 0.140 0.000 0.024 0.836
#> SRR537100 4 0.8730 0.0885 0.204 0.336 0.052 0.408
#> SRR537101 2 0.5389 0.6404 0.032 0.756 0.036 0.176
#> SRR537102 4 0.1743 0.7577 0.000 0.004 0.056 0.940
#> SRR537104 4 0.4804 0.3265 0.000 0.000 0.384 0.616
#> SRR537105 4 0.0657 0.7621 0.000 0.004 0.012 0.984
#> SRR537106 4 0.1474 0.7576 0.000 0.000 0.052 0.948
#> SRR537107 4 0.3105 0.7144 0.000 0.004 0.140 0.856
#> SRR537108 4 0.1743 0.7570 0.000 0.004 0.056 0.940
#> SRR537109 3 0.3569 0.6654 0.000 0.000 0.804 0.196
#> SRR537110 4 0.4891 0.4860 0.000 0.012 0.308 0.680
#> SRR537111 4 0.2593 0.7161 0.004 0.000 0.104 0.892
#> SRR537113 3 0.4673 0.5178 0.000 0.008 0.700 0.292
#> SRR537114 2 0.5863 0.6748 0.000 0.700 0.120 0.180
#> SRR537115 3 0.8156 -0.0996 0.220 0.344 0.420 0.016
#> SRR537116 3 0.2385 0.7898 0.000 0.028 0.920 0.052
#> SRR537117 1 0.1824 0.7626 0.936 0.004 0.060 0.000
#> SRR537118 4 0.7542 0.2653 0.280 0.004 0.204 0.512
#> SRR537119 2 0.7004 0.6807 0.072 0.632 0.248 0.048
#> SRR537120 2 0.6514 0.6667 0.152 0.636 0.212 0.000
#> SRR537121 1 0.4891 0.4099 0.680 0.000 0.012 0.308
#> SRR537122 4 0.1492 0.7598 0.004 0.004 0.036 0.956
#> SRR537123 1 0.0336 0.7789 0.992 0.000 0.008 0.000
#> SRR537124 1 0.4245 0.7063 0.820 0.116 0.064 0.000
#> SRR537125 4 0.6461 0.5299 0.168 0.004 0.168 0.660
#> SRR537126 4 0.6929 0.3520 0.308 0.004 0.120 0.568
#> SRR537127 4 0.5060 0.3491 0.412 0.000 0.004 0.584
#> SRR537128 4 0.4608 0.5358 0.304 0.000 0.004 0.692
#> SRR537129 4 0.4936 0.4236 0.372 0.000 0.004 0.624
#> SRR537130 4 0.2197 0.7331 0.080 0.000 0.004 0.916
#> SRR537131 4 0.4889 0.4491 0.360 0.000 0.004 0.636
#> SRR537132 4 0.4584 0.5398 0.300 0.000 0.004 0.696
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR191639 5 0.8310 0.0487 0.264 0.012 0.092 0.248 0.384
#> SRR191640 1 0.5731 0.2802 0.480 0.000 0.084 0.436 0.000
#> SRR191641 1 0.3604 0.5302 0.840 0.008 0.044 0.104 0.004
#> SRR191642 1 0.4986 0.4539 0.608 0.004 0.032 0.356 0.000
#> SRR191643 4 0.1306 0.7093 0.016 0.016 0.008 0.960 0.000
#> SRR191644 4 0.5135 0.4409 0.204 0.008 0.088 0.700 0.000
#> SRR191645 1 0.6087 0.3828 0.528 0.008 0.088 0.372 0.004
#> SRR191646 1 0.5754 0.4362 0.564 0.000 0.088 0.344 0.004
#> SRR191647 4 0.2043 0.7158 0.008 0.004 0.048 0.928 0.012
#> SRR191648 4 0.1757 0.7152 0.004 0.000 0.048 0.936 0.012
#> SRR191649 1 0.5840 0.4570 0.596 0.008 0.084 0.308 0.004
#> SRR191650 4 0.3779 0.6339 0.068 0.004 0.096 0.828 0.004
#> SRR191651 4 0.4018 0.6339 0.064 0.012 0.092 0.824 0.008
#> SRR191652 1 0.4286 0.5352 0.804 0.004 0.076 0.100 0.016
#> SRR191653 4 0.0162 0.7147 0.000 0.000 0.004 0.996 0.000
#> SRR191654 4 0.0566 0.7159 0.000 0.012 0.004 0.984 0.000
#> SRR191655 4 0.2144 0.6884 0.068 0.000 0.020 0.912 0.000
#> SRR191656 5 0.1168 0.6592 0.032 0.000 0.008 0.000 0.960
#> SRR191657 1 0.6580 0.4350 0.564 0.012 0.112 0.292 0.020
#> SRR191658 1 0.7524 0.4214 0.548 0.012 0.112 0.204 0.124
#> SRR191659 1 0.6936 0.4152 0.532 0.012 0.104 0.312 0.040
#> SRR191660 1 0.6001 0.4822 0.648 0.012 0.100 0.224 0.016
#> SRR191661 4 0.5263 0.4551 0.188 0.008 0.096 0.704 0.004
#> SRR191662 4 0.1774 0.6950 0.016 0.000 0.052 0.932 0.000
#> SRR191663 4 0.6180 0.0713 0.332 0.008 0.096 0.556 0.008
#> SRR191664 1 0.7028 0.2963 0.468 0.012 0.104 0.380 0.036
#> SRR191665 1 0.7341 0.2425 0.504 0.012 0.100 0.072 0.312
#> SRR191666 1 0.6203 0.4408 0.624 0.008 0.056 0.260 0.052
#> SRR191667 1 0.4965 0.5060 0.732 0.008 0.032 0.200 0.028
#> SRR191668 5 0.3885 0.5778 0.176 0.000 0.040 0.000 0.784
#> SRR191669 5 0.3409 0.6092 0.144 0.000 0.032 0.000 0.824
#> SRR191670 5 0.5999 -0.0197 0.456 0.008 0.072 0.004 0.460
#> SRR191671 1 0.5996 -0.0158 0.472 0.008 0.072 0.004 0.444
#> SRR191672 5 0.0880 0.6612 0.032 0.000 0.000 0.000 0.968
#> SRR191673 5 0.0880 0.6612 0.032 0.000 0.000 0.000 0.968
#> SRR191674 5 0.2929 0.6327 0.008 0.152 0.000 0.000 0.840
#> SRR191675 5 0.3814 0.5068 0.004 0.276 0.000 0.000 0.720
#> SRR191677 2 0.2563 0.7452 0.120 0.872 0.000 0.000 0.008
#> SRR191678 1 0.3011 0.5012 0.876 0.036 0.076 0.000 0.012
#> SRR191679 3 0.4065 0.8458 0.180 0.048 0.772 0.000 0.000
#> SRR191680 2 0.3022 0.7501 0.136 0.848 0.004 0.000 0.012
#> SRR191681 2 0.5604 0.3364 0.460 0.480 0.008 0.000 0.052
#> SRR191682 1 0.8030 -0.1019 0.432 0.136 0.188 0.000 0.244
#> SRR191683 2 0.5983 0.6666 0.168 0.656 0.020 0.004 0.152
#> SRR191684 3 0.4998 0.8167 0.172 0.108 0.716 0.004 0.000
#> SRR191685 2 0.3305 0.7283 0.088 0.864 0.008 0.012 0.028
#> SRR191686 2 0.6402 0.4509 0.168 0.508 0.004 0.000 0.320
#> SRR191687 2 0.3347 0.7343 0.100 0.856 0.004 0.012 0.028
#> SRR191688 1 0.3113 0.5350 0.864 0.100 0.020 0.016 0.000
#> SRR191689 1 0.4826 0.4532 0.760 0.140 0.068 0.000 0.032
#> SRR191690 1 0.1549 0.5251 0.944 0.016 0.040 0.000 0.000
#> SRR191691 2 0.4475 0.7284 0.180 0.756 0.008 0.056 0.000
#> SRR191692 1 0.6284 0.3499 0.660 0.128 0.092 0.000 0.120
#> SRR191693 5 0.3031 0.6367 0.016 0.128 0.004 0.000 0.852
#> SRR191694 5 0.3635 0.5479 0.004 0.248 0.000 0.000 0.748
#> SRR191695 1 0.4687 0.4547 0.780 0.068 0.108 0.000 0.044
#> SRR191696 1 0.5135 0.4320 0.752 0.076 0.108 0.000 0.064
#> SRR191697 1 0.6187 0.2222 0.588 0.288 0.096 0.000 0.028
#> SRR191698 1 0.5115 0.4134 0.720 0.132 0.140 0.004 0.004
#> SRR191699 1 0.5525 0.3365 0.664 0.212 0.116 0.008 0.000
#> SRR191700 1 0.4596 0.4535 0.780 0.076 0.116 0.000 0.028
#> SRR191701 2 0.4387 0.6363 0.328 0.660 0.004 0.004 0.004
#> SRR191702 2 0.3963 0.7265 0.256 0.732 0.004 0.000 0.008
#> SRR191703 2 0.2674 0.7457 0.140 0.856 0.000 0.000 0.004
#> SRR191704 3 0.5255 0.7891 0.304 0.072 0.624 0.000 0.000
#> SRR191705 1 0.3815 0.3509 0.764 0.012 0.220 0.000 0.004
#> SRR191706 2 0.4196 0.3474 0.004 0.640 0.000 0.000 0.356
#> SRR191707 1 0.5468 0.1607 0.608 0.328 0.016 0.048 0.000
#> SRR191708 1 0.2166 0.5139 0.912 0.012 0.072 0.004 0.000
#> SRR191709 2 0.4129 0.7331 0.204 0.756 0.000 0.040 0.000
#> SRR191710 2 0.4009 0.6837 0.312 0.684 0.000 0.000 0.004
#> SRR191711 2 0.4084 0.6506 0.328 0.668 0.000 0.004 0.000
#> SRR191712 1 0.1471 0.5321 0.952 0.024 0.020 0.000 0.004
#> SRR191713 3 0.5598 0.8180 0.248 0.112 0.636 0.000 0.004
#> SRR191714 2 0.3818 0.7002 0.128 0.824 0.008 0.028 0.012
#> SRR191715 2 0.3409 0.6780 0.052 0.836 0.000 0.000 0.112
#> SRR191716 1 0.1372 0.5335 0.956 0.024 0.016 0.000 0.004
#> SRR191717 2 0.5153 0.7094 0.204 0.684 0.000 0.000 0.112
#> SRR191718 1 0.3410 0.4988 0.856 0.052 0.076 0.000 0.016
#> SRR537099 4 0.6172 0.6179 0.008 0.084 0.132 0.684 0.092
#> SRR537100 4 0.8605 0.3439 0.180 0.056 0.132 0.472 0.160
#> SRR537101 1 0.3724 0.5244 0.848 0.004 0.028 0.064 0.056
#> SRR537102 4 0.1883 0.7135 0.012 0.048 0.008 0.932 0.000
#> SRR537104 4 0.3508 0.5764 0.000 0.252 0.000 0.748 0.000
#> SRR537105 4 0.0833 0.7161 0.004 0.016 0.004 0.976 0.000
#> SRR537106 4 0.1393 0.7101 0.008 0.024 0.012 0.956 0.000
#> SRR537107 4 0.2359 0.6999 0.036 0.060 0.000 0.904 0.000
#> SRR537108 4 0.1408 0.7130 0.008 0.044 0.000 0.948 0.000
#> SRR537109 2 0.4180 0.5605 0.036 0.744 0.000 0.220 0.000
#> SRR537110 4 0.4224 0.6220 0.080 0.120 0.008 0.792 0.000
#> SRR537111 4 0.5505 0.5606 0.040 0.148 0.080 0.724 0.008
#> SRR537113 2 0.7269 0.1994 0.196 0.464 0.032 0.304 0.004
#> SRR537114 1 0.2032 0.5468 0.924 0.004 0.020 0.052 0.000
#> SRR537115 1 0.6039 0.2932 0.604 0.232 0.000 0.008 0.156
#> SRR537116 2 0.3495 0.7495 0.160 0.812 0.000 0.028 0.000
#> SRR537117 5 0.2494 0.6533 0.032 0.056 0.008 0.000 0.904
#> SRR537118 4 0.8568 0.4115 0.112 0.116 0.136 0.504 0.132
#> SRR537119 1 0.7603 0.2520 0.576 0.128 0.148 0.116 0.032
#> SRR537120 1 0.6313 0.3645 0.668 0.104 0.124 0.004 0.100
#> SRR537121 5 0.7557 -0.1589 0.004 0.080 0.124 0.384 0.408
#> SRR537122 4 0.5448 0.6493 0.016 0.084 0.112 0.744 0.044
#> SRR537123 5 0.2948 0.5950 0.008 0.020 0.092 0.004 0.876
#> SRR537124 5 0.4878 0.4783 0.208 0.060 0.012 0.000 0.720
#> SRR537125 4 0.7615 0.5150 0.104 0.096 0.132 0.596 0.072
#> SRR537126 4 0.7981 0.4719 0.056 0.096 0.140 0.548 0.160
#> SRR537127 4 0.6297 0.3768 0.000 0.008 0.128 0.508 0.356
#> SRR537128 4 0.6142 0.4634 0.000 0.008 0.128 0.560 0.304
#> SRR537129 4 0.6122 0.4049 0.000 0.004 0.124 0.528 0.344
#> SRR537130 4 0.4214 0.6717 0.000 0.004 0.120 0.788 0.088
#> SRR537131 4 0.6111 0.4121 0.000 0.004 0.124 0.532 0.340
#> SRR537132 4 0.6013 0.4705 0.000 0.004 0.128 0.568 0.300
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR191639 1 0.4058 0.408 0.672 0.012 0.004 0.004 0.308 0.000
#> SRR191640 1 0.3694 0.628 0.740 0.232 0.000 0.028 0.000 0.000
#> SRR191641 2 0.4320 0.516 0.280 0.684 0.012 0.000 0.016 0.008
#> SRR191642 2 0.4763 0.354 0.336 0.608 0.000 0.048 0.000 0.008
#> SRR191643 4 0.4987 0.377 0.472 0.016 0.000 0.476 0.000 0.036
#> SRR191644 1 0.2126 0.645 0.904 0.020 0.000 0.072 0.000 0.004
#> SRR191645 1 0.3373 0.619 0.744 0.248 0.000 0.008 0.000 0.000
#> SRR191646 1 0.3565 0.539 0.692 0.304 0.000 0.004 0.000 0.000
#> SRR191647 4 0.4090 0.539 0.384 0.000 0.008 0.604 0.004 0.000
#> SRR191648 4 0.4118 0.526 0.396 0.000 0.008 0.592 0.004 0.000
#> SRR191649 1 0.3725 0.517 0.676 0.316 0.000 0.000 0.008 0.000
#> SRR191650 1 0.1663 0.620 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR191651 1 0.2113 0.611 0.896 0.000 0.004 0.092 0.008 0.000
#> SRR191652 2 0.4753 0.344 0.356 0.600 0.008 0.000 0.028 0.008
#> SRR191653 4 0.4316 0.484 0.432 0.004 0.004 0.552 0.000 0.008
#> SRR191654 4 0.4171 0.550 0.380 0.004 0.000 0.604 0.000 0.012
#> SRR191655 1 0.5027 -0.128 0.552 0.068 0.000 0.376 0.000 0.004
#> SRR191656 5 0.1007 0.706 0.016 0.008 0.000 0.004 0.968 0.004
#> SRR191657 1 0.3964 0.637 0.776 0.164 0.016 0.004 0.040 0.000
#> SRR191658 1 0.4724 0.549 0.708 0.104 0.008 0.000 0.176 0.004
#> SRR191659 1 0.3379 0.664 0.832 0.100 0.008 0.004 0.056 0.000
#> SRR191660 1 0.4713 0.537 0.672 0.264 0.016 0.000 0.044 0.004
#> SRR191661 1 0.1674 0.639 0.924 0.004 0.000 0.068 0.004 0.000
#> SRR191662 1 0.3583 0.320 0.728 0.000 0.004 0.260 0.000 0.008
#> SRR191663 1 0.1633 0.670 0.932 0.044 0.000 0.024 0.000 0.000
#> SRR191664 1 0.2514 0.676 0.896 0.044 0.008 0.008 0.044 0.000
#> SRR191665 1 0.4781 0.303 0.608 0.072 0.000 0.000 0.320 0.000
#> SRR191666 2 0.5755 0.425 0.308 0.580 0.012 0.052 0.048 0.000
#> SRR191667 2 0.4836 0.563 0.252 0.684 0.012 0.024 0.020 0.008
#> SRR191668 5 0.3470 0.609 0.200 0.028 0.000 0.000 0.772 0.000
#> SRR191669 5 0.2973 0.657 0.136 0.024 0.000 0.004 0.836 0.000
#> SRR191670 5 0.5136 0.170 0.420 0.084 0.000 0.000 0.496 0.000
#> SRR191671 5 0.5252 0.144 0.424 0.096 0.000 0.000 0.480 0.000
#> SRR191672 5 0.0964 0.704 0.012 0.016 0.000 0.004 0.968 0.000
#> SRR191673 5 0.0912 0.705 0.008 0.012 0.000 0.004 0.972 0.004
#> SRR191674 5 0.2001 0.691 0.000 0.004 0.000 0.004 0.900 0.092
#> SRR191675 5 0.3429 0.558 0.000 0.004 0.000 0.004 0.740 0.252
#> SRR191677 6 0.3757 0.704 0.000 0.120 0.000 0.052 0.024 0.804
#> SRR191678 2 0.0582 0.718 0.004 0.984 0.004 0.000 0.004 0.004
#> SRR191679 3 0.4469 0.809 0.016 0.088 0.780 0.072 0.000 0.044
#> SRR191680 6 0.5127 0.706 0.000 0.176 0.040 0.056 0.020 0.708
#> SRR191681 2 0.4248 0.481 0.000 0.708 0.000 0.004 0.052 0.236
#> SRR191682 2 0.5327 0.574 0.000 0.712 0.028 0.124 0.096 0.040
#> SRR191683 6 0.7065 0.350 0.000 0.352 0.004 0.092 0.156 0.396
#> SRR191684 3 0.6487 0.751 0.028 0.112 0.612 0.112 0.000 0.136
#> SRR191685 6 0.5080 0.616 0.008 0.080 0.012 0.176 0.016 0.708
#> SRR191686 2 0.7102 -0.243 0.000 0.392 0.000 0.092 0.200 0.316
#> SRR191687 6 0.5337 0.640 0.004 0.104 0.000 0.160 0.048 0.684
#> SRR191688 2 0.3456 0.686 0.112 0.824 0.008 0.000 0.004 0.052
#> SRR191689 2 0.2007 0.712 0.008 0.924 0.000 0.012 0.016 0.040
#> SRR191690 2 0.1965 0.715 0.040 0.924 0.024 0.000 0.004 0.008
#> SRR191691 2 0.5858 -0.200 0.016 0.452 0.000 0.124 0.000 0.408
#> SRR191692 2 0.2643 0.701 0.000 0.888 0.000 0.040 0.036 0.036
#> SRR191693 5 0.3219 0.669 0.000 0.040 0.000 0.028 0.848 0.084
#> SRR191694 5 0.2989 0.642 0.000 0.008 0.000 0.004 0.812 0.176
#> SRR191695 2 0.1967 0.716 0.004 0.928 0.008 0.004 0.028 0.028
#> SRR191696 2 0.2311 0.714 0.004 0.912 0.004 0.016 0.028 0.036
#> SRR191697 2 0.3133 0.678 0.000 0.852 0.000 0.064 0.016 0.068
#> SRR191698 2 0.3077 0.674 0.004 0.848 0.004 0.112 0.004 0.028
#> SRR191699 2 0.2263 0.700 0.004 0.908 0.008 0.036 0.000 0.044
#> SRR191700 2 0.2365 0.694 0.000 0.892 0.004 0.084 0.008 0.012
#> SRR191701 2 0.4447 0.415 0.004 0.680 0.000 0.044 0.004 0.268
#> SRR191702 6 0.4675 0.634 0.008 0.324 0.036 0.000 0.004 0.628
#> SRR191703 6 0.3219 0.705 0.008 0.168 0.000 0.000 0.016 0.808
#> SRR191704 3 0.4324 0.783 0.020 0.108 0.784 0.024 0.000 0.064
#> SRR191705 2 0.4520 0.479 0.028 0.660 0.296 0.000 0.004 0.012
#> SRR191706 6 0.4051 0.189 0.000 0.008 0.000 0.000 0.432 0.560
#> SRR191707 2 0.3706 0.634 0.024 0.796 0.000 0.032 0.000 0.148
#> SRR191708 2 0.3765 0.648 0.048 0.780 0.164 0.000 0.000 0.008
#> SRR191709 6 0.3732 0.700 0.024 0.228 0.004 0.000 0.000 0.744
#> SRR191710 6 0.4015 0.562 0.012 0.372 0.000 0.000 0.000 0.616
#> SRR191711 6 0.3799 0.689 0.008 0.280 0.000 0.008 0.000 0.704
#> SRR191712 2 0.2359 0.715 0.056 0.904 0.020 0.000 0.012 0.008
#> SRR191713 3 0.4526 0.786 0.040 0.064 0.772 0.016 0.000 0.108
#> SRR191714 6 0.4817 0.612 0.048 0.092 0.052 0.020 0.016 0.772
#> SRR191715 6 0.3487 0.613 0.004 0.036 0.000 0.008 0.140 0.812
#> SRR191716 2 0.1909 0.715 0.052 0.920 0.024 0.000 0.004 0.000
#> SRR191717 6 0.6316 0.510 0.000 0.312 0.000 0.032 0.176 0.480
#> SRR191718 2 0.1527 0.719 0.020 0.948 0.012 0.000 0.012 0.008
#> SRR537099 4 0.2231 0.627 0.048 0.020 0.000 0.912 0.012 0.008
#> SRR537100 4 0.5268 0.142 0.012 0.360 0.008 0.564 0.056 0.000
#> SRR537101 2 0.4484 0.626 0.176 0.748 0.028 0.004 0.036 0.008
#> SRR537102 4 0.4875 0.532 0.368 0.028 0.000 0.580 0.000 0.024
#> SRR537104 4 0.5935 0.469 0.276 0.004 0.000 0.492 0.000 0.228
#> SRR537105 4 0.4315 0.542 0.384 0.004 0.004 0.596 0.000 0.012
#> SRR537106 1 0.4700 -0.412 0.488 0.008 0.000 0.476 0.000 0.028
#> SRR537107 4 0.5350 0.529 0.356 0.040 0.000 0.560 0.000 0.044
#> SRR537108 4 0.4954 0.516 0.388 0.008 0.000 0.552 0.000 0.052
#> SRR537109 6 0.4622 0.476 0.132 0.012 0.000 0.136 0.000 0.720
#> SRR537110 4 0.6046 0.474 0.332 0.068 0.000 0.524 0.000 0.076
#> SRR537111 1 0.3384 0.597 0.840 0.004 0.000 0.088 0.020 0.048
#> SRR537113 1 0.5618 0.360 0.584 0.064 0.000 0.012 0.028 0.312
#> SRR537114 2 0.3019 0.683 0.128 0.840 0.020 0.000 0.000 0.012
#> SRR537115 2 0.6264 0.466 0.108 0.576 0.000 0.000 0.212 0.104
#> SRR537116 6 0.4280 0.710 0.012 0.228 0.000 0.044 0.000 0.716
#> SRR537117 5 0.3748 0.642 0.000 0.084 0.000 0.060 0.816 0.040
#> SRR537118 4 0.3259 0.534 0.000 0.104 0.000 0.836 0.048 0.012
#> SRR537119 2 0.3627 0.605 0.000 0.760 0.004 0.216 0.004 0.016
#> SRR537120 2 0.3172 0.656 0.000 0.820 0.000 0.152 0.016 0.012
#> SRR537121 4 0.2926 0.581 0.004 0.008 0.000 0.844 0.132 0.012
#> SRR537122 4 0.2362 0.641 0.080 0.012 0.000 0.892 0.000 0.016
#> SRR537123 5 0.3885 0.509 0.000 0.044 0.000 0.220 0.736 0.000
#> SRR537124 2 0.5136 0.310 0.000 0.544 0.000 0.068 0.380 0.008
#> SRR537125 4 0.2892 0.591 0.028 0.068 0.000 0.876 0.016 0.012
#> SRR537126 4 0.2768 0.574 0.008 0.060 0.000 0.880 0.044 0.008
#> SRR537127 4 0.4488 0.516 0.044 0.000 0.016 0.692 0.248 0.000
#> SRR537128 4 0.4321 0.569 0.048 0.000 0.020 0.732 0.200 0.000
#> SRR537129 4 0.4234 0.557 0.044 0.000 0.016 0.732 0.208 0.000
#> SRR537130 4 0.3438 0.648 0.144 0.000 0.020 0.812 0.024 0.000
#> SRR537131 4 0.4295 0.563 0.048 0.000 0.016 0.728 0.208 0.000
#> SRR537132 4 0.4292 0.570 0.048 0.000 0.020 0.736 0.196 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)
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