Date: 2019-12-25 22:48:07 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 17780 rows and 119 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] 17780 119
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:kmeans | 2 | 1.000 | 0.985 | 0.991 | ** | |
| SD:skmeans | 2 | 1.000 | 0.956 | 0.983 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.984 | 0.993 | ** | |
| SD:pam | 2 | 0.989 | 0.958 | 0.969 | ** | |
| MAD:kmeans | 2 | 0.984 | 0.960 | 0.982 | ** | |
| ATC:NMF | 2 | 0.948 | 0.941 | 0.976 | * | |
| MAD:skmeans | 3 | 0.945 | 0.937 | 0.971 | * | 2 |
| ATC:pam | 5 | 0.942 | 0.906 | 0.961 | * | 4 |
| ATC:skmeans | 3 | 0.905 | 0.913 | 0.963 | * | 2 |
| MAD:NMF | 2 | 0.897 | 0.944 | 0.975 | ||
| CV:skmeans | 2 | 0.895 | 0.936 | 0.973 | ||
| CV:mclust | 2 | 0.891 | 0.935 | 0.971 | ||
| ATC:mclust | 4 | 0.863 | 0.893 | 0.945 | ||
| SD:NMF | 2 | 0.848 | 0.906 | 0.962 | ||
| CV:NMF | 2 | 0.787 | 0.891 | 0.955 | ||
| MAD:mclust | 2 | 0.752 | 0.926 | 0.949 | ||
| SD:mclust | 2 | 0.706 | 0.921 | 0.925 | ||
| CV:kmeans | 2 | 0.659 | 0.831 | 0.913 | ||
| MAD:pam | 2 | 0.593 | 0.852 | 0.922 | ||
| ATC:hclust | 2 | 0.550 | 0.771 | 0.900 | ||
| CV:hclust | 5 | 0.496 | 0.677 | 0.790 | ||
| MAD:hclust | 4 | 0.492 | 0.631 | 0.780 | ||
| SD:hclust | 3 | 0.400 | 0.673 | 0.807 | ||
| CV:pam | 3 | 0.393 | 0.694 | 0.829 |
**: 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 0.848 0.906 0.962 0.469 0.530 0.530
#> CV:NMF 2 0.787 0.891 0.955 0.490 0.515 0.515
#> MAD:NMF 2 0.897 0.944 0.975 0.482 0.511 0.511
#> ATC:NMF 2 0.948 0.941 0.976 0.405 0.596 0.596
#> SD:skmeans 2 1.000 0.956 0.983 0.497 0.504 0.504
#> CV:skmeans 2 0.895 0.936 0.973 0.504 0.497 0.497
#> MAD:skmeans 2 0.982 0.967 0.985 0.503 0.499 0.499
#> ATC:skmeans 2 1.000 0.989 0.995 0.493 0.509 0.509
#> SD:mclust 2 0.706 0.921 0.925 0.436 0.550 0.550
#> CV:mclust 2 0.891 0.935 0.971 0.441 0.550 0.550
#> MAD:mclust 2 0.752 0.926 0.949 0.438 0.550 0.550
#> ATC:mclust 2 0.295 0.600 0.814 0.432 0.496 0.496
#> SD:kmeans 2 1.000 0.985 0.991 0.434 0.562 0.562
#> CV:kmeans 2 0.659 0.831 0.913 0.468 0.526 0.526
#> MAD:kmeans 2 0.984 0.960 0.982 0.449 0.556 0.556
#> ATC:kmeans 2 1.000 0.984 0.993 0.444 0.556 0.556
#> SD:pam 2 0.989 0.958 0.969 0.428 0.574 0.574
#> CV:pam 2 0.453 0.814 0.900 0.303 0.765 0.765
#> MAD:pam 2 0.593 0.852 0.922 0.438 0.562 0.562
#> ATC:pam 2 0.746 0.816 0.926 0.414 0.550 0.550
#> SD:hclust 2 0.505 0.755 0.889 0.388 0.596 0.596
#> CV:hclust 2 0.232 0.546 0.775 0.381 0.611 0.611
#> MAD:hclust 2 0.417 0.783 0.888 0.420 0.581 0.581
#> ATC:hclust 2 0.550 0.771 0.900 0.462 0.539 0.539
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.799 0.832 0.929 0.395 0.718 0.512
#> CV:NMF 3 0.835 0.889 0.952 0.323 0.710 0.497
#> MAD:NMF 3 0.721 0.790 0.904 0.363 0.734 0.525
#> ATC:NMF 3 0.716 0.857 0.922 0.592 0.677 0.489
#> SD:skmeans 3 0.838 0.895 0.950 0.324 0.769 0.571
#> CV:skmeans 3 0.741 0.854 0.929 0.319 0.738 0.521
#> MAD:skmeans 3 0.945 0.937 0.971 0.309 0.765 0.563
#> ATC:skmeans 3 0.905 0.913 0.963 0.340 0.754 0.549
#> SD:mclust 3 0.768 0.871 0.942 0.354 0.827 0.696
#> CV:mclust 3 0.877 0.904 0.964 0.253 0.856 0.746
#> MAD:mclust 3 0.713 0.808 0.913 0.389 0.856 0.740
#> ATC:mclust 3 0.747 0.856 0.923 0.471 0.770 0.574
#> SD:kmeans 3 0.762 0.805 0.901 0.472 0.729 0.539
#> CV:kmeans 3 0.691 0.805 0.912 0.351 0.700 0.494
#> MAD:kmeans 3 0.753 0.876 0.929 0.441 0.729 0.536
#> ATC:kmeans 3 0.682 0.722 0.880 0.466 0.700 0.501
#> SD:pam 3 0.577 0.809 0.885 0.416 0.805 0.665
#> CV:pam 3 0.393 0.694 0.829 0.857 0.663 0.568
#> MAD:pam 3 0.436 0.392 0.705 0.400 0.716 0.551
#> ATC:pam 3 0.886 0.924 0.965 0.550 0.641 0.433
#> SD:hclust 3 0.400 0.673 0.807 0.485 0.722 0.566
#> CV:hclust 3 0.425 0.490 0.759 0.474 0.695 0.548
#> MAD:hclust 3 0.414 0.572 0.664 0.433 0.919 0.867
#> ATC:hclust 3 0.563 0.662 0.775 0.256 0.800 0.646
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.846 0.850 0.932 0.1163 0.866 0.640
#> CV:NMF 4 0.849 0.857 0.938 0.1280 0.830 0.568
#> MAD:NMF 4 0.853 0.873 0.941 0.1173 0.868 0.643
#> ATC:NMF 4 0.823 0.853 0.929 0.1072 0.852 0.620
#> SD:skmeans 4 0.738 0.720 0.868 0.1105 0.877 0.663
#> CV:skmeans 4 0.692 0.612 0.819 0.1083 0.927 0.791
#> MAD:skmeans 4 0.712 0.585 0.755 0.1190 0.889 0.695
#> ATC:skmeans 4 0.886 0.900 0.950 0.0945 0.900 0.714
#> SD:mclust 4 0.783 0.797 0.906 0.1663 0.780 0.535
#> CV:mclust 4 0.599 0.776 0.811 0.1881 0.970 0.934
#> MAD:mclust 4 0.774 0.857 0.925 0.1355 0.787 0.544
#> ATC:mclust 4 0.863 0.893 0.945 0.0949 0.922 0.786
#> SD:kmeans 4 0.659 0.723 0.820 0.1233 0.898 0.722
#> CV:kmeans 4 0.533 0.565 0.755 0.1193 0.848 0.619
#> MAD:kmeans 4 0.677 0.771 0.821 0.1144 0.921 0.778
#> ATC:kmeans 4 0.637 0.682 0.829 0.1186 0.800 0.505
#> SD:pam 4 0.560 0.677 0.799 0.1230 0.896 0.753
#> CV:pam 4 0.519 0.735 0.822 0.2577 0.809 0.596
#> MAD:pam 4 0.462 0.524 0.727 0.1589 0.694 0.393
#> ATC:pam 4 0.912 0.925 0.966 0.1132 0.876 0.677
#> SD:hclust 4 0.486 0.541 0.733 0.1790 0.849 0.642
#> CV:hclust 4 0.474 0.671 0.808 0.1098 0.835 0.646
#> MAD:hclust 4 0.492 0.631 0.780 0.1620 0.713 0.498
#> ATC:hclust 4 0.613 0.672 0.841 0.0865 0.899 0.761
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.704 0.569 0.760 0.0689 0.880 0.612
#> CV:NMF 5 0.735 0.773 0.868 0.0701 0.847 0.524
#> MAD:NMF 5 0.732 0.705 0.818 0.0614 0.889 0.632
#> ATC:NMF 5 0.691 0.651 0.812 0.0788 0.929 0.769
#> SD:skmeans 5 0.761 0.745 0.841 0.0721 0.907 0.672
#> CV:skmeans 5 0.700 0.639 0.782 0.0716 0.858 0.554
#> MAD:skmeans 5 0.783 0.775 0.884 0.0681 0.868 0.571
#> ATC:skmeans 5 0.876 0.855 0.924 0.0529 0.916 0.716
#> SD:mclust 5 0.687 0.766 0.840 0.0706 0.906 0.714
#> CV:mclust 5 0.503 0.527 0.775 0.0528 0.879 0.734
#> MAD:mclust 5 0.637 0.625 0.801 0.0706 0.930 0.785
#> ATC:mclust 5 0.744 0.760 0.850 0.0668 0.893 0.672
#> SD:kmeans 5 0.667 0.536 0.697 0.0738 0.924 0.761
#> CV:kmeans 5 0.643 0.607 0.730 0.0760 0.881 0.641
#> MAD:kmeans 5 0.670 0.603 0.733 0.0735 0.911 0.706
#> ATC:kmeans 5 0.652 0.587 0.749 0.0655 0.902 0.659
#> SD:pam 5 0.848 0.827 0.916 0.1350 0.831 0.542
#> CV:pam 5 0.562 0.658 0.766 0.0990 0.799 0.431
#> MAD:pam 5 0.722 0.761 0.883 0.0886 0.865 0.579
#> ATC:pam 5 0.942 0.906 0.961 0.0958 0.913 0.707
#> SD:hclust 5 0.557 0.567 0.754 0.0739 0.900 0.690
#> CV:hclust 5 0.496 0.677 0.790 0.1091 0.925 0.785
#> MAD:hclust 5 0.630 0.555 0.751 0.0690 0.955 0.848
#> ATC:hclust 5 0.579 0.616 0.779 0.1422 0.863 0.642
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.792 0.738 0.872 0.0515 0.857 0.477
#> CV:NMF 6 0.834 0.804 0.904 0.0603 0.887 0.550
#> MAD:NMF 6 0.777 0.698 0.857 0.0544 0.860 0.481
#> ATC:NMF 6 0.662 0.574 0.743 0.0576 0.823 0.419
#> SD:skmeans 6 0.826 0.726 0.870 0.0488 0.926 0.675
#> CV:skmeans 6 0.768 0.667 0.825 0.0500 0.919 0.646
#> MAD:skmeans 6 0.814 0.707 0.862 0.0424 0.934 0.707
#> ATC:skmeans 6 0.882 0.820 0.902 0.0366 0.969 0.873
#> SD:mclust 6 0.800 0.742 0.846 0.0570 0.954 0.830
#> CV:mclust 6 0.585 0.489 0.722 0.0826 0.760 0.433
#> MAD:mclust 6 0.773 0.715 0.823 0.0700 0.877 0.589
#> ATC:mclust 6 0.788 0.785 0.873 0.0601 0.917 0.692
#> SD:kmeans 6 0.704 0.677 0.784 0.0492 0.852 0.506
#> CV:kmeans 6 0.696 0.608 0.755 0.0569 0.891 0.602
#> MAD:kmeans 6 0.692 0.639 0.753 0.0482 0.887 0.569
#> ATC:kmeans 6 0.697 0.617 0.776 0.0480 0.958 0.813
#> SD:pam 6 0.754 0.669 0.808 0.0625 0.838 0.430
#> CV:pam 6 0.574 0.417 0.662 0.0429 0.907 0.618
#> MAD:pam 6 0.707 0.663 0.802 0.0590 0.843 0.435
#> ATC:pam 6 0.826 0.645 0.810 0.0330 0.942 0.752
#> SD:hclust 6 0.645 0.592 0.750 0.0513 0.941 0.781
#> CV:hclust 6 0.509 0.689 0.793 0.0631 0.974 0.912
#> MAD:hclust 6 0.659 0.569 0.749 0.0382 0.955 0.829
#> ATC:hclust 6 0.670 0.542 0.718 0.0542 0.963 0.872
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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.505 0.755 0.889 0.3885 0.596 0.596
#> 3 3 0.400 0.673 0.807 0.4854 0.722 0.566
#> 4 4 0.486 0.541 0.733 0.1790 0.849 0.642
#> 5 5 0.557 0.567 0.754 0.0739 0.900 0.690
#> 6 6 0.645 0.592 0.750 0.0513 0.941 0.781
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
#> SRR816969 1 0.0000 0.885 1.000 0.000
#> SRR1335605 2 0.9393 0.591 0.356 0.644
#> SRR1432014 1 0.9209 0.470 0.664 0.336
#> SRR1499215 1 0.8267 0.622 0.740 0.260
#> SRR1460409 1 0.0000 0.885 1.000 0.000
#> SRR1086441 1 0.0000 0.885 1.000 0.000
#> SRR1097344 2 0.9661 0.527 0.392 0.608
#> SRR1081789 2 0.5842 0.756 0.140 0.860
#> SRR1453005 2 0.0376 0.784 0.004 0.996
#> SRR1366985 1 0.0000 0.885 1.000 0.000
#> SRR815280 1 0.0000 0.885 1.000 0.000
#> SRR1348531 1 0.0672 0.883 0.992 0.008
#> SRR815845 1 0.9896 0.114 0.560 0.440
#> SRR1471178 1 0.0000 0.885 1.000 0.000
#> SRR1080696 1 0.1843 0.875 0.972 0.028
#> SRR1078684 1 0.8144 0.638 0.748 0.252
#> SRR1317751 1 0.0000 0.885 1.000 0.000
#> SRR1435667 1 0.9209 0.470 0.664 0.336
#> SRR1097905 1 0.1633 0.878 0.976 0.024
#> SRR1456548 1 0.1184 0.881 0.984 0.016
#> SRR1075126 1 0.0376 0.884 0.996 0.004
#> SRR813108 2 0.1633 0.782 0.024 0.976
#> SRR1479062 1 0.3584 0.848 0.932 0.068
#> SRR1408703 1 0.2043 0.873 0.968 0.032
#> SRR1332360 1 0.0000 0.885 1.000 0.000
#> SRR1098686 1 0.0672 0.883 0.992 0.008
#> SRR1434228 1 0.0000 0.885 1.000 0.000
#> SRR1467149 1 0.0938 0.883 0.988 0.012
#> SRR1399113 2 0.0000 0.783 0.000 1.000
#> SRR1476507 2 0.9552 0.560 0.376 0.624
#> SRR1092468 1 0.2948 0.859 0.948 0.052
#> SRR1441804 1 0.0672 0.883 0.992 0.008
#> SRR1326100 2 0.1843 0.782 0.028 0.972
#> SRR1398815 1 0.0000 0.885 1.000 0.000
#> SRR1436021 2 0.9775 0.470 0.412 0.588
#> SRR1480083 2 0.0000 0.783 0.000 1.000
#> SRR1472863 1 0.0938 0.882 0.988 0.012
#> SRR815542 1 0.0000 0.885 1.000 0.000
#> SRR1400100 1 0.9635 0.313 0.612 0.388
#> SRR1312002 1 0.2236 0.870 0.964 0.036
#> SRR1470253 1 0.0938 0.882 0.988 0.012
#> SRR1414332 1 0.0000 0.885 1.000 0.000
#> SRR1069209 1 0.0000 0.885 1.000 0.000
#> SRR661052 1 0.0938 0.882 0.988 0.012
#> SRR1308860 1 0.0000 0.885 1.000 0.000
#> SRR1421159 2 0.9427 0.592 0.360 0.640
#> SRR1340943 1 0.9580 0.307 0.620 0.380
#> SRR1078855 1 0.0000 0.885 1.000 0.000
#> SRR1459465 2 0.0000 0.783 0.000 1.000
#> SRR816818 2 0.0000 0.783 0.000 1.000
#> SRR1478679 1 0.8267 0.622 0.740 0.260
#> SRR1350979 1 0.8499 0.591 0.724 0.276
#> SRR1458198 1 0.0000 0.885 1.000 0.000
#> SRR1386910 2 0.9393 0.591 0.356 0.644
#> SRR1465375 2 0.9552 0.560 0.376 0.624
#> SRR1323699 1 0.8267 0.622 0.740 0.260
#> SRR1431139 1 0.8144 0.638 0.748 0.252
#> SRR1373964 1 0.9286 0.451 0.656 0.344
#> SRR1455413 1 0.1843 0.875 0.972 0.028
#> SRR1437163 1 0.0938 0.882 0.988 0.012
#> SRR1347343 1 0.9209 0.470 0.664 0.336
#> SRR1465480 2 0.0000 0.783 0.000 1.000
#> SRR1489631 1 0.1184 0.881 0.984 0.016
#> SRR1086514 2 0.9286 0.617 0.344 0.656
#> SRR1430928 1 0.0000 0.885 1.000 0.000
#> SRR1310939 1 0.5842 0.779 0.860 0.140
#> SRR1344294 2 0.0000 0.783 0.000 1.000
#> SRR1099402 1 0.0000 0.885 1.000 0.000
#> SRR1468118 1 0.0000 0.885 1.000 0.000
#> SRR1486348 1 0.0000 0.885 1.000 0.000
#> SRR1488770 2 0.0000 0.783 0.000 1.000
#> SRR1083732 1 0.0000 0.885 1.000 0.000
#> SRR1456611 2 0.0000 0.783 0.000 1.000
#> SRR1080318 1 0.0000 0.885 1.000 0.000
#> SRR1500089 1 0.0000 0.885 1.000 0.000
#> SRR1441178 1 0.0000 0.885 1.000 0.000
#> SRR1381396 1 0.0000 0.885 1.000 0.000
#> SRR1096081 1 0.0000 0.885 1.000 0.000
#> SRR1349809 2 0.9209 0.623 0.336 0.664
#> SRR1324314 1 0.4815 0.812 0.896 0.104
#> SRR1092444 1 0.0000 0.885 1.000 0.000
#> SRR1382553 1 0.7883 0.660 0.764 0.236
#> SRR1075530 2 0.8861 0.667 0.304 0.696
#> SRR1442612 1 0.9209 0.470 0.664 0.336
#> SRR1360056 1 0.1414 0.879 0.980 0.020
#> SRR1078164 1 0.0000 0.885 1.000 0.000
#> SRR1434545 1 0.9580 0.307 0.620 0.380
#> SRR1398251 1 0.0000 0.885 1.000 0.000
#> SRR1375866 1 0.0000 0.885 1.000 0.000
#> SRR1091645 2 0.9661 0.527 0.392 0.608
#> SRR1416636 1 0.1843 0.875 0.972 0.028
#> SRR1105441 1 0.9209 0.469 0.664 0.336
#> SRR1082496 2 0.0000 0.783 0.000 1.000
#> SRR1315353 2 0.1843 0.782 0.028 0.972
#> SRR1093697 2 0.0000 0.783 0.000 1.000
#> SRR1077429 1 0.1633 0.877 0.976 0.024
#> SRR1076120 1 0.0000 0.885 1.000 0.000
#> SRR1074410 1 0.0000 0.885 1.000 0.000
#> SRR1340345 2 0.8861 0.667 0.304 0.696
#> SRR1069514 1 0.9635 0.326 0.612 0.388
#> SRR1092636 1 0.1633 0.877 0.976 0.024
#> SRR1365013 2 0.5842 0.756 0.140 0.860
#> SRR1073069 1 0.0000 0.885 1.000 0.000
#> SRR1443137 1 0.0000 0.885 1.000 0.000
#> SRR1437143 2 0.0000 0.783 0.000 1.000
#> SRR1091990 1 0.0000 0.885 1.000 0.000
#> SRR820234 2 0.0376 0.784 0.004 0.996
#> SRR1338079 1 0.0938 0.882 0.988 0.012
#> SRR1390094 1 0.9754 0.246 0.592 0.408
#> SRR1340721 2 0.9209 0.623 0.336 0.664
#> SRR1335964 1 0.7453 0.692 0.788 0.212
#> SRR1086869 1 0.0000 0.885 1.000 0.000
#> SRR1453434 1 0.4022 0.837 0.920 0.080
#> SRR1402261 1 0.9580 0.307 0.620 0.380
#> SRR657809 2 0.8955 0.659 0.312 0.688
#> SRR1093075 1 0.0000 0.885 1.000 0.000
#> SRR1433329 1 0.0000 0.885 1.000 0.000
#> SRR1353418 1 0.0000 0.885 1.000 0.000
#> SRR1092913 2 0.8861 0.667 0.304 0.696
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1335605 3 0.7158 0.437 0.032 0.372 0.596
#> SRR1432014 3 0.6375 0.629 0.244 0.036 0.720
#> SRR1499215 3 0.6931 0.513 0.328 0.032 0.640
#> SRR1460409 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1086441 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1097344 3 0.4121 0.466 0.000 0.168 0.832
#> SRR1081789 2 0.6661 0.275 0.012 0.588 0.400
#> SRR1453005 2 0.2625 0.866 0.000 0.916 0.084
#> SRR1366985 1 0.0424 0.830 0.992 0.000 0.008
#> SRR815280 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1348531 1 0.5098 0.700 0.752 0.000 0.248
#> SRR815845 3 0.8405 0.591 0.264 0.132 0.604
#> SRR1471178 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1080696 1 0.5058 0.701 0.756 0.000 0.244
#> SRR1078684 3 0.7462 0.466 0.352 0.048 0.600
#> SRR1317751 1 0.4931 0.714 0.768 0.000 0.232
#> SRR1435667 3 0.6375 0.629 0.244 0.036 0.720
#> SRR1097905 1 0.5431 0.628 0.716 0.000 0.284
#> SRR1456548 1 0.5058 0.689 0.756 0.000 0.244
#> SRR1075126 1 0.5397 0.623 0.720 0.000 0.280
#> SRR813108 2 0.3116 0.851 0.000 0.892 0.108
#> SRR1479062 1 0.5659 0.683 0.740 0.012 0.248
#> SRR1408703 1 0.5098 0.694 0.752 0.000 0.248
#> SRR1332360 1 0.0424 0.830 0.992 0.000 0.008
#> SRR1098686 1 0.1643 0.817 0.956 0.000 0.044
#> SRR1434228 1 0.0424 0.830 0.992 0.000 0.008
#> SRR1467149 1 0.5785 0.585 0.668 0.000 0.332
#> SRR1399113 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1476507 3 0.5938 0.494 0.020 0.248 0.732
#> SRR1092468 1 0.6126 0.523 0.644 0.004 0.352
#> SRR1441804 1 0.5098 0.700 0.752 0.000 0.248
#> SRR1326100 2 0.4121 0.795 0.000 0.832 0.168
#> SRR1398815 1 0.0424 0.830 0.992 0.000 0.008
#> SRR1436021 3 0.6276 0.526 0.040 0.224 0.736
#> SRR1480083 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1472863 1 0.4931 0.698 0.768 0.000 0.232
#> SRR815542 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1400100 3 0.8089 0.547 0.308 0.092 0.600
#> SRR1312002 1 0.4235 0.768 0.824 0.000 0.176
#> SRR1470253 1 0.3816 0.782 0.852 0.000 0.148
#> SRR1414332 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1069209 1 0.0424 0.830 0.992 0.000 0.008
#> SRR661052 1 0.4931 0.698 0.768 0.000 0.232
#> SRR1308860 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1421159 3 0.5797 0.441 0.008 0.280 0.712
#> SRR1340943 3 0.4796 0.583 0.220 0.000 0.780
#> SRR1078855 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.901 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1478679 3 0.6931 0.513 0.328 0.032 0.640
#> SRR1350979 3 0.6264 0.407 0.380 0.004 0.616
#> SRR1458198 1 0.6008 0.500 0.628 0.000 0.372
#> SRR1386910 3 0.7158 0.437 0.032 0.372 0.596
#> SRR1465375 3 0.5938 0.494 0.020 0.248 0.732
#> SRR1323699 3 0.6931 0.513 0.328 0.032 0.640
#> SRR1431139 3 0.7462 0.466 0.352 0.048 0.600
#> SRR1373964 3 0.6443 0.634 0.240 0.040 0.720
#> SRR1455413 1 0.5016 0.708 0.760 0.000 0.240
#> SRR1437163 1 0.4931 0.698 0.768 0.000 0.232
#> SRR1347343 3 0.6375 0.629 0.244 0.036 0.720
#> SRR1465480 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1489631 1 0.5058 0.689 0.756 0.000 0.244
#> SRR1086514 3 0.5560 0.403 0.000 0.300 0.700
#> SRR1430928 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1310939 1 0.6664 0.143 0.528 0.008 0.464
#> SRR1344294 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1468118 1 0.4654 0.737 0.792 0.000 0.208
#> SRR1486348 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1083732 1 0.0237 0.831 0.996 0.000 0.004
#> SRR1456611 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1080318 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1500089 1 0.6008 0.500 0.628 0.000 0.372
#> SRR1441178 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1381396 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1096081 1 0.4931 0.714 0.768 0.000 0.232
#> SRR1349809 3 0.7263 0.389 0.032 0.400 0.568
#> SRR1324314 1 0.6095 0.328 0.608 0.000 0.392
#> SRR1092444 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1382553 3 0.7123 0.438 0.364 0.032 0.604
#> SRR1075530 3 0.6026 0.335 0.000 0.376 0.624
#> SRR1442612 3 0.6375 0.629 0.244 0.036 0.720
#> SRR1360056 1 0.3551 0.789 0.868 0.000 0.132
#> SRR1078164 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1434545 3 0.4796 0.583 0.220 0.000 0.780
#> SRR1398251 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1375866 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1091645 3 0.4121 0.466 0.000 0.168 0.832
#> SRR1416636 1 0.5058 0.701 0.756 0.000 0.244
#> SRR1105441 3 0.7599 0.618 0.260 0.084 0.656
#> SRR1082496 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1315353 2 0.4062 0.797 0.000 0.836 0.164
#> SRR1093697 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1077429 1 0.4931 0.710 0.768 0.000 0.232
#> SRR1076120 1 0.6008 0.500 0.628 0.000 0.372
#> SRR1074410 1 0.0237 0.830 0.996 0.000 0.004
#> SRR1340345 3 0.6026 0.335 0.000 0.376 0.624
#> SRR1069514 3 0.7339 0.656 0.224 0.088 0.688
#> SRR1092636 1 0.4931 0.710 0.768 0.000 0.232
#> SRR1365013 2 0.6661 0.275 0.012 0.588 0.400
#> SRR1073069 1 0.0424 0.830 0.992 0.000 0.008
#> SRR1443137 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.901 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.831 1.000 0.000 0.000
#> SRR820234 2 0.2537 0.867 0.000 0.920 0.080
#> SRR1338079 1 0.4931 0.698 0.768 0.000 0.232
#> SRR1390094 3 0.7133 0.662 0.192 0.096 0.712
#> SRR1340721 3 0.7263 0.389 0.032 0.400 0.568
#> SRR1335964 3 0.6280 0.125 0.460 0.000 0.540
#> SRR1086869 1 0.4931 0.714 0.768 0.000 0.232
#> SRR1453434 1 0.6154 0.339 0.592 0.000 0.408
#> SRR1402261 3 0.4796 0.583 0.220 0.000 0.780
#> SRR657809 3 0.6548 0.365 0.012 0.372 0.616
#> SRR1093075 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.831 1.000 0.000 0.000
#> SRR1353418 1 0.3482 0.784 0.872 0.000 0.128
#> SRR1092913 3 0.5948 0.350 0.000 0.360 0.640
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.1118 0.7835 0.964 0.000 0.036 0.000
#> SRR1335605 4 0.6863 0.4610 0.000 0.348 0.116 0.536
#> SRR1432014 4 0.6066 0.3120 0.012 0.024 0.432 0.532
#> SRR1499215 4 0.7080 0.1308 0.064 0.024 0.448 0.464
#> SRR1460409 1 0.0592 0.7784 0.984 0.000 0.016 0.000
#> SRR1086441 1 0.1022 0.7843 0.968 0.000 0.032 0.000
#> SRR1097344 4 0.5200 0.3868 0.000 0.072 0.184 0.744
#> SRR1081789 2 0.6376 0.1276 0.000 0.504 0.064 0.432
#> SRR1453005 2 0.3708 0.7975 0.000 0.832 0.020 0.148
#> SRR1366985 1 0.1256 0.7750 0.964 0.000 0.028 0.008
#> SRR815280 1 0.0469 0.7759 0.988 0.000 0.012 0.000
#> SRR1348531 1 0.6936 0.3668 0.588 0.000 0.224 0.188
#> SRR815845 3 0.6963 -0.2628 0.000 0.112 0.464 0.424
#> SRR1471178 1 0.1022 0.7843 0.968 0.000 0.032 0.000
#> SRR1080696 3 0.5772 0.6703 0.260 0.000 0.672 0.068
#> SRR1078684 3 0.7292 -0.1172 0.068 0.032 0.468 0.432
#> SRR1317751 3 0.4420 0.5977 0.240 0.000 0.748 0.012
#> SRR1435667 4 0.6066 0.3120 0.012 0.024 0.432 0.532
#> SRR1097905 1 0.6442 0.4802 0.632 0.000 0.124 0.244
#> SRR1456548 1 0.5998 0.5611 0.684 0.000 0.116 0.200
#> SRR1075126 1 0.6428 0.4631 0.624 0.000 0.112 0.264
#> SRR813108 2 0.4100 0.7848 0.000 0.816 0.036 0.148
#> SRR1479062 3 0.6140 0.6606 0.252 0.000 0.652 0.096
#> SRR1408703 3 0.5716 0.6673 0.252 0.000 0.680 0.068
#> SRR1332360 1 0.1256 0.7750 0.964 0.000 0.028 0.008
#> SRR1098686 1 0.2589 0.7554 0.912 0.000 0.044 0.044
#> SRR1434228 1 0.1256 0.7750 0.964 0.000 0.028 0.008
#> SRR1467149 1 0.7556 0.1361 0.488 0.000 0.248 0.264
#> SRR1399113 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.4199 0.5198 0.000 0.164 0.032 0.804
#> SRR1092468 1 0.7483 0.1660 0.496 0.000 0.216 0.288
#> SRR1441804 1 0.6936 0.3668 0.588 0.000 0.224 0.188
#> SRR1326100 2 0.4959 0.7217 0.000 0.752 0.052 0.196
#> SRR1398815 1 0.1635 0.7806 0.948 0.000 0.044 0.008
#> SRR1436021 4 0.4514 0.5504 0.000 0.136 0.064 0.800
#> SRR1480083 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.5820 0.5783 0.700 0.000 0.108 0.192
#> SRR815542 1 0.0336 0.7843 0.992 0.000 0.008 0.000
#> SRR1400100 3 0.6507 -0.1760 0.000 0.076 0.520 0.404
#> SRR1312002 3 0.6176 0.4432 0.424 0.000 0.524 0.052
#> SRR1470253 3 0.5771 0.3974 0.460 0.000 0.512 0.028
#> SRR1414332 1 0.1118 0.7835 0.964 0.000 0.036 0.000
#> SRR1069209 1 0.1545 0.7731 0.952 0.000 0.040 0.008
#> SRR661052 1 0.5820 0.5783 0.700 0.000 0.108 0.192
#> SRR1308860 1 0.1118 0.7837 0.964 0.000 0.036 0.000
#> SRR1421159 4 0.4500 0.4916 0.000 0.192 0.032 0.776
#> SRR1340943 4 0.5728 0.3279 0.188 0.000 0.104 0.708
#> SRR1078855 1 0.0000 0.7826 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1478679 4 0.7080 0.1308 0.064 0.024 0.448 0.464
#> SRR1350979 3 0.6187 -0.0133 0.052 0.000 0.516 0.432
#> SRR1458198 1 0.7511 0.1115 0.468 0.000 0.196 0.336
#> SRR1386910 4 0.6863 0.4610 0.000 0.348 0.116 0.536
#> SRR1465375 4 0.4104 0.5211 0.000 0.164 0.028 0.808
#> SRR1323699 4 0.7080 0.1308 0.064 0.024 0.448 0.464
#> SRR1431139 3 0.7347 -0.1166 0.072 0.032 0.464 0.432
#> SRR1373964 4 0.6147 0.3190 0.012 0.028 0.428 0.532
#> SRR1455413 3 0.6396 0.5888 0.360 0.000 0.564 0.076
#> SRR1437163 1 0.5820 0.5783 0.700 0.000 0.108 0.192
#> SRR1347343 4 0.6066 0.3120 0.012 0.024 0.432 0.532
#> SRR1465480 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.5998 0.5611 0.684 0.000 0.116 0.200
#> SRR1086514 4 0.4692 0.4683 0.000 0.212 0.032 0.756
#> SRR1430928 1 0.1022 0.7843 0.968 0.000 0.032 0.000
#> SRR1310939 3 0.7102 0.3994 0.164 0.000 0.548 0.288
#> SRR1344294 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0188 0.7842 0.996 0.000 0.004 0.000
#> SRR1468118 3 0.5062 0.6329 0.284 0.000 0.692 0.024
#> SRR1486348 1 0.1118 0.7835 0.964 0.000 0.036 0.000
#> SRR1488770 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.1576 0.7779 0.948 0.000 0.048 0.004
#> SRR1456611 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0592 0.7827 0.984 0.000 0.016 0.000
#> SRR1500089 1 0.7511 0.1115 0.468 0.000 0.196 0.336
#> SRR1441178 1 0.0469 0.7759 0.988 0.000 0.012 0.000
#> SRR1381396 1 0.1209 0.7856 0.964 0.000 0.032 0.004
#> SRR1096081 3 0.4420 0.5977 0.240 0.000 0.748 0.012
#> SRR1349809 4 0.6804 0.4201 0.000 0.376 0.104 0.520
#> SRR1324314 1 0.7392 0.0554 0.472 0.000 0.172 0.356
#> SRR1092444 1 0.0592 0.7827 0.984 0.000 0.016 0.000
#> SRR1382553 4 0.7565 0.0474 0.104 0.024 0.436 0.436
#> SRR1075530 4 0.6350 0.3777 0.000 0.296 0.092 0.612
#> SRR1442612 4 0.6066 0.3120 0.012 0.024 0.432 0.532
#> SRR1360056 3 0.5465 0.5207 0.392 0.000 0.588 0.020
#> SRR1078164 1 0.0469 0.7759 0.988 0.000 0.012 0.000
#> SRR1434545 4 0.5728 0.3279 0.188 0.000 0.104 0.708
#> SRR1398251 1 0.0000 0.7826 1.000 0.000 0.000 0.000
#> SRR1375866 1 0.0592 0.7827 0.984 0.000 0.016 0.000
#> SRR1091645 4 0.5200 0.3868 0.000 0.072 0.184 0.744
#> SRR1416636 3 0.5772 0.6703 0.260 0.000 0.672 0.068
#> SRR1105441 4 0.7122 0.2755 0.024 0.068 0.424 0.484
#> SRR1082496 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.4904 0.7198 0.000 0.744 0.040 0.216
#> SRR1093697 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.5810 0.6720 0.276 0.000 0.660 0.064
#> SRR1076120 1 0.7511 0.1115 0.468 0.000 0.196 0.336
#> SRR1074410 1 0.1209 0.7856 0.964 0.000 0.032 0.004
#> SRR1340345 4 0.6350 0.3777 0.000 0.296 0.092 0.612
#> SRR1069514 4 0.6641 0.3665 0.012 0.060 0.384 0.544
#> SRR1092636 3 0.5810 0.6720 0.276 0.000 0.660 0.064
#> SRR1365013 2 0.6376 0.1276 0.000 0.504 0.064 0.432
#> SRR1073069 1 0.1256 0.7750 0.964 0.000 0.028 0.008
#> SRR1443137 1 0.0000 0.7826 1.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.8702 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.7826 1.000 0.000 0.000 0.000
#> SRR820234 2 0.3554 0.8032 0.000 0.844 0.020 0.136
#> SRR1338079 1 0.5820 0.5783 0.700 0.000 0.108 0.192
#> SRR1390094 4 0.7205 0.4428 0.060 0.068 0.256 0.616
#> SRR1340721 4 0.6804 0.4201 0.000 0.376 0.104 0.520
#> SRR1335964 3 0.6677 0.2422 0.100 0.000 0.552 0.348
#> SRR1086869 3 0.4420 0.5977 0.240 0.000 0.748 0.012
#> SRR1453434 1 0.6400 0.3018 0.524 0.000 0.068 0.408
#> SRR1402261 4 0.5728 0.3279 0.188 0.000 0.104 0.708
#> SRR657809 4 0.5321 0.4370 0.000 0.296 0.032 0.672
#> SRR1093075 1 0.0000 0.7826 1.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.7826 1.000 0.000 0.000 0.000
#> SRR1353418 3 0.5269 0.5660 0.364 0.000 0.620 0.016
#> SRR1092913 4 0.6182 0.3829 0.000 0.276 0.088 0.636
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0963 0.7968 0.964 0.000 0.036 0.000 0.000
#> SRR1335605 3 0.6128 0.2057 0.000 0.320 0.572 0.080 0.028
#> SRR1432014 3 0.0727 0.5737 0.012 0.000 0.980 0.004 0.004
#> SRR1499215 3 0.2554 0.5284 0.036 0.000 0.892 0.000 0.072
#> SRR1460409 1 0.0671 0.7920 0.980 0.000 0.016 0.000 0.004
#> SRR1086441 1 0.0880 0.7973 0.968 0.000 0.032 0.000 0.000
#> SRR1097344 4 0.1990 0.5835 0.000 0.004 0.008 0.920 0.068
#> SRR1081789 3 0.6219 -0.1669 0.000 0.424 0.436 0.140 0.000
#> SRR1453005 2 0.4490 0.6942 0.000 0.724 0.052 0.224 0.000
#> SRR1366985 1 0.1124 0.7892 0.960 0.000 0.036 0.000 0.004
#> SRR815280 1 0.0404 0.7894 0.988 0.000 0.012 0.000 0.000
#> SRR1348531 1 0.6899 0.3130 0.508 0.000 0.260 0.024 0.208
#> SRR815845 3 0.5261 0.4237 0.000 0.100 0.728 0.032 0.140
#> SRR1471178 1 0.0880 0.7973 0.968 0.000 0.032 0.000 0.000
#> SRR1080696 5 0.5884 0.6737 0.100 0.000 0.420 0.000 0.480
#> SRR1078684 3 0.3452 0.4963 0.036 0.008 0.848 0.004 0.104
#> SRR1317751 5 0.2763 0.5961 0.000 0.000 0.148 0.004 0.848
#> SRR1435667 3 0.0727 0.5737 0.012 0.000 0.980 0.004 0.004
#> SRR1097905 1 0.5627 0.5344 0.624 0.000 0.296 0.024 0.056
#> SRR1456548 1 0.5410 0.5913 0.668 0.000 0.252 0.028 0.052
#> SRR1075126 1 0.5804 0.5093 0.604 0.000 0.304 0.020 0.072
#> SRR813108 2 0.4723 0.6965 0.000 0.736 0.132 0.132 0.000
#> SRR1479062 5 0.6740 0.6411 0.132 0.000 0.412 0.024 0.432
#> SRR1408703 5 0.5847 0.6701 0.096 0.000 0.424 0.000 0.480
#> SRR1332360 1 0.1124 0.7892 0.960 0.000 0.036 0.000 0.004
#> SRR1098686 1 0.2270 0.7710 0.904 0.000 0.076 0.000 0.020
#> SRR1434228 1 0.1124 0.7892 0.960 0.000 0.036 0.000 0.004
#> SRR1467149 1 0.7427 0.0387 0.392 0.000 0.332 0.036 0.240
#> SRR1399113 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.5007 0.6605 0.000 0.060 0.260 0.676 0.004
#> SRR1092468 1 0.7201 0.1149 0.408 0.000 0.380 0.036 0.176
#> SRR1441804 1 0.6899 0.3130 0.508 0.000 0.260 0.024 0.208
#> SRR1326100 2 0.5322 0.6029 0.000 0.672 0.188 0.140 0.000
#> SRR1398815 1 0.1270 0.7939 0.948 0.000 0.052 0.000 0.000
#> SRR1436021 4 0.5875 0.5528 0.000 0.088 0.396 0.512 0.004
#> SRR1480083 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.4878 0.6189 0.700 0.000 0.248 0.024 0.028
#> SRR815542 1 0.0451 0.7968 0.988 0.000 0.004 0.000 0.008
#> SRR1400100 3 0.4581 0.4209 0.000 0.076 0.764 0.012 0.148
#> SRR1312002 3 0.6820 -0.4051 0.344 0.000 0.344 0.000 0.312
#> SRR1470253 1 0.6787 -0.4470 0.380 0.000 0.288 0.000 0.332
#> SRR1414332 1 0.0963 0.7968 0.964 0.000 0.036 0.000 0.000
#> SRR1069209 1 0.1408 0.7873 0.948 0.000 0.044 0.000 0.008
#> SRR661052 1 0.4878 0.6189 0.700 0.000 0.248 0.024 0.028
#> SRR1308860 1 0.1168 0.7964 0.960 0.000 0.032 0.000 0.008
#> SRR1421159 4 0.5944 0.6324 0.000 0.116 0.312 0.568 0.004
#> SRR1340943 4 0.6785 0.5427 0.112 0.000 0.144 0.612 0.132
#> SRR1078855 1 0.0000 0.7949 1.000 0.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.2554 0.5284 0.036 0.000 0.892 0.000 0.072
#> SRR1350979 3 0.3755 0.4088 0.032 0.000 0.816 0.012 0.140
#> SRR1458198 1 0.8462 -0.1039 0.328 0.000 0.176 0.232 0.264
#> SRR1386910 3 0.6128 0.2057 0.000 0.320 0.572 0.080 0.028
#> SRR1465375 4 0.5030 0.6590 0.000 0.060 0.264 0.672 0.004
#> SRR1323699 3 0.2554 0.5284 0.036 0.000 0.892 0.000 0.072
#> SRR1431139 3 0.3529 0.4958 0.040 0.008 0.844 0.004 0.104
#> SRR1373964 3 0.0727 0.5746 0.012 0.004 0.980 0.004 0.000
#> SRR1455413 3 0.6863 -0.4856 0.260 0.000 0.404 0.004 0.332
#> SRR1437163 1 0.4878 0.6189 0.700 0.000 0.248 0.024 0.028
#> SRR1347343 3 0.0727 0.5737 0.012 0.000 0.980 0.004 0.004
#> SRR1465480 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.5410 0.5913 0.668 0.000 0.252 0.028 0.052
#> SRR1086514 4 0.6053 0.6404 0.000 0.136 0.292 0.568 0.004
#> SRR1430928 1 0.0880 0.7973 0.968 0.000 0.032 0.000 0.000
#> SRR1310939 3 0.5896 0.0208 0.116 0.000 0.640 0.020 0.224
#> SRR1344294 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0162 0.7964 0.996 0.000 0.004 0.000 0.000
#> SRR1468118 5 0.3875 0.6334 0.048 0.000 0.160 0.000 0.792
#> SRR1486348 1 0.0963 0.7968 0.964 0.000 0.036 0.000 0.000
#> SRR1488770 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.1661 0.7902 0.940 0.000 0.036 0.000 0.024
#> SRR1456611 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0671 0.7954 0.980 0.000 0.016 0.000 0.004
#> SRR1500089 1 0.8462 -0.1039 0.328 0.000 0.176 0.232 0.264
#> SRR1441178 1 0.0404 0.7894 0.988 0.000 0.012 0.000 0.000
#> SRR1381396 1 0.0963 0.7983 0.964 0.000 0.036 0.000 0.000
#> SRR1096081 5 0.2763 0.5961 0.000 0.000 0.148 0.004 0.848
#> SRR1349809 3 0.6218 0.1676 0.000 0.348 0.544 0.080 0.028
#> SRR1324314 3 0.5325 -0.1515 0.460 0.000 0.500 0.012 0.028
#> SRR1092444 1 0.0671 0.7954 0.980 0.000 0.016 0.000 0.004
#> SRR1382553 3 0.3242 0.4909 0.076 0.000 0.852 0.000 0.072
#> SRR1075530 4 0.5733 0.5718 0.000 0.220 0.160 0.620 0.000
#> SRR1442612 3 0.0727 0.5737 0.012 0.000 0.980 0.004 0.004
#> SRR1360056 5 0.6597 0.5716 0.244 0.000 0.296 0.000 0.460
#> SRR1078164 1 0.0404 0.7894 0.988 0.000 0.012 0.000 0.000
#> SRR1434545 4 0.6785 0.5427 0.112 0.000 0.144 0.612 0.132
#> SRR1398251 1 0.0000 0.7949 1.000 0.000 0.000 0.000 0.000
#> SRR1375866 1 0.0510 0.7953 0.984 0.000 0.016 0.000 0.000
#> SRR1091645 4 0.1990 0.5835 0.000 0.004 0.008 0.920 0.068
#> SRR1416636 5 0.5884 0.6737 0.100 0.000 0.420 0.000 0.480
#> SRR1105441 3 0.2992 0.5518 0.000 0.044 0.876 0.008 0.072
#> SRR1082496 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.5504 0.5776 0.000 0.644 0.132 0.224 0.000
#> SRR1093697 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.6055 0.6780 0.120 0.000 0.408 0.000 0.472
#> SRR1076120 1 0.8462 -0.1039 0.328 0.000 0.176 0.232 0.264
#> SRR1074410 1 0.0963 0.7983 0.964 0.000 0.036 0.000 0.000
#> SRR1340345 4 0.5733 0.5718 0.000 0.220 0.160 0.620 0.000
#> SRR1069514 3 0.1978 0.5692 0.012 0.032 0.932 0.024 0.000
#> SRR1092636 5 0.6055 0.6780 0.120 0.000 0.408 0.000 0.472
#> SRR1365013 3 0.6219 -0.1669 0.000 0.424 0.436 0.140 0.000
#> SRR1073069 1 0.1124 0.7892 0.960 0.000 0.036 0.000 0.004
#> SRR1443137 1 0.0000 0.7949 1.000 0.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0000 0.7949 1.000 0.000 0.000 0.000 0.000
#> SRR820234 2 0.4333 0.7096 0.000 0.740 0.048 0.212 0.000
#> SRR1338079 1 0.4878 0.6189 0.700 0.000 0.248 0.024 0.028
#> SRR1390094 3 0.5180 0.4203 0.044 0.000 0.724 0.180 0.052
#> SRR1340721 3 0.6218 0.1676 0.000 0.348 0.544 0.080 0.028
#> SRR1335964 3 0.4704 0.2268 0.064 0.000 0.736 0.008 0.192
#> SRR1086869 5 0.2763 0.5961 0.000 0.000 0.148 0.004 0.848
#> SRR1453434 1 0.7844 0.1184 0.432 0.000 0.132 0.300 0.136
#> SRR1402261 4 0.6785 0.5427 0.112 0.000 0.144 0.612 0.132
#> SRR657809 4 0.6619 0.4604 0.000 0.220 0.360 0.420 0.000
#> SRR1093075 1 0.0000 0.7949 1.000 0.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.7949 1.000 0.000 0.000 0.000 0.000
#> SRR1353418 5 0.6109 0.6683 0.172 0.000 0.272 0.000 0.556
#> SRR1092913 4 0.5384 0.5915 0.000 0.196 0.140 0.664 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1553 0.8249 0.944 0.000 0.004 0.008 0.032 0.012
#> SRR1335605 3 0.6919 0.1996 0.004 0.264 0.500 0.168 0.028 0.036
#> SRR1432014 3 0.0520 0.6112 0.008 0.000 0.984 0.000 0.008 0.000
#> SRR1499215 3 0.2722 0.5679 0.016 0.000 0.876 0.008 0.088 0.012
#> SRR1460409 1 0.0893 0.8202 0.972 0.000 0.004 0.004 0.004 0.016
#> SRR1086441 1 0.1476 0.8258 0.948 0.000 0.004 0.008 0.028 0.012
#> SRR1097344 4 0.3171 0.4726 0.000 0.000 0.000 0.784 0.012 0.204
#> SRR1081789 3 0.6566 -0.1876 0.000 0.364 0.388 0.224 0.012 0.012
#> SRR1453005 2 0.4819 0.6239 0.000 0.668 0.032 0.264 0.004 0.032
#> SRR1366985 1 0.1340 0.8153 0.948 0.000 0.040 0.000 0.008 0.004
#> SRR815280 1 0.0748 0.8178 0.976 0.000 0.004 0.004 0.000 0.016
#> SRR1348531 1 0.6892 0.1951 0.460 0.000 0.180 0.004 0.284 0.072
#> SRR815845 3 0.5634 0.4402 0.000 0.060 0.660 0.120 0.156 0.004
#> SRR1471178 1 0.1476 0.8258 0.948 0.000 0.004 0.008 0.028 0.012
#> SRR1080696 5 0.4813 0.5620 0.036 0.000 0.368 0.004 0.584 0.008
#> SRR1078684 3 0.3825 0.5274 0.016 0.000 0.812 0.028 0.116 0.028
#> SRR1317751 5 0.3217 0.3796 0.000 0.000 0.044 0.036 0.852 0.068
#> SRR1435667 3 0.0520 0.6112 0.008 0.000 0.984 0.000 0.008 0.000
#> SRR1097905 1 0.6686 0.4224 0.548 0.000 0.228 0.016 0.128 0.080
#> SRR1456548 1 0.5943 0.5580 0.636 0.000 0.200 0.020 0.084 0.060
#> SRR1075126 1 0.7090 0.3376 0.516 0.000 0.168 0.016 0.120 0.180
#> SRR813108 2 0.5085 0.6457 0.000 0.700 0.128 0.144 0.012 0.016
#> SRR1479062 5 0.5965 0.5594 0.064 0.000 0.352 0.028 0.532 0.024
#> SRR1408703 5 0.4914 0.5591 0.036 0.000 0.372 0.004 0.576 0.012
#> SRR1332360 1 0.1340 0.8153 0.948 0.000 0.040 0.000 0.008 0.004
#> SRR1098686 1 0.3528 0.7582 0.836 0.000 0.032 0.008 0.088 0.036
#> SRR1434228 1 0.1340 0.8153 0.948 0.000 0.040 0.000 0.008 0.004
#> SRR1467149 5 0.7590 -0.0564 0.316 0.000 0.200 0.004 0.328 0.152
#> SRR1399113 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.5472 0.6990 0.000 0.036 0.192 0.656 0.004 0.112
#> SRR1092468 1 0.8190 -0.2209 0.288 0.000 0.272 0.032 0.232 0.176
#> SRR1441804 1 0.6892 0.1951 0.460 0.000 0.180 0.004 0.284 0.072
#> SRR1326100 2 0.5703 0.5347 0.000 0.624 0.168 0.180 0.012 0.016
#> SRR1398815 1 0.2272 0.8207 0.912 0.000 0.016 0.008 0.040 0.024
#> SRR1436021 4 0.6137 0.5900 0.000 0.060 0.352 0.516 0.012 0.060
#> SRR1480083 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.5510 0.5935 0.668 0.000 0.200 0.016 0.064 0.052
#> SRR815542 1 0.1049 0.8278 0.960 0.000 0.000 0.000 0.032 0.008
#> SRR1400100 3 0.4851 0.4478 0.000 0.040 0.716 0.084 0.160 0.000
#> SRR1312002 5 0.6286 0.4412 0.284 0.000 0.312 0.000 0.396 0.008
#> SRR1470253 5 0.6492 0.4195 0.304 0.000 0.252 0.000 0.420 0.024
#> SRR1414332 1 0.1553 0.8249 0.944 0.000 0.004 0.008 0.032 0.012
#> SRR1069209 1 0.1367 0.8143 0.944 0.000 0.044 0.000 0.012 0.000
#> SRR661052 1 0.5510 0.5935 0.668 0.000 0.200 0.016 0.064 0.052
#> SRR1308860 1 0.1929 0.8187 0.924 0.000 0.004 0.008 0.048 0.016
#> SRR1421159 4 0.6223 0.6827 0.000 0.092 0.256 0.576 0.012 0.064
#> SRR1340943 6 0.2872 0.4404 0.000 0.000 0.012 0.152 0.004 0.832
#> SRR1078855 1 0.0146 0.8267 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1459465 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.2722 0.5679 0.016 0.000 0.876 0.008 0.088 0.012
#> SRR1350979 3 0.4081 0.4195 0.016 0.000 0.768 0.020 0.176 0.020
#> SRR1458198 6 0.6394 0.5208 0.184 0.000 0.012 0.016 0.308 0.480
#> SRR1386910 3 0.6919 0.1996 0.004 0.264 0.500 0.168 0.028 0.036
#> SRR1465375 4 0.5418 0.7032 0.000 0.036 0.196 0.660 0.004 0.104
#> SRR1323699 3 0.2722 0.5679 0.016 0.000 0.876 0.008 0.088 0.012
#> SRR1431139 3 0.3910 0.5267 0.020 0.000 0.808 0.028 0.116 0.028
#> SRR1373964 3 0.0551 0.6119 0.008 0.000 0.984 0.004 0.004 0.000
#> SRR1455413 5 0.6692 0.4602 0.228 0.000 0.344 0.008 0.396 0.024
#> SRR1437163 1 0.5510 0.5935 0.668 0.000 0.200 0.016 0.064 0.052
#> SRR1347343 3 0.0520 0.6112 0.008 0.000 0.984 0.000 0.008 0.000
#> SRR1465480 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.5943 0.5580 0.636 0.000 0.200 0.020 0.084 0.060
#> SRR1086514 4 0.6322 0.6900 0.000 0.112 0.236 0.576 0.012 0.064
#> SRR1430928 1 0.1476 0.8258 0.948 0.000 0.004 0.008 0.028 0.012
#> SRR1310939 3 0.6827 -0.1490 0.040 0.000 0.500 0.040 0.296 0.124
#> SRR1344294 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0520 0.8292 0.984 0.000 0.000 0.000 0.008 0.008
#> SRR1468118 5 0.3067 0.4194 0.016 0.000 0.052 0.032 0.872 0.028
#> SRR1486348 1 0.1553 0.8249 0.944 0.000 0.004 0.008 0.032 0.012
#> SRR1488770 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.2465 0.8009 0.892 0.000 0.004 0.008 0.072 0.024
#> SRR1456611 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.1109 0.8231 0.964 0.000 0.004 0.004 0.012 0.016
#> SRR1500089 6 0.6394 0.5208 0.184 0.000 0.012 0.016 0.308 0.480
#> SRR1441178 1 0.0837 0.8154 0.972 0.000 0.004 0.004 0.000 0.020
#> SRR1381396 1 0.1533 0.8284 0.948 0.000 0.012 0.008 0.016 0.016
#> SRR1096081 5 0.3217 0.3796 0.000 0.000 0.044 0.036 0.852 0.068
#> SRR1349809 3 0.7063 0.1536 0.004 0.292 0.468 0.168 0.028 0.040
#> SRR1324314 3 0.6028 -0.1191 0.424 0.000 0.460 0.016 0.068 0.032
#> SRR1092444 1 0.1015 0.8254 0.968 0.000 0.004 0.004 0.012 0.012
#> SRR1382553 3 0.3442 0.5330 0.056 0.000 0.836 0.008 0.088 0.012
#> SRR1075530 4 0.4213 0.6773 0.000 0.160 0.092 0.744 0.000 0.004
#> SRR1442612 3 0.0520 0.6112 0.008 0.000 0.984 0.000 0.008 0.000
#> SRR1360056 5 0.5569 0.5795 0.180 0.000 0.248 0.000 0.568 0.004
#> SRR1078164 1 0.0837 0.8154 0.972 0.000 0.004 0.004 0.000 0.020
#> SRR1434545 6 0.2872 0.4404 0.000 0.000 0.012 0.152 0.004 0.832
#> SRR1398251 1 0.0146 0.8267 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1375866 1 0.0893 0.8228 0.972 0.000 0.004 0.004 0.004 0.016
#> SRR1091645 4 0.3171 0.4726 0.000 0.000 0.000 0.784 0.012 0.204
#> SRR1416636 5 0.4813 0.5620 0.036 0.000 0.368 0.004 0.584 0.008
#> SRR1105441 3 0.3097 0.5916 0.000 0.012 0.852 0.064 0.072 0.000
#> SRR1082496 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.5812 0.4939 0.000 0.584 0.100 0.276 0.004 0.036
#> SRR1093697 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.5217 0.5841 0.056 0.000 0.348 0.004 0.576 0.016
#> SRR1076120 6 0.6394 0.5208 0.184 0.000 0.012 0.016 0.308 0.480
#> SRR1074410 1 0.1533 0.8284 0.948 0.000 0.012 0.008 0.016 0.016
#> SRR1340345 4 0.4213 0.6773 0.000 0.160 0.092 0.744 0.000 0.004
#> SRR1069514 3 0.1879 0.6056 0.008 0.028 0.932 0.016 0.016 0.000
#> SRR1092636 5 0.5217 0.5841 0.056 0.000 0.348 0.004 0.576 0.016
#> SRR1365013 3 0.6566 -0.1876 0.000 0.364 0.388 0.224 0.012 0.012
#> SRR1073069 1 0.1340 0.8153 0.948 0.000 0.040 0.000 0.008 0.004
#> SRR1443137 1 0.0146 0.8267 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1437143 2 0.0000 0.8752 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.0146 0.8267 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR820234 2 0.4224 0.6624 0.000 0.712 0.032 0.244 0.004 0.008
#> SRR1338079 1 0.5510 0.5935 0.668 0.000 0.200 0.016 0.064 0.052
#> SRR1390094 3 0.5535 0.4363 0.016 0.000 0.668 0.096 0.036 0.184
#> SRR1340721 3 0.7063 0.1536 0.004 0.292 0.468 0.168 0.028 0.040
#> SRR1335964 3 0.5070 0.2134 0.024 0.000 0.668 0.016 0.248 0.044
#> SRR1086869 5 0.3217 0.3796 0.000 0.000 0.044 0.036 0.852 0.068
#> SRR1453434 6 0.5339 0.5247 0.260 0.000 0.004 0.020 0.088 0.628
#> SRR1402261 6 0.2872 0.4404 0.000 0.000 0.012 0.152 0.004 0.832
#> SRR657809 4 0.5701 0.5230 0.000 0.160 0.288 0.544 0.000 0.008
#> SRR1093075 1 0.0146 0.8267 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1433329 1 0.0146 0.8267 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1353418 5 0.4725 0.5957 0.108 0.000 0.204 0.000 0.684 0.004
#> SRR1092913 4 0.4350 0.6840 0.000 0.136 0.072 0.760 0.000 0.032
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 17780 rows and 119 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 1.000 0.985 0.991 0.4340 0.562 0.562
#> 3 3 0.762 0.805 0.901 0.4720 0.729 0.539
#> 4 4 0.659 0.723 0.820 0.1233 0.898 0.722
#> 5 5 0.667 0.536 0.697 0.0738 0.924 0.761
#> 6 6 0.704 0.677 0.784 0.0492 0.852 0.506
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
#> SRR816969 1 0.0000 0.998 1.000 0.000
#> SRR1335605 1 0.0000 0.998 1.000 0.000
#> SRR1432014 1 0.0000 0.998 1.000 0.000
#> SRR1499215 1 0.0000 0.998 1.000 0.000
#> SRR1460409 1 0.0000 0.998 1.000 0.000
#> SRR1086441 1 0.0000 0.998 1.000 0.000
#> SRR1097344 2 0.0000 0.978 0.000 1.000
#> SRR1081789 2 0.0376 0.978 0.004 0.996
#> SRR1453005 2 0.0000 0.978 0.000 1.000
#> SRR1366985 1 0.0000 0.998 1.000 0.000
#> SRR815280 1 0.0000 0.998 1.000 0.000
#> SRR1348531 1 0.0000 0.998 1.000 0.000
#> SRR815845 2 0.1414 0.977 0.020 0.980
#> SRR1471178 1 0.0000 0.998 1.000 0.000
#> SRR1080696 1 0.0000 0.998 1.000 0.000
#> SRR1078684 1 0.0000 0.998 1.000 0.000
#> SRR1317751 1 0.0000 0.998 1.000 0.000
#> SRR1435667 2 0.1414 0.977 0.020 0.980
#> SRR1097905 1 0.0000 0.998 1.000 0.000
#> SRR1456548 1 0.0000 0.998 1.000 0.000
#> SRR1075126 1 0.0000 0.998 1.000 0.000
#> SRR813108 2 0.0000 0.978 0.000 1.000
#> SRR1479062 1 0.0000 0.998 1.000 0.000
#> SRR1408703 1 0.0000 0.998 1.000 0.000
#> SRR1332360 1 0.0000 0.998 1.000 0.000
#> SRR1098686 1 0.0000 0.998 1.000 0.000
#> SRR1434228 1 0.0000 0.998 1.000 0.000
#> SRR1467149 1 0.0000 0.998 1.000 0.000
#> SRR1399113 2 0.0000 0.978 0.000 1.000
#> SRR1476507 2 0.1414 0.977 0.020 0.980
#> SRR1092468 1 0.0000 0.998 1.000 0.000
#> SRR1441804 1 0.0000 0.998 1.000 0.000
#> SRR1326100 2 0.0000 0.978 0.000 1.000
#> SRR1398815 1 0.0000 0.998 1.000 0.000
#> SRR1436021 2 0.1414 0.977 0.020 0.980
#> SRR1480083 2 0.0000 0.978 0.000 1.000
#> SRR1472863 1 0.0000 0.998 1.000 0.000
#> SRR815542 1 0.0000 0.998 1.000 0.000
#> SRR1400100 2 0.1414 0.977 0.020 0.980
#> SRR1312002 1 0.0000 0.998 1.000 0.000
#> SRR1470253 1 0.0000 0.998 1.000 0.000
#> SRR1414332 1 0.0000 0.998 1.000 0.000
#> SRR1069209 1 0.0000 0.998 1.000 0.000
#> SRR661052 1 0.0000 0.998 1.000 0.000
#> SRR1308860 1 0.0000 0.998 1.000 0.000
#> SRR1421159 2 0.1414 0.977 0.020 0.980
#> SRR1340943 1 0.0000 0.998 1.000 0.000
#> SRR1078855 1 0.0000 0.998 1.000 0.000
#> SRR1459465 2 0.0000 0.978 0.000 1.000
#> SRR816818 2 0.0000 0.978 0.000 1.000
#> SRR1478679 2 0.8608 0.626 0.284 0.716
#> SRR1350979 1 0.0000 0.998 1.000 0.000
#> SRR1458198 1 0.0000 0.998 1.000 0.000
#> SRR1386910 2 0.1414 0.977 0.020 0.980
#> SRR1465375 2 0.1414 0.977 0.020 0.980
#> SRR1323699 1 0.0000 0.998 1.000 0.000
#> SRR1431139 1 0.0000 0.998 1.000 0.000
#> SRR1373964 1 0.3274 0.934 0.940 0.060
#> SRR1455413 1 0.0000 0.998 1.000 0.000
#> SRR1437163 1 0.0000 0.998 1.000 0.000
#> SRR1347343 1 0.0000 0.998 1.000 0.000
#> SRR1465480 2 0.0000 0.978 0.000 1.000
#> SRR1489631 1 0.0000 0.998 1.000 0.000
#> SRR1086514 2 0.0000 0.978 0.000 1.000
#> SRR1430928 1 0.0000 0.998 1.000 0.000
#> SRR1310939 1 0.0000 0.998 1.000 0.000
#> SRR1344294 2 0.0000 0.978 0.000 1.000
#> SRR1099402 1 0.0000 0.998 1.000 0.000
#> SRR1468118 1 0.0000 0.998 1.000 0.000
#> SRR1486348 1 0.0000 0.998 1.000 0.000
#> SRR1488770 2 0.0000 0.978 0.000 1.000
#> SRR1083732 1 0.0000 0.998 1.000 0.000
#> SRR1456611 2 0.0000 0.978 0.000 1.000
#> SRR1080318 1 0.0000 0.998 1.000 0.000
#> SRR1500089 1 0.0000 0.998 1.000 0.000
#> SRR1441178 1 0.0000 0.998 1.000 0.000
#> SRR1381396 1 0.0000 0.998 1.000 0.000
#> SRR1096081 1 0.0000 0.998 1.000 0.000
#> SRR1349809 2 0.1414 0.977 0.020 0.980
#> SRR1324314 1 0.0000 0.998 1.000 0.000
#> SRR1092444 1 0.0000 0.998 1.000 0.000
#> SRR1382553 1 0.0000 0.998 1.000 0.000
#> SRR1075530 2 0.1414 0.977 0.020 0.980
#> SRR1442612 1 0.5629 0.844 0.868 0.132
#> SRR1360056 1 0.0000 0.998 1.000 0.000
#> SRR1078164 1 0.0000 0.998 1.000 0.000
#> SRR1434545 1 0.0000 0.998 1.000 0.000
#> SRR1398251 1 0.0000 0.998 1.000 0.000
#> SRR1375866 1 0.0000 0.998 1.000 0.000
#> SRR1091645 2 0.1414 0.977 0.020 0.980
#> SRR1416636 1 0.0000 0.998 1.000 0.000
#> SRR1105441 2 0.1414 0.977 0.020 0.980
#> SRR1082496 2 0.0000 0.978 0.000 1.000
#> SRR1315353 2 0.0000 0.978 0.000 1.000
#> SRR1093697 2 0.0000 0.978 0.000 1.000
#> SRR1077429 1 0.0000 0.998 1.000 0.000
#> SRR1076120 1 0.0000 0.998 1.000 0.000
#> SRR1074410 1 0.0000 0.998 1.000 0.000
#> SRR1340345 2 0.1414 0.977 0.020 0.980
#> SRR1069514 2 0.1414 0.977 0.020 0.980
#> SRR1092636 1 0.0000 0.998 1.000 0.000
#> SRR1365013 2 0.1414 0.977 0.020 0.980
#> SRR1073069 1 0.0000 0.998 1.000 0.000
#> SRR1443137 1 0.0000 0.998 1.000 0.000
#> SRR1437143 2 0.0000 0.978 0.000 1.000
#> SRR1091990 1 0.0000 0.998 1.000 0.000
#> SRR820234 2 0.0000 0.978 0.000 1.000
#> SRR1338079 1 0.0000 0.998 1.000 0.000
#> SRR1390094 1 0.0000 0.998 1.000 0.000
#> SRR1340721 2 0.7219 0.767 0.200 0.800
#> SRR1335964 1 0.0000 0.998 1.000 0.000
#> SRR1086869 1 0.0000 0.998 1.000 0.000
#> SRR1453434 1 0.0000 0.998 1.000 0.000
#> SRR1402261 1 0.0000 0.998 1.000 0.000
#> SRR657809 2 0.1414 0.977 0.020 0.980
#> SRR1093075 1 0.0000 0.998 1.000 0.000
#> SRR1433329 1 0.0000 0.998 1.000 0.000
#> SRR1353418 1 0.0000 0.998 1.000 0.000
#> SRR1092913 2 0.1414 0.977 0.020 0.980
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1335605 3 0.1015 0.864 0.012 0.008 0.980
#> SRR1432014 3 0.1015 0.864 0.012 0.008 0.980
#> SRR1499215 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1460409 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1097344 2 0.5882 0.725 0.000 0.652 0.348
#> SRR1081789 2 0.5733 0.729 0.000 0.676 0.324
#> SRR1453005 2 0.1031 0.783 0.000 0.976 0.024
#> SRR1366985 3 0.6286 0.252 0.464 0.000 0.536
#> SRR815280 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.942 1.000 0.000 0.000
#> SRR815845 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1471178 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1080696 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1078684 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1317751 3 0.1289 0.857 0.032 0.000 0.968
#> SRR1435667 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1097905 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.942 1.000 0.000 0.000
#> SRR813108 2 0.1031 0.783 0.000 0.976 0.024
#> SRR1479062 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1408703 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1332360 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1434228 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1467149 1 0.6045 0.366 0.620 0.000 0.380
#> SRR1399113 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1476507 2 0.6168 0.647 0.000 0.588 0.412
#> SRR1092468 1 0.6079 0.347 0.612 0.000 0.388
#> SRR1441804 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1436021 2 0.6095 0.658 0.000 0.608 0.392
#> SRR1480083 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.942 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1400100 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1312002 3 0.5948 0.479 0.360 0.000 0.640
#> SRR1470253 3 0.6062 0.434 0.384 0.000 0.616
#> SRR1414332 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.942 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1421159 2 0.6079 0.664 0.000 0.612 0.388
#> SRR1340943 1 0.6095 0.362 0.608 0.000 0.392
#> SRR1078855 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.783 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1478679 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1350979 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1458198 1 0.0237 0.938 0.996 0.000 0.004
#> SRR1386910 2 0.5810 0.722 0.000 0.664 0.336
#> SRR1465375 2 0.5882 0.725 0.000 0.652 0.348
#> SRR1323699 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1431139 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1373964 3 0.1015 0.864 0.012 0.008 0.980
#> SRR1455413 1 0.2959 0.839 0.900 0.000 0.100
#> SRR1437163 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1347343 3 0.1015 0.864 0.012 0.008 0.980
#> SRR1465480 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1086514 2 0.5760 0.727 0.000 0.672 0.328
#> SRR1430928 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1310939 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1344294 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1468118 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1486348 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1500089 1 0.0592 0.933 0.988 0.000 0.012
#> SRR1441178 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1096081 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1349809 2 0.4974 0.753 0.000 0.764 0.236
#> SRR1324314 3 0.6260 0.293 0.448 0.000 0.552
#> SRR1092444 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1382553 3 0.6280 0.262 0.460 0.000 0.540
#> SRR1075530 2 0.5926 0.718 0.000 0.644 0.356
#> SRR1442612 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1360056 3 0.5882 0.500 0.348 0.000 0.652
#> SRR1078164 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1434545 1 0.6783 0.326 0.588 0.016 0.396
#> SRR1398251 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1091645 2 0.6168 0.647 0.000 0.588 0.412
#> SRR1416636 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1105441 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1082496 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1315353 2 0.6095 0.651 0.000 0.608 0.392
#> SRR1093697 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1077429 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1076120 1 0.5785 0.469 0.668 0.000 0.332
#> SRR1074410 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1340345 2 0.5882 0.725 0.000 0.652 0.348
#> SRR1069514 3 0.0892 0.853 0.000 0.020 0.980
#> SRR1092636 3 0.0892 0.867 0.020 0.000 0.980
#> SRR1365013 2 0.5810 0.722 0.000 0.664 0.336
#> SRR1073069 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.942 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.783 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1390094 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1340721 2 0.7140 0.483 0.328 0.632 0.040
#> SRR1335964 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1086869 3 0.0747 0.867 0.016 0.000 0.984
#> SRR1453434 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1402261 1 0.6095 0.362 0.608 0.000 0.392
#> SRR657809 2 0.5760 0.727 0.000 0.672 0.328
#> SRR1093075 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.942 1.000 0.000 0.000
#> SRR1353418 3 0.5882 0.500 0.348 0.000 0.652
#> SRR1092913 2 0.5882 0.725 0.000 0.652 0.348
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0336 0.9024 0.992 0.000 0.000 0.008
#> SRR1335605 3 0.2345 0.6804 0.000 0.000 0.900 0.100
#> SRR1432014 3 0.0000 0.7205 0.000 0.000 1.000 0.000
#> SRR1499215 3 0.1474 0.7140 0.000 0.000 0.948 0.052
#> SRR1460409 1 0.1302 0.9006 0.956 0.000 0.000 0.044
#> SRR1086441 1 0.0188 0.9028 0.996 0.000 0.000 0.004
#> SRR1097344 4 0.6442 0.7090 0.000 0.244 0.124 0.632
#> SRR1081789 4 0.7683 0.6568 0.000 0.244 0.304 0.452
#> SRR1453005 2 0.4973 0.2655 0.000 0.644 0.008 0.348
#> SRR1366985 1 0.7103 0.0468 0.468 0.000 0.404 0.128
#> SRR815280 1 0.2760 0.8665 0.872 0.000 0.000 0.128
#> SRR1348531 1 0.1211 0.8994 0.960 0.000 0.000 0.040
#> SRR815845 3 0.1474 0.7114 0.000 0.000 0.948 0.052
#> SRR1471178 1 0.0188 0.9028 0.996 0.000 0.000 0.004
#> SRR1080696 3 0.4072 0.6968 0.000 0.000 0.748 0.252
#> SRR1078684 3 0.2281 0.6795 0.000 0.000 0.904 0.096
#> SRR1317751 3 0.4304 0.6845 0.000 0.000 0.716 0.284
#> SRR1435667 3 0.1118 0.7136 0.000 0.000 0.964 0.036
#> SRR1097905 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1456548 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1075126 1 0.1389 0.8990 0.952 0.000 0.000 0.048
#> SRR813108 2 0.5063 0.6313 0.000 0.768 0.124 0.108
#> SRR1479062 3 0.4040 0.6970 0.000 0.000 0.752 0.248
#> SRR1408703 3 0.4164 0.6933 0.000 0.000 0.736 0.264
#> SRR1332360 1 0.2814 0.8647 0.868 0.000 0.000 0.132
#> SRR1098686 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1434228 1 0.3196 0.8585 0.856 0.000 0.008 0.136
#> SRR1467149 4 0.6871 -0.0920 0.416 0.000 0.104 0.480
#> SRR1399113 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.6433 0.7233 0.000 0.188 0.164 0.648
#> SRR1092468 3 0.7489 0.0771 0.296 0.000 0.492 0.212
#> SRR1441804 1 0.1211 0.8994 0.960 0.000 0.000 0.040
#> SRR1326100 2 0.0524 0.8919 0.000 0.988 0.008 0.004
#> SRR1398815 1 0.0336 0.9024 0.992 0.000 0.000 0.008
#> SRR1436021 4 0.7007 0.7001 0.000 0.144 0.308 0.548
#> SRR1480083 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0188 0.9028 0.996 0.000 0.000 0.004
#> SRR815542 1 0.1211 0.8991 0.960 0.000 0.000 0.040
#> SRR1400100 3 0.2469 0.6772 0.000 0.000 0.892 0.108
#> SRR1312002 3 0.6889 0.5122 0.232 0.000 0.592 0.176
#> SRR1470253 3 0.6844 0.5457 0.152 0.000 0.588 0.260
#> SRR1414332 1 0.0336 0.9024 0.992 0.000 0.000 0.008
#> SRR1069209 1 0.2868 0.8632 0.864 0.000 0.000 0.136
#> SRR661052 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1308860 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1421159 4 0.6708 0.6257 0.000 0.096 0.376 0.528
#> SRR1340943 4 0.5007 0.5644 0.172 0.000 0.068 0.760
#> SRR1078855 1 0.2814 0.8653 0.868 0.000 0.000 0.132
#> SRR1459465 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.2647 0.6553 0.000 0.000 0.880 0.120
#> SRR1350979 3 0.1940 0.7245 0.000 0.000 0.924 0.076
#> SRR1458198 1 0.5646 0.5866 0.656 0.000 0.048 0.296
#> SRR1386910 4 0.7457 0.7028 0.000 0.276 0.220 0.504
#> SRR1465375 4 0.7394 0.7266 0.008 0.248 0.188 0.556
#> SRR1323699 3 0.1389 0.7147 0.000 0.000 0.952 0.048
#> SRR1431139 3 0.2281 0.6837 0.000 0.000 0.904 0.096
#> SRR1373964 3 0.1637 0.7029 0.000 0.000 0.940 0.060
#> SRR1455413 1 0.6851 0.4535 0.584 0.000 0.148 0.268
#> SRR1437163 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1347343 3 0.1118 0.7136 0.000 0.000 0.964 0.036
#> SRR1465480 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1086514 4 0.7401 0.6885 0.000 0.300 0.196 0.504
#> SRR1430928 1 0.0188 0.9028 0.996 0.000 0.000 0.004
#> SRR1310939 3 0.4356 0.6496 0.000 0.000 0.708 0.292
#> SRR1344294 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0707 0.9029 0.980 0.000 0.000 0.020
#> SRR1468118 3 0.4331 0.6820 0.000 0.000 0.712 0.288
#> SRR1486348 1 0.0188 0.9028 0.996 0.000 0.000 0.004
#> SRR1488770 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.9028 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.1211 0.9025 0.960 0.000 0.000 0.040
#> SRR1500089 1 0.5966 0.5431 0.624 0.000 0.060 0.316
#> SRR1441178 1 0.2760 0.8665 0.872 0.000 0.000 0.128
#> SRR1381396 1 0.0336 0.9024 0.992 0.000 0.000 0.008
#> SRR1096081 3 0.4304 0.6845 0.000 0.000 0.716 0.284
#> SRR1349809 2 0.7454 -0.3127 0.004 0.516 0.180 0.300
#> SRR1324314 3 0.5492 0.4604 0.328 0.000 0.640 0.032
#> SRR1092444 1 0.2412 0.8777 0.908 0.000 0.008 0.084
#> SRR1382553 3 0.6939 0.3364 0.332 0.000 0.540 0.128
#> SRR1075530 4 0.6907 0.7320 0.000 0.240 0.172 0.588
#> SRR1442612 3 0.1118 0.7136 0.000 0.000 0.964 0.036
#> SRR1360056 3 0.5152 0.6499 0.020 0.000 0.664 0.316
#> SRR1078164 1 0.2760 0.8665 0.872 0.000 0.000 0.128
#> SRR1434545 4 0.5345 0.5753 0.156 0.012 0.072 0.760
#> SRR1398251 1 0.3196 0.8585 0.856 0.000 0.008 0.136
#> SRR1375866 1 0.0817 0.9002 0.976 0.000 0.000 0.024
#> SRR1091645 4 0.5553 0.6628 0.000 0.176 0.100 0.724
#> SRR1416636 3 0.4193 0.6917 0.000 0.000 0.732 0.268
#> SRR1105441 3 0.2469 0.6772 0.000 0.000 0.892 0.108
#> SRR1082496 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.7009 -0.5247 0.000 0.116 0.444 0.440
#> SRR1093697 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.4250 0.6884 0.000 0.000 0.724 0.276
#> SRR1076120 1 0.6747 0.3752 0.528 0.000 0.100 0.372
#> SRR1074410 1 0.0336 0.9024 0.992 0.000 0.000 0.008
#> SRR1340345 4 0.6859 0.7233 0.000 0.256 0.156 0.588
#> SRR1069514 3 0.2345 0.6757 0.000 0.000 0.900 0.100
#> SRR1092636 3 0.3726 0.7073 0.000 0.000 0.788 0.212
#> SRR1365013 4 0.7433 0.6957 0.000 0.208 0.288 0.504
#> SRR1073069 1 0.3196 0.8585 0.856 0.000 0.008 0.136
#> SRR1443137 1 0.2814 0.8653 0.868 0.000 0.000 0.132
#> SRR1437143 2 0.0000 0.9039 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.2760 0.8665 0.872 0.000 0.000 0.128
#> SRR820234 2 0.0188 0.9009 0.000 0.996 0.000 0.004
#> SRR1338079 1 0.1022 0.8986 0.968 0.000 0.000 0.032
#> SRR1390094 3 0.4543 0.2823 0.000 0.000 0.676 0.324
#> SRR1340721 4 0.8579 0.6669 0.084 0.252 0.156 0.508
#> SRR1335964 3 0.3356 0.7107 0.000 0.000 0.824 0.176
#> SRR1086869 3 0.4331 0.6820 0.000 0.000 0.712 0.288
#> SRR1453434 1 0.1557 0.8991 0.944 0.000 0.000 0.056
#> SRR1402261 4 0.5007 0.5644 0.172 0.000 0.068 0.760
#> SRR657809 4 0.7388 0.6849 0.000 0.304 0.192 0.504
#> SRR1093075 1 0.2814 0.8653 0.868 0.000 0.000 0.132
#> SRR1433329 1 0.2868 0.8632 0.864 0.000 0.000 0.136
#> SRR1353418 3 0.5496 0.6371 0.036 0.000 0.652 0.312
#> SRR1092913 4 0.6859 0.7233 0.000 0.256 0.156 0.588
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0671 0.74761 0.980 0.000 0.000 0.016 0.004
#> SRR1335605 3 0.3898 0.53894 0.004 0.000 0.812 0.108 0.076
#> SRR1432014 3 0.1430 0.57791 0.000 0.000 0.944 0.004 0.052
#> SRR1499215 3 0.1124 0.58836 0.004 0.000 0.960 0.000 0.036
#> SRR1460409 1 0.2376 0.73056 0.904 0.000 0.000 0.044 0.052
#> SRR1086441 1 0.0162 0.74895 0.996 0.000 0.000 0.000 0.004
#> SRR1097344 4 0.4345 0.78021 0.000 0.072 0.060 0.808 0.060
#> SRR1081789 3 0.6553 0.08697 0.004 0.064 0.548 0.328 0.056
#> SRR1453005 2 0.5000 0.01249 0.000 0.500 0.008 0.476 0.016
#> SRR1366985 1 0.6367 0.34747 0.516 0.000 0.128 0.012 0.344
#> SRR815280 1 0.4475 0.61034 0.692 0.000 0.000 0.032 0.276
#> SRR1348531 1 0.2735 0.71425 0.880 0.000 0.000 0.036 0.084
#> SRR815845 3 0.1907 0.59883 0.000 0.000 0.928 0.044 0.028
#> SRR1471178 1 0.0000 0.74888 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 3 0.5779 0.13644 0.000 0.000 0.508 0.092 0.400
#> SRR1078684 3 0.1116 0.59855 0.004 0.000 0.964 0.028 0.004
#> SRR1317751 3 0.5933 0.01890 0.000 0.000 0.452 0.104 0.444
#> SRR1435667 3 0.0510 0.59353 0.000 0.000 0.984 0.000 0.016
#> SRR1097905 1 0.3595 0.66717 0.816 0.000 0.000 0.044 0.140
#> SRR1456548 1 0.3366 0.67829 0.828 0.000 0.000 0.032 0.140
#> SRR1075126 1 0.2793 0.71069 0.876 0.000 0.000 0.036 0.088
#> SRR813108 2 0.6397 0.31495 0.000 0.540 0.312 0.132 0.016
#> SRR1479062 3 0.5752 0.16263 0.000 0.000 0.524 0.092 0.384
#> SRR1408703 3 0.5821 0.13200 0.000 0.000 0.504 0.096 0.400
#> SRR1332360 1 0.4356 0.57387 0.648 0.000 0.000 0.012 0.340
#> SRR1098686 1 0.2740 0.71251 0.876 0.000 0.000 0.028 0.096
#> SRR1434228 1 0.4507 0.56978 0.644 0.000 0.004 0.012 0.340
#> SRR1467149 5 0.6582 0.39499 0.280 0.000 0.004 0.220 0.496
#> SRR1399113 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.4468 0.78524 0.000 0.060 0.076 0.800 0.064
#> SRR1092468 3 0.7795 -0.05326 0.312 0.000 0.428 0.112 0.148
#> SRR1441804 1 0.2735 0.71425 0.880 0.000 0.000 0.036 0.084
#> SRR1326100 2 0.3608 0.72259 0.000 0.836 0.044 0.108 0.012
#> SRR1398815 1 0.1403 0.74309 0.952 0.000 0.000 0.024 0.024
#> SRR1436021 3 0.5673 -0.06184 0.000 0.020 0.512 0.428 0.040
#> SRR1480083 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.1638 0.72914 0.932 0.000 0.000 0.004 0.064
#> SRR815542 1 0.2676 0.71752 0.884 0.000 0.000 0.036 0.080
#> SRR1400100 3 0.2561 0.58662 0.000 0.000 0.884 0.096 0.020
#> SRR1312002 5 0.5952 0.31714 0.136 0.000 0.304 0.000 0.560
#> SRR1470253 5 0.5841 0.37729 0.084 0.000 0.236 0.032 0.648
#> SRR1414332 1 0.0798 0.74706 0.976 0.000 0.000 0.016 0.008
#> SRR1069209 1 0.4356 0.57387 0.648 0.000 0.000 0.012 0.340
#> SRR661052 1 0.3134 0.69450 0.848 0.000 0.000 0.032 0.120
#> SRR1308860 1 0.2628 0.71616 0.884 0.000 0.000 0.028 0.088
#> SRR1421159 3 0.5078 0.06814 0.000 0.008 0.564 0.404 0.024
#> SRR1340943 4 0.4452 0.60137 0.072 0.000 0.004 0.760 0.164
#> SRR1078855 1 0.4418 0.57908 0.652 0.000 0.000 0.016 0.332
#> SRR1459465 2 0.0162 0.86051 0.000 0.996 0.000 0.004 0.000
#> SRR816818 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.2899 0.57431 0.004 0.000 0.872 0.096 0.028
#> SRR1350979 3 0.2522 0.54002 0.000 0.000 0.880 0.012 0.108
#> SRR1458198 1 0.6376 -0.09622 0.500 0.000 0.000 0.192 0.308
#> SRR1386910 3 0.6797 -0.12059 0.000 0.068 0.472 0.388 0.072
#> SRR1465375 4 0.5723 0.72900 0.004 0.076 0.168 0.700 0.052
#> SRR1323699 3 0.1202 0.59073 0.004 0.000 0.960 0.004 0.032
#> SRR1431139 3 0.1564 0.59791 0.004 0.000 0.948 0.024 0.024
#> SRR1373964 3 0.0510 0.59849 0.000 0.000 0.984 0.016 0.000
#> SRR1455413 5 0.6820 0.34844 0.416 0.000 0.068 0.072 0.444
#> SRR1437163 1 0.3506 0.67484 0.824 0.000 0.000 0.044 0.132
#> SRR1347343 3 0.0609 0.59248 0.000 0.000 0.980 0.000 0.020
#> SRR1465480 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.3366 0.67829 0.828 0.000 0.000 0.032 0.140
#> SRR1086514 4 0.5390 0.69734 0.000 0.108 0.180 0.696 0.016
#> SRR1430928 1 0.0000 0.74888 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 3 0.5201 0.46635 0.000 0.000 0.684 0.188 0.128
#> SRR1344294 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0162 0.74895 0.996 0.000 0.000 0.000 0.004
#> SRR1468118 3 0.6162 0.01772 0.000 0.000 0.436 0.132 0.432
#> SRR1486348 1 0.0609 0.74687 0.980 0.000 0.000 0.000 0.020
#> SRR1488770 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0290 0.74923 0.992 0.000 0.000 0.000 0.008
#> SRR1456611 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.3146 0.71977 0.856 0.000 0.000 0.052 0.092
#> SRR1500089 5 0.6396 0.40859 0.376 0.000 0.000 0.172 0.452
#> SRR1441178 1 0.4890 0.57201 0.628 0.000 0.000 0.040 0.332
#> SRR1381396 1 0.1579 0.74200 0.944 0.000 0.000 0.024 0.032
#> SRR1096081 3 0.5929 0.05066 0.000 0.000 0.464 0.104 0.432
#> SRR1349809 2 0.8257 -0.17363 0.016 0.376 0.260 0.276 0.072
#> SRR1324314 3 0.5218 0.15970 0.296 0.000 0.632 0.000 0.072
#> SRR1092444 1 0.3692 0.68133 0.812 0.000 0.000 0.052 0.136
#> SRR1382553 3 0.6621 -0.00504 0.180 0.000 0.480 0.008 0.332
#> SRR1075530 4 0.3520 0.78014 0.000 0.076 0.080 0.840 0.004
#> SRR1442612 3 0.0510 0.59353 0.000 0.000 0.984 0.000 0.016
#> SRR1360056 5 0.5037 0.26982 0.008 0.000 0.320 0.036 0.636
#> SRR1078164 1 0.4921 0.56649 0.620 0.000 0.000 0.040 0.340
#> SRR1434545 4 0.4494 0.63468 0.048 0.000 0.020 0.768 0.164
#> SRR1398251 1 0.4639 0.55914 0.636 0.000 0.008 0.012 0.344
#> SRR1375866 1 0.1741 0.74097 0.936 0.000 0.000 0.024 0.040
#> SRR1091645 4 0.4146 0.77239 0.000 0.064 0.048 0.820 0.068
#> SRR1416636 3 0.5821 0.13200 0.000 0.000 0.504 0.096 0.400
#> SRR1105441 3 0.2505 0.58829 0.000 0.000 0.888 0.092 0.020
#> SRR1082496 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 3 0.4553 0.26792 0.000 0.004 0.652 0.328 0.016
#> SRR1093697 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.5803 0.09893 0.000 0.000 0.488 0.092 0.420
#> SRR1076120 5 0.6812 0.45201 0.324 0.000 0.012 0.200 0.464
#> SRR1074410 1 0.1661 0.74106 0.940 0.000 0.000 0.024 0.036
#> SRR1340345 4 0.3303 0.78228 0.000 0.076 0.076 0.848 0.000
#> SRR1069514 3 0.2707 0.57198 0.000 0.000 0.876 0.100 0.024
#> SRR1092636 3 0.5394 0.16718 0.000 0.000 0.540 0.060 0.400
#> SRR1365013 3 0.6308 0.04532 0.004 0.028 0.532 0.364 0.072
#> SRR1073069 1 0.4356 0.57387 0.648 0.000 0.000 0.012 0.340
#> SRR1443137 1 0.4491 0.57945 0.652 0.000 0.000 0.020 0.328
#> SRR1437143 2 0.0000 0.86230 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.4397 0.61186 0.696 0.000 0.000 0.028 0.276
#> SRR820234 2 0.1430 0.82501 0.000 0.944 0.000 0.052 0.004
#> SRR1338079 1 0.2951 0.70324 0.860 0.000 0.000 0.028 0.112
#> SRR1390094 3 0.4372 0.50601 0.004 0.000 0.776 0.100 0.120
#> SRR1340721 4 0.8657 0.35779 0.268 0.052 0.204 0.400 0.076
#> SRR1335964 3 0.4595 0.46427 0.000 0.000 0.740 0.088 0.172
#> SRR1086869 3 0.6162 0.01772 0.000 0.000 0.436 0.132 0.432
#> SRR1453434 1 0.4065 0.67581 0.772 0.000 0.000 0.048 0.180
#> SRR1402261 4 0.4452 0.60137 0.072 0.000 0.004 0.760 0.164
#> SRR657809 4 0.5590 0.70557 0.004 0.084 0.172 0.704 0.036
#> SRR1093075 1 0.4418 0.57908 0.652 0.000 0.000 0.016 0.332
#> SRR1433329 1 0.4508 0.57569 0.648 0.000 0.000 0.020 0.332
#> SRR1353418 5 0.5037 0.26982 0.008 0.000 0.320 0.036 0.636
#> SRR1092913 4 0.4148 0.78484 0.000 0.080 0.072 0.816 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.2865 0.7140 0.840 0.000 0.000 0.008 0.012 0.140
#> SRR1335605 3 0.3802 0.6450 0.024 0.000 0.824 0.040 0.024 0.088
#> SRR1432014 3 0.4005 0.6892 0.000 0.000 0.748 0.004 0.192 0.056
#> SRR1499215 3 0.3997 0.7116 0.000 0.000 0.760 0.000 0.132 0.108
#> SRR1460409 1 0.3452 0.7470 0.824 0.000 0.000 0.036 0.024 0.116
#> SRR1086441 1 0.2003 0.7483 0.884 0.000 0.000 0.000 0.000 0.116
#> SRR1097344 4 0.2620 0.7600 0.000 0.008 0.068 0.888 0.024 0.012
#> SRR1081789 3 0.3899 0.5481 0.000 0.008 0.792 0.112 0.004 0.084
#> SRR1453005 4 0.6808 0.2616 0.000 0.352 0.120 0.436 0.004 0.088
#> SRR1366985 6 0.4648 0.7042 0.192 0.000 0.076 0.000 0.020 0.712
#> SRR815280 6 0.4440 0.7473 0.376 0.000 0.000 0.012 0.016 0.596
#> SRR1348531 1 0.2271 0.7569 0.908 0.000 0.000 0.024 0.036 0.032
#> SRR815845 3 0.2868 0.7214 0.000 0.000 0.852 0.032 0.112 0.004
#> SRR1471178 1 0.1957 0.7502 0.888 0.000 0.000 0.000 0.000 0.112
#> SRR1080696 5 0.1918 0.7552 0.000 0.000 0.088 0.008 0.904 0.000
#> SRR1078684 3 0.3031 0.7325 0.004 0.000 0.844 0.000 0.108 0.044
#> SRR1317751 5 0.1785 0.7666 0.000 0.000 0.048 0.008 0.928 0.016
#> SRR1435667 3 0.3566 0.7168 0.000 0.000 0.788 0.000 0.156 0.056
#> SRR1097905 1 0.1493 0.7555 0.936 0.000 0.004 0.004 0.000 0.056
#> SRR1456548 1 0.1364 0.7615 0.944 0.000 0.004 0.004 0.000 0.048
#> SRR1075126 1 0.2453 0.7366 0.896 0.000 0.000 0.044 0.016 0.044
#> SRR813108 3 0.5691 0.1851 0.000 0.376 0.524 0.044 0.004 0.052
#> SRR1479062 5 0.3299 0.7003 0.000 0.000 0.140 0.028 0.820 0.012
#> SRR1408703 5 0.1918 0.7552 0.000 0.000 0.088 0.008 0.904 0.000
#> SRR1332360 6 0.3482 0.8438 0.316 0.000 0.000 0.000 0.000 0.684
#> SRR1098686 1 0.0146 0.7769 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1434228 6 0.3809 0.8379 0.304 0.000 0.004 0.000 0.008 0.684
#> SRR1467149 5 0.7034 0.3117 0.332 0.000 0.008 0.156 0.424 0.080
#> SRR1399113 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.2468 0.7613 0.000 0.008 0.096 0.880 0.016 0.000
#> SRR1092468 1 0.6933 0.0771 0.472 0.000 0.336 0.044 0.064 0.084
#> SRR1441804 1 0.2042 0.7621 0.920 0.000 0.000 0.024 0.024 0.032
#> SRR1326100 2 0.5334 0.4660 0.000 0.636 0.260 0.036 0.004 0.064
#> SRR1398815 1 0.3771 0.6898 0.780 0.000 0.000 0.024 0.024 0.172
#> SRR1436021 3 0.3948 0.4718 0.000 0.000 0.748 0.188 0.000 0.064
#> SRR1480083 2 0.0146 0.9535 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1472863 1 0.2196 0.7671 0.884 0.000 0.004 0.004 0.000 0.108
#> SRR815542 1 0.1458 0.7697 0.948 0.000 0.000 0.016 0.016 0.020
#> SRR1400100 3 0.2645 0.7016 0.000 0.000 0.884 0.044 0.056 0.016
#> SRR1312002 6 0.6066 -0.0442 0.060 0.000 0.076 0.000 0.396 0.468
#> SRR1470253 5 0.3858 0.6402 0.000 0.000 0.012 0.020 0.740 0.228
#> SRR1414332 1 0.3095 0.7052 0.828 0.000 0.000 0.012 0.016 0.144
#> SRR1069209 6 0.3636 0.8432 0.320 0.000 0.000 0.000 0.004 0.676
#> SRR661052 1 0.1542 0.7736 0.936 0.000 0.004 0.008 0.000 0.052
#> SRR1308860 1 0.0260 0.7765 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1421159 3 0.2983 0.5751 0.000 0.000 0.832 0.136 0.000 0.032
#> SRR1340943 4 0.4210 0.6291 0.064 0.000 0.008 0.796 0.060 0.072
#> SRR1078855 6 0.3636 0.8432 0.320 0.000 0.000 0.000 0.004 0.676
#> SRR1459465 2 0.1007 0.9366 0.000 0.968 0.004 0.008 0.004 0.016
#> SRR816818 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.2393 0.7233 0.004 0.000 0.892 0.000 0.040 0.064
#> SRR1350979 3 0.4353 0.6350 0.000 0.000 0.696 0.004 0.244 0.056
#> SRR1458198 1 0.6897 0.1407 0.492 0.000 0.004 0.172 0.240 0.092
#> SRR1386910 3 0.4830 0.4293 0.008 0.008 0.704 0.176 0.000 0.104
#> SRR1465375 4 0.4743 0.6846 0.000 0.008 0.248 0.668 0.000 0.076
#> SRR1323699 3 0.3992 0.7122 0.000 0.000 0.760 0.000 0.136 0.104
#> SRR1431139 3 0.3322 0.7329 0.012 0.000 0.832 0.000 0.104 0.052
#> SRR1373964 3 0.3295 0.7295 0.000 0.000 0.816 0.000 0.128 0.056
#> SRR1455413 5 0.5841 0.1535 0.444 0.000 0.012 0.044 0.456 0.044
#> SRR1437163 1 0.1429 0.7582 0.940 0.000 0.004 0.004 0.000 0.052
#> SRR1347343 3 0.3736 0.7122 0.000 0.000 0.776 0.000 0.156 0.068
#> SRR1465480 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.1226 0.7645 0.952 0.000 0.004 0.004 0.000 0.040
#> SRR1086514 4 0.5228 0.5465 0.000 0.012 0.360 0.556 0.000 0.072
#> SRR1430928 1 0.2048 0.7449 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1310939 3 0.7319 0.3796 0.032 0.000 0.468 0.144 0.268 0.088
#> SRR1344294 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.2562 0.6994 0.828 0.000 0.000 0.000 0.000 0.172
#> SRR1468118 5 0.1801 0.7653 0.000 0.000 0.056 0.016 0.924 0.004
#> SRR1486348 1 0.2006 0.7582 0.892 0.000 0.000 0.004 0.000 0.104
#> SRR1488770 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1663 0.7654 0.912 0.000 0.000 0.000 0.000 0.088
#> SRR1456611 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.3631 0.7326 0.820 0.000 0.000 0.040 0.040 0.100
#> SRR1500089 5 0.6933 0.3512 0.308 0.000 0.004 0.144 0.452 0.092
#> SRR1441178 6 0.4499 0.7817 0.284 0.000 0.000 0.024 0.024 0.668
#> SRR1381396 1 0.3945 0.6842 0.764 0.000 0.000 0.028 0.024 0.184
#> SRR1096081 5 0.1887 0.7664 0.000 0.000 0.048 0.012 0.924 0.016
#> SRR1349809 3 0.7273 0.0346 0.024 0.264 0.472 0.132 0.000 0.108
#> SRR1324314 3 0.7190 0.1769 0.352 0.000 0.364 0.000 0.128 0.156
#> SRR1092444 1 0.4624 0.6725 0.748 0.000 0.000 0.048 0.104 0.100
#> SRR1382553 6 0.4810 0.3799 0.056 0.000 0.260 0.000 0.020 0.664
#> SRR1075530 4 0.4625 0.7205 0.000 0.012 0.224 0.700 0.004 0.060
#> SRR1442612 3 0.3566 0.7168 0.000 0.000 0.788 0.000 0.156 0.056
#> SRR1360056 5 0.2730 0.6993 0.000 0.000 0.012 0.000 0.836 0.152
#> SRR1078164 6 0.4712 0.7705 0.284 0.000 0.000 0.032 0.028 0.656
#> SRR1434545 4 0.4193 0.6370 0.056 0.000 0.012 0.800 0.060 0.072
#> SRR1398251 6 0.3772 0.8324 0.296 0.000 0.004 0.000 0.008 0.692
#> SRR1375866 1 0.4184 0.6688 0.744 0.000 0.000 0.032 0.028 0.196
#> SRR1091645 4 0.2706 0.7584 0.000 0.008 0.068 0.880 0.040 0.004
#> SRR1416636 5 0.1918 0.7552 0.000 0.000 0.088 0.008 0.904 0.000
#> SRR1105441 3 0.2134 0.7053 0.000 0.000 0.904 0.044 0.052 0.000
#> SRR1082496 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 3 0.3621 0.5855 0.000 0.000 0.804 0.124 0.008 0.064
#> SRR1093697 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.1531 0.7641 0.000 0.000 0.068 0.000 0.928 0.004
#> SRR1076120 5 0.7086 0.3192 0.304 0.000 0.004 0.172 0.428 0.092
#> SRR1074410 1 0.4017 0.6827 0.760 0.000 0.000 0.032 0.024 0.184
#> SRR1340345 4 0.4404 0.7366 0.000 0.012 0.192 0.732 0.004 0.060
#> SRR1069514 3 0.1777 0.7185 0.000 0.000 0.928 0.004 0.024 0.044
#> SRR1092636 5 0.1858 0.7527 0.000 0.000 0.092 0.000 0.904 0.004
#> SRR1365013 3 0.4520 0.4578 0.004 0.004 0.724 0.164 0.000 0.104
#> SRR1073069 6 0.3482 0.8438 0.316 0.000 0.000 0.000 0.000 0.684
#> SRR1443137 6 0.3499 0.8433 0.320 0.000 0.000 0.000 0.000 0.680
#> SRR1437143 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.4286 0.7944 0.352 0.000 0.000 0.012 0.012 0.624
#> SRR820234 2 0.2339 0.8864 0.000 0.908 0.036 0.024 0.004 0.028
#> SRR1338079 1 0.1615 0.7745 0.928 0.000 0.004 0.004 0.000 0.064
#> SRR1390094 3 0.4715 0.6872 0.016 0.000 0.760 0.108 0.052 0.064
#> SRR1340721 1 0.6482 0.2355 0.552 0.004 0.236 0.100 0.000 0.108
#> SRR1335964 3 0.4623 0.3179 0.000 0.000 0.540 0.016 0.428 0.016
#> SRR1086869 5 0.1801 0.7653 0.000 0.000 0.056 0.016 0.924 0.004
#> SRR1453434 1 0.5198 0.4432 0.656 0.000 0.000 0.092 0.028 0.224
#> SRR1402261 4 0.4210 0.6291 0.064 0.000 0.008 0.796 0.060 0.072
#> SRR657809 4 0.5259 0.6322 0.000 0.012 0.300 0.596 0.000 0.092
#> SRR1093075 6 0.3636 0.8432 0.320 0.000 0.000 0.000 0.004 0.676
#> SRR1433329 6 0.3499 0.8433 0.320 0.000 0.000 0.000 0.000 0.680
#> SRR1353418 5 0.2768 0.6991 0.000 0.000 0.012 0.000 0.832 0.156
#> SRR1092913 4 0.3190 0.7613 0.000 0.012 0.116 0.840 0.004 0.028
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 17780 rows and 119 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 0.956 0.983 0.4975 0.504 0.504
#> 3 3 0.838 0.895 0.950 0.3235 0.769 0.571
#> 4 4 0.738 0.720 0.868 0.1105 0.877 0.663
#> 5 5 0.761 0.745 0.841 0.0721 0.907 0.672
#> 6 6 0.826 0.726 0.870 0.0488 0.926 0.675
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
#> SRR816969 1 0.0000 0.980 1.000 0.000
#> SRR1335605 2 0.0000 0.985 0.000 1.000
#> SRR1432014 2 0.0000 0.985 0.000 1.000
#> SRR1499215 2 0.0672 0.978 0.008 0.992
#> SRR1460409 1 0.0000 0.980 1.000 0.000
#> SRR1086441 1 0.0000 0.980 1.000 0.000
#> SRR1097344 2 0.0000 0.985 0.000 1.000
#> SRR1081789 2 0.0000 0.985 0.000 1.000
#> SRR1453005 2 0.0000 0.985 0.000 1.000
#> SRR1366985 1 0.0000 0.980 1.000 0.000
#> SRR815280 1 0.0000 0.980 1.000 0.000
#> SRR1348531 1 0.0000 0.980 1.000 0.000
#> SRR815845 2 0.0000 0.985 0.000 1.000
#> SRR1471178 1 0.0000 0.980 1.000 0.000
#> SRR1080696 1 0.9732 0.329 0.596 0.404
#> SRR1078684 2 0.0000 0.985 0.000 1.000
#> SRR1317751 1 0.0672 0.973 0.992 0.008
#> SRR1435667 2 0.0000 0.985 0.000 1.000
#> SRR1097905 1 0.0000 0.980 1.000 0.000
#> SRR1456548 1 0.0000 0.980 1.000 0.000
#> SRR1075126 1 0.0000 0.980 1.000 0.000
#> SRR813108 2 0.0000 0.985 0.000 1.000
#> SRR1479062 2 0.0000 0.985 0.000 1.000
#> SRR1408703 1 0.9710 0.339 0.600 0.400
#> SRR1332360 1 0.0000 0.980 1.000 0.000
#> SRR1098686 1 0.0000 0.980 1.000 0.000
#> SRR1434228 1 0.0000 0.980 1.000 0.000
#> SRR1467149 1 0.0000 0.980 1.000 0.000
#> SRR1399113 2 0.0000 0.985 0.000 1.000
#> SRR1476507 2 0.0000 0.985 0.000 1.000
#> SRR1092468 1 0.0000 0.980 1.000 0.000
#> SRR1441804 1 0.0000 0.980 1.000 0.000
#> SRR1326100 2 0.0000 0.985 0.000 1.000
#> SRR1398815 1 0.0000 0.980 1.000 0.000
#> SRR1436021 2 0.0000 0.985 0.000 1.000
#> SRR1480083 2 0.0000 0.985 0.000 1.000
#> SRR1472863 1 0.0000 0.980 1.000 0.000
#> SRR815542 1 0.0000 0.980 1.000 0.000
#> SRR1400100 2 0.0000 0.985 0.000 1.000
#> SRR1312002 1 0.0000 0.980 1.000 0.000
#> SRR1470253 1 0.0000 0.980 1.000 0.000
#> SRR1414332 1 0.0000 0.980 1.000 0.000
#> SRR1069209 1 0.0000 0.980 1.000 0.000
#> SRR661052 1 0.0000 0.980 1.000 0.000
#> SRR1308860 1 0.0000 0.980 1.000 0.000
#> SRR1421159 2 0.0000 0.985 0.000 1.000
#> SRR1340943 1 0.0376 0.976 0.996 0.004
#> SRR1078855 1 0.0000 0.980 1.000 0.000
#> SRR1459465 2 0.0000 0.985 0.000 1.000
#> SRR816818 2 0.0000 0.985 0.000 1.000
#> SRR1478679 2 0.0000 0.985 0.000 1.000
#> SRR1350979 2 0.0000 0.985 0.000 1.000
#> SRR1458198 1 0.0000 0.980 1.000 0.000
#> SRR1386910 2 0.0000 0.985 0.000 1.000
#> SRR1465375 2 0.0000 0.985 0.000 1.000
#> SRR1323699 2 0.7219 0.748 0.200 0.800
#> SRR1431139 1 0.9795 0.291 0.584 0.416
#> SRR1373964 2 0.0000 0.985 0.000 1.000
#> SRR1455413 1 0.0000 0.980 1.000 0.000
#> SRR1437163 1 0.0000 0.980 1.000 0.000
#> SRR1347343 2 0.0000 0.985 0.000 1.000
#> SRR1465480 2 0.0000 0.985 0.000 1.000
#> SRR1489631 1 0.0000 0.980 1.000 0.000
#> SRR1086514 2 0.0000 0.985 0.000 1.000
#> SRR1430928 1 0.0000 0.980 1.000 0.000
#> SRR1310939 2 0.7056 0.759 0.192 0.808
#> SRR1344294 2 0.0000 0.985 0.000 1.000
#> SRR1099402 1 0.0000 0.980 1.000 0.000
#> SRR1468118 1 0.0672 0.973 0.992 0.008
#> SRR1486348 1 0.0000 0.980 1.000 0.000
#> SRR1488770 2 0.0000 0.985 0.000 1.000
#> SRR1083732 1 0.0000 0.980 1.000 0.000
#> SRR1456611 2 0.0000 0.985 0.000 1.000
#> SRR1080318 1 0.0000 0.980 1.000 0.000
#> SRR1500089 1 0.0000 0.980 1.000 0.000
#> SRR1441178 1 0.0000 0.980 1.000 0.000
#> SRR1381396 1 0.0000 0.980 1.000 0.000
#> SRR1096081 1 0.0672 0.973 0.992 0.008
#> SRR1349809 2 0.0000 0.985 0.000 1.000
#> SRR1324314 1 0.0000 0.980 1.000 0.000
#> SRR1092444 1 0.0000 0.980 1.000 0.000
#> SRR1382553 1 0.0000 0.980 1.000 0.000
#> SRR1075530 2 0.0000 0.985 0.000 1.000
#> SRR1442612 2 0.0000 0.985 0.000 1.000
#> SRR1360056 1 0.0000 0.980 1.000 0.000
#> SRR1078164 1 0.0000 0.980 1.000 0.000
#> SRR1434545 2 0.9129 0.503 0.328 0.672
#> SRR1398251 1 0.0000 0.980 1.000 0.000
#> SRR1375866 1 0.0000 0.980 1.000 0.000
#> SRR1091645 2 0.0000 0.985 0.000 1.000
#> SRR1416636 1 0.2778 0.934 0.952 0.048
#> SRR1105441 2 0.0000 0.985 0.000 1.000
#> SRR1082496 2 0.0000 0.985 0.000 1.000
#> SRR1315353 2 0.0000 0.985 0.000 1.000
#> SRR1093697 2 0.0000 0.985 0.000 1.000
#> SRR1077429 1 0.0000 0.980 1.000 0.000
#> SRR1076120 1 0.0000 0.980 1.000 0.000
#> SRR1074410 1 0.0000 0.980 1.000 0.000
#> SRR1340345 2 0.0000 0.985 0.000 1.000
#> SRR1069514 2 0.0000 0.985 0.000 1.000
#> SRR1092636 1 0.0000 0.980 1.000 0.000
#> SRR1365013 2 0.0000 0.985 0.000 1.000
#> SRR1073069 1 0.0000 0.980 1.000 0.000
#> SRR1443137 1 0.0000 0.980 1.000 0.000
#> SRR1437143 2 0.0000 0.985 0.000 1.000
#> SRR1091990 1 0.0000 0.980 1.000 0.000
#> SRR820234 2 0.0000 0.985 0.000 1.000
#> SRR1338079 1 0.0000 0.980 1.000 0.000
#> SRR1390094 2 0.0000 0.985 0.000 1.000
#> SRR1340721 2 0.0672 0.978 0.008 0.992
#> SRR1335964 2 0.1184 0.970 0.016 0.984
#> SRR1086869 1 0.0672 0.973 0.992 0.008
#> SRR1453434 1 0.0000 0.980 1.000 0.000
#> SRR1402261 1 0.0000 0.980 1.000 0.000
#> SRR657809 2 0.0000 0.985 0.000 1.000
#> SRR1093075 1 0.0000 0.980 1.000 0.000
#> SRR1433329 1 0.0000 0.980 1.000 0.000
#> SRR1353418 1 0.0000 0.980 1.000 0.000
#> SRR1092913 2 0.0000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1335605 2 0.1411 0.929 0.000 0.964 0.036
#> SRR1432014 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1499215 3 0.5663 0.803 0.096 0.096 0.808
#> SRR1460409 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1097344 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1081789 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1453005 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1366985 3 0.5785 0.584 0.332 0.000 0.668
#> SRR815280 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.966 1.000 0.000 0.000
#> SRR815845 3 0.5706 0.586 0.000 0.320 0.680
#> SRR1471178 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1078684 3 0.4555 0.756 0.000 0.200 0.800
#> SRR1317751 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1435667 3 0.4452 0.764 0.000 0.192 0.808
#> SRR1097905 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.966 1.000 0.000 0.000
#> SRR813108 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1479062 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1332360 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1434228 1 0.2165 0.907 0.936 0.000 0.064
#> SRR1467149 1 0.4452 0.788 0.808 0.000 0.192
#> SRR1399113 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1476507 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1092468 1 0.0747 0.954 0.984 0.000 0.016
#> SRR1441804 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1436021 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1480083 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.966 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1400100 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1312002 3 0.4555 0.761 0.200 0.000 0.800
#> SRR1470253 3 0.1163 0.876 0.028 0.000 0.972
#> SRR1414332 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.966 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1421159 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1340943 1 0.5167 0.773 0.792 0.016 0.192
#> SRR1078855 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.961 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1478679 3 0.5926 0.523 0.000 0.356 0.644
#> SRR1350979 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1458198 1 0.4452 0.788 0.808 0.000 0.192
#> SRR1386910 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1465375 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1323699 3 0.4399 0.773 0.188 0.000 0.812
#> SRR1431139 3 0.0592 0.882 0.012 0.000 0.988
#> SRR1373964 3 0.4452 0.764 0.000 0.192 0.808
#> SRR1455413 1 0.4452 0.788 0.808 0.000 0.192
#> SRR1437163 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1347343 3 0.3551 0.817 0.000 0.132 0.868
#> SRR1465480 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1086514 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1430928 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1500089 1 0.4452 0.788 0.808 0.000 0.192
#> SRR1441178 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1349809 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1324314 3 0.5905 0.546 0.352 0.000 0.648
#> SRR1092444 1 0.0237 0.963 0.996 0.000 0.004
#> SRR1382553 3 0.5785 0.584 0.332 0.000 0.668
#> SRR1075530 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1442612 3 0.3551 0.817 0.000 0.132 0.868
#> SRR1360056 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1078164 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1434545 2 0.4452 0.749 0.000 0.808 0.192
#> SRR1398251 1 0.2796 0.877 0.908 0.000 0.092
#> SRR1375866 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1091645 2 0.4452 0.749 0.000 0.808 0.192
#> SRR1416636 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1105441 2 0.6154 0.191 0.000 0.592 0.408
#> SRR1082496 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1315353 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1093697 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1076120 1 0.4452 0.788 0.808 0.000 0.192
#> SRR1074410 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1340345 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1069514 2 0.0237 0.958 0.000 0.996 0.004
#> SRR1092636 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1365013 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1073069 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.966 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1390094 2 0.6140 0.374 0.000 0.596 0.404
#> SRR1340721 2 0.1753 0.909 0.048 0.952 0.000
#> SRR1335964 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1402261 1 0.5167 0.773 0.792 0.016 0.192
#> SRR657809 2 0.0000 0.961 0.000 1.000 0.000
#> SRR1093075 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.966 1.000 0.000 0.000
#> SRR1353418 3 0.0000 0.886 0.000 0.000 1.000
#> SRR1092913 2 0.0000 0.961 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.1389 0.91602 0.000 0.952 0.048 0.000
#> SRR1432014 3 0.0336 0.63030 0.000 0.000 0.992 0.008
#> SRR1499215 3 0.1474 0.61877 0.000 0.000 0.948 0.052
#> SRR1460409 1 0.2408 0.89870 0.896 0.000 0.000 0.104
#> SRR1086441 1 0.2149 0.90457 0.912 0.000 0.000 0.088
#> SRR1097344 2 0.4134 0.67943 0.000 0.740 0.000 0.260
#> SRR1081789 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1453005 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1366985 3 0.5218 0.47610 0.200 0.000 0.736 0.064
#> SRR815280 1 0.0672 0.91781 0.984 0.000 0.008 0.008
#> SRR1348531 1 0.2530 0.89453 0.888 0.000 0.000 0.112
#> SRR815845 3 0.4967 0.15149 0.000 0.452 0.548 0.000
#> SRR1471178 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.4941 -0.10117 0.000 0.000 0.564 0.436
#> SRR1078684 3 0.1716 0.61846 0.000 0.064 0.936 0.000
#> SRR1317751 4 0.4817 0.45889 0.000 0.000 0.388 0.612
#> SRR1435667 3 0.0592 0.63352 0.000 0.016 0.984 0.000
#> SRR1097905 1 0.2530 0.89453 0.888 0.000 0.000 0.112
#> SRR1456548 1 0.2530 0.89453 0.888 0.000 0.000 0.112
#> SRR1075126 1 0.1022 0.91883 0.968 0.000 0.000 0.032
#> SRR813108 2 0.1792 0.89222 0.000 0.932 0.068 0.000
#> SRR1479062 4 0.4790 0.46789 0.000 0.000 0.380 0.620
#> SRR1408703 4 0.4998 0.25123 0.000 0.000 0.488 0.512
#> SRR1332360 1 0.2300 0.88977 0.920 0.000 0.016 0.064
#> SRR1098686 1 0.2345 0.90062 0.900 0.000 0.000 0.100
#> SRR1434228 1 0.4663 0.74955 0.788 0.000 0.148 0.064
#> SRR1467149 4 0.1716 0.58770 0.064 0.000 0.000 0.936
#> SRR1399113 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1476507 2 0.4500 0.58694 0.000 0.684 0.000 0.316
#> SRR1092468 4 0.5600 0.17031 0.376 0.000 0.028 0.596
#> SRR1441804 1 0.2530 0.89453 0.888 0.000 0.000 0.112
#> SRR1326100 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1436021 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1480083 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR815542 1 0.2469 0.89676 0.892 0.000 0.000 0.108
#> SRR1400100 2 0.0188 0.94664 0.000 0.996 0.004 0.000
#> SRR1312002 3 0.7200 0.26497 0.220 0.000 0.552 0.228
#> SRR1470253 4 0.7697 0.08363 0.240 0.000 0.316 0.444
#> SRR1414332 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.2300 0.88977 0.920 0.000 0.016 0.064
#> SRR661052 1 0.2281 0.90201 0.904 0.000 0.000 0.096
#> SRR1308860 1 0.2408 0.89870 0.896 0.000 0.000 0.104
#> SRR1421159 2 0.1716 0.89633 0.000 0.936 0.064 0.000
#> SRR1340943 4 0.4419 0.53797 0.084 0.104 0.000 0.812
#> SRR1078855 1 0.2300 0.88977 0.920 0.000 0.016 0.064
#> SRR1459465 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.3356 0.53699 0.000 0.176 0.824 0.000
#> SRR1350979 3 0.0592 0.62618 0.000 0.000 0.984 0.016
#> SRR1458198 4 0.3219 0.52792 0.164 0.000 0.000 0.836
#> SRR1386910 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1465375 2 0.1557 0.91440 0.000 0.944 0.000 0.056
#> SRR1323699 3 0.0469 0.63207 0.000 0.000 0.988 0.012
#> SRR1431139 3 0.1452 0.62272 0.008 0.000 0.956 0.036
#> SRR1373964 3 0.0921 0.63251 0.000 0.028 0.972 0.000
#> SRR1455413 4 0.2921 0.55858 0.140 0.000 0.000 0.860
#> SRR1437163 1 0.2408 0.89870 0.896 0.000 0.000 0.104
#> SRR1347343 3 0.0000 0.63227 0.000 0.000 1.000 0.000
#> SRR1465480 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.2530 0.89453 0.888 0.000 0.000 0.112
#> SRR1086514 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1430928 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.3266 0.55520 0.000 0.000 0.168 0.832
#> SRR1344294 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0188 0.92037 0.996 0.000 0.000 0.004
#> SRR1468118 4 0.4564 0.50974 0.000 0.000 0.328 0.672
#> SRR1486348 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.2469 0.89714 0.892 0.000 0.000 0.108
#> SRR1500089 4 0.1716 0.58770 0.064 0.000 0.000 0.936
#> SRR1441178 1 0.2142 0.89436 0.928 0.000 0.016 0.056
#> SRR1381396 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1096081 4 0.4830 0.45344 0.000 0.000 0.392 0.608
#> SRR1349809 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1324314 3 0.6919 0.21927 0.368 0.000 0.516 0.116
#> SRR1092444 1 0.2973 0.86910 0.856 0.000 0.000 0.144
#> SRR1382553 3 0.5062 0.49031 0.184 0.000 0.752 0.064
#> SRR1075530 2 0.1716 0.90882 0.000 0.936 0.000 0.064
#> SRR1442612 3 0.0592 0.63352 0.000 0.016 0.984 0.000
#> SRR1360056 3 0.4985 -0.00947 0.000 0.000 0.532 0.468
#> SRR1078164 1 0.1938 0.89829 0.936 0.000 0.012 0.052
#> SRR1434545 4 0.3982 0.45943 0.004 0.220 0.000 0.776
#> SRR1398251 1 0.4711 0.74426 0.784 0.000 0.152 0.064
#> SRR1375866 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1091645 4 0.4624 0.35245 0.000 0.340 0.000 0.660
#> SRR1416636 4 0.4996 0.26173 0.000 0.000 0.484 0.516
#> SRR1105441 2 0.4967 0.09540 0.000 0.548 0.452 0.000
#> SRR1082496 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1093697 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1077429 4 0.4830 0.45344 0.000 0.000 0.392 0.608
#> SRR1076120 4 0.1637 0.58773 0.060 0.000 0.000 0.940
#> SRR1074410 1 0.0000 0.92122 1.000 0.000 0.000 0.000
#> SRR1340345 2 0.1940 0.89901 0.000 0.924 0.000 0.076
#> SRR1069514 3 0.4992 0.01523 0.000 0.476 0.524 0.000
#> SRR1092636 3 0.4948 -0.11312 0.000 0.000 0.560 0.440
#> SRR1365013 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1073069 1 0.2623 0.88207 0.908 0.000 0.028 0.064
#> SRR1443137 1 0.2300 0.88977 0.920 0.000 0.016 0.064
#> SRR1437143 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.1118 0.91008 0.964 0.000 0.000 0.036
#> SRR820234 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.2345 0.90044 0.900 0.000 0.000 0.100
#> SRR1390094 3 0.7300 0.16549 0.000 0.180 0.516 0.304
#> SRR1340721 2 0.0817 0.92959 0.024 0.976 0.000 0.000
#> SRR1335964 4 0.4790 0.46023 0.000 0.000 0.380 0.620
#> SRR1086869 4 0.4564 0.50974 0.000 0.000 0.328 0.672
#> SRR1453434 1 0.3751 0.82073 0.800 0.000 0.004 0.196
#> SRR1402261 4 0.4419 0.53797 0.084 0.104 0.000 0.812
#> SRR657809 2 0.0000 0.94912 0.000 1.000 0.000 0.000
#> SRR1093075 1 0.2300 0.88977 0.920 0.000 0.016 0.064
#> SRR1433329 1 0.2300 0.88977 0.920 0.000 0.016 0.064
#> SRR1353418 3 0.4977 0.00421 0.000 0.000 0.540 0.460
#> SRR1092913 2 0.2469 0.87007 0.000 0.892 0.000 0.108
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1335605 2 0.2450 0.825 0.000 0.900 0.048 0.000 0.052
#> SRR1432014 3 0.0290 0.803 0.000 0.000 0.992 0.000 0.008
#> SRR1499215 3 0.3266 0.679 0.000 0.004 0.796 0.200 0.000
#> SRR1460409 1 0.0771 0.930 0.976 0.000 0.000 0.020 0.004
#> SRR1086441 1 0.0162 0.940 0.996 0.000 0.000 0.004 0.000
#> SRR1097344 2 0.6581 0.345 0.000 0.520 0.008 0.220 0.252
#> SRR1081789 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1453005 2 0.0162 0.895 0.000 0.996 0.004 0.000 0.000
#> SRR1366985 4 0.4275 0.506 0.020 0.000 0.284 0.696 0.000
#> SRR815280 1 0.3366 0.608 0.768 0.000 0.000 0.232 0.000
#> SRR1348531 1 0.0404 0.937 0.988 0.000 0.000 0.012 0.000
#> SRR815845 3 0.4823 0.506 0.000 0.316 0.644 0.000 0.040
#> SRR1471178 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1080696 5 0.4278 0.298 0.000 0.000 0.452 0.000 0.548
#> SRR1078684 3 0.1121 0.813 0.000 0.044 0.956 0.000 0.000
#> SRR1317751 5 0.3438 0.626 0.000 0.000 0.172 0.020 0.808
#> SRR1435667 3 0.0880 0.815 0.000 0.032 0.968 0.000 0.000
#> SRR1097905 1 0.0290 0.936 0.992 0.000 0.000 0.008 0.000
#> SRR1456548 1 0.0162 0.938 0.996 0.000 0.000 0.004 0.000
#> SRR1075126 1 0.3012 0.812 0.852 0.000 0.000 0.124 0.024
#> SRR813108 2 0.3857 0.471 0.000 0.688 0.312 0.000 0.000
#> SRR1479062 5 0.3274 0.605 0.000 0.000 0.220 0.000 0.780
#> SRR1408703 5 0.4126 0.433 0.000 0.000 0.380 0.000 0.620
#> SRR1332360 4 0.3774 0.785 0.296 0.000 0.000 0.704 0.000
#> SRR1098686 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000
#> SRR1434228 4 0.3906 0.786 0.292 0.000 0.004 0.704 0.000
#> SRR1467149 5 0.3659 0.640 0.012 0.000 0.000 0.220 0.768
#> SRR1399113 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 2 0.6868 0.164 0.000 0.444 0.008 0.244 0.304
#> SRR1092468 5 0.7247 0.424 0.228 0.000 0.032 0.292 0.448
#> SRR1441804 1 0.0290 0.936 0.992 0.000 0.000 0.008 0.000
#> SRR1326100 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1398815 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1436021 2 0.1557 0.869 0.000 0.940 0.008 0.052 0.000
#> SRR1480083 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR815542 1 0.0566 0.934 0.984 0.000 0.000 0.012 0.004
#> SRR1400100 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1312002 4 0.5204 0.634 0.080 0.000 0.008 0.684 0.228
#> SRR1470253 4 0.4418 0.481 0.000 0.000 0.016 0.652 0.332
#> SRR1414332 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1069209 4 0.3774 0.785 0.296 0.000 0.000 0.704 0.000
#> SRR661052 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000
#> SRR1308860 1 0.0162 0.938 0.996 0.000 0.000 0.004 0.000
#> SRR1421159 2 0.4866 0.370 0.000 0.620 0.344 0.036 0.000
#> SRR1340943 5 0.4892 0.598 0.032 0.000 0.008 0.304 0.656
#> SRR1078855 4 0.3752 0.786 0.292 0.000 0.000 0.708 0.000
#> SRR1459465 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.2843 0.752 0.000 0.144 0.848 0.008 0.000
#> SRR1350979 3 0.0290 0.803 0.000 0.000 0.992 0.000 0.008
#> SRR1458198 5 0.4276 0.628 0.032 0.000 0.000 0.244 0.724
#> SRR1386910 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1465375 2 0.3601 0.786 0.000 0.824 0.008 0.136 0.032
#> SRR1323699 3 0.2583 0.746 0.000 0.004 0.864 0.132 0.000
#> SRR1431139 3 0.1949 0.774 0.012 0.000 0.932 0.016 0.040
#> SRR1373964 3 0.0880 0.815 0.000 0.032 0.968 0.000 0.000
#> SRR1455413 5 0.3194 0.602 0.148 0.000 0.000 0.020 0.832
#> SRR1437163 1 0.0162 0.938 0.996 0.000 0.000 0.004 0.000
#> SRR1347343 3 0.0451 0.808 0.000 0.004 0.988 0.008 0.000
#> SRR1465480 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.0162 0.938 0.996 0.000 0.000 0.004 0.000
#> SRR1086514 2 0.0451 0.892 0.000 0.988 0.004 0.008 0.000
#> SRR1430928 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1310939 5 0.5738 0.576 0.000 0.000 0.132 0.264 0.604
#> SRR1344294 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.2377 0.803 0.872 0.000 0.000 0.128 0.000
#> SRR1468118 5 0.2813 0.634 0.000 0.000 0.168 0.000 0.832
#> SRR1486348 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1488770 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1456611 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0404 0.936 0.988 0.000 0.000 0.012 0.000
#> SRR1500089 5 0.3280 0.646 0.012 0.000 0.000 0.176 0.812
#> SRR1441178 4 0.4150 0.645 0.388 0.000 0.000 0.612 0.000
#> SRR1381396 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1096081 5 0.3476 0.625 0.000 0.000 0.176 0.020 0.804
#> SRR1349809 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1324314 4 0.5926 0.709 0.164 0.000 0.140 0.664 0.032
#> SRR1092444 1 0.1981 0.866 0.920 0.000 0.000 0.016 0.064
#> SRR1382553 4 0.3949 0.472 0.004 0.000 0.300 0.696 0.000
#> SRR1075530 2 0.4118 0.741 0.000 0.772 0.008 0.188 0.032
#> SRR1442612 3 0.0290 0.808 0.000 0.008 0.992 0.000 0.000
#> SRR1360056 4 0.4836 0.450 0.000 0.000 0.036 0.628 0.336
#> SRR1078164 4 0.4192 0.613 0.404 0.000 0.000 0.596 0.000
#> SRR1434545 5 0.5084 0.589 0.008 0.028 0.008 0.304 0.652
#> SRR1398251 4 0.3906 0.786 0.292 0.000 0.004 0.704 0.000
#> SRR1375866 1 0.0404 0.937 0.988 0.000 0.000 0.012 0.000
#> SRR1091645 5 0.5613 0.562 0.000 0.096 0.012 0.244 0.648
#> SRR1416636 5 0.4045 0.468 0.000 0.000 0.356 0.000 0.644
#> SRR1105441 3 0.3999 0.510 0.000 0.344 0.656 0.000 0.000
#> SRR1082496 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.1851 0.823 0.000 0.912 0.088 0.000 0.000
#> SRR1093697 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.3109 0.619 0.000 0.000 0.200 0.000 0.800
#> SRR1076120 5 0.3835 0.633 0.012 0.000 0.000 0.244 0.744
#> SRR1074410 1 0.0290 0.939 0.992 0.000 0.000 0.008 0.000
#> SRR1340345 2 0.4412 0.726 0.000 0.756 0.008 0.188 0.048
#> SRR1069514 3 0.2732 0.740 0.000 0.160 0.840 0.000 0.000
#> SRR1092636 5 0.4268 0.317 0.000 0.000 0.444 0.000 0.556
#> SRR1365013 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1073069 4 0.3774 0.785 0.296 0.000 0.000 0.704 0.000
#> SRR1443137 4 0.3774 0.785 0.296 0.000 0.000 0.704 0.000
#> SRR1437143 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.3242 0.645 0.784 0.000 0.000 0.216 0.000
#> SRR820234 2 0.0000 0.896 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000
#> SRR1390094 3 0.5564 0.437 0.000 0.008 0.656 0.112 0.224
#> SRR1340721 2 0.2074 0.803 0.104 0.896 0.000 0.000 0.000
#> SRR1335964 5 0.4781 0.371 0.000 0.000 0.428 0.020 0.552
#> SRR1086869 5 0.2813 0.634 0.000 0.000 0.168 0.000 0.832
#> SRR1453434 1 0.6284 0.138 0.508 0.000 0.000 0.320 0.172
#> SRR1402261 5 0.4892 0.598 0.032 0.000 0.008 0.304 0.656
#> SRR657809 2 0.0566 0.890 0.000 0.984 0.004 0.012 0.000
#> SRR1093075 4 0.3752 0.786 0.292 0.000 0.000 0.708 0.000
#> SRR1433329 4 0.3774 0.785 0.296 0.000 0.000 0.704 0.000
#> SRR1353418 4 0.5037 0.433 0.000 0.000 0.048 0.616 0.336
#> SRR1092913 2 0.5191 0.658 0.000 0.700 0.008 0.192 0.100
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1387 0.9033 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1335605 2 0.3562 0.6783 0.004 0.784 0.036 0.000 0.176 0.000
#> SRR1432014 3 0.0790 0.8491 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1499215 3 0.2260 0.7784 0.000 0.000 0.860 0.000 0.000 0.140
#> SRR1460409 1 0.3558 0.8515 0.828 0.000 0.004 0.056 0.020 0.092
#> SRR1086441 1 0.1267 0.9051 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1097344 4 0.3767 0.5329 0.000 0.276 0.012 0.708 0.000 0.004
#> SRR1081789 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1453005 2 0.0260 0.9255 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1366985 6 0.0865 0.8611 0.000 0.000 0.036 0.000 0.000 0.964
#> SRR815280 1 0.3982 0.2730 0.536 0.000 0.000 0.004 0.000 0.460
#> SRR1348531 1 0.3424 0.8469 0.852 0.000 0.016 0.036 0.052 0.044
#> SRR815845 3 0.4952 0.6057 0.000 0.180 0.652 0.000 0.168 0.000
#> SRR1471178 1 0.1387 0.9033 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1080696 5 0.1204 0.8039 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1078684 3 0.0937 0.8511 0.000 0.040 0.960 0.000 0.000 0.000
#> SRR1317751 5 0.0632 0.8082 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1435667 3 0.0909 0.8538 0.000 0.020 0.968 0.000 0.012 0.000
#> SRR1097905 1 0.0000 0.8940 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1456548 1 0.0000 0.8940 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075126 1 0.6313 0.2181 0.452 0.000 0.008 0.168 0.016 0.356
#> SRR813108 2 0.3867 -0.1195 0.000 0.512 0.488 0.000 0.000 0.000
#> SRR1479062 5 0.2001 0.7775 0.000 0.000 0.040 0.048 0.912 0.000
#> SRR1408703 5 0.1075 0.8071 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1332360 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1098686 1 0.0790 0.9039 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR1434228 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1467149 4 0.4860 0.1715 0.032 0.000 0.016 0.552 0.400 0.000
#> SRR1399113 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3219 0.5919 0.000 0.192 0.012 0.792 0.000 0.004
#> SRR1092468 4 0.6356 0.2909 0.176 0.000 0.044 0.540 0.236 0.004
#> SRR1441804 1 0.2864 0.8660 0.884 0.000 0.016 0.040 0.024 0.036
#> SRR1326100 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398815 1 0.1007 0.9048 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1436021 2 0.3933 0.5959 0.000 0.740 0.040 0.216 0.000 0.004
#> SRR1480083 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.0790 0.8999 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR815542 1 0.2798 0.8674 0.876 0.000 0.000 0.056 0.020 0.048
#> SRR1400100 2 0.0146 0.9281 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1312002 6 0.2196 0.7873 0.004 0.000 0.004 0.000 0.108 0.884
#> SRR1470253 6 0.3999 -0.1033 0.000 0.000 0.000 0.004 0.496 0.500
#> SRR1414332 1 0.1387 0.9033 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1069209 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR661052 1 0.0363 0.8982 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1308860 1 0.0363 0.8990 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1421159 3 0.4740 0.5028 0.000 0.300 0.632 0.064 0.000 0.004
#> SRR1340943 4 0.0146 0.5731 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1078855 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1459465 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.1265 0.8479 0.000 0.044 0.948 0.000 0.000 0.008
#> SRR1350979 3 0.0865 0.8481 0.000 0.000 0.964 0.000 0.036 0.000
#> SRR1458198 4 0.5114 0.2512 0.036 0.000 0.016 0.592 0.344 0.012
#> SRR1386910 2 0.0653 0.9197 0.004 0.980 0.012 0.000 0.000 0.004
#> SRR1465375 4 0.4293 0.2203 0.000 0.448 0.012 0.536 0.000 0.004
#> SRR1323699 3 0.2003 0.7987 0.000 0.000 0.884 0.000 0.000 0.116
#> SRR1431139 3 0.1819 0.8245 0.008 0.000 0.932 0.032 0.024 0.004
#> SRR1373964 3 0.0713 0.8536 0.000 0.028 0.972 0.000 0.000 0.000
#> SRR1455413 5 0.4381 0.5468 0.100 0.000 0.016 0.136 0.748 0.000
#> SRR1437163 1 0.0363 0.8982 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1347343 3 0.0909 0.8499 0.000 0.000 0.968 0.000 0.020 0.012
#> SRR1465480 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.0000 0.8940 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1086514 2 0.1442 0.8862 0.000 0.944 0.012 0.040 0.000 0.004
#> SRR1430928 1 0.1387 0.9033 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1310939 4 0.4544 0.3339 0.000 0.000 0.076 0.668 0.256 0.000
#> SRR1344294 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.3765 0.4179 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1468118 5 0.0632 0.8082 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1486348 1 0.0865 0.9012 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1488770 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1327 0.9043 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1456611 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.2605 0.8788 0.896 0.000 0.016 0.020 0.020 0.048
#> SRR1500089 5 0.5053 -0.0121 0.032 0.000 0.016 0.456 0.492 0.004
#> SRR1441178 6 0.1858 0.8115 0.092 0.000 0.000 0.004 0.000 0.904
#> SRR1381396 1 0.1411 0.9046 0.936 0.000 0.000 0.004 0.000 0.060
#> SRR1096081 5 0.0632 0.8082 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1349809 2 0.0291 0.9260 0.004 0.992 0.004 0.000 0.000 0.000
#> SRR1324314 6 0.1857 0.8414 0.004 0.000 0.044 0.000 0.028 0.924
#> SRR1092444 1 0.4812 0.7483 0.752 0.000 0.016 0.068 0.112 0.052
#> SRR1382553 6 0.1267 0.8421 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR1075530 4 0.4284 0.3567 0.000 0.392 0.016 0.588 0.000 0.004
#> SRR1442612 3 0.0790 0.8491 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1360056 5 0.3971 0.1823 0.000 0.000 0.004 0.000 0.548 0.448
#> SRR1078164 6 0.2053 0.7941 0.108 0.000 0.000 0.004 0.000 0.888
#> SRR1434545 4 0.0146 0.5731 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1398251 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1375866 1 0.1700 0.8985 0.916 0.000 0.000 0.004 0.000 0.080
#> SRR1091645 4 0.3865 0.5620 0.000 0.088 0.012 0.800 0.096 0.004
#> SRR1416636 5 0.1075 0.8071 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1105441 3 0.3265 0.6581 0.000 0.248 0.748 0.000 0.000 0.004
#> SRR1082496 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.1075 0.8867 0.000 0.952 0.048 0.000 0.000 0.000
#> SRR1093697 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0713 0.8065 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1076120 4 0.4755 0.2442 0.024 0.000 0.016 0.600 0.356 0.004
#> SRR1074410 1 0.1411 0.9048 0.936 0.000 0.000 0.004 0.000 0.060
#> SRR1340345 4 0.4255 0.3805 0.000 0.380 0.016 0.600 0.000 0.004
#> SRR1069514 3 0.1141 0.8454 0.000 0.052 0.948 0.000 0.000 0.000
#> SRR1092636 5 0.1141 0.8060 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1365013 2 0.0146 0.9279 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1073069 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1443137 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1437143 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.3866 -0.1696 0.484 0.000 0.000 0.000 0.000 0.516
#> SRR820234 2 0.0000 0.9300 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1338079 1 0.0363 0.8982 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1390094 3 0.4308 0.2159 0.000 0.008 0.532 0.452 0.008 0.000
#> SRR1340721 2 0.2871 0.6807 0.192 0.804 0.004 0.000 0.000 0.000
#> SRR1335964 5 0.3954 0.5409 0.000 0.000 0.296 0.016 0.684 0.004
#> SRR1086869 5 0.0632 0.8082 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1453434 4 0.6763 -0.0124 0.268 0.000 0.008 0.388 0.024 0.312
#> SRR1402261 4 0.0146 0.5731 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR657809 2 0.1625 0.8720 0.000 0.928 0.012 0.060 0.000 0.000
#> SRR1093075 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1433329 6 0.0146 0.8847 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1353418 5 0.4018 0.2750 0.000 0.000 0.008 0.000 0.580 0.412
#> SRR1092913 4 0.4119 0.4522 0.000 0.336 0.016 0.644 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17780 rows and 119 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 0.989 0.958 0.969 0.4275 0.574 0.574
#> 3 3 0.577 0.809 0.885 0.4161 0.805 0.665
#> 4 4 0.560 0.677 0.799 0.1230 0.896 0.753
#> 5 5 0.848 0.827 0.916 0.1350 0.831 0.542
#> 6 6 0.754 0.669 0.808 0.0625 0.838 0.430
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
#> SRR816969 1 0.0000 0.973 1.000 0.000
#> SRR1335605 1 0.1843 0.962 0.972 0.028
#> SRR1432014 1 0.3733 0.947 0.928 0.072
#> SRR1499215 1 0.3733 0.947 0.928 0.072
#> SRR1460409 1 0.0000 0.973 1.000 0.000
#> SRR1086441 1 0.0000 0.973 1.000 0.000
#> SRR1097344 2 0.3733 0.956 0.072 0.928
#> SRR1081789 2 0.1843 0.966 0.028 0.972
#> SRR1453005 2 0.1843 0.966 0.028 0.972
#> SRR1366985 1 0.2603 0.960 0.956 0.044
#> SRR815280 1 0.0000 0.973 1.000 0.000
#> SRR1348531 1 0.0000 0.973 1.000 0.000
#> SRR815845 2 0.0000 0.963 0.000 1.000
#> SRR1471178 1 0.0000 0.973 1.000 0.000
#> SRR1080696 1 0.3274 0.954 0.940 0.060
#> SRR1078684 1 0.3274 0.954 0.940 0.060
#> SRR1317751 1 0.3274 0.954 0.940 0.060
#> SRR1435667 2 0.1843 0.966 0.028 0.972
#> SRR1097905 1 0.0000 0.973 1.000 0.000
#> SRR1456548 1 0.0000 0.973 1.000 0.000
#> SRR1075126 1 0.0000 0.973 1.000 0.000
#> SRR813108 2 0.1633 0.966 0.024 0.976
#> SRR1479062 1 0.6801 0.825 0.820 0.180
#> SRR1408703 1 0.3274 0.954 0.940 0.060
#> SRR1332360 1 0.0376 0.972 0.996 0.004
#> SRR1098686 1 0.0000 0.973 1.000 0.000
#> SRR1434228 1 0.2603 0.960 0.956 0.044
#> SRR1467149 1 0.0000 0.973 1.000 0.000
#> SRR1399113 2 0.0938 0.964 0.012 0.988
#> SRR1476507 2 0.3733 0.956 0.072 0.928
#> SRR1092468 1 0.0000 0.973 1.000 0.000
#> SRR1441804 1 0.0000 0.973 1.000 0.000
#> SRR1326100 2 0.0000 0.963 0.000 1.000
#> SRR1398815 1 0.0000 0.973 1.000 0.000
#> SRR1436021 2 0.1843 0.966 0.028 0.972
#> SRR1480083 2 0.0000 0.963 0.000 1.000
#> SRR1472863 1 0.0000 0.973 1.000 0.000
#> SRR815542 1 0.0000 0.973 1.000 0.000
#> SRR1400100 2 0.1843 0.966 0.028 0.972
#> SRR1312002 1 0.2603 0.960 0.956 0.044
#> SRR1470253 1 0.0938 0.970 0.988 0.012
#> SRR1414332 1 0.0000 0.973 1.000 0.000
#> SRR1069209 1 0.2603 0.960 0.956 0.044
#> SRR661052 1 0.0000 0.973 1.000 0.000
#> SRR1308860 1 0.0000 0.973 1.000 0.000
#> SRR1421159 2 0.1843 0.966 0.028 0.972
#> SRR1340943 1 0.0000 0.973 1.000 0.000
#> SRR1078855 1 0.0000 0.973 1.000 0.000
#> SRR1459465 2 0.1843 0.961 0.028 0.972
#> SRR816818 2 0.2603 0.955 0.044 0.956
#> SRR1478679 1 0.3733 0.947 0.928 0.072
#> SRR1350979 1 0.3733 0.947 0.928 0.072
#> SRR1458198 1 0.0000 0.973 1.000 0.000
#> SRR1386910 2 0.2603 0.955 0.044 0.956
#> SRR1465375 2 0.4022 0.950 0.080 0.920
#> SRR1323699 1 0.3733 0.947 0.928 0.072
#> SRR1431139 1 0.3274 0.954 0.940 0.060
#> SRR1373964 1 0.3879 0.944 0.924 0.076
#> SRR1455413 1 0.0000 0.973 1.000 0.000
#> SRR1437163 1 0.0000 0.973 1.000 0.000
#> SRR1347343 1 0.3733 0.947 0.928 0.072
#> SRR1465480 2 0.2603 0.955 0.044 0.956
#> SRR1489631 1 0.0000 0.973 1.000 0.000
#> SRR1086514 2 0.1843 0.966 0.028 0.972
#> SRR1430928 1 0.0000 0.973 1.000 0.000
#> SRR1310939 1 0.3114 0.956 0.944 0.056
#> SRR1344294 2 0.0000 0.963 0.000 1.000
#> SRR1099402 1 0.0000 0.973 1.000 0.000
#> SRR1468118 1 0.1184 0.967 0.984 0.016
#> SRR1486348 1 0.0000 0.973 1.000 0.000
#> SRR1488770 2 0.0000 0.963 0.000 1.000
#> SRR1083732 1 0.0000 0.973 1.000 0.000
#> SRR1456611 2 0.0000 0.963 0.000 1.000
#> SRR1080318 1 0.0000 0.973 1.000 0.000
#> SRR1500089 1 0.0000 0.973 1.000 0.000
#> SRR1441178 1 0.0000 0.973 1.000 0.000
#> SRR1381396 1 0.0000 0.973 1.000 0.000
#> SRR1096081 1 0.3274 0.954 0.940 0.060
#> SRR1349809 1 0.2778 0.949 0.952 0.048
#> SRR1324314 1 0.2603 0.960 0.956 0.044
#> SRR1092444 1 0.0000 0.973 1.000 0.000
#> SRR1382553 1 0.2603 0.960 0.956 0.044
#> SRR1075530 2 0.3733 0.956 0.072 0.928
#> SRR1442612 2 0.7528 0.735 0.216 0.784
#> SRR1360056 1 0.0000 0.973 1.000 0.000
#> SRR1078164 1 0.0000 0.973 1.000 0.000
#> SRR1434545 1 0.8207 0.632 0.744 0.256
#> SRR1398251 1 0.0000 0.973 1.000 0.000
#> SRR1375866 1 0.0000 0.973 1.000 0.000
#> SRR1091645 2 0.3733 0.956 0.072 0.928
#> SRR1416636 1 0.3584 0.950 0.932 0.068
#> SRR1105441 2 0.1843 0.966 0.028 0.972
#> SRR1082496 2 0.0938 0.964 0.012 0.988
#> SRR1315353 2 0.1843 0.966 0.028 0.972
#> SRR1093697 2 0.0000 0.963 0.000 1.000
#> SRR1077429 1 0.3274 0.954 0.940 0.060
#> SRR1076120 1 0.0000 0.973 1.000 0.000
#> SRR1074410 1 0.0000 0.973 1.000 0.000
#> SRR1340345 2 0.3733 0.956 0.072 0.928
#> SRR1069514 2 0.1843 0.966 0.028 0.972
#> SRR1092636 1 0.3274 0.954 0.940 0.060
#> SRR1365013 2 0.3733 0.956 0.072 0.928
#> SRR1073069 1 0.2603 0.960 0.956 0.044
#> SRR1443137 1 0.0000 0.973 1.000 0.000
#> SRR1437143 2 0.0000 0.963 0.000 1.000
#> SRR1091990 1 0.0000 0.973 1.000 0.000
#> SRR820234 2 0.0000 0.963 0.000 1.000
#> SRR1338079 1 0.0000 0.973 1.000 0.000
#> SRR1390094 1 0.3431 0.952 0.936 0.064
#> SRR1340721 1 0.1843 0.955 0.972 0.028
#> SRR1335964 1 0.3584 0.950 0.932 0.068
#> SRR1086869 1 0.3274 0.954 0.940 0.060
#> SRR1453434 1 0.0000 0.973 1.000 0.000
#> SRR1402261 1 0.0000 0.973 1.000 0.000
#> SRR657809 2 0.4022 0.950 0.080 0.920
#> SRR1093075 1 0.0000 0.973 1.000 0.000
#> SRR1433329 1 0.2603 0.960 0.956 0.044
#> SRR1353418 1 0.2603 0.960 0.956 0.044
#> SRR1092913 2 0.4022 0.950 0.080 0.920
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1335605 1 0.5016 0.723 0.760 0.000 0.240
#> SRR1432014 3 0.0000 0.848 0.000 0.000 1.000
#> SRR1499215 1 0.6267 0.395 0.548 0.000 0.452
#> SRR1460409 1 0.1964 0.876 0.944 0.000 0.056
#> SRR1086441 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1097344 2 0.4289 0.835 0.040 0.868 0.092
#> SRR1081789 2 0.5967 0.804 0.032 0.752 0.216
#> SRR1453005 2 0.2625 0.835 0.000 0.916 0.084
#> SRR1366985 1 0.5733 0.605 0.676 0.000 0.324
#> SRR815280 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1348531 1 0.2356 0.870 0.928 0.000 0.072
#> SRR815845 3 0.4931 0.583 0.000 0.232 0.768
#> SRR1471178 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.848 0.000 0.000 1.000
#> SRR1078684 1 0.6154 0.475 0.592 0.000 0.408
#> SRR1317751 3 0.3816 0.802 0.148 0.000 0.852
#> SRR1435667 3 0.1753 0.809 0.000 0.048 0.952
#> SRR1097905 1 0.1163 0.882 0.972 0.000 0.028
#> SRR1456548 1 0.2261 0.872 0.932 0.000 0.068
#> SRR1075126 1 0.1031 0.883 0.976 0.000 0.024
#> SRR813108 2 0.2537 0.835 0.000 0.920 0.080
#> SRR1479062 3 0.0747 0.850 0.016 0.000 0.984
#> SRR1408703 3 0.1163 0.846 0.028 0.000 0.972
#> SRR1332360 1 0.1411 0.875 0.964 0.000 0.036
#> SRR1098686 1 0.0592 0.882 0.988 0.000 0.012
#> SRR1434228 1 0.2356 0.858 0.928 0.000 0.072
#> SRR1467149 1 0.4235 0.793 0.824 0.000 0.176
#> SRR1399113 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1476507 2 0.6096 0.811 0.040 0.752 0.208
#> SRR1092468 1 0.3941 0.813 0.844 0.000 0.156
#> SRR1441804 1 0.0592 0.883 0.988 0.000 0.012
#> SRR1326100 2 0.1860 0.835 0.000 0.948 0.052
#> SRR1398815 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1436021 2 0.5731 0.804 0.020 0.752 0.228
#> SRR1480083 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.882 1.000 0.000 0.000
#> SRR815542 1 0.1860 0.878 0.948 0.000 0.052
#> SRR1400100 2 0.5138 0.794 0.000 0.748 0.252
#> SRR1312002 1 0.5363 0.686 0.724 0.000 0.276
#> SRR1470253 1 0.5733 0.625 0.676 0.000 0.324
#> SRR1414332 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1069209 1 0.2356 0.858 0.928 0.000 0.072
#> SRR661052 1 0.1964 0.876 0.944 0.000 0.056
#> SRR1308860 1 0.0592 0.882 0.988 0.000 0.012
#> SRR1421159 2 0.5731 0.804 0.020 0.752 0.228
#> SRR1340943 1 0.5111 0.783 0.808 0.024 0.168
#> SRR1078855 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1459465 2 0.0000 0.830 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1478679 1 0.7112 0.424 0.552 0.024 0.424
#> SRR1350979 3 0.1289 0.844 0.032 0.000 0.968
#> SRR1458198 1 0.2537 0.867 0.920 0.000 0.080
#> SRR1386910 2 0.6731 0.796 0.088 0.740 0.172
#> SRR1465375 2 0.7155 0.751 0.128 0.720 0.152
#> SRR1323699 3 0.4974 0.634 0.236 0.000 0.764
#> SRR1431139 1 0.5216 0.706 0.740 0.000 0.260
#> SRR1373964 3 0.0237 0.847 0.004 0.000 0.996
#> SRR1455413 1 0.4235 0.793 0.824 0.000 0.176
#> SRR1437163 1 0.2165 0.873 0.936 0.000 0.064
#> SRR1347343 3 0.0237 0.847 0.004 0.000 0.996
#> SRR1465480 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1489631 1 0.2165 0.873 0.936 0.000 0.064
#> SRR1086514 2 0.4887 0.809 0.000 0.772 0.228
#> SRR1430928 1 0.0237 0.882 0.996 0.000 0.004
#> SRR1310939 3 0.2448 0.837 0.076 0.000 0.924
#> SRR1344294 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1468118 3 0.4796 0.750 0.220 0.000 0.780
#> SRR1486348 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1080318 1 0.1964 0.876 0.944 0.000 0.056
#> SRR1500089 1 0.3038 0.856 0.896 0.000 0.104
#> SRR1441178 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1381396 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.848 0.000 0.000 1.000
#> SRR1349809 1 0.5897 0.761 0.792 0.076 0.132
#> SRR1324314 1 0.3482 0.839 0.872 0.000 0.128
#> SRR1092444 1 0.1964 0.876 0.944 0.000 0.056
#> SRR1382553 1 0.5706 0.615 0.680 0.000 0.320
#> SRR1075530 2 0.6348 0.809 0.060 0.752 0.188
#> SRR1442612 3 0.0237 0.847 0.000 0.004 0.996
#> SRR1360056 3 0.5529 0.634 0.296 0.000 0.704
#> SRR1078164 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1434545 1 0.7044 0.682 0.724 0.108 0.168
#> SRR1398251 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1375866 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1091645 2 0.6096 0.811 0.040 0.752 0.208
#> SRR1416636 3 0.0237 0.849 0.004 0.000 0.996
#> SRR1105441 2 0.5098 0.797 0.000 0.752 0.248
#> SRR1082496 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1315353 2 0.5098 0.797 0.000 0.752 0.248
#> SRR1093697 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1077429 3 0.4452 0.787 0.192 0.000 0.808
#> SRR1076120 1 0.4504 0.775 0.804 0.000 0.196
#> SRR1074410 1 0.0000 0.882 1.000 0.000 0.000
#> SRR1340345 2 0.6348 0.809 0.060 0.752 0.188
#> SRR1069514 2 0.6832 0.607 0.020 0.604 0.376
#> SRR1092636 3 0.4504 0.784 0.196 0.000 0.804
#> SRR1365013 2 0.7011 0.779 0.092 0.720 0.188
#> SRR1073069 1 0.2356 0.858 0.928 0.000 0.072
#> SRR1443137 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1437143 2 0.0000 0.830 0.000 1.000 0.000
#> SRR1091990 1 0.1289 0.875 0.968 0.000 0.032
#> SRR820234 2 0.0747 0.832 0.000 0.984 0.016
#> SRR1338079 1 0.0237 0.882 0.996 0.000 0.004
#> SRR1390094 1 0.6168 0.457 0.588 0.000 0.412
#> SRR1340721 1 0.3267 0.833 0.884 0.000 0.116
#> SRR1335964 3 0.4291 0.793 0.180 0.000 0.820
#> SRR1086869 3 0.4291 0.793 0.180 0.000 0.820
#> SRR1453434 1 0.0424 0.883 0.992 0.000 0.008
#> SRR1402261 1 0.4235 0.793 0.824 0.000 0.176
#> SRR657809 2 0.6587 0.792 0.092 0.752 0.156
#> SRR1093075 1 0.1289 0.875 0.968 0.000 0.032
#> SRR1433329 1 0.2356 0.858 0.928 0.000 0.072
#> SRR1353418 3 0.3116 0.799 0.108 0.000 0.892
#> SRR1092913 2 0.6463 0.802 0.080 0.756 0.164
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.2921 0.784 0.860 0.000 0.000 0.140
#> SRR1335605 4 0.5861 -0.637 0.480 0.000 0.032 0.488
#> SRR1432014 3 0.1022 0.834 0.000 0.000 0.968 0.032
#> SRR1499215 1 0.6613 0.283 0.628 0.000 0.172 0.200
#> SRR1460409 1 0.5432 0.790 0.652 0.000 0.032 0.316
#> SRR1086441 1 0.4331 0.804 0.712 0.000 0.000 0.288
#> SRR1097344 4 0.5267 0.608 0.000 0.184 0.076 0.740
#> SRR1081789 4 0.6049 0.627 0.000 0.184 0.132 0.684
#> SRR1453005 4 0.6308 0.594 0.000 0.232 0.120 0.648
#> SRR1366985 1 0.3494 0.559 0.824 0.000 0.172 0.004
#> SRR815280 1 0.0592 0.736 0.984 0.000 0.000 0.016
#> SRR1348531 1 0.5473 0.787 0.644 0.000 0.032 0.324
#> SRR815845 3 0.3681 0.719 0.000 0.008 0.816 0.176
#> SRR1471178 1 0.4356 0.803 0.708 0.000 0.000 0.292
#> SRR1080696 3 0.1022 0.834 0.000 0.000 0.968 0.032
#> SRR1078684 4 0.7860 -0.297 0.340 0.000 0.276 0.384
#> SRR1317751 3 0.3913 0.774 0.148 0.000 0.824 0.028
#> SRR1435667 3 0.1022 0.834 0.000 0.000 0.968 0.032
#> SRR1097905 1 0.4382 0.803 0.704 0.000 0.000 0.296
#> SRR1456548 1 0.5453 0.788 0.648 0.000 0.032 0.320
#> SRR1075126 1 0.4382 0.803 0.704 0.000 0.000 0.296
#> SRR813108 4 0.7042 0.404 0.000 0.352 0.132 0.516
#> SRR1479062 3 0.0921 0.839 0.000 0.000 0.972 0.028
#> SRR1408703 3 0.1637 0.837 0.000 0.000 0.940 0.060
#> SRR1332360 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1098686 1 0.4356 0.803 0.708 0.000 0.000 0.292
#> SRR1434228 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1467149 1 0.5473 0.787 0.644 0.000 0.032 0.324
#> SRR1399113 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.6049 0.635 0.000 0.184 0.132 0.684
#> SRR1092468 1 0.5473 0.787 0.644 0.000 0.032 0.324
#> SRR1441804 1 0.4560 0.803 0.700 0.000 0.004 0.296
#> SRR1326100 4 0.6042 0.613 0.000 0.224 0.104 0.672
#> SRR1398815 1 0.3801 0.800 0.780 0.000 0.000 0.220
#> SRR1436021 4 0.5689 0.645 0.000 0.184 0.104 0.712
#> SRR1480083 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.4331 0.804 0.712 0.000 0.000 0.288
#> SRR815542 1 0.5152 0.794 0.664 0.000 0.020 0.316
#> SRR1400100 4 0.5744 0.643 0.000 0.184 0.108 0.708
#> SRR1312002 1 0.4194 0.573 0.800 0.000 0.172 0.028
#> SRR1470253 1 0.4872 0.417 0.728 0.000 0.244 0.028
#> SRR1414332 1 0.3764 0.800 0.784 0.000 0.000 0.216
#> SRR1069209 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR661052 1 0.5453 0.788 0.648 0.000 0.032 0.320
#> SRR1308860 1 0.4356 0.803 0.708 0.000 0.000 0.292
#> SRR1421159 4 0.5689 0.645 0.000 0.184 0.104 0.712
#> SRR1340943 1 0.5862 0.622 0.484 0.000 0.032 0.484
#> SRR1078855 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0707 0.975 0.000 0.980 0.000 0.020
#> SRR816818 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1478679 1 0.7674 0.208 0.460 0.000 0.260 0.280
#> SRR1350979 3 0.0000 0.841 0.000 0.000 1.000 0.000
#> SRR1458198 1 0.5473 0.787 0.644 0.000 0.032 0.324
#> SRR1386910 4 0.4562 0.603 0.000 0.208 0.028 0.764
#> SRR1465375 4 0.4375 0.576 0.000 0.180 0.032 0.788
#> SRR1323699 1 0.7599 -0.300 0.424 0.000 0.376 0.200
#> SRR1431139 1 0.6585 0.669 0.584 0.000 0.104 0.312
#> SRR1373964 3 0.1118 0.833 0.000 0.000 0.964 0.036
#> SRR1455413 1 0.5473 0.787 0.644 0.000 0.032 0.324
#> SRR1437163 1 0.5780 0.636 0.496 0.000 0.028 0.476
#> SRR1347343 3 0.2036 0.829 0.032 0.000 0.936 0.032
#> SRR1465480 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.5453 0.788 0.648 0.000 0.032 0.320
#> SRR1086514 4 0.5728 0.642 0.000 0.188 0.104 0.708
#> SRR1430928 1 0.4356 0.803 0.708 0.000 0.000 0.292
#> SRR1310939 3 0.2596 0.828 0.024 0.000 0.908 0.068
#> SRR1344294 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.4164 0.806 0.736 0.000 0.000 0.264
#> SRR1468118 3 0.5628 0.660 0.144 0.000 0.724 0.132
#> SRR1486348 1 0.4331 0.804 0.712 0.000 0.000 0.288
#> SRR1488770 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.3801 0.800 0.780 0.000 0.000 0.220
#> SRR1456611 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.4932 0.790 0.728 0.000 0.032 0.240
#> SRR1500089 1 0.5754 0.784 0.636 0.000 0.048 0.316
#> SRR1441178 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1381396 1 0.4304 0.805 0.716 0.000 0.000 0.284
#> SRR1096081 3 0.2546 0.826 0.060 0.000 0.912 0.028
#> SRR1349809 4 0.6148 -0.611 0.468 0.048 0.000 0.484
#> SRR1324314 1 0.2011 0.755 0.920 0.000 0.000 0.080
#> SRR1092444 1 0.4900 0.793 0.732 0.000 0.032 0.236
#> SRR1382553 1 0.4289 0.583 0.796 0.000 0.172 0.032
#> SRR1075530 4 0.5689 0.645 0.000 0.184 0.104 0.712
#> SRR1442612 3 0.1022 0.834 0.000 0.000 0.968 0.032
#> SRR1360056 3 0.5558 0.610 0.364 0.000 0.608 0.028
#> SRR1078164 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1434545 4 0.6764 -0.502 0.424 0.036 0.032 0.508
#> SRR1398251 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1375866 1 0.2868 0.783 0.864 0.000 0.000 0.136
#> SRR1091645 4 0.7299 0.464 0.000 0.184 0.296 0.520
#> SRR1416636 3 0.0592 0.842 0.000 0.000 0.984 0.016
#> SRR1105441 4 0.5744 0.643 0.000 0.184 0.108 0.708
#> SRR1082496 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1315353 4 0.6142 0.624 0.000 0.184 0.140 0.676
#> SRR1093697 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.4307 0.760 0.144 0.000 0.808 0.048
#> SRR1076120 1 0.7327 0.671 0.504 0.000 0.176 0.320
#> SRR1074410 1 0.3726 0.798 0.788 0.000 0.000 0.212
#> SRR1340345 4 0.4012 0.609 0.000 0.184 0.016 0.800
#> SRR1069514 4 0.7210 0.509 0.000 0.184 0.276 0.540
#> SRR1092636 3 0.4050 0.770 0.144 0.000 0.820 0.036
#> SRR1365013 4 0.5650 0.645 0.000 0.180 0.104 0.716
#> SRR1073069 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1443137 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR820234 4 0.6449 0.225 0.000 0.452 0.068 0.480
#> SRR1338079 1 0.4356 0.803 0.708 0.000 0.000 0.292
#> SRR1390094 4 0.7909 -0.473 0.324 0.000 0.312 0.364
#> SRR1340721 1 0.4972 0.667 0.544 0.000 0.000 0.456
#> SRR1335964 3 0.3958 0.773 0.144 0.000 0.824 0.032
#> SRR1086869 3 0.3863 0.775 0.144 0.000 0.828 0.028
#> SRR1453434 1 0.4331 0.804 0.712 0.000 0.000 0.288
#> SRR1402261 1 0.5862 0.622 0.484 0.000 0.032 0.484
#> SRR657809 4 0.3444 0.597 0.000 0.184 0.000 0.816
#> SRR1093075 1 0.2011 0.756 0.920 0.000 0.000 0.080
#> SRR1433329 1 0.0000 0.728 1.000 0.000 0.000 0.000
#> SRR1353418 3 0.2469 0.784 0.108 0.000 0.892 0.000
#> SRR1092913 4 0.4418 0.578 0.000 0.184 0.032 0.784
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 3 0.3895 0.4717 0.320 0.000 0.680 0.000 0.000
#> SRR1335605 1 0.1405 0.8644 0.956 0.000 0.008 0.020 0.016
#> SRR1432014 5 0.0671 0.9381 0.000 0.000 0.004 0.016 0.980
#> SRR1499215 3 0.1430 0.7923 0.000 0.000 0.944 0.004 0.052
#> SRR1460409 1 0.0671 0.8715 0.980 0.000 0.004 0.000 0.016
#> SRR1086441 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1097344 4 0.0510 0.9345 0.000 0.000 0.000 0.984 0.016
#> SRR1081789 4 0.0451 0.9397 0.000 0.000 0.008 0.988 0.004
#> SRR1453005 4 0.0162 0.9394 0.000 0.004 0.000 0.996 0.000
#> SRR1366985 3 0.1270 0.7943 0.000 0.000 0.948 0.000 0.052
#> SRR815280 3 0.2648 0.7416 0.152 0.000 0.848 0.000 0.000
#> SRR1348531 1 0.0609 0.8695 0.980 0.000 0.000 0.000 0.020
#> SRR815845 5 0.1608 0.9154 0.000 0.000 0.000 0.072 0.928
#> SRR1471178 1 0.1410 0.8689 0.940 0.000 0.060 0.000 0.000
#> SRR1080696 5 0.0510 0.9395 0.000 0.000 0.000 0.016 0.984
#> SRR1078684 1 0.5018 0.6268 0.692 0.000 0.040 0.020 0.248
#> SRR1317751 5 0.1270 0.9489 0.052 0.000 0.000 0.000 0.948
#> SRR1435667 5 0.0671 0.9381 0.000 0.000 0.004 0.016 0.980
#> SRR1097905 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1456548 1 0.0324 0.8726 0.992 0.000 0.004 0.000 0.004
#> SRR1075126 1 0.1410 0.8695 0.940 0.000 0.060 0.000 0.000
#> SRR813108 4 0.0865 0.9310 0.000 0.024 0.000 0.972 0.004
#> SRR1479062 5 0.1341 0.9479 0.056 0.000 0.000 0.000 0.944
#> SRR1408703 5 0.1774 0.9483 0.052 0.000 0.000 0.016 0.932
#> SRR1332360 3 0.0290 0.8257 0.008 0.000 0.992 0.000 0.000
#> SRR1098686 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1434228 3 0.0290 0.8257 0.008 0.000 0.992 0.000 0.000
#> SRR1467149 1 0.0609 0.8695 0.980 0.000 0.000 0.000 0.020
#> SRR1399113 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0404 0.9365 0.000 0.000 0.000 0.988 0.012
#> SRR1092468 1 0.0579 0.8703 0.984 0.000 0.008 0.000 0.008
#> SRR1441804 1 0.1408 0.8731 0.948 0.000 0.044 0.000 0.008
#> SRR1326100 4 0.0451 0.9387 0.000 0.008 0.000 0.988 0.004
#> SRR1398815 1 0.4210 0.3283 0.588 0.000 0.412 0.000 0.000
#> SRR1436021 4 0.0451 0.9397 0.000 0.000 0.008 0.988 0.004
#> SRR1480083 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR815542 1 0.1012 0.8746 0.968 0.000 0.020 0.000 0.012
#> SRR1400100 4 0.0960 0.9333 0.016 0.000 0.008 0.972 0.004
#> SRR1312002 3 0.1478 0.7966 0.064 0.000 0.936 0.000 0.000
#> SRR1470253 3 0.1845 0.7892 0.056 0.000 0.928 0.000 0.016
#> SRR1414332 1 0.3837 0.5716 0.692 0.000 0.308 0.000 0.000
#> SRR1069209 3 0.0510 0.8249 0.016 0.000 0.984 0.000 0.000
#> SRR661052 1 0.2462 0.8075 0.880 0.000 0.112 0.000 0.008
#> SRR1308860 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1421159 4 0.0451 0.9397 0.000 0.000 0.008 0.988 0.004
#> SRR1340943 1 0.1216 0.8654 0.960 0.000 0.000 0.020 0.020
#> SRR1078855 3 0.0290 0.8257 0.008 0.000 0.992 0.000 0.000
#> SRR1459465 2 0.1197 0.9484 0.000 0.952 0.000 0.048 0.000
#> SRR816818 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 1 0.5905 0.3806 0.600 0.000 0.308 0.036 0.056
#> SRR1350979 5 0.0613 0.9420 0.004 0.000 0.008 0.004 0.984
#> SRR1458198 1 0.0609 0.8695 0.980 0.000 0.000 0.000 0.020
#> SRR1386910 4 0.3961 0.6372 0.248 0.016 0.000 0.736 0.000
#> SRR1465375 4 0.3817 0.6409 0.252 0.000 0.004 0.740 0.004
#> SRR1323699 3 0.1430 0.7923 0.000 0.000 0.944 0.004 0.052
#> SRR1431139 1 0.1200 0.8664 0.964 0.000 0.012 0.016 0.008
#> SRR1373964 5 0.1018 0.9339 0.000 0.000 0.016 0.016 0.968
#> SRR1455413 1 0.0898 0.8677 0.972 0.000 0.008 0.000 0.020
#> SRR1437163 1 0.0613 0.8737 0.984 0.000 0.008 0.004 0.004
#> SRR1347343 5 0.0912 0.9355 0.000 0.000 0.016 0.012 0.972
#> SRR1465480 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.0290 0.8713 0.992 0.000 0.000 0.000 0.008
#> SRR1086514 4 0.0162 0.9396 0.000 0.000 0.000 0.996 0.004
#> SRR1430928 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1310939 5 0.1956 0.9306 0.076 0.000 0.008 0.000 0.916
#> SRR1344294 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.2605 0.8117 0.852 0.000 0.148 0.000 0.000
#> SRR1468118 5 0.1410 0.9447 0.060 0.000 0.000 0.000 0.940
#> SRR1486348 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1488770 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.3480 0.6605 0.752 0.000 0.248 0.000 0.000
#> SRR1456611 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.4811 0.0532 0.528 0.000 0.452 0.000 0.020
#> SRR1500089 1 0.2852 0.7523 0.828 0.000 0.000 0.000 0.172
#> SRR1441178 3 0.0510 0.8245 0.016 0.000 0.984 0.000 0.000
#> SRR1381396 1 0.2516 0.8175 0.860 0.000 0.140 0.000 0.000
#> SRR1096081 5 0.1270 0.9489 0.052 0.000 0.000 0.000 0.948
#> SRR1349809 1 0.2409 0.8497 0.908 0.056 0.008 0.028 0.000
#> SRR1324314 3 0.3752 0.5460 0.292 0.000 0.708 0.000 0.000
#> SRR1092444 1 0.4508 0.4055 0.648 0.000 0.332 0.000 0.020
#> SRR1382553 3 0.4629 0.6152 0.244 0.000 0.704 0.000 0.052
#> SRR1075530 4 0.0000 0.9391 0.000 0.000 0.000 1.000 0.000
#> SRR1442612 5 0.0671 0.9381 0.000 0.000 0.004 0.016 0.980
#> SRR1360056 3 0.5281 0.1693 0.052 0.000 0.548 0.000 0.400
#> SRR1078164 3 0.0771 0.8238 0.020 0.000 0.976 0.000 0.004
#> SRR1434545 1 0.3689 0.6332 0.740 0.000 0.000 0.256 0.004
#> SRR1398251 3 0.0451 0.8245 0.008 0.000 0.988 0.000 0.004
#> SRR1375866 3 0.3906 0.5230 0.292 0.000 0.704 0.000 0.004
#> SRR1091645 4 0.0880 0.9276 0.000 0.000 0.000 0.968 0.032
#> SRR1416636 5 0.0703 0.9490 0.024 0.000 0.000 0.000 0.976
#> SRR1105441 4 0.0451 0.9397 0.000 0.000 0.008 0.988 0.004
#> SRR1082496 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 4 0.0451 0.9397 0.000 0.000 0.008 0.988 0.004
#> SRR1093697 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.1341 0.9479 0.056 0.000 0.000 0.000 0.944
#> SRR1076120 1 0.1043 0.8627 0.960 0.000 0.000 0.000 0.040
#> SRR1074410 3 0.4306 -0.0629 0.492 0.000 0.508 0.000 0.000
#> SRR1340345 4 0.0404 0.9363 0.012 0.000 0.000 0.988 0.000
#> SRR1069514 4 0.1628 0.9044 0.000 0.000 0.008 0.936 0.056
#> SRR1092636 5 0.1341 0.9479 0.056 0.000 0.000 0.000 0.944
#> SRR1365013 4 0.1329 0.9195 0.032 0.000 0.008 0.956 0.004
#> SRR1073069 3 0.0290 0.8257 0.008 0.000 0.992 0.000 0.000
#> SRR1443137 3 0.0290 0.8257 0.008 0.000 0.992 0.000 0.000
#> SRR1437143 2 0.0000 0.9951 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 3 0.0703 0.8224 0.024 0.000 0.976 0.000 0.000
#> SRR820234 4 0.3300 0.7263 0.000 0.204 0.000 0.792 0.004
#> SRR1338079 1 0.1341 0.8695 0.944 0.000 0.056 0.000 0.000
#> SRR1390094 1 0.2886 0.8034 0.864 0.000 0.016 0.004 0.116
#> SRR1340721 1 0.1697 0.8695 0.932 0.000 0.060 0.008 0.000
#> SRR1335964 5 0.1628 0.9465 0.056 0.000 0.008 0.000 0.936
#> SRR1086869 5 0.1270 0.9489 0.052 0.000 0.000 0.000 0.948
#> SRR1453434 1 0.1768 0.8658 0.924 0.000 0.072 0.000 0.004
#> SRR1402261 1 0.0798 0.8694 0.976 0.000 0.000 0.016 0.008
#> SRR657809 4 0.0404 0.9363 0.012 0.000 0.000 0.988 0.000
#> SRR1093075 3 0.4287 0.1015 0.460 0.000 0.540 0.000 0.000
#> SRR1433329 3 0.0290 0.8257 0.008 0.000 0.992 0.000 0.000
#> SRR1353418 5 0.2377 0.8293 0.000 0.000 0.128 0.000 0.872
#> SRR1092913 4 0.0566 0.9350 0.012 0.000 0.000 0.984 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.2218 0.7314 0.884 0.000 0.000 0.000 0.012 0.104
#> SRR1335605 5 0.3073 0.7151 0.204 0.000 0.000 0.008 0.788 0.000
#> SRR1432014 3 0.0000 0.6491 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1499215 3 0.3864 0.2304 0.000 0.000 0.520 0.000 0.000 0.480
#> SRR1460409 5 0.3862 0.3997 0.476 0.000 0.000 0.000 0.524 0.000
#> SRR1086441 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1097344 4 0.0260 0.9311 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1081789 4 0.0692 0.9327 0.000 0.000 0.020 0.976 0.004 0.000
#> SRR1453005 4 0.0260 0.9311 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1366985 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815280 1 0.2793 0.6123 0.800 0.000 0.000 0.000 0.000 0.200
#> SRR1348531 5 0.2912 0.7099 0.216 0.000 0.000 0.000 0.784 0.000
#> SRR815845 4 0.4179 0.1865 0.000 0.000 0.472 0.516 0.012 0.000
#> SRR1471178 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1080696 3 0.0260 0.6466 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1078684 3 0.5554 0.1061 0.392 0.000 0.492 0.008 0.108 0.000
#> SRR1317751 5 0.3851 0.3454 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1435667 3 0.0260 0.6493 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1097905 1 0.1910 0.7609 0.892 0.000 0.000 0.000 0.108 0.000
#> SRR1456548 5 0.3076 0.6975 0.240 0.000 0.000 0.000 0.760 0.000
#> SRR1075126 1 0.4586 0.5475 0.660 0.000 0.000 0.000 0.076 0.264
#> SRR813108 4 0.0951 0.9309 0.000 0.008 0.020 0.968 0.004 0.000
#> SRR1479062 5 0.3012 0.6162 0.000 0.000 0.196 0.008 0.796 0.000
#> SRR1408703 5 0.3864 0.3125 0.000 0.000 0.480 0.000 0.520 0.000
#> SRR1332360 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1098686 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1434228 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1467149 5 0.2260 0.7220 0.140 0.000 0.000 0.000 0.860 0.000
#> SRR1399113 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.0508 0.9309 0.000 0.000 0.004 0.984 0.012 0.000
#> SRR1092468 5 0.3023 0.6996 0.232 0.000 0.000 0.000 0.768 0.000
#> SRR1441804 5 0.3817 0.3475 0.432 0.000 0.000 0.000 0.568 0.000
#> SRR1326100 4 0.0748 0.9328 0.000 0.004 0.016 0.976 0.004 0.000
#> SRR1398815 1 0.3693 0.6238 0.788 0.000 0.000 0.000 0.120 0.092
#> SRR1436021 4 0.0692 0.9327 0.000 0.000 0.020 0.976 0.004 0.000
#> SRR1480083 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR815542 1 0.3151 0.4731 0.748 0.000 0.000 0.000 0.252 0.000
#> SRR1400100 4 0.1334 0.9178 0.000 0.000 0.020 0.948 0.032 0.000
#> SRR1312002 6 0.0260 0.7639 0.000 0.000 0.000 0.000 0.008 0.992
#> SRR1470253 6 0.2260 0.6975 0.000 0.000 0.000 0.000 0.140 0.860
#> SRR1414332 1 0.0603 0.8079 0.980 0.000 0.000 0.000 0.004 0.016
#> SRR1069209 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR661052 5 0.3547 0.5913 0.332 0.000 0.000 0.000 0.668 0.000
#> SRR1308860 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1421159 4 0.0692 0.9327 0.000 0.000 0.020 0.976 0.004 0.000
#> SRR1340943 5 0.3103 0.7131 0.208 0.000 0.000 0.008 0.784 0.000
#> SRR1078855 6 0.3789 0.1497 0.416 0.000 0.000 0.000 0.000 0.584
#> SRR1459465 2 0.0363 0.9848 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR816818 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.4462 0.2812 0.008 0.000 0.540 0.016 0.000 0.436
#> SRR1350979 3 0.2996 0.4603 0.000 0.000 0.772 0.000 0.228 0.000
#> SRR1458198 5 0.2941 0.7074 0.220 0.000 0.000 0.000 0.780 0.000
#> SRR1386910 4 0.2862 0.8394 0.000 0.048 0.008 0.864 0.080 0.000
#> SRR1465375 4 0.1918 0.8597 0.008 0.000 0.000 0.904 0.088 0.000
#> SRR1323699 3 0.3864 0.2304 0.000 0.000 0.520 0.000 0.000 0.480
#> SRR1431139 5 0.3708 0.6982 0.220 0.000 0.020 0.008 0.752 0.000
#> SRR1373964 3 0.0260 0.6493 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1455413 5 0.2260 0.7220 0.140 0.000 0.000 0.000 0.860 0.000
#> SRR1437163 1 0.3634 0.2501 0.644 0.000 0.000 0.000 0.356 0.000
#> SRR1347343 3 0.0363 0.6444 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR1465480 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 5 0.2883 0.7117 0.212 0.000 0.000 0.000 0.788 0.000
#> SRR1086514 4 0.0692 0.9327 0.000 0.000 0.020 0.976 0.004 0.000
#> SRR1430928 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310939 5 0.3468 0.5905 0.004 0.000 0.284 0.000 0.712 0.000
#> SRR1344294 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.3020 0.7618 0.844 0.000 0.000 0.000 0.076 0.080
#> SRR1468118 5 0.3851 0.3454 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1486348 1 0.0000 0.8123 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.0260 0.8108 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1456611 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 5 0.1866 0.6416 0.008 0.000 0.000 0.000 0.908 0.084
#> SRR1500089 5 0.3081 0.7145 0.220 0.000 0.004 0.000 0.776 0.000
#> SRR1441178 6 0.2302 0.7148 0.008 0.000 0.000 0.000 0.120 0.872
#> SRR1381396 1 0.2902 0.7331 0.800 0.000 0.000 0.000 0.196 0.004
#> SRR1096081 5 0.3854 0.3426 0.000 0.000 0.464 0.000 0.536 0.000
#> SRR1349809 1 0.6997 0.3259 0.484 0.272 0.016 0.152 0.076 0.000
#> SRR1324314 6 0.1765 0.6858 0.096 0.000 0.000 0.000 0.000 0.904
#> SRR1092444 5 0.1838 0.6520 0.016 0.000 0.000 0.000 0.916 0.068
#> SRR1382553 3 0.4097 0.2068 0.008 0.000 0.504 0.000 0.000 0.488
#> SRR1075530 4 0.0260 0.9311 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1442612 3 0.0000 0.6491 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1360056 5 0.5902 0.0904 0.000 0.000 0.204 0.000 0.404 0.392
#> SRR1078164 6 0.4371 0.5624 0.052 0.000 0.000 0.000 0.284 0.664
#> SRR1434545 5 0.5595 0.4994 0.192 0.000 0.000 0.268 0.540 0.000
#> SRR1398251 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1375866 6 0.5377 0.4448 0.124 0.000 0.000 0.000 0.348 0.528
#> SRR1091645 4 0.0520 0.9288 0.000 0.000 0.008 0.984 0.008 0.000
#> SRR1416636 3 0.3804 -0.1577 0.000 0.000 0.576 0.000 0.424 0.000
#> SRR1105441 4 0.0692 0.9327 0.000 0.000 0.020 0.976 0.004 0.000
#> SRR1082496 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 4 0.0692 0.9327 0.000 0.000 0.020 0.976 0.004 0.000
#> SRR1093697 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.2823 0.6150 0.000 0.000 0.204 0.000 0.796 0.000
#> SRR1076120 5 0.2883 0.7117 0.212 0.000 0.000 0.000 0.788 0.000
#> SRR1074410 6 0.5910 0.2553 0.308 0.000 0.000 0.000 0.232 0.460
#> SRR1340345 4 0.0260 0.9311 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1069514 3 0.3982 0.0217 0.000 0.000 0.536 0.460 0.004 0.000
#> SRR1092636 5 0.2823 0.6150 0.000 0.000 0.204 0.000 0.796 0.000
#> SRR1365013 4 0.1926 0.8857 0.000 0.000 0.020 0.912 0.068 0.000
#> SRR1073069 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1443137 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1437143 2 0.0000 0.9985 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.3620 0.4327 0.352 0.000 0.000 0.000 0.000 0.648
#> SRR820234 4 0.3514 0.7232 0.000 0.208 0.020 0.768 0.004 0.000
#> SRR1338079 1 0.1556 0.7815 0.920 0.000 0.000 0.000 0.080 0.000
#> SRR1390094 5 0.5001 0.5755 0.308 0.000 0.096 0.000 0.596 0.000
#> SRR1340721 1 0.1757 0.7797 0.916 0.000 0.000 0.008 0.076 0.000
#> SRR1335964 5 0.3012 0.6162 0.000 0.000 0.196 0.008 0.796 0.000
#> SRR1086869 5 0.3851 0.3454 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1453434 1 0.4495 0.5352 0.660 0.000 0.000 0.000 0.064 0.276
#> SRR1402261 5 0.3103 0.7131 0.208 0.000 0.000 0.008 0.784 0.000
#> SRR657809 4 0.0260 0.9311 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1093075 1 0.3864 0.0870 0.520 0.000 0.000 0.000 0.000 0.480
#> SRR1433329 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1353418 6 0.3982 0.1359 0.000 0.000 0.460 0.000 0.004 0.536
#> SRR1092913 4 0.0260 0.9311 0.000 0.000 0.000 0.992 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["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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.706 0.921 0.925 0.4363 0.550 0.550
#> 3 3 0.768 0.871 0.942 0.3536 0.827 0.696
#> 4 4 0.783 0.797 0.906 0.1663 0.780 0.535
#> 5 5 0.687 0.766 0.840 0.0706 0.906 0.714
#> 6 6 0.800 0.742 0.846 0.0570 0.954 0.830
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
#> SRR816969 1 0.3274 0.96805 0.940 0.060
#> SRR1335605 2 0.2423 0.94488 0.040 0.960
#> SRR1432014 2 0.2236 0.94606 0.036 0.964
#> SRR1499215 2 0.2423 0.94488 0.040 0.960
#> SRR1460409 1 0.3274 0.96805 0.940 0.060
#> SRR1086441 1 0.3274 0.96805 0.940 0.060
#> SRR1097344 2 0.0376 0.94194 0.004 0.996
#> SRR1081789 2 0.0376 0.94194 0.004 0.996
#> SRR1453005 2 0.2423 0.91861 0.040 0.960
#> SRR1366985 2 0.3431 0.92965 0.064 0.936
#> SRR815280 1 0.5294 0.94859 0.880 0.120
#> SRR1348531 1 0.5629 0.93803 0.868 0.132
#> SRR815845 2 0.2236 0.94606 0.036 0.964
#> SRR1471178 1 0.3274 0.96805 0.940 0.060
#> SRR1080696 2 0.2236 0.94606 0.036 0.964
#> SRR1078684 2 0.2423 0.94488 0.040 0.960
#> SRR1317751 2 0.2236 0.94606 0.036 0.964
#> SRR1435667 2 0.2236 0.94606 0.036 0.964
#> SRR1097905 1 0.3879 0.96514 0.924 0.076
#> SRR1456548 1 0.3274 0.96805 0.940 0.060
#> SRR1075126 1 0.3274 0.96805 0.940 0.060
#> SRR813108 2 0.3274 0.90560 0.060 0.940
#> SRR1479062 2 0.2423 0.94488 0.040 0.960
#> SRR1408703 2 0.2236 0.94606 0.036 0.964
#> SRR1332360 1 0.5294 0.94859 0.880 0.120
#> SRR1098686 1 0.3274 0.96805 0.940 0.060
#> SRR1434228 2 0.9963 0.04686 0.464 0.536
#> SRR1467149 2 0.4815 0.89989 0.104 0.896
#> SRR1399113 2 0.3274 0.90560 0.060 0.940
#> SRR1476507 2 0.0376 0.94194 0.004 0.996
#> SRR1092468 2 0.4815 0.89989 0.104 0.896
#> SRR1441804 1 0.3733 0.96409 0.928 0.072
#> SRR1326100 2 0.3274 0.90560 0.060 0.940
#> SRR1398815 1 0.3274 0.96805 0.940 0.060
#> SRR1436021 2 0.0376 0.94194 0.004 0.996
#> SRR1480083 2 0.3274 0.90560 0.060 0.940
#> SRR1472863 1 0.5294 0.94859 0.880 0.120
#> SRR815542 1 0.3274 0.96805 0.940 0.060
#> SRR1400100 2 0.2236 0.94606 0.036 0.964
#> SRR1312002 2 0.3431 0.92965 0.064 0.936
#> SRR1470253 2 0.2423 0.94488 0.040 0.960
#> SRR1414332 1 0.3274 0.96805 0.940 0.060
#> SRR1069209 1 0.5294 0.94859 0.880 0.120
#> SRR661052 1 0.3274 0.96805 0.940 0.060
#> SRR1308860 1 0.3431 0.96771 0.936 0.064
#> SRR1421159 2 0.0376 0.94194 0.004 0.996
#> SRR1340943 2 0.0672 0.94177 0.008 0.992
#> SRR1078855 1 0.5178 0.95071 0.884 0.116
#> SRR1459465 2 0.3274 0.90560 0.060 0.940
#> SRR816818 2 0.3274 0.90560 0.060 0.940
#> SRR1478679 2 0.2423 0.94488 0.040 0.960
#> SRR1350979 2 0.2236 0.94606 0.036 0.964
#> SRR1458198 2 0.4815 0.89989 0.104 0.896
#> SRR1386910 2 0.0376 0.94194 0.004 0.996
#> SRR1465375 2 0.0376 0.94194 0.004 0.996
#> SRR1323699 2 0.2423 0.94488 0.040 0.960
#> SRR1431139 2 0.2423 0.94488 0.040 0.960
#> SRR1373964 2 0.2236 0.94606 0.036 0.964
#> SRR1455413 2 0.2603 0.94306 0.044 0.956
#> SRR1437163 1 0.4562 0.95877 0.904 0.096
#> SRR1347343 2 0.2236 0.94606 0.036 0.964
#> SRR1465480 2 0.3274 0.90560 0.060 0.940
#> SRR1489631 1 0.3274 0.96805 0.940 0.060
#> SRR1086514 2 0.0000 0.94071 0.000 1.000
#> SRR1430928 1 0.3431 0.96771 0.936 0.064
#> SRR1310939 2 0.2423 0.94488 0.040 0.960
#> SRR1344294 2 0.3274 0.90560 0.060 0.940
#> SRR1099402 1 0.3274 0.96805 0.940 0.060
#> SRR1468118 2 0.2236 0.94606 0.036 0.964
#> SRR1486348 1 0.3274 0.96805 0.940 0.060
#> SRR1488770 2 0.3274 0.90560 0.060 0.940
#> SRR1083732 1 0.3274 0.96805 0.940 0.060
#> SRR1456611 2 0.3274 0.90560 0.060 0.940
#> SRR1080318 1 0.3274 0.96805 0.940 0.060
#> SRR1500089 2 0.4815 0.89989 0.104 0.896
#> SRR1441178 1 0.5294 0.94859 0.880 0.120
#> SRR1381396 1 0.3274 0.96805 0.940 0.060
#> SRR1096081 2 0.2236 0.94606 0.036 0.964
#> SRR1349809 2 0.0376 0.94194 0.004 0.996
#> SRR1324314 2 0.3733 0.92345 0.072 0.928
#> SRR1092444 1 0.4431 0.95248 0.908 0.092
#> SRR1382553 2 0.3274 0.93272 0.060 0.940
#> SRR1075530 2 0.0376 0.94194 0.004 0.996
#> SRR1442612 2 0.2236 0.94606 0.036 0.964
#> SRR1360056 2 0.2236 0.94606 0.036 0.964
#> SRR1078164 1 0.5629 0.93803 0.868 0.132
#> SRR1434545 2 0.0376 0.94194 0.004 0.996
#> SRR1398251 2 0.9977 0.00812 0.472 0.528
#> SRR1375866 1 0.5294 0.94859 0.880 0.120
#> SRR1091645 2 0.0376 0.94194 0.004 0.996
#> SRR1416636 2 0.2236 0.94606 0.036 0.964
#> SRR1105441 2 0.2236 0.94606 0.036 0.964
#> SRR1082496 2 0.3274 0.90560 0.060 0.940
#> SRR1315353 2 0.0000 0.94071 0.000 1.000
#> SRR1093697 2 0.3274 0.90560 0.060 0.940
#> SRR1077429 2 0.2236 0.94606 0.036 0.964
#> SRR1076120 2 0.2603 0.94306 0.044 0.956
#> SRR1074410 1 0.3274 0.96805 0.940 0.060
#> SRR1340345 2 0.0376 0.94194 0.004 0.996
#> SRR1069514 2 0.2236 0.94606 0.036 0.964
#> SRR1092636 2 0.2423 0.94488 0.040 0.960
#> SRR1365013 2 0.0376 0.94194 0.004 0.996
#> SRR1073069 1 0.8207 0.75885 0.744 0.256
#> SRR1443137 1 0.5294 0.94859 0.880 0.120
#> SRR1437143 2 0.3274 0.90560 0.060 0.940
#> SRR1091990 1 0.5178 0.95071 0.884 0.116
#> SRR820234 2 0.3274 0.90560 0.060 0.940
#> SRR1338079 1 0.3274 0.96805 0.940 0.060
#> SRR1390094 2 0.2423 0.94488 0.040 0.960
#> SRR1340721 2 0.9460 0.39635 0.364 0.636
#> SRR1335964 2 0.2423 0.94488 0.040 0.960
#> SRR1086869 2 0.2236 0.94606 0.036 0.964
#> SRR1453434 1 0.3274 0.96805 0.940 0.060
#> SRR1402261 2 0.3733 0.92590 0.072 0.928
#> SRR657809 2 0.0376 0.94194 0.004 0.996
#> SRR1093075 1 0.5294 0.94859 0.880 0.120
#> SRR1433329 1 0.5294 0.94859 0.880 0.120
#> SRR1353418 2 0.2236 0.94606 0.036 0.964
#> SRR1092913 2 0.0376 0.94194 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1335605 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1432014 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1499215 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1460409 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1097344 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1081789 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1453005 3 0.5397 0.6923 0.000 0.280 0.720
#> SRR1366985 3 0.1964 0.8627 0.056 0.000 0.944
#> SRR815280 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR815845 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1471178 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1078684 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1317751 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1435667 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1097905 1 0.6291 0.0636 0.532 0.000 0.468
#> SRR1456548 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR813108 2 0.6168 0.2046 0.000 0.588 0.412
#> SRR1479062 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1332360 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1434228 1 0.4931 0.6646 0.768 0.000 0.232
#> SRR1467149 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1399113 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1476507 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1092468 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1441804 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1326100 2 0.3551 0.7852 0.000 0.868 0.132
#> SRR1398815 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1436021 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1480083 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1472863 1 0.5650 0.4768 0.688 0.000 0.312
#> SRR815542 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1400100 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1312002 3 0.1753 0.8707 0.048 0.000 0.952
#> SRR1470253 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1414332 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1421159 3 0.4121 0.8287 0.000 0.168 0.832
#> SRR1340943 3 0.4291 0.8212 0.000 0.180 0.820
#> SRR1078855 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1478679 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1350979 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1458198 3 0.1411 0.8902 0.036 0.000 0.964
#> SRR1386910 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1465375 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1323699 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1431139 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1373964 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1455413 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1437163 3 0.6140 0.3765 0.404 0.000 0.596
#> SRR1347343 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1465480 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1489631 1 0.0747 0.9375 0.984 0.000 0.016
#> SRR1086514 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1430928 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1500089 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1441178 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1349809 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1324314 3 0.4555 0.6652 0.200 0.000 0.800
#> SRR1092444 1 0.4399 0.7022 0.812 0.000 0.188
#> SRR1382553 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1075530 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1442612 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1360056 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1078164 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1434545 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1398251 1 0.2959 0.8325 0.900 0.000 0.100
#> SRR1375866 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1091645 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1416636 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1105441 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1082496 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1315353 3 0.3267 0.8581 0.000 0.116 0.884
#> SRR1093697 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1076120 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1074410 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1340345 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1069514 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1092636 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1365013 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1073069 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.9083 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR820234 2 0.6168 0.2046 0.000 0.588 0.412
#> SRR1338079 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1390094 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1340721 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1335964 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1402261 3 0.4291 0.8212 0.000 0.180 0.820
#> SRR657809 3 0.4399 0.8157 0.000 0.188 0.812
#> SRR1093075 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1353418 3 0.0000 0.9107 0.000 0.000 1.000
#> SRR1092913 3 0.4399 0.8157 0.000 0.188 0.812
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1335605 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1432014 3 0.0188 0.9083 0.000 0.000 0.996 0.004
#> SRR1499215 3 0.0817 0.9094 0.000 0.000 0.976 0.024
#> SRR1460409 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.1022 0.8075 0.000 0.000 0.032 0.968
#> SRR1081789 3 0.7687 -0.0467 0.000 0.348 0.428 0.224
#> SRR1453005 4 0.5036 0.5142 0.000 0.280 0.024 0.696
#> SRR1366985 1 0.4605 0.5980 0.664 0.000 0.336 0.000
#> SRR815280 1 0.0469 0.8859 0.988 0.000 0.000 0.012
#> SRR1348531 1 0.2011 0.8617 0.920 0.000 0.080 0.000
#> SRR815845 3 0.2081 0.8674 0.000 0.000 0.916 0.084
#> SRR1471178 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0707 0.9027 0.000 0.000 0.980 0.020
#> SRR1078684 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1317751 3 0.0707 0.8981 0.000 0.000 0.980 0.020
#> SRR1435667 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1097905 1 0.1118 0.8809 0.964 0.000 0.036 0.000
#> SRR1456548 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1075126 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR813108 2 0.3486 0.6520 0.000 0.812 0.188 0.000
#> SRR1479062 3 0.0592 0.9101 0.000 0.000 0.984 0.016
#> SRR1408703 3 0.0188 0.9083 0.000 0.000 0.996 0.004
#> SRR1332360 1 0.1767 0.8755 0.944 0.000 0.044 0.012
#> SRR1098686 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1434228 1 0.3625 0.7904 0.828 0.000 0.160 0.012
#> SRR1467149 1 0.4978 0.5251 0.612 0.000 0.384 0.004
#> SRR1399113 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.1022 0.8075 0.000 0.000 0.032 0.968
#> SRR1092468 3 0.2494 0.8679 0.048 0.000 0.916 0.036
#> SRR1441804 1 0.2011 0.8617 0.920 0.000 0.080 0.000
#> SRR1326100 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1436021 4 0.4948 0.2488 0.000 0.000 0.440 0.560
#> SRR1480083 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0817 0.8816 0.976 0.000 0.024 0.000
#> SRR815542 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1400100 3 0.3172 0.7797 0.000 0.000 0.840 0.160
#> SRR1312002 3 0.1389 0.8740 0.048 0.000 0.952 0.000
#> SRR1470253 1 0.4843 0.5131 0.604 0.000 0.396 0.000
#> SRR1414332 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.0657 0.8860 0.984 0.000 0.004 0.012
#> SRR661052 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1308860 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1421159 3 0.4730 0.4041 0.000 0.000 0.636 0.364
#> SRR1340943 4 0.4522 0.5749 0.000 0.000 0.320 0.680
#> SRR1078855 1 0.0188 0.8874 0.996 0.000 0.000 0.004
#> SRR1459465 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0188 0.9023 0.000 0.996 0.000 0.004
#> SRR1478679 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1350979 3 0.0188 0.9083 0.000 0.000 0.996 0.004
#> SRR1458198 1 0.4817 0.5282 0.612 0.000 0.388 0.000
#> SRR1386910 2 0.7186 -0.1689 0.000 0.444 0.136 0.420
#> SRR1465375 4 0.1940 0.8011 0.000 0.000 0.076 0.924
#> SRR1323699 3 0.0817 0.9094 0.000 0.000 0.976 0.024
#> SRR1431139 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1373964 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1455413 1 0.4817 0.5282 0.612 0.000 0.388 0.000
#> SRR1437163 1 0.0921 0.8793 0.972 0.000 0.028 0.000
#> SRR1347343 3 0.0469 0.9099 0.000 0.000 0.988 0.012
#> SRR1465480 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1086514 4 0.4105 0.6989 0.000 0.156 0.032 0.812
#> SRR1430928 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.0817 0.9097 0.000 0.000 0.976 0.024
#> SRR1344294 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.0817 0.9005 0.000 0.000 0.976 0.024
#> SRR1486348 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.1716 0.8700 0.936 0.000 0.064 0.000
#> SRR1500089 1 0.4817 0.5282 0.612 0.000 0.388 0.000
#> SRR1441178 1 0.2255 0.8653 0.920 0.000 0.068 0.012
#> SRR1381396 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.0707 0.8981 0.000 0.000 0.980 0.020
#> SRR1349809 2 0.6745 0.3025 0.000 0.612 0.176 0.212
#> SRR1324314 3 0.3024 0.7502 0.148 0.000 0.852 0.000
#> SRR1092444 1 0.3172 0.8019 0.840 0.000 0.160 0.000
#> SRR1382553 3 0.1411 0.9015 0.020 0.000 0.960 0.020
#> SRR1075530 4 0.1022 0.8075 0.000 0.000 0.032 0.968
#> SRR1442612 3 0.0707 0.9101 0.000 0.000 0.980 0.020
#> SRR1360056 3 0.0000 0.9073 0.000 0.000 1.000 0.000
#> SRR1078164 1 0.2610 0.8540 0.900 0.000 0.088 0.012
#> SRR1434545 4 0.3444 0.7335 0.000 0.000 0.184 0.816
#> SRR1398251 1 0.4844 0.6377 0.688 0.000 0.300 0.012
#> SRR1375866 1 0.0469 0.8859 0.988 0.000 0.000 0.012
#> SRR1091645 4 0.1716 0.8011 0.000 0.000 0.064 0.936
#> SRR1416636 3 0.0817 0.9005 0.000 0.000 0.976 0.024
#> SRR1105441 3 0.3219 0.7743 0.000 0.000 0.836 0.164
#> SRR1082496 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.3907 0.6721 0.000 0.000 0.768 0.232
#> SRR1093697 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.0188 0.9062 0.000 0.000 0.996 0.004
#> SRR1076120 1 0.4817 0.5282 0.612 0.000 0.388 0.000
#> SRR1074410 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1340345 4 0.1022 0.8075 0.000 0.000 0.032 0.968
#> SRR1069514 3 0.2281 0.8573 0.000 0.000 0.904 0.096
#> SRR1092636 3 0.0000 0.9073 0.000 0.000 1.000 0.000
#> SRR1365013 3 0.6831 0.1517 0.000 0.112 0.536 0.352
#> SRR1073069 1 0.2542 0.8564 0.904 0.000 0.084 0.012
#> SRR1443137 1 0.2255 0.8653 0.920 0.000 0.068 0.012
#> SRR1437143 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0469 0.8859 0.988 0.000 0.000 0.012
#> SRR820234 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0000 0.8879 1.000 0.000 0.000 0.000
#> SRR1390094 3 0.1118 0.9064 0.000 0.000 0.964 0.036
#> SRR1340721 1 0.9608 -0.1269 0.312 0.300 0.268 0.120
#> SRR1335964 3 0.0336 0.9092 0.000 0.000 0.992 0.008
#> SRR1086869 3 0.0817 0.9005 0.000 0.000 0.976 0.024
#> SRR1453434 1 0.0336 0.8874 0.992 0.000 0.008 0.000
#> SRR1402261 4 0.4643 0.5269 0.000 0.000 0.344 0.656
#> SRR657809 4 0.3215 0.7589 0.000 0.092 0.032 0.876
#> SRR1093075 1 0.0188 0.8874 0.996 0.000 0.000 0.004
#> SRR1433329 1 0.2255 0.8653 0.920 0.000 0.068 0.012
#> SRR1353418 3 0.0000 0.9073 0.000 0.000 1.000 0.000
#> SRR1092913 4 0.1022 0.8075 0.000 0.000 0.032 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1335605 3 0.4946 0.8527 0.000 0.000 0.664 0.060 0.276
#> SRR1432014 3 0.4603 0.8312 0.000 0.000 0.668 0.032 0.300
#> SRR1499215 3 0.4583 0.8327 0.000 0.000 0.672 0.032 0.296
#> SRR1460409 1 0.0404 0.8675 0.988 0.000 0.012 0.000 0.000
#> SRR1086441 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1097344 4 0.0510 0.8463 0.000 0.000 0.000 0.984 0.016
#> SRR1081789 3 0.5211 0.6630 0.000 0.004 0.664 0.256 0.076
#> SRR1453005 4 0.2392 0.7655 0.000 0.104 0.004 0.888 0.004
#> SRR1366985 1 0.4517 0.6758 0.708 0.000 0.016 0.016 0.260
#> SRR815280 1 0.2929 0.8287 0.820 0.000 0.180 0.000 0.000
#> SRR1348531 1 0.4043 0.7272 0.756 0.000 0.012 0.012 0.220
#> SRR815845 3 0.5088 0.8464 0.000 0.000 0.668 0.080 0.252
#> SRR1471178 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 5 0.0162 0.8440 0.000 0.000 0.000 0.004 0.996
#> SRR1078684 3 0.4946 0.8527 0.000 0.000 0.664 0.060 0.276
#> SRR1317751 5 0.0510 0.8336 0.000 0.000 0.000 0.016 0.984
#> SRR1435667 3 0.4885 0.8522 0.000 0.000 0.668 0.056 0.276
#> SRR1097905 1 0.0566 0.8674 0.984 0.000 0.012 0.000 0.004
#> SRR1456548 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1075126 1 0.0162 0.8681 0.996 0.000 0.004 0.000 0.000
#> SRR813108 2 0.5128 0.3241 0.000 0.580 0.380 0.036 0.004
#> SRR1479062 5 0.2504 0.7676 0.000 0.000 0.064 0.040 0.896
#> SRR1408703 5 0.0703 0.8406 0.000 0.000 0.000 0.024 0.976
#> SRR1332360 1 0.3730 0.7776 0.712 0.000 0.288 0.000 0.000
#> SRR1098686 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1434228 1 0.4639 0.7766 0.708 0.000 0.236 0.000 0.056
#> SRR1467149 1 0.5847 0.4304 0.560 0.000 0.036 0.040 0.364
#> SRR1399113 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0510 0.8463 0.000 0.000 0.000 0.984 0.016
#> SRR1092468 1 0.4844 0.6497 0.692 0.000 0.008 0.044 0.256
#> SRR1441804 1 0.3512 0.7852 0.816 0.000 0.012 0.012 0.160
#> SRR1326100 2 0.0451 0.9230 0.000 0.988 0.004 0.008 0.000
#> SRR1398815 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1436021 4 0.5650 -0.2735 0.000 0.000 0.456 0.468 0.076
#> SRR1480083 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.2756 0.8231 0.880 0.000 0.012 0.012 0.096
#> SRR815542 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1400100 3 0.5372 0.7759 0.000 0.000 0.668 0.180 0.152
#> SRR1312002 5 0.6854 0.0802 0.224 0.000 0.268 0.016 0.492
#> SRR1470253 1 0.4528 0.6323 0.680 0.000 0.012 0.012 0.296
#> SRR1414332 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1069209 1 0.3305 0.8122 0.776 0.000 0.224 0.000 0.000
#> SRR661052 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1308860 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1421159 3 0.5313 0.4404 0.000 0.000 0.556 0.388 0.056
#> SRR1340943 4 0.4109 0.6392 0.004 0.000 0.036 0.768 0.192
#> SRR1078855 1 0.0510 0.8674 0.984 0.000 0.016 0.000 0.000
#> SRR1459465 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0162 0.9279 0.000 0.996 0.000 0.004 0.000
#> SRR1478679 3 0.4924 0.8528 0.000 0.000 0.668 0.060 0.272
#> SRR1350979 5 0.4942 -0.2860 0.000 0.000 0.432 0.028 0.540
#> SRR1458198 1 0.5296 0.6571 0.680 0.000 0.048 0.028 0.244
#> SRR1386910 4 0.7626 -0.0819 0.000 0.192 0.352 0.392 0.064
#> SRR1465375 4 0.1043 0.8342 0.000 0.000 0.000 0.960 0.040
#> SRR1323699 3 0.4526 0.8248 0.000 0.000 0.672 0.028 0.300
#> SRR1431139 3 0.4677 0.8337 0.000 0.000 0.664 0.036 0.300
#> SRR1373964 3 0.4844 0.8504 0.000 0.000 0.668 0.052 0.280
#> SRR1455413 1 0.4752 0.6699 0.700 0.000 0.028 0.016 0.256
#> SRR1437163 1 0.0404 0.8652 0.988 0.000 0.000 0.012 0.000
#> SRR1347343 3 0.4708 0.8406 0.000 0.000 0.668 0.040 0.292
#> SRR1465480 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1086514 4 0.1087 0.8429 0.000 0.008 0.008 0.968 0.016
#> SRR1430928 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 5 0.1041 0.8337 0.000 0.000 0.004 0.032 0.964
#> SRR1344294 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1468118 5 0.0703 0.8347 0.000 0.000 0.000 0.024 0.976
#> SRR1486348 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.1877 0.8497 0.924 0.000 0.012 0.000 0.064
#> SRR1500089 5 0.2758 0.7994 0.032 0.000 0.048 0.024 0.896
#> SRR1441178 1 0.3480 0.7998 0.752 0.000 0.248 0.000 0.000
#> SRR1381396 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1096081 5 0.0510 0.8336 0.000 0.000 0.000 0.016 0.984
#> SRR1349809 2 0.5692 0.3947 0.000 0.640 0.024 0.264 0.072
#> SRR1324314 1 0.5758 0.5591 0.636 0.000 0.128 0.008 0.228
#> SRR1092444 1 0.4074 0.7247 0.752 0.000 0.012 0.012 0.224
#> SRR1382553 3 0.5136 0.7986 0.032 0.000 0.676 0.028 0.264
#> SRR1075530 4 0.0510 0.8463 0.000 0.000 0.000 0.984 0.016
#> SRR1442612 3 0.4756 0.8444 0.000 0.000 0.668 0.044 0.288
#> SRR1360056 5 0.1921 0.8094 0.044 0.000 0.012 0.012 0.932
#> SRR1078164 1 0.4541 0.7819 0.752 0.000 0.112 0.000 0.136
#> SRR1434545 4 0.2582 0.7936 0.004 0.000 0.024 0.892 0.080
#> SRR1398251 1 0.4380 0.7781 0.708 0.000 0.260 0.000 0.032
#> SRR1375866 1 0.2852 0.8320 0.828 0.000 0.172 0.000 0.000
#> SRR1091645 4 0.0703 0.8440 0.000 0.000 0.000 0.976 0.024
#> SRR1416636 5 0.0000 0.8430 0.000 0.000 0.000 0.000 1.000
#> SRR1105441 3 0.5379 0.7860 0.000 0.000 0.668 0.168 0.164
#> SRR1082496 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 3 0.5441 0.5753 0.000 0.000 0.596 0.324 0.080
#> SRR1093697 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.0693 0.8440 0.000 0.000 0.008 0.012 0.980
#> SRR1076120 5 0.2581 0.8081 0.020 0.000 0.048 0.028 0.904
#> SRR1074410 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1340345 4 0.0510 0.8463 0.000 0.000 0.000 0.984 0.016
#> SRR1069514 3 0.5283 0.8183 0.000 0.000 0.672 0.124 0.204
#> SRR1092636 5 0.4547 -0.0847 0.000 0.000 0.400 0.012 0.588
#> SRR1365013 3 0.5511 0.5400 0.000 0.000 0.576 0.344 0.080
#> SRR1073069 1 0.3884 0.7752 0.708 0.000 0.288 0.000 0.004
#> SRR1443137 1 0.3534 0.7957 0.744 0.000 0.256 0.000 0.000
#> SRR1437143 2 0.0000 0.9303 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.3003 0.8265 0.812 0.000 0.188 0.000 0.000
#> SRR820234 2 0.1251 0.8954 0.000 0.956 0.008 0.036 0.000
#> SRR1338079 1 0.0000 0.8682 1.000 0.000 0.000 0.000 0.000
#> SRR1390094 3 0.4983 0.8490 0.000 0.000 0.664 0.064 0.272
#> SRR1340721 1 0.7739 0.3688 0.488 0.208 0.000 0.176 0.128
#> SRR1335964 5 0.0794 0.8384 0.000 0.000 0.000 0.028 0.972
#> SRR1086869 5 0.0794 0.8314 0.000 0.000 0.000 0.028 0.972
#> SRR1453434 1 0.0404 0.8675 0.988 0.000 0.012 0.000 0.000
#> SRR1402261 4 0.4210 0.6171 0.004 0.000 0.036 0.756 0.204
#> SRR657809 4 0.0833 0.8450 0.000 0.004 0.004 0.976 0.016
#> SRR1093075 1 0.0703 0.8668 0.976 0.000 0.024 0.000 0.000
#> SRR1433329 1 0.3707 0.7800 0.716 0.000 0.284 0.000 0.000
#> SRR1353418 5 0.0807 0.8429 0.000 0.000 0.012 0.012 0.976
#> SRR1092913 4 0.0510 0.8463 0.000 0.000 0.000 0.984 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1335605 3 0.0260 0.84579 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1432014 3 0.1141 0.81417 0.000 0.000 0.948 0.000 0.052 0.000
#> SRR1499215 3 0.0260 0.84616 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1460409 1 0.0146 0.88841 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1086441 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1097344 4 0.3606 0.83414 0.000 0.000 0.016 0.728 0.000 0.256
#> SRR1081789 6 0.4704 0.43186 0.000 0.000 0.468 0.044 0.000 0.488
#> SRR1453005 6 0.5813 0.45896 0.000 0.192 0.028 0.188 0.000 0.592
#> SRR1366985 1 0.6237 0.08064 0.400 0.000 0.360 0.004 0.004 0.232
#> SRR815280 1 0.1701 0.87364 0.920 0.000 0.000 0.000 0.008 0.072
#> SRR1348531 1 0.1167 0.88293 0.960 0.000 0.008 0.020 0.012 0.000
#> SRR815845 3 0.0146 0.84794 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1471178 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1080696 5 0.2219 0.78114 0.000 0.000 0.136 0.000 0.864 0.000
#> SRR1078684 3 0.0146 0.84794 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1317751 5 0.0777 0.83879 0.000 0.000 0.024 0.004 0.972 0.000
#> SRR1435667 3 0.0000 0.84826 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1097905 1 0.0547 0.88454 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR1456548 1 0.0363 0.88667 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1075126 1 0.0146 0.88793 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR813108 6 0.5227 -0.00858 0.000 0.452 0.092 0.000 0.000 0.456
#> SRR1479062 5 0.3023 0.71641 0.000 0.000 0.212 0.004 0.784 0.000
#> SRR1408703 5 0.1124 0.83834 0.000 0.000 0.036 0.008 0.956 0.000
#> SRR1332360 1 0.3266 0.75244 0.728 0.000 0.000 0.000 0.000 0.272
#> SRR1098686 1 0.0146 0.88793 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1434228 1 0.3586 0.74589 0.720 0.000 0.012 0.000 0.000 0.268
#> SRR1467149 5 0.5998 0.50145 0.172 0.000 0.020 0.280 0.528 0.000
#> SRR1399113 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3606 0.83414 0.000 0.000 0.016 0.728 0.000 0.256
#> SRR1092468 1 0.5137 0.60743 0.664 0.000 0.024 0.212 0.100 0.000
#> SRR1441804 1 0.1434 0.87199 0.940 0.000 0.000 0.048 0.012 0.000
#> SRR1326100 2 0.3789 0.25912 0.000 0.584 0.000 0.000 0.000 0.416
#> SRR1398815 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1436021 6 0.5156 0.68346 0.000 0.000 0.232 0.152 0.000 0.616
#> SRR1480083 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.0806 0.88505 0.972 0.000 0.020 0.000 0.000 0.008
#> SRR815542 1 0.0146 0.88793 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1400100 3 0.4234 -0.31760 0.000 0.000 0.544 0.016 0.000 0.440
#> SRR1312002 3 0.6096 0.28283 0.252 0.000 0.504 0.004 0.008 0.232
#> SRR1470253 1 0.4940 0.72449 0.708 0.000 0.048 0.004 0.056 0.184
#> SRR1414332 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1069209 1 0.2883 0.79893 0.788 0.000 0.000 0.000 0.000 0.212
#> SRR661052 1 0.0000 0.88798 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1308860 1 0.0146 0.88793 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1421159 6 0.4791 0.69190 0.000 0.000 0.244 0.104 0.000 0.652
#> SRR1340943 4 0.1230 0.65737 0.008 0.000 0.028 0.956 0.008 0.000
#> SRR1078855 1 0.1007 0.88345 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1459465 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0146 0.84794 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1350979 3 0.3464 0.49644 0.000 0.000 0.688 0.000 0.312 0.000
#> SRR1458198 1 0.4245 0.62762 0.684 0.000 0.020 0.280 0.016 0.000
#> SRR1386910 6 0.5589 0.68966 0.000 0.044 0.212 0.112 0.000 0.632
#> SRR1465375 4 0.5030 0.50164 0.000 0.000 0.096 0.588 0.000 0.316
#> SRR1323699 3 0.0260 0.84616 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1431139 3 0.1262 0.82332 0.020 0.000 0.956 0.016 0.008 0.000
#> SRR1373964 3 0.0000 0.84826 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1455413 1 0.4547 0.63846 0.692 0.000 0.020 0.244 0.044 0.000
#> SRR1437163 1 0.0405 0.88764 0.988 0.000 0.008 0.004 0.000 0.000
#> SRR1347343 3 0.0146 0.84739 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1465480 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.0363 0.88667 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1086514 6 0.3816 0.38719 0.000 0.000 0.032 0.240 0.000 0.728
#> SRR1430928 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1310939 5 0.3275 0.79330 0.008 0.000 0.032 0.140 0.820 0.000
#> SRR1344294 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.88798 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468118 5 0.0632 0.83949 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1486348 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1488770 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.0000 0.88798 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.1462 0.86877 0.936 0.000 0.000 0.056 0.008 0.000
#> SRR1500089 5 0.4962 0.64620 0.060 0.000 0.020 0.280 0.640 0.000
#> SRR1441178 1 0.2178 0.84748 0.868 0.000 0.000 0.000 0.000 0.132
#> SRR1381396 1 0.0260 0.88839 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1096081 5 0.0777 0.83879 0.000 0.000 0.024 0.004 0.972 0.000
#> SRR1349809 2 0.6102 0.20376 0.000 0.568 0.212 0.044 0.000 0.176
#> SRR1324314 1 0.5829 0.15691 0.476 0.000 0.372 0.004 0.004 0.144
#> SRR1092444 1 0.3134 0.78872 0.824 0.000 0.016 0.148 0.012 0.000
#> SRR1382553 3 0.2686 0.73905 0.024 0.000 0.868 0.000 0.008 0.100
#> SRR1075530 4 0.3905 0.77472 0.000 0.000 0.016 0.668 0.000 0.316
#> SRR1442612 3 0.0000 0.84826 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1360056 5 0.4345 0.68665 0.000 0.000 0.176 0.004 0.728 0.092
#> SRR1078164 1 0.2609 0.85134 0.868 0.000 0.008 0.004 0.008 0.112
#> SRR1434545 4 0.2959 0.76844 0.008 0.000 0.024 0.844 0.000 0.124
#> SRR1398251 1 0.3674 0.74234 0.716 0.000 0.016 0.000 0.000 0.268
#> SRR1375866 1 0.1327 0.87824 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1091645 4 0.3766 0.83034 0.000 0.000 0.024 0.720 0.000 0.256
#> SRR1416636 5 0.0713 0.83958 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1105441 3 0.0806 0.82860 0.000 0.000 0.972 0.020 0.000 0.008
#> SRR1082496 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 6 0.4738 0.66881 0.000 0.000 0.336 0.064 0.000 0.600
#> SRR1093697 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0891 0.83940 0.000 0.000 0.024 0.008 0.968 0.000
#> SRR1076120 5 0.4245 0.68924 0.016 0.000 0.020 0.280 0.684 0.000
#> SRR1074410 1 0.0260 0.88839 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1340345 4 0.3606 0.83414 0.000 0.000 0.016 0.728 0.000 0.256
#> SRR1069514 3 0.0146 0.84794 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1092636 3 0.3975 0.32060 0.000 0.000 0.600 0.008 0.392 0.000
#> SRR1365013 6 0.4929 0.68823 0.000 0.000 0.300 0.092 0.000 0.608
#> SRR1073069 1 0.3512 0.74565 0.720 0.000 0.008 0.000 0.000 0.272
#> SRR1443137 1 0.2178 0.84748 0.868 0.000 0.000 0.000 0.000 0.132
#> SRR1437143 2 0.0000 0.88063 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.2092 0.85139 0.876 0.000 0.000 0.000 0.000 0.124
#> SRR820234 2 0.4396 0.07582 0.000 0.520 0.024 0.000 0.000 0.456
#> SRR1338079 1 0.0458 0.88853 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1390094 3 0.1749 0.79459 0.016 0.000 0.932 0.004 0.004 0.044
#> SRR1340721 1 0.7378 0.04583 0.456 0.196 0.112 0.016 0.000 0.220
#> SRR1335964 5 0.3014 0.79778 0.000 0.000 0.036 0.132 0.832 0.000
#> SRR1086869 5 0.0777 0.83879 0.000 0.000 0.024 0.004 0.972 0.000
#> SRR1453434 1 0.0692 0.88640 0.976 0.000 0.000 0.020 0.004 0.000
#> SRR1402261 4 0.1679 0.63589 0.028 0.000 0.028 0.936 0.008 0.000
#> SRR657809 6 0.4707 0.50424 0.000 0.000 0.092 0.252 0.000 0.656
#> SRR1093075 1 0.0922 0.88647 0.968 0.000 0.000 0.004 0.004 0.024
#> SRR1433329 1 0.3076 0.77922 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR1353418 5 0.4704 0.66879 0.000 0.000 0.160 0.004 0.696 0.140
#> SRR1092913 4 0.3606 0.83414 0.000 0.000 0.016 0.728 0.000 0.256
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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.848 0.906 0.962 0.4689 0.530 0.530
#> 3 3 0.799 0.832 0.929 0.3950 0.718 0.512
#> 4 4 0.846 0.850 0.932 0.1163 0.866 0.640
#> 5 5 0.704 0.569 0.760 0.0689 0.880 0.612
#> 6 6 0.792 0.738 0.872 0.0515 0.857 0.477
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
#> SRR816969 1 0.000 0.964 1.000 0.000
#> SRR1335605 2 0.973 0.348 0.404 0.596
#> SRR1432014 2 0.839 0.651 0.268 0.732
#> SRR1499215 1 0.118 0.949 0.984 0.016
#> SRR1460409 1 0.000 0.964 1.000 0.000
#> SRR1086441 1 0.000 0.964 1.000 0.000
#> SRR1097344 2 0.000 0.950 0.000 1.000
#> SRR1081789 2 0.000 0.950 0.000 1.000
#> SRR1453005 2 0.000 0.950 0.000 1.000
#> SRR1366985 1 0.000 0.964 1.000 0.000
#> SRR815280 1 0.000 0.964 1.000 0.000
#> SRR1348531 1 0.000 0.964 1.000 0.000
#> SRR815845 2 0.000 0.950 0.000 1.000
#> SRR1471178 1 0.000 0.964 1.000 0.000
#> SRR1080696 1 0.000 0.964 1.000 0.000
#> SRR1078684 2 0.949 0.444 0.368 0.632
#> SRR1317751 1 0.000 0.964 1.000 0.000
#> SRR1435667 2 0.000 0.950 0.000 1.000
#> SRR1097905 1 0.000 0.964 1.000 0.000
#> SRR1456548 1 0.000 0.964 1.000 0.000
#> SRR1075126 1 0.000 0.964 1.000 0.000
#> SRR813108 2 0.000 0.950 0.000 1.000
#> SRR1479062 1 0.949 0.398 0.632 0.368
#> SRR1408703 1 0.000 0.964 1.000 0.000
#> SRR1332360 1 0.000 0.964 1.000 0.000
#> SRR1098686 1 0.000 0.964 1.000 0.000
#> SRR1434228 1 0.000 0.964 1.000 0.000
#> SRR1467149 1 0.000 0.964 1.000 0.000
#> SRR1399113 2 0.000 0.950 0.000 1.000
#> SRR1476507 2 0.000 0.950 0.000 1.000
#> SRR1092468 1 0.000 0.964 1.000 0.000
#> SRR1441804 1 0.000 0.964 1.000 0.000
#> SRR1326100 2 0.000 0.950 0.000 1.000
#> SRR1398815 1 0.000 0.964 1.000 0.000
#> SRR1436021 2 0.000 0.950 0.000 1.000
#> SRR1480083 2 0.000 0.950 0.000 1.000
#> SRR1472863 1 0.000 0.964 1.000 0.000
#> SRR815542 1 0.000 0.964 1.000 0.000
#> SRR1400100 2 0.000 0.950 0.000 1.000
#> SRR1312002 1 0.000 0.964 1.000 0.000
#> SRR1470253 1 0.000 0.964 1.000 0.000
#> SRR1414332 1 0.000 0.964 1.000 0.000
#> SRR1069209 1 0.000 0.964 1.000 0.000
#> SRR661052 1 0.000 0.964 1.000 0.000
#> SRR1308860 1 0.000 0.964 1.000 0.000
#> SRR1421159 2 0.000 0.950 0.000 1.000
#> SRR1340943 1 0.000 0.964 1.000 0.000
#> SRR1078855 1 0.000 0.964 1.000 0.000
#> SRR1459465 2 0.000 0.950 0.000 1.000
#> SRR816818 2 0.000 0.950 0.000 1.000
#> SRR1478679 2 0.563 0.835 0.132 0.868
#> SRR1350979 1 0.975 0.287 0.592 0.408
#> SRR1458198 1 0.000 0.964 1.000 0.000
#> SRR1386910 2 0.000 0.950 0.000 1.000
#> SRR1465375 2 0.000 0.950 0.000 1.000
#> SRR1323699 1 0.706 0.739 0.808 0.192
#> SRR1431139 1 0.000 0.964 1.000 0.000
#> SRR1373964 2 0.653 0.796 0.168 0.832
#> SRR1455413 1 0.000 0.964 1.000 0.000
#> SRR1437163 1 0.000 0.964 1.000 0.000
#> SRR1347343 2 0.861 0.623 0.284 0.716
#> SRR1465480 2 0.000 0.950 0.000 1.000
#> SRR1489631 1 0.000 0.964 1.000 0.000
#> SRR1086514 2 0.000 0.950 0.000 1.000
#> SRR1430928 1 0.000 0.964 1.000 0.000
#> SRR1310939 1 0.993 0.143 0.548 0.452
#> SRR1344294 2 0.000 0.950 0.000 1.000
#> SRR1099402 1 0.000 0.964 1.000 0.000
#> SRR1468118 1 0.000 0.964 1.000 0.000
#> SRR1486348 1 0.000 0.964 1.000 0.000
#> SRR1488770 2 0.000 0.950 0.000 1.000
#> SRR1083732 1 0.000 0.964 1.000 0.000
#> SRR1456611 2 0.000 0.950 0.000 1.000
#> SRR1080318 1 0.000 0.964 1.000 0.000
#> SRR1500089 1 0.000 0.964 1.000 0.000
#> SRR1441178 1 0.000 0.964 1.000 0.000
#> SRR1381396 1 0.000 0.964 1.000 0.000
#> SRR1096081 1 0.000 0.964 1.000 0.000
#> SRR1349809 2 0.000 0.950 0.000 1.000
#> SRR1324314 1 0.000 0.964 1.000 0.000
#> SRR1092444 1 0.000 0.964 1.000 0.000
#> SRR1382553 1 0.000 0.964 1.000 0.000
#> SRR1075530 2 0.000 0.950 0.000 1.000
#> SRR1442612 2 0.662 0.791 0.172 0.828
#> SRR1360056 1 0.000 0.964 1.000 0.000
#> SRR1078164 1 0.000 0.964 1.000 0.000
#> SRR1434545 1 0.833 0.625 0.736 0.264
#> SRR1398251 1 0.000 0.964 1.000 0.000
#> SRR1375866 1 0.000 0.964 1.000 0.000
#> SRR1091645 2 0.000 0.950 0.000 1.000
#> SRR1416636 1 0.000 0.964 1.000 0.000
#> SRR1105441 2 0.000 0.950 0.000 1.000
#> SRR1082496 2 0.000 0.950 0.000 1.000
#> SRR1315353 2 0.000 0.950 0.000 1.000
#> SRR1093697 2 0.000 0.950 0.000 1.000
#> SRR1077429 1 0.000 0.964 1.000 0.000
#> SRR1076120 1 0.000 0.964 1.000 0.000
#> SRR1074410 1 0.000 0.964 1.000 0.000
#> SRR1340345 2 0.000 0.950 0.000 1.000
#> SRR1069514 2 0.000 0.950 0.000 1.000
#> SRR1092636 1 0.000 0.964 1.000 0.000
#> SRR1365013 2 0.000 0.950 0.000 1.000
#> SRR1073069 1 0.000 0.964 1.000 0.000
#> SRR1443137 1 0.000 0.964 1.000 0.000
#> SRR1437143 2 0.000 0.950 0.000 1.000
#> SRR1091990 1 0.000 0.964 1.000 0.000
#> SRR820234 2 0.000 0.950 0.000 1.000
#> SRR1338079 1 0.000 0.964 1.000 0.000
#> SRR1390094 1 0.988 0.199 0.564 0.436
#> SRR1340721 2 0.722 0.755 0.200 0.800
#> SRR1335964 1 0.917 0.484 0.668 0.332
#> SRR1086869 1 0.000 0.964 1.000 0.000
#> SRR1453434 1 0.000 0.964 1.000 0.000
#> SRR1402261 1 0.000 0.964 1.000 0.000
#> SRR657809 2 0.000 0.950 0.000 1.000
#> SRR1093075 1 0.000 0.964 1.000 0.000
#> SRR1433329 1 0.000 0.964 1.000 0.000
#> SRR1353418 1 0.000 0.964 1.000 0.000
#> SRR1092913 2 0.000 0.950 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1335605 2 0.5036 0.8022 0.048 0.832 0.120
#> SRR1432014 3 0.0892 0.8547 0.000 0.020 0.980
#> SRR1499215 1 0.7388 0.3452 0.600 0.044 0.356
#> SRR1460409 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1097344 3 0.3192 0.7852 0.000 0.112 0.888
#> SRR1081789 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1453005 2 0.0892 0.9195 0.000 0.980 0.020
#> SRR1366985 1 0.0892 0.9389 0.980 0.000 0.020
#> SRR815280 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR815845 3 0.1289 0.8506 0.000 0.032 0.968
#> SRR1471178 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1080696 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1078684 3 0.9649 0.0326 0.208 0.388 0.404
#> SRR1317751 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1435667 3 0.1411 0.8489 0.000 0.036 0.964
#> SRR1097905 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR813108 2 0.3412 0.8256 0.000 0.876 0.124
#> SRR1479062 3 0.0000 0.8572 0.000 0.000 1.000
#> SRR1408703 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1332360 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR1098686 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1434228 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR1467149 3 0.5591 0.5663 0.304 0.000 0.696
#> SRR1399113 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1476507 3 0.2796 0.8035 0.000 0.092 0.908
#> SRR1092468 1 0.1643 0.9162 0.956 0.000 0.044
#> SRR1441804 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1436021 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1480083 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1400100 3 0.3267 0.7914 0.000 0.116 0.884
#> SRR1312002 1 0.6274 0.1331 0.544 0.000 0.456
#> SRR1470253 1 0.5363 0.6009 0.724 0.000 0.276
#> SRR1414332 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1069209 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR661052 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1421159 2 0.5706 0.5359 0.000 0.680 0.320
#> SRR1340943 3 0.5138 0.6490 0.252 0.000 0.748
#> SRR1078855 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1478679 2 0.1620 0.9101 0.024 0.964 0.012
#> SRR1350979 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1458198 3 0.6274 0.2044 0.456 0.000 0.544
#> SRR1386910 2 0.4002 0.7884 0.000 0.840 0.160
#> SRR1465375 2 0.0424 0.9256 0.000 0.992 0.008
#> SRR1323699 3 0.7250 0.3061 0.396 0.032 0.572
#> SRR1431139 1 0.6373 0.2788 0.588 0.004 0.408
#> SRR1373964 3 0.6769 0.3276 0.016 0.392 0.592
#> SRR1455413 1 0.6192 0.2041 0.580 0.000 0.420
#> SRR1437163 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1347343 3 0.1411 0.8489 0.000 0.036 0.964
#> SRR1465480 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1086514 2 0.0747 0.9212 0.000 0.984 0.016
#> SRR1430928 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.8572 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.8572 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1500089 3 0.0592 0.8567 0.012 0.000 0.988
#> SRR1441178 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1096081 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1349809 2 0.1163 0.9105 0.028 0.972 0.000
#> SRR1324314 1 0.0592 0.9459 0.988 0.000 0.012
#> SRR1092444 1 0.0237 0.9510 0.996 0.000 0.004
#> SRR1382553 1 0.0592 0.9459 0.988 0.000 0.012
#> SRR1075530 3 0.5497 0.5336 0.000 0.292 0.708
#> SRR1442612 3 0.0892 0.8547 0.000 0.020 0.980
#> SRR1360056 3 0.1411 0.8507 0.036 0.000 0.964
#> SRR1078164 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR1434545 3 0.3116 0.7958 0.108 0.000 0.892
#> SRR1398251 1 0.0892 0.9395 0.980 0.000 0.020
#> SRR1375866 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1091645 3 0.0237 0.8565 0.000 0.004 0.996
#> SRR1416636 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1105441 3 0.1753 0.8433 0.000 0.048 0.952
#> SRR1082496 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1315353 3 0.6154 0.2906 0.000 0.408 0.592
#> SRR1093697 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1077429 3 0.0424 0.8592 0.008 0.000 0.992
#> SRR1076120 3 0.0592 0.8567 0.012 0.000 0.988
#> SRR1074410 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1340345 2 0.6180 0.2989 0.000 0.584 0.416
#> SRR1069514 2 0.2878 0.8555 0.000 0.904 0.096
#> SRR1092636 3 0.1411 0.8507 0.036 0.000 0.964
#> SRR1365013 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1073069 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR1443137 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR1437143 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1390094 3 0.4834 0.7016 0.204 0.004 0.792
#> SRR1340721 2 0.4974 0.6656 0.236 0.764 0.000
#> SRR1335964 3 0.0000 0.8572 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.8572 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1402261 3 0.5882 0.4877 0.348 0.000 0.652
#> SRR657809 2 0.0000 0.9292 0.000 1.000 0.000
#> SRR1093075 1 0.0000 0.9536 1.000 0.000 0.000
#> SRR1433329 1 0.0237 0.9517 0.996 0.000 0.004
#> SRR1353418 3 0.1411 0.8507 0.036 0.000 0.964
#> SRR1092913 2 0.4605 0.7443 0.000 0.796 0.204
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.4315 0.74925 0.008 0.800 0.172 0.020
#> SRR1432014 3 0.0000 0.90065 0.000 0.000 1.000 0.000
#> SRR1499215 3 0.0921 0.89446 0.028 0.000 0.972 0.000
#> SRR1460409 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.0707 0.90608 0.000 0.000 0.020 0.980
#> SRR1081789 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1453005 2 0.2466 0.84005 0.000 0.900 0.004 0.096
#> SRR1366985 3 0.3311 0.76018 0.172 0.000 0.828 0.000
#> SRR815280 1 0.0469 0.95472 0.988 0.000 0.012 0.000
#> SRR1348531 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR815845 3 0.0927 0.89883 0.000 0.008 0.976 0.016
#> SRR1471178 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0707 0.89873 0.000 0.000 0.980 0.020
#> SRR1078684 3 0.6050 0.62481 0.112 0.212 0.676 0.000
#> SRR1317751 3 0.1211 0.89093 0.000 0.000 0.960 0.040
#> SRR1435667 3 0.0707 0.89603 0.000 0.020 0.980 0.000
#> SRR1097905 1 0.0817 0.94766 0.976 0.000 0.000 0.024
#> SRR1456548 1 0.0469 0.95548 0.988 0.000 0.000 0.012
#> SRR1075126 1 0.0188 0.95848 0.996 0.000 0.000 0.004
#> SRR813108 2 0.2530 0.82237 0.000 0.888 0.112 0.000
#> SRR1479062 3 0.2469 0.84189 0.000 0.000 0.892 0.108
#> SRR1408703 3 0.0817 0.89786 0.000 0.000 0.976 0.024
#> SRR1332360 1 0.0592 0.95277 0.984 0.000 0.016 0.000
#> SRR1098686 1 0.0336 0.95739 0.992 0.000 0.000 0.008
#> SRR1434228 1 0.3569 0.74963 0.804 0.000 0.196 0.000
#> SRR1467149 4 0.0000 0.90390 0.000 0.000 0.000 1.000
#> SRR1399113 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.0592 0.90695 0.000 0.000 0.016 0.984
#> SRR1092468 4 0.4250 0.60670 0.276 0.000 0.000 0.724
#> SRR1441804 1 0.0336 0.95739 0.992 0.000 0.000 0.008
#> SRR1326100 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0336 0.95739 0.992 0.000 0.000 0.008
#> SRR1436021 2 0.0817 0.88737 0.000 0.976 0.000 0.024
#> SRR1480083 2 0.0188 0.89324 0.000 0.996 0.004 0.000
#> SRR1472863 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR815542 1 0.0469 0.95548 0.988 0.000 0.000 0.012
#> SRR1400100 3 0.0927 0.89883 0.000 0.008 0.976 0.016
#> SRR1312002 3 0.1022 0.89314 0.032 0.000 0.968 0.000
#> SRR1470253 3 0.1118 0.89226 0.036 0.000 0.964 0.000
#> SRR1414332 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.0817 0.94740 0.976 0.000 0.024 0.000
#> SRR661052 1 0.0336 0.95739 0.992 0.000 0.000 0.008
#> SRR1308860 1 0.0707 0.95072 0.980 0.000 0.000 0.020
#> SRR1421159 2 0.5143 0.22139 0.000 0.540 0.004 0.456
#> SRR1340943 4 0.0188 0.90583 0.000 0.000 0.004 0.996
#> SRR1078855 1 0.0469 0.95472 0.988 0.000 0.012 0.000
#> SRR1459465 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1478679 2 0.3718 0.76249 0.012 0.820 0.168 0.000
#> SRR1350979 3 0.0000 0.90065 0.000 0.000 1.000 0.000
#> SRR1458198 4 0.0469 0.90689 0.000 0.000 0.012 0.988
#> SRR1386910 2 0.2089 0.86941 0.000 0.932 0.020 0.048
#> SRR1465375 2 0.4468 0.70370 0.016 0.752 0.000 0.232
#> SRR1323699 3 0.1118 0.89111 0.036 0.000 0.964 0.000
#> SRR1431139 3 0.4976 0.50845 0.340 0.004 0.652 0.004
#> SRR1373964 3 0.0895 0.89583 0.004 0.020 0.976 0.000
#> SRR1455413 4 0.5060 0.29550 0.412 0.000 0.004 0.584
#> SRR1437163 1 0.0707 0.95072 0.980 0.000 0.000 0.020
#> SRR1347343 3 0.0779 0.89748 0.016 0.004 0.980 0.000
#> SRR1465480 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0707 0.95072 0.980 0.000 0.000 0.020
#> SRR1086514 2 0.4522 0.57581 0.000 0.680 0.000 0.320
#> SRR1430928 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.1118 0.89527 0.000 0.000 0.036 0.964
#> SRR1344294 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1468118 4 0.4817 0.35475 0.000 0.000 0.388 0.612
#> SRR1486348 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0592 0.95311 0.984 0.000 0.000 0.016
#> SRR1500089 4 0.0707 0.90608 0.000 0.000 0.020 0.980
#> SRR1441178 1 0.0469 0.95472 0.988 0.000 0.012 0.000
#> SRR1381396 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.1022 0.89478 0.000 0.000 0.968 0.032
#> SRR1349809 2 0.1174 0.88254 0.012 0.968 0.000 0.020
#> SRR1324314 1 0.4679 0.43579 0.648 0.000 0.352 0.000
#> SRR1092444 1 0.0921 0.94516 0.972 0.000 0.000 0.028
#> SRR1382553 1 0.5776 0.00534 0.504 0.028 0.468 0.000
#> SRR1075530 4 0.0592 0.90695 0.000 0.000 0.016 0.984
#> SRR1442612 3 0.0188 0.90053 0.000 0.004 0.996 0.000
#> SRR1360056 3 0.0376 0.90105 0.004 0.000 0.992 0.004
#> SRR1078164 1 0.0592 0.95277 0.984 0.000 0.016 0.000
#> SRR1434545 4 0.0188 0.90583 0.000 0.000 0.004 0.996
#> SRR1398251 3 0.2281 0.84490 0.096 0.000 0.904 0.000
#> SRR1375866 1 0.0000 0.95911 1.000 0.000 0.000 0.000
#> SRR1091645 4 0.0817 0.90395 0.000 0.000 0.024 0.976
#> SRR1416636 3 0.0707 0.89873 0.000 0.000 0.980 0.020
#> SRR1105441 3 0.1059 0.89825 0.000 0.016 0.972 0.012
#> SRR1082496 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.5097 0.26758 0.000 0.428 0.568 0.004
#> SRR1093697 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.1302 0.88864 0.000 0.000 0.956 0.044
#> SRR1076120 4 0.0707 0.90608 0.000 0.000 0.020 0.980
#> SRR1074410 1 0.0188 0.95838 0.996 0.000 0.000 0.004
#> SRR1340345 4 0.0188 0.90583 0.000 0.000 0.004 0.996
#> SRR1069514 2 0.4907 0.25027 0.000 0.580 0.420 0.000
#> SRR1092636 3 0.0469 0.90034 0.000 0.000 0.988 0.012
#> SRR1365013 2 0.1284 0.88145 0.012 0.964 0.000 0.024
#> SRR1073069 1 0.1557 0.91926 0.944 0.000 0.056 0.000
#> SRR1443137 1 0.0592 0.95277 0.984 0.000 0.016 0.000
#> SRR1437143 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0188 0.95801 0.996 0.000 0.004 0.000
#> SRR820234 2 0.0000 0.89433 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0336 0.95739 0.992 0.000 0.000 0.008
#> SRR1390094 3 0.3219 0.76652 0.164 0.000 0.836 0.000
#> SRR1340721 2 0.4630 0.60412 0.252 0.732 0.000 0.016
#> SRR1335964 3 0.4761 0.44689 0.000 0.000 0.628 0.372
#> SRR1086869 3 0.3486 0.74633 0.000 0.000 0.812 0.188
#> SRR1453434 1 0.0188 0.95832 0.996 0.000 0.000 0.004
#> SRR1402261 4 0.0524 0.90095 0.008 0.000 0.004 0.988
#> SRR657809 2 0.3172 0.78886 0.000 0.840 0.000 0.160
#> SRR1093075 1 0.0592 0.95277 0.984 0.000 0.016 0.000
#> SRR1433329 1 0.2281 0.87757 0.904 0.000 0.096 0.000
#> SRR1353418 3 0.0376 0.90105 0.004 0.000 0.992 0.004
#> SRR1092913 4 0.0000 0.90390 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.2813 0.7762 0.832 0.168 0.000 0.000 0.000
#> SRR1335605 2 0.1908 0.3876 0.000 0.908 0.000 0.000 0.092
#> SRR1432014 3 0.4305 0.0460 0.000 0.000 0.512 0.000 0.488
#> SRR1499215 3 0.5051 0.4020 0.072 0.000 0.664 0.000 0.264
#> SRR1460409 1 0.0703 0.7871 0.976 0.024 0.000 0.000 0.000
#> SRR1086441 1 0.1732 0.7898 0.920 0.080 0.000 0.000 0.000
#> SRR1097344 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1081789 3 0.4288 -0.3818 0.004 0.384 0.612 0.000 0.000
#> SRR1453005 3 0.4278 -0.1170 0.000 0.000 0.548 0.452 0.000
#> SRR1366985 1 0.3814 0.4156 0.720 0.000 0.276 0.000 0.004
#> SRR815280 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1348531 1 0.4223 0.7417 0.724 0.248 0.000 0.000 0.028
#> SRR815845 5 0.0162 0.8495 0.000 0.000 0.004 0.000 0.996
#> SRR1471178 1 0.1478 0.7905 0.936 0.064 0.000 0.000 0.000
#> SRR1080696 5 0.0609 0.8422 0.000 0.000 0.020 0.000 0.980
#> SRR1078684 3 0.3888 0.4477 0.076 0.000 0.804 0.000 0.120
#> SRR1317751 5 0.0000 0.8485 0.000 0.000 0.000 0.000 1.000
#> SRR1435667 3 0.4304 0.0541 0.000 0.000 0.516 0.000 0.484
#> SRR1097905 1 0.4307 0.5810 0.504 0.496 0.000 0.000 0.000
#> SRR1456548 1 0.4305 0.5904 0.512 0.488 0.000 0.000 0.000
#> SRR1075126 1 0.0162 0.7842 0.996 0.004 0.000 0.000 0.000
#> SRR813108 3 0.0404 0.3582 0.000 0.012 0.988 0.000 0.000
#> SRR1479062 5 0.6674 0.0774 0.000 0.000 0.248 0.324 0.428
#> SRR1408703 5 0.0162 0.8495 0.000 0.000 0.004 0.000 0.996
#> SRR1332360 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1098686 1 0.4182 0.6622 0.600 0.400 0.000 0.000 0.000
#> SRR1434228 1 0.0162 0.7811 0.996 0.000 0.004 0.000 0.000
#> SRR1467149 4 0.4918 0.6356 0.000 0.192 0.000 0.708 0.100
#> SRR1399113 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1476507 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1092468 4 0.6381 0.1927 0.276 0.212 0.000 0.512 0.000
#> SRR1441804 1 0.3969 0.7217 0.692 0.304 0.000 0.000 0.004
#> SRR1326100 2 0.4306 0.5483 0.000 0.508 0.492 0.000 0.000
#> SRR1398815 1 0.4297 0.6048 0.528 0.472 0.000 0.000 0.000
#> SRR1436021 3 0.6335 -0.1857 0.000 0.168 0.480 0.352 0.000
#> SRR1480083 3 0.4375 -0.4703 0.000 0.420 0.576 0.004 0.000
#> SRR1472863 1 0.4306 0.5859 0.508 0.492 0.000 0.000 0.000
#> SRR815542 1 0.1478 0.7903 0.936 0.064 0.000 0.000 0.000
#> SRR1400100 5 0.0703 0.8379 0.000 0.024 0.000 0.000 0.976
#> SRR1312002 3 0.6095 0.2678 0.416 0.000 0.460 0.000 0.124
#> SRR1470253 5 0.0162 0.8495 0.000 0.000 0.004 0.000 0.996
#> SRR1414332 1 0.1851 0.7892 0.912 0.088 0.000 0.000 0.000
#> SRR1069209 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR661052 1 0.4305 0.5904 0.512 0.488 0.000 0.000 0.000
#> SRR1308860 1 0.4297 0.6047 0.528 0.472 0.000 0.000 0.000
#> SRR1421159 4 0.4066 0.4921 0.000 0.000 0.324 0.672 0.004
#> SRR1340943 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1078855 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1459465 2 0.4659 0.5380 0.000 0.496 0.492 0.012 0.000
#> SRR816818 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1478679 3 0.1608 0.3861 0.072 0.000 0.928 0.000 0.000
#> SRR1350979 3 0.4449 0.0526 0.004 0.000 0.512 0.000 0.484
#> SRR1458198 4 0.0486 0.8618 0.004 0.004 0.000 0.988 0.004
#> SRR1386910 2 0.2278 0.4287 0.000 0.908 0.032 0.000 0.060
#> SRR1465375 4 0.6413 0.4922 0.040 0.240 0.120 0.600 0.000
#> SRR1323699 3 0.5382 0.4298 0.252 0.000 0.644 0.000 0.104
#> SRR1431139 5 0.6527 0.2932 0.228 0.232 0.008 0.000 0.532
#> SRR1373964 3 0.3789 0.4481 0.016 0.000 0.760 0.000 0.224
#> SRR1455413 2 0.8099 -0.1973 0.240 0.348 0.000 0.100 0.312
#> SRR1437163 1 0.4305 0.5904 0.512 0.488 0.000 0.000 0.000
#> SRR1347343 3 0.4957 0.1208 0.028 0.000 0.528 0.000 0.444
#> SRR1465480 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1489631 1 0.4305 0.5904 0.512 0.488 0.000 0.000 0.000
#> SRR1086514 4 0.4196 0.4326 0.000 0.004 0.356 0.640 0.000
#> SRR1430928 1 0.1544 0.7903 0.932 0.068 0.000 0.000 0.000
#> SRR1310939 4 0.0290 0.8614 0.000 0.000 0.008 0.992 0.000
#> SRR1344294 3 0.4305 -0.5705 0.000 0.488 0.512 0.000 0.000
#> SRR1099402 1 0.0162 0.7841 0.996 0.004 0.000 0.000 0.000
#> SRR1468118 5 0.0290 0.8452 0.000 0.000 0.000 0.008 0.992
#> SRR1486348 1 0.2929 0.7741 0.820 0.180 0.000 0.000 0.000
#> SRR1488770 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1083732 1 0.2648 0.7794 0.848 0.152 0.000 0.000 0.000
#> SRR1456611 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1080318 1 0.3586 0.7398 0.736 0.264 0.000 0.000 0.000
#> SRR1500089 4 0.0486 0.8618 0.004 0.004 0.000 0.988 0.004
#> SRR1441178 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1381396 1 0.3480 0.7476 0.752 0.248 0.000 0.000 0.000
#> SRR1096081 5 0.0162 0.8495 0.000 0.000 0.004 0.000 0.996
#> SRR1349809 2 0.0510 0.4431 0.000 0.984 0.016 0.000 0.000
#> SRR1324314 1 0.3585 0.6117 0.772 0.004 0.004 0.000 0.220
#> SRR1092444 5 0.6654 0.1702 0.284 0.232 0.000 0.004 0.480
#> SRR1382553 1 0.4219 0.0659 0.584 0.000 0.416 0.000 0.000
#> SRR1075530 4 0.1341 0.8290 0.000 0.000 0.000 0.944 0.056
#> SRR1442612 3 0.4305 0.0460 0.000 0.000 0.512 0.000 0.488
#> SRR1360056 5 0.3496 0.6116 0.012 0.000 0.200 0.000 0.788
#> SRR1078164 1 0.0162 0.7842 0.996 0.004 0.000 0.000 0.000
#> SRR1434545 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1398251 1 0.3282 0.5774 0.804 0.000 0.188 0.000 0.008
#> SRR1375866 1 0.4074 0.6863 0.636 0.364 0.000 0.000 0.000
#> SRR1091645 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1416636 5 0.0290 0.8480 0.000 0.000 0.008 0.000 0.992
#> SRR1105441 5 0.1012 0.8395 0.000 0.012 0.020 0.000 0.968
#> SRR1082496 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1315353 3 0.0566 0.3783 0.000 0.004 0.984 0.000 0.012
#> SRR1093697 2 0.4306 0.5483 0.000 0.508 0.492 0.000 0.000
#> SRR1077429 5 0.0000 0.8485 0.000 0.000 0.000 0.000 1.000
#> SRR1076120 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1074410 1 0.4101 0.6809 0.628 0.372 0.000 0.000 0.000
#> SRR1340345 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR1069514 3 0.1270 0.4133 0.000 0.000 0.948 0.000 0.052
#> SRR1092636 5 0.0162 0.8495 0.000 0.000 0.004 0.000 0.996
#> SRR1365013 2 0.0609 0.4446 0.000 0.980 0.020 0.000 0.000
#> SRR1073069 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1443137 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1437143 2 0.4305 0.5531 0.000 0.512 0.488 0.000 0.000
#> SRR1091990 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR820234 3 0.3182 0.1793 0.000 0.124 0.844 0.032 0.000
#> SRR1338079 1 0.4305 0.5904 0.512 0.488 0.000 0.000 0.000
#> SRR1390094 3 0.4964 0.1975 0.460 0.000 0.516 0.020 0.004
#> SRR1340721 2 0.1270 0.3963 0.052 0.948 0.000 0.000 0.000
#> SRR1335964 5 0.3132 0.6836 0.000 0.000 0.008 0.172 0.820
#> SRR1086869 5 0.0404 0.8425 0.000 0.000 0.000 0.012 0.988
#> SRR1453434 1 0.0162 0.7815 0.996 0.000 0.000 0.004 0.000
#> SRR1402261 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
#> SRR657809 2 0.5230 -0.1323 0.000 0.504 0.044 0.452 0.000
#> SRR1093075 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.7831 1.000 0.000 0.000 0.000 0.000
#> SRR1353418 5 0.0963 0.8302 0.000 0.000 0.036 0.000 0.964
#> SRR1092913 4 0.0000 0.8662 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 6 0.3866 0.1556 0.484 0.000 0.000 0.000 0.000 0.516
#> SRR1335605 1 0.4346 0.6348 0.760 0.112 0.024 0.000 0.104 0.000
#> SRR1432014 3 0.1285 0.8705 0.000 0.000 0.944 0.000 0.052 0.004
#> SRR1499215 3 0.3993 0.6946 0.000 0.004 0.700 0.000 0.024 0.272
#> SRR1460409 6 0.2358 0.7470 0.108 0.000 0.016 0.000 0.000 0.876
#> SRR1086441 6 0.3515 0.5349 0.324 0.000 0.000 0.000 0.000 0.676
#> SRR1097344 4 0.0363 0.8953 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1081789 2 0.1265 0.9337 0.008 0.948 0.044 0.000 0.000 0.000
#> SRR1453005 4 0.3457 0.7640 0.012 0.116 0.052 0.820 0.000 0.000
#> SRR1366985 6 0.1471 0.7536 0.000 0.000 0.064 0.000 0.004 0.932
#> SRR815280 6 0.0790 0.7862 0.032 0.000 0.000 0.000 0.000 0.968
#> SRR1348531 5 0.5997 -0.1634 0.344 0.000 0.000 0.000 0.416 0.240
#> SRR815845 5 0.0363 0.8776 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1471178 6 0.3076 0.6446 0.240 0.000 0.000 0.000 0.000 0.760
#> SRR1080696 5 0.2048 0.7739 0.000 0.000 0.120 0.000 0.880 0.000
#> SRR1078684 3 0.2629 0.8443 0.048 0.036 0.888 0.000 0.000 0.028
#> SRR1317751 5 0.0000 0.8822 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1435667 3 0.1141 0.8702 0.000 0.000 0.948 0.000 0.052 0.000
#> SRR1097905 1 0.0632 0.7993 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1456548 1 0.1714 0.7848 0.908 0.000 0.000 0.000 0.000 0.092
#> SRR1075126 6 0.0291 0.7911 0.000 0.000 0.004 0.004 0.000 0.992
#> SRR813108 3 0.2053 0.8291 0.004 0.108 0.888 0.000 0.000 0.000
#> SRR1479062 5 0.6193 0.1383 0.008 0.032 0.064 0.400 0.480 0.016
#> SRR1408703 5 0.0146 0.8814 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1332360 6 0.0551 0.7914 0.004 0.000 0.008 0.000 0.004 0.984
#> SRR1098686 1 0.3221 0.5926 0.736 0.000 0.000 0.000 0.000 0.264
#> SRR1434228 6 0.0291 0.7906 0.000 0.000 0.004 0.000 0.004 0.992
#> SRR1467149 4 0.4874 0.3794 0.308 0.000 0.000 0.608 0.084 0.000
#> SRR1399113 2 0.0363 0.9561 0.012 0.988 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1092468 1 0.3490 0.5917 0.724 0.000 0.000 0.268 0.000 0.008
#> SRR1441804 1 0.3330 0.5528 0.716 0.000 0.000 0.000 0.000 0.284
#> SRR1326100 2 0.4114 0.7070 0.072 0.732 0.196 0.000 0.000 0.000
#> SRR1398815 1 0.3136 0.6963 0.796 0.000 0.016 0.000 0.000 0.188
#> SRR1436021 4 0.4957 0.4473 0.304 0.052 0.020 0.624 0.000 0.000
#> SRR1480083 2 0.1124 0.9408 0.008 0.956 0.036 0.000 0.000 0.000
#> SRR1472863 1 0.1075 0.8001 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR815542 6 0.2854 0.6738 0.208 0.000 0.000 0.000 0.000 0.792
#> SRR1400100 5 0.0713 0.8689 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1312002 6 0.4169 0.1744 0.008 0.004 0.364 0.000 0.004 0.620
#> SRR1470253 5 0.0603 0.8745 0.000 0.000 0.016 0.000 0.980 0.004
#> SRR1414332 6 0.3464 0.5553 0.312 0.000 0.000 0.000 0.000 0.688
#> SRR1069209 6 0.0291 0.7906 0.000 0.000 0.004 0.000 0.004 0.992
#> SRR661052 1 0.1398 0.7992 0.940 0.000 0.008 0.000 0.000 0.052
#> SRR1308860 1 0.2730 0.6989 0.808 0.000 0.000 0.000 0.000 0.192
#> SRR1421159 3 0.4088 0.7148 0.012 0.044 0.744 0.200 0.000 0.000
#> SRR1340943 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1078855 6 0.0146 0.7911 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1459465 2 0.0692 0.9508 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR816818 2 0.0909 0.9469 0.012 0.968 0.020 0.000 0.000 0.000
#> SRR1478679 3 0.3043 0.7809 0.000 0.008 0.792 0.000 0.000 0.200
#> SRR1350979 3 0.1152 0.8715 0.000 0.004 0.952 0.000 0.044 0.000
#> SRR1458198 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1386910 1 0.4698 0.3450 0.648 0.296 0.028 0.000 0.028 0.000
#> SRR1465375 1 0.4923 0.3170 0.560 0.072 0.000 0.368 0.000 0.000
#> SRR1323699 3 0.3437 0.7484 0.000 0.008 0.752 0.000 0.004 0.236
#> SRR1431139 1 0.2432 0.7649 0.888 0.000 0.080 0.000 0.024 0.008
#> SRR1373964 3 0.1644 0.8690 0.000 0.004 0.932 0.000 0.012 0.052
#> SRR1455413 1 0.4050 0.7523 0.804 0.000 0.016 0.100 0.052 0.028
#> SRR1437163 1 0.0865 0.8009 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1347343 3 0.1408 0.8727 0.000 0.000 0.944 0.000 0.020 0.036
#> SRR1465480 2 0.0363 0.9561 0.012 0.988 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.0865 0.8009 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1086514 4 0.1010 0.8797 0.004 0.036 0.000 0.960 0.000 0.000
#> SRR1430928 6 0.3737 0.4089 0.392 0.000 0.000 0.000 0.000 0.608
#> SRR1310939 4 0.1296 0.8707 0.004 0.000 0.012 0.952 0.000 0.032
#> SRR1344294 2 0.0291 0.9546 0.004 0.992 0.004 0.000 0.000 0.000
#> SRR1099402 6 0.0291 0.7915 0.004 0.000 0.004 0.000 0.000 0.992
#> SRR1468118 5 0.0000 0.8822 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1486348 6 0.3866 0.1518 0.484 0.000 0.000 0.000 0.000 0.516
#> SRR1488770 2 0.0914 0.9556 0.016 0.968 0.016 0.000 0.000 0.000
#> SRR1083732 6 0.3838 0.2691 0.448 0.000 0.000 0.000 0.000 0.552
#> SRR1456611 2 0.1003 0.9453 0.016 0.964 0.020 0.000 0.000 0.000
#> SRR1080318 6 0.4605 0.3155 0.416 0.000 0.016 0.000 0.016 0.552
#> SRR1500089 4 0.0291 0.8975 0.004 0.000 0.000 0.992 0.004 0.000
#> SRR1441178 6 0.0964 0.7880 0.012 0.000 0.016 0.000 0.004 0.968
#> SRR1381396 6 0.4110 0.4293 0.376 0.000 0.016 0.000 0.000 0.608
#> SRR1096081 5 0.0000 0.8822 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1349809 1 0.2201 0.7568 0.896 0.076 0.028 0.000 0.000 0.000
#> SRR1324314 6 0.3758 0.5751 0.040 0.000 0.192 0.000 0.004 0.764
#> SRR1092444 5 0.3147 0.7061 0.160 0.000 0.016 0.000 0.816 0.008
#> SRR1382553 6 0.1148 0.7739 0.004 0.016 0.020 0.000 0.000 0.960
#> SRR1075530 4 0.0972 0.8842 0.000 0.008 0.000 0.964 0.028 0.000
#> SRR1442612 3 0.1265 0.8730 0.000 0.000 0.948 0.000 0.044 0.008
#> SRR1360056 5 0.2558 0.7424 0.000 0.000 0.004 0.000 0.840 0.156
#> SRR1078164 6 0.2108 0.7722 0.056 0.000 0.016 0.000 0.016 0.912
#> SRR1434545 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1398251 6 0.1267 0.7593 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR1375866 6 0.4492 0.5913 0.260 0.000 0.016 0.000 0.040 0.684
#> SRR1091645 4 0.0260 0.8968 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1416636 5 0.0000 0.8822 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1105441 3 0.4592 0.6319 0.080 0.000 0.664 0.000 0.256 0.000
#> SRR1082496 2 0.0622 0.9561 0.008 0.980 0.012 0.000 0.000 0.000
#> SRR1315353 3 0.1757 0.8514 0.008 0.076 0.916 0.000 0.000 0.000
#> SRR1093697 2 0.0260 0.9564 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0000 0.8822 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1076120 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1074410 1 0.4392 -0.1041 0.504 0.000 0.016 0.000 0.004 0.476
#> SRR1340345 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1069514 3 0.1075 0.8616 0.000 0.048 0.952 0.000 0.000 0.000
#> SRR1092636 5 0.0520 0.8779 0.008 0.000 0.008 0.000 0.984 0.000
#> SRR1365013 1 0.1261 0.7819 0.952 0.024 0.024 0.000 0.000 0.000
#> SRR1073069 6 0.0405 0.7907 0.000 0.000 0.008 0.000 0.004 0.988
#> SRR1443137 6 0.0146 0.7911 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1437143 2 0.0458 0.9553 0.016 0.984 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.0865 0.7852 0.036 0.000 0.000 0.000 0.000 0.964
#> SRR820234 2 0.2013 0.9035 0.008 0.908 0.076 0.008 0.000 0.000
#> SRR1338079 1 0.1610 0.7885 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1390094 3 0.1168 0.8717 0.000 0.016 0.956 0.000 0.000 0.028
#> SRR1340721 1 0.2818 0.7674 0.872 0.076 0.028 0.000 0.000 0.024
#> SRR1335964 3 0.4498 0.7661 0.104 0.000 0.756 0.100 0.040 0.000
#> SRR1086869 5 0.0146 0.8811 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1453434 6 0.0405 0.7897 0.000 0.000 0.004 0.008 0.000 0.988
#> SRR1402261 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR657809 4 0.5486 0.0688 0.440 0.096 0.008 0.456 0.000 0.000
#> SRR1093075 6 0.0146 0.7911 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1433329 6 0.0146 0.7911 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1353418 5 0.0291 0.8807 0.000 0.000 0.004 0.000 0.992 0.004
#> SRR1092913 4 0.0363 0.8954 0.012 0.000 0.000 0.988 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 17780 rows and 119 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.232 0.546 0.775 0.3812 0.611 0.611
#> 3 3 0.425 0.490 0.759 0.4740 0.695 0.548
#> 4 4 0.474 0.671 0.808 0.1098 0.835 0.646
#> 5 5 0.496 0.677 0.790 0.1091 0.925 0.785
#> 6 6 0.509 0.689 0.793 0.0631 0.974 0.912
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR816969 1 0.0000 0.72226 1.000 0.000
#> SRR1335605 1 0.9983 -0.09489 0.524 0.476
#> SRR1432014 2 0.7674 0.59344 0.224 0.776
#> SRR1499215 1 0.1843 0.71339 0.972 0.028
#> SRR1460409 1 0.0000 0.72226 1.000 0.000
#> SRR1086441 1 0.0000 0.72226 1.000 0.000
#> SRR1097344 2 0.9815 -0.01264 0.420 0.580
#> SRR1081789 1 0.9866 -0.00702 0.568 0.432
#> SRR1453005 2 0.9983 0.59243 0.476 0.524
#> SRR1366985 1 0.1843 0.71609 0.972 0.028
#> SRR815280 1 0.0000 0.72226 1.000 0.000
#> SRR1348531 1 0.6712 0.63981 0.824 0.176
#> SRR815845 2 0.9580 0.12179 0.380 0.620
#> SRR1471178 1 0.0000 0.72226 1.000 0.000
#> SRR1080696 1 0.9323 0.45648 0.652 0.348
#> SRR1078684 1 0.9248 0.43274 0.660 0.340
#> SRR1317751 1 0.9933 0.29525 0.548 0.452
#> SRR1435667 2 0.7674 0.59344 0.224 0.776
#> SRR1097905 1 0.4022 0.70082 0.920 0.080
#> SRR1456548 1 0.1843 0.72182 0.972 0.028
#> SRR1075126 1 0.1843 0.72160 0.972 0.028
#> SRR813108 2 0.7219 0.59375 0.200 0.800
#> SRR1479062 1 0.1633 0.71583 0.976 0.024
#> SRR1408703 1 0.9129 0.48393 0.672 0.328
#> SRR1332360 1 0.0000 0.72226 1.000 0.000
#> SRR1098686 1 0.1843 0.72182 0.972 0.028
#> SRR1434228 1 0.0000 0.72226 1.000 0.000
#> SRR1467149 1 0.6973 0.62963 0.812 0.188
#> SRR1399113 2 0.9996 0.59272 0.488 0.512
#> SRR1476507 1 0.9635 0.35613 0.612 0.388
#> SRR1092468 1 0.9754 0.38087 0.592 0.408
#> SRR1441804 1 0.2778 0.71795 0.952 0.048
#> SRR1326100 2 0.9850 0.40308 0.428 0.572
#> SRR1398815 1 0.0000 0.72226 1.000 0.000
#> SRR1436021 1 0.9552 0.32729 0.624 0.376
#> SRR1480083 2 0.9996 0.59272 0.488 0.512
#> SRR1472863 1 0.1843 0.72182 0.972 0.028
#> SRR815542 1 0.0376 0.72273 0.996 0.004
#> SRR1400100 1 0.8555 0.52853 0.720 0.280
#> SRR1312002 1 0.2043 0.71525 0.968 0.032
#> SRR1470253 1 0.1633 0.71583 0.976 0.024
#> SRR1414332 1 0.0000 0.72226 1.000 0.000
#> SRR1069209 1 0.0000 0.72226 1.000 0.000
#> SRR661052 1 0.1843 0.72182 0.972 0.028
#> SRR1308860 1 0.0376 0.72273 0.996 0.004
#> SRR1421159 2 0.8207 0.55688 0.256 0.744
#> SRR1340943 1 0.9850 0.33795 0.572 0.428
#> SRR1078855 1 0.0000 0.72226 1.000 0.000
#> SRR1459465 2 0.9996 0.59272 0.488 0.512
#> SRR816818 2 0.9996 0.59272 0.488 0.512
#> SRR1478679 1 0.4431 0.68273 0.908 0.092
#> SRR1350979 2 0.7602 0.59334 0.220 0.780
#> SRR1458198 1 0.9522 0.43501 0.628 0.372
#> SRR1386910 1 0.9983 -0.09489 0.524 0.476
#> SRR1465375 1 0.9608 0.32357 0.616 0.384
#> SRR1323699 1 0.4298 0.68458 0.912 0.088
#> SRR1431139 1 0.9248 0.43274 0.660 0.340
#> SRR1373964 2 0.7602 0.59334 0.220 0.780
#> SRR1455413 1 0.3274 0.71319 0.940 0.060
#> SRR1437163 1 0.1843 0.72182 0.972 0.028
#> SRR1347343 2 0.9881 0.28107 0.436 0.564
#> SRR1465480 2 0.9996 0.59272 0.488 0.512
#> SRR1489631 1 0.3114 0.71486 0.944 0.056
#> SRR1086514 1 0.9850 0.27215 0.572 0.428
#> SRR1430928 1 0.0000 0.72226 1.000 0.000
#> SRR1310939 1 0.9754 0.38087 0.592 0.408
#> SRR1344294 2 0.9996 0.59272 0.488 0.512
#> SRR1099402 1 0.0000 0.72226 1.000 0.000
#> SRR1468118 1 0.9933 0.29525 0.548 0.452
#> SRR1486348 1 0.0000 0.72226 1.000 0.000
#> SRR1488770 2 0.9996 0.59272 0.488 0.512
#> SRR1083732 1 0.0000 0.72226 1.000 0.000
#> SRR1456611 2 0.9996 0.59272 0.488 0.512
#> SRR1080318 1 0.0000 0.72226 1.000 0.000
#> SRR1500089 1 0.9754 0.38087 0.592 0.408
#> SRR1441178 1 0.0000 0.72226 1.000 0.000
#> SRR1381396 1 0.0000 0.72226 1.000 0.000
#> SRR1096081 1 0.9754 0.36365 0.592 0.408
#> SRR1349809 1 0.9896 -0.02077 0.560 0.440
#> SRR1324314 1 0.4431 0.68681 0.908 0.092
#> SRR1092444 1 0.0000 0.72226 1.000 0.000
#> SRR1382553 1 0.2236 0.69577 0.964 0.036
#> SRR1075530 2 0.9170 0.45765 0.332 0.668
#> SRR1442612 2 0.7674 0.59344 0.224 0.776
#> SRR1360056 1 0.2043 0.71525 0.968 0.032
#> SRR1078164 1 0.0000 0.72226 1.000 0.000
#> SRR1434545 1 0.9866 0.33158 0.568 0.432
#> SRR1398251 1 0.0000 0.72226 1.000 0.000
#> SRR1375866 1 0.0000 0.72226 1.000 0.000
#> SRR1091645 2 0.9815 -0.01264 0.420 0.580
#> SRR1416636 1 0.9087 0.48893 0.676 0.324
#> SRR1105441 1 0.9248 0.43274 0.660 0.340
#> SRR1082496 2 0.9996 0.59272 0.488 0.512
#> SRR1315353 2 0.9988 0.58254 0.480 0.520
#> SRR1093697 2 0.9996 0.59272 0.488 0.512
#> SRR1077429 1 0.8443 0.54387 0.728 0.272
#> SRR1076120 1 0.9522 0.43501 0.628 0.372
#> SRR1074410 1 0.0000 0.72226 1.000 0.000
#> SRR1340345 2 0.9170 0.45765 0.332 0.668
#> SRR1069514 2 0.7219 0.59375 0.200 0.800
#> SRR1092636 1 0.9129 0.48393 0.672 0.328
#> SRR1365013 1 0.9970 -0.11401 0.532 0.468
#> SRR1073069 1 0.0000 0.72226 1.000 0.000
#> SRR1443137 1 0.0000 0.72226 1.000 0.000
#> SRR1437143 2 0.9996 0.59272 0.488 0.512
#> SRR1091990 1 0.0000 0.72226 1.000 0.000
#> SRR820234 2 0.9996 0.59272 0.488 0.512
#> SRR1338079 1 0.1843 0.72182 0.972 0.028
#> SRR1390094 1 0.9427 0.38846 0.640 0.360
#> SRR1340721 1 0.9580 0.14190 0.620 0.380
#> SRR1335964 2 0.8207 0.55688 0.256 0.744
#> SRR1086869 1 0.9996 0.22298 0.512 0.488
#> SRR1453434 1 0.8555 0.53459 0.720 0.280
#> SRR1402261 1 0.9850 0.34030 0.572 0.428
#> SRR657809 2 0.9732 0.49763 0.404 0.596
#> SRR1093075 1 0.0000 0.72226 1.000 0.000
#> SRR1433329 1 0.0000 0.72226 1.000 0.000
#> SRR1353418 1 0.7602 0.59638 0.780 0.220
#> SRR1092913 1 0.9970 0.21079 0.532 0.468
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0747 0.8148 0.984 0.016 0.000
#> SRR1335605 2 0.7749 0.1796 0.312 0.616 0.072
#> SRR1432014 2 0.4353 0.3681 0.008 0.836 0.156
#> SRR1499215 1 0.3272 0.7701 0.892 0.104 0.004
#> SRR1460409 1 0.0592 0.8143 0.988 0.012 0.000
#> SRR1086441 1 0.0747 0.8148 0.984 0.016 0.000
#> SRR1097344 3 0.5948 0.5171 0.000 0.360 0.640
#> SRR1081789 2 0.6589 0.2817 0.280 0.688 0.032
#> SRR1453005 2 0.5016 0.4847 0.000 0.760 0.240
#> SRR1366985 1 0.3500 0.7595 0.880 0.116 0.004
#> SRR815280 1 0.1031 0.8154 0.976 0.024 0.000
#> SRR1348531 1 0.6148 0.6014 0.776 0.148 0.076
#> SRR815845 3 0.7736 0.4918 0.052 0.400 0.548
#> SRR1471178 1 0.0747 0.8148 0.984 0.016 0.000
#> SRR1080696 3 0.9447 0.5694 0.348 0.188 0.464
#> SRR1078684 2 0.9515 -0.1704 0.388 0.424 0.188
#> SRR1317751 3 0.7418 0.6484 0.080 0.248 0.672
#> SRR1435667 2 0.4353 0.3681 0.008 0.836 0.156
#> SRR1097905 1 0.3043 0.7766 0.908 0.084 0.008
#> SRR1456548 1 0.1711 0.8076 0.960 0.032 0.008
#> SRR1075126 1 0.2680 0.7988 0.924 0.068 0.008
#> SRR813108 2 0.3941 0.3799 0.000 0.844 0.156
#> SRR1479062 1 0.2527 0.7943 0.936 0.044 0.020
#> SRR1408703 3 0.9496 0.5528 0.372 0.188 0.440
#> SRR1332360 1 0.1163 0.8147 0.972 0.028 0.000
#> SRR1098686 1 0.1765 0.8110 0.956 0.040 0.004
#> SRR1434228 1 0.1411 0.8134 0.964 0.036 0.000
#> SRR1467149 1 0.6324 0.5789 0.764 0.160 0.076
#> SRR1399113 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1476507 2 0.8814 -0.0146 0.404 0.480 0.116
#> SRR1092468 1 0.9823 -0.2958 0.392 0.364 0.244
#> SRR1441804 1 0.2414 0.7966 0.940 0.040 0.020
#> SRR1326100 2 0.6486 0.3559 0.144 0.760 0.096
#> SRR1398815 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1436021 2 0.7920 0.1116 0.360 0.572 0.068
#> SRR1480083 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1472863 1 0.1529 0.8122 0.960 0.040 0.000
#> SRR815542 1 0.0747 0.8153 0.984 0.016 0.000
#> SRR1400100 1 0.9514 -0.2010 0.444 0.364 0.192
#> SRR1312002 1 0.3213 0.7837 0.912 0.028 0.060
#> SRR1470253 1 0.2527 0.7943 0.936 0.044 0.020
#> SRR1414332 1 0.0747 0.8148 0.984 0.016 0.000
#> SRR1069209 1 0.1411 0.8147 0.964 0.036 0.000
#> SRR661052 1 0.1529 0.8122 0.960 0.040 0.000
#> SRR1308860 1 0.0747 0.8153 0.984 0.016 0.000
#> SRR1421159 2 0.5678 0.3178 0.032 0.776 0.192
#> SRR1340943 1 0.9651 -0.2500 0.400 0.392 0.208
#> SRR1078855 1 0.1529 0.8124 0.960 0.040 0.000
#> SRR1459465 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR816818 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1478679 1 0.5053 0.6939 0.812 0.164 0.024
#> SRR1350979 2 0.4291 0.3717 0.008 0.840 0.152
#> SRR1458198 1 0.9450 -0.0499 0.492 0.296 0.212
#> SRR1386910 2 0.7749 0.1796 0.312 0.616 0.072
#> SRR1465375 2 0.8109 0.0999 0.352 0.568 0.080
#> SRR1323699 1 0.4748 0.7166 0.832 0.144 0.024
#> SRR1431139 2 0.9515 -0.1704 0.388 0.424 0.188
#> SRR1373964 2 0.4453 0.3705 0.012 0.836 0.152
#> SRR1455413 1 0.3148 0.7786 0.916 0.048 0.036
#> SRR1437163 1 0.1529 0.8122 0.960 0.040 0.000
#> SRR1347343 2 0.8808 0.0777 0.332 0.536 0.132
#> SRR1465480 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1489631 1 0.3028 0.7820 0.920 0.048 0.032
#> SRR1086514 2 0.8857 0.0113 0.344 0.524 0.132
#> SRR1430928 1 0.0592 0.8139 0.988 0.012 0.000
#> SRR1310939 1 0.9823 -0.2958 0.392 0.364 0.244
#> SRR1344294 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1099402 1 0.1289 0.8144 0.968 0.032 0.000
#> SRR1468118 3 0.7340 0.6469 0.076 0.248 0.676
#> SRR1486348 1 0.0747 0.8148 0.984 0.016 0.000
#> SRR1488770 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1083732 1 0.0237 0.8119 0.996 0.004 0.000
#> SRR1456611 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1080318 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1500089 1 0.9823 -0.2958 0.392 0.364 0.244
#> SRR1441178 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1096081 3 0.8550 0.6397 0.176 0.216 0.608
#> SRR1349809 2 0.6668 0.2745 0.264 0.696 0.040
#> SRR1324314 1 0.4799 0.7266 0.836 0.132 0.032
#> SRR1092444 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1382553 1 0.4934 0.6944 0.820 0.156 0.024
#> SRR1075530 2 0.6541 0.2054 0.056 0.732 0.212
#> SRR1442612 2 0.4353 0.3681 0.008 0.836 0.156
#> SRR1360056 1 0.3213 0.7837 0.912 0.028 0.060
#> SRR1078164 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1434545 1 0.9673 -0.2565 0.400 0.388 0.212
#> SRR1398251 1 0.1411 0.8134 0.964 0.036 0.000
#> SRR1375866 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1091645 3 0.5948 0.5171 0.000 0.360 0.640
#> SRR1416636 3 0.9502 0.5479 0.376 0.188 0.436
#> SRR1105441 2 0.9515 -0.1704 0.388 0.424 0.188
#> SRR1082496 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1315353 2 0.5595 0.4831 0.016 0.756 0.228
#> SRR1093697 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1077429 1 0.9496 -0.4402 0.440 0.188 0.372
#> SRR1076120 1 0.9450 -0.0499 0.492 0.296 0.212
#> SRR1074410 1 0.0000 0.8105 1.000 0.000 0.000
#> SRR1340345 2 0.6541 0.2054 0.056 0.732 0.212
#> SRR1069514 2 0.3941 0.3799 0.000 0.844 0.156
#> SRR1092636 3 0.9496 0.5528 0.372 0.188 0.440
#> SRR1365013 2 0.6354 0.3183 0.204 0.744 0.052
#> SRR1073069 1 0.1163 0.8147 0.972 0.028 0.000
#> SRR1443137 1 0.1411 0.8134 0.964 0.036 0.000
#> SRR1437143 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1091990 1 0.0747 0.8148 0.984 0.016 0.000
#> SRR820234 2 0.5254 0.4853 0.000 0.736 0.264
#> SRR1338079 1 0.1529 0.8122 0.960 0.040 0.000
#> SRR1390094 2 0.8264 0.0113 0.436 0.488 0.076
#> SRR1340721 2 0.6608 0.2182 0.356 0.628 0.016
#> SRR1335964 2 0.5678 0.3178 0.032 0.776 0.192
#> SRR1086869 3 0.5443 0.5823 0.004 0.260 0.736
#> SRR1453434 1 0.8536 0.2123 0.576 0.300 0.124
#> SRR1402261 1 0.9672 -0.2483 0.404 0.384 0.212
#> SRR657809 2 0.4253 0.4257 0.080 0.872 0.048
#> SRR1093075 1 0.1289 0.8144 0.968 0.032 0.000
#> SRR1433329 1 0.1411 0.8134 0.964 0.036 0.000
#> SRR1353418 1 0.8918 -0.0553 0.552 0.160 0.288
#> SRR1092913 2 0.9588 -0.1241 0.324 0.460 0.216
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1335605 4 0.7050 0.530 0.232 0.148 0.012 0.608
#> SRR1432014 4 0.4052 0.446 0.012 0.124 0.028 0.836
#> SRR1499215 1 0.2542 0.843 0.904 0.012 0.000 0.084
#> SRR1460409 1 0.0376 0.898 0.992 0.000 0.004 0.004
#> SRR1086441 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1097344 3 0.5306 0.322 0.000 0.020 0.632 0.348
#> SRR1081789 4 0.7200 0.528 0.228 0.220 0.000 0.552
#> SRR1453005 2 0.4482 0.661 0.008 0.728 0.000 0.264
#> SRR1366985 1 0.2796 0.831 0.892 0.016 0.000 0.092
#> SRR815280 1 0.0469 0.898 0.988 0.000 0.000 0.012
#> SRR1348531 1 0.5326 0.599 0.736 0.004 0.060 0.200
#> SRR815845 3 0.6120 0.316 0.036 0.008 0.564 0.392
#> SRR1471178 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1080696 3 0.6334 0.551 0.316 0.004 0.608 0.072
#> SRR1078684 4 0.8666 0.171 0.356 0.044 0.212 0.388
#> SRR1317751 3 0.2198 0.594 0.072 0.000 0.920 0.008
#> SRR1435667 4 0.4052 0.446 0.012 0.124 0.028 0.836
#> SRR1097905 1 0.2777 0.831 0.888 0.004 0.004 0.104
#> SRR1456548 1 0.1822 0.882 0.944 0.004 0.008 0.044
#> SRR1075126 1 0.2197 0.858 0.916 0.004 0.000 0.080
#> SRR813108 4 0.4111 0.445 0.012 0.144 0.020 0.824
#> SRR1479062 1 0.2658 0.857 0.904 0.004 0.012 0.080
#> SRR1408703 3 0.6820 0.519 0.340 0.004 0.556 0.100
#> SRR1332360 1 0.0817 0.897 0.976 0.000 0.000 0.024
#> SRR1098686 1 0.1211 0.886 0.960 0.000 0.000 0.040
#> SRR1434228 1 0.0817 0.896 0.976 0.000 0.000 0.024
#> SRR1467149 1 0.5471 0.562 0.720 0.004 0.060 0.216
#> SRR1399113 2 0.0188 0.934 0.004 0.996 0.000 0.000
#> SRR1476507 4 0.6656 0.531 0.348 0.068 0.012 0.572
#> SRR1092468 4 0.6351 0.478 0.332 0.000 0.080 0.588
#> SRR1441804 1 0.2521 0.870 0.916 0.004 0.020 0.060
#> SRR1326100 4 0.6167 0.484 0.096 0.256 0.000 0.648
#> SRR1398815 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1436021 4 0.7016 0.554 0.308 0.128 0.004 0.560
#> SRR1480083 2 0.0188 0.934 0.004 0.996 0.000 0.000
#> SRR1472863 1 0.1118 0.888 0.964 0.000 0.000 0.036
#> SRR815542 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1400100 1 0.8616 -0.308 0.412 0.040 0.224 0.324
#> SRR1312002 1 0.2670 0.849 0.904 0.000 0.072 0.024
#> SRR1470253 1 0.2658 0.857 0.904 0.004 0.012 0.080
#> SRR1414332 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1069209 1 0.0927 0.897 0.976 0.008 0.000 0.016
#> SRR661052 1 0.1118 0.888 0.964 0.000 0.000 0.036
#> SRR1308860 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1421159 4 0.3204 0.455 0.016 0.064 0.028 0.892
#> SRR1340943 4 0.7299 0.490 0.348 0.044 0.064 0.544
#> SRR1078855 1 0.1004 0.895 0.972 0.004 0.000 0.024
#> SRR1459465 2 0.0188 0.934 0.004 0.996 0.000 0.000
#> SRR816818 2 0.1109 0.934 0.004 0.968 0.000 0.028
#> SRR1478679 1 0.3790 0.741 0.820 0.016 0.000 0.164
#> SRR1350979 4 0.3952 0.448 0.012 0.124 0.024 0.840
#> SRR1458198 4 0.6552 0.367 0.440 0.000 0.076 0.484
#> SRR1386910 4 0.7050 0.530 0.232 0.148 0.012 0.608
#> SRR1465375 4 0.6937 0.558 0.300 0.124 0.004 0.572
#> SRR1323699 1 0.3428 0.774 0.844 0.012 0.000 0.144
#> SRR1431139 4 0.8666 0.171 0.356 0.044 0.212 0.388
#> SRR1373964 4 0.4015 0.451 0.016 0.120 0.024 0.840
#> SRR1455413 1 0.3100 0.845 0.888 0.004 0.028 0.080
#> SRR1437163 1 0.1118 0.888 0.964 0.000 0.000 0.036
#> SRR1347343 4 0.6640 0.409 0.340 0.056 0.020 0.584
#> SRR1465480 2 0.0188 0.934 0.004 0.996 0.000 0.000
#> SRR1489631 1 0.3030 0.849 0.892 0.004 0.028 0.076
#> SRR1086514 4 0.7501 0.547 0.288 0.108 0.036 0.568
#> SRR1430928 1 0.0000 0.897 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.6351 0.478 0.332 0.000 0.080 0.588
#> SRR1344294 2 0.1109 0.934 0.004 0.968 0.000 0.028
#> SRR1099402 1 0.0707 0.897 0.980 0.000 0.000 0.020
#> SRR1468118 3 0.2124 0.592 0.068 0.000 0.924 0.008
#> SRR1486348 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR1488770 2 0.1109 0.934 0.004 0.968 0.000 0.028
#> SRR1083732 1 0.0376 0.896 0.992 0.004 0.004 0.000
#> SRR1456611 2 0.0376 0.935 0.004 0.992 0.000 0.004
#> SRR1080318 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1500089 4 0.6351 0.478 0.332 0.000 0.080 0.588
#> SRR1441178 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1381396 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1096081 3 0.4149 0.602 0.168 0.000 0.804 0.028
#> SRR1349809 4 0.6819 0.546 0.208 0.188 0.000 0.604
#> SRR1324314 1 0.3490 0.771 0.836 0.004 0.004 0.156
#> SRR1092444 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1382553 1 0.3763 0.760 0.832 0.144 0.000 0.024
#> SRR1075530 4 0.6088 0.392 0.004 0.196 0.112 0.688
#> SRR1442612 4 0.4052 0.446 0.012 0.124 0.028 0.836
#> SRR1360056 1 0.2670 0.849 0.904 0.000 0.072 0.024
#> SRR1078164 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1434545 4 0.7286 0.488 0.344 0.044 0.064 0.548
#> SRR1398251 1 0.0921 0.896 0.972 0.000 0.000 0.028
#> SRR1375866 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1091645 3 0.5306 0.322 0.000 0.020 0.632 0.348
#> SRR1416636 3 0.6453 0.532 0.344 0.004 0.580 0.072
#> SRR1105441 4 0.8666 0.171 0.356 0.044 0.212 0.388
#> SRR1082496 2 0.1109 0.934 0.004 0.968 0.000 0.028
#> SRR1315353 2 0.5137 0.577 0.024 0.680 0.000 0.296
#> SRR1093697 2 0.1109 0.934 0.004 0.968 0.000 0.028
#> SRR1077429 3 0.7024 0.435 0.404 0.004 0.488 0.104
#> SRR1076120 4 0.6552 0.367 0.440 0.000 0.076 0.484
#> SRR1074410 1 0.0859 0.893 0.980 0.004 0.008 0.008
#> SRR1340345 4 0.6088 0.392 0.004 0.196 0.112 0.688
#> SRR1069514 4 0.4111 0.445 0.012 0.144 0.020 0.824
#> SRR1092636 3 0.6820 0.519 0.340 0.004 0.556 0.100
#> SRR1365013 4 0.6623 0.529 0.148 0.232 0.000 0.620
#> SRR1073069 1 0.0817 0.897 0.976 0.000 0.000 0.024
#> SRR1443137 1 0.0921 0.896 0.972 0.000 0.000 0.028
#> SRR1437143 2 0.0376 0.935 0.004 0.992 0.000 0.004
#> SRR1091990 1 0.0188 0.898 0.996 0.000 0.000 0.004
#> SRR820234 2 0.0188 0.934 0.004 0.996 0.000 0.000
#> SRR1338079 1 0.1118 0.888 0.964 0.000 0.000 0.036
#> SRR1390094 4 0.6457 0.508 0.384 0.064 0.004 0.548
#> SRR1340721 4 0.7388 0.499 0.304 0.192 0.000 0.504
#> SRR1335964 4 0.3204 0.455 0.016 0.064 0.028 0.892
#> SRR1086869 3 0.0469 0.514 0.000 0.000 0.988 0.012
#> SRR1453434 1 0.6818 -0.181 0.520 0.036 0.036 0.408
#> SRR1402261 4 0.7225 0.486 0.348 0.040 0.064 0.548
#> SRR657809 4 0.5746 0.305 0.040 0.348 0.000 0.612
#> SRR1093075 1 0.0707 0.897 0.980 0.000 0.000 0.020
#> SRR1433329 1 0.0921 0.896 0.972 0.000 0.000 0.028
#> SRR1353418 1 0.6152 -0.170 0.520 0.004 0.436 0.040
#> SRR1092913 4 0.7392 0.528 0.268 0.056 0.080 0.596
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0771 0.88221 0.976 0.000 0.004 0.020 0.000
#> SRR1335605 4 0.8161 0.39576 0.152 0.116 0.296 0.424 0.012
#> SRR1432014 3 0.0740 0.70068 0.008 0.008 0.980 0.000 0.004
#> SRR1499215 1 0.3392 0.82216 0.848 0.004 0.064 0.084 0.000
#> SRR1460409 1 0.0932 0.88247 0.972 0.000 0.004 0.020 0.004
#> SRR1086441 1 0.0771 0.88221 0.976 0.000 0.004 0.020 0.000
#> SRR1097344 4 0.5234 -0.11451 0.000 0.000 0.044 0.496 0.460
#> SRR1081789 4 0.8160 0.41268 0.148 0.188 0.260 0.404 0.000
#> SRR1453005 2 0.4575 0.64493 0.000 0.712 0.236 0.052 0.000
#> SRR1366985 1 0.3400 0.81640 0.848 0.004 0.076 0.072 0.000
#> SRR815280 1 0.1082 0.88293 0.964 0.000 0.008 0.028 0.000
#> SRR1348531 1 0.5195 0.49106 0.644 0.000 0.008 0.296 0.052
#> SRR815845 5 0.5685 0.13606 0.016 0.000 0.408 0.048 0.528
#> SRR1471178 1 0.0771 0.88221 0.976 0.000 0.004 0.020 0.000
#> SRR1080696 5 0.5951 0.66381 0.228 0.000 0.080 0.044 0.648
#> SRR1078684 3 0.8973 -0.00335 0.256 0.020 0.296 0.188 0.240
#> SRR1317751 5 0.2491 0.57243 0.036 0.000 0.000 0.068 0.896
#> SRR1435667 3 0.0740 0.70068 0.008 0.008 0.980 0.000 0.004
#> SRR1097905 1 0.3124 0.80890 0.844 0.000 0.016 0.136 0.004
#> SRR1456548 1 0.2339 0.85340 0.892 0.000 0.004 0.100 0.004
#> SRR1075126 1 0.3127 0.82101 0.848 0.000 0.020 0.128 0.004
#> SRR813108 3 0.1243 0.68992 0.008 0.028 0.960 0.004 0.000
#> SRR1479062 1 0.3443 0.81928 0.840 0.000 0.028 0.120 0.012
#> SRR1408703 5 0.6503 0.65323 0.252 0.000 0.084 0.068 0.596
#> SRR1332360 1 0.1701 0.87353 0.936 0.000 0.016 0.048 0.000
#> SRR1098686 1 0.1671 0.86490 0.924 0.000 0.000 0.076 0.000
#> SRR1434228 1 0.1728 0.87351 0.940 0.000 0.020 0.036 0.004
#> SRR1467149 1 0.5318 0.42847 0.616 0.000 0.008 0.324 0.052
#> SRR1399113 2 0.0000 0.93279 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.6279 0.57479 0.188 0.020 0.168 0.620 0.004
#> SRR1092468 4 0.4814 0.59489 0.188 0.000 0.060 0.736 0.016
#> SRR1441804 1 0.2805 0.84390 0.872 0.000 0.008 0.108 0.012
#> SRR1326100 4 0.7317 0.26352 0.032 0.220 0.352 0.396 0.000
#> SRR1398815 1 0.1492 0.87087 0.948 0.000 0.008 0.040 0.004
#> SRR1436021 4 0.7141 0.55717 0.148 0.084 0.192 0.572 0.004
#> SRR1480083 2 0.0000 0.93279 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.1608 0.86578 0.928 0.000 0.000 0.072 0.000
#> SRR815542 1 0.0703 0.88177 0.976 0.000 0.000 0.024 0.000
#> SRR1400100 1 0.8965 -0.39090 0.308 0.020 0.224 0.196 0.252
#> SRR1312002 1 0.3520 0.81141 0.852 0.000 0.020 0.064 0.064
#> SRR1470253 1 0.3443 0.81928 0.840 0.000 0.028 0.120 0.012
#> SRR1414332 1 0.0771 0.88221 0.976 0.000 0.004 0.020 0.000
#> SRR1069209 1 0.1787 0.87870 0.936 0.000 0.016 0.044 0.004
#> SRR661052 1 0.1608 0.86578 0.928 0.000 0.000 0.072 0.000
#> SRR1308860 1 0.0703 0.88177 0.976 0.000 0.000 0.024 0.000
#> SRR1421159 3 0.2414 0.65254 0.012 0.000 0.900 0.080 0.008
#> SRR1340943 4 0.4450 0.60170 0.188 0.012 0.044 0.756 0.000
#> SRR1078855 1 0.2032 0.87437 0.924 0.000 0.020 0.052 0.004
#> SRR1459465 2 0.0000 0.93279 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.1043 0.92141 0.000 0.960 0.040 0.000 0.000
#> SRR1478679 1 0.4326 0.73577 0.776 0.004 0.140 0.080 0.000
#> SRR1350979 3 0.0740 0.70003 0.008 0.008 0.980 0.004 0.000
#> SRR1458198 4 0.4516 0.53965 0.276 0.000 0.016 0.696 0.012
#> SRR1386910 4 0.8161 0.39576 0.152 0.116 0.296 0.424 0.012
#> SRR1465375 4 0.6902 0.56624 0.140 0.080 0.196 0.584 0.000
#> SRR1323699 1 0.4102 0.76290 0.796 0.004 0.120 0.080 0.000
#> SRR1431139 3 0.8973 -0.00335 0.256 0.020 0.296 0.188 0.240
#> SRR1373964 3 0.0854 0.69939 0.012 0.008 0.976 0.004 0.000
#> SRR1455413 1 0.3639 0.79351 0.808 0.000 0.008 0.164 0.020
#> SRR1437163 1 0.1608 0.86578 0.928 0.000 0.000 0.072 0.000
#> SRR1347343 3 0.5295 0.28296 0.304 0.004 0.628 0.064 0.000
#> SRR1465480 2 0.0000 0.93279 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.3599 0.79819 0.812 0.000 0.008 0.160 0.020
#> SRR1086514 4 0.6623 0.58932 0.128 0.076 0.148 0.640 0.008
#> SRR1430928 1 0.0609 0.88174 0.980 0.000 0.000 0.020 0.000
#> SRR1310939 4 0.4814 0.59489 0.188 0.000 0.060 0.736 0.016
#> SRR1344294 2 0.0794 0.93122 0.000 0.972 0.028 0.000 0.000
#> SRR1099402 1 0.1525 0.88002 0.948 0.000 0.012 0.036 0.004
#> SRR1468118 5 0.2650 0.56854 0.036 0.000 0.004 0.068 0.892
#> SRR1486348 1 0.0771 0.88221 0.976 0.000 0.004 0.020 0.000
#> SRR1488770 2 0.0794 0.93122 0.000 0.972 0.028 0.000 0.000
#> SRR1083732 1 0.1026 0.88065 0.968 0.000 0.004 0.024 0.004
#> SRR1456611 2 0.0162 0.93373 0.000 0.996 0.004 0.000 0.000
#> SRR1080318 1 0.1492 0.86522 0.948 0.000 0.008 0.040 0.004
#> SRR1500089 4 0.4814 0.59489 0.188 0.000 0.060 0.736 0.016
#> SRR1441178 1 0.1492 0.86522 0.948 0.000 0.008 0.040 0.004
#> SRR1381396 1 0.1492 0.87087 0.948 0.000 0.008 0.040 0.004
#> SRR1096081 5 0.3278 0.62020 0.092 0.000 0.020 0.028 0.860
#> SRR1349809 4 0.7857 0.43329 0.124 0.156 0.276 0.444 0.000
#> SRR1324314 1 0.4320 0.74894 0.780 0.000 0.096 0.120 0.004
#> SRR1092444 1 0.1492 0.86522 0.948 0.000 0.008 0.040 0.004
#> SRR1382553 1 0.4756 0.72256 0.768 0.140 0.024 0.064 0.004
#> SRR1075530 4 0.7293 0.29401 0.000 0.168 0.296 0.480 0.056
#> SRR1442612 3 0.0740 0.70068 0.008 0.008 0.980 0.000 0.004
#> SRR1360056 1 0.3520 0.81141 0.852 0.000 0.020 0.064 0.064
#> SRR1078164 1 0.1492 0.86522 0.948 0.000 0.008 0.040 0.004
#> SRR1434545 4 0.4303 0.60042 0.188 0.012 0.036 0.764 0.000
#> SRR1398251 1 0.1808 0.87316 0.936 0.000 0.020 0.040 0.004
#> SRR1375866 1 0.1492 0.86522 0.948 0.000 0.008 0.040 0.004
#> SRR1091645 4 0.5234 -0.11451 0.000 0.000 0.044 0.496 0.460
#> SRR1416636 5 0.6118 0.65898 0.256 0.000 0.080 0.044 0.620
#> SRR1105441 3 0.8973 -0.00335 0.256 0.020 0.296 0.188 0.240
#> SRR1082496 2 0.0794 0.93122 0.000 0.972 0.028 0.000 0.000
#> SRR1315353 2 0.5319 0.54562 0.012 0.652 0.276 0.060 0.000
#> SRR1093697 2 0.0794 0.93122 0.000 0.972 0.028 0.000 0.000
#> SRR1077429 5 0.6814 0.58911 0.312 0.000 0.084 0.072 0.532
#> SRR1076120 4 0.4516 0.53965 0.276 0.000 0.016 0.696 0.012
#> SRR1074410 1 0.1492 0.87087 0.948 0.000 0.008 0.040 0.004
#> SRR1340345 4 0.7293 0.29401 0.000 0.168 0.296 0.480 0.056
#> SRR1069514 3 0.1243 0.68992 0.008 0.028 0.960 0.004 0.000
#> SRR1092636 5 0.6503 0.65323 0.252 0.000 0.084 0.068 0.596
#> SRR1365013 4 0.7584 0.38165 0.064 0.196 0.308 0.432 0.000
#> SRR1073069 1 0.1701 0.87353 0.936 0.000 0.016 0.048 0.000
#> SRR1443137 1 0.1808 0.87316 0.936 0.000 0.020 0.040 0.004
#> SRR1437143 2 0.0162 0.93373 0.000 0.996 0.004 0.000 0.000
#> SRR1091990 1 0.0771 0.88221 0.976 0.000 0.004 0.020 0.000
#> SRR820234 2 0.0000 0.93279 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.1608 0.86578 0.928 0.000 0.000 0.072 0.000
#> SRR1390094 4 0.6924 0.51332 0.224 0.020 0.232 0.520 0.004
#> SRR1340721 4 0.8306 0.40331 0.224 0.160 0.236 0.380 0.000
#> SRR1335964 3 0.2414 0.65254 0.012 0.000 0.900 0.080 0.008
#> SRR1086869 5 0.2338 0.49191 0.000 0.000 0.004 0.112 0.884
#> SRR1453434 4 0.5201 0.46397 0.364 0.008 0.028 0.596 0.004
#> SRR1402261 4 0.4338 0.60023 0.192 0.012 0.036 0.760 0.000
#> SRR657809 4 0.7054 0.20055 0.008 0.316 0.324 0.352 0.000
#> SRR1093075 1 0.1787 0.87547 0.936 0.000 0.016 0.044 0.004
#> SRR1433329 1 0.1808 0.87316 0.936 0.000 0.020 0.040 0.004
#> SRR1353418 5 0.6439 0.47823 0.408 0.000 0.040 0.072 0.480
#> SRR1092913 4 0.6292 0.59564 0.140 0.024 0.120 0.676 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.0790 0.872 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1335605 4 0.8129 0.496 0.112 0.068 0.188 0.488 0.040 0.104
#> SRR1432014 3 0.0405 0.878 0.000 0.000 0.988 0.004 0.008 0.000
#> SRR1499215 1 0.4348 0.794 0.800 0.004 0.052 0.072 0.024 0.048
#> SRR1460409 1 0.0935 0.873 0.964 0.000 0.000 0.032 0.004 0.000
#> SRR1086441 1 0.0790 0.872 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1097344 6 0.4663 1.000 0.000 0.000 0.000 0.192 0.124 0.684
#> SRR1081789 4 0.7959 0.502 0.120 0.120 0.144 0.468 0.000 0.148
#> SRR1453005 2 0.5912 0.537 0.000 0.628 0.108 0.100 0.000 0.164
#> SRR1366985 1 0.4049 0.800 0.816 0.004 0.068 0.060 0.016 0.036
#> SRR815280 1 0.1124 0.873 0.956 0.000 0.000 0.036 0.000 0.008
#> SRR1348531 1 0.5872 0.257 0.516 0.000 0.000 0.340 0.120 0.024
#> SRR815845 5 0.5270 0.173 0.004 0.000 0.360 0.008 0.556 0.072
#> SRR1471178 1 0.0790 0.872 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1080696 5 0.2794 0.534 0.088 0.000 0.036 0.004 0.868 0.004
#> SRR1078684 5 0.8218 0.298 0.148 0.016 0.224 0.216 0.372 0.024
#> SRR1317751 5 0.3330 0.136 0.000 0.000 0.000 0.000 0.716 0.284
#> SRR1435667 3 0.0405 0.878 0.000 0.000 0.988 0.004 0.008 0.000
#> SRR1097905 1 0.3532 0.786 0.808 0.000 0.004 0.148 0.016 0.024
#> SRR1456548 1 0.2566 0.840 0.868 0.000 0.000 0.112 0.012 0.008
#> SRR1075126 1 0.3658 0.790 0.800 0.000 0.000 0.144 0.020 0.036
#> SRR813108 3 0.1049 0.858 0.000 0.000 0.960 0.008 0.000 0.032
#> SRR1479062 1 0.4385 0.773 0.772 0.000 0.004 0.100 0.088 0.036
#> SRR1408703 5 0.3603 0.555 0.108 0.000 0.040 0.020 0.824 0.008
#> SRR1332360 1 0.2074 0.860 0.920 0.000 0.004 0.028 0.012 0.036
#> SRR1098686 1 0.2165 0.842 0.884 0.000 0.000 0.108 0.008 0.000
#> SRR1434228 1 0.1965 0.861 0.924 0.000 0.004 0.024 0.008 0.040
#> SRR1467149 1 0.5927 0.178 0.488 0.000 0.000 0.368 0.120 0.024
#> SRR1399113 2 0.0146 0.915 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1476507 4 0.4553 0.585 0.088 0.008 0.120 0.760 0.004 0.020
#> SRR1092468 4 0.4210 0.513 0.088 0.000 0.008 0.788 0.028 0.088
#> SRR1441804 1 0.3375 0.806 0.816 0.000 0.000 0.136 0.040 0.008
#> SRR1326100 4 0.7572 0.415 0.012 0.160 0.236 0.448 0.008 0.136
#> SRR1398815 1 0.1962 0.860 0.924 0.000 0.000 0.028 0.028 0.020
#> SRR1436021 4 0.6058 0.590 0.080 0.044 0.136 0.672 0.008 0.060
#> SRR1480083 2 0.0146 0.915 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1472863 1 0.1958 0.846 0.896 0.000 0.000 0.100 0.004 0.000
#> SRR815542 1 0.1010 0.871 0.960 0.000 0.000 0.036 0.004 0.000
#> SRR1400100 5 0.8182 0.293 0.184 0.016 0.152 0.224 0.396 0.028
#> SRR1312002 1 0.4159 0.757 0.776 0.000 0.008 0.024 0.148 0.044
#> SRR1470253 1 0.4385 0.773 0.772 0.000 0.004 0.100 0.088 0.036
#> SRR1414332 1 0.0790 0.872 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1069209 1 0.1899 0.869 0.928 0.000 0.004 0.032 0.008 0.028
#> SRR661052 1 0.1958 0.846 0.896 0.000 0.000 0.100 0.004 0.000
#> SRR1308860 1 0.1010 0.871 0.960 0.000 0.000 0.036 0.004 0.000
#> SRR1421159 3 0.2617 0.804 0.004 0.000 0.876 0.080 0.000 0.040
#> SRR1340943 4 0.3070 0.543 0.084 0.004 0.004 0.852 0.000 0.056
#> SRR1078855 1 0.2190 0.863 0.908 0.000 0.000 0.044 0.008 0.040
#> SRR1459465 2 0.0146 0.915 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR816818 2 0.1148 0.904 0.000 0.960 0.020 0.004 0.000 0.016
#> SRR1478679 1 0.5108 0.722 0.736 0.004 0.116 0.080 0.020 0.044
#> SRR1350979 3 0.0291 0.877 0.000 0.000 0.992 0.004 0.004 0.000
#> SRR1458198 4 0.4485 0.485 0.168 0.000 0.000 0.740 0.036 0.056
#> SRR1386910 4 0.8129 0.496 0.112 0.068 0.188 0.488 0.040 0.104
#> SRR1465375 4 0.5518 0.592 0.068 0.036 0.128 0.704 0.000 0.064
#> SRR1323699 1 0.5005 0.741 0.752 0.004 0.096 0.072 0.028 0.048
#> SRR1431139 5 0.8218 0.298 0.148 0.016 0.224 0.216 0.372 0.024
#> SRR1373964 3 0.0436 0.877 0.004 0.000 0.988 0.004 0.004 0.000
#> SRR1455413 1 0.4345 0.730 0.732 0.000 0.000 0.188 0.068 0.012
#> SRR1437163 1 0.1806 0.853 0.908 0.000 0.000 0.088 0.004 0.000
#> SRR1347343 3 0.5465 0.253 0.288 0.004 0.616 0.056 0.008 0.028
#> SRR1465480 2 0.0146 0.915 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1489631 1 0.4283 0.741 0.740 0.000 0.000 0.180 0.068 0.012
#> SRR1086514 4 0.4852 0.593 0.060 0.032 0.068 0.768 0.004 0.068
#> SRR1430928 1 0.0935 0.872 0.964 0.000 0.000 0.032 0.004 0.000
#> SRR1310939 4 0.4210 0.513 0.088 0.000 0.008 0.788 0.028 0.088
#> SRR1344294 2 0.0862 0.914 0.000 0.972 0.008 0.004 0.000 0.016
#> SRR1099402 1 0.1562 0.871 0.940 0.000 0.004 0.032 0.000 0.024
#> SRR1468118 5 0.3409 0.110 0.000 0.000 0.000 0.000 0.700 0.300
#> SRR1486348 1 0.0790 0.872 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1488770 2 0.0862 0.914 0.000 0.972 0.008 0.004 0.000 0.016
#> SRR1083732 1 0.1390 0.872 0.948 0.000 0.000 0.032 0.016 0.004
#> SRR1456611 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.1875 0.853 0.928 0.000 0.000 0.020 0.032 0.020
#> SRR1500089 4 0.4210 0.513 0.088 0.000 0.008 0.788 0.028 0.088
#> SRR1441178 1 0.1875 0.853 0.928 0.000 0.000 0.020 0.032 0.020
#> SRR1381396 1 0.1962 0.860 0.924 0.000 0.000 0.028 0.028 0.020
#> SRR1096081 5 0.2100 0.334 0.004 0.000 0.000 0.000 0.884 0.112
#> SRR1349809 4 0.7594 0.523 0.096 0.084 0.148 0.520 0.004 0.148
#> SRR1324314 1 0.5003 0.727 0.740 0.000 0.072 0.120 0.032 0.036
#> SRR1092444 1 0.1875 0.853 0.928 0.000 0.000 0.020 0.032 0.020
#> SRR1382553 1 0.5136 0.717 0.732 0.116 0.004 0.048 0.016 0.084
#> SRR1075530 4 0.6892 0.252 0.000 0.084 0.128 0.484 0.012 0.292
#> SRR1442612 3 0.0405 0.878 0.000 0.000 0.988 0.004 0.008 0.000
#> SRR1360056 1 0.4159 0.757 0.776 0.000 0.008 0.024 0.148 0.044
#> SRR1078164 1 0.1875 0.853 0.928 0.000 0.000 0.020 0.032 0.020
#> SRR1434545 4 0.2866 0.540 0.084 0.004 0.000 0.860 0.000 0.052
#> SRR1398251 1 0.2113 0.859 0.916 0.000 0.004 0.028 0.008 0.044
#> SRR1375866 1 0.1875 0.853 0.928 0.000 0.000 0.020 0.032 0.020
#> SRR1091645 6 0.4663 1.000 0.000 0.000 0.000 0.192 0.124 0.684
#> SRR1416636 5 0.3036 0.549 0.108 0.000 0.036 0.004 0.848 0.004
#> SRR1105441 5 0.8218 0.298 0.148 0.016 0.224 0.216 0.372 0.024
#> SRR1082496 2 0.0862 0.914 0.000 0.972 0.008 0.004 0.000 0.016
#> SRR1315353 2 0.6544 0.449 0.000 0.568 0.148 0.116 0.004 0.164
#> SRR1093697 2 0.0862 0.914 0.000 0.972 0.008 0.004 0.000 0.016
#> SRR1077429 5 0.4555 0.536 0.152 0.000 0.040 0.024 0.756 0.028
#> SRR1076120 4 0.4485 0.485 0.168 0.000 0.000 0.740 0.036 0.056
#> SRR1074410 1 0.1962 0.860 0.924 0.000 0.000 0.028 0.028 0.020
#> SRR1340345 4 0.6892 0.252 0.000 0.084 0.128 0.484 0.012 0.292
#> SRR1069514 3 0.1049 0.858 0.000 0.000 0.960 0.008 0.000 0.032
#> SRR1092636 5 0.3603 0.555 0.108 0.000 0.040 0.020 0.824 0.008
#> SRR1365013 4 0.7355 0.487 0.024 0.124 0.188 0.516 0.008 0.140
#> SRR1073069 1 0.2074 0.860 0.920 0.000 0.004 0.028 0.012 0.036
#> SRR1443137 1 0.2034 0.860 0.920 0.000 0.004 0.024 0.008 0.044
#> SRR1437143 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.0790 0.872 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR820234 2 0.0146 0.915 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1338079 1 0.1806 0.853 0.908 0.000 0.000 0.088 0.004 0.000
#> SRR1390094 4 0.5452 0.553 0.132 0.008 0.192 0.652 0.004 0.012
#> SRR1340721 4 0.7968 0.482 0.188 0.096 0.128 0.456 0.000 0.132
#> SRR1335964 3 0.2547 0.803 0.004 0.000 0.880 0.080 0.000 0.036
#> SRR1086869 5 0.3828 -0.193 0.000 0.000 0.000 0.000 0.560 0.440
#> SRR1453434 4 0.4443 0.432 0.276 0.000 0.000 0.664 0.000 0.060
#> SRR1402261 4 0.2918 0.542 0.088 0.004 0.000 0.856 0.000 0.052
#> SRR657809 4 0.7568 0.308 0.000 0.232 0.176 0.388 0.004 0.200
#> SRR1093075 1 0.2050 0.864 0.920 0.000 0.004 0.032 0.008 0.036
#> SRR1433329 1 0.2034 0.860 0.920 0.000 0.004 0.024 0.008 0.044
#> SRR1353418 5 0.4429 0.446 0.240 0.000 0.004 0.012 0.704 0.040
#> SRR1092913 4 0.3760 0.545 0.044 0.008 0.032 0.832 0.008 0.076
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 17780 rows and 119 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 0.659 0.831 0.913 0.4677 0.526 0.526
#> 3 3 0.691 0.805 0.912 0.3512 0.700 0.494
#> 4 4 0.533 0.565 0.755 0.1193 0.848 0.619
#> 5 5 0.643 0.607 0.730 0.0760 0.881 0.641
#> 6 6 0.696 0.608 0.755 0.0569 0.891 0.602
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
#> SRR816969 1 0.0376 0.917 0.996 0.004
#> SRR1335605 1 0.9170 0.467 0.668 0.332
#> SRR1432014 2 0.8661 0.605 0.288 0.712
#> SRR1499215 1 0.0000 0.916 1.000 0.000
#> SRR1460409 1 0.0376 0.917 0.996 0.004
#> SRR1086441 1 0.0376 0.917 0.996 0.004
#> SRR1097344 2 0.0938 0.886 0.012 0.988
#> SRR1081789 2 0.2948 0.899 0.052 0.948
#> SRR1453005 2 0.2948 0.899 0.052 0.948
#> SRR1366985 1 0.0376 0.917 0.996 0.004
#> SRR815280 1 0.0376 0.917 0.996 0.004
#> SRR1348531 1 0.2948 0.889 0.948 0.052
#> SRR815845 2 0.4022 0.862 0.080 0.920
#> SRR1471178 1 0.0376 0.917 0.996 0.004
#> SRR1080696 1 0.9775 0.359 0.588 0.412
#> SRR1078684 2 0.9635 0.475 0.388 0.612
#> SRR1317751 1 0.8386 0.662 0.732 0.268
#> SRR1435667 2 0.4161 0.863 0.084 0.916
#> SRR1097905 1 0.0376 0.917 0.996 0.004
#> SRR1456548 1 0.0376 0.917 0.996 0.004
#> SRR1075126 1 0.0376 0.917 0.996 0.004
#> SRR813108 2 0.1843 0.893 0.028 0.972
#> SRR1479062 1 0.9248 0.480 0.660 0.340
#> SRR1408703 1 0.9552 0.453 0.624 0.376
#> SRR1332360 1 0.0000 0.916 1.000 0.000
#> SRR1098686 1 0.0376 0.917 0.996 0.004
#> SRR1434228 1 0.0376 0.917 0.996 0.004
#> SRR1467149 1 0.2948 0.889 0.948 0.052
#> SRR1399113 2 0.2948 0.899 0.052 0.948
#> SRR1476507 2 0.1184 0.887 0.016 0.984
#> SRR1092468 1 0.2778 0.901 0.952 0.048
#> SRR1441804 1 0.2236 0.899 0.964 0.036
#> SRR1326100 2 0.2948 0.899 0.052 0.948
#> SRR1398815 1 0.0376 0.917 0.996 0.004
#> SRR1436021 2 0.3431 0.896 0.064 0.936
#> SRR1480083 2 0.2948 0.899 0.052 0.948
#> SRR1472863 1 0.0376 0.917 0.996 0.004
#> SRR815542 1 0.0376 0.917 0.996 0.004
#> SRR1400100 2 0.2778 0.879 0.048 0.952
#> SRR1312002 1 0.0000 0.916 1.000 0.000
#> SRR1470253 1 0.1184 0.910 0.984 0.016
#> SRR1414332 1 0.0376 0.917 0.996 0.004
#> SRR1069209 1 0.0376 0.917 0.996 0.004
#> SRR661052 1 0.0000 0.916 1.000 0.000
#> SRR1308860 1 0.0376 0.917 0.996 0.004
#> SRR1421159 2 0.1184 0.887 0.016 0.984
#> SRR1340943 1 0.5294 0.825 0.880 0.120
#> SRR1078855 1 0.0376 0.917 0.996 0.004
#> SRR1459465 2 0.2948 0.899 0.052 0.948
#> SRR816818 2 0.2948 0.899 0.052 0.948
#> SRR1478679 2 0.9944 0.318 0.456 0.544
#> SRR1350979 2 0.9087 0.533 0.324 0.676
#> SRR1458198 1 0.2948 0.893 0.948 0.052
#> SRR1386910 2 0.1414 0.884 0.020 0.980
#> SRR1465375 2 0.7299 0.769 0.204 0.796
#> SRR1323699 1 0.5737 0.793 0.864 0.136
#> SRR1431139 1 0.0938 0.914 0.988 0.012
#> SRR1373964 2 0.9044 0.619 0.320 0.680
#> SRR1455413 1 0.2603 0.894 0.956 0.044
#> SRR1437163 1 0.0376 0.917 0.996 0.004
#> SRR1347343 2 0.9491 0.524 0.368 0.632
#> SRR1465480 2 0.2948 0.899 0.052 0.948
#> SRR1489631 1 0.0000 0.916 1.000 0.000
#> SRR1086514 2 0.0938 0.886 0.012 0.988
#> SRR1430928 1 0.0376 0.917 0.996 0.004
#> SRR1310939 1 0.9732 0.359 0.596 0.404
#> SRR1344294 2 0.2948 0.899 0.052 0.948
#> SRR1099402 1 0.0376 0.917 0.996 0.004
#> SRR1468118 1 0.8386 0.662 0.732 0.268
#> SRR1486348 1 0.0376 0.917 0.996 0.004
#> SRR1488770 2 0.2948 0.899 0.052 0.948
#> SRR1083732 1 0.0376 0.917 0.996 0.004
#> SRR1456611 2 0.2948 0.899 0.052 0.948
#> SRR1080318 1 0.0000 0.916 1.000 0.000
#> SRR1500089 1 0.3114 0.891 0.944 0.056
#> SRR1441178 1 0.0000 0.916 1.000 0.000
#> SRR1381396 1 0.0000 0.916 1.000 0.000
#> SRR1096081 1 0.8386 0.662 0.732 0.268
#> SRR1349809 2 0.6148 0.829 0.152 0.848
#> SRR1324314 1 0.0000 0.916 1.000 0.000
#> SRR1092444 1 0.2948 0.889 0.948 0.052
#> SRR1382553 1 0.1633 0.906 0.976 0.024
#> SRR1075530 2 0.0938 0.886 0.012 0.988
#> SRR1442612 2 0.8386 0.647 0.268 0.732
#> SRR1360056 1 0.2948 0.889 0.948 0.052
#> SRR1078164 1 0.0376 0.915 0.996 0.004
#> SRR1434545 2 0.6623 0.817 0.172 0.828
#> SRR1398251 1 0.0000 0.916 1.000 0.000
#> SRR1375866 1 0.0376 0.915 0.996 0.004
#> SRR1091645 2 0.1184 0.884 0.016 0.984
#> SRR1416636 1 0.9552 0.453 0.624 0.376
#> SRR1105441 2 0.2948 0.880 0.052 0.948
#> SRR1082496 2 0.2948 0.899 0.052 0.948
#> SRR1315353 2 0.2948 0.899 0.052 0.948
#> SRR1093697 2 0.2948 0.899 0.052 0.948
#> SRR1077429 1 0.6531 0.794 0.832 0.168
#> SRR1076120 1 0.3114 0.891 0.944 0.056
#> SRR1074410 1 0.0000 0.916 1.000 0.000
#> SRR1340345 2 0.0938 0.886 0.012 0.988
#> SRR1069514 2 0.4690 0.871 0.100 0.900
#> SRR1092636 1 0.3584 0.883 0.932 0.068
#> SRR1365013 2 0.3431 0.896 0.064 0.936
#> SRR1073069 1 0.0000 0.916 1.000 0.000
#> SRR1443137 1 0.0376 0.917 0.996 0.004
#> SRR1437143 2 0.2948 0.899 0.052 0.948
#> SRR1091990 1 0.0376 0.917 0.996 0.004
#> SRR820234 2 0.2948 0.899 0.052 0.948
#> SRR1338079 1 0.0376 0.917 0.996 0.004
#> SRR1390094 2 0.9460 0.533 0.364 0.636
#> SRR1340721 1 0.9044 0.451 0.680 0.320
#> SRR1335964 1 0.9775 0.359 0.588 0.412
#> SRR1086869 1 0.9661 0.413 0.608 0.392
#> SRR1453434 1 0.0376 0.917 0.996 0.004
#> SRR1402261 1 0.2423 0.896 0.960 0.040
#> SRR657809 2 0.2948 0.899 0.052 0.948
#> SRR1093075 1 0.0376 0.917 0.996 0.004
#> SRR1433329 1 0.0376 0.917 0.996 0.004
#> SRR1353418 1 0.2948 0.889 0.948 0.052
#> SRR1092913 2 0.0938 0.886 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1335605 3 0.4551 0.7711 0.132 0.024 0.844
#> SRR1432014 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1499215 3 0.6267 0.2663 0.452 0.000 0.548
#> SRR1460409 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1097344 2 0.6095 0.5288 0.000 0.608 0.392
#> SRR1081789 2 0.1860 0.8228 0.000 0.948 0.052
#> SRR1453005 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1366985 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR815280 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1348531 1 0.0592 0.9486 0.988 0.000 0.012
#> SRR815845 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1471178 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1080696 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1078684 3 0.5662 0.7581 0.092 0.100 0.808
#> SRR1317751 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1435667 3 0.2165 0.8162 0.000 0.064 0.936
#> SRR1097905 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR813108 2 0.5465 0.6280 0.000 0.712 0.288
#> SRR1479062 3 0.3966 0.7916 0.100 0.024 0.876
#> SRR1408703 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1332360 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1098686 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1434228 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1467149 1 0.4887 0.7043 0.772 0.000 0.228
#> SRR1399113 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1476507 2 0.6126 0.4681 0.000 0.600 0.400
#> SRR1092468 3 0.4235 0.7384 0.176 0.000 0.824
#> SRR1441804 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1326100 2 0.2261 0.8165 0.000 0.932 0.068
#> SRR1398815 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1436021 2 0.5968 0.5263 0.000 0.636 0.364
#> SRR1480083 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1400100 3 0.0747 0.8324 0.000 0.016 0.984
#> SRR1312002 1 0.3192 0.8496 0.888 0.000 0.112
#> SRR1470253 1 0.5968 0.3826 0.636 0.000 0.364
#> SRR1414332 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1069209 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR661052 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1421159 3 0.3116 0.7915 0.000 0.108 0.892
#> SRR1340943 1 0.3356 0.8842 0.908 0.056 0.036
#> SRR1078855 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1459465 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1478679 3 0.4931 0.6766 0.232 0.000 0.768
#> SRR1350979 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1458198 1 0.2165 0.9021 0.936 0.000 0.064
#> SRR1386910 3 0.3619 0.7447 0.000 0.136 0.864
#> SRR1465375 1 0.6295 0.0552 0.528 0.472 0.000
#> SRR1323699 3 0.4750 0.6936 0.216 0.000 0.784
#> SRR1431139 3 0.4629 0.7273 0.188 0.004 0.808
#> SRR1373964 3 0.2878 0.8001 0.000 0.096 0.904
#> SRR1455413 1 0.0424 0.9508 0.992 0.000 0.008
#> SRR1437163 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1347343 3 0.2711 0.8039 0.000 0.088 0.912
#> SRR1465480 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1086514 2 0.5254 0.6775 0.000 0.736 0.264
#> SRR1430928 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1310939 3 0.2846 0.8190 0.056 0.020 0.924
#> SRR1344294 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1468118 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1486348 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1500089 3 0.4931 0.6736 0.232 0.000 0.768
#> SRR1441178 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1381396 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1096081 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1349809 2 0.3941 0.7179 0.156 0.844 0.000
#> SRR1324314 3 0.5760 0.5546 0.328 0.000 0.672
#> SRR1092444 1 0.0424 0.9508 0.992 0.000 0.008
#> SRR1382553 1 0.6902 0.6858 0.732 0.168 0.100
#> SRR1075530 3 0.6309 -0.2370 0.000 0.496 0.504
#> SRR1442612 3 0.1031 0.8303 0.000 0.024 0.976
#> SRR1360056 3 0.5948 0.3971 0.360 0.000 0.640
#> SRR1078164 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1434545 2 0.8803 0.4311 0.320 0.544 0.136
#> SRR1398251 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1375866 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1091645 3 0.3192 0.7489 0.000 0.112 0.888
#> SRR1416636 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1105441 3 0.2796 0.8026 0.000 0.092 0.908
#> SRR1082496 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1315353 2 0.4399 0.7422 0.000 0.812 0.188
#> SRR1093697 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1077429 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1076120 1 0.3619 0.8203 0.864 0.000 0.136
#> SRR1074410 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1340345 3 0.6309 -0.2370 0.000 0.496 0.504
#> SRR1069514 3 0.2878 0.8001 0.000 0.096 0.904
#> SRR1092636 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1365013 2 0.7262 0.5362 0.044 0.624 0.332
#> SRR1073069 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1443137 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1437143 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.8377 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1390094 1 0.6918 0.6674 0.736 0.128 0.136
#> SRR1340721 1 0.3551 0.8248 0.868 0.132 0.000
#> SRR1335964 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1086869 3 0.0237 0.8342 0.000 0.004 0.996
#> SRR1453434 1 0.0000 0.9553 1.000 0.000 0.000
#> SRR1402261 1 0.2063 0.9158 0.948 0.008 0.044
#> SRR657809 2 0.5178 0.6793 0.000 0.744 0.256
#> SRR1093075 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1433329 1 0.0237 0.9543 0.996 0.000 0.004
#> SRR1353418 3 0.1163 0.8232 0.028 0.000 0.972
#> SRR1092913 2 0.5882 0.5814 0.000 0.652 0.348
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1335605 4 0.6625 -0.0890 0.076 0.004 0.380 0.540
#> SRR1432014 3 0.4877 0.4520 0.000 0.000 0.592 0.408
#> SRR1499215 3 0.7566 0.1990 0.212 0.000 0.468 0.320
#> SRR1460409 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.6822 0.3968 0.000 0.192 0.204 0.604
#> SRR1081789 2 0.5028 0.3115 0.000 0.596 0.004 0.400
#> SRR1453005 2 0.3726 0.6554 0.000 0.788 0.000 0.212
#> SRR1366985 1 0.4114 0.8062 0.828 0.000 0.112 0.060
#> SRR815280 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1348531 1 0.2480 0.8587 0.904 0.000 0.088 0.008
#> SRR815845 3 0.3975 0.5851 0.000 0.000 0.760 0.240
#> SRR1471178 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.3726 0.5978 0.000 0.000 0.788 0.212
#> SRR1078684 4 0.7260 -0.0672 0.084 0.028 0.344 0.544
#> SRR1317751 3 0.3172 0.5805 0.000 0.000 0.840 0.160
#> SRR1435667 4 0.5778 -0.2653 0.000 0.028 0.472 0.500
#> SRR1097905 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1456548 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1075126 1 0.0336 0.8823 0.992 0.000 0.000 0.008
#> SRR813108 4 0.7008 0.3149 0.000 0.276 0.160 0.564
#> SRR1479062 3 0.5151 0.0407 0.004 0.000 0.532 0.464
#> SRR1408703 3 0.3837 0.5972 0.000 0.000 0.776 0.224
#> SRR1332360 1 0.5032 0.7885 0.764 0.000 0.156 0.080
#> SRR1098686 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1434228 1 0.3674 0.8252 0.852 0.000 0.104 0.044
#> SRR1467149 1 0.7261 0.3064 0.536 0.000 0.268 0.196
#> SRR1399113 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.5464 0.4500 0.000 0.212 0.072 0.716
#> SRR1092468 4 0.4773 0.4274 0.120 0.000 0.092 0.788
#> SRR1441804 1 0.0188 0.8842 0.996 0.000 0.004 0.000
#> SRR1326100 2 0.5407 0.0574 0.000 0.504 0.012 0.484
#> SRR1398815 1 0.3156 0.8482 0.884 0.000 0.048 0.068
#> SRR1436021 4 0.4747 0.4839 0.016 0.180 0.024 0.780
#> SRR1480083 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR815542 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1400100 3 0.4889 0.4803 0.000 0.004 0.636 0.360
#> SRR1312002 1 0.6201 0.4005 0.564 0.000 0.376 0.060
#> SRR1470253 3 0.7065 -0.1048 0.404 0.000 0.472 0.124
#> SRR1414332 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.2973 0.8459 0.884 0.000 0.096 0.020
#> SRR661052 1 0.3156 0.8482 0.884 0.000 0.048 0.068
#> SRR1308860 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1421159 4 0.4636 0.3478 0.000 0.040 0.188 0.772
#> SRR1340943 4 0.6462 0.2542 0.416 0.008 0.052 0.524
#> SRR1078855 1 0.3037 0.8436 0.880 0.000 0.100 0.020
#> SRR1459465 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1478679 4 0.7021 -0.1100 0.120 0.000 0.400 0.480
#> SRR1350979 3 0.4888 0.4460 0.000 0.000 0.588 0.412
#> SRR1458198 1 0.5687 0.5086 0.684 0.000 0.068 0.248
#> SRR1386910 4 0.4375 0.3892 0.000 0.032 0.180 0.788
#> SRR1465375 4 0.7215 0.3821 0.244 0.136 0.020 0.600
#> SRR1323699 3 0.6921 0.1583 0.108 0.000 0.468 0.424
#> SRR1431139 4 0.7009 -0.1518 0.120 0.000 0.392 0.488
#> SRR1373964 4 0.5996 -0.2260 0.000 0.040 0.448 0.512
#> SRR1455413 1 0.3548 0.8385 0.864 0.000 0.068 0.068
#> SRR1437163 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1347343 3 0.6015 0.1967 0.004 0.032 0.512 0.452
#> SRR1465480 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0188 0.8842 0.996 0.000 0.004 0.000
#> SRR1086514 4 0.4391 0.4193 0.000 0.252 0.008 0.740
#> SRR1430928 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.3959 0.4119 0.068 0.000 0.092 0.840
#> SRR1344294 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.3444 0.5882 0.000 0.000 0.816 0.184
#> SRR1486348 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.3474 0.8401 0.868 0.000 0.064 0.068
#> SRR1500089 4 0.7238 0.2625 0.304 0.000 0.172 0.524
#> SRR1441178 1 0.4568 0.8137 0.800 0.000 0.124 0.076
#> SRR1381396 1 0.3156 0.8482 0.884 0.000 0.048 0.068
#> SRR1096081 3 0.3311 0.5902 0.000 0.000 0.828 0.172
#> SRR1349809 2 0.7474 -0.0219 0.176 0.424 0.000 0.400
#> SRR1324314 3 0.7706 0.1639 0.364 0.000 0.412 0.224
#> SRR1092444 1 0.3621 0.8362 0.860 0.000 0.072 0.068
#> SRR1382553 1 0.8874 0.2093 0.484 0.124 0.256 0.136
#> SRR1075530 4 0.5676 0.4753 0.000 0.144 0.136 0.720
#> SRR1442612 4 0.5168 -0.3040 0.000 0.004 0.492 0.504
#> SRR1360056 3 0.4499 0.4127 0.124 0.000 0.804 0.072
#> SRR1078164 1 0.4568 0.8137 0.800 0.000 0.124 0.076
#> SRR1434545 4 0.7660 0.3675 0.296 0.084 0.060 0.560
#> SRR1398251 1 0.3899 0.8157 0.840 0.000 0.108 0.052
#> SRR1375866 1 0.4344 0.8222 0.816 0.000 0.108 0.076
#> SRR1091645 4 0.5112 0.0998 0.000 0.004 0.436 0.560
#> SRR1416636 3 0.3873 0.5981 0.000 0.000 0.772 0.228
#> SRR1105441 4 0.5938 -0.2479 0.000 0.036 0.480 0.484
#> SRR1082496 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1315353 4 0.6332 0.1523 0.000 0.404 0.064 0.532
#> SRR1093697 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.3356 0.5910 0.000 0.000 0.824 0.176
#> SRR1076120 1 0.6136 0.3930 0.632 0.000 0.080 0.288
#> SRR1074410 1 0.3474 0.8401 0.868 0.000 0.064 0.068
#> SRR1340345 4 0.5676 0.4753 0.000 0.144 0.136 0.720
#> SRR1069514 4 0.6000 -0.2302 0.000 0.040 0.452 0.508
#> SRR1092636 3 0.3610 0.6032 0.000 0.000 0.800 0.200
#> SRR1365013 4 0.4988 0.4698 0.024 0.204 0.016 0.756
#> SRR1073069 1 0.4966 0.7916 0.768 0.000 0.156 0.076
#> SRR1443137 1 0.2805 0.8470 0.888 0.000 0.100 0.012
#> SRR1437143 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR820234 2 0.0000 0.8647 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0000 0.8849 1.000 0.000 0.000 0.000
#> SRR1390094 4 0.6716 0.3233 0.324 0.040 0.040 0.596
#> SRR1340721 1 0.3672 0.7216 0.824 0.012 0.000 0.164
#> SRR1335964 3 0.4985 0.3084 0.000 0.000 0.532 0.468
#> SRR1086869 3 0.3569 0.5862 0.000 0.000 0.804 0.196
#> SRR1453434 1 0.0336 0.8823 0.992 0.000 0.000 0.008
#> SRR1402261 4 0.6148 0.2806 0.408 0.000 0.052 0.540
#> SRR657809 4 0.4134 0.4063 0.000 0.260 0.000 0.740
#> SRR1093075 1 0.2973 0.8459 0.884 0.000 0.096 0.020
#> SRR1433329 1 0.2924 0.8454 0.884 0.000 0.100 0.016
#> SRR1353418 3 0.2596 0.5085 0.024 0.000 0.908 0.068
#> SRR1092913 4 0.6236 0.4380 0.012 0.208 0.096 0.684
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0000 0.8167 1.000 0.000 0.000 0.000 0.000
#> SRR1335605 3 0.6213 0.1424 0.072 0.000 0.540 0.356 0.032
#> SRR1432014 3 0.4045 0.4066 0.000 0.000 0.644 0.000 0.356
#> SRR1499215 3 0.6524 0.2574 0.104 0.000 0.636 0.152 0.108
#> SRR1460409 1 0.0880 0.8124 0.968 0.000 0.000 0.032 0.000
#> SRR1086441 1 0.0404 0.8161 0.988 0.000 0.000 0.012 0.000
#> SRR1097344 4 0.4978 0.6074 0.000 0.040 0.068 0.752 0.140
#> SRR1081789 3 0.6783 -0.0406 0.000 0.348 0.372 0.280 0.000
#> SRR1453005 2 0.5373 0.4112 0.000 0.632 0.092 0.276 0.000
#> SRR1366985 1 0.5972 0.6228 0.652 0.000 0.216 0.088 0.044
#> SRR815280 1 0.0162 0.8165 0.996 0.000 0.000 0.004 0.000
#> SRR1348531 1 0.3009 0.7813 0.876 0.000 0.016 0.028 0.080
#> SRR815845 5 0.2690 0.7350 0.000 0.000 0.156 0.000 0.844
#> SRR1471178 1 0.0510 0.8155 0.984 0.000 0.000 0.016 0.000
#> SRR1080696 5 0.1908 0.8125 0.000 0.000 0.092 0.000 0.908
#> SRR1078684 3 0.5209 0.5512 0.048 0.008 0.736 0.040 0.168
#> SRR1317751 5 0.0880 0.8277 0.000 0.000 0.000 0.032 0.968
#> SRR1435667 3 0.4127 0.4743 0.000 0.000 0.680 0.008 0.312
#> SRR1097905 1 0.1251 0.8104 0.956 0.000 0.008 0.036 0.000
#> SRR1456548 1 0.1168 0.8109 0.960 0.000 0.008 0.032 0.000
#> SRR1075126 1 0.2054 0.8024 0.920 0.000 0.028 0.052 0.000
#> SRR813108 3 0.5780 0.5180 0.000 0.120 0.692 0.048 0.140
#> SRR1479062 4 0.6779 0.0107 0.004 0.000 0.328 0.432 0.236
#> SRR1408703 5 0.1740 0.8327 0.000 0.000 0.056 0.012 0.932
#> SRR1332360 1 0.6573 0.6321 0.604 0.000 0.204 0.140 0.052
#> SRR1098686 1 0.0794 0.8133 0.972 0.000 0.000 0.028 0.000
#> SRR1434228 1 0.5682 0.6689 0.692 0.000 0.172 0.092 0.044
#> SRR1467149 1 0.6578 -0.0928 0.448 0.000 0.012 0.396 0.144
#> SRR1399113 2 0.0000 0.9642 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.4332 0.6248 0.000 0.032 0.160 0.780 0.028
#> SRR1092468 4 0.5638 0.4231 0.048 0.000 0.352 0.580 0.020
#> SRR1441804 1 0.1281 0.8128 0.956 0.000 0.012 0.032 0.000
#> SRR1326100 3 0.6193 0.1736 0.000 0.192 0.548 0.260 0.000
#> SRR1398815 1 0.3870 0.7515 0.820 0.000 0.080 0.092 0.008
#> SRR1436021 4 0.4964 0.1305 0.004 0.020 0.460 0.516 0.000
#> SRR1480083 2 0.0000 0.9642 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.0451 0.8160 0.988 0.000 0.008 0.004 0.000
#> SRR815542 1 0.0963 0.8111 0.964 0.000 0.000 0.036 0.000
#> SRR1400100 5 0.5253 0.0874 0.000 0.016 0.396 0.024 0.564
#> SRR1312002 1 0.7600 0.4187 0.500 0.000 0.204 0.104 0.192
#> SRR1470253 3 0.8457 -0.1813 0.300 0.000 0.304 0.172 0.224
#> SRR1414332 1 0.0000 0.8167 1.000 0.000 0.000 0.000 0.000
#> SRR1069209 1 0.5575 0.6805 0.704 0.000 0.160 0.092 0.044
#> SRR661052 1 0.3924 0.7512 0.816 0.000 0.080 0.096 0.008
#> SRR1308860 1 0.1082 0.8120 0.964 0.000 0.008 0.028 0.000
#> SRR1421159 3 0.5343 0.4669 0.000 0.004 0.684 0.172 0.140
#> SRR1340943 4 0.3627 0.6359 0.120 0.000 0.032 0.832 0.016
#> SRR1078855 1 0.5575 0.6805 0.704 0.000 0.160 0.092 0.044
#> SRR1459465 2 0.0000 0.9642 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0162 0.9639 0.000 0.996 0.004 0.000 0.000
#> SRR1478679 3 0.3605 0.5185 0.060 0.000 0.852 0.044 0.044
#> SRR1350979 3 0.4045 0.4066 0.000 0.000 0.644 0.000 0.356
#> SRR1458198 4 0.5529 0.1950 0.432 0.000 0.016 0.516 0.036
#> SRR1386910 3 0.5496 -0.1219 0.000 0.024 0.500 0.452 0.024
#> SRR1465375 4 0.4754 0.6280 0.112 0.016 0.112 0.760 0.000
#> SRR1323699 3 0.4924 0.4665 0.048 0.000 0.764 0.076 0.112
#> SRR1431139 3 0.5471 0.5444 0.060 0.000 0.708 0.056 0.176
#> SRR1373964 3 0.4313 0.5008 0.000 0.008 0.704 0.012 0.276
#> SRR1455413 1 0.4615 0.7375 0.776 0.000 0.080 0.120 0.024
#> SRR1437163 1 0.1251 0.8102 0.956 0.000 0.008 0.036 0.000
#> SRR1347343 3 0.3421 0.5162 0.000 0.008 0.824 0.016 0.152
#> SRR1465480 2 0.0000 0.9642 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.1281 0.8105 0.956 0.000 0.012 0.032 0.000
#> SRR1086514 4 0.4701 0.5634 0.000 0.044 0.252 0.700 0.004
#> SRR1430928 1 0.0290 0.8163 0.992 0.000 0.000 0.008 0.000
#> SRR1310939 4 0.5607 0.2197 0.004 0.000 0.408 0.524 0.064
#> SRR1344294 2 0.0162 0.9639 0.000 0.996 0.004 0.000 0.000
#> SRR1099402 1 0.0807 0.8146 0.976 0.000 0.012 0.012 0.000
#> SRR1468118 5 0.1444 0.8313 0.000 0.000 0.012 0.040 0.948
#> SRR1486348 1 0.0000 0.8167 1.000 0.000 0.000 0.000 0.000
#> SRR1488770 2 0.0162 0.9639 0.000 0.996 0.004 0.000 0.000
#> SRR1083732 1 0.0510 0.8155 0.984 0.000 0.000 0.016 0.000
#> SRR1456611 2 0.0000 0.9642 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.4128 0.7480 0.800 0.000 0.080 0.112 0.008
#> SRR1500089 4 0.6557 0.4865 0.240 0.000 0.044 0.588 0.128
#> SRR1441178 1 0.5884 0.6967 0.684 0.000 0.120 0.144 0.052
#> SRR1381396 1 0.3924 0.7509 0.816 0.000 0.080 0.096 0.008
#> SRR1096081 5 0.1041 0.8304 0.000 0.000 0.004 0.032 0.964
#> SRR1349809 1 0.8371 -0.3900 0.324 0.148 0.232 0.296 0.000
#> SRR1324314 3 0.7015 0.0650 0.380 0.000 0.460 0.080 0.080
#> SRR1092444 1 0.4605 0.7376 0.780 0.000 0.080 0.112 0.028
#> SRR1382553 3 0.8465 0.1436 0.300 0.100 0.436 0.092 0.072
#> SRR1075530 4 0.4752 0.6213 0.000 0.040 0.180 0.748 0.032
#> SRR1442612 3 0.4127 0.4743 0.000 0.000 0.680 0.008 0.312
#> SRR1360056 5 0.5674 0.5081 0.036 0.000 0.188 0.092 0.684
#> SRR1078164 1 0.5884 0.6967 0.684 0.000 0.120 0.144 0.052
#> SRR1434545 4 0.3843 0.6403 0.112 0.004 0.040 0.828 0.016
#> SRR1398251 1 0.5750 0.6614 0.684 0.000 0.180 0.092 0.044
#> SRR1375866 1 0.4718 0.7379 0.764 0.000 0.084 0.132 0.020
#> SRR1091645 4 0.4173 0.4721 0.000 0.000 0.012 0.688 0.300
#> SRR1416636 5 0.2006 0.8283 0.000 0.000 0.072 0.012 0.916
#> SRR1105441 3 0.4715 0.4903 0.000 0.012 0.672 0.020 0.296
#> SRR1082496 2 0.0162 0.9639 0.000 0.996 0.004 0.000 0.000
#> SRR1315353 3 0.6328 0.1757 0.000 0.164 0.552 0.276 0.008
#> SRR1093697 2 0.0162 0.9639 0.000 0.996 0.004 0.000 0.000
#> SRR1077429 5 0.1485 0.8340 0.000 0.000 0.020 0.032 0.948
#> SRR1076120 4 0.6038 0.2681 0.400 0.000 0.032 0.516 0.052
#> SRR1074410 1 0.3924 0.7509 0.816 0.000 0.080 0.096 0.008
#> SRR1340345 4 0.4752 0.6213 0.000 0.040 0.180 0.748 0.032
#> SRR1069514 3 0.4380 0.4941 0.000 0.008 0.692 0.012 0.288
#> SRR1092636 5 0.1764 0.8311 0.000 0.000 0.064 0.008 0.928
#> SRR1365013 3 0.5174 -0.0946 0.004 0.032 0.520 0.444 0.000
#> SRR1073069 1 0.6563 0.6330 0.604 0.000 0.208 0.136 0.052
#> SRR1443137 1 0.5417 0.6866 0.716 0.000 0.160 0.080 0.044
#> SRR1437143 2 0.0000 0.9642 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0000 0.8167 1.000 0.000 0.000 0.000 0.000
#> SRR820234 2 0.0162 0.9626 0.000 0.996 0.004 0.000 0.000
#> SRR1338079 1 0.0693 0.8154 0.980 0.000 0.008 0.012 0.000
#> SRR1390094 3 0.5710 0.3557 0.100 0.012 0.656 0.228 0.004
#> SRR1340721 1 0.5117 0.4288 0.652 0.000 0.072 0.276 0.000
#> SRR1335964 3 0.4747 0.4323 0.000 0.000 0.620 0.028 0.352
#> SRR1086869 5 0.1549 0.8292 0.000 0.000 0.016 0.040 0.944
#> SRR1453434 1 0.1943 0.8036 0.924 0.000 0.020 0.056 0.000
#> SRR1402261 4 0.3658 0.6382 0.116 0.000 0.036 0.832 0.016
#> SRR657809 4 0.5068 0.5051 0.000 0.060 0.300 0.640 0.000
#> SRR1093075 1 0.5575 0.6805 0.704 0.000 0.160 0.092 0.044
#> SRR1433329 1 0.5524 0.6832 0.708 0.000 0.160 0.088 0.044
#> SRR1353418 5 0.4428 0.6100 0.000 0.000 0.160 0.084 0.756
#> SRR1092913 4 0.4330 0.6446 0.020 0.036 0.108 0.812 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.0806 0.7626 0.972 0.000 0.000 0.008 0.000 0.020
#> SRR1335605 6 0.7183 -0.2101 0.028 0.000 0.248 0.240 0.044 0.440
#> SRR1432014 3 0.2214 0.7390 0.000 0.000 0.888 0.000 0.096 0.016
#> SRR1499215 6 0.3800 0.4386 0.044 0.000 0.164 0.000 0.012 0.780
#> SRR1460409 1 0.0363 0.7657 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1086441 1 0.0260 0.7653 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1097344 4 0.2713 0.6745 0.000 0.016 0.024 0.884 0.068 0.008
#> SRR1081789 4 0.8250 0.0300 0.028 0.228 0.268 0.296 0.004 0.176
#> SRR1453005 2 0.6825 0.0941 0.000 0.468 0.092 0.308 0.004 0.128
#> SRR1366985 6 0.4418 0.5941 0.432 0.000 0.008 0.008 0.004 0.548
#> SRR815280 1 0.1010 0.7529 0.960 0.000 0.000 0.004 0.000 0.036
#> SRR1348531 1 0.3463 0.6585 0.816 0.000 0.008 0.000 0.120 0.056
#> SRR815845 5 0.2664 0.7364 0.000 0.000 0.184 0.000 0.816 0.000
#> SRR1471178 1 0.0632 0.7617 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1080696 5 0.1588 0.8411 0.000 0.000 0.072 0.000 0.924 0.004
#> SRR1078684 3 0.3120 0.7357 0.008 0.000 0.856 0.020 0.024 0.092
#> SRR1317751 5 0.1666 0.8497 0.000 0.000 0.036 0.020 0.936 0.008
#> SRR1435667 3 0.2060 0.7471 0.000 0.000 0.900 0.000 0.084 0.016
#> SRR1097905 1 0.1498 0.7633 0.948 0.000 0.012 0.012 0.004 0.024
#> SRR1456548 1 0.1414 0.7636 0.952 0.000 0.012 0.012 0.004 0.020
#> SRR1075126 1 0.2361 0.6627 0.880 0.000 0.004 0.012 0.000 0.104
#> SRR813108 3 0.1616 0.7449 0.000 0.028 0.940 0.020 0.012 0.000
#> SRR1479062 6 0.5051 0.2682 0.000 0.000 0.024 0.176 0.116 0.684
#> SRR1408703 5 0.1429 0.8499 0.000 0.000 0.052 0.004 0.940 0.004
#> SRR1332360 6 0.3515 0.5526 0.324 0.000 0.000 0.000 0.000 0.676
#> SRR1098686 1 0.0458 0.7653 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1434228 6 0.4316 0.5924 0.432 0.000 0.004 0.008 0.004 0.552
#> SRR1467149 1 0.7010 -0.0573 0.420 0.000 0.016 0.328 0.188 0.048
#> SRR1399113 2 0.0146 0.9434 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1476507 4 0.2127 0.6734 0.004 0.012 0.060 0.912 0.012 0.000
#> SRR1092468 4 0.6631 0.3907 0.108 0.000 0.316 0.496 0.012 0.068
#> SRR1441804 1 0.1251 0.7655 0.956 0.000 0.008 0.000 0.012 0.024
#> SRR1326100 3 0.6842 0.2841 0.000 0.088 0.500 0.264 0.008 0.140
#> SRR1398815 1 0.5474 0.5052 0.648 0.000 0.020 0.060 0.032 0.240
#> SRR1436021 3 0.4488 0.2409 0.004 0.004 0.544 0.432 0.000 0.016
#> SRR1480083 2 0.0713 0.9364 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR1472863 1 0.1294 0.7664 0.956 0.000 0.008 0.008 0.004 0.024
#> SRR815542 1 0.0363 0.7657 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1400100 5 0.5769 -0.0436 0.000 0.004 0.428 0.068 0.468 0.032
#> SRR1312002 6 0.5380 0.6046 0.336 0.000 0.008 0.004 0.088 0.564
#> SRR1470253 6 0.4932 0.4212 0.064 0.000 0.028 0.040 0.124 0.744
#> SRR1414332 1 0.0692 0.7622 0.976 0.000 0.000 0.004 0.000 0.020
#> SRR1069209 6 0.4346 0.5711 0.460 0.000 0.004 0.008 0.004 0.524
#> SRR661052 1 0.4524 0.5820 0.716 0.000 0.024 0.040 0.004 0.216
#> SRR1308860 1 0.0551 0.7687 0.984 0.000 0.000 0.004 0.004 0.008
#> SRR1421159 3 0.1726 0.7404 0.000 0.000 0.932 0.044 0.012 0.012
#> SRR1340943 4 0.2202 0.6918 0.072 0.000 0.004 0.904 0.008 0.012
#> SRR1078855 6 0.4214 0.5705 0.460 0.000 0.004 0.008 0.000 0.528
#> SRR1459465 2 0.0713 0.9364 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR816818 2 0.0582 0.9420 0.000 0.984 0.004 0.004 0.004 0.004
#> SRR1478679 3 0.4497 0.6084 0.028 0.000 0.708 0.028 0.004 0.232
#> SRR1350979 3 0.2214 0.7390 0.000 0.000 0.888 0.000 0.096 0.016
#> SRR1458198 4 0.5730 0.2598 0.408 0.000 0.008 0.496 0.048 0.040
#> SRR1386910 3 0.6668 0.0303 0.000 0.012 0.420 0.376 0.036 0.156
#> SRR1465375 4 0.3966 0.6350 0.156 0.004 0.036 0.784 0.004 0.016
#> SRR1323699 6 0.5215 -0.0363 0.024 0.000 0.404 0.020 0.016 0.536
#> SRR1431139 3 0.4419 0.7090 0.028 0.000 0.784 0.036 0.048 0.104
#> SRR1373964 3 0.2152 0.7520 0.000 0.000 0.904 0.004 0.068 0.024
#> SRR1455413 1 0.5145 0.5568 0.684 0.000 0.028 0.040 0.028 0.220
#> SRR1437163 1 0.1317 0.7638 0.956 0.000 0.008 0.016 0.004 0.016
#> SRR1347343 3 0.2930 0.7060 0.000 0.000 0.840 0.000 0.036 0.124
#> SRR1465480 2 0.0146 0.9442 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1489631 1 0.2177 0.7510 0.916 0.000 0.016 0.012 0.012 0.044
#> SRR1086514 4 0.4538 0.5232 0.000 0.012 0.228 0.704 0.004 0.052
#> SRR1430928 1 0.0458 0.7642 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1310939 4 0.5442 0.2871 0.000 0.000 0.344 0.552 0.016 0.088
#> SRR1344294 2 0.0291 0.9443 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1099402 1 0.1152 0.7475 0.952 0.000 0.000 0.004 0.000 0.044
#> SRR1468118 5 0.1408 0.8497 0.000 0.000 0.036 0.020 0.944 0.000
#> SRR1486348 1 0.0547 0.7629 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1488770 2 0.0436 0.9439 0.000 0.988 0.000 0.004 0.004 0.004
#> SRR1083732 1 0.0632 0.7617 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1456611 2 0.0146 0.9442 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1080318 1 0.5658 0.4871 0.632 0.000 0.024 0.060 0.036 0.248
#> SRR1500089 4 0.6378 0.4769 0.276 0.000 0.012 0.536 0.136 0.040
#> SRR1441178 6 0.5796 0.2493 0.376 0.000 0.020 0.052 0.028 0.524
#> SRR1381396 1 0.5613 0.4885 0.632 0.000 0.024 0.060 0.032 0.252
#> SRR1096081 5 0.1577 0.8504 0.000 0.000 0.036 0.016 0.940 0.008
#> SRR1349809 1 0.7909 -0.1641 0.412 0.044 0.080 0.280 0.012 0.172
#> SRR1324314 6 0.5857 0.5526 0.236 0.000 0.144 0.012 0.016 0.592
#> SRR1092444 1 0.6033 0.4654 0.608 0.000 0.024 0.060 0.064 0.244
#> SRR1382553 6 0.6086 0.5868 0.220 0.052 0.108 0.008 0.004 0.608
#> SRR1075530 4 0.4181 0.6463 0.000 0.012 0.104 0.792 0.028 0.064
#> SRR1442612 3 0.2060 0.7471 0.000 0.000 0.900 0.000 0.084 0.016
#> SRR1360056 5 0.4314 0.1805 0.000 0.000 0.012 0.004 0.500 0.484
#> SRR1078164 6 0.5789 0.2451 0.372 0.000 0.020 0.052 0.028 0.528
#> SRR1434545 4 0.2245 0.6918 0.068 0.000 0.004 0.904 0.012 0.012
#> SRR1398251 6 0.4412 0.5961 0.428 0.000 0.008 0.008 0.004 0.552
#> SRR1375866 1 0.5931 0.3733 0.568 0.000 0.024 0.060 0.036 0.312
#> SRR1091645 4 0.3547 0.5706 0.000 0.000 0.012 0.768 0.208 0.012
#> SRR1416636 5 0.1327 0.8464 0.000 0.000 0.064 0.000 0.936 0.000
#> SRR1105441 3 0.1701 0.7517 0.000 0.000 0.920 0.008 0.072 0.000
#> SRR1082496 2 0.0291 0.9443 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1315353 3 0.6909 0.2544 0.000 0.100 0.480 0.272 0.004 0.144
#> SRR1093697 2 0.0436 0.9439 0.000 0.988 0.000 0.004 0.004 0.004
#> SRR1077429 5 0.1245 0.8489 0.000 0.000 0.032 0.016 0.952 0.000
#> SRR1076120 4 0.5817 0.2898 0.396 0.000 0.008 0.500 0.056 0.040
#> SRR1074410 1 0.5634 0.4834 0.628 0.000 0.024 0.060 0.032 0.256
#> SRR1340345 4 0.4181 0.6463 0.000 0.012 0.104 0.792 0.028 0.064
#> SRR1069514 3 0.1901 0.7515 0.000 0.000 0.912 0.004 0.076 0.008
#> SRR1092636 5 0.1477 0.8500 0.000 0.000 0.048 0.004 0.940 0.008
#> SRR1365013 3 0.6281 0.0594 0.000 0.012 0.436 0.384 0.012 0.156
#> SRR1073069 6 0.3531 0.5536 0.328 0.000 0.000 0.000 0.000 0.672
#> SRR1443137 6 0.4227 0.5309 0.492 0.000 0.004 0.008 0.000 0.496
#> SRR1437143 2 0.0146 0.9442 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1091990 1 0.0935 0.7557 0.964 0.000 0.000 0.004 0.000 0.032
#> SRR820234 2 0.0713 0.9364 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR1338079 1 0.1223 0.7657 0.960 0.000 0.008 0.012 0.004 0.016
#> SRR1390094 3 0.4216 0.6195 0.048 0.000 0.752 0.176 0.000 0.024
#> SRR1340721 1 0.6119 0.2545 0.600 0.000 0.048 0.172 0.008 0.172
#> SRR1335964 3 0.2544 0.7261 0.000 0.000 0.864 0.004 0.120 0.012
#> SRR1086869 5 0.1666 0.8489 0.000 0.000 0.036 0.020 0.936 0.008
#> SRR1453434 1 0.2094 0.6970 0.908 0.000 0.004 0.024 0.000 0.064
#> SRR1402261 4 0.2202 0.6918 0.072 0.000 0.004 0.904 0.008 0.012
#> SRR657809 4 0.5401 0.5187 0.000 0.016 0.164 0.656 0.008 0.156
#> SRR1093075 6 0.4220 0.5622 0.468 0.000 0.004 0.008 0.000 0.520
#> SRR1433329 6 0.4226 0.5374 0.484 0.000 0.004 0.008 0.000 0.504
#> SRR1353418 5 0.3534 0.6463 0.000 0.000 0.016 0.000 0.740 0.244
#> SRR1092913 4 0.1905 0.6869 0.016 0.012 0.020 0.932 0.020 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17780 rows and 119 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 0.895 0.936 0.973 0.5039 0.497 0.497
#> 3 3 0.741 0.854 0.929 0.3185 0.738 0.521
#> 4 4 0.692 0.612 0.819 0.1083 0.927 0.791
#> 5 5 0.700 0.639 0.782 0.0716 0.858 0.554
#> 6 6 0.768 0.667 0.825 0.0500 0.919 0.646
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
#> SRR816969 1 0.0000 0.969 1.000 0.000
#> SRR1335605 2 0.0000 0.973 0.000 1.000
#> SRR1432014 2 0.0000 0.973 0.000 1.000
#> SRR1499215 1 0.6247 0.809 0.844 0.156
#> SRR1460409 1 0.0000 0.969 1.000 0.000
#> SRR1086441 1 0.0000 0.969 1.000 0.000
#> SRR1097344 2 0.0000 0.973 0.000 1.000
#> SRR1081789 2 0.0000 0.973 0.000 1.000
#> SRR1453005 2 0.0000 0.973 0.000 1.000
#> SRR1366985 1 0.0000 0.969 1.000 0.000
#> SRR815280 1 0.0000 0.969 1.000 0.000
#> SRR1348531 1 0.0000 0.969 1.000 0.000
#> SRR815845 2 0.0000 0.973 0.000 1.000
#> SRR1471178 1 0.0000 0.969 1.000 0.000
#> SRR1080696 2 0.0000 0.973 0.000 1.000
#> SRR1078684 2 0.0000 0.973 0.000 1.000
#> SRR1317751 1 0.9710 0.358 0.600 0.400
#> SRR1435667 2 0.0000 0.973 0.000 1.000
#> SRR1097905 1 0.0000 0.969 1.000 0.000
#> SRR1456548 1 0.0000 0.969 1.000 0.000
#> SRR1075126 1 0.0000 0.969 1.000 0.000
#> SRR813108 2 0.0000 0.973 0.000 1.000
#> SRR1479062 2 0.0000 0.973 0.000 1.000
#> SRR1408703 2 0.0000 0.973 0.000 1.000
#> SRR1332360 1 0.0000 0.969 1.000 0.000
#> SRR1098686 1 0.0000 0.969 1.000 0.000
#> SRR1434228 1 0.0000 0.969 1.000 0.000
#> SRR1467149 1 0.0000 0.969 1.000 0.000
#> SRR1399113 2 0.0000 0.973 0.000 1.000
#> SRR1476507 2 0.0000 0.973 0.000 1.000
#> SRR1092468 2 0.9491 0.428 0.368 0.632
#> SRR1441804 1 0.0000 0.969 1.000 0.000
#> SRR1326100 2 0.0000 0.973 0.000 1.000
#> SRR1398815 1 0.0000 0.969 1.000 0.000
#> SRR1436021 2 0.0000 0.973 0.000 1.000
#> SRR1480083 2 0.0000 0.973 0.000 1.000
#> SRR1472863 1 0.0000 0.969 1.000 0.000
#> SRR815542 1 0.0000 0.969 1.000 0.000
#> SRR1400100 2 0.0000 0.973 0.000 1.000
#> SRR1312002 1 0.0000 0.969 1.000 0.000
#> SRR1470253 1 0.0000 0.969 1.000 0.000
#> SRR1414332 1 0.0000 0.969 1.000 0.000
#> SRR1069209 1 0.0000 0.969 1.000 0.000
#> SRR661052 1 0.0000 0.969 1.000 0.000
#> SRR1308860 1 0.0000 0.969 1.000 0.000
#> SRR1421159 2 0.0000 0.973 0.000 1.000
#> SRR1340943 1 0.4298 0.891 0.912 0.088
#> SRR1078855 1 0.0000 0.969 1.000 0.000
#> SRR1459465 2 0.0000 0.973 0.000 1.000
#> SRR816818 2 0.0000 0.973 0.000 1.000
#> SRR1478679 2 0.3114 0.924 0.056 0.944
#> SRR1350979 2 0.0000 0.973 0.000 1.000
#> SRR1458198 1 0.0000 0.969 1.000 0.000
#> SRR1386910 2 0.0000 0.973 0.000 1.000
#> SRR1465375 2 0.7376 0.739 0.208 0.792
#> SRR1323699 2 0.7056 0.764 0.192 0.808
#> SRR1431139 2 0.6623 0.792 0.172 0.828
#> SRR1373964 2 0.0000 0.973 0.000 1.000
#> SRR1455413 1 0.0000 0.969 1.000 0.000
#> SRR1437163 1 0.0000 0.969 1.000 0.000
#> SRR1347343 2 0.0000 0.973 0.000 1.000
#> SRR1465480 2 0.0000 0.973 0.000 1.000
#> SRR1489631 1 0.0000 0.969 1.000 0.000
#> SRR1086514 2 0.0000 0.973 0.000 1.000
#> SRR1430928 1 0.0000 0.969 1.000 0.000
#> SRR1310939 2 0.0000 0.973 0.000 1.000
#> SRR1344294 2 0.0000 0.973 0.000 1.000
#> SRR1099402 1 0.0000 0.969 1.000 0.000
#> SRR1468118 2 0.9393 0.430 0.356 0.644
#> SRR1486348 1 0.0000 0.969 1.000 0.000
#> SRR1488770 2 0.0000 0.973 0.000 1.000
#> SRR1083732 1 0.0000 0.969 1.000 0.000
#> SRR1456611 2 0.0000 0.973 0.000 1.000
#> SRR1080318 1 0.0000 0.969 1.000 0.000
#> SRR1500089 1 0.0376 0.966 0.996 0.004
#> SRR1441178 1 0.0000 0.969 1.000 0.000
#> SRR1381396 1 0.0000 0.969 1.000 0.000
#> SRR1096081 1 0.9710 0.358 0.600 0.400
#> SRR1349809 2 0.3274 0.920 0.060 0.940
#> SRR1324314 1 0.0000 0.969 1.000 0.000
#> SRR1092444 1 0.0000 0.969 1.000 0.000
#> SRR1382553 1 0.5737 0.835 0.864 0.136
#> SRR1075530 2 0.0000 0.973 0.000 1.000
#> SRR1442612 2 0.0000 0.973 0.000 1.000
#> SRR1360056 1 0.0000 0.969 1.000 0.000
#> SRR1078164 1 0.0000 0.969 1.000 0.000
#> SRR1434545 2 0.2423 0.938 0.040 0.960
#> SRR1398251 1 0.0000 0.969 1.000 0.000
#> SRR1375866 1 0.0000 0.969 1.000 0.000
#> SRR1091645 2 0.0000 0.973 0.000 1.000
#> SRR1416636 2 0.0000 0.973 0.000 1.000
#> SRR1105441 2 0.0000 0.973 0.000 1.000
#> SRR1082496 2 0.0000 0.973 0.000 1.000
#> SRR1315353 2 0.0000 0.973 0.000 1.000
#> SRR1093697 2 0.0000 0.973 0.000 1.000
#> SRR1077429 1 0.8081 0.679 0.752 0.248
#> SRR1076120 1 0.0000 0.969 1.000 0.000
#> SRR1074410 1 0.0000 0.969 1.000 0.000
#> SRR1340345 2 0.0000 0.973 0.000 1.000
#> SRR1069514 2 0.0000 0.973 0.000 1.000
#> SRR1092636 1 0.7219 0.752 0.800 0.200
#> SRR1365013 2 0.0000 0.973 0.000 1.000
#> SRR1073069 1 0.0000 0.969 1.000 0.000
#> SRR1443137 1 0.0000 0.969 1.000 0.000
#> SRR1437143 2 0.0000 0.973 0.000 1.000
#> SRR1091990 1 0.0000 0.969 1.000 0.000
#> SRR820234 2 0.0000 0.973 0.000 1.000
#> SRR1338079 1 0.0000 0.969 1.000 0.000
#> SRR1390094 2 0.0000 0.973 0.000 1.000
#> SRR1340721 1 0.5059 0.863 0.888 0.112
#> SRR1335964 2 0.0000 0.973 0.000 1.000
#> SRR1086869 2 0.0000 0.973 0.000 1.000
#> SRR1453434 1 0.0000 0.969 1.000 0.000
#> SRR1402261 1 0.3733 0.908 0.928 0.072
#> SRR657809 2 0.0000 0.973 0.000 1.000
#> SRR1093075 1 0.0000 0.969 1.000 0.000
#> SRR1433329 1 0.0000 0.969 1.000 0.000
#> SRR1353418 1 0.0000 0.969 1.000 0.000
#> SRR1092913 2 0.0000 0.973 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1335605 2 0.6280 0.315 0.000 0.540 0.460
#> SRR1432014 3 0.0592 0.871 0.000 0.012 0.988
#> SRR1499215 3 0.4346 0.765 0.184 0.000 0.816
#> SRR1460409 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1097344 2 0.4235 0.786 0.000 0.824 0.176
#> SRR1081789 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1453005 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1366985 1 0.0000 0.965 1.000 0.000 0.000
#> SRR815280 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1348531 1 0.3941 0.817 0.844 0.000 0.156
#> SRR815845 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1471178 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1078684 3 0.5785 0.601 0.000 0.332 0.668
#> SRR1317751 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1435667 3 0.4178 0.805 0.000 0.172 0.828
#> SRR1097905 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.965 1.000 0.000 0.000
#> SRR813108 2 0.5529 0.470 0.000 0.704 0.296
#> SRR1479062 3 0.5706 0.448 0.000 0.320 0.680
#> SRR1408703 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1332360 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1434228 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1467149 1 0.4504 0.771 0.804 0.000 0.196
#> SRR1399113 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1476507 2 0.1860 0.873 0.000 0.948 0.052
#> SRR1092468 3 0.0747 0.868 0.016 0.000 0.984
#> SRR1441804 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1436021 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1480083 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.965 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1400100 2 0.3752 0.805 0.000 0.856 0.144
#> SRR1312002 1 0.6280 0.032 0.540 0.000 0.460
#> SRR1470253 3 0.3482 0.797 0.128 0.000 0.872
#> SRR1414332 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.965 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1421159 3 0.4504 0.786 0.000 0.196 0.804
#> SRR1340943 2 0.3987 0.830 0.020 0.872 0.108
#> SRR1078855 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.898 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1478679 3 0.4555 0.783 0.000 0.200 0.800
#> SRR1350979 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1458198 1 0.4291 0.790 0.820 0.000 0.180
#> SRR1386910 2 0.6204 0.421 0.000 0.576 0.424
#> SRR1465375 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1323699 3 0.4178 0.775 0.172 0.000 0.828
#> SRR1431139 3 0.4618 0.820 0.024 0.136 0.840
#> SRR1373964 3 0.4235 0.802 0.000 0.176 0.824
#> SRR1455413 1 0.4062 0.808 0.836 0.000 0.164
#> SRR1437163 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1347343 3 0.4178 0.805 0.000 0.172 0.828
#> SRR1465480 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1086514 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1430928 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1310939 3 0.0592 0.869 0.000 0.012 0.988
#> SRR1344294 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1500089 3 0.3192 0.796 0.112 0.000 0.888
#> SRR1441178 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1349809 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1324314 3 0.4555 0.751 0.200 0.000 0.800
#> SRR1092444 1 0.4002 0.813 0.840 0.000 0.160
#> SRR1382553 3 0.9853 0.265 0.360 0.252 0.388
#> SRR1075530 2 0.4235 0.786 0.000 0.824 0.176
#> SRR1442612 3 0.3482 0.829 0.000 0.128 0.872
#> SRR1360056 3 0.1031 0.863 0.024 0.000 0.976
#> SRR1078164 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1434545 2 0.3340 0.830 0.000 0.880 0.120
#> SRR1398251 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1091645 2 0.6168 0.450 0.000 0.588 0.412
#> SRR1416636 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1105441 3 0.4235 0.802 0.000 0.176 0.824
#> SRR1082496 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1315353 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1093697 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1076120 1 0.4399 0.780 0.812 0.000 0.188
#> SRR1074410 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1340345 2 0.4235 0.786 0.000 0.824 0.176
#> SRR1069514 3 0.4235 0.802 0.000 0.176 0.824
#> SRR1092636 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1365013 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1073069 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.965 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1390094 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1340721 2 0.6111 0.378 0.396 0.604 0.000
#> SRR1335964 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1402261 2 0.4749 0.811 0.076 0.852 0.072
#> SRR657809 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1093075 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1353418 3 0.0000 0.873 0.000 0.000 1.000
#> SRR1092913 2 0.4178 0.789 0.000 0.828 0.172
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.6232 0.3888 0.000 0.596 0.332 0.072
#> SRR1432014 3 0.0000 0.5801 0.000 0.000 1.000 0.000
#> SRR1499215 3 0.5619 0.2293 0.040 0.000 0.640 0.320
#> SRR1460409 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1097344 2 0.5110 0.6182 0.000 0.636 0.012 0.352
#> SRR1081789 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1453005 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1366985 1 0.5389 0.6066 0.660 0.000 0.032 0.308
#> SRR815280 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1348531 1 0.6065 0.2919 0.644 0.000 0.080 0.276
#> SRR815845 3 0.1118 0.5697 0.000 0.000 0.964 0.036
#> SRR1471178 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.1302 0.5658 0.000 0.000 0.956 0.044
#> SRR1078684 3 0.4543 0.4149 0.000 0.324 0.676 0.000
#> SRR1317751 3 0.4898 0.0773 0.000 0.000 0.584 0.416
#> SRR1435667 3 0.1637 0.5884 0.000 0.060 0.940 0.000
#> SRR1097905 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1456548 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1075126 1 0.0469 0.8628 0.988 0.000 0.000 0.012
#> SRR813108 3 0.4898 0.3256 0.000 0.416 0.584 0.000
#> SRR1479062 4 0.3975 0.4075 0.000 0.000 0.240 0.760
#> SRR1408703 3 0.4643 0.2473 0.000 0.000 0.656 0.344
#> SRR1332360 1 0.4522 0.6378 0.680 0.000 0.000 0.320
#> SRR1098686 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1434228 1 0.4632 0.6396 0.688 0.000 0.004 0.308
#> SRR1467149 4 0.6182 0.4246 0.308 0.000 0.076 0.616
#> SRR1399113 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1476507 2 0.4990 0.6216 0.000 0.640 0.008 0.352
#> SRR1092468 3 0.5024 0.3036 0.008 0.000 0.632 0.360
#> SRR1441804 1 0.0188 0.8642 0.996 0.000 0.000 0.004
#> SRR1326100 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0469 0.8620 0.988 0.000 0.000 0.012
#> SRR1436021 2 0.1174 0.8010 0.000 0.968 0.012 0.020
#> SRR1480083 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR815542 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1400100 2 0.6508 0.3692 0.000 0.600 0.296 0.104
#> SRR1312002 4 0.6534 0.3558 0.148 0.000 0.220 0.632
#> SRR1470253 4 0.4855 0.3341 0.004 0.000 0.352 0.644
#> SRR1414332 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.4454 0.6442 0.692 0.000 0.000 0.308
#> SRR661052 1 0.0469 0.8620 0.988 0.000 0.000 0.012
#> SRR1308860 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1421159 3 0.5137 0.4367 0.000 0.040 0.716 0.244
#> SRR1340943 2 0.5594 0.6039 0.024 0.620 0.004 0.352
#> SRR1078855 1 0.4454 0.6442 0.692 0.000 0.000 0.308
#> SRR1459465 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.6583 0.4078 0.000 0.176 0.632 0.192
#> SRR1350979 3 0.0000 0.5801 0.000 0.000 1.000 0.000
#> SRR1458198 4 0.4814 0.4137 0.316 0.000 0.008 0.676
#> SRR1386910 2 0.5460 0.4587 0.000 0.632 0.340 0.028
#> SRR1465375 2 0.3486 0.7365 0.000 0.812 0.000 0.188
#> SRR1323699 3 0.5041 0.3669 0.040 0.000 0.728 0.232
#> SRR1431139 3 0.3703 0.5765 0.012 0.064 0.868 0.056
#> SRR1373964 3 0.2921 0.5690 0.000 0.140 0.860 0.000
#> SRR1455413 1 0.6324 0.1142 0.584 0.000 0.076 0.340
#> SRR1437163 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1347343 3 0.5477 0.4601 0.000 0.092 0.728 0.180
#> SRR1465480 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0469 0.8620 0.988 0.000 0.000 0.012
#> SRR1086514 2 0.3219 0.7466 0.000 0.836 0.000 0.164
#> SRR1430928 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.4697 0.3141 0.000 0.000 0.644 0.356
#> SRR1344294 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.4643 0.2473 0.000 0.000 0.656 0.344
#> SRR1486348 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0592 0.8599 0.984 0.000 0.000 0.016
#> SRR1500089 4 0.4522 0.1387 0.000 0.000 0.320 0.680
#> SRR1441178 1 0.2814 0.7953 0.868 0.000 0.000 0.132
#> SRR1381396 1 0.0469 0.8620 0.988 0.000 0.000 0.012
#> SRR1096081 3 0.4661 0.2401 0.000 0.000 0.652 0.348
#> SRR1349809 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1324314 3 0.7227 0.1468 0.228 0.000 0.548 0.224
#> SRR1092444 1 0.6309 0.1278 0.588 0.000 0.076 0.336
#> SRR1382553 2 0.8812 -0.2490 0.040 0.340 0.312 0.308
#> SRR1075530 2 0.5253 0.6070 0.000 0.624 0.016 0.360
#> SRR1442612 3 0.0817 0.5870 0.000 0.024 0.976 0.000
#> SRR1360056 4 0.4679 0.3360 0.000 0.000 0.352 0.648
#> SRR1078164 1 0.2814 0.7953 0.868 0.000 0.000 0.132
#> SRR1434545 2 0.5165 0.6186 0.004 0.636 0.008 0.352
#> SRR1398251 1 0.4769 0.6347 0.684 0.000 0.008 0.308
#> SRR1375866 1 0.1004 0.8543 0.972 0.000 0.004 0.024
#> SRR1091645 4 0.6240 0.2401 0.000 0.136 0.200 0.664
#> SRR1416636 3 0.4585 0.2553 0.000 0.000 0.668 0.332
#> SRR1105441 3 0.3105 0.5703 0.000 0.140 0.856 0.004
#> SRR1082496 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.0188 0.8081 0.000 0.996 0.004 0.000
#> SRR1093697 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.4643 0.2473 0.000 0.000 0.656 0.344
#> SRR1076120 4 0.4678 0.4442 0.232 0.000 0.024 0.744
#> SRR1074410 1 0.0469 0.8620 0.988 0.000 0.000 0.012
#> SRR1340345 2 0.5253 0.6070 0.000 0.624 0.016 0.360
#> SRR1069514 3 0.2868 0.5710 0.000 0.136 0.864 0.000
#> SRR1092636 3 0.4624 0.2499 0.000 0.000 0.660 0.340
#> SRR1365013 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1073069 1 0.4522 0.6378 0.680 0.000 0.000 0.320
#> SRR1443137 1 0.4454 0.6442 0.692 0.000 0.000 0.308
#> SRR1437143 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR820234 2 0.0000 0.8103 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0000 0.8660 1.000 0.000 0.000 0.000
#> SRR1390094 2 0.4054 0.6321 0.000 0.796 0.188 0.016
#> SRR1340721 2 0.4907 0.2830 0.420 0.580 0.000 0.000
#> SRR1335964 3 0.2469 0.5575 0.000 0.000 0.892 0.108
#> SRR1086869 3 0.4643 0.2473 0.000 0.000 0.656 0.344
#> SRR1453434 1 0.0336 0.8639 0.992 0.000 0.000 0.008
#> SRR1402261 2 0.6541 0.5367 0.076 0.568 0.004 0.352
#> SRR657809 2 0.3024 0.7556 0.000 0.852 0.000 0.148
#> SRR1093075 1 0.4454 0.6442 0.692 0.000 0.000 0.308
#> SRR1433329 1 0.4454 0.6442 0.692 0.000 0.000 0.308
#> SRR1353418 4 0.4713 0.3280 0.000 0.000 0.360 0.640
#> SRR1092913 2 0.5110 0.6182 0.000 0.636 0.012 0.352
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1335605 2 0.6798 0.3282 0.004 0.524 0.100 0.044 0.328
#> SRR1432014 3 0.0000 0.6641 0.000 0.000 1.000 0.000 0.000
#> SRR1499215 5 0.3790 0.3989 0.004 0.000 0.272 0.000 0.724
#> SRR1460409 1 0.0324 0.8633 0.992 0.000 0.000 0.004 0.004
#> SRR1086441 1 0.0451 0.8627 0.988 0.000 0.000 0.004 0.008
#> SRR1097344 4 0.3521 0.7299 0.000 0.232 0.000 0.764 0.004
#> SRR1081789 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1453005 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1366985 5 0.5554 0.6351 0.316 0.000 0.092 0.000 0.592
#> SRR815280 1 0.0794 0.8493 0.972 0.000 0.000 0.000 0.028
#> SRR1348531 1 0.6652 0.3508 0.564 0.000 0.036 0.144 0.256
#> SRR815845 3 0.4541 0.5952 0.000 0.000 0.744 0.084 0.172
#> SRR1471178 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1080696 3 0.5083 0.5765 0.000 0.000 0.696 0.120 0.184
#> SRR1078684 3 0.2891 0.5581 0.000 0.176 0.824 0.000 0.000
#> SRR1317751 3 0.6821 0.3409 0.000 0.000 0.352 0.328 0.320
#> SRR1435667 3 0.0162 0.6640 0.000 0.004 0.996 0.000 0.000
#> SRR1097905 1 0.0451 0.8614 0.988 0.000 0.000 0.008 0.004
#> SRR1456548 1 0.0798 0.8583 0.976 0.000 0.000 0.008 0.016
#> SRR1075126 1 0.3229 0.6858 0.840 0.000 0.000 0.032 0.128
#> SRR813108 3 0.3305 0.5125 0.000 0.224 0.776 0.000 0.000
#> SRR1479062 5 0.3362 0.3707 0.000 0.008 0.012 0.156 0.824
#> SRR1408703 3 0.6532 0.4682 0.000 0.000 0.480 0.240 0.280
#> SRR1332360 5 0.3752 0.6243 0.292 0.000 0.000 0.000 0.708
#> SRR1098686 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1434228 5 0.4210 0.6177 0.412 0.000 0.000 0.000 0.588
#> SRR1467149 4 0.5402 0.3880 0.060 0.000 0.032 0.688 0.220
#> SRR1399113 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.3579 0.7207 0.000 0.240 0.004 0.756 0.000
#> SRR1092468 4 0.4150 0.3080 0.000 0.000 0.388 0.612 0.000
#> SRR1441804 1 0.0912 0.8565 0.972 0.000 0.000 0.016 0.012
#> SRR1326100 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1398815 1 0.2488 0.7901 0.872 0.000 0.000 0.004 0.124
#> SRR1436021 2 0.5342 0.5070 0.000 0.672 0.172 0.156 0.000
#> SRR1480083 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.0290 0.8633 0.992 0.000 0.000 0.000 0.008
#> SRR815542 1 0.0324 0.8633 0.992 0.000 0.000 0.004 0.004
#> SRR1400100 2 0.6500 0.4216 0.000 0.604 0.212 0.044 0.140
#> SRR1312002 5 0.2772 0.5136 0.052 0.000 0.044 0.012 0.892
#> SRR1470253 5 0.1605 0.4520 0.004 0.000 0.012 0.040 0.944
#> SRR1414332 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1069209 5 0.4210 0.6177 0.412 0.000 0.000 0.000 0.588
#> SRR661052 1 0.2707 0.7818 0.860 0.000 0.000 0.008 0.132
#> SRR1308860 1 0.0324 0.8632 0.992 0.000 0.000 0.004 0.004
#> SRR1421159 3 0.2233 0.6142 0.000 0.016 0.904 0.080 0.000
#> SRR1340943 4 0.3877 0.7332 0.024 0.212 0.000 0.764 0.000
#> SRR1078855 5 0.4210 0.6177 0.412 0.000 0.000 0.000 0.588
#> SRR1459465 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.4775 0.5085 0.024 0.048 0.756 0.004 0.168
#> SRR1350979 3 0.0000 0.6641 0.000 0.000 1.000 0.000 0.000
#> SRR1458198 4 0.1522 0.6777 0.012 0.000 0.000 0.944 0.044
#> SRR1386910 2 0.6989 0.3414 0.000 0.556 0.220 0.060 0.164
#> SRR1465375 2 0.4686 0.1799 0.012 0.588 0.004 0.396 0.000
#> SRR1323699 3 0.4114 0.3121 0.000 0.000 0.624 0.000 0.376
#> SRR1431139 3 0.1375 0.6584 0.008 0.008 0.960 0.016 0.008
#> SRR1373964 3 0.1043 0.6559 0.000 0.040 0.960 0.000 0.000
#> SRR1455413 1 0.6269 0.2940 0.516 0.000 0.004 0.144 0.336
#> SRR1437163 1 0.0324 0.8622 0.992 0.000 0.000 0.004 0.004
#> SRR1347343 3 0.3060 0.5954 0.000 0.024 0.848 0.000 0.128
#> SRR1465480 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.2660 0.7865 0.864 0.000 0.000 0.008 0.128
#> SRR1086514 2 0.3231 0.6466 0.000 0.800 0.004 0.196 0.000
#> SRR1430928 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1310939 4 0.4367 0.2538 0.000 0.000 0.416 0.580 0.004
#> SRR1344294 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.1410 0.8190 0.940 0.000 0.000 0.000 0.060
#> SRR1468118 3 0.6767 0.3814 0.000 0.000 0.392 0.328 0.280
#> SRR1486348 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1488770 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0290 0.8621 0.992 0.000 0.000 0.000 0.008
#> SRR1456611 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.2971 0.7591 0.836 0.000 0.000 0.008 0.156
#> SRR1500089 4 0.1444 0.6668 0.000 0.000 0.012 0.948 0.040
#> SRR1441178 5 0.4273 0.3167 0.448 0.000 0.000 0.000 0.552
#> SRR1381396 1 0.2707 0.7818 0.860 0.000 0.000 0.008 0.132
#> SRR1096081 3 0.6775 0.3780 0.000 0.000 0.388 0.328 0.284
#> SRR1349809 2 0.0162 0.8510 0.004 0.996 0.000 0.000 0.000
#> SRR1324314 5 0.7005 0.2972 0.240 0.000 0.336 0.012 0.412
#> SRR1092444 1 0.6169 0.3284 0.536 0.000 0.004 0.136 0.324
#> SRR1382553 5 0.6527 0.4217 0.048 0.252 0.112 0.000 0.588
#> SRR1075530 4 0.3461 0.7325 0.000 0.224 0.000 0.772 0.004
#> SRR1442612 3 0.0162 0.6640 0.000 0.004 0.996 0.000 0.000
#> SRR1360056 5 0.3427 0.3612 0.000 0.000 0.108 0.056 0.836
#> SRR1078164 5 0.4528 0.2744 0.444 0.000 0.000 0.008 0.548
#> SRR1434545 4 0.3366 0.7301 0.000 0.232 0.000 0.768 0.000
#> SRR1398251 5 0.4321 0.6249 0.396 0.000 0.004 0.000 0.600
#> SRR1375866 1 0.4039 0.6183 0.720 0.000 0.004 0.008 0.268
#> SRR1091645 4 0.0451 0.6967 0.000 0.008 0.000 0.988 0.004
#> SRR1416636 3 0.6496 0.4730 0.000 0.000 0.488 0.232 0.280
#> SRR1105441 3 0.1121 0.6564 0.000 0.044 0.956 0.000 0.000
#> SRR1082496 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.0609 0.8383 0.000 0.980 0.020 0.000 0.000
#> SRR1093697 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.6767 0.3814 0.000 0.000 0.392 0.328 0.280
#> SRR1076120 4 0.1121 0.6754 0.000 0.000 0.000 0.956 0.044
#> SRR1074410 1 0.2707 0.7818 0.860 0.000 0.000 0.008 0.132
#> SRR1340345 4 0.3616 0.7327 0.000 0.224 0.004 0.768 0.004
#> SRR1069514 3 0.0963 0.6573 0.000 0.036 0.964 0.000 0.000
#> SRR1092636 3 0.6532 0.4682 0.000 0.000 0.480 0.240 0.280
#> SRR1365013 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1073069 5 0.3752 0.6243 0.292 0.000 0.000 0.000 0.708
#> SRR1443137 5 0.4210 0.6177 0.412 0.000 0.000 0.000 0.588
#> SRR1437143 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0703 0.8526 0.976 0.000 0.000 0.000 0.024
#> SRR820234 2 0.0000 0.8543 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.0566 0.8611 0.984 0.000 0.000 0.004 0.012
#> SRR1390094 3 0.6802 -0.0872 0.000 0.412 0.416 0.152 0.020
#> SRR1340721 2 0.4451 0.0657 0.492 0.504 0.000 0.000 0.004
#> SRR1335964 3 0.1628 0.6541 0.000 0.000 0.936 0.056 0.008
#> SRR1086869 3 0.6767 0.3814 0.000 0.000 0.392 0.328 0.280
#> SRR1453434 1 0.3362 0.7217 0.844 0.000 0.000 0.080 0.076
#> SRR1402261 4 0.4373 0.7197 0.080 0.160 0.000 0.760 0.000
#> SRR657809 2 0.3086 0.6581 0.000 0.816 0.004 0.180 0.000
#> SRR1093075 5 0.4210 0.6177 0.412 0.000 0.000 0.000 0.588
#> SRR1433329 5 0.4210 0.6177 0.412 0.000 0.000 0.000 0.588
#> SRR1353418 5 0.4313 0.2351 0.000 0.000 0.172 0.068 0.760
#> SRR1092913 4 0.3561 0.6983 0.000 0.260 0.000 0.740 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1501 0.81283 0.924 0.000 0.000 0.000 0.000 0.076
#> SRR1335605 5 0.7022 0.00597 0.012 0.384 0.008 0.056 0.404 0.136
#> SRR1432014 3 0.0363 0.88454 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1499215 6 0.2957 0.64444 0.004 0.000 0.120 0.000 0.032 0.844
#> SRR1460409 1 0.1643 0.81370 0.924 0.000 0.000 0.008 0.000 0.068
#> SRR1086441 1 0.1556 0.81163 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1097344 4 0.1644 0.81515 0.000 0.076 0.000 0.920 0.004 0.000
#> SRR1081789 2 0.1334 0.87719 0.000 0.948 0.000 0.032 0.020 0.000
#> SRR1453005 2 0.1049 0.88163 0.000 0.960 0.000 0.032 0.008 0.000
#> SRR1366985 6 0.2928 0.77458 0.084 0.000 0.056 0.004 0.000 0.856
#> SRR815280 1 0.2527 0.74322 0.832 0.000 0.000 0.000 0.000 0.168
#> SRR1348531 5 0.5245 0.09110 0.436 0.000 0.004 0.024 0.500 0.036
#> SRR815845 5 0.3995 0.12628 0.000 0.000 0.480 0.004 0.516 0.000
#> SRR1471178 1 0.1663 0.80824 0.912 0.000 0.000 0.000 0.000 0.088
#> SRR1080696 5 0.3862 0.32534 0.000 0.000 0.388 0.004 0.608 0.000
#> SRR1078684 3 0.1501 0.84923 0.000 0.076 0.924 0.000 0.000 0.000
#> SRR1317751 5 0.2001 0.65037 0.000 0.000 0.092 0.004 0.900 0.004
#> SRR1435667 3 0.0363 0.88454 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1097905 1 0.0405 0.80590 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR1456548 1 0.0725 0.80014 0.976 0.000 0.000 0.000 0.012 0.012
#> SRR1075126 1 0.4782 0.10472 0.512 0.000 0.004 0.032 0.004 0.448
#> SRR813108 3 0.1501 0.84866 0.000 0.076 0.924 0.000 0.000 0.000
#> SRR1479062 5 0.5445 0.17922 0.000 0.008 0.012 0.064 0.516 0.400
#> SRR1408703 5 0.2234 0.65084 0.000 0.000 0.124 0.004 0.872 0.000
#> SRR1332360 6 0.0622 0.73594 0.012 0.000 0.000 0.000 0.008 0.980
#> SRR1098686 1 0.1556 0.81182 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1434228 6 0.2402 0.78892 0.140 0.000 0.000 0.004 0.000 0.856
#> SRR1467149 5 0.4932 0.35342 0.088 0.000 0.000 0.240 0.660 0.012
#> SRR1399113 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.1588 0.81634 0.000 0.072 0.000 0.924 0.004 0.000
#> SRR1092468 4 0.4828 0.54217 0.000 0.000 0.276 0.640 0.080 0.004
#> SRR1441804 1 0.2039 0.78589 0.916 0.000 0.000 0.020 0.052 0.012
#> SRR1326100 2 0.1418 0.87536 0.000 0.944 0.000 0.032 0.024 0.000
#> SRR1398815 1 0.4123 0.68225 0.780 0.000 0.008 0.016 0.060 0.136
#> SRR1436021 2 0.5942 0.05213 0.000 0.424 0.356 0.220 0.000 0.000
#> SRR1480083 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.1010 0.81293 0.960 0.000 0.000 0.000 0.004 0.036
#> SRR815542 1 0.1701 0.81294 0.920 0.000 0.000 0.008 0.000 0.072
#> SRR1400100 5 0.4757 0.03757 0.000 0.468 0.048 0.000 0.484 0.000
#> SRR1312002 6 0.3633 0.56909 0.012 0.000 0.004 0.000 0.252 0.732
#> SRR1470253 6 0.4781 -0.02814 0.008 0.000 0.008 0.020 0.448 0.516
#> SRR1414332 1 0.1556 0.81173 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1069209 6 0.2482 0.78766 0.148 0.000 0.000 0.004 0.000 0.848
#> SRR661052 1 0.4123 0.68290 0.780 0.000 0.008 0.016 0.060 0.136
#> SRR1308860 1 0.0937 0.81271 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1421159 3 0.0951 0.87874 0.000 0.004 0.968 0.020 0.008 0.000
#> SRR1340943 4 0.1524 0.81810 0.000 0.060 0.000 0.932 0.008 0.000
#> SRR1078855 6 0.2482 0.78766 0.148 0.000 0.000 0.004 0.000 0.848
#> SRR1459465 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.3284 0.74216 0.000 0.000 0.800 0.000 0.032 0.168
#> SRR1350979 3 0.0363 0.88454 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1458198 4 0.3627 0.67429 0.008 0.000 0.004 0.740 0.244 0.004
#> SRR1386910 2 0.6033 0.05991 0.000 0.464 0.056 0.064 0.412 0.004
#> SRR1465375 4 0.5328 0.08225 0.076 0.440 0.004 0.476 0.000 0.004
#> SRR1323699 3 0.4250 0.19882 0.000 0.000 0.528 0.000 0.016 0.456
#> SRR1431139 3 0.1230 0.87623 0.000 0.000 0.956 0.008 0.028 0.008
#> SRR1373964 3 0.0508 0.88481 0.000 0.012 0.984 0.000 0.004 0.000
#> SRR1455413 5 0.6460 0.07657 0.380 0.000 0.008 0.032 0.436 0.144
#> SRR1437163 1 0.0291 0.80687 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1347343 3 0.1643 0.85121 0.000 0.000 0.924 0.000 0.008 0.068
#> SRR1465480 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.3434 0.71167 0.820 0.000 0.004 0.008 0.040 0.128
#> SRR1086514 2 0.3725 0.46562 0.000 0.676 0.008 0.316 0.000 0.000
#> SRR1430928 1 0.1556 0.81173 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1310939 4 0.4602 0.48740 0.000 0.000 0.320 0.628 0.048 0.004
#> SRR1344294 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.3508 0.57182 0.704 0.000 0.000 0.004 0.000 0.292
#> SRR1468118 5 0.2053 0.65292 0.000 0.000 0.108 0.004 0.888 0.000
#> SRR1486348 1 0.1610 0.81001 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1488770 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1663 0.80824 0.912 0.000 0.000 0.000 0.000 0.088
#> SRR1456611 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.4871 0.63783 0.728 0.000 0.008 0.032 0.088 0.144
#> SRR1500089 4 0.3884 0.65466 0.004 0.000 0.012 0.708 0.272 0.004
#> SRR1441178 6 0.4510 0.58105 0.148 0.000 0.008 0.024 0.068 0.752
#> SRR1381396 1 0.4343 0.67317 0.768 0.000 0.008 0.024 0.064 0.136
#> SRR1096081 5 0.2149 0.65262 0.000 0.000 0.104 0.004 0.888 0.004
#> SRR1349809 2 0.1562 0.87367 0.004 0.940 0.000 0.032 0.024 0.000
#> SRR1324314 6 0.6510 0.43871 0.104 0.000 0.264 0.008 0.084 0.540
#> SRR1092444 5 0.6470 0.02103 0.400 0.000 0.008 0.032 0.416 0.144
#> SRR1382553 6 0.3415 0.70386 0.024 0.136 0.016 0.004 0.000 0.820
#> SRR1075530 4 0.1720 0.80528 0.000 0.040 0.000 0.928 0.032 0.000
#> SRR1442612 3 0.0363 0.88454 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1360056 5 0.4026 0.26280 0.000 0.000 0.012 0.000 0.612 0.376
#> SRR1078164 6 0.4999 0.51785 0.196 0.000 0.008 0.024 0.076 0.696
#> SRR1434545 4 0.1462 0.81830 0.000 0.056 0.000 0.936 0.008 0.000
#> SRR1398251 6 0.2462 0.78926 0.132 0.000 0.004 0.004 0.000 0.860
#> SRR1375866 1 0.5873 0.46537 0.592 0.000 0.008 0.024 0.136 0.240
#> SRR1091645 4 0.1556 0.79557 0.000 0.000 0.000 0.920 0.080 0.000
#> SRR1416636 5 0.2362 0.64671 0.000 0.000 0.136 0.004 0.860 0.000
#> SRR1105441 3 0.1003 0.87965 0.000 0.016 0.964 0.000 0.020 0.000
#> SRR1082496 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.1503 0.87206 0.000 0.944 0.032 0.016 0.008 0.000
#> SRR1093697 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.2053 0.65292 0.000 0.000 0.108 0.004 0.888 0.000
#> SRR1076120 4 0.3734 0.67344 0.008 0.000 0.004 0.736 0.244 0.008
#> SRR1074410 1 0.4489 0.66309 0.756 0.000 0.008 0.024 0.072 0.140
#> SRR1340345 4 0.1864 0.80489 0.000 0.040 0.004 0.924 0.032 0.000
#> SRR1069514 3 0.0405 0.88528 0.000 0.008 0.988 0.000 0.004 0.000
#> SRR1092636 5 0.1957 0.65414 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1365013 2 0.1575 0.87142 0.000 0.936 0.000 0.032 0.032 0.000
#> SRR1073069 6 0.0508 0.74282 0.012 0.000 0.000 0.000 0.004 0.984
#> SRR1443137 6 0.2442 0.78859 0.144 0.000 0.000 0.004 0.000 0.852
#> SRR1437143 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.2300 0.76729 0.856 0.000 0.000 0.000 0.000 0.144
#> SRR820234 2 0.0000 0.89536 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1338079 1 0.0405 0.80614 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1390094 3 0.5301 0.48691 0.000 0.168 0.628 0.196 0.000 0.008
#> SRR1340721 1 0.5766 0.09460 0.492 0.416 0.000 0.032 0.024 0.036
#> SRR1335964 3 0.1753 0.83220 0.000 0.000 0.912 0.004 0.084 0.000
#> SRR1086869 5 0.2053 0.65292 0.000 0.000 0.108 0.004 0.888 0.000
#> SRR1453434 1 0.5365 0.33286 0.560 0.000 0.000 0.116 0.004 0.320
#> SRR1402261 4 0.1624 0.81494 0.012 0.044 0.000 0.936 0.008 0.000
#> SRR657809 2 0.4289 0.51992 0.000 0.660 0.004 0.304 0.032 0.000
#> SRR1093075 6 0.2482 0.78766 0.148 0.000 0.000 0.004 0.000 0.848
#> SRR1433329 6 0.2482 0.78766 0.148 0.000 0.000 0.004 0.000 0.848
#> SRR1353418 5 0.4152 0.38846 0.000 0.000 0.032 0.000 0.664 0.304
#> SRR1092913 4 0.1501 0.81449 0.000 0.076 0.000 0.924 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", "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 17780 rows and 119 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 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 0.453 0.814 0.900 0.3029 0.765 0.765
#> 3 3 0.393 0.694 0.829 0.8567 0.663 0.568
#> 4 4 0.519 0.735 0.822 0.2577 0.809 0.596
#> 5 5 0.562 0.658 0.766 0.0990 0.799 0.431
#> 6 6 0.574 0.417 0.662 0.0429 0.907 0.618
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
#> SRR816969 1 0.0000 0.876 1.000 0.000
#> SRR1335605 1 0.7056 0.814 0.808 0.192
#> SRR1432014 1 0.7056 0.814 0.808 0.192
#> SRR1499215 1 0.6148 0.833 0.848 0.152
#> SRR1460409 1 0.0000 0.876 1.000 0.000
#> SRR1086441 1 0.0000 0.876 1.000 0.000
#> SRR1097344 1 0.9686 0.528 0.604 0.396
#> SRR1081789 1 0.9491 0.557 0.632 0.368
#> SRR1453005 2 0.2778 0.877 0.048 0.952
#> SRR1366985 1 0.0000 0.876 1.000 0.000
#> SRR815280 1 0.0000 0.876 1.000 0.000
#> SRR1348531 1 0.0000 0.876 1.000 0.000
#> SRR815845 1 0.7056 0.814 0.808 0.192
#> SRR1471178 1 0.0000 0.876 1.000 0.000
#> SRR1080696 1 0.7056 0.814 0.808 0.192
#> SRR1078684 1 0.7056 0.814 0.808 0.192
#> SRR1317751 1 0.7056 0.814 0.808 0.192
#> SRR1435667 1 0.9661 0.537 0.608 0.392
#> SRR1097905 1 0.0000 0.876 1.000 0.000
#> SRR1456548 1 0.0000 0.876 1.000 0.000
#> SRR1075126 1 0.0000 0.876 1.000 0.000
#> SRR813108 2 0.9686 0.130 0.396 0.604
#> SRR1479062 1 0.7056 0.814 0.808 0.192
#> SRR1408703 1 0.7056 0.814 0.808 0.192
#> SRR1332360 1 0.0000 0.876 1.000 0.000
#> SRR1098686 1 0.0000 0.876 1.000 0.000
#> SRR1434228 1 0.0000 0.876 1.000 0.000
#> SRR1467149 1 0.4431 0.854 0.908 0.092
#> SRR1399113 2 0.0000 0.917 0.000 1.000
#> SRR1476507 1 0.9686 0.528 0.604 0.396
#> SRR1092468 1 0.0000 0.876 1.000 0.000
#> SRR1441804 1 0.0000 0.876 1.000 0.000
#> SRR1326100 2 0.9996 -0.251 0.488 0.512
#> SRR1398815 1 0.0000 0.876 1.000 0.000
#> SRR1436021 1 0.9686 0.528 0.604 0.396
#> SRR1480083 2 0.0000 0.917 0.000 1.000
#> SRR1472863 1 0.0000 0.876 1.000 0.000
#> SRR815542 1 0.0000 0.876 1.000 0.000
#> SRR1400100 1 0.9686 0.528 0.604 0.396
#> SRR1312002 1 0.0000 0.876 1.000 0.000
#> SRR1470253 1 0.5519 0.842 0.872 0.128
#> SRR1414332 1 0.0000 0.876 1.000 0.000
#> SRR1069209 1 0.0000 0.876 1.000 0.000
#> SRR661052 1 0.0000 0.876 1.000 0.000
#> SRR1308860 1 0.0000 0.876 1.000 0.000
#> SRR1421159 1 0.7219 0.807 0.800 0.200
#> SRR1340943 1 0.7056 0.699 0.808 0.192
#> SRR1078855 1 0.0000 0.876 1.000 0.000
#> SRR1459465 2 0.0000 0.917 0.000 1.000
#> SRR816818 2 0.0000 0.917 0.000 1.000
#> SRR1478679 1 0.0000 0.876 1.000 0.000
#> SRR1350979 1 0.7056 0.814 0.808 0.192
#> SRR1458198 1 0.0000 0.876 1.000 0.000
#> SRR1386910 1 0.7056 0.814 0.808 0.192
#> SRR1465375 1 0.6247 0.829 0.844 0.156
#> SRR1323699 1 0.0000 0.876 1.000 0.000
#> SRR1431139 1 0.7056 0.814 0.808 0.192
#> SRR1373964 1 0.7219 0.807 0.800 0.200
#> SRR1455413 1 0.2236 0.869 0.964 0.036
#> SRR1437163 1 0.0000 0.876 1.000 0.000
#> SRR1347343 1 0.7056 0.814 0.808 0.192
#> SRR1465480 2 0.0000 0.917 0.000 1.000
#> SRR1489631 1 0.0000 0.876 1.000 0.000
#> SRR1086514 1 0.9686 0.528 0.604 0.396
#> SRR1430928 1 0.0000 0.876 1.000 0.000
#> SRR1310939 1 0.0938 0.874 0.988 0.012
#> SRR1344294 2 0.0000 0.917 0.000 1.000
#> SRR1099402 1 0.0000 0.876 1.000 0.000
#> SRR1468118 1 0.7056 0.814 0.808 0.192
#> SRR1486348 1 0.0000 0.876 1.000 0.000
#> SRR1488770 2 0.0000 0.917 0.000 1.000
#> SRR1083732 1 0.0000 0.876 1.000 0.000
#> SRR1456611 2 0.0000 0.917 0.000 1.000
#> SRR1080318 1 0.0000 0.876 1.000 0.000
#> SRR1500089 1 0.0000 0.876 1.000 0.000
#> SRR1441178 1 0.0000 0.876 1.000 0.000
#> SRR1381396 1 0.0000 0.876 1.000 0.000
#> SRR1096081 1 0.7056 0.814 0.808 0.192
#> SRR1349809 1 0.8081 0.754 0.752 0.248
#> SRR1324314 1 0.0000 0.876 1.000 0.000
#> SRR1092444 1 0.0376 0.875 0.996 0.004
#> SRR1382553 1 0.0376 0.874 0.996 0.004
#> SRR1075530 1 0.9522 0.577 0.628 0.372
#> SRR1442612 1 0.7056 0.814 0.808 0.192
#> SRR1360056 1 0.2603 0.866 0.956 0.044
#> SRR1078164 1 0.0000 0.876 1.000 0.000
#> SRR1434545 1 0.6623 0.727 0.828 0.172
#> SRR1398251 1 0.0000 0.876 1.000 0.000
#> SRR1375866 1 0.0000 0.876 1.000 0.000
#> SRR1091645 1 0.9686 0.528 0.604 0.396
#> SRR1416636 1 0.7056 0.814 0.808 0.192
#> SRR1105441 1 0.9552 0.569 0.624 0.376
#> SRR1082496 2 0.0000 0.917 0.000 1.000
#> SRR1315353 2 0.3114 0.869 0.056 0.944
#> SRR1093697 2 0.0000 0.917 0.000 1.000
#> SRR1077429 1 0.7056 0.814 0.808 0.192
#> SRR1076120 1 0.2423 0.867 0.960 0.040
#> SRR1074410 1 0.0000 0.876 1.000 0.000
#> SRR1340345 1 0.7674 0.786 0.776 0.224
#> SRR1069514 1 0.7056 0.814 0.808 0.192
#> SRR1092636 1 0.7056 0.814 0.808 0.192
#> SRR1365013 1 0.7056 0.814 0.808 0.192
#> SRR1073069 1 0.0000 0.876 1.000 0.000
#> SRR1443137 1 0.0000 0.876 1.000 0.000
#> SRR1437143 2 0.0000 0.917 0.000 1.000
#> SRR1091990 1 0.0000 0.876 1.000 0.000
#> SRR820234 2 0.0000 0.917 0.000 1.000
#> SRR1338079 1 0.0000 0.876 1.000 0.000
#> SRR1390094 1 0.6801 0.820 0.820 0.180
#> SRR1340721 1 0.0000 0.876 1.000 0.000
#> SRR1335964 1 0.7056 0.814 0.808 0.192
#> SRR1086869 1 0.7056 0.814 0.808 0.192
#> SRR1453434 1 0.0000 0.876 1.000 0.000
#> SRR1402261 1 0.0000 0.876 1.000 0.000
#> SRR657809 1 0.9393 0.599 0.644 0.356
#> SRR1093075 1 0.0000 0.876 1.000 0.000
#> SRR1433329 1 0.0000 0.876 1.000 0.000
#> SRR1353418 1 0.7056 0.814 0.808 0.192
#> SRR1092913 1 0.7056 0.814 0.808 0.192
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1335605 1 0.4605 0.7145 0.796 0.000 0.204
#> SRR1432014 3 0.0237 0.6056 0.000 0.004 0.996
#> SRR1499215 3 0.6244 -0.2395 0.440 0.000 0.560
#> SRR1460409 1 0.0424 0.8292 0.992 0.000 0.008
#> SRR1086441 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1097344 1 0.8354 0.3697 0.576 0.320 0.104
#> SRR1081789 3 0.8374 0.5761 0.144 0.240 0.616
#> SRR1453005 2 0.6154 0.1974 0.000 0.592 0.408
#> SRR1366985 1 0.5968 0.5809 0.636 0.000 0.364
#> SRR815280 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1348531 1 0.2796 0.8128 0.908 0.000 0.092
#> SRR815845 3 0.5940 0.6512 0.204 0.036 0.760
#> SRR1471178 1 0.0237 0.8292 0.996 0.000 0.004
#> SRR1080696 3 0.0237 0.6056 0.000 0.004 0.996
#> SRR1078684 3 0.2269 0.6075 0.040 0.016 0.944
#> SRR1317751 3 0.5845 0.6377 0.308 0.004 0.688
#> SRR1435667 3 0.1529 0.5922 0.000 0.040 0.960
#> SRR1097905 1 0.1411 0.8285 0.964 0.000 0.036
#> SRR1456548 1 0.0237 0.8295 0.996 0.000 0.004
#> SRR1075126 1 0.2165 0.8234 0.936 0.000 0.064
#> SRR813108 3 0.5760 0.4047 0.000 0.328 0.672
#> SRR1479062 3 0.5754 0.6252 0.296 0.004 0.700
#> SRR1408703 3 0.5431 0.6498 0.284 0.000 0.716
#> SRR1332360 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1098686 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1434228 1 0.5621 0.6014 0.692 0.000 0.308
#> SRR1467149 1 0.3551 0.7873 0.868 0.000 0.132
#> SRR1399113 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1476507 3 0.9767 0.4190 0.248 0.320 0.432
#> SRR1092468 1 0.2400 0.8195 0.932 0.004 0.064
#> SRR1441804 1 0.1860 0.8254 0.948 0.000 0.052
#> SRR1326100 3 0.8352 0.4794 0.100 0.332 0.568
#> SRR1398815 1 0.0424 0.8292 0.992 0.000 0.008
#> SRR1436021 3 0.9171 0.4920 0.172 0.312 0.516
#> SRR1480083 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1400100 3 0.8394 0.5043 0.108 0.316 0.576
#> SRR1312002 1 0.5591 0.6933 0.696 0.000 0.304
#> SRR1470253 1 0.5058 0.7521 0.756 0.000 0.244
#> SRR1414332 1 0.0237 0.8292 0.996 0.000 0.004
#> SRR1069209 1 0.5178 0.7092 0.744 0.000 0.256
#> SRR661052 1 0.1529 0.8258 0.960 0.000 0.040
#> SRR1308860 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1421159 3 0.7671 0.6371 0.300 0.072 0.628
#> SRR1340943 1 0.6375 0.6359 0.720 0.244 0.036
#> SRR1078855 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1459465 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR816818 2 0.0747 0.9393 0.000 0.984 0.016
#> SRR1478679 1 0.6079 0.5578 0.612 0.000 0.388
#> SRR1350979 3 0.4834 0.6506 0.204 0.004 0.792
#> SRR1458198 1 0.2625 0.8157 0.916 0.000 0.084
#> SRR1386910 1 0.5521 0.7062 0.788 0.032 0.180
#> SRR1465375 1 0.5377 0.7418 0.820 0.068 0.112
#> SRR1323699 3 0.6302 -0.2368 0.480 0.000 0.520
#> SRR1431139 3 0.6148 0.6062 0.356 0.004 0.640
#> SRR1373964 3 0.1636 0.6005 0.020 0.016 0.964
#> SRR1455413 1 0.2959 0.8093 0.900 0.000 0.100
#> SRR1437163 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1347343 3 0.1491 0.6016 0.016 0.016 0.968
#> SRR1465480 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1489631 1 0.1643 0.8259 0.956 0.000 0.044
#> SRR1086514 3 0.8415 0.4976 0.108 0.320 0.572
#> SRR1430928 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1310939 1 0.4682 0.7346 0.804 0.004 0.192
#> SRR1344294 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1099402 1 0.0424 0.8290 0.992 0.000 0.008
#> SRR1468118 1 0.5158 0.6692 0.764 0.004 0.232
#> SRR1486348 1 0.0237 0.8292 0.996 0.000 0.004
#> SRR1488770 2 0.0592 0.9440 0.000 0.988 0.012
#> SRR1083732 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1080318 1 0.1529 0.8258 0.960 0.000 0.040
#> SRR1500089 1 0.2796 0.8128 0.908 0.000 0.092
#> SRR1441178 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1381396 1 0.0892 0.8280 0.980 0.000 0.020
#> SRR1096081 3 0.5024 0.6697 0.220 0.004 0.776
#> SRR1349809 1 0.4384 0.7682 0.868 0.068 0.064
#> SRR1324314 1 0.2448 0.8204 0.924 0.000 0.076
#> SRR1092444 1 0.2356 0.8214 0.928 0.000 0.072
#> SRR1382553 1 0.7043 0.3778 0.532 0.020 0.448
#> SRR1075530 3 0.8674 0.5297 0.136 0.296 0.568
#> SRR1442612 3 0.1129 0.6047 0.020 0.004 0.976
#> SRR1360056 1 0.5760 0.6804 0.672 0.000 0.328
#> SRR1078164 1 0.2261 0.8283 0.932 0.000 0.068
#> SRR1434545 1 0.5803 0.6776 0.760 0.212 0.028
#> SRR1398251 1 0.6026 0.5792 0.624 0.000 0.376
#> SRR1375866 1 0.1529 0.8258 0.960 0.000 0.040
#> SRR1091645 3 0.8223 0.5261 0.108 0.288 0.604
#> SRR1416636 3 0.0237 0.6083 0.004 0.000 0.996
#> SRR1105441 3 0.8187 0.5860 0.128 0.244 0.628
#> SRR1082496 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1315353 3 0.5785 0.4003 0.000 0.332 0.668
#> SRR1093697 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1077429 1 0.4702 0.7057 0.788 0.000 0.212
#> SRR1076120 1 0.3192 0.8156 0.888 0.000 0.112
#> SRR1074410 1 0.1411 0.8257 0.964 0.000 0.036
#> SRR1340345 1 0.7431 0.5421 0.688 0.100 0.212
#> SRR1069514 3 0.5835 0.6582 0.164 0.052 0.784
#> SRR1092636 3 0.6252 0.3653 0.444 0.000 0.556
#> SRR1365013 1 0.4862 0.7306 0.820 0.020 0.160
#> SRR1073069 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1443137 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1437143 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1091990 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR820234 2 0.0000 0.9555 0.000 1.000 0.000
#> SRR1338079 1 0.0592 0.8294 0.988 0.000 0.012
#> SRR1390094 1 0.7310 0.5673 0.600 0.040 0.360
#> SRR1340721 1 0.0000 0.8291 1.000 0.000 0.000
#> SRR1335964 3 0.5845 0.6377 0.308 0.004 0.688
#> SRR1086869 3 0.5845 0.6377 0.308 0.004 0.688
#> SRR1453434 1 0.2537 0.8238 0.920 0.000 0.080
#> SRR1402261 1 0.2165 0.8209 0.936 0.000 0.064
#> SRR657809 1 0.9644 -0.0252 0.468 0.256 0.276
#> SRR1093075 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1433329 1 0.4654 0.7190 0.792 0.000 0.208
#> SRR1353418 3 0.5291 0.3269 0.268 0.000 0.732
#> SRR1092913 1 0.5931 0.7049 0.792 0.084 0.124
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 4 0.4072 0.5544 0.252 0.000 0.000 0.748
#> SRR1335605 1 0.2882 0.7448 0.892 0.000 0.084 0.024
#> SRR1432014 3 0.0804 0.8308 0.012 0.000 0.980 0.008
#> SRR1499215 4 0.5979 0.7051 0.172 0.000 0.136 0.692
#> SRR1460409 1 0.4222 0.7397 0.728 0.000 0.000 0.272
#> SRR1086441 1 0.4277 0.7365 0.720 0.000 0.000 0.280
#> SRR1097344 1 0.6946 0.5242 0.588 0.200 0.212 0.000
#> SRR1081789 3 0.6533 0.6556 0.032 0.068 0.664 0.236
#> SRR1453005 2 0.4907 0.0649 0.000 0.580 0.420 0.000
#> SRR1366985 4 0.3497 0.7644 0.036 0.000 0.104 0.860
#> SRR815280 4 0.4134 0.5060 0.260 0.000 0.000 0.740
#> SRR1348531 1 0.0469 0.7703 0.988 0.000 0.012 0.000
#> SRR815845 3 0.0469 0.8316 0.000 0.012 0.988 0.000
#> SRR1471178 1 0.4564 0.6955 0.672 0.000 0.000 0.328
#> SRR1080696 3 0.0657 0.8272 0.012 0.000 0.984 0.004
#> SRR1078684 3 0.4081 0.8051 0.084 0.008 0.844 0.064
#> SRR1317751 3 0.3172 0.7922 0.160 0.000 0.840 0.000
#> SRR1435667 3 0.0804 0.8308 0.012 0.000 0.980 0.008
#> SRR1097905 1 0.3933 0.7773 0.792 0.000 0.008 0.200
#> SRR1456548 1 0.3649 0.7796 0.796 0.000 0.000 0.204
#> SRR1075126 1 0.4595 0.7739 0.780 0.000 0.044 0.176
#> SRR813108 3 0.3024 0.7982 0.000 0.148 0.852 0.000
#> SRR1479062 3 0.7956 0.1547 0.276 0.004 0.420 0.300
#> SRR1408703 3 0.3486 0.7770 0.188 0.000 0.812 0.000
#> SRR1332360 4 0.1807 0.7899 0.052 0.000 0.008 0.940
#> SRR1098686 1 0.3873 0.7689 0.772 0.000 0.000 0.228
#> SRR1434228 4 0.1042 0.7863 0.020 0.000 0.008 0.972
#> SRR1467149 1 0.0469 0.7703 0.988 0.000 0.012 0.000
#> SRR1399113 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1476507 3 0.6886 0.5428 0.204 0.200 0.596 0.000
#> SRR1092468 1 0.4700 0.7829 0.792 0.000 0.084 0.124
#> SRR1441804 1 0.1635 0.7801 0.948 0.000 0.008 0.044
#> SRR1326100 3 0.3764 0.7549 0.000 0.216 0.784 0.000
#> SRR1398815 1 0.3907 0.7787 0.768 0.000 0.000 0.232
#> SRR1436021 3 0.5727 0.7031 0.096 0.200 0.704 0.000
#> SRR1480083 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.4008 0.7669 0.756 0.000 0.000 0.244
#> SRR815542 1 0.4250 0.7395 0.724 0.000 0.000 0.276
#> SRR1400100 3 0.3933 0.7680 0.008 0.200 0.792 0.000
#> SRR1312002 4 0.5074 0.7302 0.236 0.000 0.040 0.724
#> SRR1470253 4 0.5453 0.6591 0.304 0.000 0.036 0.660
#> SRR1414332 4 0.4624 0.3185 0.340 0.000 0.000 0.660
#> SRR1069209 4 0.2131 0.7840 0.036 0.000 0.032 0.932
#> SRR661052 1 0.2401 0.7744 0.904 0.000 0.004 0.092
#> SRR1308860 1 0.3873 0.7689 0.772 0.000 0.000 0.228
#> SRR1421159 3 0.2021 0.8324 0.024 0.040 0.936 0.000
#> SRR1340943 1 0.3823 0.7639 0.852 0.108 0.012 0.028
#> SRR1078855 4 0.1042 0.7863 0.020 0.000 0.008 0.972
#> SRR1459465 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0817 0.9319 0.000 0.976 0.024 0.000
#> SRR1478679 1 0.5432 0.7396 0.740 0.000 0.124 0.136
#> SRR1350979 3 0.0804 0.8308 0.012 0.000 0.980 0.008
#> SRR1458198 1 0.2227 0.7801 0.928 0.000 0.036 0.036
#> SRR1386910 1 0.3377 0.7343 0.848 0.012 0.140 0.000
#> SRR1465375 1 0.2161 0.7702 0.932 0.048 0.016 0.004
#> SRR1323699 4 0.5624 0.7464 0.148 0.000 0.128 0.724
#> SRR1431139 3 0.3307 0.8129 0.104 0.000 0.868 0.028
#> SRR1373964 3 0.0927 0.8303 0.016 0.000 0.976 0.008
#> SRR1455413 1 0.1510 0.7681 0.956 0.000 0.016 0.028
#> SRR1437163 1 0.3837 0.7704 0.776 0.000 0.000 0.224
#> SRR1347343 3 0.4883 0.4921 0.016 0.000 0.696 0.288
#> SRR1465480 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.1576 0.7795 0.948 0.000 0.004 0.048
#> SRR1086514 3 0.3933 0.7680 0.008 0.200 0.792 0.000
#> SRR1430928 1 0.4277 0.7365 0.720 0.000 0.000 0.280
#> SRR1310939 1 0.5304 0.7140 0.748 0.000 0.148 0.104
#> SRR1344294 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.4804 0.6389 0.616 0.000 0.000 0.384
#> SRR1468118 1 0.3688 0.6656 0.792 0.000 0.208 0.000
#> SRR1486348 1 0.4522 0.7027 0.680 0.000 0.000 0.320
#> SRR1488770 2 0.0592 0.9397 0.000 0.984 0.016 0.000
#> SRR1083732 1 0.4277 0.7365 0.720 0.000 0.000 0.280
#> SRR1456611 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.2714 0.7581 0.884 0.000 0.004 0.112
#> SRR1500089 1 0.1305 0.7717 0.960 0.000 0.036 0.004
#> SRR1441178 4 0.0817 0.7831 0.024 0.000 0.000 0.976
#> SRR1381396 1 0.3494 0.7724 0.824 0.000 0.004 0.172
#> SRR1096081 3 0.2530 0.8124 0.112 0.000 0.888 0.000
#> SRR1349809 1 0.5845 0.7756 0.724 0.036 0.044 0.196
#> SRR1324314 4 0.4756 0.7598 0.144 0.000 0.072 0.784
#> SRR1092444 1 0.2402 0.7454 0.912 0.000 0.012 0.076
#> SRR1382553 4 0.4483 0.7517 0.036 0.032 0.104 0.828
#> SRR1075530 3 0.4245 0.7700 0.020 0.196 0.784 0.000
#> SRR1442612 3 0.0804 0.8308 0.012 0.000 0.980 0.008
#> SRR1360056 4 0.5434 0.6993 0.252 0.000 0.052 0.696
#> SRR1078164 4 0.3710 0.7208 0.192 0.000 0.004 0.804
#> SRR1434545 1 0.5513 0.7595 0.756 0.116 0.012 0.116
#> SRR1398251 4 0.4547 0.7548 0.092 0.000 0.104 0.804
#> SRR1375866 4 0.5112 0.4445 0.436 0.000 0.004 0.560
#> SRR1091645 3 0.3991 0.8022 0.048 0.120 0.832 0.000
#> SRR1416636 3 0.2773 0.8050 0.116 0.000 0.880 0.004
#> SRR1105441 3 0.3552 0.8118 0.024 0.128 0.848 0.000
#> SRR1082496 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.3726 0.7582 0.000 0.212 0.788 0.000
#> SRR1093697 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1077429 1 0.2216 0.7370 0.908 0.000 0.092 0.000
#> SRR1076120 1 0.2319 0.7800 0.924 0.000 0.036 0.040
#> SRR1074410 1 0.2944 0.7581 0.868 0.000 0.004 0.128
#> SRR1340345 1 0.6327 0.6038 0.648 0.124 0.228 0.000
#> SRR1069514 3 0.1843 0.8311 0.028 0.016 0.948 0.008
#> SRR1092636 1 0.5000 -0.3060 0.500 0.000 0.500 0.000
#> SRR1365013 1 0.5721 0.7425 0.748 0.032 0.156 0.064
#> SRR1073069 4 0.1042 0.7863 0.020 0.000 0.008 0.972
#> SRR1443137 4 0.1042 0.7863 0.020 0.000 0.008 0.972
#> SRR1437143 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1091990 4 0.2216 0.7474 0.092 0.000 0.000 0.908
#> SRR820234 2 0.0000 0.9535 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.3649 0.7819 0.796 0.000 0.000 0.204
#> SRR1390094 1 0.6408 0.6720 0.664 0.024 0.244 0.068
#> SRR1340721 1 0.4072 0.7613 0.748 0.000 0.000 0.252
#> SRR1335964 3 0.2589 0.8196 0.116 0.000 0.884 0.000
#> SRR1086869 3 0.2704 0.8081 0.124 0.000 0.876 0.000
#> SRR1453434 1 0.5055 0.7310 0.712 0.000 0.032 0.256
#> SRR1402261 1 0.3694 0.7894 0.844 0.000 0.032 0.124
#> SRR657809 1 0.7659 0.2927 0.492 0.160 0.336 0.012
#> SRR1093075 4 0.4843 0.0415 0.396 0.000 0.000 0.604
#> SRR1433329 4 0.1042 0.7863 0.020 0.000 0.008 0.972
#> SRR1353418 4 0.6063 0.6516 0.124 0.000 0.196 0.680
#> SRR1092913 1 0.3278 0.7559 0.864 0.116 0.020 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.1965 0.6888 0.904 0.000 0.096 0.000 0.000
#> SRR1335605 5 0.3433 0.7346 0.136 0.000 0.024 0.008 0.832
#> SRR1432014 4 0.4021 0.7260 0.000 0.000 0.168 0.780 0.052
#> SRR1499215 3 0.2811 0.7615 0.012 0.000 0.876 0.012 0.100
#> SRR1460409 1 0.3696 0.5941 0.772 0.000 0.016 0.000 0.212
#> SRR1086441 1 0.0000 0.7564 1.000 0.000 0.000 0.000 0.000
#> SRR1097344 5 0.8172 0.4136 0.128 0.192 0.020 0.176 0.484
#> SRR1081789 4 0.6970 0.1818 0.192 0.012 0.304 0.484 0.008
#> SRR1453005 2 0.5043 0.0346 0.000 0.552 0.012 0.420 0.016
#> SRR1366985 3 0.1934 0.7922 0.052 0.000 0.928 0.004 0.016
#> SRR815280 1 0.1608 0.6984 0.928 0.000 0.072 0.000 0.000
#> SRR1348531 5 0.4076 0.7221 0.200 0.000 0.020 0.012 0.768
#> SRR815845 4 0.1041 0.7512 0.000 0.000 0.032 0.964 0.004
#> SRR1471178 1 0.0693 0.7599 0.980 0.000 0.008 0.000 0.012
#> SRR1080696 4 0.1478 0.7563 0.000 0.000 0.064 0.936 0.000
#> SRR1078684 4 0.4431 0.7546 0.076 0.004 0.120 0.788 0.012
#> SRR1317751 5 0.5151 0.3248 0.000 0.000 0.044 0.396 0.560
#> SRR1435667 4 0.4059 0.7242 0.000 0.000 0.172 0.776 0.052
#> SRR1097905 1 0.3456 0.7047 0.800 0.000 0.016 0.000 0.184
#> SRR1456548 1 0.4147 0.5471 0.676 0.000 0.008 0.000 0.316
#> SRR1075126 1 0.6277 0.3100 0.504 0.000 0.352 0.004 0.140
#> SRR813108 4 0.2488 0.7498 0.000 0.124 0.004 0.872 0.000
#> SRR1479062 5 0.6992 0.3076 0.088 0.000 0.264 0.100 0.548
#> SRR1408703 4 0.4404 0.6243 0.000 0.000 0.036 0.712 0.252
#> SRR1332360 3 0.3527 0.7737 0.192 0.000 0.792 0.000 0.016
#> SRR1098686 1 0.1764 0.7600 0.928 0.000 0.008 0.000 0.064
#> SRR1434228 3 0.2732 0.7902 0.160 0.000 0.840 0.000 0.000
#> SRR1467149 5 0.4002 0.7313 0.144 0.000 0.024 0.028 0.804
#> SRR1399113 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.7978 0.2462 0.100 0.192 0.004 0.436 0.268
#> SRR1092468 1 0.5677 0.2258 0.516 0.000 0.060 0.008 0.416
#> SRR1441804 5 0.3421 0.7056 0.204 0.000 0.008 0.000 0.788
#> SRR1326100 4 0.3740 0.7175 0.000 0.196 0.012 0.784 0.008
#> SRR1398815 1 0.2848 0.7222 0.840 0.000 0.004 0.000 0.156
#> SRR1436021 4 0.5505 0.6855 0.056 0.192 0.012 0.708 0.032
#> SRR1480083 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.1892 0.7580 0.916 0.000 0.004 0.000 0.080
#> SRR815542 1 0.0794 0.7603 0.972 0.000 0.000 0.000 0.028
#> SRR1400100 4 0.3427 0.7213 0.000 0.192 0.012 0.796 0.000
#> SRR1312002 3 0.3798 0.7609 0.064 0.000 0.808 0.000 0.128
#> SRR1470253 5 0.4921 0.4069 0.036 0.000 0.320 0.004 0.640
#> SRR1414332 1 0.2359 0.6820 0.904 0.000 0.060 0.000 0.036
#> SRR1069209 3 0.2690 0.7918 0.156 0.000 0.844 0.000 0.000
#> SRR661052 5 0.3093 0.7280 0.168 0.000 0.008 0.000 0.824
#> SRR1308860 1 0.2179 0.7449 0.888 0.000 0.000 0.000 0.112
#> SRR1421159 4 0.3435 0.7659 0.004 0.072 0.068 0.852 0.004
#> SRR1340943 1 0.7008 -0.2308 0.436 0.068 0.052 0.016 0.428
#> SRR1078855 3 0.3561 0.7122 0.260 0.000 0.740 0.000 0.000
#> SRR1459465 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0794 0.9260 0.000 0.972 0.000 0.028 0.000
#> SRR1478679 3 0.5503 0.5597 0.124 0.000 0.680 0.012 0.184
#> SRR1350979 4 0.4021 0.7260 0.000 0.000 0.168 0.780 0.052
#> SRR1458198 5 0.3944 0.7043 0.224 0.000 0.016 0.004 0.756
#> SRR1386910 5 0.6803 0.4458 0.136 0.012 0.020 0.292 0.540
#> SRR1465375 5 0.4465 0.7224 0.148 0.016 0.004 0.052 0.780
#> SRR1323699 3 0.2605 0.7793 0.044 0.000 0.896 0.004 0.056
#> SRR1431139 4 0.4573 0.7509 0.020 0.000 0.140 0.772 0.068
#> SRR1373964 4 0.4096 0.7264 0.000 0.000 0.176 0.772 0.052
#> SRR1455413 5 0.2806 0.7312 0.152 0.000 0.004 0.000 0.844
#> SRR1437163 1 0.2806 0.7267 0.844 0.000 0.004 0.000 0.152
#> SRR1347343 3 0.5019 0.3501 0.000 0.000 0.632 0.316 0.052
#> SRR1465480 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 5 0.3109 0.7126 0.200 0.000 0.000 0.000 0.800
#> SRR1086514 4 0.3527 0.7202 0.000 0.192 0.016 0.792 0.000
#> SRR1430928 1 0.0000 0.7564 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 3 0.6380 -0.0436 0.116 0.000 0.440 0.012 0.432
#> SRR1344294 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.4450 0.7016 0.760 0.000 0.132 0.000 0.108
#> SRR1468118 5 0.5538 0.6588 0.092 0.000 0.044 0.156 0.708
#> SRR1486348 1 0.0671 0.7586 0.980 0.000 0.004 0.000 0.016
#> SRR1488770 2 0.0609 0.9354 0.000 0.980 0.000 0.020 0.000
#> SRR1083732 1 0.0000 0.7564 1.000 0.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 5 0.2462 0.7303 0.112 0.000 0.008 0.000 0.880
#> SRR1500089 5 0.4328 0.7113 0.208 0.000 0.024 0.016 0.752
#> SRR1441178 3 0.5185 0.6434 0.228 0.000 0.672 0.000 0.100
#> SRR1381396 5 0.3690 0.6351 0.224 0.000 0.012 0.000 0.764
#> SRR1096081 4 0.3460 0.6800 0.000 0.000 0.044 0.828 0.128
#> SRR1349809 1 0.4281 0.7040 0.788 0.012 0.008 0.036 0.156
#> SRR1324314 3 0.3355 0.7764 0.132 0.000 0.832 0.000 0.036
#> SRR1092444 5 0.2179 0.7326 0.100 0.000 0.004 0.000 0.896
#> SRR1382553 3 0.3355 0.7792 0.076 0.008 0.860 0.004 0.052
#> SRR1075530 4 0.4314 0.7212 0.008 0.184 0.016 0.772 0.020
#> SRR1442612 4 0.4021 0.7260 0.000 0.000 0.168 0.780 0.052
#> SRR1360056 3 0.4703 0.6703 0.004 0.000 0.748 0.124 0.124
#> SRR1078164 5 0.5142 0.2473 0.052 0.000 0.348 0.000 0.600
#> SRR1434545 1 0.5951 0.6492 0.712 0.088 0.052 0.024 0.124
#> SRR1398251 3 0.2006 0.7987 0.072 0.000 0.916 0.000 0.012
#> SRR1375866 5 0.4823 0.4768 0.052 0.000 0.276 0.000 0.672
#> SRR1091645 4 0.3781 0.7173 0.000 0.064 0.032 0.840 0.064
#> SRR1416636 5 0.5442 0.3978 0.000 0.000 0.116 0.240 0.644
#> SRR1105441 4 0.3516 0.7371 0.000 0.164 0.020 0.812 0.004
#> SRR1082496 2 0.0404 0.9419 0.000 0.988 0.000 0.012 0.000
#> SRR1315353 4 0.3807 0.7139 0.000 0.204 0.012 0.776 0.008
#> SRR1093697 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.4855 0.6956 0.096 0.000 0.040 0.096 0.768
#> SRR1076120 5 0.4443 0.7079 0.200 0.000 0.044 0.008 0.748
#> SRR1074410 5 0.2723 0.7255 0.124 0.000 0.012 0.000 0.864
#> SRR1340345 4 0.8080 0.4294 0.132 0.180 0.020 0.500 0.168
#> SRR1069514 4 0.2536 0.7582 0.000 0.000 0.128 0.868 0.004
#> SRR1092636 5 0.4757 0.5415 0.012 0.000 0.036 0.248 0.704
#> SRR1365013 4 0.7273 0.2561 0.268 0.016 0.020 0.492 0.204
#> SRR1073069 3 0.3053 0.7892 0.164 0.000 0.828 0.000 0.008
#> SRR1443137 3 0.2773 0.7896 0.164 0.000 0.836 0.000 0.000
#> SRR1437143 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.3994 0.5208 0.772 0.000 0.188 0.000 0.040
#> SRR820234 2 0.0000 0.9498 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.2852 0.7218 0.828 0.000 0.000 0.000 0.172
#> SRR1390094 1 0.7533 0.3846 0.536 0.008 0.212 0.104 0.140
#> SRR1340721 1 0.4254 0.7157 0.772 0.000 0.080 0.000 0.148
#> SRR1335964 4 0.2929 0.7280 0.000 0.000 0.008 0.840 0.152
#> SRR1086869 4 0.2645 0.7142 0.000 0.000 0.044 0.888 0.068
#> SRR1453434 1 0.4316 0.7140 0.772 0.000 0.108 0.000 0.120
#> SRR1402261 1 0.5599 0.3139 0.552 0.004 0.056 0.004 0.384
#> SRR657809 4 0.8001 0.4756 0.132 0.188 0.024 0.516 0.140
#> SRR1093075 1 0.4235 0.0328 0.576 0.000 0.424 0.000 0.000
#> SRR1433329 3 0.2773 0.7896 0.164 0.000 0.836 0.000 0.000
#> SRR1353418 3 0.4521 0.6381 0.000 0.000 0.748 0.088 0.164
#> SRR1092913 5 0.6723 0.5831 0.184 0.132 0.012 0.052 0.620
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1983 0.66175 0.908 0.000 0.000 0.000 0.020 0.072
#> SRR1335605 5 0.4617 0.41403 0.056 0.296 0.000 0.000 0.644 0.004
#> SRR1432014 3 0.3397 0.50714 0.012 0.000 0.848 0.060 0.020 0.060
#> SRR1499215 6 0.4049 0.68639 0.024 0.028 0.096 0.004 0.036 0.812
#> SRR1460409 1 0.2320 0.63477 0.864 0.000 0.000 0.000 0.132 0.004
#> SRR1086441 1 0.0458 0.69241 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1097344 2 0.5759 -0.10832 0.020 0.580 0.060 0.008 0.316 0.016
#> SRR1081789 2 0.7102 -0.38386 0.112 0.388 0.340 0.000 0.000 0.160
#> SRR1453005 2 0.3606 -0.19985 0.000 0.724 0.264 0.008 0.000 0.004
#> SRR1366985 6 0.1794 0.75113 0.040 0.000 0.036 0.000 0.000 0.924
#> SRR815280 1 0.1141 0.67655 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1348531 5 0.3510 0.55015 0.108 0.000 0.004 0.028 0.828 0.032
#> SRR815845 3 0.3536 0.37601 0.000 0.012 0.820 0.116 0.004 0.048
#> SRR1471178 1 0.1564 0.69718 0.936 0.000 0.000 0.000 0.040 0.024
#> SRR1080696 3 0.1297 0.49044 0.000 0.000 0.948 0.040 0.000 0.012
#> SRR1078684 3 0.6876 0.33225 0.184 0.080 0.572 0.000 0.112 0.052
#> SRR1317751 4 0.6810 0.67679 0.000 0.000 0.184 0.408 0.344 0.064
#> SRR1435667 3 0.3568 0.50764 0.012 0.000 0.836 0.060 0.020 0.072
#> SRR1097905 1 0.4325 0.38399 0.568 0.004 0.000 0.000 0.412 0.016
#> SRR1456548 1 0.4527 0.30544 0.516 0.004 0.000 0.000 0.456 0.024
#> SRR1075126 5 0.6249 -0.15522 0.324 0.004 0.000 0.000 0.348 0.324
#> SRR813108 3 0.1863 0.53650 0.000 0.104 0.896 0.000 0.000 0.000
#> SRR1479062 5 0.7762 -0.02719 0.176 0.044 0.084 0.012 0.468 0.216
#> SRR1408703 3 0.6474 -0.43260 0.000 0.000 0.424 0.148 0.380 0.048
#> SRR1332360 6 0.3175 0.73989 0.164 0.000 0.000 0.000 0.028 0.808
#> SRR1098686 1 0.2750 0.68111 0.844 0.000 0.000 0.000 0.136 0.020
#> SRR1434228 6 0.2003 0.76515 0.116 0.000 0.000 0.000 0.000 0.884
#> SRR1467149 5 0.3101 0.54897 0.060 0.004 0.004 0.036 0.868 0.028
#> SRR1399113 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1476507 5 0.6788 -0.20604 0.028 0.332 0.304 0.000 0.332 0.004
#> SRR1092468 5 0.7017 0.10047 0.228 0.308 0.004 0.000 0.400 0.060
#> SRR1441804 5 0.2445 0.57236 0.108 0.000 0.000 0.000 0.872 0.020
#> SRR1326100 3 0.4226 0.43945 0.000 0.484 0.504 0.004 0.000 0.008
#> SRR1398815 1 0.3712 0.63802 0.760 0.004 0.000 0.032 0.204 0.000
#> SRR1436021 3 0.6601 0.25305 0.028 0.256 0.472 0.000 0.236 0.008
#> SRR1480083 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1472863 1 0.2234 0.68148 0.872 0.004 0.000 0.000 0.124 0.000
#> SRR815542 1 0.1728 0.69437 0.924 0.004 0.000 0.000 0.064 0.008
#> SRR1400100 3 0.4516 0.47199 0.000 0.420 0.552 0.000 0.020 0.008
#> SRR1312002 6 0.3065 0.73618 0.052 0.000 0.004 0.000 0.100 0.844
#> SRR1470253 5 0.4531 -0.17327 0.000 0.000 0.000 0.032 0.504 0.464
#> SRR1414332 1 0.1257 0.67648 0.952 0.000 0.000 0.000 0.020 0.028
#> SRR1069209 6 0.2003 0.76540 0.116 0.000 0.000 0.000 0.000 0.884
#> SRR661052 5 0.2100 0.57137 0.112 0.004 0.000 0.000 0.884 0.000
#> SRR1308860 1 0.2964 0.62347 0.792 0.004 0.000 0.000 0.204 0.000
#> SRR1421159 3 0.3831 0.53022 0.000 0.268 0.712 0.000 0.012 0.008
#> SRR1340943 1 0.7174 -0.02434 0.364 0.220 0.000 0.004 0.336 0.076
#> SRR1078855 6 0.3266 0.63214 0.272 0.000 0.000 0.000 0.000 0.728
#> SRR1459465 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR816818 2 0.4253 0.59038 0.000 0.524 0.016 0.460 0.000 0.000
#> SRR1478679 6 0.8575 -0.04206 0.064 0.244 0.040 0.060 0.272 0.320
#> SRR1350979 3 0.3419 0.50634 0.012 0.000 0.848 0.060 0.024 0.056
#> SRR1458198 5 0.3718 0.55605 0.128 0.008 0.000 0.000 0.796 0.068
#> SRR1386910 5 0.5837 0.36931 0.056 0.348 0.048 0.000 0.540 0.008
#> SRR1465375 5 0.4040 0.53630 0.092 0.104 0.020 0.000 0.784 0.000
#> SRR1323699 6 0.3866 0.70734 0.032 0.016 0.036 0.060 0.020 0.836
#> SRR1431139 3 0.5849 0.14899 0.028 0.008 0.572 0.000 0.292 0.100
#> SRR1373964 3 0.3589 0.51298 0.012 0.000 0.832 0.060 0.016 0.080
#> SRR1455413 5 0.1204 0.56566 0.056 0.000 0.000 0.000 0.944 0.000
#> SRR1437163 1 0.4343 0.42575 0.592 0.028 0.000 0.000 0.380 0.000
#> SRR1347343 3 0.5725 0.11250 0.012 0.004 0.532 0.060 0.020 0.372
#> SRR1465480 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1489631 5 0.2454 0.57350 0.104 0.016 0.000 0.000 0.876 0.004
#> SRR1086514 3 0.4093 0.47187 0.000 0.440 0.552 0.004 0.000 0.004
#> SRR1430928 1 0.0458 0.69241 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1310939 6 0.8397 -0.05510 0.052 0.296 0.032 0.060 0.260 0.300
#> SRR1344294 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1099402 1 0.5722 0.39524 0.516 0.000 0.000 0.000 0.268 0.216
#> SRR1468118 5 0.6563 -0.47302 0.024 0.000 0.068 0.408 0.436 0.064
#> SRR1486348 1 0.1007 0.69449 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1488770 2 0.4095 0.59922 0.000 0.512 0.008 0.480 0.000 0.000
#> SRR1083732 1 0.0547 0.69334 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1456611 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1080318 5 0.1575 0.54455 0.032 0.000 0.000 0.032 0.936 0.000
#> SRR1500089 5 0.3452 0.56529 0.116 0.024 0.000 0.000 0.824 0.036
#> SRR1441178 6 0.5188 0.62410 0.192 0.000 0.000 0.032 0.104 0.672
#> SRR1381396 5 0.3242 0.52745 0.148 0.004 0.000 0.032 0.816 0.000
#> SRR1096081 4 0.6781 0.77983 0.000 0.000 0.352 0.408 0.176 0.064
#> SRR1349809 1 0.6194 0.14425 0.400 0.308 0.004 0.000 0.288 0.000
#> SRR1324314 6 0.2998 0.74812 0.052 0.008 0.012 0.000 0.060 0.868
#> SRR1092444 5 0.1575 0.54455 0.032 0.000 0.000 0.032 0.936 0.000
#> SRR1382553 6 0.4485 0.70267 0.092 0.004 0.032 0.068 0.020 0.784
#> SRR1075530 2 0.4131 -0.39530 0.000 0.600 0.388 0.004 0.004 0.004
#> SRR1442612 3 0.3456 0.50794 0.012 0.000 0.844 0.060 0.020 0.064
#> SRR1360056 6 0.4987 0.48252 0.004 0.000 0.044 0.100 0.132 0.720
#> SRR1078164 6 0.5596 0.25229 0.064 0.000 0.000 0.032 0.428 0.476
#> SRR1434545 1 0.6763 0.41645 0.516 0.232 0.000 0.004 0.156 0.092
#> SRR1398251 6 0.1895 0.76337 0.072 0.000 0.016 0.000 0.000 0.912
#> SRR1375866 5 0.4428 0.04580 0.000 0.000 0.000 0.032 0.580 0.388
#> SRR1091645 4 0.8216 0.71628 0.000 0.132 0.224 0.364 0.220 0.060
#> SRR1416636 5 0.7221 -0.37206 0.012 0.020 0.244 0.208 0.472 0.044
#> SRR1105441 3 0.4018 0.48596 0.000 0.412 0.580 0.000 0.000 0.008
#> SRR1082496 2 0.3810 0.57708 0.000 0.572 0.000 0.428 0.000 0.000
#> SRR1315353 3 0.4484 0.44965 0.000 0.460 0.516 0.016 0.000 0.008
#> SRR1093697 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1077429 5 0.5827 -0.27126 0.024 0.000 0.024 0.364 0.532 0.056
#> SRR1076120 5 0.3960 0.55361 0.128 0.016 0.000 0.000 0.784 0.072
#> SRR1074410 5 0.2333 0.54606 0.060 0.004 0.000 0.032 0.900 0.004
#> SRR1340345 2 0.5288 -0.13089 0.024 0.592 0.044 0.004 0.332 0.004
#> SRR1069514 3 0.1410 0.53219 0.000 0.008 0.944 0.000 0.004 0.044
#> SRR1092636 5 0.6256 -0.24999 0.012 0.000 0.240 0.140 0.568 0.040
#> SRR1365013 5 0.7103 0.15650 0.192 0.324 0.080 0.000 0.400 0.004
#> SRR1073069 6 0.2320 0.76103 0.132 0.000 0.000 0.000 0.004 0.864
#> SRR1443137 6 0.2003 0.76470 0.116 0.000 0.000 0.000 0.000 0.884
#> SRR1437143 2 0.3868 0.60659 0.000 0.504 0.000 0.496 0.000 0.000
#> SRR1091990 1 0.3025 0.54900 0.820 0.000 0.000 0.000 0.024 0.156
#> SRR820234 2 0.3868 0.60563 0.000 0.508 0.000 0.492 0.000 0.000
#> SRR1338079 1 0.3852 0.44001 0.612 0.004 0.000 0.000 0.384 0.000
#> SRR1390094 1 0.7035 0.40038 0.520 0.012 0.176 0.000 0.152 0.140
#> SRR1340721 1 0.6678 0.27876 0.460 0.160 0.000 0.000 0.312 0.068
#> SRR1335964 3 0.4942 0.33332 0.000 0.064 0.688 0.012 0.220 0.016
#> SRR1086869 4 0.6781 0.78067 0.000 0.000 0.352 0.408 0.176 0.064
#> SRR1453434 1 0.5273 0.53731 0.620 0.004 0.000 0.000 0.208 0.168
#> SRR1402261 5 0.6691 -0.00177 0.316 0.104 0.000 0.004 0.480 0.096
#> SRR657809 2 0.6095 -0.07792 0.024 0.568 0.104 0.000 0.280 0.024
#> SRR1093075 1 0.3672 0.25406 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1433329 6 0.2092 0.76351 0.124 0.000 0.000 0.000 0.000 0.876
#> SRR1353418 6 0.5482 0.49894 0.012 0.000 0.196 0.104 0.028 0.660
#> SRR1092913 2 0.5082 -0.22796 0.036 0.556 0.012 0.004 0.388 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", "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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.891 0.935 0.971 0.4412 0.550 0.550
#> 3 3 0.877 0.904 0.964 0.2527 0.856 0.746
#> 4 4 0.599 0.776 0.811 0.1881 0.970 0.934
#> 5 5 0.503 0.527 0.775 0.0528 0.879 0.734
#> 6 6 0.585 0.489 0.722 0.0826 0.760 0.433
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
#> SRR816969 1 0.0376 0.939 0.996 0.004
#> SRR1335605 2 0.0000 0.985 0.000 1.000
#> SRR1432014 2 0.0000 0.985 0.000 1.000
#> SRR1499215 2 0.0000 0.985 0.000 1.000
#> SRR1460409 1 0.0376 0.939 0.996 0.004
#> SRR1086441 1 0.0376 0.939 0.996 0.004
#> SRR1097344 2 0.0000 0.985 0.000 1.000
#> SRR1081789 2 0.0000 0.985 0.000 1.000
#> SRR1453005 2 0.0376 0.982 0.004 0.996
#> SRR1366985 2 0.0000 0.985 0.000 1.000
#> SRR815280 1 0.0376 0.939 0.996 0.004
#> SRR1348531 1 0.9661 0.432 0.608 0.392
#> SRR815845 2 0.0000 0.985 0.000 1.000
#> SRR1471178 1 0.0376 0.939 0.996 0.004
#> SRR1080696 2 0.0000 0.985 0.000 1.000
#> SRR1078684 2 0.0000 0.985 0.000 1.000
#> SRR1317751 2 0.0000 0.985 0.000 1.000
#> SRR1435667 2 0.0000 0.985 0.000 1.000
#> SRR1097905 1 0.0376 0.939 0.996 0.004
#> SRR1456548 1 0.0376 0.939 0.996 0.004
#> SRR1075126 1 0.1184 0.933 0.984 0.016
#> SRR813108 2 0.0000 0.985 0.000 1.000
#> SRR1479062 2 0.0000 0.985 0.000 1.000
#> SRR1408703 2 0.0000 0.985 0.000 1.000
#> SRR1332360 1 0.6973 0.794 0.812 0.188
#> SRR1098686 1 0.0376 0.939 0.996 0.004
#> SRR1434228 1 0.3114 0.905 0.944 0.056
#> SRR1467149 2 0.0000 0.985 0.000 1.000
#> SRR1399113 2 0.0376 0.982 0.004 0.996
#> SRR1476507 2 0.0000 0.985 0.000 1.000
#> SRR1092468 2 0.0000 0.985 0.000 1.000
#> SRR1441804 1 0.9209 0.559 0.664 0.336
#> SRR1326100 2 0.0000 0.985 0.000 1.000
#> SRR1398815 1 0.0376 0.939 0.996 0.004
#> SRR1436021 2 0.0000 0.985 0.000 1.000
#> SRR1480083 2 0.0376 0.982 0.004 0.996
#> SRR1472863 1 0.0376 0.939 0.996 0.004
#> SRR815542 1 0.0376 0.939 0.996 0.004
#> SRR1400100 2 0.0000 0.985 0.000 1.000
#> SRR1312002 2 0.0000 0.985 0.000 1.000
#> SRR1470253 2 0.2423 0.945 0.040 0.960
#> SRR1414332 1 0.0376 0.939 0.996 0.004
#> SRR1069209 1 0.0376 0.939 0.996 0.004
#> SRR661052 1 0.0376 0.939 0.996 0.004
#> SRR1308860 1 0.0376 0.939 0.996 0.004
#> SRR1421159 2 0.0000 0.985 0.000 1.000
#> SRR1340943 2 0.0000 0.985 0.000 1.000
#> SRR1078855 1 0.0376 0.939 0.996 0.004
#> SRR1459465 2 0.0376 0.982 0.004 0.996
#> SRR816818 2 0.0376 0.982 0.004 0.996
#> SRR1478679 2 0.0000 0.985 0.000 1.000
#> SRR1350979 2 0.0000 0.985 0.000 1.000
#> SRR1458198 2 0.0000 0.985 0.000 1.000
#> SRR1386910 2 0.0000 0.985 0.000 1.000
#> SRR1465375 2 0.0000 0.985 0.000 1.000
#> SRR1323699 2 0.0000 0.985 0.000 1.000
#> SRR1431139 2 0.0000 0.985 0.000 1.000
#> SRR1373964 2 0.0000 0.985 0.000 1.000
#> SRR1455413 2 0.7453 0.704 0.212 0.788
#> SRR1437163 1 0.9522 0.438 0.628 0.372
#> SRR1347343 2 0.0000 0.985 0.000 1.000
#> SRR1465480 2 0.0376 0.982 0.004 0.996
#> SRR1489631 1 0.7219 0.780 0.800 0.200
#> SRR1086514 2 0.0000 0.985 0.000 1.000
#> SRR1430928 1 0.0376 0.939 0.996 0.004
#> SRR1310939 2 0.0000 0.985 0.000 1.000
#> SRR1344294 2 0.0376 0.982 0.004 0.996
#> SRR1099402 1 0.0376 0.939 0.996 0.004
#> SRR1468118 2 0.0000 0.985 0.000 1.000
#> SRR1486348 1 0.0376 0.939 0.996 0.004
#> SRR1488770 2 0.0376 0.982 0.004 0.996
#> SRR1083732 1 0.0376 0.939 0.996 0.004
#> SRR1456611 2 0.0376 0.982 0.004 0.996
#> SRR1080318 1 0.0376 0.939 0.996 0.004
#> SRR1500089 2 0.0000 0.985 0.000 1.000
#> SRR1441178 1 0.5294 0.858 0.880 0.120
#> SRR1381396 1 0.0376 0.939 0.996 0.004
#> SRR1096081 2 0.0000 0.985 0.000 1.000
#> SRR1349809 2 0.0000 0.985 0.000 1.000
#> SRR1324314 2 0.6343 0.792 0.160 0.840
#> SRR1092444 2 1.0000 -0.111 0.496 0.504
#> SRR1382553 2 0.0000 0.985 0.000 1.000
#> SRR1075530 2 0.0000 0.985 0.000 1.000
#> SRR1442612 2 0.0000 0.985 0.000 1.000
#> SRR1360056 2 0.0000 0.985 0.000 1.000
#> SRR1078164 1 0.7219 0.780 0.800 0.200
#> SRR1434545 2 0.0000 0.985 0.000 1.000
#> SRR1398251 2 0.4562 0.879 0.096 0.904
#> SRR1375866 1 0.7219 0.780 0.800 0.200
#> SRR1091645 2 0.0000 0.985 0.000 1.000
#> SRR1416636 2 0.0000 0.985 0.000 1.000
#> SRR1105441 2 0.0000 0.985 0.000 1.000
#> SRR1082496 2 0.0376 0.982 0.004 0.996
#> SRR1315353 2 0.0000 0.985 0.000 1.000
#> SRR1093697 2 0.0376 0.982 0.004 0.996
#> SRR1077429 2 0.0000 0.985 0.000 1.000
#> SRR1076120 2 0.0000 0.985 0.000 1.000
#> SRR1074410 1 0.0376 0.939 0.996 0.004
#> SRR1340345 2 0.0000 0.985 0.000 1.000
#> SRR1069514 2 0.0000 0.985 0.000 1.000
#> SRR1092636 2 0.0000 0.985 0.000 1.000
#> SRR1365013 2 0.0000 0.985 0.000 1.000
#> SRR1073069 1 0.7528 0.760 0.784 0.216
#> SRR1443137 1 0.1414 0.931 0.980 0.020
#> SRR1437143 2 0.0376 0.982 0.004 0.996
#> SRR1091990 1 0.0376 0.939 0.996 0.004
#> SRR820234 2 0.0376 0.982 0.004 0.996
#> SRR1338079 1 0.0376 0.939 0.996 0.004
#> SRR1390094 2 0.0000 0.985 0.000 1.000
#> SRR1340721 2 0.0376 0.982 0.004 0.996
#> SRR1335964 2 0.0000 0.985 0.000 1.000
#> SRR1086869 2 0.0000 0.985 0.000 1.000
#> SRR1453434 1 0.0672 0.937 0.992 0.008
#> SRR1402261 2 0.0000 0.985 0.000 1.000
#> SRR657809 2 0.0000 0.985 0.000 1.000
#> SRR1093075 1 0.0376 0.939 0.996 0.004
#> SRR1433329 1 0.0376 0.939 0.996 0.004
#> SRR1353418 2 0.0000 0.985 0.000 1.000
#> SRR1092913 2 0.0000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1335605 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1432014 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1499215 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1460409 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1097344 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1081789 3 0.0424 0.9632 0.000 0.008 0.992
#> SRR1453005 3 0.5760 0.4855 0.000 0.328 0.672
#> SRR1366985 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR815280 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1348531 3 0.6295 0.0472 0.472 0.000 0.528
#> SRR815845 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1471178 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1078684 3 0.0237 0.9651 0.004 0.000 0.996
#> SRR1317751 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1435667 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1097905 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1075126 1 0.1163 0.9007 0.972 0.000 0.028
#> SRR813108 3 0.1163 0.9450 0.000 0.028 0.972
#> SRR1479062 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1332360 1 0.5733 0.5305 0.676 0.000 0.324
#> SRR1098686 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1434228 1 0.3816 0.7576 0.852 0.000 0.148
#> SRR1467149 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1399113 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1476507 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1092468 3 0.0237 0.9651 0.004 0.000 0.996
#> SRR1441804 3 0.6225 0.1954 0.432 0.000 0.568
#> SRR1326100 3 0.0747 0.9591 0.000 0.016 0.984
#> SRR1398815 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1436021 3 0.0237 0.9651 0.004 0.000 0.996
#> SRR1480083 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1400100 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1312002 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1470253 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1414332 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1421159 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1340943 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1078855 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR816818 2 0.6008 0.3834 0.000 0.628 0.372
#> SRR1478679 3 0.0237 0.9651 0.004 0.000 0.996
#> SRR1350979 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1458198 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1386910 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1465375 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1323699 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1431139 3 0.0237 0.9651 0.004 0.000 0.996
#> SRR1373964 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1455413 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1437163 1 0.4062 0.7357 0.836 0.000 0.164
#> SRR1347343 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1465480 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1489631 1 0.5291 0.6162 0.732 0.000 0.268
#> SRR1086514 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1430928 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1456611 2 0.1289 0.9176 0.000 0.968 0.032
#> SRR1080318 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1500089 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1441178 1 0.3686 0.7824 0.860 0.000 0.140
#> SRR1381396 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1349809 3 0.0829 0.9613 0.012 0.004 0.984
#> SRR1324314 3 0.3116 0.8489 0.108 0.000 0.892
#> SRR1092444 3 0.5497 0.5679 0.292 0.000 0.708
#> SRR1382553 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1075530 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1442612 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1360056 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1078164 1 0.5254 0.6216 0.736 0.000 0.264
#> SRR1434545 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1398251 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1375866 1 0.4555 0.7080 0.800 0.000 0.200
#> SRR1091645 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1416636 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1105441 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1082496 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1315353 3 0.0237 0.9646 0.000 0.004 0.996
#> SRR1093697 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1076120 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1074410 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1340345 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1069514 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1092636 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1365013 3 0.0237 0.9651 0.004 0.000 0.996
#> SRR1073069 1 0.5760 0.5241 0.672 0.000 0.328
#> SRR1443137 1 0.0592 0.9152 0.988 0.000 0.012
#> SRR1437143 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.9508 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1390094 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1340721 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR1335964 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1453434 1 0.0237 0.9216 0.996 0.000 0.004
#> SRR1402261 3 0.0747 0.9607 0.016 0.000 0.984
#> SRR657809 3 0.0000 0.9657 0.000 0.000 1.000
#> SRR1093075 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.9245 1.000 0.000 0.000
#> SRR1353418 3 0.0424 0.9637 0.008 0.000 0.992
#> SRR1092913 3 0.0000 0.9657 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.2216 0.873 0.908 0.000 0.000 0.092
#> SRR1335605 3 0.1792 0.792 0.000 0.000 0.932 0.068
#> SRR1432014 3 0.4992 0.605 0.000 0.000 0.524 0.476
#> SRR1499215 3 0.4817 0.675 0.000 0.000 0.612 0.388
#> SRR1460409 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1086441 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1097344 3 0.3688 0.726 0.000 0.000 0.792 0.208
#> SRR1081789 3 0.2530 0.769 0.000 0.000 0.888 0.112
#> SRR1453005 3 0.6911 0.388 0.000 0.304 0.560 0.136
#> SRR1366985 3 0.6232 0.660 0.072 0.000 0.596 0.332
#> SRR815280 1 0.0336 0.899 0.992 0.000 0.000 0.008
#> SRR1348531 1 0.6148 0.228 0.540 0.000 0.408 0.052
#> SRR815845 3 0.4331 0.739 0.000 0.000 0.712 0.288
#> SRR1471178 1 0.0000 0.899 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.4543 0.720 0.000 0.000 0.676 0.324
#> SRR1078684 3 0.3975 0.764 0.000 0.000 0.760 0.240
#> SRR1317751 3 0.3219 0.778 0.000 0.000 0.836 0.164
#> SRR1435667 3 0.4981 0.611 0.000 0.000 0.536 0.464
#> SRR1097905 1 0.0336 0.898 0.992 0.000 0.000 0.008
#> SRR1456548 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1075126 1 0.1256 0.886 0.964 0.000 0.028 0.008
#> SRR813108 3 0.4805 0.764 0.000 0.084 0.784 0.132
#> SRR1479062 3 0.1389 0.790 0.000 0.000 0.952 0.048
#> SRR1408703 3 0.3356 0.780 0.000 0.000 0.824 0.176
#> SRR1332360 1 0.6231 0.700 0.668 0.000 0.148 0.184
#> SRR1098686 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1434228 1 0.3718 0.830 0.820 0.000 0.012 0.168
#> SRR1467149 3 0.3123 0.760 0.000 0.000 0.844 0.156
#> SRR1399113 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1476507 3 0.3219 0.744 0.000 0.000 0.836 0.164
#> SRR1092468 3 0.1474 0.784 0.000 0.000 0.948 0.052
#> SRR1441804 1 0.5860 0.328 0.580 0.000 0.380 0.040
#> SRR1326100 3 0.1716 0.783 0.000 0.000 0.936 0.064
#> SRR1398815 1 0.0000 0.899 1.000 0.000 0.000 0.000
#> SRR1436021 3 0.3172 0.750 0.000 0.000 0.840 0.160
#> SRR1480083 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.2149 0.874 0.912 0.000 0.000 0.088
#> SRR815542 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1400100 3 0.1211 0.791 0.000 0.000 0.960 0.040
#> SRR1312002 3 0.4605 0.715 0.000 0.000 0.664 0.336
#> SRR1470253 3 0.5944 0.713 0.104 0.000 0.684 0.212
#> SRR1414332 1 0.0336 0.899 0.992 0.000 0.000 0.008
#> SRR1069209 1 0.3172 0.838 0.840 0.000 0.000 0.160
#> SRR661052 1 0.0000 0.899 1.000 0.000 0.000 0.000
#> SRR1308860 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1421159 3 0.1716 0.791 0.000 0.000 0.936 0.064
#> SRR1340943 3 0.3610 0.727 0.000 0.000 0.800 0.200
#> SRR1078855 1 0.1557 0.885 0.944 0.000 0.000 0.056
#> SRR1459465 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR816818 2 0.5851 0.503 0.000 0.660 0.272 0.068
#> SRR1478679 3 0.4761 0.693 0.000 0.000 0.628 0.372
#> SRR1350979 3 0.4866 0.668 0.000 0.000 0.596 0.404
#> SRR1458198 3 0.2704 0.766 0.000 0.000 0.876 0.124
#> SRR1386910 3 0.2149 0.772 0.000 0.000 0.912 0.088
#> SRR1465375 3 0.3311 0.741 0.000 0.000 0.828 0.172
#> SRR1323699 3 0.4817 0.677 0.000 0.000 0.612 0.388
#> SRR1431139 3 0.2647 0.788 0.000 0.000 0.880 0.120
#> SRR1373964 3 0.4985 0.608 0.000 0.000 0.532 0.468
#> SRR1455413 3 0.4818 0.652 0.216 0.000 0.748 0.036
#> SRR1437163 1 0.0927 0.890 0.976 0.000 0.016 0.008
#> SRR1347343 3 0.4948 0.631 0.000 0.000 0.560 0.440
#> SRR1465480 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.5227 0.608 0.704 0.000 0.256 0.040
#> SRR1086514 3 0.3266 0.744 0.000 0.000 0.832 0.168
#> SRR1430928 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1310939 3 0.1557 0.791 0.000 0.000 0.944 0.056
#> SRR1344294 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.899 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.2973 0.780 0.000 0.000 0.856 0.144
#> SRR1486348 1 0.0000 0.899 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0592 0.898 0.984 0.000 0.000 0.016
#> SRR1456611 2 0.1610 0.926 0.000 0.952 0.032 0.016
#> SRR1080318 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1500089 3 0.1940 0.783 0.000 0.000 0.924 0.076
#> SRR1441178 1 0.4840 0.793 0.784 0.000 0.100 0.116
#> SRR1381396 1 0.0336 0.899 0.992 0.000 0.000 0.008
#> SRR1096081 3 0.3610 0.773 0.000 0.000 0.800 0.200
#> SRR1349809 3 0.3266 0.745 0.000 0.000 0.832 0.168
#> SRR1324314 3 0.6578 0.670 0.108 0.000 0.592 0.300
#> SRR1092444 3 0.6179 0.291 0.392 0.000 0.552 0.056
#> SRR1382553 3 0.4406 0.733 0.000 0.000 0.700 0.300
#> SRR1075530 3 0.3837 0.708 0.000 0.000 0.776 0.224
#> SRR1442612 3 0.4981 0.611 0.000 0.000 0.536 0.464
#> SRR1360056 3 0.4053 0.758 0.004 0.000 0.768 0.228
#> SRR1078164 1 0.5574 0.739 0.728 0.000 0.148 0.124
#> SRR1434545 3 0.3486 0.732 0.000 0.000 0.812 0.188
#> SRR1398251 3 0.7456 0.428 0.256 0.000 0.508 0.236
#> SRR1375866 1 0.5522 0.742 0.732 0.000 0.148 0.120
#> SRR1091645 3 0.3219 0.752 0.000 0.000 0.836 0.164
#> SRR1416636 3 0.4406 0.734 0.000 0.000 0.700 0.300
#> SRR1105441 3 0.2760 0.784 0.000 0.000 0.872 0.128
#> SRR1082496 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.1557 0.787 0.000 0.000 0.944 0.056
#> SRR1093697 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.3074 0.783 0.000 0.000 0.848 0.152
#> SRR1076120 3 0.1716 0.782 0.000 0.000 0.936 0.064
#> SRR1074410 1 0.0921 0.895 0.972 0.000 0.000 0.028
#> SRR1340345 3 0.3907 0.703 0.000 0.000 0.768 0.232
#> SRR1069514 3 0.4972 0.614 0.000 0.000 0.544 0.456
#> SRR1092636 3 0.3907 0.760 0.000 0.000 0.768 0.232
#> SRR1365013 3 0.3074 0.753 0.000 0.000 0.848 0.152
#> SRR1073069 1 0.6473 0.672 0.644 0.000 0.168 0.188
#> SRR1443137 1 0.4332 0.816 0.792 0.000 0.032 0.176
#> SRR1437143 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.2345 0.869 0.900 0.000 0.000 0.100
#> SRR820234 2 0.0000 0.963 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1390094 3 0.2081 0.792 0.000 0.000 0.916 0.084
#> SRR1340721 3 0.4237 0.734 0.040 0.000 0.808 0.152
#> SRR1335964 3 0.2814 0.784 0.000 0.000 0.868 0.132
#> SRR1086869 3 0.2760 0.782 0.000 0.000 0.872 0.128
#> SRR1453434 1 0.0188 0.899 0.996 0.000 0.000 0.004
#> SRR1402261 3 0.3610 0.727 0.000 0.000 0.800 0.200
#> SRR657809 3 0.3400 0.737 0.000 0.000 0.820 0.180
#> SRR1093075 1 0.0000 0.899 1.000 0.000 0.000 0.000
#> SRR1433329 1 0.3356 0.831 0.824 0.000 0.000 0.176
#> SRR1353418 3 0.4817 0.681 0.000 0.000 0.612 0.388
#> SRR1092913 3 0.3837 0.706 0.000 0.000 0.776 0.224
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.2966 5.40e-01 0.816 0.000 0.000 0.000 0.184
#> SRR1335605 4 0.4602 5.10e-01 0.052 0.000 0.240 0.708 0.000
#> SRR1432014 3 0.2891 8.38e-01 0.000 0.000 0.824 0.176 0.000
#> SRR1499215 4 0.4171 2.76e-01 0.000 0.000 0.396 0.604 0.000
#> SRR1460409 1 0.2124 7.01e-01 0.900 0.000 0.004 0.000 0.096
#> SRR1086441 1 0.0000 7.39e-01 1.000 0.000 0.000 0.000 0.000
#> SRR1097344 4 0.2424 5.24e-01 0.000 0.000 0.132 0.868 0.000
#> SRR1081789 4 0.1956 5.88e-01 0.000 0.008 0.076 0.916 0.000
#> SRR1453005 2 0.4517 -1.07e-06 0.000 0.556 0.008 0.436 0.000
#> SRR1366985 4 0.8140 -9.22e-02 0.228 0.000 0.124 0.396 0.252
#> SRR815280 1 0.0963 7.22e-01 0.964 0.000 0.000 0.000 0.036
#> SRR1348531 4 0.6069 1.48e-02 0.452 0.000 0.056 0.464 0.028
#> SRR815845 4 0.4249 3.13e-01 0.000 0.000 0.432 0.568 0.000
#> SRR1471178 1 0.0000 7.39e-01 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 4 0.6203 4.14e-01 0.000 0.000 0.224 0.552 0.224
#> SRR1078684 4 0.3966 4.08e-01 0.000 0.000 0.336 0.664 0.000
#> SRR1317751 4 0.6513 2.46e-01 0.000 0.000 0.192 0.424 0.384
#> SRR1435667 3 0.3074 8.63e-01 0.000 0.000 0.804 0.196 0.000
#> SRR1097905 1 0.2233 6.96e-01 0.892 0.000 0.004 0.000 0.104
#> SRR1456548 1 0.2233 6.96e-01 0.892 0.000 0.004 0.000 0.104
#> SRR1075126 1 0.3465 6.33e-01 0.840 0.000 0.004 0.052 0.104
#> SRR813108 4 0.6008 3.57e-01 0.000 0.200 0.216 0.584 0.000
#> SRR1479062 4 0.3689 5.03e-01 0.004 0.000 0.256 0.740 0.000
#> SRR1408703 4 0.6024 4.23e-01 0.000 0.000 0.148 0.556 0.296
#> SRR1332360 5 0.6006 7.10e-01 0.404 0.000 0.012 0.080 0.504
#> SRR1098686 1 0.2179 6.99e-01 0.896 0.000 0.004 0.000 0.100
#> SRR1434228 1 0.4161 -1.48e-01 0.608 0.000 0.000 0.000 0.392
#> SRR1467149 4 0.4874 5.10e-01 0.244 0.000 0.044 0.700 0.012
#> SRR1399113 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0290 5.92e-01 0.000 0.000 0.008 0.992 0.000
#> SRR1092468 4 0.4785 5.92e-01 0.068 0.000 0.024 0.756 0.152
#> SRR1441804 4 0.5712 3.99e-02 0.452 0.000 0.032 0.488 0.028
#> SRR1326100 4 0.4026 5.01e-01 0.000 0.244 0.020 0.736 0.000
#> SRR1398815 1 0.0000 7.39e-01 1.000 0.000 0.000 0.000 0.000
#> SRR1436021 4 0.0290 5.92e-01 0.000 0.000 0.008 0.992 0.000
#> SRR1480083 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.3074 5.19e-01 0.804 0.000 0.000 0.000 0.196
#> SRR815542 1 0.2233 6.96e-01 0.892 0.000 0.004 0.000 0.104
#> SRR1400100 4 0.3109 5.44e-01 0.000 0.000 0.200 0.800 0.000
#> SRR1312002 4 0.7123 3.44e-01 0.224 0.000 0.140 0.552 0.084
#> SRR1470253 4 0.6914 3.75e-01 0.232 0.000 0.128 0.568 0.072
#> SRR1414332 1 0.0703 7.29e-01 0.976 0.000 0.000 0.000 0.024
#> SRR1069209 1 0.4114 -8.11e-02 0.624 0.000 0.000 0.000 0.376
#> SRR661052 1 0.0162 7.39e-01 0.996 0.000 0.004 0.000 0.000
#> SRR1308860 1 0.2179 6.99e-01 0.896 0.000 0.004 0.000 0.100
#> SRR1421159 4 0.2561 5.75e-01 0.000 0.000 0.144 0.856 0.000
#> SRR1340943 4 0.0955 5.97e-01 0.028 0.000 0.004 0.968 0.000
#> SRR1078855 1 0.2732 5.79e-01 0.840 0.000 0.000 0.000 0.160
#> SRR1459465 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.3579 5.42e-01 0.000 0.756 0.004 0.240 0.000
#> SRR1478679 4 0.4161 2.88e-01 0.000 0.000 0.392 0.608 0.000
#> SRR1350979 3 0.4201 3.02e-01 0.000 0.000 0.592 0.408 0.000
#> SRR1458198 4 0.4315 5.14e-01 0.244 0.000 0.016 0.728 0.012
#> SRR1386910 4 0.0510 5.96e-01 0.000 0.000 0.016 0.984 0.000
#> SRR1465375 4 0.0798 5.95e-01 0.016 0.000 0.008 0.976 0.000
#> SRR1323699 4 0.4242 1.69e-01 0.000 0.000 0.428 0.572 0.000
#> SRR1431139 4 0.4704 4.77e-01 0.016 0.000 0.276 0.688 0.020
#> SRR1373964 3 0.3336 8.56e-01 0.000 0.000 0.772 0.228 0.000
#> SRR1455413 4 0.4774 4.73e-01 0.276 0.000 0.028 0.684 0.012
#> SRR1437163 1 0.3047 6.58e-01 0.868 0.000 0.004 0.044 0.084
#> SRR1347343 3 0.3796 7.63e-01 0.000 0.000 0.700 0.300 0.000
#> SRR1465480 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.5456 -1.95e-01 0.524 0.000 0.016 0.428 0.032
#> SRR1086514 4 0.0290 5.92e-01 0.000 0.000 0.008 0.992 0.000
#> SRR1430928 1 0.0000 7.39e-01 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 4 0.3809 5.00e-01 0.000 0.000 0.256 0.736 0.008
#> SRR1344294 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 7.39e-01 1.000 0.000 0.000 0.000 0.000
#> SRR1468118 4 0.6428 2.59e-01 0.000 0.000 0.176 0.440 0.384
#> SRR1486348 1 0.0162 7.38e-01 0.996 0.000 0.000 0.000 0.004
#> SRR1488770 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.1851 6.77e-01 0.912 0.000 0.000 0.000 0.088
#> SRR1456611 2 0.0794 8.79e-01 0.000 0.972 0.000 0.028 0.000
#> SRR1080318 1 0.1205 7.30e-01 0.956 0.000 0.004 0.000 0.040
#> SRR1500089 4 0.4821 5.65e-01 0.024 0.000 0.032 0.716 0.228
#> SRR1441178 1 0.5725 -2.24e-01 0.596 0.000 0.012 0.076 0.316
#> SRR1381396 1 0.0000 7.39e-01 1.000 0.000 0.000 0.000 0.000
#> SRR1096081 4 0.6618 2.14e-01 0.000 0.000 0.216 0.400 0.384
#> SRR1349809 4 0.2177 5.89e-01 0.004 0.080 0.008 0.908 0.000
#> SRR1324314 4 0.5974 4.76e-01 0.160 0.000 0.188 0.636 0.016
#> SRR1092444 4 0.5627 4.41e-01 0.296 0.000 0.056 0.624 0.024
#> SRR1382553 4 0.4114 3.31e-01 0.000 0.000 0.376 0.624 0.000
#> SRR1075530 4 0.2230 5.33e-01 0.000 0.000 0.116 0.884 0.000
#> SRR1442612 3 0.3109 8.65e-01 0.000 0.000 0.800 0.200 0.000
#> SRR1360056 4 0.5699 4.64e-01 0.220 0.000 0.156 0.624 0.000
#> SRR1078164 1 0.5825 -2.49e-01 0.588 0.000 0.012 0.084 0.316
#> SRR1434545 4 0.1106 5.96e-01 0.024 0.000 0.012 0.964 0.000
#> SRR1398251 5 0.7779 4.48e-01 0.256 0.000 0.072 0.256 0.416
#> SRR1375866 1 0.5725 -2.24e-01 0.596 0.000 0.012 0.076 0.316
#> SRR1091645 4 0.3238 5.26e-01 0.000 0.000 0.136 0.836 0.028
#> SRR1416636 4 0.5983 4.51e-01 0.000 0.000 0.168 0.580 0.252
#> SRR1105441 4 0.4030 3.90e-01 0.000 0.000 0.352 0.648 0.000
#> SRR1082496 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 4 0.2915 5.80e-01 0.000 0.024 0.116 0.860 0.000
#> SRR1093697 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 4 0.6185 3.58e-01 0.000 0.000 0.148 0.504 0.348
#> SRR1076120 4 0.4407 5.14e-01 0.244 0.000 0.020 0.724 0.012
#> SRR1074410 1 0.2068 6.65e-01 0.904 0.000 0.000 0.004 0.092
#> SRR1340345 4 0.2230 5.33e-01 0.000 0.000 0.116 0.884 0.000
#> SRR1069514 3 0.3274 8.62e-01 0.000 0.000 0.780 0.220 0.000
#> SRR1092636 4 0.6014 4.47e-01 0.000 0.000 0.172 0.576 0.252
#> SRR1365013 4 0.0404 5.93e-01 0.000 0.000 0.012 0.988 0.000
#> SRR1073069 5 0.6006 7.10e-01 0.404 0.000 0.012 0.080 0.504
#> SRR1443137 5 0.5157 5.23e-01 0.468 0.000 0.008 0.024 0.500
#> SRR1437143 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.3143 5.03e-01 0.796 0.000 0.000 0.000 0.204
#> SRR820234 2 0.0000 9.11e-01 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.0162 7.39e-01 0.996 0.000 0.004 0.000 0.000
#> SRR1390094 4 0.5013 4.97e-01 0.084 0.000 0.232 0.684 0.000
#> SRR1340721 4 0.2574 5.90e-01 0.112 0.000 0.012 0.876 0.000
#> SRR1335964 4 0.5588 4.77e-01 0.000 0.000 0.104 0.604 0.292
#> SRR1086869 4 0.6326 2.90e-01 0.000 0.000 0.160 0.460 0.380
#> SRR1453434 1 0.2919 6.74e-01 0.868 0.000 0.004 0.024 0.104
#> SRR1402261 4 0.0992 5.97e-01 0.024 0.000 0.008 0.968 0.000
#> SRR657809 4 0.0290 5.92e-01 0.000 0.000 0.008 0.992 0.000
#> SRR1093075 1 0.0162 7.38e-01 0.996 0.000 0.000 0.000 0.004
#> SRR1433329 1 0.4306 -4.93e-01 0.508 0.000 0.000 0.000 0.492
#> SRR1353418 4 0.7260 4.10e-01 0.128 0.000 0.236 0.536 0.100
#> SRR1092913 4 0.2464 5.44e-01 0.016 0.000 0.096 0.888 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1267 0.6349 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1335605 4 0.5782 0.3462 0.032 0.000 0.180 0.628 0.008 0.152
#> SRR1432014 3 0.2122 0.6969 0.000 0.000 0.900 0.076 0.024 0.000
#> SRR1499215 3 0.6516 0.4924 0.032 0.000 0.500 0.260 0.008 0.200
#> SRR1460409 1 0.3351 0.5930 0.832 0.000 0.020 0.000 0.040 0.108
#> SRR1086441 1 0.0291 0.6506 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1097344 4 0.4468 0.4266 0.000 0.000 0.008 0.604 0.024 0.364
#> SRR1081789 4 0.2506 0.5865 0.000 0.052 0.068 0.880 0.000 0.000
#> SRR1453005 2 0.4549 0.5637 0.000 0.696 0.032 0.240 0.000 0.032
#> SRR1366985 6 0.7666 0.7100 0.312 0.000 0.200 0.172 0.004 0.312
#> SRR815280 1 0.0858 0.6465 0.968 0.000 0.004 0.000 0.000 0.028
#> SRR1348531 1 0.5349 0.0584 0.568 0.000 0.004 0.352 0.044 0.032
#> SRR815845 3 0.4686 0.5920 0.000 0.000 0.660 0.248 0.092 0.000
#> SRR1471178 1 0.0291 0.6506 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1080696 5 0.5611 0.4520 0.000 0.000 0.224 0.232 0.544 0.000
#> SRR1078684 4 0.5603 -0.0847 0.000 0.000 0.384 0.484 0.004 0.128
#> SRR1317751 5 0.1152 0.6399 0.000 0.000 0.004 0.044 0.952 0.000
#> SRR1435667 3 0.1753 0.7160 0.000 0.000 0.912 0.084 0.004 0.000
#> SRR1097905 1 0.4059 0.5744 0.796 0.000 0.048 0.004 0.044 0.108
#> SRR1456548 1 0.4059 0.5744 0.796 0.000 0.048 0.004 0.044 0.108
#> SRR1075126 1 0.4356 0.5672 0.784 0.000 0.048 0.016 0.044 0.108
#> SRR813108 3 0.6605 0.3082 0.000 0.188 0.480 0.284 0.004 0.044
#> SRR1479062 4 0.5410 0.2454 0.000 0.000 0.276 0.588 0.008 0.128
#> SRR1408703 5 0.2838 0.6692 0.000 0.000 0.004 0.188 0.808 0.000
#> SRR1332360 1 0.4672 0.1306 0.596 0.000 0.000 0.056 0.000 0.348
#> SRR1098686 1 0.3855 0.5777 0.804 0.000 0.044 0.000 0.044 0.108
#> SRR1434228 1 0.3390 0.3062 0.704 0.000 0.000 0.000 0.000 0.296
#> SRR1467149 1 0.5355 -0.0741 0.496 0.000 0.004 0.432 0.044 0.024
#> SRR1399113 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.1610 0.6031 0.000 0.000 0.000 0.916 0.000 0.084
#> SRR1092468 4 0.4729 0.4113 0.076 0.000 0.012 0.700 0.208 0.004
#> SRR1441804 1 0.5349 0.0612 0.568 0.000 0.004 0.352 0.044 0.032
#> SRR1326100 4 0.6072 0.2032 0.000 0.420 0.096 0.440 0.000 0.044
#> SRR1398815 1 0.0146 0.6506 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1436021 4 0.1265 0.6048 0.000 0.000 0.044 0.948 0.000 0.008
#> SRR1480083 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.1610 0.6274 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR815542 1 0.3855 0.5777 0.804 0.000 0.044 0.000 0.044 0.108
#> SRR1400100 4 0.3622 0.4210 0.000 0.000 0.236 0.744 0.004 0.016
#> SRR1312002 1 0.7937 -0.7397 0.308 0.000 0.200 0.228 0.012 0.252
#> SRR1470253 1 0.7983 -0.7470 0.312 0.000 0.192 0.224 0.016 0.256
#> SRR1414332 1 0.0692 0.6482 0.976 0.000 0.004 0.000 0.000 0.020
#> SRR1069209 1 0.2454 0.5802 0.840 0.000 0.000 0.000 0.000 0.160
#> SRR661052 1 0.0717 0.6513 0.976 0.000 0.000 0.000 0.008 0.016
#> SRR1308860 1 0.3498 0.5887 0.824 0.000 0.024 0.000 0.044 0.108
#> SRR1421159 4 0.5376 0.2697 0.000 0.000 0.324 0.576 0.020 0.080
#> SRR1340943 4 0.2884 0.5920 0.064 0.000 0.000 0.864 0.008 0.064
#> SRR1078855 1 0.1349 0.6382 0.940 0.000 0.004 0.000 0.000 0.056
#> SRR1459465 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.3830 0.6215 0.000 0.744 0.044 0.212 0.000 0.000
#> SRR1478679 3 0.5023 0.5488 0.000 0.000 0.600 0.312 0.004 0.084
#> SRR1350979 3 0.2706 0.7160 0.000 0.000 0.852 0.124 0.024 0.000
#> SRR1458198 1 0.5305 -0.0797 0.492 0.000 0.004 0.440 0.040 0.024
#> SRR1386910 4 0.1528 0.6125 0.000 0.000 0.028 0.944 0.016 0.012
#> SRR1465375 4 0.1991 0.6085 0.044 0.000 0.012 0.920 0.000 0.024
#> SRR1323699 3 0.5263 0.6111 0.000 0.000 0.624 0.248 0.012 0.116
#> SRR1431139 4 0.7371 0.1016 0.048 0.000 0.260 0.464 0.060 0.168
#> SRR1373964 3 0.1765 0.7219 0.000 0.000 0.904 0.096 0.000 0.000
#> SRR1455413 1 0.5272 -0.0240 0.532 0.000 0.004 0.400 0.032 0.032
#> SRR1437163 1 0.4092 0.5803 0.812 0.000 0.028 0.048 0.040 0.072
#> SRR1347343 3 0.2048 0.7283 0.000 0.000 0.880 0.120 0.000 0.000
#> SRR1465480 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.5048 0.1874 0.616 0.000 0.000 0.308 0.052 0.024
#> SRR1086514 4 0.1714 0.6066 0.000 0.000 0.000 0.908 0.000 0.092
#> SRR1430928 1 0.0291 0.6506 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1310939 4 0.5744 0.3398 0.000 0.000 0.200 0.588 0.192 0.020
#> SRR1344294 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0291 0.6506 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1468118 5 0.1285 0.6448 0.000 0.000 0.004 0.052 0.944 0.000
#> SRR1486348 1 0.0146 0.6506 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1007 0.6444 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1456611 2 0.1124 0.9115 0.000 0.956 0.036 0.008 0.000 0.000
#> SRR1080318 1 0.2769 0.6225 0.880 0.000 0.000 0.036 0.032 0.052
#> SRR1500089 5 0.5496 0.1756 0.052 0.000 0.008 0.448 0.472 0.020
#> SRR1441178 1 0.3865 0.4761 0.752 0.000 0.000 0.056 0.000 0.192
#> SRR1381396 1 0.0146 0.6511 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1096081 5 0.1152 0.6399 0.000 0.000 0.004 0.044 0.952 0.000
#> SRR1349809 4 0.3667 0.5415 0.008 0.160 0.044 0.788 0.000 0.000
#> SRR1324314 4 0.8316 -0.3500 0.248 0.000 0.164 0.324 0.052 0.212
#> SRR1092444 1 0.5380 0.0325 0.556 0.000 0.004 0.364 0.044 0.032
#> SRR1382553 3 0.6678 0.4326 0.044 0.000 0.460 0.296 0.004 0.196
#> SRR1075530 4 0.4468 0.4319 0.000 0.000 0.008 0.604 0.024 0.364
#> SRR1442612 3 0.1753 0.7160 0.000 0.000 0.912 0.084 0.004 0.000
#> SRR1360056 1 0.8243 -0.6393 0.300 0.000 0.160 0.288 0.044 0.208
#> SRR1078164 1 0.4032 0.4609 0.740 0.000 0.000 0.068 0.000 0.192
#> SRR1434545 4 0.2934 0.5927 0.044 0.000 0.000 0.864 0.016 0.076
#> SRR1398251 6 0.6849 0.6931 0.328 0.000 0.112 0.104 0.004 0.452
#> SRR1375866 1 0.3865 0.4761 0.752 0.000 0.000 0.056 0.000 0.192
#> SRR1091645 4 0.4845 0.4120 0.000 0.000 0.008 0.580 0.048 0.364
#> SRR1416636 5 0.5265 0.5136 0.000 0.000 0.176 0.220 0.604 0.000
#> SRR1105441 3 0.4265 0.4550 0.000 0.000 0.596 0.384 0.004 0.016
#> SRR1082496 2 0.0458 0.9300 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1315353 4 0.5472 0.3216 0.000 0.060 0.292 0.600 0.000 0.048
#> SRR1093697 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.1858 0.6644 0.000 0.000 0.004 0.092 0.904 0.000
#> SRR1076120 4 0.5948 -0.1984 0.432 0.000 0.004 0.440 0.100 0.024
#> SRR1074410 1 0.1610 0.6239 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1340345 4 0.4394 0.4323 0.000 0.000 0.008 0.608 0.020 0.364
#> SRR1069514 3 0.1806 0.7196 0.000 0.000 0.908 0.088 0.004 0.000
#> SRR1092636 5 0.4795 0.4860 0.000 0.000 0.072 0.324 0.604 0.000
#> SRR1365013 4 0.1075 0.6044 0.000 0.000 0.048 0.952 0.000 0.000
#> SRR1073069 1 0.4781 0.1659 0.608 0.000 0.000 0.072 0.000 0.320
#> SRR1443137 1 0.3719 0.4531 0.728 0.000 0.000 0.024 0.000 0.248
#> SRR1437143 2 0.0000 0.9354 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.1327 0.6330 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR820234 2 0.0458 0.9300 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1338079 1 0.0653 0.6502 0.980 0.000 0.004 0.000 0.012 0.004
#> SRR1390094 4 0.6413 0.2672 0.120 0.000 0.196 0.576 0.004 0.104
#> SRR1340721 4 0.3446 0.2790 0.308 0.000 0.000 0.692 0.000 0.000
#> SRR1335964 5 0.3426 0.6086 0.000 0.000 0.004 0.276 0.720 0.000
#> SRR1086869 5 0.2838 0.6489 0.000 0.000 0.004 0.188 0.808 0.000
#> SRR1453434 1 0.4105 0.5750 0.796 0.000 0.044 0.008 0.044 0.108
#> SRR1402261 4 0.2731 0.5966 0.044 0.000 0.000 0.876 0.012 0.068
#> SRR657809 4 0.1462 0.6108 0.000 0.000 0.008 0.936 0.000 0.056
#> SRR1093075 1 0.0436 0.6518 0.988 0.000 0.004 0.000 0.004 0.004
#> SRR1433329 1 0.3221 0.4705 0.736 0.000 0.000 0.000 0.000 0.264
#> SRR1353418 5 0.8758 -0.1040 0.104 0.000 0.244 0.224 0.248 0.180
#> SRR1092913 4 0.4179 0.4571 0.000 0.000 0.008 0.652 0.016 0.324
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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.787 0.891 0.955 0.4904 0.515 0.515
#> 3 3 0.835 0.889 0.952 0.3234 0.710 0.497
#> 4 4 0.849 0.857 0.938 0.1280 0.830 0.568
#> 5 5 0.735 0.773 0.868 0.0701 0.847 0.524
#> 6 6 0.834 0.804 0.904 0.0603 0.887 0.550
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
#> SRR816969 1 0.0000 0.9430 1.000 0.000
#> SRR1335605 1 0.9552 0.4359 0.624 0.376
#> SRR1432014 2 0.0000 0.9619 0.000 1.000
#> SRR1499215 1 0.0000 0.9430 1.000 0.000
#> SRR1460409 1 0.0000 0.9430 1.000 0.000
#> SRR1086441 1 0.0000 0.9430 1.000 0.000
#> SRR1097344 2 0.0000 0.9619 0.000 1.000
#> SRR1081789 2 0.0000 0.9619 0.000 1.000
#> SRR1453005 2 0.0000 0.9619 0.000 1.000
#> SRR1366985 1 0.0000 0.9430 1.000 0.000
#> SRR815280 1 0.0000 0.9430 1.000 0.000
#> SRR1348531 1 0.0000 0.9430 1.000 0.000
#> SRR815845 2 0.0000 0.9619 0.000 1.000
#> SRR1471178 1 0.0000 0.9430 1.000 0.000
#> SRR1080696 2 0.7528 0.7099 0.216 0.784
#> SRR1078684 2 0.1843 0.9395 0.028 0.972
#> SRR1317751 1 0.7219 0.7482 0.800 0.200
#> SRR1435667 2 0.0000 0.9619 0.000 1.000
#> SRR1097905 1 0.0000 0.9430 1.000 0.000
#> SRR1456548 1 0.0000 0.9430 1.000 0.000
#> SRR1075126 1 0.0000 0.9430 1.000 0.000
#> SRR813108 2 0.0000 0.9619 0.000 1.000
#> SRR1479062 1 1.0000 0.0734 0.504 0.496
#> SRR1408703 1 0.7950 0.6950 0.760 0.240
#> SRR1332360 1 0.0000 0.9430 1.000 0.000
#> SRR1098686 1 0.0000 0.9430 1.000 0.000
#> SRR1434228 1 0.0000 0.9430 1.000 0.000
#> SRR1467149 1 0.0000 0.9430 1.000 0.000
#> SRR1399113 2 0.0000 0.9619 0.000 1.000
#> SRR1476507 2 0.0000 0.9619 0.000 1.000
#> SRR1092468 1 0.2043 0.9201 0.968 0.032
#> SRR1441804 1 0.0000 0.9430 1.000 0.000
#> SRR1326100 2 0.0000 0.9619 0.000 1.000
#> SRR1398815 1 0.0000 0.9430 1.000 0.000
#> SRR1436021 2 0.0000 0.9619 0.000 1.000
#> SRR1480083 2 0.0000 0.9619 0.000 1.000
#> SRR1472863 1 0.0000 0.9430 1.000 0.000
#> SRR815542 1 0.0000 0.9430 1.000 0.000
#> SRR1400100 2 0.0000 0.9619 0.000 1.000
#> SRR1312002 1 0.0000 0.9430 1.000 0.000
#> SRR1470253 1 0.0000 0.9430 1.000 0.000
#> SRR1414332 1 0.0000 0.9430 1.000 0.000
#> SRR1069209 1 0.0000 0.9430 1.000 0.000
#> SRR661052 1 0.0000 0.9430 1.000 0.000
#> SRR1308860 1 0.0000 0.9430 1.000 0.000
#> SRR1421159 2 0.0000 0.9619 0.000 1.000
#> SRR1340943 1 0.0938 0.9352 0.988 0.012
#> SRR1078855 1 0.0000 0.9430 1.000 0.000
#> SRR1459465 2 0.0000 0.9619 0.000 1.000
#> SRR816818 2 0.0000 0.9619 0.000 1.000
#> SRR1478679 2 0.6247 0.7999 0.156 0.844
#> SRR1350979 2 0.0000 0.9619 0.000 1.000
#> SRR1458198 1 0.0000 0.9430 1.000 0.000
#> SRR1386910 2 0.0000 0.9619 0.000 1.000
#> SRR1465375 2 0.8327 0.6429 0.264 0.736
#> SRR1323699 1 0.9954 0.1345 0.540 0.460
#> SRR1431139 1 0.0938 0.9350 0.988 0.012
#> SRR1373964 2 0.0000 0.9619 0.000 1.000
#> SRR1455413 1 0.0000 0.9430 1.000 0.000
#> SRR1437163 1 0.0000 0.9430 1.000 0.000
#> SRR1347343 2 0.0000 0.9619 0.000 1.000
#> SRR1465480 2 0.0000 0.9619 0.000 1.000
#> SRR1489631 1 0.0000 0.9430 1.000 0.000
#> SRR1086514 2 0.0000 0.9619 0.000 1.000
#> SRR1430928 1 0.0000 0.9430 1.000 0.000
#> SRR1310939 2 0.0376 0.9588 0.004 0.996
#> SRR1344294 2 0.0000 0.9619 0.000 1.000
#> SRR1099402 1 0.0000 0.9430 1.000 0.000
#> SRR1468118 1 0.7745 0.7115 0.772 0.228
#> SRR1486348 1 0.0000 0.9430 1.000 0.000
#> SRR1488770 2 0.0000 0.9619 0.000 1.000
#> SRR1083732 1 0.0000 0.9430 1.000 0.000
#> SRR1456611 2 0.0000 0.9619 0.000 1.000
#> SRR1080318 1 0.0000 0.9430 1.000 0.000
#> SRR1500089 1 0.0672 0.9378 0.992 0.008
#> SRR1441178 1 0.0000 0.9430 1.000 0.000
#> SRR1381396 1 0.0000 0.9430 1.000 0.000
#> SRR1096081 1 0.7299 0.7431 0.796 0.204
#> SRR1349809 2 0.8327 0.6413 0.264 0.736
#> SRR1324314 1 0.0000 0.9430 1.000 0.000
#> SRR1092444 1 0.0000 0.9430 1.000 0.000
#> SRR1382553 1 0.9754 0.3361 0.592 0.408
#> SRR1075530 2 0.0000 0.9619 0.000 1.000
#> SRR1442612 2 0.0000 0.9619 0.000 1.000
#> SRR1360056 1 0.0000 0.9430 1.000 0.000
#> SRR1078164 1 0.0000 0.9430 1.000 0.000
#> SRR1434545 2 0.5059 0.8535 0.112 0.888
#> SRR1398251 1 0.0000 0.9430 1.000 0.000
#> SRR1375866 1 0.0000 0.9430 1.000 0.000
#> SRR1091645 2 0.0000 0.9619 0.000 1.000
#> SRR1416636 1 0.8081 0.6828 0.752 0.248
#> SRR1105441 2 0.0000 0.9619 0.000 1.000
#> SRR1082496 2 0.0000 0.9619 0.000 1.000
#> SRR1315353 2 0.0000 0.9619 0.000 1.000
#> SRR1093697 2 0.0000 0.9619 0.000 1.000
#> SRR1077429 1 0.7139 0.7531 0.804 0.196
#> SRR1076120 1 0.0000 0.9430 1.000 0.000
#> SRR1074410 1 0.0000 0.9430 1.000 0.000
#> SRR1340345 2 0.0000 0.9619 0.000 1.000
#> SRR1069514 2 0.0000 0.9619 0.000 1.000
#> SRR1092636 1 0.0938 0.9351 0.988 0.012
#> SRR1365013 2 0.0000 0.9619 0.000 1.000
#> SRR1073069 1 0.0000 0.9430 1.000 0.000
#> SRR1443137 1 0.0000 0.9430 1.000 0.000
#> SRR1437143 2 0.0000 0.9619 0.000 1.000
#> SRR1091990 1 0.0000 0.9430 1.000 0.000
#> SRR820234 2 0.0000 0.9619 0.000 1.000
#> SRR1338079 1 0.0000 0.9430 1.000 0.000
#> SRR1390094 1 0.9775 0.3473 0.588 0.412
#> SRR1340721 1 0.7602 0.7041 0.780 0.220
#> SRR1335964 2 0.6801 0.7652 0.180 0.820
#> SRR1086869 2 0.9795 0.2380 0.416 0.584
#> SRR1453434 1 0.0000 0.9430 1.000 0.000
#> SRR1402261 1 0.0376 0.9404 0.996 0.004
#> SRR657809 2 0.0000 0.9619 0.000 1.000
#> SRR1093075 1 0.0000 0.9430 1.000 0.000
#> SRR1433329 1 0.0000 0.9430 1.000 0.000
#> SRR1353418 1 0.0000 0.9430 1.000 0.000
#> SRR1092913 2 0.0000 0.9619 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1335605 2 0.5178 0.796 0.164 0.808 0.028
#> SRR1432014 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1499215 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1460409 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1097344 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1081789 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1453005 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1366985 1 0.0000 0.976 1.000 0.000 0.000
#> SRR815280 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1348531 1 0.0892 0.958 0.980 0.000 0.020
#> SRR815845 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1471178 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1078684 2 0.4339 0.863 0.084 0.868 0.048
#> SRR1317751 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1435667 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1097905 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.976 1.000 0.000 0.000
#> SRR813108 2 0.3340 0.833 0.000 0.880 0.120
#> SRR1479062 3 0.6495 0.122 0.004 0.460 0.536
#> SRR1408703 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1332360 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1434228 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1467149 3 0.1753 0.875 0.048 0.000 0.952
#> SRR1399113 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1476507 3 0.0237 0.907 0.000 0.004 0.996
#> SRR1092468 3 0.3941 0.775 0.156 0.000 0.844
#> SRR1441804 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1436021 2 0.3816 0.797 0.000 0.852 0.148
#> SRR1480083 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.976 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1400100 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1312002 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1470253 1 0.0424 0.969 0.992 0.000 0.008
#> SRR1414332 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.976 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1421159 3 0.1964 0.876 0.000 0.056 0.944
#> SRR1340943 1 0.0237 0.972 0.996 0.000 0.004
#> SRR1078855 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.940 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1478679 2 0.4931 0.723 0.232 0.768 0.000
#> SRR1350979 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1458198 1 0.5882 0.447 0.652 0.000 0.348
#> SRR1386910 3 0.2796 0.844 0.000 0.092 0.908
#> SRR1465375 2 0.4605 0.766 0.204 0.796 0.000
#> SRR1323699 1 0.6119 0.711 0.772 0.164 0.064
#> SRR1431139 3 0.4842 0.695 0.224 0.000 0.776
#> SRR1373964 3 0.4452 0.752 0.000 0.192 0.808
#> SRR1455413 1 0.2625 0.890 0.916 0.000 0.084
#> SRR1437163 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1347343 3 0.3619 0.813 0.000 0.136 0.864
#> SRR1465480 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1086514 2 0.1289 0.920 0.000 0.968 0.032
#> SRR1430928 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1500089 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1441178 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1349809 2 0.3686 0.832 0.140 0.860 0.000
#> SRR1324314 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1092444 1 0.1163 0.950 0.972 0.000 0.028
#> SRR1382553 2 0.3551 0.839 0.132 0.868 0.000
#> SRR1075530 3 0.0237 0.906 0.000 0.004 0.996
#> SRR1442612 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1360056 3 0.3412 0.805 0.124 0.000 0.876
#> SRR1078164 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1434545 3 0.6816 0.150 0.012 0.472 0.516
#> SRR1398251 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1091645 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1416636 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1105441 3 0.0237 0.907 0.000 0.004 0.996
#> SRR1082496 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1315353 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1093697 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1076120 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1074410 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1340345 3 0.0237 0.907 0.000 0.004 0.996
#> SRR1069514 3 0.3619 0.812 0.000 0.136 0.864
#> SRR1092636 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1365013 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1073069 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.976 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1390094 3 0.9626 0.159 0.392 0.204 0.404
#> SRR1340721 1 0.6225 0.191 0.568 0.432 0.000
#> SRR1335964 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1402261 3 0.5859 0.498 0.344 0.000 0.656
#> SRR657809 2 0.0000 0.940 0.000 1.000 0.000
#> SRR1093075 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.976 1.000 0.000 0.000
#> SRR1353418 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1092913 3 0.4654 0.713 0.000 0.208 0.792
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.3810 0.713891 0.008 0.804 0.188 0.000
#> SRR1432014 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1499215 3 0.1302 0.838931 0.044 0.000 0.956 0.000
#> SRR1460409 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1081789 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1453005 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1366985 3 0.4356 0.599328 0.292 0.000 0.708 0.000
#> SRR815280 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1348531 1 0.1118 0.953359 0.964 0.000 0.036 0.000
#> SRR815845 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1471178 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1078684 3 0.5004 0.397062 0.004 0.392 0.604 0.000
#> SRR1317751 3 0.4164 0.602558 0.000 0.000 0.736 0.264
#> SRR1435667 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1097905 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1456548 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1075126 1 0.0524 0.981006 0.988 0.000 0.004 0.008
#> SRR813108 3 0.4992 0.177473 0.000 0.476 0.524 0.000
#> SRR1479062 2 0.5168 -0.000806 0.000 0.500 0.496 0.004
#> SRR1408703 3 0.4543 0.507084 0.000 0.000 0.676 0.324
#> SRR1332360 1 0.0707 0.974063 0.980 0.000 0.020 0.000
#> SRR1098686 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1434228 1 0.0336 0.983400 0.992 0.000 0.008 0.000
#> SRR1467149 4 0.0188 0.951460 0.004 0.000 0.000 0.996
#> SRR1399113 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1092468 4 0.0188 0.951460 0.004 0.000 0.000 0.996
#> SRR1441804 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1326100 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1436021 2 0.4250 0.585017 0.000 0.724 0.000 0.276
#> SRR1480083 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR815542 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1400100 3 0.0336 0.855978 0.000 0.000 0.992 0.008
#> SRR1312002 3 0.0000 0.856630 0.000 0.000 1.000 0.000
#> SRR1470253 3 0.2704 0.774246 0.124 0.000 0.876 0.000
#> SRR1414332 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR661052 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1308860 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1421159 4 0.3448 0.768685 0.000 0.004 0.168 0.828
#> SRR1340943 4 0.0188 0.951460 0.004 0.000 0.000 0.996
#> SRR1078855 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1459465 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.4969 0.722144 0.140 0.088 0.772 0.000
#> SRR1350979 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1458198 4 0.0188 0.951460 0.004 0.000 0.000 0.996
#> SRR1386910 4 0.2919 0.883012 0.000 0.044 0.060 0.896
#> SRR1465375 2 0.4624 0.497683 0.340 0.660 0.000 0.000
#> SRR1323699 3 0.2868 0.770549 0.136 0.000 0.864 0.000
#> SRR1431139 3 0.4122 0.670141 0.236 0.000 0.760 0.004
#> SRR1373964 3 0.1867 0.825298 0.000 0.072 0.928 0.000
#> SRR1455413 1 0.2868 0.840593 0.864 0.000 0.000 0.136
#> SRR1437163 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1347343 3 0.0000 0.856630 0.000 0.000 1.000 0.000
#> SRR1465480 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1086514 2 0.4585 0.505246 0.000 0.668 0.000 0.332
#> SRR1430928 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.0188 0.950632 0.000 0.000 0.004 0.996
#> SRR1344294 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1468118 4 0.4477 0.550844 0.000 0.000 0.312 0.688
#> SRR1486348 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1500089 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1441178 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1381396 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.0707 0.850512 0.000 0.000 0.980 0.020
#> SRR1349809 2 0.2760 0.780923 0.128 0.872 0.000 0.000
#> SRR1324314 1 0.3873 0.678350 0.772 0.000 0.228 0.000
#> SRR1092444 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1382553 2 0.1004 0.875329 0.024 0.972 0.004 0.000
#> SRR1075530 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1442612 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1360056 3 0.0000 0.856630 0.000 0.000 1.000 0.000
#> SRR1078164 1 0.0469 0.980840 0.988 0.000 0.012 0.000
#> SRR1434545 4 0.0188 0.950752 0.000 0.004 0.000 0.996
#> SRR1398251 3 0.2011 0.818556 0.080 0.000 0.920 0.000
#> SRR1375866 1 0.0336 0.982030 0.992 0.000 0.008 0.000
#> SRR1091645 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1416636 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1105441 3 0.0376 0.856519 0.000 0.004 0.992 0.004
#> SRR1082496 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.0921 0.873885 0.000 0.972 0.028 0.000
#> SRR1093697 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.4994 0.044530 0.000 0.000 0.520 0.480
#> SRR1076120 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1074410 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1340345 4 0.0000 0.952709 0.000 0.000 0.000 1.000
#> SRR1069514 3 0.0188 0.857041 0.000 0.000 0.996 0.004
#> SRR1092636 3 0.0336 0.855978 0.000 0.000 0.992 0.008
#> SRR1365013 2 0.0524 0.886571 0.008 0.988 0.000 0.004
#> SRR1073069 1 0.0817 0.970500 0.976 0.000 0.024 0.000
#> SRR1443137 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1437143 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR820234 2 0.0000 0.891483 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0000 0.986689 1.000 0.000 0.000 0.000
#> SRR1390094 3 0.3569 0.716514 0.000 0.196 0.804 0.000
#> SRR1340721 2 0.4972 0.199740 0.456 0.544 0.000 0.000
#> SRR1335964 3 0.4522 0.536879 0.000 0.000 0.680 0.320
#> SRR1086869 4 0.3311 0.786239 0.000 0.000 0.172 0.828
#> SRR1453434 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1402261 4 0.0188 0.951460 0.004 0.000 0.000 0.996
#> SRR657809 2 0.1389 0.861731 0.000 0.952 0.000 0.048
#> SRR1093075 1 0.0188 0.985564 0.996 0.000 0.004 0.000
#> SRR1433329 1 0.0336 0.983400 0.992 0.000 0.008 0.000
#> SRR1353418 3 0.0000 0.856630 0.000 0.000 1.000 0.000
#> SRR1092913 4 0.0000 0.952709 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0880 0.8774 0.968 0.000 0.000 0.000 0.032
#> SRR1335605 5 0.4402 0.2659 0.012 0.352 0.000 0.000 0.636
#> SRR1432014 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1499215 3 0.5888 0.3344 0.124 0.000 0.560 0.000 0.316
#> SRR1460409 1 0.0510 0.8800 0.984 0.000 0.000 0.000 0.016
#> SRR1086441 1 0.0162 0.8807 0.996 0.000 0.000 0.000 0.004
#> SRR1097344 4 0.0162 0.8725 0.000 0.000 0.000 0.996 0.004
#> SRR1081789 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1453005 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1366985 1 0.5414 0.2348 0.528 0.000 0.412 0.000 0.060
#> SRR815280 1 0.0963 0.8766 0.964 0.000 0.000 0.000 0.036
#> SRR1348531 5 0.3751 0.7012 0.212 0.000 0.004 0.012 0.772
#> SRR815845 3 0.4114 0.2421 0.000 0.000 0.624 0.000 0.376
#> SRR1471178 1 0.0000 0.8809 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 3 0.1792 0.8172 0.000 0.000 0.916 0.000 0.084
#> SRR1078684 3 0.2325 0.8336 0.068 0.028 0.904 0.000 0.000
#> SRR1317751 5 0.4630 0.7338 0.000 0.000 0.088 0.176 0.736
#> SRR1435667 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1097905 1 0.2719 0.8230 0.852 0.000 0.000 0.004 0.144
#> SRR1456548 1 0.2719 0.8288 0.852 0.000 0.000 0.004 0.144
#> SRR1075126 1 0.0807 0.8811 0.976 0.000 0.000 0.012 0.012
#> SRR813108 3 0.1908 0.8248 0.000 0.092 0.908 0.000 0.000
#> SRR1479062 5 0.3381 0.6923 0.016 0.176 0.000 0.000 0.808
#> SRR1408703 5 0.4934 0.7224 0.000 0.000 0.104 0.188 0.708
#> SRR1332360 1 0.2377 0.8387 0.872 0.000 0.000 0.000 0.128
#> SRR1098686 1 0.1571 0.8665 0.936 0.000 0.000 0.004 0.060
#> SRR1434228 1 0.1410 0.8685 0.940 0.000 0.000 0.000 0.060
#> SRR1467149 5 0.4436 0.4991 0.008 0.000 0.000 0.396 0.596
#> SRR1399113 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0162 0.8725 0.000 0.000 0.000 0.996 0.004
#> SRR1092468 4 0.3507 0.7555 0.052 0.000 0.000 0.828 0.120
#> SRR1441804 1 0.1661 0.8740 0.940 0.000 0.000 0.024 0.036
#> SRR1326100 2 0.4127 0.7587 0.000 0.784 0.136 0.000 0.080
#> SRR1398815 1 0.2377 0.8424 0.872 0.000 0.000 0.000 0.128
#> SRR1436021 4 0.7872 0.3122 0.020 0.092 0.296 0.472 0.120
#> SRR1480083 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.2377 0.8411 0.872 0.000 0.000 0.000 0.128
#> SRR815542 1 0.0324 0.8811 0.992 0.000 0.000 0.004 0.004
#> SRR1400100 5 0.4935 0.6968 0.000 0.028 0.212 0.040 0.720
#> SRR1312002 5 0.4926 0.6972 0.132 0.000 0.152 0.000 0.716
#> SRR1470253 5 0.2964 0.7338 0.120 0.000 0.024 0.000 0.856
#> SRR1414332 1 0.0290 0.8809 0.992 0.000 0.000 0.000 0.008
#> SRR1069209 1 0.1197 0.8730 0.952 0.000 0.000 0.000 0.048
#> SRR661052 1 0.3305 0.7898 0.776 0.000 0.000 0.000 0.224
#> SRR1308860 1 0.1502 0.8681 0.940 0.000 0.000 0.004 0.056
#> SRR1421159 3 0.3922 0.7078 0.000 0.000 0.780 0.180 0.040
#> SRR1340943 4 0.0000 0.8735 0.000 0.000 0.000 1.000 0.000
#> SRR1078855 1 0.1043 0.8756 0.960 0.000 0.000 0.000 0.040
#> SRR1459465 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.1597 0.8555 0.048 0.012 0.940 0.000 0.000
#> SRR1350979 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1458198 4 0.0000 0.8735 0.000 0.000 0.000 1.000 0.000
#> SRR1386910 5 0.6432 -0.0571 0.020 0.108 0.000 0.380 0.492
#> SRR1465375 1 0.7125 0.2770 0.504 0.060 0.000 0.300 0.136
#> SRR1323699 3 0.0290 0.8797 0.000 0.000 0.992 0.000 0.008
#> SRR1431139 3 0.3888 0.7513 0.112 0.000 0.812 0.004 0.072
#> SRR1373964 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1455413 5 0.3759 0.7365 0.092 0.000 0.000 0.092 0.816
#> SRR1437163 1 0.2763 0.8216 0.848 0.000 0.000 0.004 0.148
#> SRR1347343 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1465480 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.3456 0.8017 0.800 0.000 0.000 0.016 0.184
#> SRR1086514 4 0.3442 0.7798 0.000 0.104 0.000 0.836 0.060
#> SRR1430928 1 0.0290 0.8804 0.992 0.000 0.000 0.000 0.008
#> SRR1310939 4 0.1270 0.8417 0.000 0.000 0.052 0.948 0.000
#> SRR1344294 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0510 0.8800 0.984 0.000 0.000 0.000 0.016
#> SRR1468118 5 0.4815 0.6879 0.000 0.000 0.064 0.244 0.692
#> SRR1486348 1 0.0510 0.8793 0.984 0.000 0.000 0.000 0.016
#> SRR1488770 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0162 0.8807 0.996 0.000 0.000 0.000 0.004
#> SRR1456611 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.4201 0.4140 0.592 0.000 0.000 0.000 0.408
#> SRR1500089 4 0.0000 0.8735 0.000 0.000 0.000 1.000 0.000
#> SRR1441178 1 0.3109 0.7827 0.800 0.000 0.000 0.000 0.200
#> SRR1381396 1 0.2690 0.8253 0.844 0.000 0.000 0.000 0.156
#> SRR1096081 5 0.4886 0.7107 0.000 0.000 0.188 0.100 0.712
#> SRR1349809 2 0.3477 0.7840 0.040 0.824 0.000 0.000 0.136
#> SRR1324314 1 0.3521 0.7945 0.820 0.000 0.140 0.000 0.040
#> SRR1092444 5 0.3061 0.7356 0.136 0.000 0.000 0.020 0.844
#> SRR1382553 2 0.3880 0.7162 0.152 0.800 0.004 0.000 0.044
#> SRR1075530 4 0.0162 0.8725 0.000 0.000 0.000 0.996 0.004
#> SRR1442612 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1360056 5 0.4016 0.7323 0.112 0.000 0.092 0.000 0.796
#> SRR1078164 5 0.3480 0.6210 0.248 0.000 0.000 0.000 0.752
#> SRR1434545 4 0.0000 0.8735 0.000 0.000 0.000 1.000 0.000
#> SRR1398251 1 0.4708 0.6637 0.712 0.000 0.220 0.000 0.068
#> SRR1375866 5 0.3242 0.6719 0.216 0.000 0.000 0.000 0.784
#> SRR1091645 4 0.0162 0.8725 0.000 0.000 0.000 0.996 0.004
#> SRR1416636 5 0.3642 0.6837 0.000 0.000 0.232 0.008 0.760
#> SRR1105441 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1082496 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.4138 0.3542 0.000 0.616 0.384 0.000 0.000
#> SRR1093697 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.4660 0.7255 0.000 0.000 0.080 0.192 0.728
#> SRR1076120 4 0.0162 0.8725 0.000 0.000 0.000 0.996 0.004
#> SRR1074410 1 0.2929 0.8029 0.820 0.000 0.000 0.000 0.180
#> SRR1340345 4 0.0404 0.8707 0.000 0.000 0.000 0.988 0.012
#> SRR1069514 3 0.0000 0.8820 0.000 0.000 1.000 0.000 0.000
#> SRR1092636 5 0.3527 0.7183 0.000 0.000 0.172 0.024 0.804
#> SRR1365013 4 0.7481 0.1212 0.048 0.384 0.008 0.408 0.152
#> SRR1073069 1 0.1908 0.8535 0.908 0.000 0.000 0.000 0.092
#> SRR1443137 1 0.1341 0.8701 0.944 0.000 0.000 0.000 0.056
#> SRR1437143 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0880 0.8775 0.968 0.000 0.000 0.000 0.032
#> SRR820234 2 0.0000 0.9367 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.2629 0.8355 0.860 0.000 0.000 0.004 0.136
#> SRR1390094 3 0.1200 0.8706 0.016 0.012 0.964 0.000 0.008
#> SRR1340721 1 0.5778 0.5331 0.620 0.244 0.000 0.004 0.132
#> SRR1335964 3 0.3242 0.6949 0.000 0.000 0.784 0.216 0.000
#> SRR1086869 5 0.5026 0.5329 0.000 0.000 0.040 0.372 0.588
#> SRR1453434 1 0.0703 0.8790 0.976 0.000 0.000 0.000 0.024
#> SRR1402261 4 0.0290 0.8707 0.008 0.000 0.000 0.992 0.000
#> SRR657809 4 0.6058 0.4204 0.004 0.312 0.000 0.556 0.128
#> SRR1093075 1 0.1121 0.8743 0.956 0.000 0.000 0.000 0.044
#> SRR1433329 1 0.1410 0.8685 0.940 0.000 0.000 0.000 0.060
#> SRR1353418 5 0.3491 0.6862 0.004 0.000 0.228 0.000 0.768
#> SRR1092913 4 0.0510 0.8688 0.000 0.000 0.000 0.984 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.3578 0.5803 0.660 0.000 0.000 0.000 0.000 0.340
#> SRR1335605 1 0.2179 0.8005 0.900 0.036 0.000 0.000 0.064 0.000
#> SRR1432014 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1499215 3 0.6834 0.2820 0.192 0.000 0.448 0.000 0.072 0.288
#> SRR1460409 6 0.0632 0.8532 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1086441 6 0.2491 0.7287 0.164 0.000 0.000 0.000 0.000 0.836
#> SRR1097344 4 0.0260 0.9742 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1081789 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1453005 2 0.0146 0.9288 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1366985 6 0.0713 0.8475 0.000 0.000 0.028 0.000 0.000 0.972
#> SRR815280 6 0.0000 0.8594 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1348531 5 0.1080 0.8752 0.032 0.000 0.000 0.004 0.960 0.004
#> SRR815845 3 0.3499 0.5630 0.000 0.000 0.680 0.000 0.320 0.000
#> SRR1471178 6 0.1267 0.8307 0.060 0.000 0.000 0.000 0.000 0.940
#> SRR1080696 3 0.3175 0.6617 0.000 0.000 0.744 0.000 0.256 0.000
#> SRR1078684 3 0.0891 0.8860 0.008 0.000 0.968 0.000 0.000 0.024
#> SRR1317751 5 0.0291 0.8886 0.000 0.000 0.004 0.004 0.992 0.000
#> SRR1435667 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1097905 1 0.1663 0.8479 0.912 0.000 0.000 0.000 0.000 0.088
#> SRR1456548 1 0.1765 0.8539 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1075126 6 0.0146 0.8592 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR813108 3 0.0146 0.8964 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1479062 2 0.5442 0.1175 0.080 0.500 0.000 0.008 0.408 0.004
#> SRR1408703 5 0.0146 0.8882 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1332360 6 0.1219 0.8344 0.048 0.000 0.000 0.000 0.004 0.948
#> SRR1098686 1 0.3464 0.6406 0.688 0.000 0.000 0.000 0.000 0.312
#> SRR1434228 6 0.0146 0.8589 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1467149 5 0.2956 0.7908 0.120 0.000 0.000 0.040 0.840 0.000
#> SRR1399113 2 0.0146 0.9302 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.0000 0.9781 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1092468 4 0.1204 0.9418 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR1441804 1 0.3756 0.6304 0.676 0.000 0.000 0.004 0.004 0.316
#> SRR1326100 3 0.2786 0.8253 0.084 0.056 0.860 0.000 0.000 0.000
#> SRR1398815 1 0.1588 0.8550 0.924 0.000 0.000 0.000 0.004 0.072
#> SRR1436021 3 0.4167 0.4396 0.024 0.000 0.632 0.344 0.000 0.000
#> SRR1480083 2 0.0146 0.9288 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.2003 0.8436 0.884 0.000 0.000 0.000 0.000 0.116
#> SRR815542 6 0.1814 0.7986 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1400100 5 0.0260 0.8877 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1312002 6 0.4127 0.4719 0.004 0.004 0.016 0.000 0.304 0.672
#> SRR1470253 5 0.2558 0.7803 0.156 0.000 0.000 0.000 0.840 0.004
#> SRR1414332 6 0.3446 0.4844 0.308 0.000 0.000 0.000 0.000 0.692
#> SRR1069209 6 0.0000 0.8594 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR661052 1 0.1010 0.8492 0.960 0.000 0.000 0.000 0.004 0.036
#> SRR1308860 1 0.3464 0.6415 0.688 0.000 0.000 0.000 0.000 0.312
#> SRR1421159 3 0.0993 0.8903 0.024 0.000 0.964 0.012 0.000 0.000
#> SRR1340943 4 0.0146 0.9761 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1078855 6 0.0146 0.8589 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1459465 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0363 0.9287 0.012 0.988 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0547 0.8927 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1350979 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1458198 4 0.0000 0.9781 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1386910 1 0.1819 0.8100 0.932 0.004 0.008 0.024 0.032 0.000
#> SRR1465375 1 0.2879 0.7613 0.816 0.004 0.000 0.176 0.000 0.004
#> SRR1323699 3 0.2706 0.7745 0.008 0.000 0.832 0.000 0.000 0.160
#> SRR1431139 3 0.1745 0.8737 0.056 0.000 0.924 0.000 0.000 0.020
#> SRR1373964 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1455413 1 0.2527 0.7323 0.832 0.000 0.000 0.000 0.168 0.000
#> SRR1437163 1 0.1556 0.8553 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1347343 3 0.0260 0.8965 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1465480 2 0.0260 0.9298 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.0458 0.8422 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1086514 4 0.0146 0.9771 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1430928 6 0.3828 0.0781 0.440 0.000 0.000 0.000 0.000 0.560
#> SRR1310939 4 0.0291 0.9755 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR1344294 2 0.0146 0.9288 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1099402 6 0.0260 0.8585 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1468118 5 0.0291 0.8886 0.000 0.000 0.004 0.004 0.992 0.000
#> SRR1486348 6 0.3620 0.3717 0.352 0.000 0.000 0.000 0.000 0.648
#> SRR1488770 2 0.0363 0.9287 0.012 0.988 0.000 0.000 0.000 0.000
#> SRR1083732 6 0.3151 0.5955 0.252 0.000 0.000 0.000 0.000 0.748
#> SRR1456611 2 0.0458 0.9266 0.016 0.984 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.3717 0.5627 0.708 0.000 0.000 0.000 0.276 0.016
#> SRR1500089 4 0.0000 0.9781 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1441178 6 0.2593 0.7379 0.148 0.000 0.000 0.000 0.008 0.844
#> SRR1381396 1 0.2053 0.8421 0.888 0.000 0.000 0.000 0.004 0.108
#> SRR1096081 5 0.0291 0.8886 0.000 0.000 0.004 0.004 0.992 0.000
#> SRR1349809 1 0.1327 0.8234 0.936 0.064 0.000 0.000 0.000 0.000
#> SRR1324314 6 0.2830 0.7328 0.020 0.000 0.144 0.000 0.000 0.836
#> SRR1092444 5 0.3717 0.4354 0.384 0.000 0.000 0.000 0.616 0.000
#> SRR1382553 6 0.4126 0.0265 0.004 0.480 0.004 0.000 0.000 0.512
#> SRR1075530 4 0.0260 0.9755 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1442612 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1360056 5 0.0436 0.8878 0.000 0.000 0.004 0.004 0.988 0.004
#> SRR1078164 5 0.5351 0.4678 0.148 0.000 0.000 0.000 0.572 0.280
#> SRR1434545 4 0.0000 0.9781 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1398251 6 0.0547 0.8518 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR1375866 5 0.3756 0.4983 0.352 0.000 0.000 0.000 0.644 0.004
#> SRR1091645 4 0.0363 0.9712 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR1416636 5 0.0146 0.8865 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1105441 3 0.0790 0.8905 0.032 0.000 0.968 0.000 0.000 0.000
#> SRR1082496 2 0.0260 0.9298 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.3961 0.1677 0.004 0.556 0.440 0.000 0.000 0.000
#> SRR1093697 2 0.0146 0.9302 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0291 0.8886 0.000 0.000 0.004 0.004 0.992 0.000
#> SRR1076120 4 0.0000 0.9781 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1074410 1 0.1895 0.8530 0.912 0.000 0.000 0.000 0.016 0.072
#> SRR1340345 4 0.0363 0.9734 0.012 0.000 0.000 0.988 0.000 0.000
#> SRR1069514 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1092636 5 0.0146 0.8883 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1365013 1 0.0405 0.8310 0.988 0.004 0.008 0.000 0.000 0.000
#> SRR1073069 6 0.0146 0.8593 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1443137 6 0.0000 0.8594 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1437143 2 0.0363 0.9287 0.012 0.988 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.0363 0.8575 0.012 0.000 0.000 0.000 0.000 0.988
#> SRR820234 2 0.0146 0.9288 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1338079 1 0.1501 0.8581 0.924 0.000 0.000 0.000 0.000 0.076
#> SRR1390094 3 0.0291 0.8966 0.000 0.004 0.992 0.000 0.000 0.004
#> SRR1340721 1 0.4023 0.7491 0.756 0.100 0.000 0.000 0.000 0.144
#> SRR1335964 3 0.1092 0.8891 0.020 0.000 0.960 0.020 0.000 0.000
#> SRR1086869 5 0.2053 0.8119 0.000 0.000 0.004 0.108 0.888 0.000
#> SRR1453434 6 0.0260 0.8574 0.000 0.000 0.000 0.008 0.000 0.992
#> SRR1402261 4 0.0000 0.9781 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR657809 4 0.4026 0.7337 0.088 0.160 0.000 0.752 0.000 0.000
#> SRR1093075 6 0.0000 0.8594 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1433329 6 0.0000 0.8594 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1353418 5 0.0260 0.8877 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1092913 4 0.0000 0.9781 0.000 0.000 0.000 1.000 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", "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 17780 rows and 119 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 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 0.417 0.783 0.888 0.4195 0.581 0.581
#> 3 3 0.414 0.572 0.664 0.4329 0.919 0.867
#> 4 4 0.492 0.631 0.780 0.1620 0.713 0.498
#> 5 5 0.630 0.555 0.751 0.0690 0.955 0.848
#> 6 6 0.659 0.569 0.749 0.0382 0.955 0.829
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
#> SRR816969 1 0.0672 0.8714 0.992 0.008
#> SRR1335605 2 0.7528 0.7752 0.216 0.784
#> SRR1432014 1 0.9248 0.5473 0.660 0.340
#> SRR1499215 1 0.8955 0.5977 0.688 0.312
#> SRR1460409 1 0.0376 0.8687 0.996 0.004
#> SRR1086441 1 0.0672 0.8714 0.992 0.008
#> SRR1097344 2 0.6887 0.8117 0.184 0.816
#> SRR1081789 2 0.4690 0.8483 0.100 0.900
#> SRR1453005 2 0.0376 0.8565 0.004 0.996
#> SRR1366985 1 0.0000 0.8699 1.000 0.000
#> SRR815280 1 0.0376 0.8687 0.996 0.004
#> SRR1348531 1 0.2778 0.8633 0.952 0.048
#> SRR815845 2 0.9944 0.1494 0.456 0.544
#> SRR1471178 1 0.0672 0.8714 0.992 0.008
#> SRR1080696 1 0.4298 0.8505 0.912 0.088
#> SRR1078684 1 0.9129 0.5735 0.672 0.328
#> SRR1317751 1 0.3114 0.8655 0.944 0.056
#> SRR1435667 1 0.9286 0.5387 0.656 0.344
#> SRR1097905 1 0.5519 0.8274 0.872 0.128
#> SRR1456548 1 0.5408 0.8299 0.876 0.124
#> SRR1075126 1 0.2423 0.8634 0.960 0.040
#> SRR813108 2 0.0376 0.8565 0.004 0.996
#> SRR1479062 1 0.5408 0.8286 0.876 0.124
#> SRR1408703 1 0.4431 0.8483 0.908 0.092
#> SRR1332360 1 0.0000 0.8699 1.000 0.000
#> SRR1098686 1 0.0938 0.8718 0.988 0.012
#> SRR1434228 1 0.0000 0.8699 1.000 0.000
#> SRR1467149 1 0.3274 0.8622 0.940 0.060
#> SRR1399113 2 0.0376 0.8565 0.004 0.996
#> SRR1476507 2 0.7376 0.7888 0.208 0.792
#> SRR1092468 1 0.4562 0.8485 0.904 0.096
#> SRR1441804 1 0.2778 0.8633 0.952 0.048
#> SRR1326100 2 0.1414 0.8572 0.020 0.980
#> SRR1398815 1 0.0672 0.8714 0.992 0.008
#> SRR1436021 2 0.7528 0.7782 0.216 0.784
#> SRR1480083 2 0.0376 0.8565 0.004 0.996
#> SRR1472863 1 0.5408 0.8299 0.876 0.124
#> SRR815542 1 0.0376 0.8687 0.996 0.004
#> SRR1400100 1 0.9833 0.3287 0.576 0.424
#> SRR1312002 1 0.2043 0.8710 0.968 0.032
#> SRR1470253 1 0.1414 0.8720 0.980 0.020
#> SRR1414332 1 0.0672 0.8714 0.992 0.008
#> SRR1069209 1 0.0000 0.8699 1.000 0.000
#> SRR661052 1 0.5408 0.8299 0.876 0.124
#> SRR1308860 1 0.0672 0.8714 0.992 0.008
#> SRR1421159 2 0.6887 0.8130 0.184 0.816
#> SRR1340943 1 0.9661 0.3696 0.608 0.392
#> SRR1078855 1 0.0376 0.8687 0.996 0.004
#> SRR1459465 2 0.0376 0.8565 0.004 0.996
#> SRR816818 2 0.0376 0.8565 0.004 0.996
#> SRR1478679 1 0.9000 0.5914 0.684 0.316
#> SRR1350979 1 0.8386 0.6701 0.732 0.268
#> SRR1458198 1 0.0672 0.8714 0.992 0.008
#> SRR1386910 2 0.7528 0.7752 0.216 0.784
#> SRR1465375 2 0.7376 0.7888 0.208 0.792
#> SRR1323699 1 0.8955 0.5977 0.688 0.312
#> SRR1431139 1 0.9129 0.5735 0.672 0.328
#> SRR1373964 1 0.9323 0.5301 0.652 0.348
#> SRR1455413 1 0.3114 0.8657 0.944 0.056
#> SRR1437163 1 0.5294 0.8325 0.880 0.120
#> SRR1347343 1 0.9286 0.5387 0.656 0.344
#> SRR1465480 2 0.0376 0.8565 0.004 0.996
#> SRR1489631 1 0.5408 0.8299 0.876 0.124
#> SRR1086514 2 0.6887 0.8130 0.184 0.816
#> SRR1430928 1 0.0672 0.8714 0.992 0.008
#> SRR1310939 1 0.7299 0.7532 0.796 0.204
#> SRR1344294 2 0.0376 0.8565 0.004 0.996
#> SRR1099402 1 0.0376 0.8687 0.996 0.004
#> SRR1468118 1 0.2603 0.8676 0.956 0.044
#> SRR1486348 1 0.0672 0.8714 0.992 0.008
#> SRR1488770 2 0.0376 0.8565 0.004 0.996
#> SRR1083732 1 0.0672 0.8714 0.992 0.008
#> SRR1456611 2 0.0376 0.8565 0.004 0.996
#> SRR1080318 1 0.0376 0.8687 0.996 0.004
#> SRR1500089 1 0.0672 0.8714 0.992 0.008
#> SRR1441178 1 0.0376 0.8687 0.996 0.004
#> SRR1381396 1 0.0376 0.8687 0.996 0.004
#> SRR1096081 1 0.3114 0.8655 0.944 0.056
#> SRR1349809 2 0.7528 0.7752 0.216 0.784
#> SRR1324314 1 0.5629 0.8254 0.868 0.132
#> SRR1092444 1 0.0376 0.8687 0.996 0.004
#> SRR1382553 1 0.8443 0.6619 0.728 0.272
#> SRR1075530 2 0.5629 0.8423 0.132 0.868
#> SRR1442612 1 0.9286 0.5387 0.656 0.344
#> SRR1360056 1 0.1843 0.8714 0.972 0.028
#> SRR1078164 1 0.0376 0.8687 0.996 0.004
#> SRR1434545 1 0.9661 0.3696 0.608 0.392
#> SRR1398251 1 0.0376 0.8687 0.996 0.004
#> SRR1375866 1 0.0376 0.8687 0.996 0.004
#> SRR1091645 2 0.6887 0.8117 0.184 0.816
#> SRR1416636 1 0.4298 0.8505 0.912 0.088
#> SRR1105441 1 0.9635 0.4381 0.612 0.388
#> SRR1082496 2 0.0376 0.8565 0.004 0.996
#> SRR1315353 2 0.1184 0.8554 0.016 0.984
#> SRR1093697 2 0.0376 0.8565 0.004 0.996
#> SRR1077429 1 0.3274 0.8641 0.940 0.060
#> SRR1076120 1 0.0672 0.8714 0.992 0.008
#> SRR1074410 1 0.0376 0.8687 0.996 0.004
#> SRR1340345 2 0.5629 0.8423 0.132 0.868
#> SRR1069514 2 0.9988 0.0261 0.480 0.520
#> SRR1092636 1 0.2948 0.8663 0.948 0.052
#> SRR1365013 2 0.4690 0.8483 0.100 0.900
#> SRR1073069 1 0.0000 0.8699 1.000 0.000
#> SRR1443137 1 0.0376 0.8687 0.996 0.004
#> SRR1437143 2 0.0376 0.8565 0.004 0.996
#> SRR1091990 1 0.0376 0.8687 0.996 0.004
#> SRR820234 2 0.0376 0.8565 0.004 0.996
#> SRR1338079 1 0.5294 0.8325 0.880 0.120
#> SRR1390094 1 0.9993 0.0720 0.516 0.484
#> SRR1340721 2 0.7745 0.7623 0.228 0.772
#> SRR1335964 1 0.7453 0.7428 0.788 0.212
#> SRR1086869 1 0.3114 0.8655 0.944 0.056
#> SRR1453434 1 0.6343 0.7639 0.840 0.160
#> SRR1402261 1 0.9522 0.4196 0.628 0.372
#> SRR657809 2 0.6887 0.8108 0.184 0.816
#> SRR1093075 1 0.0376 0.8687 0.996 0.004
#> SRR1433329 1 0.0376 0.8687 0.996 0.004
#> SRR1353418 1 0.1843 0.8697 0.972 0.028
#> SRR1092913 2 0.5629 0.8423 0.132 0.868
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.599 0.6974 0.632 0.000 NA
#> SRR1335605 2 0.711 0.6460 0.100 0.716 NA
#> SRR1432014 1 0.859 0.1546 0.560 0.320 NA
#> SRR1499215 1 0.840 0.2052 0.588 0.296 NA
#> SRR1460409 1 0.604 0.6944 0.620 0.000 NA
#> SRR1086441 1 0.597 0.6980 0.636 0.000 NA
#> SRR1097344 2 0.583 0.6595 0.076 0.796 NA
#> SRR1081789 2 0.798 0.6834 0.076 0.584 NA
#> SRR1453005 2 0.620 0.7184 0.000 0.576 NA
#> SRR1366985 1 0.632 0.6999 0.636 0.008 NA
#> SRR815280 1 0.608 0.6917 0.612 0.000 NA
#> SRR1348531 1 0.749 0.6782 0.668 0.084 NA
#> SRR815845 2 0.929 0.3144 0.372 0.464 NA
#> SRR1471178 1 0.595 0.6988 0.640 0.000 NA
#> SRR1080696 1 0.312 0.5737 0.908 0.080 NA
#> SRR1078684 1 0.813 0.2188 0.600 0.304 NA
#> SRR1317751 1 0.257 0.6034 0.936 0.032 NA
#> SRR1435667 1 0.865 0.1476 0.556 0.320 NA
#> SRR1097905 1 0.925 0.6231 0.516 0.188 NA
#> SRR1456548 1 0.923 0.6333 0.508 0.176 NA
#> SRR1075126 1 0.806 0.6770 0.604 0.092 NA
#> SRR813108 2 0.739 0.7031 0.032 0.496 NA
#> SRR1479062 1 0.445 0.5394 0.860 0.100 NA
#> SRR1408703 1 0.321 0.5707 0.904 0.084 NA
#> SRR1332360 1 0.632 0.6999 0.636 0.008 NA
#> SRR1098686 1 0.608 0.7008 0.652 0.004 NA
#> SRR1434228 1 0.632 0.6999 0.636 0.008 NA
#> SRR1467149 1 0.744 0.6612 0.692 0.108 NA
#> SRR1399113 2 0.627 0.7141 0.000 0.544 NA
#> SRR1476507 2 0.423 0.6513 0.084 0.872 NA
#> SRR1092468 1 0.851 0.6435 0.604 0.152 NA
#> SRR1441804 1 0.749 0.6782 0.668 0.084 NA
#> SRR1326100 2 0.766 0.7064 0.044 0.504 NA
#> SRR1398815 1 0.599 0.6974 0.632 0.000 NA
#> SRR1436021 2 0.406 0.6443 0.092 0.876 NA
#> SRR1480083 2 0.627 0.7141 0.000 0.544 NA
#> SRR1472863 1 0.923 0.6333 0.508 0.176 NA
#> SRR815542 1 0.599 0.6981 0.632 0.000 NA
#> SRR1400100 1 0.857 0.0321 0.508 0.392 NA
#> SRR1312002 1 0.337 0.6146 0.908 0.052 NA
#> SRR1470253 1 0.380 0.6403 0.888 0.032 NA
#> SRR1414332 1 0.599 0.6974 0.632 0.000 NA
#> SRR1069209 1 0.632 0.6999 0.636 0.008 NA
#> SRR661052 1 0.921 0.6324 0.512 0.176 NA
#> SRR1308860 1 0.593 0.6996 0.644 0.000 NA
#> SRR1421159 2 0.350 0.6669 0.072 0.900 NA
#> SRR1340943 2 0.966 -0.0852 0.220 0.440 NA
#> SRR1078855 1 0.608 0.6917 0.612 0.000 NA
#> SRR1459465 2 0.627 0.7141 0.000 0.544 NA
#> SRR816818 2 0.627 0.7141 0.000 0.544 NA
#> SRR1478679 1 0.842 0.1992 0.584 0.300 NA
#> SRR1350979 1 0.752 0.3031 0.660 0.260 NA
#> SRR1458198 1 0.742 0.6728 0.680 0.088 NA
#> SRR1386910 2 0.711 0.6460 0.100 0.716 NA
#> SRR1465375 2 0.401 0.6475 0.084 0.880 NA
#> SRR1323699 1 0.840 0.2052 0.588 0.296 NA
#> SRR1431139 1 0.820 0.2222 0.596 0.304 NA
#> SRR1373964 1 0.867 0.1423 0.552 0.324 NA
#> SRR1455413 1 0.268 0.5866 0.924 0.068 NA
#> SRR1437163 1 0.919 0.6356 0.512 0.172 NA
#> SRR1347343 1 0.865 0.1476 0.556 0.320 NA
#> SRR1465480 2 0.627 0.7141 0.000 0.544 NA
#> SRR1489631 1 0.923 0.6333 0.508 0.176 NA
#> SRR1086514 2 0.350 0.6669 0.072 0.900 NA
#> SRR1430928 1 0.597 0.6980 0.636 0.000 NA
#> SRR1310939 1 0.620 0.4235 0.748 0.208 NA
#> SRR1344294 2 0.627 0.7141 0.000 0.544 NA
#> SRR1099402 1 0.601 0.6972 0.628 0.000 NA
#> SRR1468118 1 0.178 0.6048 0.960 0.020 NA
#> SRR1486348 1 0.599 0.6974 0.632 0.000 NA
#> SRR1488770 2 0.627 0.7141 0.000 0.544 NA
#> SRR1083732 1 0.593 0.6995 0.644 0.000 NA
#> SRR1456611 2 0.627 0.7141 0.000 0.544 NA
#> SRR1080318 1 0.601 0.6967 0.628 0.000 NA
#> SRR1500089 1 0.742 0.6728 0.680 0.088 NA
#> SRR1441178 1 0.608 0.6917 0.612 0.000 NA
#> SRR1381396 1 0.606 0.6929 0.616 0.000 NA
#> SRR1096081 1 0.257 0.6034 0.936 0.032 NA
#> SRR1349809 2 0.721 0.6486 0.100 0.708 NA
#> SRR1324314 1 0.869 0.6242 0.584 0.156 NA
#> SRR1092444 1 0.601 0.6967 0.628 0.000 NA
#> SRR1382553 1 0.811 0.2847 0.628 0.256 NA
#> SRR1075530 2 0.338 0.6905 0.044 0.908 NA
#> SRR1442612 1 0.865 0.1476 0.556 0.320 NA
#> SRR1360056 1 0.266 0.6266 0.932 0.024 NA
#> SRR1078164 1 0.608 0.6917 0.612 0.000 NA
#> SRR1434545 2 0.966 -0.0852 0.220 0.440 NA
#> SRR1398251 1 0.608 0.6917 0.612 0.000 NA
#> SRR1375866 1 0.606 0.6929 0.616 0.000 NA
#> SRR1091645 2 0.583 0.6595 0.076 0.796 NA
#> SRR1416636 1 0.312 0.5737 0.908 0.080 NA
#> SRR1105441 1 0.863 0.0947 0.532 0.356 NA
#> SRR1082496 2 0.627 0.7141 0.000 0.544 NA
#> SRR1315353 2 0.671 0.7181 0.012 0.572 NA
#> SRR1093697 2 0.627 0.7141 0.000 0.544 NA
#> SRR1077429 1 0.238 0.5914 0.936 0.056 NA
#> SRR1076120 1 0.742 0.6728 0.680 0.088 NA
#> SRR1074410 1 0.606 0.6929 0.616 0.000 NA
#> SRR1340345 2 0.338 0.6905 0.044 0.908 NA
#> SRR1069514 1 0.979 -0.2764 0.388 0.376 NA
#> SRR1092636 1 0.199 0.5980 0.948 0.048 NA
#> SRR1365013 2 0.792 0.6819 0.076 0.596 NA
#> SRR1073069 1 0.632 0.6999 0.636 0.008 NA
#> SRR1443137 1 0.608 0.6917 0.612 0.000 NA
#> SRR1437143 2 0.627 0.7141 0.000 0.544 NA
#> SRR1091990 1 0.608 0.6917 0.612 0.000 NA
#> SRR820234 2 0.625 0.7151 0.000 0.556 NA
#> SRR1338079 1 0.919 0.6356 0.512 0.172 NA
#> SRR1390094 2 0.947 0.1858 0.308 0.484 NA
#> SRR1340721 2 0.741 0.6431 0.112 0.696 NA
#> SRR1335964 1 0.648 0.3937 0.728 0.224 NA
#> SRR1086869 1 0.257 0.6034 0.936 0.032 NA
#> SRR1453434 1 0.971 0.5280 0.420 0.224 NA
#> SRR1402261 2 0.974 -0.1266 0.236 0.428 NA
#> SRR657809 2 0.400 0.6644 0.060 0.884 NA
#> SRR1093075 1 0.608 0.6917 0.612 0.000 NA
#> SRR1433329 1 0.608 0.6917 0.612 0.000 NA
#> SRR1353418 1 0.127 0.6211 0.972 0.004 NA
#> SRR1092913 2 0.338 0.6905 0.044 0.908 NA
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0657 0.839 0.984 0.000 0.004 0.012
#> SRR1335605 2 0.7511 0.132 0.008 0.492 0.348 0.152
#> SRR1432014 3 0.2142 0.651 0.000 0.016 0.928 0.056
#> SRR1499215 3 0.2797 0.667 0.028 0.016 0.912 0.044
#> SRR1460409 1 0.0657 0.839 0.984 0.000 0.004 0.012
#> SRR1086441 1 0.0804 0.839 0.980 0.000 0.008 0.012
#> SRR1097344 4 0.4988 0.560 0.000 0.236 0.036 0.728
#> SRR1081789 2 0.7598 0.208 0.000 0.460 0.324 0.216
#> SRR1453005 2 0.4919 0.536 0.000 0.752 0.048 0.200
#> SRR1366985 1 0.1940 0.815 0.924 0.000 0.076 0.000
#> SRR815280 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1348531 1 0.6112 0.612 0.676 0.000 0.196 0.128
#> SRR815845 3 0.6339 0.269 0.008 0.252 0.652 0.088
#> SRR1471178 1 0.0937 0.839 0.976 0.000 0.012 0.012
#> SRR1080696 3 0.6163 0.683 0.164 0.000 0.676 0.160
#> SRR1078684 3 0.2977 0.667 0.020 0.024 0.904 0.052
#> SRR1317751 3 0.7122 0.622 0.192 0.000 0.560 0.248
#> SRR1435667 3 0.2060 0.649 0.000 0.016 0.932 0.052
#> SRR1097905 1 0.6274 0.619 0.664 0.000 0.184 0.152
#> SRR1456548 1 0.5613 0.680 0.724 0.000 0.156 0.120
#> SRR1075126 1 0.5470 0.696 0.736 0.000 0.116 0.148
#> SRR813108 2 0.5705 0.530 0.000 0.712 0.108 0.180
#> SRR1479062 3 0.6309 0.686 0.156 0.008 0.684 0.152
#> SRR1408703 3 0.6119 0.684 0.168 0.000 0.680 0.152
#> SRR1332360 1 0.1940 0.815 0.924 0.000 0.076 0.000
#> SRR1098686 1 0.2131 0.827 0.932 0.000 0.032 0.036
#> SRR1434228 1 0.1940 0.815 0.924 0.000 0.076 0.000
#> SRR1467149 1 0.7399 0.303 0.512 0.000 0.280 0.208
#> SRR1399113 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.6594 0.641 0.000 0.228 0.148 0.624
#> SRR1092468 1 0.7086 0.391 0.548 0.000 0.292 0.160
#> SRR1441804 1 0.6112 0.612 0.676 0.000 0.196 0.128
#> SRR1326100 2 0.5700 0.544 0.000 0.716 0.120 0.164
#> SRR1398815 1 0.0657 0.839 0.984 0.000 0.004 0.012
#> SRR1436021 4 0.7093 0.606 0.000 0.216 0.216 0.568
#> SRR1480083 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.5560 0.683 0.728 0.000 0.156 0.116
#> SRR815542 1 0.1059 0.840 0.972 0.000 0.016 0.012
#> SRR1400100 3 0.4179 0.560 0.004 0.104 0.832 0.060
#> SRR1312002 3 0.6494 0.504 0.340 0.000 0.572 0.088
#> SRR1470253 3 0.7103 0.324 0.404 0.000 0.468 0.128
#> SRR1414332 1 0.0657 0.839 0.984 0.000 0.004 0.012
#> SRR1069209 1 0.1940 0.815 0.924 0.000 0.076 0.000
#> SRR661052 1 0.5604 0.679 0.724 0.000 0.160 0.116
#> SRR1308860 1 0.1059 0.839 0.972 0.000 0.016 0.012
#> SRR1421159 4 0.6756 0.630 0.000 0.252 0.148 0.600
#> SRR1340943 4 0.6273 0.396 0.264 0.000 0.100 0.636
#> SRR1078855 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1459465 2 0.0188 0.706 0.000 0.996 0.004 0.000
#> SRR816818 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.2694 0.666 0.024 0.016 0.916 0.044
#> SRR1350979 3 0.2675 0.689 0.048 0.000 0.908 0.044
#> SRR1458198 1 0.7437 0.314 0.512 0.000 0.240 0.248
#> SRR1386910 2 0.7511 0.132 0.008 0.492 0.348 0.152
#> SRR1465375 4 0.6656 0.640 0.000 0.220 0.160 0.620
#> SRR1323699 3 0.2797 0.667 0.028 0.016 0.912 0.044
#> SRR1431139 3 0.3187 0.667 0.028 0.024 0.896 0.052
#> SRR1373964 3 0.2174 0.646 0.000 0.020 0.928 0.052
#> SRR1455413 3 0.6465 0.658 0.228 0.000 0.636 0.136
#> SRR1437163 1 0.5515 0.687 0.732 0.000 0.152 0.116
#> SRR1347343 3 0.2060 0.649 0.000 0.016 0.932 0.052
#> SRR1465480 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.5613 0.680 0.724 0.000 0.156 0.120
#> SRR1086514 4 0.6756 0.630 0.000 0.252 0.148 0.600
#> SRR1430928 1 0.0804 0.839 0.980 0.000 0.008 0.012
#> SRR1310939 3 0.5156 0.689 0.096 0.008 0.776 0.120
#> SRR1344294 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.1182 0.840 0.968 0.000 0.016 0.016
#> SRR1468118 3 0.7007 0.637 0.208 0.000 0.580 0.212
#> SRR1486348 1 0.0657 0.839 0.984 0.000 0.004 0.012
#> SRR1488770 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.1059 0.839 0.972 0.000 0.016 0.012
#> SRR1456611 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.1406 0.835 0.960 0.000 0.016 0.024
#> SRR1500089 1 0.7437 0.314 0.512 0.000 0.240 0.248
#> SRR1441178 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1381396 1 0.0469 0.839 0.988 0.000 0.000 0.012
#> SRR1096081 3 0.7122 0.622 0.192 0.000 0.560 0.248
#> SRR1349809 2 0.7479 0.142 0.008 0.504 0.336 0.152
#> SRR1324314 1 0.6489 0.365 0.548 0.000 0.372 0.080
#> SRR1092444 1 0.1406 0.835 0.960 0.000 0.016 0.024
#> SRR1382553 3 0.3909 0.668 0.088 0.016 0.856 0.040
#> SRR1075530 4 0.6859 0.507 0.000 0.380 0.108 0.512
#> SRR1442612 3 0.2060 0.649 0.000 0.016 0.932 0.052
#> SRR1360056 3 0.6732 0.516 0.336 0.000 0.556 0.108
#> SRR1078164 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1434545 4 0.6273 0.396 0.264 0.000 0.100 0.636
#> SRR1398251 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1375866 1 0.0469 0.839 0.988 0.000 0.000 0.012
#> SRR1091645 4 0.4988 0.560 0.000 0.236 0.036 0.728
#> SRR1416636 3 0.6163 0.683 0.164 0.000 0.676 0.160
#> SRR1105441 3 0.3497 0.605 0.008 0.060 0.876 0.056
#> SRR1082496 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.5328 0.519 0.000 0.724 0.064 0.212
#> SRR1093697 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.6626 0.663 0.216 0.000 0.624 0.160
#> SRR1076120 1 0.7437 0.314 0.512 0.000 0.240 0.248
#> SRR1074410 1 0.0469 0.839 0.988 0.000 0.000 0.012
#> SRR1340345 4 0.6859 0.507 0.000 0.380 0.108 0.512
#> SRR1069514 3 0.5292 0.422 0.000 0.168 0.744 0.088
#> SRR1092636 3 0.6616 0.657 0.220 0.000 0.624 0.156
#> SRR1365013 2 0.7426 0.243 0.000 0.488 0.324 0.188
#> SRR1073069 1 0.1940 0.815 0.924 0.000 0.076 0.000
#> SRR1443137 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1437143 2 0.0000 0.708 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR820234 2 0.4375 0.567 0.000 0.788 0.032 0.180
#> SRR1338079 1 0.5515 0.687 0.732 0.000 0.152 0.116
#> SRR1390094 3 0.7546 0.135 0.100 0.048 0.580 0.272
#> SRR1340721 2 0.7733 0.142 0.020 0.504 0.328 0.148
#> SRR1335964 3 0.4144 0.695 0.104 0.000 0.828 0.068
#> SRR1086869 3 0.7122 0.622 0.192 0.000 0.560 0.248
#> SRR1453434 1 0.6253 0.419 0.564 0.000 0.064 0.372
#> SRR1402261 4 0.6393 0.363 0.284 0.000 0.100 0.616
#> SRR657809 4 0.7593 0.467 0.000 0.300 0.228 0.472
#> SRR1093075 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1433329 1 0.0592 0.838 0.984 0.000 0.000 0.016
#> SRR1353418 3 0.6813 0.585 0.292 0.000 0.576 0.132
#> SRR1092913 4 0.6766 0.511 0.000 0.380 0.100 0.520
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0794 0.8374 0.972 0.000 0.000 0.000 0.028
#> SRR1335605 2 0.7677 0.2047 0.008 0.420 0.368 0.092 0.112
#> SRR1432014 3 0.0451 0.6189 0.000 0.000 0.988 0.008 0.004
#> SRR1499215 3 0.0968 0.6190 0.012 0.000 0.972 0.004 0.012
#> SRR1460409 1 0.0566 0.8373 0.984 0.000 0.004 0.000 0.012
#> SRR1086441 1 0.0865 0.8374 0.972 0.000 0.004 0.000 0.024
#> SRR1097344 4 0.3003 0.6659 0.000 0.040 0.016 0.880 0.064
#> SRR1081789 2 0.7057 0.1777 0.000 0.392 0.340 0.256 0.012
#> SRR1453005 2 0.5434 0.3732 0.000 0.580 0.060 0.356 0.004
#> SRR1366985 1 0.2069 0.8048 0.912 0.000 0.076 0.000 0.012
#> SRR815280 1 0.0290 0.8363 0.992 0.000 0.000 0.000 0.008
#> SRR1348531 1 0.6479 0.3507 0.572 0.000 0.152 0.024 0.252
#> SRR815845 3 0.6798 0.3685 0.008 0.180 0.616 0.072 0.124
#> SRR1471178 1 0.1026 0.8372 0.968 0.000 0.004 0.004 0.024
#> SRR1080696 3 0.5111 0.2464 0.024 0.000 0.588 0.012 0.376
#> SRR1078684 3 0.2745 0.6132 0.004 0.004 0.892 0.036 0.064
#> SRR1317751 5 0.4599 0.2752 0.000 0.000 0.356 0.020 0.624
#> SRR1435667 3 0.0290 0.6180 0.000 0.000 0.992 0.008 0.000
#> SRR1097905 1 0.6424 0.5048 0.612 0.000 0.200 0.040 0.148
#> SRR1456548 1 0.5730 0.6150 0.684 0.000 0.180 0.040 0.096
#> SRR1075126 1 0.5952 0.5368 0.656 0.000 0.120 0.032 0.192
#> SRR813108 2 0.5548 0.4856 0.000 0.652 0.124 0.220 0.004
#> SRR1479062 3 0.5561 0.3162 0.036 0.000 0.612 0.032 0.320
#> SRR1408703 3 0.5166 0.2559 0.028 0.000 0.592 0.012 0.368
#> SRR1332360 1 0.2069 0.8048 0.912 0.000 0.076 0.000 0.012
#> SRR1098686 1 0.2395 0.8087 0.904 0.000 0.016 0.008 0.072
#> SRR1434228 1 0.2069 0.8048 0.912 0.000 0.076 0.000 0.012
#> SRR1467149 5 0.7319 0.3098 0.340 0.000 0.208 0.036 0.416
#> SRR1399113 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.4200 0.7261 0.000 0.072 0.128 0.792 0.008
#> SRR1092468 1 0.7402 -0.0529 0.436 0.000 0.244 0.040 0.280
#> SRR1441804 1 0.6479 0.3507 0.572 0.000 0.152 0.024 0.252
#> SRR1326100 2 0.5691 0.4951 0.000 0.648 0.132 0.212 0.008
#> SRR1398815 1 0.0794 0.8374 0.972 0.000 0.000 0.000 0.028
#> SRR1436021 4 0.4824 0.6880 0.000 0.076 0.200 0.720 0.004
#> SRR1480083 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.5505 0.6295 0.700 0.000 0.180 0.036 0.084
#> SRR815542 1 0.1074 0.8371 0.968 0.000 0.012 0.004 0.016
#> SRR1400100 3 0.4493 0.5400 0.004 0.096 0.800 0.040 0.060
#> SRR1312002 3 0.6479 0.0719 0.196 0.000 0.512 0.004 0.288
#> SRR1470253 3 0.6734 -0.1603 0.268 0.000 0.408 0.000 0.324
#> SRR1414332 1 0.0794 0.8374 0.972 0.000 0.000 0.000 0.028
#> SRR1069209 1 0.2069 0.8048 0.912 0.000 0.076 0.000 0.012
#> SRR661052 1 0.5539 0.6251 0.696 0.000 0.184 0.036 0.084
#> SRR1308860 1 0.1243 0.8360 0.960 0.000 0.008 0.004 0.028
#> SRR1421159 4 0.4462 0.7189 0.000 0.100 0.128 0.768 0.004
#> SRR1340943 4 0.6668 0.4304 0.076 0.000 0.064 0.532 0.328
#> SRR1078855 1 0.0404 0.8372 0.988 0.000 0.000 0.000 0.012
#> SRR1459465 2 0.0162 0.6922 0.000 0.996 0.004 0.000 0.000
#> SRR816818 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.0854 0.6193 0.008 0.000 0.976 0.004 0.012
#> SRR1350979 3 0.3344 0.5896 0.012 0.000 0.848 0.028 0.112
#> SRR1458198 5 0.7705 0.4847 0.240 0.000 0.144 0.132 0.484
#> SRR1386910 2 0.7677 0.2047 0.008 0.420 0.368 0.092 0.112
#> SRR1465375 4 0.4331 0.7244 0.000 0.072 0.140 0.780 0.008
#> SRR1323699 3 0.0968 0.6190 0.012 0.000 0.972 0.004 0.012
#> SRR1431139 3 0.2981 0.6119 0.012 0.004 0.884 0.036 0.064
#> SRR1373964 3 0.0510 0.6150 0.000 0.000 0.984 0.016 0.000
#> SRR1455413 3 0.5739 0.0711 0.064 0.000 0.504 0.008 0.424
#> SRR1437163 1 0.5471 0.6343 0.704 0.000 0.176 0.036 0.084
#> SRR1347343 3 0.0290 0.6180 0.000 0.000 0.992 0.008 0.000
#> SRR1465480 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.5608 0.6231 0.692 0.000 0.180 0.036 0.092
#> SRR1086514 4 0.4462 0.7189 0.000 0.100 0.128 0.768 0.004
#> SRR1430928 1 0.1026 0.8369 0.968 0.000 0.004 0.004 0.024
#> SRR1310939 3 0.5205 0.4789 0.028 0.000 0.708 0.060 0.204
#> SRR1344294 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0981 0.8384 0.972 0.000 0.008 0.008 0.012
#> SRR1468118 5 0.5133 0.2176 0.020 0.000 0.380 0.016 0.584
#> SRR1486348 1 0.0794 0.8374 0.972 0.000 0.000 0.000 0.028
#> SRR1488770 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.1280 0.8358 0.960 0.000 0.008 0.008 0.024
#> SRR1456611 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.1106 0.8297 0.964 0.000 0.012 0.000 0.024
#> SRR1500089 5 0.7705 0.4847 0.240 0.000 0.144 0.132 0.484
#> SRR1441178 1 0.0290 0.8363 0.992 0.000 0.000 0.000 0.008
#> SRR1381396 1 0.0162 0.8369 0.996 0.000 0.000 0.000 0.004
#> SRR1096081 5 0.4599 0.2752 0.000 0.000 0.356 0.020 0.624
#> SRR1349809 2 0.7665 0.2133 0.008 0.432 0.356 0.092 0.112
#> SRR1324314 1 0.6660 0.1798 0.496 0.000 0.348 0.024 0.132
#> SRR1092444 1 0.1106 0.8297 0.964 0.000 0.012 0.000 0.024
#> SRR1382553 3 0.2166 0.5778 0.072 0.000 0.912 0.004 0.012
#> SRR1075530 4 0.4670 0.6466 0.000 0.200 0.076 0.724 0.000
#> SRR1442612 3 0.0290 0.6180 0.000 0.000 0.992 0.008 0.000
#> SRR1360056 3 0.6630 -0.0321 0.176 0.000 0.468 0.008 0.348
#> SRR1078164 1 0.0290 0.8363 0.992 0.000 0.000 0.000 0.008
#> SRR1434545 4 0.6668 0.4304 0.076 0.000 0.064 0.532 0.328
#> SRR1398251 1 0.0404 0.8372 0.988 0.000 0.000 0.000 0.012
#> SRR1375866 1 0.0162 0.8369 0.996 0.000 0.000 0.000 0.004
#> SRR1091645 4 0.3003 0.6659 0.000 0.040 0.016 0.880 0.064
#> SRR1416636 3 0.5111 0.2464 0.024 0.000 0.588 0.012 0.376
#> SRR1105441 3 0.3102 0.5875 0.004 0.040 0.884 0.036 0.036
#> SRR1082496 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.5753 0.3514 0.000 0.552 0.084 0.360 0.004
#> SRR1093697 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.5541 0.0471 0.056 0.000 0.496 0.004 0.444
#> SRR1076120 5 0.7705 0.4847 0.240 0.000 0.144 0.132 0.484
#> SRR1074410 1 0.0162 0.8369 0.996 0.000 0.000 0.000 0.004
#> SRR1340345 4 0.4670 0.6466 0.000 0.200 0.076 0.724 0.000
#> SRR1069514 3 0.3950 0.4662 0.000 0.136 0.796 0.068 0.000
#> SRR1092636 3 0.5633 0.0635 0.064 0.000 0.504 0.004 0.428
#> SRR1365013 2 0.6967 0.2143 0.000 0.420 0.340 0.228 0.012
#> SRR1073069 1 0.2069 0.8048 0.912 0.000 0.076 0.000 0.012
#> SRR1443137 1 0.0404 0.8372 0.988 0.000 0.000 0.000 0.012
#> SRR1437143 2 0.0000 0.6938 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0290 0.8363 0.992 0.000 0.000 0.000 0.008
#> SRR820234 2 0.4743 0.4974 0.000 0.700 0.048 0.248 0.004
#> SRR1338079 1 0.5471 0.6343 0.704 0.000 0.176 0.036 0.084
#> SRR1390094 3 0.6368 0.1891 0.036 0.008 0.608 0.260 0.088
#> SRR1340721 2 0.7765 0.2164 0.016 0.432 0.356 0.084 0.112
#> SRR1335964 3 0.4268 0.5319 0.020 0.000 0.760 0.020 0.200
#> SRR1086869 5 0.4599 0.2752 0.000 0.000 0.356 0.020 0.624
#> SRR1453434 1 0.7732 -0.2432 0.332 0.000 0.052 0.292 0.324
#> SRR1402261 4 0.6869 0.4119 0.096 0.000 0.064 0.520 0.320
#> SRR657809 4 0.6181 0.5308 0.000 0.200 0.220 0.576 0.004
#> SRR1093075 1 0.0404 0.8372 0.988 0.000 0.000 0.000 0.012
#> SRR1433329 1 0.0404 0.8372 0.988 0.000 0.000 0.000 0.012
#> SRR1353418 3 0.6217 -0.1173 0.108 0.000 0.448 0.008 0.436
#> SRR1092913 4 0.4555 0.6492 0.000 0.200 0.068 0.732 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1003 0.8465 0.964 0.000 0.000 0.000 0.020 0.016
#> SRR1335605 3 0.8271 -0.1650 0.008 0.336 0.336 0.112 0.080 0.128
#> SRR1432014 3 0.0405 0.6660 0.000 0.000 0.988 0.000 0.008 0.004
#> SRR1499215 3 0.0984 0.6628 0.008 0.000 0.968 0.000 0.012 0.012
#> SRR1460409 1 0.0622 0.8466 0.980 0.000 0.000 0.000 0.012 0.008
#> SRR1086441 1 0.0862 0.8462 0.972 0.000 0.004 0.000 0.016 0.008
#> SRR1097344 4 0.2312 0.5996 0.000 0.000 0.000 0.876 0.012 0.112
#> SRR1081789 2 0.7231 0.0643 0.000 0.348 0.296 0.280 0.004 0.072
#> SRR1453005 2 0.5178 0.2570 0.000 0.508 0.016 0.424 0.000 0.052
#> SRR1366985 1 0.2308 0.8127 0.896 0.000 0.076 0.000 0.016 0.012
#> SRR815280 1 0.0692 0.8431 0.976 0.000 0.000 0.000 0.004 0.020
#> SRR1348531 1 0.6497 0.3617 0.548 0.000 0.076 0.016 0.268 0.092
#> SRR815845 3 0.7424 0.3985 0.008 0.112 0.536 0.080 0.188 0.076
#> SRR1471178 1 0.1007 0.8462 0.968 0.000 0.004 0.004 0.016 0.008
#> SRR1080696 3 0.4467 0.0118 0.004 0.000 0.496 0.000 0.480 0.020
#> SRR1078684 3 0.3093 0.6435 0.004 0.000 0.864 0.036 0.064 0.032
#> SRR1317751 5 0.3415 0.4512 0.000 0.000 0.080 0.004 0.820 0.096
#> SRR1435667 3 0.0291 0.6660 0.000 0.000 0.992 0.000 0.004 0.004
#> SRR1097905 1 0.6753 0.5108 0.588 0.000 0.160 0.040 0.108 0.104
#> SRR1456548 1 0.5788 0.6292 0.676 0.000 0.152 0.040 0.056 0.076
#> SRR1075126 1 0.6244 0.5408 0.624 0.000 0.076 0.020 0.144 0.136
#> SRR813108 2 0.5559 0.4428 0.000 0.620 0.084 0.248 0.000 0.048
#> SRR1479062 3 0.5047 0.1151 0.008 0.000 0.524 0.032 0.424 0.012
#> SRR1408703 3 0.4313 0.0280 0.004 0.000 0.504 0.000 0.480 0.012
#> SRR1332360 1 0.2308 0.8127 0.896 0.000 0.076 0.000 0.016 0.012
#> SRR1098686 1 0.2529 0.8151 0.892 0.000 0.012 0.004 0.064 0.028
#> SRR1434228 1 0.2308 0.8127 0.896 0.000 0.076 0.000 0.016 0.012
#> SRR1467149 5 0.7355 0.2244 0.308 0.000 0.100 0.020 0.424 0.148
#> SRR1399113 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3021 0.7755 0.000 0.020 0.076 0.860 0.000 0.044
#> SRR1092468 1 0.7837 -0.0187 0.400 0.000 0.168 0.032 0.256 0.144
#> SRR1441804 1 0.6497 0.3617 0.548 0.000 0.076 0.016 0.268 0.092
#> SRR1326100 2 0.5909 0.4284 0.000 0.596 0.096 0.240 0.000 0.068
#> SRR1398815 1 0.1092 0.8467 0.960 0.000 0.000 0.000 0.020 0.020
#> SRR1436021 4 0.3657 0.7443 0.000 0.020 0.168 0.788 0.000 0.024
#> SRR1480083 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.5563 0.6430 0.692 0.000 0.152 0.040 0.044 0.072
#> SRR815542 1 0.1173 0.8461 0.960 0.000 0.008 0.000 0.016 0.016
#> SRR1400100 3 0.5404 0.5573 0.004 0.076 0.720 0.052 0.116 0.032
#> SRR1312002 3 0.6128 -0.1784 0.172 0.000 0.440 0.000 0.372 0.016
#> SRR1470253 5 0.6304 0.2605 0.244 0.000 0.336 0.000 0.408 0.012
#> SRR1414332 1 0.1003 0.8465 0.964 0.000 0.000 0.000 0.020 0.016
#> SRR1069209 1 0.2308 0.8127 0.896 0.000 0.076 0.000 0.016 0.012
#> SRR661052 1 0.5596 0.6388 0.688 0.000 0.156 0.040 0.044 0.072
#> SRR1308860 1 0.1350 0.8433 0.952 0.000 0.008 0.000 0.020 0.020
#> SRR1421159 4 0.3309 0.7928 0.000 0.044 0.092 0.840 0.000 0.024
#> SRR1340943 6 0.4301 0.7782 0.008 0.000 0.008 0.264 0.024 0.696
#> SRR1078855 1 0.0909 0.8439 0.968 0.000 0.000 0.000 0.012 0.020
#> SRR1459465 2 0.0146 0.6952 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR816818 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0870 0.6637 0.004 0.000 0.972 0.000 0.012 0.012
#> SRR1350979 3 0.3850 0.5566 0.004 0.000 0.772 0.020 0.184 0.020
#> SRR1458198 5 0.6428 0.0223 0.172 0.000 0.008 0.020 0.420 0.380
#> SRR1386910 2 0.8271 0.0799 0.008 0.336 0.336 0.112 0.080 0.128
#> SRR1465375 4 0.3111 0.7806 0.000 0.020 0.088 0.852 0.000 0.040
#> SRR1323699 3 0.0984 0.6628 0.008 0.000 0.968 0.000 0.012 0.012
#> SRR1431139 3 0.3304 0.6411 0.012 0.000 0.856 0.036 0.064 0.032
#> SRR1373964 3 0.0551 0.6647 0.000 0.000 0.984 0.008 0.004 0.004
#> SRR1455413 5 0.5677 0.2216 0.032 0.000 0.384 0.004 0.516 0.064
#> SRR1437163 1 0.5529 0.6477 0.696 0.000 0.148 0.040 0.044 0.072
#> SRR1347343 3 0.0291 0.6660 0.000 0.000 0.992 0.000 0.004 0.004
#> SRR1465480 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.5682 0.6370 0.684 0.000 0.152 0.040 0.052 0.072
#> SRR1086514 4 0.3309 0.7928 0.000 0.044 0.092 0.840 0.000 0.024
#> SRR1430928 1 0.0964 0.8458 0.968 0.000 0.004 0.000 0.016 0.012
#> SRR1310939 3 0.5649 0.3690 0.004 0.000 0.604 0.060 0.276 0.056
#> SRR1344294 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.1490 0.8463 0.948 0.000 0.008 0.004 0.016 0.024
#> SRR1468118 5 0.2723 0.4735 0.000 0.000 0.120 0.004 0.856 0.020
#> SRR1486348 1 0.1003 0.8465 0.964 0.000 0.000 0.000 0.020 0.016
#> SRR1488770 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1312 0.8442 0.956 0.000 0.008 0.004 0.020 0.012
#> SRR1456611 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.1124 0.8396 0.956 0.000 0.000 0.000 0.036 0.008
#> SRR1500089 5 0.6428 0.0223 0.172 0.000 0.008 0.020 0.420 0.380
#> SRR1441178 1 0.0777 0.8428 0.972 0.000 0.000 0.000 0.004 0.024
#> SRR1381396 1 0.0363 0.8454 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1096081 5 0.3415 0.4512 0.000 0.000 0.080 0.004 0.820 0.096
#> SRR1349809 2 0.8268 0.0899 0.008 0.348 0.324 0.112 0.080 0.128
#> SRR1324314 1 0.6781 0.2047 0.476 0.000 0.324 0.016 0.120 0.064
#> SRR1092444 1 0.1124 0.8396 0.956 0.000 0.000 0.000 0.036 0.008
#> SRR1382553 3 0.2102 0.6138 0.068 0.000 0.908 0.000 0.012 0.012
#> SRR1075530 4 0.4138 0.7551 0.000 0.132 0.060 0.780 0.004 0.024
#> SRR1442612 3 0.0291 0.6660 0.000 0.000 0.992 0.000 0.004 0.004
#> SRR1360056 5 0.5762 0.2324 0.152 0.000 0.380 0.000 0.464 0.004
#> SRR1078164 1 0.0777 0.8428 0.972 0.000 0.000 0.000 0.004 0.024
#> SRR1434545 6 0.4301 0.7782 0.008 0.000 0.008 0.264 0.024 0.696
#> SRR1398251 1 0.0909 0.8439 0.968 0.000 0.000 0.000 0.012 0.020
#> SRR1375866 1 0.0363 0.8454 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1091645 4 0.2312 0.5996 0.000 0.000 0.000 0.876 0.012 0.112
#> SRR1416636 3 0.4467 0.0118 0.004 0.000 0.496 0.000 0.480 0.020
#> SRR1105441 3 0.3315 0.6340 0.004 0.020 0.864 0.044 0.036 0.032
#> SRR1082496 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.5568 0.2389 0.000 0.488 0.044 0.420 0.000 0.048
#> SRR1093697 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.4780 0.2179 0.028 0.000 0.392 0.000 0.564 0.016
#> SRR1076120 5 0.6428 0.0223 0.172 0.000 0.008 0.020 0.420 0.380
#> SRR1074410 1 0.0363 0.8454 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1340345 4 0.4138 0.7551 0.000 0.132 0.060 0.780 0.004 0.024
#> SRR1069514 3 0.3803 0.5301 0.000 0.128 0.796 0.064 0.004 0.008
#> SRR1092636 5 0.4929 0.2021 0.036 0.000 0.404 0.000 0.544 0.016
#> SRR1365013 2 0.7183 0.1074 0.000 0.376 0.296 0.252 0.004 0.072
#> SRR1073069 1 0.2308 0.8127 0.896 0.000 0.076 0.000 0.016 0.012
#> SRR1443137 1 0.0909 0.8439 0.968 0.000 0.000 0.000 0.012 0.020
#> SRR1437143 2 0.0000 0.6968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.0603 0.8444 0.980 0.000 0.000 0.000 0.004 0.016
#> SRR820234 2 0.4452 0.4285 0.000 0.644 0.004 0.312 0.000 0.040
#> SRR1338079 1 0.5529 0.6477 0.696 0.000 0.148 0.040 0.044 0.072
#> SRR1390094 3 0.6101 0.2312 0.024 0.000 0.568 0.196 0.008 0.204
#> SRR1340721 2 0.8388 0.0957 0.016 0.348 0.320 0.104 0.080 0.132
#> SRR1335964 3 0.4851 0.4392 0.012 0.000 0.668 0.016 0.264 0.040
#> SRR1086869 5 0.3415 0.4512 0.000 0.000 0.080 0.004 0.820 0.096
#> SRR1453434 6 0.6205 0.4356 0.232 0.000 0.008 0.088 0.084 0.588
#> SRR1402261 6 0.4714 0.7774 0.028 0.000 0.008 0.264 0.024 0.676
#> SRR657809 4 0.5723 0.6315 0.000 0.132 0.196 0.632 0.008 0.032
#> SRR1093075 1 0.0909 0.8439 0.968 0.000 0.000 0.000 0.012 0.020
#> SRR1433329 1 0.0909 0.8439 0.968 0.000 0.000 0.000 0.012 0.020
#> SRR1353418 5 0.5076 0.3947 0.088 0.000 0.288 0.000 0.616 0.008
#> SRR1092913 4 0.3840 0.7597 0.000 0.128 0.052 0.796 0.000 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17780 rows and 119 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 0.984 0.960 0.982 0.4486 0.556 0.556
#> 3 3 0.753 0.876 0.929 0.4414 0.729 0.536
#> 4 4 0.677 0.771 0.821 0.1144 0.921 0.778
#> 5 5 0.670 0.603 0.733 0.0735 0.911 0.706
#> 6 6 0.692 0.639 0.753 0.0482 0.887 0.569
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
#> SRR816969 1 0.000 0.980 1.000 0.000
#> SRR1335605 1 0.929 0.496 0.656 0.344
#> SRR1432014 1 0.644 0.814 0.836 0.164
#> SRR1499215 1 0.402 0.913 0.920 0.080
#> SRR1460409 1 0.000 0.980 1.000 0.000
#> SRR1086441 1 0.000 0.980 1.000 0.000
#> SRR1097344 2 0.000 0.983 0.000 1.000
#> SRR1081789 2 0.000 0.983 0.000 1.000
#> SRR1453005 2 0.000 0.983 0.000 1.000
#> SRR1366985 1 0.000 0.980 1.000 0.000
#> SRR815280 1 0.000 0.980 1.000 0.000
#> SRR1348531 1 0.000 0.980 1.000 0.000
#> SRR815845 2 0.000 0.983 0.000 1.000
#> SRR1471178 1 0.000 0.980 1.000 0.000
#> SRR1080696 1 0.000 0.980 1.000 0.000
#> SRR1078684 1 0.402 0.913 0.920 0.080
#> SRR1317751 1 0.000 0.980 1.000 0.000
#> SRR1435667 2 0.000 0.983 0.000 1.000
#> SRR1097905 1 0.000 0.980 1.000 0.000
#> SRR1456548 1 0.000 0.980 1.000 0.000
#> SRR1075126 1 0.000 0.980 1.000 0.000
#> SRR813108 2 0.000 0.983 0.000 1.000
#> SRR1479062 1 0.402 0.913 0.920 0.080
#> SRR1408703 1 0.000 0.980 1.000 0.000
#> SRR1332360 1 0.000 0.980 1.000 0.000
#> SRR1098686 1 0.000 0.980 1.000 0.000
#> SRR1434228 1 0.000 0.980 1.000 0.000
#> SRR1467149 1 0.000 0.980 1.000 0.000
#> SRR1399113 2 0.000 0.983 0.000 1.000
#> SRR1476507 2 0.000 0.983 0.000 1.000
#> SRR1092468 1 0.000 0.980 1.000 0.000
#> SRR1441804 1 0.000 0.980 1.000 0.000
#> SRR1326100 2 0.000 0.983 0.000 1.000
#> SRR1398815 1 0.000 0.980 1.000 0.000
#> SRR1436021 2 0.000 0.983 0.000 1.000
#> SRR1480083 2 0.000 0.983 0.000 1.000
#> SRR1472863 1 0.000 0.980 1.000 0.000
#> SRR815542 1 0.000 0.980 1.000 0.000
#> SRR1400100 2 0.000 0.983 0.000 1.000
#> SRR1312002 1 0.000 0.980 1.000 0.000
#> SRR1470253 1 0.000 0.980 1.000 0.000
#> SRR1414332 1 0.000 0.980 1.000 0.000
#> SRR1069209 1 0.000 0.980 1.000 0.000
#> SRR661052 1 0.000 0.980 1.000 0.000
#> SRR1308860 1 0.000 0.980 1.000 0.000
#> SRR1421159 2 0.000 0.983 0.000 1.000
#> SRR1340943 1 0.000 0.980 1.000 0.000
#> SRR1078855 1 0.000 0.980 1.000 0.000
#> SRR1459465 2 0.000 0.983 0.000 1.000
#> SRR816818 2 0.000 0.983 0.000 1.000
#> SRR1478679 2 0.722 0.751 0.200 0.800
#> SRR1350979 1 0.402 0.913 0.920 0.080
#> SRR1458198 1 0.000 0.980 1.000 0.000
#> SRR1386910 2 0.000 0.983 0.000 1.000
#> SRR1465375 2 0.000 0.983 0.000 1.000
#> SRR1323699 1 0.402 0.913 0.920 0.080
#> SRR1431139 1 0.000 0.980 1.000 0.000
#> SRR1373964 1 0.943 0.458 0.640 0.360
#> SRR1455413 1 0.000 0.980 1.000 0.000
#> SRR1437163 1 0.000 0.980 1.000 0.000
#> SRR1347343 1 0.552 0.860 0.872 0.128
#> SRR1465480 2 0.000 0.983 0.000 1.000
#> SRR1489631 1 0.000 0.980 1.000 0.000
#> SRR1086514 2 0.000 0.983 0.000 1.000
#> SRR1430928 1 0.000 0.980 1.000 0.000
#> SRR1310939 1 0.278 0.941 0.952 0.048
#> SRR1344294 2 0.000 0.983 0.000 1.000
#> SRR1099402 1 0.000 0.980 1.000 0.000
#> SRR1468118 1 0.000 0.980 1.000 0.000
#> SRR1486348 1 0.000 0.980 1.000 0.000
#> SRR1488770 2 0.000 0.983 0.000 1.000
#> SRR1083732 1 0.000 0.980 1.000 0.000
#> SRR1456611 2 0.000 0.983 0.000 1.000
#> SRR1080318 1 0.000 0.980 1.000 0.000
#> SRR1500089 1 0.000 0.980 1.000 0.000
#> SRR1441178 1 0.000 0.980 1.000 0.000
#> SRR1381396 1 0.000 0.980 1.000 0.000
#> SRR1096081 1 0.000 0.980 1.000 0.000
#> SRR1349809 2 0.000 0.983 0.000 1.000
#> SRR1324314 1 0.000 0.980 1.000 0.000
#> SRR1092444 1 0.000 0.980 1.000 0.000
#> SRR1382553 1 0.000 0.980 1.000 0.000
#> SRR1075530 2 0.000 0.983 0.000 1.000
#> SRR1442612 2 0.730 0.745 0.204 0.796
#> SRR1360056 1 0.000 0.980 1.000 0.000
#> SRR1078164 1 0.000 0.980 1.000 0.000
#> SRR1434545 1 0.000 0.980 1.000 0.000
#> SRR1398251 1 0.000 0.980 1.000 0.000
#> SRR1375866 1 0.000 0.980 1.000 0.000
#> SRR1091645 2 0.000 0.983 0.000 1.000
#> SRR1416636 1 0.000 0.980 1.000 0.000
#> SRR1105441 2 0.000 0.983 0.000 1.000
#> SRR1082496 2 0.000 0.983 0.000 1.000
#> SRR1315353 2 0.000 0.983 0.000 1.000
#> SRR1093697 2 0.000 0.983 0.000 1.000
#> SRR1077429 1 0.000 0.980 1.000 0.000
#> SRR1076120 1 0.000 0.980 1.000 0.000
#> SRR1074410 1 0.000 0.980 1.000 0.000
#> SRR1340345 2 0.000 0.983 0.000 1.000
#> SRR1069514 2 0.000 0.983 0.000 1.000
#> SRR1092636 1 0.000 0.980 1.000 0.000
#> SRR1365013 2 0.000 0.983 0.000 1.000
#> SRR1073069 1 0.000 0.980 1.000 0.000
#> SRR1443137 1 0.000 0.980 1.000 0.000
#> SRR1437143 2 0.000 0.983 0.000 1.000
#> SRR1091990 1 0.000 0.980 1.000 0.000
#> SRR820234 2 0.000 0.983 0.000 1.000
#> SRR1338079 1 0.000 0.980 1.000 0.000
#> SRR1390094 1 0.402 0.913 0.920 0.080
#> SRR1340721 2 0.730 0.745 0.204 0.796
#> SRR1335964 1 0.278 0.941 0.952 0.048
#> SRR1086869 1 0.000 0.980 1.000 0.000
#> SRR1453434 1 0.000 0.980 1.000 0.000
#> SRR1402261 1 0.000 0.980 1.000 0.000
#> SRR657809 2 0.000 0.983 0.000 1.000
#> SRR1093075 1 0.000 0.980 1.000 0.000
#> SRR1433329 1 0.000 0.980 1.000 0.000
#> SRR1353418 1 0.000 0.980 1.000 0.000
#> SRR1092913 2 0.000 0.983 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1335605 3 0.0747 0.903 0.000 0.016 0.984
#> SRR1432014 3 0.0892 0.903 0.000 0.020 0.980
#> SRR1499215 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1460409 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1097344 2 0.4702 0.846 0.000 0.788 0.212
#> SRR1081789 2 0.4452 0.847 0.000 0.808 0.192
#> SRR1453005 2 0.0892 0.868 0.000 0.980 0.020
#> SRR1366985 3 0.5678 0.615 0.316 0.000 0.684
#> SRR815280 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.958 1.000 0.000 0.000
#> SRR815845 3 0.1643 0.886 0.000 0.044 0.956
#> SRR1471178 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1080696 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1078684 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1317751 3 0.1163 0.907 0.028 0.000 0.972
#> SRR1435667 3 0.0892 0.903 0.000 0.020 0.980
#> SRR1097905 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1456548 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1075126 1 0.0000 0.958 1.000 0.000 0.000
#> SRR813108 2 0.0892 0.868 0.000 0.980 0.020
#> SRR1479062 3 0.0747 0.912 0.016 0.000 0.984
#> SRR1408703 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1332360 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1434228 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1467149 1 0.5178 0.676 0.744 0.000 0.256
#> SRR1399113 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1476507 2 0.5058 0.819 0.000 0.756 0.244
#> SRR1092468 1 0.5497 0.608 0.708 0.000 0.292
#> SRR1441804 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1436021 2 0.4887 0.828 0.000 0.772 0.228
#> SRR1480083 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.958 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1400100 3 0.3038 0.825 0.000 0.104 0.896
#> SRR1312002 3 0.5363 0.672 0.276 0.000 0.724
#> SRR1470253 3 0.4702 0.742 0.212 0.000 0.788
#> SRR1414332 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.958 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1421159 2 0.4796 0.829 0.000 0.780 0.220
#> SRR1340943 1 0.5291 0.668 0.732 0.000 0.268
#> SRR1078855 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1459465 2 0.0237 0.866 0.000 0.996 0.004
#> SRR816818 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1478679 3 0.0892 0.903 0.000 0.020 0.980
#> SRR1350979 3 0.0747 0.912 0.016 0.000 0.984
#> SRR1458198 1 0.2066 0.909 0.940 0.000 0.060
#> SRR1386910 2 0.4605 0.843 0.000 0.796 0.204
#> SRR1465375 2 0.4842 0.838 0.000 0.776 0.224
#> SRR1323699 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1431139 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1373964 3 0.0892 0.903 0.000 0.020 0.980
#> SRR1455413 1 0.4121 0.797 0.832 0.000 0.168
#> SRR1437163 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1347343 3 0.0892 0.903 0.000 0.020 0.980
#> SRR1465480 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1489631 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1086514 2 0.4452 0.847 0.000 0.808 0.192
#> SRR1430928 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1310939 3 0.0747 0.912 0.016 0.000 0.984
#> SRR1344294 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1468118 3 0.0747 0.912 0.016 0.000 0.984
#> SRR1486348 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1500089 1 0.2261 0.902 0.932 0.000 0.068
#> SRR1441178 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1096081 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1349809 2 0.3038 0.863 0.000 0.896 0.104
#> SRR1324314 3 0.5678 0.616 0.316 0.000 0.684
#> SRR1092444 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1382553 3 0.5678 0.616 0.316 0.000 0.684
#> SRR1075530 2 0.4702 0.846 0.000 0.788 0.212
#> SRR1442612 3 0.0892 0.903 0.000 0.020 0.980
#> SRR1360056 3 0.4654 0.746 0.208 0.000 0.792
#> SRR1078164 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1434545 1 0.5363 0.655 0.724 0.000 0.276
#> SRR1398251 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1091645 2 0.5058 0.819 0.000 0.756 0.244
#> SRR1416636 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1105441 3 0.2959 0.830 0.000 0.100 0.900
#> SRR1082496 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1315353 2 0.6260 0.394 0.000 0.552 0.448
#> SRR1093697 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1077429 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1076120 1 0.4702 0.738 0.788 0.000 0.212
#> SRR1074410 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1340345 2 0.4702 0.846 0.000 0.788 0.212
#> SRR1069514 3 0.2448 0.855 0.000 0.076 0.924
#> SRR1092636 3 0.0892 0.913 0.020 0.000 0.980
#> SRR1365013 2 0.4555 0.845 0.000 0.800 0.200
#> SRR1073069 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.958 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.867 0.000 1.000 0.000
#> SRR1338079 1 0.0237 0.956 0.996 0.000 0.004
#> SRR1390094 3 0.0424 0.908 0.008 0.000 0.992
#> SRR1340721 2 0.6255 0.538 0.320 0.668 0.012
#> SRR1335964 3 0.0747 0.912 0.016 0.000 0.984
#> SRR1086869 3 0.0747 0.912 0.016 0.000 0.984
#> SRR1453434 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1402261 1 0.5291 0.668 0.732 0.000 0.268
#> SRR657809 2 0.4555 0.847 0.000 0.800 0.200
#> SRR1093075 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.958 1.000 0.000 0.000
#> SRR1353418 3 0.4702 0.742 0.212 0.000 0.788
#> SRR1092913 2 0.4702 0.846 0.000 0.788 0.212
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.88215 1.000 0.000 0.000 0.000
#> SRR1335605 3 0.1209 0.82766 0.000 0.004 0.964 0.032
#> SRR1432014 3 0.0188 0.83554 0.000 0.004 0.996 0.000
#> SRR1499215 3 0.1209 0.83159 0.000 0.004 0.964 0.032
#> SRR1460409 1 0.1716 0.88067 0.936 0.000 0.000 0.064
#> SRR1086441 1 0.0188 0.88232 0.996 0.000 0.000 0.004
#> SRR1097344 4 0.6280 0.69211 0.000 0.316 0.080 0.604
#> SRR1081789 4 0.7811 0.50704 0.000 0.368 0.252 0.380
#> SRR1453005 2 0.4718 0.38924 0.000 0.708 0.012 0.280
#> SRR1366985 3 0.6933 0.50250 0.244 0.000 0.584 0.172
#> SRR815280 1 0.3172 0.83074 0.840 0.000 0.000 0.160
#> SRR1348531 1 0.2011 0.86897 0.920 0.000 0.000 0.080
#> SRR815845 3 0.0804 0.83216 0.000 0.008 0.980 0.012
#> SRR1471178 1 0.0000 0.88215 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.2973 0.82136 0.000 0.000 0.856 0.144
#> SRR1078684 3 0.1004 0.82849 0.000 0.004 0.972 0.024
#> SRR1317751 3 0.3444 0.81000 0.000 0.000 0.816 0.184
#> SRR1435667 3 0.0524 0.83471 0.000 0.004 0.988 0.008
#> SRR1097905 1 0.2011 0.86769 0.920 0.000 0.000 0.080
#> SRR1456548 1 0.2011 0.86769 0.920 0.000 0.000 0.080
#> SRR1075126 1 0.1940 0.87323 0.924 0.000 0.000 0.076
#> SRR813108 2 0.3117 0.77924 0.000 0.880 0.092 0.028
#> SRR1479062 3 0.3311 0.81383 0.000 0.000 0.828 0.172
#> SRR1408703 3 0.3123 0.81921 0.000 0.000 0.844 0.156
#> SRR1332360 1 0.3356 0.82243 0.824 0.000 0.000 0.176
#> SRR1098686 1 0.2011 0.86769 0.920 0.000 0.000 0.080
#> SRR1434228 1 0.3626 0.81661 0.812 0.000 0.004 0.184
#> SRR1467149 4 0.5929 0.17531 0.356 0.000 0.048 0.596
#> SRR1399113 2 0.0188 0.91841 0.000 0.996 0.000 0.004
#> SRR1476507 4 0.6432 0.70859 0.000 0.236 0.128 0.636
#> SRR1092468 1 0.7752 0.00307 0.404 0.000 0.360 0.236
#> SRR1441804 1 0.2081 0.86732 0.916 0.000 0.000 0.084
#> SRR1326100 2 0.0524 0.91034 0.000 0.988 0.008 0.004
#> SRR1398815 1 0.0188 0.88232 0.996 0.000 0.000 0.004
#> SRR1436021 4 0.7220 0.70055 0.000 0.240 0.212 0.548
#> SRR1480083 2 0.0000 0.91926 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.88215 1.000 0.000 0.000 0.000
#> SRR815542 1 0.2216 0.86805 0.908 0.000 0.000 0.092
#> SRR1400100 3 0.2675 0.78947 0.000 0.044 0.908 0.048
#> SRR1312002 3 0.6260 0.66749 0.144 0.000 0.664 0.192
#> SRR1470253 3 0.5354 0.73280 0.056 0.000 0.712 0.232
#> SRR1414332 1 0.0188 0.88232 0.996 0.000 0.000 0.004
#> SRR1069209 1 0.3444 0.81893 0.816 0.000 0.000 0.184
#> SRR661052 1 0.1637 0.87477 0.940 0.000 0.000 0.060
#> SRR1308860 1 0.2081 0.86732 0.916 0.000 0.000 0.084
#> SRR1421159 4 0.7205 0.67308 0.000 0.200 0.252 0.548
#> SRR1340943 4 0.4685 0.53426 0.156 0.000 0.060 0.784
#> SRR1078855 1 0.3400 0.82125 0.820 0.000 0.000 0.180
#> SRR1459465 2 0.0188 0.91841 0.000 0.996 0.000 0.004
#> SRR816818 2 0.0188 0.91841 0.000 0.996 0.000 0.004
#> SRR1478679 3 0.1398 0.82435 0.000 0.004 0.956 0.040
#> SRR1350979 3 0.1022 0.83789 0.000 0.000 0.968 0.032
#> SRR1458198 1 0.4838 0.72229 0.724 0.000 0.024 0.252
#> SRR1386910 4 0.7069 0.70858 0.000 0.324 0.144 0.532
#> SRR1465375 4 0.6663 0.71880 0.000 0.280 0.124 0.596
#> SRR1323699 3 0.1209 0.83159 0.000 0.004 0.964 0.032
#> SRR1431139 3 0.0921 0.83001 0.000 0.000 0.972 0.028
#> SRR1373964 3 0.0524 0.83471 0.000 0.004 0.988 0.008
#> SRR1455413 1 0.7074 0.46185 0.568 0.000 0.192 0.240
#> SRR1437163 1 0.2081 0.86646 0.916 0.000 0.000 0.084
#> SRR1347343 3 0.0657 0.83479 0.000 0.004 0.984 0.012
#> SRR1465480 2 0.0188 0.91841 0.000 0.996 0.000 0.004
#> SRR1489631 1 0.2011 0.86769 0.920 0.000 0.000 0.080
#> SRR1086514 4 0.6953 0.70084 0.000 0.336 0.128 0.536
#> SRR1430928 1 0.0000 0.88215 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.3311 0.80898 0.000 0.000 0.828 0.172
#> SRR1344294 2 0.0000 0.91926 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0188 0.88215 0.996 0.000 0.000 0.004
#> SRR1468118 3 0.3528 0.80445 0.000 0.000 0.808 0.192
#> SRR1486348 1 0.0000 0.88215 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.91926 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.88215 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.91926 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.1716 0.87444 0.936 0.000 0.000 0.064
#> SRR1500089 1 0.5247 0.67856 0.684 0.000 0.032 0.284
#> SRR1441178 1 0.3311 0.82643 0.828 0.000 0.000 0.172
#> SRR1381396 1 0.0188 0.88232 0.996 0.000 0.000 0.004
#> SRR1096081 3 0.3356 0.81229 0.000 0.000 0.824 0.176
#> SRR1349809 2 0.6450 -0.01449 0.000 0.616 0.108 0.276
#> SRR1324314 3 0.5354 0.63237 0.232 0.000 0.712 0.056
#> SRR1092444 1 0.1940 0.87565 0.924 0.000 0.000 0.076
#> SRR1382553 3 0.6724 0.53102 0.224 0.000 0.612 0.164
#> SRR1075530 4 0.6878 0.71565 0.000 0.316 0.128 0.556
#> SRR1442612 3 0.0376 0.83442 0.000 0.004 0.992 0.004
#> SRR1360056 3 0.4661 0.75585 0.016 0.000 0.728 0.256
#> SRR1078164 1 0.3311 0.82643 0.828 0.000 0.000 0.172
#> SRR1434545 4 0.4956 0.55155 0.140 0.004 0.076 0.780
#> SRR1398251 1 0.4079 0.80852 0.800 0.000 0.020 0.180
#> SRR1375866 1 0.0188 0.88232 0.996 0.000 0.000 0.004
#> SRR1091645 4 0.6110 0.68905 0.000 0.240 0.100 0.660
#> SRR1416636 3 0.3266 0.81530 0.000 0.000 0.832 0.168
#> SRR1105441 3 0.2586 0.79271 0.000 0.040 0.912 0.048
#> SRR1082496 2 0.0188 0.91841 0.000 0.996 0.000 0.004
#> SRR1315353 3 0.7138 -0.09458 0.000 0.164 0.540 0.296
#> SRR1093697 2 0.0000 0.91926 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.3311 0.81383 0.000 0.000 0.828 0.172
#> SRR1076120 1 0.6240 0.56439 0.604 0.000 0.076 0.320
#> SRR1074410 1 0.0188 0.88232 0.996 0.000 0.000 0.004
#> SRR1340345 4 0.6878 0.71565 0.000 0.316 0.128 0.556
#> SRR1069514 3 0.1610 0.81868 0.000 0.016 0.952 0.032
#> SRR1092636 3 0.2704 0.82630 0.000 0.000 0.876 0.124
#> SRR1365013 4 0.7155 0.70998 0.000 0.300 0.164 0.536
#> SRR1073069 1 0.3400 0.82125 0.820 0.000 0.000 0.180
#> SRR1443137 1 0.3444 0.82130 0.816 0.000 0.000 0.184
#> SRR1437143 2 0.0000 0.91926 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.3172 0.83074 0.840 0.000 0.000 0.160
#> SRR820234 2 0.0469 0.91094 0.000 0.988 0.000 0.012
#> SRR1338079 1 0.1211 0.87897 0.960 0.000 0.000 0.040
#> SRR1390094 3 0.4343 0.60508 0.000 0.004 0.732 0.264
#> SRR1340721 4 0.8008 0.57344 0.172 0.180 0.068 0.580
#> SRR1335964 3 0.2216 0.82982 0.000 0.000 0.908 0.092
#> SRR1086869 3 0.3528 0.80445 0.000 0.000 0.808 0.192
#> SRR1453434 1 0.2760 0.86627 0.872 0.000 0.000 0.128
#> SRR1402261 4 0.4829 0.54018 0.156 0.000 0.068 0.776
#> SRR657809 4 0.6966 0.70607 0.000 0.328 0.132 0.540
#> SRR1093075 1 0.3400 0.82125 0.820 0.000 0.000 0.180
#> SRR1433329 1 0.3444 0.82130 0.816 0.000 0.000 0.184
#> SRR1353418 3 0.4690 0.75154 0.016 0.000 0.724 0.260
#> SRR1092913 4 0.6791 0.71413 0.000 0.316 0.120 0.564
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.2124 0.6681 0.900 0.000 0.000 0.004 0.096
#> SRR1335605 3 0.6569 0.5469 0.000 0.000 0.468 0.240 0.292
#> SRR1432014 3 0.5969 0.6888 0.000 0.008 0.608 0.140 0.244
#> SRR1499215 3 0.6002 0.6884 0.000 0.008 0.596 0.132 0.264
#> SRR1460409 1 0.1638 0.6909 0.932 0.000 0.000 0.004 0.064
#> SRR1086441 1 0.1732 0.6845 0.920 0.000 0.000 0.000 0.080
#> SRR1097344 4 0.4373 0.6442 0.000 0.088 0.048 0.804 0.060
#> SRR1081789 4 0.7135 0.2649 0.000 0.060 0.164 0.528 0.248
#> SRR1453005 2 0.4971 0.0565 0.000 0.512 0.000 0.460 0.028
#> SRR1366985 5 0.5967 0.5037 0.184 0.000 0.204 0.004 0.608
#> SRR815280 1 0.4450 -0.7055 0.508 0.000 0.000 0.004 0.488
#> SRR1348531 1 0.0000 0.7088 1.000 0.000 0.000 0.000 0.000
#> SRR815845 3 0.6483 0.6250 0.000 0.008 0.540 0.220 0.232
#> SRR1471178 1 0.1792 0.6810 0.916 0.000 0.000 0.000 0.084
#> SRR1080696 3 0.0693 0.6377 0.000 0.000 0.980 0.008 0.012
#> SRR1078684 3 0.6194 0.6749 0.000 0.008 0.580 0.164 0.248
#> SRR1317751 3 0.1830 0.5938 0.000 0.000 0.932 0.028 0.040
#> SRR1435667 3 0.5969 0.6888 0.000 0.008 0.608 0.140 0.244
#> SRR1097905 1 0.1809 0.6838 0.928 0.000 0.000 0.012 0.060
#> SRR1456548 1 0.1571 0.6892 0.936 0.000 0.000 0.004 0.060
#> SRR1075126 1 0.1386 0.6963 0.952 0.000 0.000 0.016 0.032
#> SRR813108 2 0.7042 0.3311 0.000 0.568 0.080 0.192 0.160
#> SRR1479062 3 0.0404 0.6249 0.000 0.000 0.988 0.012 0.000
#> SRR1408703 3 0.0451 0.6354 0.000 0.000 0.988 0.008 0.004
#> SRR1332360 5 0.4632 0.8112 0.448 0.000 0.012 0.000 0.540
#> SRR1098686 1 0.0510 0.7086 0.984 0.000 0.000 0.000 0.016
#> SRR1434228 5 0.5167 0.7804 0.396 0.000 0.036 0.004 0.564
#> SRR1467149 1 0.7677 0.2268 0.464 0.000 0.284 0.120 0.132
#> SRR1399113 2 0.0290 0.8995 0.000 0.992 0.000 0.008 0.000
#> SRR1476507 4 0.3991 0.6587 0.000 0.076 0.040 0.828 0.056
#> SRR1092468 1 0.7709 0.0991 0.476 0.000 0.172 0.108 0.244
#> SRR1441804 1 0.0000 0.7088 1.000 0.000 0.000 0.000 0.000
#> SRR1326100 2 0.3115 0.7715 0.000 0.852 0.000 0.112 0.036
#> SRR1398815 1 0.2389 0.6641 0.880 0.000 0.000 0.004 0.116
#> SRR1436021 4 0.5207 0.5318 0.000 0.020 0.076 0.708 0.196
#> SRR1480083 2 0.0162 0.9003 0.000 0.996 0.000 0.000 0.004
#> SRR1472863 1 0.2629 0.6818 0.860 0.000 0.000 0.004 0.136
#> SRR815542 1 0.0771 0.7017 0.976 0.000 0.000 0.004 0.020
#> SRR1400100 3 0.6619 0.5886 0.000 0.008 0.512 0.256 0.224
#> SRR1312002 3 0.5142 0.3187 0.052 0.000 0.600 0.000 0.348
#> SRR1470253 3 0.4275 0.4128 0.020 0.000 0.696 0.000 0.284
#> SRR1414332 1 0.2179 0.6651 0.896 0.000 0.000 0.004 0.100
#> SRR1069209 5 0.4764 0.8126 0.436 0.000 0.012 0.004 0.548
#> SRR661052 1 0.1831 0.6968 0.920 0.000 0.000 0.004 0.076
#> SRR1308860 1 0.0703 0.7071 0.976 0.000 0.000 0.000 0.024
#> SRR1421159 4 0.5628 0.4392 0.000 0.016 0.100 0.660 0.224
#> SRR1340943 4 0.6271 0.5312 0.116 0.000 0.088 0.660 0.136
#> SRR1078855 5 0.4425 0.8062 0.452 0.000 0.000 0.004 0.544
#> SRR1459465 2 0.0579 0.8984 0.000 0.984 0.000 0.008 0.008
#> SRR816818 2 0.0290 0.8995 0.000 0.992 0.000 0.008 0.000
#> SRR1478679 3 0.6479 0.6415 0.000 0.008 0.532 0.188 0.272
#> SRR1350979 3 0.5642 0.6921 0.000 0.000 0.624 0.136 0.240
#> SRR1458198 1 0.6368 0.3619 0.648 0.000 0.148 0.080 0.124
#> SRR1386910 4 0.6270 0.6173 0.000 0.092 0.072 0.644 0.192
#> SRR1465375 4 0.3498 0.6694 0.004 0.096 0.008 0.848 0.044
#> SRR1323699 3 0.6002 0.6884 0.000 0.008 0.596 0.132 0.264
#> SRR1431139 3 0.5896 0.6794 0.000 0.000 0.596 0.168 0.236
#> SRR1373964 3 0.5969 0.6888 0.000 0.008 0.608 0.140 0.244
#> SRR1455413 1 0.6105 0.2510 0.532 0.000 0.376 0.032 0.060
#> SRR1437163 1 0.1809 0.6838 0.928 0.000 0.000 0.012 0.060
#> SRR1347343 3 0.5969 0.6888 0.000 0.008 0.608 0.140 0.244
#> SRR1465480 2 0.0290 0.8995 0.000 0.992 0.000 0.008 0.000
#> SRR1489631 1 0.1571 0.6892 0.936 0.000 0.000 0.004 0.060
#> SRR1086514 4 0.4240 0.6422 0.000 0.148 0.016 0.788 0.048
#> SRR1430928 1 0.1908 0.6746 0.908 0.000 0.000 0.000 0.092
#> SRR1310939 3 0.5246 0.6581 0.004 0.000 0.692 0.124 0.180
#> SRR1344294 2 0.0000 0.9012 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.1908 0.6768 0.908 0.000 0.000 0.000 0.092
#> SRR1468118 3 0.2450 0.5645 0.000 0.000 0.900 0.052 0.048
#> SRR1486348 1 0.2074 0.6892 0.896 0.000 0.000 0.000 0.104
#> SRR1488770 2 0.0000 0.9012 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.1671 0.6875 0.924 0.000 0.000 0.000 0.076
#> SRR1456611 2 0.0000 0.9012 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.1357 0.7019 0.948 0.000 0.000 0.004 0.048
#> SRR1500089 1 0.7113 0.2392 0.512 0.000 0.296 0.068 0.124
#> SRR1441178 5 0.4437 0.7651 0.464 0.000 0.000 0.004 0.532
#> SRR1381396 1 0.2389 0.6641 0.880 0.000 0.000 0.004 0.116
#> SRR1096081 3 0.1668 0.5985 0.000 0.000 0.940 0.028 0.032
#> SRR1349809 4 0.6938 0.1849 0.000 0.412 0.036 0.420 0.132
#> SRR1324314 3 0.7257 0.5267 0.148 0.000 0.492 0.064 0.296
#> SRR1092444 1 0.1662 0.6962 0.936 0.000 0.004 0.004 0.056
#> SRR1382553 5 0.5116 0.0179 0.052 0.000 0.220 0.024 0.704
#> SRR1075530 4 0.3209 0.6681 0.000 0.100 0.020 0.860 0.020
#> SRR1442612 3 0.5969 0.6888 0.000 0.008 0.608 0.140 0.244
#> SRR1360056 3 0.3837 0.4345 0.000 0.000 0.692 0.000 0.308
#> SRR1078164 5 0.4434 0.7718 0.460 0.000 0.000 0.004 0.536
#> SRR1434545 4 0.6137 0.5396 0.104 0.000 0.088 0.672 0.136
#> SRR1398251 5 0.4798 0.7908 0.404 0.000 0.016 0.004 0.576
#> SRR1375866 1 0.2439 0.6602 0.876 0.000 0.000 0.004 0.120
#> SRR1091645 4 0.4704 0.6316 0.000 0.072 0.084 0.784 0.060
#> SRR1416636 3 0.0162 0.6289 0.000 0.000 0.996 0.004 0.000
#> SRR1105441 3 0.6619 0.5886 0.000 0.008 0.512 0.256 0.224
#> SRR1082496 2 0.0290 0.8995 0.000 0.992 0.000 0.008 0.000
#> SRR1315353 4 0.6888 -0.0276 0.000 0.016 0.272 0.484 0.228
#> SRR1093697 2 0.0000 0.9012 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.1310 0.6080 0.000 0.000 0.956 0.020 0.024
#> SRR1076120 1 0.7592 0.1724 0.448 0.000 0.320 0.100 0.132
#> SRR1074410 1 0.2389 0.6641 0.880 0.000 0.000 0.004 0.116
#> SRR1340345 4 0.2775 0.6683 0.000 0.100 0.020 0.876 0.004
#> SRR1069514 3 0.6778 0.5650 0.000 0.008 0.476 0.264 0.252
#> SRR1092636 3 0.1281 0.6436 0.000 0.000 0.956 0.032 0.012
#> SRR1365013 4 0.5883 0.5204 0.000 0.036 0.092 0.656 0.216
#> SRR1073069 5 0.4627 0.8133 0.444 0.000 0.012 0.000 0.544
#> SRR1443137 5 0.4287 0.7984 0.460 0.000 0.000 0.000 0.540
#> SRR1437143 2 0.0000 0.9012 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.4448 -0.6979 0.516 0.000 0.000 0.004 0.480
#> SRR820234 2 0.2110 0.8397 0.000 0.912 0.000 0.072 0.016
#> SRR1338079 1 0.1952 0.6997 0.912 0.000 0.000 0.004 0.084
#> SRR1390094 3 0.7192 0.5121 0.012 0.008 0.444 0.256 0.280
#> SRR1340721 4 0.6762 0.3465 0.356 0.044 0.000 0.496 0.104
#> SRR1335964 3 0.5224 0.6873 0.000 0.000 0.684 0.140 0.176
#> SRR1086869 3 0.2450 0.5645 0.000 0.000 0.900 0.052 0.048
#> SRR1453434 1 0.3236 0.5498 0.828 0.000 0.000 0.020 0.152
#> SRR1402261 4 0.6271 0.5312 0.116 0.000 0.088 0.660 0.136
#> SRR657809 4 0.3773 0.6649 0.000 0.100 0.020 0.832 0.048
#> SRR1093075 5 0.4425 0.8062 0.452 0.000 0.000 0.004 0.544
#> SRR1433329 5 0.4622 0.8135 0.440 0.000 0.012 0.000 0.548
#> SRR1353418 3 0.3876 0.4256 0.000 0.000 0.684 0.000 0.316
#> SRR1092913 4 0.3478 0.6599 0.000 0.100 0.024 0.848 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.3257 0.70416 0.816 0.000 0.000 0.012 0.020 0.152
#> SRR1335605 3 0.4245 0.62335 0.020 0.000 0.788 0.108 0.020 0.064
#> SRR1432014 3 0.2389 0.66226 0.000 0.000 0.864 0.000 0.128 0.008
#> SRR1499215 3 0.2793 0.66438 0.000 0.000 0.856 0.004 0.112 0.028
#> SRR1460409 1 0.3548 0.73283 0.824 0.000 0.000 0.020 0.076 0.080
#> SRR1086441 1 0.2092 0.74342 0.876 0.000 0.000 0.000 0.000 0.124
#> SRR1097344 4 0.3298 0.72862 0.000 0.028 0.012 0.856 0.040 0.064
#> SRR1081789 3 0.5251 0.36301 0.000 0.020 0.612 0.304 0.008 0.056
#> SRR1453005 4 0.6266 0.16526 0.000 0.380 0.068 0.472 0.004 0.076
#> SRR1366985 6 0.5055 0.62314 0.084 0.000 0.168 0.004 0.040 0.704
#> SRR815280 6 0.4848 0.72586 0.336 0.000 0.000 0.032 0.024 0.608
#> SRR1348531 1 0.1528 0.76039 0.936 0.000 0.000 0.000 0.048 0.016
#> SRR815845 3 0.2862 0.68513 0.000 0.000 0.872 0.056 0.052 0.020
#> SRR1471178 1 0.2135 0.74052 0.872 0.000 0.000 0.000 0.000 0.128
#> SRR1080696 5 0.3371 0.64247 0.000 0.000 0.292 0.000 0.708 0.000
#> SRR1078684 3 0.1967 0.68939 0.000 0.000 0.904 0.012 0.084 0.000
#> SRR1317751 5 0.3221 0.69061 0.000 0.000 0.188 0.000 0.792 0.020
#> SRR1435667 3 0.2278 0.66467 0.000 0.000 0.868 0.000 0.128 0.004
#> SRR1097905 1 0.2382 0.73881 0.904 0.000 0.004 0.024 0.020 0.048
#> SRR1456548 1 0.2118 0.74578 0.916 0.000 0.004 0.012 0.020 0.048
#> SRR1075126 1 0.2828 0.73078 0.872 0.000 0.004 0.012 0.080 0.032
#> SRR813108 2 0.6651 0.13628 0.000 0.432 0.376 0.116 0.004 0.072
#> SRR1479062 5 0.3915 0.62466 0.000 0.000 0.304 0.008 0.680 0.008
#> SRR1408703 5 0.3351 0.64536 0.000 0.000 0.288 0.000 0.712 0.000
#> SRR1332360 6 0.3488 0.87978 0.244 0.000 0.000 0.004 0.008 0.744
#> SRR1098686 1 0.0508 0.76925 0.984 0.000 0.004 0.000 0.000 0.012
#> SRR1434228 6 0.3702 0.86873 0.224 0.000 0.012 0.004 0.008 0.752
#> SRR1467149 5 0.6397 -0.12371 0.404 0.000 0.000 0.064 0.424 0.108
#> SRR1399113 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3287 0.72471 0.000 0.008 0.036 0.856 0.040 0.060
#> SRR1092468 1 0.7152 0.22677 0.484 0.000 0.244 0.040 0.184 0.048
#> SRR1441804 1 0.1594 0.75917 0.932 0.000 0.000 0.000 0.052 0.016
#> SRR1326100 2 0.5484 0.54119 0.000 0.672 0.156 0.116 0.004 0.052
#> SRR1398815 1 0.3590 0.70447 0.808 0.000 0.000 0.032 0.024 0.136
#> SRR1436021 3 0.4647 0.10238 0.000 0.000 0.508 0.460 0.012 0.020
#> SRR1480083 2 0.0508 0.89762 0.000 0.984 0.000 0.000 0.004 0.012
#> SRR1472863 1 0.3061 0.74441 0.840 0.000 0.004 0.008 0.020 0.128
#> SRR815542 1 0.2128 0.75227 0.908 0.000 0.000 0.004 0.056 0.032
#> SRR1400100 3 0.3060 0.66541 0.000 0.000 0.836 0.132 0.012 0.020
#> SRR1312002 5 0.6321 0.28229 0.024 0.000 0.156 0.004 0.420 0.396
#> SRR1470253 5 0.5158 0.61310 0.008 0.000 0.112 0.004 0.648 0.228
#> SRR1414332 1 0.3513 0.69667 0.804 0.000 0.000 0.024 0.020 0.152
#> SRR1069209 6 0.3463 0.87927 0.240 0.000 0.000 0.004 0.008 0.748
#> SRR661052 1 0.2239 0.75455 0.900 0.000 0.000 0.008 0.020 0.072
#> SRR1308860 1 0.0363 0.76901 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1421159 3 0.4642 0.32015 0.000 0.000 0.592 0.356 0.000 0.052
#> SRR1340943 4 0.6199 0.52718 0.088 0.000 0.000 0.580 0.216 0.116
#> SRR1078855 6 0.3290 0.87644 0.252 0.000 0.000 0.000 0.004 0.744
#> SRR1459465 2 0.0951 0.88946 0.000 0.968 0.000 0.008 0.004 0.020
#> SRR816818 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0922 0.69777 0.000 0.000 0.968 0.024 0.004 0.004
#> SRR1350979 3 0.2320 0.66128 0.000 0.000 0.864 0.000 0.132 0.004
#> SRR1458198 1 0.6064 0.31333 0.516 0.000 0.000 0.052 0.336 0.096
#> SRR1386910 3 0.6188 -0.00579 0.004 0.032 0.448 0.432 0.020 0.064
#> SRR1465375 4 0.3650 0.71372 0.004 0.032 0.072 0.840 0.016 0.036
#> SRR1323699 3 0.2793 0.66438 0.000 0.000 0.856 0.004 0.112 0.028
#> SRR1431139 3 0.2485 0.69016 0.004 0.000 0.884 0.020 0.088 0.004
#> SRR1373964 3 0.2006 0.67754 0.000 0.000 0.892 0.000 0.104 0.004
#> SRR1455413 1 0.5533 0.05948 0.464 0.000 0.012 0.020 0.456 0.048
#> SRR1437163 1 0.2382 0.73881 0.904 0.000 0.004 0.024 0.020 0.048
#> SRR1347343 3 0.2489 0.65946 0.000 0.000 0.860 0.000 0.128 0.012
#> SRR1465480 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.2118 0.74578 0.916 0.000 0.004 0.012 0.020 0.048
#> SRR1086514 4 0.5173 0.51201 0.000 0.040 0.248 0.656 0.004 0.052
#> SRR1430928 1 0.2178 0.73816 0.868 0.000 0.000 0.000 0.000 0.132
#> SRR1310939 3 0.5359 0.37286 0.008 0.000 0.608 0.044 0.304 0.036
#> SRR1344294 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.2340 0.72877 0.852 0.000 0.000 0.000 0.000 0.148
#> SRR1468118 5 0.2980 0.68753 0.000 0.000 0.192 0.000 0.800 0.008
#> SRR1486348 1 0.1910 0.75119 0.892 0.000 0.000 0.000 0.000 0.108
#> SRR1488770 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.2048 0.74582 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1456611 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.3444 0.73364 0.836 0.000 0.000 0.032 0.076 0.056
#> SRR1500089 5 0.5732 0.11323 0.348 0.000 0.000 0.032 0.532 0.088
#> SRR1441178 6 0.4770 0.82270 0.268 0.000 0.000 0.036 0.032 0.664
#> SRR1381396 1 0.3745 0.69584 0.796 0.000 0.000 0.032 0.028 0.144
#> SRR1096081 5 0.3200 0.69009 0.000 0.000 0.196 0.000 0.788 0.016
#> SRR1349809 4 0.7971 0.26111 0.028 0.280 0.260 0.348 0.020 0.064
#> SRR1324314 3 0.7288 0.07309 0.160 0.000 0.452 0.004 0.172 0.212
#> SRR1092444 1 0.3895 0.71335 0.800 0.000 0.000 0.032 0.108 0.060
#> SRR1382553 6 0.4811 0.42373 0.020 0.000 0.280 0.004 0.040 0.656
#> SRR1075530 4 0.3303 0.70182 0.000 0.040 0.092 0.844 0.004 0.020
#> SRR1442612 3 0.2278 0.66467 0.000 0.000 0.868 0.000 0.128 0.004
#> SRR1360056 5 0.4900 0.61101 0.000 0.000 0.108 0.004 0.656 0.232
#> SRR1078164 6 0.4770 0.82207 0.268 0.000 0.000 0.036 0.032 0.664
#> SRR1434545 4 0.6199 0.52718 0.088 0.000 0.000 0.580 0.216 0.116
#> SRR1398251 6 0.3411 0.87661 0.232 0.000 0.004 0.000 0.008 0.756
#> SRR1375866 1 0.3855 0.69107 0.788 0.000 0.000 0.032 0.032 0.148
#> SRR1091645 4 0.3268 0.72343 0.000 0.008 0.016 0.852 0.068 0.056
#> SRR1416636 5 0.3330 0.64873 0.000 0.000 0.284 0.000 0.716 0.000
#> SRR1105441 3 0.3048 0.67012 0.000 0.000 0.844 0.116 0.012 0.028
#> SRR1082496 2 0.0146 0.90237 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1315353 3 0.4800 0.44022 0.000 0.000 0.640 0.280 0.004 0.076
#> SRR1093697 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.3161 0.68398 0.000 0.000 0.216 0.000 0.776 0.008
#> SRR1076120 5 0.6123 0.15218 0.320 0.000 0.000 0.060 0.524 0.096
#> SRR1074410 1 0.3855 0.69107 0.788 0.000 0.000 0.032 0.032 0.148
#> SRR1340345 4 0.2536 0.72078 0.000 0.040 0.064 0.888 0.004 0.004
#> SRR1069514 3 0.1391 0.69337 0.000 0.000 0.944 0.040 0.000 0.016
#> SRR1092636 5 0.3428 0.62888 0.000 0.000 0.304 0.000 0.696 0.000
#> SRR1365013 3 0.5783 0.14663 0.004 0.008 0.500 0.396 0.016 0.076
#> SRR1073069 6 0.3488 0.87978 0.244 0.000 0.000 0.004 0.008 0.744
#> SRR1443137 6 0.3265 0.87756 0.248 0.000 0.000 0.004 0.000 0.748
#> SRR1437143 2 0.0000 0.90359 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.4720 0.72903 0.340 0.000 0.000 0.028 0.020 0.612
#> SRR820234 2 0.3798 0.75931 0.000 0.820 0.044 0.060 0.004 0.072
#> SRR1338079 1 0.2505 0.75432 0.880 0.000 0.000 0.008 0.020 0.092
#> SRR1390094 3 0.4733 0.62021 0.016 0.000 0.760 0.084 0.092 0.048
#> SRR1340721 1 0.6738 0.19384 0.536 0.004 0.112 0.264 0.020 0.064
#> SRR1335964 3 0.3437 0.53455 0.000 0.000 0.752 0.008 0.236 0.004
#> SRR1086869 5 0.3046 0.68843 0.000 0.000 0.188 0.000 0.800 0.012
#> SRR1453434 1 0.5445 0.47823 0.632 0.000 0.000 0.020 0.160 0.188
#> SRR1402261 4 0.6199 0.52718 0.088 0.000 0.000 0.580 0.216 0.116
#> SRR657809 4 0.4389 0.66151 0.000 0.040 0.132 0.772 0.012 0.044
#> SRR1093075 6 0.3240 0.87978 0.244 0.000 0.000 0.000 0.004 0.752
#> SRR1433329 6 0.3463 0.87996 0.240 0.000 0.000 0.004 0.008 0.748
#> SRR1353418 5 0.4900 0.61101 0.000 0.000 0.108 0.004 0.656 0.232
#> SRR1092913 4 0.2600 0.73307 0.000 0.036 0.024 0.896 0.008 0.036
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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.982 0.967 0.985 0.5028 0.499 0.499
#> 3 3 0.945 0.937 0.971 0.3093 0.765 0.563
#> 4 4 0.712 0.585 0.755 0.1190 0.889 0.695
#> 5 5 0.783 0.775 0.884 0.0681 0.868 0.571
#> 6 6 0.814 0.707 0.862 0.0424 0.934 0.707
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
#> SRR816969 1 0.0000 0.977 1.000 0.000
#> SRR1335605 2 0.0000 0.993 0.000 1.000
#> SRR1432014 2 0.0000 0.993 0.000 1.000
#> SRR1499215 2 0.0000 0.993 0.000 1.000
#> SRR1460409 1 0.0000 0.977 1.000 0.000
#> SRR1086441 1 0.0000 0.977 1.000 0.000
#> SRR1097344 2 0.0000 0.993 0.000 1.000
#> SRR1081789 2 0.0000 0.993 0.000 1.000
#> SRR1453005 2 0.0000 0.993 0.000 1.000
#> SRR1366985 1 0.0000 0.977 1.000 0.000
#> SRR815280 1 0.0000 0.977 1.000 0.000
#> SRR1348531 1 0.0000 0.977 1.000 0.000
#> SRR815845 2 0.0000 0.993 0.000 1.000
#> SRR1471178 1 0.0000 0.977 1.000 0.000
#> SRR1080696 2 0.0000 0.993 0.000 1.000
#> SRR1078684 2 0.0000 0.993 0.000 1.000
#> SRR1317751 1 0.5842 0.837 0.860 0.140
#> SRR1435667 2 0.0000 0.993 0.000 1.000
#> SRR1097905 1 0.0000 0.977 1.000 0.000
#> SRR1456548 1 0.0000 0.977 1.000 0.000
#> SRR1075126 1 0.0000 0.977 1.000 0.000
#> SRR813108 2 0.0000 0.993 0.000 1.000
#> SRR1479062 2 0.0000 0.993 0.000 1.000
#> SRR1408703 2 0.0000 0.993 0.000 1.000
#> SRR1332360 1 0.0000 0.977 1.000 0.000
#> SRR1098686 1 0.0000 0.977 1.000 0.000
#> SRR1434228 1 0.0000 0.977 1.000 0.000
#> SRR1467149 1 0.0000 0.977 1.000 0.000
#> SRR1399113 2 0.0000 0.993 0.000 1.000
#> SRR1476507 2 0.0000 0.993 0.000 1.000
#> SRR1092468 1 0.0000 0.977 1.000 0.000
#> SRR1441804 1 0.0000 0.977 1.000 0.000
#> SRR1326100 2 0.0000 0.993 0.000 1.000
#> SRR1398815 1 0.0000 0.977 1.000 0.000
#> SRR1436021 2 0.0000 0.993 0.000 1.000
#> SRR1480083 2 0.0000 0.993 0.000 1.000
#> SRR1472863 1 0.0000 0.977 1.000 0.000
#> SRR815542 1 0.0000 0.977 1.000 0.000
#> SRR1400100 2 0.0000 0.993 0.000 1.000
#> SRR1312002 1 0.0000 0.977 1.000 0.000
#> SRR1470253 1 0.0000 0.977 1.000 0.000
#> SRR1414332 1 0.0000 0.977 1.000 0.000
#> SRR1069209 1 0.0000 0.977 1.000 0.000
#> SRR661052 1 0.0000 0.977 1.000 0.000
#> SRR1308860 1 0.0000 0.977 1.000 0.000
#> SRR1421159 2 0.0000 0.993 0.000 1.000
#> SRR1340943 1 0.0376 0.974 0.996 0.004
#> SRR1078855 1 0.0000 0.977 1.000 0.000
#> SRR1459465 2 0.0000 0.993 0.000 1.000
#> SRR816818 2 0.0000 0.993 0.000 1.000
#> SRR1478679 2 0.0000 0.993 0.000 1.000
#> SRR1350979 2 0.0000 0.993 0.000 1.000
#> SRR1458198 1 0.0000 0.977 1.000 0.000
#> SRR1386910 2 0.0000 0.993 0.000 1.000
#> SRR1465375 2 0.0000 0.993 0.000 1.000
#> SRR1323699 2 0.3274 0.934 0.060 0.940
#> SRR1431139 2 0.3733 0.921 0.072 0.928
#> SRR1373964 2 0.0000 0.993 0.000 1.000
#> SRR1455413 1 0.0000 0.977 1.000 0.000
#> SRR1437163 1 0.0000 0.977 1.000 0.000
#> SRR1347343 2 0.0000 0.993 0.000 1.000
#> SRR1465480 2 0.0000 0.993 0.000 1.000
#> SRR1489631 1 0.0000 0.977 1.000 0.000
#> SRR1086514 2 0.0000 0.993 0.000 1.000
#> SRR1430928 1 0.0000 0.977 1.000 0.000
#> SRR1310939 2 0.0000 0.993 0.000 1.000
#> SRR1344294 2 0.0000 0.993 0.000 1.000
#> SRR1099402 1 0.0000 0.977 1.000 0.000
#> SRR1468118 1 0.7528 0.739 0.784 0.216
#> SRR1486348 1 0.0000 0.977 1.000 0.000
#> SRR1488770 2 0.0000 0.993 0.000 1.000
#> SRR1083732 1 0.0000 0.977 1.000 0.000
#> SRR1456611 2 0.0000 0.993 0.000 1.000
#> SRR1080318 1 0.0000 0.977 1.000 0.000
#> SRR1500089 1 0.0000 0.977 1.000 0.000
#> SRR1441178 1 0.0000 0.977 1.000 0.000
#> SRR1381396 1 0.0000 0.977 1.000 0.000
#> SRR1096081 1 0.7056 0.772 0.808 0.192
#> SRR1349809 2 0.0000 0.993 0.000 1.000
#> SRR1324314 1 0.0000 0.977 1.000 0.000
#> SRR1092444 1 0.0000 0.977 1.000 0.000
#> SRR1382553 1 0.0000 0.977 1.000 0.000
#> SRR1075530 2 0.0000 0.993 0.000 1.000
#> SRR1442612 2 0.0000 0.993 0.000 1.000
#> SRR1360056 1 0.0000 0.977 1.000 0.000
#> SRR1078164 1 0.0000 0.977 1.000 0.000
#> SRR1434545 1 0.9460 0.458 0.636 0.364
#> SRR1398251 1 0.0000 0.977 1.000 0.000
#> SRR1375866 1 0.0000 0.977 1.000 0.000
#> SRR1091645 2 0.0000 0.993 0.000 1.000
#> SRR1416636 2 0.3114 0.937 0.056 0.944
#> SRR1105441 2 0.0000 0.993 0.000 1.000
#> SRR1082496 2 0.0000 0.993 0.000 1.000
#> SRR1315353 2 0.0000 0.993 0.000 1.000
#> SRR1093697 2 0.0000 0.993 0.000 1.000
#> SRR1077429 1 0.0000 0.977 1.000 0.000
#> SRR1076120 1 0.0000 0.977 1.000 0.000
#> SRR1074410 1 0.0000 0.977 1.000 0.000
#> SRR1340345 2 0.0000 0.993 0.000 1.000
#> SRR1069514 2 0.0000 0.993 0.000 1.000
#> SRR1092636 1 0.8608 0.606 0.716 0.284
#> SRR1365013 2 0.0000 0.993 0.000 1.000
#> SRR1073069 1 0.0000 0.977 1.000 0.000
#> SRR1443137 1 0.0000 0.977 1.000 0.000
#> SRR1437143 2 0.0000 0.993 0.000 1.000
#> SRR1091990 1 0.0000 0.977 1.000 0.000
#> SRR820234 2 0.0000 0.993 0.000 1.000
#> SRR1338079 1 0.0000 0.977 1.000 0.000
#> SRR1390094 2 0.0000 0.993 0.000 1.000
#> SRR1340721 2 0.7219 0.749 0.200 0.800
#> SRR1335964 2 0.0000 0.993 0.000 1.000
#> SRR1086869 1 0.7219 0.761 0.800 0.200
#> SRR1453434 1 0.0000 0.977 1.000 0.000
#> SRR1402261 1 0.0000 0.977 1.000 0.000
#> SRR657809 2 0.0000 0.993 0.000 1.000
#> SRR1093075 1 0.0000 0.977 1.000 0.000
#> SRR1433329 1 0.0000 0.977 1.000 0.000
#> SRR1353418 1 0.0000 0.977 1.000 0.000
#> SRR1092913 2 0.0000 0.993 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1335605 2 0.3686 0.824 0.000 0.860 0.140
#> SRR1432014 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1499215 3 0.1289 0.924 0.000 0.032 0.968
#> SRR1460409 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1097344 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1081789 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1453005 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1366985 3 0.4555 0.771 0.200 0.000 0.800
#> SRR815280 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.987 1.000 0.000 0.000
#> SRR815845 3 0.5760 0.519 0.000 0.328 0.672
#> SRR1471178 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1078684 3 0.3551 0.838 0.000 0.132 0.868
#> SRR1317751 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1435667 3 0.1289 0.924 0.000 0.032 0.968
#> SRR1097905 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.987 1.000 0.000 0.000
#> SRR813108 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1479062 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1332360 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1434228 1 0.5098 0.649 0.752 0.000 0.248
#> SRR1467149 1 0.1289 0.964 0.968 0.000 0.032
#> SRR1399113 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1476507 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1092468 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1441804 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1436021 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1480083 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1472863 1 0.0000 0.987 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1400100 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1312002 3 0.4178 0.804 0.172 0.000 0.828
#> SRR1470253 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1414332 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.987 1.000 0.000 0.000
#> SRR661052 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1421159 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1340943 1 0.2689 0.933 0.932 0.036 0.032
#> SRR1078855 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.969 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1478679 3 0.5138 0.682 0.000 0.252 0.748
#> SRR1350979 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1458198 1 0.1289 0.964 0.968 0.000 0.032
#> SRR1386910 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1465375 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1323699 3 0.0424 0.936 0.008 0.000 0.992
#> SRR1431139 3 0.0983 0.933 0.004 0.016 0.980
#> SRR1373964 3 0.1289 0.924 0.000 0.032 0.968
#> SRR1455413 1 0.1289 0.964 0.968 0.000 0.032
#> SRR1437163 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1347343 3 0.0592 0.935 0.000 0.012 0.988
#> SRR1465480 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1086514 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1430928 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1500089 1 0.1289 0.964 0.968 0.000 0.032
#> SRR1441178 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1349809 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1324314 3 0.5216 0.681 0.260 0.000 0.740
#> SRR1092444 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1382553 3 0.4555 0.771 0.200 0.000 0.800
#> SRR1075530 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1442612 3 0.0592 0.935 0.000 0.012 0.988
#> SRR1360056 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1078164 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1434545 2 0.2313 0.921 0.024 0.944 0.032
#> SRR1398251 1 0.2448 0.913 0.924 0.000 0.076
#> SRR1375866 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1091645 2 0.0747 0.955 0.000 0.984 0.016
#> SRR1416636 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1105441 2 0.6126 0.273 0.000 0.600 0.400
#> SRR1082496 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1315353 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1093697 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1076120 1 0.1289 0.964 0.968 0.000 0.032
#> SRR1074410 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1340345 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1069514 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1092636 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1365013 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1073069 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.987 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1338079 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1390094 2 0.6235 0.262 0.000 0.564 0.436
#> SRR1340721 2 0.1753 0.919 0.048 0.952 0.000
#> SRR1335964 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1402261 1 0.2176 0.948 0.948 0.020 0.032
#> SRR657809 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1093075 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1353418 3 0.0000 0.940 0.000 0.000 1.000
#> SRR1092913 2 0.0000 0.969 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.0927 0.9156 0.000 0.976 0.016 0.008
#> SRR1432014 3 0.4500 0.5150 0.000 0.000 0.684 0.316
#> SRR1499215 3 0.3528 0.5328 0.000 0.000 0.808 0.192
#> SRR1460409 1 0.3942 0.6990 0.764 0.000 0.000 0.236
#> SRR1086441 1 0.3801 0.7053 0.780 0.000 0.000 0.220
#> SRR1097344 2 0.2704 0.8388 0.000 0.876 0.000 0.124
#> SRR1081789 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1453005 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1366985 3 0.4193 0.3541 0.268 0.000 0.732 0.000
#> SRR815280 1 0.1557 0.7326 0.944 0.000 0.056 0.000
#> SRR1348531 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR815845 2 0.7687 -0.1527 0.000 0.448 0.312 0.240
#> SRR1471178 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1080696 4 0.4977 -0.1043 0.000 0.000 0.460 0.540
#> SRR1078684 3 0.5557 0.5063 0.000 0.040 0.652 0.308
#> SRR1317751 4 0.4790 0.1226 0.000 0.000 0.380 0.620
#> SRR1435667 3 0.5206 0.5137 0.000 0.024 0.668 0.308
#> SRR1097905 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1456548 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1075126 1 0.2149 0.7384 0.912 0.000 0.000 0.088
#> SRR813108 2 0.0376 0.9265 0.000 0.992 0.004 0.004
#> SRR1479062 4 0.4790 0.1102 0.000 0.000 0.380 0.620
#> SRR1408703 4 0.4898 0.0265 0.000 0.000 0.416 0.584
#> SRR1332360 1 0.4605 0.5170 0.664 0.000 0.336 0.000
#> SRR1098686 1 0.3837 0.7041 0.776 0.000 0.000 0.224
#> SRR1434228 1 0.4981 0.2789 0.536 0.000 0.464 0.000
#> SRR1467149 4 0.4454 0.2195 0.308 0.000 0.000 0.692
#> SRR1399113 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1476507 2 0.3074 0.8105 0.000 0.848 0.000 0.152
#> SRR1092468 1 0.4999 0.3062 0.508 0.000 0.000 0.492
#> SRR1441804 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1326100 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1436021 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1480083 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR815542 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1400100 2 0.0817 0.9144 0.000 0.976 0.000 0.024
#> SRR1312002 3 0.3962 0.3934 0.152 0.000 0.820 0.028
#> SRR1470253 3 0.7220 0.2535 0.260 0.000 0.544 0.196
#> SRR1414332 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.4585 0.5219 0.668 0.000 0.332 0.000
#> SRR661052 1 0.3873 0.7023 0.772 0.000 0.000 0.228
#> SRR1308860 1 0.4008 0.6945 0.756 0.000 0.000 0.244
#> SRR1421159 2 0.0188 0.9285 0.000 0.996 0.000 0.004
#> SRR1340943 4 0.4741 0.1775 0.328 0.004 0.000 0.668
#> SRR1078855 1 0.4585 0.5221 0.668 0.000 0.332 0.000
#> SRR1459465 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.6973 0.3731 0.000 0.196 0.584 0.220
#> SRR1350979 3 0.4585 0.4954 0.000 0.000 0.668 0.332
#> SRR1458198 4 0.4585 0.1695 0.332 0.000 0.000 0.668
#> SRR1386910 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1465375 2 0.1211 0.9084 0.000 0.960 0.000 0.040
#> SRR1323699 3 0.3764 0.5353 0.000 0.000 0.784 0.216
#> SRR1431139 3 0.5112 0.4912 0.008 0.004 0.648 0.340
#> SRR1373964 3 0.5108 0.5172 0.000 0.020 0.672 0.308
#> SRR1455413 4 0.4500 0.2069 0.316 0.000 0.000 0.684
#> SRR1437163 1 0.4008 0.6945 0.756 0.000 0.000 0.244
#> SRR1347343 3 0.4454 0.5216 0.000 0.000 0.692 0.308
#> SRR1465480 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1086514 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1430928 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.4331 0.1722 0.000 0.000 0.288 0.712
#> SRR1344294 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1468118 4 0.4304 0.1807 0.000 0.000 0.284 0.716
#> SRR1486348 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1500089 4 0.4454 0.2195 0.308 0.000 0.000 0.692
#> SRR1441178 1 0.4500 0.5386 0.684 0.000 0.316 0.000
#> SRR1381396 1 0.0188 0.7528 0.996 0.000 0.000 0.004
#> SRR1096081 4 0.4790 0.1182 0.000 0.000 0.380 0.620
#> SRR1349809 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1324314 3 0.5088 0.3287 0.288 0.000 0.688 0.024
#> SRR1092444 1 0.4040 0.6922 0.752 0.000 0.000 0.248
#> SRR1382553 3 0.4193 0.3541 0.268 0.000 0.732 0.000
#> SRR1075530 2 0.0592 0.9228 0.000 0.984 0.000 0.016
#> SRR1442612 3 0.5108 0.5172 0.000 0.020 0.672 0.308
#> SRR1360056 3 0.3356 0.3668 0.000 0.000 0.824 0.176
#> SRR1078164 1 0.3942 0.6124 0.764 0.000 0.236 0.000
#> SRR1434545 4 0.5864 0.2514 0.072 0.264 0.000 0.664
#> SRR1398251 1 0.4941 0.3506 0.564 0.000 0.436 0.000
#> SRR1375866 1 0.0000 0.7530 1.000 0.000 0.000 0.000
#> SRR1091645 2 0.4454 0.5963 0.000 0.692 0.000 0.308
#> SRR1416636 4 0.4888 0.0369 0.000 0.000 0.412 0.588
#> SRR1105441 2 0.7578 -0.0558 0.000 0.480 0.284 0.236
#> SRR1082496 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.0188 0.9285 0.000 0.996 0.000 0.004
#> SRR1093697 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1077429 4 0.4776 0.1245 0.000 0.000 0.376 0.624
#> SRR1076120 4 0.5131 0.2320 0.280 0.000 0.028 0.692
#> SRR1074410 1 0.0188 0.7528 0.996 0.000 0.000 0.004
#> SRR1340345 2 0.0921 0.9162 0.000 0.972 0.000 0.028
#> SRR1069514 2 0.6790 0.4012 0.000 0.608 0.200 0.192
#> SRR1092636 4 0.4977 -0.1043 0.000 0.000 0.460 0.540
#> SRR1365013 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1073069 1 0.4661 0.5005 0.652 0.000 0.348 0.000
#> SRR1443137 1 0.4585 0.5221 0.668 0.000 0.332 0.000
#> SRR1437143 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.2149 0.7173 0.912 0.000 0.088 0.000
#> SRR820234 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.3873 0.7023 0.772 0.000 0.000 0.228
#> SRR1390094 4 0.7907 -0.1093 0.000 0.308 0.328 0.364
#> SRR1340721 2 0.2408 0.8221 0.104 0.896 0.000 0.000
#> SRR1335964 4 0.4746 0.1263 0.000 0.000 0.368 0.632
#> SRR1086869 4 0.4454 0.1731 0.000 0.000 0.308 0.692
#> SRR1453434 1 0.4539 0.6693 0.720 0.000 0.008 0.272
#> SRR1402261 4 0.4741 0.1775 0.328 0.004 0.000 0.668
#> SRR657809 2 0.0000 0.9302 0.000 1.000 0.000 0.000
#> SRR1093075 1 0.4585 0.5221 0.668 0.000 0.332 0.000
#> SRR1433329 1 0.4585 0.5221 0.668 0.000 0.332 0.000
#> SRR1353418 3 0.3311 0.3710 0.000 0.000 0.828 0.172
#> SRR1092913 2 0.1302 0.9056 0.000 0.956 0.000 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1335605 2 0.3174 0.785 0.000 0.844 0.132 0.004 0.020
#> SRR1432014 3 0.0000 0.720 0.000 0.000 1.000 0.000 0.000
#> SRR1499215 3 0.3561 0.544 0.000 0.000 0.740 0.000 0.260
#> SRR1460409 1 0.1697 0.921 0.932 0.000 0.000 0.060 0.008
#> SRR1086441 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1097344 2 0.3707 0.669 0.000 0.716 0.000 0.284 0.000
#> SRR1081789 2 0.0162 0.914 0.000 0.996 0.004 0.000 0.000
#> SRR1453005 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1366985 5 0.2136 0.861 0.008 0.000 0.088 0.000 0.904
#> SRR815280 1 0.2605 0.814 0.852 0.000 0.000 0.000 0.148
#> SRR1348531 1 0.0703 0.949 0.976 0.000 0.000 0.024 0.000
#> SRR815845 3 0.3766 0.545 0.000 0.268 0.728 0.000 0.004
#> SRR1471178 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1080696 3 0.4933 0.522 0.000 0.000 0.704 0.200 0.096
#> SRR1078684 3 0.0963 0.711 0.000 0.036 0.964 0.000 0.000
#> SRR1317751 4 0.6275 0.383 0.000 0.000 0.308 0.516 0.176
#> SRR1435667 3 0.0162 0.721 0.000 0.004 0.996 0.000 0.000
#> SRR1097905 1 0.0290 0.955 0.992 0.000 0.000 0.008 0.000
#> SRR1456548 1 0.0290 0.955 0.992 0.000 0.000 0.008 0.000
#> SRR1075126 1 0.2848 0.855 0.868 0.000 0.000 0.104 0.028
#> SRR813108 2 0.3612 0.609 0.000 0.732 0.268 0.000 0.000
#> SRR1479062 3 0.5831 0.066 0.000 0.000 0.496 0.408 0.096
#> SRR1408703 3 0.5137 0.479 0.000 0.000 0.676 0.228 0.096
#> SRR1332360 5 0.1908 0.912 0.092 0.000 0.000 0.000 0.908
#> SRR1098686 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000
#> SRR1434228 5 0.2077 0.911 0.084 0.000 0.008 0.000 0.908
#> SRR1467149 4 0.0451 0.677 0.004 0.000 0.000 0.988 0.008
#> SRR1399113 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 2 0.4060 0.539 0.000 0.640 0.000 0.360 0.000
#> SRR1092468 4 0.4173 0.453 0.300 0.000 0.012 0.688 0.000
#> SRR1441804 1 0.0703 0.949 0.976 0.000 0.000 0.024 0.000
#> SRR1326100 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1398815 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1436021 2 0.1341 0.889 0.000 0.944 0.000 0.056 0.000
#> SRR1480083 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR815542 1 0.1571 0.923 0.936 0.000 0.000 0.060 0.004
#> SRR1400100 2 0.0162 0.914 0.000 0.996 0.004 0.000 0.000
#> SRR1312002 5 0.1393 0.876 0.024 0.000 0.012 0.008 0.956
#> SRR1470253 5 0.0912 0.841 0.000 0.000 0.016 0.012 0.972
#> SRR1414332 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1069209 5 0.1908 0.912 0.092 0.000 0.000 0.000 0.908
#> SRR661052 1 0.0324 0.957 0.992 0.000 0.000 0.004 0.004
#> SRR1308860 1 0.0290 0.955 0.992 0.000 0.000 0.008 0.000
#> SRR1421159 2 0.4541 0.554 0.000 0.680 0.288 0.032 0.000
#> SRR1340943 4 0.1843 0.673 0.052 0.008 0.000 0.932 0.008
#> SRR1078855 5 0.1965 0.910 0.096 0.000 0.000 0.000 0.904
#> SRR1459465 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.3863 0.618 0.000 0.152 0.796 0.000 0.052
#> SRR1350979 3 0.0000 0.720 0.000 0.000 1.000 0.000 0.000
#> SRR1458198 4 0.2470 0.662 0.104 0.000 0.000 0.884 0.012
#> SRR1386910 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1465375 2 0.2732 0.816 0.000 0.840 0.000 0.160 0.000
#> SRR1323699 3 0.3274 0.584 0.000 0.000 0.780 0.000 0.220
#> SRR1431139 3 0.0290 0.718 0.000 0.000 0.992 0.008 0.000
#> SRR1373964 3 0.0162 0.721 0.000 0.004 0.996 0.000 0.000
#> SRR1455413 4 0.4372 0.610 0.172 0.000 0.000 0.756 0.072
#> SRR1437163 1 0.0290 0.955 0.992 0.000 0.000 0.008 0.000
#> SRR1347343 3 0.0000 0.720 0.000 0.000 1.000 0.000 0.000
#> SRR1465480 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.0290 0.955 0.992 0.000 0.000 0.008 0.000
#> SRR1086514 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1430928 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1310939 4 0.4620 0.179 0.000 0.000 0.392 0.592 0.016
#> SRR1344294 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0703 0.947 0.976 0.000 0.000 0.000 0.024
#> SRR1468118 4 0.5498 0.469 0.000 0.000 0.292 0.612 0.096
#> SRR1486348 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1488770 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1456611 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0794 0.947 0.972 0.000 0.000 0.028 0.000
#> SRR1500089 4 0.2359 0.678 0.060 0.000 0.000 0.904 0.036
#> SRR1441178 5 0.3366 0.766 0.232 0.000 0.000 0.000 0.768
#> SRR1381396 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1096081 4 0.6234 0.355 0.000 0.000 0.332 0.508 0.160
#> SRR1349809 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1324314 5 0.3616 0.843 0.052 0.000 0.116 0.004 0.828
#> SRR1092444 1 0.2338 0.873 0.884 0.000 0.000 0.112 0.004
#> SRR1382553 5 0.2136 0.861 0.008 0.000 0.088 0.000 0.904
#> SRR1075530 2 0.2230 0.852 0.000 0.884 0.000 0.116 0.000
#> SRR1442612 3 0.0000 0.720 0.000 0.000 1.000 0.000 0.000
#> SRR1360056 5 0.1012 0.838 0.000 0.000 0.020 0.012 0.968
#> SRR1078164 5 0.3684 0.701 0.280 0.000 0.000 0.000 0.720
#> SRR1434545 4 0.1788 0.653 0.004 0.056 0.000 0.932 0.008
#> SRR1398251 5 0.2077 0.911 0.084 0.000 0.008 0.000 0.908
#> SRR1375866 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1091645 4 0.3561 0.470 0.000 0.260 0.000 0.740 0.000
#> SRR1416636 3 0.5516 0.339 0.000 0.000 0.608 0.296 0.096
#> SRR1105441 3 0.3913 0.489 0.000 0.324 0.676 0.000 0.000
#> SRR1082496 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.3242 0.690 0.000 0.784 0.216 0.000 0.000
#> SRR1093697 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 4 0.5815 0.263 0.000 0.000 0.396 0.508 0.096
#> SRR1076120 4 0.0566 0.677 0.004 0.000 0.000 0.984 0.012
#> SRR1074410 1 0.0162 0.957 0.996 0.000 0.000 0.000 0.004
#> SRR1340345 2 0.2516 0.833 0.000 0.860 0.000 0.140 0.000
#> SRR1069514 3 0.3561 0.534 0.000 0.260 0.740 0.000 0.000
#> SRR1092636 3 0.4964 0.516 0.000 0.000 0.700 0.204 0.096
#> SRR1365013 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1073069 5 0.1908 0.912 0.092 0.000 0.000 0.000 0.908
#> SRR1443137 5 0.1965 0.910 0.096 0.000 0.000 0.000 0.904
#> SRR1437143 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.2179 0.862 0.888 0.000 0.000 0.000 0.112
#> SRR820234 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> SRR1338079 1 0.0451 0.956 0.988 0.000 0.000 0.008 0.004
#> SRR1390094 3 0.5405 0.266 0.000 0.076 0.596 0.328 0.000
#> SRR1340721 2 0.3305 0.658 0.224 0.776 0.000 0.000 0.000
#> SRR1335964 3 0.4612 0.550 0.000 0.000 0.736 0.180 0.084
#> SRR1086869 4 0.5534 0.459 0.000 0.000 0.300 0.604 0.096
#> SRR1453434 1 0.5928 0.355 0.548 0.000 0.000 0.328 0.124
#> SRR1402261 4 0.1788 0.673 0.056 0.004 0.000 0.932 0.008
#> SRR657809 2 0.0290 0.913 0.000 0.992 0.000 0.008 0.000
#> SRR1093075 5 0.1965 0.910 0.096 0.000 0.000 0.000 0.904
#> SRR1433329 5 0.1908 0.912 0.092 0.000 0.000 0.000 0.908
#> SRR1353418 5 0.1597 0.815 0.000 0.000 0.048 0.012 0.940
#> SRR1092913 2 0.3003 0.789 0.000 0.812 0.000 0.188 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1327 0.9002 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1335605 2 0.3799 0.6235 0.008 0.772 0.024 0.008 0.188 0.000
#> SRR1432014 3 0.0458 0.8332 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1499215 3 0.1610 0.7923 0.000 0.000 0.916 0.000 0.000 0.084
#> SRR1460409 1 0.4260 0.8013 0.776 0.000 0.004 0.104 0.024 0.092
#> SRR1086441 1 0.1204 0.9015 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1097344 4 0.4026 0.3424 0.000 0.376 0.012 0.612 0.000 0.000
#> SRR1081789 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1453005 2 0.0405 0.8838 0.000 0.988 0.004 0.008 0.000 0.000
#> SRR1366985 6 0.1007 0.8481 0.000 0.000 0.044 0.000 0.000 0.956
#> SRR815280 1 0.4165 0.4353 0.568 0.000 0.000 0.004 0.008 0.420
#> SRR1348531 1 0.3057 0.8586 0.868 0.000 0.004 0.048 0.044 0.036
#> SRR815845 3 0.5012 0.4365 0.000 0.336 0.576 0.000 0.088 0.000
#> SRR1471178 1 0.1327 0.9002 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1080696 5 0.1814 0.8334 0.000 0.000 0.100 0.000 0.900 0.000
#> SRR1078684 3 0.0725 0.8341 0.000 0.012 0.976 0.000 0.012 0.000
#> SRR1317751 5 0.0713 0.8390 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1435667 3 0.0508 0.8346 0.000 0.004 0.984 0.000 0.012 0.000
#> SRR1097905 1 0.0000 0.8926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1456548 1 0.0000 0.8926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1075126 1 0.5951 0.4491 0.536 0.000 0.004 0.212 0.008 0.240
#> SRR813108 2 0.3991 -0.0367 0.000 0.524 0.472 0.004 0.000 0.000
#> SRR1479062 5 0.2145 0.8303 0.000 0.000 0.072 0.028 0.900 0.000
#> SRR1408703 5 0.1765 0.8360 0.000 0.000 0.096 0.000 0.904 0.000
#> SRR1332360 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1098686 1 0.0547 0.8986 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1434228 6 0.0146 0.8691 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1467149 4 0.4047 0.3587 0.036 0.000 0.004 0.716 0.244 0.000
#> SRR1399113 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3819 0.4331 0.000 0.316 0.012 0.672 0.000 0.000
#> SRR1092468 4 0.5616 0.2572 0.136 0.000 0.016 0.580 0.268 0.000
#> SRR1441804 1 0.2374 0.8748 0.904 0.000 0.004 0.048 0.016 0.028
#> SRR1326100 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398815 1 0.1285 0.9016 0.944 0.000 0.000 0.000 0.004 0.052
#> SRR1436021 2 0.3855 0.4991 0.000 0.704 0.024 0.272 0.000 0.000
#> SRR1480083 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.0632 0.8963 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR815542 1 0.3377 0.8316 0.836 0.000 0.004 0.104 0.020 0.036
#> SRR1400100 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1312002 6 0.2446 0.7883 0.000 0.000 0.012 0.000 0.124 0.864
#> SRR1470253 6 0.3995 0.2183 0.000 0.000 0.000 0.004 0.480 0.516
#> SRR1414332 1 0.1327 0.9002 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1069209 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR661052 1 0.0146 0.8941 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1308860 1 0.0146 0.8941 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1421159 3 0.4903 0.3072 0.000 0.380 0.552 0.068 0.000 0.000
#> SRR1340943 4 0.0547 0.5236 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1078855 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1459465 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0993 0.8286 0.000 0.024 0.964 0.000 0.000 0.012
#> SRR1350979 3 0.0790 0.8258 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1458198 4 0.5229 0.3566 0.108 0.000 0.004 0.664 0.204 0.020
#> SRR1386910 2 0.0551 0.8826 0.000 0.984 0.004 0.008 0.004 0.000
#> SRR1465375 4 0.4428 0.1671 0.004 0.452 0.012 0.528 0.004 0.000
#> SRR1323699 3 0.1610 0.7923 0.000 0.000 0.916 0.000 0.000 0.084
#> SRR1431139 3 0.0858 0.8270 0.004 0.000 0.968 0.000 0.028 0.000
#> SRR1373964 3 0.0508 0.8346 0.000 0.004 0.984 0.000 0.012 0.000
#> SRR1455413 5 0.4233 0.5589 0.080 0.000 0.004 0.180 0.736 0.000
#> SRR1437163 1 0.0146 0.8941 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1347343 3 0.0458 0.8332 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1465480 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.0000 0.8926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1086514 2 0.1124 0.8604 0.000 0.956 0.008 0.036 0.000 0.000
#> SRR1430928 1 0.1327 0.9002 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1310939 4 0.5845 -0.0678 0.000 0.000 0.212 0.472 0.316 0.000
#> SRR1344294 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.3050 0.7556 0.764 0.000 0.000 0.000 0.000 0.236
#> SRR1468118 5 0.0777 0.8370 0.000 0.000 0.024 0.004 0.972 0.000
#> SRR1486348 1 0.0713 0.8962 0.972 0.000 0.000 0.000 0.000 0.028
#> SRR1488770 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1267 0.9014 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1456611 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.2675 0.8711 0.888 0.000 0.004 0.052 0.020 0.036
#> SRR1500089 5 0.4664 0.0819 0.024 0.000 0.004 0.468 0.500 0.004
#> SRR1441178 6 0.1901 0.8004 0.076 0.000 0.000 0.004 0.008 0.912
#> SRR1381396 1 0.1668 0.9001 0.928 0.000 0.000 0.004 0.008 0.060
#> SRR1096081 5 0.0713 0.8390 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1349809 2 0.0291 0.8862 0.000 0.992 0.000 0.004 0.004 0.000
#> SRR1324314 6 0.2680 0.8009 0.000 0.000 0.076 0.000 0.056 0.868
#> SRR1092444 1 0.4290 0.7832 0.780 0.000 0.004 0.084 0.096 0.036
#> SRR1382553 6 0.1007 0.8481 0.000 0.000 0.044 0.000 0.000 0.956
#> SRR1075530 2 0.4315 -0.1169 0.000 0.496 0.012 0.488 0.004 0.000
#> SRR1442612 3 0.0458 0.8332 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1360056 6 0.4080 0.2790 0.000 0.000 0.008 0.000 0.456 0.536
#> SRR1078164 6 0.2214 0.7797 0.092 0.000 0.000 0.004 0.012 0.892
#> SRR1434545 4 0.0547 0.5236 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1398251 6 0.0146 0.8691 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1375866 1 0.1728 0.8991 0.924 0.000 0.000 0.004 0.008 0.064
#> SRR1091645 4 0.4172 0.4501 0.000 0.300 0.012 0.672 0.016 0.000
#> SRR1416636 5 0.1765 0.8360 0.000 0.000 0.096 0.000 0.904 0.000
#> SRR1105441 3 0.3728 0.4862 0.000 0.344 0.652 0.000 0.004 0.000
#> SRR1082496 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.3073 0.6542 0.000 0.788 0.204 0.008 0.000 0.000
#> SRR1093697 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0858 0.8383 0.000 0.000 0.028 0.004 0.968 0.000
#> SRR1076120 4 0.4025 0.2737 0.016 0.000 0.004 0.668 0.312 0.000
#> SRR1074410 1 0.1728 0.8991 0.924 0.000 0.000 0.004 0.008 0.064
#> SRR1340345 4 0.4303 0.1432 0.000 0.460 0.012 0.524 0.004 0.000
#> SRR1069514 3 0.1075 0.8198 0.000 0.048 0.952 0.000 0.000 0.000
#> SRR1092636 5 0.1908 0.8354 0.000 0.000 0.096 0.004 0.900 0.000
#> SRR1365013 2 0.0405 0.8846 0.000 0.988 0.000 0.008 0.004 0.000
#> SRR1073069 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1443137 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1437143 2 0.0000 0.8900 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.4098 0.3851 0.548 0.000 0.000 0.004 0.004 0.444
#> SRR820234 2 0.0291 0.8860 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR1338079 1 0.0363 0.8954 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1390094 3 0.4375 0.2412 0.000 0.012 0.548 0.432 0.008 0.000
#> SRR1340721 2 0.3650 0.4971 0.272 0.716 0.000 0.008 0.004 0.000
#> SRR1335964 5 0.3979 0.2546 0.000 0.000 0.456 0.004 0.540 0.000
#> SRR1086869 5 0.0777 0.8370 0.000 0.000 0.024 0.004 0.972 0.000
#> SRR1453434 4 0.6353 -0.0324 0.340 0.000 0.004 0.460 0.024 0.172
#> SRR1402261 4 0.0547 0.5236 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR657809 2 0.2214 0.7992 0.000 0.892 0.012 0.092 0.004 0.000
#> SRR1093075 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1433329 6 0.0000 0.8700 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1353418 6 0.4181 0.2147 0.000 0.000 0.012 0.000 0.476 0.512
#> SRR1092913 4 0.4268 0.2288 0.000 0.428 0.012 0.556 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", "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 17780 rows and 119 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 0.593 0.852 0.922 0.4377 0.562 0.562
#> 3 3 0.436 0.392 0.705 0.4000 0.716 0.551
#> 4 4 0.462 0.524 0.727 0.1589 0.694 0.393
#> 5 5 0.722 0.761 0.883 0.0886 0.865 0.579
#> 6 6 0.707 0.663 0.802 0.0590 0.843 0.435
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
#> SRR816969 1 0.0000 0.9182 1.000 0.000
#> SRR1335605 1 0.8608 0.6459 0.716 0.284
#> SRR1432014 1 0.8955 0.6201 0.688 0.312
#> SRR1499215 1 0.8861 0.6339 0.696 0.304
#> SRR1460409 1 0.0000 0.9182 1.000 0.000
#> SRR1086441 1 0.0000 0.9182 1.000 0.000
#> SRR1097344 2 0.4939 0.8965 0.108 0.892
#> SRR1081789 2 0.7815 0.7492 0.232 0.768
#> SRR1453005 2 0.4161 0.9012 0.084 0.916
#> SRR1366985 1 0.1633 0.9088 0.976 0.024
#> SRR815280 1 0.0000 0.9182 1.000 0.000
#> SRR1348531 1 0.0000 0.9182 1.000 0.000
#> SRR815845 2 0.0000 0.8964 0.000 1.000
#> SRR1471178 1 0.0000 0.9182 1.000 0.000
#> SRR1080696 1 0.8386 0.6888 0.732 0.268
#> SRR1078684 1 0.8386 0.6888 0.732 0.268
#> SRR1317751 1 0.8386 0.6888 0.732 0.268
#> SRR1435667 2 0.4161 0.9012 0.084 0.916
#> SRR1097905 1 0.0000 0.9182 1.000 0.000
#> SRR1456548 1 0.0000 0.9182 1.000 0.000
#> SRR1075126 1 0.0000 0.9182 1.000 0.000
#> SRR813108 2 0.0000 0.8964 0.000 1.000
#> SRR1479062 2 0.9896 0.1762 0.440 0.560
#> SRR1408703 1 0.8386 0.6888 0.732 0.268
#> SRR1332360 1 0.1184 0.9122 0.984 0.016
#> SRR1098686 1 0.0000 0.9182 1.000 0.000
#> SRR1434228 1 0.1633 0.9088 0.976 0.024
#> SRR1467149 1 0.0000 0.9182 1.000 0.000
#> SRR1399113 2 0.1414 0.8956 0.020 0.980
#> SRR1476507 2 0.4939 0.8965 0.108 0.892
#> SRR1092468 1 0.0000 0.9182 1.000 0.000
#> SRR1441804 1 0.0000 0.9182 1.000 0.000
#> SRR1326100 2 0.0000 0.8964 0.000 1.000
#> SRR1398815 1 0.0000 0.9182 1.000 0.000
#> SRR1436021 2 0.4161 0.9012 0.084 0.916
#> SRR1480083 2 0.0000 0.8964 0.000 1.000
#> SRR1472863 1 0.0000 0.9182 1.000 0.000
#> SRR815542 1 0.0000 0.9182 1.000 0.000
#> SRR1400100 2 0.4161 0.9012 0.084 0.916
#> SRR1312002 1 0.1633 0.9088 0.976 0.024
#> SRR1470253 1 0.1414 0.9105 0.980 0.020
#> SRR1414332 1 0.0000 0.9182 1.000 0.000
#> SRR1069209 1 0.1633 0.9088 0.976 0.024
#> SRR661052 1 0.0000 0.9182 1.000 0.000
#> SRR1308860 1 0.0000 0.9182 1.000 0.000
#> SRR1421159 2 0.4161 0.9012 0.084 0.916
#> SRR1340943 1 0.0000 0.9182 1.000 0.000
#> SRR1078855 1 0.0000 0.9182 1.000 0.000
#> SRR1459465 2 0.1633 0.8949 0.024 0.976
#> SRR816818 2 0.1633 0.8949 0.024 0.976
#> SRR1478679 1 0.6148 0.8196 0.848 0.152
#> SRR1350979 1 0.8909 0.6271 0.692 0.308
#> SRR1458198 1 0.0000 0.9182 1.000 0.000
#> SRR1386910 2 0.1633 0.8949 0.024 0.976
#> SRR1465375 2 0.9286 0.5894 0.344 0.656
#> SRR1323699 1 0.5842 0.8297 0.860 0.140
#> SRR1431139 1 0.8386 0.6888 0.732 0.268
#> SRR1373964 2 0.9977 0.0318 0.472 0.528
#> SRR1455413 1 0.0000 0.9182 1.000 0.000
#> SRR1437163 1 0.0000 0.9182 1.000 0.000
#> SRR1347343 1 0.8955 0.6201 0.688 0.312
#> SRR1465480 2 0.1633 0.8949 0.024 0.976
#> SRR1489631 1 0.0000 0.9182 1.000 0.000
#> SRR1086514 2 0.4161 0.9012 0.084 0.916
#> SRR1430928 1 0.0000 0.9182 1.000 0.000
#> SRR1310939 1 0.3431 0.8862 0.936 0.064
#> SRR1344294 2 0.0000 0.8964 0.000 1.000
#> SRR1099402 1 0.0000 0.9182 1.000 0.000
#> SRR1468118 1 0.7815 0.7179 0.768 0.232
#> SRR1486348 1 0.0000 0.9182 1.000 0.000
#> SRR1488770 2 0.0000 0.8964 0.000 1.000
#> SRR1083732 1 0.0000 0.9182 1.000 0.000
#> SRR1456611 2 0.0000 0.8964 0.000 1.000
#> SRR1080318 1 0.0000 0.9182 1.000 0.000
#> SRR1500089 1 0.0000 0.9182 1.000 0.000
#> SRR1441178 1 0.0000 0.9182 1.000 0.000
#> SRR1381396 1 0.0000 0.9182 1.000 0.000
#> SRR1096081 1 0.8327 0.6940 0.736 0.264
#> SRR1349809 1 0.8909 0.6317 0.692 0.308
#> SRR1324314 1 0.1633 0.9088 0.976 0.024
#> SRR1092444 1 0.0000 0.9182 1.000 0.000
#> SRR1382553 1 0.1633 0.9088 0.976 0.024
#> SRR1075530 2 0.4939 0.8965 0.108 0.892
#> SRR1442612 2 0.4161 0.9012 0.084 0.916
#> SRR1360056 1 0.0000 0.9182 1.000 0.000
#> SRR1078164 1 0.0000 0.9182 1.000 0.000
#> SRR1434545 1 0.0938 0.9115 0.988 0.012
#> SRR1398251 1 0.0000 0.9182 1.000 0.000
#> SRR1375866 1 0.0000 0.9182 1.000 0.000
#> SRR1091645 2 0.4939 0.8965 0.108 0.892
#> SRR1416636 1 0.8713 0.6534 0.708 0.292
#> SRR1105441 2 0.4161 0.9012 0.084 0.916
#> SRR1082496 2 0.1184 0.8960 0.016 0.984
#> SRR1315353 2 0.4161 0.9012 0.084 0.916
#> SRR1093697 2 0.0000 0.8964 0.000 1.000
#> SRR1077429 1 0.5059 0.8508 0.888 0.112
#> SRR1076120 1 0.0000 0.9182 1.000 0.000
#> SRR1074410 1 0.0000 0.9182 1.000 0.000
#> SRR1340345 2 0.4939 0.8965 0.108 0.892
#> SRR1069514 2 0.4161 0.9012 0.084 0.916
#> SRR1092636 1 0.7815 0.7341 0.768 0.232
#> SRR1365013 2 0.4939 0.8965 0.108 0.892
#> SRR1073069 1 0.1633 0.9088 0.976 0.024
#> SRR1443137 1 0.0000 0.9182 1.000 0.000
#> SRR1437143 2 0.0000 0.8964 0.000 1.000
#> SRR1091990 1 0.0000 0.9182 1.000 0.000
#> SRR820234 2 0.0000 0.8964 0.000 1.000
#> SRR1338079 1 0.0000 0.9182 1.000 0.000
#> SRR1390094 1 0.6801 0.7870 0.820 0.180
#> SRR1340721 1 0.4161 0.8446 0.916 0.084
#> SRR1335964 1 0.9044 0.6054 0.680 0.320
#> SRR1086869 1 0.8386 0.6888 0.732 0.268
#> SRR1453434 1 0.0000 0.9182 1.000 0.000
#> SRR1402261 1 0.0000 0.9182 1.000 0.000
#> SRR657809 2 0.7815 0.7713 0.232 0.768
#> SRR1093075 1 0.1184 0.9122 0.984 0.016
#> SRR1433329 1 0.1633 0.9088 0.976 0.024
#> SRR1353418 1 0.1633 0.9088 0.976 0.024
#> SRR1092913 2 0.6712 0.8373 0.176 0.824
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0424 0.7636 0.992 0.000 0.008
#> SRR1335605 2 0.8680 -0.2574 0.424 0.472 0.104
#> SRR1432014 3 0.6299 0.7746 0.000 0.476 0.524
#> SRR1499215 3 0.9953 0.4503 0.344 0.288 0.368
#> SRR1460409 1 0.3607 0.7423 0.880 0.008 0.112
#> SRR1086441 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR1097344 2 0.4700 0.3871 0.008 0.812 0.180
#> SRR1081789 2 0.0424 0.3494 0.008 0.992 0.000
#> SRR1453005 2 0.6126 0.4180 0.000 0.600 0.400
#> SRR1366985 1 0.8926 0.1546 0.568 0.240 0.192
#> SRR815280 1 0.1753 0.7522 0.952 0.000 0.048
#> SRR1348531 1 0.3771 0.7412 0.876 0.012 0.112
#> SRR815845 2 0.4974 -0.1423 0.000 0.764 0.236
#> SRR1471178 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR1080696 3 0.6299 0.7746 0.000 0.476 0.524
#> SRR1078684 2 0.9605 -0.4276 0.260 0.476 0.264
#> SRR1317751 2 0.9103 -0.5810 0.144 0.476 0.380
#> SRR1435667 2 0.6062 -0.5272 0.000 0.616 0.384
#> SRR1097905 1 0.6318 0.6528 0.760 0.172 0.068
#> SRR1456548 1 0.7909 0.5275 0.648 0.240 0.112
#> SRR1075126 1 0.4062 0.6887 0.836 0.164 0.000
#> SRR813108 2 0.5465 0.4052 0.000 0.712 0.288
#> SRR1479062 3 0.6676 0.7719 0.008 0.476 0.516
#> SRR1408703 3 0.7395 0.7506 0.032 0.476 0.492
#> SRR1332360 1 0.3134 0.7413 0.916 0.032 0.052
#> SRR1098686 1 0.6318 0.6528 0.760 0.172 0.068
#> SRR1434228 1 0.2096 0.7490 0.944 0.004 0.052
#> SRR1467149 1 0.8774 0.1048 0.476 0.412 0.112
#> SRR1399113 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1476507 2 0.0237 0.3518 0.004 0.996 0.000
#> SRR1092468 1 0.8779 0.0918 0.472 0.416 0.112
#> SRR1441804 1 0.3670 0.7466 0.888 0.020 0.092
#> SRR1326100 2 0.6225 0.4195 0.000 0.568 0.432
#> SRR1398815 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR1436021 2 0.0424 0.3433 0.000 0.992 0.008
#> SRR1480083 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1472863 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR815542 1 0.6829 0.6399 0.736 0.168 0.096
#> SRR1400100 2 0.0000 0.3499 0.000 1.000 0.000
#> SRR1312002 1 0.9148 0.1348 0.544 0.220 0.236
#> SRR1470253 1 0.8117 0.3667 0.636 0.236 0.128
#> SRR1414332 1 0.0424 0.7636 0.992 0.000 0.008
#> SRR1069209 1 0.2280 0.7487 0.940 0.008 0.052
#> SRR661052 1 0.3129 0.7504 0.904 0.008 0.088
#> SRR1308860 1 0.6318 0.6528 0.760 0.172 0.068
#> SRR1421159 2 0.0000 0.3499 0.000 1.000 0.000
#> SRR1340943 1 0.7676 0.5674 0.672 0.216 0.112
#> SRR1078855 1 0.1860 0.7505 0.948 0.000 0.052
#> SRR1459465 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR816818 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1478679 2 0.9582 -0.4314 0.300 0.472 0.228
#> SRR1350979 3 0.7493 0.7434 0.036 0.476 0.488
#> SRR1458198 1 0.7267 0.6125 0.708 0.180 0.112
#> SRR1386910 2 0.2063 0.3227 0.044 0.948 0.008
#> SRR1465375 2 0.3375 0.2743 0.100 0.892 0.008
#> SRR1323699 3 0.9585 0.5757 0.212 0.332 0.456
#> SRR1431139 2 0.8774 -0.2672 0.412 0.476 0.112
#> SRR1373964 2 0.8260 -0.6898 0.076 0.492 0.432
#> SRR1455413 1 0.8779 0.0918 0.472 0.416 0.112
#> SRR1437163 1 0.6986 0.6299 0.724 0.180 0.096
#> SRR1347343 2 0.8277 -0.7190 0.076 0.468 0.456
#> SRR1465480 2 0.6505 0.4182 0.004 0.528 0.468
#> SRR1489631 1 0.7165 0.6221 0.716 0.172 0.112
#> SRR1086514 2 0.2625 0.3721 0.000 0.916 0.084
#> SRR1430928 1 0.1711 0.7622 0.960 0.008 0.032
#> SRR1310939 3 0.8113 0.7253 0.068 0.428 0.504
#> SRR1344294 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1099402 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR1468118 2 0.9606 -0.5047 0.212 0.448 0.340
#> SRR1486348 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR1488770 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1083732 1 0.0424 0.7636 0.992 0.000 0.008
#> SRR1456611 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1080318 1 0.3607 0.7423 0.880 0.008 0.112
#> SRR1500089 1 0.8710 0.1977 0.508 0.380 0.112
#> SRR1441178 1 0.1860 0.7505 0.948 0.000 0.052
#> SRR1381396 1 0.0424 0.7650 0.992 0.008 0.000
#> SRR1096081 3 0.6500 0.7748 0.004 0.464 0.532
#> SRR1349809 2 0.7752 -0.1740 0.456 0.496 0.048
#> SRR1324314 1 0.7624 0.2322 0.560 0.392 0.048
#> SRR1092444 1 0.3771 0.7416 0.876 0.012 0.112
#> SRR1382553 1 0.6109 0.5703 0.760 0.048 0.192
#> SRR1075530 2 0.0237 0.3518 0.004 0.996 0.000
#> SRR1442612 2 0.6309 -0.7628 0.000 0.504 0.496
#> SRR1360056 1 0.9806 -0.3707 0.408 0.244 0.348
#> SRR1078164 1 0.1860 0.7505 0.948 0.000 0.052
#> SRR1434545 1 0.7676 0.5674 0.672 0.216 0.112
#> SRR1398251 1 0.2448 0.7515 0.924 0.000 0.076
#> SRR1375866 1 0.0424 0.7636 0.992 0.000 0.008
#> SRR1091645 2 0.0661 0.3453 0.004 0.988 0.008
#> SRR1416636 3 0.6299 0.7746 0.000 0.476 0.524
#> SRR1105441 2 0.0000 0.3499 0.000 1.000 0.000
#> SRR1082496 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1315353 2 0.0000 0.3499 0.000 1.000 0.000
#> SRR1093697 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1077429 2 0.9457 -0.5246 0.192 0.468 0.340
#> SRR1076120 1 0.8774 0.1048 0.476 0.412 0.112
#> SRR1074410 1 0.0000 0.7646 1.000 0.000 0.000
#> SRR1340345 2 0.0592 0.3498 0.012 0.988 0.000
#> SRR1069514 2 0.3686 0.1101 0.000 0.860 0.140
#> SRR1092636 2 0.9424 -0.5272 0.188 0.472 0.340
#> SRR1365013 2 0.0237 0.3518 0.004 0.996 0.000
#> SRR1073069 1 0.3369 0.7372 0.908 0.040 0.052
#> SRR1443137 1 0.1860 0.7505 0.948 0.000 0.052
#> SRR1437143 2 0.6291 0.4194 0.000 0.532 0.468
#> SRR1091990 1 0.1753 0.7522 0.952 0.000 0.048
#> SRR820234 2 0.6244 0.4195 0.000 0.560 0.440
#> SRR1338079 1 0.2584 0.7546 0.928 0.008 0.064
#> SRR1390094 2 0.9813 -0.4547 0.260 0.424 0.316
#> SRR1340721 1 0.6804 0.6211 0.724 0.204 0.072
#> SRR1335964 2 0.9390 -0.5303 0.184 0.476 0.340
#> SRR1086869 2 0.9263 -0.5565 0.164 0.476 0.360
#> SRR1453434 1 0.2866 0.7549 0.916 0.008 0.076
#> SRR1402261 1 0.8779 0.0918 0.472 0.416 0.112
#> SRR657809 2 0.2066 0.3306 0.060 0.940 0.000
#> SRR1093075 1 0.1753 0.7522 0.952 0.000 0.048
#> SRR1433329 1 0.2096 0.7490 0.944 0.004 0.052
#> SRR1353418 3 0.9648 0.4982 0.292 0.244 0.464
#> SRR1092913 2 0.1860 0.3354 0.052 0.948 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 4 0.4477 0.5994 0.312 0.000 0.000 0.688
#> SRR1335605 1 0.3725 0.5678 0.812 0.000 0.180 0.008
#> SRR1432014 3 0.3105 0.5468 0.004 0.000 0.856 0.140
#> SRR1499215 4 0.4123 0.4792 0.008 0.000 0.220 0.772
#> SRR1460409 1 0.0336 0.6883 0.992 0.000 0.000 0.008
#> SRR1086441 1 0.3610 0.6188 0.800 0.000 0.000 0.200
#> SRR1097344 3 0.8956 0.3628 0.200 0.280 0.440 0.080
#> SRR1081789 3 0.8030 0.2870 0.100 0.268 0.552 0.080
#> SRR1453005 2 0.6890 0.1236 0.008 0.484 0.428 0.080
#> SRR1366985 4 0.3991 0.5522 0.020 0.000 0.172 0.808
#> SRR815280 4 0.3873 0.7191 0.228 0.000 0.000 0.772
#> SRR1348531 1 0.0336 0.6883 0.992 0.000 0.000 0.008
#> SRR815845 3 0.2469 0.5171 0.000 0.108 0.892 0.000
#> SRR1471178 1 0.3569 0.6222 0.804 0.000 0.000 0.196
#> SRR1080696 3 0.3105 0.5468 0.004 0.000 0.856 0.140
#> SRR1078684 3 0.6522 0.4877 0.224 0.000 0.632 0.144
#> SRR1317751 3 0.4564 0.4623 0.328 0.000 0.672 0.000
#> SRR1435667 3 0.2921 0.5463 0.000 0.000 0.860 0.140
#> SRR1097905 1 0.3074 0.6510 0.848 0.000 0.000 0.152
#> SRR1456548 1 0.1022 0.6789 0.968 0.000 0.032 0.000
#> SRR1075126 1 0.3486 0.6270 0.812 0.000 0.000 0.188
#> SRR813108 3 0.6013 0.3309 0.000 0.288 0.640 0.072
#> SRR1479062 3 0.6205 0.5522 0.196 0.000 0.668 0.136
#> SRR1408703 3 0.5380 0.5710 0.120 0.000 0.744 0.136
#> SRR1332360 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1098686 1 0.3123 0.6495 0.844 0.000 0.000 0.156
#> SRR1434228 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1467149 1 0.1716 0.6621 0.936 0.000 0.064 0.000
#> SRR1399113 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1476507 3 0.8569 0.4141 0.152 0.268 0.500 0.080
#> SRR1092468 1 0.1716 0.6621 0.936 0.000 0.064 0.000
#> SRR1441804 1 0.3123 0.6495 0.844 0.000 0.000 0.156
#> SRR1326100 3 0.6586 0.1602 0.000 0.420 0.500 0.080
#> SRR1398815 1 0.4981 0.0722 0.536 0.000 0.000 0.464
#> SRR1436021 3 0.7083 0.4621 0.120 0.268 0.596 0.016
#> SRR1480083 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.3610 0.6188 0.800 0.000 0.000 0.200
#> SRR815542 1 0.1637 0.6840 0.940 0.000 0.000 0.060
#> SRR1400100 3 0.6988 0.4619 0.112 0.268 0.604 0.016
#> SRR1312002 4 0.4907 0.5136 0.176 0.000 0.060 0.764
#> SRR1470253 4 0.6120 0.5504 0.296 0.000 0.076 0.628
#> SRR1414332 1 0.4925 0.0959 0.572 0.000 0.000 0.428
#> SRR1069209 4 0.3801 0.7292 0.220 0.000 0.000 0.780
#> SRR661052 1 0.0592 0.6889 0.984 0.000 0.000 0.016
#> SRR1308860 1 0.3123 0.6495 0.844 0.000 0.000 0.156
#> SRR1421159 3 0.8217 0.4125 0.116 0.268 0.536 0.080
#> SRR1340943 1 0.0524 0.6866 0.988 0.000 0.004 0.008
#> SRR1078855 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1459465 2 0.1610 0.8739 0.000 0.952 0.032 0.016
#> SRR816818 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1478679 4 0.7726 -0.0295 0.228 0.000 0.368 0.404
#> SRR1350979 3 0.4775 0.5428 0.076 0.000 0.784 0.140
#> SRR1458198 1 0.0000 0.6880 1.000 0.000 0.000 0.000
#> SRR1386910 3 0.7882 0.2449 0.176 0.336 0.472 0.016
#> SRR1465375 1 0.8022 0.1027 0.584 0.192 0.144 0.080
#> SRR1323699 4 0.4158 0.4690 0.008 0.000 0.224 0.768
#> SRR1431139 1 0.5273 -0.0499 0.536 0.000 0.456 0.008
#> SRR1373964 3 0.3300 0.5475 0.008 0.000 0.848 0.144
#> SRR1455413 1 0.1716 0.6621 0.936 0.000 0.064 0.000
#> SRR1437163 1 0.0657 0.6881 0.984 0.000 0.004 0.012
#> SRR1347343 3 0.3486 0.5258 0.000 0.000 0.812 0.188
#> SRR1465480 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0188 0.6883 0.996 0.000 0.000 0.004
#> SRR1086514 3 0.8166 0.4054 0.108 0.276 0.536 0.080
#> SRR1430928 1 0.3528 0.6261 0.808 0.000 0.000 0.192
#> SRR1310939 3 0.7443 0.2856 0.392 0.000 0.436 0.172
#> SRR1344294 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.4830 0.2897 0.608 0.000 0.000 0.392
#> SRR1468118 1 0.4977 0.0319 0.540 0.000 0.460 0.000
#> SRR1486348 1 0.3610 0.6188 0.800 0.000 0.000 0.200
#> SRR1488770 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.4843 0.2051 0.604 0.000 0.000 0.396
#> SRR1456611 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.4072 0.4061 0.748 0.000 0.000 0.252
#> SRR1500089 1 0.1867 0.6616 0.928 0.000 0.072 0.000
#> SRR1441178 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1381396 1 0.3942 0.5819 0.764 0.000 0.000 0.236
#> SRR1096081 3 0.7067 0.4697 0.188 0.000 0.568 0.244
#> SRR1349809 1 0.7161 0.4291 0.592 0.200 0.200 0.008
#> SRR1324314 4 0.5648 0.6250 0.252 0.000 0.064 0.684
#> SRR1092444 1 0.2469 0.6267 0.892 0.000 0.000 0.108
#> SRR1382553 4 0.5594 0.5900 0.164 0.000 0.112 0.724
#> SRR1075530 3 0.8217 0.4125 0.116 0.268 0.536 0.080
#> SRR1442612 3 0.2921 0.5463 0.000 0.000 0.860 0.140
#> SRR1360056 4 0.7780 0.3200 0.272 0.000 0.300 0.428
#> SRR1078164 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1434545 1 0.1388 0.6785 0.960 0.028 0.000 0.012
#> SRR1398251 4 0.3982 0.7294 0.220 0.000 0.004 0.776
#> SRR1375866 4 0.4431 0.6112 0.304 0.000 0.000 0.696
#> SRR1091645 3 0.8515 0.4193 0.152 0.268 0.504 0.076
#> SRR1416636 3 0.4969 0.5450 0.088 0.000 0.772 0.140
#> SRR1105441 3 0.6988 0.4619 0.112 0.268 0.604 0.016
#> SRR1082496 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.6015 0.3533 0.000 0.268 0.652 0.080
#> SRR1093697 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.4996 0.1391 0.484 0.000 0.516 0.000
#> SRR1076120 1 0.1867 0.6570 0.928 0.000 0.072 0.000
#> SRR1074410 1 0.4992 0.0278 0.524 0.000 0.000 0.476
#> SRR1340345 3 0.9165 0.2910 0.256 0.268 0.396 0.080
#> SRR1069514 3 0.7290 0.4351 0.012 0.264 0.572 0.152
#> SRR1092636 3 0.4843 0.3602 0.396 0.000 0.604 0.000
#> SRR1365013 3 0.8240 0.4156 0.120 0.264 0.536 0.080
#> SRR1073069 4 0.4098 0.7276 0.204 0.000 0.012 0.784
#> SRR1443137 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1437143 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR1091990 4 0.3801 0.7263 0.220 0.000 0.000 0.780
#> SRR820234 2 0.5966 0.4996 0.000 0.648 0.280 0.072
#> SRR1338079 1 0.3528 0.6261 0.808 0.000 0.000 0.192
#> SRR1390094 1 0.6723 0.1688 0.600 0.000 0.260 0.140
#> SRR1340721 1 0.3498 0.6534 0.832 0.000 0.008 0.160
#> SRR1335964 3 0.4605 0.4599 0.336 0.000 0.664 0.000
#> SRR1086869 3 0.4564 0.4623 0.328 0.000 0.672 0.000
#> SRR1453434 1 0.3444 0.6318 0.816 0.000 0.000 0.184
#> SRR1402261 1 0.2048 0.6581 0.928 0.000 0.064 0.008
#> SRR657809 1 0.9210 -0.2962 0.380 0.268 0.272 0.080
#> SRR1093075 4 0.4994 0.2031 0.480 0.000 0.000 0.520
#> SRR1433329 4 0.3764 0.7299 0.216 0.000 0.000 0.784
#> SRR1353418 4 0.6077 0.0715 0.044 0.000 0.460 0.496
#> SRR1092913 1 0.9219 -0.3456 0.376 0.268 0.276 0.080
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 3 0.2471 0.7489 0.136 0.000 0.864 0.000 0.000
#> SRR1335605 1 0.1364 0.8329 0.952 0.000 0.000 0.012 0.036
#> SRR1432014 5 0.0955 0.8181 0.000 0.000 0.004 0.028 0.968
#> SRR1499215 3 0.2605 0.7240 0.000 0.000 0.852 0.000 0.148
#> SRR1460409 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR1086441 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1097344 4 0.0992 0.8923 0.008 0.000 0.000 0.968 0.024
#> SRR1081789 4 0.0162 0.9018 0.000 0.000 0.000 0.996 0.004
#> SRR1453005 4 0.0162 0.9015 0.000 0.004 0.000 0.996 0.000
#> SRR1366985 3 0.2516 0.7279 0.000 0.000 0.860 0.000 0.140
#> SRR815280 3 0.1121 0.8104 0.044 0.000 0.956 0.000 0.000
#> SRR1348531 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR815845 5 0.2929 0.7499 0.000 0.000 0.000 0.180 0.820
#> SRR1471178 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1080696 5 0.0794 0.8195 0.000 0.000 0.000 0.028 0.972
#> SRR1078684 5 0.5218 0.1421 0.424 0.000 0.004 0.036 0.536
#> SRR1317751 5 0.2798 0.8385 0.140 0.000 0.000 0.008 0.852
#> SRR1435667 5 0.0955 0.8181 0.000 0.000 0.004 0.028 0.968
#> SRR1097905 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1456548 1 0.0794 0.8355 0.972 0.000 0.000 0.000 0.028
#> SRR1075126 1 0.2605 0.8081 0.852 0.000 0.148 0.000 0.000
#> SRR813108 4 0.0324 0.9015 0.000 0.004 0.000 0.992 0.004
#> SRR1479062 5 0.2798 0.8385 0.140 0.000 0.000 0.008 0.852
#> SRR1408703 5 0.3445 0.8358 0.140 0.000 0.000 0.036 0.824
#> SRR1332360 3 0.0162 0.8211 0.004 0.000 0.996 0.000 0.000
#> SRR1098686 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1434228 3 0.0000 0.8215 0.000 0.000 1.000 0.000 0.000
#> SRR1467149 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR1399113 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0609 0.8976 0.000 0.000 0.000 0.980 0.020
#> SRR1092468 1 0.0794 0.8355 0.972 0.000 0.000 0.000 0.028
#> SRR1441804 1 0.2753 0.8129 0.856 0.000 0.136 0.000 0.008
#> SRR1326100 4 0.0671 0.8980 0.000 0.016 0.000 0.980 0.004
#> SRR1398815 3 0.4307 -0.1654 0.500 0.000 0.500 0.000 0.000
#> SRR1436021 4 0.0451 0.9016 0.008 0.000 0.000 0.988 0.004
#> SRR1480083 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR815542 1 0.1597 0.8343 0.940 0.000 0.048 0.000 0.012
#> SRR1400100 4 0.1502 0.8656 0.056 0.000 0.000 0.940 0.004
#> SRR1312002 3 0.2516 0.7170 0.140 0.000 0.860 0.000 0.000
#> SRR1470253 3 0.2798 0.7124 0.140 0.000 0.852 0.000 0.008
#> SRR1414332 3 0.4306 -0.0393 0.492 0.000 0.508 0.000 0.000
#> SRR1069209 3 0.0000 0.8215 0.000 0.000 1.000 0.000 0.000
#> SRR661052 1 0.0798 0.8374 0.976 0.000 0.008 0.000 0.016
#> SRR1308860 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1421159 4 0.0162 0.9018 0.000 0.000 0.000 0.996 0.004
#> SRR1340943 1 0.1041 0.8339 0.964 0.000 0.000 0.004 0.032
#> SRR1078855 3 0.0000 0.8215 0.000 0.000 1.000 0.000 0.000
#> SRR1459465 2 0.3480 0.6630 0.000 0.752 0.000 0.248 0.000
#> SRR816818 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 1 0.6906 0.0774 0.460 0.000 0.360 0.028 0.152
#> SRR1350979 5 0.0000 0.8259 0.000 0.000 0.000 0.000 1.000
#> SRR1458198 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR1386910 4 0.6099 0.0385 0.424 0.124 0.000 0.452 0.000
#> SRR1465375 4 0.4878 0.1141 0.440 0.000 0.000 0.536 0.024
#> SRR1323699 3 0.2605 0.7240 0.000 0.000 0.852 0.000 0.148
#> SRR1431139 1 0.1251 0.8253 0.956 0.000 0.000 0.036 0.008
#> SRR1373964 5 0.0955 0.8181 0.000 0.000 0.004 0.028 0.968
#> SRR1455413 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR1437163 1 0.0798 0.8368 0.976 0.000 0.000 0.008 0.016
#> SRR1347343 5 0.0955 0.8138 0.000 0.000 0.028 0.004 0.968
#> SRR1465480 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.0794 0.8355 0.972 0.000 0.000 0.000 0.028
#> SRR1086514 4 0.0162 0.9018 0.000 0.000 0.000 0.996 0.004
#> SRR1430928 1 0.2674 0.8096 0.856 0.000 0.140 0.000 0.004
#> SRR1310939 5 0.2561 0.8362 0.144 0.000 0.000 0.000 0.856
#> SRR1344294 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.4192 0.4042 0.596 0.000 0.404 0.000 0.000
#> SRR1468118 5 0.3661 0.6998 0.276 0.000 0.000 0.000 0.724
#> SRR1486348 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1488770 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.4304 0.0680 0.516 0.000 0.484 0.000 0.000
#> SRR1456611 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.4822 0.3297 0.616 0.000 0.352 0.000 0.032
#> SRR1500089 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR1441178 3 0.0404 0.8198 0.012 0.000 0.988 0.000 0.000
#> SRR1381396 1 0.3143 0.7556 0.796 0.000 0.204 0.000 0.000
#> SRR1096081 5 0.2909 0.8349 0.140 0.000 0.012 0.000 0.848
#> SRR1349809 1 0.3326 0.7652 0.824 0.152 0.000 0.024 0.000
#> SRR1324314 3 0.2179 0.7649 0.112 0.000 0.888 0.000 0.000
#> SRR1092444 1 0.3616 0.6999 0.804 0.000 0.164 0.000 0.032
#> SRR1382553 3 0.4711 0.6899 0.116 0.000 0.736 0.000 0.148
#> SRR1075530 4 0.0000 0.9016 0.000 0.000 0.000 1.000 0.000
#> SRR1442612 5 0.0955 0.8181 0.000 0.000 0.004 0.028 0.968
#> SRR1360056 5 0.6157 0.3839 0.140 0.000 0.364 0.000 0.496
#> SRR1078164 3 0.0290 0.8206 0.008 0.000 0.992 0.000 0.000
#> SRR1434545 1 0.2707 0.7726 0.876 0.000 0.000 0.100 0.024
#> SRR1398251 3 0.0162 0.8202 0.000 0.000 0.996 0.000 0.004
#> SRR1375866 3 0.2377 0.7557 0.128 0.000 0.872 0.000 0.000
#> SRR1091645 4 0.1082 0.8906 0.008 0.000 0.000 0.964 0.028
#> SRR1416636 5 0.0609 0.8322 0.020 0.000 0.000 0.000 0.980
#> SRR1105441 4 0.0162 0.9018 0.000 0.000 0.000 0.996 0.004
#> SRR1082496 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 4 0.1270 0.8747 0.000 0.000 0.000 0.948 0.052
#> SRR1093697 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.2798 0.8385 0.140 0.000 0.000 0.008 0.852
#> SRR1076120 1 0.0880 0.8349 0.968 0.000 0.000 0.000 0.032
#> SRR1074410 3 0.4297 -0.0718 0.472 0.000 0.528 0.000 0.000
#> SRR1340345 4 0.0693 0.8988 0.012 0.000 0.000 0.980 0.008
#> SRR1069514 4 0.2471 0.7927 0.000 0.000 0.000 0.864 0.136
#> SRR1092636 5 0.2798 0.8385 0.140 0.000 0.000 0.008 0.852
#> SRR1365013 4 0.0566 0.9007 0.012 0.000 0.000 0.984 0.004
#> SRR1073069 3 0.0000 0.8215 0.000 0.000 1.000 0.000 0.000
#> SRR1443137 3 0.0000 0.8215 0.000 0.000 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.9739 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 3 0.0794 0.8146 0.028 0.000 0.972 0.000 0.000
#> SRR820234 4 0.2286 0.8155 0.000 0.108 0.000 0.888 0.004
#> SRR1338079 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1390094 1 0.3876 0.4247 0.684 0.000 0.000 0.000 0.316
#> SRR1340721 1 0.2516 0.8092 0.860 0.000 0.140 0.000 0.000
#> SRR1335964 5 0.2798 0.8385 0.140 0.000 0.000 0.008 0.852
#> SRR1086869 5 0.2798 0.8385 0.140 0.000 0.000 0.008 0.852
#> SRR1453434 1 0.2763 0.8088 0.848 0.000 0.148 0.000 0.004
#> SRR1402261 1 0.1082 0.8342 0.964 0.000 0.000 0.008 0.028
#> SRR657809 4 0.0404 0.9000 0.012 0.000 0.000 0.988 0.000
#> SRR1093075 3 0.4074 0.3265 0.364 0.000 0.636 0.000 0.000
#> SRR1433329 3 0.0000 0.8215 0.000 0.000 1.000 0.000 0.000
#> SRR1353418 5 0.1851 0.7833 0.000 0.000 0.088 0.000 0.912
#> SRR1092913 4 0.1741 0.8678 0.040 0.000 0.000 0.936 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.3934 0.5298 0.708 0.000 0.032 0.000 0.000 0.260
#> SRR1335605 5 0.4168 0.7092 0.256 0.000 0.000 0.048 0.696 0.000
#> SRR1432014 3 0.3539 0.6927 0.000 0.000 0.756 0.024 0.220 0.000
#> SRR1499215 3 0.3864 0.2480 0.000 0.000 0.520 0.000 0.000 0.480
#> SRR1460409 5 0.4289 0.5099 0.444 0.000 0.012 0.000 0.540 0.004
#> SRR1086441 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1097344 4 0.2768 0.8192 0.000 0.000 0.156 0.832 0.012 0.000
#> SRR1081789 4 0.0551 0.8411 0.008 0.000 0.004 0.984 0.004 0.000
#> SRR1453005 4 0.2768 0.8192 0.000 0.000 0.156 0.832 0.012 0.000
#> SRR1366985 6 0.0146 0.8198 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR815280 1 0.3853 0.4612 0.680 0.000 0.016 0.000 0.000 0.304
#> SRR1348531 5 0.3672 0.6250 0.368 0.000 0.000 0.000 0.632 0.000
#> SRR815845 4 0.4798 0.4277 0.000 0.004 0.236 0.664 0.096 0.000
#> SRR1471178 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1080696 3 0.3539 0.6927 0.000 0.000 0.756 0.024 0.220 0.000
#> SRR1078684 3 0.7073 0.3787 0.172 0.000 0.476 0.168 0.184 0.000
#> SRR1317751 5 0.3368 0.3861 0.000 0.000 0.232 0.012 0.756 0.000
#> SRR1435667 3 0.3991 0.6871 0.000 0.000 0.756 0.088 0.156 0.000
#> SRR1097905 1 0.0790 0.7623 0.968 0.000 0.000 0.000 0.032 0.000
#> SRR1456548 5 0.3371 0.7043 0.292 0.000 0.000 0.000 0.708 0.000
#> SRR1075126 1 0.2748 0.7218 0.848 0.000 0.000 0.000 0.024 0.128
#> SRR813108 4 0.0291 0.8410 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1479062 5 0.1257 0.6103 0.000 0.000 0.028 0.020 0.952 0.000
#> SRR1408703 5 0.3860 0.3410 0.000 0.000 0.236 0.036 0.728 0.000
#> SRR1332360 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1098686 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1434228 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1467149 5 0.3101 0.7240 0.244 0.000 0.000 0.000 0.756 0.000
#> SRR1399113 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.0909 0.8407 0.000 0.000 0.012 0.968 0.020 0.000
#> SRR1092468 5 0.3672 0.6250 0.368 0.000 0.000 0.000 0.632 0.000
#> SRR1441804 1 0.2793 0.5864 0.800 0.000 0.000 0.000 0.200 0.000
#> SRR1326100 4 0.0777 0.8400 0.000 0.024 0.000 0.972 0.004 0.000
#> SRR1398815 1 0.3956 0.6342 0.760 0.000 0.088 0.000 0.000 0.152
#> SRR1436021 4 0.0777 0.8353 0.000 0.000 0.004 0.972 0.024 0.000
#> SRR1480083 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR815542 1 0.2823 0.5304 0.796 0.000 0.000 0.000 0.204 0.000
#> SRR1400100 4 0.2520 0.7114 0.000 0.000 0.004 0.844 0.152 0.000
#> SRR1312002 6 0.0146 0.8203 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1470253 6 0.0937 0.7991 0.000 0.000 0.000 0.000 0.040 0.960
#> SRR1414332 1 0.2798 0.7076 0.852 0.000 0.036 0.000 0.000 0.112
#> SRR1069209 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR661052 1 0.3868 -0.4095 0.504 0.000 0.000 0.000 0.496 0.000
#> SRR1308860 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1421159 4 0.0291 0.8410 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1340943 5 0.3713 0.7078 0.284 0.000 0.004 0.008 0.704 0.000
#> SRR1078855 6 0.3774 0.1737 0.408 0.000 0.000 0.000 0.000 0.592
#> SRR1459465 2 0.2631 0.7582 0.000 0.820 0.000 0.180 0.000 0.000
#> SRR816818 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.5940 0.4254 0.032 0.000 0.524 0.116 0.000 0.328
#> SRR1350979 3 0.3843 0.5062 0.000 0.000 0.548 0.000 0.452 0.000
#> SRR1458198 5 0.3371 0.7043 0.292 0.000 0.000 0.000 0.708 0.000
#> SRR1386910 4 0.5523 0.4617 0.040 0.292 0.048 0.608 0.012 0.000
#> SRR1465375 4 0.3857 0.6745 0.160 0.000 0.004 0.772 0.064 0.000
#> SRR1323699 3 0.3864 0.2480 0.000 0.000 0.520 0.000 0.000 0.480
#> SRR1431139 5 0.5223 0.6297 0.200 0.000 0.004 0.168 0.628 0.000
#> SRR1373964 3 0.3991 0.6493 0.000 0.000 0.756 0.156 0.088 0.000
#> SRR1455413 5 0.3076 0.7248 0.240 0.000 0.000 0.000 0.760 0.000
#> SRR1437163 1 0.4034 0.1384 0.652 0.000 0.000 0.020 0.328 0.000
#> SRR1347343 3 0.3614 0.6829 0.000 0.000 0.752 0.000 0.220 0.028
#> SRR1465480 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 5 0.3409 0.6991 0.300 0.000 0.000 0.000 0.700 0.000
#> SRR1086514 4 0.0291 0.8410 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1430928 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310939 5 0.1714 0.5843 0.000 0.000 0.092 0.000 0.908 0.000
#> SRR1344294 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.2907 0.7257 0.828 0.000 0.000 0.000 0.020 0.152
#> SRR1468118 5 0.3457 0.4045 0.016 0.000 0.232 0.000 0.752 0.000
#> SRR1486348 1 0.0000 0.7732 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1863 0.7351 0.896 0.000 0.000 0.000 0.000 0.104
#> SRR1456611 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 5 0.5107 0.6758 0.132 0.000 0.088 0.000 0.708 0.072
#> SRR1500089 5 0.3198 0.7220 0.260 0.000 0.000 0.000 0.740 0.000
#> SRR1441178 6 0.2199 0.7733 0.020 0.000 0.088 0.000 0.000 0.892
#> SRR1381396 1 0.2554 0.7507 0.880 0.000 0.088 0.000 0.020 0.012
#> SRR1096081 5 0.3314 0.3586 0.000 0.000 0.256 0.000 0.740 0.004
#> SRR1349809 1 0.6019 0.2129 0.496 0.332 0.000 0.152 0.020 0.000
#> SRR1324314 6 0.1411 0.7747 0.060 0.000 0.000 0.000 0.004 0.936
#> SRR1092444 5 0.4989 0.6876 0.156 0.000 0.088 0.000 0.708 0.048
#> SRR1382553 3 0.4331 0.2641 0.020 0.000 0.516 0.000 0.000 0.464
#> SRR1075530 4 0.2768 0.8192 0.000 0.000 0.156 0.832 0.012 0.000
#> SRR1442612 3 0.3539 0.6927 0.000 0.000 0.756 0.024 0.220 0.000
#> SRR1360056 6 0.5651 0.0816 0.000 0.000 0.164 0.000 0.344 0.492
#> SRR1078164 6 0.3175 0.7317 0.080 0.000 0.088 0.000 0.000 0.832
#> SRR1434545 5 0.6111 0.5898 0.192 0.000 0.156 0.064 0.588 0.000
#> SRR1398251 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1375866 6 0.4507 0.6410 0.156 0.000 0.088 0.000 0.020 0.736
#> SRR1091645 4 0.3176 0.8110 0.000 0.000 0.156 0.812 0.032 0.000
#> SRR1416636 3 0.3857 0.3834 0.000 0.000 0.532 0.000 0.468 0.000
#> SRR1105441 4 0.0291 0.8410 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1082496 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 4 0.0405 0.8401 0.000 0.000 0.004 0.988 0.008 0.000
#> SRR1093697 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0909 0.6107 0.000 0.000 0.020 0.012 0.968 0.000
#> SRR1076120 5 0.3351 0.7071 0.288 0.000 0.000 0.000 0.712 0.000
#> SRR1074410 1 0.5204 0.4948 0.632 0.000 0.088 0.000 0.020 0.260
#> SRR1340345 4 0.2768 0.8192 0.000 0.000 0.156 0.832 0.012 0.000
#> SRR1069514 4 0.3997 -0.0554 0.000 0.000 0.488 0.508 0.004 0.000
#> SRR1092636 5 0.0909 0.6107 0.000 0.000 0.020 0.012 0.968 0.000
#> SRR1365013 4 0.0291 0.8410 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1073069 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1443137 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1437143 2 0.0000 0.9781 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 6 0.3551 0.6793 0.168 0.000 0.048 0.000 0.000 0.784
#> SRR820234 4 0.3276 0.6867 0.000 0.228 0.004 0.764 0.004 0.000
#> SRR1338079 1 0.1807 0.7618 0.920 0.000 0.060 0.000 0.020 0.000
#> SRR1390094 5 0.4173 0.7205 0.228 0.000 0.060 0.000 0.712 0.000
#> SRR1340721 1 0.0547 0.7661 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1335964 5 0.2830 0.5991 0.000 0.000 0.020 0.144 0.836 0.000
#> SRR1086869 5 0.3368 0.3861 0.000 0.000 0.232 0.012 0.756 0.000
#> SRR1453434 1 0.3315 0.6954 0.804 0.000 0.000 0.000 0.040 0.156
#> SRR1402261 5 0.3874 0.7105 0.276 0.000 0.012 0.008 0.704 0.000
#> SRR657809 4 0.2768 0.8192 0.000 0.000 0.156 0.832 0.012 0.000
#> SRR1093075 1 0.3797 0.2745 0.580 0.000 0.000 0.000 0.000 0.420
#> SRR1433329 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1353418 6 0.5609 0.1376 0.000 0.000 0.236 0.000 0.220 0.544
#> SRR1092913 4 0.2909 0.8180 0.004 0.000 0.156 0.828 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["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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.752 0.926 0.949 0.4378 0.550 0.550
#> 3 3 0.713 0.808 0.913 0.3885 0.856 0.740
#> 4 4 0.774 0.857 0.925 0.1355 0.787 0.544
#> 5 5 0.637 0.625 0.801 0.0706 0.930 0.785
#> 6 6 0.773 0.715 0.823 0.0700 0.877 0.589
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR816969 1 0.2603 0.9494 0.956 0.044
#> SRR1335605 2 0.0376 0.9647 0.004 0.996
#> SRR1432014 2 0.0376 0.9647 0.004 0.996
#> SRR1499215 2 0.0376 0.9647 0.004 0.996
#> SRR1460409 1 0.2603 0.9494 0.956 0.044
#> SRR1086441 1 0.2603 0.9494 0.956 0.044
#> SRR1097344 2 0.0000 0.9644 0.000 1.000
#> SRR1081789 2 0.0000 0.9644 0.000 1.000
#> SRR1453005 2 0.0000 0.9644 0.000 1.000
#> SRR1366985 2 0.0376 0.9647 0.004 0.996
#> SRR815280 1 0.6531 0.8900 0.832 0.168
#> SRR1348531 1 0.2778 0.9484 0.952 0.048
#> SRR815845 2 0.0000 0.9644 0.000 1.000
#> SRR1471178 1 0.2603 0.9494 0.956 0.044
#> SRR1080696 2 0.0376 0.9647 0.004 0.996
#> SRR1078684 2 0.0376 0.9647 0.004 0.996
#> SRR1317751 2 0.0376 0.9647 0.004 0.996
#> SRR1435667 2 0.0000 0.9644 0.000 1.000
#> SRR1097905 1 0.3114 0.9459 0.944 0.056
#> SRR1456548 1 0.2603 0.9494 0.956 0.044
#> SRR1075126 1 0.2603 0.9494 0.956 0.044
#> SRR813108 2 0.2603 0.9377 0.044 0.956
#> SRR1479062 2 0.0376 0.9647 0.004 0.996
#> SRR1408703 2 0.0376 0.9647 0.004 0.996
#> SRR1332360 1 0.6531 0.8900 0.832 0.168
#> SRR1098686 1 0.2603 0.9494 0.956 0.044
#> SRR1434228 2 0.6801 0.7507 0.180 0.820
#> SRR1467149 2 0.5519 0.8450 0.128 0.872
#> SRR1399113 2 0.2603 0.9377 0.044 0.956
#> SRR1476507 2 0.0000 0.9644 0.000 1.000
#> SRR1092468 2 0.4815 0.8728 0.104 0.896
#> SRR1441804 1 0.2603 0.9494 0.956 0.044
#> SRR1326100 2 0.2603 0.9377 0.044 0.956
#> SRR1398815 1 0.2603 0.9494 0.956 0.044
#> SRR1436021 2 0.0000 0.9644 0.000 1.000
#> SRR1480083 2 0.2603 0.9377 0.044 0.956
#> SRR1472863 1 0.6531 0.8900 0.832 0.168
#> SRR815542 1 0.2603 0.9494 0.956 0.044
#> SRR1400100 2 0.0000 0.9644 0.000 1.000
#> SRR1312002 2 0.0376 0.9647 0.004 0.996
#> SRR1470253 2 0.0376 0.9647 0.004 0.996
#> SRR1414332 1 0.2603 0.9494 0.956 0.044
#> SRR1069209 1 0.6623 0.8860 0.828 0.172
#> SRR661052 1 0.2603 0.9494 0.956 0.044
#> SRR1308860 1 0.2603 0.9494 0.956 0.044
#> SRR1421159 2 0.0000 0.9644 0.000 1.000
#> SRR1340943 2 0.2948 0.9264 0.052 0.948
#> SRR1078855 1 0.6531 0.8900 0.832 0.168
#> SRR1459465 2 0.2603 0.9377 0.044 0.956
#> SRR816818 2 0.2603 0.9377 0.044 0.956
#> SRR1478679 2 0.0376 0.9647 0.004 0.996
#> SRR1350979 2 0.0376 0.9647 0.004 0.996
#> SRR1458198 2 0.5519 0.8450 0.128 0.872
#> SRR1386910 2 0.0000 0.9644 0.000 1.000
#> SRR1465375 2 0.0376 0.9647 0.004 0.996
#> SRR1323699 2 0.0376 0.9647 0.004 0.996
#> SRR1431139 2 0.0376 0.9647 0.004 0.996
#> SRR1373964 2 0.0376 0.9647 0.004 0.996
#> SRR1455413 2 0.0376 0.9647 0.004 0.996
#> SRR1437163 1 0.3431 0.9429 0.936 0.064
#> SRR1347343 2 0.0376 0.9647 0.004 0.996
#> SRR1465480 2 0.2603 0.9377 0.044 0.956
#> SRR1489631 1 0.2603 0.9494 0.956 0.044
#> SRR1086514 2 0.0000 0.9644 0.000 1.000
#> SRR1430928 1 0.2603 0.9494 0.956 0.044
#> SRR1310939 2 0.0376 0.9647 0.004 0.996
#> SRR1344294 2 0.2603 0.9377 0.044 0.956
#> SRR1099402 1 0.2603 0.9494 0.956 0.044
#> SRR1468118 2 0.0376 0.9647 0.004 0.996
#> SRR1486348 1 0.2603 0.9494 0.956 0.044
#> SRR1488770 2 0.2603 0.9377 0.044 0.956
#> SRR1083732 1 0.2603 0.9494 0.956 0.044
#> SRR1456611 2 0.2603 0.9377 0.044 0.956
#> SRR1080318 1 0.2603 0.9494 0.956 0.044
#> SRR1500089 2 0.5519 0.8450 0.128 0.872
#> SRR1441178 1 0.6531 0.8900 0.832 0.168
#> SRR1381396 1 0.2603 0.9494 0.956 0.044
#> SRR1096081 2 0.0376 0.9647 0.004 0.996
#> SRR1349809 2 0.0000 0.9644 0.000 1.000
#> SRR1324314 2 0.1184 0.9563 0.016 0.984
#> SRR1092444 1 0.3274 0.9418 0.940 0.060
#> SRR1382553 2 0.0376 0.9647 0.004 0.996
#> SRR1075530 2 0.0000 0.9644 0.000 1.000
#> SRR1442612 2 0.0376 0.9647 0.004 0.996
#> SRR1360056 2 0.0376 0.9647 0.004 0.996
#> SRR1078164 1 0.6531 0.8900 0.832 0.168
#> SRR1434545 2 0.0376 0.9647 0.004 0.996
#> SRR1398251 2 0.9896 0.0575 0.440 0.560
#> SRR1375866 1 0.6343 0.8953 0.840 0.160
#> SRR1091645 2 0.0000 0.9644 0.000 1.000
#> SRR1416636 2 0.0376 0.9647 0.004 0.996
#> SRR1105441 2 0.0000 0.9644 0.000 1.000
#> SRR1082496 2 0.2603 0.9377 0.044 0.956
#> SRR1315353 2 0.0000 0.9644 0.000 1.000
#> SRR1093697 2 0.2603 0.9377 0.044 0.956
#> SRR1077429 2 0.0376 0.9647 0.004 0.996
#> SRR1076120 2 0.0672 0.9626 0.008 0.992
#> SRR1074410 1 0.2603 0.9494 0.956 0.044
#> SRR1340345 2 0.0000 0.9644 0.000 1.000
#> SRR1069514 2 0.0000 0.9644 0.000 1.000
#> SRR1092636 2 0.0376 0.9647 0.004 0.996
#> SRR1365013 2 0.0000 0.9644 0.000 1.000
#> SRR1073069 1 0.8955 0.6758 0.688 0.312
#> SRR1443137 1 0.6531 0.8900 0.832 0.168
#> SRR1437143 2 0.2603 0.9377 0.044 0.956
#> SRR1091990 1 0.6247 0.8976 0.844 0.156
#> SRR820234 2 0.2603 0.9377 0.044 0.956
#> SRR1338079 1 0.2603 0.9494 0.956 0.044
#> SRR1390094 2 0.0376 0.9647 0.004 0.996
#> SRR1340721 2 0.9896 0.0601 0.440 0.560
#> SRR1335964 2 0.0376 0.9647 0.004 0.996
#> SRR1086869 2 0.0376 0.9647 0.004 0.996
#> SRR1453434 1 0.2603 0.9494 0.956 0.044
#> SRR1402261 2 0.4690 0.8774 0.100 0.900
#> SRR657809 2 0.0000 0.9644 0.000 1.000
#> SRR1093075 1 0.6531 0.8900 0.832 0.168
#> SRR1433329 1 0.6712 0.8817 0.824 0.176
#> SRR1353418 2 0.0376 0.9647 0.004 0.996
#> SRR1092913 2 0.0000 0.9644 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1335605 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1432014 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1499215 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1460409 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1097344 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1081789 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1453005 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1366985 3 0.0747 0.8355 0.016 0.000 0.984
#> SRR815280 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR815845 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1471178 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1078684 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1317751 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1435667 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1097905 1 0.5178 0.5999 0.744 0.000 0.256
#> SRR1456548 1 0.0747 0.9261 0.984 0.000 0.016
#> SRR1075126 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR813108 2 0.5098 0.5837 0.000 0.752 0.248
#> SRR1479062 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1332360 1 0.0747 0.9280 0.984 0.000 0.016
#> SRR1098686 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1434228 3 0.6225 0.0848 0.432 0.000 0.568
#> SRR1467149 3 0.0661 0.8415 0.008 0.004 0.988
#> SRR1399113 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1476507 3 0.5948 0.5759 0.000 0.360 0.640
#> SRR1092468 3 0.0237 0.8437 0.004 0.000 0.996
#> SRR1441804 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1326100 2 0.0892 0.9309 0.000 0.980 0.020
#> SRR1398815 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1436021 3 0.5948 0.5759 0.000 0.360 0.640
#> SRR1480083 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1472863 1 0.5178 0.6044 0.744 0.000 0.256
#> SRR815542 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1400100 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1312002 3 0.1860 0.8012 0.052 0.000 0.948
#> SRR1470253 3 0.0424 0.8421 0.008 0.000 0.992
#> SRR1414332 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1069209 1 0.4605 0.7243 0.796 0.000 0.204
#> SRR661052 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1421159 3 0.5948 0.5759 0.000 0.360 0.640
#> SRR1340943 3 0.5378 0.6939 0.008 0.236 0.756
#> SRR1078855 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1478679 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1350979 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1458198 3 0.3038 0.7740 0.104 0.000 0.896
#> SRR1386910 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1465375 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1323699 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1431139 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1373964 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1455413 3 0.0424 0.8421 0.008 0.000 0.992
#> SRR1437163 1 0.8794 -0.1334 0.448 0.112 0.440
#> SRR1347343 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1465480 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1489631 1 0.0892 0.9221 0.980 0.000 0.020
#> SRR1086514 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1430928 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1468118 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1486348 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1500089 3 0.0424 0.8421 0.008 0.000 0.992
#> SRR1441178 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1096081 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1349809 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1324314 3 0.4605 0.6147 0.204 0.000 0.796
#> SRR1092444 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1382553 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1075530 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1442612 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1360056 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1078164 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1434545 3 0.6229 0.5926 0.008 0.340 0.652
#> SRR1398251 1 0.5529 0.5821 0.704 0.000 0.296
#> SRR1375866 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1091645 3 0.5948 0.5759 0.000 0.360 0.640
#> SRR1416636 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1105441 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1082496 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1315353 3 0.4291 0.7415 0.000 0.180 0.820
#> SRR1093697 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1077429 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1076120 3 0.0424 0.8421 0.008 0.000 0.992
#> SRR1074410 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1340345 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1069514 3 0.0237 0.8441 0.000 0.004 0.996
#> SRR1092636 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1365013 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1073069 1 0.4796 0.7025 0.780 0.000 0.220
#> SRR1443137 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.9490 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR820234 2 0.5016 0.6016 0.000 0.760 0.240
#> SRR1338079 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1390094 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1340721 3 0.6169 0.5724 0.004 0.360 0.636
#> SRR1335964 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1086869 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1402261 3 0.5461 0.6870 0.008 0.244 0.748
#> SRR657809 3 0.5968 0.5706 0.000 0.364 0.636
#> SRR1093075 1 0.0000 0.9404 1.000 0.000 0.000
#> SRR1433329 1 0.0592 0.9314 0.988 0.000 0.012
#> SRR1353418 3 0.0000 0.8453 0.000 0.000 1.000
#> SRR1092913 3 0.5968 0.5706 0.000 0.364 0.636
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1335605 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1432014 3 0.0188 0.9294 0.000 0.000 0.996 0.004
#> SRR1499215 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1460409 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.1792 0.8839 0.000 0.000 0.068 0.932
#> SRR1081789 3 0.6916 0.3073 0.000 0.280 0.572 0.148
#> SRR1453005 4 0.6037 0.5565 0.000 0.304 0.068 0.628
#> SRR1366985 3 0.2345 0.8115 0.100 0.000 0.900 0.000
#> SRR815280 1 0.1637 0.9110 0.940 0.000 0.000 0.060
#> SRR1348531 1 0.1118 0.9192 0.964 0.000 0.036 0.000
#> SRR815845 3 0.0707 0.9229 0.000 0.000 0.980 0.020
#> SRR1471178 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1078684 3 0.0336 0.9297 0.000 0.000 0.992 0.008
#> SRR1317751 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1435667 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1097905 1 0.0336 0.9238 0.992 0.000 0.008 0.000
#> SRR1456548 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1075126 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR813108 2 0.3074 0.7310 0.000 0.848 0.152 0.000
#> SRR1479062 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1408703 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1332360 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1098686 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1434228 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1467149 1 0.3626 0.7959 0.812 0.000 0.184 0.004
#> SRR1399113 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.1792 0.8839 0.000 0.000 0.068 0.932
#> SRR1092468 3 0.4053 0.6141 0.228 0.000 0.768 0.004
#> SRR1441804 1 0.1557 0.9114 0.944 0.000 0.056 0.000
#> SRR1326100 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1436021 4 0.3400 0.8407 0.000 0.000 0.180 0.820
#> SRR1480083 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.2469 0.8764 0.892 0.000 0.108 0.000
#> SRR815542 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1400100 3 0.1867 0.8714 0.000 0.000 0.928 0.072
#> SRR1312002 3 0.0707 0.9115 0.020 0.000 0.980 0.000
#> SRR1470253 1 0.4999 0.1825 0.508 0.000 0.492 0.000
#> SRR1414332 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.2739 0.9053 0.904 0.000 0.036 0.060
#> SRR661052 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1421159 3 0.4977 0.0423 0.000 0.000 0.540 0.460
#> SRR1340943 4 0.3356 0.8445 0.000 0.000 0.176 0.824
#> SRR1078855 1 0.1302 0.9158 0.956 0.000 0.000 0.044
#> SRR1459465 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1350979 3 0.0188 0.9294 0.000 0.000 0.996 0.004
#> SRR1458198 1 0.3725 0.8041 0.812 0.000 0.180 0.008
#> SRR1386910 2 0.6442 -0.1329 0.000 0.492 0.068 0.440
#> SRR1465375 4 0.2814 0.8719 0.000 0.000 0.132 0.868
#> SRR1323699 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1431139 3 0.0188 0.9294 0.000 0.000 0.996 0.004
#> SRR1373964 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1455413 1 0.3486 0.7982 0.812 0.000 0.188 0.000
#> SRR1437163 1 0.0188 0.9240 0.996 0.000 0.004 0.000
#> SRR1347343 3 0.0188 0.9294 0.000 0.000 0.996 0.004
#> SRR1465480 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1086514 4 0.4261 0.8209 0.000 0.112 0.068 0.820
#> SRR1430928 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1344294 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1486348 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.1022 0.9205 0.968 0.000 0.032 0.000
#> SRR1500089 1 0.3725 0.8041 0.812 0.000 0.180 0.008
#> SRR1441178 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1381396 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1349809 2 0.4194 0.6307 0.000 0.800 0.172 0.028
#> SRR1324314 3 0.4193 0.5586 0.268 0.000 0.732 0.000
#> SRR1092444 1 0.3024 0.8418 0.852 0.000 0.148 0.000
#> SRR1382553 3 0.0469 0.9196 0.012 0.000 0.988 0.000
#> SRR1075530 4 0.1792 0.8839 0.000 0.000 0.068 0.932
#> SRR1442612 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1360056 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1078164 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1434545 4 0.3356 0.8445 0.000 0.000 0.176 0.824
#> SRR1398251 1 0.4114 0.8555 0.828 0.000 0.112 0.060
#> SRR1375866 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1091645 4 0.2081 0.8848 0.000 0.000 0.084 0.916
#> SRR1416636 3 0.0336 0.9297 0.000 0.000 0.992 0.008
#> SRR1105441 3 0.1940 0.8674 0.000 0.000 0.924 0.076
#> SRR1082496 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.4406 0.5253 0.000 0.000 0.700 0.300
#> SRR1093697 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1076120 1 0.3668 0.7975 0.808 0.000 0.188 0.004
#> SRR1074410 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1340345 4 0.1792 0.8839 0.000 0.000 0.068 0.932
#> SRR1069514 3 0.0592 0.9272 0.000 0.000 0.984 0.016
#> SRR1092636 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1365013 4 0.7042 0.6135 0.000 0.188 0.240 0.572
#> SRR1073069 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1443137 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1437143 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.1637 0.9110 0.940 0.000 0.000 0.060
#> SRR820234 2 0.0000 0.9266 0.000 1.000 0.000 0.000
#> SRR1338079 1 0.0000 0.9240 1.000 0.000 0.000 0.000
#> SRR1390094 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1340721 1 0.7502 0.4264 0.548 0.264 0.176 0.012
#> SRR1335964 3 0.0469 0.9292 0.000 0.000 0.988 0.012
#> SRR1086869 3 0.0336 0.9297 0.000 0.000 0.992 0.008
#> SRR1453434 1 0.0921 0.9214 0.972 0.000 0.028 0.000
#> SRR1402261 4 0.3356 0.8445 0.000 0.000 0.176 0.824
#> SRR657809 4 0.2983 0.8712 0.000 0.040 0.068 0.892
#> SRR1093075 1 0.1388 0.9197 0.960 0.000 0.012 0.028
#> SRR1433329 1 0.3168 0.8984 0.884 0.000 0.056 0.060
#> SRR1353418 3 0.0000 0.9282 0.000 0.000 1.000 0.000
#> SRR1092913 4 0.1792 0.8839 0.000 0.000 0.068 0.932
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1335605 3 0.1341 0.5591 0.000 0.000 0.944 0.056 0.000
#> SRR1432014 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1499215 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1460409 1 0.0510 0.8476 0.984 0.000 0.000 0.016 0.000
#> SRR1086441 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1097344 4 0.1197 0.8436 0.000 0.000 0.048 0.952 0.000
#> SRR1081789 3 0.5936 0.1315 0.000 0.068 0.636 0.252 0.044
#> SRR1453005 4 0.4535 0.7828 0.000 0.096 0.068 0.792 0.044
#> SRR1366985 1 0.5295 0.0898 0.488 0.000 0.464 0.000 0.048
#> SRR815280 1 0.2690 0.8032 0.844 0.000 0.000 0.000 0.156
#> SRR1348531 1 0.0992 0.8446 0.968 0.000 0.008 0.024 0.000
#> SRR815845 3 0.1121 0.5801 0.000 0.000 0.956 0.000 0.044
#> SRR1471178 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1080696 3 0.4747 -0.0526 0.000 0.000 0.500 0.016 0.484
#> SRR1078684 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1317751 3 0.4747 -0.0622 0.000 0.000 0.496 0.016 0.488
#> SRR1435667 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1097905 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1456548 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1075126 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR813108 2 0.6541 0.4664 0.000 0.604 0.164 0.188 0.044
#> SRR1479062 3 0.4287 0.0102 0.000 0.000 0.540 0.000 0.460
#> SRR1408703 3 0.4305 -0.0432 0.000 0.000 0.512 0.000 0.488
#> SRR1332360 1 0.4249 0.6008 0.568 0.000 0.000 0.000 0.432
#> SRR1098686 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1434228 1 0.4256 0.5966 0.564 0.000 0.000 0.000 0.436
#> SRR1467149 1 0.7206 0.3070 0.556 0.000 0.152 0.192 0.100
#> SRR1399113 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.1197 0.8436 0.000 0.000 0.048 0.952 0.000
#> SRR1092468 1 0.6920 0.0703 0.484 0.000 0.332 0.152 0.032
#> SRR1441804 1 0.1310 0.8397 0.956 0.000 0.020 0.024 0.000
#> SRR1326100 2 0.1121 0.8826 0.000 0.956 0.000 0.000 0.044
#> SRR1398815 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1436021 4 0.3506 0.8142 0.000 0.000 0.132 0.824 0.044
#> SRR1480083 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.2377 0.7571 0.872 0.000 0.128 0.000 0.000
#> SRR815542 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1400100 3 0.2446 0.5423 0.000 0.000 0.900 0.056 0.044
#> SRR1312002 3 0.4307 0.4251 0.100 0.000 0.772 0.000 0.128
#> SRR1470253 3 0.4747 -0.0875 0.488 0.000 0.496 0.000 0.016
#> SRR1414332 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1069209 1 0.4015 0.6779 0.652 0.000 0.000 0.000 0.348
#> SRR661052 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1308860 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1421159 4 0.4777 0.6071 0.000 0.000 0.292 0.664 0.044
#> SRR1340943 4 0.3691 0.7415 0.000 0.000 0.156 0.804 0.040
#> SRR1078855 1 0.2329 0.8164 0.876 0.000 0.000 0.000 0.124
#> SRR1459465 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1350979 3 0.2179 0.5433 0.000 0.000 0.888 0.000 0.112
#> SRR1458198 1 0.6649 0.4137 0.600 0.000 0.148 0.196 0.056
#> SRR1386910 4 0.5113 0.7067 0.000 0.180 0.048 0.728 0.044
#> SRR1465375 4 0.2605 0.7817 0.000 0.000 0.148 0.852 0.000
#> SRR1323699 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1431139 3 0.0898 0.5962 0.008 0.000 0.972 0.000 0.020
#> SRR1373964 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1455413 1 0.6589 0.4173 0.604 0.000 0.196 0.148 0.052
#> SRR1437163 1 0.3246 0.6933 0.808 0.000 0.008 0.184 0.000
#> SRR1347343 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1465480 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1086514 4 0.3418 0.8256 0.000 0.028 0.068 0.860 0.044
#> SRR1430928 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1310939 3 0.4305 -0.0432 0.000 0.000 0.512 0.000 0.488
#> SRR1344294 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1468118 5 0.6383 0.6427 0.000 0.000 0.328 0.184 0.488
#> SRR1486348 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1488770 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0703 0.8464 0.976 0.000 0.000 0.024 0.000
#> SRR1500089 5 0.7611 0.7187 0.144 0.000 0.152 0.192 0.512
#> SRR1441178 1 0.2732 0.8014 0.840 0.000 0.000 0.000 0.160
#> SRR1381396 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1096081 3 0.4747 -0.0622 0.000 0.000 0.496 0.016 0.488
#> SRR1349809 2 0.5764 0.3036 0.000 0.612 0.152 0.236 0.000
#> SRR1324314 3 0.4736 0.0400 0.404 0.000 0.576 0.000 0.020
#> SRR1092444 1 0.3498 0.7413 0.832 0.000 0.132 0.024 0.012
#> SRR1382553 3 0.1124 0.5834 0.036 0.000 0.960 0.000 0.004
#> SRR1075530 4 0.1197 0.8436 0.000 0.000 0.048 0.952 0.000
#> SRR1442612 3 0.0000 0.6041 0.000 0.000 1.000 0.000 0.000
#> SRR1360056 3 0.4302 -0.0265 0.000 0.000 0.520 0.000 0.480
#> SRR1078164 1 0.2732 0.8014 0.840 0.000 0.000 0.000 0.160
#> SRR1434545 4 0.3368 0.7591 0.000 0.000 0.156 0.820 0.024
#> SRR1398251 1 0.4262 0.5922 0.560 0.000 0.000 0.000 0.440
#> SRR1375866 1 0.0794 0.8466 0.972 0.000 0.000 0.000 0.028
#> SRR1091645 4 0.1792 0.8360 0.000 0.000 0.084 0.916 0.000
#> SRR1416636 3 0.4747 -0.0622 0.000 0.000 0.496 0.016 0.488
#> SRR1105441 3 0.1764 0.5660 0.000 0.000 0.928 0.064 0.008
#> SRR1082496 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 3 0.5113 -0.0244 0.000 0.000 0.576 0.380 0.044
#> SRR1093697 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.4305 -0.0432 0.000 0.000 0.512 0.000 0.488
#> SRR1076120 5 0.7585 0.7261 0.140 0.000 0.156 0.188 0.516
#> SRR1074410 1 0.0000 0.8491 1.000 0.000 0.000 0.000 0.000
#> SRR1340345 4 0.1197 0.8436 0.000 0.000 0.048 0.952 0.000
#> SRR1069514 3 0.1282 0.5779 0.000 0.000 0.952 0.004 0.044
#> SRR1092636 3 0.2074 0.5519 0.000 0.000 0.896 0.000 0.104
#> SRR1365013 4 0.5085 0.6051 0.000 0.008 0.300 0.648 0.044
#> SRR1073069 1 0.4256 0.5966 0.564 0.000 0.000 0.000 0.436
#> SRR1443137 1 0.3586 0.7414 0.736 0.000 0.000 0.000 0.264
#> SRR1437143 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.2732 0.8014 0.840 0.000 0.000 0.000 0.160
#> SRR820234 2 0.4969 0.6253 0.000 0.732 0.036 0.188 0.044
#> SRR1338079 1 0.0290 0.8491 0.992 0.000 0.000 0.008 0.000
#> SRR1390094 3 0.2966 0.3360 0.000 0.000 0.816 0.184 0.000
#> SRR1340721 1 0.7411 0.3054 0.536 0.120 0.156 0.188 0.000
#> SRR1335964 3 0.4305 -0.0432 0.000 0.000 0.512 0.000 0.488
#> SRR1086869 5 0.6422 0.6511 0.000 0.000 0.316 0.196 0.488
#> SRR1453434 1 0.0703 0.8464 0.976 0.000 0.000 0.024 0.000
#> SRR1402261 4 0.3615 0.7471 0.000 0.000 0.156 0.808 0.036
#> SRR657809 4 0.2308 0.8389 0.000 0.004 0.048 0.912 0.036
#> SRR1093075 1 0.1478 0.8372 0.936 0.000 0.000 0.000 0.064
#> SRR1433329 1 0.4256 0.5966 0.564 0.000 0.000 0.000 0.436
#> SRR1353418 3 0.4304 -0.0350 0.000 0.000 0.516 0.000 0.484
#> SRR1092913 4 0.1197 0.8436 0.000 0.000 0.048 0.952 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1335605 3 0.0146 0.7945 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1432014 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1499215 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1460409 1 0.0260 0.8905 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1086441 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1097344 4 0.0260 0.7946 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1081789 3 0.5320 0.4123 0.000 0.000 0.572 0.140 0.000 0.288
#> SRR1453005 4 0.6468 0.5140 0.000 0.064 0.144 0.504 0.000 0.288
#> SRR1366985 6 0.4845 0.3020 0.060 0.000 0.400 0.000 0.000 0.540
#> SRR815280 1 0.2092 0.8232 0.876 0.000 0.000 0.000 0.000 0.124
#> SRR1348531 1 0.0937 0.8795 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR815845 3 0.0777 0.7903 0.000 0.000 0.972 0.004 0.000 0.024
#> SRR1471178 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1080696 5 0.3266 0.6818 0.000 0.000 0.272 0.000 0.728 0.000
#> SRR1078684 3 0.0146 0.7945 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1317751 5 0.1663 0.8323 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1435667 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1097905 1 0.0260 0.8905 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1456548 1 0.0146 0.8910 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1075126 1 0.0146 0.8910 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR813108 2 0.5737 0.3455 0.000 0.512 0.272 0.000 0.000 0.216
#> SRR1479062 5 0.2558 0.8084 0.000 0.000 0.156 0.004 0.840 0.000
#> SRR1408703 5 0.2912 0.7558 0.000 0.000 0.216 0.000 0.784 0.000
#> SRR1332360 6 0.3684 0.6661 0.372 0.000 0.000 0.000 0.000 0.628
#> SRR1098686 1 0.0146 0.8910 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1434228 6 0.3684 0.6661 0.372 0.000 0.000 0.000 0.000 0.628
#> SRR1467149 5 0.6146 0.2770 0.340 0.000 0.052 0.008 0.520 0.080
#> SRR1399113 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.0260 0.7946 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1092468 1 0.5872 0.0425 0.496 0.000 0.052 0.004 0.392 0.056
#> SRR1441804 1 0.0937 0.8795 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR1326100 2 0.2300 0.7846 0.000 0.856 0.000 0.000 0.000 0.144
#> SRR1398815 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1436021 4 0.5905 0.3746 0.000 0.000 0.244 0.468 0.000 0.288
#> SRR1480083 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 1 0.1578 0.8550 0.936 0.000 0.048 0.004 0.000 0.012
#> SRR815542 1 0.0146 0.8910 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1400100 3 0.5289 0.4180 0.000 0.000 0.576 0.136 0.000 0.288
#> SRR1312002 6 0.5071 0.2973 0.060 0.000 0.396 0.000 0.008 0.536
#> SRR1470253 6 0.6324 0.6345 0.332 0.000 0.128 0.000 0.052 0.488
#> SRR1414332 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1069209 6 0.3774 0.6055 0.408 0.000 0.000 0.000 0.000 0.592
#> SRR661052 1 0.0291 0.8913 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1308860 1 0.0146 0.8910 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1421159 3 0.6103 -0.0955 0.000 0.000 0.368 0.344 0.000 0.288
#> SRR1340943 4 0.4328 0.6726 0.016 0.000 0.052 0.792 0.060 0.080
#> SRR1078855 1 0.1910 0.8380 0.892 0.000 0.000 0.000 0.000 0.108
#> SRR1459465 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0146 0.7945 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1350979 3 0.3288 0.4462 0.000 0.000 0.724 0.000 0.276 0.000
#> SRR1458198 1 0.4449 0.6659 0.772 0.000 0.052 0.004 0.092 0.080
#> SRR1386910 4 0.6743 0.4026 0.000 0.236 0.048 0.428 0.000 0.288
#> SRR1465375 4 0.2506 0.7679 0.000 0.000 0.052 0.880 0.000 0.068
#> SRR1323699 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1431139 3 0.0653 0.7882 0.012 0.000 0.980 0.004 0.000 0.004
#> SRR1373964 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1455413 5 0.5625 0.3607 0.296 0.000 0.052 0.004 0.592 0.056
#> SRR1437163 1 0.1219 0.8559 0.948 0.000 0.048 0.004 0.000 0.000
#> SRR1347343 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1465480 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.0146 0.8910 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1086514 4 0.5492 0.5807 0.000 0.016 0.112 0.584 0.000 0.288
#> SRR1430928 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1310939 5 0.2630 0.8261 0.000 0.000 0.092 0.004 0.872 0.032
#> SRR1344294 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8909 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468118 5 0.1765 0.8337 0.000 0.000 0.096 0.000 0.904 0.000
#> SRR1486348 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1488770 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.0146 0.8915 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1456611 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.1196 0.8748 0.952 0.000 0.000 0.000 0.040 0.008
#> SRR1500089 5 0.3426 0.7605 0.024 0.000 0.052 0.004 0.840 0.080
#> SRR1441178 1 0.2854 0.6950 0.792 0.000 0.000 0.000 0.000 0.208
#> SRR1381396 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1096081 5 0.1663 0.8323 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1349809 2 0.5283 0.5275 0.000 0.688 0.060 0.140 0.000 0.112
#> SRR1324314 6 0.6070 0.5274 0.304 0.000 0.292 0.000 0.000 0.404
#> SRR1092444 1 0.2851 0.8122 0.876 0.000 0.044 0.000 0.040 0.040
#> SRR1382553 3 0.3595 0.3905 0.008 0.000 0.704 0.000 0.000 0.288
#> SRR1075530 4 0.0405 0.7945 0.000 0.000 0.008 0.988 0.000 0.004
#> SRR1442612 3 0.0000 0.7944 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1360056 5 0.5873 0.3731 0.000 0.000 0.248 0.000 0.480 0.272
#> SRR1078164 1 0.2793 0.7099 0.800 0.000 0.000 0.000 0.000 0.200
#> SRR1434545 4 0.2945 0.7420 0.012 0.000 0.052 0.868 0.004 0.064
#> SRR1398251 6 0.3684 0.6661 0.372 0.000 0.000 0.000 0.000 0.628
#> SRR1375866 1 0.1610 0.8579 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1091645 4 0.0713 0.7892 0.000 0.000 0.028 0.972 0.000 0.000
#> SRR1416636 5 0.2378 0.8098 0.000 0.000 0.152 0.000 0.848 0.000
#> SRR1105441 3 0.3845 0.6343 0.000 0.000 0.772 0.140 0.000 0.088
#> SRR1082496 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 3 0.5644 0.3280 0.000 0.000 0.524 0.188 0.000 0.288
#> SRR1093697 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.1714 0.8338 0.000 0.000 0.092 0.000 0.908 0.000
#> SRR1076120 5 0.3259 0.7654 0.016 0.000 0.052 0.004 0.848 0.080
#> SRR1074410 1 0.0146 0.8915 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1340345 4 0.0260 0.7946 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1069514 3 0.1908 0.7460 0.000 0.000 0.900 0.004 0.000 0.096
#> SRR1092636 3 0.3531 0.3191 0.000 0.000 0.672 0.000 0.328 0.000
#> SRR1365013 3 0.6056 0.0312 0.000 0.000 0.412 0.300 0.000 0.288
#> SRR1073069 6 0.3684 0.6661 0.372 0.000 0.000 0.000 0.000 0.628
#> SRR1443137 1 0.3330 0.5213 0.716 0.000 0.000 0.000 0.000 0.284
#> SRR1437143 2 0.0000 0.8930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.2092 0.8225 0.876 0.000 0.000 0.000 0.000 0.124
#> SRR820234 2 0.5373 0.4818 0.000 0.588 0.196 0.000 0.000 0.216
#> SRR1338079 1 0.1265 0.8868 0.948 0.000 0.000 0.008 0.044 0.000
#> SRR1390094 3 0.0935 0.7858 0.000 0.000 0.964 0.004 0.000 0.032
#> SRR1340721 1 0.6087 0.3403 0.628 0.208 0.052 0.040 0.000 0.072
#> SRR1335964 5 0.2001 0.8332 0.000 0.000 0.092 0.004 0.900 0.004
#> SRR1086869 5 0.1663 0.8323 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1453434 1 0.1082 0.8775 0.956 0.000 0.000 0.000 0.040 0.004
#> SRR1402261 4 0.4524 0.6601 0.020 0.000 0.052 0.780 0.068 0.080
#> SRR657809 4 0.3575 0.6874 0.000 0.000 0.008 0.708 0.000 0.284
#> SRR1093075 1 0.1765 0.8488 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1433329 6 0.3684 0.6661 0.372 0.000 0.000 0.000 0.000 0.628
#> SRR1353418 6 0.5817 0.0686 0.000 0.000 0.260 0.000 0.244 0.496
#> SRR1092913 4 0.0260 0.7946 0.000 0.000 0.008 0.992 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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.897 0.944 0.975 0.4816 0.511 0.511
#> 3 3 0.721 0.790 0.904 0.3626 0.734 0.525
#> 4 4 0.853 0.873 0.941 0.1173 0.868 0.643
#> 5 5 0.732 0.705 0.818 0.0614 0.889 0.632
#> 6 6 0.777 0.698 0.857 0.0544 0.860 0.481
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
#> SRR816969 1 0.0000 0.990 1.000 0.000
#> SRR1335605 2 0.7745 0.736 0.228 0.772
#> SRR1432014 2 0.3584 0.901 0.068 0.932
#> SRR1499215 1 0.5294 0.850 0.880 0.120
#> SRR1460409 1 0.0000 0.990 1.000 0.000
#> SRR1086441 1 0.0000 0.990 1.000 0.000
#> SRR1097344 2 0.0000 0.950 0.000 1.000
#> SRR1081789 2 0.0000 0.950 0.000 1.000
#> SRR1453005 2 0.0000 0.950 0.000 1.000
#> SRR1366985 1 0.0000 0.990 1.000 0.000
#> SRR815280 1 0.0000 0.990 1.000 0.000
#> SRR1348531 1 0.0000 0.990 1.000 0.000
#> SRR815845 2 0.0000 0.950 0.000 1.000
#> SRR1471178 1 0.0000 0.990 1.000 0.000
#> SRR1080696 1 0.0000 0.990 1.000 0.000
#> SRR1078684 2 0.6801 0.796 0.180 0.820
#> SRR1317751 1 0.0000 0.990 1.000 0.000
#> SRR1435667 2 0.0000 0.950 0.000 1.000
#> SRR1097905 1 0.0000 0.990 1.000 0.000
#> SRR1456548 1 0.0000 0.990 1.000 0.000
#> SRR1075126 1 0.0000 0.990 1.000 0.000
#> SRR813108 2 0.0000 0.950 0.000 1.000
#> SRR1479062 2 0.8661 0.642 0.288 0.712
#> SRR1408703 1 0.0000 0.990 1.000 0.000
#> SRR1332360 1 0.0000 0.990 1.000 0.000
#> SRR1098686 1 0.0000 0.990 1.000 0.000
#> SRR1434228 1 0.0000 0.990 1.000 0.000
#> SRR1467149 1 0.0000 0.990 1.000 0.000
#> SRR1399113 2 0.0000 0.950 0.000 1.000
#> SRR1476507 2 0.0000 0.950 0.000 1.000
#> SRR1092468 1 0.0000 0.990 1.000 0.000
#> SRR1441804 1 0.0000 0.990 1.000 0.000
#> SRR1326100 2 0.0000 0.950 0.000 1.000
#> SRR1398815 1 0.0000 0.990 1.000 0.000
#> SRR1436021 2 0.0000 0.950 0.000 1.000
#> SRR1480083 2 0.0000 0.950 0.000 1.000
#> SRR1472863 1 0.0000 0.990 1.000 0.000
#> SRR815542 1 0.0000 0.990 1.000 0.000
#> SRR1400100 2 0.0000 0.950 0.000 1.000
#> SRR1312002 1 0.0000 0.990 1.000 0.000
#> SRR1470253 1 0.0000 0.990 1.000 0.000
#> SRR1414332 1 0.0000 0.990 1.000 0.000
#> SRR1069209 1 0.0000 0.990 1.000 0.000
#> SRR661052 1 0.0000 0.990 1.000 0.000
#> SRR1308860 1 0.0000 0.990 1.000 0.000
#> SRR1421159 2 0.0000 0.950 0.000 1.000
#> SRR1340943 1 0.0000 0.990 1.000 0.000
#> SRR1078855 1 0.0000 0.990 1.000 0.000
#> SRR1459465 2 0.0000 0.950 0.000 1.000
#> SRR816818 2 0.0000 0.950 0.000 1.000
#> SRR1478679 2 0.0000 0.950 0.000 1.000
#> SRR1350979 2 0.8207 0.695 0.256 0.744
#> SRR1458198 1 0.0000 0.990 1.000 0.000
#> SRR1386910 2 0.0000 0.950 0.000 1.000
#> SRR1465375 2 0.0000 0.950 0.000 1.000
#> SRR1323699 1 0.9815 0.194 0.580 0.420
#> SRR1431139 1 0.0672 0.982 0.992 0.008
#> SRR1373964 2 0.0376 0.948 0.004 0.996
#> SRR1455413 1 0.0000 0.990 1.000 0.000
#> SRR1437163 1 0.0000 0.990 1.000 0.000
#> SRR1347343 2 0.5178 0.860 0.116 0.884
#> SRR1465480 2 0.0000 0.950 0.000 1.000
#> SRR1489631 1 0.0000 0.990 1.000 0.000
#> SRR1086514 2 0.0000 0.950 0.000 1.000
#> SRR1430928 1 0.0000 0.990 1.000 0.000
#> SRR1310939 2 0.9635 0.435 0.388 0.612
#> SRR1344294 2 0.0000 0.950 0.000 1.000
#> SRR1099402 1 0.0000 0.990 1.000 0.000
#> SRR1468118 1 0.0000 0.990 1.000 0.000
#> SRR1486348 1 0.0000 0.990 1.000 0.000
#> SRR1488770 2 0.0000 0.950 0.000 1.000
#> SRR1083732 1 0.0000 0.990 1.000 0.000
#> SRR1456611 2 0.0000 0.950 0.000 1.000
#> SRR1080318 1 0.0000 0.990 1.000 0.000
#> SRR1500089 1 0.0000 0.990 1.000 0.000
#> SRR1441178 1 0.0000 0.990 1.000 0.000
#> SRR1381396 1 0.0000 0.990 1.000 0.000
#> SRR1096081 1 0.0000 0.990 1.000 0.000
#> SRR1349809 2 0.0000 0.950 0.000 1.000
#> SRR1324314 1 0.0000 0.990 1.000 0.000
#> SRR1092444 1 0.0000 0.990 1.000 0.000
#> SRR1382553 1 0.0000 0.990 1.000 0.000
#> SRR1075530 2 0.0000 0.950 0.000 1.000
#> SRR1442612 2 0.0000 0.950 0.000 1.000
#> SRR1360056 1 0.0000 0.990 1.000 0.000
#> SRR1078164 1 0.0000 0.990 1.000 0.000
#> SRR1434545 1 0.4815 0.873 0.896 0.104
#> SRR1398251 1 0.0000 0.990 1.000 0.000
#> SRR1375866 1 0.0000 0.990 1.000 0.000
#> SRR1091645 2 0.0000 0.950 0.000 1.000
#> SRR1416636 1 0.0000 0.990 1.000 0.000
#> SRR1105441 2 0.0000 0.950 0.000 1.000
#> SRR1082496 2 0.0000 0.950 0.000 1.000
#> SRR1315353 2 0.0000 0.950 0.000 1.000
#> SRR1093697 2 0.0000 0.950 0.000 1.000
#> SRR1077429 1 0.0000 0.990 1.000 0.000
#> SRR1076120 1 0.0000 0.990 1.000 0.000
#> SRR1074410 1 0.0000 0.990 1.000 0.000
#> SRR1340345 2 0.0000 0.950 0.000 1.000
#> SRR1069514 2 0.0000 0.950 0.000 1.000
#> SRR1092636 1 0.0000 0.990 1.000 0.000
#> SRR1365013 2 0.0000 0.950 0.000 1.000
#> SRR1073069 1 0.0000 0.990 1.000 0.000
#> SRR1443137 1 0.0000 0.990 1.000 0.000
#> SRR1437143 2 0.0000 0.950 0.000 1.000
#> SRR1091990 1 0.0000 0.990 1.000 0.000
#> SRR820234 2 0.0000 0.950 0.000 1.000
#> SRR1338079 1 0.0000 0.990 1.000 0.000
#> SRR1390094 2 0.9661 0.424 0.392 0.608
#> SRR1340721 2 0.7139 0.777 0.196 0.804
#> SRR1335964 2 0.7453 0.758 0.212 0.788
#> SRR1086869 1 0.0000 0.990 1.000 0.000
#> SRR1453434 1 0.0000 0.990 1.000 0.000
#> SRR1402261 1 0.0000 0.990 1.000 0.000
#> SRR657809 2 0.0000 0.950 0.000 1.000
#> SRR1093075 1 0.0000 0.990 1.000 0.000
#> SRR1433329 1 0.0000 0.990 1.000 0.000
#> SRR1353418 1 0.0000 0.990 1.000 0.000
#> SRR1092913 2 0.0000 0.950 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1335605 2 0.3112 0.8757 0.028 0.916 0.056
#> SRR1432014 3 0.1643 0.8104 0.000 0.044 0.956
#> SRR1499215 3 0.7545 0.5817 0.272 0.076 0.652
#> SRR1460409 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1097344 3 0.6308 -0.0692 0.000 0.492 0.508
#> SRR1081789 2 0.0747 0.9137 0.000 0.984 0.016
#> SRR1453005 2 0.0892 0.9138 0.000 0.980 0.020
#> SRR1366985 1 0.6095 0.2635 0.608 0.000 0.392
#> SRR815280 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR815845 3 0.1643 0.8104 0.000 0.044 0.956
#> SRR1471178 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1080696 3 0.1643 0.8221 0.044 0.000 0.956
#> SRR1078684 3 0.8470 0.3481 0.104 0.344 0.552
#> SRR1317751 3 0.1860 0.8205 0.052 0.000 0.948
#> SRR1435667 3 0.1643 0.8104 0.000 0.044 0.956
#> SRR1097905 1 0.1491 0.9101 0.968 0.016 0.016
#> SRR1456548 1 0.1337 0.9124 0.972 0.012 0.016
#> SRR1075126 1 0.0237 0.9244 0.996 0.000 0.004
#> SRR813108 2 0.5291 0.6042 0.000 0.732 0.268
#> SRR1479062 3 0.0983 0.8144 0.004 0.016 0.980
#> SRR1408703 3 0.1529 0.8220 0.040 0.000 0.960
#> SRR1332360 1 0.0237 0.9240 0.996 0.000 0.004
#> SRR1098686 1 0.0237 0.9244 0.996 0.000 0.004
#> SRR1434228 1 0.3116 0.8243 0.892 0.000 0.108
#> SRR1467149 1 0.6566 0.4582 0.636 0.016 0.348
#> SRR1399113 2 0.0592 0.9141 0.000 0.988 0.012
#> SRR1476507 3 0.6274 0.0616 0.000 0.456 0.544
#> SRR1092468 1 0.1711 0.9056 0.960 0.008 0.032
#> SRR1441804 1 0.0424 0.9228 0.992 0.000 0.008
#> SRR1326100 2 0.0747 0.9137 0.000 0.984 0.016
#> SRR1398815 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1436021 2 0.0747 0.9052 0.000 0.984 0.016
#> SRR1480083 2 0.0747 0.9137 0.000 0.984 0.016
#> SRR1472863 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR815542 1 0.0592 0.9210 0.988 0.000 0.012
#> SRR1400100 3 0.3879 0.7380 0.000 0.152 0.848
#> SRR1312002 3 0.5560 0.5899 0.300 0.000 0.700
#> SRR1470253 3 0.5363 0.6315 0.276 0.000 0.724
#> SRR1414332 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1069209 1 0.0424 0.9217 0.992 0.000 0.008
#> SRR661052 1 0.0592 0.9210 0.988 0.000 0.012
#> SRR1308860 1 0.1337 0.9124 0.972 0.012 0.016
#> SRR1421159 2 0.1529 0.9090 0.000 0.960 0.040
#> SRR1340943 1 0.6912 0.1994 0.540 0.016 0.444
#> SRR1078855 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1459465 2 0.0237 0.9134 0.000 0.996 0.004
#> SRR816818 2 0.0237 0.9111 0.000 0.996 0.004
#> SRR1478679 2 0.5058 0.6461 0.000 0.756 0.244
#> SRR1350979 3 0.1620 0.8174 0.012 0.024 0.964
#> SRR1458198 1 0.5953 0.5956 0.708 0.012 0.280
#> SRR1386910 2 0.1529 0.8972 0.000 0.960 0.040
#> SRR1465375 2 0.1529 0.8929 0.000 0.960 0.040
#> SRR1323699 3 0.6106 0.6941 0.200 0.044 0.756
#> SRR1431139 3 0.6204 0.3295 0.424 0.000 0.576
#> SRR1373964 3 0.5058 0.6270 0.000 0.244 0.756
#> SRR1455413 1 0.5178 0.6256 0.744 0.000 0.256
#> SRR1437163 1 0.1636 0.9075 0.964 0.016 0.020
#> SRR1347343 3 0.1643 0.8104 0.000 0.044 0.956
#> SRR1465480 2 0.0237 0.9134 0.000 0.996 0.004
#> SRR1489631 1 0.1491 0.9101 0.968 0.016 0.016
#> SRR1086514 2 0.0892 0.9136 0.000 0.980 0.020
#> SRR1430928 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1310939 3 0.1015 0.8153 0.008 0.012 0.980
#> SRR1344294 2 0.0747 0.9137 0.000 0.984 0.016
#> SRR1099402 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1468118 3 0.0848 0.8101 0.008 0.008 0.984
#> SRR1486348 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1488770 2 0.0747 0.9137 0.000 0.984 0.016
#> SRR1083732 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1456611 2 0.0592 0.9141 0.000 0.988 0.012
#> SRR1080318 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1500089 3 0.5835 0.4310 0.340 0.000 0.660
#> SRR1441178 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1096081 3 0.1753 0.8212 0.048 0.000 0.952
#> SRR1349809 2 0.0592 0.9075 0.000 0.988 0.012
#> SRR1324314 1 0.3340 0.8096 0.880 0.000 0.120
#> SRR1092444 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1382553 1 0.5621 0.4890 0.692 0.000 0.308
#> SRR1075530 2 0.6244 0.2423 0.000 0.560 0.440
#> SRR1442612 3 0.1643 0.8104 0.000 0.044 0.956
#> SRR1360056 3 0.2066 0.8191 0.060 0.000 0.940
#> SRR1078164 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1434545 3 0.8574 0.0536 0.432 0.096 0.472
#> SRR1398251 1 0.5016 0.6303 0.760 0.000 0.240
#> SRR1375866 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1091645 3 0.4555 0.6379 0.000 0.200 0.800
#> SRR1416636 3 0.1529 0.8217 0.040 0.000 0.960
#> SRR1105441 3 0.2959 0.7825 0.000 0.100 0.900
#> SRR1082496 2 0.0592 0.9141 0.000 0.988 0.012
#> SRR1315353 3 0.3192 0.7741 0.000 0.112 0.888
#> SRR1093697 2 0.0747 0.9137 0.000 0.984 0.016
#> SRR1077429 3 0.1753 0.8208 0.048 0.000 0.952
#> SRR1076120 3 0.5465 0.5360 0.288 0.000 0.712
#> SRR1074410 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR1340345 2 0.4796 0.7098 0.000 0.780 0.220
#> SRR1069514 2 0.5785 0.4686 0.000 0.668 0.332
#> SRR1092636 3 0.1964 0.8202 0.056 0.000 0.944
#> SRR1365013 2 0.0592 0.9112 0.000 0.988 0.012
#> SRR1073069 1 0.0237 0.9240 0.996 0.000 0.004
#> SRR1443137 1 0.0237 0.9240 0.996 0.000 0.004
#> SRR1437143 2 0.0592 0.9141 0.000 0.988 0.012
#> SRR1091990 1 0.0000 0.9256 1.000 0.000 0.000
#> SRR820234 2 0.1163 0.9078 0.000 0.972 0.028
#> SRR1338079 1 0.1337 0.9124 0.972 0.012 0.016
#> SRR1390094 3 0.3293 0.8023 0.088 0.012 0.900
#> SRR1340721 2 0.5506 0.6507 0.220 0.764 0.016
#> SRR1335964 3 0.1015 0.8152 0.008 0.012 0.980
#> SRR1086869 3 0.1289 0.8181 0.032 0.000 0.968
#> SRR1453434 1 0.1031 0.9151 0.976 0.000 0.024
#> SRR1402261 1 0.6566 0.4575 0.636 0.016 0.348
#> SRR657809 2 0.1289 0.8978 0.000 0.968 0.032
#> SRR1093075 1 0.0237 0.9240 0.996 0.000 0.004
#> SRR1433329 1 0.0592 0.9190 0.988 0.000 0.012
#> SRR1353418 3 0.2066 0.8191 0.060 0.000 0.940
#> SRR1092913 2 0.3192 0.8421 0.000 0.888 0.112
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.3216 0.821 0.004 0.864 0.124 0.008
#> SRR1432014 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1499215 3 0.0188 0.915 0.004 0.000 0.996 0.000
#> SRR1460409 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1081789 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1453005 2 0.3681 0.769 0.000 0.816 0.008 0.176
#> SRR1366985 3 0.0592 0.911 0.016 0.000 0.984 0.000
#> SRR815280 1 0.0336 0.964 0.992 0.000 0.008 0.000
#> SRR1348531 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR815845 3 0.0817 0.909 0.000 0.024 0.976 0.000
#> SRR1471178 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0469 0.913 0.000 0.000 0.988 0.012
#> SRR1078684 3 0.3991 0.775 0.020 0.172 0.808 0.000
#> SRR1317751 3 0.1211 0.903 0.000 0.000 0.960 0.040
#> SRR1435667 3 0.0469 0.914 0.000 0.012 0.988 0.000
#> SRR1097905 1 0.0376 0.964 0.992 0.004 0.000 0.004
#> SRR1456548 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1075126 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR813108 2 0.2814 0.819 0.000 0.868 0.132 0.000
#> SRR1479062 3 0.2216 0.869 0.000 0.000 0.908 0.092
#> SRR1408703 3 0.1211 0.903 0.000 0.000 0.960 0.040
#> SRR1332360 1 0.1118 0.947 0.964 0.000 0.036 0.000
#> SRR1098686 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1434228 1 0.4866 0.325 0.596 0.000 0.404 0.000
#> SRR1467149 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1399113 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1092468 4 0.4382 0.586 0.296 0.000 0.000 0.704
#> SRR1441804 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1326100 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1436021 2 0.1389 0.891 0.000 0.952 0.000 0.048
#> SRR1480083 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR815542 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1400100 3 0.2408 0.856 0.000 0.104 0.896 0.000
#> SRR1312002 3 0.0188 0.915 0.004 0.000 0.996 0.000
#> SRR1470253 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> SRR1414332 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.1557 0.930 0.944 0.000 0.056 0.000
#> SRR661052 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1308860 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1421159 4 0.5288 -0.022 0.000 0.472 0.008 0.520
#> SRR1340943 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1078855 1 0.0817 0.955 0.976 0.000 0.024 0.000
#> SRR1459465 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1478679 2 0.3942 0.707 0.000 0.764 0.236 0.000
#> SRR1350979 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1458198 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1386910 2 0.0921 0.903 0.000 0.972 0.000 0.028
#> SRR1465375 2 0.5511 0.452 0.028 0.620 0.000 0.352
#> SRR1323699 3 0.0188 0.915 0.004 0.000 0.996 0.000
#> SRR1431139 3 0.4331 0.618 0.288 0.000 0.712 0.000
#> SRR1373964 3 0.0188 0.915 0.000 0.004 0.996 0.000
#> SRR1455413 4 0.4343 0.637 0.264 0.000 0.004 0.732
#> SRR1437163 1 0.0376 0.964 0.992 0.004 0.000 0.004
#> SRR1347343 3 0.0188 0.915 0.004 0.000 0.996 0.000
#> SRR1465480 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1086514 2 0.4164 0.649 0.000 0.736 0.000 0.264
#> SRR1430928 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1310939 4 0.3266 0.741 0.000 0.000 0.168 0.832
#> SRR1344294 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1468118 4 0.3074 0.776 0.000 0.000 0.152 0.848
#> SRR1486348 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1500089 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1441178 1 0.0921 0.953 0.972 0.000 0.028 0.000
#> SRR1381396 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.0817 0.910 0.000 0.000 0.976 0.024
#> SRR1349809 2 0.1109 0.897 0.028 0.968 0.000 0.004
#> SRR1324314 1 0.4382 0.588 0.704 0.000 0.296 0.000
#> SRR1092444 1 0.0657 0.958 0.984 0.000 0.004 0.012
#> SRR1382553 3 0.4483 0.606 0.284 0.004 0.712 0.000
#> SRR1075530 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1442612 3 0.0336 0.915 0.000 0.008 0.992 0.000
#> SRR1360056 3 0.0188 0.915 0.004 0.000 0.996 0.000
#> SRR1078164 1 0.0921 0.953 0.972 0.000 0.028 0.000
#> SRR1434545 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1398251 3 0.3266 0.754 0.168 0.000 0.832 0.000
#> SRR1375866 1 0.0188 0.965 0.996 0.000 0.004 0.000
#> SRR1091645 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1416636 3 0.1022 0.907 0.000 0.000 0.968 0.032
#> SRR1105441 3 0.1637 0.889 0.000 0.060 0.940 0.000
#> SRR1082496 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.3688 0.737 0.000 0.208 0.792 0.000
#> SRR1093697 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.1716 0.888 0.000 0.000 0.936 0.064
#> SRR1076120 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> SRR1074410 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR1340345 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1069514 2 0.3873 0.708 0.000 0.772 0.228 0.000
#> SRR1092636 3 0.0188 0.915 0.000 0.000 0.996 0.004
#> SRR1365013 2 0.0524 0.909 0.008 0.988 0.000 0.004
#> SRR1073069 1 0.2011 0.907 0.920 0.000 0.080 0.000
#> SRR1443137 1 0.1022 0.950 0.968 0.000 0.032 0.000
#> SRR1437143 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0336 0.964 0.992 0.000 0.008 0.000
#> SRR820234 2 0.0336 0.911 0.000 0.992 0.008 0.000
#> SRR1338079 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> SRR1390094 3 0.0469 0.913 0.012 0.000 0.988 0.000
#> SRR1340721 2 0.3945 0.672 0.216 0.780 0.000 0.004
#> SRR1335964 3 0.4643 0.522 0.000 0.000 0.656 0.344
#> SRR1086869 3 0.4134 0.668 0.000 0.000 0.740 0.260
#> SRR1453434 1 0.0188 0.965 0.996 0.000 0.000 0.004
#> SRR1402261 4 0.0188 0.901 0.004 0.000 0.000 0.996
#> SRR657809 2 0.2124 0.878 0.008 0.924 0.000 0.068
#> SRR1093075 1 0.0817 0.955 0.976 0.000 0.024 0.000
#> SRR1433329 1 0.2814 0.848 0.868 0.000 0.132 0.000
#> SRR1353418 3 0.0188 0.915 0.004 0.000 0.996 0.000
#> SRR1092913 4 0.0000 0.903 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.1792 0.85451 0.916 0.000 0.084 0.000 0.000
#> SRR1335605 2 0.6269 0.51632 0.008 0.520 0.344 0.000 0.128
#> SRR1432014 3 0.4045 0.60600 0.000 0.000 0.644 0.000 0.356
#> SRR1499215 3 0.6461 0.68734 0.060 0.104 0.612 0.000 0.224
#> SRR1460409 1 0.0162 0.85680 0.996 0.000 0.004 0.000 0.000
#> SRR1086441 1 0.1043 0.85887 0.960 0.000 0.040 0.000 0.000
#> SRR1097344 4 0.0162 0.83858 0.000 0.000 0.004 0.996 0.000
#> SRR1081789 2 0.3143 0.51871 0.000 0.796 0.204 0.000 0.000
#> SRR1453005 4 0.6689 0.06315 0.000 0.344 0.244 0.412 0.000
#> SRR1366985 1 0.4528 -0.07605 0.548 0.000 0.444 0.000 0.008
#> SRR815280 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1348531 1 0.2329 0.84488 0.876 0.000 0.124 0.000 0.000
#> SRR815845 5 0.0000 0.83126 0.000 0.000 0.000 0.000 1.000
#> SRR1471178 1 0.0703 0.85923 0.976 0.000 0.024 0.000 0.000
#> SRR1080696 5 0.0880 0.81124 0.000 0.000 0.032 0.000 0.968
#> SRR1078684 3 0.6850 0.68028 0.064 0.204 0.580 0.000 0.152
#> SRR1317751 5 0.0162 0.83082 0.000 0.000 0.000 0.004 0.996
#> SRR1435667 3 0.4045 0.60600 0.000 0.000 0.644 0.000 0.356
#> SRR1097905 1 0.5002 0.67549 0.612 0.044 0.344 0.000 0.000
#> SRR1456548 1 0.3999 0.72408 0.656 0.000 0.344 0.000 0.000
#> SRR1075126 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR813108 3 0.4101 0.52074 0.000 0.372 0.628 0.000 0.000
#> SRR1479062 3 0.6622 0.40906 0.000 0.000 0.440 0.328 0.232
#> SRR1408703 5 0.0000 0.83126 0.000 0.000 0.000 0.000 1.000
#> SRR1332360 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1098686 1 0.3534 0.78561 0.744 0.000 0.256 0.000 0.000
#> SRR1434228 1 0.0703 0.84723 0.976 0.000 0.024 0.000 0.000
#> SRR1467149 4 0.3718 0.73182 0.008 0.000 0.120 0.824 0.048
#> SRR1399113 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0162 0.83858 0.000 0.000 0.004 0.996 0.000
#> SRR1092468 4 0.6229 -0.04674 0.392 0.000 0.144 0.464 0.000
#> SRR1441804 1 0.3143 0.81413 0.796 0.000 0.204 0.000 0.000
#> SRR1326100 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1398815 1 0.3895 0.74278 0.680 0.000 0.320 0.000 0.000
#> SRR1436021 4 0.4450 0.18123 0.000 0.488 0.004 0.508 0.000
#> SRR1480083 2 0.4030 0.17816 0.000 0.648 0.352 0.000 0.000
#> SRR1472863 1 0.3999 0.72408 0.656 0.000 0.344 0.000 0.000
#> SRR815542 1 0.0794 0.85917 0.972 0.000 0.028 0.000 0.000
#> SRR1400100 5 0.2278 0.77962 0.000 0.032 0.060 0.000 0.908
#> SRR1312002 3 0.5934 0.58332 0.232 0.000 0.592 0.000 0.176
#> SRR1470253 5 0.0162 0.82996 0.000 0.000 0.004 0.000 0.996
#> SRR1414332 1 0.1121 0.85846 0.956 0.000 0.044 0.000 0.000
#> SRR1069209 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR661052 1 0.3999 0.72408 0.656 0.000 0.344 0.000 0.000
#> SRR1308860 1 0.3913 0.73960 0.676 0.000 0.324 0.000 0.000
#> SRR1421159 4 0.3676 0.64789 0.000 0.232 0.004 0.760 0.004
#> SRR1340943 4 0.0000 0.83918 0.000 0.000 0.000 1.000 0.000
#> SRR1078855 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1459465 2 0.0671 0.77575 0.000 0.980 0.004 0.016 0.000
#> SRR816818 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.4608 0.56990 0.012 0.336 0.644 0.000 0.008
#> SRR1350979 3 0.4030 0.60939 0.000 0.000 0.648 0.000 0.352
#> SRR1458198 4 0.0162 0.83769 0.004 0.000 0.000 0.996 0.000
#> SRR1386910 2 0.4697 0.61752 0.000 0.648 0.320 0.000 0.032
#> SRR1465375 4 0.4795 0.68720 0.012 0.120 0.116 0.752 0.000
#> SRR1323699 3 0.6481 0.69307 0.096 0.092 0.632 0.000 0.180
#> SRR1431139 5 0.4858 0.58728 0.112 0.008 0.140 0.000 0.740
#> SRR1373964 3 0.5464 0.68555 0.000 0.128 0.648 0.000 0.224
#> SRR1455413 5 0.8561 0.00998 0.264 0.000 0.196 0.264 0.276
#> SRR1437163 1 0.3999 0.72408 0.656 0.000 0.344 0.000 0.000
#> SRR1347343 3 0.4015 0.61197 0.000 0.000 0.652 0.000 0.348
#> SRR1465480 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.3999 0.72408 0.656 0.000 0.344 0.000 0.000
#> SRR1086514 4 0.4047 0.53143 0.000 0.320 0.004 0.676 0.000
#> SRR1430928 1 0.0880 0.85926 0.968 0.000 0.032 0.000 0.000
#> SRR1310939 4 0.1270 0.80866 0.000 0.000 0.052 0.948 0.000
#> SRR1344294 2 0.1043 0.76037 0.000 0.960 0.040 0.000 0.000
#> SRR1099402 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1468118 5 0.0510 0.82632 0.000 0.000 0.000 0.016 0.984
#> SRR1486348 1 0.2329 0.84596 0.876 0.000 0.124 0.000 0.000
#> SRR1488770 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.1544 0.85611 0.932 0.000 0.068 0.000 0.000
#> SRR1456611 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.2377 0.84286 0.872 0.000 0.128 0.000 0.000
#> SRR1500089 4 0.0794 0.82742 0.000 0.000 0.000 0.972 0.028
#> SRR1441178 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1381396 1 0.1908 0.85220 0.908 0.000 0.092 0.000 0.000
#> SRR1096081 5 0.0000 0.83126 0.000 0.000 0.000 0.000 1.000
#> SRR1349809 2 0.4268 0.60863 0.008 0.648 0.344 0.000 0.000
#> SRR1324314 1 0.4323 0.46007 0.656 0.000 0.012 0.000 0.332
#> SRR1092444 5 0.5564 0.41762 0.284 0.000 0.092 0.004 0.620
#> SRR1382553 3 0.4803 0.34278 0.444 0.020 0.536 0.000 0.000
#> SRR1075530 4 0.1121 0.81853 0.000 0.000 0.000 0.956 0.044
#> SRR1442612 3 0.4045 0.60600 0.000 0.000 0.644 0.000 0.356
#> SRR1360056 5 0.4166 0.19100 0.004 0.000 0.348 0.000 0.648
#> SRR1078164 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1434545 4 0.0162 0.83858 0.000 0.000 0.004 0.996 0.000
#> SRR1398251 1 0.2727 0.74596 0.868 0.000 0.116 0.000 0.016
#> SRR1375866 1 0.3196 0.81923 0.804 0.000 0.192 0.000 0.004
#> SRR1091645 4 0.0000 0.83918 0.000 0.000 0.000 1.000 0.000
#> SRR1416636 5 0.0162 0.82996 0.000 0.000 0.004 0.000 0.996
#> SRR1105441 5 0.1364 0.81470 0.000 0.012 0.036 0.000 0.952
#> SRR1082496 2 0.0162 0.78407 0.000 0.996 0.004 0.000 0.000
#> SRR1315353 3 0.4946 0.61396 0.000 0.300 0.648 0.000 0.052
#> SRR1093697 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.0162 0.83082 0.000 0.000 0.000 0.004 0.996
#> SRR1076120 4 0.0000 0.83918 0.000 0.000 0.000 1.000 0.000
#> SRR1074410 1 0.2966 0.82306 0.816 0.000 0.184 0.000 0.000
#> SRR1340345 4 0.0000 0.83918 0.000 0.000 0.000 1.000 0.000
#> SRR1069514 3 0.4873 0.60315 0.000 0.312 0.644 0.000 0.044
#> SRR1092636 5 0.0000 0.83126 0.000 0.000 0.000 0.000 1.000
#> SRR1365013 2 0.4252 0.61155 0.008 0.652 0.340 0.000 0.000
#> SRR1073069 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1443137 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1437143 2 0.0000 0.78610 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR820234 3 0.4989 0.42666 0.000 0.416 0.552 0.032 0.000
#> SRR1338079 1 0.3999 0.72408 0.656 0.000 0.344 0.000 0.000
#> SRR1390094 3 0.5743 0.58107 0.252 0.028 0.652 0.064 0.004
#> SRR1340721 2 0.5357 0.55053 0.068 0.588 0.344 0.000 0.000
#> SRR1335964 5 0.3636 0.54606 0.000 0.000 0.000 0.272 0.728
#> SRR1086869 5 0.0703 0.82224 0.000 0.000 0.000 0.024 0.976
#> SRR1453434 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1402261 4 0.0000 0.83918 0.000 0.000 0.000 1.000 0.000
#> SRR657809 2 0.6319 0.38864 0.000 0.520 0.196 0.284 0.000
#> SRR1093075 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1433329 1 0.0290 0.85624 0.992 0.000 0.008 0.000 0.000
#> SRR1353418 5 0.1341 0.78842 0.000 0.000 0.056 0.000 0.944
#> SRR1092913 4 0.0000 0.83918 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.3868 -0.0162 0.508 0.000 0.000 0.000 0.000 0.492
#> SRR1335605 1 0.5628 0.3033 0.568 0.168 0.008 0.000 0.256 0.000
#> SRR1432014 3 0.0777 0.8716 0.000 0.000 0.972 0.000 0.024 0.004
#> SRR1499215 3 0.3972 0.6981 0.004 0.004 0.724 0.000 0.024 0.244
#> SRR1460409 6 0.1866 0.7618 0.084 0.000 0.008 0.000 0.000 0.908
#> SRR1086441 6 0.3578 0.4445 0.340 0.000 0.000 0.000 0.000 0.660
#> SRR1097344 4 0.0260 0.8747 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1081789 2 0.3668 0.6918 0.028 0.744 0.228 0.000 0.000 0.000
#> SRR1453005 4 0.5398 0.3466 0.032 0.064 0.324 0.580 0.000 0.000
#> SRR1366985 6 0.1531 0.7496 0.004 0.000 0.068 0.000 0.000 0.928
#> SRR815280 6 0.1141 0.7826 0.052 0.000 0.000 0.000 0.000 0.948
#> SRR1348531 1 0.4847 0.0965 0.500 0.000 0.000 0.000 0.056 0.444
#> SRR815845 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1471178 6 0.3151 0.5938 0.252 0.000 0.000 0.000 0.000 0.748
#> SRR1080696 5 0.1814 0.8566 0.000 0.000 0.100 0.000 0.900 0.000
#> SRR1078684 3 0.2400 0.8263 0.064 0.024 0.896 0.000 0.000 0.016
#> SRR1317751 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1435667 3 0.0922 0.8713 0.000 0.004 0.968 0.000 0.024 0.004
#> SRR1097905 1 0.1296 0.7214 0.948 0.004 0.004 0.000 0.000 0.044
#> SRR1456548 1 0.1765 0.7146 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1075126 6 0.0146 0.7962 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR813108 3 0.1411 0.8448 0.004 0.060 0.936 0.000 0.000 0.000
#> SRR1479062 4 0.7198 0.2727 0.032 0.024 0.256 0.492 0.172 0.024
#> SRR1408703 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1332360 6 0.0405 0.7962 0.004 0.000 0.008 0.000 0.000 0.988
#> SRR1098686 1 0.3101 0.5874 0.756 0.000 0.000 0.000 0.000 0.244
#> SRR1434228 6 0.0725 0.7866 0.012 0.000 0.012 0.000 0.000 0.976
#> SRR1467149 4 0.4193 0.3160 0.352 0.000 0.000 0.624 0.024 0.000
#> SRR1399113 2 0.0820 0.9079 0.016 0.972 0.012 0.000 0.000 0.000
#> SRR1476507 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1092468 1 0.3468 0.5177 0.712 0.000 0.000 0.284 0.000 0.004
#> SRR1441804 1 0.2793 0.6424 0.800 0.000 0.000 0.000 0.000 0.200
#> SRR1326100 2 0.4008 0.7137 0.064 0.740 0.196 0.000 0.000 0.000
#> SRR1398815 1 0.2706 0.6758 0.832 0.000 0.008 0.000 0.000 0.160
#> SRR1436021 4 0.5021 0.4651 0.280 0.040 0.040 0.640 0.000 0.000
#> SRR1480083 2 0.1970 0.8794 0.028 0.912 0.060 0.000 0.000 0.000
#> SRR1472863 1 0.1531 0.7230 0.928 0.000 0.004 0.000 0.000 0.068
#> SRR815542 6 0.2793 0.6553 0.200 0.000 0.000 0.000 0.000 0.800
#> SRR1400100 5 0.1245 0.9193 0.016 0.032 0.000 0.000 0.952 0.000
#> SRR1312002 6 0.4983 -0.1446 0.028 0.004 0.444 0.000 0.016 0.508
#> SRR1470253 5 0.0767 0.9342 0.004 0.000 0.008 0.000 0.976 0.012
#> SRR1414332 6 0.3531 0.4757 0.328 0.000 0.000 0.000 0.000 0.672
#> SRR1069209 6 0.0291 0.7962 0.004 0.000 0.004 0.000 0.000 0.992
#> SRR661052 1 0.1327 0.7232 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1308860 1 0.2416 0.6817 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1421159 3 0.3732 0.6566 0.004 0.024 0.744 0.228 0.000 0.000
#> SRR1340943 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1078855 6 0.0146 0.7955 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1459465 2 0.1194 0.9025 0.008 0.956 0.032 0.004 0.000 0.000
#> SRR816818 2 0.0603 0.9007 0.016 0.980 0.004 0.000 0.000 0.000
#> SRR1478679 3 0.3071 0.7733 0.000 0.016 0.804 0.000 0.000 0.180
#> SRR1350979 3 0.0547 0.8714 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1458198 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1386910 1 0.4357 0.0361 0.560 0.420 0.012 0.000 0.008 0.000
#> SRR1465375 1 0.4930 0.0572 0.488 0.044 0.008 0.460 0.000 0.000
#> SRR1323699 3 0.3723 0.7076 0.012 0.004 0.736 0.000 0.004 0.244
#> SRR1431139 1 0.3985 0.5948 0.764 0.004 0.172 0.004 0.056 0.000
#> SRR1373964 3 0.1594 0.8604 0.000 0.000 0.932 0.000 0.016 0.052
#> SRR1455413 1 0.3473 0.6822 0.828 0.000 0.004 0.108 0.044 0.016
#> SRR1437163 1 0.1411 0.7231 0.936 0.000 0.004 0.000 0.000 0.060
#> SRR1347343 3 0.1232 0.8697 0.004 0.000 0.956 0.000 0.016 0.024
#> SRR1465480 2 0.0508 0.9060 0.012 0.984 0.004 0.000 0.000 0.000
#> SRR1489631 1 0.1471 0.7233 0.932 0.000 0.004 0.000 0.000 0.064
#> SRR1086514 4 0.1970 0.8256 0.000 0.060 0.028 0.912 0.000 0.000
#> SRR1430928 6 0.3817 0.2139 0.432 0.000 0.000 0.000 0.000 0.568
#> SRR1310939 4 0.2095 0.8350 0.016 0.000 0.028 0.916 0.000 0.040
#> SRR1344294 2 0.1176 0.9019 0.020 0.956 0.024 0.000 0.000 0.000
#> SRR1099402 6 0.0260 0.7958 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1468118 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1486348 1 0.3862 0.0493 0.524 0.000 0.000 0.000 0.000 0.476
#> SRR1488770 2 0.1088 0.9091 0.016 0.960 0.024 0.000 0.000 0.000
#> SRR1083732 6 0.3864 0.0576 0.480 0.000 0.000 0.000 0.000 0.520
#> SRR1456611 2 0.0547 0.9013 0.020 0.980 0.000 0.000 0.000 0.000
#> SRR1080318 6 0.4323 0.0393 0.476 0.000 0.008 0.000 0.008 0.508
#> SRR1500089 4 0.0603 0.8708 0.004 0.000 0.000 0.980 0.016 0.000
#> SRR1441178 6 0.0806 0.7934 0.020 0.000 0.008 0.000 0.000 0.972
#> SRR1381396 6 0.3955 0.3453 0.384 0.000 0.008 0.000 0.000 0.608
#> SRR1096081 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1349809 1 0.3014 0.5893 0.804 0.184 0.012 0.000 0.000 0.000
#> SRR1324314 6 0.4722 0.4243 0.056 0.000 0.244 0.000 0.020 0.680
#> SRR1092444 5 0.2921 0.7537 0.156 0.000 0.008 0.000 0.828 0.008
#> SRR1382553 6 0.1401 0.7695 0.020 0.004 0.028 0.000 0.000 0.948
#> SRR1075530 4 0.2457 0.8025 0.000 0.084 0.000 0.880 0.036 0.000
#> SRR1442612 3 0.0777 0.8716 0.000 0.000 0.972 0.000 0.024 0.004
#> SRR1360056 5 0.3641 0.6806 0.000 0.000 0.028 0.000 0.748 0.224
#> SRR1078164 6 0.1523 0.7850 0.044 0.000 0.008 0.000 0.008 0.940
#> SRR1434545 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1398251 6 0.0937 0.7760 0.000 0.000 0.040 0.000 0.000 0.960
#> SRR1375866 6 0.3764 0.5847 0.256 0.000 0.008 0.000 0.012 0.724
#> SRR1091645 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1416636 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1105441 3 0.4915 0.4911 0.072 0.004 0.604 0.000 0.320 0.000
#> SRR1082496 2 0.0632 0.9076 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1315353 3 0.1720 0.8497 0.032 0.040 0.928 0.000 0.000 0.000
#> SRR1093697 2 0.0820 0.9093 0.012 0.972 0.016 0.000 0.000 0.000
#> SRR1077429 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1076120 4 0.0146 0.8761 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1074410 1 0.4083 0.0947 0.532 0.000 0.008 0.000 0.000 0.460
#> SRR1340345 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1069514 3 0.0777 0.8644 0.000 0.024 0.972 0.000 0.004 0.000
#> SRR1092636 5 0.0458 0.9360 0.016 0.000 0.000 0.000 0.984 0.000
#> SRR1365013 1 0.1838 0.6846 0.916 0.068 0.016 0.000 0.000 0.000
#> SRR1073069 6 0.0363 0.7947 0.000 0.000 0.012 0.000 0.000 0.988
#> SRR1443137 6 0.0291 0.7962 0.004 0.000 0.004 0.000 0.000 0.992
#> SRR1437143 2 0.1003 0.9081 0.020 0.964 0.016 0.000 0.000 0.000
#> SRR1091990 6 0.1007 0.7857 0.044 0.000 0.000 0.000 0.000 0.956
#> SRR820234 2 0.4400 0.3926 0.032 0.592 0.376 0.000 0.000 0.000
#> SRR1338079 1 0.1610 0.7187 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1390094 3 0.0622 0.8695 0.000 0.008 0.980 0.000 0.000 0.012
#> SRR1340721 1 0.2905 0.6361 0.836 0.144 0.012 0.000 0.000 0.008
#> SRR1335964 3 0.3245 0.8200 0.044 0.000 0.852 0.052 0.052 0.000
#> SRR1086869 5 0.0000 0.9439 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1453434 6 0.0458 0.7919 0.000 0.000 0.000 0.016 0.000 0.984
#> SRR1402261 4 0.0000 0.8777 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR657809 1 0.5852 0.0326 0.452 0.168 0.004 0.376 0.000 0.000
#> SRR1093075 6 0.0146 0.7955 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1433329 6 0.0146 0.7955 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1353418 5 0.1789 0.8976 0.000 0.000 0.032 0.000 0.924 0.044
#> SRR1092913 4 0.0000 0.8777 0.000 0.000 0.000 1.000 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 17780 rows and 119 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 0.550 0.771 0.900 0.4616 0.539 0.539
#> 3 3 0.563 0.662 0.775 0.2561 0.800 0.646
#> 4 4 0.613 0.672 0.841 0.0865 0.899 0.761
#> 5 5 0.579 0.616 0.779 0.1422 0.863 0.642
#> 6 6 0.670 0.542 0.718 0.0542 0.963 0.872
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
#> SRR816969 1 0.0000 0.8649 1.000 0.000
#> SRR1335605 2 0.8661 0.6052 0.288 0.712
#> SRR1432014 1 0.9815 0.3575 0.580 0.420
#> SRR1499215 1 0.9170 0.5410 0.668 0.332
#> SRR1460409 1 0.0000 0.8649 1.000 0.000
#> SRR1086441 1 0.0000 0.8649 1.000 0.000
#> SRR1097344 2 0.0000 0.9066 0.000 1.000
#> SRR1081789 2 0.6623 0.8001 0.172 0.828
#> SRR1453005 2 0.0000 0.9066 0.000 1.000
#> SRR1366985 1 0.0000 0.8649 1.000 0.000
#> SRR815280 1 0.0000 0.8649 1.000 0.000
#> SRR1348531 1 0.0000 0.8649 1.000 0.000
#> SRR815845 2 0.7376 0.7527 0.208 0.792
#> SRR1471178 1 0.0000 0.8649 1.000 0.000
#> SRR1080696 1 0.0000 0.8649 1.000 0.000
#> SRR1078684 1 0.6623 0.7421 0.828 0.172
#> SRR1317751 1 0.0000 0.8649 1.000 0.000
#> SRR1435667 1 0.9850 0.3332 0.572 0.428
#> SRR1097905 1 0.9954 0.2365 0.540 0.460
#> SRR1456548 1 0.9954 0.2365 0.540 0.460
#> SRR1075126 1 0.0938 0.8597 0.988 0.012
#> SRR813108 2 0.2778 0.8928 0.048 0.952
#> SRR1479062 1 0.8763 0.5976 0.704 0.296
#> SRR1408703 1 0.0000 0.8649 1.000 0.000
#> SRR1332360 1 0.0000 0.8649 1.000 0.000
#> SRR1098686 1 0.1843 0.8520 0.972 0.028
#> SRR1434228 1 0.0000 0.8649 1.000 0.000
#> SRR1467149 1 0.9896 0.2963 0.560 0.440
#> SRR1399113 2 0.0000 0.9066 0.000 1.000
#> SRR1476507 2 0.0000 0.9066 0.000 1.000
#> SRR1092468 1 0.2778 0.8406 0.952 0.048
#> SRR1441804 1 0.0000 0.8649 1.000 0.000
#> SRR1326100 2 0.0000 0.9066 0.000 1.000
#> SRR1398815 1 0.0000 0.8649 1.000 0.000
#> SRR1436021 2 0.6801 0.7905 0.180 0.820
#> SRR1480083 2 0.0000 0.9066 0.000 1.000
#> SRR1472863 1 0.9933 0.2607 0.548 0.452
#> SRR815542 1 0.0000 0.8649 1.000 0.000
#> SRR1400100 2 0.8016 0.6928 0.244 0.756
#> SRR1312002 1 0.0376 0.8633 0.996 0.004
#> SRR1470253 1 0.0000 0.8649 1.000 0.000
#> SRR1414332 1 0.0000 0.8649 1.000 0.000
#> SRR1069209 1 0.0000 0.8649 1.000 0.000
#> SRR661052 1 0.9933 0.2607 0.548 0.452
#> SRR1308860 1 0.9170 0.5358 0.668 0.332
#> SRR1421159 2 0.6801 0.7905 0.180 0.820
#> SRR1340943 2 0.6048 0.8254 0.148 0.852
#> SRR1078855 1 0.0000 0.8649 1.000 0.000
#> SRR1459465 2 0.0000 0.9066 0.000 1.000
#> SRR816818 2 0.0000 0.9066 0.000 1.000
#> SRR1478679 2 0.9963 0.0297 0.464 0.536
#> SRR1350979 1 0.8909 0.5798 0.692 0.308
#> SRR1458198 1 0.0000 0.8649 1.000 0.000
#> SRR1386910 2 0.0000 0.9066 0.000 1.000
#> SRR1465375 2 0.0000 0.9066 0.000 1.000
#> SRR1323699 1 0.9286 0.5200 0.656 0.344
#> SRR1431139 1 0.2236 0.8477 0.964 0.036
#> SRR1373964 1 0.9815 0.3575 0.580 0.420
#> SRR1455413 1 0.2778 0.8406 0.952 0.048
#> SRR1437163 1 0.9963 0.2229 0.536 0.464
#> SRR1347343 1 0.9608 0.4402 0.616 0.384
#> SRR1465480 2 0.0000 0.9066 0.000 1.000
#> SRR1489631 1 0.9954 0.2365 0.540 0.460
#> SRR1086514 2 0.3274 0.8870 0.060 0.940
#> SRR1430928 1 0.0000 0.8649 1.000 0.000
#> SRR1310939 1 0.8763 0.5976 0.704 0.296
#> SRR1344294 2 0.0000 0.9066 0.000 1.000
#> SRR1099402 1 0.0000 0.8649 1.000 0.000
#> SRR1468118 1 0.7528 0.6964 0.784 0.216
#> SRR1486348 1 0.9170 0.5358 0.668 0.332
#> SRR1488770 2 0.0000 0.9066 0.000 1.000
#> SRR1083732 1 0.0000 0.8649 1.000 0.000
#> SRR1456611 2 0.0000 0.9066 0.000 1.000
#> SRR1080318 1 0.0000 0.8649 1.000 0.000
#> SRR1500089 1 0.0000 0.8649 1.000 0.000
#> SRR1441178 1 0.0000 0.8649 1.000 0.000
#> SRR1381396 1 0.0000 0.8649 1.000 0.000
#> SRR1096081 1 0.0000 0.8649 1.000 0.000
#> SRR1349809 2 0.0000 0.9066 0.000 1.000
#> SRR1324314 1 0.0376 0.8633 0.996 0.004
#> SRR1092444 1 0.0000 0.8649 1.000 0.000
#> SRR1382553 1 0.0000 0.8649 1.000 0.000
#> SRR1075530 2 0.2423 0.8956 0.040 0.960
#> SRR1442612 1 0.9815 0.3575 0.580 0.420
#> SRR1360056 1 0.7815 0.6773 0.768 0.232
#> SRR1078164 1 0.0000 0.8649 1.000 0.000
#> SRR1434545 2 0.6048 0.8254 0.148 0.852
#> SRR1398251 1 0.0000 0.8649 1.000 0.000
#> SRR1375866 1 0.0000 0.8649 1.000 0.000
#> SRR1091645 2 0.0000 0.9066 0.000 1.000
#> SRR1416636 1 0.0000 0.8649 1.000 0.000
#> SRR1105441 2 0.8499 0.6298 0.276 0.724
#> SRR1082496 2 0.0000 0.9066 0.000 1.000
#> SRR1315353 2 0.3274 0.8869 0.060 0.940
#> SRR1093697 2 0.0000 0.9066 0.000 1.000
#> SRR1077429 1 0.0000 0.8649 1.000 0.000
#> SRR1076120 1 0.0000 0.8649 1.000 0.000
#> SRR1074410 1 0.0000 0.8649 1.000 0.000
#> SRR1340345 2 0.0000 0.9066 0.000 1.000
#> SRR1069514 2 0.6801 0.7905 0.180 0.820
#> SRR1092636 1 0.2236 0.8477 0.964 0.036
#> SRR1365013 2 0.3274 0.8870 0.060 0.940
#> SRR1073069 1 0.0000 0.8649 1.000 0.000
#> SRR1443137 1 0.0000 0.8649 1.000 0.000
#> SRR1437143 2 0.0000 0.9066 0.000 1.000
#> SRR1091990 1 0.0000 0.8649 1.000 0.000
#> SRR820234 2 0.2603 0.8941 0.044 0.956
#> SRR1338079 1 0.9963 0.2229 0.536 0.464
#> SRR1390094 2 0.6247 0.8181 0.156 0.844
#> SRR1340721 2 0.0000 0.9066 0.000 1.000
#> SRR1335964 1 0.1633 0.8538 0.976 0.024
#> SRR1086869 1 0.0000 0.8649 1.000 0.000
#> SRR1453434 1 0.0000 0.8649 1.000 0.000
#> SRR1402261 2 0.6048 0.8254 0.148 0.852
#> SRR657809 2 0.0000 0.9066 0.000 1.000
#> SRR1093075 1 0.0000 0.8649 1.000 0.000
#> SRR1433329 1 0.0000 0.8649 1.000 0.000
#> SRR1353418 1 0.0000 0.8649 1.000 0.000
#> SRR1092913 2 0.0000 0.9066 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1335605 3 0.7814 0.5502 0.104 0.244 0.652
#> SRR1432014 3 0.8201 0.4755 0.400 0.076 0.524
#> SRR1499215 1 0.7661 -0.1960 0.504 0.044 0.452
#> SRR1460409 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1097344 2 0.4974 0.7352 0.000 0.764 0.236
#> SRR1081789 3 0.7251 0.3892 0.040 0.348 0.612
#> SRR1453005 2 0.3816 0.7603 0.000 0.852 0.148
#> SRR1366985 1 0.1964 0.8500 0.944 0.000 0.056
#> SRR815280 1 0.0592 0.8641 0.988 0.000 0.012
#> SRR1348531 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR815845 3 0.6229 0.4348 0.020 0.280 0.700
#> SRR1471178 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1080696 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1078684 1 0.6742 0.5147 0.708 0.052 0.240
#> SRR1317751 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1435667 3 0.7784 0.4835 0.388 0.056 0.556
#> SRR1097905 3 0.8909 0.5628 0.400 0.124 0.476
#> SRR1456548 3 0.8852 0.5674 0.396 0.120 0.484
#> SRR1075126 1 0.3038 0.8144 0.896 0.000 0.104
#> SRR813108 3 0.6295 -0.0621 0.000 0.472 0.528
#> SRR1479062 1 0.6302 -0.0966 0.520 0.000 0.480
#> SRR1408703 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1332360 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1098686 1 0.3129 0.8017 0.904 0.008 0.088
#> SRR1434228 1 0.1643 0.8528 0.956 0.000 0.044
#> SRR1467149 3 0.8779 0.5280 0.416 0.112 0.472
#> SRR1399113 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1476507 2 0.4974 0.7352 0.000 0.764 0.236
#> SRR1092468 1 0.4485 0.7613 0.844 0.020 0.136
#> SRR1441804 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1326100 2 0.4750 0.6754 0.000 0.784 0.216
#> SRR1398815 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1436021 3 0.7348 0.3862 0.044 0.348 0.608
#> SRR1480083 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1472863 3 0.8808 0.5549 0.400 0.116 0.484
#> SRR815542 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1400100 3 0.7065 0.4912 0.052 0.276 0.672
#> SRR1312002 1 0.3116 0.8123 0.892 0.000 0.108
#> SRR1470253 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1414332 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1069209 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR661052 3 0.8808 0.5549 0.400 0.116 0.484
#> SRR1308860 1 0.7542 -0.2317 0.528 0.040 0.432
#> SRR1421159 3 0.7348 0.3862 0.044 0.348 0.608
#> SRR1340943 3 0.3293 0.3682 0.012 0.088 0.900
#> SRR1078855 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1478679 3 0.8652 0.6284 0.284 0.140 0.576
#> SRR1350979 1 0.6308 -0.1451 0.508 0.000 0.492
#> SRR1458198 1 0.0237 0.8680 0.996 0.000 0.004
#> SRR1386910 2 0.1529 0.8551 0.000 0.960 0.040
#> SRR1465375 2 0.1529 0.8551 0.000 0.960 0.040
#> SRR1323699 1 0.7674 -0.2815 0.480 0.044 0.476
#> SRR1431139 1 0.4172 0.7567 0.840 0.004 0.156
#> SRR1373964 3 0.8201 0.4755 0.400 0.076 0.524
#> SRR1455413 1 0.4485 0.7613 0.844 0.020 0.136
#> SRR1437163 3 0.8895 0.5724 0.392 0.124 0.484
#> SRR1347343 3 0.7905 0.3545 0.444 0.056 0.500
#> SRR1465480 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1489631 3 0.8852 0.5674 0.396 0.120 0.484
#> SRR1086514 2 0.6286 0.2539 0.000 0.536 0.464
#> SRR1430928 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1310939 1 0.6302 -0.0966 0.520 0.000 0.480
#> SRR1344294 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1468118 1 0.6079 0.2538 0.612 0.000 0.388
#> SRR1486348 1 0.7542 -0.2317 0.528 0.040 0.432
#> SRR1488770 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1500089 1 0.0237 0.8680 0.996 0.000 0.004
#> SRR1441178 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1096081 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1349809 2 0.1529 0.8551 0.000 0.960 0.040
#> SRR1324314 1 0.3116 0.8123 0.892 0.000 0.108
#> SRR1092444 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1382553 1 0.1964 0.8500 0.944 0.000 0.056
#> SRR1075530 2 0.5621 0.6141 0.000 0.692 0.308
#> SRR1442612 3 0.8201 0.4755 0.400 0.076 0.524
#> SRR1360056 1 0.6154 0.1585 0.592 0.000 0.408
#> SRR1078164 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1434545 3 0.3293 0.3682 0.012 0.088 0.900
#> SRR1398251 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1091645 2 0.4974 0.7352 0.000 0.764 0.236
#> SRR1416636 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1105441 3 0.7569 0.5399 0.088 0.248 0.664
#> SRR1082496 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1315353 2 0.6252 0.2745 0.000 0.556 0.444
#> SRR1093697 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1077429 1 0.1753 0.8522 0.952 0.000 0.048
#> SRR1076120 1 0.0237 0.8680 0.996 0.000 0.004
#> SRR1074410 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1340345 2 0.2625 0.8374 0.000 0.916 0.084
#> SRR1069514 3 0.7306 0.4064 0.044 0.340 0.616
#> SRR1092636 1 0.4172 0.7567 0.840 0.004 0.156
#> SRR1365013 2 0.6286 0.2454 0.000 0.536 0.464
#> SRR1073069 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.8588 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR820234 2 0.6215 0.3212 0.000 0.572 0.428
#> SRR1338079 3 0.8895 0.5724 0.392 0.124 0.484
#> SRR1390094 3 0.4921 0.4317 0.020 0.164 0.816
#> SRR1340721 2 0.1529 0.8551 0.000 0.960 0.040
#> SRR1335964 1 0.3816 0.7696 0.852 0.000 0.148
#> SRR1086869 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1453434 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1402261 3 0.3293 0.3682 0.012 0.088 0.900
#> SRR657809 2 0.1529 0.8551 0.000 0.960 0.040
#> SRR1093075 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.8692 1.000 0.000 0.000
#> SRR1353418 1 0.2066 0.8478 0.940 0.000 0.060
#> SRR1092913 2 0.1529 0.8551 0.000 0.960 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1335605 3 0.2831 0.4012 0.044 0.008 0.908 0.040
#> SRR1432014 3 0.4999 0.5550 0.328 0.000 0.660 0.012
#> SRR1499215 3 0.5345 0.3787 0.428 0.000 0.560 0.012
#> SRR1460409 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1097344 2 0.4621 0.6799 0.000 0.708 0.008 0.284
#> SRR1081789 3 0.4039 0.3273 0.016 0.076 0.852 0.056
#> SRR1453005 2 0.5080 0.3944 0.000 0.576 0.420 0.004
#> SRR1366985 1 0.2546 0.8361 0.900 0.000 0.092 0.008
#> SRR815280 1 0.0657 0.8803 0.984 0.000 0.004 0.012
#> SRR1348531 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR815845 3 0.3123 0.2271 0.000 0.000 0.844 0.156
#> SRR1471178 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1080696 1 0.2402 0.8458 0.912 0.000 0.076 0.012
#> SRR1078684 1 0.4990 0.3619 0.640 0.000 0.352 0.008
#> SRR1317751 1 0.2255 0.8486 0.920 0.000 0.068 0.012
#> SRR1435667 3 0.5713 0.5434 0.340 0.000 0.620 0.040
#> SRR1097905 3 0.6840 0.5423 0.332 0.004 0.560 0.104
#> SRR1456548 3 0.6748 0.5401 0.328 0.000 0.560 0.112
#> SRR1075126 1 0.3695 0.7581 0.828 0.000 0.156 0.016
#> SRR813108 3 0.6279 0.0409 0.000 0.180 0.664 0.156
#> SRR1479062 3 0.5372 0.3199 0.444 0.000 0.544 0.012
#> SRR1408703 1 0.2402 0.8458 0.912 0.000 0.076 0.012
#> SRR1332360 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1098686 1 0.3355 0.7411 0.836 0.000 0.160 0.004
#> SRR1434228 1 0.1545 0.8648 0.952 0.000 0.040 0.008
#> SRR1467149 3 0.6677 0.5382 0.348 0.000 0.552 0.100
#> SRR1399113 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1476507 2 0.4621 0.6799 0.000 0.708 0.008 0.284
#> SRR1092468 1 0.4175 0.6845 0.776 0.000 0.212 0.012
#> SRR1441804 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1326100 2 0.5163 0.2720 0.000 0.516 0.480 0.004
#> SRR1398815 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1436021 3 0.4810 0.3219 0.020 0.108 0.808 0.064
#> SRR1480083 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1472863 3 0.6717 0.5439 0.332 0.000 0.560 0.108
#> SRR815542 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1400100 3 0.1557 0.3410 0.000 0.000 0.944 0.056
#> SRR1312002 1 0.3636 0.7503 0.820 0.000 0.172 0.008
#> SRR1470253 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1414332 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR661052 3 0.6717 0.5439 0.332 0.000 0.560 0.108
#> SRR1308860 1 0.6755 -0.3277 0.460 0.000 0.448 0.092
#> SRR1421159 3 0.4810 0.3219 0.020 0.108 0.808 0.064
#> SRR1340943 4 0.1389 0.9363 0.000 0.000 0.048 0.952
#> SRR1078855 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.4230 0.5474 0.212 0.004 0.776 0.008
#> SRR1350979 3 0.5353 0.3515 0.432 0.000 0.556 0.012
#> SRR1458198 1 0.0188 0.8862 0.996 0.000 0.004 0.000
#> SRR1386910 2 0.2483 0.8551 0.000 0.916 0.032 0.052
#> SRR1465375 2 0.2483 0.8551 0.000 0.916 0.032 0.052
#> SRR1323699 3 0.5290 0.4322 0.404 0.000 0.584 0.012
#> SRR1431139 1 0.4123 0.6785 0.772 0.000 0.220 0.008
#> SRR1373964 3 0.4999 0.5550 0.328 0.000 0.660 0.012
#> SRR1455413 1 0.4175 0.6845 0.776 0.000 0.212 0.012
#> SRR1437163 3 0.6904 0.5381 0.324 0.004 0.560 0.112
#> SRR1347343 3 0.5174 0.4951 0.368 0.000 0.620 0.012
#> SRR1465480 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1489631 3 0.6748 0.5401 0.328 0.000 0.560 0.112
#> SRR1086514 3 0.6040 0.0716 0.000 0.272 0.648 0.080
#> SRR1430928 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.5372 0.3199 0.444 0.000 0.544 0.012
#> SRR1344294 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1468118 1 0.6400 0.0145 0.524 0.000 0.408 0.068
#> SRR1486348 1 0.6755 -0.3277 0.460 0.000 0.448 0.092
#> SRR1488770 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1500089 1 0.0188 0.8862 0.996 0.000 0.004 0.000
#> SRR1441178 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1381396 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1096081 1 0.2255 0.8486 0.920 0.000 0.068 0.012
#> SRR1349809 2 0.2483 0.8551 0.000 0.916 0.032 0.052
#> SRR1324314 1 0.3636 0.7503 0.820 0.000 0.172 0.008
#> SRR1092444 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1382553 1 0.2546 0.8361 0.900 0.000 0.092 0.008
#> SRR1075530 2 0.6534 0.5253 0.000 0.624 0.244 0.132
#> SRR1442612 3 0.4999 0.5550 0.328 0.000 0.660 0.012
#> SRR1360056 1 0.6546 -0.0456 0.524 0.000 0.396 0.080
#> SRR1078164 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1434545 4 0.1389 0.9363 0.000 0.000 0.048 0.952
#> SRR1398251 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1375866 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1091645 2 0.4621 0.6799 0.000 0.708 0.008 0.284
#> SRR1416636 1 0.2402 0.8458 0.912 0.000 0.076 0.012
#> SRR1105441 3 0.2578 0.3859 0.036 0.000 0.912 0.052
#> SRR1082496 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.6950 -0.0762 0.000 0.272 0.572 0.156
#> SRR1093697 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1077429 1 0.1545 0.8658 0.952 0.000 0.040 0.008
#> SRR1076120 1 0.0188 0.8862 0.996 0.000 0.004 0.000
#> SRR1074410 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1340345 2 0.3342 0.8307 0.000 0.868 0.032 0.100
#> SRR1069514 3 0.4080 0.3364 0.020 0.072 0.852 0.056
#> SRR1092636 1 0.4123 0.6785 0.772 0.000 0.220 0.008
#> SRR1365013 3 0.5900 0.0844 0.000 0.260 0.664 0.076
#> SRR1073069 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1443137 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.8670 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR820234 3 0.7031 -0.0937 0.000 0.288 0.556 0.156
#> SRR1338079 3 0.6904 0.5381 0.324 0.004 0.560 0.112
#> SRR1390094 4 0.3810 0.7856 0.008 0.000 0.188 0.804
#> SRR1340721 2 0.2483 0.8551 0.000 0.916 0.032 0.052
#> SRR1335964 1 0.4011 0.6960 0.784 0.000 0.208 0.008
#> SRR1086869 1 0.2255 0.8486 0.920 0.000 0.068 0.012
#> SRR1453434 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1402261 4 0.1389 0.9363 0.000 0.000 0.048 0.952
#> SRR657809 2 0.2483 0.8551 0.000 0.916 0.032 0.052
#> SRR1093075 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.8877 1.000 0.000 0.000 0.000
#> SRR1353418 1 0.2255 0.8486 0.920 0.000 0.068 0.012
#> SRR1092913 2 0.2483 0.8551 0.000 0.916 0.032 0.052
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1335605 4 0.4661 0.48280 0.016 0.000 0.356 0.624 0.004
#> SRR1432014 3 0.4984 0.39409 0.052 0.000 0.640 0.308 0.000
#> SRR1499215 3 0.5365 0.49000 0.116 0.000 0.656 0.228 0.000
#> SRR1460409 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1097344 2 0.4706 0.68428 0.000 0.692 0.000 0.052 0.256
#> SRR1081789 4 0.4873 0.66955 0.000 0.068 0.244 0.688 0.000
#> SRR1453005 2 0.4300 -0.00335 0.000 0.524 0.000 0.476 0.000
#> SRR1366985 1 0.4126 0.42824 0.620 0.000 0.380 0.000 0.000
#> SRR815280 1 0.3002 0.73917 0.872 0.000 0.048 0.076 0.004
#> SRR1348531 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR815845 4 0.3210 0.63800 0.000 0.000 0.212 0.788 0.000
#> SRR1471178 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 1 0.4546 0.38303 0.532 0.000 0.460 0.008 0.000
#> SRR1078684 3 0.6066 0.37688 0.368 0.000 0.504 0.128 0.000
#> SRR1317751 1 0.4538 0.39788 0.540 0.000 0.452 0.008 0.000
#> SRR1435667 3 0.5152 0.37522 0.052 0.000 0.632 0.312 0.004
#> SRR1097905 3 0.6196 0.36481 0.124 0.004 0.592 0.268 0.012
#> SRR1456548 3 0.6265 0.36041 0.128 0.000 0.584 0.268 0.020
#> SRR1075126 1 0.4045 0.38479 0.644 0.000 0.356 0.000 0.000
#> SRR813108 4 0.3090 0.64375 0.000 0.104 0.040 0.856 0.000
#> SRR1479062 3 0.4836 0.49405 0.096 0.000 0.716 0.188 0.000
#> SRR1408703 1 0.4546 0.38303 0.532 0.000 0.460 0.008 0.000
#> SRR1332360 1 0.1270 0.79469 0.948 0.000 0.000 0.052 0.000
#> SRR1098686 1 0.3942 0.53032 0.748 0.000 0.232 0.020 0.000
#> SRR1434228 1 0.3752 0.58212 0.708 0.000 0.292 0.000 0.000
#> SRR1467149 3 0.5900 0.38286 0.128 0.000 0.612 0.252 0.008
#> SRR1399113 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 2 0.4706 0.68428 0.000 0.692 0.000 0.052 0.256
#> SRR1092468 1 0.4824 0.25768 0.596 0.000 0.376 0.028 0.000
#> SRR1441804 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1326100 4 0.5175 0.18433 0.000 0.464 0.040 0.496 0.000
#> SRR1398815 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1436021 4 0.5463 0.67272 0.000 0.100 0.248 0.648 0.004
#> SRR1480083 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 3 0.5945 0.37168 0.124 0.000 0.600 0.268 0.008
#> SRR815542 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1400100 4 0.3876 0.57636 0.000 0.000 0.316 0.684 0.000
#> SRR1312002 3 0.4443 -0.05654 0.472 0.000 0.524 0.004 0.000
#> SRR1470253 1 0.1478 0.79624 0.936 0.000 0.064 0.000 0.000
#> SRR1414332 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1069209 1 0.1341 0.79931 0.944 0.000 0.056 0.000 0.000
#> SRR661052 3 0.5945 0.37168 0.124 0.000 0.600 0.268 0.008
#> SRR1308860 3 0.6166 0.40817 0.272 0.000 0.564 0.160 0.004
#> SRR1421159 4 0.5463 0.67272 0.000 0.100 0.248 0.648 0.004
#> SRR1340943 5 0.0000 0.92353 0.000 0.000 0.000 0.000 1.000
#> SRR1078855 1 0.1408 0.80074 0.948 0.000 0.008 0.044 0.000
#> SRR1459465 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.4867 0.13222 0.024 0.000 0.544 0.432 0.000
#> SRR1350979 3 0.4779 0.48467 0.084 0.000 0.716 0.200 0.000
#> SRR1458198 1 0.2020 0.77915 0.900 0.000 0.100 0.000 0.000
#> SRR1386910 2 0.2349 0.86686 0.000 0.900 0.012 0.084 0.004
#> SRR1465375 2 0.2349 0.86686 0.000 0.900 0.012 0.084 0.004
#> SRR1323699 3 0.5167 0.47511 0.092 0.000 0.668 0.240 0.000
#> SRR1431139 3 0.4651 0.20854 0.372 0.000 0.608 0.020 0.000
#> SRR1373964 3 0.4984 0.39409 0.052 0.000 0.640 0.308 0.000
#> SRR1455413 1 0.4824 0.25768 0.596 0.000 0.376 0.028 0.000
#> SRR1437163 3 0.6376 0.35638 0.124 0.004 0.584 0.268 0.020
#> SRR1347343 3 0.5150 0.44756 0.076 0.000 0.652 0.272 0.000
#> SRR1465480 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 3 0.6265 0.36041 0.128 0.000 0.584 0.268 0.020
#> SRR1086514 4 0.5580 0.63836 0.000 0.256 0.096 0.640 0.008
#> SRR1430928 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 3 0.4836 0.49405 0.096 0.000 0.716 0.188 0.000
#> SRR1344294 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1468118 3 0.3780 0.45257 0.132 0.000 0.808 0.060 0.000
#> SRR1486348 3 0.6166 0.40817 0.272 0.000 0.564 0.160 0.004
#> SRR1488770 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.1270 0.80095 0.948 0.000 0.052 0.000 0.000
#> SRR1456611 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0703 0.80808 0.976 0.000 0.024 0.000 0.000
#> SRR1500089 1 0.2020 0.77915 0.900 0.000 0.100 0.000 0.000
#> SRR1441178 1 0.2616 0.75021 0.888 0.000 0.036 0.076 0.000
#> SRR1381396 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1096081 1 0.4538 0.39788 0.540 0.000 0.452 0.008 0.000
#> SRR1349809 2 0.2349 0.86686 0.000 0.900 0.012 0.084 0.004
#> SRR1324314 3 0.4443 -0.05654 0.472 0.000 0.524 0.004 0.000
#> SRR1092444 1 0.0703 0.80808 0.976 0.000 0.024 0.000 0.000
#> SRR1382553 1 0.4126 0.42824 0.620 0.000 0.380 0.000 0.000
#> SRR1075530 2 0.6135 0.47594 0.000 0.616 0.064 0.264 0.056
#> SRR1442612 3 0.4984 0.39409 0.052 0.000 0.640 0.308 0.000
#> SRR1360056 3 0.4431 0.51855 0.216 0.000 0.732 0.052 0.000
#> SRR1078164 1 0.1638 0.78552 0.932 0.000 0.004 0.064 0.000
#> SRR1434545 5 0.0000 0.92353 0.000 0.000 0.000 0.000 1.000
#> SRR1398251 1 0.1270 0.79469 0.948 0.000 0.000 0.052 0.000
#> SRR1375866 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1091645 2 0.4706 0.68428 0.000 0.692 0.000 0.052 0.256
#> SRR1416636 1 0.4546 0.38303 0.532 0.000 0.460 0.008 0.000
#> SRR1105441 4 0.4166 0.51746 0.004 0.000 0.348 0.648 0.000
#> SRR1082496 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 4 0.3530 0.55823 0.000 0.204 0.012 0.784 0.000
#> SRR1093697 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 1 0.4256 0.42432 0.564 0.000 0.436 0.000 0.000
#> SRR1076120 1 0.2020 0.77915 0.900 0.000 0.100 0.000 0.000
#> SRR1074410 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1340345 2 0.3426 0.84415 0.000 0.852 0.012 0.084 0.052
#> SRR1069514 4 0.4840 0.66420 0.000 0.064 0.248 0.688 0.000
#> SRR1092636 3 0.4651 0.20854 0.372 0.000 0.608 0.020 0.000
#> SRR1365013 4 0.5392 0.65048 0.000 0.244 0.096 0.656 0.004
#> SRR1073069 1 0.1270 0.79469 0.948 0.000 0.000 0.052 0.000
#> SRR1443137 1 0.1638 0.78552 0.932 0.000 0.004 0.064 0.000
#> SRR1437143 2 0.0000 0.88213 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.1197 0.79673 0.952 0.000 0.000 0.048 0.000
#> SRR820234 4 0.3210 0.53820 0.000 0.212 0.000 0.788 0.000
#> SRR1338079 3 0.6376 0.35638 0.124 0.004 0.584 0.268 0.020
#> SRR1390094 5 0.3474 0.72169 0.008 0.000 0.020 0.148 0.824
#> SRR1340721 2 0.2349 0.86686 0.000 0.900 0.012 0.084 0.004
#> SRR1335964 3 0.4268 0.22027 0.344 0.000 0.648 0.008 0.000
#> SRR1086869 1 0.4538 0.39788 0.540 0.000 0.452 0.008 0.000
#> SRR1453434 1 0.0000 0.81261 1.000 0.000 0.000 0.000 0.000
#> SRR1402261 5 0.0000 0.92353 0.000 0.000 0.000 0.000 1.000
#> SRR657809 2 0.2349 0.86686 0.000 0.900 0.012 0.084 0.004
#> SRR1093075 1 0.1408 0.80074 0.948 0.000 0.008 0.044 0.000
#> SRR1433329 1 0.1638 0.78552 0.932 0.000 0.004 0.064 0.000
#> SRR1353418 1 0.4538 0.39788 0.540 0.000 0.452 0.008 0.000
#> SRR1092913 2 0.2349 0.86686 0.000 0.900 0.012 0.084 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.0146 0.8139 0.996 0.000 0.000 0.000 0.000 NA
#> SRR1335605 3 0.5310 0.4777 0.000 0.012 0.512 0.000 0.404 NA
#> SRR1432014 5 0.3360 0.2720 0.000 0.000 0.264 0.000 0.732 NA
#> SRR1499215 5 0.3683 0.3761 0.044 0.000 0.192 0.000 0.764 NA
#> SRR1460409 1 0.0000 0.8135 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1086441 1 0.0146 0.8140 0.996 0.000 0.000 0.000 0.000 NA
#> SRR1097344 2 0.4901 0.3508 0.000 0.664 0.040 0.256 0.000 NA
#> SRR1081789 3 0.3555 0.6659 0.000 0.000 0.712 0.000 0.280 NA
#> SRR1453005 3 0.4711 0.3206 0.000 0.064 0.608 0.000 0.000 NA
#> SRR1366985 1 0.3971 0.2615 0.548 0.000 0.000 0.000 0.448 NA
#> SRR815280 1 0.2742 0.7387 0.856 0.000 0.008 0.004 0.008 NA
#> SRR1348531 1 0.0260 0.8141 0.992 0.000 0.000 0.000 0.000 NA
#> SRR815845 3 0.3534 0.6692 0.000 0.000 0.740 0.000 0.244 NA
#> SRR1471178 1 0.0260 0.8139 0.992 0.000 0.000 0.000 0.000 NA
#> SRR1080696 5 0.5587 -0.1722 0.424 0.000 0.000 0.000 0.436 NA
#> SRR1078684 5 0.5475 0.3955 0.296 0.000 0.116 0.000 0.576 NA
#> SRR1317751 1 0.5587 0.1490 0.432 0.000 0.000 0.000 0.428 NA
#> SRR1435667 5 0.3521 0.2529 0.000 0.000 0.268 0.004 0.724 NA
#> SRR1097905 5 0.5785 0.2965 0.004 0.052 0.048 0.004 0.556 NA
#> SRR1456548 5 0.6024 0.2925 0.008 0.048 0.048 0.012 0.548 NA
#> SRR1075126 1 0.4131 0.3178 0.600 0.000 0.000 0.000 0.384 NA
#> SRR813108 3 0.1563 0.6329 0.000 0.000 0.932 0.000 0.056 NA
#> SRR1479062 5 0.2911 0.3959 0.024 0.000 0.144 0.000 0.832 NA
#> SRR1408703 5 0.5587 -0.1722 0.424 0.000 0.000 0.000 0.436 NA
#> SRR1332360 1 0.1462 0.7944 0.936 0.000 0.008 0.000 0.000 NA
#> SRR1098686 1 0.4834 0.4473 0.672 0.008 0.004 0.000 0.240 NA
#> SRR1434228 1 0.3756 0.4628 0.644 0.000 0.000 0.000 0.352 NA
#> SRR1467149 5 0.5787 0.3094 0.012 0.044 0.044 0.004 0.568 NA
#> SRR1399113 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1476507 2 0.4837 0.3489 0.000 0.668 0.040 0.256 0.000 NA
#> SRR1092468 1 0.5610 0.1195 0.516 0.020 0.004 0.000 0.384 NA
#> SRR1441804 1 0.0363 0.8139 0.988 0.000 0.000 0.000 0.000 NA
#> SRR1326100 3 0.5944 0.3864 0.000 0.092 0.560 0.000 0.056 NA
#> SRR1398815 1 0.0260 0.8141 0.992 0.000 0.000 0.000 0.000 NA
#> SRR1436021 3 0.4838 0.6577 0.000 0.072 0.644 0.000 0.276 NA
#> SRR1480083 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1472863 5 0.5688 0.3029 0.008 0.048 0.048 0.000 0.564 NA
#> SRR815542 1 0.0000 0.8135 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1400100 3 0.4026 0.6139 0.000 0.000 0.636 0.000 0.348 NA
#> SRR1312002 5 0.3890 0.1309 0.400 0.000 0.000 0.000 0.596 NA
#> SRR1470253 1 0.1643 0.7921 0.924 0.000 0.000 0.000 0.068 NA
#> SRR1414332 1 0.0146 0.8139 0.996 0.000 0.000 0.000 0.000 NA
#> SRR1069209 1 0.1524 0.7958 0.932 0.000 0.000 0.000 0.060 NA
#> SRR661052 5 0.5688 0.3029 0.008 0.048 0.048 0.000 0.564 NA
#> SRR1308860 5 0.6766 0.3027 0.172 0.020 0.048 0.000 0.516 NA
#> SRR1421159 3 0.4838 0.6577 0.000 0.072 0.644 0.000 0.276 NA
#> SRR1340943 4 0.0000 0.9382 0.000 0.000 0.000 1.000 0.000 NA
#> SRR1078855 1 0.1757 0.8005 0.928 0.000 0.008 0.000 0.012 NA
#> SRR1459465 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR816818 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1478679 5 0.4037 0.0191 0.000 0.000 0.380 0.000 0.608 NA
#> SRR1350979 5 0.2768 0.3841 0.012 0.000 0.156 0.000 0.832 NA
#> SRR1458198 1 0.2258 0.7767 0.896 0.000 0.000 0.000 0.044 NA
#> SRR1386910 2 0.0000 0.6728 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1465375 2 0.0000 0.6728 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1323699 5 0.3454 0.3583 0.024 0.000 0.208 0.000 0.768 NA
#> SRR1431139 5 0.4666 0.3220 0.296 0.000 0.008 0.000 0.644 NA
#> SRR1373964 5 0.3360 0.2720 0.000 0.000 0.264 0.000 0.732 NA
#> SRR1455413 1 0.5610 0.1195 0.516 0.020 0.004 0.000 0.384 NA
#> SRR1437163 5 0.5976 0.2896 0.004 0.052 0.048 0.012 0.548 NA
#> SRR1347343 5 0.3627 0.3303 0.020 0.000 0.224 0.000 0.752 NA
#> SRR1465480 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1489631 5 0.6024 0.2925 0.008 0.048 0.048 0.012 0.548 NA
#> SRR1086514 3 0.4945 0.6094 0.000 0.240 0.648 0.004 0.108 NA
#> SRR1430928 1 0.0260 0.8139 0.992 0.000 0.000 0.000 0.000 NA
#> SRR1310939 5 0.2911 0.3959 0.024 0.000 0.144 0.000 0.832 NA
#> SRR1344294 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1099402 1 0.0363 0.8139 0.988 0.000 0.000 0.000 0.000 NA
#> SRR1468118 5 0.4018 0.3640 0.044 0.000 0.024 0.000 0.772 NA
#> SRR1486348 5 0.6766 0.3027 0.172 0.020 0.048 0.000 0.516 NA
#> SRR1488770 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1083732 1 0.1462 0.7976 0.936 0.000 0.000 0.000 0.056 NA
#> SRR1456611 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1080318 1 0.0972 0.8065 0.964 0.000 0.000 0.000 0.028 NA
#> SRR1500089 1 0.2258 0.7767 0.896 0.000 0.000 0.000 0.044 NA
#> SRR1441178 1 0.2400 0.7494 0.872 0.000 0.008 0.000 0.004 NA
#> SRR1381396 1 0.0260 0.8141 0.992 0.000 0.000 0.000 0.000 NA
#> SRR1096081 1 0.5587 0.1490 0.432 0.000 0.000 0.000 0.428 NA
#> SRR1349809 2 0.0000 0.6728 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1324314 5 0.3890 0.1309 0.400 0.000 0.000 0.000 0.596 NA
#> SRR1092444 1 0.0972 0.8065 0.964 0.000 0.000 0.000 0.028 NA
#> SRR1382553 1 0.3971 0.2615 0.548 0.000 0.000 0.000 0.448 NA
#> SRR1075530 2 0.5788 0.3699 0.000 0.644 0.216 0.052 0.060 NA
#> SRR1442612 5 0.3360 0.2720 0.000 0.000 0.264 0.000 0.732 NA
#> SRR1360056 5 0.4387 0.4326 0.144 0.000 0.036 0.000 0.756 NA
#> SRR1078164 1 0.1701 0.7854 0.920 0.000 0.008 0.000 0.000 NA
#> SRR1434545 4 0.0000 0.9382 0.000 0.000 0.000 1.000 0.000 NA
#> SRR1398251 1 0.1462 0.7944 0.936 0.000 0.008 0.000 0.000 NA
#> SRR1375866 1 0.0363 0.8139 0.988 0.000 0.000 0.000 0.000 NA
#> SRR1091645 2 0.4837 0.3489 0.000 0.668 0.040 0.256 0.000 NA
#> SRR1416636 5 0.5587 -0.1722 0.424 0.000 0.000 0.000 0.436 NA
#> SRR1105441 3 0.4131 0.5635 0.000 0.000 0.600 0.000 0.384 NA
#> SRR1082496 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1315353 3 0.2151 0.5582 0.000 0.024 0.912 0.000 0.016 NA
#> SRR1093697 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1077429 1 0.5353 0.2100 0.472 0.000 0.000 0.000 0.420 NA
#> SRR1076120 1 0.2258 0.7767 0.896 0.000 0.000 0.000 0.044 NA
#> SRR1074410 1 0.0260 0.8141 0.992 0.000 0.000 0.000 0.000 NA
#> SRR1340345 2 0.2401 0.6007 0.000 0.900 0.024 0.048 0.000 NA
#> SRR1069514 3 0.3670 0.6622 0.000 0.000 0.704 0.000 0.284 NA
#> SRR1092636 5 0.4666 0.3220 0.296 0.000 0.008 0.000 0.644 NA
#> SRR1365013 3 0.4697 0.6332 0.000 0.200 0.688 0.004 0.108 NA
#> SRR1073069 1 0.1462 0.7944 0.936 0.000 0.008 0.000 0.000 NA
#> SRR1443137 1 0.1701 0.7854 0.920 0.000 0.008 0.000 0.000 NA
#> SRR1437143 2 0.3737 0.7311 0.000 0.608 0.000 0.000 0.000 NA
#> SRR1091990 1 0.1196 0.8004 0.952 0.000 0.008 0.000 0.000 NA
#> SRR820234 3 0.1765 0.5358 0.000 0.024 0.924 0.000 0.000 NA
#> SRR1338079 5 0.5976 0.2896 0.004 0.052 0.048 0.012 0.548 NA
#> SRR1390094 4 0.3648 0.8028 0.000 0.048 0.008 0.820 0.016 NA
#> SRR1340721 2 0.0000 0.6728 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1335964 5 0.4626 0.3274 0.272 0.000 0.000 0.000 0.652 NA
#> SRR1086869 1 0.5587 0.1490 0.432 0.000 0.000 0.000 0.428 NA
#> SRR1453434 1 0.0000 0.8135 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1402261 4 0.0000 0.9382 0.000 0.000 0.000 1.000 0.000 NA
#> SRR657809 2 0.0000 0.6728 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1093075 1 0.1757 0.8005 0.928 0.000 0.008 0.000 0.012 NA
#> SRR1433329 1 0.1701 0.7854 0.920 0.000 0.008 0.000 0.000 NA
#> SRR1353418 1 0.5561 0.1559 0.436 0.000 0.000 0.000 0.428 NA
#> SRR1092913 2 0.0000 0.6728 0.000 1.000 0.000 0.000 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.984 0.993 0.4444 0.556 0.556
#> 3 3 0.682 0.722 0.880 0.4659 0.700 0.501
#> 4 4 0.637 0.682 0.829 0.1186 0.800 0.505
#> 5 5 0.652 0.587 0.749 0.0655 0.902 0.659
#> 6 6 0.697 0.617 0.776 0.0480 0.958 0.813
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
#> SRR816969 1 0.0000 0.995 1.000 0.000
#> SRR1335605 1 0.9580 0.370 0.620 0.380
#> SRR1432014 1 0.0000 0.995 1.000 0.000
#> SRR1499215 1 0.0000 0.995 1.000 0.000
#> SRR1460409 1 0.0000 0.995 1.000 0.000
#> SRR1086441 1 0.0000 0.995 1.000 0.000
#> SRR1097344 2 0.0000 0.988 0.000 1.000
#> SRR1081789 2 0.0000 0.988 0.000 1.000
#> SRR1453005 2 0.0000 0.988 0.000 1.000
#> SRR1366985 1 0.0000 0.995 1.000 0.000
#> SRR815280 1 0.0000 0.995 1.000 0.000
#> SRR1348531 1 0.0000 0.995 1.000 0.000
#> SRR815845 2 0.0672 0.982 0.008 0.992
#> SRR1471178 1 0.0000 0.995 1.000 0.000
#> SRR1080696 1 0.0000 0.995 1.000 0.000
#> SRR1078684 1 0.0000 0.995 1.000 0.000
#> SRR1317751 1 0.0000 0.995 1.000 0.000
#> SRR1435667 1 0.0000 0.995 1.000 0.000
#> SRR1097905 1 0.0000 0.995 1.000 0.000
#> SRR1456548 1 0.0000 0.995 1.000 0.000
#> SRR1075126 1 0.0000 0.995 1.000 0.000
#> SRR813108 2 0.0000 0.988 0.000 1.000
#> SRR1479062 1 0.0000 0.995 1.000 0.000
#> SRR1408703 1 0.0000 0.995 1.000 0.000
#> SRR1332360 1 0.0000 0.995 1.000 0.000
#> SRR1098686 1 0.0000 0.995 1.000 0.000
#> SRR1434228 1 0.0000 0.995 1.000 0.000
#> SRR1467149 1 0.0000 0.995 1.000 0.000
#> SRR1399113 2 0.0000 0.988 0.000 1.000
#> SRR1476507 2 0.0000 0.988 0.000 1.000
#> SRR1092468 1 0.0000 0.995 1.000 0.000
#> SRR1441804 1 0.0000 0.995 1.000 0.000
#> SRR1326100 2 0.0000 0.988 0.000 1.000
#> SRR1398815 1 0.0000 0.995 1.000 0.000
#> SRR1436021 2 0.2236 0.957 0.036 0.964
#> SRR1480083 2 0.0000 0.988 0.000 1.000
#> SRR1472863 1 0.0000 0.995 1.000 0.000
#> SRR815542 1 0.0000 0.995 1.000 0.000
#> SRR1400100 2 0.4022 0.914 0.080 0.920
#> SRR1312002 1 0.0000 0.995 1.000 0.000
#> SRR1470253 1 0.0000 0.995 1.000 0.000
#> SRR1414332 1 0.0000 0.995 1.000 0.000
#> SRR1069209 1 0.0000 0.995 1.000 0.000
#> SRR661052 1 0.0000 0.995 1.000 0.000
#> SRR1308860 1 0.0000 0.995 1.000 0.000
#> SRR1421159 2 0.0000 0.988 0.000 1.000
#> SRR1340943 1 0.0000 0.995 1.000 0.000
#> SRR1078855 1 0.0000 0.995 1.000 0.000
#> SRR1459465 2 0.0000 0.988 0.000 1.000
#> SRR816818 2 0.0000 0.988 0.000 1.000
#> SRR1478679 1 0.0000 0.995 1.000 0.000
#> SRR1350979 1 0.0000 0.995 1.000 0.000
#> SRR1458198 1 0.0000 0.995 1.000 0.000
#> SRR1386910 2 0.0000 0.988 0.000 1.000
#> SRR1465375 2 0.0000 0.988 0.000 1.000
#> SRR1323699 1 0.0000 0.995 1.000 0.000
#> SRR1431139 1 0.0000 0.995 1.000 0.000
#> SRR1373964 1 0.0000 0.995 1.000 0.000
#> SRR1455413 1 0.0000 0.995 1.000 0.000
#> SRR1437163 2 0.5629 0.853 0.132 0.868
#> SRR1347343 1 0.0000 0.995 1.000 0.000
#> SRR1465480 2 0.0000 0.988 0.000 1.000
#> SRR1489631 1 0.0000 0.995 1.000 0.000
#> SRR1086514 2 0.0000 0.988 0.000 1.000
#> SRR1430928 1 0.0000 0.995 1.000 0.000
#> SRR1310939 1 0.0000 0.995 1.000 0.000
#> SRR1344294 2 0.0000 0.988 0.000 1.000
#> SRR1099402 1 0.0000 0.995 1.000 0.000
#> SRR1468118 1 0.0000 0.995 1.000 0.000
#> SRR1486348 1 0.0000 0.995 1.000 0.000
#> SRR1488770 2 0.0000 0.988 0.000 1.000
#> SRR1083732 1 0.0000 0.995 1.000 0.000
#> SRR1456611 2 0.0000 0.988 0.000 1.000
#> SRR1080318 1 0.0000 0.995 1.000 0.000
#> SRR1500089 1 0.0000 0.995 1.000 0.000
#> SRR1441178 1 0.0000 0.995 1.000 0.000
#> SRR1381396 1 0.0000 0.995 1.000 0.000
#> SRR1096081 1 0.0000 0.995 1.000 0.000
#> SRR1349809 2 0.0000 0.988 0.000 1.000
#> SRR1324314 1 0.0000 0.995 1.000 0.000
#> SRR1092444 1 0.0000 0.995 1.000 0.000
#> SRR1382553 1 0.0000 0.995 1.000 0.000
#> SRR1075530 2 0.0000 0.988 0.000 1.000
#> SRR1442612 1 0.0000 0.995 1.000 0.000
#> SRR1360056 1 0.0000 0.995 1.000 0.000
#> SRR1078164 1 0.0000 0.995 1.000 0.000
#> SRR1434545 2 0.0000 0.988 0.000 1.000
#> SRR1398251 1 0.0000 0.995 1.000 0.000
#> SRR1375866 1 0.0000 0.995 1.000 0.000
#> SRR1091645 2 0.0000 0.988 0.000 1.000
#> SRR1416636 1 0.0000 0.995 1.000 0.000
#> SRR1105441 1 0.0000 0.995 1.000 0.000
#> SRR1082496 2 0.0000 0.988 0.000 1.000
#> SRR1315353 2 0.0000 0.988 0.000 1.000
#> SRR1093697 2 0.0000 0.988 0.000 1.000
#> SRR1077429 1 0.0000 0.995 1.000 0.000
#> SRR1076120 1 0.0000 0.995 1.000 0.000
#> SRR1074410 1 0.0000 0.995 1.000 0.000
#> SRR1340345 2 0.0000 0.988 0.000 1.000
#> SRR1069514 2 0.0000 0.988 0.000 1.000
#> SRR1092636 1 0.0000 0.995 1.000 0.000
#> SRR1365013 2 0.0000 0.988 0.000 1.000
#> SRR1073069 1 0.0000 0.995 1.000 0.000
#> SRR1443137 1 0.0000 0.995 1.000 0.000
#> SRR1437143 2 0.0000 0.988 0.000 1.000
#> SRR1091990 1 0.0000 0.995 1.000 0.000
#> SRR820234 2 0.0000 0.988 0.000 1.000
#> SRR1338079 1 0.0000 0.995 1.000 0.000
#> SRR1390094 2 0.0000 0.988 0.000 1.000
#> SRR1340721 2 0.0000 0.988 0.000 1.000
#> SRR1335964 1 0.0000 0.995 1.000 0.000
#> SRR1086869 1 0.0000 0.995 1.000 0.000
#> SRR1453434 1 0.0000 0.995 1.000 0.000
#> SRR1402261 2 0.6973 0.777 0.188 0.812
#> SRR657809 2 0.0000 0.988 0.000 1.000
#> SRR1093075 1 0.0000 0.995 1.000 0.000
#> SRR1433329 1 0.0000 0.995 1.000 0.000
#> SRR1353418 1 0.0000 0.995 1.000 0.000
#> SRR1092913 2 0.0000 0.988 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1335605 3 0.0000 0.7777 0.000 0.000 1.000
#> SRR1432014 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1499215 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1460409 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1097344 2 0.0747 0.9267 0.000 0.984 0.016
#> SRR1081789 3 0.2066 0.7502 0.000 0.060 0.940
#> SRR1453005 2 0.0237 0.9281 0.000 0.996 0.004
#> SRR1366985 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR815280 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR815845 3 0.0237 0.7777 0.000 0.004 0.996
#> SRR1471178 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1080696 1 0.6307 0.2408 0.512 0.000 0.488
#> SRR1078684 3 0.5216 0.4706 0.260 0.000 0.740
#> SRR1317751 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR1435667 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1097905 3 0.5650 0.5750 0.312 0.000 0.688
#> SRR1456548 3 0.6192 0.4262 0.420 0.000 0.580
#> SRR1075126 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR813108 3 0.5591 0.4417 0.000 0.304 0.696
#> SRR1479062 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1408703 1 0.6305 0.2510 0.516 0.000 0.484
#> SRR1332360 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1434228 1 0.2066 0.8056 0.940 0.000 0.060
#> SRR1467149 3 0.5678 0.5704 0.316 0.000 0.684
#> SRR1399113 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1476507 2 0.5058 0.6951 0.000 0.756 0.244
#> SRR1092468 1 0.5291 0.4533 0.732 0.000 0.268
#> SRR1441804 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1436021 3 0.4121 0.6520 0.000 0.168 0.832
#> SRR1480083 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1472863 3 0.5591 0.5860 0.304 0.000 0.696
#> SRR815542 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1400100 3 0.0237 0.7777 0.000 0.004 0.996
#> SRR1312002 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR1470253 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR1414332 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1069209 1 0.1753 0.8149 0.952 0.000 0.048
#> SRR661052 3 0.5760 0.5656 0.328 0.000 0.672
#> SRR1308860 3 0.6244 0.3968 0.440 0.000 0.560
#> SRR1421159 3 0.2165 0.7455 0.000 0.064 0.936
#> SRR1340943 3 0.6168 0.4399 0.412 0.000 0.588
#> SRR1078855 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1478679 3 0.0237 0.7798 0.004 0.000 0.996
#> SRR1350979 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1458198 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1386910 2 0.0592 0.9274 0.000 0.988 0.012
#> SRR1465375 2 0.0747 0.9267 0.000 0.984 0.016
#> SRR1323699 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1431139 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1373964 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1455413 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1437163 3 0.7114 0.4596 0.388 0.028 0.584
#> SRR1347343 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1465480 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1489631 3 0.6180 0.4335 0.416 0.000 0.584
#> SRR1086514 2 0.5397 0.6341 0.000 0.720 0.280
#> SRR1430928 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1310939 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1344294 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1468118 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1486348 1 0.5497 0.3851 0.708 0.000 0.292
#> SRR1488770 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1500089 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1441178 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1096081 1 0.6180 0.4023 0.584 0.000 0.416
#> SRR1349809 2 0.0592 0.9274 0.000 0.988 0.012
#> SRR1324314 3 0.6168 0.0511 0.412 0.000 0.588
#> SRR1092444 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1382553 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR1075530 2 0.6062 0.4344 0.000 0.616 0.384
#> SRR1442612 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1360056 3 0.0747 0.7848 0.016 0.000 0.984
#> SRR1078164 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1434545 2 0.5216 0.6725 0.000 0.740 0.260
#> SRR1398251 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1091645 2 0.2356 0.8846 0.000 0.928 0.072
#> SRR1416636 1 0.6307 0.2408 0.512 0.000 0.488
#> SRR1105441 3 0.0237 0.7798 0.004 0.000 0.996
#> SRR1082496 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1315353 3 0.4452 0.6226 0.000 0.192 0.808
#> SRR1093697 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1077429 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR1076120 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1074410 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1340345 2 0.0747 0.9267 0.000 0.984 0.016
#> SRR1069514 3 0.2261 0.7449 0.000 0.068 0.932
#> SRR1092636 3 0.5621 0.3637 0.308 0.000 0.692
#> SRR1365013 2 0.6168 0.3634 0.000 0.588 0.412
#> SRR1073069 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.9281 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR820234 2 0.0592 0.9272 0.000 0.988 0.012
#> SRR1338079 3 0.6180 0.4335 0.416 0.000 0.584
#> SRR1390094 3 0.6180 0.1569 0.000 0.416 0.584
#> SRR1340721 2 0.0747 0.9267 0.000 0.984 0.016
#> SRR1335964 3 0.0892 0.7832 0.020 0.000 0.980
#> SRR1086869 1 0.6192 0.3946 0.580 0.000 0.420
#> SRR1453434 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1402261 3 0.6540 0.4420 0.408 0.008 0.584
#> SRR657809 2 0.0592 0.9274 0.000 0.988 0.012
#> SRR1093075 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.8487 1.000 0.000 0.000
#> SRR1353418 1 0.6168 0.4097 0.588 0.000 0.412
#> SRR1092913 2 0.0747 0.9267 0.000 0.984 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0469 0.93251 0.988 0.000 0.012 0.000
#> SRR1335605 3 0.4830 0.23526 0.000 0.000 0.608 0.392
#> SRR1432014 3 0.2973 0.66865 0.000 0.000 0.856 0.144
#> SRR1499215 3 0.2281 0.68041 0.000 0.000 0.904 0.096
#> SRR1460409 1 0.0336 0.93245 0.992 0.000 0.000 0.008
#> SRR1086441 1 0.0469 0.93212 0.988 0.000 0.000 0.012
#> SRR1097344 2 0.4661 0.63164 0.000 0.652 0.000 0.348
#> SRR1081789 3 0.5163 0.00516 0.000 0.004 0.516 0.480
#> SRR1453005 2 0.0188 0.84904 0.000 0.996 0.000 0.004
#> SRR1366985 3 0.5508 0.31098 0.408 0.000 0.572 0.020
#> SRR815280 1 0.1798 0.91746 0.944 0.000 0.016 0.040
#> SRR1348531 1 0.1059 0.93139 0.972 0.000 0.016 0.012
#> SRR815845 3 0.4164 0.53774 0.000 0.000 0.736 0.264
#> SRR1471178 1 0.1297 0.92932 0.964 0.000 0.016 0.020
#> SRR1080696 3 0.3105 0.62661 0.120 0.000 0.868 0.012
#> SRR1078684 3 0.1854 0.67484 0.012 0.000 0.940 0.048
#> SRR1317751 3 0.5487 0.34097 0.400 0.000 0.580 0.020
#> SRR1435667 3 0.2973 0.66865 0.000 0.000 0.856 0.144
#> SRR1097905 4 0.4982 0.66755 0.092 0.000 0.136 0.772
#> SRR1456548 4 0.5932 0.63472 0.172 0.000 0.132 0.696
#> SRR1075126 1 0.2413 0.89309 0.916 0.000 0.064 0.020
#> SRR813108 4 0.5685 0.04805 0.000 0.024 0.460 0.516
#> SRR1479062 3 0.2760 0.67487 0.000 0.000 0.872 0.128
#> SRR1408703 3 0.3105 0.62661 0.120 0.000 0.868 0.012
#> SRR1332360 1 0.3335 0.84647 0.860 0.000 0.120 0.020
#> SRR1098686 1 0.1411 0.92856 0.960 0.000 0.020 0.020
#> SRR1434228 1 0.3554 0.82723 0.844 0.000 0.136 0.020
#> SRR1467149 4 0.6001 0.62747 0.128 0.000 0.184 0.688
#> SRR1399113 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.3873 0.45006 0.000 0.228 0.000 0.772
#> SRR1092468 1 0.4775 0.65687 0.740 0.000 0.232 0.028
#> SRR1441804 1 0.1297 0.92932 0.964 0.000 0.016 0.020
#> SRR1326100 2 0.0469 0.84668 0.000 0.988 0.000 0.012
#> SRR1398815 1 0.1297 0.92932 0.964 0.000 0.016 0.020
#> SRR1436021 4 0.3208 0.64523 0.000 0.004 0.148 0.848
#> SRR1480083 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1472863 4 0.6743 0.29819 0.096 0.000 0.392 0.512
#> SRR815542 1 0.0804 0.93259 0.980 0.000 0.008 0.012
#> SRR1400100 3 0.4008 0.55599 0.000 0.000 0.756 0.244
#> SRR1312002 3 0.5465 0.33920 0.392 0.000 0.588 0.020
#> SRR1470253 3 0.5576 0.19112 0.444 0.000 0.536 0.020
#> SRR1414332 1 0.0000 0.93211 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.3335 0.84647 0.860 0.000 0.120 0.020
#> SRR661052 4 0.7107 0.25141 0.128 0.000 0.408 0.464
#> SRR1308860 4 0.7179 0.34135 0.380 0.000 0.140 0.480
#> SRR1421159 4 0.4456 0.52189 0.000 0.004 0.280 0.716
#> SRR1340943 4 0.3266 0.65838 0.108 0.000 0.024 0.868
#> SRR1078855 1 0.1059 0.93139 0.972 0.000 0.016 0.012
#> SRR1459465 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.4661 0.34508 0.000 0.000 0.652 0.348
#> SRR1350979 3 0.2921 0.67099 0.000 0.000 0.860 0.140
#> SRR1458198 1 0.0937 0.93219 0.976 0.000 0.012 0.012
#> SRR1386910 2 0.4605 0.64254 0.000 0.664 0.000 0.336
#> SRR1465375 4 0.4697 0.14368 0.000 0.356 0.000 0.644
#> SRR1323699 3 0.2760 0.67487 0.000 0.000 0.872 0.128
#> SRR1431139 3 0.2654 0.67836 0.004 0.000 0.888 0.108
#> SRR1373964 3 0.2921 0.67099 0.000 0.000 0.860 0.140
#> SRR1455413 1 0.2413 0.89300 0.916 0.000 0.064 0.020
#> SRR1437163 4 0.3205 0.66013 0.104 0.000 0.024 0.872
#> SRR1347343 3 0.2281 0.68041 0.000 0.000 0.904 0.096
#> SRR1465480 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1489631 4 0.5624 0.65108 0.148 0.000 0.128 0.724
#> SRR1086514 4 0.4139 0.55175 0.000 0.144 0.040 0.816
#> SRR1430928 1 0.0524 0.93301 0.988 0.000 0.008 0.004
#> SRR1310939 3 0.2921 0.67099 0.000 0.000 0.860 0.140
#> SRR1344294 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.1297 0.92932 0.964 0.000 0.016 0.020
#> SRR1468118 3 0.3123 0.66232 0.000 0.000 0.844 0.156
#> SRR1486348 1 0.5676 0.61867 0.720 0.000 0.144 0.136
#> SRR1488770 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.3099 0.86299 0.876 0.000 0.104 0.020
#> SRR1456611 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0524 0.92986 0.988 0.000 0.008 0.004
#> SRR1500089 1 0.2174 0.90470 0.928 0.000 0.052 0.020
#> SRR1441178 1 0.0188 0.93156 0.996 0.000 0.004 0.000
#> SRR1381396 1 0.1297 0.92932 0.964 0.000 0.016 0.020
#> SRR1096081 3 0.5339 0.42621 0.356 0.000 0.624 0.020
#> SRR1349809 2 0.4522 0.65792 0.000 0.680 0.000 0.320
#> SRR1324314 3 0.1807 0.65434 0.052 0.000 0.940 0.008
#> SRR1092444 1 0.2174 0.90470 0.928 0.000 0.052 0.020
#> SRR1382553 3 0.5498 0.32154 0.404 0.000 0.576 0.020
#> SRR1075530 4 0.4379 0.53487 0.000 0.172 0.036 0.792
#> SRR1442612 3 0.2973 0.66865 0.000 0.000 0.856 0.144
#> SRR1360056 3 0.3266 0.65218 0.000 0.000 0.832 0.168
#> SRR1078164 1 0.2174 0.90470 0.928 0.000 0.052 0.020
#> SRR1434545 4 0.2944 0.56349 0.004 0.128 0.000 0.868
#> SRR1398251 1 0.1520 0.91755 0.956 0.000 0.024 0.020
#> SRR1375866 1 0.0779 0.93290 0.980 0.000 0.016 0.004
#> SRR1091645 4 0.4193 0.37491 0.000 0.268 0.000 0.732
#> SRR1416636 3 0.3105 0.62661 0.120 0.000 0.868 0.012
#> SRR1105441 3 0.3024 0.66588 0.000 0.000 0.852 0.148
#> SRR1082496 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1315353 4 0.5147 0.03900 0.000 0.004 0.460 0.536
#> SRR1093697 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.5476 0.33407 0.396 0.000 0.584 0.020
#> SRR1076120 1 0.2089 0.91889 0.932 0.000 0.048 0.020
#> SRR1074410 1 0.1297 0.92932 0.964 0.000 0.016 0.020
#> SRR1340345 2 0.4746 0.60263 0.000 0.632 0.000 0.368
#> SRR1069514 4 0.5151 0.10640 0.000 0.004 0.464 0.532
#> SRR1092636 3 0.1211 0.65754 0.040 0.000 0.960 0.000
#> SRR1365013 4 0.3497 0.59256 0.000 0.104 0.036 0.860
#> SRR1073069 1 0.3219 0.85507 0.868 0.000 0.112 0.020
#> SRR1443137 1 0.2174 0.90470 0.928 0.000 0.052 0.020
#> SRR1437143 2 0.0000 0.85028 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0188 0.93156 0.996 0.000 0.004 0.000
#> SRR820234 2 0.3529 0.74444 0.000 0.836 0.012 0.152
#> SRR1338079 4 0.4938 0.65094 0.148 0.000 0.080 0.772
#> SRR1390094 4 0.1584 0.66219 0.012 0.000 0.036 0.952
#> SRR1340721 2 0.4907 0.49732 0.000 0.580 0.000 0.420
#> SRR1335964 3 0.2530 0.67976 0.004 0.000 0.896 0.100
#> SRR1086869 3 0.4610 0.55230 0.236 0.000 0.744 0.020
#> SRR1453434 1 0.1610 0.92279 0.952 0.000 0.016 0.032
#> SRR1402261 4 0.3160 0.65638 0.108 0.000 0.020 0.872
#> SRR657809 2 0.4585 0.64647 0.000 0.668 0.000 0.332
#> SRR1093075 1 0.0804 0.92742 0.980 0.000 0.008 0.012
#> SRR1433329 1 0.2706 0.88382 0.900 0.000 0.080 0.020
#> SRR1353418 3 0.5517 0.31485 0.412 0.000 0.568 0.020
#> SRR1092913 2 0.4697 0.61953 0.000 0.644 0.000 0.356
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.1478 0.86695 0.936 0.000 0.000 0.000 0.064
#> SRR1335605 3 0.2408 0.61085 0.000 0.000 0.892 0.016 0.092
#> SRR1432014 3 0.3857 0.50228 0.000 0.000 0.688 0.000 0.312
#> SRR1499215 3 0.4242 0.29286 0.000 0.000 0.572 0.000 0.428
#> SRR1460409 1 0.1410 0.86756 0.940 0.000 0.000 0.000 0.060
#> SRR1086441 1 0.0981 0.86840 0.972 0.000 0.008 0.012 0.008
#> SRR1097344 4 0.5946 0.01026 0.000 0.388 0.052 0.532 0.028
#> SRR1081789 3 0.1942 0.59253 0.000 0.000 0.920 0.068 0.012
#> SRR1453005 2 0.2321 0.81465 0.000 0.912 0.056 0.024 0.008
#> SRR1366985 5 0.4623 0.48634 0.304 0.000 0.032 0.000 0.664
#> SRR815280 1 0.1211 0.86836 0.960 0.000 0.000 0.016 0.024
#> SRR1348531 1 0.1498 0.86052 0.952 0.000 0.016 0.024 0.008
#> SRR815845 3 0.2361 0.61566 0.000 0.000 0.892 0.012 0.096
#> SRR1471178 1 0.1012 0.86397 0.968 0.000 0.012 0.020 0.000
#> SRR1080696 5 0.3731 0.60060 0.040 0.000 0.160 0.000 0.800
#> SRR1078684 5 0.4452 -0.11144 0.004 0.000 0.496 0.000 0.500
#> SRR1317751 5 0.3370 0.64011 0.148 0.000 0.028 0.000 0.824
#> SRR1435667 3 0.3730 0.51507 0.000 0.000 0.712 0.000 0.288
#> SRR1097905 4 0.6808 0.46861 0.104 0.000 0.268 0.560 0.068
#> SRR1456548 4 0.6854 0.50972 0.228 0.000 0.124 0.576 0.072
#> SRR1075126 1 0.2703 0.82486 0.896 0.000 0.020 0.024 0.060
#> SRR813108 3 0.3427 0.55869 0.000 0.008 0.836 0.128 0.028
#> SRR1479062 5 0.4304 -0.06275 0.000 0.000 0.484 0.000 0.516
#> SRR1408703 5 0.3731 0.60060 0.040 0.000 0.160 0.000 0.800
#> SRR1332360 1 0.3635 0.75308 0.748 0.000 0.000 0.004 0.248
#> SRR1098686 1 0.2395 0.84102 0.912 0.000 0.016 0.024 0.048
#> SRR1434228 1 0.4426 0.52821 0.612 0.000 0.004 0.004 0.380
#> SRR1467149 4 0.7304 0.46937 0.160 0.000 0.228 0.528 0.084
#> SRR1399113 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.3869 0.52906 0.000 0.020 0.140 0.812 0.028
#> SRR1092468 1 0.4320 0.71308 0.792 0.000 0.052 0.024 0.132
#> SRR1441804 1 0.1498 0.86052 0.952 0.000 0.016 0.024 0.008
#> SRR1326100 2 0.2694 0.79965 0.000 0.892 0.068 0.032 0.008
#> SRR1398815 1 0.1012 0.86397 0.968 0.000 0.012 0.020 0.000
#> SRR1436021 3 0.4564 -0.05606 0.000 0.000 0.612 0.372 0.016
#> SRR1480083 2 0.0162 0.86753 0.000 0.996 0.004 0.000 0.000
#> SRR1472863 4 0.7177 0.26453 0.104 0.000 0.400 0.424 0.072
#> SRR815542 1 0.0771 0.87018 0.976 0.000 0.000 0.004 0.020
#> SRR1400100 3 0.2522 0.61524 0.000 0.000 0.880 0.012 0.108
#> SRR1312002 5 0.4303 0.62763 0.192 0.000 0.056 0.000 0.752
#> SRR1470253 5 0.3163 0.62599 0.164 0.000 0.012 0.000 0.824
#> SRR1414332 1 0.1041 0.87152 0.964 0.000 0.000 0.004 0.032
#> SRR1069209 1 0.4367 0.47985 0.580 0.000 0.000 0.004 0.416
#> SRR661052 4 0.7871 0.34158 0.220 0.000 0.292 0.404 0.084
#> SRR1308860 4 0.7272 0.25932 0.388 0.000 0.116 0.424 0.072
#> SRR1421159 3 0.1908 0.56574 0.000 0.000 0.908 0.092 0.000
#> SRR1340943 4 0.2353 0.59285 0.004 0.000 0.060 0.908 0.028
#> SRR1078855 1 0.0865 0.86962 0.972 0.000 0.000 0.004 0.024
#> SRR1459465 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.2280 0.61162 0.000 0.000 0.880 0.000 0.120
#> SRR1350979 3 0.4088 0.42798 0.000 0.000 0.632 0.000 0.368
#> SRR1458198 1 0.1393 0.86237 0.956 0.000 0.012 0.024 0.008
#> SRR1386910 2 0.6582 0.17798 0.000 0.484 0.108 0.380 0.028
#> SRR1465375 4 0.5203 0.37727 0.000 0.212 0.056 0.704 0.028
#> SRR1323699 3 0.4161 0.37677 0.000 0.000 0.608 0.000 0.392
#> SRR1431139 3 0.4242 0.29011 0.000 0.000 0.572 0.000 0.428
#> SRR1373964 3 0.4060 0.44166 0.000 0.000 0.640 0.000 0.360
#> SRR1455413 1 0.3226 0.80077 0.864 0.000 0.024 0.024 0.088
#> SRR1437163 4 0.3427 0.59196 0.004 0.000 0.096 0.844 0.056
#> SRR1347343 3 0.4242 0.29286 0.000 0.000 0.572 0.000 0.428
#> SRR1465480 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 4 0.6744 0.52670 0.188 0.000 0.140 0.600 0.072
#> SRR1086514 3 0.5408 -0.04202 0.000 0.020 0.584 0.364 0.032
#> SRR1430928 1 0.1341 0.86808 0.944 0.000 0.000 0.000 0.056
#> SRR1310939 3 0.4060 0.44177 0.000 0.000 0.640 0.000 0.360
#> SRR1344294 2 0.0162 0.86753 0.000 0.996 0.004 0.000 0.000
#> SRR1099402 1 0.1211 0.86131 0.960 0.000 0.016 0.024 0.000
#> SRR1468118 5 0.5056 0.22502 0.000 0.000 0.360 0.044 0.596
#> SRR1486348 1 0.6538 0.30972 0.600 0.000 0.088 0.240 0.072
#> SRR1488770 2 0.0162 0.86753 0.000 0.996 0.004 0.000 0.000
#> SRR1083732 1 0.3196 0.80682 0.804 0.000 0.000 0.004 0.192
#> SRR1456611 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.2136 0.86195 0.904 0.000 0.000 0.008 0.088
#> SRR1500089 1 0.3123 0.81035 0.812 0.000 0.000 0.004 0.184
#> SRR1441178 1 0.2179 0.85718 0.896 0.000 0.000 0.004 0.100
#> SRR1381396 1 0.1012 0.86397 0.968 0.000 0.012 0.020 0.000
#> SRR1096081 5 0.3339 0.64396 0.124 0.000 0.040 0.000 0.836
#> SRR1349809 2 0.5938 0.30251 0.000 0.556 0.056 0.360 0.028
#> SRR1324314 5 0.4522 0.41039 0.024 0.000 0.316 0.000 0.660
#> SRR1092444 1 0.3196 0.80682 0.804 0.000 0.000 0.004 0.192
#> SRR1382553 5 0.4949 0.52763 0.288 0.000 0.056 0.000 0.656
#> SRR1075530 4 0.5423 0.35749 0.000 0.020 0.400 0.552 0.028
#> SRR1442612 3 0.3774 0.51427 0.000 0.000 0.704 0.000 0.296
#> SRR1360056 5 0.5774 0.13189 0.004 0.000 0.368 0.084 0.544
#> SRR1078164 1 0.3266 0.80163 0.796 0.000 0.000 0.004 0.200
#> SRR1434545 4 0.1365 0.57401 0.000 0.004 0.040 0.952 0.004
#> SRR1398251 1 0.2389 0.85089 0.880 0.000 0.000 0.004 0.116
#> SRR1375866 1 0.1405 0.86230 0.956 0.000 0.016 0.020 0.008
#> SRR1091645 4 0.4863 0.48574 0.000 0.076 0.140 0.756 0.028
#> SRR1416636 5 0.3731 0.60060 0.040 0.000 0.160 0.000 0.800
#> SRR1105441 3 0.2848 0.60333 0.000 0.000 0.840 0.004 0.156
#> SRR1082496 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 3 0.3351 0.55711 0.000 0.004 0.836 0.132 0.028
#> SRR1093697 2 0.0162 0.86753 0.000 0.996 0.004 0.000 0.000
#> SRR1077429 5 0.3326 0.63887 0.152 0.000 0.024 0.000 0.824
#> SRR1076120 1 0.3489 0.82281 0.824 0.000 0.012 0.016 0.148
#> SRR1074410 1 0.0693 0.86635 0.980 0.000 0.008 0.012 0.000
#> SRR1340345 4 0.6107 -0.07525 0.000 0.424 0.060 0.488 0.028
#> SRR1069514 3 0.1830 0.58964 0.000 0.000 0.924 0.068 0.008
#> SRR1092636 5 0.4206 0.44776 0.016 0.000 0.288 0.000 0.696
#> SRR1365013 4 0.5390 0.32172 0.000 0.016 0.432 0.524 0.028
#> SRR1073069 1 0.3266 0.80163 0.796 0.000 0.000 0.004 0.200
#> SRR1443137 1 0.3266 0.80163 0.796 0.000 0.000 0.004 0.200
#> SRR1437143 2 0.0000 0.86852 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.2179 0.85718 0.896 0.000 0.000 0.004 0.100
#> SRR820234 3 0.6126 -0.13447 0.000 0.408 0.484 0.100 0.008
#> SRR1338079 4 0.6397 0.54418 0.156 0.000 0.136 0.640 0.068
#> SRR1390094 4 0.2325 0.59081 0.000 0.000 0.068 0.904 0.028
#> SRR1340721 4 0.6089 -0.00739 0.000 0.408 0.060 0.504 0.028
#> SRR1335964 5 0.4192 0.20497 0.000 0.000 0.404 0.000 0.596
#> SRR1086869 5 0.3593 0.63660 0.084 0.000 0.088 0.000 0.828
#> SRR1453434 1 0.1168 0.86184 0.960 0.000 0.008 0.032 0.000
#> SRR1402261 4 0.2284 0.59211 0.004 0.000 0.056 0.912 0.028
#> SRR657809 2 0.6442 0.17863 0.000 0.488 0.092 0.392 0.028
#> SRR1093075 1 0.2389 0.85089 0.880 0.000 0.000 0.004 0.116
#> SRR1433329 1 0.3266 0.80163 0.796 0.000 0.000 0.004 0.200
#> SRR1353418 5 0.3475 0.60905 0.180 0.000 0.012 0.004 0.804
#> SRR1092913 4 0.5832 -0.10274 0.000 0.436 0.040 0.496 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1268 0.7472 0.952 0.000 0.000 0.008 0.004 0.036
#> SRR1335605 3 0.2853 0.6794 0.000 0.000 0.868 0.048 0.012 0.072
#> SRR1432014 3 0.3679 0.6348 0.000 0.000 0.772 0.000 0.176 0.052
#> SRR1499215 3 0.4668 0.5151 0.000 0.000 0.652 0.008 0.284 0.056
#> SRR1460409 1 0.1296 0.7431 0.952 0.000 0.000 0.012 0.004 0.032
#> SRR1086441 1 0.3152 0.7134 0.792 0.000 0.000 0.008 0.004 0.196
#> SRR1097344 4 0.3733 0.5593 0.000 0.208 0.004 0.760 0.004 0.024
#> SRR1081789 3 0.2581 0.6372 0.000 0.000 0.860 0.120 0.000 0.020
#> SRR1453005 2 0.3503 0.7327 0.000 0.816 0.068 0.108 0.000 0.008
#> SRR1366985 5 0.6059 0.4312 0.356 0.000 0.072 0.032 0.520 0.020
#> SRR815280 1 0.1888 0.7422 0.916 0.000 0.000 0.012 0.004 0.068
#> SRR1348531 1 0.3774 0.6365 0.664 0.000 0.000 0.000 0.008 0.328
#> SRR815845 3 0.2114 0.6710 0.000 0.000 0.904 0.076 0.008 0.012
#> SRR1471178 1 0.3390 0.6598 0.704 0.000 0.000 0.000 0.000 0.296
#> SRR1080696 5 0.1411 0.7200 0.004 0.000 0.060 0.000 0.936 0.000
#> SRR1078684 3 0.5202 0.2523 0.004 0.000 0.532 0.008 0.396 0.060
#> SRR1317751 5 0.1644 0.7359 0.052 0.000 0.012 0.000 0.932 0.004
#> SRR1435667 3 0.3003 0.6461 0.000 0.000 0.812 0.000 0.172 0.016
#> SRR1097905 6 0.2307 0.8144 0.004 0.000 0.068 0.032 0.000 0.896
#> SRR1456548 6 0.2341 0.8099 0.032 0.000 0.012 0.056 0.000 0.900
#> SRR1075126 1 0.4967 0.4647 0.540 0.000 0.008 0.012 0.028 0.412
#> SRR813108 3 0.3088 0.5902 0.000 0.000 0.808 0.172 0.000 0.020
#> SRR1479062 3 0.4850 0.1114 0.000 0.000 0.496 0.000 0.448 0.056
#> SRR1408703 5 0.1411 0.7200 0.004 0.000 0.060 0.000 0.936 0.000
#> SRR1332360 1 0.3853 0.6322 0.780 0.000 0.000 0.052 0.156 0.012
#> SRR1098686 1 0.4389 0.5581 0.596 0.000 0.000 0.000 0.032 0.372
#> SRR1434228 1 0.4724 0.4042 0.648 0.000 0.000 0.052 0.288 0.012
#> SRR1467149 6 0.2386 0.8184 0.012 0.000 0.064 0.028 0.000 0.896
#> SRR1399113 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.2405 0.5773 0.000 0.016 0.004 0.892 0.008 0.080
#> SRR1092468 1 0.5833 0.3058 0.464 0.000 0.032 0.000 0.088 0.416
#> SRR1441804 1 0.3804 0.6273 0.656 0.000 0.000 0.000 0.008 0.336
#> SRR1326100 2 0.3578 0.7252 0.000 0.812 0.088 0.092 0.000 0.008
#> SRR1398815 1 0.3464 0.6477 0.688 0.000 0.000 0.000 0.000 0.312
#> SRR1436021 3 0.4011 0.5057 0.000 0.000 0.736 0.204 0.000 0.060
#> SRR1480083 2 0.0146 0.9151 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1472863 6 0.2466 0.7830 0.008 0.000 0.112 0.008 0.000 0.872
#> SRR815542 1 0.2163 0.7425 0.892 0.000 0.000 0.008 0.004 0.096
#> SRR1400100 3 0.2001 0.6742 0.000 0.000 0.912 0.068 0.008 0.012
#> SRR1312002 5 0.4950 0.6651 0.148 0.000 0.088 0.020 0.724 0.020
#> SRR1470253 5 0.2195 0.7273 0.068 0.000 0.000 0.016 0.904 0.012
#> SRR1414332 1 0.1663 0.7450 0.912 0.000 0.000 0.000 0.000 0.088
#> SRR1069209 1 0.4883 0.3391 0.608 0.000 0.000 0.052 0.328 0.012
#> SRR661052 6 0.2526 0.7916 0.024 0.000 0.096 0.004 0.000 0.876
#> SRR1308860 6 0.2501 0.7591 0.108 0.000 0.016 0.004 0.000 0.872
#> SRR1421159 3 0.2572 0.6301 0.000 0.000 0.852 0.136 0.000 0.012
#> SRR1340943 4 0.4307 0.2779 0.004 0.000 0.008 0.604 0.008 0.376
#> SRR1078855 1 0.1577 0.7428 0.940 0.000 0.000 0.016 0.008 0.036
#> SRR1459465 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.2030 0.6787 0.000 0.000 0.908 0.000 0.028 0.064
#> SRR1350979 3 0.4147 0.5962 0.000 0.000 0.716 0.000 0.224 0.060
#> SRR1458198 1 0.3804 0.6322 0.656 0.000 0.000 0.000 0.008 0.336
#> SRR1386910 4 0.5996 0.2404 0.000 0.424 0.072 0.448 0.000 0.056
#> SRR1465375 4 0.5190 0.5754 0.000 0.204 0.012 0.648 0.000 0.136
#> SRR1323699 3 0.4467 0.5758 0.000 0.000 0.696 0.008 0.236 0.060
#> SRR1431139 3 0.5365 0.4126 0.004 0.000 0.584 0.012 0.316 0.084
#> SRR1373964 3 0.4065 0.6028 0.000 0.000 0.724 0.000 0.220 0.056
#> SRR1455413 1 0.5071 0.4600 0.536 0.000 0.008 0.000 0.060 0.396
#> SRR1437163 6 0.3690 0.3162 0.000 0.000 0.008 0.308 0.000 0.684
#> SRR1347343 3 0.4668 0.5151 0.000 0.000 0.652 0.008 0.284 0.056
#> SRR1465480 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 6 0.2463 0.8072 0.020 0.000 0.020 0.068 0.000 0.892
#> SRR1086514 3 0.4419 0.2796 0.000 0.008 0.620 0.348 0.000 0.024
#> SRR1430928 1 0.1333 0.7462 0.944 0.000 0.000 0.008 0.000 0.048
#> SRR1310939 3 0.4147 0.5962 0.000 0.000 0.716 0.000 0.224 0.060
#> SRR1344294 2 0.0146 0.9151 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1099402 1 0.3563 0.6288 0.664 0.000 0.000 0.000 0.000 0.336
#> SRR1468118 5 0.5518 0.4144 0.000 0.000 0.136 0.004 0.544 0.316
#> SRR1486348 6 0.3259 0.5855 0.216 0.000 0.012 0.000 0.000 0.772
#> SRR1488770 2 0.0146 0.9151 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1083732 1 0.4327 0.7029 0.768 0.000 0.000 0.040 0.120 0.072
#> SRR1456611 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.2231 0.7476 0.900 0.000 0.000 0.004 0.028 0.068
#> SRR1500089 1 0.3456 0.7280 0.816 0.000 0.000 0.004 0.104 0.076
#> SRR1441178 1 0.1924 0.7241 0.920 0.000 0.000 0.048 0.028 0.004
#> SRR1381396 1 0.3653 0.6570 0.692 0.000 0.000 0.008 0.000 0.300
#> SRR1096081 5 0.1578 0.7366 0.048 0.000 0.012 0.000 0.936 0.004
#> SRR1349809 2 0.4970 -0.0846 0.000 0.536 0.012 0.408 0.000 0.044
#> SRR1324314 5 0.5245 0.5195 0.016 0.000 0.248 0.016 0.652 0.068
#> SRR1092444 1 0.4121 0.7180 0.784 0.000 0.000 0.032 0.104 0.080
#> SRR1382553 5 0.6211 0.4544 0.336 0.000 0.084 0.032 0.524 0.024
#> SRR1075530 4 0.5146 0.4058 0.000 0.020 0.312 0.604 0.000 0.064
#> SRR1442612 3 0.3388 0.6419 0.000 0.000 0.792 0.000 0.172 0.036
#> SRR1360056 5 0.6319 0.2587 0.000 0.000 0.248 0.012 0.400 0.340
#> SRR1078164 1 0.3112 0.6873 0.840 0.000 0.000 0.052 0.104 0.004
#> SRR1434545 4 0.3468 0.4436 0.000 0.000 0.000 0.728 0.008 0.264
#> SRR1398251 1 0.2209 0.7185 0.904 0.000 0.000 0.052 0.040 0.004
#> SRR1375866 1 0.3741 0.6435 0.672 0.000 0.000 0.000 0.008 0.320
#> SRR1091645 4 0.2270 0.5815 0.000 0.020 0.004 0.900 0.004 0.072
#> SRR1416636 5 0.1555 0.7176 0.004 0.000 0.060 0.000 0.932 0.004
#> SRR1105441 3 0.3019 0.6806 0.000 0.000 0.860 0.036 0.080 0.024
#> SRR1082496 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 3 0.3088 0.5902 0.000 0.000 0.808 0.172 0.000 0.020
#> SRR1093697 2 0.0146 0.9151 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1077429 5 0.1719 0.7354 0.056 0.000 0.008 0.000 0.928 0.008
#> SRR1076120 1 0.5180 0.6054 0.616 0.000 0.000 0.004 0.124 0.256
#> SRR1074410 1 0.3536 0.6864 0.736 0.000 0.000 0.008 0.004 0.252
#> SRR1340345 4 0.5150 0.4459 0.000 0.344 0.020 0.580 0.000 0.056
#> SRR1069514 3 0.2402 0.6415 0.000 0.000 0.868 0.120 0.000 0.012
#> SRR1092636 5 0.4368 0.5710 0.000 0.000 0.212 0.008 0.716 0.064
#> SRR1365013 4 0.5151 0.2019 0.000 0.008 0.420 0.508 0.000 0.064
#> SRR1073069 1 0.3159 0.6784 0.836 0.000 0.000 0.052 0.108 0.004
#> SRR1443137 1 0.3017 0.6927 0.848 0.000 0.000 0.052 0.096 0.004
#> SRR1437143 2 0.0000 0.9163 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.1844 0.7254 0.924 0.000 0.000 0.048 0.024 0.004
#> SRR820234 3 0.5589 0.3299 0.000 0.176 0.612 0.192 0.000 0.020
#> SRR1338079 6 0.2502 0.7892 0.012 0.000 0.020 0.084 0.000 0.884
#> SRR1390094 4 0.4183 0.2798 0.000 0.000 0.008 0.604 0.008 0.380
#> SRR1340721 4 0.5678 0.4358 0.000 0.340 0.012 0.524 0.000 0.124
#> SRR1335964 5 0.4787 0.3956 0.000 0.000 0.312 0.004 0.620 0.064
#> SRR1086869 5 0.0964 0.7351 0.016 0.000 0.012 0.000 0.968 0.004
#> SRR1453434 1 0.3791 0.6566 0.688 0.000 0.000 0.008 0.004 0.300
#> SRR1402261 4 0.4307 0.2803 0.004 0.000 0.008 0.604 0.008 0.376
#> SRR657809 4 0.5723 0.2598 0.000 0.424 0.056 0.472 0.000 0.048
#> SRR1093075 1 0.1989 0.7226 0.916 0.000 0.000 0.052 0.028 0.004
#> SRR1433329 1 0.3112 0.6873 0.840 0.000 0.000 0.052 0.104 0.004
#> SRR1353418 5 0.2296 0.7172 0.068 0.000 0.004 0.024 0.900 0.004
#> SRR1092913 4 0.5095 0.4345 0.000 0.352 0.016 0.576 0.000 0.056
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17780 rows and 119 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 0.989 0.995 0.4930 0.509 0.509
#> 3 3 0.905 0.913 0.963 0.3400 0.754 0.549
#> 4 4 0.886 0.900 0.950 0.0945 0.900 0.714
#> 5 5 0.876 0.855 0.924 0.0529 0.916 0.716
#> 6 6 0.882 0.820 0.902 0.0366 0.969 0.873
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
#> SRR816969 1 0.0000 0.992 1.000 0.000
#> SRR1335605 2 0.0000 0.999 0.000 1.000
#> SRR1432014 2 0.1633 0.976 0.024 0.976
#> SRR1499215 1 0.0000 0.992 1.000 0.000
#> SRR1460409 1 0.0000 0.992 1.000 0.000
#> SRR1086441 1 0.0000 0.992 1.000 0.000
#> SRR1097344 2 0.0000 0.999 0.000 1.000
#> SRR1081789 2 0.0000 0.999 0.000 1.000
#> SRR1453005 2 0.0000 0.999 0.000 1.000
#> SRR1366985 1 0.0000 0.992 1.000 0.000
#> SRR815280 1 0.0000 0.992 1.000 0.000
#> SRR1348531 1 0.0000 0.992 1.000 0.000
#> SRR815845 2 0.0000 0.999 0.000 1.000
#> SRR1471178 1 0.0000 0.992 1.000 0.000
#> SRR1080696 1 0.0000 0.992 1.000 0.000
#> SRR1078684 1 0.0000 0.992 1.000 0.000
#> SRR1317751 1 0.0000 0.992 1.000 0.000
#> SRR1435667 2 0.0672 0.992 0.008 0.992
#> SRR1097905 2 0.0000 0.999 0.000 1.000
#> SRR1456548 1 0.9209 0.498 0.664 0.336
#> SRR1075126 1 0.0000 0.992 1.000 0.000
#> SRR813108 2 0.0000 0.999 0.000 1.000
#> SRR1479062 1 0.0000 0.992 1.000 0.000
#> SRR1408703 1 0.0000 0.992 1.000 0.000
#> SRR1332360 1 0.0000 0.992 1.000 0.000
#> SRR1098686 1 0.0000 0.992 1.000 0.000
#> SRR1434228 1 0.0000 0.992 1.000 0.000
#> SRR1467149 1 0.0672 0.985 0.992 0.008
#> SRR1399113 2 0.0000 0.999 0.000 1.000
#> SRR1476507 2 0.0000 0.999 0.000 1.000
#> SRR1092468 1 0.0000 0.992 1.000 0.000
#> SRR1441804 1 0.0000 0.992 1.000 0.000
#> SRR1326100 2 0.0000 0.999 0.000 1.000
#> SRR1398815 1 0.0000 0.992 1.000 0.000
#> SRR1436021 2 0.0000 0.999 0.000 1.000
#> SRR1480083 2 0.0000 0.999 0.000 1.000
#> SRR1472863 2 0.0672 0.992 0.008 0.992
#> SRR815542 1 0.0000 0.992 1.000 0.000
#> SRR1400100 2 0.0000 0.999 0.000 1.000
#> SRR1312002 1 0.0000 0.992 1.000 0.000
#> SRR1470253 1 0.0000 0.992 1.000 0.000
#> SRR1414332 1 0.0000 0.992 1.000 0.000
#> SRR1069209 1 0.0000 0.992 1.000 0.000
#> SRR661052 1 0.0000 0.992 1.000 0.000
#> SRR1308860 1 0.0000 0.992 1.000 0.000
#> SRR1421159 2 0.0000 0.999 0.000 1.000
#> SRR1340943 2 0.0000 0.999 0.000 1.000
#> SRR1078855 1 0.0000 0.992 1.000 0.000
#> SRR1459465 2 0.0000 0.999 0.000 1.000
#> SRR816818 2 0.0000 0.999 0.000 1.000
#> SRR1478679 2 0.0000 0.999 0.000 1.000
#> SRR1350979 1 0.0000 0.992 1.000 0.000
#> SRR1458198 1 0.0000 0.992 1.000 0.000
#> SRR1386910 2 0.0000 0.999 0.000 1.000
#> SRR1465375 2 0.0000 0.999 0.000 1.000
#> SRR1323699 1 0.0000 0.992 1.000 0.000
#> SRR1431139 1 0.0000 0.992 1.000 0.000
#> SRR1373964 1 0.0000 0.992 1.000 0.000
#> SRR1455413 1 0.0000 0.992 1.000 0.000
#> SRR1437163 2 0.0000 0.999 0.000 1.000
#> SRR1347343 1 0.0000 0.992 1.000 0.000
#> SRR1465480 2 0.0000 0.999 0.000 1.000
#> SRR1489631 2 0.0000 0.999 0.000 1.000
#> SRR1086514 2 0.0000 0.999 0.000 1.000
#> SRR1430928 1 0.0000 0.992 1.000 0.000
#> SRR1310939 1 0.6623 0.792 0.828 0.172
#> SRR1344294 2 0.0000 0.999 0.000 1.000
#> SRR1099402 1 0.0000 0.992 1.000 0.000
#> SRR1468118 1 0.0000 0.992 1.000 0.000
#> SRR1486348 1 0.0000 0.992 1.000 0.000
#> SRR1488770 2 0.0000 0.999 0.000 1.000
#> SRR1083732 1 0.0000 0.992 1.000 0.000
#> SRR1456611 2 0.0000 0.999 0.000 1.000
#> SRR1080318 1 0.0000 0.992 1.000 0.000
#> SRR1500089 1 0.0000 0.992 1.000 0.000
#> SRR1441178 1 0.0000 0.992 1.000 0.000
#> SRR1381396 1 0.0000 0.992 1.000 0.000
#> SRR1096081 1 0.0000 0.992 1.000 0.000
#> SRR1349809 2 0.0000 0.999 0.000 1.000
#> SRR1324314 1 0.0000 0.992 1.000 0.000
#> SRR1092444 1 0.0000 0.992 1.000 0.000
#> SRR1382553 1 0.0000 0.992 1.000 0.000
#> SRR1075530 2 0.0000 0.999 0.000 1.000
#> SRR1442612 2 0.1633 0.976 0.024 0.976
#> SRR1360056 1 0.0000 0.992 1.000 0.000
#> SRR1078164 1 0.0000 0.992 1.000 0.000
#> SRR1434545 2 0.0000 0.999 0.000 1.000
#> SRR1398251 1 0.0000 0.992 1.000 0.000
#> SRR1375866 1 0.0000 0.992 1.000 0.000
#> SRR1091645 2 0.0000 0.999 0.000 1.000
#> SRR1416636 1 0.0000 0.992 1.000 0.000
#> SRR1105441 2 0.0000 0.999 0.000 1.000
#> SRR1082496 2 0.0000 0.999 0.000 1.000
#> SRR1315353 2 0.0000 0.999 0.000 1.000
#> SRR1093697 2 0.0000 0.999 0.000 1.000
#> SRR1077429 1 0.0000 0.992 1.000 0.000
#> SRR1076120 1 0.0000 0.992 1.000 0.000
#> SRR1074410 1 0.0000 0.992 1.000 0.000
#> SRR1340345 2 0.0000 0.999 0.000 1.000
#> SRR1069514 2 0.0000 0.999 0.000 1.000
#> SRR1092636 1 0.0000 0.992 1.000 0.000
#> SRR1365013 2 0.0000 0.999 0.000 1.000
#> SRR1073069 1 0.0000 0.992 1.000 0.000
#> SRR1443137 1 0.0000 0.992 1.000 0.000
#> SRR1437143 2 0.0000 0.999 0.000 1.000
#> SRR1091990 1 0.0000 0.992 1.000 0.000
#> SRR820234 2 0.0000 0.999 0.000 1.000
#> SRR1338079 2 0.0000 0.999 0.000 1.000
#> SRR1390094 2 0.0000 0.999 0.000 1.000
#> SRR1340721 2 0.0000 0.999 0.000 1.000
#> SRR1335964 1 0.0000 0.992 1.000 0.000
#> SRR1086869 1 0.0000 0.992 1.000 0.000
#> SRR1453434 1 0.0000 0.992 1.000 0.000
#> SRR1402261 2 0.0000 0.999 0.000 1.000
#> SRR657809 2 0.0000 0.999 0.000 1.000
#> SRR1093075 1 0.0000 0.992 1.000 0.000
#> SRR1433329 1 0.0000 0.992 1.000 0.000
#> SRR1353418 1 0.0000 0.992 1.000 0.000
#> SRR1092913 2 0.0000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1335605 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1432014 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1499215 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1460409 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1097344 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1081789 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1453005 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1366985 3 0.4555 0.7979 0.200 0.000 0.800
#> SRR815280 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR815845 3 0.6274 0.1666 0.000 0.456 0.544
#> SRR1471178 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1080696 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1078684 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1317751 3 0.2165 0.9027 0.064 0.000 0.936
#> SRR1435667 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1097905 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1456548 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1075126 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR813108 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1479062 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1332360 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1098686 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1434228 1 0.6260 0.0644 0.552 0.000 0.448
#> SRR1467149 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1399113 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1476507 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1092468 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1441804 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1326100 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1398815 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1436021 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1480083 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1472863 1 0.5733 0.5111 0.676 0.324 0.000
#> SRR815542 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1400100 2 0.5882 0.4397 0.000 0.652 0.348
#> SRR1312002 3 0.4555 0.7979 0.200 0.000 0.800
#> SRR1470253 3 0.4555 0.7979 0.200 0.000 0.800
#> SRR1414332 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1069209 1 0.6026 0.3079 0.624 0.000 0.376
#> SRR661052 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1421159 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1340943 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1078855 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1478679 2 0.4605 0.7325 0.000 0.796 0.204
#> SRR1350979 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1458198 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1386910 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1465375 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1323699 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1431139 3 0.0237 0.9278 0.004 0.000 0.996
#> SRR1373964 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1455413 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1437163 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1347343 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1465480 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1489631 1 0.4062 0.7669 0.836 0.164 0.000
#> SRR1086514 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1430928 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1310939 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1344294 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1468118 3 0.1753 0.9116 0.048 0.000 0.952
#> SRR1486348 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1083732 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1456611 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1500089 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1441178 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1096081 3 0.1753 0.9116 0.048 0.000 0.952
#> SRR1349809 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1324314 3 0.4399 0.8094 0.188 0.000 0.812
#> SRR1092444 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1382553 3 0.4555 0.7979 0.200 0.000 0.800
#> SRR1075530 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1442612 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1360056 3 0.0237 0.9278 0.004 0.000 0.996
#> SRR1078164 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1434545 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1398251 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1091645 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1416636 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1105441 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1082496 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1315353 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1093697 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1077429 3 0.4555 0.7979 0.200 0.000 0.800
#> SRR1076120 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1074410 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1340345 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1069514 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1092636 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1365013 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1073069 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR820234 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1338079 1 0.6244 0.2129 0.560 0.440 0.000
#> SRR1390094 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1340721 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1335964 3 0.0000 0.9288 0.000 0.000 1.000
#> SRR1086869 3 0.1753 0.9116 0.048 0.000 0.952
#> SRR1453434 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1402261 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR657809 2 0.0000 0.9858 0.000 1.000 0.000
#> SRR1093075 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.9547 1.000 0.000 0.000
#> SRR1353418 3 0.4504 0.8019 0.196 0.000 0.804
#> SRR1092913 2 0.0000 0.9858 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1335605 2 0.0336 0.974 0.000 0.992 0.000 0.008
#> SRR1432014 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1499215 3 0.0188 0.857 0.000 0.000 0.996 0.004
#> SRR1460409 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1097344 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1081789 2 0.0336 0.974 0.000 0.992 0.000 0.008
#> SRR1453005 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1366985 3 0.4843 0.516 0.396 0.000 0.604 0.000
#> SRR815280 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1348531 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR815845 2 0.5099 0.347 0.000 0.612 0.380 0.008
#> SRR1471178 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0000 0.857 0.000 0.000 1.000 0.000
#> SRR1078684 3 0.0000 0.857 0.000 0.000 1.000 0.000
#> SRR1317751 3 0.3610 0.770 0.200 0.000 0.800 0.000
#> SRR1435667 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1097905 4 0.0336 0.841 0.000 0.008 0.000 0.992
#> SRR1456548 4 0.0336 0.841 0.008 0.000 0.000 0.992
#> SRR1075126 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR813108 2 0.0336 0.974 0.000 0.992 0.000 0.008
#> SRR1479062 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1408703 3 0.0000 0.857 0.000 0.000 1.000 0.000
#> SRR1332360 1 0.0336 0.991 0.992 0.000 0.008 0.000
#> SRR1098686 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1434228 1 0.0336 0.991 0.992 0.000 0.008 0.000
#> SRR1467149 4 0.0336 0.841 0.008 0.000 0.000 0.992
#> SRR1399113 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1476507 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1092468 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1441804 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1326100 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1398815 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1436021 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1480083 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1472863 4 0.0376 0.841 0.004 0.004 0.000 0.992
#> SRR815542 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1400100 2 0.1356 0.937 0.000 0.960 0.032 0.008
#> SRR1312002 3 0.4843 0.516 0.396 0.000 0.604 0.000
#> SRR1470253 3 0.4855 0.507 0.400 0.000 0.600 0.000
#> SRR1414332 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.0336 0.991 0.992 0.000 0.008 0.000
#> SRR661052 4 0.0336 0.841 0.008 0.000 0.000 0.992
#> SRR1308860 4 0.0336 0.841 0.008 0.000 0.000 0.992
#> SRR1421159 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1340943 4 0.4382 0.684 0.000 0.296 0.000 0.704
#> SRR1078855 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1478679 2 0.1722 0.918 0.000 0.944 0.048 0.008
#> SRR1350979 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1458198 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1386910 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1465375 4 0.4916 0.458 0.000 0.424 0.000 0.576
#> SRR1323699 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1431139 3 0.1637 0.838 0.060 0.000 0.940 0.000
#> SRR1373964 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1455413 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1437163 4 0.0336 0.841 0.000 0.008 0.000 0.992
#> SRR1347343 3 0.0188 0.857 0.000 0.000 0.996 0.004
#> SRR1465480 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1489631 4 0.0336 0.841 0.008 0.000 0.000 0.992
#> SRR1086514 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1430928 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1344294 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.3610 0.726 0.000 0.000 0.800 0.200
#> SRR1486348 4 0.1118 0.821 0.036 0.000 0.000 0.964
#> SRR1488770 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1500089 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1441178 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1381396 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.3569 0.773 0.196 0.000 0.804 0.000
#> SRR1349809 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1324314 3 0.3610 0.770 0.200 0.000 0.800 0.000
#> SRR1092444 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1382553 3 0.4843 0.516 0.396 0.000 0.604 0.000
#> SRR1075530 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1442612 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1360056 3 0.0000 0.857 0.000 0.000 1.000 0.000
#> SRR1078164 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1434545 4 0.4406 0.680 0.000 0.300 0.000 0.700
#> SRR1398251 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1375866 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1091645 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1416636 3 0.0000 0.857 0.000 0.000 1.000 0.000
#> SRR1105441 3 0.0336 0.857 0.000 0.000 0.992 0.008
#> SRR1082496 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1315353 2 0.0336 0.974 0.000 0.992 0.000 0.008
#> SRR1093697 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.4564 0.632 0.328 0.000 0.672 0.000
#> SRR1076120 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1074410 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1340345 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1069514 2 0.0188 0.977 0.000 0.996 0.000 0.004
#> SRR1092636 3 0.0188 0.857 0.004 0.000 0.996 0.000
#> SRR1365013 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1073069 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1443137 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR820234 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1338079 4 0.0336 0.841 0.000 0.008 0.000 0.992
#> SRR1390094 4 0.4500 0.659 0.000 0.316 0.000 0.684
#> SRR1340721 4 0.4898 0.476 0.000 0.416 0.000 0.584
#> SRR1335964 3 0.0188 0.857 0.004 0.000 0.996 0.000
#> SRR1086869 3 0.3569 0.773 0.196 0.000 0.804 0.000
#> SRR1453434 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1402261 4 0.3907 0.740 0.000 0.232 0.000 0.768
#> SRR657809 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1093075 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1353418 3 0.3688 0.764 0.208 0.000 0.792 0.000
#> SRR1092913 2 0.0000 0.980 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1335605 2 0.4192 0.341 0.000 0.596 0.404 0.000 0.000
#> SRR1432014 3 0.2516 0.849 0.000 0.000 0.860 0.000 0.140
#> SRR1499215 3 0.3210 0.817 0.000 0.000 0.788 0.000 0.212
#> SRR1460409 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1097344 2 0.2389 0.832 0.000 0.880 0.116 0.004 0.000
#> SRR1081789 2 0.4242 0.271 0.000 0.572 0.428 0.000 0.000
#> SRR1453005 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1366985 5 0.4733 0.444 0.348 0.000 0.028 0.000 0.624
#> SRR815280 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1348531 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR815845 3 0.5238 0.580 0.000 0.260 0.652 0.000 0.088
#> SRR1471178 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000
#> SRR1078684 5 0.1270 0.866 0.000 0.000 0.052 0.000 0.948
#> SRR1317751 5 0.0510 0.905 0.016 0.000 0.000 0.000 0.984
#> SRR1435667 3 0.2516 0.849 0.000 0.000 0.860 0.000 0.140
#> SRR1097905 4 0.0162 0.997 0.000 0.000 0.004 0.996 0.000
#> SRR1456548 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1075126 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR813108 2 0.4278 0.203 0.000 0.548 0.452 0.000 0.000
#> SRR1479062 3 0.4307 0.262 0.000 0.000 0.504 0.000 0.496
#> SRR1408703 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000
#> SRR1332360 1 0.1671 0.896 0.924 0.000 0.000 0.000 0.076
#> SRR1098686 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1434228 1 0.4015 0.427 0.652 0.000 0.000 0.000 0.348
#> SRR1467149 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1399113 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 2 0.2629 0.820 0.000 0.860 0.136 0.004 0.000
#> SRR1092468 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1441804 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1326100 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1398815 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1436021 2 0.0162 0.884 0.000 0.996 0.004 0.000 0.000
#> SRR1480083 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 4 0.0162 0.997 0.000 0.000 0.004 0.996 0.000
#> SRR815542 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1400100 3 0.4030 0.401 0.000 0.352 0.648 0.000 0.000
#> SRR1312002 5 0.1571 0.862 0.060 0.000 0.004 0.000 0.936
#> SRR1470253 5 0.0963 0.890 0.036 0.000 0.000 0.000 0.964
#> SRR1414332 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1069209 1 0.4138 0.331 0.616 0.000 0.000 0.000 0.384
#> SRR661052 4 0.0162 0.997 0.000 0.000 0.004 0.996 0.000
#> SRR1308860 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1421159 2 0.1671 0.839 0.000 0.924 0.076 0.000 0.000
#> SRR1340943 2 0.4808 0.711 0.000 0.728 0.136 0.136 0.000
#> SRR1078855 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1459465 2 0.0162 0.884 0.000 0.996 0.004 0.000 0.000
#> SRR816818 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.2516 0.721 0.000 0.140 0.860 0.000 0.000
#> SRR1350979 3 0.2891 0.843 0.000 0.000 0.824 0.000 0.176
#> SRR1458198 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1386910 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1465375 2 0.2871 0.828 0.000 0.872 0.040 0.088 0.000
#> SRR1323699 3 0.3003 0.837 0.000 0.000 0.812 0.000 0.188
#> SRR1431139 5 0.0162 0.904 0.004 0.000 0.000 0.000 0.996
#> SRR1373964 3 0.2516 0.849 0.000 0.000 0.860 0.000 0.140
#> SRR1455413 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1437163 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1347343 3 0.3210 0.817 0.000 0.000 0.788 0.000 0.212
#> SRR1465480 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1086514 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1430928 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 3 0.2891 0.843 0.000 0.000 0.824 0.000 0.176
#> SRR1344294 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1468118 5 0.1124 0.881 0.000 0.000 0.004 0.036 0.960
#> SRR1486348 4 0.0162 0.994 0.004 0.000 0.000 0.996 0.000
#> SRR1488770 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1500089 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1441178 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1381396 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1096081 5 0.0404 0.906 0.012 0.000 0.000 0.000 0.988
#> SRR1349809 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1324314 5 0.0671 0.905 0.016 0.000 0.004 0.000 0.980
#> SRR1092444 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1382553 5 0.4909 0.313 0.412 0.000 0.028 0.000 0.560
#> SRR1075530 2 0.0162 0.884 0.000 0.996 0.004 0.000 0.000
#> SRR1442612 3 0.2516 0.849 0.000 0.000 0.860 0.000 0.140
#> SRR1360056 5 0.1121 0.874 0.000 0.000 0.044 0.000 0.956
#> SRR1078164 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1434545 2 0.4541 0.740 0.000 0.752 0.136 0.112 0.000
#> SRR1398251 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1375866 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1091645 2 0.2488 0.828 0.000 0.872 0.124 0.004 0.000
#> SRR1416636 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000
#> SRR1105441 3 0.2852 0.840 0.000 0.000 0.828 0.000 0.172
#> SRR1082496 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 2 0.4268 0.228 0.000 0.556 0.444 0.000 0.000
#> SRR1093697 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.0703 0.900 0.024 0.000 0.000 0.000 0.976
#> SRR1076120 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1074410 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1340345 2 0.0162 0.884 0.000 0.996 0.004 0.000 0.000
#> SRR1069514 2 0.4074 0.429 0.000 0.636 0.364 0.000 0.000
#> SRR1092636 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000
#> SRR1365013 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1073069 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1443137 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR820234 2 0.1478 0.848 0.000 0.936 0.064 0.000 0.000
#> SRR1338079 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1390094 2 0.4183 0.765 0.000 0.780 0.136 0.084 0.000
#> SRR1340721 2 0.2561 0.807 0.000 0.856 0.000 0.144 0.000
#> SRR1335964 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000
#> SRR1086869 5 0.0404 0.906 0.012 0.000 0.000 0.000 0.988
#> SRR1453434 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1402261 2 0.5150 0.666 0.000 0.692 0.136 0.172 0.000
#> SRR657809 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> SRR1093075 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> SRR1353418 5 0.0510 0.905 0.016 0.000 0.000 0.000 0.984
#> SRR1092913 2 0.1831 0.853 0.000 0.920 0.076 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.1655 0.933 0.936 0.000 0.004 0.044 0.012 0.004
#> SRR1335605 2 0.4853 0.621 0.000 0.700 0.132 0.152 0.000 0.016
#> SRR1432014 3 0.0713 0.800 0.000 0.000 0.972 0.000 0.028 0.000
#> SRR1499215 3 0.1983 0.777 0.000 0.000 0.908 0.020 0.072 0.000
#> SRR1460409 1 0.0260 0.953 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1086441 1 0.0146 0.954 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1097344 4 0.3857 0.545 0.000 0.468 0.000 0.532 0.000 0.000
#> SRR1081789 2 0.4957 0.552 0.000 0.648 0.204 0.148 0.000 0.000
#> SRR1453005 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1366985 5 0.6550 0.324 0.332 0.000 0.140 0.064 0.464 0.000
#> SRR815280 1 0.0935 0.945 0.964 0.000 0.004 0.032 0.000 0.000
#> SRR1348531 1 0.0291 0.953 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR815845 3 0.6617 0.315 0.000 0.292 0.492 0.152 0.060 0.004
#> SRR1471178 1 0.0260 0.953 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1080696 5 0.0632 0.858 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1078684 5 0.4079 0.571 0.000 0.000 0.288 0.032 0.680 0.000
#> SRR1317751 5 0.0363 0.860 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1435667 3 0.0547 0.799 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1097905 6 0.0000 0.969 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1456548 6 0.1387 0.961 0.000 0.000 0.000 0.068 0.000 0.932
#> SRR1075126 1 0.1870 0.932 0.928 0.000 0.004 0.044 0.012 0.012
#> SRR813108 2 0.5498 0.325 0.000 0.528 0.324 0.148 0.000 0.000
#> SRR1479062 3 0.3991 0.158 0.000 0.000 0.524 0.004 0.472 0.000
#> SRR1408703 5 0.0547 0.859 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR1332360 1 0.3677 0.794 0.804 0.000 0.012 0.064 0.120 0.000
#> SRR1098686 1 0.0260 0.953 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1434228 1 0.5111 0.395 0.596 0.000 0.016 0.064 0.324 0.000
#> SRR1467149 6 0.0363 0.970 0.000 0.000 0.000 0.012 0.000 0.988
#> SRR1399113 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3266 0.857 0.000 0.272 0.000 0.728 0.000 0.000
#> SRR1092468 1 0.0000 0.954 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1441804 1 0.0260 0.953 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1326100 2 0.0458 0.856 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1398815 1 0.0363 0.952 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1436021 2 0.1644 0.817 0.000 0.920 0.004 0.076 0.000 0.000
#> SRR1480083 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 6 0.0000 0.969 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR815542 1 0.0000 0.954 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1400100 3 0.5741 0.149 0.000 0.364 0.480 0.152 0.004 0.000
#> SRR1312002 5 0.2860 0.779 0.068 0.000 0.012 0.052 0.868 0.000
#> SRR1470253 5 0.1577 0.840 0.008 0.000 0.016 0.036 0.940 0.000
#> SRR1414332 1 0.0260 0.953 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1069209 1 0.5078 0.361 0.584 0.000 0.012 0.064 0.340 0.000
#> SRR661052 6 0.0000 0.969 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1308860 6 0.0790 0.971 0.000 0.000 0.000 0.032 0.000 0.968
#> SRR1421159 2 0.3551 0.714 0.000 0.792 0.060 0.148 0.000 0.000
#> SRR1340943 4 0.3418 0.861 0.008 0.192 0.000 0.784 0.000 0.016
#> SRR1078855 1 0.1370 0.938 0.948 0.000 0.004 0.036 0.012 0.000
#> SRR1459465 2 0.0146 0.861 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR816818 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.3066 0.698 0.000 0.044 0.832 0.124 0.000 0.000
#> SRR1350979 3 0.1387 0.796 0.000 0.000 0.932 0.000 0.068 0.000
#> SRR1458198 1 0.0291 0.953 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1386910 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1465375 2 0.2571 0.733 0.000 0.876 0.000 0.060 0.000 0.064
#> SRR1323699 3 0.1682 0.792 0.000 0.000 0.928 0.020 0.052 0.000
#> SRR1431139 5 0.1320 0.852 0.000 0.000 0.036 0.016 0.948 0.000
#> SRR1373964 3 0.0632 0.800 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR1455413 1 0.0146 0.954 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1437163 6 0.1387 0.961 0.000 0.000 0.000 0.068 0.000 0.932
#> SRR1347343 3 0.1867 0.782 0.000 0.000 0.916 0.020 0.064 0.000
#> SRR1465480 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 6 0.1327 0.963 0.000 0.000 0.000 0.064 0.000 0.936
#> SRR1086514 2 0.2704 0.757 0.000 0.844 0.016 0.140 0.000 0.000
#> SRR1430928 1 0.0363 0.953 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1310939 3 0.1327 0.798 0.000 0.000 0.936 0.000 0.064 0.000
#> SRR1344294 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0260 0.953 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1468118 5 0.1923 0.816 0.000 0.000 0.016 0.004 0.916 0.064
#> SRR1486348 6 0.0458 0.959 0.016 0.000 0.000 0.000 0.000 0.984
#> SRR1488770 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.1577 0.936 0.940 0.000 0.000 0.036 0.016 0.008
#> SRR1456611 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.0000 0.954 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500089 1 0.0146 0.954 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1441178 1 0.0508 0.952 0.984 0.000 0.004 0.012 0.000 0.000
#> SRR1381396 1 0.0146 0.954 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1096081 5 0.0363 0.860 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1349809 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1324314 5 0.1367 0.841 0.000 0.000 0.012 0.044 0.944 0.000
#> SRR1092444 1 0.0146 0.954 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1382553 5 0.6663 0.241 0.360 0.000 0.152 0.064 0.424 0.000
#> SRR1075530 2 0.0146 0.861 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1442612 3 0.0713 0.800 0.000 0.000 0.972 0.000 0.028 0.000
#> SRR1360056 5 0.4089 0.608 0.000 0.000 0.264 0.040 0.696 0.000
#> SRR1078164 1 0.0260 0.953 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1434545 4 0.3231 0.869 0.000 0.200 0.000 0.784 0.000 0.016
#> SRR1398251 1 0.1442 0.936 0.944 0.000 0.004 0.040 0.012 0.000
#> SRR1375866 1 0.0260 0.953 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1091645 4 0.3634 0.774 0.000 0.356 0.000 0.644 0.000 0.000
#> SRR1416636 5 0.0632 0.858 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1105441 3 0.4050 0.678 0.000 0.000 0.752 0.152 0.096 0.000
#> SRR1082496 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 2 0.5556 0.257 0.000 0.504 0.348 0.148 0.000 0.000
#> SRR1093697 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.0363 0.860 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1076120 1 0.0363 0.952 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1074410 1 0.0000 0.954 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1340345 2 0.0260 0.858 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1069514 2 0.4701 0.599 0.000 0.684 0.168 0.148 0.000 0.000
#> SRR1092636 5 0.0458 0.860 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1365013 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1073069 1 0.2224 0.908 0.904 0.000 0.012 0.064 0.020 0.000
#> SRR1443137 1 0.1442 0.936 0.944 0.000 0.004 0.040 0.012 0.000
#> SRR1437143 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.0260 0.953 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR820234 2 0.3608 0.711 0.000 0.788 0.064 0.148 0.000 0.000
#> SRR1338079 6 0.0937 0.970 0.000 0.000 0.000 0.040 0.000 0.960
#> SRR1390094 4 0.3171 0.871 0.000 0.204 0.000 0.784 0.000 0.012
#> SRR1340721 2 0.2100 0.740 0.000 0.884 0.000 0.004 0.000 0.112
#> SRR1335964 5 0.0458 0.859 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1086869 5 0.0458 0.859 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1453434 1 0.0363 0.951 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1402261 4 0.3284 0.866 0.000 0.196 0.000 0.784 0.000 0.020
#> SRR657809 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1093075 1 0.1442 0.936 0.944 0.000 0.004 0.040 0.012 0.000
#> SRR1433329 1 0.1442 0.936 0.944 0.000 0.004 0.040 0.012 0.000
#> SRR1353418 5 0.1257 0.850 0.000 0.000 0.020 0.028 0.952 0.000
#> SRR1092913 2 0.1387 0.795 0.000 0.932 0.000 0.068 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", "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 17780 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.746 0.816 0.926 0.4142 0.550 0.550
#> 3 3 0.886 0.924 0.965 0.5495 0.641 0.433
#> 4 4 0.912 0.925 0.966 0.1132 0.876 0.677
#> 5 5 0.942 0.906 0.961 0.0958 0.913 0.707
#> 6 6 0.826 0.645 0.810 0.0330 0.942 0.752
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 4
There is also optional best \(k\) = 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR816969 1 0.000 0.96152 1.000 0.000
#> SRR1335605 2 1.000 0.29243 0.492 0.508
#> SRR1432014 1 0.000 0.96152 1.000 0.000
#> SRR1499215 1 0.000 0.96152 1.000 0.000
#> SRR1460409 1 0.000 0.96152 1.000 0.000
#> SRR1086441 1 0.000 0.96152 1.000 0.000
#> SRR1097344 2 0.000 0.81760 0.000 1.000
#> SRR1081789 2 1.000 0.29243 0.492 0.508
#> SRR1453005 2 0.000 0.81760 0.000 1.000
#> SRR1366985 1 0.000 0.96152 1.000 0.000
#> SRR815280 1 0.000 0.96152 1.000 0.000
#> SRR1348531 1 0.000 0.96152 1.000 0.000
#> SRR815845 2 1.000 0.29243 0.492 0.508
#> SRR1471178 1 0.000 0.96152 1.000 0.000
#> SRR1080696 1 0.000 0.96152 1.000 0.000
#> SRR1078684 1 0.000 0.96152 1.000 0.000
#> SRR1317751 1 0.000 0.96152 1.000 0.000
#> SRR1435667 1 1.000 -0.25457 0.512 0.488
#> SRR1097905 1 0.998 -0.21297 0.524 0.476
#> SRR1456548 1 0.000 0.96152 1.000 0.000
#> SRR1075126 1 0.000 0.96152 1.000 0.000
#> SRR813108 2 0.871 0.62741 0.292 0.708
#> SRR1479062 1 0.000 0.96152 1.000 0.000
#> SRR1408703 1 0.000 0.96152 1.000 0.000
#> SRR1332360 1 0.000 0.96152 1.000 0.000
#> SRR1098686 1 0.000 0.96152 1.000 0.000
#> SRR1434228 1 0.000 0.96152 1.000 0.000
#> SRR1467149 1 0.000 0.96152 1.000 0.000
#> SRR1399113 2 0.000 0.81760 0.000 1.000
#> SRR1476507 2 0.000 0.81760 0.000 1.000
#> SRR1092468 1 0.000 0.96152 1.000 0.000
#> SRR1441804 1 0.000 0.96152 1.000 0.000
#> SRR1326100 2 0.000 0.81760 0.000 1.000
#> SRR1398815 1 0.000 0.96152 1.000 0.000
#> SRR1436021 2 1.000 0.29243 0.492 0.508
#> SRR1480083 2 0.000 0.81760 0.000 1.000
#> SRR1472863 1 0.443 0.84597 0.908 0.092
#> SRR815542 1 0.000 0.96152 1.000 0.000
#> SRR1400100 2 1.000 0.29243 0.492 0.508
#> SRR1312002 1 0.000 0.96152 1.000 0.000
#> SRR1470253 1 0.000 0.96152 1.000 0.000
#> SRR1414332 1 0.000 0.96152 1.000 0.000
#> SRR1069209 1 0.000 0.96152 1.000 0.000
#> SRR661052 1 0.000 0.96152 1.000 0.000
#> SRR1308860 1 0.000 0.96152 1.000 0.000
#> SRR1421159 2 1.000 0.29243 0.492 0.508
#> SRR1340943 1 0.469 0.83504 0.900 0.100
#> SRR1078855 1 0.000 0.96152 1.000 0.000
#> SRR1459465 2 0.000 0.81760 0.000 1.000
#> SRR816818 2 0.000 0.81760 0.000 1.000
#> SRR1478679 1 0.987 -0.04293 0.568 0.432
#> SRR1350979 1 0.000 0.96152 1.000 0.000
#> SRR1458198 1 0.000 0.96152 1.000 0.000
#> SRR1386910 2 0.000 0.81760 0.000 1.000
#> SRR1465375 2 0.000 0.81760 0.000 1.000
#> SRR1323699 1 0.000 0.96152 1.000 0.000
#> SRR1431139 1 0.000 0.96152 1.000 0.000
#> SRR1373964 1 0.000 0.96152 1.000 0.000
#> SRR1455413 1 0.000 0.96152 1.000 0.000
#> SRR1437163 2 1.000 0.29243 0.492 0.508
#> SRR1347343 1 0.000 0.96152 1.000 0.000
#> SRR1465480 2 0.000 0.81760 0.000 1.000
#> SRR1489631 1 0.000 0.96152 1.000 0.000
#> SRR1086514 2 0.871 0.62741 0.292 0.708
#> SRR1430928 1 0.000 0.96152 1.000 0.000
#> SRR1310939 1 0.000 0.96152 1.000 0.000
#> SRR1344294 2 0.000 0.81760 0.000 1.000
#> SRR1099402 1 0.000 0.96152 1.000 0.000
#> SRR1468118 1 0.000 0.96152 1.000 0.000
#> SRR1486348 1 0.000 0.96152 1.000 0.000
#> SRR1488770 2 0.000 0.81760 0.000 1.000
#> SRR1083732 1 0.000 0.96152 1.000 0.000
#> SRR1456611 2 0.000 0.81760 0.000 1.000
#> SRR1080318 1 0.000 0.96152 1.000 0.000
#> SRR1500089 1 0.000 0.96152 1.000 0.000
#> SRR1441178 1 0.000 0.96152 1.000 0.000
#> SRR1381396 1 0.000 0.96152 1.000 0.000
#> SRR1096081 1 0.000 0.96152 1.000 0.000
#> SRR1349809 2 0.000 0.81760 0.000 1.000
#> SRR1324314 1 0.000 0.96152 1.000 0.000
#> SRR1092444 1 0.000 0.96152 1.000 0.000
#> SRR1382553 1 0.000 0.96152 1.000 0.000
#> SRR1075530 2 0.871 0.62741 0.292 0.708
#> SRR1442612 2 1.000 0.29243 0.492 0.508
#> SRR1360056 1 0.000 0.96152 1.000 0.000
#> SRR1078164 1 0.000 0.96152 1.000 0.000
#> SRR1434545 2 0.224 0.80079 0.036 0.964
#> SRR1398251 1 0.000 0.96152 1.000 0.000
#> SRR1375866 1 0.000 0.96152 1.000 0.000
#> SRR1091645 2 0.000 0.81760 0.000 1.000
#> SRR1416636 1 0.000 0.96152 1.000 0.000
#> SRR1105441 1 0.981 0.00966 0.580 0.420
#> SRR1082496 2 0.000 0.81760 0.000 1.000
#> SRR1315353 2 0.871 0.62741 0.292 0.708
#> SRR1093697 2 0.000 0.81760 0.000 1.000
#> SRR1077429 1 0.000 0.96152 1.000 0.000
#> SRR1076120 1 0.000 0.96152 1.000 0.000
#> SRR1074410 1 0.000 0.96152 1.000 0.000
#> SRR1340345 2 0.000 0.81760 0.000 1.000
#> SRR1069514 2 1.000 0.29243 0.492 0.508
#> SRR1092636 1 0.000 0.96152 1.000 0.000
#> SRR1365013 2 0.871 0.62741 0.292 0.708
#> SRR1073069 1 0.000 0.96152 1.000 0.000
#> SRR1443137 1 0.000 0.96152 1.000 0.000
#> SRR1437143 2 0.000 0.81760 0.000 1.000
#> SRR1091990 1 0.000 0.96152 1.000 0.000
#> SRR820234 2 0.000 0.81760 0.000 1.000
#> SRR1338079 1 0.000 0.96152 1.000 0.000
#> SRR1390094 2 1.000 0.29243 0.492 0.508
#> SRR1340721 2 0.000 0.81760 0.000 1.000
#> SRR1335964 1 0.000 0.96152 1.000 0.000
#> SRR1086869 1 0.000 0.96152 1.000 0.000
#> SRR1453434 1 0.000 0.96152 1.000 0.000
#> SRR1402261 1 0.939 0.25692 0.644 0.356
#> SRR657809 2 0.000 0.81760 0.000 1.000
#> SRR1093075 1 0.000 0.96152 1.000 0.000
#> SRR1433329 1 0.000 0.96152 1.000 0.000
#> SRR1353418 1 0.000 0.96152 1.000 0.000
#> SRR1092913 2 0.000 0.81760 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.000 0.932 1.000 0.000 0.000
#> SRR1335605 3 0.000 0.963 0.000 0.000 1.000
#> SRR1432014 3 0.000 0.963 0.000 0.000 1.000
#> SRR1499215 3 0.000 0.963 0.000 0.000 1.000
#> SRR1460409 1 0.000 0.932 1.000 0.000 0.000
#> SRR1086441 1 0.000 0.932 1.000 0.000 0.000
#> SRR1097344 2 0.000 0.996 0.000 1.000 0.000
#> SRR1081789 3 0.000 0.963 0.000 0.000 1.000
#> SRR1453005 2 0.000 0.996 0.000 1.000 0.000
#> SRR1366985 3 0.186 0.925 0.052 0.000 0.948
#> SRR815280 1 0.175 0.911 0.952 0.000 0.048
#> SRR1348531 1 0.000 0.932 1.000 0.000 0.000
#> SRR815845 3 0.000 0.963 0.000 0.000 1.000
#> SRR1471178 1 0.186 0.908 0.948 0.000 0.052
#> SRR1080696 3 0.000 0.963 0.000 0.000 1.000
#> SRR1078684 3 0.000 0.963 0.000 0.000 1.000
#> SRR1317751 3 0.455 0.756 0.200 0.000 0.800
#> SRR1435667 3 0.000 0.963 0.000 0.000 1.000
#> SRR1097905 3 0.000 0.963 0.000 0.000 1.000
#> SRR1456548 1 0.630 0.194 0.524 0.000 0.476
#> SRR1075126 1 0.455 0.797 0.800 0.000 0.200
#> SRR813108 3 0.445 0.755 0.000 0.192 0.808
#> SRR1479062 3 0.000 0.963 0.000 0.000 1.000
#> SRR1408703 3 0.000 0.963 0.000 0.000 1.000
#> SRR1332360 1 0.341 0.828 0.876 0.000 0.124
#> SRR1098686 1 0.455 0.797 0.800 0.000 0.200
#> SRR1434228 1 0.000 0.932 1.000 0.000 0.000
#> SRR1467149 3 0.000 0.963 0.000 0.000 1.000
#> SRR1399113 2 0.000 0.996 0.000 1.000 0.000
#> SRR1476507 2 0.000 0.996 0.000 1.000 0.000
#> SRR1092468 3 0.000 0.963 0.000 0.000 1.000
#> SRR1441804 1 0.186 0.908 0.948 0.000 0.052
#> SRR1326100 2 0.000 0.996 0.000 1.000 0.000
#> SRR1398815 1 0.455 0.797 0.800 0.000 0.200
#> SRR1436021 3 0.000 0.963 0.000 0.000 1.000
#> SRR1480083 2 0.000 0.996 0.000 1.000 0.000
#> SRR1472863 3 0.000 0.963 0.000 0.000 1.000
#> SRR815542 1 0.000 0.932 1.000 0.000 0.000
#> SRR1400100 3 0.000 0.963 0.000 0.000 1.000
#> SRR1312002 3 0.175 0.928 0.048 0.000 0.952
#> SRR1470253 3 0.186 0.925 0.052 0.000 0.948
#> SRR1414332 1 0.000 0.932 1.000 0.000 0.000
#> SRR1069209 1 0.000 0.932 1.000 0.000 0.000
#> SRR661052 3 0.000 0.963 0.000 0.000 1.000
#> SRR1308860 1 0.455 0.797 0.800 0.000 0.200
#> SRR1421159 3 0.000 0.963 0.000 0.000 1.000
#> SRR1340943 3 0.000 0.963 0.000 0.000 1.000
#> SRR1078855 1 0.000 0.932 1.000 0.000 0.000
#> SRR1459465 2 0.000 0.996 0.000 1.000 0.000
#> SRR816818 2 0.000 0.996 0.000 1.000 0.000
#> SRR1478679 3 0.000 0.963 0.000 0.000 1.000
#> SRR1350979 3 0.000 0.963 0.000 0.000 1.000
#> SRR1458198 1 0.186 0.908 0.948 0.000 0.052
#> SRR1386910 2 0.000 0.996 0.000 1.000 0.000
#> SRR1465375 2 0.000 0.996 0.000 1.000 0.000
#> SRR1323699 3 0.000 0.963 0.000 0.000 1.000
#> SRR1431139 3 0.000 0.963 0.000 0.000 1.000
#> SRR1373964 3 0.000 0.963 0.000 0.000 1.000
#> SRR1455413 3 0.000 0.963 0.000 0.000 1.000
#> SRR1437163 3 0.583 0.426 0.340 0.000 0.660
#> SRR1347343 3 0.000 0.963 0.000 0.000 1.000
#> SRR1465480 2 0.000 0.996 0.000 1.000 0.000
#> SRR1489631 1 0.455 0.797 0.800 0.000 0.200
#> SRR1086514 3 0.590 0.471 0.000 0.352 0.648
#> SRR1430928 1 0.000 0.932 1.000 0.000 0.000
#> SRR1310939 3 0.000 0.963 0.000 0.000 1.000
#> SRR1344294 2 0.000 0.996 0.000 1.000 0.000
#> SRR1099402 1 0.000 0.932 1.000 0.000 0.000
#> SRR1468118 3 0.000 0.963 0.000 0.000 1.000
#> SRR1486348 1 0.455 0.797 0.800 0.000 0.200
#> SRR1488770 2 0.000 0.996 0.000 1.000 0.000
#> SRR1083732 1 0.000 0.932 1.000 0.000 0.000
#> SRR1456611 2 0.000 0.996 0.000 1.000 0.000
#> SRR1080318 1 0.000 0.932 1.000 0.000 0.000
#> SRR1500089 1 0.000 0.932 1.000 0.000 0.000
#> SRR1441178 1 0.000 0.932 1.000 0.000 0.000
#> SRR1381396 1 0.103 0.922 0.976 0.000 0.024
#> SRR1096081 3 0.186 0.925 0.052 0.000 0.948
#> SRR1349809 2 0.000 0.996 0.000 1.000 0.000
#> SRR1324314 3 0.000 0.963 0.000 0.000 1.000
#> SRR1092444 1 0.000 0.932 1.000 0.000 0.000
#> SRR1382553 3 0.245 0.906 0.076 0.000 0.924
#> SRR1075530 3 0.000 0.963 0.000 0.000 1.000
#> SRR1442612 3 0.000 0.963 0.000 0.000 1.000
#> SRR1360056 3 0.000 0.963 0.000 0.000 1.000
#> SRR1078164 1 0.000 0.932 1.000 0.000 0.000
#> SRR1434545 2 0.245 0.909 0.000 0.924 0.076
#> SRR1398251 1 0.000 0.932 1.000 0.000 0.000
#> SRR1375866 1 0.000 0.932 1.000 0.000 0.000
#> SRR1091645 2 0.000 0.996 0.000 1.000 0.000
#> SRR1416636 3 0.000 0.963 0.000 0.000 1.000
#> SRR1105441 3 0.000 0.963 0.000 0.000 1.000
#> SRR1082496 2 0.000 0.996 0.000 1.000 0.000
#> SRR1315353 3 0.000 0.963 0.000 0.000 1.000
#> SRR1093697 2 0.000 0.996 0.000 1.000 0.000
#> SRR1077429 3 0.164 0.931 0.044 0.000 0.956
#> SRR1076120 3 0.000 0.963 0.000 0.000 1.000
#> SRR1074410 1 0.000 0.932 1.000 0.000 0.000
#> SRR1340345 2 0.000 0.996 0.000 1.000 0.000
#> SRR1069514 3 0.000 0.963 0.000 0.000 1.000
#> SRR1092636 3 0.000 0.963 0.000 0.000 1.000
#> SRR1365013 3 0.000 0.963 0.000 0.000 1.000
#> SRR1073069 1 0.000 0.932 1.000 0.000 0.000
#> SRR1443137 1 0.000 0.932 1.000 0.000 0.000
#> SRR1437143 2 0.000 0.996 0.000 1.000 0.000
#> SRR1091990 1 0.000 0.932 1.000 0.000 0.000
#> SRR820234 2 0.000 0.996 0.000 1.000 0.000
#> SRR1338079 1 0.455 0.797 0.800 0.000 0.200
#> SRR1390094 3 0.000 0.963 0.000 0.000 1.000
#> SRR1340721 2 0.000 0.996 0.000 1.000 0.000
#> SRR1335964 3 0.000 0.963 0.000 0.000 1.000
#> SRR1086869 3 0.000 0.963 0.000 0.000 1.000
#> SRR1453434 1 0.141 0.917 0.964 0.000 0.036
#> SRR1402261 3 0.455 0.721 0.200 0.000 0.800
#> SRR657809 2 0.000 0.996 0.000 1.000 0.000
#> SRR1093075 1 0.000 0.932 1.000 0.000 0.000
#> SRR1433329 1 0.000 0.932 1.000 0.000 0.000
#> SRR1353418 3 0.506 0.695 0.244 0.000 0.756
#> SRR1092913 2 0.000 0.996 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1335605 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1432014 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1499215 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1460409 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.0707 0.959 0.000 0.020 0.000 0.980
#> SRR1081789 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1453005 4 0.4008 0.672 0.000 0.244 0.000 0.756
#> SRR1366985 3 0.1474 0.925 0.052 0.000 0.948 0.000
#> SRR815280 1 0.1389 0.906 0.952 0.000 0.048 0.000
#> SRR1348531 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR815845 3 0.4746 0.420 0.000 0.000 0.632 0.368
#> SRR1471178 1 0.1474 0.904 0.948 0.000 0.052 0.000
#> SRR1080696 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1078684 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1317751 3 0.3610 0.744 0.200 0.000 0.800 0.000
#> SRR1435667 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1097905 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1456548 1 0.4992 0.222 0.524 0.000 0.476 0.000
#> SRR1075126 1 0.3610 0.780 0.800 0.000 0.200 0.000
#> SRR813108 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1479062 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1408703 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1332360 1 0.2704 0.815 0.876 0.000 0.124 0.000
#> SRR1098686 1 0.3610 0.780 0.800 0.000 0.200 0.000
#> SRR1434228 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1467149 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1399113 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1092468 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1441804 1 0.1474 0.904 0.948 0.000 0.052 0.000
#> SRR1326100 4 0.0188 0.971 0.000 0.004 0.000 0.996
#> SRR1398815 1 0.3610 0.780 0.800 0.000 0.200 0.000
#> SRR1436021 3 0.1211 0.932 0.000 0.000 0.960 0.040
#> SRR1480083 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1472863 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR815542 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1400100 4 0.0817 0.948 0.000 0.000 0.024 0.976
#> SRR1312002 3 0.1389 0.929 0.048 0.000 0.952 0.000
#> SRR1470253 3 0.1474 0.925 0.052 0.000 0.948 0.000
#> SRR1414332 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1069209 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR661052 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1308860 1 0.3610 0.780 0.800 0.000 0.200 0.000
#> SRR1421159 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1340943 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1078855 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1350979 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1458198 1 0.1474 0.904 0.948 0.000 0.052 0.000
#> SRR1386910 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1465375 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1323699 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1431139 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1373964 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1455413 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1437163 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1347343 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1465480 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.3610 0.780 0.800 0.000 0.200 0.000
#> SRR1086514 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1430928 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1344294 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1486348 1 0.3610 0.780 0.800 0.000 0.200 0.000
#> SRR1488770 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1456611 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1500089 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1441178 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1381396 1 0.0817 0.919 0.976 0.000 0.024 0.000
#> SRR1096081 3 0.1474 0.925 0.052 0.000 0.948 0.000
#> SRR1349809 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1324314 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1092444 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1382553 3 0.1940 0.906 0.076 0.000 0.924 0.000
#> SRR1075530 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1442612 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1360056 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1078164 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1434545 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1398251 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1375866 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1091645 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1416636 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1105441 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1082496 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1315353 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1093697 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.1302 0.932 0.044 0.000 0.956 0.000
#> SRR1076120 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1074410 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1340345 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1069514 4 0.4103 0.610 0.000 0.000 0.256 0.744
#> SRR1092636 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1365013 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1073069 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1443137 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR820234 4 0.0707 0.958 0.000 0.020 0.000 0.980
#> SRR1338079 1 0.4591 0.793 0.800 0.000 0.116 0.084
#> SRR1390094 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1340721 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1335964 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1086869 3 0.0000 0.966 0.000 0.000 1.000 0.000
#> SRR1453434 1 0.1118 0.913 0.964 0.000 0.036 0.000
#> SRR1402261 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR657809 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> SRR1093075 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR1353418 3 0.4008 0.687 0.244 0.000 0.756 0.000
#> SRR1092913 4 0.0188 0.971 0.000 0.004 0.000 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1335605 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1432014 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1499215 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1460409 1 0.0404 0.901 0.988 0.000 0.000 0.000 0.012
#> SRR1086441 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1097344 4 0.0609 0.960 0.000 0.020 0.000 0.980 0.000
#> SRR1081789 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1453005 4 0.3452 0.673 0.000 0.244 0.000 0.756 0.000
#> SRR1366985 5 0.0404 0.953 0.000 0.000 0.012 0.000 0.988
#> SRR815280 1 0.0404 0.901 0.988 0.000 0.000 0.000 0.012
#> SRR1348531 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR815845 3 0.4088 0.403 0.000 0.000 0.632 0.368 0.000
#> SRR1471178 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1080696 3 0.4291 0.136 0.000 0.000 0.536 0.000 0.464
#> SRR1078684 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1317751 5 0.0404 0.953 0.000 0.000 0.012 0.000 0.988
#> SRR1435667 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1097905 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1456548 1 0.4300 0.170 0.524 0.000 0.476 0.000 0.000
#> SRR1075126 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR813108 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1479062 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1408703 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1332360 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1098686 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1434228 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1467149 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1399113 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1092468 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1441804 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1326100 4 0.0162 0.972 0.000 0.004 0.000 0.996 0.000
#> SRR1398815 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1436021 3 0.1043 0.925 0.000 0.000 0.960 0.040 0.000
#> SRR1480083 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR815542 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1400100 4 0.0703 0.951 0.000 0.000 0.024 0.976 0.000
#> SRR1312002 5 0.1117 0.939 0.020 0.000 0.016 0.000 0.964
#> SRR1470253 5 0.0404 0.953 0.000 0.000 0.012 0.000 0.988
#> SRR1414332 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1069209 5 0.0162 0.955 0.004 0.000 0.000 0.000 0.996
#> SRR661052 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1308860 1 0.1197 0.873 0.952 0.000 0.048 0.000 0.000
#> SRR1421159 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1340943 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1078855 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1350979 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1458198 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1386910 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1465375 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1323699 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1431139 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1373964 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1455413 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1437163 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1347343 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1465480 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.3109 0.725 0.800 0.000 0.200 0.000 0.000
#> SRR1086514 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1430928 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1310939 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1344294 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1468118 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1486348 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.4161 0.416 0.608 0.000 0.000 0.000 0.392
#> SRR1456611 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.3074 0.746 0.804 0.000 0.000 0.000 0.196
#> SRR1500089 5 0.4150 0.283 0.388 0.000 0.000 0.000 0.612
#> SRR1441178 1 0.3480 0.692 0.752 0.000 0.000 0.000 0.248
#> SRR1381396 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1096081 5 0.1270 0.911 0.000 0.000 0.052 0.000 0.948
#> SRR1349809 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1324314 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1092444 1 0.4249 0.316 0.568 0.000 0.000 0.000 0.432
#> SRR1382553 5 0.0693 0.951 0.008 0.000 0.012 0.000 0.980
#> SRR1075530 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1442612 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1360056 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1078164 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1434545 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1398251 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1375866 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1091645 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1416636 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1105441 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1082496 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1093697 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.2561 0.803 0.000 0.000 0.856 0.000 0.144
#> SRR1076120 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1074410 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1340345 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1069514 4 0.3534 0.631 0.000 0.000 0.256 0.744 0.000
#> SRR1092636 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1365013 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1073069 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1443137 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1437143 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.3480 0.692 0.752 0.000 0.000 0.000 0.248
#> SRR820234 4 0.0609 0.959 0.000 0.020 0.000 0.980 0.000
#> SRR1338079 1 0.3806 0.771 0.812 0.000 0.104 0.084 0.000
#> SRR1390094 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1340721 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1335964 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1086869 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000
#> SRR1453434 1 0.0000 0.907 1.000 0.000 0.000 0.000 0.000
#> SRR1402261 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR657809 4 0.0000 0.975 0.000 0.000 0.000 1.000 0.000
#> SRR1093075 5 0.0703 0.939 0.024 0.000 0.000 0.000 0.976
#> SRR1433329 5 0.0000 0.956 0.000 0.000 0.000 0.000 1.000
#> SRR1353418 5 0.0404 0.953 0.000 0.000 0.012 0.000 0.988
#> SRR1092913 4 0.0162 0.972 0.000 0.004 0.000 0.996 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1335605 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1432014 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1499215 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1460409 1 0.3620 0.49154 0.648 0.000 0.000 0.000 0.352 0.000
#> SRR1086441 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1097344 5 0.3857 0.02358 0.000 0.000 0.000 0.468 0.532 0.000
#> SRR1081789 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1453005 4 0.6000 0.19365 0.000 0.244 0.000 0.420 0.336 0.000
#> SRR1366985 6 0.3817 0.36365 0.000 0.000 0.432 0.000 0.000 0.568
#> SRR815280 1 0.1556 0.83201 0.920 0.000 0.000 0.000 0.080 0.000
#> SRR1348531 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR815845 3 0.4378 0.40315 0.000 0.000 0.632 0.328 0.040 0.000
#> SRR1471178 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1080696 6 0.3888 0.34862 0.000 0.000 0.252 0.000 0.032 0.716
#> SRR1078684 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1317751 6 0.0790 0.57860 0.000 0.000 0.000 0.000 0.032 0.968
#> SRR1435667 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1097905 3 0.3728 0.47887 0.000 0.000 0.652 0.344 0.004 0.000
#> SRR1456548 3 0.6097 0.14162 0.232 0.000 0.420 0.344 0.004 0.000
#> SRR1075126 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR813108 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1479062 3 0.2003 0.79360 0.000 0.000 0.884 0.000 0.000 0.116
#> SRR1408703 3 0.4504 0.31989 0.000 0.000 0.536 0.000 0.032 0.432
#> SRR1332360 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1098686 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1434228 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1467149 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1399113 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1092468 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1441804 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326100 4 0.3728 0.51049 0.000 0.004 0.000 0.652 0.344 0.000
#> SRR1398815 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1436021 3 0.0937 0.86102 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1480083 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR815542 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1400100 4 0.4078 0.49728 0.000 0.000 0.024 0.656 0.320 0.000
#> SRR1312002 6 0.4305 0.34316 0.020 0.000 0.436 0.000 0.000 0.544
#> SRR1470253 6 0.2178 0.61357 0.000 0.000 0.132 0.000 0.000 0.868
#> SRR1414332 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1069209 6 0.3547 0.68835 0.000 0.000 0.000 0.000 0.332 0.668
#> SRR661052 3 0.0146 0.89400 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1308860 1 0.1152 0.85125 0.952 0.000 0.044 0.000 0.004 0.000
#> SRR1421159 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1340943 4 0.0146 0.48188 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1078855 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1350979 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1458198 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1386910 5 0.3857 0.02358 0.000 0.000 0.000 0.468 0.532 0.000
#> SRR1465375 4 0.2730 0.33346 0.000 0.000 0.000 0.808 0.192 0.000
#> SRR1323699 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1431139 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1373964 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1455413 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1437163 4 0.0146 0.48188 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1347343 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1465480 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 1 0.5953 0.29523 0.456 0.000 0.196 0.344 0.004 0.000
#> SRR1086514 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1430928 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310939 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1344294 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468118 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1486348 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1488770 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.3198 0.63161 0.740 0.000 0.000 0.000 0.000 0.260
#> SRR1456611 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.2762 0.71154 0.804 0.000 0.000 0.000 0.000 0.196
#> SRR1500089 1 0.3409 0.56980 0.700 0.000 0.000 0.000 0.000 0.300
#> SRR1441178 5 0.5933 -0.39084 0.348 0.000 0.000 0.000 0.432 0.220
#> SRR1381396 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1096081 6 0.1245 0.57562 0.000 0.000 0.016 0.000 0.032 0.952
#> SRR1349809 4 0.3866 0.04979 0.000 0.000 0.000 0.516 0.484 0.000
#> SRR1324314 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1092444 1 0.5902 0.10604 0.472 0.000 0.000 0.000 0.268 0.260
#> SRR1382553 6 0.4348 0.38292 0.024 0.000 0.416 0.000 0.000 0.560
#> SRR1075530 5 0.3867 -0.06025 0.000 0.000 0.000 0.488 0.512 0.000
#> SRR1442612 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1360056 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1078164 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1434545 4 0.0000 0.48338 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1398251 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1375866 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1091645 5 0.3862 -0.00601 0.000 0.000 0.000 0.476 0.524 0.000
#> SRR1416636 3 0.4504 0.31989 0.000 0.000 0.536 0.000 0.032 0.432
#> SRR1105441 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1082496 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1093697 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 6 0.4517 -0.09527 0.000 0.000 0.444 0.000 0.032 0.524
#> SRR1076120 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1074410 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1340345 5 0.3857 0.02358 0.000 0.000 0.000 0.468 0.532 0.000
#> SRR1069514 4 0.5277 0.28478 0.000 0.000 0.256 0.592 0.152 0.000
#> SRR1092636 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1365013 4 0.3592 0.51472 0.000 0.000 0.000 0.656 0.344 0.000
#> SRR1073069 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1443137 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1437143 2 0.0000 1.00000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 5 0.5933 -0.39084 0.348 0.000 0.000 0.000 0.432 0.220
#> SRR820234 4 0.4092 0.49112 0.000 0.020 0.000 0.636 0.344 0.000
#> SRR1338079 1 0.5419 0.33503 0.468 0.000 0.100 0.428 0.004 0.000
#> SRR1390094 4 0.0000 0.48338 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1340721 4 0.2697 0.33464 0.000 0.000 0.000 0.812 0.188 0.000
#> SRR1335964 3 0.0000 0.89697 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1086869 3 0.4504 0.31989 0.000 0.000 0.536 0.000 0.032 0.432
#> SRR1453434 1 0.0000 0.88898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1402261 4 0.0146 0.48188 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR657809 5 0.3857 0.02358 0.000 0.000 0.000 0.468 0.532 0.000
#> SRR1093075 6 0.4051 0.69535 0.008 0.000 0.000 0.000 0.432 0.560
#> SRR1433329 6 0.3817 0.70069 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1353418 6 0.0000 0.59473 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1092913 5 0.3857 0.02358 0.000 0.000 0.000 0.468 0.532 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 17780 rows and 119 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 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 0.295 0.600 0.814 0.4315 0.496 0.496
#> 3 3 0.747 0.856 0.923 0.4710 0.770 0.574
#> 4 4 0.863 0.893 0.945 0.0949 0.922 0.786
#> 5 5 0.744 0.760 0.850 0.0668 0.893 0.672
#> 6 6 0.788 0.785 0.873 0.0601 0.917 0.692
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
#> SRR816969 1 0.0000 0.80001 1.000 0.000
#> SRR1335605 2 0.9460 0.60128 0.364 0.636
#> SRR1432014 2 0.9393 0.60741 0.356 0.644
#> SRR1499215 2 0.9393 0.60741 0.356 0.644
#> SRR1460409 1 0.0000 0.80001 1.000 0.000
#> SRR1086441 1 0.0000 0.80001 1.000 0.000
#> SRR1097344 2 0.6887 0.54248 0.184 0.816
#> SRR1081789 2 0.9358 0.60964 0.352 0.648
#> SRR1453005 2 0.0376 0.63978 0.004 0.996
#> SRR1366985 1 0.9710 0.14477 0.600 0.400
#> SRR815280 1 0.0672 0.80124 0.992 0.008
#> SRR1348531 1 0.0938 0.80136 0.988 0.012
#> SRR815845 2 0.9732 0.52677 0.404 0.596
#> SRR1471178 1 0.0000 0.80001 1.000 0.000
#> SRR1080696 2 0.9393 0.60741 0.356 0.644
#> SRR1078684 2 0.9710 0.53517 0.400 0.600
#> SRR1317751 2 0.9754 0.51775 0.408 0.592
#> SRR1435667 2 0.9358 0.60964 0.352 0.648
#> SRR1097905 1 0.9087 0.38494 0.676 0.324
#> SRR1456548 1 0.0938 0.80136 0.988 0.012
#> SRR1075126 1 0.0376 0.80073 0.996 0.004
#> SRR813108 2 0.7453 0.64210 0.212 0.788
#> SRR1479062 2 0.9393 0.60741 0.356 0.644
#> SRR1408703 2 0.9491 0.59108 0.368 0.632
#> SRR1332360 1 0.0938 0.80136 0.988 0.012
#> SRR1098686 1 0.0938 0.80136 0.988 0.012
#> SRR1434228 1 0.8608 0.47521 0.716 0.284
#> SRR1467149 1 0.9170 0.36067 0.668 0.332
#> SRR1399113 2 0.0376 0.63978 0.004 0.996
#> SRR1476507 2 0.8016 0.47307 0.244 0.756
#> SRR1092468 1 0.7815 0.57340 0.768 0.232
#> SRR1441804 1 0.0938 0.80136 0.988 0.012
#> SRR1326100 2 0.0938 0.64181 0.012 0.988
#> SRR1398815 1 0.0000 0.80001 1.000 0.000
#> SRR1436021 1 0.9866 0.00154 0.568 0.432
#> SRR1480083 2 0.0376 0.63978 0.004 0.996
#> SRR1472863 1 0.8386 0.49696 0.732 0.268
#> SRR815542 1 0.0000 0.80001 1.000 0.000
#> SRR1400100 2 0.9393 0.60741 0.356 0.644
#> SRR1312002 1 0.9710 0.14477 0.600 0.400
#> SRR1470253 1 0.9635 0.19022 0.612 0.388
#> SRR1414332 1 0.0000 0.80001 1.000 0.000
#> SRR1069209 1 0.7219 0.62712 0.800 0.200
#> SRR661052 1 0.7528 0.59658 0.784 0.216
#> SRR1308860 1 0.0938 0.80136 0.988 0.012
#> SRR1421159 2 0.9393 0.60842 0.356 0.644
#> SRR1340943 1 0.7139 0.65802 0.804 0.196
#> SRR1078855 1 0.0000 0.80001 1.000 0.000
#> SRR1459465 2 0.0376 0.63978 0.004 0.996
#> SRR816818 2 0.0376 0.63978 0.004 0.996
#> SRR1478679 2 0.9427 0.60541 0.360 0.640
#> SRR1350979 2 0.9460 0.59717 0.364 0.636
#> SRR1458198 1 0.9170 0.36067 0.668 0.332
#> SRR1386910 2 0.6712 0.55421 0.176 0.824
#> SRR1465375 2 0.8909 0.38721 0.308 0.692
#> SRR1323699 2 0.9393 0.60741 0.356 0.644
#> SRR1431139 1 0.9970 -0.15760 0.532 0.468
#> SRR1373964 2 0.9393 0.60741 0.356 0.644
#> SRR1455413 1 0.4939 0.73028 0.892 0.108
#> SRR1437163 1 0.4690 0.74549 0.900 0.100
#> SRR1347343 2 0.9393 0.60741 0.356 0.644
#> SRR1465480 2 0.0376 0.63978 0.004 0.996
#> SRR1489631 1 0.0938 0.80136 0.988 0.012
#> SRR1086514 2 0.2043 0.64576 0.032 0.968
#> SRR1430928 1 0.0000 0.80001 1.000 0.000
#> SRR1310939 2 0.9427 0.60270 0.360 0.640
#> SRR1344294 2 0.0376 0.63978 0.004 0.996
#> SRR1099402 1 0.0000 0.80001 1.000 0.000
#> SRR1468118 1 0.9977 -0.17661 0.528 0.472
#> SRR1486348 1 0.0000 0.80001 1.000 0.000
#> SRR1488770 2 0.0376 0.63978 0.004 0.996
#> SRR1083732 1 0.0938 0.80136 0.988 0.012
#> SRR1456611 2 0.0376 0.63978 0.004 0.996
#> SRR1080318 1 0.0938 0.80136 0.988 0.012
#> SRR1500089 1 0.9129 0.37164 0.672 0.328
#> SRR1441178 1 0.0000 0.80001 1.000 0.000
#> SRR1381396 1 0.0000 0.80001 1.000 0.000
#> SRR1096081 2 0.9710 0.53517 0.400 0.600
#> SRR1349809 2 0.4431 0.62489 0.092 0.908
#> SRR1324314 2 0.9933 0.42024 0.452 0.548
#> SRR1092444 1 0.1843 0.79273 0.972 0.028
#> SRR1382553 1 0.9933 -0.08866 0.548 0.452
#> SRR1075530 2 0.6247 0.58929 0.156 0.844
#> SRR1442612 2 0.9393 0.60741 0.356 0.644
#> SRR1360056 1 0.9491 0.25101 0.632 0.368
#> SRR1078164 1 0.0000 0.80001 1.000 0.000
#> SRR1434545 1 0.8499 0.51953 0.724 0.276
#> SRR1398251 1 0.0000 0.80001 1.000 0.000
#> SRR1375866 1 0.0000 0.80001 1.000 0.000
#> SRR1091645 2 0.7950 0.47708 0.240 0.760
#> SRR1416636 2 0.9427 0.60270 0.360 0.640
#> SRR1105441 2 0.9393 0.60741 0.356 0.644
#> SRR1082496 2 0.0376 0.63978 0.004 0.996
#> SRR1315353 2 0.7674 0.64082 0.224 0.776
#> SRR1093697 2 0.0376 0.63978 0.004 0.996
#> SRR1077429 2 0.9998 0.28689 0.492 0.508
#> SRR1076120 1 0.9087 0.38207 0.676 0.324
#> SRR1074410 1 0.0000 0.80001 1.000 0.000
#> SRR1340345 2 0.7950 0.47708 0.240 0.760
#> SRR1069514 2 0.9393 0.60842 0.356 0.644
#> SRR1092636 2 0.9732 0.52677 0.404 0.596
#> SRR1365013 2 0.2236 0.64634 0.036 0.964
#> SRR1073069 1 0.0938 0.80136 0.988 0.012
#> SRR1443137 1 0.0000 0.80001 1.000 0.000
#> SRR1437143 2 0.0376 0.63978 0.004 0.996
#> SRR1091990 1 0.0000 0.80001 1.000 0.000
#> SRR820234 2 0.1184 0.64308 0.016 0.984
#> SRR1338079 1 0.0938 0.80136 0.988 0.012
#> SRR1390094 1 0.9460 0.31206 0.636 0.364
#> SRR1340721 2 0.8713 0.41524 0.292 0.708
#> SRR1335964 2 0.9710 0.53517 0.400 0.600
#> SRR1086869 2 0.9754 0.51775 0.408 0.592
#> SRR1453434 1 0.1184 0.79960 0.984 0.016
#> SRR1402261 1 0.6048 0.71031 0.852 0.148
#> SRR657809 2 0.7219 0.52859 0.200 0.800
#> SRR1093075 1 0.0000 0.80001 1.000 0.000
#> SRR1433329 1 0.0672 0.80124 0.992 0.008
#> SRR1353418 2 0.9977 0.35505 0.472 0.528
#> SRR1092913 2 0.7950 0.47708 0.240 0.760
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1335605 3 0.1525 0.9289 0.004 0.032 0.964
#> SRR1432014 3 0.0829 0.9391 0.004 0.012 0.984
#> SRR1499215 3 0.0237 0.9372 0.004 0.000 0.996
#> SRR1460409 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1086441 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1097344 2 0.5465 0.7646 0.000 0.712 0.288
#> SRR1081789 3 0.1267 0.9343 0.004 0.024 0.972
#> SRR1453005 2 0.1753 0.8301 0.000 0.952 0.048
#> SRR1366985 3 0.0892 0.9315 0.020 0.000 0.980
#> SRR815280 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1348531 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR815845 3 0.0892 0.9328 0.000 0.020 0.980
#> SRR1471178 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1080696 3 0.0829 0.9391 0.004 0.012 0.984
#> SRR1078684 3 0.0237 0.9372 0.004 0.000 0.996
#> SRR1317751 3 0.0592 0.9381 0.000 0.012 0.988
#> SRR1435667 3 0.3193 0.8706 0.004 0.100 0.896
#> SRR1097905 1 0.5826 0.7069 0.764 0.032 0.204
#> SRR1456548 1 0.0237 0.9248 0.996 0.000 0.004
#> SRR1075126 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR813108 3 0.5327 0.6369 0.000 0.272 0.728
#> SRR1479062 3 0.0592 0.9381 0.000 0.012 0.988
#> SRR1408703 3 0.0592 0.9381 0.000 0.012 0.988
#> SRR1332360 1 0.6291 0.0528 0.532 0.000 0.468
#> SRR1098686 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1434228 3 0.5327 0.6039 0.272 0.000 0.728
#> SRR1467149 1 0.5521 0.7401 0.788 0.032 0.180
#> SRR1399113 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1476507 2 0.5497 0.7649 0.000 0.708 0.292
#> SRR1092468 1 0.2584 0.8845 0.928 0.008 0.064
#> SRR1441804 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1326100 2 0.1753 0.8301 0.000 0.952 0.048
#> SRR1398815 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1436021 1 0.9984 -0.2228 0.356 0.308 0.336
#> SRR1480083 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1472863 1 0.6542 0.6734 0.736 0.060 0.204
#> SRR815542 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1400100 3 0.0661 0.9391 0.004 0.008 0.988
#> SRR1312002 3 0.1860 0.9023 0.052 0.000 0.948
#> SRR1470253 3 0.0892 0.9316 0.020 0.000 0.980
#> SRR1414332 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1069209 3 0.5397 0.5906 0.280 0.000 0.720
#> SRR661052 1 0.5092 0.7508 0.804 0.020 0.176
#> SRR1308860 1 0.0237 0.9248 0.996 0.000 0.004
#> SRR1421159 3 0.1289 0.9268 0.000 0.032 0.968
#> SRR1340943 1 0.4357 0.8434 0.868 0.080 0.052
#> SRR1078855 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1459465 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1478679 3 0.0475 0.9379 0.004 0.004 0.992
#> SRR1350979 3 0.0661 0.9391 0.004 0.008 0.988
#> SRR1458198 1 0.2773 0.8890 0.928 0.024 0.048
#> SRR1386910 2 0.5560 0.7566 0.000 0.700 0.300
#> SRR1465375 2 0.7388 0.7420 0.100 0.692 0.208
#> SRR1323699 3 0.0237 0.9372 0.004 0.000 0.996
#> SRR1431139 3 0.0424 0.9371 0.008 0.000 0.992
#> SRR1373964 3 0.0829 0.9391 0.004 0.012 0.984
#> SRR1455413 1 0.1711 0.9058 0.960 0.008 0.032
#> SRR1437163 1 0.3967 0.8576 0.884 0.072 0.044
#> SRR1347343 3 0.0475 0.9386 0.004 0.004 0.992
#> SRR1465480 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1489631 1 0.0983 0.9177 0.980 0.016 0.004
#> SRR1086514 3 0.2959 0.8519 0.000 0.100 0.900
#> SRR1430928 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1310939 3 0.0592 0.9381 0.000 0.012 0.988
#> SRR1344294 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1099402 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1468118 3 0.2031 0.9168 0.032 0.016 0.952
#> SRR1486348 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1083732 1 0.0237 0.9248 0.996 0.000 0.004
#> SRR1456611 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1080318 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1500089 1 0.2774 0.8827 0.920 0.008 0.072
#> SRR1441178 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1381396 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1096081 3 0.0661 0.9389 0.004 0.008 0.988
#> SRR1349809 2 0.5431 0.7659 0.000 0.716 0.284
#> SRR1324314 3 0.0592 0.9351 0.012 0.000 0.988
#> SRR1092444 1 0.0237 0.9249 0.996 0.000 0.004
#> SRR1382553 3 0.0424 0.9371 0.008 0.000 0.992
#> SRR1075530 2 0.5560 0.7566 0.000 0.700 0.300
#> SRR1442612 3 0.0829 0.9391 0.004 0.012 0.984
#> SRR1360056 3 0.4784 0.7006 0.200 0.004 0.796
#> SRR1078164 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1434545 1 0.5981 0.7575 0.788 0.080 0.132
#> SRR1398251 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1375866 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1091645 2 0.5497 0.7649 0.000 0.708 0.292
#> SRR1416636 3 0.0592 0.9381 0.000 0.012 0.988
#> SRR1105441 3 0.0424 0.9378 0.000 0.008 0.992
#> SRR1082496 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1315353 3 0.2261 0.9046 0.000 0.068 0.932
#> SRR1093697 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1077429 3 0.1950 0.9125 0.040 0.008 0.952
#> SRR1076120 1 0.2680 0.8849 0.924 0.008 0.068
#> SRR1074410 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1340345 2 0.5497 0.7649 0.000 0.708 0.292
#> SRR1069514 3 0.1647 0.9259 0.004 0.036 0.960
#> SRR1092636 3 0.0475 0.9376 0.004 0.004 0.992
#> SRR1365013 2 0.5560 0.7518 0.000 0.700 0.300
#> SRR1073069 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1443137 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1437143 2 0.0000 0.8335 0.000 1.000 0.000
#> SRR1091990 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR820234 3 0.5733 0.5550 0.000 0.324 0.676
#> SRR1338079 1 0.2590 0.8807 0.924 0.072 0.004
#> SRR1390094 1 0.6922 0.6559 0.720 0.080 0.200
#> SRR1340721 2 0.7344 0.7438 0.100 0.696 0.204
#> SRR1335964 3 0.0661 0.9389 0.004 0.008 0.988
#> SRR1086869 3 0.0661 0.9389 0.004 0.008 0.988
#> SRR1453434 1 0.0475 0.9235 0.992 0.004 0.004
#> SRR1402261 1 0.3678 0.8622 0.892 0.080 0.028
#> SRR657809 2 0.5497 0.7649 0.000 0.708 0.292
#> SRR1093075 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1433329 1 0.0000 0.9262 1.000 0.000 0.000
#> SRR1353418 3 0.0592 0.9361 0.012 0.000 0.988
#> SRR1092913 2 0.5497 0.7649 0.000 0.708 0.292
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1335605 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1432014 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1499215 3 0.0469 0.941 0.000 0.000 0.988 0.012
#> SRR1460409 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1086441 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.0927 0.770 0.000 0.016 0.008 0.976
#> SRR1081789 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1453005 2 0.0672 0.981 0.000 0.984 0.008 0.008
#> SRR1366985 3 0.0779 0.936 0.016 0.000 0.980 0.004
#> SRR815280 1 0.0524 0.972 0.988 0.000 0.004 0.008
#> SRR1348531 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR815845 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1471178 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0188 0.942 0.000 0.000 0.996 0.004
#> SRR1078684 3 0.0469 0.941 0.000 0.000 0.988 0.012
#> SRR1317751 3 0.0376 0.942 0.004 0.000 0.992 0.004
#> SRR1435667 3 0.3105 0.852 0.000 0.120 0.868 0.012
#> SRR1097905 4 0.4485 0.668 0.248 0.000 0.012 0.740
#> SRR1456548 1 0.0469 0.970 0.988 0.000 0.012 0.000
#> SRR1075126 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR813108 3 0.3710 0.776 0.000 0.192 0.804 0.004
#> SRR1479062 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1408703 3 0.0188 0.942 0.000 0.000 0.996 0.004
#> SRR1332360 1 0.0469 0.970 0.988 0.000 0.012 0.000
#> SRR1098686 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR1434228 3 0.4088 0.676 0.232 0.000 0.764 0.004
#> SRR1467149 1 0.3271 0.824 0.856 0.000 0.012 0.132
#> SRR1399113 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.0927 0.770 0.000 0.016 0.008 0.976
#> SRR1092468 1 0.0707 0.967 0.980 0.000 0.020 0.000
#> SRR1441804 1 0.0336 0.975 0.992 0.000 0.008 0.000
#> SRR1326100 2 0.0657 0.978 0.000 0.984 0.012 0.004
#> SRR1398815 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1436021 4 0.4679 0.510 0.000 0.000 0.352 0.648
#> SRR1480083 2 0.0188 0.993 0.000 0.996 0.000 0.004
#> SRR1472863 1 0.1677 0.936 0.948 0.000 0.012 0.040
#> SRR815542 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1400100 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1312002 3 0.1661 0.907 0.052 0.000 0.944 0.004
#> SRR1470253 3 0.0895 0.934 0.020 0.000 0.976 0.004
#> SRR1414332 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1069209 3 0.4122 0.670 0.236 0.000 0.760 0.004
#> SRR661052 1 0.3047 0.847 0.872 0.000 0.012 0.116
#> SRR1308860 1 0.0469 0.970 0.988 0.000 0.012 0.000
#> SRR1421159 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1340943 4 0.4212 0.691 0.216 0.000 0.012 0.772
#> SRR1078855 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1478679 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1350979 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1458198 1 0.0469 0.973 0.988 0.000 0.012 0.000
#> SRR1386910 4 0.3450 0.726 0.000 0.156 0.008 0.836
#> SRR1465375 4 0.2727 0.767 0.004 0.084 0.012 0.900
#> SRR1323699 3 0.0469 0.941 0.000 0.000 0.988 0.012
#> SRR1431139 3 0.0376 0.942 0.004 0.000 0.992 0.004
#> SRR1373964 3 0.0469 0.941 0.000 0.000 0.988 0.012
#> SRR1455413 1 0.0592 0.970 0.984 0.000 0.016 0.000
#> SRR1437163 4 0.4820 0.636 0.296 0.000 0.012 0.692
#> SRR1347343 3 0.0469 0.941 0.000 0.000 0.988 0.012
#> SRR1465480 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0657 0.968 0.984 0.000 0.012 0.004
#> SRR1086514 3 0.0524 0.940 0.000 0.004 0.988 0.008
#> SRR1430928 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1310939 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1344294 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1468118 3 0.3946 0.781 0.020 0.000 0.812 0.168
#> SRR1486348 1 0.0188 0.975 0.996 0.000 0.004 0.000
#> SRR1488770 2 0.0188 0.993 0.000 0.996 0.000 0.004
#> SRR1083732 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR1456611 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR1500089 1 0.0469 0.973 0.988 0.000 0.012 0.000
#> SRR1441178 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR1381396 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1096081 3 0.0376 0.942 0.004 0.000 0.992 0.004
#> SRR1349809 4 0.4137 0.674 0.000 0.208 0.012 0.780
#> SRR1324314 3 0.0657 0.938 0.012 0.000 0.984 0.004
#> SRR1092444 1 0.0469 0.973 0.988 0.000 0.012 0.000
#> SRR1382553 3 0.0657 0.937 0.012 0.000 0.984 0.004
#> SRR1075530 4 0.2943 0.753 0.000 0.032 0.076 0.892
#> SRR1442612 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1360056 3 0.4800 0.475 0.340 0.000 0.656 0.004
#> SRR1078164 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1434545 4 0.4318 0.697 0.208 0.004 0.012 0.776
#> SRR1398251 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1375866 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR1091645 4 0.0927 0.770 0.000 0.016 0.008 0.976
#> SRR1416636 3 0.0188 0.942 0.000 0.000 0.996 0.004
#> SRR1105441 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1082496 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.2466 0.876 0.000 0.096 0.900 0.004
#> SRR1093697 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.2888 0.817 0.124 0.000 0.872 0.004
#> SRR1076120 1 0.0592 0.970 0.984 0.000 0.016 0.000
#> SRR1074410 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1340345 4 0.2124 0.770 0.000 0.068 0.008 0.924
#> SRR1069514 3 0.0188 0.943 0.000 0.000 0.996 0.004
#> SRR1092636 3 0.0376 0.942 0.004 0.000 0.992 0.004
#> SRR1365013 4 0.6107 0.581 0.000 0.088 0.264 0.648
#> SRR1073069 1 0.0188 0.976 0.996 0.000 0.004 0.000
#> SRR1443137 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1437143 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR820234 3 0.4188 0.705 0.000 0.244 0.752 0.004
#> SRR1338079 1 0.5127 0.312 0.632 0.000 0.012 0.356
#> SRR1390094 4 0.5408 0.169 0.488 0.000 0.012 0.500
#> SRR1340721 4 0.2989 0.762 0.004 0.100 0.012 0.884
#> SRR1335964 3 0.0376 0.942 0.004 0.000 0.992 0.004
#> SRR1086869 3 0.0376 0.942 0.004 0.000 0.992 0.004
#> SRR1453434 1 0.0336 0.973 0.992 0.000 0.008 0.000
#> SRR1402261 4 0.4248 0.689 0.220 0.000 0.012 0.768
#> SRR657809 4 0.2976 0.751 0.000 0.120 0.008 0.872
#> SRR1093075 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1433329 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1353418 3 0.0524 0.940 0.008 0.000 0.988 0.004
#> SRR1092913 4 0.1807 0.771 0.000 0.052 0.008 0.940
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.0579 0.8983 0.984 0.000 0.008 0.000 0.008
#> SRR1335605 3 0.3796 0.8687 0.000 0.000 0.700 0.000 0.300
#> SRR1432014 3 0.3752 0.8963 0.000 0.000 0.708 0.000 0.292
#> SRR1499215 3 0.4275 0.8629 0.020 0.000 0.696 0.000 0.284
#> SRR1460409 1 0.0162 0.9008 0.996 0.000 0.004 0.000 0.000
#> SRR1086441 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> SRR1097344 4 0.0566 0.7600 0.000 0.012 0.004 0.984 0.000
#> SRR1081789 3 0.3612 0.8961 0.000 0.000 0.732 0.000 0.268
#> SRR1453005 2 0.2407 0.8908 0.000 0.896 0.012 0.088 0.004
#> SRR1366985 5 0.6368 0.2988 0.400 0.000 0.164 0.000 0.436
#> SRR815280 1 0.0963 0.8933 0.964 0.000 0.036 0.000 0.000
#> SRR1348531 1 0.0451 0.8996 0.988 0.000 0.004 0.000 0.008
#> SRR815845 3 0.3684 0.8959 0.000 0.000 0.720 0.000 0.280
#> SRR1471178 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> SRR1080696 5 0.2773 0.5382 0.000 0.000 0.164 0.000 0.836
#> SRR1078684 3 0.4638 0.7938 0.028 0.000 0.648 0.000 0.324
#> SRR1317751 5 0.0771 0.6678 0.004 0.000 0.020 0.000 0.976
#> SRR1435667 3 0.3957 0.8954 0.000 0.008 0.712 0.000 0.280
#> SRR1097905 4 0.7329 0.4804 0.300 0.000 0.116 0.492 0.092
#> SRR1456548 1 0.3819 0.7285 0.772 0.000 0.208 0.004 0.016
#> SRR1075126 1 0.0880 0.8920 0.968 0.000 0.000 0.000 0.032
#> SRR813108 3 0.6138 0.6308 0.000 0.176 0.552 0.000 0.272
#> SRR1479062 3 0.4088 0.8074 0.000 0.000 0.632 0.000 0.368
#> SRR1408703 5 0.1671 0.6413 0.000 0.000 0.076 0.000 0.924
#> SRR1332360 1 0.1469 0.8807 0.948 0.000 0.016 0.000 0.036
#> SRR1098686 1 0.0880 0.8920 0.968 0.000 0.000 0.000 0.032
#> SRR1434228 1 0.4382 0.5266 0.688 0.000 0.024 0.000 0.288
#> SRR1467149 1 0.6493 0.5070 0.632 0.000 0.104 0.176 0.088
#> SRR1399113 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0566 0.7600 0.000 0.012 0.004 0.984 0.000
#> SRR1092468 1 0.2377 0.8244 0.872 0.000 0.000 0.000 0.128
#> SRR1441804 1 0.0451 0.8993 0.988 0.000 0.004 0.000 0.008
#> SRR1326100 2 0.2291 0.9051 0.000 0.908 0.012 0.072 0.008
#> SRR1398815 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> SRR1436021 4 0.5267 0.5591 0.016 0.000 0.156 0.712 0.116
#> SRR1480083 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1472863 1 0.6889 0.2921 0.528 0.000 0.280 0.040 0.152
#> SRR815542 1 0.0000 0.9000 1.000 0.000 0.000 0.000 0.000
#> SRR1400100 3 0.3684 0.8959 0.000 0.000 0.720 0.000 0.280
#> SRR1312002 5 0.5793 0.4006 0.292 0.000 0.124 0.000 0.584
#> SRR1470253 1 0.5088 0.0901 0.528 0.000 0.036 0.000 0.436
#> SRR1414332 1 0.0000 0.9000 1.000 0.000 0.000 0.000 0.000
#> SRR1069209 1 0.3821 0.6635 0.764 0.000 0.020 0.000 0.216
#> SRR661052 1 0.4714 0.7006 0.756 0.000 0.032 0.044 0.168
#> SRR1308860 1 0.3730 0.7745 0.808 0.000 0.152 0.004 0.036
#> SRR1421159 3 0.3684 0.8959 0.000 0.000 0.720 0.000 0.280
#> SRR1340943 4 0.6498 0.6431 0.164 0.000 0.224 0.584 0.028
#> SRR1078855 1 0.0290 0.9004 0.992 0.000 0.008 0.000 0.000
#> SRR1459465 2 0.0162 0.9809 0.000 0.996 0.000 0.004 0.000
#> SRR816818 2 0.0162 0.9809 0.000 0.996 0.000 0.004 0.000
#> SRR1478679 3 0.3612 0.8931 0.000 0.000 0.732 0.000 0.268
#> SRR1350979 3 0.3752 0.8963 0.000 0.000 0.708 0.000 0.292
#> SRR1458198 1 0.2068 0.8573 0.904 0.000 0.004 0.000 0.092
#> SRR1386910 4 0.2179 0.7377 0.000 0.100 0.004 0.896 0.000
#> SRR1465375 4 0.3338 0.7550 0.000 0.068 0.076 0.852 0.004
#> SRR1323699 3 0.3707 0.8900 0.000 0.000 0.716 0.000 0.284
#> SRR1431139 3 0.5083 0.5286 0.036 0.000 0.532 0.000 0.432
#> SRR1373964 3 0.3661 0.8944 0.000 0.000 0.724 0.000 0.276
#> SRR1455413 1 0.1197 0.8840 0.952 0.000 0.000 0.000 0.048
#> SRR1437163 4 0.6919 0.6082 0.208 0.000 0.220 0.536 0.036
#> SRR1347343 3 0.3684 0.8926 0.000 0.000 0.720 0.000 0.280
#> SRR1465480 2 0.0162 0.9809 0.000 0.996 0.000 0.004 0.000
#> SRR1489631 1 0.5686 0.5699 0.664 0.000 0.224 0.084 0.028
#> SRR1086514 3 0.5059 0.7423 0.000 0.008 0.712 0.092 0.188
#> SRR1430928 1 0.0451 0.9004 0.988 0.000 0.008 0.000 0.004
#> SRR1310939 3 0.3796 0.8943 0.000 0.000 0.700 0.000 0.300
#> SRR1344294 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0324 0.9001 0.992 0.000 0.004 0.000 0.004
#> SRR1468118 5 0.3606 0.5758 0.008 0.000 0.152 0.024 0.816
#> SRR1486348 1 0.1461 0.8878 0.952 0.000 0.028 0.004 0.016
#> SRR1488770 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1083732 1 0.0794 0.8958 0.972 0.000 0.000 0.000 0.028
#> SRR1456611 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.0000 0.9000 1.000 0.000 0.000 0.000 0.000
#> SRR1500089 1 0.2020 0.8529 0.900 0.000 0.000 0.000 0.100
#> SRR1441178 1 0.0404 0.8999 0.988 0.000 0.012 0.000 0.000
#> SRR1381396 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> SRR1096081 5 0.0771 0.6678 0.004 0.000 0.020 0.000 0.976
#> SRR1349809 4 0.3783 0.5939 0.000 0.252 0.000 0.740 0.008
#> SRR1324314 5 0.5313 -0.0737 0.056 0.000 0.388 0.000 0.556
#> SRR1092444 1 0.0404 0.8997 0.988 0.000 0.000 0.000 0.012
#> SRR1382553 5 0.6659 0.1432 0.248 0.000 0.316 0.000 0.436
#> SRR1075530 4 0.3285 0.7361 0.000 0.032 0.036 0.868 0.064
#> SRR1442612 3 0.3774 0.8951 0.000 0.000 0.704 0.000 0.296
#> SRR1360056 1 0.6694 -0.3130 0.408 0.000 0.244 0.000 0.348
#> SRR1078164 1 0.0579 0.8983 0.984 0.000 0.008 0.000 0.008
#> SRR1434545 4 0.4494 0.7140 0.028 0.000 0.232 0.728 0.012
#> SRR1398251 1 0.0451 0.8991 0.988 0.000 0.008 0.000 0.004
#> SRR1375866 1 0.0451 0.8996 0.988 0.000 0.004 0.000 0.008
#> SRR1091645 4 0.0566 0.7600 0.000 0.012 0.004 0.984 0.000
#> SRR1416636 5 0.2179 0.6093 0.000 0.000 0.112 0.000 0.888
#> SRR1105441 3 0.3752 0.8959 0.000 0.000 0.708 0.000 0.292
#> SRR1082496 2 0.0162 0.9809 0.000 0.996 0.000 0.004 0.000
#> SRR1315353 3 0.4138 0.8812 0.000 0.016 0.708 0.000 0.276
#> SRR1093697 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 5 0.1281 0.6603 0.032 0.000 0.012 0.000 0.956
#> SRR1076120 1 0.2329 0.8357 0.876 0.000 0.000 0.000 0.124
#> SRR1074410 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> SRR1340345 4 0.1270 0.7567 0.000 0.052 0.000 0.948 0.000
#> SRR1069514 3 0.3612 0.8931 0.000 0.000 0.732 0.000 0.268
#> SRR1092636 5 0.4876 -0.1263 0.028 0.000 0.396 0.000 0.576
#> SRR1365013 4 0.6157 0.4978 0.000 0.060 0.268 0.612 0.060
#> SRR1073069 1 0.0807 0.8954 0.976 0.000 0.012 0.000 0.012
#> SRR1443137 1 0.0579 0.8983 0.984 0.000 0.008 0.000 0.008
#> SRR1437143 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0290 0.8996 0.992 0.000 0.008 0.000 0.000
#> SRR820234 3 0.7200 0.3886 0.000 0.236 0.540 0.088 0.136
#> SRR1338079 4 0.7238 0.5378 0.268 0.000 0.228 0.468 0.036
#> SRR1390094 4 0.5550 0.7103 0.068 0.000 0.280 0.636 0.016
#> SRR1340721 4 0.3520 0.7507 0.000 0.080 0.076 0.840 0.004
#> SRR1335964 5 0.2230 0.6201 0.000 0.000 0.116 0.000 0.884
#> SRR1086869 5 0.0771 0.6678 0.004 0.000 0.020 0.000 0.976
#> SRR1453434 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> SRR1402261 4 0.6481 0.6468 0.156 0.000 0.232 0.584 0.028
#> SRR657809 4 0.1851 0.7443 0.000 0.088 0.000 0.912 0.000
#> SRR1093075 1 0.0451 0.8991 0.988 0.000 0.008 0.000 0.004
#> SRR1433329 1 0.0579 0.8983 0.984 0.000 0.008 0.000 0.008
#> SRR1353418 5 0.2793 0.6398 0.088 0.000 0.036 0.000 0.876
#> SRR1092913 4 0.1121 0.7582 0.000 0.044 0.000 0.956 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 1 0.0291 0.9148 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1335605 3 0.0914 0.8421 0.000 0.000 0.968 0.016 0.016 0.000
#> SRR1432014 3 0.1088 0.8419 0.000 0.000 0.960 0.000 0.024 0.016
#> SRR1499215 3 0.2230 0.8106 0.000 0.000 0.892 0.000 0.024 0.084
#> SRR1460409 1 0.0891 0.9089 0.968 0.000 0.000 0.000 0.008 0.024
#> SRR1086441 1 0.0363 0.9142 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1097344 4 0.1745 0.8727 0.000 0.012 0.000 0.920 0.000 0.068
#> SRR1081789 3 0.1584 0.8279 0.000 0.000 0.928 0.064 0.008 0.000
#> SRR1453005 2 0.4838 0.3081 0.000 0.564 0.064 0.372 0.000 0.000
#> SRR1366985 1 0.6308 0.2405 0.540 0.000 0.104 0.000 0.272 0.084
#> SRR815280 1 0.1049 0.9059 0.960 0.000 0.000 0.000 0.008 0.032
#> SRR1348531 1 0.0260 0.9149 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR815845 3 0.1584 0.8245 0.000 0.000 0.928 0.064 0.008 0.000
#> SRR1471178 1 0.0363 0.9142 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1080696 5 0.2527 0.6952 0.000 0.000 0.168 0.000 0.832 0.000
#> SRR1078684 3 0.2737 0.7921 0.004 0.000 0.868 0.000 0.044 0.084
#> SRR1317751 5 0.1049 0.7582 0.008 0.000 0.032 0.000 0.960 0.000
#> SRR1435667 3 0.1995 0.8378 0.000 0.000 0.912 0.000 0.036 0.052
#> SRR1097905 6 0.4579 0.7360 0.072 0.000 0.004 0.088 0.072 0.764
#> SRR1456548 6 0.3584 0.6895 0.308 0.000 0.000 0.004 0.000 0.688
#> SRR1075126 1 0.0405 0.9137 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR813108 3 0.4780 0.6629 0.000 0.168 0.720 0.068 0.044 0.000
#> SRR1479062 3 0.2730 0.7169 0.000 0.000 0.808 0.000 0.192 0.000
#> SRR1408703 5 0.1714 0.7492 0.000 0.000 0.092 0.000 0.908 0.000
#> SRR1332360 1 0.2585 0.8211 0.880 0.000 0.024 0.000 0.012 0.084
#> SRR1098686 1 0.0146 0.9147 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1434228 1 0.5188 0.5571 0.676 0.000 0.044 0.000 0.196 0.084
#> SRR1467149 6 0.4978 0.7337 0.224 0.000 0.004 0.044 0.044 0.684
#> SRR1399113 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1476507 4 0.1745 0.8727 0.000 0.012 0.000 0.920 0.000 0.068
#> SRR1092468 1 0.2773 0.7574 0.828 0.000 0.004 0.000 0.164 0.004
#> SRR1441804 1 0.0260 0.9142 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1326100 2 0.3432 0.7518 0.000 0.800 0.052 0.148 0.000 0.000
#> SRR1398815 1 0.0458 0.9133 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1436021 3 0.3338 0.7488 0.004 0.000 0.832 0.096 0.004 0.064
#> SRR1480083 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1472863 6 0.7066 0.4893 0.148 0.000 0.212 0.024 0.096 0.520
#> SRR815542 1 0.0508 0.9132 0.984 0.000 0.000 0.000 0.004 0.012
#> SRR1400100 3 0.0547 0.8413 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1312002 5 0.6578 0.3284 0.348 0.000 0.112 0.000 0.456 0.084
#> SRR1470253 1 0.5981 0.0526 0.488 0.000 0.048 0.000 0.380 0.084
#> SRR1414332 1 0.0405 0.9150 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR1069209 1 0.4149 0.7213 0.784 0.000 0.036 0.000 0.096 0.084
#> SRR661052 6 0.5195 0.6578 0.296 0.000 0.008 0.028 0.044 0.624
#> SRR1308860 6 0.3652 0.6746 0.324 0.000 0.000 0.004 0.000 0.672
#> SRR1421159 3 0.1531 0.8264 0.000 0.000 0.928 0.068 0.004 0.000
#> SRR1340943 6 0.2841 0.6905 0.012 0.000 0.000 0.164 0.000 0.824
#> SRR1078855 1 0.0405 0.9150 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR1459465 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 3 0.1462 0.8325 0.000 0.000 0.936 0.000 0.008 0.056
#> SRR1350979 3 0.1594 0.8337 0.000 0.000 0.932 0.000 0.016 0.052
#> SRR1458198 1 0.1588 0.8677 0.924 0.000 0.000 0.000 0.072 0.004
#> SRR1386910 4 0.1700 0.8404 0.000 0.080 0.004 0.916 0.000 0.000
#> SRR1465375 4 0.3279 0.8433 0.000 0.028 0.000 0.796 0.000 0.176
#> SRR1323699 3 0.2039 0.8180 0.000 0.000 0.904 0.000 0.020 0.076
#> SRR1431139 3 0.4354 0.5739 0.004 0.000 0.720 0.000 0.196 0.080
#> SRR1373964 3 0.1757 0.8240 0.000 0.000 0.916 0.000 0.008 0.076
#> SRR1455413 1 0.0603 0.9088 0.980 0.000 0.000 0.000 0.016 0.004
#> SRR1437163 6 0.2997 0.7406 0.060 0.000 0.000 0.096 0.000 0.844
#> SRR1347343 3 0.2255 0.8231 0.000 0.000 0.892 0.000 0.028 0.080
#> SRR1465480 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489631 6 0.2814 0.7586 0.172 0.000 0.000 0.008 0.000 0.820
#> SRR1086514 3 0.4617 0.1894 0.000 0.016 0.544 0.424 0.016 0.000
#> SRR1430928 1 0.0405 0.9149 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1310939 3 0.0937 0.8404 0.000 0.000 0.960 0.000 0.040 0.000
#> SRR1344294 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1099402 1 0.0458 0.9133 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1468118 5 0.4412 0.5824 0.012 0.000 0.320 0.008 0.648 0.012
#> SRR1486348 1 0.2340 0.7646 0.852 0.000 0.000 0.000 0.000 0.148
#> SRR1488770 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083732 1 0.0405 0.9127 0.988 0.000 0.008 0.000 0.000 0.004
#> SRR1456611 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1080318 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500089 1 0.1556 0.8627 0.920 0.000 0.000 0.000 0.080 0.000
#> SRR1441178 1 0.0405 0.9151 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1381396 1 0.0458 0.9133 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1096081 5 0.1049 0.7582 0.008 0.000 0.032 0.000 0.960 0.000
#> SRR1349809 4 0.3626 0.8294 0.000 0.144 0.000 0.788 0.000 0.068
#> SRR1324314 5 0.6109 0.4956 0.068 0.000 0.324 0.000 0.524 0.084
#> SRR1092444 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1382553 1 0.6431 0.2434 0.540 0.000 0.132 0.000 0.244 0.084
#> SRR1075530 4 0.3225 0.8688 0.000 0.036 0.048 0.852 0.000 0.064
#> SRR1442612 3 0.0632 0.8404 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR1360056 3 0.6072 0.3093 0.184 0.000 0.588 0.000 0.172 0.056
#> SRR1078164 1 0.0260 0.9147 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1434545 6 0.2841 0.6905 0.012 0.000 0.000 0.164 0.000 0.824
#> SRR1398251 1 0.0405 0.9150 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR1375866 1 0.0363 0.9152 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1091645 4 0.1745 0.8727 0.000 0.012 0.000 0.920 0.000 0.068
#> SRR1416636 5 0.2048 0.7362 0.000 0.000 0.120 0.000 0.880 0.000
#> SRR1105441 3 0.0632 0.8404 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR1082496 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1315353 3 0.2237 0.8125 0.000 0.000 0.896 0.068 0.036 0.000
#> SRR1093697 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077429 5 0.1434 0.7562 0.012 0.000 0.048 0.000 0.940 0.000
#> SRR1076120 1 0.1753 0.8577 0.912 0.000 0.004 0.000 0.084 0.000
#> SRR1074410 1 0.0458 0.9133 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1340345 4 0.2448 0.8814 0.000 0.052 0.000 0.884 0.000 0.064
#> SRR1069514 3 0.1584 0.8279 0.000 0.000 0.928 0.064 0.008 0.000
#> SRR1092636 5 0.5141 0.5497 0.008 0.000 0.316 0.000 0.592 0.084
#> SRR1365013 4 0.4649 0.2980 0.000 0.048 0.380 0.572 0.000 0.000
#> SRR1073069 1 0.1138 0.8962 0.960 0.000 0.004 0.000 0.012 0.024
#> SRR1443137 1 0.0260 0.9147 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1437143 2 0.0000 0.9453 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.0291 0.9148 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR820234 3 0.5673 0.5577 0.000 0.176 0.628 0.156 0.040 0.000
#> SRR1338079 6 0.3261 0.7588 0.104 0.000 0.000 0.072 0.000 0.824
#> SRR1390094 6 0.2928 0.7396 0.056 0.000 0.004 0.084 0.000 0.856
#> SRR1340721 4 0.3283 0.8523 0.000 0.036 0.000 0.804 0.000 0.160
#> SRR1335964 5 0.3373 0.7014 0.008 0.000 0.248 0.000 0.744 0.000
#> SRR1086869 5 0.1049 0.7582 0.008 0.000 0.032 0.000 0.960 0.000
#> SRR1453434 1 0.0405 0.9139 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR1402261 6 0.2805 0.6921 0.012 0.000 0.000 0.160 0.000 0.828
#> SRR657809 4 0.2905 0.8715 0.000 0.084 0.000 0.852 0.000 0.064
#> SRR1093075 1 0.0405 0.9149 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1433329 1 0.0260 0.9147 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1353418 5 0.3632 0.7220 0.044 0.000 0.048 0.000 0.824 0.084
#> SRR1092913 4 0.2179 0.8815 0.000 0.036 0.000 0.900 0.000 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["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 17780 rows and 119 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 0.948 0.941 0.976 0.4046 0.596 0.596
#> 3 3 0.716 0.857 0.922 0.5916 0.677 0.489
#> 4 4 0.823 0.853 0.929 0.1072 0.852 0.620
#> 5 5 0.691 0.651 0.812 0.0788 0.929 0.769
#> 6 6 0.662 0.574 0.743 0.0576 0.823 0.419
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
#> SRR816969 1 0.0000 0.981 1.000 0.000
#> SRR1335605 1 0.9427 0.408 0.640 0.360
#> SRR1432014 1 0.0376 0.978 0.996 0.004
#> SRR1499215 1 0.0000 0.981 1.000 0.000
#> SRR1460409 1 0.0000 0.981 1.000 0.000
#> SRR1086441 1 0.0000 0.981 1.000 0.000
#> SRR1097344 2 0.0000 0.957 0.000 1.000
#> SRR1081789 2 0.8267 0.665 0.260 0.740
#> SRR1453005 2 0.0000 0.957 0.000 1.000
#> SRR1366985 1 0.0000 0.981 1.000 0.000
#> SRR815280 1 0.0000 0.981 1.000 0.000
#> SRR1348531 1 0.0000 0.981 1.000 0.000
#> SRR815845 2 0.6343 0.808 0.160 0.840
#> SRR1471178 1 0.0000 0.981 1.000 0.000
#> SRR1080696 1 0.0000 0.981 1.000 0.000
#> SRR1078684 1 0.0000 0.981 1.000 0.000
#> SRR1317751 1 0.0000 0.981 1.000 0.000
#> SRR1435667 1 0.0000 0.981 1.000 0.000
#> SRR1097905 1 0.0000 0.981 1.000 0.000
#> SRR1456548 1 0.0000 0.981 1.000 0.000
#> SRR1075126 1 0.0000 0.981 1.000 0.000
#> SRR813108 2 0.0000 0.957 0.000 1.000
#> SRR1479062 1 0.0000 0.981 1.000 0.000
#> SRR1408703 1 0.0000 0.981 1.000 0.000
#> SRR1332360 1 0.0000 0.981 1.000 0.000
#> SRR1098686 1 0.0000 0.981 1.000 0.000
#> SRR1434228 1 0.0000 0.981 1.000 0.000
#> SRR1467149 1 0.0000 0.981 1.000 0.000
#> SRR1399113 2 0.0000 0.957 0.000 1.000
#> SRR1476507 2 0.0672 0.953 0.008 0.992
#> SRR1092468 1 0.0000 0.981 1.000 0.000
#> SRR1441804 1 0.0000 0.981 1.000 0.000
#> SRR1326100 2 0.0000 0.957 0.000 1.000
#> SRR1398815 1 0.0000 0.981 1.000 0.000
#> SRR1436021 1 0.9850 0.211 0.572 0.428
#> SRR1480083 2 0.0000 0.957 0.000 1.000
#> SRR1472863 1 0.0000 0.981 1.000 0.000
#> SRR815542 1 0.0000 0.981 1.000 0.000
#> SRR1400100 1 0.9866 0.198 0.568 0.432
#> SRR1312002 1 0.0000 0.981 1.000 0.000
#> SRR1470253 1 0.0000 0.981 1.000 0.000
#> SRR1414332 1 0.0000 0.981 1.000 0.000
#> SRR1069209 1 0.0000 0.981 1.000 0.000
#> SRR661052 1 0.0000 0.981 1.000 0.000
#> SRR1308860 1 0.0000 0.981 1.000 0.000
#> SRR1421159 2 0.9491 0.447 0.368 0.632
#> SRR1340943 1 0.0000 0.981 1.000 0.000
#> SRR1078855 1 0.0000 0.981 1.000 0.000
#> SRR1459465 2 0.0000 0.957 0.000 1.000
#> SRR816818 2 0.0000 0.957 0.000 1.000
#> SRR1478679 1 0.7528 0.705 0.784 0.216
#> SRR1350979 1 0.0000 0.981 1.000 0.000
#> SRR1458198 1 0.0000 0.981 1.000 0.000
#> SRR1386910 2 0.0000 0.957 0.000 1.000
#> SRR1465375 2 0.0000 0.957 0.000 1.000
#> SRR1323699 1 0.0000 0.981 1.000 0.000
#> SRR1431139 1 0.0000 0.981 1.000 0.000
#> SRR1373964 1 0.0000 0.981 1.000 0.000
#> SRR1455413 1 0.0000 0.981 1.000 0.000
#> SRR1437163 1 0.0000 0.981 1.000 0.000
#> SRR1347343 1 0.0000 0.981 1.000 0.000
#> SRR1465480 2 0.0000 0.957 0.000 1.000
#> SRR1489631 1 0.0000 0.981 1.000 0.000
#> SRR1086514 2 0.0000 0.957 0.000 1.000
#> SRR1430928 1 0.0000 0.981 1.000 0.000
#> SRR1310939 1 0.0000 0.981 1.000 0.000
#> SRR1344294 2 0.0000 0.957 0.000 1.000
#> SRR1099402 1 0.0000 0.981 1.000 0.000
#> SRR1468118 1 0.0000 0.981 1.000 0.000
#> SRR1486348 1 0.0000 0.981 1.000 0.000
#> SRR1488770 2 0.0000 0.957 0.000 1.000
#> SRR1083732 1 0.0000 0.981 1.000 0.000
#> SRR1456611 2 0.0000 0.957 0.000 1.000
#> SRR1080318 1 0.0000 0.981 1.000 0.000
#> SRR1500089 1 0.0000 0.981 1.000 0.000
#> SRR1441178 1 0.0000 0.981 1.000 0.000
#> SRR1381396 1 0.0000 0.981 1.000 0.000
#> SRR1096081 1 0.0000 0.981 1.000 0.000
#> SRR1349809 2 0.0000 0.957 0.000 1.000
#> SRR1324314 1 0.0000 0.981 1.000 0.000
#> SRR1092444 1 0.0000 0.981 1.000 0.000
#> SRR1382553 1 0.0000 0.981 1.000 0.000
#> SRR1075530 2 0.0938 0.950 0.012 0.988
#> SRR1442612 1 0.0672 0.974 0.992 0.008
#> SRR1360056 1 0.0000 0.981 1.000 0.000
#> SRR1078164 1 0.0000 0.981 1.000 0.000
#> SRR1434545 1 0.0000 0.981 1.000 0.000
#> SRR1398251 1 0.0000 0.981 1.000 0.000
#> SRR1375866 1 0.0000 0.981 1.000 0.000
#> SRR1091645 2 0.0000 0.957 0.000 1.000
#> SRR1416636 1 0.0000 0.981 1.000 0.000
#> SRR1105441 1 0.2603 0.937 0.956 0.044
#> SRR1082496 2 0.0000 0.957 0.000 1.000
#> SRR1315353 2 0.0938 0.950 0.012 0.988
#> SRR1093697 2 0.0000 0.957 0.000 1.000
#> SRR1077429 1 0.0000 0.981 1.000 0.000
#> SRR1076120 1 0.0000 0.981 1.000 0.000
#> SRR1074410 1 0.0000 0.981 1.000 0.000
#> SRR1340345 2 0.0000 0.957 0.000 1.000
#> SRR1069514 2 0.9323 0.495 0.348 0.652
#> SRR1092636 1 0.0000 0.981 1.000 0.000
#> SRR1365013 2 0.5842 0.831 0.140 0.860
#> SRR1073069 1 0.0000 0.981 1.000 0.000
#> SRR1443137 1 0.0000 0.981 1.000 0.000
#> SRR1437143 2 0.0000 0.957 0.000 1.000
#> SRR1091990 1 0.0000 0.981 1.000 0.000
#> SRR820234 2 0.0000 0.957 0.000 1.000
#> SRR1338079 1 0.0000 0.981 1.000 0.000
#> SRR1390094 1 0.0000 0.981 1.000 0.000
#> SRR1340721 2 0.0000 0.957 0.000 1.000
#> SRR1335964 1 0.0000 0.981 1.000 0.000
#> SRR1086869 1 0.0000 0.981 1.000 0.000
#> SRR1453434 1 0.0000 0.981 1.000 0.000
#> SRR1402261 1 0.0000 0.981 1.000 0.000
#> SRR657809 2 0.0000 0.957 0.000 1.000
#> SRR1093075 1 0.0000 0.981 1.000 0.000
#> SRR1433329 1 0.0000 0.981 1.000 0.000
#> SRR1353418 1 0.0000 0.981 1.000 0.000
#> SRR1092913 2 0.0000 0.957 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR816969 1 0.3752 0.890 0.856 0.000 0.144
#> SRR1335605 2 0.7222 0.616 0.220 0.696 0.084
#> SRR1432014 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1499215 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1460409 1 0.2878 0.917 0.904 0.000 0.096
#> SRR1086441 1 0.1163 0.925 0.972 0.000 0.028
#> SRR1097344 2 0.1643 0.919 0.044 0.956 0.000
#> SRR1081789 3 0.5216 0.616 0.000 0.260 0.740
#> SRR1453005 2 0.2261 0.881 0.000 0.932 0.068
#> SRR1366985 3 0.0747 0.887 0.016 0.000 0.984
#> SRR815280 1 0.2448 0.924 0.924 0.000 0.076
#> SRR1348531 1 0.1643 0.927 0.956 0.000 0.044
#> SRR815845 3 0.2959 0.819 0.000 0.100 0.900
#> SRR1471178 1 0.1753 0.927 0.952 0.000 0.048
#> SRR1080696 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1078684 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1317751 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1435667 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1097905 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1456548 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1075126 1 0.3116 0.912 0.892 0.000 0.108
#> SRR813108 3 0.3412 0.783 0.000 0.124 0.876
#> SRR1479062 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1408703 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1332360 1 0.5363 0.723 0.724 0.000 0.276
#> SRR1098686 1 0.2261 0.926 0.932 0.000 0.068
#> SRR1434228 3 0.4399 0.734 0.188 0.000 0.812
#> SRR1467149 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1399113 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1476507 2 0.2356 0.906 0.072 0.928 0.000
#> SRR1092468 1 0.3816 0.888 0.852 0.000 0.148
#> SRR1441804 1 0.0237 0.918 0.996 0.000 0.004
#> SRR1326100 2 0.5988 0.381 0.000 0.632 0.368
#> SRR1398815 1 0.1643 0.927 0.956 0.000 0.044
#> SRR1436021 2 0.6521 0.611 0.248 0.712 0.040
#> SRR1480083 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1472863 1 0.1163 0.925 0.972 0.000 0.028
#> SRR815542 1 0.2261 0.926 0.932 0.000 0.068
#> SRR1400100 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1312002 3 0.3551 0.802 0.132 0.000 0.868
#> SRR1470253 3 0.0892 0.885 0.020 0.000 0.980
#> SRR1414332 1 0.2261 0.926 0.932 0.000 0.068
#> SRR1069209 3 0.5926 0.399 0.356 0.000 0.644
#> SRR661052 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1308860 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1421159 2 0.5024 0.703 0.004 0.776 0.220
#> SRR1340943 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1078855 1 0.2356 0.925 0.928 0.000 0.072
#> SRR1459465 2 0.0000 0.928 0.000 1.000 0.000
#> SRR816818 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1478679 3 0.5375 0.783 0.128 0.056 0.816
#> SRR1350979 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1458198 1 0.0592 0.921 0.988 0.000 0.012
#> SRR1386910 2 0.1289 0.922 0.032 0.968 0.000
#> SRR1465375 2 0.3551 0.860 0.132 0.868 0.000
#> SRR1323699 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1431139 3 0.5178 0.640 0.256 0.000 0.744
#> SRR1373964 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1455413 1 0.3116 0.912 0.892 0.000 0.108
#> SRR1437163 1 0.0237 0.915 0.996 0.004 0.000
#> SRR1347343 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1465480 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1489631 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1086514 2 0.1753 0.901 0.000 0.952 0.048
#> SRR1430928 1 0.4452 0.842 0.808 0.000 0.192
#> SRR1310939 3 0.0592 0.889 0.012 0.000 0.988
#> SRR1344294 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1099402 1 0.0424 0.920 0.992 0.000 0.008
#> SRR1468118 1 0.4235 0.859 0.824 0.000 0.176
#> SRR1486348 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1488770 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1083732 1 0.3752 0.890 0.856 0.000 0.144
#> SRR1456611 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1080318 1 0.2448 0.924 0.924 0.000 0.076
#> SRR1500089 1 0.3941 0.881 0.844 0.000 0.156
#> SRR1441178 1 0.2711 0.920 0.912 0.000 0.088
#> SRR1381396 1 0.1031 0.924 0.976 0.000 0.024
#> SRR1096081 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1349809 2 0.0237 0.928 0.004 0.996 0.000
#> SRR1324314 3 0.5216 0.630 0.260 0.000 0.740
#> SRR1092444 1 0.3192 0.910 0.888 0.000 0.112
#> SRR1382553 3 0.0747 0.887 0.016 0.000 0.984
#> SRR1075530 2 0.0237 0.928 0.004 0.996 0.000
#> SRR1442612 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1360056 3 0.5650 0.537 0.312 0.000 0.688
#> SRR1078164 1 0.3686 0.893 0.860 0.000 0.140
#> SRR1434545 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1398251 1 0.3686 0.894 0.860 0.000 0.140
#> SRR1375866 1 0.1753 0.927 0.952 0.000 0.048
#> SRR1091645 2 0.2066 0.913 0.060 0.940 0.000
#> SRR1416636 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1105441 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1082496 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1315353 3 0.3619 0.780 0.000 0.136 0.864
#> SRR1093697 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1077429 3 0.6154 0.249 0.408 0.000 0.592
#> SRR1076120 1 0.3752 0.891 0.856 0.000 0.144
#> SRR1074410 1 0.1411 0.926 0.964 0.000 0.036
#> SRR1340345 2 0.1753 0.918 0.048 0.952 0.000
#> SRR1069514 3 0.6154 0.302 0.000 0.408 0.592
#> SRR1092636 3 0.0424 0.890 0.008 0.000 0.992
#> SRR1365013 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1073069 1 0.5591 0.676 0.696 0.000 0.304
#> SRR1443137 1 0.3941 0.880 0.844 0.000 0.156
#> SRR1437143 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1091990 1 0.1964 0.926 0.944 0.000 0.056
#> SRR820234 3 0.5178 0.616 0.000 0.256 0.744
#> SRR1338079 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1390094 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1340721 2 0.3267 0.874 0.116 0.884 0.000
#> SRR1335964 3 0.1163 0.881 0.028 0.000 0.972
#> SRR1086869 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1453434 1 0.0000 0.917 1.000 0.000 0.000
#> SRR1402261 1 0.0000 0.917 1.000 0.000 0.000
#> SRR657809 2 0.1643 0.919 0.044 0.956 0.000
#> SRR1093075 1 0.3941 0.880 0.844 0.000 0.156
#> SRR1433329 1 0.5591 0.677 0.696 0.000 0.304
#> SRR1353418 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1092913 2 0.2066 0.913 0.060 0.940 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR816969 1 0.0524 0.954 0.988 0.000 0.004 0.008
#> SRR1335605 2 0.3568 0.785 0.116 0.856 0.004 0.024
#> SRR1432014 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1499215 3 0.2266 0.821 0.084 0.000 0.912 0.004
#> SRR1460409 1 0.0188 0.954 0.996 0.000 0.004 0.000
#> SRR1086441 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> SRR1097344 4 0.1118 0.930 0.000 0.036 0.000 0.964
#> SRR1081789 2 0.3726 0.707 0.000 0.788 0.212 0.000
#> SRR1453005 2 0.0469 0.909 0.000 0.988 0.012 0.000
#> SRR1366985 3 0.4584 0.600 0.300 0.000 0.696 0.004
#> SRR815280 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR1348531 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> SRR815845 3 0.1151 0.853 0.000 0.008 0.968 0.024
#> SRR1471178 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> SRR1080696 3 0.0188 0.861 0.000 0.000 0.996 0.004
#> SRR1078684 3 0.2334 0.819 0.088 0.000 0.908 0.004
#> SRR1317751 3 0.1118 0.852 0.000 0.000 0.964 0.036
#> SRR1435667 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1097905 1 0.1584 0.936 0.952 0.012 0.000 0.036
#> SRR1456548 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> SRR1075126 1 0.0469 0.953 0.988 0.000 0.000 0.012
#> SRR813108 3 0.0592 0.857 0.000 0.016 0.984 0.000
#> SRR1479062 3 0.0469 0.860 0.000 0.000 0.988 0.012
#> SRR1408703 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1332360 1 0.1109 0.942 0.968 0.000 0.028 0.004
#> SRR1098686 1 0.0376 0.954 0.992 0.000 0.004 0.004
#> SRR1434228 1 0.3870 0.724 0.788 0.000 0.208 0.004
#> SRR1467149 4 0.2345 0.845 0.100 0.000 0.000 0.900
#> SRR1399113 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1476507 4 0.0921 0.934 0.000 0.028 0.000 0.972
#> SRR1092468 1 0.2197 0.894 0.916 0.000 0.080 0.004
#> SRR1441804 1 0.0921 0.948 0.972 0.000 0.000 0.028
#> SRR1326100 2 0.1474 0.883 0.000 0.948 0.052 0.000
#> SRR1398815 1 0.0895 0.950 0.976 0.000 0.004 0.020
#> SRR1436021 2 0.6040 0.423 0.052 0.620 0.004 0.324
#> SRR1480083 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1472863 1 0.0817 0.949 0.976 0.000 0.000 0.024
#> SRR815542 1 0.0188 0.954 0.996 0.000 0.004 0.000
#> SRR1400100 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1312002 3 0.4950 0.443 0.376 0.000 0.620 0.004
#> SRR1470253 3 0.3355 0.755 0.160 0.000 0.836 0.004
#> SRR1414332 1 0.0188 0.954 0.996 0.000 0.004 0.000
#> SRR1069209 1 0.5097 0.165 0.568 0.000 0.428 0.004
#> SRR661052 1 0.1022 0.946 0.968 0.000 0.000 0.032
#> SRR1308860 1 0.1022 0.946 0.968 0.000 0.000 0.032
#> SRR1421159 3 0.5108 0.523 0.000 0.308 0.672 0.020
#> SRR1340943 4 0.0817 0.937 0.024 0.000 0.000 0.976
#> SRR1078855 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> SRR1459465 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR816818 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1478679 2 0.6602 0.413 0.324 0.584 0.088 0.004
#> SRR1350979 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1458198 4 0.0817 0.937 0.024 0.000 0.000 0.976
#> SRR1386910 2 0.2760 0.842 0.000 0.872 0.000 0.128
#> SRR1465375 2 0.2918 0.847 0.008 0.876 0.000 0.116
#> SRR1323699 3 0.1557 0.839 0.056 0.000 0.944 0.000
#> SRR1431139 3 0.4647 0.616 0.288 0.000 0.704 0.008
#> SRR1373964 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1455413 1 0.0672 0.953 0.984 0.000 0.008 0.008
#> SRR1437163 1 0.4319 0.697 0.760 0.012 0.000 0.228
#> SRR1347343 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1465480 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1489631 1 0.0592 0.952 0.984 0.000 0.000 0.016
#> SRR1086514 3 0.6979 0.274 0.000 0.344 0.528 0.128
#> SRR1430928 1 0.0657 0.950 0.984 0.000 0.012 0.004
#> SRR1310939 3 0.2868 0.781 0.000 0.000 0.864 0.136
#> SRR1344294 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1099402 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR1468118 4 0.0336 0.929 0.000 0.000 0.008 0.992
#> SRR1486348 1 0.0817 0.949 0.976 0.000 0.000 0.024
#> SRR1488770 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1083732 1 0.0592 0.950 0.984 0.000 0.016 0.000
#> SRR1456611 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1080318 1 0.0376 0.954 0.992 0.000 0.004 0.004
#> SRR1500089 4 0.5464 0.661 0.212 0.000 0.072 0.716
#> SRR1441178 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> SRR1381396 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR1096081 3 0.0707 0.857 0.000 0.000 0.980 0.020
#> SRR1349809 2 0.0188 0.913 0.000 0.996 0.000 0.004
#> SRR1324314 1 0.4328 0.665 0.748 0.000 0.244 0.008
#> SRR1092444 1 0.1059 0.949 0.972 0.000 0.016 0.012
#> SRR1382553 3 0.5088 0.327 0.424 0.000 0.572 0.004
#> SRR1075530 4 0.1209 0.931 0.000 0.032 0.004 0.964
#> SRR1442612 3 0.0000 0.861 0.000 0.000 1.000 0.000
#> SRR1360056 3 0.5112 0.447 0.384 0.000 0.608 0.008
#> SRR1078164 1 0.0376 0.953 0.992 0.000 0.004 0.004
#> SRR1434545 4 0.0817 0.937 0.024 0.000 0.000 0.976
#> SRR1398251 1 0.0188 0.954 0.996 0.000 0.004 0.000
#> SRR1375866 1 0.0817 0.949 0.976 0.000 0.000 0.024
#> SRR1091645 4 0.0921 0.933 0.000 0.028 0.000 0.972
#> SRR1416636 3 0.0188 0.861 0.000 0.000 0.996 0.004
#> SRR1105441 3 0.0188 0.861 0.000 0.000 0.996 0.004
#> SRR1082496 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1315353 3 0.2266 0.816 0.000 0.004 0.912 0.084
#> SRR1093697 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1077429 3 0.2840 0.830 0.056 0.000 0.900 0.044
#> SRR1076120 4 0.0817 0.937 0.024 0.000 0.000 0.976
#> SRR1074410 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR1340345 4 0.2530 0.866 0.000 0.112 0.000 0.888
#> SRR1069514 2 0.2255 0.866 0.012 0.920 0.068 0.000
#> SRR1092636 3 0.0469 0.859 0.012 0.000 0.988 0.000
#> SRR1365013 2 0.2345 0.855 0.000 0.900 0.000 0.100
#> SRR1073069 1 0.1398 0.931 0.956 0.000 0.040 0.004
#> SRR1443137 1 0.0376 0.953 0.992 0.000 0.004 0.004
#> SRR1437143 2 0.0000 0.914 0.000 1.000 0.000 0.000
#> SRR1091990 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> SRR820234 3 0.3024 0.758 0.000 0.148 0.852 0.000
#> SRR1338079 1 0.1118 0.944 0.964 0.000 0.000 0.036
#> SRR1390094 1 0.2589 0.855 0.884 0.000 0.000 0.116
#> SRR1340721 2 0.1388 0.900 0.012 0.960 0.000 0.028
#> SRR1335964 3 0.1389 0.847 0.000 0.000 0.952 0.048
#> SRR1086869 3 0.4250 0.613 0.000 0.000 0.724 0.276
#> SRR1453434 1 0.0592 0.951 0.984 0.000 0.000 0.016
#> SRR1402261 4 0.0817 0.937 0.024 0.000 0.000 0.976
#> SRR657809 2 0.1118 0.902 0.000 0.964 0.000 0.036
#> SRR1093075 1 0.0376 0.953 0.992 0.000 0.004 0.004
#> SRR1433329 1 0.1661 0.920 0.944 0.000 0.052 0.004
#> SRR1353418 3 0.0188 0.861 0.004 0.000 0.996 0.000
#> SRR1092913 4 0.1557 0.917 0.000 0.056 0.000 0.944
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR816969 1 0.1478 0.80150 0.936 0.000 0.000 0.000 0.064
#> SRR1335605 2 0.5552 0.28762 0.008 0.588 0.064 0.000 0.340
#> SRR1432014 3 0.0162 0.71470 0.000 0.000 0.996 0.000 0.004
#> SRR1499215 3 0.5004 0.60929 0.092 0.000 0.692 0.000 0.216
#> SRR1460409 1 0.2074 0.79437 0.896 0.000 0.000 0.000 0.104
#> SRR1086441 1 0.0290 0.79958 0.992 0.000 0.000 0.000 0.008
#> SRR1097344 4 0.0404 0.86114 0.000 0.012 0.000 0.988 0.000
#> SRR1081789 2 0.3508 0.61251 0.000 0.748 0.252 0.000 0.000
#> SRR1453005 2 0.3177 0.66289 0.000 0.792 0.208 0.000 0.000
#> SRR1366985 1 0.6369 0.34458 0.520 0.000 0.240 0.000 0.240
#> SRR815280 1 0.0162 0.79994 0.996 0.000 0.000 0.000 0.004
#> SRR1348531 1 0.1478 0.80243 0.936 0.000 0.000 0.000 0.064
#> SRR815845 3 0.3857 0.68322 0.000 0.000 0.688 0.000 0.312
#> SRR1471178 1 0.0162 0.79934 0.996 0.000 0.000 0.000 0.004
#> SRR1080696 3 0.2891 0.73646 0.000 0.000 0.824 0.000 0.176
#> SRR1078684 3 0.5798 0.49735 0.156 0.000 0.608 0.000 0.236
#> SRR1317751 3 0.4375 0.67993 0.004 0.000 0.628 0.004 0.364
#> SRR1435667 3 0.2020 0.69879 0.000 0.000 0.900 0.000 0.100
#> SRR1097905 5 0.6495 0.36439 0.380 0.148 0.000 0.008 0.464
#> SRR1456548 1 0.0290 0.79889 0.992 0.000 0.000 0.000 0.008
#> SRR1075126 1 0.0290 0.79889 0.992 0.000 0.000 0.000 0.008
#> SRR813108 3 0.3639 0.65778 0.000 0.024 0.792 0.000 0.184
#> SRR1479062 3 0.1851 0.72661 0.000 0.000 0.912 0.000 0.088
#> SRR1408703 3 0.3534 0.72354 0.000 0.000 0.744 0.000 0.256
#> SRR1332360 1 0.2249 0.79395 0.896 0.000 0.008 0.000 0.096
#> SRR1098686 1 0.1732 0.79804 0.920 0.000 0.000 0.000 0.080
#> SRR1434228 1 0.4197 0.65429 0.728 0.000 0.028 0.000 0.244
#> SRR1467149 5 0.5387 0.47778 0.040 0.000 0.048 0.224 0.688
#> SRR1399113 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1476507 4 0.0162 0.86195 0.000 0.004 0.000 0.996 0.000
#> SRR1092468 1 0.3491 0.70978 0.768 0.000 0.004 0.000 0.228
#> SRR1441804 1 0.4015 0.34981 0.652 0.000 0.000 0.000 0.348
#> SRR1326100 2 0.0290 0.81233 0.000 0.992 0.008 0.000 0.000
#> SRR1398815 1 0.2230 0.73116 0.884 0.000 0.000 0.000 0.116
#> SRR1436021 4 0.5340 0.47092 0.036 0.324 0.000 0.620 0.020
#> SRR1480083 2 0.0609 0.80711 0.000 0.980 0.020 0.000 0.000
#> SRR1472863 1 0.4787 0.00394 0.548 0.020 0.000 0.000 0.432
#> SRR815542 1 0.2583 0.78183 0.864 0.000 0.000 0.004 0.132
#> SRR1400100 3 0.3876 0.69505 0.000 0.000 0.684 0.000 0.316
#> SRR1312002 3 0.6279 0.36946 0.280 0.000 0.528 0.000 0.192
#> SRR1470253 3 0.5320 0.65277 0.052 0.000 0.524 0.000 0.424
#> SRR1414332 1 0.0404 0.80130 0.988 0.000 0.000 0.000 0.012
#> SRR1069209 1 0.4801 0.58655 0.668 0.000 0.048 0.000 0.284
#> SRR661052 5 0.4806 0.50373 0.328 0.028 0.004 0.000 0.640
#> SRR1308860 1 0.4341 0.14284 0.592 0.000 0.000 0.004 0.404
#> SRR1421159 2 0.6422 0.30502 0.008 0.576 0.060 0.308 0.048
#> SRR1340943 4 0.0000 0.86171 0.000 0.000 0.000 1.000 0.000
#> SRR1078855 1 0.0162 0.79994 0.996 0.000 0.000 0.000 0.004
#> SRR1459465 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1478679 2 0.7219 0.17933 0.288 0.504 0.140 0.000 0.068
#> SRR1350979 3 0.1671 0.70497 0.000 0.000 0.924 0.000 0.076
#> SRR1458198 4 0.0290 0.86020 0.000 0.000 0.000 0.992 0.008
#> SRR1386910 5 0.6604 0.21677 0.000 0.352 0.116 0.028 0.504
#> SRR1465375 2 0.4675 0.32963 0.004 0.620 0.000 0.016 0.360
#> SRR1323699 3 0.1965 0.70542 0.000 0.000 0.904 0.000 0.096
#> SRR1431139 3 0.5834 0.62738 0.104 0.000 0.532 0.000 0.364
#> SRR1373964 3 0.2561 0.68629 0.000 0.000 0.856 0.000 0.144
#> SRR1455413 1 0.1965 0.79414 0.904 0.000 0.000 0.000 0.096
#> SRR1437163 1 0.5250 -0.04191 0.536 0.000 0.000 0.048 0.416
#> SRR1347343 3 0.2230 0.69221 0.000 0.000 0.884 0.000 0.116
#> SRR1465480 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1489631 1 0.3109 0.61598 0.800 0.000 0.000 0.000 0.200
#> SRR1086514 4 0.4696 0.23584 0.000 0.428 0.016 0.556 0.000
#> SRR1430928 1 0.2230 0.78759 0.884 0.000 0.000 0.000 0.116
#> SRR1310939 3 0.3506 0.65946 0.000 0.000 0.824 0.132 0.044
#> SRR1344294 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1099402 1 0.0290 0.79889 0.992 0.000 0.000 0.000 0.008
#> SRR1468118 5 0.4599 0.42801 0.000 0.000 0.156 0.100 0.744
#> SRR1486348 1 0.3274 0.58511 0.780 0.000 0.000 0.000 0.220
#> SRR1488770 2 0.0404 0.81114 0.000 0.988 0.012 0.000 0.000
#> SRR1083732 1 0.2338 0.78861 0.884 0.000 0.004 0.000 0.112
#> SRR1456611 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1080318 1 0.2329 0.78409 0.876 0.000 0.000 0.000 0.124
#> SRR1500089 4 0.6515 0.29910 0.212 0.000 0.016 0.560 0.212
#> SRR1441178 1 0.0162 0.79994 0.996 0.000 0.000 0.000 0.004
#> SRR1381396 1 0.0290 0.79889 0.992 0.000 0.000 0.000 0.008
#> SRR1096081 3 0.4084 0.69140 0.000 0.000 0.668 0.004 0.328
#> SRR1349809 2 0.0162 0.81259 0.000 0.996 0.000 0.000 0.004
#> SRR1324314 3 0.6605 0.42263 0.236 0.000 0.452 0.000 0.312
#> SRR1092444 1 0.2719 0.77273 0.852 0.000 0.004 0.000 0.144
#> SRR1382553 1 0.6128 0.41859 0.564 0.000 0.204 0.000 0.232
#> SRR1075530 4 0.1701 0.83972 0.000 0.028 0.016 0.944 0.012
#> SRR1442612 3 0.0290 0.71456 0.000 0.000 0.992 0.000 0.008
#> SRR1360056 3 0.4219 0.53689 0.000 0.000 0.584 0.000 0.416
#> SRR1078164 1 0.2286 0.78870 0.888 0.000 0.004 0.000 0.108
#> SRR1434545 4 0.0162 0.86100 0.000 0.000 0.000 0.996 0.004
#> SRR1398251 1 0.0162 0.79994 0.996 0.000 0.000 0.000 0.004
#> SRR1375866 1 0.4201 0.28668 0.592 0.000 0.000 0.000 0.408
#> SRR1091645 4 0.0000 0.86171 0.000 0.000 0.000 1.000 0.000
#> SRR1416636 3 0.3636 0.71304 0.000 0.000 0.728 0.000 0.272
#> SRR1105441 3 0.3074 0.73591 0.000 0.000 0.804 0.000 0.196
#> SRR1082496 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1315353 3 0.1430 0.69556 0.000 0.052 0.944 0.000 0.004
#> SRR1093697 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1077429 3 0.5161 0.65146 0.024 0.000 0.568 0.012 0.396
#> SRR1076120 4 0.0703 0.84635 0.000 0.000 0.000 0.976 0.024
#> SRR1074410 1 0.0162 0.79934 0.996 0.000 0.000 0.000 0.004
#> SRR1340345 4 0.1043 0.84889 0.000 0.040 0.000 0.960 0.000
#> SRR1069514 2 0.1952 0.76514 0.004 0.912 0.084 0.000 0.000
#> SRR1092636 3 0.4045 0.68074 0.000 0.000 0.644 0.000 0.356
#> SRR1365013 2 0.3174 0.72870 0.000 0.868 0.080 0.036 0.016
#> SRR1073069 1 0.2763 0.76750 0.848 0.000 0.004 0.000 0.148
#> SRR1443137 1 0.1608 0.79969 0.928 0.000 0.000 0.000 0.072
#> SRR1437143 2 0.0000 0.81421 0.000 1.000 0.000 0.000 0.000
#> SRR1091990 1 0.0510 0.80225 0.984 0.000 0.000 0.000 0.016
#> SRR820234 3 0.3884 0.43339 0.000 0.288 0.708 0.000 0.004
#> SRR1338079 1 0.4510 0.03343 0.560 0.000 0.000 0.008 0.432
#> SRR1390094 1 0.5689 0.36687 0.604 0.000 0.060 0.316 0.020
#> SRR1340721 2 0.4403 0.19100 0.000 0.560 0.000 0.004 0.436
#> SRR1335964 3 0.4232 0.69732 0.000 0.000 0.676 0.012 0.312
#> SRR1086869 3 0.5714 0.61074 0.000 0.000 0.580 0.108 0.312
#> SRR1453434 1 0.3550 0.65317 0.796 0.000 0.000 0.184 0.020
#> SRR1402261 4 0.0000 0.86171 0.000 0.000 0.000 1.000 0.000
#> SRR657809 2 0.4517 0.18224 0.000 0.556 0.000 0.008 0.436
#> SRR1093075 1 0.1043 0.80239 0.960 0.000 0.000 0.000 0.040
#> SRR1433329 1 0.2806 0.76497 0.844 0.000 0.004 0.000 0.152
#> SRR1353418 3 0.3849 0.72926 0.016 0.000 0.752 0.000 0.232
#> SRR1092913 4 0.1251 0.84820 0.000 0.036 0.000 0.956 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR816969 6 0.3728 0.02420 0.344 0.000 0.004 0.000 0.000 0.652
#> SRR1335605 2 0.4964 0.47995 0.080 0.608 0.000 0.000 0.308 0.004
#> SRR1432014 3 0.2100 0.76527 0.000 0.000 0.884 0.000 0.112 0.004
#> SRR1499215 3 0.4834 -0.00919 0.000 0.004 0.484 0.000 0.044 0.468
#> SRR1460409 6 0.2845 0.51761 0.172 0.000 0.004 0.000 0.004 0.820
#> SRR1086441 1 0.4025 0.62523 0.576 0.000 0.008 0.000 0.000 0.416
#> SRR1097344 4 0.0146 0.86681 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1081789 3 0.3076 0.63276 0.000 0.240 0.760 0.000 0.000 0.000
#> SRR1453005 3 0.2946 0.69950 0.004 0.184 0.808 0.004 0.000 0.000
#> SRR1366985 6 0.2282 0.69621 0.020 0.000 0.068 0.000 0.012 0.900
#> SRR815280 1 0.3971 0.61111 0.548 0.000 0.000 0.000 0.004 0.448
#> SRR1348531 6 0.4002 -0.25169 0.404 0.000 0.000 0.000 0.008 0.588
#> SRR815845 5 0.2114 0.63499 0.012 0.000 0.076 0.000 0.904 0.008
#> SRR1471178 1 0.3923 0.63528 0.580 0.000 0.004 0.000 0.000 0.416
#> SRR1080696 5 0.4985 0.28470 0.000 0.000 0.376 0.000 0.548 0.076
#> SRR1078684 6 0.4787 0.38282 0.004 0.000 0.312 0.000 0.064 0.620
#> SRR1317751 5 0.4165 0.59542 0.000 0.000 0.036 0.000 0.672 0.292
#> SRR1435667 3 0.1265 0.78277 0.000 0.000 0.948 0.000 0.044 0.008
#> SRR1097905 1 0.6143 -0.05172 0.516 0.192 0.000 0.000 0.268 0.024
#> SRR1456548 1 0.4090 0.64474 0.604 0.000 0.004 0.000 0.008 0.384
#> SRR1075126 1 0.3547 0.64366 0.696 0.000 0.000 0.000 0.004 0.300
#> SRR813108 3 0.3304 0.69130 0.004 0.008 0.820 0.000 0.024 0.144
#> SRR1479062 3 0.3482 0.50056 0.000 0.000 0.684 0.000 0.316 0.000
#> SRR1408703 5 0.4557 0.61148 0.000 0.000 0.072 0.000 0.660 0.268
#> SRR1332360 6 0.1918 0.65893 0.088 0.000 0.008 0.000 0.000 0.904
#> SRR1098686 6 0.2841 0.59933 0.164 0.000 0.012 0.000 0.000 0.824
#> SRR1434228 6 0.0909 0.70365 0.020 0.000 0.012 0.000 0.000 0.968
#> SRR1467149 5 0.3933 0.52514 0.220 0.000 0.000 0.032 0.740 0.008
#> SRR1399113 2 0.0146 0.78106 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1476507 4 0.0436 0.86645 0.004 0.000 0.004 0.988 0.004 0.000
#> SRR1092468 6 0.3018 0.68809 0.036 0.000 0.040 0.032 0.016 0.876
#> SRR1441804 1 0.4466 0.50124 0.708 0.000 0.000 0.000 0.116 0.176
#> SRR1326100 2 0.1700 0.76141 0.000 0.916 0.080 0.000 0.004 0.000
#> SRR1398815 1 0.3240 0.62592 0.752 0.000 0.004 0.000 0.000 0.244
#> SRR1436021 2 0.4577 0.61712 0.012 0.732 0.000 0.152 0.004 0.100
#> SRR1480083 2 0.3198 0.53360 0.000 0.740 0.260 0.000 0.000 0.000
#> SRR1472863 1 0.3492 0.46112 0.796 0.004 0.000 0.000 0.160 0.040
#> SRR815542 6 0.2398 0.62747 0.104 0.000 0.020 0.000 0.000 0.876
#> SRR1400100 5 0.3088 0.68919 0.000 0.000 0.048 0.000 0.832 0.120
#> SRR1312002 3 0.4220 0.50054 0.028 0.000 0.664 0.000 0.004 0.304
#> SRR1470253 6 0.5004 0.00166 0.000 0.000 0.084 0.000 0.348 0.568
#> SRR1414332 1 0.4025 0.60830 0.576 0.000 0.008 0.000 0.000 0.416
#> SRR1069209 6 0.2066 0.69347 0.000 0.000 0.052 0.000 0.040 0.908
#> SRR661052 5 0.3989 0.25021 0.468 0.000 0.000 0.000 0.528 0.004
#> SRR1308860 1 0.2843 0.50235 0.848 0.000 0.000 0.000 0.116 0.036
#> SRR1421159 2 0.6296 -0.04686 0.004 0.432 0.036 0.432 0.012 0.084
#> SRR1340943 4 0.0291 0.86693 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1078855 1 0.3881 0.64183 0.600 0.000 0.000 0.000 0.004 0.396
#> SRR1459465 2 0.0146 0.78106 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR816818 2 0.0000 0.78101 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478679 2 0.7128 0.04583 0.092 0.376 0.204 0.000 0.000 0.328
#> SRR1350979 3 0.2094 0.78221 0.004 0.000 0.908 0.000 0.064 0.024
#> SRR1458198 4 0.0551 0.86431 0.004 0.000 0.000 0.984 0.008 0.004
#> SRR1386910 5 0.5133 0.12785 0.108 0.312 0.000 0.000 0.580 0.000
#> SRR1465375 2 0.4377 0.53837 0.368 0.608 0.004 0.008 0.012 0.000
#> SRR1323699 3 0.2197 0.77808 0.000 0.000 0.900 0.000 0.056 0.044
#> SRR1431139 6 0.4616 0.35543 0.004 0.000 0.080 0.000 0.236 0.680
#> SRR1373964 3 0.1934 0.77554 0.000 0.000 0.916 0.000 0.044 0.040
#> SRR1455413 6 0.1624 0.70487 0.040 0.000 0.004 0.000 0.020 0.936
#> SRR1437163 1 0.2502 0.51208 0.884 0.000 0.000 0.012 0.084 0.020
#> SRR1347343 3 0.1196 0.78330 0.000 0.000 0.952 0.000 0.040 0.008
#> SRR1465480 2 0.0146 0.78106 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1489631 1 0.3110 0.63525 0.792 0.000 0.000 0.000 0.012 0.196
#> SRR1086514 4 0.4171 0.27664 0.004 0.380 0.012 0.604 0.000 0.000
#> SRR1430928 6 0.2445 0.61871 0.108 0.000 0.020 0.000 0.000 0.872
#> SRR1310939 3 0.3381 0.75586 0.004 0.000 0.836 0.092 0.056 0.012
#> SRR1344294 2 0.0146 0.78091 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1099402 1 0.4076 0.62818 0.564 0.000 0.004 0.000 0.004 0.428
#> SRR1468118 5 0.2086 0.62218 0.064 0.000 0.004 0.012 0.912 0.008
#> SRR1486348 1 0.2312 0.60005 0.876 0.000 0.000 0.000 0.012 0.112
#> SRR1488770 2 0.1523 0.77507 0.008 0.940 0.044 0.000 0.008 0.000
#> SRR1083732 6 0.2282 0.69293 0.068 0.000 0.020 0.000 0.012 0.900
#> SRR1456611 2 0.0984 0.77867 0.012 0.968 0.012 0.000 0.008 0.000
#> SRR1080318 6 0.1851 0.70485 0.036 0.000 0.012 0.000 0.024 0.928
#> SRR1500089 6 0.4857 0.44299 0.004 0.000 0.012 0.228 0.076 0.680
#> SRR1441178 1 0.3971 0.61111 0.548 0.000 0.000 0.000 0.004 0.448
#> SRR1381396 1 0.3852 0.64116 0.612 0.000 0.000 0.000 0.004 0.384
#> SRR1096081 5 0.3618 0.68958 0.000 0.000 0.048 0.000 0.776 0.176
#> SRR1349809 2 0.1285 0.77298 0.052 0.944 0.000 0.000 0.004 0.000
#> SRR1324314 6 0.6205 -0.08503 0.056 0.000 0.100 0.000 0.364 0.480
#> SRR1092444 6 0.2358 0.69690 0.028 0.000 0.016 0.000 0.056 0.900
#> SRR1382553 6 0.2112 0.67818 0.016 0.000 0.088 0.000 0.000 0.896
#> SRR1075530 2 0.6135 0.05651 0.004 0.396 0.000 0.360 0.240 0.000
#> SRR1442612 3 0.1957 0.76530 0.000 0.000 0.888 0.000 0.112 0.000
#> SRR1360056 5 0.4582 0.49713 0.116 0.000 0.160 0.000 0.716 0.008
#> SRR1078164 6 0.0790 0.69543 0.032 0.000 0.000 0.000 0.000 0.968
#> SRR1434545 4 0.0291 0.86704 0.004 0.000 0.004 0.992 0.000 0.000
#> SRR1398251 1 0.3971 0.61111 0.548 0.000 0.000 0.000 0.004 0.448
#> SRR1375866 1 0.4963 0.45484 0.612 0.000 0.000 0.000 0.100 0.288
#> SRR1091645 4 0.0000 0.86699 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1416636 5 0.3530 0.62659 0.000 0.000 0.152 0.000 0.792 0.056
#> SRR1105441 5 0.5260 0.26156 0.008 0.000 0.072 0.000 0.464 0.456
#> SRR1082496 2 0.0146 0.78106 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1315353 3 0.2149 0.77885 0.004 0.016 0.900 0.000 0.080 0.000
#> SRR1093697 2 0.0603 0.78004 0.000 0.980 0.016 0.000 0.004 0.000
#> SRR1077429 5 0.4692 0.29322 0.000 0.000 0.044 0.000 0.512 0.444
#> SRR1076120 4 0.1511 0.83314 0.004 0.000 0.000 0.940 0.012 0.044
#> SRR1074410 1 0.4076 0.60563 0.564 0.000 0.004 0.000 0.004 0.428
#> SRR1340345 4 0.3593 0.70856 0.008 0.176 0.004 0.788 0.024 0.000
#> SRR1069514 2 0.2619 0.75334 0.008 0.884 0.072 0.000 0.004 0.032
#> SRR1092636 5 0.3662 0.69223 0.004 0.000 0.044 0.000 0.780 0.172
#> SRR1365013 2 0.3703 0.70201 0.072 0.792 0.004 0.000 0.132 0.000
#> SRR1073069 6 0.1196 0.69491 0.040 0.000 0.008 0.000 0.000 0.952
#> SRR1443137 6 0.3265 0.33711 0.248 0.000 0.000 0.000 0.004 0.748
#> SRR1437143 2 0.0000 0.78101 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1091990 1 0.4086 0.58285 0.528 0.000 0.008 0.000 0.000 0.464
#> SRR820234 3 0.2717 0.75045 0.004 0.100 0.868 0.004 0.024 0.000
#> SRR1338079 1 0.2197 0.55330 0.900 0.000 0.000 0.000 0.056 0.044
#> SRR1390094 3 0.7685 0.19950 0.244 0.004 0.392 0.248 0.020 0.092
#> SRR1340721 2 0.5502 0.41371 0.404 0.480 0.000 0.004 0.112 0.000
#> SRR1335964 5 0.3978 0.68154 0.000 0.000 0.064 0.000 0.744 0.192
#> SRR1086869 5 0.3433 0.68980 0.004 0.000 0.032 0.024 0.832 0.108
#> SRR1453434 4 0.5661 0.16985 0.152 0.000 0.004 0.556 0.004 0.284
#> SRR1402261 4 0.0291 0.86704 0.004 0.000 0.004 0.992 0.000 0.000
#> SRR657809 2 0.5543 0.47474 0.204 0.556 0.000 0.000 0.240 0.000
#> SRR1093075 1 0.4114 0.59497 0.532 0.000 0.004 0.000 0.004 0.460
#> SRR1433329 6 0.0790 0.69620 0.032 0.000 0.000 0.000 0.000 0.968
#> SRR1353418 6 0.5099 -0.23278 0.000 0.000 0.080 0.000 0.424 0.496
#> SRR1092913 4 0.2558 0.80886 0.012 0.084 0.004 0.884 0.016 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