Date: 2019-12-25 22:47:52 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 17851 rows and 124 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] 17851 124
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 | 0.989 | 0.996 | ** | |
| SD:skmeans | 3 | 1.000 | 0.963 | 0.986 | ** | 2 |
| SD:NMF | 3 | 1.000 | 0.962 | 0.983 | ** | 2 |
| CV:kmeans | 2 | 1.000 | 0.975 | 0.990 | ** | |
| CV:skmeans | 3 | 1.000 | 0.989 | 0.995 | ** | 2 |
| CV:pam | 2 | 1.000 | 0.982 | 0.992 | ** | |
| ATC:hclust | 2 | 1.000 | 0.972 | 0.987 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.990 | 0.996 | ** | |
| ATC:skmeans | 2 | 1.000 | 0.993 | 0.997 | ** | |
| ATC:pam | 2 | 1.000 | 0.993 | 0.997 | ** | |
| ATC:mclust | 2 | 1.000 | 0.991 | 0.996 | ** | |
| ATC:NMF | 2 | 1.000 | 0.971 | 0.988 | ** | |
| CV:NMF | 3 | 1.000 | 0.939 | 0.978 | ** | 2 |
| MAD:skmeans | 3 | 0.978 | 0.953 | 0.980 | ** | 2 |
| CV:hclust | 2 | 0.965 | 0.947 | 0.976 | ** | |
| MAD:NMF | 3 | 0.962 | 0.923 | 0.965 | ** | |
| SD:hclust | 2 | 0.935 | 0.927 | 0.968 | * | |
| MAD:pam | 5 | 0.934 | 0.913 | 0.962 | * | 2,3 |
| SD:pam | 3 | 0.923 | 0.918 | 0.967 | * | 2 |
| MAD:kmeans | 2 | 0.904 | 0.973 | 0.987 | * | |
| SD:mclust | 2 | 0.900 | 0.939 | 0.971 | * | |
| CV:mclust | 2 | 0.871 | 0.924 | 0.967 | ||
| MAD:hclust | 2 | 0.735 | 0.885 | 0.949 | ||
| MAD:mclust | 5 | 0.701 | 0.686 | 0.809 |
**: 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 1.000 0.980 0.992 0.361 0.639 0.639
#> CV:NMF 2 0.983 0.952 0.981 0.398 0.606 0.606
#> MAD:NMF 2 0.886 0.917 0.968 0.383 0.622 0.622
#> ATC:NMF 2 1.000 0.971 0.988 0.375 0.622 0.622
#> SD:skmeans 2 1.000 0.993 0.997 0.445 0.554 0.554
#> CV:skmeans 2 1.000 0.978 0.991 0.444 0.554 0.554
#> MAD:skmeans 2 1.000 0.965 0.985 0.452 0.548 0.548
#> ATC:skmeans 2 1.000 0.993 0.997 0.425 0.578 0.578
#> SD:mclust 2 0.900 0.939 0.971 0.433 0.559 0.559
#> CV:mclust 2 0.871 0.924 0.967 0.459 0.534 0.534
#> MAD:mclust 2 0.852 0.862 0.947 0.388 0.648 0.648
#> ATC:mclust 2 1.000 0.991 0.996 0.428 0.571 0.571
#> SD:kmeans 2 1.000 0.989 0.996 0.329 0.666 0.666
#> CV:kmeans 2 1.000 0.975 0.990 0.344 0.666 0.666
#> MAD:kmeans 2 0.904 0.973 0.987 0.339 0.675 0.675
#> ATC:kmeans 2 1.000 0.990 0.996 0.330 0.666 0.666
#> SD:pam 2 1.000 0.988 0.995 0.330 0.666 0.666
#> CV:pam 2 1.000 0.982 0.992 0.357 0.639 0.639
#> MAD:pam 2 0.966 0.966 0.984 0.365 0.639 0.639
#> ATC:pam 2 1.000 0.993 0.997 0.319 0.685 0.685
#> SD:hclust 2 0.935 0.927 0.968 0.308 0.695 0.695
#> CV:hclust 2 0.965 0.947 0.976 0.318 0.706 0.706
#> MAD:hclust 2 0.735 0.885 0.949 0.327 0.695 0.695
#> ATC:hclust 2 1.000 0.972 0.987 0.303 0.685 0.685
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 1.000 0.962 0.983 0.792 0.699 0.533
#> CV:NMF 3 1.000 0.939 0.978 0.651 0.671 0.486
#> MAD:NMF 3 0.962 0.923 0.965 0.690 0.666 0.489
#> ATC:NMF 3 0.831 0.898 0.941 0.617 0.720 0.565
#> SD:skmeans 3 1.000 0.963 0.986 0.508 0.745 0.551
#> CV:skmeans 3 1.000 0.989 0.995 0.511 0.743 0.549
#> MAD:skmeans 3 0.978 0.953 0.980 0.486 0.749 0.554
#> ATC:skmeans 3 0.823 0.942 0.964 0.548 0.756 0.577
#> SD:mclust 3 0.651 0.808 0.839 0.447 0.727 0.531
#> CV:mclust 3 0.669 0.740 0.803 0.325 0.770 0.584
#> MAD:mclust 3 0.560 0.723 0.769 0.611 0.627 0.448
#> ATC:mclust 3 0.543 0.692 0.852 0.294 0.808 0.678
#> SD:kmeans 3 0.623 0.883 0.879 0.833 0.671 0.515
#> CV:kmeans 3 0.628 0.851 0.872 0.774 0.686 0.528
#> MAD:kmeans 3 0.634 0.908 0.909 0.816 0.683 0.530
#> ATC:kmeans 3 0.607 0.795 0.795 0.608 0.839 0.762
#> SD:pam 3 0.923 0.918 0.967 0.920 0.687 0.534
#> CV:pam 3 0.882 0.934 0.970 0.814 0.675 0.507
#> MAD:pam 3 0.917 0.939 0.973 0.780 0.699 0.533
#> ATC:pam 3 0.668 0.813 0.811 0.775 0.681 0.534
#> SD:hclust 3 0.760 0.850 0.945 0.147 0.987 0.981
#> CV:hclust 3 0.869 0.899 0.958 0.117 0.987 0.981
#> MAD:hclust 3 0.752 0.807 0.926 0.104 0.987 0.981
#> ATC:hclust 3 0.825 0.881 0.949 0.257 0.987 0.981
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.773 0.822 0.875 0.1296 0.889 0.694
#> CV:NMF 4 0.752 0.724 0.865 0.1158 0.906 0.734
#> MAD:NMF 4 0.742 0.779 0.871 0.1361 0.854 0.615
#> ATC:NMF 4 0.621 0.713 0.858 0.1380 0.843 0.624
#> SD:skmeans 4 0.886 0.861 0.922 0.0977 0.911 0.737
#> CV:skmeans 4 0.813 0.799 0.899 0.1096 0.888 0.677
#> MAD:skmeans 4 0.879 0.871 0.927 0.1012 0.916 0.751
#> ATC:skmeans 4 0.721 0.757 0.777 0.0754 0.944 0.837
#> SD:mclust 4 0.508 0.695 0.760 0.1146 0.918 0.767
#> CV:mclust 4 0.666 0.636 0.799 0.1875 0.803 0.522
#> MAD:mclust 4 0.519 0.582 0.741 0.1340 0.833 0.568
#> ATC:mclust 4 0.589 0.690 0.824 0.1566 0.722 0.475
#> SD:kmeans 4 0.719 0.718 0.812 0.1809 0.892 0.716
#> CV:kmeans 4 0.689 0.806 0.853 0.1622 0.907 0.743
#> MAD:kmeans 4 0.701 0.652 0.776 0.1569 0.910 0.755
#> ATC:kmeans 4 0.663 0.747 0.878 0.2566 0.805 0.630
#> SD:pam 4 0.815 0.847 0.880 0.0625 0.980 0.944
#> CV:pam 4 0.774 0.813 0.858 0.0707 0.981 0.947
#> MAD:pam 4 0.828 0.767 0.817 0.0760 0.949 0.856
#> ATC:pam 4 0.561 0.718 0.837 0.2492 0.735 0.416
#> SD:hclust 4 0.764 0.800 0.908 0.1778 0.896 0.848
#> CV:hclust 4 0.827 0.879 0.921 0.0497 0.985 0.979
#> MAD:hclust 4 0.705 0.835 0.907 0.2762 0.805 0.718
#> ATC:hclust 4 0.819 0.879 0.946 0.0411 0.988 0.981
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.644 0.613 0.767 0.0626 0.914 0.708
#> CV:NMF 5 0.709 0.663 0.832 0.0749 0.900 0.661
#> MAD:NMF 5 0.686 0.662 0.815 0.0736 0.855 0.522
#> ATC:NMF 5 0.646 0.599 0.779 0.0785 0.934 0.787
#> SD:skmeans 5 0.846 0.837 0.906 0.0577 0.957 0.839
#> CV:skmeans 5 0.798 0.667 0.857 0.0539 0.939 0.774
#> MAD:skmeans 5 0.787 0.815 0.880 0.0621 0.946 0.798
#> ATC:skmeans 5 0.727 0.654 0.827 0.0971 0.800 0.452
#> SD:mclust 5 0.628 0.521 0.728 0.0802 0.880 0.619
#> CV:mclust 5 0.602 0.581 0.757 0.0614 0.868 0.598
#> MAD:mclust 5 0.701 0.686 0.809 0.0652 0.833 0.511
#> ATC:mclust 5 0.483 0.590 0.742 0.0611 0.941 0.853
#> SD:kmeans 5 0.714 0.699 0.800 0.0774 0.884 0.627
#> CV:kmeans 5 0.735 0.684 0.811 0.0866 0.911 0.695
#> MAD:kmeans 5 0.699 0.720 0.817 0.0850 0.864 0.578
#> ATC:kmeans 5 0.658 0.563 0.757 0.1208 0.926 0.783
#> SD:pam 5 0.864 0.877 0.942 0.1038 0.888 0.688
#> CV:pam 5 0.790 0.798 0.904 0.0824 0.860 0.611
#> MAD:pam 5 0.934 0.913 0.962 0.0728 0.919 0.746
#> ATC:pam 5 0.733 0.810 0.859 0.0999 0.859 0.546
#> SD:hclust 5 0.739 0.779 0.890 0.1479 0.871 0.787
#> CV:hclust 5 0.765 0.811 0.915 0.2788 0.840 0.764
#> MAD:hclust 5 0.556 0.792 0.879 0.1433 0.975 0.951
#> ATC:hclust 5 0.772 0.845 0.920 0.3281 0.829 0.741
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.735 0.738 0.845 0.0468 0.879 0.554
#> CV:NMF 6 0.810 0.773 0.859 0.0438 0.877 0.521
#> MAD:NMF 6 0.694 0.626 0.798 0.0404 0.915 0.633
#> ATC:NMF 6 0.648 0.650 0.796 0.0511 0.891 0.617
#> SD:skmeans 6 0.810 0.764 0.863 0.0479 0.943 0.756
#> CV:skmeans 6 0.766 0.674 0.812 0.0398 0.934 0.722
#> MAD:skmeans 6 0.777 0.760 0.865 0.0507 0.939 0.731
#> ATC:skmeans 6 0.864 0.845 0.904 0.0491 0.910 0.643
#> SD:mclust 6 0.712 0.562 0.743 0.0543 0.904 0.645
#> CV:mclust 6 0.784 0.696 0.802 0.0493 0.903 0.640
#> MAD:mclust 6 0.721 0.507 0.769 0.0488 0.961 0.851
#> ATC:mclust 6 0.781 0.823 0.886 0.1792 0.749 0.381
#> SD:kmeans 6 0.752 0.623 0.793 0.0532 0.971 0.875
#> CV:kmeans 6 0.732 0.569 0.738 0.0429 0.963 0.841
#> MAD:kmeans 6 0.737 0.646 0.786 0.0507 0.978 0.902
#> ATC:kmeans 6 0.680 0.470 0.699 0.0630 0.859 0.543
#> SD:pam 6 0.775 0.728 0.832 0.0653 0.877 0.576
#> CV:pam 6 0.754 0.653 0.829 0.0486 0.964 0.856
#> MAD:pam 6 0.835 0.713 0.875 0.0560 0.935 0.756
#> ATC:pam 6 0.794 0.608 0.791 0.0501 0.900 0.584
#> SD:hclust 6 0.507 0.686 0.807 0.1352 0.973 0.945
#> CV:hclust 6 0.725 0.742 0.875 0.0813 0.990 0.981
#> MAD:hclust 6 0.424 0.596 0.775 0.2250 0.896 0.786
#> ATC:hclust 6 0.772 0.825 0.910 0.0222 0.991 0.981
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.935 0.927 0.968 0.308 0.695 0.695
#> 3 3 0.760 0.850 0.945 0.147 0.987 0.981
#> 4 4 0.764 0.800 0.908 0.178 0.896 0.848
#> 5 5 0.739 0.779 0.890 0.148 0.871 0.787
#> 6 6 0.507 0.686 0.807 0.135 0.973 0.945
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
#> SRR1442087 1 0.0000 0.974 1.000 0.000
#> SRR1390119 2 0.0000 0.919 0.000 1.000
#> SRR1436127 1 0.0000 0.974 1.000 0.000
#> SRR1347278 1 0.0000 0.974 1.000 0.000
#> SRR1332904 2 0.8081 0.709 0.248 0.752
#> SRR1444179 1 0.0000 0.974 1.000 0.000
#> SRR1082685 1 0.0000 0.974 1.000 0.000
#> SRR1362287 1 0.0000 0.974 1.000 0.000
#> SRR1339007 1 0.0000 0.974 1.000 0.000
#> SRR1376557 2 0.0000 0.919 0.000 1.000
#> SRR1468700 2 0.0376 0.919 0.004 0.996
#> SRR1077455 1 0.0000 0.974 1.000 0.000
#> SRR1413978 1 0.0000 0.974 1.000 0.000
#> SRR1439896 1 0.0000 0.974 1.000 0.000
#> SRR1317963 2 0.8909 0.621 0.308 0.692
#> SRR1431865 1 0.0000 0.974 1.000 0.000
#> SRR1394253 1 0.0000 0.974 1.000 0.000
#> SRR1082664 1 0.0000 0.974 1.000 0.000
#> SRR1077968 1 0.0000 0.974 1.000 0.000
#> SRR1076393 1 0.0000 0.974 1.000 0.000
#> SRR1477476 2 0.0000 0.919 0.000 1.000
#> SRR1398057 1 0.0000 0.974 1.000 0.000
#> SRR1485042 1 0.0000 0.974 1.000 0.000
#> SRR1385453 1 0.4431 0.891 0.908 0.092
#> SRR1348074 1 0.2948 0.932 0.948 0.052
#> SRR813959 1 0.9580 0.348 0.620 0.380
#> SRR665442 1 0.3114 0.929 0.944 0.056
#> SRR1378068 1 0.0000 0.974 1.000 0.000
#> SRR1485237 1 0.2948 0.932 0.948 0.052
#> SRR1350792 1 0.0000 0.974 1.000 0.000
#> SRR1326797 1 0.0000 0.974 1.000 0.000
#> SRR808994 1 0.0000 0.974 1.000 0.000
#> SRR1474041 1 0.0000 0.974 1.000 0.000
#> SRR1405641 1 0.0000 0.974 1.000 0.000
#> SRR1362245 1 0.0000 0.974 1.000 0.000
#> SRR1500194 1 0.0000 0.974 1.000 0.000
#> SRR1414876 2 0.0000 0.919 0.000 1.000
#> SRR1478523 1 0.3274 0.924 0.940 0.060
#> SRR1325161 1 0.0000 0.974 1.000 0.000
#> SRR1318026 1 0.2778 0.936 0.952 0.048
#> SRR1343778 1 0.0000 0.974 1.000 0.000
#> SRR1441287 1 0.0000 0.974 1.000 0.000
#> SRR1430991 1 0.0000 0.974 1.000 0.000
#> SRR1499722 1 0.0000 0.974 1.000 0.000
#> SRR1351368 1 0.0000 0.974 1.000 0.000
#> SRR1441785 1 0.0000 0.974 1.000 0.000
#> SRR1096101 1 0.0000 0.974 1.000 0.000
#> SRR808375 1 0.0000 0.974 1.000 0.000
#> SRR1452842 1 0.0000 0.974 1.000 0.000
#> SRR1311709 1 0.0000 0.974 1.000 0.000
#> SRR1433352 1 0.0000 0.974 1.000 0.000
#> SRR1340241 2 0.0376 0.919 0.004 0.996
#> SRR1456754 1 0.0000 0.974 1.000 0.000
#> SRR1465172 1 0.0000 0.974 1.000 0.000
#> SRR1499284 1 0.0000 0.974 1.000 0.000
#> SRR1499607 2 0.8909 0.621 0.308 0.692
#> SRR812342 1 0.0000 0.974 1.000 0.000
#> SRR1405374 1 0.0000 0.974 1.000 0.000
#> SRR1403565 1 0.0000 0.974 1.000 0.000
#> SRR1332024 1 0.0000 0.974 1.000 0.000
#> SRR1471633 1 0.0000 0.974 1.000 0.000
#> SRR1325944 2 0.0000 0.919 0.000 1.000
#> SRR1429450 2 0.0000 0.919 0.000 1.000
#> SRR821573 1 0.0000 0.974 1.000 0.000
#> SRR1435372 1 0.0000 0.974 1.000 0.000
#> SRR1324184 2 0.0000 0.919 0.000 1.000
#> SRR816517 1 0.5519 0.846 0.872 0.128
#> SRR1324141 1 0.2948 0.932 0.948 0.052
#> SRR1101612 1 0.0000 0.974 1.000 0.000
#> SRR1356531 1 0.0000 0.974 1.000 0.000
#> SRR1089785 1 0.0000 0.974 1.000 0.000
#> SRR1077708 1 0.0000 0.974 1.000 0.000
#> SRR1343720 1 0.0000 0.974 1.000 0.000
#> SRR1477499 2 0.0000 0.919 0.000 1.000
#> SRR1347236 1 0.0000 0.974 1.000 0.000
#> SRR1326408 1 0.0000 0.974 1.000 0.000
#> SRR1336529 1 0.0000 0.974 1.000 0.000
#> SRR1440643 1 0.4431 0.891 0.908 0.092
#> SRR662354 1 0.0000 0.974 1.000 0.000
#> SRR1310817 1 0.0000 0.974 1.000 0.000
#> SRR1347389 2 0.0376 0.918 0.004 0.996
#> SRR1353097 1 0.0000 0.974 1.000 0.000
#> SRR1384737 1 0.2948 0.932 0.948 0.052
#> SRR1096339 1 0.0000 0.974 1.000 0.000
#> SRR1345329 1 0.2948 0.932 0.948 0.052
#> SRR1414771 1 0.0000 0.974 1.000 0.000
#> SRR1309119 1 0.0000 0.974 1.000 0.000
#> SRR1470438 1 0.0000 0.974 1.000 0.000
#> SRR1343221 1 0.0000 0.974 1.000 0.000
#> SRR1410847 1 0.0000 0.974 1.000 0.000
#> SRR807949 1 0.0000 0.974 1.000 0.000
#> SRR1442332 1 0.0000 0.974 1.000 0.000
#> SRR815920 1 0.0000 0.974 1.000 0.000
#> SRR1471524 1 0.0000 0.974 1.000 0.000
#> SRR1477221 1 0.0000 0.974 1.000 0.000
#> SRR1445046 2 0.8909 0.621 0.308 0.692
#> SRR1331962 2 0.0376 0.919 0.004 0.996
#> SRR1319946 1 0.9661 0.312 0.608 0.392
#> SRR1311599 1 0.0000 0.974 1.000 0.000
#> SRR1323977 1 0.9427 0.404 0.640 0.360
#> SRR1445132 2 0.0000 0.919 0.000 1.000
#> SRR1337321 1 0.0000 0.974 1.000 0.000
#> SRR1366390 2 0.0376 0.918 0.004 0.996
#> SRR1343012 1 0.2948 0.932 0.948 0.052
#> SRR1311958 2 0.6438 0.800 0.164 0.836
#> SRR1388234 1 0.9209 0.465 0.664 0.336
#> SRR1370384 1 0.0000 0.974 1.000 0.000
#> SRR1321650 1 0.0000 0.974 1.000 0.000
#> SRR1485117 2 0.0000 0.919 0.000 1.000
#> SRR1384713 1 0.0000 0.974 1.000 0.000
#> SRR816609 1 0.2948 0.932 0.948 0.052
#> SRR1486239 2 0.8861 0.628 0.304 0.696
#> SRR1309638 1 0.0000 0.974 1.000 0.000
#> SRR1356660 1 0.0000 0.974 1.000 0.000
#> SRR1392883 2 0.0000 0.919 0.000 1.000
#> SRR808130 1 0.0000 0.974 1.000 0.000
#> SRR816677 1 0.2778 0.936 0.952 0.048
#> SRR1455722 1 0.0000 0.974 1.000 0.000
#> SRR1336029 1 0.0000 0.974 1.000 0.000
#> SRR808452 1 0.0000 0.974 1.000 0.000
#> SRR1352169 1 0.0000 0.974 1.000 0.000
#> SRR1366707 1 0.0000 0.974 1.000 0.000
#> SRR1328143 1 0.0000 0.974 1.000 0.000
#> SRR1473567 2 0.0000 0.919 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1390119 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1436127 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1347278 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1332904 2 0.7804 0.5351 0.120 0.664 0.216
#> SRR1444179 1 0.0592 0.9344 0.988 0.000 0.012
#> SRR1082685 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1376557 2 0.0747 0.8463 0.000 0.984 0.016
#> SRR1468700 2 0.1529 0.8358 0.000 0.960 0.040
#> SRR1077455 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1317963 2 0.8587 0.4222 0.176 0.604 0.220
#> SRR1431865 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1082664 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1077968 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1076393 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1477476 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1398057 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1485042 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1385453 1 0.4842 0.6848 0.776 0.000 0.224
#> SRR1348074 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR813959 1 0.9501 -0.0639 0.488 0.288 0.224
#> SRR665442 3 0.4504 0.0000 0.196 0.000 0.804
#> SRR1378068 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1485237 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR1350792 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR808994 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1474041 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1405641 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1362245 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1500194 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1478523 1 0.4452 0.7338 0.808 0.000 0.192
#> SRR1325161 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1318026 1 0.3941 0.7829 0.844 0.000 0.156
#> SRR1343778 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1441287 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1430991 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1499722 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1351368 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1441785 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR808375 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1452842 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1311709 1 0.0424 0.9379 0.992 0.000 0.008
#> SRR1433352 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1340241 2 0.1964 0.8264 0.000 0.944 0.056
#> SRR1456754 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1465172 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1499284 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1499607 2 0.8587 0.4222 0.176 0.604 0.220
#> SRR812342 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1332024 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1471633 1 0.0592 0.9344 0.988 0.000 0.012
#> SRR1325944 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR821573 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1435372 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1324184 2 0.1031 0.8391 0.000 0.976 0.024
#> SRR816517 1 0.6211 0.6133 0.736 0.036 0.228
#> SRR1324141 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR1101612 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1089785 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1077708 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1343720 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1477499 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1347236 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1326408 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1336529 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1440643 1 0.4796 0.6911 0.780 0.000 0.220
#> SRR662354 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1310817 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1347389 2 0.0592 0.8472 0.000 0.988 0.012
#> SRR1353097 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1384737 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR1096339 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1345329 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR1414771 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1309119 1 0.0592 0.9344 0.988 0.000 0.012
#> SRR1470438 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1343221 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR807949 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1442332 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR815920 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1471524 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1477221 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1445046 2 0.8587 0.4222 0.176 0.604 0.220
#> SRR1331962 2 0.1529 0.8358 0.000 0.960 0.040
#> SRR1319946 1 0.9582 -0.1092 0.472 0.300 0.228
#> SRR1311599 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1323977 1 0.9292 0.0373 0.520 0.272 0.208
#> SRR1445132 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1337321 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1366390 2 0.0592 0.8472 0.000 0.988 0.012
#> SRR1343012 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR1311958 2 0.5798 0.6868 0.044 0.780 0.176
#> SRR1388234 1 0.9055 0.1383 0.552 0.252 0.196
#> SRR1370384 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1321650 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1485117 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR816609 1 0.4002 0.7783 0.840 0.000 0.160
#> SRR1486239 2 0.8542 0.4309 0.172 0.608 0.220
#> SRR1309638 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1356660 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.8499 0.000 1.000 0.000
#> SRR808130 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR816677 1 0.3941 0.7829 0.844 0.000 0.156
#> SRR1455722 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1352169 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1366707 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1328143 1 0.0000 0.9445 1.000 0.000 0.000
#> SRR1473567 2 0.0000 0.8499 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1390119 2 0.1118 0.80254 0.000 0.964 0.036 0.000
#> SRR1436127 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1347278 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1332904 3 0.4356 0.00796 0.000 0.292 0.708 0.000
#> SRR1444179 1 0.1118 0.91669 0.964 0.000 0.036 0.000
#> SRR1082685 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.3873 0.82713 0.000 0.772 0.228 0.000
#> SRR1468700 2 0.4843 0.66232 0.000 0.604 0.396 0.000
#> SRR1077455 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1413978 1 0.0336 0.94254 0.992 0.000 0.008 0.000
#> SRR1439896 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1317963 3 0.3907 0.13640 0.000 0.232 0.768 0.000
#> SRR1431865 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1082664 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1077968 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1076393 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1477476 2 0.1118 0.80254 0.000 0.964 0.036 0.000
#> SRR1398057 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1485042 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1385453 3 0.4941 0.35571 0.436 0.000 0.564 0.000
#> SRR1348074 1 0.4500 0.45099 0.684 0.000 0.316 0.000
#> SRR813959 3 0.6351 0.45117 0.268 0.104 0.628 0.000
#> SRR665442 4 0.0000 0.00000 0.000 0.000 0.000 1.000
#> SRR1378068 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1485237 1 0.4500 0.45099 0.684 0.000 0.316 0.000
#> SRR1350792 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR808994 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1474041 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1405641 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1362245 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1500194 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.1302 0.84185 0.000 0.956 0.044 0.000
#> SRR1478523 3 0.4994 0.23134 0.480 0.000 0.520 0.000
#> SRR1325161 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1318026 1 0.4454 0.46967 0.692 0.000 0.308 0.000
#> SRR1343778 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1441287 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1430991 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1499722 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1351368 1 0.2011 0.86660 0.920 0.000 0.080 0.000
#> SRR1441785 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR808375 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1452842 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1311709 1 0.1474 0.90055 0.948 0.000 0.052 0.000
#> SRR1433352 1 0.0188 0.94573 0.996 0.000 0.004 0.000
#> SRR1340241 2 0.4008 0.80961 0.000 0.756 0.244 0.000
#> SRR1456754 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1465172 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1499284 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1499607 3 0.3837 0.14136 0.000 0.224 0.776 0.000
#> SRR812342 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1332024 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1471633 1 0.1389 0.90475 0.952 0.000 0.048 0.000
#> SRR1325944 2 0.0921 0.83845 0.000 0.972 0.028 0.000
#> SRR1429450 2 0.0000 0.82632 0.000 1.000 0.000 0.000
#> SRR821573 1 0.0336 0.94252 0.992 0.000 0.008 0.000
#> SRR1435372 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.4426 0.82958 0.000 0.772 0.204 0.024
#> SRR816517 3 0.4843 0.41407 0.396 0.000 0.604 0.000
#> SRR1324141 1 0.4431 0.48019 0.696 0.000 0.304 0.000
#> SRR1101612 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1089785 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1077708 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1343720 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1477499 2 0.0336 0.83043 0.000 0.992 0.008 0.000
#> SRR1347236 1 0.0188 0.94573 0.996 0.000 0.004 0.000
#> SRR1326408 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1336529 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1440643 1 0.4994 -0.14591 0.520 0.000 0.480 0.000
#> SRR662354 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1310817 1 0.0469 0.93924 0.988 0.000 0.012 0.000
#> SRR1347389 2 0.3975 0.82692 0.000 0.760 0.240 0.000
#> SRR1353097 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1384737 1 0.4431 0.48019 0.696 0.000 0.304 0.000
#> SRR1096339 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1345329 1 0.4500 0.45099 0.684 0.000 0.316 0.000
#> SRR1414771 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1309119 1 0.1389 0.90475 0.952 0.000 0.048 0.000
#> SRR1470438 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1343221 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR807949 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1442332 1 0.0188 0.94573 0.996 0.000 0.004 0.000
#> SRR815920 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1471524 1 0.2011 0.86660 0.920 0.000 0.080 0.000
#> SRR1477221 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1445046 3 0.3907 0.13640 0.000 0.232 0.768 0.000
#> SRR1331962 2 0.4843 0.66232 0.000 0.604 0.396 0.000
#> SRR1319946 3 0.6042 0.42777 0.224 0.104 0.672 0.000
#> SRR1311599 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1323977 3 0.6627 0.46213 0.348 0.096 0.556 0.000
#> SRR1445132 2 0.1118 0.80254 0.000 0.964 0.036 0.000
#> SRR1337321 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1366390 2 0.3975 0.82692 0.000 0.760 0.240 0.000
#> SRR1343012 1 0.4431 0.48019 0.696 0.000 0.304 0.000
#> SRR1311958 3 0.4877 -0.29640 0.000 0.408 0.592 0.000
#> SRR1388234 3 0.6735 0.45250 0.388 0.096 0.516 0.000
#> SRR1370384 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1321650 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1485117 2 0.3172 0.84408 0.000 0.840 0.160 0.000
#> SRR1384713 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR816609 1 0.4500 0.45099 0.684 0.000 0.316 0.000
#> SRR1486239 3 0.3942 0.13085 0.000 0.236 0.764 0.000
#> SRR1309638 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1356660 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1392883 2 0.0817 0.83738 0.000 0.976 0.024 0.000
#> SRR808130 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR816677 1 0.4193 0.55306 0.732 0.000 0.268 0.000
#> SRR1455722 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0592 0.93588 0.984 0.000 0.016 0.000
#> SRR808452 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1352169 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1366707 1 0.2011 0.86660 0.920 0.000 0.080 0.000
#> SRR1328143 1 0.0000 0.94880 1.000 0.000 0.000 0.000
#> SRR1473567 2 0.3486 0.84014 0.000 0.812 0.188 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 1 0.0290 0.9676 0.992 0.000 0.000 0.008 0.000
#> SRR1390119 3 0.0000 0.7741 0.000 0.000 1.000 0.000 0.000
#> SRR1436127 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1347278 1 0.0290 0.9682 0.992 0.000 0.000 0.008 0.000
#> SRR1332904 2 0.3990 0.5155 0.000 0.688 0.004 0.308 0.000
#> SRR1444179 1 0.1732 0.8806 0.920 0.000 0.000 0.080 0.000
#> SRR1082685 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1362287 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1339007 1 0.0404 0.9677 0.988 0.000 0.000 0.012 0.000
#> SRR1376557 2 0.4375 -0.0517 0.000 0.576 0.420 0.004 0.000
#> SRR1468700 2 0.2471 0.4746 0.000 0.864 0.136 0.000 0.000
#> SRR1077455 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1413978 1 0.0510 0.9648 0.984 0.000 0.000 0.016 0.000
#> SRR1439896 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1317963 2 0.4088 0.4902 0.000 0.632 0.000 0.368 0.000
#> SRR1431865 1 0.0290 0.9693 0.992 0.000 0.000 0.008 0.000
#> SRR1394253 1 0.0290 0.9693 0.992 0.000 0.000 0.008 0.000
#> SRR1082664 1 0.0162 0.9688 0.996 0.000 0.000 0.004 0.000
#> SRR1077968 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1076393 1 0.0510 0.9618 0.984 0.000 0.000 0.016 0.000
#> SRR1477476 3 0.0000 0.7741 0.000 0.000 1.000 0.000 0.000
#> SRR1398057 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1485042 1 0.0404 0.9677 0.988 0.000 0.000 0.012 0.000
#> SRR1385453 4 0.1908 0.2697 0.092 0.000 0.000 0.908 0.000
#> SRR1348074 4 0.4249 0.6505 0.432 0.000 0.000 0.568 0.000
#> SRR813959 4 0.5062 -0.0110 0.068 0.276 0.000 0.656 0.000
#> SRR665442 5 0.0000 0.0000 0.000 0.000 0.000 0.000 1.000
#> SRR1378068 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1485237 4 0.4249 0.6505 0.432 0.000 0.000 0.568 0.000
#> SRR1350792 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1326797 1 0.0162 0.9695 0.996 0.000 0.000 0.004 0.000
#> SRR808994 1 0.0510 0.9637 0.984 0.000 0.000 0.016 0.000
#> SRR1474041 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1405641 1 0.0510 0.9637 0.984 0.000 0.000 0.016 0.000
#> SRR1362245 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1500194 1 0.0290 0.9693 0.992 0.000 0.000 0.008 0.000
#> SRR1414876 3 0.3612 0.6954 0.000 0.268 0.732 0.000 0.000
#> SRR1478523 4 0.2471 0.3430 0.136 0.000 0.000 0.864 0.000
#> SRR1325161 1 0.0000 0.9692 1.000 0.000 0.000 0.000 0.000
#> SRR1318026 4 0.4262 0.6390 0.440 0.000 0.000 0.560 0.000
#> SRR1343778 1 0.0290 0.9676 0.992 0.000 0.000 0.008 0.000
#> SRR1441287 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1430991 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1499722 1 0.0162 0.9695 0.996 0.000 0.000 0.004 0.000
#> SRR1351368 1 0.2074 0.8462 0.896 0.000 0.000 0.104 0.000
#> SRR1441785 1 0.0290 0.9693 0.992 0.000 0.000 0.008 0.000
#> SRR1096101 1 0.0404 0.9677 0.988 0.000 0.000 0.012 0.000
#> SRR808375 1 0.0000 0.9692 1.000 0.000 0.000 0.000 0.000
#> SRR1452842 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1311709 1 0.2648 0.7479 0.848 0.000 0.000 0.152 0.000
#> SRR1433352 1 0.0290 0.9682 0.992 0.000 0.000 0.008 0.000
#> SRR1340241 3 0.5458 0.1189 0.000 0.464 0.476 0.060 0.000
#> SRR1456754 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1465172 1 0.0000 0.9692 1.000 0.000 0.000 0.000 0.000
#> SRR1499284 1 0.0000 0.9692 1.000 0.000 0.000 0.000 0.000
#> SRR1499607 2 0.4114 0.4845 0.000 0.624 0.000 0.376 0.000
#> SRR812342 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1405374 1 0.0404 0.9677 0.988 0.000 0.000 0.012 0.000
#> SRR1403565 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1332024 1 0.0510 0.9637 0.984 0.000 0.000 0.016 0.000
#> SRR1471633 1 0.2280 0.8137 0.880 0.000 0.000 0.120 0.000
#> SRR1325944 3 0.3177 0.7582 0.000 0.208 0.792 0.000 0.000
#> SRR1429450 3 0.1478 0.7958 0.000 0.064 0.936 0.000 0.000
#> SRR821573 1 0.0510 0.9629 0.984 0.000 0.000 0.016 0.000
#> SRR1435372 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1324184 2 0.4571 0.3433 0.000 0.736 0.216 0.024 0.024
#> SRR816517 4 0.1697 0.1806 0.060 0.008 0.000 0.932 0.000
#> SRR1324141 4 0.4294 0.5893 0.468 0.000 0.000 0.532 0.000
#> SRR1101612 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1356531 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1089785 1 0.0000 0.9692 1.000 0.000 0.000 0.000 0.000
#> SRR1077708 1 0.0162 0.9688 0.996 0.000 0.000 0.004 0.000
#> SRR1343720 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1477499 3 0.2690 0.7717 0.000 0.156 0.844 0.000 0.000
#> SRR1347236 1 0.0162 0.9695 0.996 0.000 0.000 0.004 0.000
#> SRR1326408 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1336529 1 0.0510 0.9637 0.984 0.000 0.000 0.016 0.000
#> SRR1440643 4 0.3452 0.4966 0.244 0.000 0.000 0.756 0.000
#> SRR662354 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1310817 1 0.1043 0.9351 0.960 0.000 0.000 0.040 0.000
#> SRR1347389 2 0.4337 0.3649 0.000 0.744 0.204 0.052 0.000
#> SRR1353097 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1384737 4 0.4294 0.5893 0.468 0.000 0.000 0.532 0.000
#> SRR1096339 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1345329 4 0.4249 0.6505 0.432 0.000 0.000 0.568 0.000
#> SRR1414771 1 0.0510 0.9637 0.984 0.000 0.000 0.016 0.000
#> SRR1309119 1 0.2280 0.8137 0.880 0.000 0.000 0.120 0.000
#> SRR1470438 1 0.0510 0.9637 0.984 0.000 0.000 0.016 0.000
#> SRR1343221 1 0.0404 0.9677 0.988 0.000 0.000 0.012 0.000
#> SRR1410847 1 0.0404 0.9677 0.988 0.000 0.000 0.012 0.000
#> SRR807949 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1442332 1 0.0290 0.9682 0.992 0.000 0.000 0.008 0.000
#> SRR815920 1 0.0404 0.9663 0.988 0.000 0.000 0.012 0.000
#> SRR1471524 1 0.2074 0.8462 0.896 0.000 0.000 0.104 0.000
#> SRR1477221 1 0.0290 0.9697 0.992 0.000 0.000 0.008 0.000
#> SRR1445046 2 0.4088 0.4902 0.000 0.632 0.000 0.368 0.000
#> SRR1331962 2 0.2329 0.4844 0.000 0.876 0.124 0.000 0.000
#> SRR1319946 4 0.3684 -0.1895 0.000 0.280 0.000 0.720 0.000
#> SRR1311599 1 0.0290 0.9693 0.992 0.000 0.000 0.008 0.000
#> SRR1323977 4 0.6062 0.3074 0.168 0.268 0.000 0.564 0.000
#> SRR1445132 3 0.0000 0.7741 0.000 0.000 1.000 0.000 0.000
#> SRR1337321 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1366390 2 0.4337 0.3649 0.000 0.744 0.204 0.052 0.000
#> SRR1343012 4 0.4294 0.5893 0.468 0.000 0.000 0.532 0.000
#> SRR1311958 2 0.2852 0.5416 0.000 0.828 0.000 0.172 0.000
#> SRR1388234 4 0.6231 0.4057 0.204 0.252 0.000 0.544 0.000
#> SRR1370384 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR1321650 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1485117 2 0.4304 -0.2254 0.000 0.516 0.484 0.000 0.000
#> SRR1384713 1 0.0162 0.9696 0.996 0.000 0.000 0.004 0.000
#> SRR816609 4 0.4249 0.6505 0.432 0.000 0.000 0.568 0.000
#> SRR1486239 2 0.4060 0.4945 0.000 0.640 0.000 0.360 0.000
#> SRR1309638 1 0.0290 0.9668 0.992 0.000 0.000 0.008 0.000
#> SRR1356660 1 0.0290 0.9693 0.992 0.000 0.000 0.008 0.000
#> SRR1392883 3 0.2966 0.7708 0.000 0.184 0.816 0.000 0.000
#> SRR808130 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR816677 1 0.4307 -0.5236 0.504 0.000 0.000 0.496 0.000
#> SRR1455722 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1336029 1 0.0794 0.9534 0.972 0.000 0.000 0.028 0.000
#> SRR808452 1 0.0290 0.9689 0.992 0.000 0.000 0.008 0.000
#> SRR1352169 1 0.0290 0.9682 0.992 0.000 0.000 0.008 0.000
#> SRR1366707 1 0.2074 0.8462 0.896 0.000 0.000 0.104 0.000
#> SRR1328143 1 0.0162 0.9686 0.996 0.000 0.000 0.004 0.000
#> SRR1473567 2 0.4210 -0.0408 0.000 0.588 0.412 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 1 0.1788 0.8746 0.928 0.000 0.004 0.028 0.040 0.000
#> SRR1390119 3 0.3774 0.9584 0.000 0.408 0.592 0.000 0.000 0.000
#> SRR1436127 1 0.2454 0.8329 0.876 0.000 0.004 0.016 0.104 0.000
#> SRR1347278 1 0.1405 0.8811 0.948 0.000 0.004 0.024 0.024 0.000
#> SRR1332904 5 0.4244 0.8726 0.000 0.080 0.000 0.200 0.720 0.000
#> SRR1444179 1 0.2491 0.7930 0.836 0.000 0.000 0.164 0.000 0.000
#> SRR1082685 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1362287 1 0.1349 0.8871 0.940 0.000 0.004 0.056 0.000 0.000
#> SRR1339007 1 0.1501 0.8769 0.924 0.000 0.000 0.076 0.000 0.000
#> SRR1376557 2 0.4326 0.2660 0.000 0.656 0.044 0.000 0.300 0.000
#> SRR1468700 2 0.3737 0.1411 0.000 0.608 0.000 0.000 0.392 0.000
#> SRR1077455 1 0.1411 0.8849 0.936 0.000 0.004 0.060 0.000 0.000
#> SRR1413978 1 0.1444 0.8830 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR1439896 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1317963 5 0.3534 0.9117 0.000 0.016 0.000 0.244 0.740 0.000
#> SRR1431865 1 0.1141 0.8854 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1394253 1 0.1141 0.8854 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1082664 1 0.1059 0.8873 0.964 0.000 0.004 0.016 0.016 0.000
#> SRR1077968 1 0.1411 0.8849 0.936 0.000 0.004 0.060 0.000 0.000
#> SRR1076393 1 0.2641 0.8389 0.876 0.000 0.004 0.048 0.072 0.000
#> SRR1477476 3 0.3774 0.9584 0.000 0.408 0.592 0.000 0.000 0.000
#> SRR1398057 1 0.1826 0.8682 0.924 0.000 0.004 0.020 0.052 0.000
#> SRR1485042 1 0.1444 0.8789 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR1385453 4 0.2774 0.1165 0.040 0.000 0.012 0.872 0.076 0.000
#> SRR1348074 4 0.3699 0.6561 0.336 0.000 0.000 0.660 0.004 0.000
#> SRR813959 4 0.5165 -0.3180 0.068 0.000 0.008 0.528 0.396 0.000
#> SRR665442 6 0.0000 0.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1378068 1 0.2405 0.8373 0.880 0.000 0.004 0.016 0.100 0.000
#> SRR1485237 4 0.3699 0.6561 0.336 0.000 0.000 0.660 0.004 0.000
#> SRR1350792 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1326797 1 0.0767 0.8869 0.976 0.000 0.004 0.012 0.008 0.000
#> SRR808994 1 0.3702 0.7339 0.784 0.000 0.008 0.044 0.164 0.000
#> SRR1474041 1 0.1401 0.8791 0.948 0.000 0.004 0.020 0.028 0.000
#> SRR1405641 1 0.3702 0.7339 0.784 0.000 0.008 0.044 0.164 0.000
#> SRR1362245 1 0.3388 0.7587 0.804 0.000 0.004 0.036 0.156 0.000
#> SRR1500194 1 0.1141 0.8854 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1414876 2 0.4236 -0.3789 0.000 0.656 0.308 0.000 0.036 0.000
#> SRR1478523 4 0.3272 0.1876 0.080 0.000 0.008 0.836 0.076 0.000
#> SRR1325161 1 0.0862 0.8853 0.972 0.000 0.004 0.008 0.016 0.000
#> SRR1318026 4 0.3728 0.6537 0.344 0.000 0.000 0.652 0.004 0.000
#> SRR1343778 1 0.1788 0.8746 0.928 0.000 0.004 0.028 0.040 0.000
#> SRR1441287 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1430991 1 0.1562 0.8780 0.940 0.000 0.004 0.024 0.032 0.000
#> SRR1499722 1 0.0767 0.8869 0.976 0.000 0.004 0.012 0.008 0.000
#> SRR1351368 1 0.3852 0.7304 0.784 0.000 0.008 0.136 0.072 0.000
#> SRR1441785 1 0.1141 0.8854 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1096101 1 0.1501 0.8769 0.924 0.000 0.000 0.076 0.000 0.000
#> SRR808375 1 0.0862 0.8853 0.972 0.000 0.004 0.008 0.016 0.000
#> SRR1452842 1 0.1411 0.8849 0.936 0.000 0.004 0.060 0.000 0.000
#> SRR1311709 1 0.3163 0.6816 0.764 0.000 0.000 0.232 0.004 0.000
#> SRR1433352 1 0.1074 0.8854 0.960 0.000 0.000 0.028 0.012 0.000
#> SRR1340241 2 0.5556 0.1617 0.000 0.592 0.108 0.024 0.276 0.000
#> SRR1456754 1 0.1155 0.8897 0.956 0.000 0.004 0.036 0.004 0.000
#> SRR1465172 1 0.0862 0.8853 0.972 0.000 0.004 0.008 0.016 0.000
#> SRR1499284 1 0.0862 0.8853 0.972 0.000 0.004 0.008 0.016 0.000
#> SRR1499607 5 0.3373 0.9058 0.000 0.008 0.000 0.248 0.744 0.000
#> SRR812342 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1405374 1 0.1714 0.8668 0.908 0.000 0.000 0.092 0.000 0.000
#> SRR1403565 1 0.1552 0.8906 0.940 0.000 0.004 0.036 0.020 0.000
#> SRR1332024 1 0.3702 0.7339 0.784 0.000 0.008 0.044 0.164 0.000
#> SRR1471633 1 0.2823 0.7335 0.796 0.000 0.000 0.204 0.000 0.000
#> SRR1325944 2 0.3955 -0.5774 0.000 0.608 0.384 0.000 0.008 0.000
#> SRR1429450 3 0.3860 0.8677 0.000 0.472 0.528 0.000 0.000 0.000
#> SRR821573 1 0.1296 0.8843 0.952 0.000 0.004 0.032 0.012 0.000
#> SRR1435372 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1324184 2 0.5644 0.1635 0.000 0.500 0.392 0.000 0.084 0.024
#> SRR816517 4 0.2617 0.0379 0.016 0.000 0.012 0.872 0.100 0.000
#> SRR1324141 4 0.3672 0.6427 0.368 0.000 0.000 0.632 0.000 0.000
#> SRR1101612 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1356531 1 0.1444 0.8806 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR1089785 1 0.1218 0.8843 0.956 0.000 0.004 0.012 0.028 0.000
#> SRR1077708 1 0.1059 0.8873 0.964 0.000 0.004 0.016 0.016 0.000
#> SRR1343720 1 0.1168 0.8842 0.956 0.000 0.000 0.016 0.028 0.000
#> SRR1477499 2 0.4660 -0.6422 0.000 0.540 0.416 0.000 0.044 0.000
#> SRR1347236 1 0.0951 0.8866 0.968 0.000 0.004 0.020 0.008 0.000
#> SRR1326408 1 0.1411 0.8849 0.936 0.000 0.004 0.060 0.000 0.000
#> SRR1336529 1 0.3702 0.7339 0.784 0.000 0.008 0.044 0.164 0.000
#> SRR1440643 4 0.4433 0.4269 0.200 0.000 0.012 0.720 0.068 0.000
#> SRR662354 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1310817 1 0.1769 0.8737 0.924 0.000 0.004 0.060 0.012 0.000
#> SRR1347389 2 0.5489 0.1726 0.000 0.496 0.396 0.008 0.100 0.000
#> SRR1353097 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1384737 4 0.3672 0.6427 0.368 0.000 0.000 0.632 0.000 0.000
#> SRR1096339 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1345329 4 0.3699 0.6561 0.336 0.000 0.000 0.660 0.004 0.000
#> SRR1414771 1 0.3702 0.7339 0.784 0.000 0.008 0.044 0.164 0.000
#> SRR1309119 1 0.2823 0.7335 0.796 0.000 0.000 0.204 0.000 0.000
#> SRR1470438 1 0.3702 0.7339 0.784 0.000 0.008 0.044 0.164 0.000
#> SRR1343221 1 0.1714 0.8668 0.908 0.000 0.000 0.092 0.000 0.000
#> SRR1410847 1 0.1501 0.8772 0.924 0.000 0.000 0.076 0.000 0.000
#> SRR807949 1 0.1401 0.8791 0.948 0.000 0.004 0.020 0.028 0.000
#> SRR1442332 1 0.1218 0.8845 0.956 0.000 0.004 0.028 0.012 0.000
#> SRR815920 1 0.2341 0.8596 0.900 0.000 0.012 0.032 0.056 0.000
#> SRR1471524 1 0.3852 0.7304 0.784 0.000 0.008 0.136 0.072 0.000
#> SRR1477221 1 0.1844 0.8888 0.924 0.000 0.004 0.048 0.024 0.000
#> SRR1445046 5 0.3534 0.9117 0.000 0.016 0.000 0.244 0.740 0.000
#> SRR1331962 2 0.3765 0.1078 0.000 0.596 0.000 0.000 0.404 0.000
#> SRR1319946 4 0.4018 -0.4743 0.000 0.000 0.008 0.580 0.412 0.000
#> SRR1311599 1 0.1141 0.8854 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1323977 4 0.5741 0.0949 0.152 0.000 0.004 0.480 0.364 0.000
#> SRR1445132 3 0.3774 0.9584 0.000 0.408 0.592 0.000 0.000 0.000
#> SRR1337321 1 0.3317 0.7635 0.808 0.000 0.004 0.032 0.156 0.000
#> SRR1366390 2 0.5489 0.1726 0.000 0.496 0.396 0.008 0.100 0.000
#> SRR1343012 4 0.3672 0.6427 0.368 0.000 0.000 0.632 0.000 0.000
#> SRR1311958 5 0.4952 0.6288 0.000 0.252 0.000 0.116 0.632 0.000
#> SRR1388234 4 0.5635 0.2202 0.152 0.000 0.004 0.528 0.316 0.000
#> SRR1370384 1 0.1411 0.8849 0.936 0.000 0.004 0.060 0.000 0.000
#> SRR1321650 1 0.3353 0.7587 0.804 0.000 0.004 0.032 0.160 0.000
#> SRR1485117 2 0.0508 0.2540 0.000 0.984 0.004 0.000 0.012 0.000
#> SRR1384713 1 0.1411 0.8849 0.936 0.000 0.004 0.060 0.000 0.000
#> SRR816609 4 0.3699 0.6561 0.336 0.000 0.000 0.660 0.004 0.000
#> SRR1486239 5 0.3670 0.9115 0.000 0.024 0.000 0.240 0.736 0.000
#> SRR1309638 1 0.2913 0.8053 0.848 0.000 0.004 0.032 0.116 0.000
#> SRR1356660 1 0.1141 0.8854 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1392883 2 0.3774 -0.6241 0.000 0.592 0.408 0.000 0.000 0.000
#> SRR808130 1 0.1401 0.8791 0.948 0.000 0.004 0.020 0.028 0.000
#> SRR816677 4 0.3915 0.5693 0.412 0.000 0.000 0.584 0.004 0.000
#> SRR1455722 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1336029 1 0.1957 0.8566 0.888 0.000 0.000 0.112 0.000 0.000
#> SRR808452 1 0.1663 0.8690 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1352169 1 0.1405 0.8811 0.948 0.000 0.004 0.024 0.024 0.000
#> SRR1366707 1 0.3852 0.7304 0.784 0.000 0.008 0.136 0.072 0.000
#> SRR1328143 1 0.1401 0.8791 0.948 0.000 0.004 0.020 0.028 0.000
#> SRR1473567 2 0.2597 0.3342 0.000 0.824 0.000 0.000 0.176 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17851 rows and 124 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 0.989 0.996 0.3294 0.666 0.666
#> 3 3 0.623 0.883 0.879 0.8327 0.671 0.515
#> 4 4 0.719 0.718 0.812 0.1809 0.892 0.716
#> 5 5 0.714 0.699 0.800 0.0774 0.884 0.627
#> 6 6 0.752 0.623 0.793 0.0532 0.971 0.875
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1442087 1 0.000 1.000 1.000 0.000
#> SRR1390119 2 0.000 0.978 0.000 1.000
#> SRR1436127 1 0.000 1.000 1.000 0.000
#> SRR1347278 1 0.000 1.000 1.000 0.000
#> SRR1332904 2 0.000 0.978 0.000 1.000
#> SRR1444179 1 0.000 1.000 1.000 0.000
#> SRR1082685 1 0.000 1.000 1.000 0.000
#> SRR1362287 1 0.000 1.000 1.000 0.000
#> SRR1339007 1 0.000 1.000 1.000 0.000
#> SRR1376557 2 0.000 0.978 0.000 1.000
#> SRR1468700 2 0.000 0.978 0.000 1.000
#> SRR1077455 1 0.000 1.000 1.000 0.000
#> SRR1413978 1 0.000 1.000 1.000 0.000
#> SRR1439896 1 0.000 1.000 1.000 0.000
#> SRR1317963 2 0.000 0.978 0.000 1.000
#> SRR1431865 1 0.000 1.000 1.000 0.000
#> SRR1394253 1 0.000 1.000 1.000 0.000
#> SRR1082664 1 0.000 1.000 1.000 0.000
#> SRR1077968 1 0.000 1.000 1.000 0.000
#> SRR1076393 1 0.000 1.000 1.000 0.000
#> SRR1477476 2 0.000 0.978 0.000 1.000
#> SRR1398057 1 0.000 1.000 1.000 0.000
#> SRR1485042 1 0.000 1.000 1.000 0.000
#> SRR1385453 1 0.000 1.000 1.000 0.000
#> SRR1348074 1 0.000 1.000 1.000 0.000
#> SRR813959 1 0.000 1.000 1.000 0.000
#> SRR665442 1 0.000 1.000 1.000 0.000
#> SRR1378068 1 0.000 1.000 1.000 0.000
#> SRR1485237 1 0.000 1.000 1.000 0.000
#> SRR1350792 1 0.000 1.000 1.000 0.000
#> SRR1326797 1 0.000 1.000 1.000 0.000
#> SRR808994 1 0.000 1.000 1.000 0.000
#> SRR1474041 1 0.000 1.000 1.000 0.000
#> SRR1405641 1 0.000 1.000 1.000 0.000
#> SRR1362245 1 0.000 1.000 1.000 0.000
#> SRR1500194 1 0.000 1.000 1.000 0.000
#> SRR1414876 2 0.000 0.978 0.000 1.000
#> SRR1478523 1 0.000 1.000 1.000 0.000
#> SRR1325161 1 0.000 1.000 1.000 0.000
#> SRR1318026 1 0.000 1.000 1.000 0.000
#> SRR1343778 1 0.000 1.000 1.000 0.000
#> SRR1441287 1 0.000 1.000 1.000 0.000
#> SRR1430991 1 0.000 1.000 1.000 0.000
#> SRR1499722 1 0.000 1.000 1.000 0.000
#> SRR1351368 1 0.000 1.000 1.000 0.000
#> SRR1441785 1 0.000 1.000 1.000 0.000
#> SRR1096101 1 0.000 1.000 1.000 0.000
#> SRR808375 1 0.000 1.000 1.000 0.000
#> SRR1452842 1 0.000 1.000 1.000 0.000
#> SRR1311709 1 0.000 1.000 1.000 0.000
#> SRR1433352 1 0.000 1.000 1.000 0.000
#> SRR1340241 2 0.000 0.978 0.000 1.000
#> SRR1456754 1 0.000 1.000 1.000 0.000
#> SRR1465172 1 0.000 1.000 1.000 0.000
#> SRR1499284 1 0.000 1.000 1.000 0.000
#> SRR1499607 2 0.000 0.978 0.000 1.000
#> SRR812342 1 0.000 1.000 1.000 0.000
#> SRR1405374 1 0.000 1.000 1.000 0.000
#> SRR1403565 1 0.000 1.000 1.000 0.000
#> SRR1332024 1 0.000 1.000 1.000 0.000
#> SRR1471633 1 0.000 1.000 1.000 0.000
#> SRR1325944 2 0.000 0.978 0.000 1.000
#> SRR1429450 2 0.000 0.978 0.000 1.000
#> SRR821573 1 0.000 1.000 1.000 0.000
#> SRR1435372 1 0.000 1.000 1.000 0.000
#> SRR1324184 2 0.000 0.978 0.000 1.000
#> SRR816517 2 0.745 0.734 0.212 0.788
#> SRR1324141 1 0.000 1.000 1.000 0.000
#> SRR1101612 1 0.000 1.000 1.000 0.000
#> SRR1356531 1 0.000 1.000 1.000 0.000
#> SRR1089785 1 0.000 1.000 1.000 0.000
#> SRR1077708 1 0.000 1.000 1.000 0.000
#> SRR1343720 1 0.000 1.000 1.000 0.000
#> SRR1477499 2 0.000 0.978 0.000 1.000
#> SRR1347236 1 0.000 1.000 1.000 0.000
#> SRR1326408 1 0.000 1.000 1.000 0.000
#> SRR1336529 1 0.000 1.000 1.000 0.000
#> SRR1440643 1 0.000 1.000 1.000 0.000
#> SRR662354 1 0.000 1.000 1.000 0.000
#> SRR1310817 1 0.000 1.000 1.000 0.000
#> SRR1347389 2 0.000 0.978 0.000 1.000
#> SRR1353097 1 0.000 1.000 1.000 0.000
#> SRR1384737 1 0.000 1.000 1.000 0.000
#> SRR1096339 1 0.000 1.000 1.000 0.000
#> SRR1345329 1 0.000 1.000 1.000 0.000
#> SRR1414771 1 0.000 1.000 1.000 0.000
#> SRR1309119 1 0.000 1.000 1.000 0.000
#> SRR1470438 1 0.000 1.000 1.000 0.000
#> SRR1343221 1 0.000 1.000 1.000 0.000
#> SRR1410847 1 0.000 1.000 1.000 0.000
#> SRR807949 1 0.000 1.000 1.000 0.000
#> SRR1442332 1 0.000 1.000 1.000 0.000
#> SRR815920 1 0.000 1.000 1.000 0.000
#> SRR1471524 1 0.000 1.000 1.000 0.000
#> SRR1477221 1 0.000 1.000 1.000 0.000
#> SRR1445046 2 0.000 0.978 0.000 1.000
#> SRR1331962 2 0.000 0.978 0.000 1.000
#> SRR1319946 2 0.000 0.978 0.000 1.000
#> SRR1311599 1 0.000 1.000 1.000 0.000
#> SRR1323977 1 0.000 1.000 1.000 0.000
#> SRR1445132 2 0.000 0.978 0.000 1.000
#> SRR1337321 1 0.000 1.000 1.000 0.000
#> SRR1366390 2 0.000 0.978 0.000 1.000
#> SRR1343012 1 0.000 1.000 1.000 0.000
#> SRR1311958 2 0.000 0.978 0.000 1.000
#> SRR1388234 2 0.929 0.490 0.344 0.656
#> SRR1370384 1 0.000 1.000 1.000 0.000
#> SRR1321650 1 0.000 1.000 1.000 0.000
#> SRR1485117 2 0.000 0.978 0.000 1.000
#> SRR1384713 1 0.000 1.000 1.000 0.000
#> SRR816609 1 0.000 1.000 1.000 0.000
#> SRR1486239 2 0.000 0.978 0.000 1.000
#> SRR1309638 1 0.000 1.000 1.000 0.000
#> SRR1356660 1 0.000 1.000 1.000 0.000
#> SRR1392883 2 0.000 0.978 0.000 1.000
#> SRR808130 1 0.000 1.000 1.000 0.000
#> SRR816677 1 0.000 1.000 1.000 0.000
#> SRR1455722 1 0.000 1.000 1.000 0.000
#> SRR1336029 1 0.000 1.000 1.000 0.000
#> SRR808452 1 0.000 1.000 1.000 0.000
#> SRR1352169 1 0.000 1.000 1.000 0.000
#> SRR1366707 1 0.000 1.000 1.000 0.000
#> SRR1328143 1 0.000 1.000 1.000 0.000
#> SRR1473567 2 0.000 0.978 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1390119 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1436127 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1347278 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1332904 2 0.1031 0.9480 0.000 0.976 0.024
#> SRR1444179 1 0.0237 0.9116 0.996 0.000 0.004
#> SRR1082685 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1413978 1 0.0424 0.9094 0.992 0.000 0.008
#> SRR1439896 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1317963 2 0.4931 0.8563 0.000 0.768 0.232
#> SRR1431865 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1082664 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1077968 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1076393 3 0.5098 0.9457 0.248 0.000 0.752
#> SRR1477476 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1398057 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1485042 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1385453 3 0.0747 0.6716 0.016 0.000 0.984
#> SRR1348074 1 0.5058 0.7210 0.756 0.000 0.244
#> SRR813959 3 0.0747 0.6716 0.016 0.000 0.984
#> SRR665442 1 0.3038 0.8598 0.896 0.000 0.104
#> SRR1378068 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1485237 1 0.5058 0.7210 0.756 0.000 0.244
#> SRR1350792 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1326797 1 0.1031 0.8894 0.976 0.000 0.024
#> SRR808994 3 0.5098 0.9457 0.248 0.000 0.752
#> SRR1474041 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1405641 3 0.5138 0.9474 0.252 0.000 0.748
#> SRR1362245 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1500194 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1478523 3 0.3038 0.7927 0.104 0.000 0.896
#> SRR1325161 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1318026 1 0.5058 0.7210 0.756 0.000 0.244
#> SRR1343778 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1441287 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1430991 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1499722 3 0.5291 0.9399 0.268 0.000 0.732
#> SRR1351368 3 0.2796 0.7813 0.092 0.000 0.908
#> SRR1441785 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR808375 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1452842 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1311709 1 0.2448 0.8665 0.924 0.000 0.076
#> SRR1433352 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1340241 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1465172 1 0.5621 0.2715 0.692 0.000 0.308
#> SRR1499284 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1499607 2 0.4931 0.8563 0.000 0.768 0.232
#> SRR812342 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1332024 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1471633 1 0.3412 0.8311 0.876 0.000 0.124
#> SRR1325944 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR821573 3 0.5138 0.9459 0.252 0.000 0.748
#> SRR1435372 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1324184 2 0.0592 0.9484 0.000 0.988 0.012
#> SRR816517 3 0.5529 0.0615 0.000 0.296 0.704
#> SRR1324141 1 0.5098 0.7202 0.752 0.000 0.248
#> SRR1101612 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1089785 3 0.5178 0.9477 0.256 0.000 0.744
#> SRR1077708 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1343720 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1477499 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1347236 1 0.3686 0.7215 0.860 0.000 0.140
#> SRR1326408 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1336529 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1440643 3 0.0747 0.6716 0.016 0.000 0.984
#> SRR662354 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1310817 3 0.5098 0.9435 0.248 0.000 0.752
#> SRR1347389 2 0.4062 0.8943 0.000 0.836 0.164
#> SRR1353097 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1384737 1 0.5058 0.7210 0.756 0.000 0.244
#> SRR1096339 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1345329 1 0.5058 0.7210 0.756 0.000 0.244
#> SRR1414771 3 0.5098 0.9457 0.248 0.000 0.752
#> SRR1309119 1 0.2066 0.8774 0.940 0.000 0.060
#> SRR1470438 3 0.5098 0.9457 0.248 0.000 0.752
#> SRR1343221 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR807949 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1442332 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR815920 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1471524 3 0.5058 0.9427 0.244 0.000 0.756
#> SRR1477221 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1445046 2 0.4931 0.8563 0.000 0.768 0.232
#> SRR1331962 2 0.1643 0.9428 0.000 0.956 0.044
#> SRR1319946 2 0.5016 0.8504 0.000 0.760 0.240
#> SRR1311599 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1323977 1 0.5098 0.7202 0.752 0.000 0.248
#> SRR1445132 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1337321 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1366390 2 0.1529 0.9430 0.000 0.960 0.040
#> SRR1343012 1 0.4399 0.7801 0.812 0.000 0.188
#> SRR1311958 2 0.4291 0.8862 0.000 0.820 0.180
#> SRR1388234 1 0.9664 0.1552 0.460 0.296 0.244
#> SRR1370384 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1321650 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1485117 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR816609 1 0.5058 0.7210 0.756 0.000 0.244
#> SRR1486239 2 0.1964 0.9390 0.000 0.944 0.056
#> SRR1309638 3 0.5178 0.9488 0.256 0.000 0.744
#> SRR1356660 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9523 0.000 1.000 0.000
#> SRR808130 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR816677 1 0.3551 0.8254 0.868 0.000 0.132
#> SRR1455722 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1336029 1 0.0237 0.9116 0.996 0.000 0.004
#> SRR808452 1 0.0000 0.9138 1.000 0.000 0.000
#> SRR1352169 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1366707 3 0.5058 0.9427 0.244 0.000 0.756
#> SRR1328143 3 0.5216 0.9482 0.260 0.000 0.740
#> SRR1473567 2 0.0000 0.9523 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.2816 0.8523 0.036 0.064 0.900 0.000
#> SRR1390119 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1436127 3 0.4775 0.8386 0.028 0.232 0.740 0.000
#> SRR1347278 3 0.4370 0.8507 0.044 0.156 0.800 0.000
#> SRR1332904 4 0.4697 -0.6285 0.000 0.356 0.000 0.644
#> SRR1444179 1 0.1610 0.8759 0.952 0.032 0.016 0.000
#> SRR1082685 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.2081 0.8669 0.916 0.084 0.000 0.000
#> SRR1339007 1 0.0336 0.8896 0.992 0.008 0.000 0.000
#> SRR1376557 2 0.4967 0.9684 0.000 0.548 0.000 0.452
#> SRR1468700 2 0.4967 0.9684 0.000 0.548 0.000 0.452
#> SRR1077455 1 0.2816 0.8260 0.900 0.036 0.064 0.000
#> SRR1413978 1 0.2542 0.8622 0.904 0.084 0.012 0.000
#> SRR1439896 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1317963 4 0.0000 0.2795 0.000 0.000 0.000 1.000
#> SRR1431865 1 0.2081 0.8669 0.916 0.084 0.000 0.000
#> SRR1394253 1 0.2149 0.8671 0.912 0.088 0.000 0.000
#> SRR1082664 3 0.1635 0.8397 0.044 0.008 0.948 0.000
#> SRR1077968 1 0.0592 0.8877 0.984 0.016 0.000 0.000
#> SRR1076393 3 0.3447 0.8531 0.020 0.128 0.852 0.000
#> SRR1477476 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1398057 3 0.4671 0.8430 0.028 0.220 0.752 0.000
#> SRR1485042 1 0.1118 0.8850 0.964 0.036 0.000 0.000
#> SRR1385453 3 0.6770 0.5693 0.000 0.160 0.604 0.236
#> SRR1348074 4 0.6444 0.4591 0.388 0.032 0.024 0.556
#> SRR813959 4 0.6070 0.2126 0.000 0.048 0.404 0.548
#> SRR665442 1 0.9763 -0.2357 0.328 0.228 0.160 0.284
#> SRR1378068 3 0.5105 0.8233 0.028 0.276 0.696 0.000
#> SRR1485237 4 0.6380 0.4411 0.400 0.032 0.020 0.548
#> SRR1350792 1 0.0188 0.8905 0.996 0.004 0.000 0.000
#> SRR1326797 1 0.5639 0.4701 0.636 0.040 0.324 0.000
#> SRR808994 3 0.5322 0.8102 0.028 0.312 0.660 0.000
#> SRR1474041 3 0.1489 0.8407 0.044 0.004 0.952 0.000
#> SRR1405641 3 0.5277 0.8138 0.028 0.304 0.668 0.000
#> SRR1362245 3 0.5022 0.8258 0.028 0.264 0.708 0.000
#> SRR1500194 1 0.1302 0.8841 0.956 0.044 0.000 0.000
#> SRR1414876 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1478523 3 0.4644 0.8147 0.004 0.164 0.788 0.044
#> SRR1325161 3 0.2965 0.8084 0.072 0.036 0.892 0.000
#> SRR1318026 4 0.6464 0.4475 0.396 0.032 0.024 0.548
#> SRR1343778 3 0.3144 0.8513 0.044 0.072 0.884 0.000
#> SRR1441287 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1430991 3 0.1635 0.8396 0.044 0.008 0.948 0.000
#> SRR1499722 3 0.4818 0.6273 0.216 0.036 0.748 0.000
#> SRR1351368 3 0.6358 0.7188 0.004 0.204 0.664 0.128
#> SRR1441785 1 0.2081 0.8669 0.916 0.084 0.000 0.000
#> SRR1096101 1 0.1118 0.8866 0.964 0.036 0.000 0.000
#> SRR808375 3 0.2408 0.8270 0.044 0.036 0.920 0.000
#> SRR1452842 1 0.2578 0.8380 0.912 0.036 0.052 0.000
#> SRR1311709 1 0.1362 0.8707 0.964 0.004 0.020 0.012
#> SRR1433352 3 0.1302 0.8416 0.044 0.000 0.956 0.000
#> SRR1340241 2 0.4941 0.9785 0.000 0.564 0.000 0.436
#> SRR1456754 1 0.1256 0.8767 0.964 0.028 0.008 0.000
#> SRR1465172 1 0.6011 0.1033 0.484 0.040 0.476 0.000
#> SRR1499284 1 0.5168 0.5811 0.712 0.040 0.248 0.000
#> SRR1499607 4 0.0000 0.2795 0.000 0.000 0.000 1.000
#> SRR812342 1 0.0188 0.8905 0.996 0.004 0.000 0.000
#> SRR1405374 1 0.1302 0.8841 0.956 0.044 0.000 0.000
#> SRR1403565 1 0.2830 0.8394 0.900 0.060 0.040 0.000
#> SRR1332024 3 0.5364 0.8064 0.028 0.320 0.652 0.000
#> SRR1471633 1 0.2310 0.8575 0.932 0.032 0.020 0.016
#> SRR1325944 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1429450 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR821573 3 0.1833 0.8245 0.024 0.032 0.944 0.000
#> SRR1435372 1 0.0336 0.8899 0.992 0.008 0.000 0.000
#> SRR1324184 2 0.5172 0.9579 0.000 0.588 0.008 0.404
#> SRR816517 4 0.6650 0.3738 0.000 0.200 0.176 0.624
#> SRR1324141 4 0.7405 0.4990 0.332 0.040 0.080 0.548
#> SRR1101612 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0188 0.8905 0.996 0.004 0.000 0.000
#> SRR1089785 3 0.1635 0.8431 0.044 0.008 0.948 0.000
#> SRR1077708 3 0.3464 0.8376 0.032 0.108 0.860 0.000
#> SRR1343720 3 0.2111 0.8330 0.044 0.024 0.932 0.000
#> SRR1477499 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1347236 1 0.5971 0.2480 0.532 0.040 0.428 0.000
#> SRR1326408 1 0.1209 0.8835 0.964 0.032 0.004 0.000
#> SRR1336529 3 0.5254 0.8155 0.028 0.300 0.672 0.000
#> SRR1440643 3 0.7067 0.4652 0.000 0.160 0.552 0.288
#> SRR662354 1 0.0188 0.8905 0.996 0.004 0.000 0.000
#> SRR1310817 3 0.1256 0.8356 0.028 0.008 0.964 0.000
#> SRR1347389 4 0.2814 -0.0239 0.000 0.132 0.000 0.868
#> SRR1353097 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1384737 4 0.6753 0.4626 0.380 0.040 0.032 0.548
#> SRR1096339 1 0.1118 0.8850 0.964 0.036 0.000 0.000
#> SRR1345329 4 0.6380 0.4411 0.400 0.032 0.020 0.548
#> SRR1414771 3 0.5322 0.8102 0.028 0.312 0.660 0.000
#> SRR1309119 1 0.1624 0.8734 0.952 0.028 0.020 0.000
#> SRR1470438 3 0.5322 0.8102 0.028 0.312 0.660 0.000
#> SRR1343221 1 0.0188 0.8905 0.996 0.004 0.000 0.000
#> SRR1410847 1 0.1211 0.8847 0.960 0.040 0.000 0.000
#> SRR807949 3 0.1767 0.8380 0.044 0.012 0.944 0.000
#> SRR1442332 3 0.1489 0.8407 0.044 0.004 0.952 0.000
#> SRR815920 3 0.4964 0.8300 0.028 0.256 0.716 0.000
#> SRR1471524 3 0.4323 0.8354 0.020 0.204 0.776 0.000
#> SRR1477221 3 0.5022 0.8258 0.028 0.264 0.708 0.000
#> SRR1445046 4 0.0000 0.2795 0.000 0.000 0.000 1.000
#> SRR1331962 4 0.4522 -0.5493 0.000 0.320 0.000 0.680
#> SRR1319946 4 0.0188 0.2836 0.000 0.000 0.004 0.996
#> SRR1311599 1 0.2149 0.8671 0.912 0.088 0.000 0.000
#> SRR1323977 4 0.7422 0.4993 0.332 0.044 0.076 0.548
#> SRR1445132 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1337321 3 0.4406 0.8476 0.028 0.192 0.780 0.000
#> SRR1366390 4 0.4961 -0.8036 0.000 0.448 0.000 0.552
#> SRR1343012 4 0.8180 0.4328 0.344 0.040 0.148 0.468
#> SRR1311958 4 0.0921 0.2301 0.000 0.028 0.000 0.972
#> SRR1388234 4 0.5449 0.5494 0.288 0.004 0.032 0.676
#> SRR1370384 1 0.1452 0.8734 0.956 0.036 0.008 0.000
#> SRR1321650 3 0.4775 0.8341 0.028 0.232 0.740 0.000
#> SRR1485117 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR1384713 1 0.2578 0.8380 0.912 0.036 0.052 0.000
#> SRR816609 4 0.6464 0.4475 0.396 0.032 0.024 0.548
#> SRR1486239 4 0.3649 -0.2502 0.000 0.204 0.000 0.796
#> SRR1309638 3 0.4840 0.8315 0.028 0.240 0.732 0.000
#> SRR1356660 1 0.2081 0.8669 0.916 0.084 0.000 0.000
#> SRR1392883 2 0.4907 0.9869 0.000 0.580 0.000 0.420
#> SRR808130 3 0.1489 0.8407 0.044 0.004 0.952 0.000
#> SRR816677 1 0.4766 0.6931 0.800 0.040 0.020 0.140
#> SRR1455722 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.1510 0.8768 0.956 0.028 0.016 0.000
#> SRR808452 1 0.0000 0.8908 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.3612 0.8529 0.044 0.100 0.856 0.000
#> SRR1366707 3 0.4706 0.8283 0.020 0.248 0.732 0.000
#> SRR1328143 3 0.1489 0.8407 0.044 0.004 0.952 0.000
#> SRR1473567 2 0.4967 0.9684 0.000 0.548 0.000 0.452
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.4936 0.62250 0.012 0.000 0.412 0.012 0.564
#> SRR1390119 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.2833 0.64009 0.004 0.000 0.852 0.004 0.140
#> SRR1347278 3 0.5912 -0.03120 0.016 0.000 0.508 0.064 0.412
#> SRR1332904 2 0.6229 0.12768 0.000 0.464 0.000 0.392 0.144
#> SRR1444179 1 0.0404 0.92876 0.988 0.000 0.000 0.012 0.000
#> SRR1082685 1 0.0162 0.93087 0.996 0.000 0.000 0.000 0.004
#> SRR1362287 1 0.3753 0.86352 0.844 0.000 0.052 0.056 0.048
#> SRR1339007 1 0.0898 0.92716 0.972 0.000 0.000 0.008 0.020
#> SRR1376557 2 0.3309 0.83018 0.000 0.836 0.000 0.036 0.128
#> SRR1468700 2 0.4376 0.76956 0.000 0.764 0.000 0.092 0.144
#> SRR1077455 1 0.2616 0.86988 0.880 0.000 0.000 0.020 0.100
#> SRR1413978 1 0.4146 0.84752 0.820 0.000 0.056 0.072 0.052
#> SRR1439896 1 0.0162 0.93087 0.996 0.000 0.000 0.000 0.004
#> SRR1317963 4 0.4343 0.65708 0.000 0.096 0.000 0.768 0.136
#> SRR1431865 1 0.3753 0.86352 0.844 0.000 0.052 0.056 0.048
#> SRR1394253 1 0.3606 0.87205 0.852 0.000 0.040 0.056 0.052
#> SRR1082664 5 0.4701 0.69074 0.016 0.000 0.368 0.004 0.612
#> SRR1077968 1 0.1872 0.90559 0.928 0.000 0.000 0.020 0.052
#> SRR1076393 3 0.4644 0.12727 0.004 0.000 0.604 0.012 0.380
#> SRR1477476 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.5015 0.33814 0.004 0.000 0.668 0.056 0.272
#> SRR1485042 1 0.0771 0.92779 0.976 0.000 0.000 0.004 0.020
#> SRR1385453 4 0.6622 -0.00746 0.000 0.000 0.328 0.440 0.232
#> SRR1348074 4 0.2305 0.74986 0.092 0.000 0.000 0.896 0.012
#> SRR813959 4 0.3449 0.68117 0.000 0.000 0.024 0.812 0.164
#> SRR665442 3 0.7836 0.17830 0.068 0.000 0.376 0.260 0.296
#> SRR1378068 3 0.1502 0.67120 0.004 0.000 0.940 0.000 0.056
#> SRR1485237 4 0.2536 0.74320 0.128 0.000 0.000 0.868 0.004
#> SRR1350792 1 0.0451 0.93074 0.988 0.000 0.000 0.004 0.008
#> SRR1326797 5 0.5034 0.37836 0.308 0.000 0.028 0.016 0.648
#> SRR808994 3 0.0162 0.67984 0.004 0.000 0.996 0.000 0.000
#> SRR1474041 5 0.4893 0.69553 0.016 0.000 0.360 0.012 0.612
#> SRR1405641 3 0.1041 0.67925 0.004 0.000 0.964 0.000 0.032
#> SRR1362245 3 0.3826 0.61051 0.004 0.000 0.812 0.056 0.128
#> SRR1500194 1 0.1981 0.91002 0.924 0.000 0.000 0.028 0.048
#> SRR1414876 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.6430 0.20666 0.000 0.000 0.480 0.192 0.328
#> SRR1325161 5 0.5138 0.64361 0.048 0.000 0.260 0.016 0.676
#> SRR1318026 4 0.3759 0.73216 0.136 0.000 0.000 0.808 0.056
#> SRR1343778 5 0.4956 0.59530 0.016 0.000 0.428 0.008 0.548
#> SRR1441287 1 0.0000 0.93080 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.4791 0.69697 0.016 0.000 0.360 0.008 0.616
#> SRR1499722 5 0.5348 0.59051 0.108 0.000 0.200 0.008 0.684
#> SRR1351368 3 0.6236 0.36204 0.000 0.000 0.544 0.248 0.208
#> SRR1441785 1 0.3753 0.86352 0.844 0.000 0.052 0.056 0.048
#> SRR1096101 1 0.0955 0.92773 0.968 0.000 0.000 0.004 0.028
#> SRR808375 5 0.4520 0.67447 0.016 0.000 0.296 0.008 0.680
#> SRR1452842 1 0.2448 0.88045 0.892 0.000 0.000 0.020 0.088
#> SRR1311709 1 0.0771 0.92502 0.976 0.000 0.000 0.020 0.004
#> SRR1433352 5 0.4804 0.69380 0.016 0.000 0.364 0.008 0.612
#> SRR1340241 2 0.1341 0.88079 0.000 0.944 0.000 0.000 0.056
#> SRR1456754 1 0.1774 0.90695 0.932 0.000 0.000 0.016 0.052
#> SRR1465172 5 0.5368 0.43439 0.252 0.000 0.060 0.020 0.668
#> SRR1499284 5 0.4736 0.26540 0.404 0.000 0.000 0.020 0.576
#> SRR1499607 4 0.4300 0.65963 0.000 0.096 0.000 0.772 0.132
#> SRR812342 1 0.0693 0.92935 0.980 0.000 0.000 0.008 0.012
#> SRR1405374 1 0.1741 0.91539 0.936 0.000 0.000 0.024 0.040
#> SRR1403565 1 0.4431 0.83106 0.800 0.000 0.052 0.056 0.092
#> SRR1332024 3 0.0324 0.67835 0.004 0.000 0.992 0.000 0.004
#> SRR1471633 1 0.0609 0.92521 0.980 0.000 0.000 0.020 0.000
#> SRR1325944 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.5122 0.47753 0.008 0.000 0.140 0.136 0.716
#> SRR1435372 1 0.0807 0.92863 0.976 0.000 0.000 0.012 0.012
#> SRR1324184 2 0.2463 0.86047 0.000 0.888 0.004 0.008 0.100
#> SRR816517 4 0.3574 0.70827 0.000 0.004 0.088 0.836 0.072
#> SRR1324141 4 0.3872 0.73826 0.116 0.000 0.008 0.816 0.060
#> SRR1101612 1 0.0162 0.93087 0.996 0.000 0.000 0.000 0.004
#> SRR1356531 1 0.0451 0.93074 0.988 0.000 0.000 0.004 0.008
#> SRR1089785 5 0.4943 0.67801 0.016 0.000 0.376 0.012 0.596
#> SRR1077708 5 0.4657 0.59547 0.008 0.000 0.380 0.008 0.604
#> SRR1343720 5 0.4791 0.69239 0.020 0.000 0.336 0.008 0.636
#> SRR1477499 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.5253 0.42724 0.264 0.000 0.060 0.012 0.664
#> SRR1326408 1 0.0912 0.92727 0.972 0.000 0.000 0.012 0.016
#> SRR1336529 3 0.1041 0.67925 0.004 0.000 0.964 0.000 0.032
#> SRR1440643 4 0.6545 0.04574 0.000 0.000 0.324 0.460 0.216
#> SRR662354 1 0.0451 0.93074 0.988 0.000 0.000 0.004 0.008
#> SRR1310817 5 0.4692 0.65860 0.004 0.000 0.320 0.024 0.652
#> SRR1347389 4 0.5867 0.39791 0.000 0.268 0.000 0.588 0.144
#> SRR1353097 1 0.0451 0.93051 0.988 0.000 0.000 0.008 0.004
#> SRR1384737 4 0.3758 0.73801 0.112 0.000 0.008 0.824 0.056
#> SRR1096339 1 0.0609 0.92820 0.980 0.000 0.000 0.000 0.020
#> SRR1345329 4 0.2280 0.74643 0.120 0.000 0.000 0.880 0.000
#> SRR1414771 3 0.0162 0.67984 0.004 0.000 0.996 0.000 0.000
#> SRR1309119 1 0.0898 0.92665 0.972 0.000 0.000 0.008 0.020
#> SRR1470438 3 0.0162 0.67984 0.004 0.000 0.996 0.000 0.000
#> SRR1343221 1 0.0404 0.93116 0.988 0.000 0.000 0.000 0.012
#> SRR1410847 1 0.2193 0.90845 0.920 0.000 0.008 0.028 0.044
#> SRR807949 5 0.4852 0.69920 0.016 0.000 0.348 0.012 0.624
#> SRR1442332 5 0.4893 0.69553 0.016 0.000 0.360 0.012 0.612
#> SRR815920 3 0.2881 0.62659 0.004 0.000 0.860 0.012 0.124
#> SRR1471524 3 0.4017 0.49635 0.004 0.000 0.736 0.012 0.248
#> SRR1477221 3 0.3779 0.61357 0.004 0.000 0.816 0.056 0.124
#> SRR1445046 4 0.4428 0.65159 0.000 0.096 0.000 0.760 0.144
#> SRR1331962 4 0.6247 -0.10117 0.000 0.428 0.000 0.428 0.144
#> SRR1319946 4 0.2824 0.70343 0.000 0.096 0.000 0.872 0.032
#> SRR1311599 1 0.3823 0.86343 0.840 0.000 0.052 0.056 0.052
#> SRR1323977 4 0.3242 0.74489 0.116 0.000 0.000 0.844 0.040
#> SRR1445132 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.5098 0.48270 0.004 0.000 0.660 0.060 0.276
#> SRR1366390 2 0.5335 0.63282 0.000 0.668 0.000 0.200 0.132
#> SRR1343012 4 0.5796 0.65816 0.112 0.000 0.052 0.692 0.144
#> SRR1311958 4 0.4889 0.60767 0.000 0.136 0.000 0.720 0.144
#> SRR1388234 4 0.2673 0.74497 0.076 0.016 0.000 0.892 0.016
#> SRR1370384 1 0.2331 0.88669 0.900 0.000 0.000 0.020 0.080
#> SRR1321650 3 0.3124 0.62497 0.004 0.000 0.844 0.016 0.136
#> SRR1485117 2 0.0510 0.89342 0.000 0.984 0.000 0.000 0.016
#> SRR1384713 1 0.2390 0.88365 0.896 0.000 0.000 0.020 0.084
#> SRR816609 4 0.2377 0.74356 0.128 0.000 0.000 0.872 0.000
#> SRR1486239 4 0.5923 0.36507 0.000 0.280 0.000 0.576 0.144
#> SRR1309638 3 0.4365 0.52412 0.004 0.000 0.736 0.036 0.224
#> SRR1356660 1 0.3753 0.86352 0.844 0.000 0.052 0.056 0.048
#> SRR1392883 2 0.0000 0.89630 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.4893 0.69553 0.016 0.000 0.360 0.012 0.612
#> SRR816677 1 0.5815 0.43754 0.600 0.000 0.036 0.316 0.048
#> SRR1455722 1 0.0162 0.93087 0.996 0.000 0.000 0.000 0.004
#> SRR1336029 1 0.1106 0.92563 0.964 0.000 0.000 0.012 0.024
#> SRR808452 1 0.0324 0.93048 0.992 0.000 0.000 0.004 0.004
#> SRR1352169 3 0.5020 -0.21209 0.016 0.000 0.564 0.012 0.408
#> SRR1366707 3 0.2787 0.62653 0.004 0.000 0.856 0.004 0.136
#> SRR1328143 5 0.4893 0.69553 0.016 0.000 0.360 0.012 0.612
#> SRR1473567 2 0.3309 0.83018 0.000 0.836 0.000 0.036 0.128
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.2968 0.70841 0.000 0.000 0.168 0.000 0.816 0.016
#> SRR1390119 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.3368 0.51248 0.000 0.000 0.756 0.000 0.232 0.012
#> SRR1347278 5 0.6003 -0.11590 0.000 0.000 0.272 0.000 0.436 0.292
#> SRR1332904 4 0.5986 0.22821 0.000 0.304 0.008 0.488 0.000 0.200
#> SRR1444179 1 0.0260 0.82899 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1082685 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.4166 0.57946 0.648 0.000 0.028 0.000 0.000 0.324
#> SRR1339007 1 0.2784 0.75205 0.848 0.000 0.000 0.008 0.012 0.132
#> SRR1376557 2 0.3613 0.77653 0.000 0.772 0.008 0.024 0.000 0.196
#> SRR1468700 2 0.5516 0.56703 0.000 0.596 0.008 0.196 0.000 0.200
#> SRR1077455 1 0.4437 0.61913 0.724 0.000 0.000 0.008 0.088 0.180
#> SRR1413978 1 0.4436 0.56317 0.632 0.000 0.028 0.008 0.000 0.332
#> SRR1439896 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.3529 0.63734 0.000 0.028 0.008 0.788 0.000 0.176
#> SRR1431865 1 0.4150 0.58496 0.652 0.000 0.028 0.000 0.000 0.320
#> SRR1394253 1 0.4002 0.59521 0.660 0.000 0.020 0.000 0.000 0.320
#> SRR1082664 5 0.2699 0.76207 0.000 0.000 0.108 0.008 0.864 0.020
#> SRR1077968 1 0.3353 0.71317 0.808 0.000 0.000 0.008 0.028 0.156
#> SRR1076393 5 0.5651 -0.02865 0.000 0.000 0.392 0.016 0.492 0.100
#> SRR1477476 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 3 0.6012 0.14043 0.000 0.000 0.424 0.000 0.320 0.256
#> SRR1485042 1 0.1267 0.81975 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1385453 3 0.7527 -0.09414 0.000 0.000 0.324 0.312 0.180 0.184
#> SRR1348074 4 0.1716 0.69047 0.036 0.000 0.004 0.932 0.000 0.028
#> SRR813959 4 0.4411 0.57600 0.000 0.000 0.016 0.740 0.084 0.160
#> SRR665442 6 0.6625 0.00000 0.004 0.000 0.216 0.104 0.132 0.544
#> SRR1378068 3 0.2100 0.58050 0.000 0.000 0.884 0.000 0.112 0.004
#> SRR1485237 4 0.1500 0.68628 0.052 0.000 0.000 0.936 0.000 0.012
#> SRR1350792 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.3423 0.60868 0.088 0.000 0.000 0.000 0.812 0.100
#> SRR808994 3 0.1501 0.57774 0.000 0.000 0.924 0.000 0.076 0.000
#> SRR1474041 5 0.2094 0.77324 0.000 0.000 0.080 0.000 0.900 0.020
#> SRR1405641 3 0.1610 0.58050 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1362245 3 0.5715 0.13613 0.000 0.000 0.536 0.008 0.156 0.300
#> SRR1500194 1 0.2482 0.77283 0.848 0.000 0.004 0.000 0.000 0.148
#> SRR1414876 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 3 0.7389 0.04082 0.000 0.000 0.404 0.188 0.236 0.172
#> SRR1325161 5 0.2791 0.67253 0.016 0.000 0.000 0.008 0.852 0.124
#> SRR1318026 4 0.4041 0.61791 0.060 0.000 0.012 0.764 0.000 0.164
#> SRR1343778 5 0.2912 0.70632 0.000 0.000 0.172 0.000 0.816 0.012
#> SRR1441287 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.1387 0.78071 0.000 0.000 0.068 0.000 0.932 0.000
#> SRR1499722 5 0.2560 0.68378 0.036 0.000 0.000 0.000 0.872 0.092
#> SRR1351368 3 0.7095 0.08064 0.000 0.000 0.472 0.184 0.168 0.176
#> SRR1441785 1 0.4166 0.57946 0.648 0.000 0.028 0.000 0.000 0.324
#> SRR1096101 1 0.1267 0.81975 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR808375 5 0.1333 0.74847 0.000 0.000 0.008 0.000 0.944 0.048
#> SRR1452842 1 0.4389 0.62500 0.728 0.000 0.000 0.008 0.084 0.180
#> SRR1311709 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1433352 5 0.1700 0.77613 0.000 0.000 0.080 0.000 0.916 0.004
#> SRR1340241 2 0.1584 0.86101 0.000 0.928 0.008 0.000 0.000 0.064
#> SRR1456754 1 0.3702 0.68896 0.784 0.000 0.000 0.008 0.044 0.164
#> SRR1465172 5 0.4115 0.57090 0.076 0.000 0.000 0.012 0.764 0.148
#> SRR1499284 5 0.5286 0.34408 0.168 0.000 0.000 0.012 0.640 0.180
#> SRR1499607 4 0.3161 0.65530 0.000 0.028 0.008 0.828 0.000 0.136
#> SRR812342 1 0.1194 0.81873 0.956 0.000 0.000 0.004 0.008 0.032
#> SRR1405374 1 0.1753 0.80836 0.912 0.000 0.004 0.000 0.000 0.084
#> SRR1403565 1 0.4467 0.55608 0.632 0.000 0.028 0.004 0.004 0.332
#> SRR1332024 3 0.1701 0.57017 0.000 0.000 0.920 0.000 0.072 0.008
#> SRR1471633 1 0.0603 0.82781 0.980 0.000 0.000 0.004 0.000 0.016
#> SRR1325944 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.2847 0.69331 0.000 0.000 0.036 0.048 0.876 0.040
#> SRR1435372 1 0.1049 0.81943 0.960 0.000 0.000 0.000 0.008 0.032
#> SRR1324184 2 0.3651 0.79088 0.000 0.792 0.032 0.016 0.000 0.160
#> SRR816517 4 0.4849 0.57288 0.000 0.008 0.092 0.712 0.016 0.172
#> SRR1324141 4 0.4088 0.62130 0.032 0.000 0.016 0.776 0.016 0.160
#> SRR1101612 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0862 0.82470 0.972 0.000 0.000 0.004 0.008 0.016
#> SRR1089785 5 0.2402 0.75195 0.000 0.000 0.120 0.000 0.868 0.012
#> SRR1077708 5 0.3605 0.70569 0.000 0.000 0.060 0.008 0.804 0.128
#> SRR1343720 5 0.1845 0.77565 0.000 0.000 0.052 0.000 0.920 0.028
#> SRR1477499 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 5 0.3563 0.60548 0.088 0.000 0.000 0.004 0.808 0.100
#> SRR1326408 1 0.2501 0.77207 0.872 0.000 0.000 0.004 0.016 0.108
#> SRR1336529 3 0.1714 0.58150 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1440643 4 0.7410 -0.06294 0.000 0.000 0.288 0.372 0.156 0.184
#> SRR662354 1 0.0291 0.82868 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1310817 5 0.2318 0.76099 0.000 0.000 0.064 0.000 0.892 0.044
#> SRR1347389 4 0.5537 0.52137 0.000 0.152 0.024 0.624 0.000 0.200
#> SRR1353097 1 0.1049 0.81943 0.960 0.000 0.000 0.000 0.008 0.032
#> SRR1384737 4 0.4082 0.59082 0.024 0.000 0.020 0.748 0.004 0.204
#> SRR1096339 1 0.0937 0.82326 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1345329 4 0.1500 0.68628 0.052 0.000 0.000 0.936 0.000 0.012
#> SRR1414771 3 0.1444 0.57612 0.000 0.000 0.928 0.000 0.072 0.000
#> SRR1309119 1 0.1285 0.82026 0.944 0.000 0.000 0.004 0.000 0.052
#> SRR1470438 3 0.1444 0.57612 0.000 0.000 0.928 0.000 0.072 0.000
#> SRR1343221 1 0.0551 0.82728 0.984 0.000 0.000 0.004 0.008 0.004
#> SRR1410847 1 0.2402 0.77847 0.856 0.000 0.004 0.000 0.000 0.140
#> SRR807949 5 0.1285 0.78093 0.000 0.000 0.052 0.000 0.944 0.004
#> SRR1442332 5 0.1701 0.77785 0.000 0.000 0.072 0.000 0.920 0.008
#> SRR815920 3 0.2805 0.56616 0.000 0.000 0.828 0.000 0.160 0.012
#> SRR1471524 3 0.5411 0.31597 0.000 0.000 0.560 0.000 0.288 0.152
#> SRR1477221 3 0.5655 0.15977 0.000 0.000 0.536 0.004 0.164 0.296
#> SRR1445046 4 0.3719 0.62543 0.000 0.028 0.008 0.764 0.000 0.200
#> SRR1331962 4 0.5872 0.31878 0.000 0.268 0.008 0.524 0.000 0.200
#> SRR1319946 4 0.1755 0.68887 0.000 0.028 0.008 0.932 0.000 0.032
#> SRR1311599 1 0.4150 0.58496 0.652 0.000 0.028 0.000 0.000 0.320
#> SRR1323977 4 0.3774 0.64194 0.040 0.000 0.016 0.804 0.008 0.132
#> SRR1445132 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 3 0.6307 -0.00627 0.000 0.000 0.384 0.008 0.300 0.308
#> SRR1366390 2 0.5583 0.59600 0.000 0.620 0.024 0.180 0.000 0.176
#> SRR1343012 4 0.5128 0.52085 0.024 0.000 0.024 0.688 0.052 0.212
#> SRR1311958 4 0.4114 0.60886 0.000 0.052 0.008 0.740 0.000 0.200
#> SRR1388234 4 0.0964 0.69236 0.016 0.012 0.000 0.968 0.000 0.004
#> SRR1370384 1 0.4132 0.64767 0.748 0.000 0.000 0.008 0.064 0.180
#> SRR1321650 3 0.4565 0.42711 0.000 0.000 0.680 0.004 0.244 0.072
#> SRR1485117 2 0.1267 0.86660 0.000 0.940 0.000 0.000 0.000 0.060
#> SRR1384713 1 0.4389 0.62500 0.728 0.000 0.000 0.008 0.084 0.180
#> SRR816609 4 0.1500 0.68628 0.052 0.000 0.000 0.936 0.000 0.012
#> SRR1486239 4 0.5267 0.51308 0.000 0.160 0.008 0.632 0.000 0.200
#> SRR1309638 3 0.6312 0.09152 0.000 0.000 0.460 0.020 0.296 0.224
#> SRR1356660 1 0.4150 0.58496 0.652 0.000 0.028 0.000 0.000 0.320
#> SRR1392883 2 0.0000 0.88304 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.1588 0.77902 0.000 0.000 0.072 0.000 0.924 0.004
#> SRR816677 1 0.5879 0.11662 0.468 0.000 0.004 0.348 0.000 0.180
#> SRR1455722 1 0.0146 0.82917 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1336029 1 0.2051 0.80503 0.896 0.000 0.004 0.004 0.000 0.096
#> SRR808452 1 0.0000 0.82964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352169 5 0.4234 0.50108 0.000 0.000 0.280 0.000 0.676 0.044
#> SRR1366707 3 0.3947 0.48149 0.000 0.000 0.732 0.004 0.228 0.036
#> SRR1328143 5 0.1701 0.77785 0.000 0.000 0.072 0.000 0.920 0.008
#> SRR1473567 2 0.3613 0.77653 0.000 0.772 0.008 0.024 0.000 0.196
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.993 0.997 0.4449 0.554 0.554
#> 3 3 1.000 0.963 0.986 0.5078 0.745 0.551
#> 4 4 0.886 0.861 0.922 0.0977 0.911 0.737
#> 5 5 0.846 0.837 0.906 0.0577 0.957 0.839
#> 6 6 0.810 0.764 0.863 0.0479 0.943 0.756
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
#> SRR1442087 1 0.000 1.000 1.000 0.000
#> SRR1390119 2 0.000 0.991 0.000 1.000
#> SRR1436127 1 0.000 1.000 1.000 0.000
#> SRR1347278 1 0.000 1.000 1.000 0.000
#> SRR1332904 2 0.000 0.991 0.000 1.000
#> SRR1444179 1 0.000 1.000 1.000 0.000
#> SRR1082685 1 0.000 1.000 1.000 0.000
#> SRR1362287 1 0.000 1.000 1.000 0.000
#> SRR1339007 1 0.000 1.000 1.000 0.000
#> SRR1376557 2 0.000 0.991 0.000 1.000
#> SRR1468700 2 0.000 0.991 0.000 1.000
#> SRR1077455 1 0.000 1.000 1.000 0.000
#> SRR1413978 1 0.000 1.000 1.000 0.000
#> SRR1439896 1 0.000 1.000 1.000 0.000
#> SRR1317963 2 0.000 0.991 0.000 1.000
#> SRR1431865 1 0.000 1.000 1.000 0.000
#> SRR1394253 1 0.000 1.000 1.000 0.000
#> SRR1082664 1 0.000 1.000 1.000 0.000
#> SRR1077968 1 0.000 1.000 1.000 0.000
#> SRR1076393 1 0.000 1.000 1.000 0.000
#> SRR1477476 2 0.000 0.991 0.000 1.000
#> SRR1398057 1 0.000 1.000 1.000 0.000
#> SRR1485042 1 0.000 1.000 1.000 0.000
#> SRR1385453 2 0.000 0.991 0.000 1.000
#> SRR1348074 2 0.000 0.991 0.000 1.000
#> SRR813959 2 0.000 0.991 0.000 1.000
#> SRR665442 2 0.000 0.991 0.000 1.000
#> SRR1378068 1 0.000 1.000 1.000 0.000
#> SRR1485237 2 0.000 0.991 0.000 1.000
#> SRR1350792 1 0.000 1.000 1.000 0.000
#> SRR1326797 1 0.000 1.000 1.000 0.000
#> SRR808994 1 0.000 1.000 1.000 0.000
#> SRR1474041 1 0.000 1.000 1.000 0.000
#> SRR1405641 1 0.000 1.000 1.000 0.000
#> SRR1362245 1 0.000 1.000 1.000 0.000
#> SRR1500194 1 0.000 1.000 1.000 0.000
#> SRR1414876 2 0.000 0.991 0.000 1.000
#> SRR1478523 2 0.932 0.466 0.348 0.652
#> SRR1325161 1 0.000 1.000 1.000 0.000
#> SRR1318026 2 0.000 0.991 0.000 1.000
#> SRR1343778 1 0.000 1.000 1.000 0.000
#> SRR1441287 1 0.000 1.000 1.000 0.000
#> SRR1430991 1 0.000 1.000 1.000 0.000
#> SRR1499722 1 0.000 1.000 1.000 0.000
#> SRR1351368 2 0.000 0.991 0.000 1.000
#> SRR1441785 1 0.000 1.000 1.000 0.000
#> SRR1096101 1 0.000 1.000 1.000 0.000
#> SRR808375 1 0.000 1.000 1.000 0.000
#> SRR1452842 1 0.000 1.000 1.000 0.000
#> SRR1311709 1 0.000 1.000 1.000 0.000
#> SRR1433352 1 0.000 1.000 1.000 0.000
#> SRR1340241 2 0.000 0.991 0.000 1.000
#> SRR1456754 1 0.000 1.000 1.000 0.000
#> SRR1465172 1 0.000 1.000 1.000 0.000
#> SRR1499284 1 0.000 1.000 1.000 0.000
#> SRR1499607 2 0.000 0.991 0.000 1.000
#> SRR812342 1 0.000 1.000 1.000 0.000
#> SRR1405374 1 0.000 1.000 1.000 0.000
#> SRR1403565 1 0.000 1.000 1.000 0.000
#> SRR1332024 1 0.000 1.000 1.000 0.000
#> SRR1471633 1 0.000 1.000 1.000 0.000
#> SRR1325944 2 0.000 0.991 0.000 1.000
#> SRR1429450 2 0.000 0.991 0.000 1.000
#> SRR821573 1 0.000 1.000 1.000 0.000
#> SRR1435372 1 0.000 1.000 1.000 0.000
#> SRR1324184 2 0.000 0.991 0.000 1.000
#> SRR816517 2 0.000 0.991 0.000 1.000
#> SRR1324141 2 0.000 0.991 0.000 1.000
#> SRR1101612 1 0.000 1.000 1.000 0.000
#> SRR1356531 1 0.000 1.000 1.000 0.000
#> SRR1089785 1 0.000 1.000 1.000 0.000
#> SRR1077708 1 0.000 1.000 1.000 0.000
#> SRR1343720 1 0.000 1.000 1.000 0.000
#> SRR1477499 2 0.000 0.991 0.000 1.000
#> SRR1347236 1 0.000 1.000 1.000 0.000
#> SRR1326408 1 0.000 1.000 1.000 0.000
#> SRR1336529 1 0.000 1.000 1.000 0.000
#> SRR1440643 2 0.000 0.991 0.000 1.000
#> SRR662354 1 0.000 1.000 1.000 0.000
#> SRR1310817 1 0.000 1.000 1.000 0.000
#> SRR1347389 2 0.000 0.991 0.000 1.000
#> SRR1353097 1 0.000 1.000 1.000 0.000
#> SRR1384737 2 0.000 0.991 0.000 1.000
#> SRR1096339 1 0.000 1.000 1.000 0.000
#> SRR1345329 2 0.000 0.991 0.000 1.000
#> SRR1414771 1 0.000 1.000 1.000 0.000
#> SRR1309119 1 0.000 1.000 1.000 0.000
#> SRR1470438 1 0.000 1.000 1.000 0.000
#> SRR1343221 1 0.000 1.000 1.000 0.000
#> SRR1410847 1 0.000 1.000 1.000 0.000
#> SRR807949 1 0.000 1.000 1.000 0.000
#> SRR1442332 1 0.000 1.000 1.000 0.000
#> SRR815920 1 0.000 1.000 1.000 0.000
#> SRR1471524 1 0.000 1.000 1.000 0.000
#> SRR1477221 1 0.000 1.000 1.000 0.000
#> SRR1445046 2 0.000 0.991 0.000 1.000
#> SRR1331962 2 0.000 0.991 0.000 1.000
#> SRR1319946 2 0.000 0.991 0.000 1.000
#> SRR1311599 1 0.000 1.000 1.000 0.000
#> SRR1323977 2 0.000 0.991 0.000 1.000
#> SRR1445132 2 0.000 0.991 0.000 1.000
#> SRR1337321 1 0.000 1.000 1.000 0.000
#> SRR1366390 2 0.000 0.991 0.000 1.000
#> SRR1343012 2 0.000 0.991 0.000 1.000
#> SRR1311958 2 0.000 0.991 0.000 1.000
#> SRR1388234 2 0.000 0.991 0.000 1.000
#> SRR1370384 1 0.000 1.000 1.000 0.000
#> SRR1321650 1 0.000 1.000 1.000 0.000
#> SRR1485117 2 0.000 0.991 0.000 1.000
#> SRR1384713 1 0.000 1.000 1.000 0.000
#> SRR816609 2 0.000 0.991 0.000 1.000
#> SRR1486239 2 0.000 0.991 0.000 1.000
#> SRR1309638 1 0.000 1.000 1.000 0.000
#> SRR1356660 1 0.000 1.000 1.000 0.000
#> SRR1392883 2 0.000 0.991 0.000 1.000
#> SRR808130 1 0.000 1.000 1.000 0.000
#> SRR816677 1 0.141 0.979 0.980 0.020
#> SRR1455722 1 0.000 1.000 1.000 0.000
#> SRR1336029 1 0.000 1.000 1.000 0.000
#> SRR808452 1 0.000 1.000 1.000 0.000
#> SRR1352169 1 0.000 1.000 1.000 0.000
#> SRR1366707 1 0.000 1.000 1.000 0.000
#> SRR1328143 1 0.000 1.000 1.000 0.000
#> SRR1473567 2 0.000 0.991 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1347278 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1332904 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1082664 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1077968 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1485042 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1385453 3 0.6267 0.147 0.000 0.452 0.548
#> SRR1348074 2 0.0000 0.980 0.000 1.000 0.000
#> SRR813959 2 0.0000 0.980 0.000 1.000 0.000
#> SRR665442 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1378068 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1485237 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1350792 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1326797 1 0.1289 0.962 0.968 0.000 0.032
#> SRR808994 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1474041 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1500194 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1478523 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1325161 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1318026 2 0.1643 0.938 0.044 0.956 0.000
#> SRR1343778 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1441287 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1430991 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1499722 3 0.0424 0.974 0.008 0.000 0.992
#> SRR1351368 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1441785 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.993 1.000 0.000 0.000
#> SRR808375 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1452842 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1311709 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1433352 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1465172 3 0.5138 0.653 0.252 0.000 0.748
#> SRR1499284 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1499607 2 0.0000 0.980 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1403565 1 0.0747 0.978 0.984 0.000 0.016
#> SRR1332024 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1471633 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.980 0.000 1.000 0.000
#> SRR821573 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1435372 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.980 0.000 1.000 0.000
#> SRR816517 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1324141 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1101612 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1089785 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1347236 1 0.5216 0.644 0.740 0.000 0.260
#> SRR1326408 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1440643 2 0.6280 0.135 0.000 0.540 0.460
#> SRR662354 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1310817 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1347389 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1384737 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1096339 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1345329 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1414771 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1309119 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1470438 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1343221 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.993 1.000 0.000 0.000
#> SRR807949 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.982 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1477221 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1445046 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1319946 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1323977 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1445132 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1337321 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1366390 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1343012 2 0.4654 0.729 0.000 0.792 0.208
#> SRR1311958 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1388234 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1321650 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.993 1.000 0.000 0.000
#> SRR816609 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1486239 2 0.0000 0.980 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1356660 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.980 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.982 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.993 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.993 1.000 0.000 0.000
#> SRR1352169 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1366707 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1328143 3 0.0000 0.982 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.980 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 4 0.4277 0.5235 0.000 0.000 0.280 0.720
#> SRR1390119 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.4585 0.6685 0.000 0.000 0.668 0.332
#> SRR1347278 3 0.4989 0.4216 0.000 0.000 0.528 0.472
#> SRR1332904 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0707 0.9740 0.980 0.000 0.020 0.000
#> SRR1339007 1 0.0592 0.9724 0.984 0.000 0.000 0.016
#> SRR1376557 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.3074 0.8363 0.848 0.000 0.000 0.152
#> SRR1413978 1 0.1557 0.9536 0.944 0.000 0.056 0.000
#> SRR1439896 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1431865 1 0.0707 0.9740 0.980 0.000 0.020 0.000
#> SRR1394253 1 0.0707 0.9740 0.980 0.000 0.020 0.000
#> SRR1082664 4 0.0592 0.8708 0.000 0.000 0.016 0.984
#> SRR1077968 1 0.0817 0.9686 0.976 0.000 0.000 0.024
#> SRR1076393 3 0.4972 0.3048 0.000 0.000 0.544 0.456
#> SRR1477476 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.4072 0.7264 0.000 0.000 0.748 0.252
#> SRR1485042 1 0.0469 0.9764 0.988 0.000 0.012 0.000
#> SRR1385453 3 0.6924 0.4351 0.000 0.232 0.588 0.180
#> SRR1348074 2 0.1637 0.9362 0.000 0.940 0.060 0.000
#> SRR813959 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR665442 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1378068 3 0.2760 0.7840 0.000 0.000 0.872 0.128
#> SRR1485237 2 0.1890 0.9334 0.008 0.936 0.056 0.000
#> SRR1350792 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1326797 4 0.1940 0.8063 0.076 0.000 0.000 0.924
#> SRR808994 3 0.2647 0.7842 0.000 0.000 0.880 0.120
#> SRR1474041 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR1405641 3 0.2704 0.7845 0.000 0.000 0.876 0.124
#> SRR1362245 3 0.4500 0.6702 0.000 0.000 0.684 0.316
#> SRR1500194 1 0.0469 0.9764 0.988 0.000 0.012 0.000
#> SRR1414876 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.4304 0.5925 0.000 0.000 0.716 0.284
#> SRR1325161 4 0.0469 0.8679 0.012 0.000 0.000 0.988
#> SRR1318026 2 0.3734 0.8587 0.044 0.848 0.108 0.000
#> SRR1343778 4 0.4585 0.4034 0.000 0.000 0.332 0.668
#> SRR1441287 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1430991 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR1499722 4 0.0707 0.8638 0.020 0.000 0.000 0.980
#> SRR1351368 3 0.1637 0.7450 0.000 0.000 0.940 0.060
#> SRR1441785 1 0.0707 0.9740 0.980 0.000 0.020 0.000
#> SRR1096101 1 0.0336 0.9771 0.992 0.000 0.008 0.000
#> SRR808375 4 0.0000 0.8716 0.000 0.000 0.000 1.000
#> SRR1452842 1 0.2081 0.9181 0.916 0.000 0.000 0.084
#> SRR1311709 1 0.1022 0.9628 0.968 0.000 0.032 0.000
#> SRR1433352 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR1340241 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.0817 0.9686 0.976 0.000 0.000 0.024
#> SRR1465172 4 0.1474 0.8351 0.052 0.000 0.000 0.948
#> SRR1499284 4 0.2814 0.7237 0.132 0.000 0.000 0.868
#> SRR1499607 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR812342 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0469 0.9764 0.988 0.000 0.012 0.000
#> SRR1403565 1 0.2813 0.8974 0.896 0.000 0.024 0.080
#> SRR1332024 3 0.2704 0.7845 0.000 0.000 0.876 0.124
#> SRR1471633 1 0.1118 0.9601 0.964 0.000 0.036 0.000
#> SRR1325944 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR821573 4 0.1474 0.8413 0.000 0.000 0.052 0.948
#> SRR1435372 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR816517 2 0.0707 0.9584 0.000 0.980 0.020 0.000
#> SRR1324141 2 0.2530 0.8984 0.000 0.888 0.112 0.000
#> SRR1101612 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1089785 4 0.1302 0.8616 0.000 0.000 0.044 0.956
#> SRR1077708 4 0.3486 0.6354 0.000 0.000 0.188 0.812
#> SRR1343720 4 0.0000 0.8716 0.000 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1347236 4 0.1557 0.8317 0.056 0.000 0.000 0.944
#> SRR1326408 1 0.0817 0.9686 0.976 0.000 0.000 0.024
#> SRR1336529 3 0.2704 0.7845 0.000 0.000 0.876 0.124
#> SRR1440643 3 0.6547 0.3757 0.000 0.340 0.568 0.092
#> SRR662354 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1310817 4 0.1637 0.8568 0.000 0.000 0.060 0.940
#> SRR1347389 2 0.0188 0.9677 0.000 0.996 0.004 0.000
#> SRR1353097 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1384737 2 0.2589 0.8953 0.000 0.884 0.116 0.000
#> SRR1096339 1 0.0469 0.9764 0.988 0.000 0.012 0.000
#> SRR1345329 2 0.1557 0.9381 0.000 0.944 0.056 0.000
#> SRR1414771 3 0.2647 0.7842 0.000 0.000 0.880 0.120
#> SRR1309119 1 0.0188 0.9775 0.996 0.000 0.004 0.000
#> SRR1470438 3 0.2647 0.7842 0.000 0.000 0.880 0.120
#> SRR1343221 1 0.0817 0.9686 0.976 0.000 0.000 0.024
#> SRR1410847 1 0.0469 0.9764 0.988 0.000 0.012 0.000
#> SRR807949 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR1442332 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR815920 3 0.2814 0.7830 0.000 0.000 0.868 0.132
#> SRR1471524 3 0.2081 0.7598 0.000 0.000 0.916 0.084
#> SRR1477221 3 0.4522 0.6656 0.000 0.000 0.680 0.320
#> SRR1445046 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1331962 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.0707 0.9740 0.980 0.000 0.020 0.000
#> SRR1323977 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.4713 0.6139 0.000 0.000 0.640 0.360
#> SRR1366390 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1343012 2 0.7581 0.0333 0.000 0.424 0.380 0.196
#> SRR1311958 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1370384 1 0.0817 0.9686 0.976 0.000 0.000 0.024
#> SRR1321650 3 0.4697 0.6367 0.000 0.000 0.644 0.356
#> SRR1485117 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.1940 0.9262 0.924 0.000 0.000 0.076
#> SRR816609 2 0.1557 0.9381 0.000 0.944 0.056 0.000
#> SRR1486239 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR1309638 4 0.4907 -0.0917 0.000 0.000 0.420 0.580
#> SRR1356660 1 0.0707 0.9740 0.980 0.000 0.020 0.000
#> SRR1392883 2 0.0000 0.9698 0.000 1.000 0.000 0.000
#> SRR808130 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR816677 1 0.1792 0.9414 0.932 0.000 0.068 0.000
#> SRR1455722 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0469 0.9767 0.988 0.000 0.012 0.000
#> SRR808452 1 0.0000 0.9777 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.4998 0.3560 0.000 0.000 0.512 0.488
#> SRR1366707 3 0.2408 0.7715 0.000 0.000 0.896 0.104
#> SRR1328143 4 0.0817 0.8732 0.000 0.000 0.024 0.976
#> SRR1473567 2 0.0000 0.9698 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
#> SRR1442087 5 0.4088 0.408 0.000 0.000 0.368 0.000 0.632
#> SRR1390119 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.3366 0.697 0.000 0.000 0.768 0.000 0.232
#> SRR1347278 3 0.5233 0.571 0.000 0.000 0.636 0.076 0.288
#> SRR1332904 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1444179 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.2793 0.872 0.876 0.000 0.036 0.088 0.000
#> SRR1339007 1 0.0898 0.910 0.972 0.000 0.000 0.008 0.020
#> SRR1376557 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1077455 1 0.3455 0.729 0.784 0.000 0.000 0.008 0.208
#> SRR1413978 1 0.4637 0.633 0.672 0.000 0.036 0.292 0.000
#> SRR1439896 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.2127 0.874 0.000 0.892 0.000 0.108 0.000
#> SRR1431865 1 0.2793 0.872 0.876 0.000 0.036 0.088 0.000
#> SRR1394253 1 0.2793 0.872 0.876 0.000 0.036 0.088 0.000
#> SRR1082664 5 0.1041 0.896 0.000 0.000 0.032 0.004 0.964
#> SRR1077968 1 0.1251 0.903 0.956 0.000 0.000 0.008 0.036
#> SRR1076393 3 0.4576 0.441 0.000 0.000 0.608 0.016 0.376
#> SRR1477476 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.4088 0.718 0.000 0.000 0.776 0.056 0.168
#> SRR1485042 1 0.1018 0.912 0.968 0.000 0.016 0.016 0.000
#> SRR1385453 3 0.7352 0.284 0.000 0.320 0.476 0.096 0.108
#> SRR1348074 4 0.2773 0.897 0.000 0.164 0.000 0.836 0.000
#> SRR813959 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR665442 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1378068 3 0.1043 0.776 0.000 0.000 0.960 0.000 0.040
#> SRR1485237 4 0.3141 0.898 0.016 0.152 0.000 0.832 0.000
#> SRR1350792 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.1251 0.876 0.036 0.000 0.000 0.008 0.956
#> SRR808994 3 0.0794 0.775 0.000 0.000 0.972 0.000 0.028
#> SRR1474041 5 0.1410 0.889 0.000 0.000 0.060 0.000 0.940
#> SRR1405641 3 0.0880 0.776 0.000 0.000 0.968 0.000 0.032
#> SRR1362245 3 0.4693 0.667 0.000 0.000 0.724 0.080 0.196
#> SRR1500194 1 0.2236 0.889 0.908 0.000 0.024 0.068 0.000
#> SRR1414876 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.4302 0.649 0.000 0.000 0.744 0.048 0.208
#> SRR1325161 5 0.0992 0.886 0.024 0.000 0.000 0.008 0.968
#> SRR1318026 4 0.2547 0.866 0.048 0.040 0.004 0.904 0.004
#> SRR1343778 5 0.4262 0.203 0.000 0.000 0.440 0.000 0.560
#> SRR1441287 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.1121 0.896 0.000 0.000 0.044 0.000 0.956
#> SRR1499722 5 0.0992 0.886 0.024 0.000 0.000 0.008 0.968
#> SRR1351368 3 0.2754 0.736 0.000 0.000 0.880 0.080 0.040
#> SRR1441785 1 0.2793 0.872 0.876 0.000 0.036 0.088 0.000
#> SRR1096101 1 0.1173 0.915 0.964 0.000 0.012 0.020 0.004
#> SRR808375 5 0.0162 0.897 0.000 0.000 0.004 0.000 0.996
#> SRR1452842 1 0.2249 0.861 0.896 0.000 0.000 0.008 0.096
#> SRR1311709 1 0.4101 0.380 0.628 0.000 0.000 0.372 0.000
#> SRR1433352 5 0.0963 0.898 0.000 0.000 0.036 0.000 0.964
#> SRR1340241 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 1 0.1484 0.897 0.944 0.000 0.000 0.008 0.048
#> SRR1465172 5 0.1168 0.880 0.032 0.000 0.000 0.008 0.960
#> SRR1499284 5 0.1484 0.865 0.048 0.000 0.000 0.008 0.944
#> SRR1499607 2 0.2127 0.874 0.000 0.892 0.000 0.108 0.000
#> SRR812342 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> SRR1405374 1 0.1493 0.906 0.948 0.000 0.024 0.028 0.000
#> SRR1403565 1 0.4339 0.809 0.804 0.000 0.036 0.088 0.072
#> SRR1332024 3 0.0703 0.774 0.000 0.000 0.976 0.000 0.024
#> SRR1471633 1 0.4262 0.194 0.560 0.000 0.000 0.440 0.000
#> SRR1325944 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.1041 0.889 0.000 0.000 0.004 0.032 0.964
#> SRR1435372 1 0.0451 0.915 0.988 0.000 0.000 0.004 0.008
#> SRR1324184 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR816517 2 0.1365 0.936 0.000 0.952 0.004 0.040 0.004
#> SRR1324141 4 0.2170 0.900 0.000 0.088 0.004 0.904 0.004
#> SRR1101612 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.1792 0.872 0.000 0.000 0.084 0.000 0.916
#> SRR1077708 5 0.3053 0.737 0.000 0.000 0.164 0.008 0.828
#> SRR1343720 5 0.0579 0.896 0.000 0.000 0.008 0.008 0.984
#> SRR1477499 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.1168 0.880 0.032 0.000 0.000 0.008 0.960
#> SRR1326408 1 0.1082 0.907 0.964 0.000 0.000 0.008 0.028
#> SRR1336529 3 0.0880 0.776 0.000 0.000 0.968 0.000 0.032
#> SRR1440643 3 0.6969 0.260 0.000 0.348 0.488 0.104 0.060
#> SRR662354 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.1300 0.892 0.000 0.000 0.016 0.028 0.956
#> SRR1347389 2 0.0162 0.979 0.000 0.996 0.000 0.004 0.000
#> SRR1353097 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> SRR1384737 4 0.1864 0.895 0.000 0.068 0.004 0.924 0.004
#> SRR1096339 1 0.0807 0.914 0.976 0.000 0.012 0.012 0.000
#> SRR1345329 4 0.2813 0.895 0.000 0.168 0.000 0.832 0.000
#> SRR1414771 3 0.0794 0.775 0.000 0.000 0.972 0.000 0.028
#> SRR1309119 1 0.0703 0.914 0.976 0.000 0.000 0.024 0.000
#> SRR1470438 3 0.0794 0.775 0.000 0.000 0.972 0.000 0.028
#> SRR1343221 1 0.1251 0.903 0.956 0.000 0.000 0.008 0.036
#> SRR1410847 1 0.2362 0.885 0.900 0.000 0.024 0.076 0.000
#> SRR807949 5 0.1121 0.896 0.000 0.000 0.044 0.000 0.956
#> SRR1442332 5 0.1341 0.890 0.000 0.000 0.056 0.000 0.944
#> SRR815920 3 0.1043 0.776 0.000 0.000 0.960 0.000 0.040
#> SRR1471524 3 0.2304 0.755 0.000 0.000 0.908 0.044 0.048
#> SRR1477221 3 0.4701 0.663 0.000 0.000 0.720 0.076 0.204
#> SRR1445046 2 0.2127 0.874 0.000 0.892 0.000 0.108 0.000
#> SRR1331962 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1319946 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1311599 1 0.2793 0.872 0.876 0.000 0.036 0.088 0.000
#> SRR1323977 2 0.0703 0.963 0.000 0.976 0.000 0.024 0.000
#> SRR1445132 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.5181 0.589 0.000 0.000 0.652 0.080 0.268
#> SRR1366390 2 0.0162 0.979 0.000 0.996 0.000 0.004 0.000
#> SRR1343012 4 0.2199 0.889 0.000 0.060 0.016 0.916 0.008
#> SRR1311958 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1388234 2 0.1197 0.941 0.000 0.952 0.000 0.048 0.000
#> SRR1370384 1 0.1557 0.894 0.940 0.000 0.000 0.008 0.052
#> SRR1321650 3 0.4029 0.602 0.000 0.000 0.680 0.004 0.316
#> SRR1485117 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1384713 1 0.2077 0.871 0.908 0.000 0.000 0.008 0.084
#> SRR816609 4 0.2813 0.895 0.000 0.168 0.000 0.832 0.000
#> SRR1486239 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR1309638 3 0.4650 0.296 0.000 0.000 0.520 0.012 0.468
#> SRR1356660 1 0.2793 0.872 0.876 0.000 0.036 0.088 0.000
#> SRR1392883 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.1341 0.890 0.000 0.000 0.056 0.000 0.944
#> SRR816677 4 0.2616 0.750 0.100 0.000 0.020 0.880 0.000
#> SRR1455722 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0992 0.913 0.968 0.000 0.008 0.024 0.000
#> SRR808452 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.4045 0.531 0.000 0.000 0.644 0.000 0.356
#> SRR1366707 3 0.1697 0.773 0.000 0.000 0.932 0.008 0.060
#> SRR1328143 5 0.1341 0.890 0.000 0.000 0.056 0.000 0.944
#> SRR1473567 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.4136 0.1635 0.000 0.000 0.428 0.000 0.560 0.012
#> SRR1390119 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.3551 0.6542 0.000 0.000 0.792 0.000 0.148 0.060
#> SRR1347278 6 0.4918 0.3022 0.000 0.000 0.308 0.000 0.088 0.604
#> SRR1332904 2 0.0146 0.9622 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1444179 1 0.1196 0.8375 0.952 0.000 0.000 0.008 0.000 0.040
#> SRR1082685 1 0.0458 0.8459 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1362287 6 0.3390 0.7180 0.296 0.000 0.000 0.000 0.000 0.704
#> SRR1339007 1 0.1867 0.8311 0.916 0.000 0.000 0.000 0.020 0.064
#> SRR1376557 2 0.0146 0.9622 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1468700 2 0.0146 0.9622 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1077455 1 0.3627 0.7216 0.792 0.000 0.000 0.000 0.128 0.080
#> SRR1413978 6 0.4500 0.6877 0.224 0.000 0.000 0.088 0.000 0.688
#> SRR1439896 1 0.0937 0.8373 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1317963 2 0.2823 0.7690 0.000 0.796 0.000 0.204 0.000 0.000
#> SRR1431865 6 0.3499 0.7085 0.320 0.000 0.000 0.000 0.000 0.680
#> SRR1394253 6 0.3499 0.7085 0.320 0.000 0.000 0.000 0.000 0.680
#> SRR1082664 5 0.1779 0.8711 0.000 0.000 0.064 0.000 0.920 0.016
#> SRR1077968 1 0.2608 0.8045 0.872 0.000 0.000 0.000 0.048 0.080
#> SRR1076393 3 0.5057 0.4130 0.000 0.000 0.560 0.000 0.352 0.088
#> SRR1477476 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 3 0.4726 0.1238 0.000 0.000 0.528 0.000 0.048 0.424
#> SRR1485042 1 0.1610 0.8134 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1385453 3 0.7220 0.5080 0.000 0.124 0.544 0.064 0.096 0.172
#> SRR1348074 4 0.0937 0.9320 0.000 0.040 0.000 0.960 0.000 0.000
#> SRR813959 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR665442 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1378068 3 0.0146 0.7328 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1485237 4 0.1285 0.9270 0.004 0.052 0.000 0.944 0.000 0.000
#> SRR1350792 1 0.0632 0.8437 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1326797 5 0.1745 0.8571 0.012 0.000 0.000 0.000 0.920 0.068
#> SRR808994 3 0.0363 0.7317 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR1474041 5 0.2058 0.8735 0.000 0.000 0.036 0.000 0.908 0.056
#> SRR1405641 3 0.0363 0.7307 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR1362245 6 0.4584 0.1817 0.000 0.000 0.404 0.000 0.040 0.556
#> SRR1500194 6 0.3843 0.4218 0.452 0.000 0.000 0.000 0.000 0.548
#> SRR1414876 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 3 0.5734 0.6032 0.000 0.000 0.628 0.048 0.168 0.156
#> SRR1325161 5 0.1327 0.8658 0.000 0.000 0.000 0.000 0.936 0.064
#> SRR1318026 4 0.0363 0.9299 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1343778 3 0.4157 0.1807 0.000 0.000 0.544 0.000 0.444 0.012
#> SRR1441287 1 0.0547 0.8447 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1430991 5 0.1575 0.8841 0.000 0.000 0.032 0.000 0.936 0.032
#> SRR1499722 5 0.1643 0.8702 0.008 0.000 0.000 0.000 0.924 0.068
#> SRR1351368 3 0.4440 0.6608 0.000 0.000 0.748 0.060 0.036 0.156
#> SRR1441785 6 0.3464 0.7133 0.312 0.000 0.000 0.000 0.000 0.688
#> SRR1096101 1 0.2692 0.7734 0.840 0.000 0.000 0.000 0.012 0.148
#> SRR808375 5 0.0000 0.8859 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1452842 1 0.3175 0.7701 0.832 0.000 0.000 0.000 0.088 0.080
#> SRR1311709 1 0.3168 0.7060 0.792 0.000 0.000 0.192 0.000 0.016
#> SRR1433352 5 0.1633 0.8837 0.000 0.000 0.044 0.000 0.932 0.024
#> SRR1340241 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1456754 1 0.2786 0.7989 0.860 0.000 0.000 0.000 0.056 0.084
#> SRR1465172 5 0.1895 0.8517 0.016 0.000 0.000 0.000 0.912 0.072
#> SRR1499284 5 0.3458 0.7413 0.112 0.000 0.000 0.000 0.808 0.080
#> SRR1499607 2 0.2793 0.7739 0.000 0.800 0.000 0.200 0.000 0.000
#> SRR812342 1 0.0000 0.8464 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.3765 0.0717 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1403565 6 0.3468 0.7174 0.284 0.000 0.000 0.000 0.004 0.712
#> SRR1332024 3 0.0632 0.7261 0.000 0.000 0.976 0.000 0.000 0.024
#> SRR1471633 1 0.3404 0.6647 0.760 0.000 0.000 0.224 0.000 0.016
#> SRR1325944 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.0935 0.8844 0.000 0.000 0.004 0.000 0.964 0.032
#> SRR1435372 1 0.1719 0.8319 0.924 0.000 0.000 0.000 0.016 0.060
#> SRR1324184 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816517 2 0.3336 0.7860 0.000 0.812 0.000 0.056 0.000 0.132
#> SRR1324141 4 0.0713 0.9269 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1101612 1 0.0458 0.8459 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1356531 1 0.0260 0.8462 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1089785 5 0.2376 0.8609 0.000 0.000 0.068 0.000 0.888 0.044
#> SRR1077708 5 0.3709 0.6785 0.000 0.000 0.204 0.000 0.756 0.040
#> SRR1343720 5 0.1320 0.8850 0.000 0.000 0.016 0.000 0.948 0.036
#> SRR1477499 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 5 0.1686 0.8591 0.012 0.000 0.000 0.000 0.924 0.064
#> SRR1326408 1 0.2728 0.8072 0.872 0.000 0.000 0.008 0.040 0.080
#> SRR1336529 3 0.0363 0.7307 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR1440643 3 0.7510 0.4449 0.000 0.168 0.504 0.080 0.076 0.172
#> SRR662354 1 0.0937 0.8373 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1310817 5 0.1745 0.8770 0.000 0.000 0.020 0.000 0.924 0.056
#> SRR1347389 2 0.0363 0.9557 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1353097 1 0.1010 0.8420 0.960 0.000 0.000 0.000 0.004 0.036
#> SRR1384737 4 0.0632 0.9282 0.000 0.000 0.000 0.976 0.000 0.024
#> SRR1096339 1 0.1501 0.8144 0.924 0.000 0.000 0.000 0.000 0.076
#> SRR1345329 4 0.1141 0.9279 0.000 0.052 0.000 0.948 0.000 0.000
#> SRR1414771 3 0.0458 0.7292 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR1309119 1 0.1757 0.8148 0.916 0.000 0.000 0.008 0.000 0.076
#> SRR1470438 3 0.0458 0.7292 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR1343221 1 0.2009 0.8329 0.908 0.000 0.000 0.000 0.024 0.068
#> SRR1410847 1 0.3857 -0.2454 0.532 0.000 0.000 0.000 0.000 0.468
#> SRR807949 5 0.1498 0.8848 0.000 0.000 0.032 0.000 0.940 0.028
#> SRR1442332 5 0.1921 0.8765 0.000 0.000 0.032 0.000 0.916 0.052
#> SRR815920 3 0.0717 0.7333 0.000 0.000 0.976 0.000 0.016 0.008
#> SRR1471524 3 0.4021 0.6803 0.000 0.000 0.780 0.032 0.044 0.144
#> SRR1477221 6 0.4493 0.2762 0.000 0.000 0.364 0.000 0.040 0.596
#> SRR1445046 2 0.2823 0.7690 0.000 0.796 0.000 0.204 0.000 0.000
#> SRR1331962 2 0.0146 0.9622 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1319946 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1311599 6 0.3499 0.7085 0.320 0.000 0.000 0.000 0.000 0.680
#> SRR1323977 2 0.0713 0.9453 0.000 0.972 0.000 0.028 0.000 0.000
#> SRR1445132 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 6 0.4567 0.3214 0.000 0.000 0.332 0.000 0.052 0.616
#> SRR1366390 2 0.0146 0.9610 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1343012 4 0.1007 0.9192 0.000 0.000 0.000 0.956 0.000 0.044
#> SRR1311958 2 0.0146 0.9622 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1388234 2 0.2135 0.8587 0.000 0.872 0.000 0.128 0.000 0.000
#> SRR1370384 1 0.2794 0.7957 0.860 0.000 0.000 0.000 0.060 0.080
#> SRR1321650 3 0.5246 0.4720 0.000 0.000 0.596 0.000 0.256 0.148
#> SRR1485117 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 1 0.3073 0.7783 0.840 0.000 0.000 0.000 0.080 0.080
#> SRR816609 4 0.1204 0.9246 0.000 0.056 0.000 0.944 0.000 0.000
#> SRR1486239 2 0.0363 0.9581 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1309638 3 0.5750 0.3252 0.012 0.000 0.512 0.000 0.344 0.132
#> SRR1356660 6 0.3499 0.7085 0.320 0.000 0.000 0.000 0.000 0.680
#> SRR1392883 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.1575 0.8841 0.000 0.000 0.032 0.000 0.936 0.032
#> SRR816677 4 0.2896 0.7887 0.016 0.000 0.000 0.824 0.000 0.160
#> SRR1455722 1 0.0458 0.8459 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1336029 1 0.3244 0.5296 0.732 0.000 0.000 0.000 0.000 0.268
#> SRR808452 1 0.0458 0.8459 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1352169 3 0.5042 0.3941 0.000 0.000 0.576 0.000 0.332 0.092
#> SRR1366707 3 0.2789 0.7150 0.000 0.000 0.864 0.004 0.044 0.088
#> SRR1328143 5 0.1575 0.8841 0.000 0.000 0.032 0.000 0.936 0.032
#> SRR1473567 2 0.0146 0.9622 0.000 0.996 0.000 0.004 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "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 17851 rows and 124 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 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 0.988 0.995 0.3300 0.666 0.666
#> 3 3 0.923 0.918 0.967 0.9197 0.687 0.534
#> 4 4 0.815 0.847 0.880 0.0625 0.980 0.944
#> 5 5 0.864 0.877 0.942 0.1038 0.888 0.688
#> 6 6 0.775 0.728 0.832 0.0653 0.877 0.576
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
#> SRR1442087 1 0.0000 1.000 1.000 0.000
#> SRR1390119 2 0.0000 0.979 0.000 1.000
#> SRR1436127 1 0.0000 1.000 1.000 0.000
#> SRR1347278 1 0.0000 1.000 1.000 0.000
#> SRR1332904 2 0.0000 0.979 0.000 1.000
#> SRR1444179 1 0.0000 1.000 1.000 0.000
#> SRR1082685 1 0.0000 1.000 1.000 0.000
#> SRR1362287 1 0.0000 1.000 1.000 0.000
#> SRR1339007 1 0.0000 1.000 1.000 0.000
#> SRR1376557 2 0.0000 0.979 0.000 1.000
#> SRR1468700 2 0.0000 0.979 0.000 1.000
#> SRR1077455 1 0.0000 1.000 1.000 0.000
#> SRR1413978 1 0.0000 1.000 1.000 0.000
#> SRR1439896 1 0.0000 1.000 1.000 0.000
#> SRR1317963 2 0.0000 0.979 0.000 1.000
#> SRR1431865 1 0.0000 1.000 1.000 0.000
#> SRR1394253 1 0.0000 1.000 1.000 0.000
#> SRR1082664 1 0.0000 1.000 1.000 0.000
#> SRR1077968 1 0.0000 1.000 1.000 0.000
#> SRR1076393 1 0.0000 1.000 1.000 0.000
#> SRR1477476 2 0.0000 0.979 0.000 1.000
#> SRR1398057 1 0.0000 1.000 1.000 0.000
#> SRR1485042 1 0.0000 1.000 1.000 0.000
#> SRR1385453 1 0.0000 1.000 1.000 0.000
#> SRR1348074 1 0.0672 0.992 0.992 0.008
#> SRR813959 1 0.0000 1.000 1.000 0.000
#> SRR665442 1 0.0000 1.000 1.000 0.000
#> SRR1378068 1 0.0000 1.000 1.000 0.000
#> SRR1485237 1 0.0672 0.992 0.992 0.008
#> SRR1350792 1 0.0000 1.000 1.000 0.000
#> SRR1326797 1 0.0000 1.000 1.000 0.000
#> SRR808994 1 0.0000 1.000 1.000 0.000
#> SRR1474041 1 0.0000 1.000 1.000 0.000
#> SRR1405641 1 0.0000 1.000 1.000 0.000
#> SRR1362245 1 0.0000 1.000 1.000 0.000
#> SRR1500194 1 0.0000 1.000 1.000 0.000
#> SRR1414876 2 0.0000 0.979 0.000 1.000
#> SRR1478523 1 0.0000 1.000 1.000 0.000
#> SRR1325161 1 0.0000 1.000 1.000 0.000
#> SRR1318026 1 0.0000 1.000 1.000 0.000
#> SRR1343778 1 0.0000 1.000 1.000 0.000
#> SRR1441287 1 0.0000 1.000 1.000 0.000
#> SRR1430991 1 0.0000 1.000 1.000 0.000
#> SRR1499722 1 0.0000 1.000 1.000 0.000
#> SRR1351368 1 0.0000 1.000 1.000 0.000
#> SRR1441785 1 0.0000 1.000 1.000 0.000
#> SRR1096101 1 0.0000 1.000 1.000 0.000
#> SRR808375 1 0.0000 1.000 1.000 0.000
#> SRR1452842 1 0.0000 1.000 1.000 0.000
#> SRR1311709 1 0.0000 1.000 1.000 0.000
#> SRR1433352 1 0.0000 1.000 1.000 0.000
#> SRR1340241 2 0.0000 0.979 0.000 1.000
#> SRR1456754 1 0.0000 1.000 1.000 0.000
#> SRR1465172 1 0.0000 1.000 1.000 0.000
#> SRR1499284 1 0.0000 1.000 1.000 0.000
#> SRR1499607 2 0.0000 0.979 0.000 1.000
#> SRR812342 1 0.0000 1.000 1.000 0.000
#> SRR1405374 1 0.0000 1.000 1.000 0.000
#> SRR1403565 1 0.0000 1.000 1.000 0.000
#> SRR1332024 1 0.0000 1.000 1.000 0.000
#> SRR1471633 1 0.0000 1.000 1.000 0.000
#> SRR1325944 2 0.0000 0.979 0.000 1.000
#> SRR1429450 2 0.0000 0.979 0.000 1.000
#> SRR821573 1 0.0000 1.000 1.000 0.000
#> SRR1435372 1 0.0000 1.000 1.000 0.000
#> SRR1324184 2 0.0000 0.979 0.000 1.000
#> SRR816517 2 0.0000 0.979 0.000 1.000
#> SRR1324141 1 0.0000 1.000 1.000 0.000
#> SRR1101612 1 0.0000 1.000 1.000 0.000
#> SRR1356531 1 0.0000 1.000 1.000 0.000
#> SRR1089785 1 0.0000 1.000 1.000 0.000
#> SRR1077708 1 0.0000 1.000 1.000 0.000
#> SRR1343720 1 0.0000 1.000 1.000 0.000
#> SRR1477499 2 0.0000 0.979 0.000 1.000
#> SRR1347236 1 0.0000 1.000 1.000 0.000
#> SRR1326408 1 0.0000 1.000 1.000 0.000
#> SRR1336529 1 0.0000 1.000 1.000 0.000
#> SRR1440643 1 0.0000 1.000 1.000 0.000
#> SRR662354 1 0.0000 1.000 1.000 0.000
#> SRR1310817 1 0.0000 1.000 1.000 0.000
#> SRR1347389 2 0.0000 0.979 0.000 1.000
#> SRR1353097 1 0.0000 1.000 1.000 0.000
#> SRR1384737 1 0.0000 1.000 1.000 0.000
#> SRR1096339 1 0.0000 1.000 1.000 0.000
#> SRR1345329 1 0.0672 0.992 0.992 0.008
#> SRR1414771 1 0.0000 1.000 1.000 0.000
#> SRR1309119 1 0.0000 1.000 1.000 0.000
#> SRR1470438 1 0.0000 1.000 1.000 0.000
#> SRR1343221 1 0.0000 1.000 1.000 0.000
#> SRR1410847 1 0.0000 1.000 1.000 0.000
#> SRR807949 1 0.0000 1.000 1.000 0.000
#> SRR1442332 1 0.0000 1.000 1.000 0.000
#> SRR815920 1 0.0000 1.000 1.000 0.000
#> SRR1471524 1 0.0000 1.000 1.000 0.000
#> SRR1477221 1 0.0000 1.000 1.000 0.000
#> SRR1445046 2 0.0000 0.979 0.000 1.000
#> SRR1331962 2 0.0000 0.979 0.000 1.000
#> SRR1319946 2 0.5294 0.855 0.120 0.880
#> SRR1311599 1 0.0000 1.000 1.000 0.000
#> SRR1323977 1 0.0000 1.000 1.000 0.000
#> SRR1445132 2 0.0000 0.979 0.000 1.000
#> SRR1337321 1 0.0000 1.000 1.000 0.000
#> SRR1366390 2 0.0000 0.979 0.000 1.000
#> SRR1343012 1 0.0000 1.000 1.000 0.000
#> SRR1311958 2 0.0000 0.979 0.000 1.000
#> SRR1388234 2 0.9775 0.309 0.412 0.588
#> SRR1370384 1 0.0000 1.000 1.000 0.000
#> SRR1321650 1 0.0000 1.000 1.000 0.000
#> SRR1485117 2 0.0000 0.979 0.000 1.000
#> SRR1384713 1 0.0000 1.000 1.000 0.000
#> SRR816609 1 0.1184 0.984 0.984 0.016
#> SRR1486239 2 0.0000 0.979 0.000 1.000
#> SRR1309638 1 0.0000 1.000 1.000 0.000
#> SRR1356660 1 0.0000 1.000 1.000 0.000
#> SRR1392883 2 0.0000 0.979 0.000 1.000
#> SRR808130 1 0.0000 1.000 1.000 0.000
#> SRR816677 1 0.0000 1.000 1.000 0.000
#> SRR1455722 1 0.0000 1.000 1.000 0.000
#> SRR1336029 1 0.0000 1.000 1.000 0.000
#> SRR808452 1 0.0000 1.000 1.000 0.000
#> SRR1352169 1 0.0000 1.000 1.000 0.000
#> SRR1366707 1 0.0000 1.000 1.000 0.000
#> SRR1328143 1 0.0000 1.000 1.000 0.000
#> SRR1473567 2 0.0000 0.979 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1347278 1 0.5859 0.4856 0.656 0.000 0.344
#> SRR1332904 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1077455 1 0.0424 0.9619 0.992 0.000 0.008
#> SRR1413978 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1082664 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1077968 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1485042 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1385453 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1348074 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR813959 3 0.5591 0.5564 0.304 0.000 0.696
#> SRR665442 1 0.2165 0.9130 0.936 0.000 0.064
#> SRR1378068 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1485237 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1350792 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1326797 1 0.4654 0.7449 0.792 0.000 0.208
#> SRR808994 3 0.0747 0.9354 0.016 0.000 0.984
#> SRR1474041 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1362245 3 0.6154 0.3226 0.408 0.000 0.592
#> SRR1500194 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1478523 3 0.1031 0.9292 0.024 0.000 0.976
#> SRR1325161 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1318026 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1343778 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1441287 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1430991 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1499722 1 0.5291 0.6463 0.732 0.000 0.268
#> SRR1351368 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1441785 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR808375 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1452842 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1311709 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1433352 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1465172 1 0.3267 0.8641 0.884 0.000 0.116
#> SRR1499284 1 0.2261 0.9092 0.932 0.000 0.068
#> SRR1499607 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1332024 3 0.2537 0.8790 0.080 0.000 0.920
#> SRR1471633 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR821573 3 0.6192 0.2709 0.420 0.000 0.580
#> SRR1435372 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR816517 3 0.2356 0.8844 0.000 0.072 0.928
#> SRR1324141 1 0.0892 0.9522 0.980 0.000 0.020
#> SRR1101612 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1089785 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1347236 1 0.4504 0.7592 0.804 0.000 0.196
#> SRR1326408 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1440643 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR662354 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1310817 3 0.4555 0.7293 0.200 0.000 0.800
#> SRR1347389 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1384737 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1096339 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1345329 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1414771 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1309119 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1470438 3 0.2261 0.8894 0.068 0.000 0.932
#> SRR1343221 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR807949 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1477221 3 0.3686 0.8157 0.140 0.000 0.860
#> SRR1445046 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1319946 2 0.4110 0.8036 0.152 0.844 0.004
#> SRR1311599 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1323977 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1337321 1 0.6008 0.4264 0.628 0.000 0.372
#> SRR1366390 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1343012 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1311958 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1388234 2 0.6302 0.0802 0.480 0.520 0.000
#> SRR1370384 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1321650 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR816609 1 0.0237 0.9649 0.996 0.004 0.000
#> SRR1486239 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1356660 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9696 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.9682 1.000 0.000 0.000
#> SRR1352169 3 0.0892 0.9324 0.020 0.000 0.980
#> SRR1366707 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1328143 3 0.0000 0.9465 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.9696 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.0592 0.862 0.000 0.016 0.984 0.000
#> SRR1390119 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1436127 3 0.2647 0.866 0.000 0.120 0.880 0.000
#> SRR1347278 1 0.6868 0.370 0.584 0.152 0.264 0.000
#> SRR1332904 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR1444179 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.4304 0.818 0.000 0.716 0.000 0.284
#> SRR1468700 2 0.4304 0.818 0.000 0.716 0.000 0.284
#> SRR1077455 1 0.0469 0.931 0.988 0.000 0.012 0.000
#> SRR1413978 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR1431865 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1082664 3 0.0592 0.862 0.000 0.016 0.984 0.000
#> SRR1077968 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1076393 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1477476 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1398057 3 0.3219 0.859 0.000 0.164 0.836 0.000
#> SRR1485042 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1385453 3 0.3610 0.852 0.000 0.200 0.800 0.000
#> SRR1348074 1 0.2149 0.875 0.912 0.088 0.000 0.000
#> SRR813959 3 0.7627 0.379 0.292 0.240 0.468 0.000
#> SRR665442 1 0.4174 0.799 0.816 0.044 0.140 0.000
#> SRR1378068 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR1485237 1 0.2149 0.875 0.912 0.088 0.000 0.000
#> SRR1350792 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.4624 0.564 0.660 0.000 0.340 0.000
#> SRR808994 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR1474041 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1405641 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR1362245 3 0.7093 0.547 0.272 0.172 0.556 0.000
#> SRR1500194 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1414876 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1478523 3 0.3751 0.853 0.004 0.196 0.800 0.000
#> SRR1325161 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1318026 1 0.0188 0.937 0.996 0.004 0.000 0.000
#> SRR1343778 3 0.3444 0.857 0.000 0.184 0.816 0.000
#> SRR1441287 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1430991 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1499722 1 0.5016 0.430 0.600 0.004 0.396 0.000
#> SRR1351368 3 0.3610 0.852 0.000 0.200 0.800 0.000
#> SRR1441785 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR808375 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1452842 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1311709 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1433352 3 0.1792 0.867 0.000 0.068 0.932 0.000
#> SRR1340241 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1456754 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1465172 1 0.3975 0.722 0.760 0.000 0.240 0.000
#> SRR1499284 1 0.3649 0.762 0.796 0.000 0.204 0.000
#> SRR1499607 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR812342 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1332024 3 0.4136 0.847 0.016 0.196 0.788 0.000
#> SRR1471633 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1325944 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1429450 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR821573 3 0.4907 0.138 0.420 0.000 0.580 0.000
#> SRR1435372 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1324184 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR816517 3 0.5404 0.692 0.000 0.328 0.644 0.028
#> SRR1324141 1 0.3099 0.854 0.876 0.104 0.020 0.000
#> SRR1101612 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1089785 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1077708 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1343720 3 0.0921 0.864 0.000 0.028 0.972 0.000
#> SRR1477499 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1347236 1 0.3688 0.761 0.792 0.000 0.208 0.000
#> SRR1326408 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1336529 3 0.3528 0.856 0.000 0.192 0.808 0.000
#> SRR1440643 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR662354 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1310817 3 0.3569 0.631 0.196 0.000 0.804 0.000
#> SRR1347389 2 0.4888 0.579 0.000 0.588 0.000 0.412
#> SRR1353097 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1384737 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1096339 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1345329 1 0.2149 0.875 0.912 0.088 0.000 0.000
#> SRR1414771 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR1309119 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1470438 3 0.3893 0.851 0.008 0.196 0.796 0.000
#> SRR1343221 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR807949 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1442332 3 0.2408 0.866 0.000 0.104 0.896 0.000
#> SRR815920 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR1471524 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1477221 3 0.5339 0.790 0.100 0.156 0.744 0.000
#> SRR1445046 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR1331962 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR1319946 2 0.1545 0.718 0.008 0.952 0.000 0.040
#> SRR1311599 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1323977 1 0.3688 0.768 0.792 0.208 0.000 0.000
#> SRR1445132 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1337321 1 0.6323 0.117 0.500 0.060 0.440 0.000
#> SRR1366390 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR1343012 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1311958 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR1388234 2 0.1902 0.683 0.064 0.932 0.000 0.004
#> SRR1370384 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1321650 3 0.1389 0.866 0.000 0.048 0.952 0.000
#> SRR1485117 4 0.4888 -0.123 0.000 0.412 0.000 0.588
#> SRR1384713 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR816609 1 0.3356 0.784 0.824 0.176 0.000 0.000
#> SRR1486239 2 0.3569 0.890 0.000 0.804 0.000 0.196
#> SRR1309638 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1356660 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1392883 4 0.0000 0.950 0.000 0.000 0.000 1.000
#> SRR808130 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR816677 1 0.0336 0.934 0.992 0.008 0.000 0.000
#> SRR1455722 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.3569 0.854 0.000 0.196 0.804 0.000
#> SRR1366707 3 0.1302 0.863 0.000 0.044 0.956 0.000
#> SRR1328143 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> SRR1473567 2 0.4250 0.827 0.000 0.724 0.000 0.276
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.1544 0.8896 0.000 0.000 0.068 0.000 0.932
#> SRR1390119 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1347278 3 0.4879 0.7016 0.176 0.000 0.716 0.000 0.108
#> SRR1332904 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.0404 0.9523 0.988 0.000 0.012 0.000 0.000
#> SRR1339007 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1376557 4 0.3109 0.7385 0.000 0.200 0.000 0.800 0.000
#> SRR1468700 4 0.3109 0.7385 0.000 0.200 0.000 0.800 0.000
#> SRR1077455 1 0.0510 0.9493 0.984 0.000 0.000 0.000 0.016
#> SRR1413978 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1082664 5 0.1851 0.8697 0.000 0.000 0.088 0.000 0.912
#> SRR1077968 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1076393 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.3242 0.7454 0.000 0.000 0.784 0.000 0.216
#> SRR1485042 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1385453 3 0.0404 0.8720 0.000 0.000 0.988 0.000 0.012
#> SRR1348074 1 0.3305 0.7439 0.776 0.000 0.000 0.224 0.000
#> SRR813959 3 0.5122 0.6932 0.000 0.000 0.688 0.200 0.112
#> SRR665442 1 0.3794 0.7750 0.800 0.000 0.000 0.048 0.152
#> SRR1378068 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1485237 1 0.3109 0.7728 0.800 0.000 0.000 0.200 0.000
#> SRR1350792 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0404 0.9233 0.012 0.000 0.000 0.000 0.988
#> SRR808994 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1474041 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1362245 3 0.2473 0.8367 0.072 0.000 0.896 0.000 0.032
#> SRR1500194 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1414876 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.1197 0.8535 0.048 0.000 0.952 0.000 0.000
#> SRR1325161 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1318026 1 0.0162 0.9586 0.996 0.000 0.000 0.004 0.000
#> SRR1343778 3 0.2723 0.8253 0.012 0.000 0.864 0.000 0.124
#> SRR1441287 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1499722 5 0.3177 0.6789 0.000 0.000 0.208 0.000 0.792
#> SRR1351368 3 0.0290 0.8728 0.000 0.000 0.992 0.000 0.008
#> SRR1441785 1 0.0404 0.9523 0.988 0.000 0.012 0.000 0.000
#> SRR1096101 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1452842 1 0.0162 0.9583 0.996 0.000 0.000 0.000 0.004
#> SRR1311709 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1433352 3 0.4453 0.7158 0.048 0.000 0.724 0.000 0.228
#> SRR1340241 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1465172 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1499284 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1499607 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1403565 1 0.0404 0.9523 0.988 0.000 0.012 0.000 0.000
#> SRR1332024 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1471633 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1325944 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1435372 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1324184 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR816517 3 0.3837 0.6150 0.000 0.000 0.692 0.308 0.000
#> SRR1324141 1 0.3266 0.7694 0.796 0.000 0.000 0.200 0.004
#> SRR1101612 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1077708 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1343720 5 0.4297 -0.0748 0.000 0.000 0.472 0.000 0.528
#> SRR1477499 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 1 0.3430 0.7281 0.776 0.000 0.004 0.000 0.220
#> SRR1326408 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1440643 3 0.1725 0.8518 0.044 0.000 0.936 0.020 0.000
#> SRR662354 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1347389 4 0.3586 0.6242 0.000 0.264 0.000 0.736 0.000
#> SRR1353097 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1384737 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1096339 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 1 0.3305 0.7439 0.776 0.000 0.000 0.224 0.000
#> SRR1414771 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1309119 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1470438 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1343221 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1442332 3 0.3837 0.6440 0.000 0.000 0.692 0.000 0.308
#> SRR815920 3 0.0000 0.8735 0.000 0.000 1.000 0.000 0.000
#> SRR1471524 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1477221 3 0.4502 0.7162 0.180 0.000 0.744 0.000 0.076
#> SRR1445046 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1331962 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1319946 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1323977 1 0.4832 0.6669 0.712 0.000 0.088 0.200 0.000
#> SRR1445132 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.3816 0.6487 0.000 0.000 0.696 0.000 0.304
#> SRR1366390 2 0.0510 0.9403 0.000 0.984 0.000 0.016 0.000
#> SRR1343012 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1311958 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1388234 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1321650 3 0.2732 0.7930 0.000 0.000 0.840 0.000 0.160
#> SRR1485117 2 0.4219 0.1782 0.000 0.584 0.000 0.416 0.000
#> SRR1384713 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR816609 1 0.3913 0.5868 0.676 0.000 0.000 0.324 0.000
#> SRR1486239 4 0.0000 0.9158 0.000 0.000 0.000 1.000 0.000
#> SRR1309638 5 0.1197 0.9045 0.000 0.000 0.048 0.000 0.952
#> SRR1356660 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9557 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1455722 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.1041 0.8687 0.004 0.000 0.964 0.000 0.032
#> SRR1366707 5 0.3796 0.5928 0.000 0.000 0.300 0.000 0.700
#> SRR1328143 5 0.0000 0.9349 0.000 0.000 0.000 0.000 1.000
#> SRR1473567 4 0.2813 0.7784 0.000 0.168 0.000 0.832 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.2996 0.71534 0.000 0.000 0.228 0.000 0.772 0.000
#> SRR1390119 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.0547 0.85870 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1347278 6 0.3307 0.55987 0.000 0.000 0.072 0.000 0.108 0.820
#> SRR1332904 4 0.0000 0.80965 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1082685 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1362287 6 0.2070 0.62937 0.092 0.000 0.012 0.000 0.000 0.896
#> SRR1339007 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1376557 4 0.0000 0.80965 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1468700 4 0.0000 0.80965 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.4978 0.77545 0.532 0.000 0.000 0.000 0.072 0.396
#> SRR1413978 6 0.1957 0.61617 0.112 0.000 0.000 0.000 0.000 0.888
#> SRR1439896 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1317963 4 0.2491 0.82418 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR1431865 6 0.1957 0.61617 0.112 0.000 0.000 0.000 0.000 0.888
#> SRR1394253 6 0.1957 0.61617 0.112 0.000 0.000 0.000 0.000 0.888
#> SRR1082664 5 0.1471 0.87514 0.000 0.000 0.064 0.000 0.932 0.004
#> SRR1077968 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1076393 5 0.1285 0.88990 0.000 0.000 0.052 0.000 0.944 0.004
#> SRR1477476 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 6 0.5095 0.19004 0.000 0.000 0.104 0.000 0.312 0.584
#> SRR1485042 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1385453 3 0.3931 0.77262 0.100 0.000 0.800 0.000 0.036 0.064
#> SRR1348074 1 0.0000 0.44739 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR813959 5 0.5269 0.51023 0.284 0.000 0.040 0.000 0.620 0.056
#> SRR665442 1 0.5111 0.63223 0.624 0.000 0.000 0.000 0.152 0.224
#> SRR1378068 3 0.0146 0.85916 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1485237 1 0.0000 0.44739 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1350792 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1326797 5 0.0363 0.89961 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR808994 3 0.0146 0.85845 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1474041 5 0.1285 0.88187 0.000 0.000 0.004 0.000 0.944 0.052
#> SRR1405641 3 0.0146 0.85845 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1362245 6 0.4135 0.25754 0.000 0.000 0.300 0.000 0.032 0.668
#> SRR1500194 6 0.2003 0.60965 0.116 0.000 0.000 0.000 0.000 0.884
#> SRR1414876 2 0.2762 0.83986 0.000 0.804 0.000 0.196 0.000 0.000
#> SRR1478523 6 0.4242 -0.05893 0.016 0.000 0.448 0.000 0.000 0.536
#> SRR1325161 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1318026 1 0.2491 0.60067 0.836 0.000 0.000 0.000 0.000 0.164
#> SRR1343778 3 0.6575 0.22757 0.136 0.000 0.476 0.000 0.316 0.072
#> SRR1441287 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1430991 5 0.1075 0.89286 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1499722 5 0.0146 0.90544 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1351368 3 0.6473 0.45786 0.184 0.000 0.528 0.000 0.224 0.064
#> SRR1441785 6 0.1745 0.63372 0.068 0.000 0.012 0.000 0.000 0.920
#> SRR1096101 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR808375 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1452842 1 0.4863 0.78635 0.528 0.000 0.000 0.000 0.060 0.412
#> SRR1311709 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1433352 5 0.6421 0.32701 0.292 0.000 0.124 0.000 0.512 0.072
#> SRR1340241 2 0.2793 0.83662 0.000 0.800 0.000 0.200 0.000 0.000
#> SRR1456754 1 0.4410 0.81723 0.560 0.000 0.000 0.000 0.028 0.412
#> SRR1465172 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499284 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499607 4 0.2416 0.82482 0.156 0.000 0.000 0.844 0.000 0.000
#> SRR812342 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1405374 6 0.2003 0.60955 0.116 0.000 0.000 0.000 0.000 0.884
#> SRR1403565 6 0.1895 0.62488 0.072 0.000 0.016 0.000 0.000 0.912
#> SRR1332024 3 0.2823 0.70025 0.000 0.000 0.796 0.000 0.000 0.204
#> SRR1471633 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1325944 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1435372 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1324184 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816517 4 0.6883 0.44213 0.368 0.000 0.092 0.396 0.000 0.144
#> SRR1324141 1 0.0935 0.41333 0.964 0.000 0.004 0.000 0.032 0.000
#> SRR1101612 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1356531 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1089785 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1077708 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1343720 5 0.2448 0.83940 0.000 0.000 0.064 0.000 0.884 0.052
#> SRR1477499 2 0.2491 0.86077 0.000 0.836 0.000 0.164 0.000 0.000
#> SRR1347236 1 0.5742 0.49323 0.532 0.000 0.004 0.000 0.272 0.192
#> SRR1326408 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1336529 3 0.0632 0.85685 0.000 0.000 0.976 0.000 0.000 0.024
#> SRR1440643 6 0.5726 0.14435 0.316 0.000 0.188 0.000 0.000 0.496
#> SRR662354 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1310817 5 0.0790 0.89923 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1347389 4 0.5380 0.52658 0.412 0.112 0.000 0.476 0.000 0.000
#> SRR1353097 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1384737 6 0.3756 0.52548 0.352 0.000 0.004 0.000 0.000 0.644
#> SRR1096339 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1345329 1 0.0000 0.44739 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414771 3 0.1141 0.84565 0.000 0.000 0.948 0.000 0.000 0.052
#> SRR1309119 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1470438 3 0.1141 0.84565 0.000 0.000 0.948 0.000 0.000 0.052
#> SRR1343221 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1410847 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR807949 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1442332 5 0.2962 0.82214 0.000 0.000 0.084 0.000 0.848 0.068
#> SRR815920 3 0.0458 0.85950 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR1471524 5 0.1285 0.88990 0.000 0.000 0.052 0.000 0.944 0.004
#> SRR1477221 6 0.3020 0.58426 0.000 0.000 0.080 0.000 0.076 0.844
#> SRR1445046 4 0.0146 0.81108 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1331962 4 0.2491 0.82418 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR1319946 4 0.2491 0.82418 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR1311599 6 0.1957 0.61617 0.112 0.000 0.000 0.000 0.000 0.888
#> SRR1323977 1 0.0146 0.44237 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1445132 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 6 0.4707 0.13610 0.000 0.000 0.056 0.000 0.360 0.584
#> SRR1366390 2 0.3566 0.83403 0.096 0.800 0.000 0.104 0.000 0.000
#> SRR1343012 1 0.3562 0.56459 0.788 0.000 0.004 0.000 0.040 0.168
#> SRR1311958 4 0.2491 0.82418 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR1388234 4 0.2697 0.81248 0.188 0.000 0.000 0.812 0.000 0.000
#> SRR1370384 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1321650 3 0.2826 0.80183 0.000 0.000 0.856 0.000 0.092 0.052
#> SRR1485117 4 0.3823 0.02915 0.000 0.436 0.000 0.564 0.000 0.000
#> SRR1384713 1 0.4814 0.79014 0.532 0.000 0.000 0.000 0.056 0.412
#> SRR816609 1 0.1196 0.38344 0.952 0.000 0.000 0.008 0.000 0.040
#> SRR1486239 4 0.0458 0.81383 0.016 0.000 0.000 0.984 0.000 0.000
#> SRR1309638 5 0.3572 0.68158 0.000 0.000 0.204 0.000 0.764 0.032
#> SRR1356660 6 0.1957 0.61617 0.112 0.000 0.000 0.000 0.000 0.888
#> SRR1392883 2 0.0000 0.92978 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.0000 0.90599 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR816677 6 0.1957 0.61617 0.112 0.000 0.000 0.000 0.000 0.888
#> SRR1455722 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1336029 6 0.2003 0.60965 0.116 0.000 0.000 0.000 0.000 0.884
#> SRR808452 1 0.3782 0.84108 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR1352169 6 0.4084 0.00737 0.000 0.000 0.400 0.000 0.012 0.588
#> SRR1366707 3 0.1285 0.83124 0.000 0.000 0.944 0.000 0.052 0.004
#> SRR1328143 5 0.1075 0.89286 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1473567 4 0.0000 0.80965 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["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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.900 0.939 0.971 0.4326 0.559 0.559
#> 3 3 0.651 0.808 0.839 0.4468 0.727 0.531
#> 4 4 0.508 0.695 0.760 0.1146 0.918 0.767
#> 5 5 0.628 0.521 0.728 0.0802 0.880 0.619
#> 6 6 0.712 0.562 0.743 0.0543 0.904 0.645
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
#> SRR1442087 1 0.0000 0.982 1.000 0.000
#> SRR1390119 2 0.0000 0.940 0.000 1.000
#> SRR1436127 1 0.0000 0.982 1.000 0.000
#> SRR1347278 1 0.0000 0.982 1.000 0.000
#> SRR1332904 2 0.0000 0.940 0.000 1.000
#> SRR1444179 1 0.0000 0.982 1.000 0.000
#> SRR1082685 1 0.0000 0.982 1.000 0.000
#> SRR1362287 1 0.0000 0.982 1.000 0.000
#> SRR1339007 1 0.0000 0.982 1.000 0.000
#> SRR1376557 2 0.0000 0.940 0.000 1.000
#> SRR1468700 2 0.0000 0.940 0.000 1.000
#> SRR1077455 1 0.0000 0.982 1.000 0.000
#> SRR1413978 1 0.0000 0.982 1.000 0.000
#> SRR1439896 1 0.0000 0.982 1.000 0.000
#> SRR1317963 2 0.0000 0.940 0.000 1.000
#> SRR1431865 1 0.0000 0.982 1.000 0.000
#> SRR1394253 1 0.0000 0.982 1.000 0.000
#> SRR1082664 1 0.0000 0.982 1.000 0.000
#> SRR1077968 1 0.0000 0.982 1.000 0.000
#> SRR1076393 1 0.0000 0.982 1.000 0.000
#> SRR1477476 2 0.0000 0.940 0.000 1.000
#> SRR1398057 1 0.0000 0.982 1.000 0.000
#> SRR1485042 1 0.0000 0.982 1.000 0.000
#> SRR1385453 2 0.8081 0.735 0.248 0.752
#> SRR1348074 2 0.0938 0.936 0.012 0.988
#> SRR813959 2 0.4022 0.905 0.080 0.920
#> SRR665442 2 0.3431 0.914 0.064 0.936
#> SRR1378068 1 0.0000 0.982 1.000 0.000
#> SRR1485237 2 0.3879 0.907 0.076 0.924
#> SRR1350792 1 0.0000 0.982 1.000 0.000
#> SRR1326797 1 0.0000 0.982 1.000 0.000
#> SRR808994 1 0.0000 0.982 1.000 0.000
#> SRR1474041 1 0.0000 0.982 1.000 0.000
#> SRR1405641 1 0.0000 0.982 1.000 0.000
#> SRR1362245 1 0.0000 0.982 1.000 0.000
#> SRR1500194 1 0.0000 0.982 1.000 0.000
#> SRR1414876 2 0.0000 0.940 0.000 1.000
#> SRR1478523 1 0.9087 0.467 0.676 0.324
#> SRR1325161 1 0.0000 0.982 1.000 0.000
#> SRR1318026 2 0.7453 0.784 0.212 0.788
#> SRR1343778 1 0.0000 0.982 1.000 0.000
#> SRR1441287 1 0.0000 0.982 1.000 0.000
#> SRR1430991 1 0.0000 0.982 1.000 0.000
#> SRR1499722 1 0.0000 0.982 1.000 0.000
#> SRR1351368 2 0.8327 0.709 0.264 0.736
#> SRR1441785 1 0.0000 0.982 1.000 0.000
#> SRR1096101 1 0.0000 0.982 1.000 0.000
#> SRR808375 1 0.0000 0.982 1.000 0.000
#> SRR1452842 1 0.0000 0.982 1.000 0.000
#> SRR1311709 1 0.3733 0.906 0.928 0.072
#> SRR1433352 1 0.0000 0.982 1.000 0.000
#> SRR1340241 2 0.0000 0.940 0.000 1.000
#> SRR1456754 1 0.0000 0.982 1.000 0.000
#> SRR1465172 1 0.0000 0.982 1.000 0.000
#> SRR1499284 1 0.0000 0.982 1.000 0.000
#> SRR1499607 2 0.0000 0.940 0.000 1.000
#> SRR812342 1 0.0000 0.982 1.000 0.000
#> SRR1405374 1 0.0000 0.982 1.000 0.000
#> SRR1403565 1 0.0000 0.982 1.000 0.000
#> SRR1332024 1 0.0000 0.982 1.000 0.000
#> SRR1471633 1 0.8499 0.587 0.724 0.276
#> SRR1325944 2 0.0000 0.940 0.000 1.000
#> SRR1429450 2 0.0000 0.940 0.000 1.000
#> SRR821573 1 0.7815 0.672 0.768 0.232
#> SRR1435372 1 0.0000 0.982 1.000 0.000
#> SRR1324184 2 0.0000 0.940 0.000 1.000
#> SRR816517 2 0.5737 0.855 0.136 0.864
#> SRR1324141 2 0.7453 0.784 0.212 0.788
#> SRR1101612 1 0.0000 0.982 1.000 0.000
#> SRR1356531 1 0.0000 0.982 1.000 0.000
#> SRR1089785 1 0.0000 0.982 1.000 0.000
#> SRR1077708 1 0.0000 0.982 1.000 0.000
#> SRR1343720 1 0.0000 0.982 1.000 0.000
#> SRR1477499 2 0.0000 0.940 0.000 1.000
#> SRR1347236 1 0.0000 0.982 1.000 0.000
#> SRR1326408 1 0.0000 0.982 1.000 0.000
#> SRR1336529 1 0.0000 0.982 1.000 0.000
#> SRR1440643 2 0.8144 0.729 0.252 0.748
#> SRR662354 1 0.0000 0.982 1.000 0.000
#> SRR1310817 1 0.3114 0.924 0.944 0.056
#> SRR1347389 2 0.0000 0.940 0.000 1.000
#> SRR1353097 1 0.0000 0.982 1.000 0.000
#> SRR1384737 2 0.7453 0.784 0.212 0.788
#> SRR1096339 1 0.0000 0.982 1.000 0.000
#> SRR1345329 2 0.3879 0.907 0.076 0.924
#> SRR1414771 1 0.0000 0.982 1.000 0.000
#> SRR1309119 1 0.0938 0.971 0.988 0.012
#> SRR1470438 1 0.0000 0.982 1.000 0.000
#> SRR1343221 1 0.0000 0.982 1.000 0.000
#> SRR1410847 1 0.0000 0.982 1.000 0.000
#> SRR807949 1 0.0000 0.982 1.000 0.000
#> SRR1442332 1 0.0000 0.982 1.000 0.000
#> SRR815920 1 0.0000 0.982 1.000 0.000
#> SRR1471524 1 0.0000 0.982 1.000 0.000
#> SRR1477221 1 0.0000 0.982 1.000 0.000
#> SRR1445046 2 0.0000 0.940 0.000 1.000
#> SRR1331962 2 0.0000 0.940 0.000 1.000
#> SRR1319946 2 0.0000 0.940 0.000 1.000
#> SRR1311599 1 0.0000 0.982 1.000 0.000
#> SRR1323977 2 0.3733 0.910 0.072 0.928
#> SRR1445132 2 0.0000 0.940 0.000 1.000
#> SRR1337321 1 0.0000 0.982 1.000 0.000
#> SRR1366390 2 0.0000 0.940 0.000 1.000
#> SRR1343012 2 0.7883 0.753 0.236 0.764
#> SRR1311958 2 0.0000 0.940 0.000 1.000
#> SRR1388234 2 0.0376 0.939 0.004 0.996
#> SRR1370384 1 0.0000 0.982 1.000 0.000
#> SRR1321650 1 0.0000 0.982 1.000 0.000
#> SRR1485117 2 0.0000 0.940 0.000 1.000
#> SRR1384713 1 0.0000 0.982 1.000 0.000
#> SRR816609 2 0.4815 0.888 0.104 0.896
#> SRR1486239 2 0.0000 0.940 0.000 1.000
#> SRR1309638 1 0.0000 0.982 1.000 0.000
#> SRR1356660 1 0.0000 0.982 1.000 0.000
#> SRR1392883 2 0.0000 0.940 0.000 1.000
#> SRR808130 1 0.0000 0.982 1.000 0.000
#> SRR816677 1 0.9580 0.328 0.620 0.380
#> SRR1455722 1 0.0000 0.982 1.000 0.000
#> SRR1336029 1 0.0000 0.982 1.000 0.000
#> SRR808452 1 0.0000 0.982 1.000 0.000
#> SRR1352169 1 0.0000 0.982 1.000 0.000
#> SRR1366707 1 0.0000 0.982 1.000 0.000
#> SRR1328143 1 0.0000 0.982 1.000 0.000
#> SRR1473567 2 0.0000 0.940 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1390119 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1436127 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1347278 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1332904 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1444179 1 0.3619 0.6971 0.864 0.000 0.136
#> SRR1082685 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1362287 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1339007 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1077455 1 0.4702 0.7019 0.788 0.000 0.212
#> SRR1413978 1 0.4235 0.7551 0.824 0.000 0.176
#> SRR1439896 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1317963 2 0.0237 0.8981 0.004 0.996 0.000
#> SRR1431865 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1394253 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1082664 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1077968 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1076393 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1477476 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1398057 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1485042 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1385453 3 0.5733 -0.1493 0.000 0.324 0.676
#> SRR1348074 2 0.5956 0.8327 0.016 0.720 0.264
#> SRR813959 2 0.5397 0.8313 0.000 0.720 0.280
#> SRR665442 2 0.5363 0.8332 0.000 0.724 0.276
#> SRR1378068 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1485237 2 0.5956 0.8327 0.016 0.720 0.264
#> SRR1350792 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1326797 3 0.5178 0.9227 0.256 0.000 0.744
#> SRR808994 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1474041 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1405641 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1362245 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1500194 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1478523 3 0.2749 0.4989 0.012 0.064 0.924
#> SRR1325161 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1318026 2 0.5956 0.8327 0.016 0.720 0.264
#> SRR1343778 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1441287 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1430991 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1499722 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1351368 3 0.5529 -0.0608 0.000 0.296 0.704
#> SRR1441785 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1096101 1 0.4346 0.7445 0.816 0.000 0.184
#> SRR808375 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1452842 1 0.6309 -0.2920 0.504 0.000 0.496
#> SRR1311709 1 0.8233 0.4389 0.616 0.120 0.264
#> SRR1433352 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1340241 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1456754 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1465172 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1499284 3 0.5178 0.9219 0.256 0.000 0.744
#> SRR1499607 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1405374 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1403565 1 0.6267 -0.0926 0.548 0.000 0.452
#> SRR1332024 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1471633 1 0.9431 0.2177 0.500 0.220 0.280
#> SRR1325944 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR821573 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1435372 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR816517 2 0.5178 0.8412 0.000 0.744 0.256
#> SRR1324141 2 0.5397 0.8313 0.000 0.720 0.280
#> SRR1101612 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1089785 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1077708 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1343720 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1477499 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1347236 3 0.5178 0.9227 0.256 0.000 0.744
#> SRR1326408 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1336529 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1440643 3 0.5733 -0.1493 0.000 0.324 0.676
#> SRR662354 1 0.2066 0.8103 0.940 0.000 0.060
#> SRR1310817 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1347389 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1384737 2 0.5397 0.8313 0.000 0.720 0.280
#> SRR1096339 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1345329 2 0.5956 0.8327 0.016 0.720 0.264
#> SRR1414771 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1309119 1 0.5529 0.5954 0.704 0.000 0.296
#> SRR1470438 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1343221 1 0.4654 0.7086 0.792 0.000 0.208
#> SRR1410847 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR807949 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1442332 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR815920 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1471524 3 0.5098 0.9227 0.248 0.000 0.752
#> SRR1477221 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1445046 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1319946 2 0.5480 0.8374 0.004 0.732 0.264
#> SRR1311599 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1323977 2 0.5848 0.8329 0.012 0.720 0.268
#> SRR1445132 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1337321 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1366390 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1343012 2 0.5733 0.7933 0.000 0.676 0.324
#> SRR1311958 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1388234 2 0.5848 0.8329 0.012 0.720 0.268
#> SRR1370384 1 0.0747 0.8224 0.984 0.000 0.016
#> SRR1321650 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1485117 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1384713 1 0.3816 0.7736 0.852 0.000 0.148
#> SRR816609 2 0.5956 0.8327 0.016 0.720 0.264
#> SRR1486239 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR1309638 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1356660 1 0.4178 0.7598 0.828 0.000 0.172
#> SRR1392883 2 0.0000 0.8995 0.000 1.000 0.000
#> SRR808130 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR816677 2 0.9599 0.3833 0.200 0.412 0.388
#> SRR1455722 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1336029 1 0.7984 0.5876 0.652 0.132 0.216
#> SRR808452 1 0.0000 0.8256 1.000 0.000 0.000
#> SRR1352169 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1366707 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1328143 3 0.5138 0.9282 0.252 0.000 0.748
#> SRR1473567 2 0.0000 0.8995 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.4401 0.7353 0.272 0.000 0.724 0.004
#> SRR1390119 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.4675 0.7463 0.244 0.000 0.736 0.020
#> SRR1347278 3 0.5300 0.7042 0.308 0.000 0.664 0.028
#> SRR1332904 2 0.2530 0.8822 0.000 0.888 0.000 0.112
#> SRR1444179 1 0.4008 0.5677 0.756 0.000 0.000 0.244
#> SRR1082685 1 0.2589 0.7242 0.884 0.000 0.000 0.116
#> SRR1362287 1 0.4150 0.7589 0.824 0.000 0.120 0.056
#> SRR1339007 1 0.0592 0.8026 0.984 0.000 0.000 0.016
#> SRR1376557 2 0.1389 0.9000 0.000 0.952 0.000 0.048
#> SRR1468700 2 0.2216 0.8930 0.000 0.908 0.000 0.092
#> SRR1077455 1 0.5560 0.6332 0.728 0.000 0.156 0.116
#> SRR1413978 1 0.5909 0.7020 0.736 0.024 0.144 0.096
#> SRR1439896 1 0.0336 0.8012 0.992 0.000 0.000 0.008
#> SRR1317963 2 0.4040 0.7158 0.000 0.752 0.000 0.248
#> SRR1431865 1 0.4344 0.7678 0.816 0.000 0.108 0.076
#> SRR1394253 1 0.4525 0.7756 0.804 0.000 0.116 0.080
#> SRR1082664 3 0.5131 0.7314 0.280 0.000 0.692 0.028
#> SRR1077968 1 0.0188 0.7996 0.996 0.000 0.004 0.000
#> SRR1076393 3 0.2408 0.6729 0.036 0.000 0.920 0.044
#> SRR1477476 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.4720 0.7403 0.264 0.000 0.720 0.016
#> SRR1485042 1 0.1211 0.8028 0.960 0.000 0.000 0.040
#> SRR1385453 3 0.4454 0.2855 0.000 0.000 0.692 0.308
#> SRR1348074 4 0.5927 0.8425 0.052 0.164 0.048 0.736
#> SRR813959 3 0.7702 -0.5304 0.000 0.224 0.416 0.360
#> SRR665442 4 0.8235 0.5228 0.012 0.304 0.292 0.392
#> SRR1378068 3 0.4188 0.7496 0.244 0.000 0.752 0.004
#> SRR1485237 4 0.5854 0.8415 0.044 0.172 0.048 0.736
#> SRR1350792 1 0.0000 0.8002 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.7811 -0.1741 0.380 0.000 0.368 0.252
#> SRR808994 3 0.4869 0.7093 0.132 0.000 0.780 0.088
#> SRR1474041 3 0.3695 0.7547 0.156 0.000 0.828 0.016
#> SRR1405641 3 0.4532 0.7313 0.156 0.000 0.792 0.052
#> SRR1362245 3 0.4849 0.7284 0.164 0.000 0.772 0.064
#> SRR1500194 1 0.1867 0.7821 0.928 0.000 0.000 0.072
#> SRR1414876 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.4262 0.4230 0.008 0.000 0.756 0.236
#> SRR1325161 3 0.6049 0.6984 0.184 0.000 0.684 0.132
#> SRR1318026 4 0.6216 0.8382 0.076 0.152 0.048 0.724
#> SRR1343778 3 0.5157 0.7283 0.284 0.000 0.688 0.028
#> SRR1441287 1 0.1389 0.7831 0.952 0.000 0.000 0.048
#> SRR1430991 3 0.3852 0.7575 0.192 0.000 0.800 0.008
#> SRR1499722 3 0.6764 0.5165 0.332 0.000 0.556 0.112
#> SRR1351368 3 0.4304 0.3327 0.000 0.000 0.716 0.284
#> SRR1441785 1 0.4318 0.7684 0.816 0.000 0.116 0.068
#> SRR1096101 1 0.4127 0.7507 0.824 0.000 0.124 0.052
#> SRR808375 3 0.3895 0.7576 0.184 0.000 0.804 0.012
#> SRR1452842 1 0.6083 0.5157 0.672 0.000 0.216 0.112
#> SRR1311709 1 0.5016 0.2225 0.600 0.000 0.004 0.396
#> SRR1433352 3 0.5108 0.6934 0.308 0.000 0.672 0.020
#> SRR1340241 2 0.0188 0.9183 0.000 0.996 0.000 0.004
#> SRR1456754 1 0.3032 0.7565 0.868 0.000 0.124 0.008
#> SRR1465172 3 0.7122 0.4339 0.340 0.000 0.516 0.144
#> SRR1499284 3 0.7269 0.2642 0.396 0.000 0.456 0.148
#> SRR1499607 2 0.3831 0.7645 0.004 0.792 0.000 0.204
#> SRR812342 1 0.2530 0.7273 0.888 0.000 0.000 0.112
#> SRR1405374 1 0.3919 0.7714 0.840 0.000 0.104 0.056
#> SRR1403565 1 0.5672 0.4652 0.668 0.000 0.276 0.056
#> SRR1332024 3 0.4514 0.7207 0.136 0.000 0.800 0.064
#> SRR1471633 1 0.5332 0.0289 0.512 0.004 0.004 0.480
#> SRR1325944 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR821573 3 0.7098 0.4851 0.132 0.008 0.572 0.288
#> SRR1435372 1 0.2216 0.7479 0.908 0.000 0.000 0.092
#> SRR1324184 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR816517 3 0.7910 -0.5510 0.000 0.316 0.364 0.320
#> SRR1324141 4 0.6160 0.8401 0.068 0.152 0.052 0.728
#> SRR1101612 1 0.0336 0.8012 0.992 0.000 0.000 0.008
#> SRR1356531 1 0.0336 0.8012 0.992 0.000 0.000 0.008
#> SRR1089785 3 0.4004 0.7563 0.164 0.000 0.812 0.024
#> SRR1077708 3 0.4576 0.7434 0.260 0.000 0.728 0.012
#> SRR1343720 3 0.4706 0.7317 0.248 0.000 0.732 0.020
#> SRR1477499 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1347236 1 0.7681 -0.1623 0.404 0.000 0.380 0.216
#> SRR1326408 1 0.3074 0.7060 0.848 0.000 0.000 0.152
#> SRR1336529 3 0.4328 0.7496 0.244 0.000 0.748 0.008
#> SRR1440643 3 0.4454 0.2855 0.000 0.000 0.692 0.308
#> SRR662354 1 0.1389 0.7942 0.952 0.000 0.048 0.000
#> SRR1310817 3 0.4188 0.5737 0.040 0.000 0.812 0.148
#> SRR1347389 2 0.2814 0.8679 0.000 0.868 0.000 0.132
#> SRR1353097 1 0.1557 0.7794 0.944 0.000 0.000 0.056
#> SRR1384737 4 0.7800 0.8000 0.084 0.152 0.152 0.612
#> SRR1096339 1 0.1022 0.8030 0.968 0.000 0.000 0.032
#> SRR1345329 4 0.5854 0.8415 0.044 0.172 0.048 0.736
#> SRR1414771 3 0.4724 0.7165 0.136 0.000 0.788 0.076
#> SRR1309119 1 0.4964 0.2959 0.616 0.004 0.000 0.380
#> SRR1470438 3 0.4817 0.7073 0.128 0.000 0.784 0.088
#> SRR1343221 1 0.4070 0.7433 0.824 0.000 0.132 0.044
#> SRR1410847 1 0.3818 0.7651 0.844 0.000 0.108 0.048
#> SRR807949 3 0.4418 0.7550 0.184 0.000 0.784 0.032
#> SRR1442332 3 0.4214 0.7543 0.204 0.000 0.780 0.016
#> SRR815920 3 0.4328 0.7497 0.244 0.000 0.748 0.008
#> SRR1471524 3 0.3117 0.6327 0.028 0.000 0.880 0.092
#> SRR1477221 3 0.4630 0.7459 0.252 0.000 0.732 0.016
#> SRR1445046 2 0.3311 0.8247 0.000 0.828 0.000 0.172
#> SRR1331962 2 0.2216 0.8930 0.000 0.908 0.000 0.092
#> SRR1319946 4 0.5834 0.7221 0.008 0.288 0.044 0.660
#> SRR1311599 1 0.3934 0.7650 0.836 0.000 0.116 0.048
#> SRR1323977 4 0.5724 0.8015 0.016 0.228 0.048 0.708
#> SRR1445132 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.4136 0.7562 0.196 0.000 0.788 0.016
#> SRR1366390 2 0.0592 0.9172 0.000 0.984 0.000 0.016
#> SRR1343012 4 0.7598 0.7936 0.068 0.152 0.156 0.624
#> SRR1311958 2 0.2814 0.8679 0.000 0.868 0.000 0.132
#> SRR1388234 4 0.5644 0.7740 0.008 0.248 0.048 0.696
#> SRR1370384 1 0.1610 0.7939 0.952 0.000 0.032 0.016
#> SRR1321650 3 0.4194 0.7561 0.228 0.000 0.764 0.008
#> SRR1485117 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.2859 0.7622 0.880 0.000 0.112 0.008
#> SRR816609 4 0.6431 0.8360 0.084 0.160 0.048 0.708
#> SRR1486239 2 0.2814 0.8679 0.000 0.868 0.000 0.132
#> SRR1309638 3 0.4661 0.7436 0.256 0.000 0.728 0.016
#> SRR1356660 1 0.4344 0.7678 0.816 0.000 0.108 0.076
#> SRR1392883 2 0.0000 0.9188 0.000 1.000 0.000 0.000
#> SRR808130 3 0.3768 0.7579 0.184 0.000 0.808 0.008
#> SRR816677 4 0.7690 0.5237 0.280 0.092 0.060 0.568
#> SRR1455722 1 0.0336 0.8012 0.992 0.000 0.000 0.008
#> SRR1336029 1 0.6617 0.5894 0.628 0.004 0.124 0.244
#> SRR808452 1 0.0707 0.7981 0.980 0.000 0.000 0.020
#> SRR1352169 3 0.4365 0.7572 0.188 0.000 0.784 0.028
#> SRR1366707 3 0.3392 0.7040 0.072 0.000 0.872 0.056
#> SRR1328143 3 0.3810 0.7579 0.188 0.000 0.804 0.008
#> SRR1473567 2 0.1867 0.9021 0.000 0.928 0.000 0.072
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.4811 0.5167 0.020 0.000 0.452 0.000 0.528
#> SRR1390119 2 0.0000 0.8364 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.1186 0.6457 0.020 0.000 0.964 0.008 0.008
#> SRR1347278 3 0.6609 0.2661 0.172 0.000 0.600 0.048 0.180
#> SRR1332904 2 0.4674 0.5209 0.000 0.568 0.000 0.416 0.016
#> SRR1444179 1 0.2230 0.8120 0.884 0.000 0.000 0.116 0.000
#> SRR1082685 1 0.0000 0.8170 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.4275 0.7984 0.784 0.000 0.012 0.148 0.056
#> SRR1339007 1 0.0451 0.8187 0.988 0.000 0.000 0.008 0.004
#> SRR1376557 2 0.3264 0.7928 0.000 0.820 0.000 0.164 0.016
#> SRR1468700 2 0.4517 0.5700 0.000 0.600 0.000 0.388 0.012
#> SRR1077455 1 0.5471 0.5458 0.576 0.000 0.016 0.040 0.368
#> SRR1413978 1 0.5763 0.5492 0.560 0.000 0.016 0.364 0.060
#> SRR1439896 1 0.0162 0.8164 0.996 0.000 0.000 0.000 0.004
#> SRR1317963 4 0.4321 -0.1662 0.000 0.396 0.000 0.600 0.004
#> SRR1431865 1 0.4878 0.7632 0.720 0.000 0.012 0.208 0.060
#> SRR1394253 1 0.4298 0.7983 0.784 0.000 0.012 0.144 0.060
#> SRR1082664 5 0.5756 0.5250 0.036 0.000 0.424 0.028 0.512
#> SRR1077968 1 0.0609 0.8112 0.980 0.000 0.000 0.000 0.020
#> SRR1076393 3 0.4972 -0.3840 0.004 0.000 0.500 0.020 0.476
#> SRR1477476 2 0.0000 0.8364 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.3227 0.6066 0.040 0.000 0.868 0.020 0.072
#> SRR1485042 1 0.1965 0.8178 0.904 0.000 0.000 0.096 0.000
#> SRR1385453 3 0.6915 0.3182 0.000 0.040 0.488 0.132 0.340
#> SRR1348074 4 0.5302 0.3891 0.220 0.052 0.020 0.700 0.008
#> SRR813959 5 0.7442 -0.0492 0.000 0.256 0.040 0.280 0.424
#> SRR665442 4 0.8572 0.0775 0.008 0.276 0.132 0.304 0.280
#> SRR1378068 3 0.1299 0.6459 0.020 0.000 0.960 0.012 0.008
#> SRR1485237 4 0.5858 0.1464 0.384 0.040 0.020 0.548 0.008
#> SRR1350792 1 0.0510 0.8128 0.984 0.000 0.000 0.000 0.016
#> SRR1326797 5 0.5166 0.2779 0.172 0.000 0.040 0.060 0.728
#> SRR808994 3 0.2278 0.6033 0.000 0.000 0.908 0.032 0.060
#> SRR1474041 5 0.4954 0.5191 0.020 0.000 0.448 0.004 0.528
#> SRR1405641 3 0.0609 0.6360 0.000 0.000 0.980 0.020 0.000
#> SRR1362245 3 0.0609 0.6360 0.000 0.000 0.980 0.020 0.000
#> SRR1500194 1 0.2127 0.8153 0.892 0.000 0.000 0.108 0.000
#> SRR1414876 2 0.0162 0.8367 0.000 0.996 0.000 0.004 0.000
#> SRR1478523 3 0.6759 0.3233 0.000 0.040 0.500 0.112 0.348
#> SRR1325161 5 0.4160 0.3797 0.024 0.000 0.168 0.024 0.784
#> SRR1318026 4 0.5866 0.1342 0.388 0.040 0.020 0.544 0.008
#> SRR1343778 5 0.5684 0.5140 0.040 0.000 0.440 0.020 0.500
#> SRR1441287 1 0.0000 0.8170 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.4811 0.5167 0.020 0.000 0.452 0.000 0.528
#> SRR1499722 5 0.4014 0.3619 0.060 0.000 0.096 0.024 0.820
#> SRR1351368 3 0.6908 0.3114 0.000 0.040 0.480 0.128 0.352
#> SRR1441785 1 0.4752 0.7714 0.732 0.000 0.012 0.200 0.056
#> SRR1096101 1 0.4189 0.8006 0.788 0.000 0.008 0.144 0.060
#> SRR808375 5 0.5415 0.5170 0.020 0.000 0.448 0.024 0.508
#> SRR1452842 1 0.5973 0.3863 0.468 0.000 0.020 0.060 0.452
#> SRR1311709 1 0.3291 0.7625 0.840 0.040 0.000 0.120 0.000
#> SRR1433352 5 0.5284 0.5177 0.040 0.000 0.424 0.004 0.532
#> SRR1340241 2 0.1444 0.8280 0.000 0.948 0.000 0.040 0.012
#> SRR1456754 1 0.3359 0.7976 0.848 0.000 0.004 0.052 0.096
#> SRR1465172 5 0.3804 0.3404 0.100 0.000 0.040 0.028 0.832
#> SRR1499284 5 0.3644 0.3300 0.120 0.000 0.024 0.024 0.832
#> SRR1499607 4 0.4574 -0.1944 0.000 0.412 0.000 0.576 0.012
#> SRR812342 1 0.1732 0.7837 0.920 0.000 0.000 0.000 0.080
#> SRR1405374 1 0.4275 0.7984 0.784 0.000 0.012 0.148 0.056
#> SRR1403565 1 0.5502 0.7255 0.704 0.000 0.028 0.128 0.140
#> SRR1332024 3 0.0609 0.6360 0.000 0.000 0.980 0.020 0.000
#> SRR1471633 1 0.4752 0.6282 0.684 0.040 0.000 0.272 0.004
#> SRR1325944 2 0.0000 0.8364 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.8364 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.5978 0.4906 0.020 0.000 0.400 0.064 0.516
#> SRR1435372 1 0.0404 0.8142 0.988 0.000 0.000 0.000 0.012
#> SRR1324184 2 0.3160 0.7811 0.000 0.808 0.000 0.188 0.004
#> SRR816517 4 0.8525 0.0758 0.000 0.260 0.196 0.312 0.232
#> SRR1324141 4 0.6057 0.1341 0.388 0.040 0.020 0.536 0.016
#> SRR1101612 1 0.0162 0.8164 0.996 0.000 0.000 0.000 0.004
#> SRR1356531 1 0.0162 0.8164 0.996 0.000 0.000 0.000 0.004
#> SRR1089785 5 0.5470 0.4863 0.016 0.000 0.440 0.032 0.512
#> SRR1077708 5 0.5075 0.4928 0.020 0.000 0.460 0.008 0.512
#> SRR1343720 5 0.5250 0.4891 0.040 0.000 0.404 0.004 0.552
#> SRR1477499 2 0.0162 0.8367 0.000 0.996 0.000 0.004 0.000
#> SRR1347236 5 0.6087 -0.3143 0.388 0.000 0.024 0.068 0.520
#> SRR1326408 1 0.1965 0.8182 0.904 0.000 0.000 0.096 0.000
#> SRR1336529 3 0.1074 0.6467 0.016 0.000 0.968 0.012 0.004
#> SRR1440643 3 0.7195 0.2746 0.000 0.040 0.448 0.172 0.340
#> SRR662354 1 0.2464 0.7787 0.892 0.000 0.004 0.012 0.092
#> SRR1310817 5 0.5672 0.4855 0.008 0.000 0.412 0.060 0.520
#> SRR1347389 4 0.4718 -0.2756 0.000 0.444 0.000 0.540 0.016
#> SRR1353097 1 0.0162 0.8164 0.996 0.000 0.000 0.000 0.004
#> SRR1384737 4 0.6451 0.1098 0.392 0.040 0.024 0.512 0.032
#> SRR1096339 1 0.1965 0.8178 0.904 0.000 0.000 0.096 0.000
#> SRR1345329 4 0.6020 0.2148 0.352 0.056 0.020 0.564 0.008
#> SRR1414771 3 0.2209 0.6058 0.000 0.000 0.912 0.032 0.056
#> SRR1309119 1 0.3579 0.7150 0.756 0.004 0.000 0.240 0.000
#> SRR1470438 3 0.2278 0.6033 0.000 0.000 0.908 0.032 0.060
#> SRR1343221 1 0.4154 0.8039 0.796 0.000 0.008 0.124 0.072
#> SRR1410847 1 0.4109 0.8006 0.788 0.000 0.004 0.148 0.060
#> SRR807949 5 0.5246 0.5202 0.020 0.000 0.440 0.016 0.524
#> SRR1442332 5 0.5464 0.5206 0.020 0.000 0.424 0.028 0.528
#> SRR815920 3 0.3513 0.5280 0.000 0.000 0.800 0.020 0.180
#> SRR1471524 3 0.4873 0.3600 0.000 0.012 0.676 0.032 0.280
#> SRR1477221 3 0.2374 0.6316 0.016 0.000 0.912 0.020 0.052
#> SRR1445046 4 0.4383 -0.2213 0.000 0.424 0.000 0.572 0.004
#> SRR1331962 2 0.4622 0.4810 0.000 0.548 0.000 0.440 0.012
#> SRR1319946 4 0.4986 0.0730 0.004 0.268 0.020 0.684 0.024
#> SRR1311599 1 0.4403 0.7987 0.776 0.000 0.012 0.148 0.064
#> SRR1323977 4 0.7662 0.3466 0.344 0.200 0.020 0.408 0.028
#> SRR1445132 2 0.0000 0.8364 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.3937 0.5205 0.012 0.000 0.784 0.020 0.184
#> SRR1366390 2 0.3163 0.7877 0.000 0.824 0.000 0.164 0.012
#> SRR1343012 4 0.6773 0.1260 0.380 0.040 0.020 0.500 0.060
#> SRR1311958 4 0.4610 -0.2442 0.000 0.432 0.000 0.556 0.012
#> SRR1388234 4 0.4283 0.1462 0.004 0.220 0.020 0.748 0.008
#> SRR1370384 1 0.3612 0.6668 0.732 0.000 0.000 0.000 0.268
#> SRR1321650 3 0.1815 0.6441 0.020 0.000 0.940 0.024 0.016
#> SRR1485117 2 0.2179 0.8166 0.000 0.888 0.000 0.112 0.000
#> SRR1384713 1 0.3633 0.7393 0.812 0.004 0.004 0.020 0.160
#> SRR816609 4 0.5866 0.1362 0.388 0.040 0.020 0.544 0.008
#> SRR1486239 4 0.4617 -0.2540 0.000 0.436 0.000 0.552 0.012
#> SRR1309638 3 0.5226 0.1885 0.040 0.000 0.656 0.020 0.284
#> SRR1356660 1 0.4815 0.7658 0.724 0.000 0.012 0.208 0.056
#> SRR1392883 2 0.0000 0.8364 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.4807 0.5189 0.020 0.000 0.448 0.000 0.532
#> SRR816677 1 0.6211 0.0866 0.460 0.040 0.020 0.460 0.020
#> SRR1455722 1 0.0162 0.8164 0.996 0.000 0.000 0.000 0.004
#> SRR1336029 1 0.5101 0.6899 0.672 0.000 0.024 0.272 0.032
#> SRR808452 1 0.0162 0.8164 0.996 0.000 0.000 0.000 0.004
#> SRR1352169 3 0.5331 0.1550 0.008 0.000 0.600 0.048 0.344
#> SRR1366707 3 0.4382 0.3519 0.000 0.000 0.688 0.024 0.288
#> SRR1328143 5 0.4811 0.5167 0.020 0.000 0.452 0.000 0.528
#> SRR1473567 2 0.3890 0.7265 0.000 0.736 0.000 0.252 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.0508 0.61768 0.004 0.000 0.012 0.000 0.984 NA
#> SRR1390119 2 0.3847 0.65456 0.000 0.544 0.000 0.000 0.000 NA
#> SRR1436127 5 0.4357 -0.41513 0.004 0.000 0.484 0.004 0.500 NA
#> SRR1347278 5 0.5907 0.00823 0.228 0.000 0.196 0.008 0.560 NA
#> SRR1332904 2 0.1141 0.64924 0.000 0.948 0.000 0.052 0.000 NA
#> SRR1444179 1 0.1958 0.84385 0.896 0.004 0.000 0.100 0.000 NA
#> SRR1082685 1 0.1007 0.84295 0.956 0.000 0.000 0.000 0.000 NA
#> SRR1362287 1 0.4040 0.79870 0.756 0.000 0.000 0.112 0.000 NA
#> SRR1339007 1 0.0363 0.84689 0.988 0.000 0.000 0.000 0.000 NA
#> SRR1376557 2 0.0622 0.66836 0.000 0.980 0.000 0.012 0.000 NA
#> SRR1468700 2 0.0508 0.66377 0.000 0.984 0.000 0.012 0.000 NA
#> SRR1077455 1 0.6519 0.45960 0.484 0.000 0.356 0.024 0.088 NA
#> SRR1413978 1 0.5434 0.47319 0.512 0.000 0.000 0.360 0.000 NA
#> SRR1439896 1 0.1141 0.84111 0.948 0.000 0.000 0.000 0.000 NA
#> SRR1317963 2 0.3890 0.12340 0.000 0.596 0.000 0.400 0.000 NA
#> SRR1431865 1 0.4279 0.78369 0.732 0.000 0.000 0.140 0.000 NA
#> SRR1394253 1 0.3657 0.81719 0.792 0.000 0.000 0.108 0.000 NA
#> SRR1082664 5 0.0862 0.61976 0.016 0.000 0.008 0.000 0.972 NA
#> SRR1077968 1 0.1327 0.83836 0.936 0.000 0.000 0.000 0.000 NA
#> SRR1076393 5 0.1624 0.58694 0.000 0.000 0.044 0.008 0.936 NA
#> SRR1477476 2 0.3847 0.65456 0.000 0.544 0.000 0.000 0.000 NA
#> SRR1398057 5 0.4208 -0.33868 0.004 0.000 0.452 0.008 0.536 NA
#> SRR1485042 1 0.1806 0.84627 0.908 0.000 0.000 0.088 0.000 NA
#> SRR1385453 5 0.7492 -0.11057 0.000 0.004 0.208 0.132 0.368 NA
#> SRR1348074 4 0.1699 0.75430 0.040 0.012 0.000 0.936 0.008 NA
#> SRR813959 4 0.4069 0.64548 0.000 0.148 0.008 0.764 0.080 NA
#> SRR665442 4 0.3663 0.64759 0.000 0.156 0.012 0.792 0.040 NA
#> SRR1378068 3 0.4357 0.37154 0.004 0.000 0.496 0.004 0.488 NA
#> SRR1485237 4 0.1699 0.75430 0.040 0.012 0.000 0.936 0.008 NA
#> SRR1350792 1 0.1327 0.83829 0.936 0.000 0.000 0.000 0.000 NA
#> SRR1326797 5 0.5994 0.30684 0.092 0.000 0.364 0.024 0.508 NA
#> SRR808994 3 0.4736 0.49591 0.000 0.000 0.620 0.000 0.072 NA
#> SRR1474041 5 0.0405 0.62089 0.004 0.000 0.008 0.000 0.988 NA
#> SRR1405641 3 0.4002 0.53358 0.000 0.000 0.588 0.000 0.404 NA
#> SRR1362245 3 0.4010 0.53249 0.000 0.000 0.584 0.000 0.408 NA
#> SRR1500194 1 0.1949 0.84581 0.904 0.004 0.000 0.088 0.000 NA
#> SRR1414876 2 0.3823 0.65829 0.000 0.564 0.000 0.000 0.000 NA
#> SRR1478523 5 0.6913 -0.09714 0.000 0.000 0.244 0.060 0.404 NA
#> SRR1325161 5 0.4580 0.36353 0.004 0.000 0.368 0.028 0.596 NA
#> SRR1318026 4 0.1590 0.74733 0.048 0.008 0.000 0.936 0.008 NA
#> SRR1343778 5 0.1223 0.61669 0.016 0.000 0.012 0.008 0.960 NA
#> SRR1441287 1 0.1007 0.84295 0.956 0.000 0.000 0.000 0.000 NA
#> SRR1430991 5 0.0551 0.62080 0.004 0.000 0.008 0.004 0.984 NA
#> SRR1499722 5 0.5354 0.35119 0.032 0.000 0.360 0.036 0.564 NA
#> SRR1351368 3 0.7353 0.22362 0.000 0.004 0.340 0.088 0.276 NA
#> SRR1441785 1 0.4318 0.78126 0.728 0.000 0.000 0.140 0.000 NA
#> SRR1096101 1 0.2829 0.84217 0.864 0.000 0.000 0.096 0.024 NA
#> SRR808375 5 0.1381 0.61351 0.004 0.000 0.020 0.020 0.952 NA
#> SRR1452842 3 0.7430 -0.25967 0.344 0.000 0.356 0.036 0.216 NA
#> SRR1311709 1 0.2325 0.84186 0.884 0.008 0.000 0.100 0.000 NA
#> SRR1433352 5 0.1180 0.61700 0.024 0.000 0.008 0.004 0.960 NA
#> SRR1340241 2 0.3923 0.66408 0.000 0.620 0.000 0.008 0.000 NA
#> SRR1456754 1 0.3261 0.82337 0.852 0.000 0.048 0.012 0.012 NA
#> SRR1465172 5 0.5949 0.32377 0.056 0.000 0.364 0.036 0.524 NA
#> SRR1499284 5 0.6086 0.31860 0.056 0.000 0.364 0.036 0.516 NA
#> SRR1499607 4 0.3862 0.10663 0.000 0.476 0.000 0.524 0.000 NA
#> SRR812342 1 0.3384 0.76551 0.812 0.000 0.120 0.000 0.000 NA
#> SRR1405374 1 0.3567 0.82078 0.800 0.000 0.000 0.100 0.000 NA
#> SRR1403565 1 0.4233 0.75505 0.752 0.000 0.000 0.100 0.140 NA
#> SRR1332024 3 0.4292 0.53723 0.000 0.000 0.588 0.000 0.388 NA
#> SRR1471633 1 0.3030 0.81218 0.816 0.008 0.000 0.168 0.000 NA
#> SRR1325944 2 0.3847 0.65456 0.000 0.544 0.000 0.000 0.000 NA
#> SRR1429450 2 0.3847 0.65456 0.000 0.544 0.000 0.000 0.000 NA
#> SRR821573 5 0.1321 0.61050 0.000 0.000 0.020 0.024 0.952 NA
#> SRR1435372 1 0.1267 0.83921 0.940 0.000 0.000 0.000 0.000 NA
#> SRR1324184 2 0.1196 0.67278 0.000 0.952 0.000 0.008 0.000 NA
#> SRR816517 4 0.4857 0.61687 0.000 0.152 0.096 0.716 0.036 NA
#> SRR1324141 4 0.1453 0.75361 0.040 0.008 0.000 0.944 0.008 NA
#> SRR1101612 1 0.1141 0.84296 0.948 0.000 0.000 0.000 0.000 NA
#> SRR1356531 1 0.1075 0.84191 0.952 0.000 0.000 0.000 0.000 NA
#> SRR1089785 5 0.0508 0.61662 0.004 0.000 0.012 0.000 0.984 NA
#> SRR1077708 5 0.0951 0.61268 0.004 0.000 0.020 0.008 0.968 NA
#> SRR1343720 5 0.1180 0.61551 0.024 0.000 0.004 0.008 0.960 NA
#> SRR1477499 2 0.3838 0.65585 0.000 0.552 0.000 0.000 0.000 NA
#> SRR1347236 5 0.6391 0.28292 0.104 0.000 0.364 0.028 0.480 NA
#> SRR1326408 1 0.1663 0.84666 0.912 0.000 0.000 0.088 0.000 NA
#> SRR1336529 3 0.3915 0.52472 0.000 0.000 0.584 0.004 0.412 NA
#> SRR1440643 4 0.7472 0.01398 0.000 0.004 0.144 0.380 0.184 NA
#> SRR662354 1 0.3770 0.73495 0.776 0.000 0.148 0.000 0.000 NA
#> SRR1310817 5 0.0767 0.61851 0.000 0.000 0.008 0.012 0.976 NA
#> SRR1347389 2 0.3288 0.39160 0.000 0.724 0.000 0.276 0.000 NA
#> SRR1353097 1 0.1267 0.83921 0.940 0.000 0.000 0.000 0.000 NA
#> SRR1384737 4 0.1649 0.75227 0.040 0.008 0.000 0.936 0.016 NA
#> SRR1096339 1 0.1806 0.84627 0.908 0.000 0.000 0.088 0.000 NA
#> SRR1345329 4 0.1881 0.75315 0.040 0.020 0.000 0.928 0.008 NA
#> SRR1414771 3 0.4736 0.49591 0.000 0.000 0.620 0.000 0.072 NA
#> SRR1309119 1 0.2655 0.82892 0.848 0.004 0.000 0.140 0.000 NA
#> SRR1470438 3 0.4736 0.49591 0.000 0.000 0.620 0.000 0.072 NA
#> SRR1343221 1 0.2806 0.83795 0.872 0.000 0.000 0.060 0.056 NA
#> SRR1410847 1 0.2728 0.84026 0.860 0.000 0.000 0.100 0.000 NA
#> SRR807949 5 0.0798 0.62036 0.004 0.000 0.012 0.004 0.976 NA
#> SRR1442332 5 0.0665 0.62010 0.008 0.000 0.008 0.000 0.980 NA
#> SRR815920 5 0.4320 -0.36773 0.000 0.000 0.468 0.008 0.516 NA
#> SRR1471524 5 0.4351 0.22736 0.000 0.000 0.256 0.008 0.692 NA
#> SRR1477221 3 0.4080 0.46144 0.000 0.000 0.536 0.008 0.456 NA
#> SRR1445046 2 0.3668 0.28292 0.000 0.668 0.000 0.328 0.000 NA
#> SRR1331962 2 0.1858 0.61654 0.000 0.904 0.000 0.092 0.000 NA
#> SRR1319946 4 0.2980 0.63646 0.000 0.192 0.000 0.800 0.008 NA
#> SRR1311599 1 0.3888 0.81244 0.780 0.000 0.000 0.108 0.004 NA
#> SRR1323977 4 0.3165 0.70592 0.040 0.116 0.000 0.836 0.008 NA
#> SRR1445132 2 0.3847 0.65456 0.000 0.544 0.000 0.000 0.000 NA
#> SRR1337321 5 0.4158 -0.24175 0.000 0.000 0.416 0.008 0.572 NA
#> SRR1366390 2 0.2506 0.65227 0.000 0.880 0.000 0.068 0.000 NA
#> SRR1343012 4 0.4233 0.58136 0.168 0.004 0.000 0.740 0.088 NA
#> SRR1311958 2 0.3619 0.30992 0.000 0.680 0.000 0.316 0.000 NA
#> SRR1388234 4 0.2669 0.67564 0.000 0.156 0.000 0.836 0.008 NA
#> SRR1370384 1 0.4743 0.57705 0.600 0.000 0.348 0.008 0.000 NA
#> SRR1321650 3 0.4220 0.44754 0.004 0.000 0.520 0.008 0.468 NA
#> SRR1485117 2 0.2805 0.67573 0.000 0.812 0.000 0.004 0.000 NA
#> SRR1384713 1 0.4754 0.67030 0.704 0.000 0.196 0.008 0.008 NA
#> SRR816609 4 0.1699 0.75430 0.040 0.012 0.000 0.936 0.008 NA
#> SRR1486239 2 0.3565 0.33434 0.000 0.692 0.000 0.304 0.000 NA
#> SRR1309638 5 0.3897 0.10158 0.008 0.000 0.300 0.008 0.684 NA
#> SRR1356660 1 0.4281 0.78423 0.732 0.000 0.000 0.136 0.000 NA
#> SRR1392883 2 0.3847 0.65456 0.000 0.544 0.000 0.000 0.000 NA
#> SRR808130 5 0.0405 0.62089 0.004 0.000 0.008 0.000 0.988 NA
#> SRR816677 4 0.4354 -0.24134 0.476 0.004 0.000 0.508 0.008 NA
#> SRR1455722 1 0.1267 0.83921 0.940 0.000 0.000 0.000 0.000 NA
#> SRR1336029 1 0.3671 0.79957 0.784 0.000 0.000 0.168 0.040 NA
#> SRR808452 1 0.1141 0.84111 0.948 0.000 0.000 0.000 0.000 NA
#> SRR1352169 5 0.3518 0.38912 0.000 0.000 0.184 0.008 0.784 NA
#> SRR1366707 5 0.4109 0.04709 0.000 0.000 0.328 0.008 0.652 NA
#> SRR1328143 5 0.0405 0.62089 0.004 0.000 0.008 0.000 0.988 NA
#> SRR1473567 2 0.0520 0.66824 0.000 0.984 0.000 0.008 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["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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.980 0.992 0.3615 0.639 0.639
#> 3 3 1.000 0.962 0.983 0.7919 0.699 0.533
#> 4 4 0.773 0.822 0.875 0.1296 0.889 0.694
#> 5 5 0.644 0.613 0.767 0.0626 0.914 0.708
#> 6 6 0.735 0.738 0.845 0.0468 0.879 0.554
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
#> SRR1442087 1 0.0000 0.994 1.000 0.000
#> SRR1390119 2 0.0000 0.981 0.000 1.000
#> SRR1436127 1 0.0000 0.994 1.000 0.000
#> SRR1347278 1 0.0000 0.994 1.000 0.000
#> SRR1332904 2 0.0000 0.981 0.000 1.000
#> SRR1444179 1 0.0000 0.994 1.000 0.000
#> SRR1082685 1 0.0000 0.994 1.000 0.000
#> SRR1362287 1 0.0000 0.994 1.000 0.000
#> SRR1339007 1 0.0000 0.994 1.000 0.000
#> SRR1376557 2 0.0000 0.981 0.000 1.000
#> SRR1468700 2 0.0000 0.981 0.000 1.000
#> SRR1077455 1 0.0000 0.994 1.000 0.000
#> SRR1413978 1 0.0000 0.994 1.000 0.000
#> SRR1439896 1 0.0000 0.994 1.000 0.000
#> SRR1317963 2 0.0000 0.981 0.000 1.000
#> SRR1431865 1 0.0000 0.994 1.000 0.000
#> SRR1394253 1 0.0000 0.994 1.000 0.000
#> SRR1082664 1 0.0000 0.994 1.000 0.000
#> SRR1077968 1 0.0000 0.994 1.000 0.000
#> SRR1076393 1 0.0000 0.994 1.000 0.000
#> SRR1477476 2 0.0000 0.981 0.000 1.000
#> SRR1398057 1 0.0000 0.994 1.000 0.000
#> SRR1485042 1 0.0000 0.994 1.000 0.000
#> SRR1385453 1 0.0938 0.983 0.988 0.012
#> SRR1348074 1 0.9248 0.469 0.660 0.340
#> SRR813959 2 0.8327 0.649 0.264 0.736
#> SRR665442 2 0.0000 0.981 0.000 1.000
#> SRR1378068 1 0.0000 0.994 1.000 0.000
#> SRR1485237 1 0.4562 0.890 0.904 0.096
#> SRR1350792 1 0.0000 0.994 1.000 0.000
#> SRR1326797 1 0.0000 0.994 1.000 0.000
#> SRR808994 1 0.0000 0.994 1.000 0.000
#> SRR1474041 1 0.0000 0.994 1.000 0.000
#> SRR1405641 1 0.0000 0.994 1.000 0.000
#> SRR1362245 1 0.0000 0.994 1.000 0.000
#> SRR1500194 1 0.0000 0.994 1.000 0.000
#> SRR1414876 2 0.0000 0.981 0.000 1.000
#> SRR1478523 1 0.0000 0.994 1.000 0.000
#> SRR1325161 1 0.0000 0.994 1.000 0.000
#> SRR1318026 1 0.0000 0.994 1.000 0.000
#> SRR1343778 1 0.0000 0.994 1.000 0.000
#> SRR1441287 1 0.0000 0.994 1.000 0.000
#> SRR1430991 1 0.0000 0.994 1.000 0.000
#> SRR1499722 1 0.0000 0.994 1.000 0.000
#> SRR1351368 1 0.0000 0.994 1.000 0.000
#> SRR1441785 1 0.0000 0.994 1.000 0.000
#> SRR1096101 1 0.0000 0.994 1.000 0.000
#> SRR808375 1 0.0000 0.994 1.000 0.000
#> SRR1452842 1 0.0000 0.994 1.000 0.000
#> SRR1311709 1 0.0000 0.994 1.000 0.000
#> SRR1433352 1 0.0000 0.994 1.000 0.000
#> SRR1340241 2 0.0000 0.981 0.000 1.000
#> SRR1456754 1 0.0000 0.994 1.000 0.000
#> SRR1465172 1 0.0000 0.994 1.000 0.000
#> SRR1499284 1 0.0000 0.994 1.000 0.000
#> SRR1499607 2 0.0000 0.981 0.000 1.000
#> SRR812342 1 0.0000 0.994 1.000 0.000
#> SRR1405374 1 0.0000 0.994 1.000 0.000
#> SRR1403565 1 0.0000 0.994 1.000 0.000
#> SRR1332024 1 0.0000 0.994 1.000 0.000
#> SRR1471633 1 0.0000 0.994 1.000 0.000
#> SRR1325944 2 0.0000 0.981 0.000 1.000
#> SRR1429450 2 0.0000 0.981 0.000 1.000
#> SRR821573 1 0.0000 0.994 1.000 0.000
#> SRR1435372 1 0.0000 0.994 1.000 0.000
#> SRR1324184 2 0.0000 0.981 0.000 1.000
#> SRR816517 2 0.0000 0.981 0.000 1.000
#> SRR1324141 1 0.0000 0.994 1.000 0.000
#> SRR1101612 1 0.0000 0.994 1.000 0.000
#> SRR1356531 1 0.0000 0.994 1.000 0.000
#> SRR1089785 1 0.0000 0.994 1.000 0.000
#> SRR1077708 1 0.0000 0.994 1.000 0.000
#> SRR1343720 1 0.0000 0.994 1.000 0.000
#> SRR1477499 2 0.0000 0.981 0.000 1.000
#> SRR1347236 1 0.0000 0.994 1.000 0.000
#> SRR1326408 1 0.0000 0.994 1.000 0.000
#> SRR1336529 1 0.0000 0.994 1.000 0.000
#> SRR1440643 1 0.0000 0.994 1.000 0.000
#> SRR662354 1 0.0000 0.994 1.000 0.000
#> SRR1310817 1 0.0000 0.994 1.000 0.000
#> SRR1347389 2 0.0000 0.981 0.000 1.000
#> SRR1353097 1 0.0000 0.994 1.000 0.000
#> SRR1384737 1 0.0000 0.994 1.000 0.000
#> SRR1096339 1 0.0000 0.994 1.000 0.000
#> SRR1345329 1 0.3114 0.937 0.944 0.056
#> SRR1414771 1 0.0000 0.994 1.000 0.000
#> SRR1309119 1 0.0000 0.994 1.000 0.000
#> SRR1470438 1 0.0000 0.994 1.000 0.000
#> SRR1343221 1 0.0000 0.994 1.000 0.000
#> SRR1410847 1 0.0000 0.994 1.000 0.000
#> SRR807949 1 0.0000 0.994 1.000 0.000
#> SRR1442332 1 0.0000 0.994 1.000 0.000
#> SRR815920 1 0.0000 0.994 1.000 0.000
#> SRR1471524 1 0.0000 0.994 1.000 0.000
#> SRR1477221 1 0.0000 0.994 1.000 0.000
#> SRR1445046 2 0.0000 0.981 0.000 1.000
#> SRR1331962 2 0.0000 0.981 0.000 1.000
#> SRR1319946 2 0.0000 0.981 0.000 1.000
#> SRR1311599 1 0.0000 0.994 1.000 0.000
#> SRR1323977 2 0.8267 0.656 0.260 0.740
#> SRR1445132 2 0.0000 0.981 0.000 1.000
#> SRR1337321 1 0.0000 0.994 1.000 0.000
#> SRR1366390 2 0.0000 0.981 0.000 1.000
#> SRR1343012 1 0.0000 0.994 1.000 0.000
#> SRR1311958 2 0.0000 0.981 0.000 1.000
#> SRR1388234 2 0.0000 0.981 0.000 1.000
#> SRR1370384 1 0.0000 0.994 1.000 0.000
#> SRR1321650 1 0.0000 0.994 1.000 0.000
#> SRR1485117 2 0.0000 0.981 0.000 1.000
#> SRR1384713 1 0.0000 0.994 1.000 0.000
#> SRR816609 1 0.0000 0.994 1.000 0.000
#> SRR1486239 2 0.0000 0.981 0.000 1.000
#> SRR1309638 1 0.0000 0.994 1.000 0.000
#> SRR1356660 1 0.0000 0.994 1.000 0.000
#> SRR1392883 2 0.0000 0.981 0.000 1.000
#> SRR808130 1 0.0000 0.994 1.000 0.000
#> SRR816677 1 0.0000 0.994 1.000 0.000
#> SRR1455722 1 0.0000 0.994 1.000 0.000
#> SRR1336029 1 0.0000 0.994 1.000 0.000
#> SRR808452 1 0.0000 0.994 1.000 0.000
#> SRR1352169 1 0.0000 0.994 1.000 0.000
#> SRR1366707 1 0.0000 0.994 1.000 0.000
#> SRR1328143 1 0.0000 0.994 1.000 0.000
#> SRR1473567 2 0.0000 0.981 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1390119 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1347278 3 0.0892 0.961 0.020 0.000 0.980
#> SRR1332904 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1082664 3 0.0892 0.961 0.020 0.000 0.980
#> SRR1077968 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1398057 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1485042 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1385453 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1348074 1 0.0892 0.977 0.980 0.020 0.000
#> SRR813959 2 0.5591 0.551 0.000 0.696 0.304
#> SRR665442 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1378068 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1485237 1 0.0237 0.993 0.996 0.004 0.000
#> SRR1350792 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.996 1.000 0.000 0.000
#> SRR808994 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1474041 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1405641 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1500194 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1478523 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1325161 3 0.4062 0.802 0.164 0.000 0.836
#> SRR1318026 1 0.0237 0.993 0.996 0.004 0.000
#> SRR1343778 3 0.0747 0.963 0.016 0.000 0.984
#> SRR1441287 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1430991 3 0.0592 0.965 0.012 0.000 0.988
#> SRR1499722 3 0.6154 0.353 0.408 0.000 0.592
#> SRR1351368 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1441785 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.996 1.000 0.000 0.000
#> SRR808375 3 0.1163 0.955 0.028 0.000 0.972
#> SRR1452842 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1311709 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1433352 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1340241 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1465172 1 0.0424 0.989 0.992 0.000 0.008
#> SRR1499284 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1499607 2 0.0000 0.975 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1332024 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1471633 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.975 0.000 1.000 0.000
#> SRR821573 3 0.5591 0.591 0.304 0.000 0.696
#> SRR1435372 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.975 0.000 1.000 0.000
#> SRR816517 3 0.1643 0.930 0.000 0.044 0.956
#> SRR1324141 1 0.2945 0.900 0.908 0.088 0.004
#> SRR1101612 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1089785 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1077708 3 0.1031 0.958 0.024 0.000 0.976
#> SRR1343720 3 0.1643 0.940 0.044 0.000 0.956
#> SRR1477499 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1347236 1 0.1411 0.959 0.964 0.000 0.036
#> SRR1326408 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1440643 3 0.0000 0.965 0.000 0.000 1.000
#> SRR662354 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1310817 3 0.0892 0.961 0.020 0.000 0.980
#> SRR1347389 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1384737 1 0.0237 0.993 0.996 0.004 0.000
#> SRR1096339 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1345329 1 0.0747 0.981 0.984 0.016 0.000
#> SRR1414771 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1309119 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1470438 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1343221 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.996 1.000 0.000 0.000
#> SRR807949 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1442332 3 0.0592 0.965 0.012 0.000 0.988
#> SRR815920 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1477221 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1445046 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1319946 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1323977 2 0.5706 0.522 0.320 0.680 0.000
#> SRR1445132 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1337321 3 0.0592 0.965 0.012 0.000 0.988
#> SRR1366390 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1343012 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1311958 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1388234 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1321650 3 0.0237 0.966 0.004 0.000 0.996
#> SRR1485117 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.996 1.000 0.000 0.000
#> SRR816609 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1486239 2 0.0000 0.975 0.000 1.000 0.000
#> SRR1309638 3 0.1163 0.955 0.028 0.000 0.972
#> SRR1356660 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.975 0.000 1.000 0.000
#> SRR808130 3 0.0424 0.966 0.008 0.000 0.992
#> SRR816677 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.996 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1352169 3 0.0747 0.963 0.016 0.000 0.984
#> SRR1366707 3 0.0000 0.965 0.000 0.000 1.000
#> SRR1328143 3 0.0424 0.966 0.008 0.000 0.992
#> SRR1473567 2 0.0000 0.975 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.4888 0.521 0.000 0.000 0.588 0.412
#> SRR1390119 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.3528 0.845 0.000 0.000 0.808 0.192
#> SRR1347278 3 0.3219 0.854 0.000 0.000 0.836 0.164
#> SRR1332904 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.0336 0.911 0.992 0.000 0.000 0.008
#> SRR1082685 1 0.0336 0.910 0.992 0.000 0.000 0.008
#> SRR1362287 1 0.1706 0.902 0.948 0.000 0.016 0.036
#> SRR1339007 1 0.0779 0.910 0.980 0.000 0.004 0.016
#> SRR1376557 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1077455 4 0.4776 0.379 0.376 0.000 0.000 0.624
#> SRR1413978 1 0.4462 0.819 0.804 0.000 0.064 0.132
#> SRR1439896 1 0.0336 0.910 0.992 0.000 0.000 0.008
#> SRR1317963 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1431865 1 0.3557 0.859 0.856 0.000 0.036 0.108
#> SRR1394253 1 0.1182 0.907 0.968 0.000 0.016 0.016
#> SRR1082664 4 0.4134 0.614 0.000 0.000 0.260 0.740
#> SRR1077968 1 0.3649 0.750 0.796 0.000 0.000 0.204
#> SRR1076393 3 0.4661 0.656 0.000 0.000 0.652 0.348
#> SRR1477476 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.2921 0.857 0.000 0.000 0.860 0.140
#> SRR1485042 1 0.1824 0.899 0.936 0.000 0.004 0.060
#> SRR1385453 3 0.4843 0.505 0.000 0.000 0.604 0.396
#> SRR1348074 1 0.3611 0.866 0.860 0.060 0.000 0.080
#> SRR813959 2 0.4948 0.189 0.000 0.560 0.000 0.440
#> SRR665442 2 0.1557 0.916 0.056 0.944 0.000 0.000
#> SRR1378068 3 0.3266 0.853 0.000 0.000 0.832 0.168
#> SRR1485237 1 0.2565 0.883 0.912 0.056 0.000 0.032
#> SRR1350792 1 0.0817 0.907 0.976 0.000 0.000 0.024
#> SRR1326797 4 0.3400 0.698 0.180 0.000 0.000 0.820
#> SRR808994 3 0.0592 0.796 0.000 0.000 0.984 0.016
#> SRR1474041 4 0.3907 0.657 0.000 0.000 0.232 0.768
#> SRR1405641 3 0.2081 0.844 0.000 0.000 0.916 0.084
#> SRR1362245 3 0.1489 0.791 0.004 0.000 0.952 0.044
#> SRR1500194 1 0.1356 0.905 0.960 0.000 0.008 0.032
#> SRR1414876 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.1474 0.829 0.000 0.000 0.948 0.052
#> SRR1325161 4 0.3463 0.735 0.096 0.000 0.040 0.864
#> SRR1318026 1 0.3667 0.869 0.856 0.056 0.000 0.088
#> SRR1343778 3 0.3837 0.822 0.000 0.000 0.776 0.224
#> SRR1441287 1 0.0336 0.909 0.992 0.000 0.000 0.008
#> SRR1430991 4 0.3400 0.719 0.000 0.000 0.180 0.820
#> SRR1499722 4 0.3501 0.724 0.132 0.000 0.020 0.848
#> SRR1351368 3 0.1118 0.822 0.000 0.000 0.964 0.036
#> SRR1441785 1 0.3818 0.851 0.844 0.000 0.048 0.108
#> SRR1096101 1 0.1743 0.907 0.940 0.000 0.004 0.056
#> SRR808375 4 0.3161 0.741 0.012 0.000 0.124 0.864
#> SRR1452842 4 0.4992 0.056 0.476 0.000 0.000 0.524
#> SRR1311709 1 0.0469 0.909 0.988 0.000 0.000 0.012
#> SRR1433352 4 0.3400 0.719 0.000 0.000 0.180 0.820
#> SRR1340241 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.2921 0.834 0.860 0.000 0.000 0.140
#> SRR1465172 4 0.3402 0.709 0.164 0.000 0.004 0.832
#> SRR1499284 4 0.3688 0.673 0.208 0.000 0.000 0.792
#> SRR1499607 2 0.0707 0.964 0.000 0.980 0.000 0.020
#> SRR812342 1 0.0817 0.907 0.976 0.000 0.000 0.024
#> SRR1405374 1 0.1356 0.906 0.960 0.000 0.008 0.032
#> SRR1403565 1 0.1022 0.910 0.968 0.000 0.000 0.032
#> SRR1332024 3 0.1557 0.829 0.000 0.000 0.944 0.056
#> SRR1471633 1 0.0707 0.910 0.980 0.000 0.000 0.020
#> SRR1325944 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR821573 4 0.3404 0.743 0.032 0.000 0.104 0.864
#> SRR1435372 1 0.2281 0.871 0.904 0.000 0.000 0.096
#> SRR1324184 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR816517 3 0.3390 0.714 0.000 0.132 0.852 0.016
#> SRR1324141 4 0.6587 0.496 0.136 0.204 0.008 0.652
#> SRR1101612 1 0.0336 0.910 0.992 0.000 0.000 0.008
#> SRR1356531 1 0.0592 0.908 0.984 0.000 0.000 0.016
#> SRR1089785 4 0.4222 0.589 0.000 0.000 0.272 0.728
#> SRR1077708 3 0.4925 0.487 0.000 0.000 0.572 0.428
#> SRR1343720 4 0.3024 0.735 0.000 0.000 0.148 0.852
#> SRR1477499 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1347236 4 0.3498 0.712 0.160 0.000 0.008 0.832
#> SRR1326408 1 0.1792 0.892 0.932 0.000 0.000 0.068
#> SRR1336529 3 0.3074 0.856 0.000 0.000 0.848 0.152
#> SRR1440643 3 0.4431 0.712 0.000 0.000 0.696 0.304
#> SRR662354 1 0.1302 0.903 0.956 0.000 0.000 0.044
#> SRR1310817 4 0.3024 0.736 0.000 0.000 0.148 0.852
#> SRR1347389 2 0.1118 0.951 0.000 0.964 0.000 0.036
#> SRR1353097 1 0.1022 0.905 0.968 0.000 0.000 0.032
#> SRR1384737 1 0.4176 0.841 0.832 0.008 0.044 0.116
#> SRR1096339 1 0.0921 0.908 0.972 0.000 0.000 0.028
#> SRR1345329 1 0.3190 0.874 0.880 0.016 0.008 0.096
#> SRR1414771 3 0.0336 0.796 0.000 0.000 0.992 0.008
#> SRR1309119 1 0.2081 0.890 0.916 0.000 0.000 0.084
#> SRR1470438 3 0.0469 0.793 0.000 0.000 0.988 0.012
#> SRR1343221 1 0.2760 0.841 0.872 0.000 0.000 0.128
#> SRR1410847 1 0.0592 0.910 0.984 0.000 0.000 0.016
#> SRR807949 4 0.3266 0.726 0.000 0.000 0.168 0.832
#> SRR1442332 4 0.3873 0.662 0.000 0.000 0.228 0.772
#> SRR815920 3 0.3219 0.854 0.000 0.000 0.836 0.164
#> SRR1471524 3 0.3400 0.844 0.000 0.000 0.820 0.180
#> SRR1477221 3 0.3024 0.854 0.000 0.000 0.852 0.148
#> SRR1445046 2 0.0376 0.972 0.004 0.992 0.000 0.004
#> SRR1331962 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.1297 0.908 0.964 0.000 0.016 0.020
#> SRR1323977 2 0.0188 0.974 0.004 0.996 0.000 0.000
#> SRR1445132 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.3052 0.848 0.004 0.000 0.860 0.136
#> SRR1366390 2 0.0188 0.976 0.000 0.996 0.000 0.004
#> SRR1343012 1 0.6439 0.414 0.528 0.044 0.012 0.416
#> SRR1311958 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1370384 1 0.4134 0.665 0.740 0.000 0.000 0.260
#> SRR1321650 3 0.3486 0.847 0.000 0.000 0.812 0.188
#> SRR1485117 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.4972 0.151 0.544 0.000 0.000 0.456
#> SRR816609 1 0.2179 0.885 0.924 0.064 0.000 0.012
#> SRR1486239 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR1309638 3 0.3108 0.849 0.016 0.000 0.872 0.112
#> SRR1356660 1 0.4127 0.836 0.824 0.000 0.052 0.124
#> SRR1392883 2 0.0000 0.978 0.000 1.000 0.000 0.000
#> SRR808130 4 0.3486 0.712 0.000 0.000 0.188 0.812
#> SRR816677 1 0.3143 0.871 0.876 0.000 0.024 0.100
#> SRR1455722 1 0.0817 0.907 0.976 0.000 0.000 0.024
#> SRR1336029 1 0.2976 0.868 0.872 0.000 0.008 0.120
#> SRR808452 1 0.1389 0.899 0.952 0.000 0.000 0.048
#> SRR1352169 3 0.3801 0.826 0.000 0.000 0.780 0.220
#> SRR1366707 3 0.3356 0.849 0.000 0.000 0.824 0.176
#> SRR1328143 4 0.4072 0.623 0.000 0.000 0.252 0.748
#> SRR1473567 2 0.0000 0.978 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
#> SRR1442087 5 0.3242 0.4583 0.000 0.000 0.216 0.000 0.784
#> SRR1390119 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.5460 0.5127 0.004 0.000 0.524 0.052 0.420
#> SRR1347278 3 0.7431 0.5283 0.068 0.000 0.488 0.188 0.256
#> SRR1332904 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1444179 1 0.2798 0.5726 0.852 0.000 0.000 0.140 0.008
#> SRR1082685 1 0.2608 0.6617 0.888 0.000 0.004 0.088 0.020
#> SRR1362287 1 0.6210 0.5449 0.540 0.000 0.184 0.276 0.000
#> SRR1339007 1 0.2321 0.6410 0.916 0.000 0.016 0.044 0.024
#> SRR1376557 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1077455 1 0.4908 0.3621 0.608 0.000 0.000 0.036 0.356
#> SRR1413978 1 0.5905 0.4747 0.572 0.000 0.292 0.136 0.000
#> SRR1439896 1 0.4818 0.6334 0.708 0.000 0.080 0.212 0.000
#> SRR1317963 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1431865 1 0.6287 0.5345 0.528 0.000 0.196 0.276 0.000
#> SRR1394253 1 0.6127 0.5557 0.552 0.000 0.172 0.276 0.000
#> SRR1082664 5 0.3959 0.5842 0.028 0.000 0.140 0.024 0.808
#> SRR1077968 1 0.4254 0.5309 0.740 0.000 0.000 0.040 0.220
#> SRR1076393 5 0.5867 -0.1220 0.028 0.000 0.412 0.044 0.516
#> SRR1477476 2 0.0404 0.9251 0.000 0.988 0.000 0.012 0.000
#> SRR1398057 3 0.6055 0.6207 0.068 0.000 0.668 0.092 0.172
#> SRR1485042 1 0.2754 0.6424 0.880 0.000 0.080 0.040 0.000
#> SRR1385453 5 0.5600 0.1013 0.000 0.000 0.316 0.096 0.588
#> SRR1348074 4 0.4418 0.7490 0.332 0.016 0.000 0.652 0.000
#> SRR813959 2 0.3424 0.6150 0.000 0.760 0.000 0.000 0.240
#> SRR665442 2 0.2866 0.7966 0.024 0.872 0.004 0.100 0.000
#> SRR1378068 3 0.4585 0.5833 0.000 0.000 0.628 0.020 0.352
#> SRR1485237 1 0.3951 0.5527 0.812 0.016 0.000 0.128 0.044
#> SRR1350792 1 0.4210 0.6693 0.784 0.000 0.004 0.140 0.072
#> SRR1326797 5 0.3305 0.5617 0.224 0.000 0.000 0.000 0.776
#> SRR808994 3 0.2583 0.6560 0.000 0.000 0.864 0.004 0.132
#> SRR1474041 5 0.2046 0.6550 0.000 0.000 0.068 0.016 0.916
#> SRR1405641 3 0.3790 0.6494 0.000 0.000 0.724 0.004 0.272
#> SRR1362245 3 0.4634 0.5505 0.072 0.000 0.740 0.184 0.004
#> SRR1500194 1 0.5770 0.5839 0.604 0.000 0.140 0.256 0.000
#> SRR1414876 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.4573 0.6242 0.000 0.000 0.700 0.044 0.256
#> SRR1325161 5 0.2516 0.6372 0.140 0.000 0.000 0.000 0.860
#> SRR1318026 4 0.4066 0.7602 0.324 0.000 0.000 0.672 0.004
#> SRR1343778 5 0.4305 -0.3464 0.000 0.000 0.488 0.000 0.512
#> SRR1441287 1 0.2818 0.6632 0.860 0.000 0.008 0.128 0.004
#> SRR1430991 5 0.1106 0.6884 0.012 0.000 0.024 0.000 0.964
#> SRR1499722 5 0.2773 0.6207 0.164 0.000 0.000 0.000 0.836
#> SRR1351368 3 0.5190 0.6062 0.000 0.000 0.668 0.096 0.236
#> SRR1441785 1 0.6463 0.5015 0.496 0.000 0.228 0.276 0.000
#> SRR1096101 1 0.3584 0.6710 0.848 0.000 0.020 0.064 0.068
#> SRR808375 5 0.1638 0.6796 0.064 0.000 0.004 0.000 0.932
#> SRR1452842 1 0.4752 0.4236 0.648 0.000 0.000 0.036 0.316
#> SRR1311709 1 0.2351 0.6184 0.896 0.000 0.000 0.088 0.016
#> SRR1433352 5 0.1493 0.6894 0.024 0.000 0.028 0.000 0.948
#> SRR1340241 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 1 0.3810 0.5848 0.788 0.000 0.000 0.036 0.176
#> SRR1465172 5 0.3639 0.5885 0.184 0.000 0.000 0.024 0.792
#> SRR1499284 5 0.5009 0.0778 0.428 0.000 0.000 0.032 0.540
#> SRR1499607 2 0.2824 0.8294 0.024 0.880 0.008 0.088 0.000
#> SRR812342 1 0.2819 0.6706 0.884 0.000 0.004 0.052 0.060
#> SRR1405374 1 0.5811 0.5820 0.596 0.000 0.140 0.264 0.000
#> SRR1403565 1 0.6036 0.5638 0.564 0.000 0.160 0.276 0.000
#> SRR1332024 3 0.5871 0.5794 0.084 0.000 0.680 0.176 0.060
#> SRR1471633 1 0.3160 0.5295 0.808 0.000 0.000 0.188 0.004
#> SRR1325944 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.1798 0.6795 0.064 0.000 0.004 0.004 0.928
#> SRR1435372 1 0.3489 0.5977 0.820 0.000 0.000 0.036 0.144
#> SRR1324184 2 0.4256 0.1743 0.000 0.564 0.000 0.436 0.000
#> SRR816517 3 0.5006 0.6281 0.000 0.016 0.712 0.060 0.212
#> SRR1324141 4 0.4425 0.7677 0.296 0.000 0.000 0.680 0.024
#> SRR1101612 1 0.4863 0.6320 0.708 0.000 0.088 0.204 0.000
#> SRR1356531 1 0.1901 0.6625 0.932 0.000 0.024 0.040 0.004
#> SRR1089785 5 0.1638 0.6672 0.000 0.000 0.064 0.004 0.932
#> SRR1077708 5 0.5133 -0.0135 0.044 0.000 0.388 0.000 0.568
#> SRR1343720 5 0.1670 0.6851 0.052 0.000 0.012 0.000 0.936
#> SRR1477499 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.2813 0.6178 0.168 0.000 0.000 0.000 0.832
#> SRR1326408 1 0.3437 0.5830 0.832 0.000 0.000 0.048 0.120
#> SRR1336529 3 0.5543 0.6303 0.024 0.000 0.612 0.044 0.320
#> SRR1440643 5 0.6309 0.0654 0.000 0.000 0.240 0.228 0.532
#> SRR662354 1 0.4701 0.6380 0.708 0.000 0.028 0.248 0.016
#> SRR1310817 5 0.1041 0.6827 0.000 0.000 0.032 0.004 0.964
#> SRR1347389 4 0.4938 0.4654 0.048 0.312 0.000 0.640 0.000
#> SRR1353097 1 0.2850 0.6124 0.872 0.000 0.000 0.036 0.092
#> SRR1384737 4 0.4046 0.7699 0.296 0.000 0.008 0.696 0.000
#> SRR1096339 1 0.5274 0.6184 0.676 0.000 0.132 0.192 0.000
#> SRR1345329 1 0.4194 0.3971 0.708 0.004 0.012 0.276 0.000
#> SRR1414771 3 0.2672 0.6633 0.004 0.000 0.872 0.008 0.116
#> SRR1309119 1 0.3999 0.5091 0.656 0.000 0.000 0.344 0.000
#> SRR1470438 3 0.3374 0.6660 0.004 0.000 0.844 0.044 0.108
#> SRR1343221 1 0.3988 0.5760 0.768 0.000 0.000 0.036 0.196
#> SRR1410847 1 0.5541 0.6046 0.636 0.000 0.128 0.236 0.000
#> SRR807949 5 0.0955 0.6856 0.004 0.000 0.028 0.000 0.968
#> SRR1442332 5 0.1410 0.6704 0.000 0.000 0.060 0.000 0.940
#> SRR815920 3 0.4540 0.5960 0.000 0.000 0.640 0.020 0.340
#> SRR1471524 3 0.5077 0.4370 0.000 0.000 0.536 0.036 0.428
#> SRR1477221 3 0.6591 0.5374 0.076 0.000 0.600 0.232 0.092
#> SRR1445046 2 0.5030 0.5087 0.104 0.696 0.000 0.200 0.000
#> SRR1331962 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1319946 2 0.0162 0.9293 0.000 0.996 0.000 0.000 0.004
#> SRR1311599 1 0.6097 0.5582 0.556 0.000 0.168 0.276 0.000
#> SRR1323977 2 0.2653 0.8120 0.024 0.880 0.000 0.000 0.096
#> SRR1445132 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.6903 0.5727 0.056 0.000 0.556 0.244 0.144
#> SRR1366390 4 0.4114 0.3132 0.000 0.376 0.000 0.624 0.000
#> SRR1343012 4 0.4400 0.7617 0.308 0.000 0.000 0.672 0.020
#> SRR1311958 2 0.1043 0.9036 0.000 0.960 0.000 0.040 0.000
#> SRR1388234 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1370384 1 0.4240 0.5354 0.736 0.000 0.000 0.036 0.228
#> SRR1321650 3 0.6462 0.6095 0.068 0.000 0.608 0.088 0.236
#> SRR1485117 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1384713 1 0.4616 0.4634 0.676 0.000 0.000 0.036 0.288
#> SRR816609 1 0.4439 0.5371 0.788 0.120 0.000 0.068 0.024
#> SRR1486239 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR1309638 3 0.6308 0.5628 0.144 0.000 0.632 0.044 0.180
#> SRR1356660 1 0.6349 0.5145 0.524 0.000 0.232 0.244 0.000
#> SRR1392883 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.1121 0.6788 0.000 0.000 0.044 0.000 0.956
#> SRR816677 1 0.4203 0.5964 0.760 0.000 0.188 0.052 0.000
#> SRR1455722 1 0.3389 0.6737 0.836 0.000 0.000 0.116 0.048
#> SRR1336029 1 0.3884 0.4393 0.708 0.000 0.004 0.288 0.000
#> SRR808452 1 0.3702 0.6643 0.820 0.000 0.000 0.096 0.084
#> SRR1352169 5 0.6634 -0.4351 0.060 0.000 0.428 0.064 0.448
#> SRR1366707 3 0.4893 0.4999 0.000 0.000 0.568 0.028 0.404
#> SRR1328143 5 0.1478 0.6688 0.000 0.000 0.064 0.000 0.936
#> SRR1473567 2 0.0000 0.9322 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.1863 0.782 0.000 0.000 0.104 0.000 0.896 0.000
#> SRR1390119 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 5 0.4450 0.494 0.000 0.000 0.236 0.012 0.700 0.052
#> SRR1347278 6 0.4548 0.447 0.000 0.000 0.080 0.000 0.248 0.672
#> SRR1332904 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 1 0.2660 0.804 0.868 0.000 0.000 0.084 0.000 0.048
#> SRR1082685 1 0.3372 0.789 0.804 0.000 0.000 0.016 0.016 0.164
#> SRR1362287 6 0.0622 0.767 0.012 0.000 0.008 0.000 0.000 0.980
#> SRR1339007 1 0.2100 0.818 0.916 0.000 0.024 0.008 0.048 0.004
#> SRR1376557 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077455 1 0.2643 0.800 0.856 0.000 0.000 0.008 0.128 0.008
#> SRR1413978 6 0.6435 0.219 0.212 0.000 0.368 0.024 0.000 0.396
#> SRR1439896 6 0.3349 0.592 0.244 0.000 0.000 0.008 0.000 0.748
#> SRR1317963 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1431865 6 0.0777 0.770 0.024 0.000 0.004 0.000 0.000 0.972
#> SRR1394253 6 0.0632 0.771 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1082664 3 0.5671 0.567 0.148 0.000 0.540 0.008 0.304 0.000
#> SRR1077968 1 0.2218 0.816 0.884 0.000 0.000 0.000 0.104 0.012
#> SRR1076393 3 0.4234 0.737 0.028 0.000 0.724 0.024 0.224 0.000
#> SRR1477476 2 0.1434 0.912 0.000 0.940 0.012 0.048 0.000 0.000
#> SRR1398057 6 0.5261 -0.148 0.000 0.000 0.444 0.000 0.096 0.460
#> SRR1485042 1 0.2255 0.813 0.892 0.000 0.016 0.004 0.000 0.088
#> SRR1385453 5 0.5500 0.354 0.000 0.000 0.224 0.188 0.584 0.004
#> SRR1348074 4 0.2473 0.847 0.136 0.000 0.000 0.856 0.000 0.008
#> SRR813959 2 0.3515 0.499 0.000 0.676 0.000 0.000 0.324 0.000
#> SRR665442 2 0.2536 0.829 0.000 0.864 0.000 0.020 0.000 0.116
#> SRR1378068 3 0.3921 0.676 0.000 0.000 0.676 0.004 0.308 0.012
#> SRR1485237 1 0.1242 0.819 0.960 0.012 0.000 0.008 0.012 0.008
#> SRR1350792 1 0.4682 0.443 0.556 0.000 0.000 0.000 0.048 0.396
#> SRR1326797 5 0.2146 0.763 0.116 0.000 0.000 0.000 0.880 0.004
#> SRR808994 3 0.0870 0.697 0.000 0.000 0.972 0.004 0.012 0.012
#> SRR1474041 5 0.1321 0.830 0.000 0.000 0.020 0.024 0.952 0.004
#> SRR1405641 3 0.2806 0.757 0.000 0.000 0.844 0.004 0.136 0.016
#> SRR1362245 3 0.4216 0.429 0.008 0.000 0.676 0.008 0.012 0.296
#> SRR1500194 6 0.1398 0.766 0.052 0.000 0.000 0.008 0.000 0.940
#> SRR1414876 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 3 0.4928 0.618 0.000 0.000 0.640 0.096 0.260 0.004
#> SRR1325161 5 0.1204 0.824 0.056 0.000 0.000 0.000 0.944 0.000
#> SRR1318026 4 0.1957 0.856 0.112 0.000 0.000 0.888 0.000 0.000
#> SRR1343778 3 0.4884 0.622 0.064 0.000 0.592 0.004 0.340 0.000
#> SRR1441287 1 0.3934 0.622 0.676 0.000 0.000 0.020 0.000 0.304
#> SRR1430991 5 0.0291 0.843 0.004 0.000 0.004 0.000 0.992 0.000
#> SRR1499722 5 0.1075 0.830 0.048 0.000 0.000 0.000 0.952 0.000
#> SRR1351368 3 0.3757 0.700 0.000 0.000 0.780 0.136 0.084 0.000
#> SRR1441785 6 0.0603 0.767 0.016 0.000 0.004 0.000 0.000 0.980
#> SRR1096101 1 0.4163 0.811 0.776 0.000 0.016 0.004 0.080 0.124
#> SRR808375 5 0.0713 0.838 0.028 0.000 0.000 0.000 0.972 0.000
#> SRR1452842 1 0.2261 0.810 0.884 0.000 0.000 0.008 0.104 0.004
#> SRR1311709 1 0.2412 0.816 0.880 0.000 0.000 0.028 0.000 0.092
#> SRR1433352 5 0.0993 0.843 0.012 0.000 0.024 0.000 0.964 0.000
#> SRR1340241 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1456754 1 0.2068 0.818 0.904 0.000 0.000 0.008 0.080 0.008
#> SRR1465172 5 0.2562 0.701 0.172 0.000 0.000 0.000 0.828 0.000
#> SRR1499284 1 0.3314 0.670 0.740 0.000 0.000 0.000 0.256 0.004
#> SRR1499607 2 0.3299 0.818 0.048 0.844 0.080 0.028 0.000 0.000
#> SRR812342 1 0.4038 0.754 0.728 0.000 0.000 0.000 0.056 0.216
#> SRR1405374 6 0.1285 0.767 0.052 0.000 0.000 0.004 0.000 0.944
#> SRR1403565 6 0.0798 0.769 0.012 0.000 0.004 0.004 0.004 0.976
#> SRR1332024 6 0.5031 -0.110 0.000 0.000 0.448 0.000 0.072 0.480
#> SRR1471633 1 0.2595 0.803 0.872 0.000 0.000 0.084 0.000 0.044
#> SRR1325944 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.1572 0.828 0.036 0.000 0.000 0.028 0.936 0.000
#> SRR1435372 1 0.2509 0.826 0.876 0.000 0.000 0.000 0.088 0.036
#> SRR1324184 4 0.3445 0.636 0.000 0.260 0.000 0.732 0.000 0.008
#> SRR816517 3 0.3291 0.697 0.000 0.000 0.828 0.104 0.064 0.004
#> SRR1324141 4 0.3430 0.805 0.208 0.000 0.004 0.772 0.016 0.000
#> SRR1101612 6 0.3265 0.590 0.248 0.000 0.000 0.004 0.000 0.748
#> SRR1356531 1 0.2353 0.830 0.896 0.000 0.004 0.004 0.024 0.072
#> SRR1089785 5 0.1285 0.827 0.004 0.000 0.052 0.000 0.944 0.000
#> SRR1077708 3 0.5629 0.584 0.088 0.000 0.552 0.004 0.336 0.020
#> SRR1343720 5 0.0858 0.839 0.028 0.000 0.004 0.000 0.968 0.000
#> SRR1477499 2 0.0146 0.950 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1347236 5 0.1714 0.792 0.092 0.000 0.000 0.000 0.908 0.000
#> SRR1326408 1 0.1462 0.818 0.936 0.000 0.000 0.008 0.056 0.000
#> SRR1336529 3 0.3823 0.750 0.000 0.000 0.764 0.004 0.184 0.048
#> SRR1440643 5 0.4818 0.514 0.000 0.000 0.076 0.284 0.636 0.004
#> SRR662354 6 0.3121 0.676 0.180 0.000 0.000 0.012 0.004 0.804
#> SRR1310817 5 0.0777 0.841 0.004 0.000 0.000 0.024 0.972 0.000
#> SRR1347389 4 0.1686 0.845 0.064 0.012 0.000 0.924 0.000 0.000
#> SRR1353097 1 0.2389 0.832 0.888 0.000 0.000 0.000 0.052 0.060
#> SRR1384737 4 0.3284 0.816 0.168 0.000 0.032 0.800 0.000 0.000
#> SRR1096339 6 0.3136 0.624 0.228 0.000 0.000 0.004 0.000 0.768
#> SRR1345329 1 0.2507 0.781 0.892 0.012 0.016 0.072 0.000 0.008
#> SRR1414771 3 0.1777 0.719 0.000 0.000 0.928 0.004 0.044 0.024
#> SRR1309119 1 0.5907 0.219 0.444 0.000 0.000 0.340 0.000 0.216
#> SRR1470438 3 0.1296 0.696 0.000 0.000 0.952 0.004 0.012 0.032
#> SRR1343221 1 0.3078 0.820 0.836 0.000 0.000 0.000 0.108 0.056
#> SRR1410847 6 0.1588 0.761 0.072 0.000 0.000 0.004 0.000 0.924
#> SRR807949 5 0.0260 0.843 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1442332 5 0.0692 0.840 0.000 0.000 0.020 0.004 0.976 0.000
#> SRR815920 3 0.4048 0.690 0.000 0.000 0.684 0.012 0.292 0.012
#> SRR1471524 5 0.5145 -0.222 0.000 0.000 0.424 0.072 0.500 0.004
#> SRR1477221 6 0.3088 0.657 0.000 0.000 0.120 0.000 0.048 0.832
#> SRR1445046 2 0.2801 0.827 0.068 0.860 0.000 0.072 0.000 0.000
#> SRR1331962 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1319946 2 0.0363 0.944 0.000 0.988 0.000 0.000 0.012 0.000
#> SRR1311599 6 0.0547 0.771 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR1323977 2 0.1700 0.879 0.004 0.916 0.000 0.000 0.080 0.000
#> SRR1445132 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 6 0.4468 0.560 0.000 0.000 0.088 0.008 0.184 0.720
#> SRR1366390 4 0.1196 0.809 0.000 0.040 0.008 0.952 0.000 0.000
#> SRR1343012 1 0.3960 0.565 0.736 0.000 0.040 0.220 0.004 0.000
#> SRR1311958 2 0.1141 0.919 0.000 0.948 0.000 0.052 0.000 0.000
#> SRR1388234 2 0.0632 0.932 0.024 0.976 0.000 0.000 0.000 0.000
#> SRR1370384 1 0.2122 0.820 0.900 0.000 0.000 0.008 0.084 0.008
#> SRR1321650 3 0.5620 0.637 0.000 0.000 0.564 0.004 0.220 0.212
#> SRR1485117 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 1 0.2070 0.810 0.892 0.000 0.000 0.008 0.100 0.000
#> SRR816609 1 0.2212 0.760 0.880 0.112 0.000 0.008 0.000 0.000
#> SRR1486239 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1309638 3 0.4324 0.486 0.264 0.000 0.696 0.016 0.008 0.016
#> SRR1356660 6 0.1995 0.761 0.052 0.000 0.036 0.000 0.000 0.912
#> SRR1392883 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.0603 0.843 0.004 0.000 0.016 0.000 0.980 0.000
#> SRR816677 1 0.3755 0.713 0.780 0.000 0.172 0.016 0.000 0.032
#> SRR1455722 1 0.3997 0.664 0.688 0.000 0.000 0.004 0.020 0.288
#> SRR1336029 1 0.3120 0.778 0.840 0.000 0.008 0.112 0.000 0.040
#> SRR808452 1 0.4545 0.713 0.688 0.000 0.000 0.008 0.064 0.240
#> SRR1352169 5 0.3985 0.637 0.004 0.000 0.088 0.000 0.768 0.140
#> SRR1366707 3 0.4527 0.634 0.000 0.000 0.624 0.040 0.332 0.004
#> SRR1328143 5 0.0632 0.839 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1473567 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.965 0.947 0.976 0.3179 0.706 0.706
#> 3 3 0.869 0.899 0.958 0.1170 0.987 0.981
#> 4 4 0.827 0.879 0.921 0.0497 0.985 0.979
#> 5 5 0.765 0.811 0.915 0.2788 0.840 0.764
#> 6 6 0.725 0.742 0.875 0.0813 0.990 0.981
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
#> SRR1442087 1 0.0000 0.973 1.000 0.000
#> SRR1390119 2 0.0000 0.988 0.000 1.000
#> SRR1436127 1 0.0000 0.973 1.000 0.000
#> SRR1347278 1 0.0000 0.973 1.000 0.000
#> SRR1332904 2 0.1184 0.981 0.016 0.984
#> SRR1444179 1 0.0000 0.973 1.000 0.000
#> SRR1082685 1 0.0000 0.973 1.000 0.000
#> SRR1362287 1 0.0000 0.973 1.000 0.000
#> SRR1339007 1 0.0000 0.973 1.000 0.000
#> SRR1376557 2 0.0000 0.988 0.000 1.000
#> SRR1468700 2 0.0000 0.988 0.000 1.000
#> SRR1077455 1 0.0000 0.973 1.000 0.000
#> SRR1413978 1 0.0000 0.973 1.000 0.000
#> SRR1439896 1 0.0000 0.973 1.000 0.000
#> SRR1317963 1 0.9635 0.409 0.612 0.388
#> SRR1431865 1 0.0000 0.973 1.000 0.000
#> SRR1394253 1 0.0000 0.973 1.000 0.000
#> SRR1082664 1 0.0000 0.973 1.000 0.000
#> SRR1077968 1 0.0000 0.973 1.000 0.000
#> SRR1076393 1 0.0000 0.973 1.000 0.000
#> SRR1477476 2 0.0000 0.988 0.000 1.000
#> SRR1398057 1 0.0000 0.973 1.000 0.000
#> SRR1485042 1 0.0000 0.973 1.000 0.000
#> SRR1385453 1 0.1414 0.958 0.980 0.020
#> SRR1348074 1 0.2778 0.934 0.952 0.048
#> SRR813959 1 0.9393 0.484 0.644 0.356
#> SRR665442 1 0.6438 0.807 0.836 0.164
#> SRR1378068 1 0.0000 0.973 1.000 0.000
#> SRR1485237 1 0.6148 0.822 0.848 0.152
#> SRR1350792 1 0.0000 0.973 1.000 0.000
#> SRR1326797 1 0.0000 0.973 1.000 0.000
#> SRR808994 1 0.0000 0.973 1.000 0.000
#> SRR1474041 1 0.0000 0.973 1.000 0.000
#> SRR1405641 1 0.0000 0.973 1.000 0.000
#> SRR1362245 1 0.0000 0.973 1.000 0.000
#> SRR1500194 1 0.0000 0.973 1.000 0.000
#> SRR1414876 2 0.0376 0.987 0.004 0.996
#> SRR1478523 1 0.1414 0.958 0.980 0.020
#> SRR1325161 1 0.0000 0.973 1.000 0.000
#> SRR1318026 1 0.1414 0.958 0.980 0.020
#> SRR1343778 1 0.0000 0.973 1.000 0.000
#> SRR1441287 1 0.0000 0.973 1.000 0.000
#> SRR1430991 1 0.0000 0.973 1.000 0.000
#> SRR1499722 1 0.0000 0.973 1.000 0.000
#> SRR1351368 1 0.0000 0.973 1.000 0.000
#> SRR1441785 1 0.0000 0.973 1.000 0.000
#> SRR1096101 1 0.0000 0.973 1.000 0.000
#> SRR808375 1 0.0000 0.973 1.000 0.000
#> SRR1452842 1 0.0000 0.973 1.000 0.000
#> SRR1311709 1 0.0376 0.970 0.996 0.004
#> SRR1433352 1 0.0000 0.973 1.000 0.000
#> SRR1340241 2 0.3584 0.931 0.068 0.932
#> SRR1456754 1 0.0000 0.973 1.000 0.000
#> SRR1465172 1 0.0000 0.973 1.000 0.000
#> SRR1499284 1 0.0000 0.973 1.000 0.000
#> SRR1499607 1 0.9732 0.367 0.596 0.404
#> SRR812342 1 0.0000 0.973 1.000 0.000
#> SRR1405374 1 0.0000 0.973 1.000 0.000
#> SRR1403565 1 0.0000 0.973 1.000 0.000
#> SRR1332024 1 0.0000 0.973 1.000 0.000
#> SRR1471633 1 0.0000 0.973 1.000 0.000
#> SRR1325944 2 0.0000 0.988 0.000 1.000
#> SRR1429450 2 0.0000 0.988 0.000 1.000
#> SRR821573 1 0.0000 0.973 1.000 0.000
#> SRR1435372 1 0.0000 0.973 1.000 0.000
#> SRR1324184 2 0.0376 0.987 0.004 0.996
#> SRR816517 1 0.6247 0.818 0.844 0.156
#> SRR1324141 1 0.1414 0.958 0.980 0.020
#> SRR1101612 1 0.0000 0.973 1.000 0.000
#> SRR1356531 1 0.0000 0.973 1.000 0.000
#> SRR1089785 1 0.0000 0.973 1.000 0.000
#> SRR1077708 1 0.0000 0.973 1.000 0.000
#> SRR1343720 1 0.0000 0.973 1.000 0.000
#> SRR1477499 2 0.0000 0.988 0.000 1.000
#> SRR1347236 1 0.0000 0.973 1.000 0.000
#> SRR1326408 1 0.0000 0.973 1.000 0.000
#> SRR1336529 1 0.0000 0.973 1.000 0.000
#> SRR1440643 1 0.0376 0.970 0.996 0.004
#> SRR662354 1 0.0000 0.973 1.000 0.000
#> SRR1310817 1 0.0000 0.973 1.000 0.000
#> SRR1347389 2 0.1633 0.975 0.024 0.976
#> SRR1353097 1 0.0000 0.973 1.000 0.000
#> SRR1384737 1 0.1414 0.958 0.980 0.020
#> SRR1096339 1 0.0000 0.973 1.000 0.000
#> SRR1345329 1 0.2778 0.934 0.952 0.048
#> SRR1414771 1 0.0000 0.973 1.000 0.000
#> SRR1309119 1 0.0000 0.973 1.000 0.000
#> SRR1470438 1 0.0000 0.973 1.000 0.000
#> SRR1343221 1 0.0000 0.973 1.000 0.000
#> SRR1410847 1 0.0000 0.973 1.000 0.000
#> SRR807949 1 0.0000 0.973 1.000 0.000
#> SRR1442332 1 0.0000 0.973 1.000 0.000
#> SRR815920 1 0.0000 0.973 1.000 0.000
#> SRR1471524 1 0.0000 0.973 1.000 0.000
#> SRR1477221 1 0.0000 0.973 1.000 0.000
#> SRR1445046 2 0.0000 0.988 0.000 1.000
#> SRR1331962 2 0.0000 0.988 0.000 1.000
#> SRR1319946 2 0.3879 0.922 0.076 0.924
#> SRR1311599 1 0.0000 0.973 1.000 0.000
#> SRR1323977 1 0.9323 0.502 0.652 0.348
#> SRR1445132 2 0.0000 0.988 0.000 1.000
#> SRR1337321 1 0.0000 0.973 1.000 0.000
#> SRR1366390 2 0.1633 0.975 0.024 0.976
#> SRR1343012 1 0.1414 0.958 0.980 0.020
#> SRR1311958 2 0.0000 0.988 0.000 1.000
#> SRR1388234 1 0.8955 0.575 0.688 0.312
#> SRR1370384 1 0.0000 0.973 1.000 0.000
#> SRR1321650 1 0.0000 0.973 1.000 0.000
#> SRR1485117 2 0.0000 0.988 0.000 1.000
#> SRR1384713 1 0.0000 0.973 1.000 0.000
#> SRR816609 1 0.6801 0.788 0.820 0.180
#> SRR1486239 2 0.1184 0.981 0.016 0.984
#> SRR1309638 1 0.0000 0.973 1.000 0.000
#> SRR1356660 1 0.0000 0.973 1.000 0.000
#> SRR1392883 2 0.0000 0.988 0.000 1.000
#> SRR808130 1 0.0000 0.973 1.000 0.000
#> SRR816677 1 0.0938 0.965 0.988 0.012
#> SRR1455722 1 0.0000 0.973 1.000 0.000
#> SRR1336029 1 0.0000 0.973 1.000 0.000
#> SRR808452 1 0.0000 0.973 1.000 0.000
#> SRR1352169 1 0.0000 0.973 1.000 0.000
#> SRR1366707 1 0.0000 0.973 1.000 0.000
#> SRR1328143 1 0.0000 0.973 1.000 0.000
#> SRR1473567 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
#> SRR1442087 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1390119 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1436127 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1347278 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1332904 2 0.1765 0.947 0.004 0.956 0.040
#> SRR1444179 1 0.0424 0.946 0.992 0.000 0.008
#> SRR1082685 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1413978 1 0.1031 0.937 0.976 0.000 0.024
#> SRR1439896 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1317963 1 0.8955 0.166 0.516 0.344 0.140
#> SRR1431865 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1082664 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1077968 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1076393 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1477476 2 0.0237 0.969 0.000 0.996 0.004
#> SRR1398057 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1485042 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1385453 1 0.3610 0.869 0.888 0.016 0.096
#> SRR1348074 1 0.4483 0.827 0.848 0.024 0.128
#> SRR813959 1 0.8863 0.245 0.544 0.312 0.144
#> SRR665442 3 0.2066 0.000 0.060 0.000 0.940
#> SRR1378068 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1485237 1 0.6856 0.673 0.740 0.132 0.128
#> SRR1350792 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.951 1.000 0.000 0.000
#> SRR808994 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1474041 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1405641 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1362245 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1500194 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1414876 2 0.0237 0.967 0.004 0.996 0.000
#> SRR1478523 1 0.3610 0.869 0.888 0.016 0.096
#> SRR1325161 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1318026 1 0.3116 0.872 0.892 0.000 0.108
#> SRR1343778 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1441287 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1430991 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1499722 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1351368 1 0.0592 0.944 0.988 0.000 0.012
#> SRR1441785 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.951 1.000 0.000 0.000
#> SRR808375 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1452842 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1311709 1 0.2261 0.906 0.932 0.000 0.068
#> SRR1433352 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1340241 2 0.3482 0.870 0.000 0.872 0.128
#> SRR1456754 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1465172 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1499284 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1499607 1 0.9091 0.126 0.504 0.344 0.152
#> SRR812342 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1332024 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1471633 1 0.1529 0.927 0.960 0.000 0.040
#> SRR1325944 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.971 0.000 1.000 0.000
#> SRR821573 1 0.1860 0.917 0.948 0.000 0.052
#> SRR1435372 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1324184 2 0.2537 0.922 0.000 0.920 0.080
#> SRR816517 1 0.6737 0.682 0.744 0.100 0.156
#> SRR1324141 1 0.3116 0.872 0.892 0.000 0.108
#> SRR1101612 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1089785 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1077708 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1343720 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1477499 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1347236 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1326408 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1336529 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1440643 1 0.2301 0.909 0.936 0.004 0.060
#> SRR662354 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1310817 1 0.1860 0.917 0.948 0.000 0.052
#> SRR1347389 2 0.2448 0.929 0.000 0.924 0.076
#> SRR1353097 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1384737 1 0.3116 0.872 0.892 0.000 0.108
#> SRR1096339 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1345329 1 0.4483 0.827 0.848 0.024 0.128
#> SRR1414771 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1309119 1 0.1529 0.927 0.960 0.000 0.040
#> SRR1470438 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1343221 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.951 1.000 0.000 0.000
#> SRR807949 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1442332 1 0.0000 0.951 1.000 0.000 0.000
#> SRR815920 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1471524 1 0.2165 0.909 0.936 0.000 0.064
#> SRR1477221 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1445046 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1319946 2 0.3695 0.868 0.012 0.880 0.108
#> SRR1311599 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1323977 1 0.8821 0.268 0.552 0.304 0.144
#> SRR1445132 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1337321 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1366390 2 0.2448 0.929 0.000 0.924 0.076
#> SRR1343012 1 0.3116 0.872 0.892 0.000 0.108
#> SRR1311958 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1388234 1 0.8770 0.331 0.572 0.272 0.156
#> SRR1370384 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1321650 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1485117 2 0.0000 0.971 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.951 1.000 0.000 0.000
#> SRR816609 1 0.7397 0.616 0.704 0.148 0.148
#> SRR1486239 2 0.1765 0.947 0.004 0.956 0.040
#> SRR1309638 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1356660 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.971 0.000 1.000 0.000
#> SRR808130 1 0.0000 0.951 1.000 0.000 0.000
#> SRR816677 1 0.2878 0.884 0.904 0.000 0.096
#> SRR1455722 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.951 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1352169 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1366707 1 0.0592 0.944 0.988 0.000 0.012
#> SRR1328143 1 0.0000 0.951 1.000 0.000 0.000
#> SRR1473567 2 0.0000 0.971 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1390119 3 0.4103 0.876 0.000 0.256 0.744 0.000
#> SRR1436127 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1347278 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1332904 2 0.3545 0.769 0.000 0.828 0.164 0.008
#> SRR1444179 1 0.0336 0.948 0.992 0.000 0.008 0.000
#> SRR1082685 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.1302 0.846 0.000 0.956 0.044 0.000
#> SRR1468700 2 0.0000 0.873 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1413978 1 0.0817 0.940 0.976 0.000 0.024 0.000
#> SRR1439896 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1317963 1 0.7878 0.225 0.500 0.200 0.284 0.016
#> SRR1431865 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1082664 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1077968 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1076393 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1477476 3 0.4072 0.874 0.000 0.252 0.748 0.000
#> SRR1398057 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1485042 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1385453 1 0.2704 0.867 0.876 0.000 0.124 0.000
#> SRR1348074 1 0.3963 0.828 0.836 0.016 0.132 0.016
#> SRR813959 1 0.7529 0.308 0.532 0.144 0.308 0.016
#> SRR665442 4 0.0000 0.000 0.000 0.000 0.000 1.000
#> SRR1378068 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1485237 1 0.5615 0.685 0.724 0.044 0.212 0.020
#> SRR1350792 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR808994 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1474041 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1405641 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1362245 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1500194 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.3444 0.680 0.000 0.816 0.184 0.000
#> SRR1478523 1 0.2704 0.867 0.876 0.000 0.124 0.000
#> SRR1325161 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1318026 1 0.2859 0.870 0.880 0.000 0.112 0.008
#> SRR1343778 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1441287 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1430991 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1499722 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1351368 1 0.0469 0.946 0.988 0.000 0.012 0.000
#> SRR1441785 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR808375 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1452842 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1311709 1 0.2255 0.902 0.920 0.000 0.068 0.012
#> SRR1433352 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1340241 3 0.3545 0.551 0.000 0.164 0.828 0.008
#> SRR1456754 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1465172 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1499284 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1499607 1 0.7665 0.188 0.488 0.180 0.324 0.008
#> SRR812342 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1332024 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1471633 1 0.1305 0.930 0.960 0.000 0.036 0.004
#> SRR1325944 3 0.4624 0.881 0.000 0.340 0.660 0.000
#> SRR1429450 3 0.4605 0.881 0.000 0.336 0.664 0.000
#> SRR821573 1 0.1474 0.922 0.948 0.000 0.052 0.000
#> SRR1435372 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.2830 0.818 0.000 0.900 0.060 0.040
#> SRR816517 1 0.4710 0.698 0.732 0.008 0.252 0.008
#> SRR1324141 1 0.2859 0.870 0.880 0.000 0.112 0.008
#> SRR1101612 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1089785 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1077708 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1343720 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1477499 3 0.4624 0.881 0.000 0.340 0.660 0.000
#> SRR1347236 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1326408 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1336529 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1440643 1 0.1716 0.914 0.936 0.000 0.064 0.000
#> SRR662354 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1310817 1 0.1474 0.922 0.948 0.000 0.052 0.000
#> SRR1347389 2 0.2704 0.810 0.000 0.876 0.124 0.000
#> SRR1353097 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1384737 1 0.2859 0.870 0.880 0.000 0.112 0.008
#> SRR1096339 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1345329 1 0.3963 0.828 0.836 0.016 0.132 0.016
#> SRR1414771 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1309119 1 0.1305 0.930 0.960 0.000 0.036 0.004
#> SRR1470438 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1343221 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR807949 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1442332 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR815920 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1471524 1 0.1716 0.914 0.936 0.000 0.064 0.000
#> SRR1477221 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1445046 2 0.0000 0.873 0.000 1.000 0.000 0.000
#> SRR1331962 2 0.0000 0.873 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.4831 0.621 0.000 0.704 0.280 0.016
#> SRR1311599 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1323977 1 0.7474 0.328 0.540 0.140 0.304 0.016
#> SRR1445132 3 0.4193 0.880 0.000 0.268 0.732 0.000
#> SRR1337321 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1366390 2 0.2704 0.810 0.000 0.876 0.124 0.000
#> SRR1343012 1 0.2859 0.870 0.880 0.000 0.112 0.008
#> SRR1311958 2 0.0000 0.873 0.000 1.000 0.000 0.000
#> SRR1388234 1 0.7462 0.375 0.556 0.136 0.288 0.020
#> SRR1370384 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1321650 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1485117 2 0.0000 0.873 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR816609 1 0.6083 0.632 0.688 0.060 0.232 0.020
#> SRR1486239 2 0.3545 0.769 0.000 0.828 0.164 0.008
#> SRR1309638 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1356660 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1392883 3 0.4624 0.881 0.000 0.340 0.660 0.000
#> SRR808130 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR816677 1 0.2741 0.880 0.892 0.000 0.096 0.012
#> SRR1455722 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1352169 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1366707 1 0.0469 0.946 0.988 0.000 0.012 0.000
#> SRR1328143 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1473567 2 0.0000 0.873 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
#> SRR1442087 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1390119 3 0.0451 0.8343 0.000 0.004 0.988 0.008 0.00
#> SRR1436127 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1347278 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1332904 2 0.4170 0.7613 0.000 0.780 0.080 0.140 0.00
#> SRR1444179 1 0.1851 0.8614 0.912 0.000 0.000 0.088 0.00
#> SRR1082685 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1362287 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1339007 1 0.0290 0.9395 0.992 0.000 0.000 0.008 0.00
#> SRR1376557 2 0.1768 0.8318 0.000 0.924 0.072 0.004 0.00
#> SRR1468700 2 0.0162 0.8570 0.000 0.996 0.000 0.004 0.00
#> SRR1077455 1 0.0162 0.9400 0.996 0.000 0.000 0.004 0.00
#> SRR1413978 1 0.1121 0.9181 0.956 0.000 0.000 0.044 0.00
#> SRR1439896 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1317963 4 0.3398 0.0867 0.004 0.144 0.024 0.828 0.00
#> SRR1431865 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1394253 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1082664 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1077968 1 0.0290 0.9395 0.992 0.000 0.000 0.008 0.00
#> SRR1076393 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1477476 3 0.0290 0.8312 0.000 0.000 0.992 0.008 0.00
#> SRR1398057 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1485042 1 0.0510 0.9378 0.984 0.000 0.000 0.016 0.00
#> SRR1385453 4 0.4201 0.6690 0.408 0.000 0.000 0.592 0.00
#> SRR1348074 4 0.3990 0.7095 0.308 0.004 0.000 0.688 0.00
#> SRR813959 1 0.6930 -0.3484 0.460 0.084 0.068 0.388 0.00
#> SRR665442 5 0.0000 0.0000 0.000 0.000 0.000 0.000 1.00
#> SRR1378068 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1485237 4 0.3578 0.6020 0.204 0.008 0.004 0.784 0.00
#> SRR1350792 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1326797 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR808994 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1474041 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1405641 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1362245 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1500194 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1414876 2 0.4404 0.5987 0.000 0.684 0.292 0.024 0.00
#> SRR1478523 4 0.4201 0.6690 0.408 0.000 0.000 0.592 0.00
#> SRR1325161 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1318026 4 0.4101 0.7121 0.372 0.000 0.000 0.628 0.00
#> SRR1343778 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1441287 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1430991 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1499722 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1351368 1 0.0703 0.9223 0.976 0.000 0.000 0.024 0.00
#> SRR1441785 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1096101 1 0.0510 0.9375 0.984 0.000 0.000 0.016 0.00
#> SRR808375 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1452842 1 0.0162 0.9400 0.996 0.000 0.000 0.004 0.00
#> SRR1311709 1 0.4150 0.0149 0.612 0.000 0.000 0.388 0.00
#> SRR1433352 1 0.0162 0.9400 0.996 0.000 0.000 0.004 0.00
#> SRR1340241 3 0.5296 0.4350 0.000 0.084 0.636 0.280 0.00
#> SRR1456754 1 0.0162 0.9400 0.996 0.000 0.000 0.004 0.00
#> SRR1465172 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1499284 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1499607 4 0.3060 0.0500 0.000 0.128 0.024 0.848 0.00
#> SRR812342 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1405374 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1403565 1 0.0404 0.9387 0.988 0.000 0.000 0.012 0.00
#> SRR1332024 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1471633 1 0.3508 0.5358 0.748 0.000 0.000 0.252 0.00
#> SRR1325944 3 0.2179 0.8661 0.000 0.112 0.888 0.000 0.00
#> SRR1429450 3 0.2179 0.8645 0.000 0.112 0.888 0.000 0.00
#> SRR821573 1 0.2329 0.7867 0.876 0.000 0.000 0.124 0.00
#> SRR1435372 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1324184 2 0.3473 0.7929 0.000 0.840 0.008 0.112 0.04
#> SRR816517 4 0.3861 0.6171 0.264 0.000 0.008 0.728 0.00
#> SRR1324141 4 0.4101 0.7121 0.372 0.000 0.000 0.628 0.00
#> SRR1101612 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1356531 1 0.0510 0.9378 0.984 0.000 0.000 0.016 0.00
#> SRR1089785 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1077708 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1343720 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1477499 3 0.2230 0.8636 0.000 0.116 0.884 0.000 0.00
#> SRR1347236 1 0.0162 0.9400 0.996 0.000 0.000 0.004 0.00
#> SRR1326408 1 0.0290 0.9395 0.992 0.000 0.000 0.008 0.00
#> SRR1336529 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1440643 1 0.2852 0.7045 0.828 0.000 0.000 0.172 0.00
#> SRR662354 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1310817 1 0.2329 0.7867 0.876 0.000 0.000 0.124 0.00
#> SRR1347389 2 0.3318 0.7881 0.000 0.800 0.008 0.192 0.00
#> SRR1353097 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1384737 4 0.4101 0.7121 0.372 0.000 0.000 0.628 0.00
#> SRR1096339 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1345329 4 0.3990 0.7095 0.308 0.004 0.000 0.688 0.00
#> SRR1414771 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1309119 1 0.3480 0.5459 0.752 0.000 0.000 0.248 0.00
#> SRR1470438 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1343221 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1410847 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR807949 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1442332 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR815920 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1471524 1 0.2813 0.7052 0.832 0.000 0.000 0.168 0.00
#> SRR1477221 1 0.0404 0.9387 0.988 0.000 0.000 0.012 0.00
#> SRR1445046 2 0.0162 0.8570 0.000 0.996 0.000 0.004 0.00
#> SRR1331962 2 0.0162 0.8570 0.000 0.996 0.000 0.004 0.00
#> SRR1319946 2 0.5163 0.6025 0.000 0.636 0.068 0.296 0.00
#> SRR1311599 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1323977 1 0.6839 -0.3348 0.468 0.080 0.064 0.388 0.00
#> SRR1445132 3 0.0955 0.8461 0.000 0.028 0.968 0.004 0.00
#> SRR1337321 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1366390 2 0.3318 0.7881 0.000 0.800 0.008 0.192 0.00
#> SRR1343012 4 0.4101 0.7121 0.372 0.000 0.000 0.628 0.00
#> SRR1311958 2 0.0162 0.8570 0.000 0.996 0.000 0.004 0.00
#> SRR1388234 4 0.3009 0.2216 0.028 0.080 0.016 0.876 0.00
#> SRR1370384 1 0.0510 0.9375 0.984 0.000 0.000 0.016 0.00
#> SRR1321650 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1485117 2 0.0000 0.8566 0.000 1.000 0.000 0.000 0.00
#> SRR1384713 1 0.0162 0.9400 0.996 0.000 0.000 0.004 0.00
#> SRR816609 4 0.3081 0.5357 0.156 0.012 0.000 0.832 0.00
#> SRR1486239 2 0.4170 0.7613 0.000 0.780 0.080 0.140 0.00
#> SRR1309638 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1356660 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1392883 3 0.2179 0.8661 0.000 0.112 0.888 0.000 0.00
#> SRR808130 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR816677 1 0.4227 -0.1337 0.580 0.000 0.000 0.420 0.00
#> SRR1455722 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1336029 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR808452 1 0.0703 0.9348 0.976 0.000 0.000 0.024 0.00
#> SRR1352169 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1366707 1 0.0703 0.9223 0.976 0.000 0.000 0.024 0.00
#> SRR1328143 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.00
#> SRR1473567 2 0.0000 0.8566 0.000 1.000 0.000 0.000 0.00
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR1390119 6 0.0146 0.8414 0.000 0.004 0.000 0.000 0.00 0.996
#> SRR1436127 1 0.1082 0.9031 0.956 0.000 0.040 0.004 0.00 0.000
#> SRR1347278 1 0.0790 0.9081 0.968 0.000 0.032 0.000 0.00 0.000
#> SRR1332904 2 0.4621 0.7073 0.000 0.728 0.052 0.176 0.00 0.044
#> SRR1444179 1 0.1910 0.8512 0.892 0.000 0.000 0.108 0.00 0.000
#> SRR1082685 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1362287 1 0.0865 0.9100 0.964 0.000 0.000 0.036 0.00 0.000
#> SRR1339007 1 0.0547 0.9132 0.980 0.000 0.000 0.020 0.00 0.000
#> SRR1376557 2 0.1444 0.8074 0.000 0.928 0.000 0.000 0.00 0.072
#> SRR1468700 2 0.0000 0.8402 0.000 1.000 0.000 0.000 0.00 0.000
#> SRR1077455 1 0.0547 0.9141 0.980 0.000 0.000 0.020 0.00 0.000
#> SRR1413978 1 0.1471 0.8939 0.932 0.000 0.004 0.064 0.00 0.000
#> SRR1439896 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1317963 4 0.3239 0.2263 0.000 0.100 0.044 0.840 0.00 0.016
#> SRR1431865 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1394253 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1082664 1 0.0458 0.9125 0.984 0.000 0.016 0.000 0.00 0.000
#> SRR1077968 1 0.0632 0.9137 0.976 0.000 0.000 0.024 0.00 0.000
#> SRR1076393 1 0.0603 0.9115 0.980 0.000 0.016 0.004 0.00 0.000
#> SRR1477476 6 0.0000 0.8383 0.000 0.000 0.000 0.000 0.00 1.000
#> SRR1398057 1 0.0937 0.9053 0.960 0.000 0.040 0.000 0.00 0.000
#> SRR1485042 1 0.0713 0.9120 0.972 0.000 0.000 0.028 0.00 0.000
#> SRR1385453 3 0.5945 -0.2192 0.220 0.000 0.420 0.360 0.00 0.000
#> SRR1348074 4 0.3394 0.6256 0.236 0.000 0.012 0.752 0.00 0.000
#> SRR813959 1 0.6883 -0.2467 0.448 0.040 0.132 0.348 0.00 0.032
#> SRR665442 5 0.0000 0.0000 0.000 0.000 0.000 0.000 1.00 0.000
#> SRR1378068 1 0.1152 0.9011 0.952 0.000 0.044 0.004 0.00 0.000
#> SRR1485237 4 0.3625 0.5565 0.184 0.004 0.028 0.780 0.00 0.004
#> SRR1350792 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1326797 1 0.0547 0.9115 0.980 0.000 0.020 0.000 0.00 0.000
#> SRR808994 1 0.1644 0.8791 0.920 0.000 0.076 0.004 0.00 0.000
#> SRR1474041 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR1405641 1 0.1757 0.8763 0.916 0.000 0.076 0.008 0.00 0.000
#> SRR1362245 1 0.0363 0.9127 0.988 0.000 0.012 0.000 0.00 0.000
#> SRR1500194 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1414876 2 0.4531 0.6128 0.000 0.680 0.036 0.020 0.00 0.264
#> SRR1478523 3 0.5959 -0.2223 0.224 0.000 0.416 0.360 0.00 0.000
#> SRR1325161 1 0.0363 0.9127 0.988 0.000 0.012 0.000 0.00 0.000
#> SRR1318026 4 0.4705 0.6123 0.260 0.000 0.088 0.652 0.00 0.000
#> SRR1343778 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR1441287 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1430991 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR1499722 1 0.0547 0.9115 0.980 0.000 0.020 0.000 0.00 0.000
#> SRR1351368 1 0.2001 0.8721 0.912 0.000 0.048 0.040 0.00 0.000
#> SRR1441785 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1096101 1 0.0790 0.9111 0.968 0.000 0.000 0.032 0.00 0.000
#> SRR808375 1 0.0363 0.9127 0.988 0.000 0.012 0.000 0.00 0.000
#> SRR1452842 1 0.0458 0.9135 0.984 0.000 0.000 0.016 0.00 0.000
#> SRR1311709 1 0.3797 0.1233 0.580 0.000 0.000 0.420 0.00 0.000
#> SRR1433352 1 0.1049 0.9082 0.960 0.000 0.032 0.008 0.00 0.000
#> SRR1340241 6 0.5796 0.4320 0.000 0.064 0.148 0.156 0.00 0.632
#> SRR1456754 1 0.0603 0.9140 0.980 0.000 0.004 0.016 0.00 0.000
#> SRR1465172 1 0.0405 0.9138 0.988 0.000 0.008 0.004 0.00 0.000
#> SRR1499284 1 0.0260 0.9133 0.992 0.000 0.008 0.000 0.00 0.000
#> SRR1499607 4 0.4839 0.1440 0.000 0.112 0.164 0.704 0.00 0.020
#> SRR812342 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1405374 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1403565 1 0.0508 0.9147 0.984 0.000 0.004 0.012 0.00 0.000
#> SRR1332024 1 0.1644 0.8791 0.920 0.000 0.076 0.004 0.00 0.000
#> SRR1471633 1 0.3710 0.5050 0.696 0.000 0.012 0.292 0.00 0.000
#> SRR1325944 6 0.2053 0.8704 0.000 0.108 0.004 0.000 0.00 0.888
#> SRR1429450 6 0.2053 0.8690 0.000 0.108 0.004 0.000 0.00 0.888
#> SRR821573 1 0.3698 0.6932 0.788 0.000 0.116 0.096 0.00 0.000
#> SRR1435372 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1324184 3 0.4649 -0.2165 0.000 0.468 0.492 0.000 0.04 0.000
#> SRR816517 3 0.5339 -0.0892 0.080 0.000 0.464 0.448 0.00 0.008
#> SRR1324141 4 0.4705 0.6123 0.260 0.000 0.088 0.652 0.00 0.000
#> SRR1101612 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1356531 1 0.0713 0.9120 0.972 0.000 0.000 0.028 0.00 0.000
#> SRR1089785 1 0.0603 0.9116 0.980 0.000 0.016 0.004 0.00 0.000
#> SRR1077708 1 0.0622 0.9133 0.980 0.000 0.012 0.008 0.00 0.000
#> SRR1343720 1 0.0458 0.9125 0.984 0.000 0.016 0.000 0.00 0.000
#> SRR1477499 6 0.2100 0.8681 0.000 0.112 0.004 0.000 0.00 0.884
#> SRR1347236 1 0.0692 0.9128 0.976 0.000 0.020 0.004 0.00 0.000
#> SRR1326408 1 0.0632 0.9135 0.976 0.000 0.000 0.024 0.00 0.000
#> SRR1336529 1 0.1644 0.8791 0.920 0.000 0.076 0.004 0.00 0.000
#> SRR1440643 1 0.4500 0.5385 0.708 0.000 0.148 0.144 0.00 0.000
#> SRR662354 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1310817 1 0.3698 0.6932 0.788 0.000 0.116 0.096 0.00 0.000
#> SRR1347389 3 0.4361 -0.1419 0.000 0.424 0.552 0.024 0.00 0.000
#> SRR1353097 1 0.1007 0.9069 0.956 0.000 0.000 0.044 0.00 0.000
#> SRR1384737 4 0.4705 0.6123 0.260 0.000 0.088 0.652 0.00 0.000
#> SRR1096339 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1345329 4 0.3394 0.6256 0.236 0.000 0.012 0.752 0.00 0.000
#> SRR1414771 1 0.1757 0.8763 0.916 0.000 0.076 0.008 0.00 0.000
#> SRR1309119 1 0.3690 0.5146 0.700 0.000 0.012 0.288 0.00 0.000
#> SRR1470438 1 0.1644 0.8791 0.920 0.000 0.076 0.004 0.00 0.000
#> SRR1343221 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1410847 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR807949 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR1442332 1 0.0790 0.9081 0.968 0.000 0.032 0.000 0.00 0.000
#> SRR815920 1 0.1152 0.9011 0.952 0.000 0.044 0.004 0.00 0.000
#> SRR1471524 1 0.4374 0.5691 0.712 0.000 0.192 0.096 0.00 0.000
#> SRR1477221 1 0.0508 0.9147 0.984 0.000 0.004 0.012 0.00 0.000
#> SRR1445046 2 0.0000 0.8402 0.000 1.000 0.000 0.000 0.00 0.000
#> SRR1331962 2 0.0000 0.8402 0.000 1.000 0.000 0.000 0.00 0.000
#> SRR1319946 2 0.5424 0.5322 0.000 0.592 0.072 0.304 0.00 0.032
#> SRR1311599 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1323977 1 0.6778 -0.2479 0.448 0.036 0.132 0.356 0.00 0.028
#> SRR1445132 6 0.0713 0.8532 0.000 0.028 0.000 0.000 0.00 0.972
#> SRR1337321 1 0.0363 0.9127 0.988 0.000 0.012 0.000 0.00 0.000
#> SRR1366390 3 0.4361 -0.1419 0.000 0.424 0.552 0.024 0.00 0.000
#> SRR1343012 4 0.4705 0.6123 0.260 0.000 0.088 0.652 0.00 0.000
#> SRR1311958 2 0.0000 0.8402 0.000 1.000 0.000 0.000 0.00 0.000
#> SRR1388234 4 0.2337 0.3083 0.008 0.036 0.036 0.908 0.00 0.012
#> SRR1370384 1 0.0790 0.9110 0.968 0.000 0.000 0.032 0.00 0.000
#> SRR1321650 1 0.0363 0.9127 0.988 0.000 0.012 0.000 0.00 0.000
#> SRR1485117 2 0.0146 0.8392 0.000 0.996 0.004 0.000 0.00 0.000
#> SRR1384713 1 0.0520 0.9142 0.984 0.000 0.008 0.008 0.00 0.000
#> SRR816609 4 0.2882 0.5157 0.120 0.004 0.028 0.848 0.00 0.000
#> SRR1486239 2 0.4621 0.7073 0.000 0.728 0.052 0.176 0.00 0.044
#> SRR1309638 1 0.0622 0.9133 0.980 0.000 0.012 0.008 0.00 0.000
#> SRR1356660 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1392883 6 0.2053 0.8704 0.000 0.108 0.004 0.000 0.00 0.888
#> SRR808130 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR816677 1 0.3854 -0.0723 0.536 0.000 0.000 0.464 0.00 0.000
#> SRR1455722 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1336029 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR808452 1 0.0937 0.9087 0.960 0.000 0.000 0.040 0.00 0.000
#> SRR1352169 1 0.0790 0.9081 0.968 0.000 0.032 0.000 0.00 0.000
#> SRR1366707 1 0.2001 0.8721 0.912 0.000 0.048 0.040 0.00 0.000
#> SRR1328143 1 0.0935 0.9065 0.964 0.000 0.032 0.004 0.00 0.000
#> SRR1473567 2 0.0146 0.8392 0.000 0.996 0.004 0.000 0.00 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17851 rows and 124 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 1.000 0.975 0.990 0.3444 0.666 0.666
#> 3 3 0.628 0.851 0.872 0.7742 0.686 0.528
#> 4 4 0.689 0.806 0.853 0.1622 0.907 0.743
#> 5 5 0.735 0.684 0.811 0.0866 0.911 0.695
#> 6 6 0.732 0.569 0.738 0.0429 0.963 0.841
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
#> SRR1442087 1 0.0000 0.988 1.000 0.000
#> SRR1390119 2 0.0000 0.997 0.000 1.000
#> SRR1436127 1 0.0000 0.988 1.000 0.000
#> SRR1347278 1 0.0000 0.988 1.000 0.000
#> SRR1332904 2 0.0000 0.997 0.000 1.000
#> SRR1444179 1 0.0000 0.988 1.000 0.000
#> SRR1082685 1 0.0000 0.988 1.000 0.000
#> SRR1362287 1 0.0000 0.988 1.000 0.000
#> SRR1339007 1 0.0000 0.988 1.000 0.000
#> SRR1376557 2 0.0000 0.997 0.000 1.000
#> SRR1468700 2 0.0000 0.997 0.000 1.000
#> SRR1077455 1 0.0000 0.988 1.000 0.000
#> SRR1413978 1 0.0000 0.988 1.000 0.000
#> SRR1439896 1 0.0000 0.988 1.000 0.000
#> SRR1317963 2 0.0000 0.997 0.000 1.000
#> SRR1431865 1 0.0000 0.988 1.000 0.000
#> SRR1394253 1 0.0000 0.988 1.000 0.000
#> SRR1082664 1 0.0000 0.988 1.000 0.000
#> SRR1077968 1 0.0000 0.988 1.000 0.000
#> SRR1076393 1 0.0000 0.988 1.000 0.000
#> SRR1477476 2 0.0000 0.997 0.000 1.000
#> SRR1398057 1 0.0000 0.988 1.000 0.000
#> SRR1485042 1 0.0000 0.988 1.000 0.000
#> SRR1385453 1 0.0000 0.988 1.000 0.000
#> SRR1348074 1 0.1843 0.961 0.972 0.028
#> SRR813959 1 0.0938 0.977 0.988 0.012
#> SRR665442 1 0.9833 0.276 0.576 0.424
#> SRR1378068 1 0.0000 0.988 1.000 0.000
#> SRR1485237 1 0.0000 0.988 1.000 0.000
#> SRR1350792 1 0.0000 0.988 1.000 0.000
#> SRR1326797 1 0.0000 0.988 1.000 0.000
#> SRR808994 1 0.0000 0.988 1.000 0.000
#> SRR1474041 1 0.0000 0.988 1.000 0.000
#> SRR1405641 1 0.0000 0.988 1.000 0.000
#> SRR1362245 1 0.0000 0.988 1.000 0.000
#> SRR1500194 1 0.0000 0.988 1.000 0.000
#> SRR1414876 2 0.0000 0.997 0.000 1.000
#> SRR1478523 1 0.0000 0.988 1.000 0.000
#> SRR1325161 1 0.0000 0.988 1.000 0.000
#> SRR1318026 1 0.0000 0.988 1.000 0.000
#> SRR1343778 1 0.0000 0.988 1.000 0.000
#> SRR1441287 1 0.0000 0.988 1.000 0.000
#> SRR1430991 1 0.0000 0.988 1.000 0.000
#> SRR1499722 1 0.0000 0.988 1.000 0.000
#> SRR1351368 1 0.0000 0.988 1.000 0.000
#> SRR1441785 1 0.0000 0.988 1.000 0.000
#> SRR1096101 1 0.0000 0.988 1.000 0.000
#> SRR808375 1 0.0000 0.988 1.000 0.000
#> SRR1452842 1 0.0000 0.988 1.000 0.000
#> SRR1311709 1 0.0000 0.988 1.000 0.000
#> SRR1433352 1 0.0000 0.988 1.000 0.000
#> SRR1340241 2 0.0000 0.997 0.000 1.000
#> SRR1456754 1 0.0000 0.988 1.000 0.000
#> SRR1465172 1 0.0000 0.988 1.000 0.000
#> SRR1499284 1 0.0000 0.988 1.000 0.000
#> SRR1499607 2 0.0000 0.997 0.000 1.000
#> SRR812342 1 0.0000 0.988 1.000 0.000
#> SRR1405374 1 0.0000 0.988 1.000 0.000
#> SRR1403565 1 0.0000 0.988 1.000 0.000
#> SRR1332024 1 0.0000 0.988 1.000 0.000
#> SRR1471633 1 0.0000 0.988 1.000 0.000
#> SRR1325944 2 0.0000 0.997 0.000 1.000
#> SRR1429450 2 0.0000 0.997 0.000 1.000
#> SRR821573 1 0.0000 0.988 1.000 0.000
#> SRR1435372 1 0.0000 0.988 1.000 0.000
#> SRR1324184 2 0.0000 0.997 0.000 1.000
#> SRR816517 2 0.3584 0.925 0.068 0.932
#> SRR1324141 1 0.0000 0.988 1.000 0.000
#> SRR1101612 1 0.0000 0.988 1.000 0.000
#> SRR1356531 1 0.0000 0.988 1.000 0.000
#> SRR1089785 1 0.0000 0.988 1.000 0.000
#> SRR1077708 1 0.0000 0.988 1.000 0.000
#> SRR1343720 1 0.0000 0.988 1.000 0.000
#> SRR1477499 2 0.0000 0.997 0.000 1.000
#> SRR1347236 1 0.0000 0.988 1.000 0.000
#> SRR1326408 1 0.0000 0.988 1.000 0.000
#> SRR1336529 1 0.0000 0.988 1.000 0.000
#> SRR1440643 1 0.0000 0.988 1.000 0.000
#> SRR662354 1 0.0000 0.988 1.000 0.000
#> SRR1310817 1 0.0000 0.988 1.000 0.000
#> SRR1347389 2 0.0000 0.997 0.000 1.000
#> SRR1353097 1 0.0000 0.988 1.000 0.000
#> SRR1384737 1 0.0000 0.988 1.000 0.000
#> SRR1096339 1 0.0000 0.988 1.000 0.000
#> SRR1345329 1 0.0000 0.988 1.000 0.000
#> SRR1414771 1 0.0000 0.988 1.000 0.000
#> SRR1309119 1 0.0000 0.988 1.000 0.000
#> SRR1470438 1 0.0000 0.988 1.000 0.000
#> SRR1343221 1 0.0000 0.988 1.000 0.000
#> SRR1410847 1 0.0000 0.988 1.000 0.000
#> SRR807949 1 0.0000 0.988 1.000 0.000
#> SRR1442332 1 0.0000 0.988 1.000 0.000
#> SRR815920 1 0.0000 0.988 1.000 0.000
#> SRR1471524 1 0.0000 0.988 1.000 0.000
#> SRR1477221 1 0.0000 0.988 1.000 0.000
#> SRR1445046 2 0.0000 0.997 0.000 1.000
#> SRR1331962 2 0.0000 0.997 0.000 1.000
#> SRR1319946 2 0.0000 0.997 0.000 1.000
#> SRR1311599 1 0.0000 0.988 1.000 0.000
#> SRR1323977 1 0.9710 0.343 0.600 0.400
#> SRR1445132 2 0.0000 0.997 0.000 1.000
#> SRR1337321 1 0.0000 0.988 1.000 0.000
#> SRR1366390 2 0.0000 0.997 0.000 1.000
#> SRR1343012 1 0.0000 0.988 1.000 0.000
#> SRR1311958 2 0.0000 0.997 0.000 1.000
#> SRR1388234 2 0.0000 0.997 0.000 1.000
#> SRR1370384 1 0.0000 0.988 1.000 0.000
#> SRR1321650 1 0.0000 0.988 1.000 0.000
#> SRR1485117 2 0.0000 0.997 0.000 1.000
#> SRR1384713 1 0.0000 0.988 1.000 0.000
#> SRR816609 1 0.8386 0.636 0.732 0.268
#> SRR1486239 2 0.0000 0.997 0.000 1.000
#> SRR1309638 1 0.0000 0.988 1.000 0.000
#> SRR1356660 1 0.0000 0.988 1.000 0.000
#> SRR1392883 2 0.0000 0.997 0.000 1.000
#> SRR808130 1 0.0000 0.988 1.000 0.000
#> SRR816677 1 0.0000 0.988 1.000 0.000
#> SRR1455722 1 0.0000 0.988 1.000 0.000
#> SRR1336029 1 0.0000 0.988 1.000 0.000
#> SRR808452 1 0.0000 0.988 1.000 0.000
#> SRR1352169 1 0.0000 0.988 1.000 0.000
#> SRR1366707 1 0.0000 0.988 1.000 0.000
#> SRR1328143 1 0.0000 0.988 1.000 0.000
#> SRR1473567 2 0.0000 0.997 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1347278 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1332904 2 0.0237 0.944 0.004 0.996 0.000
#> SRR1444179 1 0.4504 0.810 0.804 0.000 0.196
#> SRR1082685 1 0.4346 0.803 0.816 0.000 0.184
#> SRR1362287 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1339007 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1376557 2 0.0237 0.944 0.004 0.996 0.000
#> SRR1468700 2 0.0747 0.942 0.016 0.984 0.000
#> SRR1077455 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1413978 1 0.5465 0.858 0.712 0.000 0.288
#> SRR1439896 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1317963 2 0.4931 0.817 0.232 0.768 0.000
#> SRR1431865 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1394253 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1082664 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1077968 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1076393 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1485042 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1385453 3 0.5529 0.601 0.296 0.000 0.704
#> SRR1348074 1 0.0424 0.679 0.992 0.000 0.008
#> SRR813959 3 0.5431 0.620 0.284 0.000 0.716
#> SRR665442 1 0.5497 0.127 0.708 0.292 0.000
#> SRR1378068 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1485237 1 0.0424 0.679 0.992 0.000 0.008
#> SRR1350792 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1326797 3 0.5138 0.454 0.252 0.000 0.748
#> SRR808994 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1474041 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1500194 1 0.5591 0.865 0.696 0.000 0.304
#> SRR1414876 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1478523 3 0.4887 0.695 0.228 0.000 0.772
#> SRR1325161 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1318026 1 0.0892 0.693 0.980 0.000 0.020
#> SRR1343778 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1441287 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1430991 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1499722 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1351368 3 0.3412 0.817 0.124 0.000 0.876
#> SRR1441785 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1096101 1 0.5621 0.866 0.692 0.000 0.308
#> SRR808375 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1452842 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1311709 1 0.3116 0.760 0.892 0.000 0.108
#> SRR1433352 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1456754 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1465172 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1499284 3 0.3482 0.757 0.128 0.000 0.872
#> SRR1499607 2 0.5465 0.774 0.288 0.712 0.000
#> SRR812342 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1405374 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1403565 1 0.6008 0.776 0.628 0.000 0.372
#> SRR1332024 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1471633 1 0.1163 0.702 0.972 0.000 0.028
#> SRR1325944 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.944 0.000 1.000 0.000
#> SRR821573 3 0.3941 0.781 0.156 0.000 0.844
#> SRR1435372 1 0.5591 0.865 0.696 0.000 0.304
#> SRR1324184 2 0.1031 0.941 0.024 0.976 0.000
#> SRR816517 2 0.9972 0.243 0.300 0.364 0.336
#> SRR1324141 1 0.1031 0.698 0.976 0.000 0.024
#> SRR1101612 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1356531 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1089785 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1347236 3 0.3482 0.757 0.128 0.000 0.872
#> SRR1326408 1 0.5560 0.863 0.700 0.000 0.300
#> SRR1336529 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1440643 3 0.5178 0.661 0.256 0.000 0.744
#> SRR662354 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1310817 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1347389 2 0.3482 0.890 0.128 0.872 0.000
#> SRR1353097 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1384737 1 0.1031 0.698 0.976 0.000 0.024
#> SRR1096339 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1345329 1 0.0424 0.679 0.992 0.000 0.008
#> SRR1414771 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1309119 1 0.1163 0.702 0.972 0.000 0.028
#> SRR1470438 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1343221 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1410847 1 0.5621 0.866 0.692 0.000 0.308
#> SRR807949 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.941 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1471524 3 0.2165 0.880 0.064 0.000 0.936
#> SRR1477221 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1445046 2 0.1529 0.935 0.040 0.960 0.000
#> SRR1331962 2 0.0747 0.942 0.016 0.984 0.000
#> SRR1319946 2 0.1529 0.935 0.040 0.960 0.000
#> SRR1311599 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1323977 1 0.6082 0.146 0.692 0.296 0.012
#> SRR1445132 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1337321 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1366390 2 0.2959 0.906 0.100 0.900 0.000
#> SRR1343012 1 0.1163 0.697 0.972 0.000 0.028
#> SRR1311958 2 0.0747 0.942 0.016 0.984 0.000
#> SRR1388234 2 0.5431 0.776 0.284 0.716 0.000
#> SRR1370384 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1321650 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.944 0.000 1.000 0.000
#> SRR1384713 1 0.5621 0.866 0.692 0.000 0.308
#> SRR816609 1 0.0424 0.679 0.992 0.000 0.008
#> SRR1486239 2 0.0747 0.942 0.016 0.984 0.000
#> SRR1309638 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1356660 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1392883 2 0.0000 0.944 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.941 0.000 0.000 1.000
#> SRR816677 1 0.2796 0.751 0.908 0.000 0.092
#> SRR1455722 1 0.5621 0.866 0.692 0.000 0.308
#> SRR1336029 1 0.5621 0.866 0.692 0.000 0.308
#> SRR808452 1 0.5560 0.863 0.700 0.000 0.300
#> SRR1352169 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1366707 3 0.0747 0.926 0.016 0.000 0.984
#> SRR1328143 3 0.0000 0.941 0.000 0.000 1.000
#> SRR1473567 2 0.0237 0.944 0.004 0.996 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.2773 0.8710 0.004 0.000 0.880 0.116
#> SRR1390119 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.3725 0.8542 0.008 0.000 0.812 0.180
#> SRR1347278 3 0.2048 0.8759 0.008 0.000 0.928 0.064
#> SRR1332904 2 0.1004 0.9206 0.004 0.972 0.000 0.024
#> SRR1444179 1 0.1022 0.9164 0.968 0.000 0.032 0.000
#> SRR1082685 1 0.1209 0.9158 0.964 0.000 0.032 0.004
#> SRR1362287 1 0.1938 0.9335 0.936 0.000 0.052 0.012
#> SRR1339007 1 0.3745 0.8823 0.852 0.000 0.060 0.088
#> SRR1376557 2 0.1635 0.9167 0.008 0.948 0.000 0.044
#> SRR1468700 2 0.3217 0.8783 0.012 0.860 0.000 0.128
#> SRR1077455 1 0.5280 0.7675 0.748 0.000 0.156 0.096
#> SRR1413978 1 0.1975 0.9338 0.936 0.000 0.048 0.016
#> SRR1439896 1 0.1661 0.9354 0.944 0.000 0.052 0.004
#> SRR1317963 4 0.5110 0.2927 0.012 0.352 0.000 0.636
#> SRR1431865 1 0.1938 0.9335 0.936 0.000 0.052 0.012
#> SRR1394253 1 0.1938 0.9335 0.936 0.000 0.052 0.012
#> SRR1082664 3 0.2197 0.8416 0.004 0.000 0.916 0.080
#> SRR1077968 1 0.3667 0.8887 0.856 0.000 0.056 0.088
#> SRR1076393 3 0.2593 0.8685 0.004 0.000 0.892 0.104
#> SRR1477476 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.3351 0.8625 0.008 0.000 0.844 0.148
#> SRR1485042 1 0.1661 0.9358 0.944 0.000 0.052 0.004
#> SRR1385453 4 0.5442 0.3723 0.028 0.000 0.336 0.636
#> SRR1348074 4 0.4746 0.6101 0.368 0.000 0.000 0.632
#> SRR813959 4 0.5673 0.2915 0.024 0.000 0.448 0.528
#> SRR665442 4 0.6134 0.6079 0.236 0.104 0.000 0.660
#> SRR1378068 3 0.3933 0.8458 0.008 0.000 0.792 0.200
#> SRR1485237 4 0.4964 0.5908 0.380 0.000 0.004 0.616
#> SRR1350792 1 0.1661 0.9354 0.944 0.000 0.052 0.004
#> SRR1326797 3 0.5018 0.6741 0.144 0.000 0.768 0.088
#> SRR808994 3 0.3972 0.8445 0.008 0.000 0.788 0.204
#> SRR1474041 3 0.0188 0.8694 0.004 0.000 0.996 0.000
#> SRR1405641 3 0.3791 0.8443 0.004 0.000 0.796 0.200
#> SRR1362245 3 0.3545 0.8626 0.008 0.000 0.828 0.164
#> SRR1500194 1 0.1807 0.9345 0.940 0.000 0.052 0.008
#> SRR1414876 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.4387 0.7867 0.024 0.000 0.776 0.200
#> SRR1325161 3 0.2197 0.8416 0.004 0.000 0.916 0.080
#> SRR1318026 4 0.5581 0.4374 0.448 0.000 0.020 0.532
#> SRR1343778 3 0.3539 0.8572 0.004 0.000 0.820 0.176
#> SRR1441287 1 0.1557 0.9351 0.944 0.000 0.056 0.000
#> SRR1430991 3 0.0376 0.8702 0.004 0.000 0.992 0.004
#> SRR1499722 3 0.2266 0.8393 0.004 0.000 0.912 0.084
#> SRR1351368 3 0.3547 0.8411 0.016 0.000 0.840 0.144
#> SRR1441785 1 0.1938 0.9335 0.936 0.000 0.052 0.012
#> SRR1096101 1 0.3168 0.9068 0.884 0.000 0.056 0.060
#> SRR808375 3 0.2197 0.8416 0.004 0.000 0.916 0.080
#> SRR1452842 1 0.5031 0.7916 0.768 0.000 0.140 0.092
#> SRR1311709 1 0.1406 0.9052 0.960 0.000 0.024 0.016
#> SRR1433352 3 0.1743 0.8753 0.004 0.000 0.940 0.056
#> SRR1340241 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.4817 0.8091 0.784 0.000 0.128 0.088
#> SRR1465172 3 0.2334 0.8371 0.004 0.000 0.908 0.088
#> SRR1499284 3 0.4920 0.6869 0.136 0.000 0.776 0.088
#> SRR1499607 4 0.5403 0.3382 0.024 0.348 0.000 0.628
#> SRR812342 1 0.1474 0.9355 0.948 0.000 0.052 0.000
#> SRR1405374 1 0.1807 0.9345 0.940 0.000 0.052 0.008
#> SRR1403565 1 0.5248 0.7585 0.748 0.000 0.164 0.088
#> SRR1332024 3 0.4049 0.8419 0.008 0.000 0.780 0.212
#> SRR1471633 1 0.1510 0.8903 0.956 0.000 0.016 0.028
#> SRR1325944 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR821573 3 0.3862 0.7653 0.024 0.000 0.824 0.152
#> SRR1435372 1 0.2021 0.9331 0.932 0.000 0.056 0.012
#> SRR1324184 2 0.4050 0.8516 0.024 0.808 0.000 0.168
#> SRR816517 4 0.5994 0.5142 0.028 0.068 0.184 0.720
#> SRR1324141 4 0.6680 0.5917 0.260 0.000 0.136 0.604
#> SRR1101612 1 0.1474 0.9355 0.948 0.000 0.052 0.000
#> SRR1356531 1 0.1661 0.9358 0.944 0.000 0.052 0.004
#> SRR1089785 3 0.0188 0.8694 0.004 0.000 0.996 0.000
#> SRR1077708 3 0.3725 0.8444 0.008 0.000 0.812 0.180
#> SRR1343720 3 0.2197 0.8416 0.004 0.000 0.916 0.080
#> SRR1477499 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1347236 3 0.4419 0.7386 0.104 0.000 0.812 0.084
#> SRR1326408 1 0.3533 0.8927 0.864 0.000 0.056 0.080
#> SRR1336529 3 0.3933 0.8458 0.008 0.000 0.792 0.200
#> SRR1440643 3 0.5602 0.3009 0.024 0.000 0.568 0.408
#> SRR662354 1 0.1661 0.9354 0.944 0.000 0.052 0.004
#> SRR1310817 3 0.1716 0.8494 0.000 0.000 0.936 0.064
#> SRR1347389 4 0.5004 0.1978 0.004 0.392 0.000 0.604
#> SRR1353097 1 0.1557 0.9351 0.944 0.000 0.056 0.000
#> SRR1384737 4 0.5517 0.4672 0.412 0.000 0.020 0.568
#> SRR1096339 1 0.1474 0.9355 0.948 0.000 0.052 0.000
#> SRR1345329 4 0.4746 0.6101 0.368 0.000 0.000 0.632
#> SRR1414771 3 0.3831 0.8430 0.004 0.000 0.792 0.204
#> SRR1309119 1 0.1297 0.8923 0.964 0.000 0.016 0.020
#> SRR1470438 3 0.3972 0.8445 0.008 0.000 0.788 0.204
#> SRR1343221 1 0.3833 0.8759 0.848 0.000 0.080 0.072
#> SRR1410847 1 0.1474 0.9355 0.948 0.000 0.052 0.000
#> SRR807949 3 0.0524 0.8704 0.004 0.000 0.988 0.008
#> SRR1442332 3 0.0895 0.8710 0.004 0.000 0.976 0.020
#> SRR815920 3 0.3933 0.8458 0.008 0.000 0.792 0.200
#> SRR1471524 3 0.2799 0.8530 0.008 0.000 0.884 0.108
#> SRR1477221 3 0.3450 0.8614 0.008 0.000 0.836 0.156
#> SRR1445046 2 0.4576 0.7164 0.012 0.728 0.000 0.260
#> SRR1331962 2 0.3217 0.8783 0.012 0.860 0.000 0.128
#> SRR1319946 2 0.4372 0.7098 0.004 0.728 0.000 0.268
#> SRR1311599 1 0.1938 0.9335 0.936 0.000 0.052 0.012
#> SRR1323977 4 0.7343 0.6177 0.156 0.104 0.088 0.652
#> SRR1445132 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.1970 0.8577 0.008 0.000 0.932 0.060
#> SRR1366390 4 0.5158 -0.0556 0.004 0.472 0.000 0.524
#> SRR1343012 4 0.6834 0.5912 0.224 0.000 0.176 0.600
#> SRR1311958 2 0.3377 0.8690 0.012 0.848 0.000 0.140
#> SRR1388234 4 0.5478 0.3411 0.028 0.344 0.000 0.628
#> SRR1370384 1 0.3948 0.8756 0.840 0.000 0.064 0.096
#> SRR1321650 3 0.3351 0.8651 0.008 0.000 0.844 0.148
#> SRR1485117 2 0.1151 0.9194 0.008 0.968 0.000 0.024
#> SRR1384713 1 0.5477 0.7345 0.728 0.000 0.180 0.092
#> SRR816609 4 0.4730 0.6116 0.364 0.000 0.000 0.636
#> SRR1486239 2 0.3217 0.8783 0.012 0.860 0.000 0.128
#> SRR1309638 3 0.4011 0.8424 0.008 0.000 0.784 0.208
#> SRR1356660 1 0.1938 0.9335 0.936 0.000 0.052 0.012
#> SRR1392883 2 0.0000 0.9220 0.000 1.000 0.000 0.000
#> SRR808130 3 0.0188 0.8694 0.004 0.000 0.996 0.000
#> SRR816677 1 0.3606 0.7517 0.844 0.000 0.024 0.132
#> SRR1455722 1 0.1557 0.9351 0.944 0.000 0.056 0.000
#> SRR1336029 1 0.1557 0.9351 0.944 0.000 0.056 0.000
#> SRR808452 1 0.1557 0.9351 0.944 0.000 0.056 0.000
#> SRR1352169 3 0.1970 0.8746 0.008 0.000 0.932 0.060
#> SRR1366707 3 0.2868 0.8599 0.000 0.000 0.864 0.136
#> SRR1328143 3 0.1661 0.8754 0.004 0.000 0.944 0.052
#> SRR1473567 2 0.1767 0.9158 0.012 0.944 0.000 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 3 0.4542 0.58483 0.008 0.000 0.536 0.000 0.456
#> SRR1390119 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.4108 0.79362 0.008 0.000 0.684 0.000 0.308
#> SRR1347278 5 0.4565 0.07114 0.008 0.000 0.352 0.008 0.632
#> SRR1332904 2 0.2046 0.84464 0.000 0.916 0.016 0.068 0.000
#> SRR1444179 1 0.0451 0.92535 0.988 0.000 0.008 0.004 0.000
#> SRR1082685 1 0.0609 0.92423 0.980 0.000 0.020 0.000 0.000
#> SRR1362287 1 0.1087 0.92394 0.968 0.000 0.016 0.008 0.008
#> SRR1339007 1 0.3795 0.78066 0.780 0.000 0.000 0.028 0.192
#> SRR1376557 2 0.3692 0.83024 0.000 0.812 0.136 0.052 0.000
#> SRR1468700 2 0.4968 0.78779 0.000 0.712 0.136 0.152 0.000
#> SRR1077455 5 0.5335 -0.00694 0.408 0.000 0.012 0.032 0.548
#> SRR1413978 1 0.1588 0.92015 0.948 0.000 0.016 0.028 0.008
#> SRR1439896 1 0.0404 0.92676 0.988 0.000 0.012 0.000 0.000
#> SRR1317963 4 0.4094 0.66139 0.000 0.084 0.128 0.788 0.000
#> SRR1431865 1 0.0960 0.92396 0.972 0.000 0.016 0.008 0.004
#> SRR1394253 1 0.1200 0.92331 0.964 0.000 0.016 0.012 0.008
#> SRR1082664 5 0.1369 0.58533 0.008 0.000 0.028 0.008 0.956
#> SRR1077968 1 0.4470 0.75032 0.744 0.000 0.012 0.036 0.208
#> SRR1076393 5 0.3855 0.40614 0.008 0.000 0.240 0.004 0.748
#> SRR1477476 2 0.0162 0.84660 0.000 0.996 0.004 0.000 0.000
#> SRR1398057 3 0.4688 0.49718 0.008 0.000 0.532 0.004 0.456
#> SRR1485042 1 0.0579 0.92820 0.984 0.000 0.000 0.008 0.008
#> SRR1385453 4 0.5331 0.49393 0.000 0.000 0.372 0.568 0.060
#> SRR1348074 4 0.1894 0.79520 0.072 0.000 0.008 0.920 0.000
#> SRR813959 4 0.5166 0.63028 0.000 0.000 0.108 0.680 0.212
#> SRR665442 4 0.2616 0.77832 0.008 0.016 0.068 0.900 0.008
#> SRR1378068 3 0.4025 0.80182 0.008 0.000 0.700 0.000 0.292
#> SRR1485237 4 0.3166 0.77845 0.112 0.000 0.020 0.856 0.012
#> SRR1350792 1 0.0404 0.92676 0.988 0.000 0.012 0.000 0.000
#> SRR1326797 5 0.1877 0.55365 0.064 0.000 0.000 0.012 0.924
#> SRR808994 3 0.4134 0.80010 0.008 0.000 0.704 0.004 0.284
#> SRR1474041 5 0.3611 0.45686 0.008 0.000 0.208 0.004 0.780
#> SRR1405641 3 0.4003 0.80147 0.008 0.000 0.704 0.000 0.288
#> SRR1362245 5 0.4771 -0.15537 0.008 0.000 0.432 0.008 0.552
#> SRR1500194 1 0.0404 0.92707 0.988 0.000 0.012 0.000 0.000
#> SRR1414876 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.5581 0.52371 0.000 0.000 0.636 0.140 0.224
#> SRR1325161 5 0.0740 0.58519 0.008 0.000 0.008 0.004 0.980
#> SRR1318026 4 0.4532 0.74365 0.152 0.000 0.052 0.772 0.024
#> SRR1343778 3 0.4252 0.75284 0.008 0.000 0.652 0.000 0.340
#> SRR1441287 1 0.0162 0.92843 0.996 0.000 0.000 0.000 0.004
#> SRR1430991 5 0.3643 0.45080 0.008 0.000 0.212 0.004 0.776
#> SRR1499722 5 0.0740 0.58611 0.008 0.000 0.008 0.004 0.980
#> SRR1351368 3 0.4309 0.68911 0.000 0.000 0.676 0.016 0.308
#> SRR1441785 1 0.0960 0.92396 0.972 0.000 0.016 0.008 0.004
#> SRR1096101 1 0.2570 0.87429 0.888 0.000 0.000 0.028 0.084
#> SRR808375 5 0.1243 0.58455 0.008 0.000 0.028 0.004 0.960
#> SRR1452842 5 0.5088 -0.08583 0.436 0.000 0.000 0.036 0.528
#> SRR1311709 1 0.0898 0.92161 0.972 0.000 0.020 0.008 0.000
#> SRR1433352 5 0.4403 0.08800 0.008 0.000 0.340 0.004 0.648
#> SRR1340241 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 1 0.4445 0.64176 0.676 0.000 0.000 0.024 0.300
#> SRR1465172 5 0.0798 0.57882 0.008 0.000 0.000 0.016 0.976
#> SRR1499284 5 0.2110 0.54298 0.072 0.000 0.000 0.016 0.912
#> SRR1499607 4 0.2017 0.75782 0.000 0.080 0.008 0.912 0.000
#> SRR812342 1 0.0162 0.92843 0.996 0.000 0.000 0.000 0.004
#> SRR1405374 1 0.0404 0.92707 0.988 0.000 0.012 0.000 0.000
#> SRR1403565 1 0.4410 0.60687 0.700 0.000 0.016 0.008 0.276
#> SRR1332024 3 0.4088 0.79405 0.008 0.000 0.712 0.004 0.276
#> SRR1471633 1 0.1216 0.91495 0.960 0.000 0.020 0.020 0.000
#> SRR1325944 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.4197 0.41107 0.000 0.000 0.076 0.148 0.776
#> SRR1435372 1 0.2124 0.89876 0.924 0.000 0.020 0.012 0.044
#> SRR1324184 2 0.5999 0.72752 0.000 0.616 0.188 0.188 0.008
#> SRR816517 4 0.3786 0.75042 0.000 0.004 0.204 0.776 0.016
#> SRR1324141 4 0.4522 0.76339 0.040 0.000 0.056 0.788 0.116
#> SRR1101612 1 0.0000 0.92802 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0451 0.92827 0.988 0.000 0.000 0.004 0.008
#> SRR1089785 5 0.3578 0.45994 0.008 0.000 0.204 0.004 0.784
#> SRR1077708 5 0.3634 0.45353 0.008 0.000 0.184 0.012 0.796
#> SRR1343720 5 0.0981 0.58553 0.008 0.000 0.012 0.008 0.972
#> SRR1477499 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.1996 0.57039 0.048 0.000 0.012 0.012 0.928
#> SRR1326408 1 0.3944 0.76736 0.768 0.000 0.000 0.032 0.200
#> SRR1336529 3 0.4025 0.80182 0.008 0.000 0.700 0.000 0.292
#> SRR1440643 3 0.6465 0.26646 0.000 0.000 0.484 0.308 0.208
#> SRR662354 1 0.0162 0.92858 0.996 0.000 0.000 0.004 0.000
#> SRR1310817 5 0.3035 0.54778 0.008 0.000 0.136 0.008 0.848
#> SRR1347389 4 0.4738 0.61194 0.000 0.140 0.112 0.744 0.004
#> SRR1353097 1 0.0162 0.92843 0.996 0.000 0.000 0.000 0.004
#> SRR1384737 4 0.4587 0.75731 0.104 0.000 0.056 0.788 0.052
#> SRR1096339 1 0.0000 0.92802 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.1894 0.79577 0.072 0.000 0.008 0.920 0.000
#> SRR1414771 3 0.4111 0.79850 0.008 0.000 0.708 0.004 0.280
#> SRR1309119 1 0.0579 0.92283 0.984 0.000 0.008 0.008 0.000
#> SRR1470438 3 0.4134 0.80010 0.008 0.000 0.704 0.004 0.284
#> SRR1343221 1 0.2806 0.83562 0.844 0.000 0.000 0.004 0.152
#> SRR1410847 1 0.0451 0.92814 0.988 0.000 0.000 0.004 0.008
#> SRR807949 5 0.3675 0.44463 0.008 0.000 0.216 0.004 0.772
#> SRR1442332 5 0.4178 0.26065 0.008 0.000 0.292 0.004 0.696
#> SRR815920 3 0.4025 0.80182 0.008 0.000 0.700 0.000 0.292
#> SRR1471524 3 0.4613 0.61341 0.000 0.000 0.620 0.020 0.360
#> SRR1477221 3 0.4792 0.47924 0.008 0.000 0.536 0.008 0.448
#> SRR1445046 2 0.6069 0.55355 0.000 0.524 0.136 0.340 0.000
#> SRR1331962 2 0.4968 0.78779 0.000 0.712 0.136 0.152 0.000
#> SRR1319946 2 0.5717 0.59689 0.000 0.572 0.104 0.324 0.000
#> SRR1311599 1 0.1087 0.92394 0.968 0.000 0.016 0.008 0.008
#> SRR1323977 4 0.3773 0.79534 0.024 0.016 0.056 0.852 0.052
#> SRR1445132 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 5 0.3293 0.52275 0.008 0.000 0.160 0.008 0.824
#> SRR1366390 4 0.5436 0.47908 0.000 0.216 0.116 0.664 0.004
#> SRR1343012 4 0.5032 0.69292 0.016 0.000 0.056 0.704 0.224
#> SRR1311958 2 0.5775 0.67552 0.000 0.600 0.136 0.264 0.000
#> SRR1388234 4 0.1956 0.76056 0.000 0.076 0.008 0.916 0.000
#> SRR1370384 1 0.4592 0.73188 0.728 0.000 0.012 0.036 0.224
#> SRR1321650 5 0.4664 -0.20398 0.008 0.000 0.436 0.004 0.552
#> SRR1485117 2 0.2864 0.83652 0.000 0.852 0.136 0.012 0.000
#> SRR1384713 5 0.4966 0.03126 0.404 0.000 0.000 0.032 0.564
#> SRR816609 4 0.1956 0.79482 0.076 0.000 0.008 0.916 0.000
#> SRR1486239 2 0.5043 0.78286 0.000 0.704 0.136 0.160 0.000
#> SRR1309638 5 0.4495 0.35874 0.008 0.000 0.236 0.032 0.724
#> SRR1356660 1 0.0960 0.92396 0.972 0.000 0.016 0.008 0.004
#> SRR1392883 2 0.0000 0.84835 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.3611 0.45686 0.008 0.000 0.208 0.004 0.780
#> SRR816677 1 0.3764 0.69060 0.772 0.000 0.008 0.212 0.008
#> SRR1455722 1 0.0162 0.92843 0.996 0.000 0.000 0.000 0.004
#> SRR1336029 1 0.0290 0.92851 0.992 0.000 0.000 0.000 0.008
#> SRR808452 1 0.0000 0.92802 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 5 0.4425 -0.14945 0.008 0.000 0.392 0.000 0.600
#> SRR1366707 3 0.4389 0.72565 0.004 0.000 0.624 0.004 0.368
#> SRR1328143 5 0.4220 0.23640 0.008 0.000 0.300 0.004 0.688
#> SRR1473567 2 0.3759 0.82919 0.000 0.808 0.136 0.056 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.3684 -0.0471 0.000 0.000 0.332 0.000 0.664 0.004
#> SRR1390119 2 0.3865 0.7797 0.000 0.720 0.032 0.000 0.000 0.248
#> SRR1436127 3 0.3890 0.6751 0.000 0.000 0.596 0.000 0.400 0.004
#> SRR1347278 5 0.3867 0.3836 0.000 0.000 0.200 0.000 0.748 0.052
#> SRR1332904 2 0.4286 0.7761 0.000 0.752 0.028 0.052 0.000 0.168
#> SRR1444179 1 0.0653 0.8275 0.980 0.000 0.004 0.004 0.000 0.012
#> SRR1082685 1 0.1442 0.8160 0.944 0.000 0.012 0.004 0.000 0.040
#> SRR1362287 1 0.2201 0.7940 0.900 0.000 0.052 0.000 0.000 0.048
#> SRR1339007 1 0.4504 -0.1819 0.536 0.000 0.000 0.000 0.032 0.432
#> SRR1376557 2 0.1074 0.7524 0.000 0.960 0.012 0.028 0.000 0.000
#> SRR1468700 2 0.2135 0.7058 0.000 0.872 0.000 0.128 0.000 0.000
#> SRR1077455 6 0.6249 0.9313 0.244 0.000 0.008 0.004 0.284 0.460
#> SRR1413978 1 0.2776 0.7751 0.860 0.000 0.052 0.000 0.000 0.088
#> SRR1439896 1 0.1194 0.8197 0.956 0.000 0.008 0.004 0.000 0.032
#> SRR1317963 4 0.3519 0.5938 0.000 0.232 0.008 0.752 0.000 0.008
#> SRR1431865 1 0.2070 0.7983 0.908 0.000 0.044 0.000 0.000 0.048
#> SRR1394253 1 0.2201 0.7940 0.900 0.000 0.052 0.000 0.000 0.048
#> SRR1082664 5 0.2996 0.4612 0.000 0.000 0.000 0.000 0.772 0.228
#> SRR1077968 1 0.4825 -0.2451 0.500 0.000 0.008 0.004 0.028 0.460
#> SRR1076393 5 0.3645 0.4863 0.000 0.000 0.152 0.000 0.784 0.064
#> SRR1477476 2 0.4151 0.7664 0.000 0.692 0.044 0.000 0.000 0.264
#> SRR1398057 5 0.4460 -0.3334 0.000 0.000 0.452 0.000 0.520 0.028
#> SRR1485042 1 0.0865 0.8224 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1385453 4 0.6897 0.4108 0.000 0.000 0.348 0.416 0.108 0.128
#> SRR1348074 4 0.1363 0.7758 0.028 0.004 0.012 0.952 0.000 0.004
#> SRR813959 4 0.5969 0.5326 0.000 0.000 0.076 0.568 0.280 0.076
#> SRR665442 4 0.3963 0.7399 0.000 0.012 0.076 0.780 0.000 0.132
#> SRR1378068 3 0.3547 0.7665 0.000 0.000 0.668 0.000 0.332 0.000
#> SRR1485237 4 0.2862 0.7573 0.052 0.000 0.020 0.872 0.000 0.056
#> SRR1350792 1 0.1367 0.8145 0.944 0.000 0.012 0.000 0.000 0.044
#> SRR1326797 5 0.3952 0.3548 0.052 0.000 0.000 0.000 0.736 0.212
#> SRR808994 3 0.3482 0.7637 0.000 0.000 0.684 0.000 0.316 0.000
#> SRR1474041 5 0.1049 0.5617 0.000 0.000 0.032 0.000 0.960 0.008
#> SRR1405641 3 0.3547 0.7665 0.000 0.000 0.668 0.000 0.332 0.000
#> SRR1362245 5 0.5102 -0.1481 0.000 0.000 0.428 0.000 0.492 0.080
#> SRR1500194 1 0.0862 0.8273 0.972 0.000 0.008 0.004 0.000 0.016
#> SRR1414876 2 0.3841 0.7806 0.000 0.724 0.032 0.000 0.000 0.244
#> SRR1478523 3 0.6480 0.3394 0.000 0.000 0.512 0.092 0.288 0.108
#> SRR1325161 5 0.3175 0.4261 0.000 0.000 0.000 0.000 0.744 0.256
#> SRR1318026 4 0.4472 0.7604 0.044 0.000 0.096 0.760 0.000 0.100
#> SRR1343778 3 0.3950 0.6224 0.000 0.000 0.564 0.000 0.432 0.004
#> SRR1441287 1 0.0146 0.8280 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1430991 5 0.0790 0.5597 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1499722 5 0.2994 0.4738 0.004 0.000 0.000 0.000 0.788 0.208
#> SRR1351368 3 0.5527 0.3397 0.000 0.000 0.512 0.016 0.384 0.088
#> SRR1441785 1 0.2201 0.7940 0.900 0.000 0.052 0.000 0.000 0.048
#> SRR1096101 1 0.3426 0.6385 0.784 0.000 0.012 0.000 0.012 0.192
#> SRR808375 5 0.2416 0.5227 0.000 0.000 0.000 0.000 0.844 0.156
#> SRR1452842 6 0.6131 0.9216 0.288 0.000 0.000 0.004 0.276 0.432
#> SRR1311709 1 0.1750 0.8116 0.932 0.000 0.016 0.012 0.000 0.040
#> SRR1433352 5 0.2389 0.4633 0.000 0.000 0.128 0.000 0.864 0.008
#> SRR1340241 2 0.3911 0.7767 0.000 0.712 0.032 0.000 0.000 0.256
#> SRR1456754 1 0.5108 -0.3949 0.484 0.000 0.000 0.000 0.080 0.436
#> SRR1465172 5 0.3695 0.1792 0.000 0.000 0.000 0.000 0.624 0.376
#> SRR1499284 5 0.4712 -0.1093 0.052 0.000 0.000 0.000 0.564 0.384
#> SRR1499607 4 0.1340 0.7602 0.000 0.040 0.004 0.948 0.000 0.008
#> SRR812342 1 0.0146 0.8280 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1405374 1 0.0964 0.8269 0.968 0.000 0.012 0.004 0.000 0.016
#> SRR1403565 1 0.5192 0.3896 0.672 0.000 0.052 0.000 0.208 0.068
#> SRR1332024 3 0.3508 0.7442 0.000 0.000 0.704 0.000 0.292 0.004
#> SRR1471633 1 0.2045 0.8011 0.916 0.000 0.016 0.016 0.000 0.052
#> SRR1325944 2 0.3841 0.7806 0.000 0.724 0.032 0.000 0.000 0.244
#> SRR1429450 2 0.3884 0.7808 0.000 0.724 0.036 0.000 0.000 0.240
#> SRR821573 5 0.5514 0.3271 0.000 0.000 0.112 0.060 0.660 0.168
#> SRR1435372 1 0.2658 0.7618 0.864 0.000 0.016 0.008 0.000 0.112
#> SRR1324184 2 0.5293 0.5740 0.000 0.680 0.056 0.168 0.000 0.096
#> SRR816517 4 0.5660 0.7036 0.000 0.000 0.188 0.628 0.040 0.144
#> SRR1324141 4 0.4376 0.7604 0.008 0.000 0.096 0.760 0.012 0.124
#> SRR1101612 1 0.0291 0.8281 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR1356531 1 0.0632 0.8269 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1089785 5 0.0935 0.5613 0.000 0.000 0.032 0.000 0.964 0.004
#> SRR1077708 5 0.5461 0.1892 0.000 0.000 0.136 0.000 0.520 0.344
#> SRR1343720 5 0.3023 0.4567 0.000 0.000 0.000 0.000 0.768 0.232
#> SRR1477499 2 0.3925 0.7808 0.000 0.724 0.040 0.000 0.000 0.236
#> SRR1347236 5 0.3802 0.3823 0.044 0.000 0.000 0.000 0.748 0.208
#> SRR1326408 1 0.4402 -0.0794 0.564 0.000 0.000 0.004 0.020 0.412
#> SRR1336529 3 0.3547 0.7665 0.000 0.000 0.668 0.000 0.332 0.000
#> SRR1440643 3 0.7218 0.0992 0.000 0.000 0.396 0.200 0.292 0.112
#> SRR662354 1 0.0405 0.8293 0.988 0.000 0.004 0.000 0.000 0.008
#> SRR1310817 5 0.2134 0.5735 0.000 0.000 0.052 0.000 0.904 0.044
#> SRR1347389 4 0.5354 0.5390 0.000 0.236 0.072 0.644 0.000 0.048
#> SRR1353097 1 0.0547 0.8268 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1384737 4 0.4357 0.7610 0.016 0.000 0.096 0.760 0.004 0.124
#> SRR1096339 1 0.0291 0.8281 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR1345329 4 0.1180 0.7748 0.024 0.004 0.004 0.960 0.000 0.008
#> SRR1414771 3 0.3428 0.7606 0.000 0.000 0.696 0.000 0.304 0.000
#> SRR1309119 1 0.0665 0.8274 0.980 0.000 0.004 0.008 0.000 0.008
#> SRR1470438 3 0.3464 0.7627 0.000 0.000 0.688 0.000 0.312 0.000
#> SRR1343221 1 0.2402 0.7265 0.868 0.000 0.000 0.000 0.012 0.120
#> SRR1410847 1 0.0603 0.8279 0.980 0.000 0.004 0.000 0.000 0.016
#> SRR807949 5 0.0790 0.5597 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1442332 5 0.1970 0.5020 0.000 0.000 0.092 0.000 0.900 0.008
#> SRR815920 3 0.3684 0.7652 0.000 0.000 0.664 0.000 0.332 0.004
#> SRR1471524 5 0.5288 -0.2043 0.000 0.000 0.404 0.004 0.504 0.088
#> SRR1477221 5 0.4901 -0.2880 0.000 0.000 0.456 0.000 0.484 0.060
#> SRR1445046 2 0.4126 0.3744 0.000 0.624 0.008 0.360 0.000 0.008
#> SRR1331962 2 0.2135 0.7058 0.000 0.872 0.000 0.128 0.000 0.000
#> SRR1319946 2 0.4720 0.5158 0.000 0.640 0.012 0.300 0.000 0.048
#> SRR1311599 1 0.2201 0.7940 0.900 0.000 0.052 0.000 0.000 0.048
#> SRR1323977 4 0.3487 0.7777 0.000 0.000 0.060 0.824 0.016 0.100
#> SRR1445132 2 0.3841 0.7806 0.000 0.724 0.032 0.000 0.000 0.244
#> SRR1337321 5 0.2712 0.5713 0.000 0.000 0.048 0.000 0.864 0.088
#> SRR1366390 4 0.5990 0.4468 0.000 0.276 0.088 0.568 0.000 0.068
#> SRR1343012 4 0.5540 0.6874 0.008 0.000 0.096 0.664 0.048 0.184
#> SRR1311958 2 0.3628 0.5442 0.000 0.720 0.004 0.268 0.000 0.008
#> SRR1388234 4 0.1453 0.7584 0.000 0.040 0.008 0.944 0.000 0.008
#> SRR1370384 1 0.4951 -0.3124 0.480 0.000 0.008 0.004 0.036 0.472
#> SRR1321650 5 0.4300 -0.2115 0.000 0.000 0.432 0.000 0.548 0.020
#> SRR1485117 2 0.0665 0.7570 0.000 0.980 0.008 0.008 0.000 0.004
#> SRR1384713 6 0.6109 0.9225 0.248 0.000 0.000 0.004 0.316 0.432
#> SRR816609 4 0.1261 0.7733 0.028 0.004 0.008 0.956 0.000 0.004
#> SRR1486239 2 0.2632 0.6903 0.000 0.832 0.004 0.164 0.000 0.000
#> SRR1309638 5 0.5677 0.0344 0.000 0.000 0.156 0.000 0.440 0.404
#> SRR1356660 1 0.2201 0.7940 0.900 0.000 0.052 0.000 0.000 0.048
#> SRR1392883 2 0.3841 0.7806 0.000 0.724 0.032 0.000 0.000 0.244
#> SRR808130 5 0.0790 0.5597 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR816677 1 0.4259 0.3963 0.648 0.000 0.008 0.324 0.000 0.020
#> SRR1455722 1 0.0146 0.8280 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1336029 1 0.0520 0.8286 0.984 0.000 0.008 0.000 0.000 0.008
#> SRR808452 1 0.0405 0.8279 0.988 0.000 0.000 0.004 0.000 0.008
#> SRR1352169 5 0.3073 0.3326 0.000 0.000 0.204 0.000 0.788 0.008
#> SRR1366707 5 0.4592 -0.4513 0.000 0.000 0.468 0.000 0.496 0.036
#> SRR1328143 5 0.1970 0.5035 0.000 0.000 0.092 0.000 0.900 0.008
#> SRR1473567 2 0.1074 0.7524 0.000 0.960 0.012 0.028 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17851 rows and 124 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 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 0.978 0.991 0.4440 0.554 0.554
#> 3 3 1.000 0.989 0.995 0.5106 0.743 0.549
#> 4 4 0.813 0.799 0.899 0.1096 0.888 0.677
#> 5 5 0.798 0.667 0.857 0.0539 0.939 0.774
#> 6 6 0.766 0.674 0.812 0.0398 0.934 0.722
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
#> SRR1442087 1 0.0000 0.996 1.000 0.000
#> SRR1390119 2 0.0000 0.980 0.000 1.000
#> SRR1436127 1 0.0000 0.996 1.000 0.000
#> SRR1347278 1 0.0000 0.996 1.000 0.000
#> SRR1332904 2 0.0000 0.980 0.000 1.000
#> SRR1444179 1 0.0000 0.996 1.000 0.000
#> SRR1082685 1 0.0000 0.996 1.000 0.000
#> SRR1362287 1 0.0000 0.996 1.000 0.000
#> SRR1339007 1 0.0000 0.996 1.000 0.000
#> SRR1376557 2 0.0000 0.980 0.000 1.000
#> SRR1468700 2 0.0000 0.980 0.000 1.000
#> SRR1077455 1 0.0000 0.996 1.000 0.000
#> SRR1413978 1 0.0000 0.996 1.000 0.000
#> SRR1439896 1 0.0000 0.996 1.000 0.000
#> SRR1317963 2 0.0000 0.980 0.000 1.000
#> SRR1431865 1 0.0000 0.996 1.000 0.000
#> SRR1394253 1 0.0000 0.996 1.000 0.000
#> SRR1082664 1 0.0000 0.996 1.000 0.000
#> SRR1077968 1 0.0000 0.996 1.000 0.000
#> SRR1076393 1 0.0000 0.996 1.000 0.000
#> SRR1477476 2 0.0000 0.980 0.000 1.000
#> SRR1398057 1 0.0000 0.996 1.000 0.000
#> SRR1485042 1 0.0000 0.996 1.000 0.000
#> SRR1385453 2 0.0000 0.980 0.000 1.000
#> SRR1348074 2 0.0000 0.980 0.000 1.000
#> SRR813959 2 0.0000 0.980 0.000 1.000
#> SRR665442 2 0.0000 0.980 0.000 1.000
#> SRR1378068 1 0.0000 0.996 1.000 0.000
#> SRR1485237 2 0.0000 0.980 0.000 1.000
#> SRR1350792 1 0.0000 0.996 1.000 0.000
#> SRR1326797 1 0.0000 0.996 1.000 0.000
#> SRR808994 1 0.0000 0.996 1.000 0.000
#> SRR1474041 1 0.0000 0.996 1.000 0.000
#> SRR1405641 1 0.0000 0.996 1.000 0.000
#> SRR1362245 1 0.0000 0.996 1.000 0.000
#> SRR1500194 1 0.0000 0.996 1.000 0.000
#> SRR1414876 2 0.0000 0.980 0.000 1.000
#> SRR1478523 2 0.7219 0.753 0.200 0.800
#> SRR1325161 1 0.0000 0.996 1.000 0.000
#> SRR1318026 2 0.0000 0.980 0.000 1.000
#> SRR1343778 1 0.0000 0.996 1.000 0.000
#> SRR1441287 1 0.0000 0.996 1.000 0.000
#> SRR1430991 1 0.0000 0.996 1.000 0.000
#> SRR1499722 1 0.0000 0.996 1.000 0.000
#> SRR1351368 1 0.0000 0.996 1.000 0.000
#> SRR1441785 1 0.0000 0.996 1.000 0.000
#> SRR1096101 1 0.0000 0.996 1.000 0.000
#> SRR808375 1 0.0000 0.996 1.000 0.000
#> SRR1452842 1 0.0000 0.996 1.000 0.000
#> SRR1311709 1 0.0938 0.984 0.988 0.012
#> SRR1433352 1 0.0000 0.996 1.000 0.000
#> SRR1340241 2 0.0000 0.980 0.000 1.000
#> SRR1456754 1 0.0000 0.996 1.000 0.000
#> SRR1465172 1 0.0000 0.996 1.000 0.000
#> SRR1499284 1 0.0000 0.996 1.000 0.000
#> SRR1499607 2 0.0000 0.980 0.000 1.000
#> SRR812342 1 0.0000 0.996 1.000 0.000
#> SRR1405374 1 0.0000 0.996 1.000 0.000
#> SRR1403565 1 0.0000 0.996 1.000 0.000
#> SRR1332024 1 0.0000 0.996 1.000 0.000
#> SRR1471633 1 0.8608 0.590 0.716 0.284
#> SRR1325944 2 0.0000 0.980 0.000 1.000
#> SRR1429450 2 0.0000 0.980 0.000 1.000
#> SRR821573 1 0.2043 0.963 0.968 0.032
#> SRR1435372 1 0.0000 0.996 1.000 0.000
#> SRR1324184 2 0.0000 0.980 0.000 1.000
#> SRR816517 2 0.0000 0.980 0.000 1.000
#> SRR1324141 2 0.0000 0.980 0.000 1.000
#> SRR1101612 1 0.0000 0.996 1.000 0.000
#> SRR1356531 1 0.0000 0.996 1.000 0.000
#> SRR1089785 1 0.0000 0.996 1.000 0.000
#> SRR1077708 1 0.0000 0.996 1.000 0.000
#> SRR1343720 1 0.0000 0.996 1.000 0.000
#> SRR1477499 2 0.0000 0.980 0.000 1.000
#> SRR1347236 1 0.0000 0.996 1.000 0.000
#> SRR1326408 1 0.0000 0.996 1.000 0.000
#> SRR1336529 1 0.0000 0.996 1.000 0.000
#> SRR1440643 2 0.7056 0.764 0.192 0.808
#> SRR662354 1 0.0000 0.996 1.000 0.000
#> SRR1310817 1 0.0000 0.996 1.000 0.000
#> SRR1347389 2 0.0000 0.980 0.000 1.000
#> SRR1353097 1 0.0000 0.996 1.000 0.000
#> SRR1384737 2 0.0000 0.980 0.000 1.000
#> SRR1096339 1 0.0000 0.996 1.000 0.000
#> SRR1345329 2 0.0000 0.980 0.000 1.000
#> SRR1414771 1 0.0000 0.996 1.000 0.000
#> SRR1309119 1 0.1184 0.980 0.984 0.016
#> SRR1470438 1 0.0000 0.996 1.000 0.000
#> SRR1343221 1 0.0000 0.996 1.000 0.000
#> SRR1410847 1 0.0000 0.996 1.000 0.000
#> SRR807949 1 0.0000 0.996 1.000 0.000
#> SRR1442332 1 0.0000 0.996 1.000 0.000
#> SRR815920 1 0.0000 0.996 1.000 0.000
#> SRR1471524 1 0.0000 0.996 1.000 0.000
#> SRR1477221 1 0.0000 0.996 1.000 0.000
#> SRR1445046 2 0.0000 0.980 0.000 1.000
#> SRR1331962 2 0.0000 0.980 0.000 1.000
#> SRR1319946 2 0.0000 0.980 0.000 1.000
#> SRR1311599 1 0.0000 0.996 1.000 0.000
#> SRR1323977 2 0.0000 0.980 0.000 1.000
#> SRR1445132 2 0.0000 0.980 0.000 1.000
#> SRR1337321 1 0.0000 0.996 1.000 0.000
#> SRR1366390 2 0.0000 0.980 0.000 1.000
#> SRR1343012 2 0.0000 0.980 0.000 1.000
#> SRR1311958 2 0.0000 0.980 0.000 1.000
#> SRR1388234 2 0.0000 0.980 0.000 1.000
#> SRR1370384 1 0.0000 0.996 1.000 0.000
#> SRR1321650 1 0.0000 0.996 1.000 0.000
#> SRR1485117 2 0.0000 0.980 0.000 1.000
#> SRR1384713 1 0.0000 0.996 1.000 0.000
#> SRR816609 2 0.0000 0.980 0.000 1.000
#> SRR1486239 2 0.0000 0.980 0.000 1.000
#> SRR1309638 1 0.0000 0.996 1.000 0.000
#> SRR1356660 1 0.0000 0.996 1.000 0.000
#> SRR1392883 2 0.0000 0.980 0.000 1.000
#> SRR808130 1 0.0000 0.996 1.000 0.000
#> SRR816677 2 0.9775 0.315 0.412 0.588
#> SRR1455722 1 0.0000 0.996 1.000 0.000
#> SRR1336029 1 0.0000 0.996 1.000 0.000
#> SRR808452 1 0.0000 0.996 1.000 0.000
#> SRR1352169 1 0.0000 0.996 1.000 0.000
#> SRR1366707 1 0.0000 0.996 1.000 0.000
#> SRR1328143 1 0.0000 0.996 1.000 0.000
#> SRR1473567 2 0.0000 0.980 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.000 0.996 0.000 0.000 1.000
#> SRR1390119 2 0.000 0.991 0.000 1.000 0.000
#> SRR1436127 3 0.000 0.996 0.000 0.000 1.000
#> SRR1347278 3 0.000 0.996 0.000 0.000 1.000
#> SRR1332904 2 0.000 0.991 0.000 1.000 0.000
#> SRR1444179 1 0.000 0.997 1.000 0.000 0.000
#> SRR1082685 1 0.000 0.997 1.000 0.000 0.000
#> SRR1362287 1 0.000 0.997 1.000 0.000 0.000
#> SRR1339007 1 0.000 0.997 1.000 0.000 0.000
#> SRR1376557 2 0.000 0.991 0.000 1.000 0.000
#> SRR1468700 2 0.000 0.991 0.000 1.000 0.000
#> SRR1077455 1 0.000 0.997 1.000 0.000 0.000
#> SRR1413978 1 0.000 0.997 1.000 0.000 0.000
#> SRR1439896 1 0.000 0.997 1.000 0.000 0.000
#> SRR1317963 2 0.000 0.991 0.000 1.000 0.000
#> SRR1431865 1 0.000 0.997 1.000 0.000 0.000
#> SRR1394253 1 0.000 0.997 1.000 0.000 0.000
#> SRR1082664 3 0.000 0.996 0.000 0.000 1.000
#> SRR1077968 1 0.000 0.997 1.000 0.000 0.000
#> SRR1076393 3 0.000 0.996 0.000 0.000 1.000
#> SRR1477476 2 0.000 0.991 0.000 1.000 0.000
#> SRR1398057 3 0.000 0.996 0.000 0.000 1.000
#> SRR1485042 1 0.000 0.997 1.000 0.000 0.000
#> SRR1385453 2 0.280 0.897 0.000 0.908 0.092
#> SRR1348074 2 0.000 0.991 0.000 1.000 0.000
#> SRR813959 2 0.000 0.991 0.000 1.000 0.000
#> SRR665442 2 0.000 0.991 0.000 1.000 0.000
#> SRR1378068 3 0.000 0.996 0.000 0.000 1.000
#> SRR1485237 2 0.000 0.991 0.000 1.000 0.000
#> SRR1350792 1 0.000 0.997 1.000 0.000 0.000
#> SRR1326797 3 0.000 0.996 0.000 0.000 1.000
#> SRR808994 3 0.000 0.996 0.000 0.000 1.000
#> SRR1474041 3 0.000 0.996 0.000 0.000 1.000
#> SRR1405641 3 0.000 0.996 0.000 0.000 1.000
#> SRR1362245 3 0.000 0.996 0.000 0.000 1.000
#> SRR1500194 1 0.000 0.997 1.000 0.000 0.000
#> SRR1414876 2 0.000 0.991 0.000 1.000 0.000
#> SRR1478523 3 0.000 0.996 0.000 0.000 1.000
#> SRR1325161 3 0.000 0.996 0.000 0.000 1.000
#> SRR1318026 2 0.455 0.751 0.200 0.800 0.000
#> SRR1343778 3 0.000 0.996 0.000 0.000 1.000
#> SRR1441287 1 0.000 0.997 1.000 0.000 0.000
#> SRR1430991 3 0.000 0.996 0.000 0.000 1.000
#> SRR1499722 3 0.000 0.996 0.000 0.000 1.000
#> SRR1351368 3 0.000 0.996 0.000 0.000 1.000
#> SRR1441785 1 0.000 0.997 1.000 0.000 0.000
#> SRR1096101 1 0.000 0.997 1.000 0.000 0.000
#> SRR808375 3 0.000 0.996 0.000 0.000 1.000
#> SRR1452842 1 0.000 0.997 1.000 0.000 0.000
#> SRR1311709 1 0.000 0.997 1.000 0.000 0.000
#> SRR1433352 3 0.000 0.996 0.000 0.000 1.000
#> SRR1340241 2 0.000 0.991 0.000 1.000 0.000
#> SRR1456754 1 0.000 0.997 1.000 0.000 0.000
#> SRR1465172 3 0.000 0.996 0.000 0.000 1.000
#> SRR1499284 3 0.000 0.996 0.000 0.000 1.000
#> SRR1499607 2 0.000 0.991 0.000 1.000 0.000
#> SRR812342 1 0.000 0.997 1.000 0.000 0.000
#> SRR1405374 1 0.000 0.997 1.000 0.000 0.000
#> SRR1403565 1 0.319 0.874 0.888 0.000 0.112
#> SRR1332024 3 0.000 0.996 0.000 0.000 1.000
#> SRR1471633 1 0.000 0.997 1.000 0.000 0.000
#> SRR1325944 2 0.000 0.991 0.000 1.000 0.000
#> SRR1429450 2 0.000 0.991 0.000 1.000 0.000
#> SRR821573 3 0.000 0.996 0.000 0.000 1.000
#> SRR1435372 1 0.000 0.997 1.000 0.000 0.000
#> SRR1324184 2 0.000 0.991 0.000 1.000 0.000
#> SRR816517 2 0.000 0.991 0.000 1.000 0.000
#> SRR1324141 2 0.000 0.991 0.000 1.000 0.000
#> SRR1101612 1 0.000 0.997 1.000 0.000 0.000
#> SRR1356531 1 0.000 0.997 1.000 0.000 0.000
#> SRR1089785 3 0.000 0.996 0.000 0.000 1.000
#> SRR1077708 3 0.000 0.996 0.000 0.000 1.000
#> SRR1343720 3 0.000 0.996 0.000 0.000 1.000
#> SRR1477499 2 0.000 0.991 0.000 1.000 0.000
#> SRR1347236 3 0.000 0.996 0.000 0.000 1.000
#> SRR1326408 1 0.000 0.997 1.000 0.000 0.000
#> SRR1336529 3 0.000 0.996 0.000 0.000 1.000
#> SRR1440643 3 0.424 0.784 0.000 0.176 0.824
#> SRR662354 1 0.000 0.997 1.000 0.000 0.000
#> SRR1310817 3 0.000 0.996 0.000 0.000 1.000
#> SRR1347389 2 0.000 0.991 0.000 1.000 0.000
#> SRR1353097 1 0.000 0.997 1.000 0.000 0.000
#> SRR1384737 2 0.000 0.991 0.000 1.000 0.000
#> SRR1096339 1 0.000 0.997 1.000 0.000 0.000
#> SRR1345329 2 0.000 0.991 0.000 1.000 0.000
#> SRR1414771 3 0.000 0.996 0.000 0.000 1.000
#> SRR1309119 1 0.000 0.997 1.000 0.000 0.000
#> SRR1470438 3 0.000 0.996 0.000 0.000 1.000
#> SRR1343221 1 0.000 0.997 1.000 0.000 0.000
#> SRR1410847 1 0.000 0.997 1.000 0.000 0.000
#> SRR807949 3 0.000 0.996 0.000 0.000 1.000
#> SRR1442332 3 0.000 0.996 0.000 0.000 1.000
#> SRR815920 3 0.000 0.996 0.000 0.000 1.000
#> SRR1471524 3 0.000 0.996 0.000 0.000 1.000
#> SRR1477221 3 0.000 0.996 0.000 0.000 1.000
#> SRR1445046 2 0.000 0.991 0.000 1.000 0.000
#> SRR1331962 2 0.000 0.991 0.000 1.000 0.000
#> SRR1319946 2 0.000 0.991 0.000 1.000 0.000
#> SRR1311599 1 0.000 0.997 1.000 0.000 0.000
#> SRR1323977 2 0.000 0.991 0.000 1.000 0.000
#> SRR1445132 2 0.000 0.991 0.000 1.000 0.000
#> SRR1337321 3 0.000 0.996 0.000 0.000 1.000
#> SRR1366390 2 0.000 0.991 0.000 1.000 0.000
#> SRR1343012 2 0.153 0.953 0.000 0.960 0.040
#> SRR1311958 2 0.000 0.991 0.000 1.000 0.000
#> SRR1388234 2 0.000 0.991 0.000 1.000 0.000
#> SRR1370384 1 0.000 0.997 1.000 0.000 0.000
#> SRR1321650 3 0.000 0.996 0.000 0.000 1.000
#> SRR1485117 2 0.000 0.991 0.000 1.000 0.000
#> SRR1384713 1 0.000 0.997 1.000 0.000 0.000
#> SRR816609 2 0.000 0.991 0.000 1.000 0.000
#> SRR1486239 2 0.000 0.991 0.000 1.000 0.000
#> SRR1309638 3 0.000 0.996 0.000 0.000 1.000
#> SRR1356660 1 0.000 0.997 1.000 0.000 0.000
#> SRR1392883 2 0.000 0.991 0.000 1.000 0.000
#> SRR808130 3 0.000 0.996 0.000 0.000 1.000
#> SRR816677 1 0.000 0.997 1.000 0.000 0.000
#> SRR1455722 1 0.000 0.997 1.000 0.000 0.000
#> SRR1336029 1 0.000 0.997 1.000 0.000 0.000
#> SRR808452 1 0.000 0.997 1.000 0.000 0.000
#> SRR1352169 3 0.000 0.996 0.000 0.000 1.000
#> SRR1366707 3 0.000 0.996 0.000 0.000 1.000
#> SRR1328143 3 0.000 0.996 0.000 0.000 1.000
#> SRR1473567 2 0.000 0.991 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.3074 0.8455 0.000 0.000 0.848 0.152
#> SRR1390119 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1347278 3 0.3528 0.8328 0.000 0.000 0.808 0.192
#> SRR1332904 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.4996 -0.0363 0.516 0.000 0.000 0.484
#> SRR1376557 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1077455 4 0.3024 0.6874 0.148 0.000 0.000 0.852
#> SRR1413978 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1431865 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1082664 4 0.1118 0.7008 0.000 0.000 0.036 0.964
#> SRR1077968 4 0.5000 0.0353 0.496 0.000 0.000 0.504
#> SRR1076393 4 0.4941 -0.1820 0.000 0.000 0.436 0.564
#> SRR1477476 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.3311 0.8393 0.000 0.000 0.828 0.172
#> SRR1485042 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1385453 2 0.4250 0.7106 0.000 0.724 0.276 0.000
#> SRR1348074 2 0.0469 0.9698 0.000 0.988 0.000 0.012
#> SRR813959 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR665442 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1378068 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1485237 2 0.0469 0.9698 0.000 0.988 0.000 0.012
#> SRR1350792 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1326797 4 0.0524 0.7163 0.004 0.000 0.008 0.988
#> SRR808994 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1474041 3 0.4998 0.5294 0.000 0.000 0.512 0.488
#> SRR1405641 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1362245 3 0.4304 0.7527 0.000 0.000 0.716 0.284
#> SRR1500194 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.0000 0.7468 0.000 0.000 1.000 0.000
#> SRR1325161 4 0.0469 0.7146 0.000 0.000 0.012 0.988
#> SRR1318026 2 0.6083 0.7093 0.124 0.716 0.144 0.016
#> SRR1343778 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1441287 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1430991 3 0.4996 0.5369 0.000 0.000 0.516 0.484
#> SRR1499722 4 0.0469 0.7146 0.000 0.000 0.012 0.988
#> SRR1351368 3 0.0336 0.7466 0.000 0.000 0.992 0.008
#> SRR1441785 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.3569 0.7064 0.804 0.000 0.000 0.196
#> SRR808375 4 0.0592 0.7120 0.000 0.000 0.016 0.984
#> SRR1452842 4 0.3219 0.6798 0.164 0.000 0.000 0.836
#> SRR1311709 1 0.0469 0.9383 0.988 0.000 0.000 0.012
#> SRR1433352 3 0.4522 0.7412 0.000 0.000 0.680 0.320
#> SRR1340241 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1456754 4 0.4761 0.3807 0.372 0.000 0.000 0.628
#> SRR1465172 4 0.0469 0.7146 0.000 0.000 0.012 0.988
#> SRR1499284 4 0.0524 0.7163 0.004 0.000 0.008 0.988
#> SRR1499607 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR812342 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.4382 0.5168 0.704 0.000 0.000 0.296
#> SRR1332024 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1471633 1 0.0469 0.9383 0.988 0.000 0.000 0.012
#> SRR1325944 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR821573 4 0.3219 0.6640 0.000 0.000 0.164 0.836
#> SRR1435372 1 0.1211 0.9113 0.960 0.000 0.000 0.040
#> SRR1324184 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR816517 2 0.0592 0.9664 0.000 0.984 0.016 0.000
#> SRR1324141 2 0.3763 0.8422 0.000 0.832 0.144 0.024
#> SRR1101612 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1089785 3 0.4998 0.5294 0.000 0.000 0.512 0.488
#> SRR1077708 4 0.3074 0.5983 0.000 0.000 0.152 0.848
#> SRR1343720 4 0.0469 0.7146 0.000 0.000 0.012 0.988
#> SRR1477499 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1347236 4 0.0524 0.7163 0.004 0.000 0.008 0.988
#> SRR1326408 4 0.4981 0.1337 0.464 0.000 0.000 0.536
#> SRR1336529 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1440643 3 0.2408 0.6559 0.000 0.104 0.896 0.000
#> SRR662354 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1310817 4 0.4888 0.2200 0.000 0.000 0.412 0.588
#> SRR1347389 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1353097 1 0.0188 0.9441 0.996 0.000 0.000 0.004
#> SRR1384737 2 0.3547 0.8494 0.000 0.840 0.144 0.016
#> SRR1096339 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1345329 2 0.0469 0.9698 0.000 0.988 0.000 0.012
#> SRR1414771 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1309119 1 0.0188 0.9441 0.996 0.000 0.000 0.004
#> SRR1470438 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1343221 1 0.4961 0.1092 0.552 0.000 0.000 0.448
#> SRR1410847 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR807949 3 0.4996 0.5369 0.000 0.000 0.516 0.484
#> SRR1442332 3 0.4804 0.6814 0.000 0.000 0.616 0.384
#> SRR815920 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1471524 3 0.0188 0.7468 0.000 0.000 0.996 0.004
#> SRR1477221 3 0.3356 0.8378 0.000 0.000 0.824 0.176
#> SRR1445046 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1331962 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1323977 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1337321 4 0.4843 -0.1643 0.000 0.000 0.396 0.604
#> SRR1366390 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1343012 4 0.4614 0.6291 0.000 0.064 0.144 0.792
#> SRR1311958 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1370384 4 0.4977 0.1583 0.460 0.000 0.000 0.540
#> SRR1321650 3 0.4134 0.7800 0.000 0.000 0.740 0.260
#> SRR1485117 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1384713 4 0.3074 0.6856 0.152 0.000 0.000 0.848
#> SRR816609 2 0.0469 0.9698 0.000 0.988 0.000 0.012
#> SRR1486239 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR1309638 4 0.3649 0.5316 0.000 0.000 0.204 0.796
#> SRR1356660 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9763 0.000 1.000 0.000 0.000
#> SRR808130 3 0.4996 0.5369 0.000 0.000 0.516 0.484
#> SRR816677 1 0.0469 0.9383 0.988 0.000 0.000 0.012
#> SRR1455722 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.9470 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.2973 0.8467 0.000 0.000 0.856 0.144
#> SRR1366707 3 0.1557 0.7872 0.000 0.000 0.944 0.056
#> SRR1328143 3 0.4804 0.6814 0.000 0.000 0.616 0.384
#> SRR1473567 2 0.0000 0.9763 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
#> SRR1442087 3 0.1124 0.7817 0.000 0.000 0.960 0.004 0.036
#> SRR1390119 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1436127 3 0.0703 0.7843 0.000 0.000 0.976 0.000 0.024
#> SRR1347278 3 0.3519 0.6445 0.000 0.000 0.776 0.008 0.216
#> SRR1332904 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1444179 1 0.0566 0.8884 0.984 0.000 0.000 0.012 0.004
#> SRR1082685 1 0.0162 0.8940 0.996 0.000 0.000 0.000 0.004
#> SRR1362287 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1339007 1 0.5694 0.0622 0.464 0.000 0.000 0.080 0.456
#> SRR1376557 2 0.0162 0.9205 0.000 0.996 0.000 0.004 0.000
#> SRR1468700 2 0.0404 0.9187 0.000 0.988 0.000 0.012 0.000
#> SRR1077455 5 0.2754 0.6141 0.040 0.000 0.000 0.080 0.880
#> SRR1413978 1 0.2377 0.8121 0.872 0.000 0.000 0.128 0.000
#> SRR1439896 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.1544 0.8693 0.000 0.932 0.000 0.068 0.000
#> SRR1431865 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1082664 5 0.2771 0.6106 0.000 0.000 0.128 0.012 0.860
#> SRR1077968 5 0.5684 -0.0226 0.432 0.000 0.000 0.080 0.488
#> SRR1076393 3 0.4659 0.0198 0.000 0.000 0.500 0.012 0.488
#> SRR1477476 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1398057 3 0.2230 0.7376 0.000 0.000 0.884 0.000 0.116
#> SRR1485042 1 0.0992 0.8806 0.968 0.000 0.000 0.024 0.008
#> SRR1385453 2 0.6381 -0.1323 0.000 0.464 0.172 0.364 0.000
#> SRR1348074 4 0.4403 0.3561 0.000 0.436 0.000 0.560 0.004
#> SRR813959 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR665442 2 0.0290 0.9199 0.000 0.992 0.000 0.008 0.000
#> SRR1378068 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1485237 2 0.4443 -0.2244 0.000 0.524 0.000 0.472 0.004
#> SRR1350792 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0451 0.6557 0.000 0.000 0.004 0.008 0.988
#> SRR808994 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1474041 5 0.4803 0.0384 0.000 0.000 0.444 0.020 0.536
#> SRR1405641 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1362245 3 0.3684 0.5554 0.000 0.000 0.720 0.000 0.280
#> SRR1500194 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1414876 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1478523 3 0.3398 0.6490 0.000 0.004 0.780 0.216 0.000
#> SRR1325161 5 0.0579 0.6558 0.000 0.000 0.008 0.008 0.984
#> SRR1318026 4 0.1478 0.7572 0.000 0.064 0.000 0.936 0.000
#> SRR1343778 3 0.0510 0.7855 0.000 0.000 0.984 0.000 0.016
#> SRR1441287 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.4807 0.0316 0.000 0.000 0.448 0.020 0.532
#> SRR1499722 5 0.0807 0.6543 0.000 0.000 0.012 0.012 0.976
#> SRR1351368 3 0.4252 0.5109 0.000 0.000 0.652 0.340 0.008
#> SRR1441785 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1096101 1 0.5368 0.3980 0.596 0.000 0.000 0.072 0.332
#> SRR808375 5 0.2144 0.6270 0.000 0.000 0.068 0.020 0.912
#> SRR1452842 5 0.3239 0.5974 0.068 0.000 0.000 0.080 0.852
#> SRR1311709 1 0.3969 0.5567 0.692 0.000 0.000 0.304 0.004
#> SRR1433352 3 0.4183 0.4716 0.000 0.000 0.668 0.008 0.324
#> SRR1340241 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1456754 5 0.5393 0.3192 0.312 0.000 0.000 0.080 0.608
#> SRR1465172 5 0.0671 0.6547 0.000 0.000 0.004 0.016 0.980
#> SRR1499284 5 0.1041 0.6511 0.000 0.000 0.004 0.032 0.964
#> SRR1499607 2 0.1341 0.8824 0.000 0.944 0.000 0.056 0.000
#> SRR812342 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1403565 1 0.4503 0.4533 0.664 0.000 0.024 0.000 0.312
#> SRR1332024 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1471633 1 0.3928 0.5740 0.700 0.000 0.000 0.296 0.004
#> SRR1325944 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1429450 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR821573 5 0.4416 0.3875 0.000 0.000 0.012 0.356 0.632
#> SRR1435372 1 0.3182 0.7865 0.844 0.000 0.000 0.032 0.124
#> SRR1324184 2 0.0290 0.9199 0.000 0.992 0.000 0.008 0.000
#> SRR816517 2 0.2127 0.8068 0.000 0.892 0.000 0.108 0.000
#> SRR1324141 4 0.1410 0.7572 0.000 0.060 0.000 0.940 0.000
#> SRR1101612 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0807 0.8856 0.976 0.000 0.000 0.012 0.012
#> SRR1089785 5 0.4818 -0.0064 0.000 0.000 0.460 0.020 0.520
#> SRR1077708 5 0.3890 0.4912 0.000 0.000 0.252 0.012 0.736
#> SRR1343720 5 0.1082 0.6520 0.000 0.000 0.028 0.008 0.964
#> SRR1477499 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1347236 5 0.0579 0.6556 0.000 0.000 0.008 0.008 0.984
#> SRR1326408 5 0.6515 0.1334 0.208 0.000 0.000 0.328 0.464
#> SRR1336529 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1440643 3 0.6574 0.2201 0.000 0.124 0.476 0.380 0.020
#> SRR662354 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.6225 0.2660 0.000 0.000 0.200 0.256 0.544
#> SRR1347389 2 0.1732 0.8614 0.000 0.920 0.000 0.080 0.000
#> SRR1353097 1 0.1568 0.8662 0.944 0.000 0.000 0.020 0.036
#> SRR1384737 4 0.1341 0.7546 0.000 0.056 0.000 0.944 0.000
#> SRR1096339 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.4420 0.3294 0.000 0.448 0.000 0.548 0.004
#> SRR1414771 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1309119 1 0.0955 0.8799 0.968 0.000 0.000 0.028 0.004
#> SRR1470438 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1343221 1 0.4401 0.4934 0.656 0.000 0.000 0.016 0.328
#> SRR1410847 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.4811 0.0157 0.000 0.000 0.452 0.020 0.528
#> SRR1442332 3 0.4736 0.3006 0.000 0.000 0.576 0.020 0.404
#> SRR815920 3 0.0000 0.7870 0.000 0.000 1.000 0.000 0.000
#> SRR1471524 3 0.4575 0.5240 0.000 0.000 0.648 0.328 0.024
#> SRR1477221 3 0.2329 0.7314 0.000 0.000 0.876 0.000 0.124
#> SRR1445046 2 0.1043 0.8977 0.000 0.960 0.000 0.040 0.000
#> SRR1331962 2 0.0404 0.9187 0.000 0.988 0.000 0.012 0.000
#> SRR1319946 2 0.0162 0.9205 0.000 0.996 0.000 0.004 0.000
#> SRR1311599 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1323977 2 0.0404 0.9184 0.000 0.988 0.000 0.012 0.000
#> SRR1445132 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR1337321 5 0.4760 0.1335 0.000 0.000 0.416 0.020 0.564
#> SRR1366390 2 0.0703 0.9079 0.000 0.976 0.000 0.024 0.000
#> SRR1343012 4 0.0609 0.6790 0.000 0.000 0.000 0.980 0.020
#> SRR1311958 2 0.0404 0.9187 0.000 0.988 0.000 0.012 0.000
#> SRR1388234 2 0.0404 0.9187 0.000 0.988 0.000 0.012 0.000
#> SRR1370384 5 0.5616 0.1338 0.384 0.000 0.000 0.080 0.536
#> SRR1321650 3 0.3305 0.6357 0.000 0.000 0.776 0.000 0.224
#> SRR1485117 2 0.0162 0.9205 0.000 0.996 0.000 0.004 0.000
#> SRR1384713 5 0.2423 0.6216 0.024 0.000 0.000 0.080 0.896
#> SRR816609 2 0.4171 0.0939 0.000 0.604 0.000 0.396 0.000
#> SRR1486239 2 0.0404 0.9187 0.000 0.988 0.000 0.012 0.000
#> SRR1309638 5 0.5255 0.4008 0.000 0.000 0.304 0.072 0.624
#> SRR1356660 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1392883 2 0.0162 0.9203 0.000 0.996 0.000 0.004 0.000
#> SRR808130 5 0.4803 0.0384 0.000 0.000 0.444 0.020 0.536
#> SRR816677 1 0.4560 0.1544 0.508 0.000 0.000 0.484 0.008
#> SRR1455722 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0162 0.8943 0.996 0.000 0.000 0.004 0.000
#> SRR808452 1 0.0000 0.8958 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.1478 0.7707 0.000 0.000 0.936 0.000 0.064
#> SRR1366707 3 0.2358 0.7405 0.000 0.000 0.888 0.104 0.008
#> SRR1328143 3 0.4787 0.2211 0.000 0.000 0.548 0.020 0.432
#> SRR1473567 2 0.0290 0.9199 0.000 0.992 0.000 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 3 0.2482 0.6961 0.000 0.000 0.848 0.000 0.148 0.004
#> SRR1390119 2 0.0806 0.9273 0.000 0.972 0.000 0.000 0.008 0.020
#> SRR1436127 3 0.2558 0.6945 0.000 0.000 0.840 0.000 0.156 0.004
#> SRR1347278 3 0.5563 0.2346 0.036 0.000 0.512 0.004 0.400 0.048
#> SRR1332904 2 0.0405 0.9285 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR1444179 1 0.2608 0.8221 0.872 0.000 0.000 0.048 0.000 0.080
#> SRR1082685 1 0.3032 0.7982 0.840 0.000 0.000 0.056 0.000 0.104
#> SRR1362287 1 0.1536 0.8243 0.940 0.000 0.000 0.004 0.016 0.040
#> SRR1339007 6 0.3081 0.6734 0.220 0.000 0.000 0.000 0.004 0.776
#> SRR1376557 2 0.0291 0.9261 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1468700 2 0.0717 0.9203 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR1077455 6 0.2768 0.6619 0.012 0.000 0.000 0.000 0.156 0.832
#> SRR1413978 1 0.4672 0.6613 0.720 0.000 0.000 0.128 0.016 0.136
#> SRR1439896 1 0.1753 0.8322 0.912 0.000 0.000 0.004 0.000 0.084
#> SRR1317963 2 0.2165 0.8358 0.000 0.884 0.000 0.108 0.000 0.008
#> SRR1431865 1 0.1464 0.8259 0.944 0.000 0.000 0.004 0.016 0.036
#> SRR1394253 1 0.1536 0.8252 0.940 0.000 0.000 0.004 0.016 0.040
#> SRR1082664 5 0.5283 0.5286 0.000 0.000 0.148 0.000 0.588 0.264
#> SRR1077968 6 0.2595 0.7147 0.160 0.000 0.000 0.000 0.004 0.836
#> SRR1076393 5 0.5478 0.1703 0.000 0.000 0.424 0.000 0.452 0.124
#> SRR1477476 2 0.0909 0.9257 0.000 0.968 0.000 0.000 0.012 0.020
#> SRR1398057 3 0.3287 0.6282 0.000 0.000 0.768 0.000 0.220 0.012
#> SRR1485042 1 0.3141 0.7527 0.788 0.000 0.000 0.000 0.012 0.200
#> SRR1385453 2 0.8399 -0.2027 0.000 0.352 0.188 0.216 0.172 0.072
#> SRR1348074 4 0.3658 0.6718 0.000 0.216 0.000 0.752 0.000 0.032
#> SRR813959 2 0.0909 0.9258 0.000 0.968 0.000 0.000 0.012 0.020
#> SRR665442 2 0.0146 0.9271 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1378068 3 0.0260 0.7531 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1485237 4 0.4859 0.5233 0.000 0.344 0.000 0.584 0.000 0.072
#> SRR1350792 1 0.2006 0.8267 0.892 0.000 0.000 0.004 0.000 0.104
#> SRR1326797 5 0.3464 0.5272 0.000 0.000 0.000 0.000 0.688 0.312
#> SRR808994 3 0.0000 0.7532 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1474041 5 0.2948 0.6447 0.000 0.000 0.188 0.000 0.804 0.008
#> SRR1405641 3 0.0260 0.7512 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1362245 3 0.4849 0.4959 0.012 0.000 0.644 0.000 0.280 0.064
#> SRR1500194 1 0.0508 0.8366 0.984 0.000 0.000 0.004 0.000 0.012
#> SRR1414876 2 0.0717 0.9278 0.000 0.976 0.000 0.000 0.008 0.016
#> SRR1478523 3 0.5073 0.5785 0.000 0.000 0.712 0.084 0.128 0.076
#> SRR1325161 5 0.3330 0.5586 0.000 0.000 0.000 0.000 0.716 0.284
#> SRR1318026 4 0.1674 0.6681 0.000 0.004 0.000 0.924 0.068 0.004
#> SRR1343778 3 0.1082 0.7469 0.000 0.000 0.956 0.000 0.040 0.004
#> SRR1441287 1 0.1444 0.8358 0.928 0.000 0.000 0.000 0.000 0.072
#> SRR1430991 5 0.3110 0.6497 0.000 0.000 0.196 0.000 0.792 0.012
#> SRR1499722 5 0.3337 0.5811 0.000 0.000 0.004 0.000 0.736 0.260
#> SRR1351368 3 0.6350 0.4035 0.000 0.000 0.532 0.176 0.240 0.052
#> SRR1441785 1 0.1464 0.8259 0.944 0.000 0.000 0.004 0.016 0.036
#> SRR1096101 6 0.4187 0.3921 0.356 0.000 0.000 0.004 0.016 0.624
#> SRR808375 5 0.3301 0.6313 0.000 0.000 0.024 0.000 0.788 0.188
#> SRR1452842 6 0.3307 0.6807 0.044 0.000 0.000 0.000 0.148 0.808
#> SRR1311709 1 0.5219 0.3881 0.552 0.000 0.000 0.340 0.000 0.108
#> SRR1433352 3 0.4218 0.1524 0.000 0.000 0.556 0.000 0.428 0.016
#> SRR1340241 2 0.0806 0.9273 0.000 0.972 0.000 0.000 0.008 0.020
#> SRR1456754 6 0.3307 0.7194 0.148 0.000 0.000 0.000 0.044 0.808
#> SRR1465172 5 0.3838 0.2401 0.000 0.000 0.000 0.000 0.552 0.448
#> SRR1499284 6 0.3659 0.2964 0.000 0.000 0.000 0.000 0.364 0.636
#> SRR1499607 2 0.2070 0.8453 0.000 0.892 0.000 0.100 0.000 0.008
#> SRR812342 1 0.1556 0.8338 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1405374 1 0.0508 0.8364 0.984 0.000 0.000 0.004 0.000 0.012
#> SRR1403565 1 0.4948 0.3866 0.612 0.000 0.000 0.004 0.304 0.080
#> SRR1332024 3 0.0146 0.7527 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1471633 1 0.5279 0.3855 0.548 0.000 0.000 0.336 0.000 0.116
#> SRR1325944 2 0.0806 0.9273 0.000 0.972 0.000 0.000 0.008 0.020
#> SRR1429450 2 0.0806 0.9273 0.000 0.972 0.000 0.000 0.008 0.020
#> SRR821573 5 0.4039 0.4543 0.000 0.000 0.000 0.208 0.732 0.060
#> SRR1435372 1 0.4808 0.1703 0.480 0.000 0.000 0.052 0.000 0.468
#> SRR1324184 2 0.0146 0.9271 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR816517 2 0.3791 0.7593 0.000 0.816 0.000 0.064 0.056 0.064
#> SRR1324141 4 0.2162 0.6656 0.000 0.004 0.000 0.896 0.088 0.012
#> SRR1101612 1 0.1007 0.8391 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1356531 1 0.3175 0.6930 0.744 0.000 0.000 0.000 0.000 0.256
#> SRR1089785 5 0.3230 0.6443 0.000 0.000 0.212 0.000 0.776 0.012
#> SRR1077708 6 0.5966 -0.0992 0.000 0.000 0.232 0.000 0.340 0.428
#> SRR1343720 5 0.4389 0.4214 0.000 0.000 0.032 0.000 0.596 0.372
#> SRR1477499 2 0.0806 0.9273 0.000 0.972 0.000 0.000 0.008 0.020
#> SRR1347236 5 0.3601 0.5367 0.000 0.000 0.004 0.000 0.684 0.312
#> SRR1326408 6 0.3962 0.6952 0.128 0.000 0.000 0.096 0.004 0.772
#> SRR1336529 3 0.0000 0.7532 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1440643 5 0.8138 -0.1766 0.000 0.076 0.288 0.212 0.340 0.084
#> SRR662354 1 0.1141 0.8395 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1310817 5 0.2345 0.6376 0.000 0.000 0.028 0.028 0.904 0.040
#> SRR1347389 2 0.2631 0.7827 0.000 0.840 0.000 0.152 0.000 0.008
#> SRR1353097 1 0.3023 0.7122 0.768 0.000 0.000 0.000 0.000 0.232
#> SRR1384737 4 0.2162 0.6656 0.000 0.004 0.000 0.896 0.088 0.012
#> SRR1096339 1 0.0937 0.8398 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1345329 4 0.3766 0.6675 0.000 0.232 0.000 0.736 0.000 0.032
#> SRR1414771 3 0.0508 0.7486 0.000 0.000 0.984 0.000 0.012 0.004
#> SRR1309119 1 0.2190 0.8265 0.900 0.000 0.000 0.040 0.000 0.060
#> SRR1470438 3 0.0000 0.7532 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1343221 1 0.4141 0.1616 0.556 0.000 0.000 0.000 0.012 0.432
#> SRR1410847 1 0.1410 0.8414 0.944 0.000 0.000 0.004 0.008 0.044
#> SRR807949 5 0.3012 0.6488 0.000 0.000 0.196 0.000 0.796 0.008
#> SRR1442332 5 0.3652 0.5406 0.000 0.000 0.264 0.000 0.720 0.016
#> SRR815920 3 0.0146 0.7533 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1471524 3 0.6452 0.3499 0.000 0.000 0.492 0.172 0.288 0.048
#> SRR1477221 3 0.5056 0.4996 0.028 0.000 0.636 0.004 0.288 0.044
#> SRR1445046 2 0.1757 0.8715 0.000 0.916 0.000 0.076 0.000 0.008
#> SRR1331962 2 0.0717 0.9203 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR1319946 2 0.0508 0.9289 0.000 0.984 0.000 0.000 0.004 0.012
#> SRR1311599 1 0.1464 0.8259 0.944 0.000 0.000 0.004 0.016 0.036
#> SRR1323977 2 0.0862 0.9270 0.000 0.972 0.000 0.008 0.004 0.016
#> SRR1445132 2 0.0806 0.9273 0.000 0.972 0.000 0.000 0.008 0.020
#> SRR1337321 5 0.4650 0.6139 0.016 0.000 0.172 0.000 0.716 0.096
#> SRR1366390 2 0.1167 0.9217 0.000 0.960 0.000 0.012 0.008 0.020
#> SRR1343012 4 0.2333 0.6534 0.000 0.000 0.000 0.884 0.092 0.024
#> SRR1311958 2 0.0717 0.9203 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR1388234 2 0.1196 0.9046 0.000 0.952 0.000 0.040 0.000 0.008
#> SRR1370384 6 0.2734 0.7183 0.148 0.000 0.000 0.004 0.008 0.840
#> SRR1321650 3 0.3405 0.5657 0.000 0.000 0.724 0.000 0.272 0.004
#> SRR1485117 2 0.0146 0.9271 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1384713 6 0.2980 0.6333 0.008 0.000 0.000 0.000 0.192 0.800
#> SRR816609 4 0.4263 0.2113 0.000 0.480 0.000 0.504 0.000 0.016
#> SRR1486239 2 0.0717 0.9203 0.000 0.976 0.000 0.016 0.000 0.008
#> SRR1309638 6 0.4851 0.4091 0.000 0.000 0.272 0.000 0.096 0.632
#> SRR1356660 1 0.1464 0.8259 0.944 0.000 0.000 0.004 0.016 0.036
#> SRR1392883 2 0.0717 0.9278 0.000 0.976 0.000 0.000 0.008 0.016
#> SRR808130 5 0.2838 0.6509 0.000 0.000 0.188 0.000 0.808 0.004
#> SRR816677 4 0.4887 0.3634 0.280 0.000 0.000 0.624 0.000 0.096
#> SRR1455722 1 0.1556 0.8338 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1336029 1 0.1010 0.8401 0.960 0.000 0.000 0.004 0.000 0.036
#> SRR808452 1 0.1387 0.8366 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1352169 3 0.3445 0.6141 0.000 0.000 0.744 0.000 0.244 0.012
#> SRR1366707 3 0.4448 0.5876 0.000 0.000 0.724 0.040 0.204 0.032
#> SRR1328143 5 0.3629 0.5400 0.000 0.000 0.276 0.000 0.712 0.012
#> SRR1473567 2 0.0405 0.9250 0.000 0.988 0.000 0.008 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["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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.982 0.992 0.3566 0.639 0.639
#> 3 3 0.882 0.934 0.970 0.8136 0.675 0.507
#> 4 4 0.774 0.813 0.858 0.0707 0.981 0.947
#> 5 5 0.790 0.798 0.904 0.0824 0.860 0.611
#> 6 6 0.754 0.653 0.829 0.0486 0.964 0.856
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
#> SRR1442087 1 0.0000 0.998 1.000 0.000
#> SRR1390119 2 0.0000 0.972 0.000 1.000
#> SRR1436127 1 0.0000 0.998 1.000 0.000
#> SRR1347278 1 0.0000 0.998 1.000 0.000
#> SRR1332904 2 0.0000 0.972 0.000 1.000
#> SRR1444179 1 0.0000 0.998 1.000 0.000
#> SRR1082685 1 0.0000 0.998 1.000 0.000
#> SRR1362287 1 0.0000 0.998 1.000 0.000
#> SRR1339007 1 0.0000 0.998 1.000 0.000
#> SRR1376557 2 0.0000 0.972 0.000 1.000
#> SRR1468700 2 0.0000 0.972 0.000 1.000
#> SRR1077455 1 0.0000 0.998 1.000 0.000
#> SRR1413978 1 0.0000 0.998 1.000 0.000
#> SRR1439896 1 0.0000 0.998 1.000 0.000
#> SRR1317963 2 0.0000 0.972 0.000 1.000
#> SRR1431865 1 0.0000 0.998 1.000 0.000
#> SRR1394253 1 0.0000 0.998 1.000 0.000
#> SRR1082664 1 0.0000 0.998 1.000 0.000
#> SRR1077968 1 0.0000 0.998 1.000 0.000
#> SRR1076393 1 0.0000 0.998 1.000 0.000
#> SRR1477476 2 0.0000 0.972 0.000 1.000
#> SRR1398057 1 0.0000 0.998 1.000 0.000
#> SRR1485042 1 0.0000 0.998 1.000 0.000
#> SRR1385453 1 0.0000 0.998 1.000 0.000
#> SRR1348074 2 0.9686 0.375 0.396 0.604
#> SRR813959 1 0.0000 0.998 1.000 0.000
#> SRR665442 1 0.0000 0.998 1.000 0.000
#> SRR1378068 1 0.0000 0.998 1.000 0.000
#> SRR1485237 2 0.1184 0.960 0.016 0.984
#> SRR1350792 1 0.0000 0.998 1.000 0.000
#> SRR1326797 1 0.0000 0.998 1.000 0.000
#> SRR808994 1 0.0000 0.998 1.000 0.000
#> SRR1474041 1 0.0000 0.998 1.000 0.000
#> SRR1405641 1 0.0000 0.998 1.000 0.000
#> SRR1362245 1 0.0000 0.998 1.000 0.000
#> SRR1500194 1 0.0000 0.998 1.000 0.000
#> SRR1414876 2 0.0000 0.972 0.000 1.000
#> SRR1478523 1 0.0000 0.998 1.000 0.000
#> SRR1325161 1 0.0000 0.998 1.000 0.000
#> SRR1318026 1 0.0000 0.998 1.000 0.000
#> SRR1343778 1 0.0000 0.998 1.000 0.000
#> SRR1441287 1 0.0000 0.998 1.000 0.000
#> SRR1430991 1 0.0000 0.998 1.000 0.000
#> SRR1499722 1 0.0000 0.998 1.000 0.000
#> SRR1351368 1 0.0000 0.998 1.000 0.000
#> SRR1441785 1 0.0000 0.998 1.000 0.000
#> SRR1096101 1 0.0000 0.998 1.000 0.000
#> SRR808375 1 0.0000 0.998 1.000 0.000
#> SRR1452842 1 0.0000 0.998 1.000 0.000
#> SRR1311709 1 0.0000 0.998 1.000 0.000
#> SRR1433352 1 0.0000 0.998 1.000 0.000
#> SRR1340241 2 0.0000 0.972 0.000 1.000
#> SRR1456754 1 0.0000 0.998 1.000 0.000
#> SRR1465172 1 0.0000 0.998 1.000 0.000
#> SRR1499284 1 0.0000 0.998 1.000 0.000
#> SRR1499607 2 0.0000 0.972 0.000 1.000
#> SRR812342 1 0.0000 0.998 1.000 0.000
#> SRR1405374 1 0.0000 0.998 1.000 0.000
#> SRR1403565 1 0.0000 0.998 1.000 0.000
#> SRR1332024 1 0.0000 0.998 1.000 0.000
#> SRR1471633 1 0.0000 0.998 1.000 0.000
#> SRR1325944 2 0.0000 0.972 0.000 1.000
#> SRR1429450 2 0.0000 0.972 0.000 1.000
#> SRR821573 1 0.0000 0.998 1.000 0.000
#> SRR1435372 1 0.0000 0.998 1.000 0.000
#> SRR1324184 2 0.0000 0.972 0.000 1.000
#> SRR816517 1 0.0000 0.998 1.000 0.000
#> SRR1324141 1 0.0000 0.998 1.000 0.000
#> SRR1101612 1 0.0000 0.998 1.000 0.000
#> SRR1356531 1 0.0000 0.998 1.000 0.000
#> SRR1089785 1 0.0000 0.998 1.000 0.000
#> SRR1077708 1 0.0000 0.998 1.000 0.000
#> SRR1343720 1 0.0000 0.998 1.000 0.000
#> SRR1477499 2 0.0000 0.972 0.000 1.000
#> SRR1347236 1 0.0000 0.998 1.000 0.000
#> SRR1326408 1 0.0000 0.998 1.000 0.000
#> SRR1336529 1 0.0000 0.998 1.000 0.000
#> SRR1440643 1 0.0000 0.998 1.000 0.000
#> SRR662354 1 0.0000 0.998 1.000 0.000
#> SRR1310817 1 0.0000 0.998 1.000 0.000
#> SRR1347389 2 0.6973 0.777 0.188 0.812
#> SRR1353097 1 0.0000 0.998 1.000 0.000
#> SRR1384737 1 0.0000 0.998 1.000 0.000
#> SRR1096339 1 0.0000 0.998 1.000 0.000
#> SRR1345329 2 0.5946 0.834 0.144 0.856
#> SRR1414771 1 0.0000 0.998 1.000 0.000
#> SRR1309119 1 0.0000 0.998 1.000 0.000
#> SRR1470438 1 0.0000 0.998 1.000 0.000
#> SRR1343221 1 0.0000 0.998 1.000 0.000
#> SRR1410847 1 0.0000 0.998 1.000 0.000
#> SRR807949 1 0.0000 0.998 1.000 0.000
#> SRR1442332 1 0.0000 0.998 1.000 0.000
#> SRR815920 1 0.0000 0.998 1.000 0.000
#> SRR1471524 1 0.0000 0.998 1.000 0.000
#> SRR1477221 1 0.0000 0.998 1.000 0.000
#> SRR1445046 2 0.0000 0.972 0.000 1.000
#> SRR1331962 2 0.0000 0.972 0.000 1.000
#> SRR1319946 2 0.0000 0.972 0.000 1.000
#> SRR1311599 1 0.0000 0.998 1.000 0.000
#> SRR1323977 1 0.6973 0.762 0.812 0.188
#> SRR1445132 2 0.0000 0.972 0.000 1.000
#> SRR1337321 1 0.0000 0.998 1.000 0.000
#> SRR1366390 2 0.2603 0.937 0.044 0.956
#> SRR1343012 1 0.0000 0.998 1.000 0.000
#> SRR1311958 2 0.0000 0.972 0.000 1.000
#> SRR1388234 2 0.0000 0.972 0.000 1.000
#> SRR1370384 1 0.0000 0.998 1.000 0.000
#> SRR1321650 1 0.0000 0.998 1.000 0.000
#> SRR1485117 2 0.0000 0.972 0.000 1.000
#> SRR1384713 1 0.0000 0.998 1.000 0.000
#> SRR816609 2 0.0376 0.969 0.004 0.996
#> SRR1486239 2 0.0000 0.972 0.000 1.000
#> SRR1309638 1 0.0000 0.998 1.000 0.000
#> SRR1356660 1 0.0000 0.998 1.000 0.000
#> SRR1392883 2 0.0000 0.972 0.000 1.000
#> SRR808130 1 0.0000 0.998 1.000 0.000
#> SRR816677 1 0.0000 0.998 1.000 0.000
#> SRR1455722 1 0.0000 0.998 1.000 0.000
#> SRR1336029 1 0.0000 0.998 1.000 0.000
#> SRR808452 1 0.0000 0.998 1.000 0.000
#> SRR1352169 1 0.0000 0.998 1.000 0.000
#> SRR1366707 1 0.0000 0.998 1.000 0.000
#> SRR1328143 1 0.0000 0.998 1.000 0.000
#> SRR1473567 2 0.0000 0.972 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1347278 1 0.5706 0.582 0.680 0.000 0.320
#> SRR1332904 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1077455 1 0.4605 0.767 0.796 0.000 0.204
#> SRR1413978 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1082664 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1077968 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1398057 3 0.0424 0.970 0.008 0.000 0.992
#> SRR1485042 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1385453 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1348074 1 0.0000 0.945 1.000 0.000 0.000
#> SRR813959 3 0.0000 0.976 0.000 0.000 1.000
#> SRR665442 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1378068 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1485237 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1350792 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1326797 1 0.4750 0.753 0.784 0.000 0.216
#> SRR808994 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1474041 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1362245 3 0.2625 0.895 0.084 0.000 0.916
#> SRR1500194 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1478523 3 0.0892 0.960 0.020 0.000 0.980
#> SRR1325161 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1318026 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1343778 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1441287 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1430991 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1499722 1 0.6225 0.317 0.568 0.000 0.432
#> SRR1351368 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1441785 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.945 1.000 0.000 0.000
#> SRR808375 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1452842 1 0.4291 0.794 0.820 0.000 0.180
#> SRR1311709 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1433352 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1456754 1 0.0424 0.940 0.992 0.000 0.008
#> SRR1465172 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1499284 1 0.4750 0.753 0.784 0.000 0.216
#> SRR1499607 2 0.0000 0.991 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1332024 3 0.4750 0.722 0.216 0.000 0.784
#> SRR1471633 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.991 0.000 1.000 0.000
#> SRR821573 3 0.1753 0.932 0.048 0.000 0.952
#> SRR1435372 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.991 0.000 1.000 0.000
#> SRR816517 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1324141 1 0.4750 0.753 0.784 0.000 0.216
#> SRR1101612 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1089785 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1347236 3 0.4931 0.678 0.232 0.000 0.768
#> SRR1326408 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1440643 3 0.0424 0.970 0.008 0.000 0.992
#> SRR662354 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1310817 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1347389 2 0.4504 0.763 0.196 0.804 0.000
#> SRR1353097 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1384737 1 0.0237 0.942 0.996 0.000 0.004
#> SRR1096339 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1345329 1 0.3116 0.846 0.892 0.108 0.000
#> SRR1414771 3 0.0424 0.970 0.008 0.000 0.992
#> SRR1309119 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1470438 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1343221 1 0.4555 0.742 0.800 0.000 0.200
#> SRR1410847 1 0.0000 0.945 1.000 0.000 0.000
#> SRR807949 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.976 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1477221 3 0.4002 0.803 0.160 0.000 0.840
#> SRR1445046 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1319946 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1323977 1 0.2796 0.872 0.908 0.092 0.000
#> SRR1445132 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1337321 3 0.2878 0.882 0.096 0.000 0.904
#> SRR1366390 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1343012 1 0.4654 0.763 0.792 0.000 0.208
#> SRR1311958 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1388234 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1370384 1 0.0592 0.937 0.988 0.000 0.012
#> SRR1321650 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1384713 1 0.4750 0.753 0.784 0.000 0.216
#> SRR816609 2 0.0592 0.980 0.012 0.988 0.000
#> SRR1486239 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1356660 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.991 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.976 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.945 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.945 1.000 0.000 0.000
#> SRR1352169 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1366707 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1328143 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.991 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.2081 0.840 0.000 0.000 0.916 0.084
#> SRR1390119 4 0.4697 0.980 0.000 0.356 0.000 0.644
#> SRR1436127 3 0.0592 0.826 0.000 0.000 0.984 0.016
#> SRR1347278 1 0.6955 0.404 0.560 0.000 0.296 0.144
#> SRR1332904 2 0.2281 0.852 0.000 0.904 0.000 0.096
#> SRR1444179 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.2149 0.861 0.000 0.912 0.000 0.088
#> SRR1468700 2 0.2149 0.861 0.000 0.912 0.000 0.088
#> SRR1077455 1 0.6770 0.510 0.604 0.000 0.160 0.236
#> SRR1413978 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.0336 0.880 0.000 0.992 0.000 0.008
#> SRR1431865 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1082664 3 0.4454 0.810 0.000 0.000 0.692 0.308
#> SRR1077968 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1076393 3 0.4477 0.809 0.000 0.000 0.688 0.312
#> SRR1477476 4 0.4697 0.980 0.000 0.356 0.000 0.644
#> SRR1398057 3 0.2376 0.837 0.016 0.000 0.916 0.068
#> SRR1485042 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1385453 3 0.2882 0.748 0.000 0.084 0.892 0.024
#> SRR1348074 1 0.2048 0.846 0.928 0.064 0.000 0.008
#> SRR813959 3 0.6300 0.766 0.000 0.084 0.608 0.308
#> SRR665442 1 0.2216 0.832 0.908 0.092 0.000 0.000
#> SRR1378068 3 0.0817 0.813 0.000 0.000 0.976 0.024
#> SRR1485237 1 0.2480 0.830 0.904 0.088 0.000 0.008
#> SRR1350792 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.7212 0.364 0.516 0.000 0.160 0.324
#> SRR808994 3 0.0817 0.813 0.000 0.000 0.976 0.024
#> SRR1474041 3 0.4543 0.803 0.000 0.000 0.676 0.324
#> SRR1405641 3 0.0817 0.813 0.000 0.000 0.976 0.024
#> SRR1362245 3 0.5394 0.800 0.060 0.000 0.712 0.228
#> SRR1500194 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.3688 0.639 0.000 0.792 0.000 0.208
#> SRR1478523 3 0.1284 0.813 0.012 0.000 0.964 0.024
#> SRR1325161 3 0.4543 0.803 0.000 0.000 0.676 0.324
#> SRR1318026 1 0.0336 0.886 0.992 0.000 0.000 0.008
#> SRR1343778 3 0.1389 0.835 0.000 0.000 0.952 0.048
#> SRR1441287 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1430991 3 0.3444 0.836 0.000 0.000 0.816 0.184
#> SRR1499722 1 0.7748 0.102 0.428 0.000 0.248 0.324
#> SRR1351368 3 0.1211 0.825 0.000 0.000 0.960 0.040
#> SRR1441785 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR808375 3 0.4543 0.803 0.000 0.000 0.676 0.324
#> SRR1452842 1 0.6420 0.565 0.640 0.000 0.132 0.228
#> SRR1311709 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1433352 3 0.1940 0.839 0.000 0.000 0.924 0.076
#> SRR1340241 4 0.4730 0.968 0.000 0.364 0.000 0.636
#> SRR1456754 1 0.3626 0.754 0.812 0.000 0.004 0.184
#> SRR1465172 3 0.4543 0.803 0.000 0.000 0.676 0.324
#> SRR1499284 1 0.7212 0.364 0.516 0.000 0.160 0.324
#> SRR1499607 2 0.0336 0.880 0.000 0.992 0.000 0.008
#> SRR812342 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1332024 3 0.3659 0.677 0.136 0.000 0.840 0.024
#> SRR1471633 1 0.0336 0.886 0.992 0.000 0.000 0.008
#> SRR1325944 4 0.4697 0.980 0.000 0.356 0.000 0.644
#> SRR1429450 4 0.4697 0.980 0.000 0.356 0.000 0.644
#> SRR821573 3 0.5793 0.766 0.048 0.000 0.628 0.324
#> SRR1435372 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.0000 0.883 0.000 1.000 0.000 0.000
#> SRR816517 3 0.3149 0.737 0.000 0.088 0.880 0.032
#> SRR1324141 1 0.7256 0.411 0.540 0.004 0.156 0.300
#> SRR1101612 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1089785 3 0.4454 0.811 0.000 0.000 0.692 0.308
#> SRR1077708 3 0.4500 0.806 0.000 0.000 0.684 0.316
#> SRR1343720 3 0.2281 0.841 0.000 0.000 0.904 0.096
#> SRR1477499 4 0.4925 0.859 0.000 0.428 0.000 0.572
#> SRR1347236 3 0.7324 0.530 0.240 0.000 0.532 0.228
#> SRR1326408 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1336529 3 0.0817 0.813 0.000 0.000 0.976 0.024
#> SRR1440643 3 0.2021 0.813 0.040 0.000 0.936 0.024
#> SRR662354 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1310817 3 0.4543 0.803 0.000 0.000 0.676 0.324
#> SRR1347389 2 0.2611 0.695 0.096 0.896 0.000 0.008
#> SRR1353097 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1384737 1 0.0524 0.885 0.988 0.000 0.004 0.008
#> SRR1096339 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1345329 1 0.3351 0.761 0.844 0.148 0.000 0.008
#> SRR1414771 3 0.0817 0.813 0.000 0.000 0.976 0.024
#> SRR1309119 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1470438 3 0.1004 0.811 0.004 0.000 0.972 0.024
#> SRR1343221 1 0.3528 0.691 0.808 0.000 0.192 0.000
#> SRR1410847 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR807949 3 0.4382 0.815 0.000 0.000 0.704 0.296
#> SRR1442332 3 0.4103 0.825 0.000 0.000 0.744 0.256
#> SRR815920 3 0.0817 0.813 0.000 0.000 0.976 0.024
#> SRR1471524 3 0.2973 0.837 0.000 0.000 0.856 0.144
#> SRR1477221 3 0.3528 0.680 0.192 0.000 0.808 0.000
#> SRR1445046 2 0.0336 0.880 0.000 0.992 0.000 0.008
#> SRR1331962 2 0.2081 0.864 0.000 0.916 0.000 0.084
#> SRR1319946 2 0.0336 0.883 0.000 0.992 0.000 0.008
#> SRR1311599 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1323977 1 0.3351 0.779 0.844 0.148 0.000 0.008
#> SRR1445132 4 0.4697 0.980 0.000 0.356 0.000 0.644
#> SRR1337321 3 0.5558 0.779 0.036 0.000 0.640 0.324
#> SRR1366390 2 0.0817 0.875 0.000 0.976 0.000 0.024
#> SRR1343012 1 0.7013 0.436 0.556 0.000 0.152 0.292
#> SRR1311958 2 0.0000 0.883 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0336 0.880 0.000 0.992 0.000 0.008
#> SRR1370384 1 0.3768 0.751 0.808 0.000 0.008 0.184
#> SRR1321650 3 0.2647 0.839 0.000 0.000 0.880 0.120
#> SRR1485117 2 0.2149 0.861 0.000 0.912 0.000 0.088
#> SRR1384713 1 0.7198 0.371 0.520 0.000 0.160 0.320
#> SRR816609 2 0.1722 0.809 0.048 0.944 0.000 0.008
#> SRR1486239 2 0.2081 0.864 0.000 0.916 0.000 0.084
#> SRR1309638 3 0.4500 0.806 0.000 0.000 0.684 0.316
#> SRR1356660 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1392883 4 0.4697 0.980 0.000 0.356 0.000 0.644
#> SRR808130 3 0.4477 0.809 0.000 0.000 0.688 0.312
#> SRR816677 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1455722 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.2053 0.838 0.004 0.000 0.924 0.072
#> SRR1366707 3 0.1474 0.821 0.000 0.000 0.948 0.052
#> SRR1328143 3 0.3266 0.838 0.000 0.000 0.832 0.168
#> SRR1473567 2 0.2081 0.864 0.000 0.916 0.000 0.084
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 3 0.3707 0.6824 0.000 0.000 0.716 0.000 0.284
#> SRR1390119 2 0.0000 0.9731 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.2280 0.8062 0.000 0.000 0.880 0.000 0.120
#> SRR1347278 5 0.6554 0.1628 0.392 0.000 0.200 0.000 0.408
#> SRR1332904 4 0.1952 0.9032 0.000 0.084 0.004 0.912 0.000
#> SRR1444179 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1376557 4 0.1410 0.9147 0.000 0.060 0.000 0.940 0.000
#> SRR1468700 4 0.1410 0.9147 0.000 0.060 0.000 0.940 0.000
#> SRR1077455 5 0.3895 0.5057 0.320 0.000 0.000 0.000 0.680
#> SRR1413978 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.0162 0.9205 0.000 0.000 0.004 0.996 0.000
#> SRR1431865 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1082664 5 0.1043 0.7690 0.000 0.000 0.040 0.000 0.960
#> SRR1077968 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1076393 5 0.2891 0.6200 0.000 0.000 0.176 0.000 0.824
#> SRR1477476 2 0.0000 0.9731 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.3391 0.7654 0.012 0.000 0.800 0.000 0.188
#> SRR1485042 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1385453 3 0.0162 0.7879 0.000 0.000 0.996 0.000 0.004
#> SRR1348074 1 0.2423 0.8706 0.896 0.000 0.024 0.080 0.000
#> SRR813959 5 0.3803 0.6707 0.000 0.000 0.140 0.056 0.804
#> SRR665442 1 0.2940 0.8535 0.876 0.000 0.048 0.072 0.004
#> SRR1378068 3 0.1270 0.8191 0.000 0.000 0.948 0.000 0.052
#> SRR1485237 1 0.2659 0.8648 0.888 0.000 0.052 0.060 0.000
#> SRR1350792 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR808994 3 0.1270 0.8191 0.000 0.000 0.948 0.000 0.052
#> SRR1474041 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1405641 3 0.1270 0.8191 0.000 0.000 0.948 0.000 0.052
#> SRR1362245 3 0.5289 0.1678 0.048 0.000 0.500 0.000 0.452
#> SRR1500194 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1414876 4 0.3913 0.6175 0.000 0.324 0.000 0.676 0.000
#> SRR1478523 3 0.3555 0.7439 0.124 0.000 0.824 0.000 0.052
#> SRR1325161 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1318026 1 0.0162 0.9555 0.996 0.000 0.004 0.000 0.000
#> SRR1343778 3 0.2773 0.7836 0.000 0.000 0.836 0.000 0.164
#> SRR1441287 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.4150 0.0951 0.000 0.000 0.388 0.000 0.612
#> SRR1499722 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1351368 3 0.3143 0.7612 0.000 0.000 0.796 0.000 0.204
#> SRR1441785 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1452842 5 0.4060 0.4600 0.360 0.000 0.000 0.000 0.640
#> SRR1311709 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1433352 3 0.4960 0.6728 0.080 0.000 0.688 0.000 0.232
#> SRR1340241 2 0.0510 0.9592 0.000 0.984 0.000 0.016 0.000
#> SRR1456754 1 0.4287 0.0723 0.540 0.000 0.000 0.000 0.460
#> SRR1465172 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1499284 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1499607 4 0.0404 0.9201 0.000 0.000 0.012 0.988 0.000
#> SRR812342 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1332024 3 0.1270 0.7827 0.052 0.000 0.948 0.000 0.000
#> SRR1471633 1 0.0162 0.9555 0.996 0.000 0.004 0.000 0.000
#> SRR1325944 2 0.0000 0.9731 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9731 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.0000 0.7797 0.000 0.000 0.000 0.000 1.000
#> SRR1435372 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1324184 4 0.1197 0.9083 0.000 0.000 0.048 0.952 0.000
#> SRR816517 3 0.1410 0.7607 0.000 0.000 0.940 0.060 0.000
#> SRR1324141 5 0.3647 0.6053 0.228 0.000 0.004 0.004 0.764
#> SRR1101612 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.1544 0.7514 0.000 0.000 0.068 0.000 0.932
#> SRR1077708 5 0.1410 0.7617 0.000 0.000 0.060 0.000 0.940
#> SRR1343720 3 0.4273 0.3543 0.000 0.000 0.552 0.000 0.448
#> SRR1477499 2 0.2732 0.8032 0.000 0.840 0.000 0.160 0.000
#> SRR1347236 5 0.5435 0.5137 0.188 0.000 0.152 0.000 0.660
#> SRR1326408 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.1270 0.8191 0.000 0.000 0.948 0.000 0.052
#> SRR1440643 3 0.4035 0.7112 0.156 0.000 0.784 0.000 0.060
#> SRR662354 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.0162 0.7793 0.000 0.000 0.004 0.000 0.996
#> SRR1347389 4 0.2790 0.8431 0.068 0.000 0.052 0.880 0.000
#> SRR1353097 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1384737 1 0.0324 0.9526 0.992 0.000 0.004 0.000 0.004
#> SRR1096339 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 1 0.3284 0.7906 0.828 0.000 0.024 0.148 0.000
#> SRR1414771 3 0.1270 0.8191 0.000 0.000 0.948 0.000 0.052
#> SRR1309119 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1470438 3 0.1357 0.8179 0.004 0.000 0.948 0.000 0.048
#> SRR1343221 1 0.1341 0.9051 0.944 0.000 0.056 0.000 0.000
#> SRR1410847 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.1732 0.7400 0.000 0.000 0.080 0.000 0.920
#> SRR1442332 5 0.3452 0.5442 0.000 0.000 0.244 0.000 0.756
#> SRR815920 3 0.1270 0.8191 0.000 0.000 0.948 0.000 0.052
#> SRR1471524 5 0.4283 -0.1229 0.000 0.000 0.456 0.000 0.544
#> SRR1477221 3 0.4756 0.5474 0.288 0.000 0.668 0.000 0.044
#> SRR1445046 4 0.0000 0.9204 0.000 0.000 0.000 1.000 0.000
#> SRR1331962 4 0.1197 0.9176 0.000 0.048 0.000 0.952 0.000
#> SRR1319946 4 0.1981 0.9109 0.000 0.028 0.048 0.924 0.000
#> SRR1311599 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1323977 1 0.3516 0.8113 0.836 0.000 0.052 0.108 0.004
#> SRR1445132 2 0.0000 0.9731 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 5 0.0510 0.7774 0.000 0.000 0.016 0.000 0.984
#> SRR1366390 4 0.2221 0.8966 0.000 0.036 0.052 0.912 0.000
#> SRR1343012 5 0.3395 0.6014 0.236 0.000 0.000 0.000 0.764
#> SRR1311958 4 0.0290 0.9204 0.000 0.000 0.008 0.992 0.000
#> SRR1388234 4 0.1197 0.9081 0.000 0.000 0.048 0.952 0.000
#> SRR1370384 1 0.4291 0.0569 0.536 0.000 0.000 0.000 0.464
#> SRR1321650 3 0.3949 0.5705 0.000 0.000 0.668 0.000 0.332
#> SRR1485117 4 0.1544 0.9117 0.000 0.068 0.000 0.932 0.000
#> SRR1384713 5 0.1121 0.7637 0.044 0.000 0.000 0.000 0.956
#> SRR816609 4 0.3146 0.8108 0.092 0.000 0.052 0.856 0.000
#> SRR1486239 4 0.1478 0.9147 0.000 0.064 0.000 0.936 0.000
#> SRR1309638 5 0.1851 0.7451 0.000 0.000 0.088 0.000 0.912
#> SRR1356660 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9731 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.1197 0.7639 0.000 0.000 0.048 0.000 0.952
#> SRR816677 1 0.0162 0.9551 0.996 0.000 0.000 0.000 0.004
#> SRR1455722 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.9580 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.3766 0.6980 0.004 0.000 0.728 0.000 0.268
#> SRR1366707 3 0.3039 0.7432 0.000 0.000 0.808 0.000 0.192
#> SRR1328143 5 0.4278 -0.1293 0.000 0.000 0.452 0.000 0.548
#> SRR1473567 4 0.1341 0.9159 0.000 0.056 0.000 0.944 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 3 0.3847 0.2225 0.000 0.000 0.644 0.008 0.348 0.000
#> SRR1390119 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1436127 3 0.3611 0.5476 0.000 0.000 0.796 0.108 0.096 0.000
#> SRR1347278 4 0.6662 0.5728 0.128 0.000 0.108 0.512 0.252 0.000
#> SRR1332904 2 0.1858 0.8518 0.000 0.912 0.000 0.076 0.000 0.012
#> SRR1444179 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0146 0.9009 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1362287 1 0.1556 0.8504 0.920 0.000 0.000 0.080 0.000 0.000
#> SRR1339007 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1376557 2 0.0146 0.8546 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1468700 2 0.0146 0.8546 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1077455 5 0.3699 0.3157 0.336 0.000 0.000 0.004 0.660 0.000
#> SRR1413978 1 0.0146 0.9009 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1439896 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.1444 0.8543 0.000 0.928 0.000 0.072 0.000 0.000
#> SRR1431865 1 0.0458 0.8944 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR1394253 1 0.0865 0.8829 0.964 0.000 0.000 0.036 0.000 0.000
#> SRR1082664 5 0.3268 0.5284 0.000 0.000 0.044 0.144 0.812 0.000
#> SRR1077968 1 0.0146 0.9009 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1076393 5 0.2805 0.5036 0.000 0.000 0.184 0.004 0.812 0.000
#> SRR1477476 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1398057 4 0.6147 0.4893 0.020 0.000 0.260 0.508 0.212 0.000
#> SRR1485042 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1385453 3 0.2092 0.6154 0.000 0.000 0.876 0.124 0.000 0.000
#> SRR1348074 1 0.3819 0.6062 0.672 0.012 0.000 0.316 0.000 0.000
#> SRR813959 5 0.3426 0.4218 0.000 0.004 0.000 0.276 0.720 0.000
#> SRR665442 1 0.4009 0.5385 0.632 0.008 0.000 0.356 0.004 0.000
#> SRR1378068 3 0.0000 0.6481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1485237 1 0.3843 0.4193 0.548 0.000 0.000 0.452 0.000 0.000
#> SRR1350792 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0713 0.6506 0.000 0.000 0.000 0.028 0.972 0.000
#> SRR808994 3 0.0632 0.6464 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1474041 5 0.3101 0.3440 0.000 0.000 0.000 0.244 0.756 0.000
#> SRR1405641 3 0.0632 0.6464 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1362245 4 0.6138 0.5637 0.044 0.000 0.120 0.512 0.324 0.000
#> SRR1500194 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1414876 2 0.3482 0.5909 0.000 0.684 0.000 0.000 0.000 0.316
#> SRR1478523 3 0.3678 0.5241 0.084 0.000 0.788 0.128 0.000 0.000
#> SRR1325161 5 0.0000 0.6568 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1318026 1 0.1267 0.8701 0.940 0.000 0.000 0.060 0.000 0.000
#> SRR1343778 3 0.4293 0.3810 0.000 0.000 0.716 0.084 0.200 0.000
#> SRR1441287 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.3742 0.2380 0.000 0.000 0.348 0.004 0.648 0.000
#> SRR1499722 5 0.0146 0.6563 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1351368 3 0.4406 0.4195 0.000 0.000 0.696 0.224 0.080 0.000
#> SRR1441785 1 0.1863 0.8271 0.896 0.000 0.000 0.104 0.000 0.000
#> SRR1096101 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.0000 0.6568 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1452842 5 0.4551 0.2620 0.344 0.000 0.000 0.048 0.608 0.000
#> SRR1311709 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1433352 3 0.5818 -0.0350 0.000 0.000 0.496 0.256 0.248 0.000
#> SRR1340241 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1456754 5 0.5585 0.0518 0.416 0.000 0.000 0.140 0.444 0.000
#> SRR1465172 5 0.0000 0.6568 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499284 5 0.0000 0.6568 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499607 2 0.3126 0.7819 0.000 0.752 0.000 0.248 0.000 0.000
#> SRR812342 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.1556 0.8474 0.920 0.000 0.000 0.080 0.000 0.000
#> SRR1403565 1 0.3804 0.2741 0.576 0.000 0.000 0.424 0.000 0.000
#> SRR1332024 3 0.3672 0.2595 0.000 0.000 0.632 0.368 0.000 0.000
#> SRR1471633 1 0.1075 0.8773 0.952 0.000 0.000 0.048 0.000 0.000
#> SRR1325944 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1429450 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR821573 5 0.0260 0.6564 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1435372 1 0.0146 0.9009 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1324184 2 0.2664 0.8163 0.000 0.816 0.000 0.184 0.000 0.000
#> SRR816517 3 0.3996 0.2888 0.000 0.004 0.512 0.484 0.000 0.000
#> SRR1324141 5 0.5322 0.3174 0.216 0.000 0.000 0.188 0.596 0.000
#> SRR1101612 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.1204 0.6379 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1077708 5 0.2843 0.5779 0.000 0.000 0.036 0.116 0.848 0.000
#> SRR1343720 5 0.5127 0.0149 0.000 0.000 0.364 0.092 0.544 0.000
#> SRR1477499 6 0.2178 0.8498 0.000 0.132 0.000 0.000 0.000 0.868
#> SRR1347236 5 0.5058 0.3716 0.188 0.000 0.124 0.016 0.672 0.000
#> SRR1326408 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.0632 0.6464 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1440643 4 0.4640 0.0753 0.032 0.000 0.436 0.528 0.004 0.000
#> SRR662354 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.0260 0.6567 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1347389 2 0.3737 0.6930 0.000 0.608 0.000 0.392 0.000 0.000
#> SRR1353097 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384737 1 0.2933 0.7448 0.796 0.000 0.000 0.200 0.004 0.000
#> SRR1096339 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1345329 1 0.5083 0.5073 0.604 0.116 0.000 0.280 0.000 0.000
#> SRR1414771 3 0.3221 0.3944 0.000 0.000 0.736 0.264 0.000 0.000
#> SRR1309119 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1470438 3 0.3221 0.3944 0.000 0.000 0.736 0.264 0.000 0.000
#> SRR1343221 1 0.3893 0.6839 0.768 0.000 0.092 0.140 0.000 0.000
#> SRR1410847 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.1219 0.6393 0.000 0.000 0.048 0.004 0.948 0.000
#> SRR1442332 4 0.4644 0.3213 0.000 0.000 0.040 0.504 0.456 0.000
#> SRR815920 3 0.0146 0.6476 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1471524 5 0.3966 0.0575 0.000 0.000 0.444 0.004 0.552 0.000
#> SRR1477221 4 0.6434 0.3675 0.200 0.000 0.240 0.512 0.048 0.000
#> SRR1445046 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1331962 2 0.0146 0.8546 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1319946 2 0.3101 0.8066 0.000 0.756 0.000 0.244 0.000 0.000
#> SRR1311599 1 0.2562 0.7504 0.828 0.000 0.000 0.172 0.000 0.000
#> SRR1323977 1 0.4100 0.4967 0.600 0.008 0.000 0.388 0.004 0.000
#> SRR1445132 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1337321 5 0.3847 -0.2034 0.000 0.000 0.000 0.456 0.544 0.000
#> SRR1366390 2 0.3838 0.6706 0.000 0.552 0.000 0.448 0.000 0.000
#> SRR1343012 5 0.4503 0.4185 0.100 0.000 0.000 0.204 0.696 0.000
#> SRR1311958 2 0.0713 0.8563 0.000 0.972 0.000 0.028 0.000 0.000
#> SRR1388234 2 0.3101 0.8079 0.000 0.756 0.000 0.244 0.000 0.000
#> SRR1370384 1 0.3971 0.0966 0.548 0.000 0.000 0.004 0.448 0.000
#> SRR1321650 4 0.6111 0.3831 0.000 0.000 0.304 0.372 0.324 0.000
#> SRR1485117 2 0.0260 0.8540 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1384713 5 0.0937 0.6451 0.040 0.000 0.000 0.000 0.960 0.000
#> SRR816609 2 0.3934 0.7227 0.008 0.616 0.000 0.376 0.000 0.000
#> SRR1486239 2 0.1524 0.8530 0.000 0.932 0.000 0.060 0.000 0.008
#> SRR1309638 5 0.3102 0.5391 0.000 0.000 0.028 0.156 0.816 0.000
#> SRR1356660 1 0.0146 0.9005 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1392883 6 0.0000 0.9805 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR808130 5 0.1010 0.6476 0.000 0.000 0.036 0.004 0.960 0.000
#> SRR816677 1 0.0508 0.8958 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR1455722 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.9022 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.5900 -0.0422 0.004 0.000 0.496 0.288 0.212 0.000
#> SRR1366707 3 0.1341 0.6410 0.000 0.000 0.948 0.024 0.028 0.000
#> SRR1328143 5 0.4228 0.1331 0.000 0.000 0.392 0.020 0.588 0.000
#> SRR1473567 2 0.0146 0.8546 0.000 0.996 0.000 0.000 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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 17851 rows and 124 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 0.871 0.924 0.967 0.4592 0.534 0.534
#> 3 3 0.669 0.740 0.803 0.3254 0.770 0.584
#> 4 4 0.666 0.636 0.799 0.1875 0.803 0.522
#> 5 5 0.602 0.581 0.757 0.0614 0.868 0.598
#> 6 6 0.784 0.696 0.802 0.0493 0.903 0.640
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
#> SRR1442087 1 0.0000 0.97848 1.000 0.000
#> SRR1390119 2 0.0000 0.94036 0.000 1.000
#> SRR1436127 1 0.0000 0.97848 1.000 0.000
#> SRR1347278 1 0.0000 0.97848 1.000 0.000
#> SRR1332904 2 0.0000 0.94036 0.000 1.000
#> SRR1444179 1 0.0000 0.97848 1.000 0.000
#> SRR1082685 1 0.3733 0.90460 0.928 0.072
#> SRR1362287 1 0.0000 0.97848 1.000 0.000
#> SRR1339007 1 0.0000 0.97848 1.000 0.000
#> SRR1376557 2 0.0000 0.94036 0.000 1.000
#> SRR1468700 2 0.0000 0.94036 0.000 1.000
#> SRR1077455 1 0.0000 0.97848 1.000 0.000
#> SRR1413978 1 0.1633 0.95652 0.976 0.024
#> SRR1439896 1 0.0000 0.97848 1.000 0.000
#> SRR1317963 2 0.0000 0.94036 0.000 1.000
#> SRR1431865 1 0.0000 0.97848 1.000 0.000
#> SRR1394253 1 0.0000 0.97848 1.000 0.000
#> SRR1082664 1 0.0000 0.97848 1.000 0.000
#> SRR1077968 1 0.0000 0.97848 1.000 0.000
#> SRR1076393 1 0.0000 0.97848 1.000 0.000
#> SRR1477476 2 0.0000 0.94036 0.000 1.000
#> SRR1398057 1 0.0000 0.97848 1.000 0.000
#> SRR1485042 1 0.0000 0.97848 1.000 0.000
#> SRR1385453 2 0.0938 0.93550 0.012 0.988
#> SRR1348074 2 0.0938 0.93550 0.012 0.988
#> SRR813959 2 0.0000 0.94036 0.000 1.000
#> SRR665442 2 0.0000 0.94036 0.000 1.000
#> SRR1378068 1 0.0000 0.97848 1.000 0.000
#> SRR1485237 2 0.0672 0.93736 0.008 0.992
#> SRR1350792 1 0.0000 0.97848 1.000 0.000
#> SRR1326797 1 0.2778 0.93199 0.952 0.048
#> SRR808994 1 0.0000 0.97848 1.000 0.000
#> SRR1474041 1 0.0000 0.97848 1.000 0.000
#> SRR1405641 1 0.0000 0.97848 1.000 0.000
#> SRR1362245 1 0.0000 0.97848 1.000 0.000
#> SRR1500194 1 0.0000 0.97848 1.000 0.000
#> SRR1414876 2 0.0000 0.94036 0.000 1.000
#> SRR1478523 2 0.7950 0.72125 0.240 0.760
#> SRR1325161 1 0.0000 0.97848 1.000 0.000
#> SRR1318026 2 0.1184 0.93295 0.016 0.984
#> SRR1343778 1 0.0000 0.97848 1.000 0.000
#> SRR1441287 1 0.0000 0.97848 1.000 0.000
#> SRR1430991 1 0.0000 0.97848 1.000 0.000
#> SRR1499722 1 0.0000 0.97848 1.000 0.000
#> SRR1351368 2 0.7950 0.72125 0.240 0.760
#> SRR1441785 1 0.0000 0.97848 1.000 0.000
#> SRR1096101 1 0.0000 0.97848 1.000 0.000
#> SRR808375 1 0.0000 0.97848 1.000 0.000
#> SRR1452842 1 0.0000 0.97848 1.000 0.000
#> SRR1311709 1 0.9977 0.00181 0.528 0.472
#> SRR1433352 1 0.0000 0.97848 1.000 0.000
#> SRR1340241 2 0.0000 0.94036 0.000 1.000
#> SRR1456754 1 0.0000 0.97848 1.000 0.000
#> SRR1465172 1 0.0000 0.97848 1.000 0.000
#> SRR1499284 1 0.0000 0.97848 1.000 0.000
#> SRR1499607 2 0.0000 0.94036 0.000 1.000
#> SRR812342 1 0.0000 0.97848 1.000 0.000
#> SRR1405374 1 0.0000 0.97848 1.000 0.000
#> SRR1403565 1 0.0000 0.97848 1.000 0.000
#> SRR1332024 1 0.0000 0.97848 1.000 0.000
#> SRR1471633 2 0.8555 0.65941 0.280 0.720
#> SRR1325944 2 0.0000 0.94036 0.000 1.000
#> SRR1429450 2 0.0000 0.94036 0.000 1.000
#> SRR821573 1 0.9775 0.23793 0.588 0.412
#> SRR1435372 1 0.0000 0.97848 1.000 0.000
#> SRR1324184 2 0.0000 0.94036 0.000 1.000
#> SRR816517 2 0.0376 0.93913 0.004 0.996
#> SRR1324141 2 0.7950 0.72125 0.240 0.760
#> SRR1101612 1 0.0000 0.97848 1.000 0.000
#> SRR1356531 1 0.0000 0.97848 1.000 0.000
#> SRR1089785 1 0.0000 0.97848 1.000 0.000
#> SRR1077708 1 0.0000 0.97848 1.000 0.000
#> SRR1343720 1 0.0000 0.97848 1.000 0.000
#> SRR1477499 2 0.0000 0.94036 0.000 1.000
#> SRR1347236 1 0.0000 0.97848 1.000 0.000
#> SRR1326408 1 0.4690 0.87252 0.900 0.100
#> SRR1336529 1 0.0000 0.97848 1.000 0.000
#> SRR1440643 2 0.6887 0.78646 0.184 0.816
#> SRR662354 1 0.0000 0.97848 1.000 0.000
#> SRR1310817 1 0.0000 0.97848 1.000 0.000
#> SRR1347389 2 0.0376 0.93913 0.004 0.996
#> SRR1353097 1 0.0000 0.97848 1.000 0.000
#> SRR1384737 2 0.0938 0.93550 0.012 0.988
#> SRR1096339 1 0.0000 0.97848 1.000 0.000
#> SRR1345329 2 0.0938 0.93550 0.012 0.988
#> SRR1414771 1 0.0672 0.97155 0.992 0.008
#> SRR1309119 2 0.9580 0.45449 0.380 0.620
#> SRR1470438 1 0.0672 0.97149 0.992 0.008
#> SRR1343221 1 0.0000 0.97848 1.000 0.000
#> SRR1410847 1 0.0000 0.97848 1.000 0.000
#> SRR807949 1 0.0000 0.97848 1.000 0.000
#> SRR1442332 1 0.0000 0.97848 1.000 0.000
#> SRR815920 1 0.0000 0.97848 1.000 0.000
#> SRR1471524 2 0.9522 0.47287 0.372 0.628
#> SRR1477221 1 0.0000 0.97848 1.000 0.000
#> SRR1445046 2 0.0000 0.94036 0.000 1.000
#> SRR1331962 2 0.0000 0.94036 0.000 1.000
#> SRR1319946 2 0.0000 0.94036 0.000 1.000
#> SRR1311599 1 0.0000 0.97848 1.000 0.000
#> SRR1323977 2 0.0000 0.94036 0.000 1.000
#> SRR1445132 2 0.0000 0.94036 0.000 1.000
#> SRR1337321 1 0.0000 0.97848 1.000 0.000
#> SRR1366390 2 0.0376 0.93913 0.004 0.996
#> SRR1343012 2 0.7950 0.72125 0.240 0.760
#> SRR1311958 2 0.0000 0.94036 0.000 1.000
#> SRR1388234 2 0.0000 0.94036 0.000 1.000
#> SRR1370384 1 0.0000 0.97848 1.000 0.000
#> SRR1321650 1 0.0000 0.97848 1.000 0.000
#> SRR1485117 2 0.0000 0.94036 0.000 1.000
#> SRR1384713 1 0.0000 0.97848 1.000 0.000
#> SRR816609 2 0.0000 0.94036 0.000 1.000
#> SRR1486239 2 0.0000 0.94036 0.000 1.000
#> SRR1309638 1 0.0000 0.97848 1.000 0.000
#> SRR1356660 1 0.0000 0.97848 1.000 0.000
#> SRR1392883 2 0.0000 0.94036 0.000 1.000
#> SRR808130 1 0.0000 0.97848 1.000 0.000
#> SRR816677 2 0.7950 0.72125 0.240 0.760
#> SRR1455722 1 0.0000 0.97848 1.000 0.000
#> SRR1336029 1 0.0000 0.97848 1.000 0.000
#> SRR808452 1 0.0000 0.97848 1.000 0.000
#> SRR1352169 1 0.6973 0.75021 0.812 0.188
#> SRR1366707 1 0.7453 0.71114 0.788 0.212
#> SRR1328143 1 0.0000 0.97848 1.000 0.000
#> SRR1473567 2 0.0000 0.94036 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1347278 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1332904 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1444179 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1082685 1 0.4555 0.515 0.800 0.000 0.200
#> SRR1362287 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1339007 3 0.6309 -0.681 0.496 0.000 0.504
#> SRR1376557 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1077455 3 0.3038 0.748 0.104 0.000 0.896
#> SRR1413978 1 0.6460 0.811 0.556 0.004 0.440
#> SRR1439896 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1317963 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1431865 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1394253 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1082664 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1077968 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1076393 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1485042 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1385453 2 0.6460 0.758 0.440 0.556 0.004
#> SRR1348074 2 0.6244 0.761 0.440 0.560 0.000
#> SRR813959 2 0.6244 0.761 0.440 0.560 0.000
#> SRR665442 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1378068 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1485237 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1350792 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1326797 3 0.0475 0.885 0.004 0.004 0.992
#> SRR808994 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1474041 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1500194 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1414876 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1478523 2 0.6625 0.756 0.440 0.552 0.008
#> SRR1325161 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1318026 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1343778 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1441287 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1430991 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1499722 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1351368 2 0.7029 0.747 0.440 0.540 0.020
#> SRR1441785 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1096101 3 0.5560 0.143 0.300 0.000 0.700
#> SRR808375 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1452842 3 0.2711 0.775 0.088 0.000 0.912
#> SRR1311709 1 0.5016 -0.334 0.760 0.240 0.000
#> SRR1433352 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1456754 3 0.4399 0.563 0.188 0.000 0.812
#> SRR1465172 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1499284 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1499607 2 0.0000 0.822 0.000 1.000 0.000
#> SRR812342 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1405374 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1403565 3 0.3551 0.695 0.132 0.000 0.868
#> SRR1332024 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1471633 1 0.6225 -0.641 0.568 0.432 0.000
#> SRR1325944 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.822 0.000 1.000 0.000
#> SRR821573 2 0.8538 0.220 0.100 0.520 0.380
#> SRR1435372 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1324184 2 0.0000 0.822 0.000 1.000 0.000
#> SRR816517 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1324141 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1101612 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1356531 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1089785 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1347236 3 0.0237 0.888 0.004 0.000 0.996
#> SRR1326408 1 0.8691 0.636 0.452 0.104 0.444
#> SRR1336529 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1440643 2 0.6771 0.753 0.440 0.548 0.012
#> SRR662354 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1310817 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1347389 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1353097 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1384737 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1096339 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1345329 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1414771 3 0.0237 0.887 0.000 0.004 0.996
#> SRR1309119 1 0.5835 -0.511 0.660 0.340 0.000
#> SRR1470438 3 0.0592 0.878 0.000 0.012 0.988
#> SRR1343221 3 0.4291 0.584 0.180 0.000 0.820
#> SRR1410847 1 0.6244 0.817 0.560 0.000 0.440
#> SRR807949 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.892 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1471524 3 0.8108 0.056 0.072 0.392 0.536
#> SRR1477221 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1445046 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1319946 2 0.3192 0.808 0.112 0.888 0.000
#> SRR1311599 3 0.6295 -0.611 0.472 0.000 0.528
#> SRR1323977 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1445132 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1337321 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1366390 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1343012 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1311958 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1388234 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1370384 3 0.6008 -0.236 0.372 0.000 0.628
#> SRR1321650 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1384713 3 0.1964 0.824 0.056 0.000 0.944
#> SRR816609 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1486239 2 0.0000 0.822 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1356660 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1392883 2 0.0000 0.822 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.892 0.000 0.000 1.000
#> SRR816677 2 0.6244 0.761 0.440 0.560 0.000
#> SRR1455722 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1336029 1 0.6244 0.817 0.560 0.000 0.440
#> SRR808452 1 0.6244 0.817 0.560 0.000 0.440
#> SRR1352169 3 0.4605 0.538 0.000 0.204 0.796
#> SRR1366707 3 0.2066 0.805 0.000 0.060 0.940
#> SRR1328143 3 0.0000 0.892 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.822 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.1151 0.8440 0.024 0.000 0.968 0.008
#> SRR1390119 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.1929 0.8411 0.024 0.000 0.940 0.036
#> SRR1347278 3 0.5213 0.3694 0.328 0.000 0.652 0.020
#> SRR1332904 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.1022 0.7630 0.968 0.000 0.000 0.032
#> SRR1082685 1 0.1474 0.7503 0.948 0.000 0.000 0.052
#> SRR1362287 1 0.1302 0.8028 0.956 0.000 0.044 0.000
#> SRR1339007 1 0.6474 0.6151 0.624 0.000 0.120 0.256
#> SRR1376557 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.7726 0.3669 0.404 0.000 0.228 0.368
#> SRR1413978 1 0.2993 0.7815 0.904 0.016 0.040 0.040
#> SRR1439896 1 0.1118 0.8029 0.964 0.000 0.036 0.000
#> SRR1317963 2 0.1209 0.8009 0.032 0.964 0.000 0.004
#> SRR1431865 1 0.1489 0.8028 0.952 0.000 0.044 0.004
#> SRR1394253 1 0.1302 0.8028 0.956 0.000 0.044 0.000
#> SRR1082664 3 0.3910 0.8030 0.024 0.000 0.820 0.156
#> SRR1077968 1 0.5410 0.6954 0.728 0.000 0.080 0.192
#> SRR1076393 3 0.3325 0.8262 0.024 0.000 0.864 0.112
#> SRR1477476 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.1520 0.8450 0.024 0.000 0.956 0.020
#> SRR1485042 1 0.1022 0.8024 0.968 0.000 0.032 0.000
#> SRR1385453 4 0.5700 0.3328 0.000 0.412 0.028 0.560
#> SRR1348074 2 0.5833 -0.2398 0.032 0.528 0.000 0.440
#> SRR813959 2 0.5668 -0.2546 0.000 0.532 0.024 0.444
#> SRR665442 2 0.5143 -0.2261 0.004 0.540 0.000 0.456
#> SRR1378068 3 0.1733 0.8425 0.024 0.000 0.948 0.028
#> SRR1485237 4 0.5862 0.2917 0.032 0.484 0.000 0.484
#> SRR1350792 1 0.1022 0.8024 0.968 0.000 0.032 0.000
#> SRR1326797 1 0.7823 0.2993 0.372 0.000 0.256 0.372
#> SRR808994 3 0.2670 0.8264 0.024 0.000 0.904 0.072
#> SRR1474041 3 0.3015 0.8324 0.024 0.000 0.884 0.092
#> SRR1405641 3 0.2742 0.8245 0.024 0.000 0.900 0.076
#> SRR1362245 3 0.3015 0.8370 0.024 0.000 0.884 0.092
#> SRR1500194 1 0.0524 0.7905 0.988 0.000 0.008 0.004
#> SRR1414876 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.7044 0.0543 0.000 0.120 0.452 0.428
#> SRR1325161 3 0.4535 0.7448 0.016 0.000 0.744 0.240
#> SRR1318026 4 0.5917 0.3697 0.036 0.444 0.000 0.520
#> SRR1343778 3 0.0817 0.8445 0.024 0.000 0.976 0.000
#> SRR1441287 1 0.0921 0.8010 0.972 0.000 0.028 0.000
#> SRR1430991 3 0.3080 0.8309 0.024 0.000 0.880 0.096
#> SRR1499722 4 0.7919 -0.3516 0.324 0.000 0.324 0.352
#> SRR1351368 3 0.5367 0.5410 0.000 0.032 0.664 0.304
#> SRR1441785 1 0.1302 0.8028 0.956 0.000 0.044 0.000
#> SRR1096101 1 0.4636 0.7261 0.792 0.000 0.140 0.068
#> SRR808375 3 0.4348 0.7778 0.024 0.000 0.780 0.196
#> SRR1452842 1 0.7743 0.3589 0.400 0.000 0.232 0.368
#> SRR1311709 1 0.5111 0.5467 0.740 0.056 0.000 0.204
#> SRR1433352 3 0.4348 0.6826 0.196 0.000 0.780 0.024
#> SRR1340241 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.7384 0.4789 0.476 0.000 0.172 0.352
#> SRR1465172 3 0.6919 0.4610 0.116 0.000 0.516 0.368
#> SRR1499284 4 0.7853 -0.2971 0.268 0.000 0.364 0.368
#> SRR1499607 2 0.0336 0.8274 0.000 0.992 0.000 0.008
#> SRR812342 1 0.0469 0.7930 0.988 0.000 0.012 0.000
#> SRR1405374 1 0.1489 0.8028 0.952 0.000 0.044 0.004
#> SRR1403565 1 0.7382 0.4600 0.520 0.000 0.260 0.220
#> SRR1332024 3 0.2111 0.8378 0.024 0.000 0.932 0.044
#> SRR1471633 1 0.7145 0.1548 0.556 0.252 0.000 0.192
#> SRR1325944 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR821573 3 0.7007 0.3103 0.000 0.144 0.548 0.308
#> SRR1435372 1 0.1297 0.7911 0.964 0.000 0.020 0.016
#> SRR1324184 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR816517 4 0.5696 0.2669 0.000 0.480 0.024 0.496
#> SRR1324141 4 0.4898 0.4079 0.000 0.416 0.000 0.584
#> SRR1101612 1 0.1022 0.8024 0.968 0.000 0.032 0.000
#> SRR1356531 1 0.1022 0.8024 0.968 0.000 0.032 0.000
#> SRR1089785 3 0.1520 0.8444 0.024 0.000 0.956 0.020
#> SRR1077708 3 0.3895 0.7888 0.012 0.000 0.804 0.184
#> SRR1343720 3 0.4735 0.7779 0.068 0.000 0.784 0.148
#> SRR1477499 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1347236 1 0.7869 0.2716 0.368 0.000 0.276 0.356
#> SRR1326408 1 0.5897 0.6893 0.728 0.044 0.044 0.184
#> SRR1336529 3 0.2521 0.8326 0.024 0.000 0.912 0.064
#> SRR1440643 4 0.6969 0.3133 0.000 0.224 0.192 0.584
#> SRR662354 1 0.1004 0.7995 0.972 0.000 0.024 0.004
#> SRR1310817 3 0.1118 0.8391 0.000 0.000 0.964 0.036
#> SRR1347389 2 0.0921 0.8092 0.000 0.972 0.000 0.028
#> SRR1353097 1 0.1706 0.8011 0.948 0.000 0.036 0.016
#> SRR1384737 4 0.4989 0.3342 0.000 0.472 0.000 0.528
#> SRR1096339 1 0.1022 0.8024 0.968 0.000 0.032 0.000
#> SRR1345329 2 0.5821 -0.2171 0.032 0.536 0.000 0.432
#> SRR1414771 3 0.3552 0.7662 0.000 0.024 0.848 0.128
#> SRR1309119 1 0.6508 0.3561 0.640 0.192 0.000 0.168
#> SRR1470438 3 0.3494 0.8053 0.016 0.028 0.876 0.080
#> SRR1343221 1 0.6497 0.6124 0.640 0.000 0.200 0.160
#> SRR1410847 1 0.1635 0.8026 0.948 0.000 0.044 0.008
#> SRR807949 3 0.3325 0.8246 0.024 0.000 0.864 0.112
#> SRR1442332 3 0.1406 0.8429 0.024 0.000 0.960 0.016
#> SRR815920 3 0.2670 0.8264 0.024 0.000 0.904 0.072
#> SRR1471524 3 0.4567 0.6426 0.000 0.016 0.740 0.244
#> SRR1477221 3 0.1629 0.8452 0.024 0.000 0.952 0.024
#> SRR1445046 2 0.0188 0.8309 0.004 0.996 0.000 0.000
#> SRR1331962 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.4004 0.5726 0.024 0.812 0.000 0.164
#> SRR1311599 1 0.2125 0.7914 0.920 0.000 0.076 0.004
#> SRR1323977 4 0.4967 0.3693 0.000 0.452 0.000 0.548
#> SRR1445132 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.3143 0.8303 0.024 0.000 0.876 0.100
#> SRR1366390 2 0.1118 0.8031 0.000 0.964 0.000 0.036
#> SRR1343012 4 0.5050 0.4120 0.000 0.408 0.004 0.588
#> SRR1311958 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.5792 -0.1808 0.032 0.552 0.000 0.416
#> SRR1370384 1 0.6587 0.5786 0.576 0.000 0.100 0.324
#> SRR1321650 3 0.1940 0.8392 0.000 0.000 0.924 0.076
#> SRR1485117 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.7799 0.3211 0.384 0.000 0.248 0.368
#> SRR816609 2 0.5821 -0.2171 0.032 0.536 0.000 0.432
#> SRR1486239 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR1309638 3 0.4501 0.7671 0.024 0.000 0.764 0.212
#> SRR1356660 1 0.1302 0.8028 0.956 0.000 0.044 0.000
#> SRR1392883 2 0.0000 0.8338 0.000 1.000 0.000 0.000
#> SRR808130 3 0.3015 0.8323 0.024 0.000 0.884 0.092
#> SRR816677 1 0.7502 -0.1269 0.456 0.188 0.000 0.356
#> SRR1455722 1 0.1022 0.8024 0.968 0.000 0.032 0.000
#> SRR1336029 1 0.1489 0.8028 0.952 0.000 0.044 0.004
#> SRR808452 1 0.0000 0.7843 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.5248 0.7172 0.024 0.112 0.784 0.080
#> SRR1366707 3 0.3672 0.7416 0.000 0.012 0.824 0.164
#> SRR1328143 3 0.1629 0.8446 0.024 0.000 0.952 0.024
#> SRR1473567 2 0.0000 0.8338 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
#> SRR1442087 5 0.3461 0.4966 0.004 0.000 0.224 0.000 0.772
#> SRR1390119 2 0.1403 0.8905 0.000 0.952 0.024 0.024 0.000
#> SRR1436127 5 0.4430 0.2541 0.004 0.000 0.456 0.000 0.540
#> SRR1347278 5 0.6909 0.3239 0.296 0.000 0.216 0.016 0.472
#> SRR1332904 2 0.2233 0.8748 0.000 0.904 0.016 0.080 0.000
#> SRR1444179 1 0.1478 0.7989 0.936 0.000 0.000 0.064 0.000
#> SRR1082685 1 0.1851 0.7851 0.912 0.000 0.000 0.088 0.000
#> SRR1362287 1 0.2609 0.8231 0.896 0.000 0.028 0.008 0.068
#> SRR1339007 1 0.5983 0.6055 0.656 0.000 0.036 0.116 0.192
#> SRR1376557 2 0.0693 0.8936 0.000 0.980 0.012 0.008 0.000
#> SRR1468700 2 0.0451 0.8928 0.000 0.988 0.004 0.008 0.000
#> SRR1077455 5 0.6811 0.3679 0.220 0.000 0.052 0.156 0.572
#> SRR1413978 1 0.2389 0.7813 0.880 0.000 0.000 0.116 0.004
#> SRR1439896 1 0.1117 0.8403 0.964 0.000 0.000 0.016 0.020
#> SRR1317963 2 0.4585 0.1595 0.004 0.592 0.008 0.396 0.000
#> SRR1431865 1 0.1195 0.8415 0.960 0.000 0.000 0.012 0.028
#> SRR1394253 1 0.1764 0.8309 0.928 0.000 0.000 0.008 0.064
#> SRR1082664 5 0.1012 0.5549 0.012 0.000 0.020 0.000 0.968
#> SRR1077968 1 0.5466 0.5567 0.648 0.000 0.024 0.052 0.276
#> SRR1076393 5 0.2338 0.5423 0.004 0.000 0.112 0.000 0.884
#> SRR1477476 2 0.1043 0.8891 0.000 0.960 0.040 0.000 0.000
#> SRR1398057 5 0.4359 0.3196 0.004 0.000 0.412 0.000 0.584
#> SRR1485042 1 0.1168 0.8416 0.960 0.000 0.000 0.008 0.032
#> SRR1385453 3 0.6512 -0.4201 0.000 0.180 0.484 0.332 0.004
#> SRR1348074 4 0.4335 0.8361 0.072 0.168 0.000 0.760 0.000
#> SRR813959 4 0.6615 0.6409 0.000 0.236 0.268 0.492 0.004
#> SRR665442 4 0.5977 0.7885 0.040 0.252 0.076 0.632 0.000
#> SRR1378068 5 0.4420 0.2750 0.004 0.000 0.448 0.000 0.548
#> SRR1485237 4 0.4010 0.8404 0.056 0.160 0.000 0.784 0.000
#> SRR1350792 1 0.1082 0.8419 0.964 0.000 0.000 0.008 0.028
#> SRR1326797 5 0.6438 0.4229 0.148 0.000 0.068 0.148 0.636
#> SRR808994 3 0.4437 -0.1264 0.004 0.000 0.532 0.000 0.464
#> SRR1474041 5 0.1764 0.5604 0.008 0.000 0.064 0.000 0.928
#> SRR1405641 5 0.4452 0.1452 0.004 0.000 0.496 0.000 0.500
#> SRR1362245 5 0.3491 0.4399 0.004 0.000 0.228 0.000 0.768
#> SRR1500194 1 0.0404 0.8309 0.988 0.000 0.000 0.012 0.000
#> SRR1414876 2 0.1211 0.8921 0.000 0.960 0.016 0.024 0.000
#> SRR1478523 3 0.6528 0.2277 0.120 0.020 0.644 0.176 0.040
#> SRR1325161 5 0.3401 0.5084 0.008 0.000 0.072 0.068 0.852
#> SRR1318026 4 0.4252 0.8297 0.072 0.144 0.004 0.780 0.000
#> SRR1343778 5 0.5531 0.4134 0.120 0.000 0.248 0.000 0.632
#> SRR1441287 1 0.0290 0.8360 0.992 0.000 0.000 0.000 0.008
#> SRR1430991 5 0.2970 0.5328 0.004 0.000 0.168 0.000 0.828
#> SRR1499722 5 0.5441 0.4694 0.128 0.000 0.052 0.096 0.724
#> SRR1351368 3 0.5821 0.3885 0.020 0.000 0.636 0.096 0.248
#> SRR1441785 1 0.3718 0.7769 0.824 0.000 0.120 0.008 0.048
#> SRR1096101 1 0.3402 0.7235 0.804 0.000 0.004 0.008 0.184
#> SRR808375 5 0.2077 0.5549 0.008 0.000 0.084 0.000 0.908
#> SRR1452842 5 0.6846 0.3698 0.216 0.000 0.056 0.156 0.572
#> SRR1311709 1 0.3509 0.6790 0.792 0.000 0.008 0.196 0.004
#> SRR1433352 5 0.5541 0.4940 0.128 0.000 0.236 0.000 0.636
#> SRR1340241 2 0.1043 0.8891 0.000 0.960 0.040 0.000 0.000
#> SRR1456754 5 0.7179 0.0382 0.380 0.000 0.052 0.136 0.432
#> SRR1465172 5 0.5114 0.4815 0.080 0.000 0.056 0.112 0.752
#> SRR1499284 5 0.5890 0.4520 0.108 0.000 0.056 0.152 0.684
#> SRR1499607 2 0.3398 0.6513 0.000 0.780 0.004 0.216 0.000
#> SRR812342 1 0.0693 0.8384 0.980 0.000 0.000 0.008 0.012
#> SRR1405374 1 0.1364 0.8411 0.952 0.000 0.000 0.012 0.036
#> SRR1403565 1 0.5193 0.3358 0.588 0.000 0.016 0.024 0.372
#> SRR1332024 5 0.4434 0.2498 0.004 0.000 0.460 0.000 0.536
#> SRR1471633 1 0.5156 0.3973 0.620 0.048 0.004 0.328 0.000
#> SRR1325944 2 0.0963 0.8900 0.000 0.964 0.036 0.000 0.000
#> SRR1429450 2 0.1310 0.8913 0.000 0.956 0.020 0.024 0.000
#> SRR821573 3 0.7198 0.3063 0.000 0.024 0.436 0.272 0.268
#> SRR1435372 1 0.2723 0.7716 0.864 0.000 0.000 0.012 0.124
#> SRR1324184 2 0.0898 0.8914 0.000 0.972 0.008 0.020 0.000
#> SRR816517 3 0.6660 -0.4337 0.000 0.216 0.468 0.312 0.004
#> SRR1324141 4 0.4280 0.8073 0.000 0.140 0.088 0.772 0.000
#> SRR1101612 1 0.0992 0.8408 0.968 0.000 0.000 0.008 0.024
#> SRR1356531 1 0.1670 0.8356 0.936 0.000 0.000 0.012 0.052
#> SRR1089785 5 0.3274 0.4929 0.000 0.000 0.220 0.000 0.780
#> SRR1077708 5 0.1956 0.5237 0.008 0.000 0.076 0.000 0.916
#> SRR1343720 5 0.2529 0.5600 0.056 0.000 0.040 0.004 0.900
#> SRR1477499 2 0.0963 0.8900 0.000 0.964 0.036 0.000 0.000
#> SRR1347236 5 0.5544 0.4578 0.148 0.000 0.052 0.088 0.712
#> SRR1326408 1 0.5837 0.6322 0.664 0.000 0.024 0.140 0.172
#> SRR1336529 5 0.4440 0.2271 0.004 0.000 0.468 0.000 0.528
#> SRR1440643 3 0.5091 0.1267 0.000 0.004 0.624 0.328 0.044
#> SRR662354 1 0.0451 0.8345 0.988 0.000 0.000 0.004 0.008
#> SRR1310817 5 0.4276 0.1917 0.000 0.000 0.380 0.004 0.616
#> SRR1347389 2 0.3087 0.7643 0.004 0.836 0.008 0.152 0.000
#> SRR1353097 1 0.2707 0.8061 0.876 0.000 0.000 0.024 0.100
#> SRR1384737 4 0.5454 0.8329 0.044 0.164 0.080 0.712 0.000
#> SRR1096339 1 0.1082 0.8414 0.964 0.000 0.000 0.008 0.028
#> SRR1345329 4 0.4820 0.8247 0.088 0.180 0.004 0.728 0.000
#> SRR1414771 3 0.4402 0.1619 0.000 0.000 0.636 0.012 0.352
#> SRR1309119 1 0.4938 0.4616 0.648 0.040 0.004 0.308 0.000
#> SRR1470438 3 0.4331 0.0767 0.000 0.004 0.596 0.000 0.400
#> SRR1343221 1 0.4774 0.4662 0.644 0.000 0.016 0.012 0.328
#> SRR1410847 1 0.1502 0.8355 0.940 0.000 0.000 0.004 0.056
#> SRR807949 5 0.3053 0.5377 0.008 0.000 0.164 0.000 0.828
#> SRR1442332 5 0.3246 0.5280 0.008 0.000 0.184 0.000 0.808
#> SRR815920 3 0.4449 -0.1860 0.004 0.000 0.512 0.000 0.484
#> SRR1471524 3 0.4797 0.3380 0.000 0.000 0.660 0.044 0.296
#> SRR1477221 5 0.4359 0.3187 0.004 0.000 0.412 0.000 0.584
#> SRR1445046 2 0.2017 0.8610 0.000 0.912 0.008 0.080 0.000
#> SRR1331962 2 0.1408 0.8905 0.000 0.948 0.008 0.044 0.000
#> SRR1319946 2 0.4280 0.4450 0.004 0.676 0.008 0.312 0.000
#> SRR1311599 1 0.4436 0.7194 0.768 0.000 0.068 0.008 0.156
#> SRR1323977 4 0.4933 0.7864 0.000 0.228 0.080 0.692 0.000
#> SRR1445132 2 0.1205 0.8898 0.000 0.956 0.040 0.004 0.000
#> SRR1337321 5 0.3921 0.4947 0.128 0.000 0.072 0.000 0.800
#> SRR1366390 2 0.3141 0.7847 0.000 0.852 0.040 0.108 0.000
#> SRR1343012 4 0.6204 0.6488 0.000 0.124 0.096 0.668 0.112
#> SRR1311958 2 0.1764 0.8714 0.000 0.928 0.008 0.064 0.000
#> SRR1388234 4 0.4491 0.6458 0.004 0.336 0.012 0.648 0.000
#> SRR1370384 1 0.7153 0.1678 0.448 0.000 0.044 0.152 0.356
#> SRR1321650 5 0.4114 0.1602 0.000 0.000 0.376 0.000 0.624
#> SRR1485117 2 0.0865 0.8945 0.000 0.972 0.004 0.024 0.000
#> SRR1384713 5 0.6853 0.3662 0.212 0.000 0.056 0.160 0.572
#> SRR816609 4 0.4360 0.8408 0.064 0.184 0.000 0.752 0.000
#> SRR1486239 2 0.1697 0.8749 0.000 0.932 0.008 0.060 0.000
#> SRR1309638 5 0.3052 0.5349 0.092 0.000 0.032 0.008 0.868
#> SRR1356660 1 0.3669 0.7799 0.828 0.000 0.116 0.008 0.048
#> SRR1392883 2 0.1310 0.8913 0.000 0.956 0.020 0.024 0.000
#> SRR808130 5 0.3132 0.5324 0.008 0.000 0.172 0.000 0.820
#> SRR816677 1 0.4594 0.3985 0.620 0.012 0.004 0.364 0.000
#> SRR1455722 1 0.1205 0.8399 0.956 0.000 0.000 0.004 0.040
#> SRR1336029 1 0.0671 0.8385 0.980 0.000 0.000 0.004 0.016
#> SRR808452 1 0.0162 0.8297 0.996 0.000 0.000 0.004 0.000
#> SRR1352169 5 0.7337 0.1124 0.124 0.000 0.308 0.084 0.484
#> SRR1366707 3 0.4890 0.0549 0.000 0.000 0.524 0.024 0.452
#> SRR1328143 5 0.3109 0.5083 0.000 0.000 0.200 0.000 0.800
#> SRR1473567 2 0.0955 0.8941 0.000 0.968 0.004 0.028 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.3695 0.4193 0.000 0.000 0.376 0.000 0.624 0.000
#> SRR1390119 6 0.0146 0.8893 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1436127 3 0.1863 0.8163 0.000 0.000 0.896 0.000 0.104 0.000
#> SRR1347278 1 0.5260 -0.0163 0.464 0.000 0.440 0.000 0.096 0.000
#> SRR1332904 6 0.4294 -0.7073 0.000 0.428 0.000 0.020 0.000 0.552
#> SRR1444179 1 0.0520 0.8886 0.984 0.008 0.000 0.008 0.000 0.000
#> SRR1082685 1 0.0603 0.8898 0.980 0.004 0.000 0.016 0.000 0.000
#> SRR1362287 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1339007 1 0.4553 0.6192 0.620 0.328 0.000 0.000 0.052 0.000
#> SRR1376557 2 0.4093 0.8677 0.000 0.516 0.000 0.008 0.000 0.476
#> SRR1468700 2 0.4250 0.8947 0.000 0.528 0.000 0.016 0.000 0.456
#> SRR1077455 5 0.4528 0.5286 0.012 0.380 0.000 0.020 0.588 0.000
#> SRR1413978 1 0.2165 0.8362 0.884 0.008 0.000 0.108 0.000 0.000
#> SRR1439896 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1317963 2 0.4788 0.8448 0.000 0.548 0.000 0.056 0.000 0.396
#> SRR1431865 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1394253 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1082664 5 0.0458 0.6654 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1077968 1 0.4132 0.7199 0.736 0.212 0.000 0.016 0.036 0.000
#> SRR1076393 5 0.1663 0.6643 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1477476 6 0.1155 0.8553 0.000 0.036 0.004 0.004 0.000 0.956
#> SRR1398057 3 0.2003 0.8110 0.000 0.000 0.884 0.000 0.116 0.000
#> SRR1485042 1 0.0458 0.8883 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR1385453 4 0.3739 0.7868 0.000 0.036 0.144 0.796 0.000 0.024
#> SRR1348074 4 0.1713 0.8367 0.000 0.044 0.000 0.928 0.000 0.028
#> SRR813959 4 0.4121 0.8121 0.000 0.068 0.060 0.792 0.000 0.080
#> SRR665442 4 0.3268 0.8109 0.000 0.100 0.000 0.824 0.000 0.076
#> SRR1378068 3 0.1863 0.8163 0.000 0.000 0.896 0.000 0.104 0.000
#> SRR1485237 4 0.1572 0.8381 0.000 0.036 0.000 0.936 0.000 0.028
#> SRR1350792 1 0.0146 0.8898 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.4453 0.5463 0.000 0.332 0.000 0.044 0.624 0.000
#> SRR808994 3 0.1714 0.8182 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1474041 5 0.1910 0.6591 0.000 0.000 0.108 0.000 0.892 0.000
#> SRR1405641 3 0.1714 0.8182 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1362245 3 0.3789 0.4372 0.000 0.000 0.584 0.000 0.416 0.000
#> SRR1500194 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1414876 6 0.0260 0.8880 0.000 0.000 0.000 0.008 0.000 0.992
#> SRR1478523 3 0.4234 0.2763 0.000 0.044 0.676 0.280 0.000 0.000
#> SRR1325161 5 0.1501 0.6554 0.000 0.076 0.000 0.000 0.924 0.000
#> SRR1318026 4 0.1176 0.8351 0.000 0.020 0.000 0.956 0.000 0.024
#> SRR1343778 5 0.3851 0.2445 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1441287 1 0.0363 0.8893 0.988 0.012 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.2941 0.6178 0.000 0.000 0.220 0.000 0.780 0.000
#> SRR1499722 5 0.2805 0.6175 0.000 0.184 0.000 0.004 0.812 0.000
#> SRR1351368 3 0.4866 -0.2341 0.000 0.008 0.508 0.040 0.444 0.000
#> SRR1441785 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1096101 1 0.1320 0.8776 0.948 0.036 0.000 0.000 0.016 0.000
#> SRR808375 5 0.1967 0.6709 0.000 0.012 0.084 0.000 0.904 0.000
#> SRR1452842 5 0.4752 0.5233 0.024 0.376 0.000 0.020 0.580 0.000
#> SRR1311709 1 0.1444 0.8582 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR1433352 5 0.3819 0.4424 0.004 0.000 0.372 0.000 0.624 0.000
#> SRR1340241 6 0.0777 0.8696 0.000 0.024 0.000 0.004 0.000 0.972
#> SRR1456754 1 0.6049 0.3794 0.468 0.340 0.000 0.012 0.180 0.000
#> SRR1465172 5 0.3421 0.5875 0.000 0.256 0.000 0.008 0.736 0.000
#> SRR1499284 5 0.4018 0.5557 0.000 0.324 0.000 0.020 0.656 0.000
#> SRR1499607 4 0.5337 0.2032 0.000 0.116 0.000 0.524 0.000 0.360
#> SRR812342 1 0.0790 0.8851 0.968 0.032 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.0508 0.8899 0.984 0.004 0.000 0.012 0.000 0.000
#> SRR1403565 1 0.2001 0.8596 0.912 0.040 0.000 0.000 0.048 0.000
#> SRR1332024 3 0.1863 0.8155 0.000 0.000 0.896 0.000 0.104 0.000
#> SRR1471633 1 0.3878 0.5591 0.644 0.004 0.000 0.348 0.000 0.004
#> SRR1325944 6 0.0260 0.8877 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1429450 6 0.0000 0.8890 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR821573 5 0.6428 0.4268 0.000 0.064 0.132 0.304 0.500 0.000
#> SRR1435372 1 0.0000 0.8898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324184 2 0.4224 0.8881 0.000 0.552 0.000 0.016 0.000 0.432
#> SRR816517 4 0.4128 0.8065 0.000 0.048 0.100 0.788 0.000 0.064
#> SRR1324141 4 0.1625 0.8122 0.000 0.060 0.000 0.928 0.000 0.012
#> SRR1101612 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1356531 1 0.1007 0.8821 0.956 0.044 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.3023 0.6111 0.000 0.000 0.232 0.000 0.768 0.000
#> SRR1077708 5 0.0622 0.6639 0.000 0.008 0.012 0.000 0.980 0.000
#> SRR1343720 5 0.1204 0.6696 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1477499 6 0.0260 0.8877 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1347236 5 0.3514 0.5930 0.000 0.228 0.000 0.020 0.752 0.000
#> SRR1326408 1 0.5456 0.6116 0.620 0.264 0.000 0.064 0.052 0.000
#> SRR1336529 3 0.1765 0.8184 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1440643 4 0.4493 0.4720 0.000 0.040 0.364 0.596 0.000 0.000
#> SRR662354 1 0.0000 0.8898 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.2762 0.6351 0.000 0.000 0.196 0.000 0.804 0.000
#> SRR1347389 2 0.5347 0.6634 0.000 0.560 0.000 0.136 0.000 0.304
#> SRR1353097 1 0.1610 0.8659 0.916 0.084 0.000 0.000 0.000 0.000
#> SRR1384737 4 0.1341 0.8361 0.000 0.028 0.000 0.948 0.000 0.024
#> SRR1096339 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1345329 4 0.1780 0.8366 0.000 0.048 0.000 0.924 0.000 0.028
#> SRR1414771 3 0.2013 0.7993 0.000 0.008 0.908 0.008 0.076 0.000
#> SRR1309119 1 0.3864 0.5652 0.648 0.004 0.000 0.344 0.000 0.004
#> SRR1470438 3 0.1700 0.8110 0.000 0.000 0.916 0.004 0.080 0.000
#> SRR1343221 1 0.2066 0.8454 0.904 0.024 0.000 0.000 0.072 0.000
#> SRR1410847 1 0.0260 0.8894 0.992 0.008 0.000 0.000 0.000 0.000
#> SRR807949 5 0.2941 0.6178 0.000 0.000 0.220 0.000 0.780 0.000
#> SRR1442332 5 0.3330 0.5614 0.000 0.000 0.284 0.000 0.716 0.000
#> SRR815920 3 0.1714 0.8182 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1471524 3 0.4613 -0.2222 0.000 0.008 0.528 0.024 0.440 0.000
#> SRR1477221 3 0.2048 0.8088 0.000 0.000 0.880 0.000 0.120 0.000
#> SRR1445046 2 0.4234 0.8925 0.000 0.544 0.000 0.016 0.000 0.440
#> SRR1331962 2 0.4250 0.8947 0.000 0.528 0.000 0.016 0.000 0.456
#> SRR1319946 2 0.4967 0.8277 0.000 0.512 0.000 0.068 0.000 0.420
#> SRR1311599 1 0.0508 0.8901 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR1323977 4 0.3405 0.8139 0.000 0.112 0.000 0.812 0.000 0.076
#> SRR1445132 6 0.0000 0.8890 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1337321 5 0.2219 0.6305 0.000 0.000 0.136 0.000 0.864 0.000
#> SRR1366390 2 0.5498 0.6202 0.000 0.504 0.004 0.116 0.000 0.376
#> SRR1343012 4 0.4488 0.5269 0.000 0.060 0.000 0.704 0.224 0.012
#> SRR1311958 2 0.4234 0.8925 0.000 0.544 0.000 0.016 0.000 0.440
#> SRR1388234 4 0.4273 0.6594 0.000 0.080 0.000 0.716 0.000 0.204
#> SRR1370384 1 0.5846 0.4650 0.520 0.332 0.000 0.020 0.128 0.000
#> SRR1321650 5 0.3997 -0.3223 0.000 0.004 0.488 0.000 0.508 0.000
#> SRR1485117 2 0.4253 0.8916 0.000 0.524 0.000 0.016 0.000 0.460
#> SRR1384713 5 0.4591 0.5295 0.016 0.372 0.000 0.020 0.592 0.000
#> SRR816609 4 0.2384 0.8348 0.000 0.084 0.000 0.884 0.000 0.032
#> SRR1486239 2 0.4250 0.8947 0.000 0.528 0.000 0.016 0.000 0.456
#> SRR1309638 5 0.0914 0.6629 0.000 0.016 0.016 0.000 0.968 0.000
#> SRR1356660 1 0.0363 0.8904 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1392883 6 0.0260 0.8880 0.000 0.000 0.000 0.008 0.000 0.992
#> SRR808130 5 0.2969 0.6144 0.000 0.000 0.224 0.000 0.776 0.000
#> SRR816677 1 0.3607 0.5519 0.652 0.000 0.000 0.348 0.000 0.000
#> SRR1455722 1 0.0363 0.8893 0.988 0.012 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0146 0.8900 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR808452 1 0.0260 0.8894 0.992 0.008 0.000 0.000 0.000 0.000
#> SRR1352169 5 0.5290 0.0235 0.000 0.000 0.428 0.100 0.472 0.000
#> SRR1366707 5 0.4427 0.3552 0.000 0.008 0.412 0.016 0.564 0.000
#> SRR1328143 5 0.3330 0.5491 0.000 0.000 0.284 0.000 0.716 0.000
#> SRR1473567 2 0.4250 0.8947 0.000 0.528 0.000 0.016 0.000 0.456
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
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 17851 rows and 124 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 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 0.983 0.952 0.981 0.3977 0.606 0.606
#> 3 3 1.000 0.939 0.978 0.6508 0.671 0.486
#> 4 4 0.752 0.724 0.865 0.1158 0.906 0.734
#> 5 5 0.709 0.663 0.832 0.0749 0.900 0.661
#> 6 6 0.810 0.773 0.859 0.0438 0.877 0.521
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
#> SRR1442087 1 0.000 0.983 1.000 0.000
#> SRR1390119 2 0.000 0.970 0.000 1.000
#> SRR1436127 1 0.000 0.983 1.000 0.000
#> SRR1347278 1 0.000 0.983 1.000 0.000
#> SRR1332904 2 0.000 0.970 0.000 1.000
#> SRR1444179 1 0.000 0.983 1.000 0.000
#> SRR1082685 1 0.000 0.983 1.000 0.000
#> SRR1362287 1 0.000 0.983 1.000 0.000
#> SRR1339007 1 0.000 0.983 1.000 0.000
#> SRR1376557 2 0.000 0.970 0.000 1.000
#> SRR1468700 2 0.000 0.970 0.000 1.000
#> SRR1077455 1 0.000 0.983 1.000 0.000
#> SRR1413978 1 0.000 0.983 1.000 0.000
#> SRR1439896 1 0.000 0.983 1.000 0.000
#> SRR1317963 2 0.000 0.970 0.000 1.000
#> SRR1431865 1 0.000 0.983 1.000 0.000
#> SRR1394253 1 0.000 0.983 1.000 0.000
#> SRR1082664 1 0.000 0.983 1.000 0.000
#> SRR1077968 1 0.000 0.983 1.000 0.000
#> SRR1076393 1 0.000 0.983 1.000 0.000
#> SRR1477476 2 0.000 0.970 0.000 1.000
#> SRR1398057 1 0.000 0.983 1.000 0.000
#> SRR1485042 1 0.000 0.983 1.000 0.000
#> SRR1385453 2 0.714 0.758 0.196 0.804
#> SRR1348074 2 0.625 0.815 0.156 0.844
#> SRR813959 2 0.000 0.970 0.000 1.000
#> SRR665442 2 0.000 0.970 0.000 1.000
#> SRR1378068 1 0.000 0.983 1.000 0.000
#> SRR1485237 1 0.975 0.301 0.592 0.408
#> SRR1350792 1 0.000 0.983 1.000 0.000
#> SRR1326797 1 0.000 0.983 1.000 0.000
#> SRR808994 1 0.000 0.983 1.000 0.000
#> SRR1474041 1 0.000 0.983 1.000 0.000
#> SRR1405641 1 0.000 0.983 1.000 0.000
#> SRR1362245 1 0.000 0.983 1.000 0.000
#> SRR1500194 1 0.000 0.983 1.000 0.000
#> SRR1414876 2 0.000 0.970 0.000 1.000
#> SRR1478523 1 0.000 0.983 1.000 0.000
#> SRR1325161 1 0.000 0.983 1.000 0.000
#> SRR1318026 1 0.000 0.983 1.000 0.000
#> SRR1343778 1 0.000 0.983 1.000 0.000
#> SRR1441287 1 0.000 0.983 1.000 0.000
#> SRR1430991 1 0.000 0.983 1.000 0.000
#> SRR1499722 1 0.000 0.983 1.000 0.000
#> SRR1351368 1 0.000 0.983 1.000 0.000
#> SRR1441785 1 0.000 0.983 1.000 0.000
#> SRR1096101 1 0.000 0.983 1.000 0.000
#> SRR808375 1 0.000 0.983 1.000 0.000
#> SRR1452842 1 0.000 0.983 1.000 0.000
#> SRR1311709 1 0.000 0.983 1.000 0.000
#> SRR1433352 1 0.000 0.983 1.000 0.000
#> SRR1340241 2 0.000 0.970 0.000 1.000
#> SRR1456754 1 0.000 0.983 1.000 0.000
#> SRR1465172 1 0.000 0.983 1.000 0.000
#> SRR1499284 1 0.000 0.983 1.000 0.000
#> SRR1499607 2 0.000 0.970 0.000 1.000
#> SRR812342 1 0.000 0.983 1.000 0.000
#> SRR1405374 1 0.000 0.983 1.000 0.000
#> SRR1403565 1 0.000 0.983 1.000 0.000
#> SRR1332024 1 0.000 0.983 1.000 0.000
#> SRR1471633 1 0.000 0.983 1.000 0.000
#> SRR1325944 2 0.000 0.970 0.000 1.000
#> SRR1429450 2 0.000 0.970 0.000 1.000
#> SRR821573 1 0.644 0.793 0.836 0.164
#> SRR1435372 1 0.000 0.983 1.000 0.000
#> SRR1324184 2 0.000 0.970 0.000 1.000
#> SRR816517 2 0.000 0.970 0.000 1.000
#> SRR1324141 1 0.891 0.547 0.692 0.308
#> SRR1101612 1 0.000 0.983 1.000 0.000
#> SRR1356531 1 0.000 0.983 1.000 0.000
#> SRR1089785 1 0.000 0.983 1.000 0.000
#> SRR1077708 1 0.000 0.983 1.000 0.000
#> SRR1343720 1 0.000 0.983 1.000 0.000
#> SRR1477499 2 0.000 0.970 0.000 1.000
#> SRR1347236 1 0.000 0.983 1.000 0.000
#> SRR1326408 1 0.000 0.983 1.000 0.000
#> SRR1336529 1 0.000 0.983 1.000 0.000
#> SRR1440643 1 0.000 0.983 1.000 0.000
#> SRR662354 1 0.000 0.983 1.000 0.000
#> SRR1310817 1 0.000 0.983 1.000 0.000
#> SRR1347389 2 0.000 0.970 0.000 1.000
#> SRR1353097 1 0.000 0.983 1.000 0.000
#> SRR1384737 1 0.456 0.881 0.904 0.096
#> SRR1096339 1 0.000 0.983 1.000 0.000
#> SRR1345329 2 0.991 0.191 0.444 0.556
#> SRR1414771 1 0.000 0.983 1.000 0.000
#> SRR1309119 1 0.000 0.983 1.000 0.000
#> SRR1470438 1 0.000 0.983 1.000 0.000
#> SRR1343221 1 0.000 0.983 1.000 0.000
#> SRR1410847 1 0.000 0.983 1.000 0.000
#> SRR807949 1 0.000 0.983 1.000 0.000
#> SRR1442332 1 0.000 0.983 1.000 0.000
#> SRR815920 1 0.000 0.983 1.000 0.000
#> SRR1471524 1 0.000 0.983 1.000 0.000
#> SRR1477221 1 0.000 0.983 1.000 0.000
#> SRR1445046 2 0.000 0.970 0.000 1.000
#> SRR1331962 2 0.000 0.970 0.000 1.000
#> SRR1319946 2 0.000 0.970 0.000 1.000
#> SRR1311599 1 0.000 0.983 1.000 0.000
#> SRR1323977 2 0.000 0.970 0.000 1.000
#> SRR1445132 2 0.000 0.970 0.000 1.000
#> SRR1337321 1 0.000 0.983 1.000 0.000
#> SRR1366390 2 0.000 0.970 0.000 1.000
#> SRR1343012 1 0.994 0.148 0.544 0.456
#> SRR1311958 2 0.000 0.970 0.000 1.000
#> SRR1388234 2 0.000 0.970 0.000 1.000
#> SRR1370384 1 0.000 0.983 1.000 0.000
#> SRR1321650 1 0.000 0.983 1.000 0.000
#> SRR1485117 2 0.000 0.970 0.000 1.000
#> SRR1384713 1 0.000 0.983 1.000 0.000
#> SRR816609 2 0.552 0.845 0.128 0.872
#> SRR1486239 2 0.000 0.970 0.000 1.000
#> SRR1309638 1 0.000 0.983 1.000 0.000
#> SRR1356660 1 0.000 0.983 1.000 0.000
#> SRR1392883 2 0.000 0.970 0.000 1.000
#> SRR808130 1 0.000 0.983 1.000 0.000
#> SRR816677 1 0.000 0.983 1.000 0.000
#> SRR1455722 1 0.000 0.983 1.000 0.000
#> SRR1336029 1 0.000 0.983 1.000 0.000
#> SRR808452 1 0.000 0.983 1.000 0.000
#> SRR1352169 1 0.000 0.983 1.000 0.000
#> SRR1366707 1 0.000 0.983 1.000 0.000
#> SRR1328143 1 0.000 0.983 1.000 0.000
#> SRR1473567 2 0.000 0.970 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1347278 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1332904 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1082664 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1077968 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1485042 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1385453 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1348074 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR813959 2 0.6140 0.3086 0.000 0.596 0.404
#> SRR665442 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1378068 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1485237 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1350792 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1326797 1 0.1643 0.9315 0.956 0.000 0.044
#> SRR808994 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1474041 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1500194 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1478523 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1325161 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1318026 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1343778 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1441287 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1430991 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1499722 3 0.0892 0.9603 0.020 0.000 0.980
#> SRR1351368 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1441785 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR808375 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1452842 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1311709 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1433352 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1465172 3 0.5098 0.6579 0.248 0.000 0.752
#> SRR1499284 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1499607 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1332024 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1471633 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR821573 3 0.0592 0.9696 0.000 0.012 0.988
#> SRR1435372 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR816517 3 0.6274 0.1378 0.000 0.456 0.544
#> SRR1324141 1 0.6308 0.0229 0.508 0.492 0.000
#> SRR1101612 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1089785 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1347236 1 0.5810 0.4949 0.664 0.000 0.336
#> SRR1326408 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1440643 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR662354 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1310817 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1347389 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1384737 1 0.2711 0.8878 0.912 0.088 0.000
#> SRR1096339 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1345329 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1414771 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1309119 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1470438 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1343221 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR807949 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1477221 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1445046 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1319946 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1323977 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1445132 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1337321 3 0.0592 0.9690 0.012 0.000 0.988
#> SRR1366390 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1343012 2 0.6235 0.1933 0.436 0.564 0.000
#> SRR1311958 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1388234 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1321650 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR816609 1 0.4555 0.7393 0.800 0.200 0.000
#> SRR1486239 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR1309638 3 0.1289 0.9480 0.032 0.000 0.968
#> SRR1356660 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9686 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.9754 1.000 0.000 0.000
#> SRR1352169 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1366707 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1328143 3 0.0000 0.9802 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.9686 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.0469 0.83150 0.000 0.000 0.988 0.012
#> SRR1390119 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.0188 0.83127 0.000 0.000 0.996 0.004
#> SRR1347278 3 0.0937 0.83095 0.012 0.000 0.976 0.012
#> SRR1332904 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.1716 0.84787 0.936 0.000 0.000 0.064
#> SRR1082685 1 0.0188 0.86497 0.996 0.000 0.000 0.004
#> SRR1362287 1 0.0188 0.86432 0.996 0.000 0.000 0.004
#> SRR1339007 1 0.4103 0.64109 0.744 0.000 0.000 0.256
#> SRR1376557 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1077455 4 0.4888 0.36581 0.412 0.000 0.000 0.588
#> SRR1413978 1 0.3528 0.66034 0.808 0.000 0.000 0.192
#> SRR1439896 1 0.0188 0.86432 0.996 0.000 0.000 0.004
#> SRR1317963 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1431865 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1082664 3 0.4776 0.53529 0.000 0.000 0.624 0.376
#> SRR1077968 1 0.4972 0.12560 0.544 0.000 0.000 0.456
#> SRR1076393 3 0.2814 0.78644 0.000 0.000 0.868 0.132
#> SRR1477476 2 0.0336 0.92766 0.000 0.992 0.000 0.008
#> SRR1398057 3 0.0524 0.83079 0.004 0.000 0.988 0.008
#> SRR1485042 1 0.0469 0.86524 0.988 0.000 0.000 0.012
#> SRR1385453 3 0.4718 0.55986 0.000 0.012 0.708 0.280
#> SRR1348074 4 0.7370 -0.02141 0.412 0.160 0.000 0.428
#> SRR813959 2 0.2125 0.85758 0.000 0.920 0.004 0.076
#> SRR665442 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1378068 3 0.0000 0.83085 0.000 0.000 1.000 0.000
#> SRR1485237 1 0.5478 0.57890 0.696 0.056 0.000 0.248
#> SRR1350792 1 0.1118 0.85860 0.964 0.000 0.000 0.036
#> SRR1326797 4 0.5326 0.42484 0.380 0.000 0.016 0.604
#> SRR808994 3 0.0000 0.83085 0.000 0.000 1.000 0.000
#> SRR1474041 3 0.4509 0.65738 0.004 0.000 0.708 0.288
#> SRR1405641 3 0.0000 0.83085 0.000 0.000 1.000 0.000
#> SRR1362245 3 0.0817 0.82431 0.024 0.000 0.976 0.000
#> SRR1500194 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.1022 0.81949 0.000 0.000 0.968 0.032
#> SRR1325161 4 0.5517 0.00258 0.020 0.000 0.412 0.568
#> SRR1318026 4 0.4072 0.35208 0.252 0.000 0.000 0.748
#> SRR1343778 3 0.0188 0.83127 0.000 0.000 0.996 0.004
#> SRR1441287 1 0.0469 0.86558 0.988 0.000 0.000 0.012
#> SRR1430991 3 0.4661 0.57948 0.000 0.000 0.652 0.348
#> SRR1499722 4 0.7142 0.31905 0.152 0.000 0.324 0.524
#> SRR1351368 3 0.1792 0.79651 0.000 0.000 0.932 0.068
#> SRR1441785 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.3444 0.73723 0.816 0.000 0.000 0.184
#> SRR808375 3 0.4941 0.41089 0.000 0.000 0.564 0.436
#> SRR1452842 4 0.4855 0.38441 0.400 0.000 0.000 0.600
#> SRR1311709 1 0.2760 0.79286 0.872 0.000 0.000 0.128
#> SRR1433352 3 0.2888 0.79186 0.004 0.000 0.872 0.124
#> SRR1340241 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.4585 0.47290 0.668 0.000 0.000 0.332
#> SRR1465172 4 0.6661 0.43836 0.132 0.000 0.264 0.604
#> SRR1499284 4 0.4830 0.40364 0.392 0.000 0.000 0.608
#> SRR1499607 2 0.4193 0.71254 0.000 0.732 0.000 0.268
#> SRR812342 1 0.0469 0.86507 0.988 0.000 0.000 0.012
#> SRR1405374 1 0.0188 0.86497 0.996 0.000 0.000 0.004
#> SRR1403565 1 0.0188 0.86564 0.996 0.000 0.000 0.004
#> SRR1332024 3 0.0921 0.82134 0.028 0.000 0.972 0.000
#> SRR1471633 1 0.1389 0.85597 0.952 0.000 0.000 0.048
#> SRR1325944 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR821573 4 0.3450 0.42395 0.000 0.008 0.156 0.836
#> SRR1435372 1 0.4008 0.65532 0.756 0.000 0.000 0.244
#> SRR1324184 2 0.3444 0.79584 0.000 0.816 0.000 0.184
#> SRR816517 3 0.5990 0.53727 0.000 0.144 0.692 0.164
#> SRR1324141 4 0.0895 0.53497 0.004 0.020 0.000 0.976
#> SRR1101612 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.2149 0.82567 0.912 0.000 0.000 0.088
#> SRR1089785 3 0.4277 0.66528 0.000 0.000 0.720 0.280
#> SRR1077708 3 0.4576 0.68371 0.012 0.000 0.728 0.260
#> SRR1343720 3 0.5161 0.47275 0.008 0.000 0.592 0.400
#> SRR1477499 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1347236 4 0.7037 0.44713 0.168 0.000 0.268 0.564
#> SRR1326408 1 0.4790 0.37896 0.620 0.000 0.000 0.380
#> SRR1336529 3 0.0188 0.83127 0.000 0.000 0.996 0.004
#> SRR1440643 3 0.3610 0.67294 0.000 0.000 0.800 0.200
#> SRR662354 1 0.0188 0.86564 0.996 0.000 0.000 0.004
#> SRR1310817 3 0.4888 0.47008 0.000 0.000 0.588 0.412
#> SRR1347389 2 0.4830 0.56119 0.000 0.608 0.000 0.392
#> SRR1353097 1 0.3528 0.72927 0.808 0.000 0.000 0.192
#> SRR1384737 4 0.6706 0.13030 0.124 0.288 0.000 0.588
#> SRR1096339 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1345329 1 0.5690 0.58329 0.700 0.084 0.000 0.216
#> SRR1414771 3 0.0000 0.83085 0.000 0.000 1.000 0.000
#> SRR1309119 1 0.2081 0.80261 0.916 0.000 0.000 0.084
#> SRR1470438 3 0.0188 0.83099 0.000 0.000 0.996 0.004
#> SRR1343221 1 0.1716 0.85099 0.936 0.000 0.000 0.064
#> SRR1410847 1 0.0188 0.86497 0.996 0.000 0.000 0.004
#> SRR807949 3 0.4585 0.60231 0.000 0.000 0.668 0.332
#> SRR1442332 3 0.3172 0.76888 0.000 0.000 0.840 0.160
#> SRR815920 3 0.0000 0.83085 0.000 0.000 1.000 0.000
#> SRR1471524 3 0.0707 0.83008 0.000 0.000 0.980 0.020
#> SRR1477221 3 0.2048 0.79557 0.064 0.000 0.928 0.008
#> SRR1445046 2 0.1716 0.89207 0.000 0.936 0.000 0.064
#> SRR1331962 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.0000 0.86539 1.000 0.000 0.000 0.000
#> SRR1323977 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.2111 0.82190 0.024 0.000 0.932 0.044
#> SRR1366390 2 0.4817 0.56701 0.000 0.612 0.000 0.388
#> SRR1343012 4 0.1807 0.53211 0.008 0.052 0.000 0.940
#> SRR1311958 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1370384 1 0.4916 0.19421 0.576 0.000 0.000 0.424
#> SRR1321650 3 0.1042 0.82556 0.020 0.000 0.972 0.008
#> SRR1485117 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1384713 4 0.4866 0.37884 0.404 0.000 0.000 0.596
#> SRR816609 2 0.6052 0.14587 0.396 0.556 0.000 0.048
#> SRR1486239 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR1309638 3 0.6547 0.28843 0.124 0.000 0.616 0.260
#> SRR1356660 1 0.0188 0.86497 0.996 0.000 0.000 0.004
#> SRR1392883 2 0.0000 0.93233 0.000 1.000 0.000 0.000
#> SRR808130 3 0.4477 0.62838 0.000 0.000 0.688 0.312
#> SRR816677 1 0.2760 0.79629 0.872 0.000 0.000 0.128
#> SRR1455722 1 0.0336 0.86521 0.992 0.000 0.000 0.008
#> SRR1336029 1 0.1474 0.84061 0.948 0.000 0.000 0.052
#> SRR808452 1 0.0592 0.86532 0.984 0.000 0.000 0.016
#> SRR1352169 3 0.0672 0.83116 0.000 0.008 0.984 0.008
#> SRR1366707 3 0.0188 0.83135 0.000 0.000 0.996 0.004
#> SRR1328143 3 0.2773 0.79681 0.004 0.000 0.880 0.116
#> SRR1473567 2 0.0000 0.93233 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
#> SRR1442087 3 0.2505 0.7827 0.000 0.000 0.888 0.092 0.020
#> SRR1390119 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.1522 0.7939 0.000 0.000 0.944 0.044 0.012
#> SRR1347278 3 0.3792 0.7126 0.180 0.000 0.792 0.020 0.008
#> SRR1332904 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1444179 1 0.6275 0.3842 0.520 0.000 0.000 0.300 0.180
#> SRR1082685 1 0.3814 0.7645 0.808 0.000 0.000 0.124 0.068
#> SRR1362287 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1339007 5 0.3909 0.5071 0.216 0.000 0.000 0.024 0.760
#> SRR1376557 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1468700 2 0.0162 0.9338 0.000 0.996 0.000 0.000 0.004
#> SRR1077455 5 0.1041 0.5821 0.032 0.000 0.000 0.004 0.964
#> SRR1413978 1 0.5742 0.2858 0.508 0.000 0.000 0.404 0.088
#> SRR1439896 1 0.0000 0.8600 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.0404 0.9276 0.000 0.988 0.000 0.012 0.000
#> SRR1431865 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1394253 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1082664 3 0.4996 0.3748 0.000 0.000 0.548 0.032 0.420
#> SRR1077968 5 0.3012 0.5652 0.104 0.000 0.000 0.036 0.860
#> SRR1076393 3 0.2439 0.7750 0.000 0.000 0.876 0.004 0.120
#> SRR1477476 2 0.1341 0.8905 0.000 0.944 0.000 0.056 0.000
#> SRR1398057 3 0.2304 0.7653 0.100 0.000 0.892 0.008 0.000
#> SRR1485042 1 0.2624 0.8050 0.872 0.000 0.000 0.012 0.116
#> SRR1385453 4 0.4440 -0.1194 0.000 0.004 0.468 0.528 0.000
#> SRR1348074 4 0.3880 0.6380 0.028 0.004 0.000 0.784 0.184
#> SRR813959 2 0.2833 0.7566 0.000 0.852 0.004 0.140 0.004
#> SRR665442 2 0.0451 0.9304 0.000 0.988 0.000 0.008 0.004
#> SRR1378068 3 0.0000 0.7918 0.000 0.000 1.000 0.000 0.000
#> SRR1485237 5 0.5708 0.4333 0.152 0.060 0.000 0.088 0.700
#> SRR1350792 1 0.2280 0.7970 0.880 0.000 0.000 0.000 0.120
#> SRR1326797 5 0.4126 0.5512 0.028 0.000 0.032 0.140 0.800
#> SRR808994 3 0.0162 0.7912 0.000 0.000 0.996 0.004 0.000
#> SRR1474041 3 0.5703 0.5934 0.000 0.000 0.616 0.140 0.244
#> SRR1405641 3 0.0162 0.7912 0.000 0.000 0.996 0.004 0.000
#> SRR1362245 3 0.2179 0.7567 0.100 0.000 0.896 0.004 0.000
#> SRR1500194 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1414876 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.1544 0.7652 0.000 0.000 0.932 0.068 0.000
#> SRR1325161 5 0.5224 0.4575 0.000 0.000 0.176 0.140 0.684
#> SRR1318026 4 0.2930 0.6544 0.004 0.000 0.000 0.832 0.164
#> SRR1343778 3 0.0162 0.7924 0.000 0.000 0.996 0.000 0.004
#> SRR1441287 1 0.1469 0.8467 0.948 0.000 0.000 0.016 0.036
#> SRR1430991 3 0.5906 0.5296 0.000 0.000 0.576 0.140 0.284
#> SRR1499722 5 0.5888 0.2443 0.000 0.000 0.280 0.140 0.580
#> SRR1351368 3 0.3274 0.6450 0.000 0.000 0.780 0.220 0.000
#> SRR1441785 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1096101 5 0.4627 0.1391 0.444 0.000 0.000 0.012 0.544
#> SRR808375 5 0.6215 -0.1702 0.000 0.000 0.412 0.140 0.448
#> SRR1452842 5 0.0955 0.5807 0.028 0.000 0.000 0.004 0.968
#> SRR1311709 1 0.5365 0.2718 0.528 0.000 0.000 0.056 0.416
#> SRR1433352 3 0.4766 0.7040 0.000 0.000 0.732 0.132 0.136
#> SRR1340241 2 0.0510 0.9257 0.000 0.984 0.000 0.016 0.000
#> SRR1456754 5 0.2890 0.5643 0.160 0.000 0.000 0.004 0.836
#> SRR1465172 5 0.3477 0.5445 0.000 0.000 0.040 0.136 0.824
#> SRR1499284 5 0.3080 0.5614 0.020 0.000 0.004 0.124 0.852
#> SRR1499607 4 0.4567 0.0895 0.004 0.448 0.000 0.544 0.004
#> SRR812342 1 0.0404 0.8580 0.988 0.000 0.000 0.000 0.012
#> SRR1405374 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1403565 1 0.0324 0.8584 0.992 0.000 0.000 0.004 0.004
#> SRR1332024 3 0.2763 0.7161 0.148 0.000 0.848 0.004 0.000
#> SRR1471633 1 0.5979 0.5289 0.588 0.000 0.000 0.192 0.220
#> SRR1325944 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR821573 4 0.4648 -0.1690 0.000 0.000 0.012 0.524 0.464
#> SRR1435372 5 0.3934 0.4995 0.244 0.000 0.000 0.016 0.740
#> SRR1324184 2 0.4367 0.2565 0.000 0.580 0.000 0.416 0.004
#> SRR816517 3 0.3452 0.5904 0.000 0.000 0.756 0.244 0.000
#> SRR1324141 4 0.3333 0.6418 0.004 0.000 0.000 0.788 0.208
#> SRR1101612 1 0.0000 0.8600 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.3814 0.6292 0.720 0.000 0.000 0.004 0.276
#> SRR1089785 3 0.5190 0.6739 0.000 0.000 0.688 0.140 0.172
#> SRR1077708 3 0.5175 0.2180 0.000 0.000 0.496 0.040 0.464
#> SRR1343720 5 0.6219 -0.2111 0.000 0.000 0.424 0.140 0.436
#> SRR1477499 2 0.0162 0.9334 0.000 0.996 0.000 0.004 0.000
#> SRR1347236 5 0.5581 0.3804 0.000 0.000 0.224 0.140 0.636
#> SRR1326408 5 0.3595 0.5549 0.140 0.000 0.000 0.044 0.816
#> SRR1336529 3 0.0162 0.7921 0.000 0.000 0.996 0.004 0.000
#> SRR1440643 3 0.4304 0.3586 0.000 0.000 0.516 0.484 0.000
#> SRR662354 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1310817 3 0.6178 0.4896 0.000 0.000 0.536 0.168 0.296
#> SRR1347389 4 0.2890 0.6187 0.000 0.160 0.000 0.836 0.004
#> SRR1353097 5 0.4774 0.1228 0.424 0.000 0.000 0.020 0.556
#> SRR1384737 4 0.2763 0.6550 0.004 0.000 0.000 0.848 0.148
#> SRR1096339 1 0.0000 0.8600 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.6817 0.1124 0.236 0.004 0.000 0.400 0.360
#> SRR1414771 3 0.0162 0.7912 0.000 0.000 0.996 0.004 0.000
#> SRR1309119 1 0.2818 0.7925 0.856 0.000 0.000 0.132 0.012
#> SRR1470438 3 0.0324 0.7910 0.004 0.000 0.992 0.004 0.000
#> SRR1343221 1 0.3804 0.7477 0.796 0.000 0.000 0.044 0.160
#> SRR1410847 1 0.0290 0.8593 0.992 0.000 0.000 0.000 0.008
#> SRR807949 3 0.5770 0.5742 0.000 0.000 0.604 0.140 0.256
#> SRR1442332 3 0.4630 0.7185 0.000 0.000 0.744 0.140 0.116
#> SRR815920 3 0.0162 0.7912 0.000 0.000 0.996 0.004 0.000
#> SRR1471524 3 0.2843 0.7718 0.000 0.000 0.848 0.144 0.008
#> SRR1477221 3 0.3969 0.5471 0.304 0.000 0.692 0.004 0.000
#> SRR1445046 2 0.3160 0.7242 0.000 0.808 0.000 0.188 0.004
#> SRR1331962 2 0.0324 0.9323 0.000 0.992 0.000 0.004 0.004
#> SRR1319946 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1311599 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1323977 2 0.0290 0.9307 0.000 0.992 0.000 0.000 0.008
#> SRR1445132 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.4406 0.7518 0.020 0.000 0.784 0.136 0.060
#> SRR1366390 4 0.2930 0.6166 0.000 0.164 0.004 0.832 0.000
#> SRR1343012 4 0.3491 0.6293 0.004 0.000 0.000 0.768 0.228
#> SRR1311958 2 0.0324 0.9323 0.000 0.992 0.000 0.004 0.004
#> SRR1388234 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1370384 5 0.2798 0.5728 0.140 0.000 0.000 0.008 0.852
#> SRR1321650 3 0.1988 0.7857 0.048 0.000 0.928 0.008 0.016
#> SRR1485117 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1384713 5 0.1701 0.5825 0.048 0.000 0.000 0.016 0.936
#> SRR816609 2 0.7242 0.0655 0.112 0.492 0.000 0.084 0.312
#> SRR1486239 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR1309638 5 0.4152 0.4169 0.012 0.000 0.296 0.000 0.692
#> SRR1356660 1 0.0162 0.8598 0.996 0.000 0.000 0.004 0.000
#> SRR1392883 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
#> SRR808130 3 0.5581 0.6186 0.000 0.000 0.636 0.140 0.224
#> SRR816677 1 0.5473 0.2862 0.520 0.000 0.000 0.064 0.416
#> SRR1455722 1 0.0880 0.8541 0.968 0.000 0.000 0.000 0.032
#> SRR1336029 1 0.4385 0.7159 0.752 0.000 0.000 0.180 0.068
#> SRR808452 1 0.1195 0.8512 0.960 0.000 0.000 0.012 0.028
#> SRR1352169 3 0.2860 0.7907 0.036 0.008 0.896 0.044 0.016
#> SRR1366707 3 0.0290 0.7931 0.000 0.000 0.992 0.000 0.008
#> SRR1328143 3 0.4334 0.7362 0.000 0.000 0.768 0.140 0.092
#> SRR1473567 2 0.0000 0.9350 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.3868 0.2027 0.000 0.000 0.492 0.000 0.508 0.000
#> SRR1390119 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.3371 0.5159 0.000 0.000 0.708 0.000 0.292 0.000
#> SRR1347278 1 0.5918 -0.0635 0.456 0.000 0.232 0.000 0.312 0.000
#> SRR1332904 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 1 0.5442 0.2807 0.532 0.000 0.000 0.116 0.004 0.348
#> SRR1082685 1 0.4089 0.5598 0.696 0.000 0.000 0.040 0.000 0.264
#> SRR1362287 1 0.0000 0.8517 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1339007 6 0.1036 0.8267 0.024 0.000 0.000 0.004 0.008 0.964
#> SRR1376557 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077455 6 0.1858 0.8122 0.004 0.000 0.000 0.000 0.092 0.904
#> SRR1413978 1 0.5948 0.0959 0.464 0.000 0.016 0.120 0.004 0.396
#> SRR1439896 1 0.0000 0.8517 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1431865 1 0.0000 0.8517 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.8517 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082664 3 0.5454 0.3346 0.000 0.000 0.552 0.000 0.292 0.156
#> SRR1077968 6 0.0870 0.8255 0.012 0.000 0.000 0.004 0.012 0.972
#> SRR1076393 3 0.1501 0.8065 0.000 0.000 0.924 0.000 0.076 0.000
#> SRR1477476 2 0.0972 0.9509 0.000 0.964 0.008 0.028 0.000 0.000
#> SRR1398057 3 0.4011 0.5464 0.304 0.000 0.672 0.000 0.024 0.000
#> SRR1485042 1 0.3867 0.0342 0.512 0.000 0.000 0.000 0.000 0.488
#> SRR1385453 4 0.3707 0.7408 0.000 0.000 0.136 0.784 0.080 0.000
#> SRR1348074 4 0.2482 0.7920 0.004 0.000 0.000 0.848 0.000 0.148
#> SRR813959 2 0.1663 0.8851 0.000 0.912 0.000 0.000 0.088 0.000
#> SRR665442 2 0.3188 0.8629 0.016 0.860 0.000 0.072 0.036 0.016
#> SRR1378068 3 0.0547 0.8312 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1485237 6 0.3070 0.7881 0.016 0.072 0.000 0.000 0.056 0.856
#> SRR1350792 1 0.3520 0.7374 0.804 0.000 0.000 0.000 0.100 0.096
#> SRR1326797 5 0.2553 0.7634 0.008 0.000 0.000 0.000 0.848 0.144
#> SRR808994 3 0.0260 0.8315 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1474041 5 0.2378 0.8834 0.000 0.000 0.152 0.000 0.848 0.000
#> SRR1405641 3 0.0146 0.8315 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1362245 3 0.1588 0.8028 0.072 0.000 0.924 0.000 0.004 0.000
#> SRR1500194 1 0.0146 0.8500 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1414876 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 3 0.1367 0.8173 0.000 0.000 0.944 0.044 0.012 0.000
#> SRR1325161 5 0.2857 0.8479 0.000 0.000 0.072 0.000 0.856 0.072
#> SRR1318026 4 0.0458 0.8524 0.000 0.000 0.000 0.984 0.000 0.016
#> SRR1343778 3 0.0547 0.8308 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1441287 1 0.1152 0.8394 0.952 0.000 0.000 0.000 0.004 0.044
#> SRR1430991 5 0.2527 0.8792 0.000 0.000 0.168 0.000 0.832 0.000
#> SRR1499722 5 0.2633 0.8741 0.000 0.000 0.104 0.000 0.864 0.032
#> SRR1351368 3 0.1897 0.7869 0.000 0.000 0.908 0.084 0.004 0.004
#> SRR1441785 1 0.0146 0.8499 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1096101 1 0.3993 0.1411 0.520 0.000 0.000 0.000 0.004 0.476
#> SRR808375 5 0.2748 0.8828 0.000 0.000 0.128 0.000 0.848 0.024
#> SRR1452842 6 0.0922 0.8200 0.004 0.000 0.000 0.004 0.024 0.968
#> SRR1311709 6 0.2706 0.7759 0.160 0.000 0.000 0.000 0.008 0.832
#> SRR1433352 5 0.3390 0.7138 0.000 0.000 0.296 0.000 0.704 0.000
#> SRR1340241 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1456754 6 0.1151 0.8275 0.032 0.000 0.000 0.000 0.012 0.956
#> SRR1465172 5 0.2667 0.7873 0.000 0.000 0.020 0.000 0.852 0.128
#> SRR1499284 6 0.3817 0.2393 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1499607 2 0.4113 0.7224 0.000 0.768 0.020 0.164 0.004 0.044
#> SRR812342 1 0.0858 0.8470 0.968 0.000 0.000 0.000 0.004 0.028
#> SRR1405374 1 0.0000 0.8517 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1403565 1 0.0508 0.8468 0.984 0.000 0.000 0.000 0.012 0.004
#> SRR1332024 3 0.1814 0.7793 0.100 0.000 0.900 0.000 0.000 0.000
#> SRR1471633 6 0.5528 0.5722 0.252 0.000 0.000 0.036 0.096 0.616
#> SRR1325944 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.3740 0.6406 0.000 0.000 0.000 0.228 0.740 0.032
#> SRR1435372 6 0.2842 0.8039 0.044 0.000 0.000 0.000 0.104 0.852
#> SRR1324184 4 0.3372 0.7630 0.000 0.124 0.000 0.824 0.036 0.016
#> SRR816517 3 0.1957 0.7568 0.000 0.000 0.888 0.112 0.000 0.000
#> SRR1324141 4 0.2553 0.8029 0.000 0.000 0.000 0.848 0.008 0.144
#> SRR1101612 1 0.0508 0.8505 0.984 0.000 0.000 0.000 0.004 0.012
#> SRR1356531 6 0.2941 0.7223 0.220 0.000 0.000 0.000 0.000 0.780
#> SRR1089785 5 0.2597 0.8723 0.000 0.000 0.176 0.000 0.824 0.000
#> SRR1077708 3 0.4854 0.5624 0.000 0.000 0.636 0.000 0.264 0.100
#> SRR1343720 5 0.2950 0.8837 0.000 0.000 0.148 0.000 0.828 0.024
#> SRR1477499 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 5 0.2870 0.8683 0.004 0.000 0.100 0.000 0.856 0.040
#> SRR1326408 6 0.1321 0.8262 0.024 0.000 0.000 0.004 0.020 0.952
#> SRR1336529 3 0.0363 0.8319 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1440643 4 0.3740 0.7068 0.000 0.000 0.120 0.784 0.096 0.000
#> SRR662354 1 0.1448 0.8266 0.948 0.000 0.000 0.012 0.024 0.016
#> SRR1310817 5 0.2750 0.8830 0.000 0.000 0.136 0.020 0.844 0.000
#> SRR1347389 4 0.0405 0.8517 0.000 0.008 0.000 0.988 0.004 0.000
#> SRR1353097 6 0.3133 0.7254 0.212 0.000 0.000 0.000 0.008 0.780
#> SRR1384737 4 0.0951 0.8521 0.000 0.000 0.008 0.968 0.004 0.020
#> SRR1096339 1 0.0260 0.8514 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1345329 6 0.2113 0.8111 0.032 0.000 0.000 0.048 0.008 0.912
#> SRR1414771 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1309119 1 0.1686 0.8285 0.924 0.000 0.000 0.064 0.000 0.012
#> SRR1470438 3 0.0363 0.8322 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1343221 1 0.2060 0.8140 0.900 0.000 0.000 0.000 0.016 0.084
#> SRR1410847 1 0.0405 0.8512 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR807949 5 0.2416 0.8826 0.000 0.000 0.156 0.000 0.844 0.000
#> SRR1442332 5 0.2527 0.8761 0.000 0.000 0.168 0.000 0.832 0.000
#> SRR815920 3 0.0363 0.8319 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1471524 3 0.5421 0.1638 0.000 0.000 0.528 0.132 0.340 0.000
#> SRR1477221 1 0.3686 0.5941 0.748 0.000 0.220 0.000 0.032 0.000
#> SRR1445046 2 0.1003 0.9550 0.000 0.964 0.000 0.020 0.016 0.000
#> SRR1331962 2 0.0520 0.9672 0.000 0.984 0.000 0.008 0.008 0.000
#> SRR1319946 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1311599 1 0.0000 0.8517 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1323977 2 0.0777 0.9549 0.000 0.972 0.000 0.000 0.024 0.004
#> SRR1445132 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 5 0.3130 0.8606 0.048 0.000 0.124 0.000 0.828 0.000
#> SRR1366390 4 0.0405 0.8517 0.000 0.008 0.000 0.988 0.004 0.000
#> SRR1343012 4 0.4004 0.4700 0.000 0.000 0.000 0.620 0.012 0.368
#> SRR1311958 2 0.0914 0.9583 0.000 0.968 0.000 0.016 0.016 0.000
#> SRR1388234 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1370384 6 0.2263 0.8098 0.016 0.000 0.000 0.000 0.100 0.884
#> SRR1321650 3 0.2540 0.7798 0.020 0.000 0.872 0.000 0.104 0.004
#> SRR1485117 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 6 0.1297 0.8195 0.012 0.000 0.000 0.000 0.040 0.948
#> SRR816609 6 0.3287 0.6495 0.012 0.220 0.000 0.000 0.000 0.768
#> SRR1486239 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1309638 6 0.4863 0.4833 0.000 0.000 0.284 0.000 0.092 0.624
#> SRR1356660 1 0.0146 0.8518 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1392883 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.2416 0.8826 0.000 0.000 0.156 0.000 0.844 0.000
#> SRR816677 6 0.2094 0.8160 0.080 0.000 0.000 0.000 0.020 0.900
#> SRR1455722 1 0.1010 0.8438 0.960 0.000 0.000 0.000 0.004 0.036
#> SRR1336029 1 0.3024 0.7691 0.844 0.000 0.000 0.032 0.008 0.116
#> SRR808452 1 0.0858 0.8464 0.968 0.000 0.000 0.000 0.004 0.028
#> SRR1352169 3 0.5182 -0.1267 0.024 0.040 0.484 0.000 0.452 0.000
#> SRR1366707 3 0.0547 0.8315 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1328143 5 0.2454 0.8705 0.000 0.000 0.160 0.000 0.840 0.000
#> SRR1473567 2 0.0000 0.9747 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.735 0.885 0.949 0.327 0.695 0.695
#> 3 3 0.752 0.807 0.926 0.104 0.987 0.981
#> 4 4 0.705 0.835 0.907 0.276 0.805 0.718
#> 5 5 0.556 0.792 0.879 0.143 0.975 0.951
#> 6 6 0.424 0.596 0.775 0.225 0.896 0.786
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1442087 1 0.0000 0.947 1.000 0.000
#> SRR1390119 2 0.0000 0.917 0.000 1.000
#> SRR1436127 1 0.0000 0.947 1.000 0.000
#> SRR1347278 1 0.0000 0.947 1.000 0.000
#> SRR1332904 2 0.1414 0.907 0.020 0.980
#> SRR1444179 1 0.0376 0.945 0.996 0.004
#> SRR1082685 1 0.0000 0.947 1.000 0.000
#> SRR1362287 1 0.0000 0.947 1.000 0.000
#> SRR1339007 1 0.0000 0.947 1.000 0.000
#> SRR1376557 2 0.0000 0.917 0.000 1.000
#> SRR1468700 2 0.0376 0.916 0.004 0.996
#> SRR1077455 1 0.0000 0.947 1.000 0.000
#> SRR1413978 1 0.0000 0.947 1.000 0.000
#> SRR1439896 1 0.0000 0.947 1.000 0.000
#> SRR1317963 2 0.9286 0.494 0.344 0.656
#> SRR1431865 1 0.0000 0.947 1.000 0.000
#> SRR1394253 1 0.0000 0.947 1.000 0.000
#> SRR1082664 1 0.0000 0.947 1.000 0.000
#> SRR1077968 1 0.0000 0.947 1.000 0.000
#> SRR1076393 1 0.0000 0.947 1.000 0.000
#> SRR1477476 2 0.0000 0.917 0.000 1.000
#> SRR1398057 1 0.0000 0.947 1.000 0.000
#> SRR1485042 1 0.0000 0.947 1.000 0.000
#> SRR1385453 1 0.9129 0.540 0.672 0.328
#> SRR1348074 1 0.8081 0.690 0.752 0.248
#> SRR813959 1 0.7815 0.715 0.768 0.232
#> SRR665442 1 0.5178 0.840 0.884 0.116
#> SRR1378068 1 0.0000 0.947 1.000 0.000
#> SRR1485237 1 0.8016 0.696 0.756 0.244
#> SRR1350792 1 0.0000 0.947 1.000 0.000
#> SRR1326797 1 0.0000 0.947 1.000 0.000
#> SRR808994 1 0.0000 0.947 1.000 0.000
#> SRR1474041 1 0.0000 0.947 1.000 0.000
#> SRR1405641 1 0.0000 0.947 1.000 0.000
#> SRR1362245 1 0.0000 0.947 1.000 0.000
#> SRR1500194 1 0.0000 0.947 1.000 0.000
#> SRR1414876 2 0.0000 0.917 0.000 1.000
#> SRR1478523 1 0.8861 0.588 0.696 0.304
#> SRR1325161 1 0.0000 0.947 1.000 0.000
#> SRR1318026 1 0.8081 0.690 0.752 0.248
#> SRR1343778 1 0.0000 0.947 1.000 0.000
#> SRR1441287 1 0.0000 0.947 1.000 0.000
#> SRR1430991 1 0.0000 0.947 1.000 0.000
#> SRR1499722 1 0.0000 0.947 1.000 0.000
#> SRR1351368 1 0.5178 0.850 0.884 0.116
#> SRR1441785 1 0.0000 0.947 1.000 0.000
#> SRR1096101 1 0.0000 0.947 1.000 0.000
#> SRR808375 1 0.0000 0.947 1.000 0.000
#> SRR1452842 1 0.0000 0.947 1.000 0.000
#> SRR1311709 1 0.0376 0.945 0.996 0.004
#> SRR1433352 1 0.0000 0.947 1.000 0.000
#> SRR1340241 2 0.0000 0.917 0.000 1.000
#> SRR1456754 1 0.0000 0.947 1.000 0.000
#> SRR1465172 1 0.0000 0.947 1.000 0.000
#> SRR1499284 1 0.0000 0.947 1.000 0.000
#> SRR1499607 2 0.9170 0.519 0.332 0.668
#> SRR812342 1 0.0000 0.947 1.000 0.000
#> SRR1405374 1 0.0000 0.947 1.000 0.000
#> SRR1403565 1 0.0000 0.947 1.000 0.000
#> SRR1332024 1 0.0000 0.947 1.000 0.000
#> SRR1471633 1 0.0376 0.945 0.996 0.004
#> SRR1325944 2 0.0000 0.917 0.000 1.000
#> SRR1429450 2 0.0000 0.917 0.000 1.000
#> SRR821573 1 0.3274 0.901 0.940 0.060
#> SRR1435372 1 0.0000 0.947 1.000 0.000
#> SRR1324184 2 0.7376 0.729 0.208 0.792
#> SRR816517 1 0.9460 0.457 0.636 0.364
#> SRR1324141 1 0.8081 0.690 0.752 0.248
#> SRR1101612 1 0.0000 0.947 1.000 0.000
#> SRR1356531 1 0.0000 0.947 1.000 0.000
#> SRR1089785 1 0.0000 0.947 1.000 0.000
#> SRR1077708 1 0.0000 0.947 1.000 0.000
#> SRR1343720 1 0.0000 0.947 1.000 0.000
#> SRR1477499 2 0.0000 0.917 0.000 1.000
#> SRR1347236 1 0.0000 0.947 1.000 0.000
#> SRR1326408 1 0.0000 0.947 1.000 0.000
#> SRR1336529 1 0.0000 0.947 1.000 0.000
#> SRR1440643 1 0.6887 0.777 0.816 0.184
#> SRR662354 1 0.0000 0.947 1.000 0.000
#> SRR1310817 1 0.2603 0.915 0.956 0.044
#> SRR1347389 2 0.0000 0.917 0.000 1.000
#> SRR1353097 1 0.0000 0.947 1.000 0.000
#> SRR1384737 1 0.8081 0.690 0.752 0.248
#> SRR1096339 1 0.0000 0.947 1.000 0.000
#> SRR1345329 1 0.8081 0.690 0.752 0.248
#> SRR1414771 1 0.0000 0.947 1.000 0.000
#> SRR1309119 1 0.0376 0.945 0.996 0.004
#> SRR1470438 1 0.0000 0.947 1.000 0.000
#> SRR1343221 1 0.0000 0.947 1.000 0.000
#> SRR1410847 1 0.0000 0.947 1.000 0.000
#> SRR807949 1 0.0000 0.947 1.000 0.000
#> SRR1442332 1 0.0000 0.947 1.000 0.000
#> SRR815920 1 0.0000 0.947 1.000 0.000
#> SRR1471524 1 0.1184 0.937 0.984 0.016
#> SRR1477221 1 0.0000 0.947 1.000 0.000
#> SRR1445046 2 0.9286 0.494 0.344 0.656
#> SRR1331962 2 0.0376 0.916 0.004 0.996
#> SRR1319946 1 0.9608 0.404 0.616 0.384
#> SRR1311599 1 0.0000 0.947 1.000 0.000
#> SRR1323977 1 0.6623 0.791 0.828 0.172
#> SRR1445132 2 0.0000 0.917 0.000 1.000
#> SRR1337321 1 0.0000 0.947 1.000 0.000
#> SRR1366390 2 0.0000 0.917 0.000 1.000
#> SRR1343012 1 0.8081 0.690 0.752 0.248
#> SRR1311958 2 0.2043 0.899 0.032 0.968
#> SRR1388234 1 0.8081 0.690 0.752 0.248
#> SRR1370384 1 0.0000 0.947 1.000 0.000
#> SRR1321650 1 0.0000 0.947 1.000 0.000
#> SRR1485117 2 0.0000 0.917 0.000 1.000
#> SRR1384713 1 0.0000 0.947 1.000 0.000
#> SRR816609 1 0.8081 0.690 0.752 0.248
#> SRR1486239 2 0.9286 0.494 0.344 0.656
#> SRR1309638 1 0.0000 0.947 1.000 0.000
#> SRR1356660 1 0.0000 0.947 1.000 0.000
#> SRR1392883 2 0.0000 0.917 0.000 1.000
#> SRR808130 1 0.0000 0.947 1.000 0.000
#> SRR816677 1 0.5737 0.828 0.864 0.136
#> SRR1455722 1 0.0000 0.947 1.000 0.000
#> SRR1336029 1 0.0000 0.947 1.000 0.000
#> SRR808452 1 0.0000 0.947 1.000 0.000
#> SRR1352169 1 0.0000 0.947 1.000 0.000
#> SRR1366707 1 0.1184 0.937 0.984 0.016
#> SRR1328143 1 0.0000 0.947 1.000 0.000
#> SRR1473567 2 0.0000 0.917 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 1 0.0424 0.9172 0.992 0.000 0.008
#> SRR1390119 2 0.0592 0.8099 0.000 0.988 0.012
#> SRR1436127 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1347278 1 0.0592 0.9151 0.988 0.000 0.012
#> SRR1332904 2 0.3995 0.7384 0.016 0.868 0.116
#> SRR1444179 1 0.1289 0.8998 0.968 0.000 0.032
#> SRR1082685 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1376557 2 0.1289 0.8017 0.000 0.968 0.032
#> SRR1468700 2 0.1031 0.8057 0.000 0.976 0.024
#> SRR1077455 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1317963 2 0.9657 0.0460 0.300 0.460 0.240
#> SRR1431865 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1082664 1 0.0424 0.9174 0.992 0.000 0.008
#> SRR1077968 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1076393 1 0.0747 0.9126 0.984 0.000 0.016
#> SRR1477476 2 0.0592 0.8099 0.000 0.988 0.012
#> SRR1398057 1 0.0424 0.9172 0.992 0.000 0.008
#> SRR1485042 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1385453 1 0.8287 0.3777 0.616 0.128 0.256
#> SRR1348074 1 0.7412 0.5578 0.700 0.124 0.176
#> SRR813959 1 0.7102 0.5924 0.724 0.132 0.144
#> SRR665442 3 0.5254 0.0000 0.264 0.000 0.736
#> SRR1378068 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1485237 1 0.7412 0.5588 0.700 0.124 0.176
#> SRR1350792 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1326797 1 0.0424 0.9170 0.992 0.000 0.008
#> SRR808994 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1474041 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1405641 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1362245 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1500194 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.8120 0.000 1.000 0.000
#> SRR1478523 1 0.7918 0.4289 0.640 0.104 0.256
#> SRR1325161 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1318026 1 0.7412 0.5578 0.700 0.124 0.176
#> SRR1343778 1 0.0424 0.9172 0.992 0.000 0.008
#> SRR1441287 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1430991 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1499722 1 0.0424 0.9170 0.992 0.000 0.008
#> SRR1351368 1 0.4689 0.7717 0.852 0.096 0.052
#> SRR1441785 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR808375 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1452842 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1311709 1 0.0892 0.9088 0.980 0.000 0.020
#> SRR1433352 1 0.0592 0.9153 0.988 0.000 0.012
#> SRR1340241 2 0.2878 0.7644 0.000 0.904 0.096
#> SRR1456754 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1465172 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1499284 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1499607 2 0.9565 0.0609 0.296 0.476 0.228
#> SRR812342 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1332024 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1471633 1 0.1289 0.8998 0.968 0.000 0.032
#> SRR1325944 2 0.0592 0.8099 0.000 0.988 0.012
#> SRR1429450 2 0.0592 0.8099 0.000 0.988 0.012
#> SRR821573 1 0.3325 0.8365 0.904 0.020 0.076
#> SRR1435372 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1324184 2 0.5397 0.5412 0.000 0.720 0.280
#> SRR816517 1 0.8750 0.2807 0.580 0.164 0.256
#> SRR1324141 1 0.7462 0.5519 0.696 0.124 0.180
#> SRR1101612 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1089785 1 0.0424 0.9174 0.992 0.000 0.008
#> SRR1077708 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1343720 1 0.0424 0.9174 0.992 0.000 0.008
#> SRR1477499 2 0.0000 0.8120 0.000 1.000 0.000
#> SRR1347236 1 0.0237 0.9189 0.996 0.000 0.004
#> SRR1326408 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1336529 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1440643 1 0.6393 0.6635 0.768 0.112 0.120
#> SRR662354 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1310817 1 0.2280 0.8717 0.940 0.008 0.052
#> SRR1347389 2 0.0592 0.8115 0.000 0.988 0.012
#> SRR1353097 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1384737 1 0.7462 0.5519 0.696 0.124 0.180
#> SRR1096339 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1345329 1 0.7412 0.5578 0.700 0.124 0.176
#> SRR1414771 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1309119 1 0.1289 0.8998 0.968 0.000 0.032
#> SRR1470438 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1343221 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR807949 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1442332 1 0.0592 0.9153 0.988 0.000 0.012
#> SRR815920 1 0.0424 0.9172 0.992 0.000 0.008
#> SRR1471524 1 0.1453 0.8995 0.968 0.008 0.024
#> SRR1477221 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1445046 2 0.9657 0.0460 0.300 0.460 0.240
#> SRR1331962 2 0.1031 0.8057 0.000 0.976 0.024
#> SRR1319946 1 0.8907 0.2349 0.568 0.184 0.248
#> SRR1311599 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1323977 1 0.6037 0.6902 0.788 0.112 0.100
#> SRR1445132 2 0.0592 0.8099 0.000 0.988 0.012
#> SRR1337321 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1366390 2 0.0592 0.8115 0.000 0.988 0.012
#> SRR1343012 1 0.7462 0.5519 0.696 0.124 0.180
#> SRR1311958 2 0.2527 0.7755 0.020 0.936 0.044
#> SRR1388234 1 0.7462 0.5519 0.696 0.124 0.180
#> SRR1370384 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1321650 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1485117 2 0.0237 0.8118 0.000 0.996 0.004
#> SRR1384713 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR816609 1 0.7462 0.5519 0.696 0.124 0.180
#> SRR1486239 2 0.9657 0.0460 0.300 0.460 0.240
#> SRR1309638 1 0.0237 0.9186 0.996 0.000 0.004
#> SRR1356660 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1392883 2 0.0592 0.8099 0.000 0.988 0.012
#> SRR808130 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR816677 1 0.5058 0.7338 0.820 0.032 0.148
#> SRR1455722 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1352169 1 0.0592 0.9151 0.988 0.000 0.012
#> SRR1366707 1 0.1453 0.8995 0.968 0.008 0.024
#> SRR1328143 1 0.0000 0.9207 1.000 0.000 0.000
#> SRR1473567 2 0.0237 0.8118 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.0469 0.96100 0.988 0.000 0.000 0.012
#> SRR1390119 2 0.0336 0.79923 0.000 0.992 0.000 0.008
#> SRR1436127 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1347278 1 0.1557 0.91772 0.944 0.000 0.000 0.056
#> SRR1332904 2 0.4776 0.70977 0.000 0.624 0.000 0.376
#> SRR1444179 1 0.2408 0.84598 0.896 0.000 0.000 0.104
#> SRR1082685 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1362287 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.3873 0.83485 0.000 0.772 0.000 0.228
#> SRR1468700 2 0.4250 0.81673 0.000 0.724 0.000 0.276
#> SRR1077455 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1413978 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1439896 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1317963 4 0.3219 0.07047 0.000 0.164 0.000 0.836
#> SRR1431865 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1082664 1 0.0469 0.96106 0.988 0.000 0.000 0.012
#> SRR1077968 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1076393 1 0.0921 0.94922 0.972 0.000 0.000 0.028
#> SRR1477476 2 0.0336 0.79923 0.000 0.992 0.000 0.008
#> SRR1398057 1 0.0469 0.96100 0.988 0.000 0.000 0.012
#> SRR1485042 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1385453 4 0.3649 0.60823 0.204 0.000 0.000 0.796
#> SRR1348074 4 0.4624 0.72932 0.340 0.000 0.000 0.660
#> SRR813959 4 0.5143 0.52800 0.456 0.004 0.000 0.540
#> SRR665442 3 0.0000 0.00000 0.000 0.000 1.000 0.000
#> SRR1378068 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1485237 4 0.4624 0.72887 0.340 0.000 0.000 0.660
#> SRR1350792 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.1557 0.91260 0.944 0.000 0.000 0.056
#> SRR808994 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1474041 1 0.0469 0.96113 0.988 0.000 0.000 0.012
#> SRR1405641 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1362245 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1500194 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.1637 0.82472 0.000 0.940 0.000 0.060
#> SRR1478523 4 0.3873 0.62444 0.228 0.000 0.000 0.772
#> SRR1325161 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1318026 4 0.4624 0.72932 0.340 0.000 0.000 0.660
#> SRR1343778 1 0.0469 0.96100 0.988 0.000 0.000 0.012
#> SRR1441287 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1430991 1 0.0469 0.96113 0.988 0.000 0.000 0.012
#> SRR1499722 1 0.1557 0.91260 0.944 0.000 0.000 0.056
#> SRR1351368 1 0.3528 0.68049 0.808 0.000 0.000 0.192
#> SRR1441785 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR808375 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1452842 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1311709 1 0.2281 0.85568 0.904 0.000 0.000 0.096
#> SRR1433352 1 0.1022 0.94602 0.968 0.000 0.000 0.032
#> SRR1340241 2 0.4250 0.78731 0.000 0.724 0.000 0.276
#> SRR1456754 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1465172 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1499284 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1499607 4 0.3726 -0.00656 0.000 0.212 0.000 0.788
#> SRR812342 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1405374 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1403565 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1332024 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1471633 1 0.2408 0.84598 0.896 0.000 0.000 0.104
#> SRR1325944 2 0.0000 0.80322 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0336 0.79923 0.000 0.992 0.000 0.008
#> SRR821573 1 0.3975 0.56237 0.760 0.000 0.000 0.240
#> SRR1435372 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1324184 2 0.6524 0.59604 0.000 0.616 0.264 0.120
#> SRR816517 4 0.4423 0.56570 0.176 0.036 0.000 0.788
#> SRR1324141 4 0.4605 0.72901 0.336 0.000 0.000 0.664
#> SRR1101612 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1356531 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1089785 1 0.0707 0.95687 0.980 0.000 0.000 0.020
#> SRR1077708 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1343720 1 0.0707 0.95687 0.980 0.000 0.000 0.020
#> SRR1477499 2 0.2647 0.83807 0.000 0.880 0.000 0.120
#> SRR1347236 1 0.0707 0.95256 0.980 0.000 0.000 0.020
#> SRR1326408 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1336529 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1440643 4 0.5000 0.42082 0.496 0.000 0.000 0.504
#> SRR662354 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1310817 1 0.3266 0.72516 0.832 0.000 0.000 0.168
#> SRR1347389 2 0.3907 0.83391 0.000 0.768 0.000 0.232
#> SRR1353097 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1384737 4 0.4605 0.72901 0.336 0.000 0.000 0.664
#> SRR1096339 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1345329 4 0.4624 0.72932 0.340 0.000 0.000 0.660
#> SRR1414771 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1309119 1 0.2408 0.84598 0.896 0.000 0.000 0.104
#> SRR1470438 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1343221 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1410847 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR807949 1 0.0469 0.96113 0.988 0.000 0.000 0.012
#> SRR1442332 1 0.1022 0.94602 0.968 0.000 0.000 0.032
#> SRR815920 1 0.0592 0.95895 0.984 0.000 0.000 0.016
#> SRR1471524 1 0.1557 0.91426 0.944 0.000 0.000 0.056
#> SRR1477221 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1445046 4 0.3219 0.07047 0.000 0.164 0.000 0.836
#> SRR1331962 2 0.4250 0.81673 0.000 0.724 0.000 0.276
#> SRR1319946 4 0.3450 0.52411 0.156 0.008 0.000 0.836
#> SRR1311599 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1323977 1 0.4989 -0.35688 0.528 0.000 0.000 0.472
#> SRR1445132 2 0.0336 0.79923 0.000 0.992 0.000 0.008
#> SRR1337321 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1366390 2 0.3907 0.83391 0.000 0.768 0.000 0.232
#> SRR1343012 4 0.4624 0.72767 0.340 0.000 0.000 0.660
#> SRR1311958 2 0.4697 0.73791 0.000 0.644 0.000 0.356
#> SRR1388234 4 0.4605 0.72967 0.336 0.000 0.000 0.664
#> SRR1370384 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1321650 1 0.0188 0.96347 0.996 0.000 0.000 0.004
#> SRR1485117 2 0.3610 0.84212 0.000 0.800 0.000 0.200
#> SRR1384713 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR816609 4 0.4605 0.72967 0.336 0.000 0.000 0.664
#> SRR1486239 4 0.3219 0.07047 0.000 0.164 0.000 0.836
#> SRR1309638 1 0.0469 0.95996 0.988 0.000 0.000 0.012
#> SRR1356660 1 0.0000 0.96462 1.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.80322 0.000 1.000 0.000 0.000
#> SRR808130 1 0.0469 0.96113 0.988 0.000 0.000 0.012
#> SRR816677 4 0.4994 0.50603 0.480 0.000 0.000 0.520
#> SRR1455722 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1336029 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR808452 1 0.0188 0.96377 0.996 0.000 0.000 0.004
#> SRR1352169 1 0.1474 0.92270 0.948 0.000 0.000 0.052
#> SRR1366707 1 0.1637 0.91394 0.940 0.000 0.000 0.060
#> SRR1328143 1 0.0469 0.96113 0.988 0.000 0.000 0.012
#> SRR1473567 2 0.3764 0.83951 0.000 0.784 0.000 0.216
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 1 0.2077 0.91097 0.920 0.040 0.000 0.040 0.000
#> SRR1390119 3 0.0404 0.85108 0.000 0.012 0.988 0.000 0.000
#> SRR1436127 1 0.2067 0.90964 0.920 0.048 0.000 0.032 0.000
#> SRR1347278 1 0.2616 0.88910 0.888 0.036 0.000 0.076 0.000
#> SRR1332904 2 0.5673 0.59368 0.000 0.628 0.216 0.156 0.000
#> SRR1444179 1 0.3039 0.80600 0.836 0.012 0.000 0.152 0.000
#> SRR1082685 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1362287 1 0.0955 0.92265 0.968 0.028 0.000 0.004 0.000
#> SRR1339007 1 0.1267 0.91749 0.960 0.012 0.004 0.024 0.000
#> SRR1376557 2 0.4735 0.64665 0.000 0.680 0.272 0.048 0.000
#> SRR1468700 2 0.2139 0.80345 0.000 0.916 0.052 0.032 0.000
#> SRR1077455 1 0.1267 0.91749 0.960 0.012 0.004 0.024 0.000
#> SRR1413978 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1439896 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1317963 4 0.4201 0.00421 0.000 0.408 0.000 0.592 0.000
#> SRR1431865 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1394253 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1082664 1 0.1836 0.91534 0.932 0.032 0.000 0.036 0.000
#> SRR1077968 1 0.1267 0.91749 0.960 0.012 0.004 0.024 0.000
#> SRR1076393 1 0.2940 0.88882 0.876 0.048 0.004 0.072 0.000
#> SRR1477476 3 0.0404 0.85108 0.000 0.012 0.988 0.000 0.000
#> SRR1398057 1 0.1907 0.91511 0.928 0.044 0.000 0.028 0.000
#> SRR1485042 1 0.0771 0.92186 0.976 0.000 0.004 0.020 0.000
#> SRR1385453 4 0.1913 0.51837 0.044 0.016 0.008 0.932 0.000
#> SRR1348074 4 0.3630 0.69622 0.204 0.016 0.000 0.780 0.000
#> SRR813959 4 0.5533 0.52378 0.320 0.068 0.008 0.604 0.000
#> SRR665442 5 0.0000 0.00000 0.000 0.000 0.000 0.000 1.000
#> SRR1378068 1 0.2067 0.91031 0.920 0.048 0.000 0.032 0.000
#> SRR1485237 4 0.3530 0.69599 0.204 0.012 0.000 0.784 0.000
#> SRR1350792 1 0.1356 0.91649 0.956 0.012 0.004 0.028 0.000
#> SRR1326797 1 0.2673 0.88541 0.892 0.028 0.008 0.072 0.000
#> SRR808994 1 0.2376 0.90337 0.904 0.052 0.000 0.044 0.000
#> SRR1474041 1 0.2078 0.91196 0.924 0.036 0.004 0.036 0.000
#> SRR1405641 1 0.2450 0.90235 0.900 0.052 0.000 0.048 0.000
#> SRR1362245 1 0.1818 0.91378 0.932 0.044 0.000 0.024 0.000
#> SRR1500194 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1414876 3 0.3496 0.70818 0.000 0.200 0.788 0.012 0.000
#> SRR1478523 4 0.2390 0.52283 0.060 0.024 0.008 0.908 0.000
#> SRR1325161 1 0.0451 0.92269 0.988 0.008 0.000 0.004 0.000
#> SRR1318026 4 0.3530 0.69618 0.204 0.012 0.000 0.784 0.000
#> SRR1343778 1 0.2077 0.91097 0.920 0.040 0.000 0.040 0.000
#> SRR1441287 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1430991 1 0.2078 0.91196 0.924 0.036 0.004 0.036 0.000
#> SRR1499722 1 0.2673 0.88541 0.892 0.028 0.008 0.072 0.000
#> SRR1351368 1 0.4451 0.65998 0.724 0.036 0.004 0.236 0.000
#> SRR1441785 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1096101 1 0.1329 0.91672 0.956 0.008 0.004 0.032 0.000
#> SRR808375 1 0.0451 0.92269 0.988 0.008 0.000 0.004 0.000
#> SRR1452842 1 0.1267 0.91749 0.960 0.012 0.004 0.024 0.000
#> SRR1311709 1 0.3031 0.82997 0.852 0.016 0.004 0.128 0.000
#> SRR1433352 1 0.2673 0.89945 0.892 0.044 0.004 0.060 0.000
#> SRR1340241 2 0.5708 0.53666 0.000 0.588 0.300 0.112 0.000
#> SRR1456754 1 0.1267 0.91749 0.960 0.012 0.004 0.024 0.000
#> SRR1465172 1 0.0451 0.92269 0.988 0.008 0.000 0.004 0.000
#> SRR1499284 1 0.0451 0.92269 0.988 0.008 0.000 0.004 0.000
#> SRR1499607 4 0.5246 -0.06305 0.000 0.384 0.052 0.564 0.000
#> SRR812342 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1405374 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1403565 1 0.0880 0.92145 0.968 0.032 0.000 0.000 0.000
#> SRR1332024 1 0.2450 0.90235 0.900 0.052 0.000 0.048 0.000
#> SRR1471633 1 0.3039 0.80600 0.836 0.012 0.000 0.152 0.000
#> SRR1325944 3 0.1608 0.85588 0.000 0.072 0.928 0.000 0.000
#> SRR1429450 3 0.0963 0.85836 0.000 0.036 0.964 0.000 0.000
#> SRR821573 1 0.5419 0.34078 0.600 0.048 0.012 0.340 0.000
#> SRR1435372 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1324184 2 0.5600 0.42595 0.000 0.628 0.104 0.004 0.264
#> SRR816517 4 0.2777 0.49523 0.040 0.028 0.036 0.896 0.000
#> SRR1324141 4 0.3474 0.69396 0.192 0.008 0.004 0.796 0.000
#> SRR1101612 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1356531 1 0.1356 0.91649 0.956 0.012 0.004 0.028 0.000
#> SRR1089785 1 0.2308 0.90826 0.912 0.036 0.004 0.048 0.000
#> SRR1077708 1 0.1579 0.91526 0.944 0.032 0.000 0.024 0.000
#> SRR1343720 1 0.2308 0.90826 0.912 0.036 0.004 0.048 0.000
#> SRR1477499 3 0.4588 0.22787 0.000 0.380 0.604 0.016 0.000
#> SRR1347236 1 0.2122 0.91485 0.924 0.032 0.008 0.036 0.000
#> SRR1326408 1 0.1173 0.92020 0.964 0.012 0.004 0.020 0.000
#> SRR1336529 1 0.2300 0.90435 0.908 0.052 0.000 0.040 0.000
#> SRR1440643 4 0.4961 0.48529 0.360 0.024 0.008 0.608 0.000
#> SRR662354 1 0.1356 0.91649 0.956 0.012 0.004 0.028 0.000
#> SRR1310817 1 0.4861 0.59410 0.696 0.048 0.008 0.248 0.000
#> SRR1347389 2 0.2873 0.79693 0.000 0.856 0.128 0.016 0.000
#> SRR1353097 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1384737 4 0.3474 0.69396 0.192 0.008 0.004 0.796 0.000
#> SRR1096339 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1345329 4 0.3630 0.69622 0.204 0.016 0.000 0.780 0.000
#> SRR1414771 1 0.2450 0.90235 0.900 0.052 0.000 0.048 0.000
#> SRR1309119 1 0.3039 0.80600 0.836 0.012 0.000 0.152 0.000
#> SRR1470438 1 0.2376 0.90337 0.904 0.052 0.000 0.044 0.000
#> SRR1343221 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1410847 1 0.0671 0.92332 0.980 0.016 0.000 0.004 0.000
#> SRR807949 1 0.2078 0.91196 0.924 0.036 0.004 0.036 0.000
#> SRR1442332 1 0.2605 0.90216 0.896 0.044 0.004 0.056 0.000
#> SRR815920 1 0.2153 0.90983 0.916 0.040 0.000 0.044 0.000
#> SRR1471524 1 0.3516 0.84595 0.836 0.052 0.004 0.108 0.000
#> SRR1477221 1 0.1041 0.92065 0.964 0.032 0.000 0.004 0.000
#> SRR1445046 4 0.4182 0.02163 0.000 0.400 0.000 0.600 0.000
#> SRR1331962 2 0.2139 0.80345 0.000 0.916 0.052 0.032 0.000
#> SRR1319946 4 0.3265 0.49589 0.040 0.096 0.008 0.856 0.000
#> SRR1311599 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1323977 4 0.5302 0.45681 0.392 0.032 0.012 0.564 0.000
#> SRR1445132 3 0.0404 0.85108 0.000 0.012 0.988 0.000 0.000
#> SRR1337321 1 0.1741 0.91449 0.936 0.040 0.000 0.024 0.000
#> SRR1366390 2 0.2873 0.79693 0.000 0.856 0.128 0.016 0.000
#> SRR1343012 4 0.3509 0.69384 0.196 0.008 0.004 0.792 0.000
#> SRR1311958 2 0.2280 0.72805 0.000 0.880 0.000 0.120 0.000
#> SRR1388234 4 0.3496 0.69683 0.200 0.012 0.000 0.788 0.000
#> SRR1370384 1 0.1267 0.91749 0.960 0.012 0.004 0.024 0.000
#> SRR1321650 1 0.2300 0.90435 0.908 0.052 0.000 0.040 0.000
#> SRR1485117 2 0.2969 0.79547 0.000 0.852 0.128 0.020 0.000
#> SRR1384713 1 0.1173 0.92020 0.964 0.012 0.004 0.020 0.000
#> SRR816609 4 0.3496 0.69683 0.200 0.012 0.000 0.788 0.000
#> SRR1486239 4 0.4201 0.00421 0.000 0.408 0.000 0.592 0.000
#> SRR1309638 1 0.2234 0.90882 0.916 0.036 0.004 0.044 0.000
#> SRR1356660 1 0.0451 0.92311 0.988 0.008 0.000 0.004 0.000
#> SRR1392883 3 0.1608 0.85588 0.000 0.072 0.928 0.000 0.000
#> SRR808130 1 0.2078 0.91196 0.924 0.036 0.004 0.036 0.000
#> SRR816677 4 0.4196 0.54947 0.356 0.004 0.000 0.640 0.000
#> SRR1455722 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1336029 1 0.1329 0.91672 0.956 0.008 0.004 0.032 0.000
#> SRR808452 1 0.1442 0.91506 0.952 0.012 0.004 0.032 0.000
#> SRR1352169 1 0.2632 0.89103 0.888 0.040 0.000 0.072 0.000
#> SRR1366707 1 0.3567 0.84379 0.832 0.052 0.004 0.112 0.000
#> SRR1328143 1 0.2078 0.91196 0.924 0.036 0.004 0.036 0.000
#> SRR1473567 2 0.2824 0.80160 0.000 0.864 0.116 0.020 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 1 0.4218 0.22945 0.556 0.000 0.428 0.016 0.000 0.000
#> SRR1390119 6 0.0260 0.84522 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1436127 3 0.3428 0.71963 0.304 0.000 0.696 0.000 0.000 0.000
#> SRR1347278 1 0.4926 0.23116 0.540 0.000 0.392 0.068 0.000 0.000
#> SRR1332904 2 0.5096 0.56312 0.000 0.652 0.008 0.136 0.000 0.204
#> SRR1444179 1 0.2278 0.66255 0.868 0.000 0.004 0.128 0.000 0.000
#> SRR1082685 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1362287 1 0.2823 0.66699 0.796 0.000 0.204 0.000 0.000 0.000
#> SRR1339007 1 0.0146 0.73956 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1376557 2 0.3991 0.62370 0.000 0.724 0.008 0.028 0.000 0.240
#> SRR1468700 2 0.0964 0.77064 0.000 0.968 0.004 0.012 0.000 0.016
#> SRR1077455 1 0.0146 0.73956 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1413978 1 0.2631 0.68386 0.820 0.000 0.180 0.000 0.000 0.000
#> SRR1439896 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1317963 4 0.4025 0.06373 0.000 0.416 0.008 0.576 0.000 0.000
#> SRR1431865 1 0.2597 0.68373 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR1394253 1 0.2597 0.68373 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR1082664 1 0.3707 0.53339 0.680 0.000 0.312 0.008 0.000 0.000
#> SRR1077968 1 0.0146 0.73956 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1076393 1 0.4616 0.32136 0.576 0.000 0.384 0.036 0.000 0.004
#> SRR1477476 6 0.0260 0.84522 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1398057 1 0.4136 0.21343 0.560 0.000 0.428 0.012 0.000 0.000
#> SRR1485042 1 0.1327 0.73625 0.936 0.000 0.064 0.000 0.000 0.000
#> SRR1385453 4 0.1973 0.50500 0.012 0.004 0.064 0.916 0.000 0.004
#> SRR1348074 4 0.3370 0.68295 0.212 0.012 0.004 0.772 0.000 0.000
#> SRR813959 4 0.5421 0.48476 0.276 0.040 0.060 0.620 0.000 0.004
#> SRR665442 5 0.0000 0.00000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1378068 3 0.3989 0.09472 0.468 0.000 0.528 0.004 0.000 0.000
#> SRR1485237 4 0.3273 0.68305 0.212 0.008 0.004 0.776 0.000 0.000
#> SRR1350792 1 0.0000 0.73884 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 1 0.3749 0.68083 0.796 0.004 0.128 0.068 0.000 0.004
#> SRR808994 3 0.2738 0.87776 0.176 0.000 0.820 0.004 0.000 0.000
#> SRR1474041 1 0.3888 0.52987 0.672 0.000 0.312 0.016 0.000 0.000
#> SRR1405641 3 0.2848 0.87763 0.176 0.000 0.816 0.008 0.000 0.000
#> SRR1362245 3 0.3221 0.80972 0.264 0.000 0.736 0.000 0.000 0.000
#> SRR1500194 1 0.2597 0.68373 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR1414876 6 0.2912 0.70376 0.000 0.216 0.000 0.000 0.000 0.784
#> SRR1478523 4 0.2407 0.49933 0.012 0.004 0.096 0.884 0.000 0.004
#> SRR1325161 1 0.1663 0.72739 0.912 0.000 0.088 0.000 0.000 0.000
#> SRR1318026 4 0.3273 0.68297 0.212 0.008 0.004 0.776 0.000 0.000
#> SRR1343778 1 0.4218 0.22945 0.556 0.000 0.428 0.016 0.000 0.000
#> SRR1441287 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1430991 1 0.3905 0.52352 0.668 0.000 0.316 0.016 0.000 0.000
#> SRR1499722 1 0.3749 0.68083 0.796 0.004 0.128 0.068 0.000 0.004
#> SRR1351368 1 0.5882 0.24177 0.532 0.004 0.252 0.208 0.000 0.004
#> SRR1441785 1 0.2597 0.68373 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR1096101 1 0.0622 0.73871 0.980 0.000 0.012 0.008 0.000 0.000
#> SRR808375 1 0.1663 0.72739 0.912 0.000 0.088 0.000 0.000 0.000
#> SRR1452842 1 0.0146 0.73956 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1311709 1 0.2053 0.65489 0.888 0.004 0.000 0.108 0.000 0.000
#> SRR1433352 1 0.4676 0.43386 0.616 0.004 0.336 0.040 0.000 0.004
#> SRR1340241 2 0.4924 0.52917 0.000 0.636 0.004 0.092 0.000 0.268
#> SRR1456754 1 0.0458 0.74025 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1465172 1 0.1663 0.72739 0.912 0.000 0.088 0.000 0.000 0.000
#> SRR1499284 1 0.1663 0.72739 0.912 0.000 0.088 0.000 0.000 0.000
#> SRR1499607 4 0.4868 0.00116 0.000 0.400 0.008 0.548 0.000 0.044
#> SRR812342 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1405374 1 0.0508 0.73523 0.984 0.000 0.004 0.012 0.000 0.000
#> SRR1403565 1 0.3499 0.51067 0.680 0.000 0.320 0.000 0.000 0.000
#> SRR1332024 3 0.2848 0.87763 0.176 0.000 0.816 0.008 0.000 0.000
#> SRR1471633 1 0.2320 0.65879 0.864 0.000 0.004 0.132 0.000 0.000
#> SRR1325944 6 0.1387 0.84999 0.000 0.068 0.000 0.000 0.000 0.932
#> SRR1429450 6 0.0790 0.85210 0.000 0.032 0.000 0.000 0.000 0.968
#> SRR821573 1 0.6259 0.14170 0.460 0.008 0.200 0.324 0.000 0.008
#> SRR1435372 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1324184 2 0.6202 0.26444 0.000 0.572 0.124 0.000 0.228 0.076
#> SRR816517 4 0.2727 0.48827 0.012 0.016 0.052 0.888 0.000 0.032
#> SRR1324141 4 0.3401 0.68078 0.204 0.004 0.016 0.776 0.000 0.000
#> SRR1101612 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1356531 1 0.0000 0.73884 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089785 1 0.4078 0.50978 0.656 0.000 0.320 0.024 0.000 0.000
#> SRR1077708 1 0.3464 0.53896 0.688 0.000 0.312 0.000 0.000 0.000
#> SRR1343720 1 0.4078 0.50978 0.656 0.000 0.320 0.024 0.000 0.000
#> SRR1477499 6 0.3804 0.18709 0.000 0.424 0.000 0.000 0.000 0.576
#> SRR1347236 1 0.3192 0.69932 0.828 0.004 0.136 0.028 0.000 0.004
#> SRR1326408 1 0.0632 0.73971 0.976 0.000 0.024 0.000 0.000 0.000
#> SRR1336529 3 0.2730 0.87253 0.192 0.000 0.808 0.000 0.000 0.000
#> SRR1440643 4 0.5186 0.39982 0.308 0.004 0.088 0.596 0.000 0.004
#> SRR662354 1 0.0000 0.73884 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310817 1 0.6146 0.19097 0.492 0.004 0.252 0.244 0.000 0.008
#> SRR1347389 2 0.3017 0.72853 0.000 0.844 0.084 0.000 0.000 0.072
#> SRR1353097 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1384737 4 0.3401 0.68078 0.204 0.004 0.016 0.776 0.000 0.000
#> SRR1096339 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1345329 4 0.3370 0.68295 0.212 0.012 0.004 0.772 0.000 0.000
#> SRR1414771 3 0.2848 0.87763 0.176 0.000 0.816 0.008 0.000 0.000
#> SRR1309119 1 0.2320 0.65879 0.864 0.000 0.004 0.132 0.000 0.000
#> SRR1470438 3 0.2738 0.87776 0.176 0.000 0.820 0.004 0.000 0.000
#> SRR1343221 1 0.0508 0.73523 0.984 0.000 0.004 0.012 0.000 0.000
#> SRR1410847 1 0.2597 0.68853 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR807949 1 0.3888 0.52987 0.672 0.000 0.312 0.016 0.000 0.000
#> SRR1442332 1 0.4640 0.42736 0.612 0.004 0.344 0.036 0.000 0.004
#> SRR815920 1 0.4300 0.19816 0.548 0.000 0.432 0.020 0.000 0.000
#> SRR1471524 1 0.5258 0.29009 0.552 0.004 0.360 0.080 0.000 0.004
#> SRR1477221 1 0.3717 0.37780 0.616 0.000 0.384 0.000 0.000 0.000
#> SRR1445046 4 0.3993 0.09725 0.000 0.400 0.008 0.592 0.000 0.000
#> SRR1331962 2 0.0964 0.77064 0.000 0.968 0.004 0.012 0.000 0.016
#> SRR1319946 4 0.2800 0.49471 0.020 0.076 0.024 0.876 0.000 0.004
#> SRR1311599 1 0.2597 0.68373 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR1323977 4 0.5251 0.36371 0.336 0.008 0.076 0.576 0.000 0.004
#> SRR1445132 6 0.0260 0.84522 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1337321 3 0.3266 0.79750 0.272 0.000 0.728 0.000 0.000 0.000
#> SRR1366390 2 0.3017 0.72853 0.000 0.844 0.084 0.000 0.000 0.072
#> SRR1343012 4 0.3430 0.67962 0.208 0.004 0.016 0.772 0.000 0.000
#> SRR1311958 2 0.2053 0.70354 0.000 0.888 0.004 0.108 0.000 0.000
#> SRR1388234 4 0.3243 0.68372 0.208 0.008 0.004 0.780 0.000 0.000
#> SRR1370384 1 0.0146 0.73956 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1321650 3 0.2854 0.86691 0.208 0.000 0.792 0.000 0.000 0.000
#> SRR1485117 2 0.2163 0.76733 0.000 0.892 0.016 0.000 0.000 0.092
#> SRR1384713 1 0.0632 0.73971 0.976 0.000 0.024 0.000 0.000 0.000
#> SRR816609 4 0.3243 0.68372 0.208 0.008 0.004 0.780 0.000 0.000
#> SRR1486239 4 0.4025 0.06373 0.000 0.416 0.008 0.576 0.000 0.000
#> SRR1309638 1 0.4097 -0.17164 0.500 0.000 0.492 0.008 0.000 0.000
#> SRR1356660 1 0.2597 0.68373 0.824 0.000 0.176 0.000 0.000 0.000
#> SRR1392883 6 0.1387 0.84999 0.000 0.068 0.000 0.000 0.000 0.932
#> SRR808130 1 0.3888 0.52987 0.672 0.000 0.312 0.016 0.000 0.000
#> SRR816677 4 0.4632 0.54385 0.328 0.004 0.048 0.620 0.000 0.000
#> SRR1455722 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1336029 1 0.0717 0.73773 0.976 0.000 0.016 0.008 0.000 0.000
#> SRR808452 1 0.0260 0.73652 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1352169 1 0.4886 0.23169 0.540 0.000 0.396 0.064 0.000 0.000
#> SRR1366707 1 0.5223 0.28808 0.552 0.004 0.364 0.076 0.000 0.004
#> SRR1328143 1 0.3888 0.52987 0.672 0.000 0.312 0.016 0.000 0.000
#> SRR1473567 2 0.1700 0.77398 0.000 0.916 0.004 0.000 0.000 0.080
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 17851 rows and 124 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 0.904 0.973 0.987 0.3389 0.675 0.675
#> 3 3 0.634 0.908 0.909 0.8156 0.683 0.530
#> 4 4 0.701 0.652 0.776 0.1569 0.910 0.755
#> 5 5 0.699 0.720 0.817 0.0850 0.864 0.578
#> 6 6 0.737 0.646 0.786 0.0507 0.978 0.902
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
#> SRR1442087 1 0.000 0.983 1.000 0.000
#> SRR1390119 2 0.000 1.000 0.000 1.000
#> SRR1436127 1 0.000 0.983 1.000 0.000
#> SRR1347278 1 0.000 0.983 1.000 0.000
#> SRR1332904 2 0.000 1.000 0.000 1.000
#> SRR1444179 1 0.000 0.983 1.000 0.000
#> SRR1082685 1 0.000 0.983 1.000 0.000
#> SRR1362287 1 0.000 0.983 1.000 0.000
#> SRR1339007 1 0.000 0.983 1.000 0.000
#> SRR1376557 2 0.000 1.000 0.000 1.000
#> SRR1468700 2 0.000 1.000 0.000 1.000
#> SRR1077455 1 0.000 0.983 1.000 0.000
#> SRR1413978 1 0.000 0.983 1.000 0.000
#> SRR1439896 1 0.000 0.983 1.000 0.000
#> SRR1317963 2 0.000 1.000 0.000 1.000
#> SRR1431865 1 0.000 0.983 1.000 0.000
#> SRR1394253 1 0.000 0.983 1.000 0.000
#> SRR1082664 1 0.000 0.983 1.000 0.000
#> SRR1077968 1 0.000 0.983 1.000 0.000
#> SRR1076393 1 0.000 0.983 1.000 0.000
#> SRR1477476 2 0.000 1.000 0.000 1.000
#> SRR1398057 1 0.000 0.983 1.000 0.000
#> SRR1485042 1 0.000 0.983 1.000 0.000
#> SRR1385453 1 0.605 0.840 0.852 0.148
#> SRR1348074 1 0.738 0.761 0.792 0.208
#> SRR813959 1 0.000 0.983 1.000 0.000
#> SRR665442 1 0.000 0.983 1.000 0.000
#> SRR1378068 1 0.000 0.983 1.000 0.000
#> SRR1485237 1 0.000 0.983 1.000 0.000
#> SRR1350792 1 0.000 0.983 1.000 0.000
#> SRR1326797 1 0.000 0.983 1.000 0.000
#> SRR808994 1 0.000 0.983 1.000 0.000
#> SRR1474041 1 0.000 0.983 1.000 0.000
#> SRR1405641 1 0.000 0.983 1.000 0.000
#> SRR1362245 1 0.000 0.983 1.000 0.000
#> SRR1500194 1 0.000 0.983 1.000 0.000
#> SRR1414876 2 0.000 1.000 0.000 1.000
#> SRR1478523 1 0.000 0.983 1.000 0.000
#> SRR1325161 1 0.000 0.983 1.000 0.000
#> SRR1318026 1 0.595 0.845 0.856 0.144
#> SRR1343778 1 0.000 0.983 1.000 0.000
#> SRR1441287 1 0.000 0.983 1.000 0.000
#> SRR1430991 1 0.000 0.983 1.000 0.000
#> SRR1499722 1 0.000 0.983 1.000 0.000
#> SRR1351368 1 0.605 0.840 0.852 0.148
#> SRR1441785 1 0.000 0.983 1.000 0.000
#> SRR1096101 1 0.000 0.983 1.000 0.000
#> SRR808375 1 0.000 0.983 1.000 0.000
#> SRR1452842 1 0.000 0.983 1.000 0.000
#> SRR1311709 1 0.000 0.983 1.000 0.000
#> SRR1433352 1 0.000 0.983 1.000 0.000
#> SRR1340241 2 0.000 1.000 0.000 1.000
#> SRR1456754 1 0.000 0.983 1.000 0.000
#> SRR1465172 1 0.000 0.983 1.000 0.000
#> SRR1499284 1 0.000 0.983 1.000 0.000
#> SRR1499607 2 0.000 1.000 0.000 1.000
#> SRR812342 1 0.000 0.983 1.000 0.000
#> SRR1405374 1 0.000 0.983 1.000 0.000
#> SRR1403565 1 0.000 0.983 1.000 0.000
#> SRR1332024 1 0.000 0.983 1.000 0.000
#> SRR1471633 1 0.000 0.983 1.000 0.000
#> SRR1325944 2 0.000 1.000 0.000 1.000
#> SRR1429450 2 0.000 1.000 0.000 1.000
#> SRR821573 1 0.000 0.983 1.000 0.000
#> SRR1435372 1 0.000 0.983 1.000 0.000
#> SRR1324184 2 0.000 1.000 0.000 1.000
#> SRR816517 2 0.000 1.000 0.000 1.000
#> SRR1324141 1 0.595 0.845 0.856 0.144
#> SRR1101612 1 0.000 0.983 1.000 0.000
#> SRR1356531 1 0.000 0.983 1.000 0.000
#> SRR1089785 1 0.000 0.983 1.000 0.000
#> SRR1077708 1 0.000 0.983 1.000 0.000
#> SRR1343720 1 0.000 0.983 1.000 0.000
#> SRR1477499 2 0.000 1.000 0.000 1.000
#> SRR1347236 1 0.000 0.983 1.000 0.000
#> SRR1326408 1 0.000 0.983 1.000 0.000
#> SRR1336529 1 0.000 0.983 1.000 0.000
#> SRR1440643 1 0.595 0.845 0.856 0.144
#> SRR662354 1 0.000 0.983 1.000 0.000
#> SRR1310817 1 0.000 0.983 1.000 0.000
#> SRR1347389 2 0.000 1.000 0.000 1.000
#> SRR1353097 1 0.000 0.983 1.000 0.000
#> SRR1384737 1 0.595 0.845 0.856 0.144
#> SRR1096339 1 0.000 0.983 1.000 0.000
#> SRR1345329 1 0.595 0.845 0.856 0.144
#> SRR1414771 1 0.000 0.983 1.000 0.000
#> SRR1309119 1 0.000 0.983 1.000 0.000
#> SRR1470438 1 0.000 0.983 1.000 0.000
#> SRR1343221 1 0.000 0.983 1.000 0.000
#> SRR1410847 1 0.000 0.983 1.000 0.000
#> SRR807949 1 0.000 0.983 1.000 0.000
#> SRR1442332 1 0.000 0.983 1.000 0.000
#> SRR815920 1 0.000 0.983 1.000 0.000
#> SRR1471524 1 0.000 0.983 1.000 0.000
#> SRR1477221 1 0.000 0.983 1.000 0.000
#> SRR1445046 2 0.000 1.000 0.000 1.000
#> SRR1331962 2 0.000 1.000 0.000 1.000
#> SRR1319946 2 0.000 1.000 0.000 1.000
#> SRR1311599 1 0.000 0.983 1.000 0.000
#> SRR1323977 1 0.595 0.845 0.856 0.144
#> SRR1445132 2 0.000 1.000 0.000 1.000
#> SRR1337321 1 0.000 0.983 1.000 0.000
#> SRR1366390 2 0.000 1.000 0.000 1.000
#> SRR1343012 1 0.000 0.983 1.000 0.000
#> SRR1311958 2 0.000 1.000 0.000 1.000
#> SRR1388234 1 0.814 0.692 0.748 0.252
#> SRR1370384 1 0.000 0.983 1.000 0.000
#> SRR1321650 1 0.000 0.983 1.000 0.000
#> SRR1485117 2 0.000 1.000 0.000 1.000
#> SRR1384713 1 0.000 0.983 1.000 0.000
#> SRR816609 1 0.000 0.983 1.000 0.000
#> SRR1486239 2 0.000 1.000 0.000 1.000
#> SRR1309638 1 0.000 0.983 1.000 0.000
#> SRR1356660 1 0.000 0.983 1.000 0.000
#> SRR1392883 2 0.000 1.000 0.000 1.000
#> SRR808130 1 0.000 0.983 1.000 0.000
#> SRR816677 1 0.000 0.983 1.000 0.000
#> SRR1455722 1 0.000 0.983 1.000 0.000
#> SRR1336029 1 0.000 0.983 1.000 0.000
#> SRR808452 1 0.000 0.983 1.000 0.000
#> SRR1352169 1 0.000 0.983 1.000 0.000
#> SRR1366707 1 0.000 0.983 1.000 0.000
#> SRR1328143 1 0.000 0.983 1.000 0.000
#> SRR1473567 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1390119 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1436127 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1347278 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1332904 2 0.0237 0.947 0.004 0.996 0.000
#> SRR1444179 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1082685 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1362287 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1339007 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1376557 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1077455 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1413978 1 0.3816 0.920 0.852 0.000 0.148
#> SRR1439896 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1317963 2 0.4172 0.884 0.156 0.840 0.004
#> SRR1431865 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1394253 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1082664 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1077968 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1076393 3 0.0237 0.956 0.004 0.000 0.996
#> SRR1477476 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1398057 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1485042 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1385453 3 0.4062 0.788 0.164 0.000 0.836
#> SRR1348074 1 0.2187 0.808 0.948 0.028 0.024
#> SRR813959 3 0.3412 0.839 0.124 0.000 0.876
#> SRR665442 1 0.5859 0.697 0.656 0.000 0.344
#> SRR1378068 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1485237 1 0.1031 0.827 0.976 0.000 0.024
#> SRR1350792 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1326797 1 0.5098 0.865 0.752 0.000 0.248
#> SRR808994 3 0.0000 0.959 0.000 0.000 1.000
#> SRR1474041 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1405641 3 0.0000 0.959 0.000 0.000 1.000
#> SRR1362245 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1500194 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1414876 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1478523 3 0.4062 0.788 0.164 0.000 0.836
#> SRR1325161 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1318026 1 0.1031 0.827 0.976 0.000 0.024
#> SRR1343778 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1441287 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1430991 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1499722 3 0.3752 0.789 0.144 0.000 0.856
#> SRR1351368 3 0.3752 0.801 0.144 0.000 0.856
#> SRR1441785 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1096101 1 0.4235 0.935 0.824 0.000 0.176
#> SRR808375 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1452842 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1311709 1 0.2261 0.870 0.932 0.000 0.068
#> SRR1433352 3 0.1031 0.949 0.024 0.000 0.976
#> SRR1340241 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1456754 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1465172 3 0.5363 0.509 0.276 0.000 0.724
#> SRR1499284 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1499607 2 0.4172 0.884 0.156 0.840 0.004
#> SRR812342 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1405374 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1403565 1 0.4974 0.877 0.764 0.000 0.236
#> SRR1332024 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1471633 1 0.0892 0.830 0.980 0.000 0.020
#> SRR1325944 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.947 0.000 1.000 0.000
#> SRR821573 3 0.1031 0.950 0.024 0.000 0.976
#> SRR1435372 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1324184 2 0.0892 0.940 0.020 0.980 0.000
#> SRR816517 2 0.9252 0.232 0.156 0.448 0.396
#> SRR1324141 1 0.3590 0.789 0.896 0.028 0.076
#> SRR1101612 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1356531 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1089785 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1077708 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1343720 3 0.1031 0.949 0.024 0.000 0.976
#> SRR1477499 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1347236 1 0.5733 0.758 0.676 0.000 0.324
#> SRR1326408 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1336529 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1440643 3 0.4062 0.788 0.164 0.000 0.836
#> SRR662354 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1310817 3 0.0592 0.958 0.012 0.000 0.988
#> SRR1347389 2 0.1031 0.941 0.024 0.976 0.000
#> SRR1353097 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1384737 1 0.2187 0.808 0.948 0.028 0.024
#> SRR1096339 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1345329 1 0.1031 0.827 0.976 0.000 0.024
#> SRR1414771 3 0.0000 0.959 0.000 0.000 1.000
#> SRR1309119 1 0.2356 0.874 0.928 0.000 0.072
#> SRR1470438 3 0.0000 0.959 0.000 0.000 1.000
#> SRR1343221 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1410847 1 0.4235 0.935 0.824 0.000 0.176
#> SRR807949 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1442332 3 0.0592 0.959 0.012 0.000 0.988
#> SRR815920 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1471524 3 0.0237 0.956 0.004 0.000 0.996
#> SRR1477221 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1445046 2 0.3879 0.888 0.152 0.848 0.000
#> SRR1331962 2 0.1031 0.941 0.024 0.976 0.000
#> SRR1319946 2 0.4172 0.884 0.156 0.840 0.004
#> SRR1311599 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1323977 1 0.3590 0.789 0.896 0.028 0.076
#> SRR1445132 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1337321 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1366390 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1343012 1 0.3551 0.789 0.868 0.000 0.132
#> SRR1311958 2 0.3879 0.888 0.152 0.848 0.000
#> SRR1388234 1 0.3722 0.751 0.888 0.088 0.024
#> SRR1370384 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1321650 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1485117 2 0.0000 0.947 0.000 1.000 0.000
#> SRR1384713 1 0.4235 0.935 0.824 0.000 0.176
#> SRR816609 1 0.1031 0.827 0.976 0.000 0.024
#> SRR1486239 2 0.3816 0.890 0.148 0.852 0.000
#> SRR1309638 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1356660 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1392883 2 0.0000 0.947 0.000 1.000 0.000
#> SRR808130 3 0.0592 0.959 0.012 0.000 0.988
#> SRR816677 1 0.1289 0.835 0.968 0.000 0.032
#> SRR1455722 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1336029 1 0.4235 0.935 0.824 0.000 0.176
#> SRR808452 1 0.4235 0.935 0.824 0.000 0.176
#> SRR1352169 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1366707 3 0.0237 0.956 0.004 0.000 0.996
#> SRR1328143 3 0.0592 0.959 0.012 0.000 0.988
#> SRR1473567 2 0.0000 0.947 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.5222 0.762 0.032 0.000 0.688 0.280
#> SRR1390119 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1436127 3 0.2565 0.755 0.032 0.000 0.912 0.056
#> SRR1347278 3 0.4990 0.762 0.060 0.000 0.756 0.184
#> SRR1332904 2 0.4955 0.677 0.000 0.648 0.008 0.344
#> SRR1444179 1 0.1389 0.825 0.952 0.000 0.000 0.048
#> SRR1082685 1 0.0000 0.846 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.2443 0.807 0.916 0.000 0.060 0.024
#> SRR1339007 1 0.0817 0.841 0.976 0.000 0.000 0.024
#> SRR1376557 2 0.1042 0.814 0.000 0.972 0.008 0.020
#> SRR1468700 2 0.2611 0.793 0.000 0.896 0.008 0.096
#> SRR1077455 1 0.3074 0.720 0.848 0.000 0.000 0.152
#> SRR1413978 1 0.3247 0.783 0.880 0.000 0.060 0.060
#> SRR1439896 1 0.0000 0.846 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.5281 0.577 0.000 0.528 0.008 0.464
#> SRR1431865 1 0.2546 0.806 0.912 0.000 0.060 0.028
#> SRR1394253 1 0.2483 0.813 0.916 0.000 0.052 0.032
#> SRR1082664 3 0.6214 0.731 0.064 0.000 0.576 0.360
#> SRR1077968 1 0.1389 0.831 0.952 0.000 0.000 0.048
#> SRR1076393 3 0.4599 0.763 0.028 0.000 0.760 0.212
#> SRR1477476 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1398057 3 0.2224 0.745 0.032 0.000 0.928 0.040
#> SRR1485042 1 0.0707 0.841 0.980 0.000 0.000 0.020
#> SRR1385453 3 0.5112 0.579 0.004 0.000 0.560 0.436
#> SRR1348074 4 0.4994 0.334 0.480 0.000 0.000 0.520
#> SRR813959 4 0.5080 -0.519 0.004 0.000 0.420 0.576
#> SRR665442 3 0.7023 0.206 0.272 0.000 0.564 0.164
#> SRR1378068 3 0.2313 0.747 0.032 0.000 0.924 0.044
#> SRR1485237 1 0.5000 -0.357 0.504 0.000 0.000 0.496
#> SRR1350792 1 0.0469 0.844 0.988 0.000 0.000 0.012
#> SRR1326797 1 0.7342 -0.103 0.432 0.000 0.156 0.412
#> SRR808994 3 0.1610 0.729 0.032 0.000 0.952 0.016
#> SRR1474041 3 0.6228 0.730 0.064 0.000 0.572 0.364
#> SRR1405641 3 0.1356 0.734 0.032 0.000 0.960 0.008
#> SRR1362245 3 0.1936 0.739 0.032 0.000 0.940 0.028
#> SRR1500194 1 0.0707 0.841 0.980 0.000 0.000 0.020
#> SRR1414876 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1478523 3 0.5217 0.664 0.012 0.000 0.608 0.380
#> SRR1325161 3 0.6655 0.653 0.084 0.000 0.476 0.440
#> SRR1318026 1 0.5000 -0.365 0.500 0.000 0.000 0.500
#> SRR1343778 3 0.5358 0.759 0.048 0.000 0.700 0.252
#> SRR1441287 1 0.0000 0.846 1.000 0.000 0.000 0.000
#> SRR1430991 3 0.6276 0.722 0.064 0.000 0.556 0.380
#> SRR1499722 4 0.7698 -0.410 0.236 0.000 0.324 0.440
#> SRR1351368 3 0.4897 0.614 0.008 0.000 0.660 0.332
#> SRR1441785 1 0.2546 0.806 0.912 0.000 0.060 0.028
#> SRR1096101 1 0.0817 0.844 0.976 0.000 0.000 0.024
#> SRR808375 3 0.6380 0.680 0.064 0.000 0.500 0.436
#> SRR1452842 1 0.2868 0.743 0.864 0.000 0.000 0.136
#> SRR1311709 1 0.1637 0.798 0.940 0.000 0.000 0.060
#> SRR1433352 3 0.6453 0.721 0.080 0.000 0.560 0.360
#> SRR1340241 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1456754 1 0.1867 0.807 0.928 0.000 0.000 0.072
#> SRR1465172 4 0.7710 -0.264 0.296 0.000 0.256 0.448
#> SRR1499284 1 0.5172 0.224 0.588 0.000 0.008 0.404
#> SRR1499607 2 0.5277 0.582 0.000 0.532 0.008 0.460
#> SRR812342 1 0.0817 0.841 0.976 0.000 0.000 0.024
#> SRR1405374 1 0.0592 0.842 0.984 0.000 0.000 0.016
#> SRR1403565 1 0.3828 0.746 0.848 0.000 0.084 0.068
#> SRR1332024 3 0.1610 0.729 0.032 0.000 0.952 0.016
#> SRR1471633 1 0.2011 0.788 0.920 0.000 0.000 0.080
#> SRR1325944 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1429450 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR821573 3 0.5987 0.686 0.040 0.000 0.520 0.440
#> SRR1435372 1 0.0921 0.841 0.972 0.000 0.000 0.028
#> SRR1324184 2 0.1388 0.806 0.000 0.960 0.012 0.028
#> SRR816517 4 0.7001 -0.165 0.000 0.244 0.180 0.576
#> SRR1324141 4 0.5159 0.459 0.364 0.000 0.012 0.624
#> SRR1101612 1 0.0000 0.846 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0469 0.844 0.988 0.000 0.000 0.012
#> SRR1089785 3 0.6171 0.734 0.064 0.000 0.588 0.348
#> SRR1077708 3 0.4995 0.730 0.032 0.000 0.720 0.248
#> SRR1343720 3 0.6600 0.693 0.084 0.000 0.520 0.396
#> SRR1477499 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1347236 1 0.7426 -0.130 0.416 0.000 0.168 0.416
#> SRR1326408 1 0.1118 0.838 0.964 0.000 0.000 0.036
#> SRR1336529 3 0.1356 0.734 0.032 0.000 0.960 0.008
#> SRR1440643 4 0.5016 -0.282 0.004 0.000 0.396 0.600
#> SRR662354 1 0.0592 0.844 0.984 0.000 0.000 0.016
#> SRR1310817 3 0.5969 0.717 0.044 0.000 0.564 0.392
#> SRR1347389 2 0.4722 0.702 0.000 0.692 0.008 0.300
#> SRR1353097 1 0.0469 0.845 0.988 0.000 0.000 0.012
#> SRR1384737 4 0.5294 0.323 0.484 0.000 0.008 0.508
#> SRR1096339 1 0.0592 0.842 0.984 0.000 0.000 0.016
#> SRR1345329 4 0.4998 0.322 0.488 0.000 0.000 0.512
#> SRR1414771 3 0.1610 0.729 0.032 0.000 0.952 0.016
#> SRR1309119 1 0.1389 0.818 0.952 0.000 0.000 0.048
#> SRR1470438 3 0.1610 0.729 0.032 0.000 0.952 0.016
#> SRR1343221 1 0.0707 0.843 0.980 0.000 0.000 0.020
#> SRR1410847 1 0.0707 0.841 0.980 0.000 0.000 0.020
#> SRR807949 3 0.6296 0.717 0.064 0.000 0.548 0.388
#> SRR1442332 3 0.6228 0.730 0.064 0.000 0.572 0.364
#> SRR815920 3 0.2797 0.752 0.032 0.000 0.900 0.068
#> SRR1471524 3 0.4323 0.760 0.028 0.000 0.788 0.184
#> SRR1477221 3 0.1936 0.739 0.032 0.000 0.940 0.028
#> SRR1445046 2 0.5277 0.582 0.000 0.532 0.008 0.460
#> SRR1331962 2 0.4990 0.672 0.000 0.640 0.008 0.352
#> SRR1319946 2 0.4996 0.558 0.000 0.516 0.000 0.484
#> SRR1311599 1 0.2644 0.806 0.908 0.000 0.060 0.032
#> SRR1323977 4 0.5174 0.456 0.368 0.000 0.012 0.620
#> SRR1445132 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR1337321 3 0.2797 0.753 0.032 0.000 0.900 0.068
#> SRR1366390 2 0.0188 0.816 0.000 0.996 0.004 0.000
#> SRR1343012 4 0.6163 0.444 0.364 0.000 0.060 0.576
#> SRR1311958 2 0.5277 0.582 0.000 0.532 0.008 0.460
#> SRR1388234 4 0.6055 0.376 0.436 0.044 0.000 0.520
#> SRR1370384 1 0.2149 0.798 0.912 0.000 0.000 0.088
#> SRR1321650 3 0.1936 0.744 0.032 0.000 0.940 0.028
#> SRR1485117 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.2647 0.763 0.880 0.000 0.000 0.120
#> SRR816609 4 0.5000 0.298 0.500 0.000 0.000 0.500
#> SRR1486239 2 0.5277 0.582 0.000 0.532 0.008 0.460
#> SRR1309638 3 0.3523 0.721 0.032 0.000 0.856 0.112
#> SRR1356660 1 0.2546 0.806 0.912 0.000 0.060 0.028
#> SRR1392883 2 0.0592 0.817 0.000 0.984 0.016 0.000
#> SRR808130 3 0.6228 0.730 0.064 0.000 0.572 0.364
#> SRR816677 1 0.4040 0.485 0.752 0.000 0.000 0.248
#> SRR1455722 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1336029 1 0.1256 0.837 0.964 0.000 0.008 0.028
#> SRR808452 1 0.0000 0.846 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.5608 0.759 0.060 0.000 0.684 0.256
#> SRR1366707 3 0.3598 0.758 0.028 0.000 0.848 0.124
#> SRR1328143 3 0.6228 0.730 0.064 0.000 0.572 0.364
#> SRR1473567 2 0.1042 0.814 0.000 0.972 0.008 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.4870 0.4753 0.004 0.000 0.448 0.016 0.532
#> SRR1390119 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.2352 0.7230 0.004 0.000 0.896 0.008 0.092
#> SRR1347278 3 0.5651 0.2359 0.012 0.000 0.596 0.068 0.324
#> SRR1332904 4 0.5718 0.4476 0.000 0.320 0.004 0.584 0.092
#> SRR1444179 1 0.0609 0.9050 0.980 0.000 0.000 0.020 0.000
#> SRR1082685 1 0.0992 0.9086 0.968 0.000 0.000 0.008 0.024
#> SRR1362287 1 0.3907 0.8359 0.832 0.000 0.068 0.068 0.032
#> SRR1339007 1 0.1522 0.9039 0.944 0.000 0.000 0.012 0.044
#> SRR1376557 2 0.3161 0.8741 0.000 0.860 0.004 0.044 0.092
#> SRR1468700 2 0.5240 0.5551 0.000 0.660 0.000 0.244 0.096
#> SRR1077455 1 0.3877 0.7628 0.764 0.000 0.000 0.024 0.212
#> SRR1413978 1 0.4411 0.8135 0.796 0.000 0.068 0.104 0.032
#> SRR1439896 1 0.0865 0.9089 0.972 0.000 0.000 0.004 0.024
#> SRR1317963 4 0.4250 0.7304 0.000 0.128 0.004 0.784 0.084
#> SRR1431865 1 0.3844 0.8386 0.836 0.000 0.068 0.064 0.032
#> SRR1394253 1 0.3806 0.8444 0.840 0.000 0.056 0.064 0.040
#> SRR1082664 5 0.5020 0.6519 0.016 0.000 0.344 0.020 0.620
#> SRR1077968 1 0.2270 0.8848 0.904 0.000 0.000 0.020 0.076
#> SRR1076393 3 0.4422 0.4090 0.004 0.000 0.680 0.016 0.300
#> SRR1477476 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.3546 0.6733 0.004 0.000 0.832 0.048 0.116
#> SRR1485042 1 0.0671 0.9066 0.980 0.000 0.000 0.004 0.016
#> SRR1385453 3 0.6817 -0.1276 0.000 0.000 0.348 0.308 0.344
#> SRR1348074 4 0.2230 0.7810 0.116 0.000 0.000 0.884 0.000
#> SRR813959 5 0.6589 0.2567 0.000 0.000 0.224 0.328 0.448
#> SRR665442 3 0.7040 0.3539 0.084 0.000 0.536 0.104 0.276
#> SRR1378068 3 0.1571 0.7257 0.004 0.000 0.936 0.000 0.060
#> SRR1485237 4 0.2773 0.7603 0.164 0.000 0.000 0.836 0.000
#> SRR1350792 1 0.1041 0.9082 0.964 0.000 0.000 0.004 0.032
#> SRR1326797 5 0.4567 0.4899 0.216 0.000 0.028 0.020 0.736
#> SRR808994 3 0.0451 0.7445 0.004 0.000 0.988 0.000 0.008
#> SRR1474041 5 0.4703 0.6598 0.016 0.000 0.336 0.008 0.640
#> SRR1405641 3 0.0771 0.7442 0.004 0.000 0.976 0.000 0.020
#> SRR1362245 3 0.2885 0.7068 0.004 0.000 0.880 0.064 0.052
#> SRR1500194 1 0.1915 0.8905 0.928 0.000 0.000 0.040 0.032
#> SRR1414876 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 5 0.6500 0.1219 0.000 0.000 0.404 0.188 0.408
#> SRR1325161 5 0.4319 0.6304 0.028 0.000 0.176 0.024 0.772
#> SRR1318026 4 0.3326 0.7654 0.152 0.000 0.000 0.824 0.024
#> SRR1343778 5 0.4889 0.4030 0.004 0.000 0.476 0.016 0.504
#> SRR1441287 1 0.0324 0.9081 0.992 0.000 0.000 0.004 0.004
#> SRR1430991 5 0.4384 0.6713 0.016 0.000 0.324 0.000 0.660
#> SRR1499722 5 0.4424 0.5731 0.128 0.000 0.080 0.012 0.780
#> SRR1351368 3 0.5680 0.4528 0.000 0.000 0.628 0.160 0.212
#> SRR1441785 1 0.3907 0.8359 0.832 0.000 0.068 0.068 0.032
#> SRR1096101 1 0.0898 0.9074 0.972 0.000 0.000 0.008 0.020
#> SRR808375 5 0.4142 0.6561 0.016 0.000 0.220 0.012 0.752
#> SRR1452842 1 0.3961 0.7590 0.760 0.000 0.000 0.028 0.212
#> SRR1311709 1 0.1485 0.9024 0.948 0.000 0.000 0.032 0.020
#> SRR1433352 5 0.5036 0.6745 0.040 0.000 0.304 0.008 0.648
#> SRR1340241 2 0.0771 0.9349 0.000 0.976 0.004 0.000 0.020
#> SRR1456754 1 0.2900 0.8605 0.864 0.000 0.000 0.028 0.108
#> SRR1465172 5 0.4572 0.5463 0.156 0.000 0.052 0.024 0.768
#> SRR1499284 5 0.4540 0.3720 0.320 0.000 0.000 0.024 0.656
#> SRR1499607 4 0.4305 0.7290 0.000 0.128 0.004 0.780 0.088
#> SRR812342 1 0.1408 0.9045 0.948 0.000 0.000 0.008 0.044
#> SRR1405374 1 0.1741 0.8942 0.936 0.000 0.000 0.040 0.024
#> SRR1403565 1 0.4389 0.8172 0.804 0.000 0.076 0.072 0.048
#> SRR1332024 3 0.1074 0.7364 0.004 0.000 0.968 0.012 0.016
#> SRR1471633 1 0.1043 0.8968 0.960 0.000 0.000 0.040 0.000
#> SRR1325944 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.4582 0.6220 0.016 0.000 0.144 0.072 0.768
#> SRR1435372 1 0.1484 0.9040 0.944 0.000 0.000 0.008 0.048
#> SRR1324184 2 0.2482 0.9072 0.000 0.892 0.000 0.024 0.084
#> SRR816517 4 0.4867 0.7131 0.000 0.036 0.116 0.764 0.084
#> SRR1324141 4 0.3839 0.7662 0.108 0.000 0.004 0.816 0.072
#> SRR1101612 1 0.0865 0.9089 0.972 0.000 0.000 0.004 0.024
#> SRR1356531 1 0.0955 0.9087 0.968 0.000 0.000 0.004 0.028
#> SRR1089785 5 0.4602 0.6539 0.016 0.000 0.340 0.004 0.640
#> SRR1077708 5 0.4922 0.3264 0.004 0.000 0.424 0.020 0.552
#> SRR1343720 5 0.4705 0.6782 0.040 0.000 0.264 0.004 0.692
#> SRR1477499 2 0.0290 0.9382 0.000 0.992 0.000 0.000 0.008
#> SRR1347236 5 0.4361 0.5084 0.204 0.000 0.032 0.012 0.752
#> SRR1326408 1 0.1522 0.9059 0.944 0.000 0.000 0.012 0.044
#> SRR1336529 3 0.0771 0.7442 0.004 0.000 0.976 0.000 0.020
#> SRR1440643 4 0.6638 -0.0377 0.000 0.000 0.276 0.452 0.272
#> SRR662354 1 0.1168 0.9077 0.960 0.000 0.000 0.008 0.032
#> SRR1310817 5 0.4653 0.6493 0.008 0.000 0.288 0.024 0.680
#> SRR1347389 4 0.5778 0.3075 0.000 0.376 0.000 0.528 0.096
#> SRR1353097 1 0.1281 0.9077 0.956 0.000 0.000 0.012 0.032
#> SRR1384737 4 0.3781 0.7658 0.108 0.000 0.016 0.828 0.048
#> SRR1096339 1 0.0671 0.9066 0.980 0.000 0.000 0.004 0.016
#> SRR1345329 4 0.2471 0.7759 0.136 0.000 0.000 0.864 0.000
#> SRR1414771 3 0.0162 0.7437 0.004 0.000 0.996 0.000 0.000
#> SRR1309119 1 0.1195 0.8999 0.960 0.000 0.000 0.028 0.012
#> SRR1470438 3 0.0162 0.7437 0.004 0.000 0.996 0.000 0.000
#> SRR1343221 1 0.1364 0.9081 0.952 0.000 0.000 0.012 0.036
#> SRR1410847 1 0.1216 0.9023 0.960 0.000 0.000 0.020 0.020
#> SRR807949 5 0.4227 0.6766 0.016 0.000 0.292 0.000 0.692
#> SRR1442332 5 0.4686 0.6628 0.016 0.000 0.332 0.008 0.644
#> SRR815920 3 0.2352 0.7013 0.004 0.000 0.896 0.008 0.092
#> SRR1471524 3 0.4089 0.5252 0.004 0.000 0.736 0.016 0.244
#> SRR1477221 3 0.2949 0.7045 0.004 0.000 0.876 0.068 0.052
#> SRR1445046 4 0.4359 0.7271 0.000 0.128 0.004 0.776 0.092
#> SRR1331962 4 0.5461 0.5170 0.000 0.284 0.000 0.620 0.096
#> SRR1319946 4 0.2964 0.7498 0.000 0.120 0.000 0.856 0.024
#> SRR1311599 1 0.3937 0.8383 0.832 0.000 0.064 0.064 0.040
#> SRR1323977 4 0.3814 0.7681 0.116 0.000 0.004 0.816 0.064
#> SRR1445132 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.3574 0.6934 0.004 0.000 0.836 0.072 0.088
#> SRR1366390 2 0.1894 0.9151 0.000 0.920 0.000 0.008 0.072
#> SRR1343012 4 0.4454 0.7393 0.092 0.000 0.016 0.784 0.108
#> SRR1311958 4 0.4255 0.7265 0.000 0.128 0.000 0.776 0.096
#> SRR1388234 4 0.2358 0.7828 0.104 0.008 0.000 0.888 0.000
#> SRR1370384 1 0.3284 0.8290 0.828 0.000 0.000 0.024 0.148
#> SRR1321650 3 0.2196 0.7297 0.004 0.000 0.916 0.024 0.056
#> SRR1485117 2 0.1484 0.9245 0.000 0.944 0.000 0.008 0.048
#> SRR1384713 1 0.3779 0.7771 0.776 0.000 0.000 0.024 0.200
#> SRR816609 4 0.2690 0.7662 0.156 0.000 0.000 0.844 0.000
#> SRR1486239 4 0.4359 0.7271 0.000 0.128 0.004 0.776 0.092
#> SRR1309638 3 0.3510 0.6815 0.008 0.000 0.832 0.032 0.128
#> SRR1356660 1 0.3844 0.8386 0.836 0.000 0.068 0.064 0.032
#> SRR1392883 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.4418 0.6659 0.016 0.000 0.332 0.000 0.652
#> SRR816677 1 0.5037 0.3787 0.612 0.000 0.012 0.352 0.024
#> SRR1455722 1 0.0992 0.9089 0.968 0.000 0.000 0.008 0.024
#> SRR1336029 1 0.1648 0.8974 0.940 0.000 0.000 0.040 0.020
#> SRR808452 1 0.1106 0.9079 0.964 0.000 0.000 0.012 0.024
#> SRR1352169 3 0.5049 -0.2193 0.016 0.000 0.548 0.012 0.424
#> SRR1366707 3 0.3500 0.6073 0.004 0.000 0.808 0.016 0.172
#> SRR1328143 5 0.4570 0.6645 0.016 0.000 0.332 0.004 0.648
#> SRR1473567 2 0.3130 0.8708 0.000 0.856 0.000 0.048 0.096
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.4315 0.5794 0.000 0.000 0.244 0.012 0.704 0.040
#> SRR1390119 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.3202 0.5810 0.000 0.000 0.800 0.000 0.176 0.024
#> SRR1347278 3 0.6046 0.2417 0.000 0.000 0.416 0.000 0.280 0.304
#> SRR1332904 4 0.2692 0.6215 0.000 0.148 0.000 0.840 0.000 0.012
#> SRR1444179 1 0.1204 0.8247 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1082685 1 0.0146 0.8305 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1362287 1 0.4696 0.6155 0.588 0.000 0.056 0.000 0.000 0.356
#> SRR1339007 1 0.1501 0.8078 0.924 0.000 0.000 0.000 0.000 0.076
#> SRR1376557 2 0.3705 0.7649 0.000 0.740 0.004 0.236 0.000 0.020
#> SRR1468700 4 0.4624 -0.1787 0.000 0.452 0.008 0.516 0.000 0.024
#> SRR1077455 1 0.4351 0.6410 0.720 0.000 0.000 0.000 0.172 0.108
#> SRR1413978 1 0.5028 0.5680 0.536 0.000 0.056 0.008 0.000 0.400
#> SRR1439896 1 0.0458 0.8307 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1317963 4 0.0692 0.7125 0.000 0.020 0.000 0.976 0.000 0.004
#> SRR1431865 1 0.4696 0.6155 0.588 0.000 0.056 0.000 0.000 0.356
#> SRR1394253 1 0.4696 0.6155 0.588 0.000 0.056 0.000 0.000 0.356
#> SRR1082664 5 0.3141 0.7457 0.000 0.000 0.112 0.004 0.836 0.048
#> SRR1077968 1 0.2537 0.7743 0.872 0.000 0.000 0.000 0.032 0.096
#> SRR1076393 3 0.6114 -0.1075 0.000 0.000 0.440 0.008 0.340 0.212
#> SRR1477476 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 3 0.4989 0.4994 0.000 0.000 0.640 0.000 0.140 0.220
#> SRR1485042 1 0.1753 0.8201 0.912 0.000 0.004 0.000 0.000 0.084
#> SRR1385453 6 0.7466 0.5250 0.000 0.000 0.288 0.132 0.248 0.332
#> SRR1348074 4 0.3134 0.7179 0.024 0.000 0.000 0.808 0.000 0.168
#> SRR813959 5 0.6564 -0.2985 0.000 0.000 0.052 0.172 0.464 0.312
#> SRR665442 6 0.6689 -0.2991 0.028 0.000 0.348 0.012 0.188 0.424
#> SRR1378068 3 0.1753 0.6083 0.000 0.000 0.912 0.000 0.084 0.004
#> SRR1485237 4 0.4044 0.6868 0.076 0.000 0.000 0.744 0.000 0.180
#> SRR1350792 1 0.0000 0.8306 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.2724 0.6615 0.084 0.000 0.000 0.000 0.864 0.052
#> SRR808994 3 0.1196 0.6159 0.000 0.000 0.952 0.000 0.040 0.008
#> SRR1474041 5 0.2839 0.7549 0.000 0.000 0.100 0.008 0.860 0.032
#> SRR1405641 3 0.1531 0.6188 0.000 0.000 0.928 0.000 0.068 0.004
#> SRR1362245 3 0.4735 0.4528 0.000 0.000 0.628 0.000 0.076 0.296
#> SRR1500194 1 0.3104 0.7762 0.800 0.000 0.016 0.000 0.000 0.184
#> SRR1414876 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 6 0.7183 0.4761 0.000 0.000 0.316 0.080 0.276 0.328
#> SRR1325161 5 0.2362 0.7065 0.012 0.000 0.016 0.000 0.892 0.080
#> SRR1318026 4 0.4203 0.6815 0.056 0.000 0.000 0.720 0.004 0.220
#> SRR1343778 5 0.4540 0.4654 0.000 0.000 0.308 0.008 0.644 0.040
#> SRR1441287 1 0.0260 0.8308 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1430991 5 0.1970 0.7649 0.000 0.000 0.092 0.000 0.900 0.008
#> SRR1499722 5 0.2325 0.7023 0.048 0.000 0.008 0.000 0.900 0.044
#> SRR1351368 3 0.6452 -0.3460 0.000 0.000 0.460 0.048 0.152 0.340
#> SRR1441785 1 0.4696 0.6155 0.588 0.000 0.056 0.000 0.000 0.356
#> SRR1096101 1 0.2278 0.8088 0.868 0.000 0.004 0.000 0.000 0.128
#> SRR808375 5 0.1088 0.7404 0.000 0.000 0.016 0.000 0.960 0.024
#> SRR1452842 1 0.4218 0.6623 0.736 0.000 0.000 0.000 0.156 0.108
#> SRR1311709 1 0.1007 0.8249 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1433352 5 0.2635 0.7604 0.004 0.000 0.100 0.004 0.872 0.020
#> SRR1340241 2 0.0790 0.9213 0.000 0.968 0.000 0.032 0.000 0.000
#> SRR1456754 1 0.3352 0.7423 0.816 0.000 0.000 0.000 0.072 0.112
#> SRR1465172 5 0.2956 0.6542 0.064 0.000 0.000 0.000 0.848 0.088
#> SRR1499284 5 0.4691 0.4050 0.220 0.000 0.000 0.000 0.672 0.108
#> SRR1499607 4 0.0806 0.7134 0.000 0.020 0.000 0.972 0.000 0.008
#> SRR812342 1 0.0363 0.8293 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1405374 1 0.2631 0.7964 0.840 0.000 0.008 0.000 0.000 0.152
#> SRR1403565 1 0.5412 0.5391 0.524 0.000 0.052 0.000 0.032 0.392
#> SRR1332024 3 0.1720 0.6150 0.000 0.000 0.928 0.000 0.032 0.040
#> SRR1471633 1 0.1267 0.8194 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1325944 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.2398 0.7047 0.000 0.000 0.028 0.004 0.888 0.080
#> SRR1435372 1 0.0858 0.8244 0.968 0.000 0.000 0.000 0.004 0.028
#> SRR1324184 2 0.3940 0.8511 0.000 0.800 0.020 0.080 0.004 0.096
#> SRR816517 4 0.6470 0.1092 0.000 0.000 0.148 0.464 0.052 0.336
#> SRR1324141 4 0.5073 0.5722 0.048 0.000 0.004 0.596 0.016 0.336
#> SRR1101612 1 0.0000 0.8306 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0260 0.8299 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1089785 5 0.2781 0.7483 0.000 0.000 0.108 0.008 0.860 0.024
#> SRR1077708 5 0.4250 0.6361 0.000 0.000 0.144 0.004 0.744 0.108
#> SRR1343720 5 0.2307 0.7599 0.004 0.000 0.068 0.000 0.896 0.032
#> SRR1477499 2 0.0458 0.9249 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1347236 5 0.2420 0.6795 0.076 0.000 0.000 0.000 0.884 0.040
#> SRR1326408 1 0.1918 0.8026 0.904 0.000 0.000 0.000 0.008 0.088
#> SRR1336529 3 0.1387 0.6188 0.000 0.000 0.932 0.000 0.068 0.000
#> SRR1440643 6 0.7596 0.4571 0.000 0.000 0.216 0.236 0.200 0.348
#> SRR662354 1 0.0547 0.8317 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1310817 5 0.3837 0.6402 0.000 0.000 0.068 0.008 0.784 0.140
#> SRR1347389 4 0.3702 0.5258 0.000 0.208 0.008 0.760 0.000 0.024
#> SRR1353097 1 0.0458 0.8285 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1384737 4 0.4644 0.5550 0.024 0.000 0.004 0.584 0.008 0.380
#> SRR1096339 1 0.1411 0.8243 0.936 0.000 0.004 0.000 0.000 0.060
#> SRR1345329 4 0.3245 0.7161 0.028 0.000 0.000 0.800 0.000 0.172
#> SRR1414771 3 0.1320 0.6193 0.000 0.000 0.948 0.000 0.036 0.016
#> SRR1309119 1 0.1957 0.8146 0.888 0.000 0.000 0.000 0.000 0.112
#> SRR1470438 3 0.1320 0.6186 0.000 0.000 0.948 0.000 0.036 0.016
#> SRR1343221 1 0.0603 0.8309 0.980 0.000 0.000 0.000 0.004 0.016
#> SRR1410847 1 0.2744 0.7968 0.840 0.000 0.016 0.000 0.000 0.144
#> SRR807949 5 0.1956 0.7649 0.000 0.000 0.080 0.008 0.908 0.004
#> SRR1442332 5 0.2839 0.7541 0.000 0.000 0.100 0.008 0.860 0.032
#> SRR815920 3 0.2790 0.5555 0.000 0.000 0.840 0.000 0.140 0.020
#> SRR1471524 3 0.5822 0.0232 0.000 0.000 0.540 0.008 0.236 0.216
#> SRR1477221 3 0.4798 0.4476 0.000 0.000 0.620 0.000 0.080 0.300
#> SRR1445046 4 0.1092 0.7057 0.000 0.020 0.000 0.960 0.000 0.020
#> SRR1331962 4 0.2781 0.6440 0.000 0.108 0.008 0.860 0.000 0.024
#> SRR1319946 4 0.3025 0.7068 0.000 0.008 0.004 0.820 0.004 0.164
#> SRR1311599 1 0.4696 0.6155 0.588 0.000 0.056 0.000 0.000 0.356
#> SRR1323977 4 0.5134 0.5793 0.052 0.000 0.008 0.596 0.012 0.332
#> SRR1445132 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 3 0.5409 0.3918 0.000 0.000 0.540 0.000 0.136 0.324
#> SRR1366390 2 0.2826 0.8722 0.000 0.856 0.008 0.112 0.000 0.024
#> SRR1343012 4 0.5584 0.4409 0.028 0.000 0.012 0.508 0.044 0.408
#> SRR1311958 4 0.1350 0.7038 0.000 0.020 0.008 0.952 0.000 0.020
#> SRR1388234 4 0.3088 0.7175 0.020 0.000 0.000 0.808 0.000 0.172
#> SRR1370384 1 0.3361 0.7391 0.816 0.000 0.000 0.000 0.076 0.108
#> SRR1321650 3 0.4012 0.5701 0.000 0.000 0.748 0.000 0.176 0.076
#> SRR1485117 2 0.2265 0.8951 0.000 0.900 0.008 0.068 0.000 0.024
#> SRR1384713 1 0.4183 0.6673 0.740 0.000 0.000 0.000 0.152 0.108
#> SRR816609 4 0.3835 0.6982 0.056 0.000 0.000 0.756 0.000 0.188
#> SRR1486239 4 0.1092 0.7057 0.000 0.020 0.000 0.960 0.000 0.020
#> SRR1309638 3 0.5051 0.4997 0.000 0.000 0.652 0.004 0.188 0.156
#> SRR1356660 1 0.4696 0.6155 0.588 0.000 0.056 0.000 0.000 0.356
#> SRR1392883 2 0.0000 0.9275 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.2051 0.7633 0.000 0.000 0.096 0.004 0.896 0.004
#> SRR816677 1 0.6492 0.2177 0.444 0.000 0.028 0.252 0.000 0.276
#> SRR1455722 1 0.0000 0.8306 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.3240 0.7574 0.752 0.000 0.000 0.004 0.000 0.244
#> SRR808452 1 0.0146 0.8305 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1352169 5 0.4782 0.3036 0.000 0.000 0.380 0.004 0.568 0.048
#> SRR1366707 3 0.4841 0.3106 0.000 0.000 0.680 0.004 0.156 0.160
#> SRR1328143 5 0.2686 0.7563 0.000 0.000 0.100 0.008 0.868 0.024
#> SRR1473567 2 0.3892 0.7578 0.000 0.732 0.008 0.236 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", "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 17851 rows and 124 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 1.000 0.965 0.985 0.4520 0.548 0.548
#> 3 3 0.978 0.953 0.980 0.4856 0.749 0.554
#> 4 4 0.879 0.871 0.927 0.1012 0.916 0.751
#> 5 5 0.787 0.815 0.880 0.0621 0.946 0.798
#> 6 6 0.777 0.760 0.865 0.0507 0.939 0.731
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
#> SRR1442087 1 0.0000 0.988 1.000 0.000
#> SRR1390119 2 0.0000 0.978 0.000 1.000
#> SRR1436127 1 0.0000 0.988 1.000 0.000
#> SRR1347278 1 0.0000 0.988 1.000 0.000
#> SRR1332904 2 0.0000 0.978 0.000 1.000
#> SRR1444179 1 0.0000 0.988 1.000 0.000
#> SRR1082685 1 0.0000 0.988 1.000 0.000
#> SRR1362287 1 0.0000 0.988 1.000 0.000
#> SRR1339007 1 0.0000 0.988 1.000 0.000
#> SRR1376557 2 0.0000 0.978 0.000 1.000
#> SRR1468700 2 0.0000 0.978 0.000 1.000
#> SRR1077455 1 0.0000 0.988 1.000 0.000
#> SRR1413978 1 0.0376 0.985 0.996 0.004
#> SRR1439896 1 0.0000 0.988 1.000 0.000
#> SRR1317963 2 0.0000 0.978 0.000 1.000
#> SRR1431865 1 0.0000 0.988 1.000 0.000
#> SRR1394253 1 0.0000 0.988 1.000 0.000
#> SRR1082664 1 0.0000 0.988 1.000 0.000
#> SRR1077968 1 0.0000 0.988 1.000 0.000
#> SRR1076393 1 0.5408 0.859 0.876 0.124
#> SRR1477476 2 0.0000 0.978 0.000 1.000
#> SRR1398057 1 0.0000 0.988 1.000 0.000
#> SRR1485042 1 0.0000 0.988 1.000 0.000
#> SRR1385453 2 0.0000 0.978 0.000 1.000
#> SRR1348074 2 0.0000 0.978 0.000 1.000
#> SRR813959 2 0.0000 0.978 0.000 1.000
#> SRR665442 2 0.9635 0.384 0.388 0.612
#> SRR1378068 1 0.0000 0.988 1.000 0.000
#> SRR1485237 2 0.0000 0.978 0.000 1.000
#> SRR1350792 1 0.0000 0.988 1.000 0.000
#> SRR1326797 1 0.0000 0.988 1.000 0.000
#> SRR808994 1 0.0672 0.982 0.992 0.008
#> SRR1474041 1 0.0000 0.988 1.000 0.000
#> SRR1405641 1 0.0000 0.988 1.000 0.000
#> SRR1362245 1 0.0000 0.988 1.000 0.000
#> SRR1500194 1 0.0000 0.988 1.000 0.000
#> SRR1414876 2 0.0000 0.978 0.000 1.000
#> SRR1478523 2 0.3733 0.906 0.072 0.928
#> SRR1325161 1 0.0000 0.988 1.000 0.000
#> SRR1318026 2 0.0000 0.978 0.000 1.000
#> SRR1343778 1 0.0000 0.988 1.000 0.000
#> SRR1441287 1 0.0000 0.988 1.000 0.000
#> SRR1430991 1 0.0000 0.988 1.000 0.000
#> SRR1499722 1 0.0000 0.988 1.000 0.000
#> SRR1351368 2 0.0000 0.978 0.000 1.000
#> SRR1441785 1 0.0000 0.988 1.000 0.000
#> SRR1096101 1 0.0000 0.988 1.000 0.000
#> SRR808375 1 0.0000 0.988 1.000 0.000
#> SRR1452842 1 0.0000 0.988 1.000 0.000
#> SRR1311709 1 0.0000 0.988 1.000 0.000
#> SRR1433352 1 0.0000 0.988 1.000 0.000
#> SRR1340241 2 0.0000 0.978 0.000 1.000
#> SRR1456754 1 0.0000 0.988 1.000 0.000
#> SRR1465172 1 0.0000 0.988 1.000 0.000
#> SRR1499284 1 0.0000 0.988 1.000 0.000
#> SRR1499607 2 0.0000 0.978 0.000 1.000
#> SRR812342 1 0.0000 0.988 1.000 0.000
#> SRR1405374 1 0.0000 0.988 1.000 0.000
#> SRR1403565 1 0.0000 0.988 1.000 0.000
#> SRR1332024 1 0.0000 0.988 1.000 0.000
#> SRR1471633 1 0.2423 0.951 0.960 0.040
#> SRR1325944 2 0.0000 0.978 0.000 1.000
#> SRR1429450 2 0.0000 0.978 0.000 1.000
#> SRR821573 1 0.7139 0.762 0.804 0.196
#> SRR1435372 1 0.0000 0.988 1.000 0.000
#> SRR1324184 2 0.0000 0.978 0.000 1.000
#> SRR816517 2 0.0000 0.978 0.000 1.000
#> SRR1324141 2 0.0000 0.978 0.000 1.000
#> SRR1101612 1 0.0000 0.988 1.000 0.000
#> SRR1356531 1 0.0000 0.988 1.000 0.000
#> SRR1089785 1 0.0000 0.988 1.000 0.000
#> SRR1077708 1 0.0000 0.988 1.000 0.000
#> SRR1343720 1 0.0000 0.988 1.000 0.000
#> SRR1477499 2 0.0000 0.978 0.000 1.000
#> SRR1347236 1 0.0000 0.988 1.000 0.000
#> SRR1326408 1 0.0000 0.988 1.000 0.000
#> SRR1336529 1 0.0000 0.988 1.000 0.000
#> SRR1440643 2 0.0000 0.978 0.000 1.000
#> SRR662354 1 0.0000 0.988 1.000 0.000
#> SRR1310817 1 0.5842 0.839 0.860 0.140
#> SRR1347389 2 0.0000 0.978 0.000 1.000
#> SRR1353097 1 0.0000 0.988 1.000 0.000
#> SRR1384737 2 0.0000 0.978 0.000 1.000
#> SRR1096339 1 0.0000 0.988 1.000 0.000
#> SRR1345329 2 0.0000 0.978 0.000 1.000
#> SRR1414771 1 0.0938 0.978 0.988 0.012
#> SRR1309119 1 0.0672 0.982 0.992 0.008
#> SRR1470438 1 0.0672 0.982 0.992 0.008
#> SRR1343221 1 0.0000 0.988 1.000 0.000
#> SRR1410847 1 0.0000 0.988 1.000 0.000
#> SRR807949 1 0.0000 0.988 1.000 0.000
#> SRR1442332 1 0.0000 0.988 1.000 0.000
#> SRR815920 1 0.0000 0.988 1.000 0.000
#> SRR1471524 1 0.7219 0.756 0.800 0.200
#> SRR1477221 1 0.0000 0.988 1.000 0.000
#> SRR1445046 2 0.0000 0.978 0.000 1.000
#> SRR1331962 2 0.0000 0.978 0.000 1.000
#> SRR1319946 2 0.0000 0.978 0.000 1.000
#> SRR1311599 1 0.0000 0.988 1.000 0.000
#> SRR1323977 2 0.0000 0.978 0.000 1.000
#> SRR1445132 2 0.0000 0.978 0.000 1.000
#> SRR1337321 1 0.0000 0.988 1.000 0.000
#> SRR1366390 2 0.0000 0.978 0.000 1.000
#> SRR1343012 2 0.0000 0.978 0.000 1.000
#> SRR1311958 2 0.0000 0.978 0.000 1.000
#> SRR1388234 2 0.0000 0.978 0.000 1.000
#> SRR1370384 1 0.0000 0.988 1.000 0.000
#> SRR1321650 1 0.0000 0.988 1.000 0.000
#> SRR1485117 2 0.0000 0.978 0.000 1.000
#> SRR1384713 1 0.0000 0.988 1.000 0.000
#> SRR816609 2 0.0000 0.978 0.000 1.000
#> SRR1486239 2 0.0000 0.978 0.000 1.000
#> SRR1309638 1 0.0000 0.988 1.000 0.000
#> SRR1356660 1 0.0000 0.988 1.000 0.000
#> SRR1392883 2 0.0000 0.978 0.000 1.000
#> SRR808130 1 0.0000 0.988 1.000 0.000
#> SRR816677 2 0.9896 0.235 0.440 0.560
#> SRR1455722 1 0.0000 0.988 1.000 0.000
#> SRR1336029 1 0.0000 0.988 1.000 0.000
#> SRR808452 1 0.0000 0.988 1.000 0.000
#> SRR1352169 1 0.0000 0.988 1.000 0.000
#> SRR1366707 1 0.7056 0.768 0.808 0.192
#> SRR1328143 1 0.0000 0.988 1.000 0.000
#> SRR1473567 2 0.0000 0.978 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1347278 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1332904 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1082664 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1077968 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1485042 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1385453 2 0.1643 0.938 0.000 0.956 0.044
#> SRR1348074 2 0.0000 0.977 0.000 1.000 0.000
#> SRR813959 2 0.4555 0.746 0.000 0.800 0.200
#> SRR665442 2 0.8250 0.544 0.140 0.628 0.232
#> SRR1378068 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1485237 2 0.3267 0.860 0.116 0.884 0.000
#> SRR1350792 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1326797 1 0.5988 0.434 0.632 0.000 0.368
#> SRR808994 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1474041 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1500194 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1478523 3 0.4504 0.745 0.000 0.196 0.804
#> SRR1325161 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1318026 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1343778 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1441287 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1430991 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1499722 3 0.2066 0.922 0.060 0.000 0.940
#> SRR1351368 3 0.6062 0.366 0.000 0.384 0.616
#> SRR1441785 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.978 1.000 0.000 0.000
#> SRR808375 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1452842 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1311709 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1433352 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1465172 3 0.0592 0.972 0.012 0.000 0.988
#> SRR1499284 1 0.0237 0.974 0.996 0.000 0.004
#> SRR1499607 2 0.0000 0.977 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1403565 1 0.4178 0.784 0.828 0.000 0.172
#> SRR1332024 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1471633 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.977 0.000 1.000 0.000
#> SRR821573 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1435372 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.977 0.000 1.000 0.000
#> SRR816517 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1324141 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1101612 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1089785 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1347236 1 0.6008 0.424 0.628 0.000 0.372
#> SRR1326408 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1440643 2 0.0000 0.977 0.000 1.000 0.000
#> SRR662354 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1310817 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1347389 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1384737 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1096339 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1345329 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1414771 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1309119 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1470438 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1343221 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.978 1.000 0.000 0.000
#> SRR807949 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.983 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1477221 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1445046 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1319946 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1323977 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1445132 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1337321 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1366390 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1343012 2 0.4062 0.799 0.000 0.836 0.164
#> SRR1311958 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1388234 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1321650 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.978 1.000 0.000 0.000
#> SRR816609 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1486239 2 0.0000 0.977 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1356660 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.977 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.983 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.978 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.978 1.000 0.000 0.000
#> SRR1352169 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1366707 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1328143 3 0.0000 0.983 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.977 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.4977 0.178 0.000 0.000 0.540 0.460
#> SRR1390119 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.2469 0.809 0.000 0.000 0.892 0.108
#> SRR1347278 3 0.4564 0.474 0.000 0.000 0.672 0.328
#> SRR1332904 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.1867 0.924 0.928 0.000 0.072 0.000
#> SRR1339007 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.4331 0.654 0.712 0.000 0.000 0.288
#> SRR1413978 1 0.1792 0.927 0.932 0.000 0.068 0.000
#> SRR1439896 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1431865 1 0.1792 0.927 0.932 0.000 0.068 0.000
#> SRR1394253 1 0.1792 0.927 0.932 0.000 0.068 0.000
#> SRR1082664 4 0.2216 0.885 0.000 0.000 0.092 0.908
#> SRR1077968 1 0.1302 0.936 0.956 0.000 0.000 0.044
#> SRR1076393 3 0.4164 0.647 0.000 0.000 0.736 0.264
#> SRR1477476 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.1022 0.816 0.000 0.000 0.968 0.032
#> SRR1485042 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1385453 2 0.5849 0.621 0.000 0.704 0.164 0.132
#> SRR1348074 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR813959 2 0.4415 0.758 0.000 0.804 0.056 0.140
#> SRR665442 3 0.8363 0.260 0.020 0.300 0.408 0.272
#> SRR1378068 3 0.1716 0.818 0.000 0.000 0.936 0.064
#> SRR1485237 2 0.2197 0.887 0.080 0.916 0.000 0.004
#> SRR1350792 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1326797 4 0.0469 0.890 0.012 0.000 0.000 0.988
#> SRR808994 3 0.0469 0.814 0.000 0.000 0.988 0.012
#> SRR1474041 4 0.2281 0.896 0.000 0.000 0.096 0.904
#> SRR1405641 3 0.1557 0.819 0.000 0.000 0.944 0.056
#> SRR1362245 3 0.0817 0.814 0.000 0.000 0.976 0.024
#> SRR1500194 1 0.0336 0.954 0.992 0.000 0.008 0.000
#> SRR1414876 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.4990 0.417 0.000 0.008 0.640 0.352
#> SRR1325161 4 0.0188 0.898 0.000 0.000 0.004 0.996
#> SRR1318026 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1343778 3 0.4790 0.427 0.000 0.000 0.620 0.380
#> SRR1441287 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1430991 4 0.2216 0.898 0.000 0.000 0.092 0.908
#> SRR1499722 4 0.0376 0.896 0.004 0.000 0.004 0.992
#> SRR1351368 3 0.4579 0.649 0.000 0.200 0.768 0.032
#> SRR1441785 1 0.1792 0.927 0.932 0.000 0.068 0.000
#> SRR1096101 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR808375 4 0.0188 0.898 0.000 0.000 0.004 0.996
#> SRR1452842 1 0.4040 0.719 0.752 0.000 0.000 0.248
#> SRR1311709 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1433352 4 0.2469 0.887 0.000 0.000 0.108 0.892
#> SRR1340241 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.2011 0.912 0.920 0.000 0.000 0.080
#> SRR1465172 4 0.0376 0.896 0.004 0.000 0.004 0.992
#> SRR1499284 4 0.1557 0.840 0.056 0.000 0.000 0.944
#> SRR1499607 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR812342 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0188 0.955 0.996 0.000 0.004 0.000
#> SRR1403565 1 0.4054 0.782 0.796 0.000 0.188 0.016
#> SRR1332024 3 0.0000 0.811 0.000 0.000 1.000 0.000
#> SRR1471633 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1325944 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR821573 4 0.0188 0.898 0.000 0.000 0.004 0.996
#> SRR1435372 1 0.0469 0.952 0.988 0.000 0.000 0.012
#> SRR1324184 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR816517 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1324141 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1101612 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1089785 4 0.3024 0.847 0.000 0.000 0.148 0.852
#> SRR1077708 4 0.4776 0.265 0.000 0.000 0.376 0.624
#> SRR1343720 4 0.1389 0.900 0.000 0.000 0.048 0.952
#> SRR1477499 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1347236 4 0.0469 0.890 0.012 0.000 0.000 0.988
#> SRR1326408 1 0.0817 0.947 0.976 0.000 0.000 0.024
#> SRR1336529 3 0.1557 0.819 0.000 0.000 0.944 0.056
#> SRR1440643 2 0.2281 0.880 0.000 0.904 0.096 0.000
#> SRR662354 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1310817 4 0.2011 0.902 0.000 0.000 0.080 0.920
#> SRR1347389 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1353097 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1384737 2 0.0376 0.967 0.000 0.992 0.004 0.004
#> SRR1096339 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1345329 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1414771 3 0.0000 0.811 0.000 0.000 1.000 0.000
#> SRR1309119 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1470438 3 0.0000 0.811 0.000 0.000 1.000 0.000
#> SRR1343221 1 0.1867 0.918 0.928 0.000 0.000 0.072
#> SRR1410847 1 0.0469 0.953 0.988 0.000 0.012 0.000
#> SRR807949 4 0.2081 0.901 0.000 0.000 0.084 0.916
#> SRR1442332 4 0.2281 0.896 0.000 0.000 0.096 0.904
#> SRR815920 3 0.1867 0.816 0.000 0.000 0.928 0.072
#> SRR1471524 3 0.2149 0.812 0.000 0.000 0.912 0.088
#> SRR1477221 3 0.0707 0.814 0.000 0.000 0.980 0.020
#> SRR1445046 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1331962 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.1792 0.927 0.932 0.000 0.068 0.000
#> SRR1323977 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.2011 0.800 0.000 0.000 0.920 0.080
#> SRR1366390 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1343012 2 0.4936 0.498 0.000 0.672 0.316 0.012
#> SRR1311958 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1370384 1 0.2216 0.902 0.908 0.000 0.000 0.092
#> SRR1321650 3 0.2589 0.804 0.000 0.000 0.884 0.116
#> SRR1485117 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.3649 0.781 0.796 0.000 0.000 0.204
#> SRR816609 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1486239 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1309638 3 0.3356 0.772 0.000 0.000 0.824 0.176
#> SRR1356660 1 0.1792 0.927 0.932 0.000 0.068 0.000
#> SRR1392883 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR808130 4 0.2281 0.896 0.000 0.000 0.096 0.904
#> SRR816677 1 0.1661 0.934 0.944 0.000 0.052 0.004
#> SRR1455722 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0188 0.955 0.996 0.000 0.004 0.000
#> SRR808452 1 0.0000 0.956 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.4804 0.401 0.000 0.000 0.616 0.384
#> SRR1366707 3 0.2469 0.803 0.000 0.000 0.892 0.108
#> SRR1328143 4 0.2281 0.896 0.000 0.000 0.096 0.904
#> SRR1473567 2 0.0000 0.971 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
#> SRR1442087 3 0.4210 0.305 0.000 0.000 0.588 0.000 0.412
#> SRR1390119 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.1965 0.821 0.000 0.000 0.904 0.000 0.096
#> SRR1347278 3 0.6076 0.549 0.000 0.000 0.572 0.196 0.232
#> SRR1332904 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1444179 1 0.0162 0.894 0.996 0.000 0.000 0.004 0.000
#> SRR1082685 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.3790 0.801 0.744 0.000 0.004 0.248 0.004
#> SRR1339007 1 0.0693 0.891 0.980 0.000 0.000 0.012 0.008
#> SRR1376557 2 0.0162 0.886 0.000 0.996 0.000 0.004 0.000
#> SRR1468700 2 0.1908 0.797 0.000 0.908 0.000 0.092 0.000
#> SRR1077455 1 0.3934 0.671 0.740 0.000 0.000 0.016 0.244
#> SRR1413978 1 0.4142 0.748 0.684 0.000 0.004 0.308 0.004
#> SRR1439896 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.4201 0.722 0.000 0.408 0.000 0.592 0.000
#> SRR1431865 1 0.3790 0.801 0.744 0.000 0.004 0.248 0.004
#> SRR1394253 1 0.3790 0.801 0.744 0.000 0.004 0.248 0.004
#> SRR1082664 5 0.2179 0.888 0.000 0.000 0.112 0.000 0.888
#> SRR1077968 1 0.1300 0.883 0.956 0.000 0.000 0.016 0.028
#> SRR1076393 3 0.4080 0.649 0.000 0.000 0.728 0.020 0.252
#> SRR1477476 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.2570 0.816 0.000 0.000 0.888 0.084 0.028
#> SRR1485042 1 0.1502 0.888 0.940 0.000 0.000 0.056 0.004
#> SRR1385453 2 0.5383 0.517 0.000 0.704 0.192 0.036 0.068
#> SRR1348074 4 0.3730 0.830 0.000 0.288 0.000 0.712 0.000
#> SRR813959 2 0.2439 0.747 0.000 0.876 0.000 0.004 0.120
#> SRR665442 2 0.6124 0.510 0.036 0.684 0.040 0.060 0.180
#> SRR1378068 3 0.0162 0.840 0.000 0.000 0.996 0.000 0.004
#> SRR1485237 4 0.4959 0.681 0.160 0.128 0.000 0.712 0.000
#> SRR1350792 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0566 0.928 0.004 0.000 0.000 0.012 0.984
#> SRR808994 3 0.0162 0.840 0.000 0.000 0.996 0.000 0.004
#> SRR1474041 5 0.1197 0.938 0.000 0.000 0.048 0.000 0.952
#> SRR1405641 3 0.0162 0.840 0.000 0.000 0.996 0.000 0.004
#> SRR1362245 3 0.3690 0.756 0.000 0.000 0.780 0.200 0.020
#> SRR1500194 1 0.3647 0.813 0.764 0.000 0.004 0.228 0.004
#> SRR1414876 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.5015 0.589 0.000 0.032 0.676 0.020 0.272
#> SRR1325161 5 0.0451 0.932 0.000 0.000 0.004 0.008 0.988
#> SRR1318026 4 0.3684 0.830 0.000 0.280 0.000 0.720 0.000
#> SRR1343778 3 0.3990 0.550 0.000 0.000 0.688 0.004 0.308
#> SRR1441287 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.1197 0.938 0.000 0.000 0.048 0.000 0.952
#> SRR1499722 5 0.0451 0.932 0.000 0.000 0.004 0.008 0.988
#> SRR1351368 3 0.3645 0.702 0.000 0.168 0.804 0.024 0.004
#> SRR1441785 1 0.3790 0.801 0.744 0.000 0.004 0.248 0.004
#> SRR1096101 1 0.2011 0.881 0.908 0.000 0.000 0.088 0.004
#> SRR808375 5 0.0162 0.934 0.000 0.000 0.004 0.000 0.996
#> SRR1452842 1 0.3183 0.786 0.828 0.000 0.000 0.016 0.156
#> SRR1311709 1 0.2773 0.780 0.836 0.000 0.000 0.164 0.000
#> SRR1433352 5 0.1410 0.932 0.000 0.000 0.060 0.000 0.940
#> SRR1340241 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 1 0.1493 0.885 0.948 0.000 0.000 0.024 0.028
#> SRR1465172 5 0.0671 0.926 0.004 0.000 0.000 0.016 0.980
#> SRR1499284 5 0.1469 0.896 0.036 0.000 0.000 0.016 0.948
#> SRR1499607 4 0.4302 0.564 0.000 0.480 0.000 0.520 0.000
#> SRR812342 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.2674 0.861 0.856 0.000 0.000 0.140 0.004
#> SRR1403565 1 0.4592 0.780 0.716 0.000 0.016 0.244 0.024
#> SRR1332024 3 0.0162 0.840 0.000 0.000 0.996 0.004 0.000
#> SRR1471633 1 0.2966 0.756 0.816 0.000 0.000 0.184 0.000
#> SRR1325944 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.0162 0.934 0.000 0.000 0.004 0.000 0.996
#> SRR1435372 1 0.0693 0.891 0.980 0.000 0.000 0.012 0.008
#> SRR1324184 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR816517 2 0.0609 0.873 0.000 0.980 0.000 0.020 0.000
#> SRR1324141 4 0.3612 0.827 0.000 0.268 0.000 0.732 0.000
#> SRR1101612 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.2280 0.879 0.000 0.000 0.120 0.000 0.880
#> SRR1077708 5 0.4101 0.462 0.000 0.000 0.332 0.004 0.664
#> SRR1343720 5 0.1341 0.934 0.000 0.000 0.056 0.000 0.944
#> SRR1477499 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.0451 0.930 0.004 0.000 0.000 0.008 0.988
#> SRR1326408 1 0.1648 0.879 0.940 0.000 0.000 0.040 0.020
#> SRR1336529 3 0.0162 0.840 0.000 0.000 0.996 0.000 0.004
#> SRR1440643 2 0.3096 0.755 0.000 0.868 0.084 0.040 0.008
#> SRR662354 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.1168 0.939 0.000 0.000 0.032 0.008 0.960
#> SRR1347389 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1353097 1 0.0290 0.894 0.992 0.000 0.000 0.008 0.000
#> SRR1384737 4 0.3424 0.813 0.000 0.240 0.000 0.760 0.000
#> SRR1096339 1 0.1124 0.891 0.960 0.000 0.000 0.036 0.004
#> SRR1345329 4 0.3730 0.830 0.000 0.288 0.000 0.712 0.000
#> SRR1414771 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000
#> SRR1309119 1 0.1124 0.891 0.960 0.000 0.000 0.036 0.004
#> SRR1470438 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000
#> SRR1343221 1 0.1399 0.888 0.952 0.000 0.000 0.020 0.028
#> SRR1410847 1 0.3205 0.839 0.816 0.000 0.004 0.176 0.004
#> SRR807949 5 0.0880 0.939 0.000 0.000 0.032 0.000 0.968
#> SRR1442332 5 0.1197 0.938 0.000 0.000 0.048 0.000 0.952
#> SRR815920 3 0.0290 0.840 0.000 0.000 0.992 0.000 0.008
#> SRR1471524 3 0.1485 0.833 0.000 0.000 0.948 0.020 0.032
#> SRR1477221 3 0.3779 0.754 0.000 0.000 0.776 0.200 0.024
#> SRR1445046 4 0.4182 0.733 0.000 0.400 0.000 0.600 0.000
#> SRR1331962 2 0.3366 0.531 0.000 0.768 0.000 0.232 0.000
#> SRR1319946 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1311599 1 0.3790 0.801 0.744 0.000 0.004 0.248 0.004
#> SRR1323977 2 0.1965 0.793 0.000 0.904 0.000 0.096 0.000
#> SRR1445132 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.4613 0.730 0.000 0.000 0.728 0.200 0.072
#> SRR1366390 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1343012 4 0.4367 0.772 0.000 0.192 0.060 0.748 0.000
#> SRR1311958 2 0.3508 0.477 0.000 0.748 0.000 0.252 0.000
#> SRR1388234 4 0.4235 0.690 0.000 0.424 0.000 0.576 0.000
#> SRR1370384 1 0.1626 0.876 0.940 0.000 0.000 0.016 0.044
#> SRR1321650 3 0.2136 0.822 0.000 0.000 0.904 0.008 0.088
#> SRR1485117 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR1384713 1 0.3011 0.803 0.844 0.000 0.000 0.016 0.140
#> SRR816609 4 0.3730 0.830 0.000 0.288 0.000 0.712 0.000
#> SRR1486239 2 0.3452 0.499 0.000 0.756 0.000 0.244 0.000
#> SRR1309638 3 0.2624 0.808 0.000 0.000 0.872 0.012 0.116
#> SRR1356660 1 0.3790 0.801 0.744 0.000 0.004 0.248 0.004
#> SRR1392883 2 0.0000 0.889 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.1197 0.938 0.000 0.000 0.048 0.000 0.952
#> SRR816677 4 0.2787 0.545 0.136 0.000 0.004 0.856 0.004
#> SRR1455722 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.2286 0.873 0.888 0.000 0.000 0.108 0.004
#> SRR808452 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.3949 0.530 0.000 0.000 0.668 0.000 0.332
#> SRR1366707 3 0.1740 0.827 0.000 0.000 0.932 0.012 0.056
#> SRR1328143 5 0.1197 0.938 0.000 0.000 0.048 0.000 0.952
#> SRR1473567 2 0.1851 0.802 0.000 0.912 0.000 0.088 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 3 0.4184 0.3393 0.000 0.000 0.576 0.000 0.408 0.016
#> SRR1390119 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.1950 0.7936 0.000 0.000 0.912 0.000 0.064 0.024
#> SRR1347278 6 0.5127 0.1975 0.000 0.000 0.364 0.000 0.092 0.544
#> SRR1332904 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 1 0.2121 0.8810 0.892 0.000 0.000 0.012 0.000 0.096
#> SRR1082685 1 0.1714 0.8849 0.908 0.000 0.000 0.000 0.000 0.092
#> SRR1362287 6 0.2346 0.7427 0.124 0.000 0.008 0.000 0.000 0.868
#> SRR1339007 1 0.0508 0.8783 0.984 0.000 0.000 0.000 0.004 0.012
#> SRR1376557 2 0.0146 0.8799 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1468700 2 0.2048 0.8005 0.000 0.880 0.000 0.120 0.000 0.000
#> SRR1077455 1 0.2118 0.7798 0.888 0.000 0.000 0.000 0.104 0.008
#> SRR1413978 6 0.2581 0.7401 0.128 0.000 0.000 0.016 0.000 0.856
#> SRR1439896 1 0.1910 0.8788 0.892 0.000 0.000 0.000 0.000 0.108
#> SRR1317963 4 0.2730 0.8057 0.000 0.192 0.000 0.808 0.000 0.000
#> SRR1431865 6 0.2219 0.7426 0.136 0.000 0.000 0.000 0.000 0.864
#> SRR1394253 6 0.2260 0.7413 0.140 0.000 0.000 0.000 0.000 0.860
#> SRR1082664 5 0.3101 0.8138 0.012 0.000 0.136 0.000 0.832 0.020
#> SRR1077968 1 0.0458 0.8685 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1076393 3 0.5143 0.6574 0.000 0.000 0.672 0.036 0.208 0.084
#> SRR1477476 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 3 0.3409 0.6528 0.000 0.000 0.780 0.000 0.028 0.192
#> SRR1485042 1 0.3126 0.7243 0.752 0.000 0.000 0.000 0.000 0.248
#> SRR1385453 2 0.7233 0.3810 0.000 0.532 0.196 0.056 0.120 0.096
#> SRR1348074 4 0.1007 0.8911 0.000 0.044 0.000 0.956 0.000 0.000
#> SRR813959 2 0.2834 0.7919 0.000 0.852 0.008 0.000 0.120 0.020
#> SRR665442 2 0.4237 0.7285 0.068 0.780 0.008 0.000 0.120 0.024
#> SRR1378068 3 0.0260 0.8108 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1485237 4 0.1176 0.8788 0.024 0.020 0.000 0.956 0.000 0.000
#> SRR1350792 1 0.1765 0.8848 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1326797 5 0.1643 0.8720 0.068 0.000 0.000 0.000 0.924 0.008
#> SRR808994 3 0.0551 0.8092 0.000 0.000 0.984 0.004 0.004 0.008
#> SRR1474041 5 0.1480 0.8957 0.000 0.000 0.040 0.000 0.940 0.020
#> SRR1405641 3 0.0291 0.8105 0.000 0.000 0.992 0.004 0.004 0.000
#> SRR1362245 6 0.4263 0.0383 0.000 0.000 0.480 0.000 0.016 0.504
#> SRR1500194 6 0.3198 0.6154 0.260 0.000 0.000 0.000 0.000 0.740
#> SRR1414876 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 3 0.6290 0.4688 0.000 0.024 0.556 0.040 0.284 0.096
#> SRR1325161 5 0.1196 0.8892 0.040 0.000 0.000 0.000 0.952 0.008
#> SRR1318026 4 0.0790 0.8889 0.000 0.032 0.000 0.968 0.000 0.000
#> SRR1343778 3 0.3936 0.5869 0.000 0.000 0.688 0.000 0.288 0.024
#> SRR1441287 1 0.1765 0.8840 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1430991 5 0.1196 0.8983 0.000 0.000 0.040 0.000 0.952 0.008
#> SRR1499722 5 0.1196 0.8886 0.040 0.000 0.000 0.000 0.952 0.008
#> SRR1351368 3 0.5425 0.6450 0.000 0.144 0.696 0.044 0.020 0.096
#> SRR1441785 6 0.2178 0.7431 0.132 0.000 0.000 0.000 0.000 0.868
#> SRR1096101 1 0.3371 0.5550 0.708 0.000 0.000 0.000 0.000 0.292
#> SRR808375 5 0.0146 0.8990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1452842 1 0.1643 0.8230 0.924 0.000 0.000 0.000 0.068 0.008
#> SRR1311709 1 0.3183 0.8255 0.828 0.000 0.000 0.112 0.000 0.060
#> SRR1433352 5 0.1888 0.8890 0.004 0.000 0.068 0.000 0.916 0.012
#> SRR1340241 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1456754 1 0.1575 0.8564 0.936 0.000 0.000 0.000 0.032 0.032
#> SRR1465172 5 0.1584 0.8770 0.064 0.000 0.000 0.000 0.928 0.008
#> SRR1499284 5 0.3323 0.6835 0.240 0.000 0.000 0.000 0.752 0.008
#> SRR1499607 4 0.3023 0.7574 0.000 0.232 0.000 0.768 0.000 0.000
#> SRR812342 1 0.1327 0.8880 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1405374 6 0.3634 0.4495 0.356 0.000 0.000 0.000 0.000 0.644
#> SRR1403565 6 0.1863 0.7364 0.104 0.000 0.000 0.000 0.000 0.896
#> SRR1332024 3 0.0653 0.8081 0.000 0.000 0.980 0.004 0.004 0.012
#> SRR1471633 1 0.3468 0.8072 0.804 0.000 0.000 0.128 0.000 0.068
#> SRR1325944 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.0696 0.8953 0.008 0.000 0.004 0.004 0.980 0.004
#> SRR1435372 1 0.0520 0.8754 0.984 0.000 0.000 0.000 0.008 0.008
#> SRR1324184 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816517 2 0.2781 0.8005 0.000 0.868 0.008 0.040 0.000 0.084
#> SRR1324141 4 0.0405 0.8741 0.000 0.004 0.000 0.988 0.000 0.008
#> SRR1101612 1 0.1765 0.8842 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1356531 1 0.1387 0.8879 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1089785 5 0.2212 0.8506 0.000 0.000 0.112 0.000 0.880 0.008
#> SRR1077708 5 0.4593 0.1676 0.012 0.000 0.412 0.000 0.556 0.020
#> SRR1343720 5 0.1942 0.8850 0.012 0.000 0.064 0.000 0.916 0.008
#> SRR1477499 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 5 0.1333 0.8849 0.048 0.000 0.000 0.000 0.944 0.008
#> SRR1326408 1 0.0717 0.8659 0.976 0.000 0.000 0.008 0.016 0.000
#> SRR1336529 3 0.0260 0.8108 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1440643 2 0.5203 0.6879 0.000 0.736 0.056 0.068 0.044 0.096
#> SRR662354 1 0.1863 0.8816 0.896 0.000 0.000 0.000 0.000 0.104
#> SRR1310817 5 0.1483 0.8894 0.000 0.000 0.012 0.008 0.944 0.036
#> SRR1347389 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1353097 1 0.0632 0.8828 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1384737 4 0.0603 0.8730 0.000 0.004 0.000 0.980 0.000 0.016
#> SRR1096339 1 0.2854 0.7779 0.792 0.000 0.000 0.000 0.000 0.208
#> SRR1345329 4 0.1007 0.8911 0.000 0.044 0.000 0.956 0.000 0.000
#> SRR1414771 3 0.0653 0.8084 0.000 0.000 0.980 0.004 0.004 0.012
#> SRR1309119 1 0.3602 0.7545 0.760 0.000 0.000 0.032 0.000 0.208
#> SRR1470438 3 0.0653 0.8084 0.000 0.000 0.980 0.004 0.004 0.012
#> SRR1343221 1 0.1701 0.8745 0.920 0.000 0.000 0.000 0.008 0.072
#> SRR1410847 6 0.3756 0.3274 0.400 0.000 0.000 0.000 0.000 0.600
#> SRR807949 5 0.0777 0.9001 0.000 0.000 0.024 0.000 0.972 0.004
#> SRR1442332 5 0.1480 0.8957 0.000 0.000 0.040 0.000 0.940 0.020
#> SRR815920 3 0.0692 0.8113 0.000 0.000 0.976 0.000 0.020 0.004
#> SRR1471524 3 0.4121 0.7532 0.000 0.000 0.788 0.040 0.084 0.088
#> SRR1477221 6 0.4242 0.1349 0.000 0.000 0.448 0.000 0.016 0.536
#> SRR1445046 4 0.2631 0.8164 0.000 0.180 0.000 0.820 0.000 0.000
#> SRR1331962 2 0.3547 0.4890 0.000 0.668 0.000 0.332 0.000 0.000
#> SRR1319946 2 0.0951 0.8704 0.000 0.968 0.000 0.020 0.004 0.008
#> SRR1311599 6 0.2260 0.7413 0.140 0.000 0.000 0.000 0.000 0.860
#> SRR1323977 2 0.2845 0.7540 0.000 0.820 0.000 0.172 0.004 0.004
#> SRR1445132 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 6 0.4445 0.2330 0.000 0.000 0.396 0.000 0.032 0.572
#> SRR1366390 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1343012 4 0.1706 0.8509 0.004 0.004 0.024 0.936 0.000 0.032
#> SRR1311958 2 0.3756 0.3228 0.000 0.600 0.000 0.400 0.000 0.000
#> SRR1388234 4 0.2883 0.7766 0.000 0.212 0.000 0.788 0.000 0.000
#> SRR1370384 1 0.1049 0.8534 0.960 0.000 0.000 0.000 0.032 0.008
#> SRR1321650 3 0.2390 0.7780 0.000 0.000 0.888 0.000 0.056 0.056
#> SRR1485117 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 1 0.1584 0.8276 0.928 0.000 0.000 0.000 0.064 0.008
#> SRR816609 4 0.1007 0.8911 0.000 0.044 0.000 0.956 0.000 0.000
#> SRR1486239 2 0.3717 0.3646 0.000 0.616 0.000 0.384 0.000 0.000
#> SRR1309638 3 0.3379 0.7730 0.008 0.000 0.832 0.004 0.100 0.056
#> SRR1356660 6 0.2260 0.7413 0.140 0.000 0.000 0.000 0.000 0.860
#> SRR1392883 2 0.0000 0.8815 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.1297 0.8975 0.000 0.000 0.040 0.000 0.948 0.012
#> SRR816677 4 0.3686 0.6390 0.032 0.000 0.000 0.748 0.000 0.220
#> SRR1455722 1 0.1814 0.8826 0.900 0.000 0.000 0.000 0.000 0.100
#> SRR1336029 6 0.4242 0.1354 0.448 0.000 0.000 0.016 0.000 0.536
#> SRR808452 1 0.1663 0.8862 0.912 0.000 0.000 0.000 0.000 0.088
#> SRR1352169 3 0.4419 0.3412 0.000 0.000 0.584 0.000 0.384 0.032
#> SRR1366707 3 0.3821 0.7629 0.000 0.000 0.804 0.024 0.100 0.072
#> SRR1328143 5 0.1480 0.8957 0.000 0.000 0.040 0.000 0.940 0.020
#> SRR1473567 2 0.2003 0.8031 0.000 0.884 0.000 0.116 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", "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 17851 rows and 124 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 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 0.966 0.966 0.984 0.3653 0.639 0.639
#> 3 3 0.917 0.939 0.973 0.7798 0.699 0.533
#> 4 4 0.828 0.767 0.817 0.0760 0.949 0.856
#> 5 5 0.934 0.913 0.962 0.0728 0.919 0.746
#> 6 6 0.835 0.713 0.875 0.0560 0.935 0.756
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 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
#> SRR1442087 1 0.0000 0.987 1.000 0.000
#> SRR1390119 2 0.0000 0.974 0.000 1.000
#> SRR1436127 1 0.0000 0.987 1.000 0.000
#> SRR1347278 1 0.0000 0.987 1.000 0.000
#> SRR1332904 2 0.0000 0.974 0.000 1.000
#> SRR1444179 1 0.0000 0.987 1.000 0.000
#> SRR1082685 1 0.0000 0.987 1.000 0.000
#> SRR1362287 1 0.0000 0.987 1.000 0.000
#> SRR1339007 1 0.0000 0.987 1.000 0.000
#> SRR1376557 2 0.0000 0.974 0.000 1.000
#> SRR1468700 2 0.0000 0.974 0.000 1.000
#> SRR1077455 1 0.0000 0.987 1.000 0.000
#> SRR1413978 1 0.0000 0.987 1.000 0.000
#> SRR1439896 1 0.0000 0.987 1.000 0.000
#> SRR1317963 2 0.4298 0.904 0.088 0.912
#> SRR1431865 1 0.0000 0.987 1.000 0.000
#> SRR1394253 1 0.0000 0.987 1.000 0.000
#> SRR1082664 1 0.0000 0.987 1.000 0.000
#> SRR1077968 1 0.0000 0.987 1.000 0.000
#> SRR1076393 1 0.0376 0.984 0.996 0.004
#> SRR1477476 2 0.0000 0.974 0.000 1.000
#> SRR1398057 1 0.0000 0.987 1.000 0.000
#> SRR1485042 1 0.0000 0.987 1.000 0.000
#> SRR1385453 1 0.6343 0.813 0.840 0.160
#> SRR1348074 2 0.8386 0.651 0.268 0.732
#> SRR813959 1 0.0376 0.984 0.996 0.004
#> SRR665442 1 0.0000 0.987 1.000 0.000
#> SRR1378068 1 0.0376 0.984 0.996 0.004
#> SRR1485237 1 0.8713 0.583 0.708 0.292
#> SRR1350792 1 0.0000 0.987 1.000 0.000
#> SRR1326797 1 0.0000 0.987 1.000 0.000
#> SRR808994 1 0.0376 0.984 0.996 0.004
#> SRR1474041 1 0.0000 0.987 1.000 0.000
#> SRR1405641 1 0.0000 0.987 1.000 0.000
#> SRR1362245 1 0.0000 0.987 1.000 0.000
#> SRR1500194 1 0.0000 0.987 1.000 0.000
#> SRR1414876 2 0.0000 0.974 0.000 1.000
#> SRR1478523 1 0.0376 0.984 0.996 0.004
#> SRR1325161 1 0.0000 0.987 1.000 0.000
#> SRR1318026 1 0.8661 0.591 0.712 0.288
#> SRR1343778 1 0.0376 0.984 0.996 0.004
#> SRR1441287 1 0.0000 0.987 1.000 0.000
#> SRR1430991 1 0.0000 0.987 1.000 0.000
#> SRR1499722 1 0.0000 0.987 1.000 0.000
#> SRR1351368 1 0.6531 0.802 0.832 0.168
#> SRR1441785 1 0.0000 0.987 1.000 0.000
#> SRR1096101 1 0.0000 0.987 1.000 0.000
#> SRR808375 1 0.0000 0.987 1.000 0.000
#> SRR1452842 1 0.0000 0.987 1.000 0.000
#> SRR1311709 1 0.0000 0.987 1.000 0.000
#> SRR1433352 1 0.0000 0.987 1.000 0.000
#> SRR1340241 2 0.0000 0.974 0.000 1.000
#> SRR1456754 1 0.0000 0.987 1.000 0.000
#> SRR1465172 1 0.0000 0.987 1.000 0.000
#> SRR1499284 1 0.0000 0.987 1.000 0.000
#> SRR1499607 2 0.0000 0.974 0.000 1.000
#> SRR812342 1 0.0000 0.987 1.000 0.000
#> SRR1405374 1 0.0000 0.987 1.000 0.000
#> SRR1403565 1 0.0000 0.987 1.000 0.000
#> SRR1332024 1 0.0000 0.987 1.000 0.000
#> SRR1471633 1 0.0000 0.987 1.000 0.000
#> SRR1325944 2 0.0000 0.974 0.000 1.000
#> SRR1429450 2 0.0000 0.974 0.000 1.000
#> SRR821573 1 0.0376 0.984 0.996 0.004
#> SRR1435372 1 0.0000 0.987 1.000 0.000
#> SRR1324184 2 0.0000 0.974 0.000 1.000
#> SRR816517 2 0.0000 0.974 0.000 1.000
#> SRR1324141 1 0.4815 0.881 0.896 0.104
#> SRR1101612 1 0.0000 0.987 1.000 0.000
#> SRR1356531 1 0.0000 0.987 1.000 0.000
#> SRR1089785 1 0.0376 0.984 0.996 0.004
#> SRR1077708 1 0.0000 0.987 1.000 0.000
#> SRR1343720 1 0.0000 0.987 1.000 0.000
#> SRR1477499 2 0.0000 0.974 0.000 1.000
#> SRR1347236 1 0.0000 0.987 1.000 0.000
#> SRR1326408 1 0.0000 0.987 1.000 0.000
#> SRR1336529 1 0.0000 0.987 1.000 0.000
#> SRR1440643 1 0.0376 0.984 0.996 0.004
#> SRR662354 1 0.0000 0.987 1.000 0.000
#> SRR1310817 1 0.0376 0.984 0.996 0.004
#> SRR1347389 2 0.0000 0.974 0.000 1.000
#> SRR1353097 1 0.0000 0.987 1.000 0.000
#> SRR1384737 1 0.5737 0.842 0.864 0.136
#> SRR1096339 1 0.0000 0.987 1.000 0.000
#> SRR1345329 2 0.5294 0.871 0.120 0.880
#> SRR1414771 1 0.0000 0.987 1.000 0.000
#> SRR1309119 1 0.0000 0.987 1.000 0.000
#> SRR1470438 1 0.0376 0.984 0.996 0.004
#> SRR1343221 1 0.0000 0.987 1.000 0.000
#> SRR1410847 1 0.0000 0.987 1.000 0.000
#> SRR807949 1 0.0000 0.987 1.000 0.000
#> SRR1442332 1 0.0000 0.987 1.000 0.000
#> SRR815920 1 0.0000 0.987 1.000 0.000
#> SRR1471524 1 0.0376 0.984 0.996 0.004
#> SRR1477221 1 0.0000 0.987 1.000 0.000
#> SRR1445046 2 0.0000 0.974 0.000 1.000
#> SRR1331962 2 0.0000 0.974 0.000 1.000
#> SRR1319946 2 0.0376 0.971 0.004 0.996
#> SRR1311599 1 0.0000 0.987 1.000 0.000
#> SRR1323977 1 0.0376 0.984 0.996 0.004
#> SRR1445132 2 0.0000 0.974 0.000 1.000
#> SRR1337321 1 0.0000 0.987 1.000 0.000
#> SRR1366390 2 0.0000 0.974 0.000 1.000
#> SRR1343012 1 0.0376 0.984 0.996 0.004
#> SRR1311958 2 0.0000 0.974 0.000 1.000
#> SRR1388234 2 0.4298 0.904 0.088 0.912
#> SRR1370384 1 0.0000 0.987 1.000 0.000
#> SRR1321650 1 0.0000 0.987 1.000 0.000
#> SRR1485117 2 0.0000 0.974 0.000 1.000
#> SRR1384713 1 0.0000 0.987 1.000 0.000
#> SRR816609 2 0.6048 0.838 0.148 0.852
#> SRR1486239 2 0.0000 0.974 0.000 1.000
#> SRR1309638 1 0.0000 0.987 1.000 0.000
#> SRR1356660 1 0.0000 0.987 1.000 0.000
#> SRR1392883 2 0.0000 0.974 0.000 1.000
#> SRR808130 1 0.0000 0.987 1.000 0.000
#> SRR816677 1 0.0672 0.980 0.992 0.008
#> SRR1455722 1 0.0000 0.987 1.000 0.000
#> SRR1336029 1 0.0000 0.987 1.000 0.000
#> SRR808452 1 0.0000 0.987 1.000 0.000
#> SRR1352169 1 0.0000 0.987 1.000 0.000
#> SRR1366707 1 0.0376 0.984 0.996 0.004
#> SRR1328143 1 0.0000 0.987 1.000 0.000
#> SRR1473567 2 0.0000 0.974 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1347278 3 0.4291 0.790 0.180 0.000 0.820
#> SRR1332904 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1317963 2 0.2165 0.926 0.064 0.936 0.000
#> SRR1431865 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1082664 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1077968 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1485042 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1385453 3 0.0237 0.960 0.000 0.004 0.996
#> SRR1348074 2 0.5138 0.684 0.252 0.748 0.000
#> SRR813959 3 0.0424 0.958 0.008 0.000 0.992
#> SRR665442 1 0.0892 0.956 0.980 0.000 0.020
#> SRR1378068 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1485237 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1350792 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1326797 1 0.6204 0.234 0.576 0.000 0.424
#> SRR808994 3 0.0237 0.960 0.004 0.000 0.996
#> SRR1474041 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1362245 3 0.3412 0.850 0.124 0.000 0.876
#> SRR1500194 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1414876 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1478523 3 0.0892 0.950 0.020 0.000 0.980
#> SRR1325161 3 0.0747 0.952 0.016 0.000 0.984
#> SRR1318026 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1343778 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1441287 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1430991 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1499722 3 0.4750 0.742 0.216 0.000 0.784
#> SRR1351368 3 0.0424 0.957 0.000 0.008 0.992
#> SRR1441785 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.973 1.000 0.000 0.000
#> SRR808375 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1452842 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1311709 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1433352 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1340241 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1465172 1 0.3619 0.839 0.864 0.000 0.136
#> SRR1499284 1 0.1163 0.950 0.972 0.000 0.028
#> SRR1499607 2 0.0000 0.974 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1332024 3 0.1163 0.943 0.028 0.000 0.972
#> SRR1471633 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1325944 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.974 0.000 1.000 0.000
#> SRR821573 3 0.4887 0.707 0.228 0.000 0.772
#> SRR1435372 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.974 0.000 1.000 0.000
#> SRR816517 3 0.5706 0.547 0.000 0.320 0.680
#> SRR1324141 1 0.2031 0.936 0.952 0.032 0.016
#> SRR1101612 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1089785 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1077708 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1343720 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1477499 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1347236 1 0.4452 0.767 0.808 0.000 0.192
#> SRR1326408 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1440643 3 0.0000 0.962 0.000 0.000 1.000
#> SRR662354 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1310817 1 0.5810 0.516 0.664 0.000 0.336
#> SRR1347389 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1384737 1 0.2959 0.872 0.900 0.100 0.000
#> SRR1096339 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1345329 2 0.3038 0.891 0.104 0.896 0.000
#> SRR1414771 3 0.0424 0.958 0.008 0.000 0.992
#> SRR1309119 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1470438 3 0.0892 0.950 0.020 0.000 0.980
#> SRR1343221 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.973 1.000 0.000 0.000
#> SRR807949 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1442332 3 0.0000 0.962 0.000 0.000 1.000
#> SRR815920 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1477221 3 0.0747 0.953 0.016 0.000 0.984
#> SRR1445046 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1319946 2 0.0661 0.967 0.008 0.988 0.004
#> SRR1311599 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1323977 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1337321 3 0.4750 0.742 0.216 0.000 0.784
#> SRR1366390 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1343012 1 0.0237 0.970 0.996 0.000 0.004
#> SRR1311958 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1388234 2 0.2356 0.919 0.072 0.928 0.000
#> SRR1370384 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1321650 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.973 1.000 0.000 0.000
#> SRR816609 2 0.3551 0.860 0.132 0.868 0.000
#> SRR1486239 2 0.0000 0.974 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1356660 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.974 0.000 1.000 0.000
#> SRR808130 3 0.0000 0.962 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1336029 1 0.0000 0.973 1.000 0.000 0.000
#> SRR808452 1 0.0000 0.973 1.000 0.000 0.000
#> SRR1352169 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1366707 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1328143 3 0.0000 0.962 0.000 0.000 1.000
#> SRR1473567 2 0.0000 0.974 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.4713 0.707 0.000 0.000 0.640 0.360
#> SRR1390119 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.0000 0.776 0.000 0.000 1.000 0.000
#> SRR1347278 3 0.4134 0.566 0.260 0.000 0.740 0.000
#> SRR1332904 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1444179 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0707 0.957 0.980 0.000 0.020 0.000
#> SRR1339007 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1376557 4 0.4998 0.436 0.000 0.488 0.000 0.512
#> SRR1468700 4 0.4994 0.458 0.000 0.480 0.000 0.520
#> SRR1077455 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1413978 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1317963 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1431865 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1082664 3 0.4679 0.726 0.000 0.000 0.648 0.352
#> SRR1077968 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1076393 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1477476 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.0000 0.776 0.000 0.000 1.000 0.000
#> SRR1485042 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1385453 3 0.1637 0.766 0.000 0.000 0.940 0.060
#> SRR1348074 4 0.7235 0.307 0.180 0.288 0.000 0.532
#> SRR813959 3 0.3142 0.770 0.008 0.000 0.860 0.132
#> SRR665442 1 0.1356 0.939 0.960 0.000 0.032 0.008
#> SRR1378068 3 0.0000 0.776 0.000 0.000 1.000 0.000
#> SRR1485237 1 0.1302 0.938 0.956 0.000 0.000 0.044
#> SRR1350792 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1326797 4 0.7307 -0.182 0.404 0.000 0.152 0.444
#> SRR808994 3 0.0921 0.766 0.000 0.000 0.972 0.028
#> SRR1474041 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1405641 3 0.0000 0.776 0.000 0.000 1.000 0.000
#> SRR1362245 3 0.2704 0.700 0.124 0.000 0.876 0.000
#> SRR1500194 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.3463 0.730 0.096 0.000 0.864 0.040
#> SRR1325161 3 0.5257 0.687 0.008 0.000 0.548 0.444
#> SRR1318026 1 0.1302 0.938 0.956 0.000 0.000 0.044
#> SRR1343778 3 0.1042 0.775 0.020 0.000 0.972 0.008
#> SRR1441287 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1430991 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1499722 3 0.6613 0.602 0.200 0.000 0.628 0.172
#> SRR1351368 3 0.2469 0.769 0.000 0.000 0.892 0.108
#> SRR1441785 1 0.0707 0.957 0.980 0.000 0.020 0.000
#> SRR1096101 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR808375 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1452842 1 0.0672 0.961 0.984 0.000 0.008 0.008
#> SRR1311709 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1433352 3 0.2466 0.750 0.096 0.000 0.900 0.004
#> SRR1340241 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1465172 4 0.7218 -0.150 0.416 0.000 0.140 0.444
#> SRR1499284 1 0.6064 0.249 0.512 0.000 0.044 0.444
#> SRR1499607 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR812342 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.0707 0.957 0.980 0.000 0.020 0.000
#> SRR1332024 3 0.1302 0.761 0.044 0.000 0.956 0.000
#> SRR1471633 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1325944 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR821573 3 0.6607 0.619 0.080 0.000 0.476 0.444
#> SRR1435372 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR816517 3 0.6890 0.313 0.000 0.268 0.580 0.152
#> SRR1324141 1 0.2125 0.907 0.920 0.000 0.004 0.076
#> SRR1101612 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1089785 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1077708 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1343720 3 0.2704 0.775 0.000 0.000 0.876 0.124
#> SRR1477499 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1347236 1 0.4095 0.717 0.792 0.000 0.192 0.016
#> SRR1326408 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1336529 3 0.0000 0.776 0.000 0.000 1.000 0.000
#> SRR1440643 3 0.3128 0.740 0.076 0.000 0.884 0.040
#> SRR662354 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1310817 4 0.7711 -0.390 0.248 0.000 0.308 0.444
#> SRR1347389 2 0.2868 0.707 0.000 0.864 0.000 0.136
#> SRR1353097 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1384737 1 0.3401 0.805 0.840 0.008 0.000 0.152
#> SRR1096339 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1345329 4 0.5800 0.503 0.032 0.420 0.000 0.548
#> SRR1414771 3 0.0592 0.773 0.016 0.000 0.984 0.000
#> SRR1309119 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1470438 3 0.1388 0.763 0.012 0.000 0.960 0.028
#> SRR1343221 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR807949 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1442332 3 0.2281 0.775 0.000 0.000 0.904 0.096
#> SRR815920 3 0.0000 0.776 0.000 0.000 1.000 0.000
#> SRR1471524 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1477221 3 0.1022 0.768 0.032 0.000 0.968 0.000
#> SRR1445046 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1331962 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1319946 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1311599 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1323977 1 0.1302 0.938 0.956 0.000 0.000 0.044
#> SRR1445132 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.5486 0.611 0.200 0.000 0.720 0.080
#> SRR1366390 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR1343012 1 0.1489 0.936 0.952 0.000 0.004 0.044
#> SRR1311958 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1388234 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1370384 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1321650 3 0.0188 0.777 0.000 0.000 0.996 0.004
#> SRR1485117 2 0.4999 -0.463 0.000 0.508 0.000 0.492
#> SRR1384713 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR816609 4 0.6243 0.465 0.060 0.392 0.000 0.548
#> SRR1486239 4 0.4955 0.538 0.000 0.444 0.000 0.556
#> SRR1309638 3 0.4661 0.707 0.000 0.000 0.652 0.348
#> SRR1356660 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.918 0.000 1.000 0.000 0.000
#> SRR808130 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR816677 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1455722 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> SRR1352169 3 0.2222 0.778 0.016 0.000 0.924 0.060
#> SRR1366707 3 0.4746 0.704 0.000 0.000 0.632 0.368
#> SRR1328143 3 0.4955 0.692 0.000 0.000 0.556 0.444
#> SRR1473567 4 0.4955 0.538 0.000 0.444 0.000 0.556
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.3707 0.6219 0.000 0.000 0.284 0.000 0.716
#> SRR1390119 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1347278 3 0.1792 0.8575 0.084 0.000 0.916 0.000 0.000
#> SRR1332904 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.0609 0.9684 0.980 0.000 0.020 0.000 0.000
#> SRR1339007 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1376557 4 0.0290 0.9602 0.000 0.008 0.000 0.992 0.000
#> SRR1468700 4 0.0290 0.9602 0.000 0.008 0.000 0.992 0.000
#> SRR1077455 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1413978 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1082664 5 0.4307 -0.0939 0.000 0.000 0.500 0.000 0.500
#> SRR1077968 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1076393 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1485042 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1385453 3 0.0898 0.9062 0.000 0.000 0.972 0.008 0.020
#> SRR1348074 4 0.2929 0.6981 0.180 0.000 0.000 0.820 0.000
#> SRR813959 3 0.2074 0.8687 0.000 0.000 0.896 0.000 0.104
#> SRR665442 1 0.2732 0.8158 0.840 0.000 0.000 0.000 0.160
#> SRR1378068 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1485237 1 0.0290 0.9794 0.992 0.000 0.000 0.008 0.000
#> SRR1350792 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR808994 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1474041 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1405641 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1362245 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1500194 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1414876 2 0.1341 0.9198 0.000 0.944 0.000 0.056 0.000
#> SRR1478523 3 0.2074 0.8387 0.104 0.000 0.896 0.000 0.000
#> SRR1325161 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1318026 1 0.0290 0.9794 0.992 0.000 0.000 0.008 0.000
#> SRR1343778 3 0.0609 0.9043 0.020 0.000 0.980 0.000 0.000
#> SRR1441287 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1499722 3 0.4300 0.1827 0.000 0.000 0.524 0.000 0.476
#> SRR1351368 3 0.2017 0.8624 0.000 0.000 0.912 0.008 0.080
#> SRR1441785 1 0.0609 0.9684 0.980 0.000 0.020 0.000 0.000
#> SRR1096101 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1452842 1 0.0880 0.9586 0.968 0.000 0.000 0.000 0.032
#> SRR1311709 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1433352 3 0.2669 0.8340 0.104 0.000 0.876 0.000 0.020
#> SRR1340241 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1465172 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1499284 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1499607 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1403565 1 0.0609 0.9684 0.980 0.000 0.020 0.000 0.000
#> SRR1332024 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1471633 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1325944 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1435372 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1324184 2 0.1341 0.9198 0.000 0.944 0.000 0.056 0.000
#> SRR816517 3 0.5642 0.5461 0.000 0.180 0.636 0.184 0.000
#> SRR1324141 1 0.2067 0.9184 0.920 0.000 0.000 0.048 0.032
#> SRR1101612 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1077708 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1343720 3 0.3586 0.6825 0.000 0.000 0.736 0.000 0.264
#> SRR1477499 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 1 0.3210 0.7442 0.788 0.000 0.000 0.000 0.212
#> SRR1326408 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1440643 3 0.1792 0.8575 0.084 0.000 0.916 0.000 0.000
#> SRR662354 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1347389 2 0.4242 0.2952 0.000 0.572 0.000 0.428 0.000
#> SRR1353097 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1384737 1 0.2471 0.8468 0.864 0.000 0.000 0.136 0.000
#> SRR1096339 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.0880 0.9318 0.032 0.000 0.000 0.968 0.000
#> SRR1414771 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1309119 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1470438 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1343221 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1442332 3 0.2074 0.8687 0.000 0.000 0.896 0.000 0.104
#> SRR815920 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1471524 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1477221 3 0.0000 0.9123 0.000 0.000 1.000 0.000 0.000
#> SRR1445046 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1331962 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1319946 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1311599 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1323977 1 0.0290 0.9794 0.992 0.000 0.000 0.008 0.000
#> SRR1445132 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.1908 0.8769 0.000 0.000 0.908 0.000 0.092
#> SRR1366390 2 0.1341 0.9198 0.000 0.944 0.000 0.056 0.000
#> SRR1343012 1 0.0290 0.9794 0.992 0.000 0.000 0.008 0.000
#> SRR1311958 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1388234 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1321650 3 0.1341 0.8888 0.000 0.000 0.944 0.000 0.056
#> SRR1485117 4 0.1043 0.9327 0.000 0.040 0.000 0.960 0.000
#> SRR1384713 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR816609 4 0.1410 0.8933 0.060 0.000 0.000 0.940 0.000
#> SRR1486239 4 0.0000 0.9655 0.000 0.000 0.000 1.000 0.000
#> SRR1309638 5 0.2074 0.8506 0.000 0.000 0.104 0.000 0.896
#> SRR1356660 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR816677 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1455722 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR808452 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000
#> SRR1352169 3 0.2046 0.8862 0.016 0.000 0.916 0.000 0.068
#> SRR1366707 5 0.1792 0.8673 0.000 0.000 0.084 0.000 0.916
#> SRR1328143 5 0.0000 0.9398 0.000 0.000 0.000 0.000 1.000
#> SRR1473567 4 0.0000 0.9655 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
#> SRR1442087 3 0.4933 0.2021 0.000 0.000 0.504 0.000 0.432 0.064
#> SRR1390119 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.3531 0.6657 0.000 0.000 0.672 0.000 0.000 0.328
#> SRR1347278 6 0.0000 0.6043 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1332904 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1339007 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1376557 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1468700 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.0363 0.8447 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1413978 1 0.3899 0.3888 0.592 0.000 0.004 0.000 0.000 0.404
#> SRR1439896 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1431865 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1394253 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1082664 5 0.5341 0.3726 0.000 0.000 0.132 0.000 0.556 0.312
#> SRR1077968 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1076393 5 0.1444 0.8162 0.000 0.000 0.072 0.000 0.928 0.000
#> SRR1477476 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 6 0.0000 0.6043 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1485042 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1385453 3 0.3221 0.6266 0.000 0.000 0.736 0.000 0.000 0.264
#> SRR1348074 4 0.3523 0.6861 0.180 0.000 0.040 0.780 0.000 0.000
#> SRR813959 5 0.3975 0.3736 0.000 0.000 0.004 0.000 0.544 0.452
#> SRR665442 1 0.3602 0.6554 0.784 0.000 0.000 0.000 0.160 0.056
#> SRR1378068 3 0.1863 0.7508 0.000 0.000 0.896 0.000 0.000 0.104
#> SRR1485237 1 0.0865 0.8293 0.964 0.000 0.036 0.000 0.000 0.000
#> SRR1350792 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR808994 3 0.1007 0.7343 0.000 0.000 0.956 0.000 0.000 0.044
#> SRR1474041 5 0.1387 0.8120 0.000 0.000 0.000 0.000 0.932 0.068
#> SRR1405641 3 0.1863 0.7508 0.000 0.000 0.896 0.000 0.000 0.104
#> SRR1362245 6 0.0000 0.6043 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1500194 1 0.3747 0.4074 0.604 0.000 0.000 0.000 0.000 0.396
#> SRR1414876 2 0.1204 0.9107 0.000 0.944 0.000 0.056 0.000 0.000
#> SRR1478523 6 0.2260 0.4699 0.000 0.000 0.140 0.000 0.000 0.860
#> SRR1325161 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1318026 1 0.1007 0.8240 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1343778 3 0.4596 0.4769 0.004 0.000 0.508 0.000 0.028 0.460
#> SRR1441287 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.0363 0.8501 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1499722 5 0.3076 0.6515 0.000 0.000 0.000 0.000 0.760 0.240
#> SRR1351368 3 0.4263 0.5156 0.000 0.000 0.600 0.000 0.024 0.376
#> SRR1441785 1 0.3789 0.3717 0.584 0.000 0.000 0.000 0.000 0.416
#> SRR1096101 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1452842 1 0.1007 0.8219 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1311709 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1433352 6 0.6740 0.0412 0.332 0.000 0.144 0.000 0.080 0.444
#> SRR1340241 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1456754 1 0.0260 0.8470 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1465172 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499284 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499607 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR812342 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405374 1 0.3747 0.4074 0.604 0.000 0.000 0.000 0.000 0.396
#> SRR1403565 6 0.3864 -0.1660 0.480 0.000 0.000 0.000 0.000 0.520
#> SRR1332024 6 0.3810 -0.0297 0.000 0.000 0.428 0.000 0.000 0.572
#> SRR1471633 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1325944 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.0146 0.8526 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1435372 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324184 2 0.1204 0.9107 0.000 0.944 0.000 0.056 0.000 0.000
#> SRR816517 4 0.6684 0.2522 0.000 0.164 0.068 0.464 0.000 0.304
#> SRR1324141 1 0.3323 0.7125 0.836 0.000 0.104 0.028 0.032 0.000
#> SRR1101612 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1077708 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1343720 5 0.5465 0.2959 0.000 0.000 0.132 0.000 0.508 0.360
#> SRR1477499 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 1 0.2969 0.6028 0.776 0.000 0.000 0.000 0.224 0.000
#> SRR1326408 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.1863 0.7508 0.000 0.000 0.896 0.000 0.000 0.104
#> SRR1440643 6 0.4455 0.3389 0.232 0.000 0.080 0.000 0.000 0.688
#> SRR662354 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.0260 0.8517 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1347389 2 0.4499 0.2123 0.000 0.540 0.032 0.428 0.000 0.000
#> SRR1353097 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384737 6 0.6628 -0.0328 0.400 0.000 0.104 0.092 0.000 0.404
#> SRR1096339 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.1498 0.8969 0.032 0.000 0.028 0.940 0.000 0.000
#> SRR1414771 3 0.3050 0.6947 0.000 0.000 0.764 0.000 0.000 0.236
#> SRR1309119 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1470438 3 0.3126 0.6852 0.000 0.000 0.752 0.000 0.000 0.248
#> SRR1343221 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1442332 5 0.4325 0.3475 0.000 0.000 0.020 0.000 0.524 0.456
#> SRR815920 3 0.3446 0.6761 0.000 0.000 0.692 0.000 0.000 0.308
#> SRR1471524 5 0.0363 0.8501 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1477221 6 0.0000 0.6043 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1445046 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1331962 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1319946 4 0.0937 0.9137 0.000 0.000 0.040 0.960 0.000 0.000
#> SRR1311599 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1323977 1 0.0937 0.8265 0.960 0.000 0.040 0.000 0.000 0.000
#> SRR1445132 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 6 0.0146 0.6025 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1366390 2 0.1204 0.9107 0.000 0.944 0.000 0.056 0.000 0.000
#> SRR1343012 1 0.2218 0.7626 0.884 0.000 0.104 0.000 0.012 0.000
#> SRR1311958 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1388234 4 0.0713 0.9205 0.000 0.000 0.028 0.972 0.000 0.000
#> SRR1370384 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1321650 3 0.3794 0.6866 0.000 0.000 0.744 0.000 0.040 0.216
#> SRR1485117 4 0.0790 0.9101 0.000 0.032 0.000 0.968 0.000 0.000
#> SRR1384713 1 0.0363 0.8447 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR816609 4 0.1411 0.8747 0.060 0.000 0.004 0.936 0.000 0.000
#> SRR1486239 4 0.0000 0.9320 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1309638 5 0.3695 0.2623 0.000 0.000 0.376 0.000 0.624 0.000
#> SRR1356660 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1392883 2 0.0000 0.9365 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.0000 0.8539 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR816677 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR1455722 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.3765 0.3948 0.596 0.000 0.000 0.000 0.000 0.404
#> SRR808452 1 0.0000 0.8513 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352169 6 0.0632 0.5897 0.000 0.000 0.024 0.000 0.000 0.976
#> SRR1366707 3 0.1007 0.7130 0.000 0.000 0.956 0.000 0.044 0.000
#> SRR1328143 5 0.0363 0.8501 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1473567 4 0.0000 0.9320 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", "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 17851 rows and 124 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 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 0.852 0.862 0.947 0.3876 0.648 0.648
#> 3 3 0.560 0.723 0.769 0.6115 0.627 0.448
#> 4 4 0.519 0.582 0.741 0.1340 0.833 0.568
#> 5 5 0.701 0.686 0.809 0.0652 0.833 0.511
#> 6 6 0.721 0.507 0.769 0.0488 0.961 0.851
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
#> SRR1442087 1 0.0000 0.9343 1.000 0.000
#> SRR1390119 2 0.0000 0.9656 0.000 1.000
#> SRR1436127 1 0.0000 0.9343 1.000 0.000
#> SRR1347278 1 0.0000 0.9343 1.000 0.000
#> SRR1332904 2 0.0000 0.9656 0.000 1.000
#> SRR1444179 1 0.0000 0.9343 1.000 0.000
#> SRR1082685 1 0.0000 0.9343 1.000 0.000
#> SRR1362287 1 0.0000 0.9343 1.000 0.000
#> SRR1339007 1 0.0000 0.9343 1.000 0.000
#> SRR1376557 2 0.0000 0.9656 0.000 1.000
#> SRR1468700 2 0.0000 0.9656 0.000 1.000
#> SRR1077455 1 0.0000 0.9343 1.000 0.000
#> SRR1413978 1 0.0000 0.9343 1.000 0.000
#> SRR1439896 1 0.0000 0.9343 1.000 0.000
#> SRR1317963 2 0.0000 0.9656 0.000 1.000
#> SRR1431865 1 0.0000 0.9343 1.000 0.000
#> SRR1394253 1 0.0000 0.9343 1.000 0.000
#> SRR1082664 1 0.0000 0.9343 1.000 0.000
#> SRR1077968 1 0.0000 0.9343 1.000 0.000
#> SRR1076393 1 0.1633 0.9174 0.976 0.024
#> SRR1477476 2 0.0000 0.9656 0.000 1.000
#> SRR1398057 1 0.0000 0.9343 1.000 0.000
#> SRR1485042 1 0.0000 0.9343 1.000 0.000
#> SRR1385453 1 0.9970 0.1859 0.532 0.468
#> SRR1348074 2 0.6048 0.8085 0.148 0.852
#> SRR813959 1 0.9522 0.4340 0.628 0.372
#> SRR665442 1 0.8555 0.6095 0.720 0.280
#> SRR1378068 1 0.0000 0.9343 1.000 0.000
#> SRR1485237 1 0.9963 0.1983 0.536 0.464
#> SRR1350792 1 0.0000 0.9343 1.000 0.000
#> SRR1326797 1 0.0000 0.9343 1.000 0.000
#> SRR808994 1 0.0000 0.9343 1.000 0.000
#> SRR1474041 1 0.0000 0.9343 1.000 0.000
#> SRR1405641 1 0.0000 0.9343 1.000 0.000
#> SRR1362245 1 0.0000 0.9343 1.000 0.000
#> SRR1500194 1 0.0000 0.9343 1.000 0.000
#> SRR1414876 2 0.0000 0.9656 0.000 1.000
#> SRR1478523 1 0.6148 0.7873 0.848 0.152
#> SRR1325161 1 0.0000 0.9343 1.000 0.000
#> SRR1318026 1 1.0000 0.0747 0.500 0.500
#> SRR1343778 1 0.0000 0.9343 1.000 0.000
#> SRR1441287 1 0.0000 0.9343 1.000 0.000
#> SRR1430991 1 0.0000 0.9343 1.000 0.000
#> SRR1499722 1 0.0000 0.9343 1.000 0.000
#> SRR1351368 1 0.9944 0.2222 0.544 0.456
#> SRR1441785 1 0.0000 0.9343 1.000 0.000
#> SRR1096101 1 0.0000 0.9343 1.000 0.000
#> SRR808375 1 0.0000 0.9343 1.000 0.000
#> SRR1452842 1 0.0000 0.9343 1.000 0.000
#> SRR1311709 1 0.3584 0.8799 0.932 0.068
#> SRR1433352 1 0.0000 0.9343 1.000 0.000
#> SRR1340241 2 0.0000 0.9656 0.000 1.000
#> SRR1456754 1 0.0000 0.9343 1.000 0.000
#> SRR1465172 1 0.0000 0.9343 1.000 0.000
#> SRR1499284 1 0.0000 0.9343 1.000 0.000
#> SRR1499607 2 0.0000 0.9656 0.000 1.000
#> SRR812342 1 0.0000 0.9343 1.000 0.000
#> SRR1405374 1 0.0000 0.9343 1.000 0.000
#> SRR1403565 1 0.0000 0.9343 1.000 0.000
#> SRR1332024 1 0.0000 0.9343 1.000 0.000
#> SRR1471633 1 0.4161 0.8646 0.916 0.084
#> SRR1325944 2 0.0000 0.9656 0.000 1.000
#> SRR1429450 2 0.0000 0.9656 0.000 1.000
#> SRR821573 1 0.1843 0.9143 0.972 0.028
#> SRR1435372 1 0.0000 0.9343 1.000 0.000
#> SRR1324184 2 0.0000 0.9656 0.000 1.000
#> SRR816517 2 0.0672 0.9592 0.008 0.992
#> SRR1324141 1 0.9983 0.1601 0.524 0.476
#> SRR1101612 1 0.0000 0.9343 1.000 0.000
#> SRR1356531 1 0.0000 0.9343 1.000 0.000
#> SRR1089785 1 0.0000 0.9343 1.000 0.000
#> SRR1077708 1 0.0000 0.9343 1.000 0.000
#> SRR1343720 1 0.0000 0.9343 1.000 0.000
#> SRR1477499 2 0.0000 0.9656 0.000 1.000
#> SRR1347236 1 0.0000 0.9343 1.000 0.000
#> SRR1326408 1 0.0000 0.9343 1.000 0.000
#> SRR1336529 1 0.0000 0.9343 1.000 0.000
#> SRR1440643 1 0.9909 0.2569 0.556 0.444
#> SRR662354 1 0.0000 0.9343 1.000 0.000
#> SRR1310817 1 0.2603 0.9014 0.956 0.044
#> SRR1347389 2 0.0000 0.9656 0.000 1.000
#> SRR1353097 1 0.0000 0.9343 1.000 0.000
#> SRR1384737 1 0.9988 0.1468 0.520 0.480
#> SRR1096339 1 0.0000 0.9343 1.000 0.000
#> SRR1345329 2 0.9833 0.1775 0.424 0.576
#> SRR1414771 1 0.0000 0.9343 1.000 0.000
#> SRR1309119 1 0.2236 0.9081 0.964 0.036
#> SRR1470438 1 0.0000 0.9343 1.000 0.000
#> SRR1343221 1 0.0000 0.9343 1.000 0.000
#> SRR1410847 1 0.0000 0.9343 1.000 0.000
#> SRR807949 1 0.0000 0.9343 1.000 0.000
#> SRR1442332 1 0.0000 0.9343 1.000 0.000
#> SRR815920 1 0.0000 0.9343 1.000 0.000
#> SRR1471524 1 0.1414 0.9205 0.980 0.020
#> SRR1477221 1 0.0000 0.9343 1.000 0.000
#> SRR1445046 2 0.0000 0.9656 0.000 1.000
#> SRR1331962 2 0.0000 0.9656 0.000 1.000
#> SRR1319946 2 0.4562 0.8727 0.096 0.904
#> SRR1311599 1 0.0000 0.9343 1.000 0.000
#> SRR1323977 1 0.9977 0.1733 0.528 0.472
#> SRR1445132 2 0.0000 0.9656 0.000 1.000
#> SRR1337321 1 0.0000 0.9343 1.000 0.000
#> SRR1366390 2 0.0000 0.9656 0.000 1.000
#> SRR1343012 1 0.9833 0.3102 0.576 0.424
#> SRR1311958 2 0.0000 0.9656 0.000 1.000
#> SRR1388234 2 0.6048 0.8085 0.148 0.852
#> SRR1370384 1 0.0000 0.9343 1.000 0.000
#> SRR1321650 1 0.0000 0.9343 1.000 0.000
#> SRR1485117 2 0.0000 0.9656 0.000 1.000
#> SRR1384713 1 0.0000 0.9343 1.000 0.000
#> SRR816609 1 0.9983 0.1602 0.524 0.476
#> SRR1486239 2 0.0000 0.9656 0.000 1.000
#> SRR1309638 1 0.0000 0.9343 1.000 0.000
#> SRR1356660 1 0.0000 0.9343 1.000 0.000
#> SRR1392883 2 0.0000 0.9656 0.000 1.000
#> SRR808130 1 0.0000 0.9343 1.000 0.000
#> SRR816677 1 0.1633 0.9173 0.976 0.024
#> SRR1455722 1 0.0000 0.9343 1.000 0.000
#> SRR1336029 1 0.0000 0.9343 1.000 0.000
#> SRR808452 1 0.0000 0.9343 1.000 0.000
#> SRR1352169 1 0.0000 0.9343 1.000 0.000
#> SRR1366707 1 0.1184 0.9232 0.984 0.016
#> SRR1328143 1 0.0000 0.9343 1.000 0.000
#> SRR1473567 2 0.0000 0.9656 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1390119 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1436127 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1347278 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1332904 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1444179 1 0.3816 0.67948 0.852 0.000 0.148
#> SRR1082685 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1362287 1 0.3879 0.76976 0.848 0.000 0.152
#> SRR1339007 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1077455 1 0.4504 0.70921 0.804 0.000 0.196
#> SRR1413978 1 0.5677 0.72745 0.792 0.048 0.160
#> SRR1439896 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1317963 2 0.1525 0.85547 0.032 0.964 0.004
#> SRR1431865 1 0.3816 0.77369 0.852 0.000 0.148
#> SRR1394253 1 0.3816 0.77369 0.852 0.000 0.148
#> SRR1082664 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1077968 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1076393 3 0.4399 0.74882 0.188 0.000 0.812
#> SRR1477476 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1398057 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1485042 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1385453 3 0.6140 -0.34769 0.000 0.404 0.596
#> SRR1348074 2 0.8107 0.72514 0.096 0.604 0.300
#> SRR813959 3 0.6079 -0.31593 0.000 0.388 0.612
#> SRR665442 3 0.6543 -0.15872 0.016 0.344 0.640
#> SRR1378068 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1485237 2 0.8132 0.72314 0.096 0.600 0.304
#> SRR1350792 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1326797 3 0.6260 0.46058 0.448 0.000 0.552
#> SRR808994 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1474041 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR1405641 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1362245 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1500194 1 0.0237 0.83540 0.996 0.000 0.004
#> SRR1414876 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1478523 3 0.3784 0.37902 0.004 0.132 0.864
#> SRR1325161 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR1318026 2 0.8132 0.72314 0.096 0.600 0.304
#> SRR1343778 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1441287 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1430991 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR1499722 3 0.5968 0.71531 0.364 0.000 0.636
#> SRR1351368 3 0.6111 -0.32989 0.000 0.396 0.604
#> SRR1441785 1 0.4291 0.73569 0.820 0.000 0.180
#> SRR1096101 1 0.4178 0.74511 0.828 0.000 0.172
#> SRR808375 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR1452842 1 0.4654 0.68743 0.792 0.000 0.208
#> SRR1311709 1 0.8372 0.43321 0.580 0.108 0.312
#> SRR1433352 3 0.5591 0.79807 0.304 0.000 0.696
#> SRR1340241 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1456754 1 0.3816 0.77369 0.852 0.000 0.148
#> SRR1465172 3 0.5497 0.76466 0.292 0.000 0.708
#> SRR1499284 3 0.6280 0.48638 0.460 0.000 0.540
#> SRR1499607 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1405374 1 0.3816 0.77369 0.852 0.000 0.148
#> SRR1403565 1 0.5465 0.50195 0.712 0.000 0.288
#> SRR1332024 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1471633 2 0.9963 0.31016 0.312 0.376 0.312
#> SRR1325944 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR821573 3 0.7047 0.68849 0.204 0.084 0.712
#> SRR1435372 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR816517 2 0.5465 0.76937 0.000 0.712 0.288
#> SRR1324141 2 0.8071 0.68208 0.076 0.564 0.360
#> SRR1101612 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1089785 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR1077708 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1343720 3 0.5591 0.79807 0.304 0.000 0.696
#> SRR1477499 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1347236 3 0.6260 0.47602 0.448 0.000 0.552
#> SRR1326408 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1336529 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1440643 3 0.6460 -0.43844 0.004 0.440 0.556
#> SRR662354 1 0.0237 0.83557 0.996 0.000 0.004
#> SRR1310817 3 0.4840 0.71799 0.168 0.016 0.816
#> SRR1347389 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1384737 2 0.7945 0.64856 0.064 0.548 0.388
#> SRR1096339 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1345329 2 0.8107 0.72514 0.096 0.604 0.300
#> SRR1414771 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1309119 1 0.7637 0.48342 0.640 0.076 0.284
#> SRR1470438 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1343221 1 0.4504 0.70926 0.804 0.000 0.196
#> SRR1410847 1 0.3752 0.77626 0.856 0.000 0.144
#> SRR807949 3 0.5016 0.79186 0.240 0.000 0.760
#> SRR1442332 3 0.5291 0.80158 0.268 0.000 0.732
#> SRR815920 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1471524 3 0.3340 0.67603 0.120 0.000 0.880
#> SRR1477221 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1445046 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1319946 2 0.7260 0.74261 0.048 0.636 0.316
#> SRR1311599 1 0.3816 0.77369 0.852 0.000 0.148
#> SRR1323977 2 0.7997 0.72416 0.084 0.600 0.316
#> SRR1445132 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1337321 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1366390 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1343012 2 0.7807 0.59483 0.052 0.516 0.432
#> SRR1311958 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1388234 2 0.8132 0.72314 0.096 0.600 0.304
#> SRR1370384 1 0.0237 0.83557 0.996 0.000 0.004
#> SRR1321650 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1485117 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1384713 1 0.2878 0.80326 0.904 0.000 0.096
#> SRR816609 2 0.8132 0.72314 0.096 0.600 0.304
#> SRR1486239 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR1309638 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1356660 1 0.3816 0.77369 0.852 0.000 0.148
#> SRR1392883 2 0.0000 0.86918 0.000 1.000 0.000
#> SRR808130 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR816677 1 0.9919 -0.00102 0.372 0.272 0.356
#> SRR1455722 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1336029 1 0.7380 0.63065 0.684 0.088 0.228
#> SRR808452 1 0.0000 0.83605 1.000 0.000 0.000
#> SRR1352169 3 0.5529 0.80642 0.296 0.000 0.704
#> SRR1366707 3 0.4931 0.78678 0.232 0.000 0.768
#> SRR1328143 3 0.5058 0.79453 0.244 0.000 0.756
#> SRR1473567 2 0.0000 0.86918 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.1743 0.709 0.056 0.000 0.940 0.004
#> SRR1390119 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.0592 0.713 0.000 0.000 0.984 0.016
#> SRR1347278 3 0.1798 0.705 0.040 0.000 0.944 0.016
#> SRR1332904 2 0.2593 0.922 0.004 0.892 0.000 0.104
#> SRR1444179 1 0.0188 0.598 0.996 0.000 0.004 0.000
#> SRR1082685 1 0.0817 0.618 0.976 0.000 0.024 0.000
#> SRR1362287 1 0.4955 0.564 0.556 0.000 0.444 0.000
#> SRR1339007 1 0.4382 0.657 0.704 0.000 0.296 0.000
#> SRR1376557 2 0.1474 0.940 0.000 0.948 0.000 0.052
#> SRR1468700 2 0.2081 0.932 0.000 0.916 0.000 0.084
#> SRR1077455 1 0.7488 0.149 0.436 0.000 0.384 0.180
#> SRR1413978 1 0.4933 0.573 0.568 0.000 0.432 0.000
#> SRR1439896 1 0.4454 0.649 0.692 0.000 0.308 0.000
#> SRR1317963 2 0.6420 0.612 0.132 0.640 0.000 0.228
#> SRR1431865 1 0.4955 0.564 0.556 0.000 0.444 0.000
#> SRR1394253 1 0.4817 0.616 0.612 0.000 0.388 0.000
#> SRR1082664 3 0.3716 0.674 0.052 0.000 0.852 0.096
#> SRR1077968 1 0.4776 0.627 0.712 0.000 0.272 0.016
#> SRR1076393 4 0.6207 0.281 0.052 0.000 0.452 0.496
#> SRR1477476 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.0672 0.713 0.008 0.000 0.984 0.008
#> SRR1485042 1 0.3801 0.679 0.780 0.000 0.220 0.000
#> SRR1385453 4 0.5427 0.596 0.004 0.044 0.248 0.704
#> SRR1348074 4 0.7398 0.344 0.440 0.060 0.044 0.456
#> SRR813959 4 0.4428 0.569 0.004 0.000 0.276 0.720
#> SRR665442 4 0.5510 0.618 0.048 0.032 0.164 0.756
#> SRR1378068 3 0.0592 0.713 0.000 0.000 0.984 0.016
#> SRR1485237 1 0.7242 -0.244 0.528 0.056 0.044 0.372
#> SRR1350792 1 0.5026 0.638 0.672 0.000 0.312 0.016
#> SRR1326797 3 0.7550 0.219 0.332 0.000 0.464 0.204
#> SRR808994 3 0.1474 0.698 0.000 0.000 0.948 0.052
#> SRR1474041 3 0.6055 0.235 0.052 0.000 0.576 0.372
#> SRR1405641 3 0.1389 0.700 0.000 0.000 0.952 0.048
#> SRR1362245 3 0.1302 0.701 0.000 0.000 0.956 0.044
#> SRR1500194 1 0.3311 0.681 0.828 0.000 0.172 0.000
#> SRR1414876 2 0.0188 0.940 0.004 0.996 0.000 0.000
#> SRR1478523 4 0.6324 0.236 0.036 0.012 0.440 0.512
#> SRR1325161 3 0.6150 0.463 0.060 0.000 0.580 0.360
#> SRR1318026 1 0.7399 -0.376 0.452 0.060 0.044 0.444
#> SRR1343778 3 0.1978 0.707 0.068 0.000 0.928 0.004
#> SRR1441287 1 0.2081 0.655 0.916 0.000 0.084 0.000
#> SRR1430991 3 0.5144 0.580 0.052 0.000 0.732 0.216
#> SRR1499722 3 0.6201 0.593 0.124 0.000 0.664 0.212
#> SRR1351368 4 0.6334 0.582 0.016 0.060 0.284 0.640
#> SRR1441785 1 0.4941 0.571 0.564 0.000 0.436 0.000
#> SRR1096101 3 0.5512 -0.381 0.492 0.000 0.492 0.016
#> SRR808375 3 0.5519 0.543 0.052 0.000 0.684 0.264
#> SRR1452842 3 0.7210 -0.031 0.360 0.000 0.492 0.148
#> SRR1311709 1 0.1610 0.575 0.952 0.000 0.016 0.032
#> SRR1433352 3 0.3840 0.684 0.052 0.000 0.844 0.104
#> SRR1340241 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.5459 0.503 0.552 0.000 0.432 0.016
#> SRR1465172 3 0.7092 0.463 0.148 0.000 0.532 0.320
#> SRR1499284 3 0.7493 0.413 0.200 0.000 0.480 0.320
#> SRR1499607 2 0.3751 0.834 0.004 0.800 0.000 0.196
#> SRR812342 1 0.1297 0.612 0.964 0.000 0.020 0.016
#> SRR1405374 1 0.4925 0.572 0.572 0.000 0.428 0.000
#> SRR1403565 3 0.4697 0.106 0.356 0.000 0.644 0.000
#> SRR1332024 3 0.1389 0.700 0.000 0.000 0.952 0.048
#> SRR1471633 1 0.2450 0.533 0.912 0.000 0.016 0.072
#> SRR1325944 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR821573 4 0.7059 0.536 0.184 0.000 0.248 0.568
#> SRR1435372 1 0.1389 0.636 0.952 0.000 0.048 0.000
#> SRR1324184 2 0.1211 0.941 0.000 0.960 0.000 0.040
#> SRR816517 4 0.6083 0.610 0.004 0.144 0.156 0.696
#> SRR1324141 4 0.7273 0.477 0.360 0.060 0.044 0.536
#> SRR1101612 1 0.4431 0.652 0.696 0.000 0.304 0.000
#> SRR1356531 1 0.4406 0.654 0.700 0.000 0.300 0.000
#> SRR1089785 3 0.6130 0.147 0.052 0.000 0.548 0.400
#> SRR1077708 3 0.2385 0.708 0.052 0.000 0.920 0.028
#> SRR1343720 3 0.4713 0.626 0.052 0.000 0.776 0.172
#> SRR1477499 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1347236 3 0.7402 0.379 0.264 0.000 0.516 0.220
#> SRR1326408 1 0.0469 0.608 0.988 0.000 0.012 0.000
#> SRR1336529 3 0.0707 0.711 0.000 0.000 0.980 0.020
#> SRR1440643 4 0.5405 0.592 0.004 0.040 0.256 0.700
#> SRR662354 1 0.4699 0.638 0.676 0.000 0.320 0.004
#> SRR1310817 4 0.5955 0.445 0.056 0.000 0.328 0.616
#> SRR1347389 2 0.2081 0.932 0.000 0.916 0.000 0.084
#> SRR1353097 1 0.0921 0.621 0.972 0.000 0.028 0.000
#> SRR1384737 4 0.7403 0.493 0.260 0.056 0.084 0.600
#> SRR1096339 1 0.4500 0.643 0.684 0.000 0.316 0.000
#> SRR1345329 1 0.7248 -0.197 0.536 0.096 0.020 0.348
#> SRR1414771 3 0.1474 0.698 0.000 0.000 0.948 0.052
#> SRR1309119 1 0.1256 0.576 0.964 0.000 0.008 0.028
#> SRR1470438 3 0.1474 0.698 0.000 0.000 0.948 0.052
#> SRR1343221 3 0.5506 -0.332 0.472 0.000 0.512 0.016
#> SRR1410847 1 0.4804 0.589 0.616 0.000 0.384 0.000
#> SRR807949 3 0.5742 0.452 0.052 0.000 0.648 0.300
#> SRR1442332 3 0.4959 0.600 0.052 0.000 0.752 0.196
#> SRR815920 3 0.0376 0.714 0.004 0.000 0.992 0.004
#> SRR1471524 4 0.6204 0.293 0.052 0.000 0.448 0.500
#> SRR1477221 3 0.0336 0.714 0.000 0.000 0.992 0.008
#> SRR1445046 2 0.3205 0.908 0.024 0.872 0.000 0.104
#> SRR1331962 2 0.2149 0.932 0.000 0.912 0.000 0.088
#> SRR1319946 4 0.7299 0.485 0.348 0.064 0.044 0.544
#> SRR1311599 1 0.4933 0.582 0.568 0.000 0.432 0.000
#> SRR1323977 4 0.7262 0.482 0.356 0.060 0.044 0.540
#> SRR1445132 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.0524 0.715 0.008 0.000 0.988 0.004
#> SRR1366390 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR1343012 4 0.6295 0.617 0.116 0.056 0.100 0.728
#> SRR1311958 2 0.2676 0.924 0.012 0.896 0.000 0.092
#> SRR1388234 4 0.7249 0.482 0.352 0.060 0.044 0.544
#> SRR1370384 1 0.6136 0.497 0.632 0.000 0.288 0.080
#> SRR1321650 3 0.1305 0.701 0.004 0.000 0.960 0.036
#> SRR1485117 2 0.1211 0.941 0.000 0.960 0.000 0.040
#> SRR1384713 1 0.6779 0.392 0.560 0.000 0.324 0.116
#> SRR816609 1 0.6901 -0.172 0.564 0.040 0.044 0.352
#> SRR1486239 2 0.2530 0.924 0.004 0.896 0.000 0.100
#> SRR1309638 3 0.0804 0.716 0.012 0.000 0.980 0.008
#> SRR1356660 1 0.4941 0.571 0.564 0.000 0.436 0.000
#> SRR1392883 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> SRR808130 3 0.5343 0.552 0.052 0.000 0.708 0.240
#> SRR816677 1 0.3652 0.553 0.856 0.000 0.092 0.052
#> SRR1455722 1 0.3528 0.683 0.808 0.000 0.192 0.000
#> SRR1336029 1 0.4936 0.615 0.652 0.000 0.340 0.008
#> SRR808452 1 0.2408 0.667 0.896 0.000 0.104 0.000
#> SRR1352169 3 0.1305 0.715 0.036 0.000 0.960 0.004
#> SRR1366707 3 0.6207 -0.187 0.052 0.000 0.496 0.452
#> SRR1328143 3 0.5279 0.562 0.052 0.000 0.716 0.232
#> SRR1473567 2 0.1716 0.938 0.000 0.936 0.000 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.2286 0.6885 0.004 0.000 0.108 0.000 0.888
#> SRR1390119 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.3676 0.8622 0.004 0.000 0.760 0.004 0.232
#> SRR1347278 5 0.7586 -0.0653 0.340 0.000 0.256 0.044 0.360
#> SRR1332904 4 0.4378 0.5214 0.000 0.248 0.000 0.716 0.036
#> SRR1444179 1 0.0290 0.8755 0.992 0.000 0.000 0.008 0.000
#> SRR1082685 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.3333 0.8475 0.856 0.000 0.028 0.096 0.020
#> SRR1339007 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1376557 2 0.3741 0.6587 0.000 0.732 0.000 0.264 0.004
#> SRR1468700 4 0.3816 0.4566 0.000 0.304 0.000 0.696 0.000
#> SRR1077455 1 0.6654 0.5575 0.616 0.000 0.180 0.088 0.116
#> SRR1413978 1 0.2956 0.8523 0.872 0.000 0.012 0.096 0.020
#> SRR1439896 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.4098 0.5721 0.000 0.156 0.000 0.780 0.064
#> SRR1431865 1 0.3059 0.8513 0.868 0.000 0.016 0.096 0.020
#> SRR1394253 1 0.3333 0.8475 0.856 0.000 0.028 0.096 0.020
#> SRR1082664 5 0.1329 0.7192 0.004 0.000 0.032 0.008 0.956
#> SRR1077968 1 0.0880 0.8691 0.968 0.000 0.000 0.000 0.032
#> SRR1076393 5 0.2624 0.6709 0.000 0.000 0.116 0.012 0.872
#> SRR1477476 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 3 0.4141 0.8257 0.024 0.000 0.728 0.000 0.248
#> SRR1485042 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1385453 5 0.4880 0.4988 0.004 0.004 0.188 0.076 0.728
#> SRR1348074 4 0.3960 0.5513 0.140 0.004 0.000 0.800 0.056
#> SRR813959 5 0.2934 0.6797 0.004 0.004 0.036 0.076 0.880
#> SRR665442 3 0.6311 -0.2487 0.004 0.004 0.464 0.116 0.412
#> SRR1378068 3 0.3522 0.8731 0.004 0.000 0.780 0.004 0.212
#> SRR1485237 1 0.5100 0.5191 0.652 0.004 0.000 0.288 0.056
#> SRR1350792 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.6390 0.5435 0.076 0.000 0.208 0.088 0.628
#> SRR808994 3 0.3177 0.8747 0.000 0.000 0.792 0.000 0.208
#> SRR1474041 5 0.1341 0.7104 0.000 0.000 0.056 0.000 0.944
#> SRR1405641 3 0.3210 0.8745 0.000 0.000 0.788 0.000 0.212
#> SRR1362245 3 0.3177 0.8747 0.000 0.000 0.792 0.000 0.208
#> SRR1500194 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1414876 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 5 0.5162 0.4291 0.028 0.000 0.256 0.036 0.680
#> SRR1325161 5 0.4302 0.6086 0.000 0.000 0.208 0.048 0.744
#> SRR1318026 4 0.5547 0.0377 0.456 0.004 0.000 0.484 0.056
#> SRR1343778 5 0.3770 0.6786 0.044 0.000 0.092 0.028 0.836
#> SRR1441287 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.0609 0.7178 0.000 0.000 0.020 0.000 0.980
#> SRR1499722 5 0.4991 0.6043 0.024 0.000 0.212 0.048 0.716
#> SRR1351368 5 0.5456 0.2246 0.000 0.004 0.316 0.072 0.608
#> SRR1441785 1 0.3496 0.8439 0.848 0.000 0.036 0.096 0.020
#> SRR1096101 1 0.2775 0.8563 0.888 0.000 0.008 0.068 0.036
#> SRR808375 5 0.2708 0.6979 0.000 0.000 0.072 0.044 0.884
#> SRR1452842 1 0.7504 0.3619 0.504 0.000 0.200 0.088 0.208
#> SRR1311709 1 0.1197 0.8558 0.952 0.000 0.000 0.048 0.000
#> SRR1433352 5 0.2140 0.7120 0.024 0.000 0.012 0.040 0.924
#> SRR1340241 2 0.0162 0.8848 0.000 0.996 0.000 0.004 0.000
#> SRR1456754 1 0.3002 0.8498 0.876 0.000 0.008 0.068 0.048
#> SRR1465172 5 0.5514 0.5890 0.024 0.000 0.204 0.088 0.684
#> SRR1499284 5 0.5595 0.5888 0.028 0.000 0.204 0.088 0.680
#> SRR1499607 4 0.4385 0.5647 0.000 0.180 0.000 0.752 0.068
#> SRR812342 1 0.0693 0.8755 0.980 0.000 0.000 0.012 0.008
#> SRR1405374 1 0.2845 0.8531 0.876 0.000 0.008 0.096 0.020
#> SRR1403565 1 0.3521 0.8289 0.844 0.000 0.008 0.068 0.080
#> SRR1332024 3 0.3177 0.8747 0.000 0.000 0.792 0.000 0.208
#> SRR1471633 1 0.1410 0.8493 0.940 0.000 0.000 0.060 0.000
#> SRR1325944 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.3764 0.6782 0.000 0.000 0.156 0.044 0.800
#> SRR1435372 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1324184 2 0.3949 0.5515 0.000 0.668 0.000 0.332 0.000
#> SRR816517 5 0.7714 0.1755 0.000 0.188 0.080 0.304 0.428
#> SRR1324141 4 0.5588 0.1126 0.436 0.004 0.000 0.500 0.060
#> SRR1101612 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.2179 0.6688 0.000 0.000 0.112 0.000 0.888
#> SRR1077708 5 0.2358 0.6900 0.008 0.000 0.104 0.000 0.888
#> SRR1343720 5 0.2011 0.7132 0.020 0.000 0.008 0.044 0.928
#> SRR1477499 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.7015 0.4778 0.136 0.000 0.204 0.088 0.572
#> SRR1326408 1 0.0162 0.8772 0.996 0.000 0.000 0.000 0.004
#> SRR1336529 3 0.3366 0.8742 0.000 0.000 0.784 0.004 0.212
#> SRR1440643 5 0.4968 0.4898 0.004 0.004 0.192 0.080 0.720
#> SRR662354 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.1557 0.7174 0.000 0.000 0.008 0.052 0.940
#> SRR1347389 4 0.3752 0.4787 0.000 0.292 0.000 0.708 0.000
#> SRR1353097 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1384737 4 0.6289 0.0819 0.400 0.004 0.000 0.464 0.132
#> SRR1096339 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1345329 1 0.6105 0.0128 0.492 0.016 0.000 0.412 0.080
#> SRR1414771 3 0.3177 0.8747 0.000 0.000 0.792 0.000 0.208
#> SRR1309119 1 0.0609 0.8695 0.980 0.000 0.000 0.020 0.000
#> SRR1470438 3 0.3177 0.8747 0.000 0.000 0.792 0.000 0.208
#> SRR1343221 1 0.3320 0.8425 0.860 0.000 0.012 0.068 0.060
#> SRR1410847 1 0.2492 0.8598 0.900 0.000 0.008 0.072 0.020
#> SRR807949 5 0.1800 0.7153 0.000 0.000 0.020 0.048 0.932
#> SRR1442332 5 0.1041 0.7186 0.000 0.000 0.032 0.004 0.964
#> SRR815920 3 0.4402 0.6471 0.004 0.000 0.620 0.004 0.372
#> SRR1471524 5 0.4536 0.3321 0.004 0.000 0.324 0.016 0.656
#> SRR1477221 3 0.3305 0.8693 0.000 0.000 0.776 0.000 0.224
#> SRR1445046 4 0.3452 0.5450 0.000 0.244 0.000 0.756 0.000
#> SRR1331962 4 0.3480 0.5430 0.000 0.248 0.000 0.752 0.000
#> SRR1319946 4 0.2827 0.5742 0.044 0.020 0.000 0.892 0.044
#> SRR1311599 1 0.3496 0.8439 0.848 0.000 0.036 0.096 0.020
#> SRR1323977 4 0.5882 0.2613 0.376 0.004 0.000 0.528 0.092
#> SRR1445132 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.4353 0.7417 0.008 0.000 0.660 0.004 0.328
#> SRR1366390 2 0.0162 0.8861 0.000 0.996 0.000 0.004 0.000
#> SRR1343012 1 0.6417 0.3687 0.528 0.004 0.000 0.196 0.272
#> SRR1311958 4 0.3607 0.5459 0.000 0.244 0.000 0.752 0.004
#> SRR1388234 4 0.3018 0.5716 0.068 0.004 0.000 0.872 0.056
#> SRR1370384 1 0.3643 0.7850 0.848 0.000 0.072 0.044 0.036
#> SRR1321650 3 0.3336 0.8686 0.000 0.000 0.772 0.000 0.228
#> SRR1485117 2 0.3452 0.6813 0.000 0.756 0.000 0.244 0.000
#> SRR1384713 1 0.3731 0.7855 0.844 0.000 0.068 0.040 0.048
#> SRR816609 1 0.5403 0.4503 0.628 0.004 0.000 0.292 0.076
#> SRR1486239 4 0.3480 0.5430 0.000 0.248 0.000 0.752 0.000
#> SRR1309638 3 0.4597 0.5529 0.012 0.000 0.564 0.000 0.424
#> SRR1356660 1 0.3416 0.8457 0.852 0.000 0.032 0.096 0.020
#> SRR1392883 2 0.0000 0.8879 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.0794 0.7166 0.000 0.000 0.028 0.000 0.972
#> SRR816677 1 0.2544 0.8501 0.900 0.000 0.008 0.028 0.064
#> SRR1455722 1 0.0000 0.8776 1.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.3002 0.8498 0.872 0.000 0.004 0.076 0.048
#> SRR808452 1 0.0162 0.8773 0.996 0.000 0.000 0.000 0.004
#> SRR1352169 5 0.4902 0.4322 0.032 0.000 0.268 0.016 0.684
#> SRR1366707 5 0.4086 0.3963 0.000 0.000 0.284 0.012 0.704
#> SRR1328143 5 0.0703 0.7170 0.000 0.000 0.024 0.000 0.976
#> SRR1473567 2 0.3983 0.5361 0.000 0.660 0.000 0.340 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.1511 0.5899 0.004 0.000 0.044 0.012 0.940 0.000
#> SRR1390119 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR1436127 3 0.3161 0.7132 0.000 0.000 0.776 0.008 0.216 0.000
#> SRR1347278 5 0.8050 -0.2604 0.200 0.000 0.300 0.028 0.308 0.164
#> SRR1332904 4 0.2821 0.5482 0.000 0.152 0.000 0.832 0.000 0.016
#> SRR1444179 1 0.0363 0.7879 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1082685 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1362287 1 0.3810 0.5361 0.572 0.000 0.000 0.000 0.000 0.428
#> SRR1339007 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1376557 4 0.3765 0.1561 0.000 0.404 0.000 0.596 0.000 0.000
#> SRR1468700 4 0.2260 0.5606 0.000 0.140 0.000 0.860 0.000 0.000
#> SRR1077455 1 0.5538 0.3406 0.564 0.004 0.000 0.008 0.112 0.312
#> SRR1413978 1 0.3966 0.5195 0.552 0.000 0.004 0.000 0.000 0.444
#> SRR1439896 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317963 4 0.2019 0.5823 0.000 0.088 0.000 0.900 0.012 0.000
#> SRR1431865 1 0.3950 0.5291 0.564 0.000 0.004 0.000 0.000 0.432
#> SRR1394253 1 0.4032 0.5370 0.572 0.000 0.000 0.000 0.008 0.420
#> SRR1082664 5 0.0363 0.6130 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR1077968 1 0.0405 0.7901 0.988 0.000 0.000 0.008 0.004 0.000
#> SRR1076393 5 0.2572 0.4851 0.000 0.000 0.136 0.012 0.852 0.000
#> SRR1477476 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR1398057 3 0.3992 0.6703 0.016 0.000 0.708 0.012 0.264 0.000
#> SRR1485042 1 0.0146 0.7917 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1385453 5 0.6563 -0.4320 0.000 0.000 0.292 0.060 0.484 0.164
#> SRR1348074 4 0.6108 0.2383 0.200 0.000 0.000 0.528 0.024 0.248
#> SRR813959 5 0.4431 -0.1271 0.000 0.000 0.004 0.060 0.688 0.248
#> SRR665442 6 0.6888 0.0000 0.000 0.004 0.112 0.104 0.356 0.424
#> SRR1378068 3 0.2969 0.7118 0.000 0.000 0.776 0.000 0.224 0.000
#> SRR1485237 1 0.6500 -0.0976 0.412 0.000 0.000 0.316 0.024 0.248
#> SRR1350792 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.4867 0.0607 0.052 0.004 0.000 0.008 0.624 0.312
#> SRR808994 3 0.2411 0.6089 0.000 0.032 0.900 0.024 0.044 0.000
#> SRR1474041 5 0.0260 0.6145 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1405641 3 0.4006 0.7123 0.000 0.032 0.748 0.016 0.204 0.000
#> SRR1362245 3 0.3985 0.7093 0.000 0.032 0.764 0.024 0.180 0.000
#> SRR1500194 1 0.1765 0.7591 0.904 0.000 0.000 0.000 0.000 0.096
#> SRR1414876 2 0.1267 0.9638 0.000 0.940 0.000 0.060 0.000 0.000
#> SRR1478523 3 0.6137 -0.4310 0.000 0.000 0.436 0.052 0.420 0.092
#> SRR1325161 5 0.3713 0.1863 0.000 0.004 0.000 0.008 0.704 0.284
#> SRR1318026 4 0.6534 0.1610 0.336 0.000 0.000 0.388 0.024 0.252
#> SRR1343778 5 0.1887 0.5856 0.016 0.000 0.048 0.012 0.924 0.000
#> SRR1441287 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.0000 0.6146 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499722 5 0.3827 0.2302 0.020 0.004 0.000 0.000 0.720 0.256
#> SRR1351368 3 0.5487 -0.3095 0.000 0.000 0.540 0.052 0.368 0.040
#> SRR1441785 1 0.3950 0.5291 0.564 0.000 0.004 0.000 0.000 0.432
#> SRR1096101 1 0.2325 0.7620 0.892 0.000 0.000 0.000 0.048 0.060
#> SRR808375 5 0.0665 0.6097 0.000 0.004 0.000 0.008 0.980 0.008
#> SRR1452842 1 0.5804 0.2493 0.516 0.004 0.000 0.008 0.136 0.336
#> SRR1311709 1 0.0547 0.7847 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR1433352 5 0.0717 0.6077 0.016 0.000 0.000 0.000 0.976 0.008
#> SRR1340241 2 0.1267 0.9638 0.000 0.940 0.000 0.060 0.000 0.000
#> SRR1456754 1 0.2328 0.7603 0.892 0.000 0.000 0.000 0.052 0.056
#> SRR1465172 5 0.4315 0.1197 0.016 0.004 0.000 0.008 0.648 0.324
#> SRR1499284 5 0.4395 0.1123 0.020 0.004 0.000 0.008 0.644 0.324
#> SRR1499607 4 0.2568 0.5776 0.000 0.096 0.000 0.876 0.012 0.016
#> SRR812342 1 0.0458 0.7860 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR1405374 1 0.3672 0.5913 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1403565 1 0.4846 0.6128 0.708 0.000 0.008 0.008 0.140 0.136
#> SRR1332024 3 0.3851 0.7026 0.000 0.032 0.780 0.024 0.164 0.000
#> SRR1471633 1 0.0547 0.7847 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR1325944 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR1429450 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR821573 5 0.1410 0.5862 0.000 0.004 0.008 0.000 0.944 0.044
#> SRR1435372 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324184 4 0.3727 0.1890 0.000 0.388 0.000 0.612 0.000 0.000
#> SRR816517 3 0.8405 -0.4975 0.000 0.048 0.284 0.212 0.220 0.236
#> SRR1324141 4 0.6530 0.1663 0.332 0.000 0.000 0.392 0.024 0.252
#> SRR1101612 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.0458 0.6125 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1077708 5 0.1983 0.5781 0.012 0.000 0.060 0.012 0.916 0.000
#> SRR1343720 5 0.0717 0.6077 0.016 0.000 0.000 0.000 0.976 0.008
#> SRR1477499 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR1347236 5 0.5213 -0.0331 0.076 0.004 0.000 0.008 0.588 0.324
#> SRR1326408 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.2854 0.7148 0.000 0.000 0.792 0.000 0.208 0.000
#> SRR1440643 5 0.6973 -0.5108 0.000 0.000 0.304 0.064 0.388 0.244
#> SRR662354 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.0405 0.6134 0.000 0.000 0.000 0.008 0.988 0.004
#> SRR1347389 4 0.2527 0.5376 0.000 0.168 0.000 0.832 0.000 0.000
#> SRR1353097 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384737 4 0.6975 0.1175 0.324 0.000 0.000 0.340 0.056 0.280
#> SRR1096339 1 0.0260 0.7916 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1345329 1 0.6764 -0.2286 0.364 0.004 0.000 0.356 0.032 0.244
#> SRR1414771 3 0.2411 0.6089 0.000 0.032 0.900 0.024 0.044 0.000
#> SRR1309119 1 0.0603 0.7866 0.980 0.000 0.000 0.016 0.000 0.004
#> SRR1470438 3 0.2122 0.5891 0.000 0.032 0.916 0.024 0.028 0.000
#> SRR1343221 1 0.2488 0.7483 0.880 0.000 0.000 0.000 0.076 0.044
#> SRR1410847 1 0.1714 0.7716 0.908 0.000 0.000 0.000 0.000 0.092
#> SRR807949 5 0.0551 0.6121 0.000 0.004 0.000 0.008 0.984 0.004
#> SRR1442332 5 0.0000 0.6146 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR815920 3 0.3586 0.6743 0.000 0.000 0.720 0.012 0.268 0.000
#> SRR1471524 5 0.3921 0.2313 0.000 0.000 0.308 0.012 0.676 0.004
#> SRR1477221 3 0.3141 0.7142 0.000 0.000 0.788 0.012 0.200 0.000
#> SRR1445046 4 0.1814 0.5825 0.000 0.100 0.000 0.900 0.000 0.000
#> SRR1331962 4 0.1910 0.5801 0.000 0.108 0.000 0.892 0.000 0.000
#> SRR1319946 4 0.3989 0.3237 0.008 0.000 0.000 0.716 0.024 0.252
#> SRR1311599 1 0.3810 0.5361 0.572 0.000 0.000 0.000 0.000 0.428
#> SRR1323977 4 0.6522 0.1707 0.324 0.000 0.000 0.400 0.024 0.252
#> SRR1445132 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR1337321 3 0.3956 0.6265 0.000 0.000 0.684 0.024 0.292 0.000
#> SRR1366390 2 0.2378 0.8536 0.000 0.848 0.000 0.152 0.000 0.000
#> SRR1343012 1 0.7711 -0.3923 0.288 0.000 0.000 0.220 0.244 0.248
#> SRR1311958 4 0.1863 0.5819 0.000 0.104 0.000 0.896 0.000 0.000
#> SRR1388234 4 0.5984 0.2497 0.176 0.000 0.000 0.548 0.024 0.252
#> SRR1370384 1 0.3651 0.5767 0.736 0.004 0.000 0.008 0.004 0.248
#> SRR1321650 3 0.4405 0.7037 0.000 0.032 0.704 0.024 0.240 0.000
#> SRR1485117 4 0.3823 0.0919 0.000 0.436 0.000 0.564 0.000 0.000
#> SRR1384713 1 0.4457 0.5471 0.700 0.004 0.000 0.008 0.048 0.240
#> SRR816609 1 0.6620 -0.1191 0.404 0.000 0.000 0.316 0.032 0.248
#> SRR1486239 4 0.1863 0.5819 0.000 0.104 0.000 0.896 0.000 0.000
#> SRR1309638 5 0.4297 -0.1698 0.004 0.000 0.452 0.012 0.532 0.000
#> SRR1356660 1 0.3950 0.5291 0.564 0.000 0.004 0.000 0.000 0.432
#> SRR1392883 2 0.0865 0.9781 0.000 0.964 0.000 0.036 0.000 0.000
#> SRR808130 5 0.0000 0.6146 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR816677 1 0.3988 0.7209 0.788 0.000 0.000 0.068 0.024 0.120
#> SRR1455722 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 1 0.3634 0.7295 0.820 0.000 0.000 0.028 0.060 0.092
#> SRR808452 1 0.0000 0.7920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352169 5 0.4173 0.2740 0.012 0.000 0.268 0.024 0.696 0.000
#> SRR1366707 5 0.3518 0.3075 0.000 0.000 0.256 0.012 0.732 0.000
#> SRR1328143 5 0.0000 0.6146 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1473567 4 0.3706 0.2102 0.000 0.380 0.000 0.620 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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.886 0.917 0.968 0.3830 0.622 0.622
#> 3 3 0.962 0.923 0.965 0.6900 0.666 0.489
#> 4 4 0.742 0.779 0.871 0.1361 0.854 0.615
#> 5 5 0.686 0.662 0.815 0.0736 0.855 0.522
#> 6 6 0.694 0.626 0.798 0.0404 0.915 0.633
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
#> SRR1442087 1 0.0000 0.9715 1.000 0.000
#> SRR1390119 2 0.0000 0.9454 0.000 1.000
#> SRR1436127 1 0.0000 0.9715 1.000 0.000
#> SRR1347278 1 0.0000 0.9715 1.000 0.000
#> SRR1332904 2 0.0000 0.9454 0.000 1.000
#> SRR1444179 1 0.0000 0.9715 1.000 0.000
#> SRR1082685 1 0.0000 0.9715 1.000 0.000
#> SRR1362287 1 0.0000 0.9715 1.000 0.000
#> SRR1339007 1 0.0000 0.9715 1.000 0.000
#> SRR1376557 2 0.0000 0.9454 0.000 1.000
#> SRR1468700 2 0.0000 0.9454 0.000 1.000
#> SRR1077455 1 0.0000 0.9715 1.000 0.000
#> SRR1413978 1 0.0000 0.9715 1.000 0.000
#> SRR1439896 1 0.0000 0.9715 1.000 0.000
#> SRR1317963 2 0.0000 0.9454 0.000 1.000
#> SRR1431865 1 0.0000 0.9715 1.000 0.000
#> SRR1394253 1 0.0000 0.9715 1.000 0.000
#> SRR1082664 1 0.0000 0.9715 1.000 0.000
#> SRR1077968 1 0.0000 0.9715 1.000 0.000
#> SRR1076393 1 0.1184 0.9583 0.984 0.016
#> SRR1477476 2 0.0000 0.9454 0.000 1.000
#> SRR1398057 1 0.0000 0.9715 1.000 0.000
#> SRR1485042 1 0.0000 0.9715 1.000 0.000
#> SRR1385453 2 0.8763 0.5712 0.296 0.704
#> SRR1348074 2 0.8713 0.5891 0.292 0.708
#> SRR813959 2 0.9977 0.1056 0.472 0.528
#> SRR665442 2 0.0000 0.9454 0.000 1.000
#> SRR1378068 1 0.0000 0.9715 1.000 0.000
#> SRR1485237 1 0.0000 0.9715 1.000 0.000
#> SRR1350792 1 0.0000 0.9715 1.000 0.000
#> SRR1326797 1 0.0000 0.9715 1.000 0.000
#> SRR808994 1 0.0672 0.9652 0.992 0.008
#> SRR1474041 1 0.0000 0.9715 1.000 0.000
#> SRR1405641 1 0.0672 0.9652 0.992 0.008
#> SRR1362245 1 0.0000 0.9715 1.000 0.000
#> SRR1500194 1 0.0000 0.9715 1.000 0.000
#> SRR1414876 2 0.0000 0.9454 0.000 1.000
#> SRR1478523 1 0.6973 0.7641 0.812 0.188
#> SRR1325161 1 0.0000 0.9715 1.000 0.000
#> SRR1318026 1 0.5178 0.8571 0.884 0.116
#> SRR1343778 1 0.0000 0.9715 1.000 0.000
#> SRR1441287 1 0.0000 0.9715 1.000 0.000
#> SRR1430991 1 0.0000 0.9715 1.000 0.000
#> SRR1499722 1 0.0000 0.9715 1.000 0.000
#> SRR1351368 2 0.9996 0.0409 0.488 0.512
#> SRR1441785 1 0.0000 0.9715 1.000 0.000
#> SRR1096101 1 0.0000 0.9715 1.000 0.000
#> SRR808375 1 0.0000 0.9715 1.000 0.000
#> SRR1452842 1 0.0000 0.9715 1.000 0.000
#> SRR1311709 1 0.0000 0.9715 1.000 0.000
#> SRR1433352 1 0.0000 0.9715 1.000 0.000
#> SRR1340241 2 0.0000 0.9454 0.000 1.000
#> SRR1456754 1 0.0000 0.9715 1.000 0.000
#> SRR1465172 1 0.0000 0.9715 1.000 0.000
#> SRR1499284 1 0.0000 0.9715 1.000 0.000
#> SRR1499607 2 0.0000 0.9454 0.000 1.000
#> SRR812342 1 0.0000 0.9715 1.000 0.000
#> SRR1405374 1 0.0000 0.9715 1.000 0.000
#> SRR1403565 1 0.0000 0.9715 1.000 0.000
#> SRR1332024 1 0.0000 0.9715 1.000 0.000
#> SRR1471633 1 0.0000 0.9715 1.000 0.000
#> SRR1325944 2 0.0000 0.9454 0.000 1.000
#> SRR1429450 2 0.0000 0.9454 0.000 1.000
#> SRR821573 1 0.0376 0.9684 0.996 0.004
#> SRR1435372 1 0.0000 0.9715 1.000 0.000
#> SRR1324184 2 0.0000 0.9454 0.000 1.000
#> SRR816517 2 0.0000 0.9454 0.000 1.000
#> SRR1324141 1 0.7674 0.7096 0.776 0.224
#> SRR1101612 1 0.0000 0.9715 1.000 0.000
#> SRR1356531 1 0.0000 0.9715 1.000 0.000
#> SRR1089785 1 0.0000 0.9715 1.000 0.000
#> SRR1077708 1 0.0000 0.9715 1.000 0.000
#> SRR1343720 1 0.0000 0.9715 1.000 0.000
#> SRR1477499 2 0.0000 0.9454 0.000 1.000
#> SRR1347236 1 0.0000 0.9715 1.000 0.000
#> SRR1326408 1 0.0000 0.9715 1.000 0.000
#> SRR1336529 1 0.0000 0.9715 1.000 0.000
#> SRR1440643 1 0.9833 0.2490 0.576 0.424
#> SRR662354 1 0.0000 0.9715 1.000 0.000
#> SRR1310817 1 0.0000 0.9715 1.000 0.000
#> SRR1347389 2 0.0000 0.9454 0.000 1.000
#> SRR1353097 1 0.0000 0.9715 1.000 0.000
#> SRR1384737 1 0.8144 0.6620 0.748 0.252
#> SRR1096339 1 0.0000 0.9715 1.000 0.000
#> SRR1345329 1 0.1184 0.9579 0.984 0.016
#> SRR1414771 1 0.4939 0.8652 0.892 0.108
#> SRR1309119 1 0.0000 0.9715 1.000 0.000
#> SRR1470438 1 0.0376 0.9684 0.996 0.004
#> SRR1343221 1 0.0000 0.9715 1.000 0.000
#> SRR1410847 1 0.0000 0.9715 1.000 0.000
#> SRR807949 1 0.0000 0.9715 1.000 0.000
#> SRR1442332 1 0.0000 0.9715 1.000 0.000
#> SRR815920 1 0.0000 0.9715 1.000 0.000
#> SRR1471524 1 0.7219 0.7468 0.800 0.200
#> SRR1477221 1 0.0000 0.9715 1.000 0.000
#> SRR1445046 2 0.0000 0.9454 0.000 1.000
#> SRR1331962 2 0.0000 0.9454 0.000 1.000
#> SRR1319946 2 0.0000 0.9454 0.000 1.000
#> SRR1311599 1 0.0000 0.9715 1.000 0.000
#> SRR1323977 1 1.0000 -0.0340 0.500 0.500
#> SRR1445132 2 0.0000 0.9454 0.000 1.000
#> SRR1337321 1 0.0000 0.9715 1.000 0.000
#> SRR1366390 2 0.0000 0.9454 0.000 1.000
#> SRR1343012 1 0.6801 0.7751 0.820 0.180
#> SRR1311958 2 0.0000 0.9454 0.000 1.000
#> SRR1388234 2 0.0000 0.9454 0.000 1.000
#> SRR1370384 1 0.0000 0.9715 1.000 0.000
#> SRR1321650 1 0.0000 0.9715 1.000 0.000
#> SRR1485117 2 0.0000 0.9454 0.000 1.000
#> SRR1384713 1 0.0000 0.9715 1.000 0.000
#> SRR816609 1 0.0000 0.9715 1.000 0.000
#> SRR1486239 2 0.0000 0.9454 0.000 1.000
#> SRR1309638 1 0.0000 0.9715 1.000 0.000
#> SRR1356660 1 0.0000 0.9715 1.000 0.000
#> SRR1392883 2 0.0000 0.9454 0.000 1.000
#> SRR808130 1 0.0000 0.9715 1.000 0.000
#> SRR816677 1 0.0000 0.9715 1.000 0.000
#> SRR1455722 1 0.0000 0.9715 1.000 0.000
#> SRR1336029 1 0.0000 0.9715 1.000 0.000
#> SRR808452 1 0.0000 0.9715 1.000 0.000
#> SRR1352169 1 0.0000 0.9715 1.000 0.000
#> SRR1366707 1 0.7219 0.7468 0.800 0.200
#> SRR1328143 1 0.0000 0.9715 1.000 0.000
#> SRR1473567 2 0.0000 0.9454 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.1031 0.9528 0.024 0.000 0.976
#> SRR1390119 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1436127 3 0.0237 0.9496 0.004 0.000 0.996
#> SRR1347278 3 0.1753 0.9448 0.048 0.000 0.952
#> SRR1332904 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1444179 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1082685 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1362287 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1413978 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1439896 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1431865 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1394253 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1082664 3 0.1643 0.9469 0.044 0.000 0.956
#> SRR1077968 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1477476 2 0.0892 0.9566 0.000 0.980 0.020
#> SRR1398057 3 0.1411 0.9504 0.036 0.000 0.964
#> SRR1485042 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1385453 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1348074 1 0.2878 0.8808 0.904 0.096 0.000
#> SRR813959 3 0.2903 0.9228 0.028 0.048 0.924
#> SRR665442 2 0.1964 0.9173 0.056 0.944 0.000
#> SRR1378068 3 0.0237 0.9496 0.004 0.000 0.996
#> SRR1485237 1 0.1163 0.9462 0.972 0.028 0.000
#> SRR1350792 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR808994 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1474041 3 0.1289 0.9515 0.032 0.000 0.968
#> SRR1405641 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1362245 3 0.0592 0.9515 0.012 0.000 0.988
#> SRR1500194 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1414876 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1478523 3 0.0237 0.9499 0.004 0.000 0.996
#> SRR1325161 3 0.2796 0.9044 0.092 0.000 0.908
#> SRR1318026 1 0.2066 0.9180 0.940 0.060 0.000
#> SRR1343778 3 0.1529 0.9489 0.040 0.000 0.960
#> SRR1441287 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1430991 3 0.1529 0.9489 0.040 0.000 0.960
#> SRR1499722 3 0.6062 0.4233 0.384 0.000 0.616
#> SRR1351368 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1441785 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR808375 3 0.1860 0.9415 0.052 0.000 0.948
#> SRR1452842 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1311709 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1433352 3 0.2165 0.9316 0.064 0.000 0.936
#> SRR1340241 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1456754 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1465172 1 0.4452 0.7467 0.808 0.000 0.192
#> SRR1499284 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1499607 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1332024 3 0.0237 0.9496 0.004 0.000 0.996
#> SRR1471633 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1325944 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1429450 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR821573 3 0.5760 0.5514 0.328 0.000 0.672
#> SRR1435372 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR816517 3 0.5706 0.4927 0.000 0.320 0.680
#> SRR1324141 1 0.5529 0.5845 0.704 0.296 0.000
#> SRR1101612 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1089785 3 0.1031 0.9528 0.024 0.000 0.976
#> SRR1077708 3 0.1753 0.9448 0.048 0.000 0.952
#> SRR1343720 3 0.2261 0.9281 0.068 0.000 0.932
#> SRR1477499 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1347236 1 0.3038 0.8623 0.896 0.000 0.104
#> SRR1326408 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1336529 3 0.0237 0.9496 0.004 0.000 0.996
#> SRR1440643 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR662354 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1310817 3 0.1529 0.9481 0.040 0.000 0.960
#> SRR1347389 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1353097 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1384737 1 0.6274 0.1651 0.544 0.456 0.000
#> SRR1096339 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1345329 1 0.1753 0.9273 0.952 0.048 0.000
#> SRR1414771 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1309119 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR1470438 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1343221 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR807949 3 0.0892 0.9528 0.020 0.000 0.980
#> SRR1442332 3 0.1529 0.9489 0.040 0.000 0.960
#> SRR815920 3 0.0237 0.9496 0.004 0.000 0.996
#> SRR1471524 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1477221 3 0.1031 0.9528 0.024 0.000 0.976
#> SRR1445046 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1319946 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1311599 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1323977 2 0.6291 0.0686 0.468 0.532 0.000
#> SRR1445132 2 0.0424 0.9651 0.000 0.992 0.008
#> SRR1337321 3 0.1289 0.9516 0.032 0.000 0.968
#> SRR1366390 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR1343012 1 0.5016 0.6846 0.760 0.240 0.000
#> SRR1311958 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1388234 2 0.4291 0.7618 0.180 0.820 0.000
#> SRR1370384 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1321650 3 0.0892 0.9528 0.020 0.000 0.980
#> SRR1485117 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR816609 1 0.0424 0.9631 0.992 0.008 0.000
#> SRR1486239 2 0.0000 0.9676 0.000 1.000 0.000
#> SRR1309638 3 0.1529 0.9493 0.040 0.000 0.960
#> SRR1356660 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1392883 2 0.0237 0.9673 0.000 0.996 0.004
#> SRR808130 3 0.1031 0.9528 0.024 0.000 0.976
#> SRR816677 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1336029 1 0.0237 0.9661 0.996 0.004 0.000
#> SRR808452 1 0.0000 0.9678 1.000 0.000 0.000
#> SRR1352169 3 0.1411 0.9504 0.036 0.000 0.964
#> SRR1366707 3 0.0000 0.9482 0.000 0.000 1.000
#> SRR1328143 3 0.0892 0.9528 0.020 0.000 0.980
#> SRR1473567 2 0.0000 0.9676 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.4977 0.5158 0.000 0.000 0.540 0.460
#> SRR1390119 2 0.0469 0.9278 0.000 0.988 0.012 0.000
#> SRR1436127 3 0.4103 0.8460 0.000 0.000 0.744 0.256
#> SRR1347278 3 0.4920 0.8332 0.052 0.000 0.756 0.192
#> SRR1332904 2 0.0188 0.9293 0.000 0.996 0.004 0.000
#> SRR1444179 1 0.1109 0.8969 0.968 0.004 0.000 0.028
#> SRR1082685 1 0.1022 0.8962 0.968 0.000 0.000 0.032
#> SRR1362287 1 0.2814 0.8602 0.868 0.000 0.132 0.000
#> SRR1339007 1 0.0927 0.9001 0.976 0.000 0.008 0.016
#> SRR1376557 2 0.0188 0.9293 0.000 0.996 0.004 0.000
#> SRR1468700 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1077455 4 0.4382 0.5241 0.296 0.000 0.000 0.704
#> SRR1413978 1 0.3172 0.8414 0.840 0.000 0.160 0.000
#> SRR1439896 1 0.0657 0.9001 0.984 0.000 0.012 0.004
#> SRR1317963 2 0.0592 0.9199 0.016 0.984 0.000 0.000
#> SRR1431865 1 0.2921 0.8549 0.860 0.000 0.140 0.000
#> SRR1394253 1 0.2704 0.8646 0.876 0.000 0.124 0.000
#> SRR1082664 4 0.1389 0.7341 0.000 0.000 0.048 0.952
#> SRR1077968 1 0.3837 0.7187 0.776 0.000 0.000 0.224
#> SRR1076393 4 0.4830 -0.0594 0.000 0.000 0.392 0.608
#> SRR1477476 2 0.0817 0.9217 0.000 0.976 0.024 0.000
#> SRR1398057 3 0.3763 0.8605 0.024 0.000 0.832 0.144
#> SRR1485042 1 0.1635 0.8961 0.948 0.000 0.044 0.008
#> SRR1385453 4 0.5995 0.3989 0.000 0.096 0.232 0.672
#> SRR1348074 1 0.4164 0.6662 0.736 0.264 0.000 0.000
#> SRR813959 4 0.4353 0.5633 0.000 0.232 0.012 0.756
#> SRR665442 2 0.5165 -0.0375 0.484 0.512 0.004 0.000
#> SRR1378068 3 0.4188 0.8533 0.004 0.000 0.752 0.244
#> SRR1485237 1 0.3107 0.8567 0.884 0.080 0.000 0.036
#> SRR1350792 1 0.1211 0.8937 0.960 0.000 0.000 0.040
#> SRR1326797 4 0.2868 0.6898 0.136 0.000 0.000 0.864
#> SRR808994 3 0.2654 0.8457 0.004 0.000 0.888 0.108
#> SRR1474041 4 0.2589 0.6859 0.000 0.000 0.116 0.884
#> SRR1405641 3 0.3448 0.8629 0.004 0.000 0.828 0.168
#> SRR1362245 3 0.0895 0.7606 0.020 0.000 0.976 0.004
#> SRR1500194 1 0.2408 0.8744 0.896 0.000 0.104 0.000
#> SRR1414876 2 0.0336 0.9287 0.000 0.992 0.008 0.000
#> SRR1478523 3 0.2921 0.8558 0.000 0.000 0.860 0.140
#> SRR1325161 4 0.0592 0.7424 0.016 0.000 0.000 0.984
#> SRR1318026 1 0.5367 0.5557 0.664 0.304 0.000 0.032
#> SRR1343778 3 0.4500 0.7912 0.000 0.000 0.684 0.316
#> SRR1441287 1 0.0779 0.8997 0.980 0.000 0.004 0.016
#> SRR1430991 4 0.1474 0.7336 0.000 0.000 0.052 0.948
#> SRR1499722 4 0.1211 0.7370 0.040 0.000 0.000 0.960
#> SRR1351368 3 0.3790 0.8547 0.000 0.016 0.820 0.164
#> SRR1441785 1 0.3123 0.8443 0.844 0.000 0.156 0.000
#> SRR1096101 1 0.2466 0.8963 0.916 0.000 0.056 0.028
#> SRR808375 4 0.0469 0.7425 0.000 0.000 0.012 0.988
#> SRR1452842 4 0.4804 0.3363 0.384 0.000 0.000 0.616
#> SRR1311709 1 0.1004 0.8976 0.972 0.004 0.000 0.024
#> SRR1433352 4 0.2011 0.7195 0.000 0.000 0.080 0.920
#> SRR1340241 2 0.0592 0.9263 0.000 0.984 0.016 0.000
#> SRR1456754 1 0.1867 0.8805 0.928 0.000 0.000 0.072
#> SRR1465172 4 0.2345 0.7110 0.100 0.000 0.000 0.900
#> SRR1499284 4 0.3569 0.6429 0.196 0.000 0.000 0.804
#> SRR1499607 2 0.0707 0.9235 0.000 0.980 0.020 0.000
#> SRR812342 1 0.1118 0.8949 0.964 0.000 0.000 0.036
#> SRR1405374 1 0.2281 0.8778 0.904 0.000 0.096 0.000
#> SRR1403565 1 0.2589 0.8686 0.884 0.000 0.116 0.000
#> SRR1332024 3 0.2089 0.7605 0.048 0.000 0.932 0.020
#> SRR1471633 1 0.1042 0.8978 0.972 0.008 0.000 0.020
#> SRR1325944 2 0.0336 0.9287 0.000 0.992 0.008 0.000
#> SRR1429450 2 0.0336 0.9287 0.000 0.992 0.008 0.000
#> SRR821573 4 0.0817 0.7413 0.024 0.000 0.000 0.976
#> SRR1435372 1 0.2530 0.8505 0.888 0.000 0.000 0.112
#> SRR1324184 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR816517 2 0.5964 0.2052 0.000 0.536 0.424 0.040
#> SRR1324141 4 0.6395 0.0559 0.064 0.460 0.000 0.476
#> SRR1101612 1 0.0188 0.8999 0.996 0.000 0.004 0.000
#> SRR1356531 1 0.0707 0.8984 0.980 0.000 0.000 0.020
#> SRR1089785 4 0.2408 0.6993 0.000 0.000 0.104 0.896
#> SRR1077708 3 0.4746 0.7137 0.000 0.000 0.632 0.368
#> SRR1343720 4 0.1022 0.7414 0.000 0.000 0.032 0.968
#> SRR1477499 2 0.0469 0.9278 0.000 0.988 0.012 0.000
#> SRR1347236 4 0.2704 0.6977 0.124 0.000 0.000 0.876
#> SRR1326408 1 0.2530 0.8502 0.888 0.000 0.000 0.112
#> SRR1336529 3 0.3791 0.8638 0.004 0.000 0.796 0.200
#> SRR1440643 4 0.7830 -0.2481 0.000 0.260 0.356 0.384
#> SRR662354 1 0.1004 0.8990 0.972 0.000 0.004 0.024
#> SRR1310817 4 0.0707 0.7419 0.000 0.000 0.020 0.980
#> SRR1347389 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1353097 1 0.1940 0.8771 0.924 0.000 0.000 0.076
#> SRR1384737 1 0.4855 0.4939 0.644 0.352 0.004 0.000
#> SRR1096339 1 0.1940 0.8851 0.924 0.000 0.076 0.000
#> SRR1345329 1 0.1022 0.8968 0.968 0.032 0.000 0.000
#> SRR1414771 3 0.2048 0.8236 0.008 0.000 0.928 0.064
#> SRR1309119 1 0.1339 0.8998 0.964 0.004 0.024 0.008
#> SRR1470438 3 0.2198 0.8285 0.008 0.000 0.920 0.072
#> SRR1343221 1 0.2921 0.8288 0.860 0.000 0.000 0.140
#> SRR1410847 1 0.2266 0.8840 0.912 0.000 0.084 0.004
#> SRR807949 4 0.1211 0.7371 0.000 0.000 0.040 0.960
#> SRR1442332 4 0.2469 0.6935 0.000 0.000 0.108 0.892
#> SRR815920 3 0.4008 0.8523 0.000 0.000 0.756 0.244
#> SRR1471524 3 0.4103 0.8467 0.000 0.000 0.744 0.256
#> SRR1477221 3 0.3697 0.8005 0.048 0.000 0.852 0.100
#> SRR1445046 2 0.1557 0.8781 0.056 0.944 0.000 0.000
#> SRR1331962 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.2704 0.8646 0.876 0.000 0.124 0.000
#> SRR1323977 2 0.2335 0.8619 0.020 0.920 0.000 0.060
#> SRR1445132 2 0.0469 0.9278 0.000 0.988 0.012 0.000
#> SRR1337321 3 0.2867 0.8195 0.012 0.000 0.884 0.104
#> SRR1366390 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1343012 2 0.7511 0.1727 0.336 0.468 0.000 0.196
#> SRR1311958 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0707 0.9166 0.020 0.980 0.000 0.000
#> SRR1370384 1 0.4040 0.6868 0.752 0.000 0.000 0.248
#> SRR1321650 3 0.4502 0.8564 0.016 0.000 0.748 0.236
#> SRR1485117 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1384713 4 0.4972 0.1217 0.456 0.000 0.000 0.544
#> SRR816609 1 0.2546 0.8743 0.912 0.060 0.000 0.028
#> SRR1486239 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR1309638 3 0.3638 0.8485 0.032 0.000 0.848 0.120
#> SRR1356660 1 0.3074 0.8472 0.848 0.000 0.152 0.000
#> SRR1392883 2 0.0000 0.9297 0.000 1.000 0.000 0.000
#> SRR808130 4 0.1716 0.7274 0.000 0.000 0.064 0.936
#> SRR816677 1 0.2589 0.8696 0.884 0.000 0.116 0.000
#> SRR1455722 1 0.0921 0.8970 0.972 0.000 0.000 0.028
#> SRR1336029 1 0.0592 0.8993 0.984 0.000 0.016 0.000
#> SRR808452 1 0.1867 0.8789 0.928 0.000 0.000 0.072
#> SRR1352169 3 0.4222 0.8358 0.000 0.000 0.728 0.272
#> SRR1366707 3 0.4164 0.8409 0.000 0.000 0.736 0.264
#> SRR1328143 4 0.2704 0.6757 0.000 0.000 0.124 0.876
#> SRR1473567 2 0.0000 0.9297 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
#> SRR1442087 5 0.4264 0.1857 0.004 0.000 0.376 0.000 0.620
#> SRR1390119 2 0.0671 0.9477 0.000 0.980 0.016 0.004 0.000
#> SRR1436127 3 0.4674 0.4769 0.000 0.000 0.568 0.016 0.416
#> SRR1347278 4 0.6222 0.0383 0.000 0.000 0.236 0.548 0.216
#> SRR1332904 2 0.0609 0.9476 0.000 0.980 0.020 0.000 0.000
#> SRR1444179 1 0.2674 0.7373 0.856 0.000 0.004 0.140 0.000
#> SRR1082685 1 0.4616 0.6237 0.676 0.000 0.000 0.288 0.036
#> SRR1362287 4 0.1992 0.7172 0.044 0.000 0.032 0.924 0.000
#> SRR1339007 1 0.2125 0.7503 0.920 0.000 0.004 0.052 0.024
#> SRR1376557 2 0.0162 0.9495 0.000 0.996 0.004 0.000 0.000
#> SRR1468700 2 0.0740 0.9480 0.004 0.980 0.008 0.008 0.000
#> SRR1077455 1 0.4165 0.5719 0.672 0.000 0.000 0.008 0.320
#> SRR1413978 1 0.6372 -0.0725 0.456 0.000 0.168 0.376 0.000
#> SRR1439896 4 0.3661 0.5302 0.276 0.000 0.000 0.724 0.000
#> SRR1317963 2 0.1041 0.9412 0.032 0.964 0.004 0.000 0.000
#> SRR1431865 4 0.2124 0.7189 0.056 0.000 0.028 0.916 0.000
#> SRR1394253 4 0.1894 0.7211 0.072 0.000 0.008 0.920 0.000
#> SRR1082664 5 0.5579 -0.1662 0.072 0.000 0.420 0.000 0.508
#> SRR1077968 1 0.3586 0.7054 0.792 0.000 0.000 0.020 0.188
#> SRR1076393 3 0.4062 0.7072 0.040 0.000 0.764 0.000 0.196
#> SRR1477476 2 0.1205 0.9380 0.000 0.956 0.040 0.004 0.000
#> SRR1398057 3 0.5880 0.5355 0.000 0.000 0.568 0.304 0.128
#> SRR1485042 1 0.3612 0.6437 0.732 0.000 0.000 0.268 0.000
#> SRR1385453 5 0.3399 0.6434 0.000 0.012 0.172 0.004 0.812
#> SRR1348074 1 0.5734 0.4933 0.648 0.248 0.028 0.076 0.000
#> SRR813959 5 0.3586 0.5326 0.000 0.264 0.000 0.000 0.736
#> SRR665442 4 0.5236 0.1912 0.048 0.408 0.000 0.544 0.000
#> SRR1378068 3 0.3752 0.6747 0.000 0.000 0.708 0.000 0.292
#> SRR1485237 1 0.1869 0.7486 0.936 0.016 0.000 0.036 0.012
#> SRR1350792 4 0.5473 -0.0624 0.416 0.000 0.000 0.520 0.064
#> SRR1326797 5 0.1908 0.7305 0.092 0.000 0.000 0.000 0.908
#> SRR808994 3 0.1267 0.7214 0.004 0.000 0.960 0.024 0.012
#> SRR1474041 5 0.0794 0.7937 0.000 0.000 0.028 0.000 0.972
#> SRR1405641 3 0.2513 0.7446 0.000 0.000 0.876 0.008 0.116
#> SRR1362245 3 0.3861 0.5269 0.008 0.000 0.728 0.264 0.000
#> SRR1500194 4 0.1908 0.7170 0.092 0.000 0.000 0.908 0.000
#> SRR1414876 2 0.0000 0.9493 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 3 0.3937 0.6552 0.000 0.004 0.736 0.008 0.252
#> SRR1325161 5 0.1043 0.7789 0.040 0.000 0.000 0.000 0.960
#> SRR1318026 1 0.5608 0.5135 0.668 0.228 0.028 0.076 0.000
#> SRR1343778 3 0.4356 0.6174 0.012 0.000 0.648 0.000 0.340
#> SRR1441287 1 0.4599 0.4650 0.600 0.000 0.000 0.384 0.016
#> SRR1430991 5 0.0703 0.7946 0.000 0.000 0.024 0.000 0.976
#> SRR1499722 5 0.0963 0.7786 0.036 0.000 0.000 0.000 0.964
#> SRR1351368 3 0.3702 0.7159 0.040 0.012 0.848 0.016 0.084
#> SRR1441785 4 0.1997 0.7157 0.040 0.000 0.036 0.924 0.000
#> SRR1096101 1 0.5473 0.3693 0.520 0.000 0.000 0.416 0.064
#> SRR808375 5 0.0162 0.7931 0.004 0.000 0.000 0.000 0.996
#> SRR1452842 1 0.3421 0.6834 0.788 0.000 0.000 0.008 0.204
#> SRR1311709 1 0.2848 0.7326 0.840 0.000 0.000 0.156 0.004
#> SRR1433352 5 0.0703 0.7955 0.000 0.000 0.024 0.000 0.976
#> SRR1340241 2 0.0771 0.9465 0.000 0.976 0.020 0.004 0.000
#> SRR1456754 1 0.2983 0.7416 0.864 0.000 0.000 0.040 0.096
#> SRR1465172 5 0.1908 0.7370 0.092 0.000 0.000 0.000 0.908
#> SRR1499284 5 0.4375 0.0849 0.420 0.000 0.000 0.004 0.576
#> SRR1499607 2 0.4693 0.8060 0.132 0.768 0.076 0.024 0.000
#> SRR812342 1 0.5080 0.5820 0.628 0.000 0.000 0.316 0.056
#> SRR1405374 4 0.1908 0.7171 0.092 0.000 0.000 0.908 0.000
#> SRR1403565 4 0.2189 0.7209 0.084 0.000 0.012 0.904 0.000
#> SRR1332024 4 0.5236 -0.1808 0.000 0.000 0.464 0.492 0.044
#> SRR1471633 1 0.2629 0.7382 0.860 0.000 0.004 0.136 0.000
#> SRR1325944 2 0.0000 0.9493 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0451 0.9492 0.000 0.988 0.008 0.004 0.000
#> SRR821573 5 0.0609 0.7885 0.020 0.000 0.000 0.000 0.980
#> SRR1435372 1 0.4444 0.7272 0.760 0.000 0.000 0.136 0.104
#> SRR1324184 2 0.1830 0.9312 0.004 0.932 0.012 0.052 0.000
#> SRR816517 3 0.4316 0.5835 0.000 0.208 0.748 0.004 0.040
#> SRR1324141 1 0.5631 0.5747 0.724 0.148 0.028 0.072 0.028
#> SRR1101612 4 0.3395 0.5914 0.236 0.000 0.000 0.764 0.000
#> SRR1356531 1 0.3284 0.7383 0.828 0.000 0.000 0.148 0.024
#> SRR1089785 5 0.1197 0.7872 0.000 0.000 0.048 0.000 0.952
#> SRR1077708 5 0.4443 -0.2394 0.004 0.000 0.472 0.000 0.524
#> SRR1343720 5 0.0451 0.7933 0.004 0.000 0.008 0.000 0.988
#> SRR1477499 2 0.0771 0.9465 0.000 0.976 0.020 0.004 0.000
#> SRR1347236 5 0.1410 0.7605 0.060 0.000 0.000 0.000 0.940
#> SRR1326408 1 0.1764 0.7440 0.928 0.000 0.000 0.008 0.064
#> SRR1336529 3 0.3171 0.7386 0.000 0.000 0.816 0.008 0.176
#> SRR1440643 5 0.5603 0.4838 0.000 0.096 0.192 0.028 0.684
#> SRR662354 4 0.3882 0.5893 0.224 0.000 0.000 0.756 0.020
#> SRR1310817 5 0.0992 0.7915 0.008 0.000 0.024 0.000 0.968
#> SRR1347389 2 0.3536 0.8818 0.052 0.852 0.024 0.072 0.000
#> SRR1353097 1 0.3779 0.7372 0.804 0.000 0.000 0.144 0.052
#> SRR1384737 1 0.4237 0.6396 0.812 0.040 0.064 0.084 0.000
#> SRR1096339 4 0.3508 0.5641 0.252 0.000 0.000 0.748 0.000
#> SRR1345329 1 0.2971 0.7246 0.880 0.016 0.032 0.072 0.000
#> SRR1414771 3 0.1836 0.7303 0.000 0.000 0.932 0.036 0.032
#> SRR1309119 4 0.4907 -0.1958 0.488 0.000 0.024 0.488 0.000
#> SRR1470438 3 0.1525 0.7210 0.004 0.000 0.948 0.036 0.012
#> SRR1343221 1 0.4849 0.7101 0.724 0.000 0.000 0.140 0.136
#> SRR1410847 4 0.2813 0.6670 0.168 0.000 0.000 0.832 0.000
#> SRR807949 5 0.0609 0.7950 0.000 0.000 0.020 0.000 0.980
#> SRR1442332 5 0.1571 0.7757 0.004 0.000 0.060 0.000 0.936
#> SRR815920 3 0.3707 0.6819 0.000 0.000 0.716 0.000 0.284
#> SRR1471524 3 0.4182 0.5250 0.000 0.000 0.600 0.000 0.400
#> SRR1477221 4 0.4891 0.2499 0.000 0.000 0.316 0.640 0.044
#> SRR1445046 2 0.4928 0.7729 0.152 0.748 0.028 0.072 0.000
#> SRR1331962 2 0.1173 0.9438 0.012 0.964 0.004 0.020 0.000
#> SRR1319946 2 0.0880 0.9366 0.000 0.968 0.000 0.000 0.032
#> SRR1311599 4 0.1732 0.7201 0.080 0.000 0.000 0.920 0.000
#> SRR1323977 2 0.2831 0.8985 0.016 0.892 0.004 0.024 0.064
#> SRR1445132 2 0.0451 0.9492 0.000 0.988 0.008 0.004 0.000
#> SRR1337321 4 0.4730 0.3740 0.000 0.000 0.260 0.688 0.052
#> SRR1366390 2 0.2395 0.9170 0.008 0.904 0.016 0.072 0.000
#> SRR1343012 1 0.3160 0.6669 0.872 0.024 0.032 0.072 0.000
#> SRR1311958 2 0.2347 0.9218 0.016 0.912 0.016 0.056 0.000
#> SRR1388234 2 0.2439 0.8677 0.120 0.876 0.000 0.004 0.000
#> SRR1370384 1 0.3151 0.7250 0.836 0.000 0.000 0.020 0.144
#> SRR1321650 3 0.5215 0.6761 0.000 0.000 0.656 0.088 0.256
#> SRR1485117 2 0.0162 0.9493 0.004 0.996 0.000 0.000 0.000
#> SRR1384713 1 0.3132 0.7058 0.820 0.000 0.000 0.008 0.172
#> SRR816609 1 0.1891 0.7483 0.936 0.016 0.000 0.032 0.016
#> SRR1486239 2 0.0000 0.9493 0.000 1.000 0.000 0.000 0.000
#> SRR1309638 3 0.4450 0.6038 0.236 0.000 0.728 0.016 0.020
#> SRR1356660 4 0.2473 0.7175 0.072 0.000 0.032 0.896 0.000
#> SRR1392883 2 0.0000 0.9493 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.0880 0.7923 0.000 0.000 0.032 0.000 0.968
#> SRR816677 1 0.3734 0.7039 0.812 0.000 0.060 0.128 0.000
#> SRR1455722 1 0.4824 0.4827 0.596 0.000 0.000 0.376 0.028
#> SRR1336029 1 0.4028 0.6975 0.776 0.000 0.048 0.176 0.000
#> SRR808452 1 0.5422 0.5941 0.616 0.000 0.000 0.296 0.088
#> SRR1352169 5 0.5235 0.2317 0.000 0.000 0.312 0.068 0.620
#> SRR1366707 3 0.4066 0.6440 0.004 0.000 0.672 0.000 0.324
#> SRR1328143 5 0.1270 0.7806 0.000 0.000 0.052 0.000 0.948
#> SRR1473567 2 0.0324 0.9491 0.004 0.992 0.000 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.3805 0.4638 0.004 0.000 0.328 0.004 0.664 0.000
#> SRR1390119 2 0.0146 0.8171 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1436127 5 0.5006 0.3592 0.000 0.000 0.304 0.008 0.612 0.076
#> SRR1347278 6 0.4336 0.5038 0.000 0.000 0.064 0.020 0.172 0.744
#> SRR1332904 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 1 0.2934 0.7220 0.844 0.000 0.000 0.112 0.000 0.044
#> SRR1082685 1 0.2222 0.7550 0.896 0.000 0.000 0.012 0.008 0.084
#> SRR1362287 6 0.1515 0.7209 0.028 0.000 0.008 0.020 0.000 0.944
#> SRR1339007 1 0.3104 0.7244 0.824 0.000 0.004 0.152 0.004 0.016
#> SRR1376557 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468700 2 0.1075 0.7992 0.000 0.952 0.000 0.048 0.000 0.000
#> SRR1077455 1 0.3893 0.6903 0.768 0.000 0.000 0.092 0.140 0.000
#> SRR1413978 3 0.7095 0.1135 0.076 0.000 0.376 0.252 0.000 0.296
#> SRR1439896 6 0.4177 0.1803 0.468 0.000 0.000 0.012 0.000 0.520
#> SRR1317963 2 0.0547 0.8127 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR1431865 6 0.2685 0.7337 0.080 0.000 0.004 0.044 0.000 0.872
#> SRR1394253 6 0.1333 0.7359 0.048 0.000 0.000 0.008 0.000 0.944
#> SRR1082664 3 0.6195 0.5207 0.108 0.000 0.536 0.064 0.292 0.000
#> SRR1077968 1 0.3274 0.7296 0.824 0.000 0.000 0.080 0.096 0.000
#> SRR1076393 3 0.4575 0.7094 0.004 0.000 0.724 0.080 0.180 0.012
#> SRR1477476 2 0.2748 0.6874 0.000 0.848 0.128 0.024 0.000 0.000
#> SRR1398057 6 0.5080 -0.0125 0.000 0.000 0.392 0.016 0.048 0.544
#> SRR1485042 1 0.3167 0.7621 0.832 0.000 0.000 0.096 0.000 0.072
#> SRR1385453 5 0.4283 0.3464 0.000 0.000 0.384 0.024 0.592 0.000
#> SRR1348074 4 0.4449 0.6523 0.216 0.088 0.000 0.696 0.000 0.000
#> SRR813959 5 0.3620 0.4161 0.000 0.352 0.000 0.000 0.648 0.000
#> SRR665442 2 0.6172 0.3233 0.080 0.592 0.004 0.108 0.000 0.216
#> SRR1378068 3 0.3419 0.7157 0.000 0.000 0.792 0.004 0.176 0.028
#> SRR1485237 1 0.1863 0.7504 0.896 0.000 0.000 0.104 0.000 0.000
#> SRR1350792 1 0.3418 0.6579 0.784 0.000 0.000 0.008 0.016 0.192
#> SRR1326797 5 0.2494 0.7485 0.120 0.000 0.000 0.016 0.864 0.000
#> SRR808994 3 0.1864 0.7120 0.000 0.000 0.924 0.040 0.004 0.032
#> SRR1474041 5 0.0964 0.8252 0.000 0.000 0.012 0.004 0.968 0.016
#> SRR1405641 3 0.2294 0.7401 0.000 0.000 0.892 0.000 0.072 0.036
#> SRR1362245 3 0.5720 0.2819 0.000 0.000 0.492 0.152 0.004 0.352
#> SRR1500194 6 0.2738 0.7117 0.176 0.000 0.000 0.004 0.000 0.820
#> SRR1414876 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 3 0.4371 0.5242 0.004 0.012 0.668 0.020 0.296 0.000
#> SRR1325161 5 0.1268 0.8211 0.036 0.000 0.004 0.008 0.952 0.000
#> SRR1318026 4 0.4533 0.6564 0.208 0.088 0.004 0.700 0.000 0.000
#> SRR1343778 3 0.3892 0.6981 0.020 0.000 0.752 0.020 0.208 0.000
#> SRR1441287 1 0.2790 0.7145 0.840 0.000 0.000 0.020 0.000 0.140
#> SRR1430991 5 0.0551 0.8296 0.004 0.000 0.008 0.004 0.984 0.000
#> SRR1499722 5 0.1657 0.8078 0.056 0.000 0.000 0.016 0.928 0.000
#> SRR1351368 3 0.3218 0.7180 0.000 0.000 0.840 0.080 0.072 0.008
#> SRR1441785 6 0.1075 0.7368 0.048 0.000 0.000 0.000 0.000 0.952
#> SRR1096101 1 0.4628 0.6123 0.712 0.000 0.000 0.064 0.024 0.200
#> SRR808375 5 0.0653 0.8281 0.012 0.000 0.004 0.004 0.980 0.000
#> SRR1452842 1 0.3642 0.7100 0.800 0.000 0.000 0.116 0.080 0.004
#> SRR1311709 1 0.1984 0.7592 0.912 0.000 0.000 0.032 0.000 0.056
#> SRR1433352 5 0.2320 0.7980 0.024 0.000 0.080 0.004 0.892 0.000
#> SRR1340241 2 0.0508 0.8134 0.000 0.984 0.004 0.012 0.000 0.000
#> SRR1456754 1 0.3272 0.7289 0.824 0.000 0.000 0.124 0.048 0.004
#> SRR1465172 5 0.2692 0.7228 0.148 0.000 0.000 0.012 0.840 0.000
#> SRR1499284 1 0.4420 0.4763 0.620 0.000 0.000 0.040 0.340 0.000
#> SRR1499607 2 0.6349 0.0808 0.008 0.488 0.304 0.180 0.000 0.020
#> SRR812342 1 0.2834 0.7411 0.852 0.000 0.000 0.008 0.020 0.120
#> SRR1405374 6 0.2902 0.7024 0.196 0.000 0.000 0.004 0.000 0.800
#> SRR1403565 6 0.2056 0.7368 0.080 0.000 0.000 0.004 0.012 0.904
#> SRR1332024 6 0.4242 0.1824 0.000 0.000 0.368 0.008 0.012 0.612
#> SRR1471633 1 0.2462 0.7396 0.876 0.000 0.000 0.096 0.000 0.028
#> SRR1325944 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0146 0.8173 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR821573 5 0.0717 0.8269 0.016 0.000 0.000 0.008 0.976 0.000
#> SRR1435372 1 0.2038 0.7718 0.920 0.000 0.000 0.028 0.032 0.020
#> SRR1324184 2 0.3989 -0.0610 0.000 0.528 0.000 0.468 0.000 0.004
#> SRR816517 3 0.3020 0.7029 0.000 0.032 0.864 0.040 0.064 0.000
#> SRR1324141 4 0.4335 0.6505 0.124 0.052 0.000 0.768 0.056 0.000
#> SRR1101612 1 0.4080 -0.0102 0.536 0.000 0.000 0.008 0.000 0.456
#> SRR1356531 1 0.1926 0.7655 0.912 0.000 0.000 0.068 0.000 0.020
#> SRR1089785 5 0.1442 0.8248 0.012 0.000 0.040 0.004 0.944 0.000
#> SRR1077708 3 0.6704 0.5639 0.048 0.000 0.528 0.068 0.292 0.064
#> SRR1343720 5 0.2182 0.8069 0.068 0.000 0.020 0.008 0.904 0.000
#> SRR1477499 2 0.0622 0.8120 0.000 0.980 0.008 0.012 0.000 0.000
#> SRR1347236 5 0.1327 0.8100 0.064 0.000 0.000 0.000 0.936 0.000
#> SRR1326408 1 0.3153 0.7297 0.832 0.000 0.000 0.128 0.032 0.008
#> SRR1336529 3 0.3353 0.7423 0.000 0.000 0.836 0.016 0.080 0.068
#> SRR1440643 5 0.4674 0.5902 0.000 0.004 0.180 0.120 0.696 0.000
#> SRR662354 6 0.3769 0.4735 0.356 0.000 0.000 0.004 0.000 0.640
#> SRR1310817 5 0.0820 0.8273 0.000 0.000 0.012 0.016 0.972 0.000
#> SRR1347389 4 0.4031 0.4931 0.008 0.332 0.008 0.652 0.000 0.000
#> SRR1353097 1 0.1635 0.7722 0.940 0.000 0.000 0.020 0.020 0.020
#> SRR1384737 4 0.3939 0.5161 0.080 0.000 0.116 0.788 0.000 0.016
#> SRR1096339 6 0.3966 0.2694 0.444 0.000 0.000 0.004 0.000 0.552
#> SRR1345329 1 0.4499 0.3707 0.604 0.024 0.004 0.364 0.000 0.004
#> SRR1414771 3 0.2043 0.7194 0.000 0.000 0.912 0.012 0.012 0.064
#> SRR1309119 1 0.5583 0.2370 0.500 0.000 0.000 0.348 0.000 0.152
#> SRR1470438 3 0.2393 0.7079 0.000 0.000 0.892 0.040 0.004 0.064
#> SRR1343221 1 0.2174 0.7698 0.912 0.000 0.000 0.016 0.036 0.036
#> SRR1410847 6 0.3103 0.6916 0.208 0.000 0.000 0.008 0.000 0.784
#> SRR807949 5 0.0405 0.8292 0.004 0.000 0.008 0.000 0.988 0.000
#> SRR1442332 5 0.1471 0.8043 0.000 0.000 0.064 0.004 0.932 0.000
#> SRR815920 3 0.3010 0.7265 0.000 0.000 0.828 0.004 0.148 0.020
#> SRR1471524 3 0.4396 0.2076 0.000 0.000 0.520 0.024 0.456 0.000
#> SRR1477221 6 0.2784 0.6259 0.000 0.000 0.092 0.020 0.020 0.868
#> SRR1445046 4 0.4379 0.4013 0.028 0.396 0.000 0.576 0.000 0.000
#> SRR1331962 2 0.1444 0.7816 0.000 0.928 0.000 0.072 0.000 0.000
#> SRR1319946 2 0.2300 0.6806 0.000 0.856 0.000 0.000 0.144 0.000
#> SRR1311599 6 0.1204 0.7388 0.056 0.000 0.000 0.000 0.000 0.944
#> SRR1323977 2 0.6355 0.0461 0.056 0.448 0.000 0.116 0.380 0.000
#> SRR1445132 2 0.0260 0.8163 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1337321 6 0.5093 0.5036 0.000 0.000 0.060 0.088 0.148 0.704
#> SRR1366390 4 0.4305 0.4024 0.004 0.380 0.012 0.600 0.004 0.000
#> SRR1343012 4 0.5533 0.5121 0.196 0.020 0.092 0.668 0.008 0.016
#> SRR1311958 2 0.3989 -0.0962 0.004 0.528 0.000 0.468 0.000 0.000
#> SRR1388234 2 0.3161 0.5200 0.216 0.776 0.000 0.008 0.000 0.000
#> SRR1370384 1 0.2923 0.7386 0.848 0.000 0.000 0.100 0.052 0.000
#> SRR1321650 3 0.5708 0.5368 0.000 0.000 0.552 0.016 0.132 0.300
#> SRR1485117 2 0.0937 0.8034 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1384713 1 0.3716 0.7048 0.792 0.000 0.000 0.128 0.076 0.004
#> SRR816609 1 0.3106 0.7260 0.832 0.012 0.000 0.140 0.004 0.012
#> SRR1486239 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1309638 3 0.5724 0.4715 0.096 0.000 0.608 0.252 0.004 0.040
#> SRR1356660 6 0.4317 0.7048 0.132 0.000 0.024 0.084 0.000 0.760
#> SRR1392883 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.0405 0.8292 0.004 0.000 0.008 0.000 0.988 0.000
#> SRR816677 1 0.5549 0.5612 0.624 0.000 0.080 0.244 0.000 0.052
#> SRR1455722 1 0.2362 0.7221 0.860 0.000 0.000 0.004 0.000 0.136
#> SRR1336029 1 0.4644 0.2556 0.564 0.000 0.012 0.400 0.000 0.024
#> SRR808452 1 0.3021 0.7549 0.860 0.000 0.000 0.020 0.044 0.076
#> SRR1352169 5 0.5363 0.4270 0.000 0.000 0.148 0.008 0.612 0.232
#> SRR1366707 3 0.3271 0.6689 0.000 0.000 0.760 0.008 0.232 0.000
#> SRR1328143 5 0.1010 0.8208 0.000 0.000 0.036 0.004 0.960 0.000
#> SRR1473567 2 0.0937 0.8029 0.000 0.960 0.000 0.040 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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.987 0.3028 0.685 0.685
#> 3 3 0.825 0.881 0.949 0.2568 0.987 0.981
#> 4 4 0.819 0.879 0.946 0.0411 0.988 0.981
#> 5 5 0.772 0.845 0.920 0.3281 0.829 0.741
#> 6 6 0.772 0.825 0.910 0.0222 0.991 0.981
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
#> SRR1442087 1 0.0000 0.997 1.000 0.000
#> SRR1390119 2 0.0000 0.938 0.000 1.000
#> SRR1436127 1 0.0000 0.997 1.000 0.000
#> SRR1347278 1 0.0000 0.997 1.000 0.000
#> SRR1332904 2 0.0000 0.938 0.000 1.000
#> SRR1444179 1 0.0000 0.997 1.000 0.000
#> SRR1082685 1 0.0000 0.997 1.000 0.000
#> SRR1362287 1 0.0000 0.997 1.000 0.000
#> SRR1339007 1 0.0000 0.997 1.000 0.000
#> SRR1376557 2 0.0000 0.938 0.000 1.000
#> SRR1468700 2 0.0000 0.938 0.000 1.000
#> SRR1077455 1 0.0000 0.997 1.000 0.000
#> SRR1413978 1 0.0000 0.997 1.000 0.000
#> SRR1439896 1 0.0000 0.997 1.000 0.000
#> SRR1317963 2 0.9491 0.490 0.368 0.632
#> SRR1431865 1 0.0000 0.997 1.000 0.000
#> SRR1394253 1 0.0000 0.997 1.000 0.000
#> SRR1082664 1 0.0000 0.997 1.000 0.000
#> SRR1077968 1 0.0000 0.997 1.000 0.000
#> SRR1076393 1 0.0000 0.997 1.000 0.000
#> SRR1477476 2 0.0000 0.938 0.000 1.000
#> SRR1398057 1 0.0000 0.997 1.000 0.000
#> SRR1485042 1 0.0000 0.997 1.000 0.000
#> SRR1385453 1 0.0000 0.997 1.000 0.000
#> SRR1348074 1 0.0672 0.990 0.992 0.008
#> SRR813959 1 0.1184 0.982 0.984 0.016
#> SRR665442 1 0.5519 0.844 0.872 0.128
#> SRR1378068 1 0.0000 0.997 1.000 0.000
#> SRR1485237 1 0.0672 0.990 0.992 0.008
#> SRR1350792 1 0.0000 0.997 1.000 0.000
#> SRR1326797 1 0.0000 0.997 1.000 0.000
#> SRR808994 1 0.0000 0.997 1.000 0.000
#> SRR1474041 1 0.0000 0.997 1.000 0.000
#> SRR1405641 1 0.0000 0.997 1.000 0.000
#> SRR1362245 1 0.0000 0.997 1.000 0.000
#> SRR1500194 1 0.0000 0.997 1.000 0.000
#> SRR1414876 2 0.0000 0.938 0.000 1.000
#> SRR1478523 1 0.0000 0.997 1.000 0.000
#> SRR1325161 1 0.0000 0.997 1.000 0.000
#> SRR1318026 1 0.0000 0.997 1.000 0.000
#> SRR1343778 1 0.0000 0.997 1.000 0.000
#> SRR1441287 1 0.0000 0.997 1.000 0.000
#> SRR1430991 1 0.0000 0.997 1.000 0.000
#> SRR1499722 1 0.0000 0.997 1.000 0.000
#> SRR1351368 1 0.0000 0.997 1.000 0.000
#> SRR1441785 1 0.0000 0.997 1.000 0.000
#> SRR1096101 1 0.0000 0.997 1.000 0.000
#> SRR808375 1 0.0000 0.997 1.000 0.000
#> SRR1452842 1 0.0000 0.997 1.000 0.000
#> SRR1311709 1 0.0000 0.997 1.000 0.000
#> SRR1433352 1 0.0000 0.997 1.000 0.000
#> SRR1340241 2 0.0000 0.938 0.000 1.000
#> SRR1456754 1 0.0000 0.997 1.000 0.000
#> SRR1465172 1 0.0000 0.997 1.000 0.000
#> SRR1499284 1 0.0000 0.997 1.000 0.000
#> SRR1499607 2 0.9491 0.490 0.368 0.632
#> SRR812342 1 0.0000 0.997 1.000 0.000
#> SRR1405374 1 0.0000 0.997 1.000 0.000
#> SRR1403565 1 0.0000 0.997 1.000 0.000
#> SRR1332024 1 0.0000 0.997 1.000 0.000
#> SRR1471633 1 0.0000 0.997 1.000 0.000
#> SRR1325944 2 0.0000 0.938 0.000 1.000
#> SRR1429450 2 0.0000 0.938 0.000 1.000
#> SRR821573 1 0.0000 0.997 1.000 0.000
#> SRR1435372 1 0.0000 0.997 1.000 0.000
#> SRR1324184 2 0.0000 0.938 0.000 1.000
#> SRR816517 1 0.1184 0.983 0.984 0.016
#> SRR1324141 1 0.0000 0.997 1.000 0.000
#> SRR1101612 1 0.0000 0.997 1.000 0.000
#> SRR1356531 1 0.0000 0.997 1.000 0.000
#> SRR1089785 1 0.0000 0.997 1.000 0.000
#> SRR1077708 1 0.0000 0.997 1.000 0.000
#> SRR1343720 1 0.0000 0.997 1.000 0.000
#> SRR1477499 2 0.0000 0.938 0.000 1.000
#> SRR1347236 1 0.0000 0.997 1.000 0.000
#> SRR1326408 1 0.0000 0.997 1.000 0.000
#> SRR1336529 1 0.0000 0.997 1.000 0.000
#> SRR1440643 1 0.0000 0.997 1.000 0.000
#> SRR662354 1 0.0000 0.997 1.000 0.000
#> SRR1310817 1 0.0000 0.997 1.000 0.000
#> SRR1347389 2 0.0000 0.938 0.000 1.000
#> SRR1353097 1 0.0000 0.997 1.000 0.000
#> SRR1384737 1 0.0000 0.997 1.000 0.000
#> SRR1096339 1 0.0000 0.997 1.000 0.000
#> SRR1345329 1 0.0672 0.990 0.992 0.008
#> SRR1414771 1 0.0000 0.997 1.000 0.000
#> SRR1309119 1 0.0000 0.997 1.000 0.000
#> SRR1470438 1 0.0000 0.997 1.000 0.000
#> SRR1343221 1 0.0000 0.997 1.000 0.000
#> SRR1410847 1 0.0000 0.997 1.000 0.000
#> SRR807949 1 0.0000 0.997 1.000 0.000
#> SRR1442332 1 0.0000 0.997 1.000 0.000
#> SRR815920 1 0.0000 0.997 1.000 0.000
#> SRR1471524 1 0.0000 0.997 1.000 0.000
#> SRR1477221 1 0.0000 0.997 1.000 0.000
#> SRR1445046 2 0.7602 0.747 0.220 0.780
#> SRR1331962 2 0.0000 0.938 0.000 1.000
#> SRR1319946 2 0.7815 0.731 0.232 0.768
#> SRR1311599 1 0.0000 0.997 1.000 0.000
#> SRR1323977 1 0.1184 0.982 0.984 0.016
#> SRR1445132 2 0.0000 0.938 0.000 1.000
#> SRR1337321 1 0.0000 0.997 1.000 0.000
#> SRR1366390 2 0.0000 0.938 0.000 1.000
#> SRR1343012 1 0.0000 0.997 1.000 0.000
#> SRR1311958 2 0.7602 0.747 0.220 0.780
#> SRR1388234 1 0.1184 0.982 0.984 0.016
#> SRR1370384 1 0.0000 0.997 1.000 0.000
#> SRR1321650 1 0.0000 0.997 1.000 0.000
#> SRR1485117 2 0.0000 0.938 0.000 1.000
#> SRR1384713 1 0.0000 0.997 1.000 0.000
#> SRR816609 1 0.1633 0.974 0.976 0.024
#> SRR1486239 2 0.0000 0.938 0.000 1.000
#> SRR1309638 1 0.0000 0.997 1.000 0.000
#> SRR1356660 1 0.0000 0.997 1.000 0.000
#> SRR1392883 2 0.0000 0.938 0.000 1.000
#> SRR808130 1 0.0000 0.997 1.000 0.000
#> SRR816677 1 0.0000 0.997 1.000 0.000
#> SRR1455722 1 0.0000 0.997 1.000 0.000
#> SRR1336029 1 0.0000 0.997 1.000 0.000
#> SRR808452 1 0.0000 0.997 1.000 0.000
#> SRR1352169 1 0.0000 0.997 1.000 0.000
#> SRR1366707 1 0.0000 0.997 1.000 0.000
#> SRR1328143 1 0.0000 0.997 1.000 0.000
#> SRR1473567 2 0.0000 0.938 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1390119 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1436127 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1347278 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1332904 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1444179 1 0.0892 0.936 0.980 0.000 0.020
#> SRR1082685 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1362287 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1339007 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1376557 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1413978 1 0.3267 0.856 0.884 0.000 0.116
#> SRR1439896 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1317963 2 0.7104 0.354 0.032 0.608 0.360
#> SRR1431865 1 0.0237 0.946 0.996 0.000 0.004
#> SRR1394253 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1082664 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1077968 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1076393 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1477476 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1398057 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1485042 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1385453 1 0.5178 0.704 0.744 0.000 0.256
#> SRR1348074 1 0.5733 0.601 0.676 0.000 0.324
#> SRR813959 1 0.5785 0.587 0.668 0.000 0.332
#> SRR665442 3 0.0000 0.000 0.000 0.000 1.000
#> SRR1378068 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1485237 1 0.5733 0.601 0.676 0.000 0.324
#> SRR1350792 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.949 1.000 0.000 0.000
#> SRR808994 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1474041 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1405641 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1362245 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1500194 1 0.0592 0.942 0.988 0.000 0.012
#> SRR1414876 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1478523 1 0.5098 0.715 0.752 0.000 0.248
#> SRR1325161 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1318026 1 0.5216 0.699 0.740 0.000 0.260
#> SRR1343778 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1441287 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1430991 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1499722 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1351368 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1441785 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1096101 1 0.0000 0.949 1.000 0.000 0.000
#> SRR808375 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1452842 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1311709 1 0.2261 0.900 0.932 0.000 0.068
#> SRR1433352 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1340241 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1456754 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1465172 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1499284 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1499607 2 0.7104 0.354 0.032 0.608 0.360
#> SRR812342 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1405374 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1403565 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1332024 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1471633 1 0.2261 0.900 0.932 0.000 0.068
#> SRR1325944 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.918 0.000 1.000 0.000
#> SRR821573 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1435372 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1324184 2 0.0000 0.918 0.000 1.000 0.000
#> SRR816517 1 0.5722 0.647 0.704 0.004 0.292
#> SRR1324141 1 0.5216 0.699 0.740 0.000 0.260
#> SRR1101612 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1089785 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1077708 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1343720 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1477499 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1347236 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1326408 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1336529 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1440643 1 0.5098 0.715 0.752 0.000 0.248
#> SRR662354 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1310817 1 0.3482 0.845 0.872 0.000 0.128
#> SRR1347389 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1384737 1 0.4555 0.773 0.800 0.000 0.200
#> SRR1096339 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1345329 1 0.5733 0.601 0.676 0.000 0.324
#> SRR1414771 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1309119 1 0.2261 0.900 0.932 0.000 0.068
#> SRR1470438 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1343221 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1410847 1 0.0000 0.949 1.000 0.000 0.000
#> SRR807949 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1442332 1 0.0000 0.949 1.000 0.000 0.000
#> SRR815920 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1471524 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1477221 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1445046 2 0.5058 0.657 0.000 0.756 0.244
#> SRR1331962 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1319946 2 0.5178 0.639 0.000 0.744 0.256
#> SRR1311599 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1323977 1 0.5785 0.587 0.668 0.000 0.332
#> SRR1445132 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1337321 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1366390 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1343012 1 0.4291 0.795 0.820 0.000 0.180
#> SRR1311958 2 0.5058 0.657 0.000 0.756 0.244
#> SRR1388234 1 0.5785 0.587 0.668 0.000 0.332
#> SRR1370384 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1321650 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1485117 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.949 1.000 0.000 0.000
#> SRR816609 1 0.5948 0.532 0.640 0.000 0.360
#> SRR1486239 2 0.0000 0.918 0.000 1.000 0.000
#> SRR1309638 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1356660 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.918 0.000 1.000 0.000
#> SRR808130 1 0.0000 0.949 1.000 0.000 0.000
#> SRR816677 1 0.1163 0.931 0.972 0.000 0.028
#> SRR1455722 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1336029 1 0.0424 0.944 0.992 0.000 0.008
#> SRR808452 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1352169 1 0.0237 0.946 0.996 0.000 0.004
#> SRR1366707 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1328143 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1473567 2 0.0000 0.918 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1390119 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1436127 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1347278 1 0.0336 0.940 0.992 0.000 0.008 0.000
#> SRR1332904 2 0.4877 0.307 0.000 0.592 0.000 0.408
#> SRR1444179 1 0.1022 0.924 0.968 0.000 0.032 0.000
#> SRR1082685 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1362287 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1339007 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0817 0.933 0.000 0.976 0.000 0.024
#> SRR1077455 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1413978 1 0.2647 0.853 0.880 0.000 0.120 0.000
#> SRR1439896 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1317963 3 0.5039 0.757 0.004 0.000 0.592 0.404
#> SRR1431865 1 0.0188 0.943 0.996 0.000 0.004 0.000
#> SRR1394253 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1082664 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1077968 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1076393 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1477476 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1398057 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1485042 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1385453 1 0.4304 0.677 0.716 0.000 0.284 0.000
#> SRR1348074 1 0.4679 0.572 0.648 0.000 0.352 0.000
#> SRR813959 1 0.4713 0.558 0.640 0.000 0.360 0.000
#> SRR665442 4 0.4877 0.000 0.000 0.000 0.408 0.592
#> SRR1378068 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1485237 1 0.4679 0.572 0.648 0.000 0.352 0.000
#> SRR1350792 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR808994 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1474041 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1405641 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1362245 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1500194 1 0.0469 0.938 0.988 0.000 0.012 0.000
#> SRR1414876 2 0.0336 0.940 0.000 0.992 0.000 0.008
#> SRR1478523 1 0.4250 0.687 0.724 0.000 0.276 0.000
#> SRR1325161 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1318026 1 0.4304 0.676 0.716 0.000 0.284 0.000
#> SRR1343778 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1441287 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1430991 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1499722 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1351368 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1441785 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR808375 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1452842 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1311709 1 0.2216 0.879 0.908 0.000 0.092 0.000
#> SRR1433352 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1340241 2 0.0817 0.928 0.000 0.976 0.000 0.024
#> SRR1456754 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1465172 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1499284 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1499607 3 0.5039 0.757 0.004 0.000 0.592 0.404
#> SRR812342 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1403565 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1332024 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1471633 1 0.2216 0.879 0.908 0.000 0.092 0.000
#> SRR1325944 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR821573 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1435372 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR816517 1 0.4543 0.619 0.676 0.000 0.324 0.000
#> SRR1324141 1 0.4304 0.676 0.716 0.000 0.284 0.000
#> SRR1101612 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1089785 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1077708 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1343720 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1477499 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1347236 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1326408 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1336529 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1440643 1 0.4250 0.687 0.724 0.000 0.276 0.000
#> SRR662354 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1310817 1 0.2760 0.846 0.872 0.000 0.128 0.000
#> SRR1347389 2 0.0592 0.936 0.000 0.984 0.000 0.016
#> SRR1353097 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1384737 1 0.3837 0.750 0.776 0.000 0.224 0.000
#> SRR1096339 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1345329 1 0.4679 0.572 0.648 0.000 0.352 0.000
#> SRR1414771 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1309119 1 0.2216 0.879 0.908 0.000 0.092 0.000
#> SRR1470438 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1343221 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1410847 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR807949 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1442332 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR815920 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1471524 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1477221 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1445046 3 0.7246 0.826 0.000 0.144 0.448 0.408
#> SRR1331962 2 0.0817 0.933 0.000 0.976 0.000 0.024
#> SRR1319946 3 0.7143 0.831 0.000 0.132 0.460 0.408
#> SRR1311599 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1323977 1 0.4713 0.558 0.640 0.000 0.360 0.000
#> SRR1445132 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1337321 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1366390 2 0.0592 0.936 0.000 0.984 0.000 0.016
#> SRR1343012 1 0.3649 0.773 0.796 0.000 0.204 0.000
#> SRR1311958 3 0.7246 0.826 0.000 0.144 0.448 0.408
#> SRR1388234 1 0.4713 0.558 0.640 0.000 0.360 0.000
#> SRR1370384 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1321650 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1485117 2 0.0188 0.942 0.000 0.996 0.000 0.004
#> SRR1384713 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR816609 1 0.4817 0.503 0.612 0.000 0.388 0.000
#> SRR1486239 2 0.4877 0.307 0.000 0.592 0.000 0.408
#> SRR1309638 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1356660 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1392883 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR808130 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR816677 1 0.0921 0.927 0.972 0.000 0.028 0.000
#> SRR1455722 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1336029 1 0.0336 0.940 0.992 0.000 0.008 0.000
#> SRR808452 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1352169 1 0.0188 0.943 0.996 0.000 0.004 0.000
#> SRR1366707 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1328143 1 0.0000 0.945 1.000 0.000 0.000 0.000
#> SRR1473567 2 0.0188 0.942 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 1 0.0510 0.9398 0.984 0.000 0.000 0.016 0
#> SRR1390119 2 0.2020 0.8844 0.000 0.900 0.100 0.000 0
#> SRR1436127 1 0.1671 0.9189 0.924 0.000 0.000 0.076 0
#> SRR1347278 1 0.1851 0.9142 0.912 0.000 0.000 0.088 0
#> SRR1332904 3 0.4287 0.1278 0.000 0.460 0.540 0.000 0
#> SRR1444179 1 0.1792 0.8968 0.916 0.000 0.000 0.084 0
#> SRR1082685 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1362287 1 0.1121 0.9334 0.956 0.000 0.000 0.044 0
#> SRR1339007 1 0.0880 0.9355 0.968 0.000 0.000 0.032 0
#> SRR1376557 2 0.0290 0.9341 0.000 0.992 0.008 0.000 0
#> SRR1468700 2 0.2561 0.8321 0.000 0.856 0.144 0.000 0
#> SRR1077455 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1413978 1 0.3534 0.6891 0.744 0.000 0.000 0.256 0
#> SRR1439896 1 0.0963 0.9336 0.964 0.000 0.000 0.036 0
#> SRR1317963 3 0.3730 0.5063 0.000 0.000 0.712 0.288 0
#> SRR1431865 1 0.1410 0.9304 0.940 0.000 0.000 0.060 0
#> SRR1394253 1 0.1121 0.9321 0.956 0.000 0.000 0.044 0
#> SRR1082664 1 0.0404 0.9402 0.988 0.000 0.000 0.012 0
#> SRR1077968 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1076393 1 0.1671 0.9203 0.924 0.000 0.000 0.076 0
#> SRR1477476 2 0.2020 0.8844 0.000 0.900 0.100 0.000 0
#> SRR1398057 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1485042 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1385453 4 0.1043 0.6991 0.040 0.000 0.000 0.960 0
#> SRR1348074 4 0.2446 0.7610 0.056 0.000 0.044 0.900 0
#> SRR813959 4 0.2592 0.7594 0.056 0.000 0.052 0.892 0
#> SRR665442 5 0.0000 0.0000 0.000 0.000 0.000 0.000 1
#> SRR1378068 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1485237 4 0.2446 0.7610 0.056 0.000 0.044 0.900 0
#> SRR1350792 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1326797 1 0.0510 0.9405 0.984 0.000 0.000 0.016 0
#> SRR808994 1 0.1908 0.9107 0.908 0.000 0.000 0.092 0
#> SRR1474041 1 0.0290 0.9404 0.992 0.000 0.000 0.008 0
#> SRR1405641 1 0.1908 0.9107 0.908 0.000 0.000 0.092 0
#> SRR1362245 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1500194 1 0.1270 0.9246 0.948 0.000 0.000 0.052 0
#> SRR1414876 2 0.2127 0.8663 0.000 0.892 0.108 0.000 0
#> SRR1478523 4 0.1341 0.7011 0.056 0.000 0.000 0.944 0
#> SRR1325161 1 0.0000 0.9406 1.000 0.000 0.000 0.000 0
#> SRR1318026 4 0.3238 0.7257 0.136 0.000 0.028 0.836 0
#> SRR1343778 1 0.0510 0.9398 0.984 0.000 0.000 0.016 0
#> SRR1441287 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1430991 1 0.0290 0.9404 0.992 0.000 0.000 0.008 0
#> SRR1499722 1 0.0404 0.9402 0.988 0.000 0.000 0.012 0
#> SRR1351368 1 0.1671 0.9203 0.924 0.000 0.000 0.076 0
#> SRR1441785 1 0.1121 0.9321 0.956 0.000 0.000 0.044 0
#> SRR1096101 1 0.0963 0.9336 0.964 0.000 0.000 0.036 0
#> SRR808375 1 0.0000 0.9406 1.000 0.000 0.000 0.000 0
#> SRR1452842 1 0.0404 0.9413 0.988 0.000 0.000 0.012 0
#> SRR1311709 1 0.3452 0.6581 0.756 0.000 0.000 0.244 0
#> SRR1433352 1 0.0880 0.9357 0.968 0.000 0.000 0.032 0
#> SRR1340241 2 0.2773 0.8531 0.000 0.836 0.164 0.000 0
#> SRR1456754 1 0.0290 0.9405 0.992 0.000 0.000 0.008 0
#> SRR1465172 1 0.0000 0.9406 1.000 0.000 0.000 0.000 0
#> SRR1499284 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1499607 3 0.3730 0.5063 0.000 0.000 0.712 0.288 0
#> SRR812342 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1405374 1 0.1121 0.9321 0.956 0.000 0.000 0.044 0
#> SRR1403565 1 0.0290 0.9405 0.992 0.000 0.000 0.008 0
#> SRR1332024 1 0.1908 0.9107 0.908 0.000 0.000 0.092 0
#> SRR1471633 1 0.3452 0.6581 0.756 0.000 0.000 0.244 0
#> SRR1325944 2 0.0000 0.9341 0.000 1.000 0.000 0.000 0
#> SRR1429450 2 0.0000 0.9341 0.000 1.000 0.000 0.000 0
#> SRR821573 1 0.0510 0.9407 0.984 0.000 0.000 0.016 0
#> SRR1435372 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1324184 2 0.0290 0.9336 0.000 0.992 0.008 0.000 0
#> SRR816517 4 0.1018 0.6676 0.016 0.000 0.016 0.968 0
#> SRR1324141 4 0.4374 0.5600 0.272 0.000 0.028 0.700 0
#> SRR1101612 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1356531 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1089785 1 0.0510 0.9407 0.984 0.000 0.000 0.016 0
#> SRR1077708 1 0.0404 0.9402 0.988 0.000 0.000 0.012 0
#> SRR1343720 1 0.0290 0.9404 0.992 0.000 0.000 0.008 0
#> SRR1477499 2 0.0000 0.9341 0.000 1.000 0.000 0.000 0
#> SRR1347236 1 0.0880 0.9357 0.968 0.000 0.000 0.032 0
#> SRR1326408 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1336529 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1440643 4 0.2439 0.7339 0.120 0.000 0.004 0.876 0
#> SRR662354 1 0.0963 0.9336 0.964 0.000 0.000 0.036 0
#> SRR1310817 1 0.4294 0.0203 0.532 0.000 0.000 0.468 0
#> SRR1347389 2 0.0794 0.9277 0.000 0.972 0.028 0.000 0
#> SRR1353097 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1384737 4 0.3774 0.4935 0.296 0.000 0.000 0.704 0
#> SRR1096339 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1345329 4 0.2446 0.7610 0.056 0.000 0.044 0.900 0
#> SRR1414771 1 0.1908 0.9107 0.908 0.000 0.000 0.092 0
#> SRR1309119 1 0.3452 0.6581 0.756 0.000 0.000 0.244 0
#> SRR1470438 1 0.1908 0.9107 0.908 0.000 0.000 0.092 0
#> SRR1343221 1 0.1121 0.9321 0.956 0.000 0.000 0.044 0
#> SRR1410847 1 0.1270 0.9371 0.948 0.000 0.000 0.052 0
#> SRR807949 1 0.0290 0.9404 0.992 0.000 0.000 0.008 0
#> SRR1442332 1 0.0880 0.9357 0.968 0.000 0.000 0.032 0
#> SRR815920 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1471524 1 0.1732 0.9199 0.920 0.000 0.000 0.080 0
#> SRR1477221 1 0.1792 0.9143 0.916 0.000 0.000 0.084 0
#> SRR1445046 3 0.2020 0.5875 0.000 0.000 0.900 0.100 0
#> SRR1331962 2 0.2690 0.8232 0.000 0.844 0.156 0.000 0
#> SRR1319946 3 0.2179 0.5866 0.000 0.000 0.888 0.112 0
#> SRR1311599 1 0.1121 0.9321 0.956 0.000 0.000 0.044 0
#> SRR1323977 4 0.2592 0.7594 0.056 0.000 0.052 0.892 0
#> SRR1445132 2 0.2020 0.8844 0.000 0.900 0.100 0.000 0
#> SRR1337321 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1366390 2 0.0794 0.9277 0.000 0.972 0.028 0.000 0
#> SRR1343012 4 0.4045 0.4128 0.356 0.000 0.000 0.644 0
#> SRR1311958 3 0.2020 0.5875 0.000 0.000 0.900 0.100 0
#> SRR1388234 4 0.2592 0.7594 0.056 0.000 0.052 0.892 0
#> SRR1370384 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1321650 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1485117 2 0.0162 0.9343 0.000 0.996 0.004 0.000 0
#> SRR1384713 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR816609 4 0.3033 0.7336 0.052 0.000 0.084 0.864 0
#> SRR1486239 3 0.4287 0.1278 0.000 0.460 0.540 0.000 0
#> SRR1309638 1 0.1851 0.9120 0.912 0.000 0.000 0.088 0
#> SRR1356660 1 0.1121 0.9321 0.956 0.000 0.000 0.044 0
#> SRR1392883 2 0.0000 0.9341 0.000 1.000 0.000 0.000 0
#> SRR808130 1 0.0000 0.9406 1.000 0.000 0.000 0.000 0
#> SRR816677 1 0.2179 0.8945 0.888 0.000 0.000 0.112 0
#> SRR1455722 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1336029 1 0.1908 0.9163 0.908 0.000 0.000 0.092 0
#> SRR808452 1 0.0609 0.9388 0.980 0.000 0.000 0.020 0
#> SRR1352169 1 0.1544 0.9287 0.932 0.000 0.000 0.068 0
#> SRR1366707 1 0.1732 0.9199 0.920 0.000 0.000 0.080 0
#> SRR1328143 1 0.0290 0.9404 0.992 0.000 0.000 0.008 0
#> SRR1473567 2 0.0404 0.9332 0.000 0.988 0.012 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 1 0.0458 0.93838 0.984 0.000 0.000 0.016 0.000 0
#> SRR1390119 3 0.0458 0.74386 0.000 0.016 0.984 0.000 0.000 0
#> SRR1436127 1 0.1501 0.91811 0.924 0.000 0.000 0.076 0.000 0
#> SRR1347278 1 0.1765 0.90844 0.904 0.000 0.000 0.096 0.000 0
#> SRR1332904 5 0.4399 0.03624 0.000 0.460 0.024 0.000 0.516 0
#> SRR1444179 1 0.1714 0.88986 0.908 0.000 0.000 0.092 0.000 0
#> SRR1082685 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1362287 1 0.1007 0.93221 0.956 0.000 0.000 0.044 0.000 0
#> SRR1339007 1 0.0790 0.93383 0.968 0.000 0.000 0.032 0.000 0
#> SRR1376557 2 0.3634 0.56333 0.000 0.696 0.296 0.000 0.008 0
#> SRR1468700 2 0.3014 0.81401 0.000 0.832 0.036 0.000 0.132 0
#> SRR1077455 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1413978 1 0.3175 0.69336 0.744 0.000 0.000 0.256 0.000 0
#> SRR1439896 1 0.0865 0.93207 0.964 0.000 0.000 0.036 0.000 0
#> SRR1317963 5 0.2527 0.53487 0.000 0.000 0.000 0.168 0.832 0
#> SRR1431865 1 0.1267 0.92952 0.940 0.000 0.000 0.060 0.000 0
#> SRR1394253 1 0.1007 0.93086 0.956 0.000 0.000 0.044 0.000 0
#> SRR1082664 1 0.0363 0.93881 0.988 0.000 0.000 0.012 0.000 0
#> SRR1077968 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1076393 1 0.1588 0.91889 0.924 0.000 0.004 0.072 0.000 0
#> SRR1477476 3 0.0458 0.74386 0.000 0.016 0.984 0.000 0.000 0
#> SRR1398057 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1485042 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1385453 4 0.1232 0.68858 0.024 0.000 0.016 0.956 0.004 0
#> SRR1348074 4 0.2451 0.77512 0.056 0.000 0.000 0.884 0.060 0
#> SRR813959 4 0.2680 0.77264 0.056 0.000 0.000 0.868 0.076 0
#> SRR665442 6 0.0000 0.00000 0.000 0.000 0.000 0.000 0.000 1
#> SRR1378068 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1485237 4 0.2451 0.77512 0.056 0.000 0.000 0.884 0.060 0
#> SRR1350792 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1326797 1 0.0632 0.93799 0.976 0.000 0.000 0.024 0.000 0
#> SRR808994 1 0.1806 0.90948 0.908 0.000 0.004 0.088 0.000 0
#> SRR1474041 1 0.0260 0.93875 0.992 0.000 0.000 0.008 0.000 0
#> SRR1405641 1 0.1806 0.90948 0.908 0.000 0.004 0.088 0.000 0
#> SRR1362245 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1500194 1 0.1204 0.92151 0.944 0.000 0.000 0.056 0.000 0
#> SRR1414876 2 0.2937 0.83124 0.000 0.848 0.056 0.000 0.096 0
#> SRR1478523 4 0.1536 0.69316 0.040 0.000 0.016 0.940 0.004 0
#> SRR1325161 1 0.0000 0.93904 1.000 0.000 0.000 0.000 0.000 0
#> SRR1318026 4 0.2740 0.73936 0.120 0.000 0.000 0.852 0.028 0
#> SRR1343778 1 0.0458 0.93838 0.984 0.000 0.000 0.016 0.000 0
#> SRR1441287 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1430991 1 0.0260 0.93875 0.992 0.000 0.000 0.008 0.000 0
#> SRR1499722 1 0.0363 0.93856 0.988 0.000 0.000 0.012 0.000 0
#> SRR1351368 1 0.1588 0.91889 0.924 0.000 0.004 0.072 0.000 0
#> SRR1441785 1 0.1007 0.93086 0.956 0.000 0.000 0.044 0.000 0
#> SRR1096101 1 0.0865 0.93207 0.964 0.000 0.000 0.036 0.000 0
#> SRR808375 1 0.0000 0.93904 1.000 0.000 0.000 0.000 0.000 0
#> SRR1452842 1 0.0363 0.93981 0.988 0.000 0.000 0.012 0.000 0
#> SRR1311709 1 0.3198 0.63685 0.740 0.000 0.000 0.260 0.000 0
#> SRR1433352 1 0.0865 0.93313 0.964 0.000 0.000 0.036 0.000 0
#> SRR1340241 3 0.1921 0.69986 0.000 0.032 0.916 0.000 0.052 0
#> SRR1456754 1 0.0260 0.93895 0.992 0.000 0.000 0.008 0.000 0
#> SRR1465172 1 0.0000 0.93904 1.000 0.000 0.000 0.000 0.000 0
#> SRR1499284 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1499607 5 0.2527 0.53487 0.000 0.000 0.000 0.168 0.832 0
#> SRR812342 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1405374 1 0.1007 0.93086 0.956 0.000 0.000 0.044 0.000 0
#> SRR1403565 1 0.0260 0.93895 0.992 0.000 0.000 0.008 0.000 0
#> SRR1332024 1 0.1806 0.90948 0.908 0.000 0.004 0.088 0.000 0
#> SRR1471633 1 0.3198 0.63685 0.740 0.000 0.000 0.260 0.000 0
#> SRR1325944 3 0.3309 0.72891 0.000 0.280 0.720 0.000 0.000 0
#> SRR1429450 3 0.3309 0.72891 0.000 0.280 0.720 0.000 0.000 0
#> SRR821573 1 0.0713 0.93626 0.972 0.000 0.000 0.028 0.000 0
#> SRR1435372 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1324184 2 0.0790 0.82305 0.000 0.968 0.032 0.000 0.000 0
#> SRR816517 4 0.1773 0.67147 0.016 0.000 0.016 0.932 0.036 0
#> SRR1324141 4 0.3841 0.56654 0.256 0.000 0.000 0.716 0.028 0
#> SRR1101612 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1356531 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1089785 1 0.0632 0.93767 0.976 0.000 0.000 0.024 0.000 0
#> SRR1077708 1 0.0363 0.93881 0.988 0.000 0.000 0.012 0.000 0
#> SRR1343720 1 0.0260 0.93875 0.992 0.000 0.000 0.008 0.000 0
#> SRR1477499 3 0.3309 0.72891 0.000 0.280 0.720 0.000 0.000 0
#> SRR1347236 1 0.0865 0.93313 0.964 0.000 0.000 0.036 0.000 0
#> SRR1326408 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1336529 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1440643 4 0.2006 0.74623 0.104 0.000 0.000 0.892 0.004 0
#> SRR662354 1 0.0865 0.93207 0.964 0.000 0.000 0.036 0.000 0
#> SRR1310817 1 0.3864 -0.00288 0.520 0.000 0.000 0.480 0.000 0
#> SRR1347389 2 0.1168 0.82411 0.000 0.956 0.028 0.000 0.016 0
#> SRR1353097 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1384737 4 0.3309 0.49613 0.280 0.000 0.000 0.720 0.000 0
#> SRR1096339 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1345329 4 0.2451 0.77512 0.056 0.000 0.000 0.884 0.060 0
#> SRR1414771 1 0.1806 0.90948 0.908 0.000 0.004 0.088 0.000 0
#> SRR1309119 1 0.3198 0.63685 0.740 0.000 0.000 0.260 0.000 0
#> SRR1470438 1 0.1806 0.90948 0.908 0.000 0.004 0.088 0.000 0
#> SRR1343221 1 0.1007 0.93086 0.956 0.000 0.000 0.044 0.000 0
#> SRR1410847 1 0.1141 0.93591 0.948 0.000 0.000 0.052 0.000 0
#> SRR807949 1 0.0260 0.93875 0.992 0.000 0.000 0.008 0.000 0
#> SRR1442332 1 0.0937 0.93148 0.960 0.000 0.000 0.040 0.000 0
#> SRR815920 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1471524 1 0.1806 0.91479 0.908 0.000 0.004 0.088 0.000 0
#> SRR1477221 1 0.1610 0.91364 0.916 0.000 0.000 0.084 0.000 0
#> SRR1445046 5 0.0692 0.62894 0.000 0.020 0.000 0.004 0.976 0
#> SRR1331962 2 0.2790 0.80596 0.000 0.844 0.024 0.000 0.132 0
#> SRR1319946 5 0.0405 0.62684 0.000 0.008 0.000 0.004 0.988 0
#> SRR1311599 1 0.1007 0.93086 0.956 0.000 0.000 0.044 0.000 0
#> SRR1323977 4 0.2625 0.77320 0.056 0.000 0.000 0.872 0.072 0
#> SRR1445132 3 0.0865 0.75180 0.000 0.036 0.964 0.000 0.000 0
#> SRR1337321 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1366390 2 0.1168 0.82411 0.000 0.956 0.028 0.000 0.016 0
#> SRR1343012 4 0.3578 0.41270 0.340 0.000 0.000 0.660 0.000 0
#> SRR1311958 5 0.0692 0.62894 0.000 0.020 0.000 0.004 0.976 0
#> SRR1388234 4 0.2625 0.77320 0.056 0.000 0.000 0.872 0.072 0
#> SRR1370384 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1321650 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1485117 2 0.2823 0.75463 0.000 0.796 0.204 0.000 0.000 0
#> SRR1384713 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR816609 4 0.3062 0.74844 0.052 0.000 0.000 0.836 0.112 0
#> SRR1486239 5 0.4399 0.03624 0.000 0.460 0.024 0.000 0.516 0
#> SRR1309638 1 0.1663 0.91142 0.912 0.000 0.000 0.088 0.000 0
#> SRR1356660 1 0.1007 0.93086 0.956 0.000 0.000 0.044 0.000 0
#> SRR1392883 3 0.3309 0.72891 0.000 0.280 0.720 0.000 0.000 0
#> SRR808130 1 0.0000 0.93904 1.000 0.000 0.000 0.000 0.000 0
#> SRR816677 1 0.2234 0.88128 0.872 0.000 0.000 0.124 0.004 0
#> SRR1455722 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1336029 1 0.1714 0.91616 0.908 0.000 0.000 0.092 0.000 0
#> SRR808452 1 0.0547 0.93738 0.980 0.000 0.000 0.020 0.000 0
#> SRR1352169 1 0.1556 0.92146 0.920 0.000 0.000 0.080 0.000 0
#> SRR1366707 1 0.1806 0.91479 0.908 0.000 0.004 0.088 0.000 0
#> SRR1328143 1 0.0260 0.93875 0.992 0.000 0.000 0.008 0.000 0
#> SRR1473567 2 0.2631 0.80241 0.000 0.840 0.152 0.000 0.008 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17851 rows and 124 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 0.990 0.996 0.330 0.666 0.666
#> 3 3 0.607 0.795 0.795 0.608 0.839 0.762
#> 4 4 0.663 0.747 0.878 0.257 0.805 0.630
#> 5 5 0.658 0.563 0.757 0.121 0.926 0.783
#> 6 6 0.680 0.470 0.699 0.063 0.859 0.543
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
#> SRR1442087 1 0.0000 1.000 1.000 0.000
#> SRR1390119 2 0.0000 0.982 0.000 1.000
#> SRR1436127 1 0.0000 1.000 1.000 0.000
#> SRR1347278 1 0.0000 1.000 1.000 0.000
#> SRR1332904 2 0.0000 0.982 0.000 1.000
#> SRR1444179 1 0.0000 1.000 1.000 0.000
#> SRR1082685 1 0.0000 1.000 1.000 0.000
#> SRR1362287 1 0.0000 1.000 1.000 0.000
#> SRR1339007 1 0.0000 1.000 1.000 0.000
#> SRR1376557 2 0.0000 0.982 0.000 1.000
#> SRR1468700 2 0.0000 0.982 0.000 1.000
#> SRR1077455 1 0.0000 1.000 1.000 0.000
#> SRR1413978 1 0.0000 1.000 1.000 0.000
#> SRR1439896 1 0.0000 1.000 1.000 0.000
#> SRR1317963 2 0.0000 0.982 0.000 1.000
#> SRR1431865 1 0.0000 1.000 1.000 0.000
#> SRR1394253 1 0.0000 1.000 1.000 0.000
#> SRR1082664 1 0.0000 1.000 1.000 0.000
#> SRR1077968 1 0.0000 1.000 1.000 0.000
#> SRR1076393 1 0.0000 1.000 1.000 0.000
#> SRR1477476 2 0.0000 0.982 0.000 1.000
#> SRR1398057 1 0.0000 1.000 1.000 0.000
#> SRR1485042 1 0.0000 1.000 1.000 0.000
#> SRR1385453 1 0.0000 1.000 1.000 0.000
#> SRR1348074 1 0.0000 1.000 1.000 0.000
#> SRR813959 1 0.0000 1.000 1.000 0.000
#> SRR665442 2 0.0938 0.971 0.012 0.988
#> SRR1378068 1 0.0000 1.000 1.000 0.000
#> SRR1485237 1 0.0000 1.000 1.000 0.000
#> SRR1350792 1 0.0000 1.000 1.000 0.000
#> SRR1326797 1 0.0000 1.000 1.000 0.000
#> SRR808994 1 0.0000 1.000 1.000 0.000
#> SRR1474041 1 0.0000 1.000 1.000 0.000
#> SRR1405641 1 0.0000 1.000 1.000 0.000
#> SRR1362245 1 0.0000 1.000 1.000 0.000
#> SRR1500194 1 0.0000 1.000 1.000 0.000
#> SRR1414876 2 0.0000 0.982 0.000 1.000
#> SRR1478523 1 0.0000 1.000 1.000 0.000
#> SRR1325161 1 0.0000 1.000 1.000 0.000
#> SRR1318026 1 0.0000 1.000 1.000 0.000
#> SRR1343778 1 0.0000 1.000 1.000 0.000
#> SRR1441287 1 0.0000 1.000 1.000 0.000
#> SRR1430991 1 0.0000 1.000 1.000 0.000
#> SRR1499722 1 0.0000 1.000 1.000 0.000
#> SRR1351368 1 0.0000 1.000 1.000 0.000
#> SRR1441785 1 0.0000 1.000 1.000 0.000
#> SRR1096101 1 0.0000 1.000 1.000 0.000
#> SRR808375 1 0.0000 1.000 1.000 0.000
#> SRR1452842 1 0.0000 1.000 1.000 0.000
#> SRR1311709 1 0.0000 1.000 1.000 0.000
#> SRR1433352 1 0.0000 1.000 1.000 0.000
#> SRR1340241 2 0.0000 0.982 0.000 1.000
#> SRR1456754 1 0.0000 1.000 1.000 0.000
#> SRR1465172 1 0.0000 1.000 1.000 0.000
#> SRR1499284 1 0.0000 1.000 1.000 0.000
#> SRR1499607 2 0.0000 0.982 0.000 1.000
#> SRR812342 1 0.0000 1.000 1.000 0.000
#> SRR1405374 1 0.0000 1.000 1.000 0.000
#> SRR1403565 1 0.0000 1.000 1.000 0.000
#> SRR1332024 1 0.0000 1.000 1.000 0.000
#> SRR1471633 1 0.0000 1.000 1.000 0.000
#> SRR1325944 2 0.0000 0.982 0.000 1.000
#> SRR1429450 2 0.0000 0.982 0.000 1.000
#> SRR821573 1 0.0000 1.000 1.000 0.000
#> SRR1435372 1 0.0000 1.000 1.000 0.000
#> SRR1324184 2 0.0000 0.982 0.000 1.000
#> SRR816517 1 0.0000 1.000 1.000 0.000
#> SRR1324141 1 0.0000 1.000 1.000 0.000
#> SRR1101612 1 0.0000 1.000 1.000 0.000
#> SRR1356531 1 0.0000 1.000 1.000 0.000
#> SRR1089785 1 0.0000 1.000 1.000 0.000
#> SRR1077708 1 0.0000 1.000 1.000 0.000
#> SRR1343720 1 0.0000 1.000 1.000 0.000
#> SRR1477499 2 0.0000 0.982 0.000 1.000
#> SRR1347236 1 0.0000 1.000 1.000 0.000
#> SRR1326408 1 0.0000 1.000 1.000 0.000
#> SRR1336529 1 0.0000 1.000 1.000 0.000
#> SRR1440643 1 0.0000 1.000 1.000 0.000
#> SRR662354 1 0.0000 1.000 1.000 0.000
#> SRR1310817 1 0.0000 1.000 1.000 0.000
#> SRR1347389 2 0.0000 0.982 0.000 1.000
#> SRR1353097 1 0.0000 1.000 1.000 0.000
#> SRR1384737 1 0.0000 1.000 1.000 0.000
#> SRR1096339 1 0.0000 1.000 1.000 0.000
#> SRR1345329 1 0.0000 1.000 1.000 0.000
#> SRR1414771 1 0.0000 1.000 1.000 0.000
#> SRR1309119 1 0.0000 1.000 1.000 0.000
#> SRR1470438 1 0.0000 1.000 1.000 0.000
#> SRR1343221 1 0.0000 1.000 1.000 0.000
#> SRR1410847 1 0.0000 1.000 1.000 0.000
#> SRR807949 1 0.0000 1.000 1.000 0.000
#> SRR1442332 1 0.0000 1.000 1.000 0.000
#> SRR815920 1 0.0000 1.000 1.000 0.000
#> SRR1471524 1 0.0000 1.000 1.000 0.000
#> SRR1477221 1 0.0000 1.000 1.000 0.000
#> SRR1445046 2 0.0000 0.982 0.000 1.000
#> SRR1331962 2 0.0000 0.982 0.000 1.000
#> SRR1319946 2 0.0000 0.982 0.000 1.000
#> SRR1311599 1 0.0000 1.000 1.000 0.000
#> SRR1323977 1 0.0000 1.000 1.000 0.000
#> SRR1445132 2 0.0000 0.982 0.000 1.000
#> SRR1337321 1 0.0000 1.000 1.000 0.000
#> SRR1366390 2 0.0000 0.982 0.000 1.000
#> SRR1343012 1 0.0000 1.000 1.000 0.000
#> SRR1311958 2 0.0000 0.982 0.000 1.000
#> SRR1388234 2 0.9896 0.215 0.440 0.560
#> SRR1370384 1 0.0000 1.000 1.000 0.000
#> SRR1321650 1 0.0000 1.000 1.000 0.000
#> SRR1485117 2 0.0000 0.982 0.000 1.000
#> SRR1384713 1 0.0000 1.000 1.000 0.000
#> SRR816609 1 0.0000 1.000 1.000 0.000
#> SRR1486239 2 0.0000 0.982 0.000 1.000
#> SRR1309638 1 0.0000 1.000 1.000 0.000
#> SRR1356660 1 0.0000 1.000 1.000 0.000
#> SRR1392883 2 0.0000 0.982 0.000 1.000
#> SRR808130 1 0.0000 1.000 1.000 0.000
#> SRR816677 1 0.0000 1.000 1.000 0.000
#> SRR1455722 1 0.0000 1.000 1.000 0.000
#> SRR1336029 1 0.0000 1.000 1.000 0.000
#> SRR808452 1 0.0000 1.000 1.000 0.000
#> SRR1352169 1 0.0000 1.000 1.000 0.000
#> SRR1366707 1 0.0000 1.000 1.000 0.000
#> SRR1328143 1 0.0000 1.000 1.000 0.000
#> SRR1473567 2 0.0000 0.982 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 1 0.5678 0.772 0.684 0.000 0.316
#> SRR1390119 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1436127 1 0.5785 0.765 0.668 0.000 0.332
#> SRR1347278 1 0.5621 0.777 0.692 0.000 0.308
#> SRR1332904 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1362287 1 0.5733 0.768 0.676 0.000 0.324
#> SRR1339007 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1376557 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1468700 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1413978 1 0.5254 0.787 0.736 0.000 0.264
#> SRR1439896 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1317963 3 0.5859 0.436 0.000 0.344 0.656
#> SRR1431865 1 0.5465 0.783 0.712 0.000 0.288
#> SRR1394253 1 0.3116 0.804 0.892 0.000 0.108
#> SRR1082664 1 0.5497 0.782 0.708 0.000 0.292
#> SRR1077968 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1076393 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1477476 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1398057 1 0.5706 0.770 0.680 0.000 0.320
#> SRR1485042 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1385453 3 0.5733 0.803 0.324 0.000 0.676
#> SRR1348074 3 0.5785 0.804 0.332 0.000 0.668
#> SRR813959 3 0.5733 0.803 0.324 0.000 0.676
#> SRR665442 3 0.5733 0.459 0.000 0.324 0.676
#> SRR1378068 1 0.5760 0.766 0.672 0.000 0.328
#> SRR1485237 3 0.5785 0.804 0.332 0.000 0.668
#> SRR1350792 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.801 1.000 0.000 0.000
#> SRR808994 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1474041 1 0.2537 0.805 0.920 0.000 0.080
#> SRR1405641 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1362245 1 0.5835 0.761 0.660 0.000 0.340
#> SRR1500194 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1414876 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1478523 1 0.1411 0.793 0.964 0.000 0.036
#> SRR1325161 1 0.3267 0.804 0.884 0.000 0.116
#> SRR1318026 3 0.5785 0.804 0.332 0.000 0.668
#> SRR1343778 1 0.5497 0.782 0.708 0.000 0.292
#> SRR1441287 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1430991 1 0.0237 0.802 0.996 0.000 0.004
#> SRR1499722 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1351368 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1441785 1 0.5560 0.779 0.700 0.000 0.300
#> SRR1096101 1 0.0000 0.801 1.000 0.000 0.000
#> SRR808375 1 0.3340 0.804 0.880 0.000 0.120
#> SRR1452842 1 0.3116 0.804 0.892 0.000 0.108
#> SRR1311709 1 0.6280 -0.429 0.540 0.000 0.460
#> SRR1433352 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1340241 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1456754 1 0.5465 0.783 0.712 0.000 0.288
#> SRR1465172 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1499284 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1499607 3 0.5785 0.453 0.000 0.332 0.668
#> SRR812342 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1405374 1 0.5465 0.783 0.712 0.000 0.288
#> SRR1403565 1 0.5465 0.783 0.712 0.000 0.288
#> SRR1332024 1 0.5810 0.763 0.664 0.000 0.336
#> SRR1471633 1 0.2165 0.732 0.936 0.000 0.064
#> SRR1325944 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1429450 2 0.0000 1.000 0.000 1.000 0.000
#> SRR821573 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1435372 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1324184 2 0.0000 1.000 0.000 1.000 0.000
#> SRR816517 3 0.1411 0.544 0.036 0.000 0.964
#> SRR1324141 3 0.5785 0.804 0.332 0.000 0.668
#> SRR1101612 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1089785 1 0.0237 0.801 0.996 0.000 0.004
#> SRR1077708 1 0.5706 0.770 0.680 0.000 0.320
#> SRR1343720 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1477499 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1347236 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1326408 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1336529 1 0.5810 0.763 0.664 0.000 0.336
#> SRR1440643 3 0.5733 0.803 0.324 0.000 0.676
#> SRR662354 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1310817 1 0.0892 0.794 0.980 0.000 0.020
#> SRR1347389 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1384737 3 0.3941 0.586 0.156 0.000 0.844
#> SRR1096339 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1345329 3 0.5785 0.804 0.332 0.000 0.668
#> SRR1414771 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1309119 1 0.2165 0.732 0.936 0.000 0.064
#> SRR1470438 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1343221 1 0.5465 0.783 0.712 0.000 0.288
#> SRR1410847 1 0.0000 0.801 1.000 0.000 0.000
#> SRR807949 1 0.0237 0.801 0.996 0.000 0.004
#> SRR1442332 1 0.0000 0.801 1.000 0.000 0.000
#> SRR815920 1 0.5785 0.765 0.668 0.000 0.332
#> SRR1471524 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1477221 1 0.5760 0.766 0.672 0.000 0.328
#> SRR1445046 3 0.5948 0.408 0.000 0.360 0.640
#> SRR1331962 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1319946 3 0.5882 0.430 0.000 0.348 0.652
#> SRR1311599 1 0.5497 0.782 0.708 0.000 0.292
#> SRR1323977 3 0.5785 0.804 0.332 0.000 0.668
#> SRR1445132 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1337321 1 0.5835 0.761 0.660 0.000 0.340
#> SRR1366390 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1343012 1 0.5138 0.783 0.748 0.000 0.252
#> SRR1311958 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1388234 3 0.6369 0.797 0.316 0.016 0.668
#> SRR1370384 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1321650 1 0.5760 0.766 0.672 0.000 0.328
#> SRR1485117 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.801 1.000 0.000 0.000
#> SRR816609 3 0.5785 0.804 0.332 0.000 0.668
#> SRR1486239 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1309638 1 0.5733 0.768 0.676 0.000 0.324
#> SRR1356660 1 0.5497 0.782 0.708 0.000 0.292
#> SRR1392883 2 0.0000 1.000 0.000 1.000 0.000
#> SRR808130 1 0.4702 0.796 0.788 0.000 0.212
#> SRR816677 1 0.0747 0.793 0.984 0.000 0.016
#> SRR1455722 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1336029 1 0.5591 0.779 0.696 0.000 0.304
#> SRR808452 1 0.0000 0.801 1.000 0.000 0.000
#> SRR1352169 1 0.5497 0.782 0.708 0.000 0.292
#> SRR1366707 1 0.5859 0.759 0.656 0.000 0.344
#> SRR1328143 1 0.3340 0.804 0.880 0.000 0.120
#> SRR1473567 2 0.0000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.5366 0.0276 0.548 0.000 0.440 0.012
#> SRR1390119 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR1436127 3 0.3172 0.8341 0.160 0.000 0.840 0.000
#> SRR1347278 3 0.5313 0.4602 0.376 0.000 0.608 0.016
#> SRR1332904 2 0.2342 0.9399 0.000 0.912 0.080 0.008
#> SRR1444179 1 0.2714 0.7330 0.884 0.000 0.112 0.004
#> SRR1082685 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1362287 3 0.4453 0.7416 0.244 0.000 0.744 0.012
#> SRR1339007 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.0000 0.9779 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0469 0.9758 0.000 0.988 0.012 0.000
#> SRR1077455 1 0.0188 0.8208 0.996 0.000 0.000 0.004
#> SRR1413978 3 0.5396 0.1879 0.464 0.000 0.524 0.012
#> SRR1439896 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1317963 4 0.2670 0.9230 0.000 0.024 0.072 0.904
#> SRR1431865 1 0.5402 -0.0741 0.516 0.000 0.472 0.012
#> SRR1394253 1 0.0804 0.8155 0.980 0.000 0.008 0.012
#> SRR1082664 1 0.5093 0.3319 0.640 0.000 0.348 0.012
#> SRR1077968 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1076393 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1477476 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR1398057 1 0.5392 -0.0583 0.528 0.000 0.460 0.012
#> SRR1485042 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1385453 4 0.1767 0.9368 0.012 0.000 0.044 0.944
#> SRR1348074 4 0.1042 0.9500 0.020 0.000 0.008 0.972
#> SRR813959 4 0.0804 0.9492 0.012 0.000 0.008 0.980
#> SRR665442 4 0.2861 0.9149 0.000 0.016 0.096 0.888
#> SRR1378068 3 0.3764 0.7826 0.216 0.000 0.784 0.000
#> SRR1485237 4 0.1004 0.9479 0.024 0.000 0.004 0.972
#> SRR1350792 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.0188 0.8208 0.996 0.000 0.000 0.004
#> SRR808994 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1474041 1 0.4290 0.6335 0.772 0.000 0.212 0.016
#> SRR1405641 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1362245 3 0.2921 0.8431 0.140 0.000 0.860 0.000
#> SRR1500194 1 0.2469 0.7395 0.892 0.000 0.108 0.000
#> SRR1414876 2 0.0000 0.9779 0.000 1.000 0.000 0.000
#> SRR1478523 1 0.6242 0.3967 0.612 0.000 0.308 0.080
#> SRR1325161 1 0.1406 0.8073 0.960 0.000 0.024 0.016
#> SRR1318026 4 0.2002 0.9364 0.020 0.000 0.044 0.936
#> SRR1343778 1 0.4999 0.3788 0.660 0.000 0.328 0.012
#> SRR1441287 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1430991 1 0.0657 0.8176 0.984 0.000 0.012 0.004
#> SRR1499722 1 0.0524 0.8191 0.988 0.000 0.008 0.004
#> SRR1351368 3 0.2983 0.7950 0.068 0.000 0.892 0.040
#> SRR1441785 1 0.5256 0.1967 0.596 0.000 0.392 0.012
#> SRR1096101 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR808375 1 0.1406 0.8073 0.960 0.000 0.024 0.016
#> SRR1452842 1 0.1406 0.8073 0.960 0.000 0.024 0.016
#> SRR1311709 1 0.7181 0.1597 0.512 0.000 0.152 0.336
#> SRR1433352 1 0.0188 0.8208 0.996 0.000 0.000 0.004
#> SRR1340241 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR1456754 1 0.5038 0.3599 0.652 0.000 0.336 0.012
#> SRR1465172 1 0.0672 0.8184 0.984 0.000 0.008 0.008
#> SRR1499284 1 0.0524 0.8191 0.988 0.000 0.008 0.004
#> SRR1499607 4 0.2670 0.9230 0.000 0.024 0.072 0.904
#> SRR812342 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1405374 1 0.5057 0.3409 0.648 0.000 0.340 0.012
#> SRR1403565 1 0.5204 0.2534 0.612 0.000 0.376 0.012
#> SRR1332024 3 0.3105 0.8432 0.140 0.000 0.856 0.004
#> SRR1471633 1 0.4535 0.6548 0.804 0.000 0.112 0.084
#> SRR1325944 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR1429450 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR821573 1 0.1902 0.7789 0.932 0.000 0.064 0.004
#> SRR1435372 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.0524 0.9760 0.000 0.988 0.008 0.004
#> SRR816517 4 0.0817 0.9443 0.000 0.000 0.024 0.976
#> SRR1324141 4 0.3099 0.8820 0.020 0.000 0.104 0.876
#> SRR1101612 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1089785 1 0.0524 0.8191 0.988 0.000 0.008 0.004
#> SRR1077708 1 0.5337 0.0883 0.564 0.000 0.424 0.012
#> SRR1343720 1 0.0376 0.8202 0.992 0.000 0.004 0.004
#> SRR1477499 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR1347236 1 0.0188 0.8208 0.996 0.000 0.000 0.004
#> SRR1326408 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1336529 3 0.3157 0.8426 0.144 0.000 0.852 0.004
#> SRR1440643 4 0.1767 0.9368 0.012 0.000 0.044 0.944
#> SRR662354 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1310817 1 0.4434 0.6079 0.756 0.000 0.228 0.016
#> SRR1347389 2 0.2342 0.9413 0.000 0.912 0.080 0.008
#> SRR1353097 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1384737 3 0.5143 0.1468 0.004 0.000 0.540 0.456
#> SRR1096339 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1345329 4 0.1042 0.9500 0.020 0.000 0.008 0.972
#> SRR1414771 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1309119 1 0.4535 0.6548 0.804 0.000 0.112 0.084
#> SRR1470438 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1343221 1 0.5110 0.3221 0.636 0.000 0.352 0.012
#> SRR1410847 1 0.0469 0.8179 0.988 0.000 0.000 0.012
#> SRR807949 1 0.0657 0.8176 0.984 0.000 0.012 0.004
#> SRR1442332 1 0.0188 0.8208 0.996 0.000 0.000 0.004
#> SRR815920 3 0.3306 0.8374 0.156 0.000 0.840 0.004
#> SRR1471524 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1477221 3 0.3377 0.8420 0.140 0.000 0.848 0.012
#> SRR1445046 4 0.3107 0.9094 0.000 0.036 0.080 0.884
#> SRR1331962 2 0.2197 0.9422 0.000 0.916 0.080 0.004
#> SRR1319946 4 0.2670 0.9230 0.000 0.024 0.072 0.904
#> SRR1311599 1 0.5159 0.2870 0.624 0.000 0.364 0.012
#> SRR1323977 4 0.1042 0.9500 0.020 0.000 0.008 0.972
#> SRR1445132 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR1337321 3 0.3377 0.8420 0.140 0.000 0.848 0.012
#> SRR1366390 2 0.0524 0.9760 0.000 0.988 0.008 0.004
#> SRR1343012 3 0.6532 0.3652 0.368 0.000 0.548 0.084
#> SRR1311958 2 0.2342 0.9399 0.000 0.912 0.080 0.008
#> SRR1388234 4 0.2413 0.9357 0.020 0.000 0.064 0.916
#> SRR1370384 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1321650 3 0.3837 0.7744 0.224 0.000 0.776 0.000
#> SRR1485117 2 0.0000 0.9779 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR816609 4 0.1042 0.9500 0.020 0.000 0.008 0.972
#> SRR1486239 2 0.2342 0.9399 0.000 0.912 0.080 0.008
#> SRR1309638 3 0.4072 0.7386 0.252 0.000 0.748 0.000
#> SRR1356660 1 0.5244 0.2096 0.600 0.000 0.388 0.012
#> SRR1392883 2 0.0188 0.9780 0.000 0.996 0.004 0.000
#> SRR808130 1 0.2142 0.7853 0.928 0.000 0.056 0.016
#> SRR816677 1 0.5291 0.5818 0.740 0.000 0.180 0.080
#> SRR1455722 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1336029 3 0.5372 0.2519 0.444 0.000 0.544 0.012
#> SRR808452 1 0.0000 0.8213 1.000 0.000 0.000 0.000
#> SRR1352169 1 0.5508 -0.0838 0.508 0.000 0.476 0.016
#> SRR1366707 3 0.2593 0.8407 0.104 0.000 0.892 0.004
#> SRR1328143 1 0.1798 0.7974 0.944 0.000 0.040 0.016
#> SRR1473567 2 0.0336 0.9766 0.000 0.992 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 1 0.6225 -0.02264 0.544 0.000 0.200 0.000 0.256
#> SRR1390119 2 0.0290 0.92068 0.000 0.992 0.008 0.000 0.000
#> SRR1436127 3 0.3075 0.81434 0.048 0.000 0.860 0.000 0.092
#> SRR1347278 3 0.6660 -0.06923 0.228 0.000 0.388 0.000 0.384
#> SRR1332904 2 0.3884 0.77656 0.000 0.708 0.000 0.004 0.288
#> SRR1444179 1 0.5671 0.20154 0.536 0.000 0.016 0.048 0.400
#> SRR1082685 1 0.4268 0.51553 0.708 0.000 0.000 0.024 0.268
#> SRR1362287 3 0.5409 0.50776 0.084 0.000 0.612 0.000 0.304
#> SRR1339007 1 0.3970 0.53367 0.752 0.000 0.000 0.024 0.224
#> SRR1376557 2 0.0000 0.92112 0.000 1.000 0.000 0.000 0.000
#> SRR1468700 2 0.2127 0.88568 0.000 0.892 0.000 0.000 0.108
#> SRR1077455 1 0.1732 0.55428 0.920 0.000 0.000 0.000 0.080
#> SRR1413978 5 0.6253 0.37589 0.204 0.000 0.228 0.004 0.564
#> SRR1439896 1 0.4292 0.51179 0.704 0.000 0.000 0.024 0.272
#> SRR1317963 4 0.4157 0.72363 0.000 0.020 0.000 0.716 0.264
#> SRR1431865 5 0.6557 0.24148 0.340 0.000 0.212 0.000 0.448
#> SRR1394253 1 0.4242 0.28976 0.572 0.000 0.000 0.000 0.428
#> SRR1082664 1 0.5496 0.17020 0.652 0.000 0.152 0.000 0.196
#> SRR1077968 1 0.3940 0.53531 0.756 0.000 0.000 0.024 0.220
#> SRR1076393 3 0.0960 0.84732 0.016 0.000 0.972 0.004 0.008
#> SRR1477476 2 0.0290 0.92068 0.000 0.992 0.008 0.000 0.000
#> SRR1398057 1 0.6405 -0.06823 0.512 0.000 0.252 0.000 0.236
#> SRR1485042 1 0.4193 0.52195 0.720 0.000 0.000 0.024 0.256
#> SRR1385453 4 0.3203 0.76730 0.008 0.000 0.020 0.848 0.124
#> SRR1348074 4 0.0162 0.82341 0.000 0.000 0.004 0.996 0.000
#> SRR813959 4 0.0290 0.82283 0.008 0.000 0.000 0.992 0.000
#> SRR665442 4 0.4735 0.70306 0.000 0.020 0.012 0.668 0.300
#> SRR1378068 3 0.1671 0.84085 0.076 0.000 0.924 0.000 0.000
#> SRR1485237 4 0.1430 0.80929 0.000 0.000 0.004 0.944 0.052
#> SRR1350792 1 0.3970 0.53367 0.752 0.000 0.000 0.024 0.224
#> SRR1326797 1 0.1908 0.53921 0.908 0.000 0.000 0.000 0.092
#> SRR808994 3 0.0771 0.85021 0.020 0.000 0.976 0.004 0.000
#> SRR1474041 1 0.5236 0.28254 0.684 0.000 0.152 0.000 0.164
#> SRR1405641 3 0.0771 0.85021 0.020 0.000 0.976 0.004 0.000
#> SRR1362245 3 0.2304 0.84568 0.044 0.000 0.908 0.000 0.048
#> SRR1500194 1 0.5253 0.26399 0.564 0.000 0.016 0.024 0.396
#> SRR1414876 2 0.0510 0.92078 0.000 0.984 0.000 0.000 0.016
#> SRR1478523 1 0.8010 -0.27134 0.352 0.000 0.084 0.272 0.292
#> SRR1325161 1 0.2612 0.47053 0.868 0.000 0.008 0.000 0.124
#> SRR1318026 4 0.2921 0.76624 0.000 0.000 0.020 0.856 0.124
#> SRR1343778 1 0.5452 0.17867 0.656 0.000 0.144 0.000 0.200
#> SRR1441287 1 0.4292 0.51179 0.704 0.000 0.000 0.024 0.272
#> SRR1430991 1 0.1082 0.52957 0.964 0.000 0.008 0.000 0.028
#> SRR1499722 1 0.0771 0.53457 0.976 0.000 0.004 0.000 0.020
#> SRR1351368 3 0.3403 0.78152 0.008 0.000 0.820 0.012 0.160
#> SRR1441785 5 0.6605 0.23708 0.348 0.000 0.220 0.000 0.432
#> SRR1096101 1 0.4268 0.51528 0.708 0.000 0.000 0.024 0.268
#> SRR808375 1 0.2798 0.45492 0.852 0.000 0.008 0.000 0.140
#> SRR1452842 1 0.3835 0.37045 0.732 0.000 0.008 0.000 0.260
#> SRR1311709 5 0.7162 0.29455 0.288 0.000 0.016 0.300 0.396
#> SRR1433352 1 0.1410 0.54739 0.940 0.000 0.000 0.000 0.060
#> SRR1340241 2 0.0290 0.92068 0.000 0.992 0.008 0.000 0.000
#> SRR1456754 1 0.6082 0.00106 0.540 0.000 0.148 0.000 0.312
#> SRR1465172 1 0.0865 0.53338 0.972 0.000 0.004 0.000 0.024
#> SRR1499284 1 0.0880 0.54761 0.968 0.000 0.000 0.000 0.032
#> SRR1499607 4 0.4132 0.72570 0.000 0.020 0.000 0.720 0.260
#> SRR812342 1 0.3877 0.53621 0.764 0.000 0.000 0.024 0.212
#> SRR1405374 5 0.6347 0.14192 0.376 0.000 0.164 0.000 0.460
#> SRR1403565 1 0.6544 -0.16739 0.440 0.000 0.204 0.000 0.356
#> SRR1332024 3 0.1043 0.85094 0.040 0.000 0.960 0.000 0.000
#> SRR1471633 5 0.7152 0.29335 0.304 0.000 0.016 0.280 0.400
#> SRR1325944 2 0.0000 0.92112 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.92112 0.000 1.000 0.000 0.000 0.000
#> SRR821573 1 0.3759 0.37506 0.764 0.000 0.016 0.000 0.220
#> SRR1435372 1 0.4268 0.51553 0.708 0.000 0.000 0.024 0.268
#> SRR1324184 2 0.0898 0.91923 0.000 0.972 0.008 0.000 0.020
#> SRR816517 4 0.0703 0.81926 0.000 0.000 0.024 0.976 0.000
#> SRR1324141 4 0.3319 0.73032 0.000 0.000 0.020 0.820 0.160
#> SRR1101612 1 0.4000 0.53144 0.748 0.000 0.000 0.024 0.228
#> SRR1356531 1 0.3940 0.53531 0.756 0.000 0.000 0.024 0.220
#> SRR1089785 1 0.2536 0.47508 0.868 0.000 0.004 0.000 0.128
#> SRR1077708 1 0.5849 0.09748 0.608 0.000 0.196 0.000 0.196
#> SRR1343720 1 0.0609 0.53603 0.980 0.000 0.000 0.000 0.020
#> SRR1477499 2 0.0000 0.92112 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 1 0.1908 0.54548 0.908 0.000 0.000 0.000 0.092
#> SRR1326408 1 0.4268 0.51553 0.708 0.000 0.000 0.024 0.268
#> SRR1336529 3 0.1043 0.85094 0.040 0.000 0.960 0.000 0.000
#> SRR1440643 4 0.3203 0.76730 0.008 0.000 0.020 0.848 0.124
#> SRR662354 1 0.4268 0.51553 0.708 0.000 0.000 0.024 0.268
#> SRR1310817 1 0.6322 0.03258 0.552 0.000 0.104 0.024 0.320
#> SRR1347389 2 0.3980 0.77931 0.000 0.708 0.008 0.000 0.284
#> SRR1353097 1 0.4219 0.51931 0.716 0.000 0.000 0.024 0.260
#> SRR1384737 4 0.6346 0.12882 0.000 0.000 0.160 0.436 0.404
#> SRR1096339 1 0.4219 0.51931 0.716 0.000 0.000 0.024 0.260
#> SRR1345329 4 0.0162 0.82341 0.000 0.000 0.004 0.996 0.000
#> SRR1414771 3 0.0833 0.84662 0.016 0.000 0.976 0.004 0.004
#> SRR1309119 5 0.7159 0.28787 0.308 0.000 0.016 0.280 0.396
#> SRR1470438 3 0.0833 0.84662 0.016 0.000 0.976 0.004 0.004
#> SRR1343221 1 0.6175 -0.03489 0.528 0.000 0.160 0.000 0.312
#> SRR1410847 1 0.4165 0.51184 0.672 0.000 0.000 0.008 0.320
#> SRR807949 1 0.2411 0.48161 0.884 0.000 0.008 0.000 0.108
#> SRR1442332 1 0.2488 0.53275 0.872 0.000 0.004 0.000 0.124
#> SRR815920 3 0.1768 0.84073 0.072 0.000 0.924 0.000 0.004
#> SRR1471524 3 0.3031 0.80600 0.016 0.000 0.852 0.004 0.128
#> SRR1477221 3 0.4640 0.67113 0.048 0.000 0.696 0.000 0.256
#> SRR1445046 4 0.4297 0.70708 0.000 0.020 0.000 0.692 0.288
#> SRR1331962 2 0.3730 0.77942 0.000 0.712 0.000 0.000 0.288
#> SRR1319946 4 0.4181 0.72207 0.000 0.020 0.000 0.712 0.268
#> SRR1311599 1 0.6445 -0.14490 0.456 0.000 0.184 0.000 0.360
#> SRR1323977 4 0.0000 0.82313 0.000 0.000 0.000 1.000 0.000
#> SRR1445132 2 0.0290 0.92068 0.000 0.992 0.008 0.000 0.000
#> SRR1337321 3 0.4573 0.67378 0.044 0.000 0.700 0.000 0.256
#> SRR1366390 2 0.1168 0.91657 0.000 0.960 0.008 0.000 0.032
#> SRR1343012 5 0.7380 0.23996 0.068 0.000 0.188 0.244 0.500
#> SRR1311958 2 0.3884 0.77656 0.000 0.708 0.000 0.004 0.288
#> SRR1388234 4 0.2813 0.78415 0.000 0.000 0.000 0.832 0.168
#> SRR1370384 1 0.3381 0.53878 0.808 0.000 0.000 0.016 0.176
#> SRR1321650 3 0.1732 0.83886 0.080 0.000 0.920 0.000 0.000
#> SRR1485117 2 0.0609 0.92029 0.000 0.980 0.000 0.000 0.020
#> SRR1384713 1 0.2513 0.55243 0.876 0.000 0.000 0.008 0.116
#> SRR816609 4 0.0162 0.82341 0.000 0.000 0.004 0.996 0.000
#> SRR1486239 2 0.3884 0.77656 0.000 0.708 0.000 0.004 0.288
#> SRR1309638 3 0.3575 0.78412 0.120 0.000 0.824 0.000 0.056
#> SRR1356660 5 0.6605 0.23708 0.348 0.000 0.220 0.000 0.432
#> SRR1392883 2 0.0000 0.92112 0.000 1.000 0.000 0.000 0.000
#> SRR808130 1 0.3011 0.44682 0.844 0.000 0.016 0.000 0.140
#> SRR816677 5 0.7152 0.29335 0.304 0.000 0.016 0.280 0.400
#> SRR1455722 1 0.4268 0.51528 0.708 0.000 0.000 0.024 0.268
#> SRR1336029 5 0.6352 0.31945 0.148 0.000 0.288 0.012 0.552
#> SRR808452 1 0.4243 0.51829 0.712 0.000 0.000 0.024 0.264
#> SRR1352169 1 0.5993 0.09488 0.584 0.000 0.184 0.000 0.232
#> SRR1366707 3 0.0960 0.84732 0.016 0.000 0.972 0.004 0.008
#> SRR1328143 1 0.3011 0.44682 0.844 0.000 0.016 0.000 0.140
#> SRR1473567 2 0.0609 0.92029 0.000 0.980 0.000 0.000 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.6200 0.17376 0.144 0.000 0.084 0.000 0.588 0.184
#> SRR1390119 2 0.0146 0.85306 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1436127 3 0.2961 0.72675 0.020 0.000 0.840 0.000 0.132 0.008
#> SRR1347278 5 0.7201 -0.51468 0.088 0.000 0.252 0.000 0.360 0.300
#> SRR1332904 2 0.3998 0.61010 0.000 0.504 0.000 0.004 0.000 0.492
#> SRR1444179 1 0.3298 0.53888 0.856 0.000 0.012 0.024 0.056 0.052
#> SRR1082685 1 0.0291 0.63025 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1362287 3 0.7321 -0.26477 0.104 0.000 0.348 0.000 0.264 0.284
#> SRR1339007 1 0.1082 0.61503 0.956 0.000 0.000 0.000 0.040 0.004
#> SRR1376557 2 0.1082 0.85416 0.000 0.956 0.000 0.000 0.004 0.040
#> SRR1468700 2 0.3298 0.78687 0.000 0.756 0.000 0.000 0.008 0.236
#> SRR1077455 1 0.3741 0.15500 0.672 0.000 0.000 0.000 0.320 0.008
#> SRR1413978 1 0.7349 -0.64423 0.312 0.000 0.104 0.000 0.276 0.308
#> SRR1439896 1 0.0146 0.63002 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1317963 4 0.4477 0.60477 0.000 0.004 0.000 0.588 0.028 0.380
#> SRR1431865 1 0.7417 -0.53998 0.348 0.000 0.100 0.004 0.268 0.280
#> SRR1394253 1 0.6001 -0.20223 0.436 0.000 0.000 0.000 0.296 0.268
#> SRR1082664 5 0.4573 0.57065 0.208 0.000 0.056 0.000 0.712 0.024
#> SRR1077968 1 0.1265 0.61071 0.948 0.000 0.000 0.000 0.044 0.008
#> SRR1076393 3 0.1180 0.76763 0.012 0.000 0.960 0.000 0.012 0.016
#> SRR1477476 2 0.0146 0.85306 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1398057 5 0.5459 0.48816 0.180 0.000 0.084 0.000 0.664 0.072
#> SRR1485042 1 0.0260 0.63022 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1385453 4 0.3147 0.69244 0.000 0.000 0.012 0.844 0.044 0.100
#> SRR1348074 4 0.0436 0.76030 0.004 0.000 0.000 0.988 0.004 0.004
#> SRR813959 4 0.0865 0.75853 0.000 0.000 0.000 0.964 0.036 0.000
#> SRR665442 4 0.5182 0.57509 0.000 0.000 0.000 0.532 0.096 0.372
#> SRR1378068 3 0.2622 0.74188 0.024 0.000 0.868 0.000 0.104 0.004
#> SRR1485237 4 0.1116 0.75329 0.004 0.000 0.000 0.960 0.008 0.028
#> SRR1350792 1 0.0972 0.61986 0.964 0.000 0.000 0.000 0.028 0.008
#> SRR1326797 1 0.3860 -0.29926 0.528 0.000 0.000 0.000 0.472 0.000
#> SRR808994 3 0.0508 0.76790 0.012 0.000 0.984 0.000 0.000 0.004
#> SRR1474041 5 0.4806 0.55454 0.236 0.000 0.072 0.000 0.676 0.016
#> SRR1405641 3 0.0508 0.76874 0.012 0.000 0.984 0.000 0.004 0.000
#> SRR1362245 3 0.3732 0.69685 0.020 0.000 0.808 0.000 0.068 0.104
#> SRR1500194 1 0.2247 0.57294 0.904 0.000 0.012 0.000 0.060 0.024
#> SRR1414876 2 0.1531 0.85224 0.000 0.928 0.000 0.000 0.004 0.068
#> SRR1478523 5 0.6984 -0.02494 0.068 0.000 0.032 0.304 0.484 0.112
#> SRR1325161 5 0.3802 0.57246 0.312 0.000 0.000 0.000 0.676 0.012
#> SRR1318026 4 0.3054 0.69105 0.004 0.000 0.012 0.852 0.028 0.104
#> SRR1343778 5 0.4730 0.56874 0.212 0.000 0.060 0.000 0.700 0.028
#> SRR1441287 1 0.0000 0.63060 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.3881 0.48959 0.396 0.000 0.000 0.000 0.600 0.004
#> SRR1499722 5 0.3923 0.46103 0.416 0.000 0.000 0.000 0.580 0.004
#> SRR1351368 3 0.5408 0.44471 0.004 0.000 0.624 0.012 0.124 0.236
#> SRR1441785 1 0.7386 -0.48685 0.352 0.000 0.096 0.004 0.260 0.288
#> SRR1096101 1 0.0000 0.63060 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.3584 0.57451 0.308 0.000 0.000 0.000 0.688 0.004
#> SRR1452842 5 0.5768 0.40070 0.316 0.000 0.000 0.000 0.488 0.196
#> SRR1311709 1 0.6297 0.06404 0.500 0.000 0.008 0.344 0.048 0.100
#> SRR1433352 1 0.3860 -0.31843 0.528 0.000 0.000 0.000 0.472 0.000
#> SRR1340241 2 0.0291 0.85346 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1456754 5 0.7064 -0.17496 0.256 0.000 0.076 0.000 0.396 0.272
#> SRR1465172 5 0.3915 0.46817 0.412 0.000 0.000 0.000 0.584 0.004
#> SRR1499284 1 0.3955 -0.03702 0.608 0.000 0.000 0.000 0.384 0.008
#> SRR1499607 4 0.4450 0.63028 0.000 0.004 0.000 0.632 0.036 0.328
#> SRR812342 1 0.1333 0.60816 0.944 0.000 0.000 0.000 0.048 0.008
#> SRR1405374 1 0.7217 -0.45094 0.376 0.000 0.076 0.004 0.256 0.288
#> SRR1403565 5 0.7303 -0.35994 0.292 0.000 0.096 0.000 0.320 0.292
#> SRR1332024 3 0.0806 0.77085 0.020 0.000 0.972 0.000 0.008 0.000
#> SRR1471633 1 0.6125 0.16799 0.572 0.000 0.012 0.272 0.048 0.096
#> SRR1325944 2 0.0000 0.85327 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.85327 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.5625 0.35298 0.376 0.000 0.008 0.004 0.508 0.104
#> SRR1435372 1 0.0146 0.63042 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1324184 2 0.2341 0.84577 0.000 0.900 0.012 0.000 0.032 0.056
#> SRR816517 4 0.1364 0.75685 0.000 0.000 0.004 0.944 0.048 0.004
#> SRR1324141 4 0.4034 0.64188 0.024 0.000 0.012 0.800 0.056 0.108
#> SRR1101612 1 0.0993 0.62226 0.964 0.000 0.000 0.000 0.024 0.012
#> SRR1356531 1 0.1049 0.61788 0.960 0.000 0.000 0.000 0.032 0.008
#> SRR1089785 5 0.3684 0.52712 0.372 0.000 0.000 0.000 0.628 0.000
#> SRR1077708 5 0.4937 0.54917 0.188 0.000 0.084 0.000 0.696 0.032
#> SRR1343720 5 0.3789 0.46253 0.416 0.000 0.000 0.000 0.584 0.000
#> SRR1477499 2 0.0000 0.85327 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 1 0.3804 -0.22281 0.576 0.000 0.000 0.000 0.424 0.000
#> SRR1326408 1 0.0146 0.63042 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1336529 3 0.0806 0.77085 0.020 0.000 0.972 0.000 0.008 0.000
#> SRR1440643 4 0.3127 0.68997 0.000 0.000 0.012 0.844 0.040 0.104
#> SRR662354 1 0.0405 0.62987 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR1310817 5 0.6097 0.40654 0.228 0.000 0.040 0.012 0.592 0.128
#> SRR1347389 2 0.4844 0.61945 0.000 0.500 0.012 0.000 0.032 0.456
#> SRR1353097 1 0.0508 0.62831 0.984 0.000 0.000 0.000 0.012 0.004
#> SRR1384737 4 0.7010 -0.35764 0.028 0.000 0.052 0.428 0.136 0.356
#> SRR1096339 1 0.0520 0.62872 0.984 0.000 0.000 0.000 0.008 0.008
#> SRR1345329 4 0.0291 0.76054 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR1414771 3 0.0508 0.76790 0.012 0.000 0.984 0.000 0.000 0.004
#> SRR1309119 1 0.6108 0.17504 0.576 0.000 0.012 0.268 0.048 0.096
#> SRR1470438 3 0.0508 0.76790 0.012 0.000 0.984 0.000 0.000 0.004
#> SRR1343221 5 0.7286 -0.27430 0.276 0.000 0.080 0.004 0.352 0.288
#> SRR1410847 1 0.1745 0.57406 0.920 0.000 0.000 0.000 0.068 0.012
#> SRR807949 5 0.3634 0.53885 0.356 0.000 0.000 0.000 0.644 0.000
#> SRR1442332 5 0.3993 0.38728 0.476 0.000 0.000 0.000 0.520 0.004
#> SRR815920 3 0.2443 0.74859 0.020 0.000 0.880 0.000 0.096 0.004
#> SRR1471524 3 0.4720 0.58213 0.012 0.000 0.720 0.004 0.124 0.140
#> SRR1477221 3 0.6457 -0.00946 0.020 0.000 0.416 0.000 0.280 0.284
#> SRR1445046 4 0.3998 0.51732 0.000 0.004 0.000 0.504 0.000 0.492
#> SRR1331962 2 0.4226 0.61377 0.000 0.504 0.000 0.004 0.008 0.484
#> SRR1319946 4 0.4242 0.59370 0.000 0.004 0.000 0.572 0.012 0.412
#> SRR1311599 5 0.7393 -0.30283 0.304 0.000 0.092 0.004 0.320 0.280
#> SRR1323977 4 0.0653 0.76074 0.004 0.000 0.000 0.980 0.012 0.004
#> SRR1445132 2 0.0146 0.85306 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1337321 3 0.6510 0.00740 0.020 0.000 0.440 0.004 0.252 0.284
#> SRR1366390 2 0.3210 0.82896 0.000 0.832 0.012 0.000 0.032 0.124
#> SRR1343012 6 0.8030 0.28582 0.064 0.000 0.100 0.304 0.172 0.360
#> SRR1311958 2 0.4097 0.60566 0.000 0.500 0.000 0.008 0.000 0.492
#> SRR1388234 4 0.3010 0.72092 0.004 0.000 0.000 0.836 0.028 0.132
#> SRR1370384 1 0.2257 0.53671 0.876 0.000 0.000 0.000 0.116 0.008
#> SRR1321650 3 0.2588 0.74535 0.024 0.000 0.876 0.000 0.092 0.008
#> SRR1485117 2 0.1643 0.85177 0.000 0.924 0.000 0.000 0.008 0.068
#> SRR1384713 1 0.3323 0.34322 0.752 0.000 0.000 0.000 0.240 0.008
#> SRR816609 4 0.0291 0.76000 0.004 0.000 0.000 0.992 0.004 0.000
#> SRR1486239 2 0.4097 0.60566 0.000 0.500 0.000 0.008 0.000 0.492
#> SRR1309638 3 0.4738 0.61859 0.032 0.000 0.712 0.000 0.188 0.068
#> SRR1356660 1 0.7386 -0.48685 0.352 0.000 0.096 0.004 0.260 0.288
#> SRR1392883 2 0.0000 0.85327 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.3351 0.58356 0.288 0.000 0.000 0.000 0.712 0.000
#> SRR816677 1 0.6358 0.09297 0.520 0.000 0.012 0.312 0.044 0.112
#> SRR1455722 1 0.0000 0.63060 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 6 0.7569 0.31925 0.272 0.000 0.124 0.004 0.280 0.320
#> SRR808452 1 0.0146 0.63042 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1352169 5 0.4995 0.51873 0.236 0.000 0.088 0.000 0.660 0.016
#> SRR1366707 3 0.0622 0.76928 0.012 0.000 0.980 0.000 0.008 0.000
#> SRR1328143 5 0.3390 0.58114 0.296 0.000 0.000 0.000 0.704 0.000
#> SRR1473567 2 0.1643 0.85177 0.000 0.924 0.000 0.000 0.008 0.068
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.993 0.997 0.4249 0.578 0.578
#> 3 3 0.823 0.942 0.964 0.5484 0.756 0.577
#> 4 4 0.721 0.757 0.777 0.0754 0.944 0.837
#> 5 5 0.727 0.654 0.827 0.0971 0.800 0.452
#> 6 6 0.864 0.845 0.904 0.0491 0.910 0.643
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
#> SRR1442087 1 0.000 0.996 1.00 0.00
#> SRR1390119 2 0.000 1.000 0.00 1.00
#> SRR1436127 1 0.000 0.996 1.00 0.00
#> SRR1347278 1 0.000 0.996 1.00 0.00
#> SRR1332904 2 0.000 1.000 0.00 1.00
#> SRR1444179 1 0.000 0.996 1.00 0.00
#> SRR1082685 1 0.000 0.996 1.00 0.00
#> SRR1362287 1 0.000 0.996 1.00 0.00
#> SRR1339007 1 0.000 0.996 1.00 0.00
#> SRR1376557 2 0.000 1.000 0.00 1.00
#> SRR1468700 2 0.000 1.000 0.00 1.00
#> SRR1077455 1 0.000 0.996 1.00 0.00
#> SRR1413978 1 0.000 0.996 1.00 0.00
#> SRR1439896 1 0.000 0.996 1.00 0.00
#> SRR1317963 2 0.000 1.000 0.00 1.00
#> SRR1431865 1 0.000 0.996 1.00 0.00
#> SRR1394253 1 0.000 0.996 1.00 0.00
#> SRR1082664 1 0.000 0.996 1.00 0.00
#> SRR1077968 1 0.000 0.996 1.00 0.00
#> SRR1076393 1 0.000 0.996 1.00 0.00
#> SRR1477476 2 0.000 1.000 0.00 1.00
#> SRR1398057 1 0.000 0.996 1.00 0.00
#> SRR1485042 1 0.000 0.996 1.00 0.00
#> SRR1385453 2 0.000 1.000 0.00 1.00
#> SRR1348074 2 0.000 1.000 0.00 1.00
#> SRR813959 2 0.000 1.000 0.00 1.00
#> SRR665442 2 0.000 1.000 0.00 1.00
#> SRR1378068 1 0.000 0.996 1.00 0.00
#> SRR1485237 2 0.000 1.000 0.00 1.00
#> SRR1350792 1 0.000 0.996 1.00 0.00
#> SRR1326797 1 0.000 0.996 1.00 0.00
#> SRR808994 1 0.000 0.996 1.00 0.00
#> SRR1474041 1 0.000 0.996 1.00 0.00
#> SRR1405641 1 0.000 0.996 1.00 0.00
#> SRR1362245 1 0.000 0.996 1.00 0.00
#> SRR1500194 1 0.000 0.996 1.00 0.00
#> SRR1414876 2 0.000 1.000 0.00 1.00
#> SRR1478523 1 0.000 0.996 1.00 0.00
#> SRR1325161 1 0.000 0.996 1.00 0.00
#> SRR1318026 2 0.000 1.000 0.00 1.00
#> SRR1343778 1 0.000 0.996 1.00 0.00
#> SRR1441287 1 0.000 0.996 1.00 0.00
#> SRR1430991 1 0.000 0.996 1.00 0.00
#> SRR1499722 1 0.000 0.996 1.00 0.00
#> SRR1351368 1 0.000 0.996 1.00 0.00
#> SRR1441785 1 0.000 0.996 1.00 0.00
#> SRR1096101 1 0.000 0.996 1.00 0.00
#> SRR808375 1 0.000 0.996 1.00 0.00
#> SRR1452842 1 0.000 0.996 1.00 0.00
#> SRR1311709 1 0.000 0.996 1.00 0.00
#> SRR1433352 1 0.000 0.996 1.00 0.00
#> SRR1340241 2 0.000 1.000 0.00 1.00
#> SRR1456754 1 0.000 0.996 1.00 0.00
#> SRR1465172 1 0.000 0.996 1.00 0.00
#> SRR1499284 1 0.000 0.996 1.00 0.00
#> SRR1499607 2 0.000 1.000 0.00 1.00
#> SRR812342 1 0.000 0.996 1.00 0.00
#> SRR1405374 1 0.000 0.996 1.00 0.00
#> SRR1403565 1 0.000 0.996 1.00 0.00
#> SRR1332024 1 0.000 0.996 1.00 0.00
#> SRR1471633 1 0.000 0.996 1.00 0.00
#> SRR1325944 2 0.000 1.000 0.00 1.00
#> SRR1429450 2 0.000 1.000 0.00 1.00
#> SRR821573 1 0.000 0.996 1.00 0.00
#> SRR1435372 1 0.000 0.996 1.00 0.00
#> SRR1324184 2 0.000 1.000 0.00 1.00
#> SRR816517 2 0.000 1.000 0.00 1.00
#> SRR1324141 2 0.000 1.000 0.00 1.00
#> SRR1101612 1 0.000 0.996 1.00 0.00
#> SRR1356531 1 0.000 0.996 1.00 0.00
#> SRR1089785 1 0.000 0.996 1.00 0.00
#> SRR1077708 1 0.000 0.996 1.00 0.00
#> SRR1343720 1 0.000 0.996 1.00 0.00
#> SRR1477499 2 0.000 1.000 0.00 1.00
#> SRR1347236 1 0.000 0.996 1.00 0.00
#> SRR1326408 1 0.000 0.996 1.00 0.00
#> SRR1336529 1 0.000 0.996 1.00 0.00
#> SRR1440643 2 0.000 1.000 0.00 1.00
#> SRR662354 1 0.000 0.996 1.00 0.00
#> SRR1310817 1 0.000 0.996 1.00 0.00
#> SRR1347389 2 0.000 1.000 0.00 1.00
#> SRR1353097 1 0.000 0.996 1.00 0.00
#> SRR1384737 1 0.925 0.485 0.66 0.34
#> SRR1096339 1 0.000 0.996 1.00 0.00
#> SRR1345329 2 0.000 1.000 0.00 1.00
#> SRR1414771 1 0.000 0.996 1.00 0.00
#> SRR1309119 1 0.000 0.996 1.00 0.00
#> SRR1470438 1 0.000 0.996 1.00 0.00
#> SRR1343221 1 0.000 0.996 1.00 0.00
#> SRR1410847 1 0.000 0.996 1.00 0.00
#> SRR807949 1 0.000 0.996 1.00 0.00
#> SRR1442332 1 0.000 0.996 1.00 0.00
#> SRR815920 1 0.000 0.996 1.00 0.00
#> SRR1471524 1 0.000 0.996 1.00 0.00
#> SRR1477221 1 0.000 0.996 1.00 0.00
#> SRR1445046 2 0.000 1.000 0.00 1.00
#> SRR1331962 2 0.000 1.000 0.00 1.00
#> SRR1319946 2 0.000 1.000 0.00 1.00
#> SRR1311599 1 0.000 0.996 1.00 0.00
#> SRR1323977 2 0.000 1.000 0.00 1.00
#> SRR1445132 2 0.000 1.000 0.00 1.00
#> SRR1337321 1 0.000 0.996 1.00 0.00
#> SRR1366390 2 0.000 1.000 0.00 1.00
#> SRR1343012 1 0.000 0.996 1.00 0.00
#> SRR1311958 2 0.000 1.000 0.00 1.00
#> SRR1388234 2 0.000 1.000 0.00 1.00
#> SRR1370384 1 0.000 0.996 1.00 0.00
#> SRR1321650 1 0.000 0.996 1.00 0.00
#> SRR1485117 2 0.000 1.000 0.00 1.00
#> SRR1384713 1 0.000 0.996 1.00 0.00
#> SRR816609 2 0.000 1.000 0.00 1.00
#> SRR1486239 2 0.000 1.000 0.00 1.00
#> SRR1309638 1 0.000 0.996 1.00 0.00
#> SRR1356660 1 0.000 0.996 1.00 0.00
#> SRR1392883 2 0.000 1.000 0.00 1.00
#> SRR808130 1 0.000 0.996 1.00 0.00
#> SRR816677 1 0.000 0.996 1.00 0.00
#> SRR1455722 1 0.000 0.996 1.00 0.00
#> SRR1336029 1 0.000 0.996 1.00 0.00
#> SRR808452 1 0.000 0.996 1.00 0.00
#> SRR1352169 1 0.000 0.996 1.00 0.00
#> SRR1366707 1 0.000 0.996 1.00 0.00
#> SRR1328143 1 0.000 0.996 1.00 0.00
#> SRR1473567 2 0.000 1.000 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.4346 0.860 0.184 0.000 0.816
#> SRR1390119 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1347278 3 0.2066 0.896 0.060 0.000 0.940
#> SRR1332904 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1444179 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1082685 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1362287 3 0.0424 0.907 0.008 0.000 0.992
#> SRR1339007 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1376557 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1468700 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1077455 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1413978 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1439896 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1317963 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1431865 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1394253 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1082664 3 0.4346 0.860 0.184 0.000 0.816
#> SRR1077968 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1076393 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1477476 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1398057 3 0.4346 0.860 0.184 0.000 0.816
#> SRR1485042 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1385453 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1348074 2 0.0000 1.000 0.000 1.000 0.000
#> SRR813959 2 0.0000 1.000 0.000 1.000 0.000
#> SRR665442 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1378068 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1485237 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1350792 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1326797 1 0.0000 0.970 1.000 0.000 0.000
#> SRR808994 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1474041 1 0.4974 0.725 0.764 0.000 0.236
#> SRR1405641 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1500194 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1414876 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1478523 1 0.5835 0.523 0.660 0.000 0.340
#> SRR1325161 1 0.3192 0.866 0.888 0.000 0.112
#> SRR1318026 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1343778 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1441287 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1430991 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1499722 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1351368 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1441785 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1096101 1 0.0000 0.970 1.000 0.000 0.000
#> SRR808375 1 0.3267 0.863 0.884 0.000 0.116
#> SRR1452842 1 0.2959 0.879 0.900 0.000 0.100
#> SRR1311709 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1433352 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1340241 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1456754 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1465172 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1499284 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1499607 2 0.0000 1.000 0.000 1.000 0.000
#> SRR812342 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1405374 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1403565 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1332024 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1471633 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1325944 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1429450 2 0.0000 1.000 0.000 1.000 0.000
#> SRR821573 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1435372 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1324184 2 0.0000 1.000 0.000 1.000 0.000
#> SRR816517 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1324141 2 0.0237 0.996 0.004 0.996 0.000
#> SRR1101612 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1356531 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1089785 1 0.0892 0.956 0.980 0.000 0.020
#> SRR1077708 3 0.4346 0.860 0.184 0.000 0.816
#> SRR1343720 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1477499 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1347236 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1326408 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1336529 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1440643 2 0.0000 1.000 0.000 1.000 0.000
#> SRR662354 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1310817 1 0.3482 0.850 0.872 0.000 0.128
#> SRR1347389 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1353097 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1384737 3 0.0592 0.902 0.000 0.012 0.988
#> SRR1096339 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1345329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1414771 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1309119 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1470438 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1343221 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1410847 1 0.0000 0.970 1.000 0.000 0.000
#> SRR807949 1 0.0892 0.956 0.980 0.000 0.020
#> SRR1442332 1 0.0000 0.970 1.000 0.000 0.000
#> SRR815920 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1477221 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1445046 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1331962 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1319946 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1311599 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1323977 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1445132 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1337321 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1366390 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1343012 3 0.0424 0.907 0.008 0.000 0.992
#> SRR1311958 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1388234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1370384 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1321650 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1485117 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1384713 1 0.0000 0.970 1.000 0.000 0.000
#> SRR816609 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1486239 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1356660 3 0.4452 0.856 0.192 0.000 0.808
#> SRR1392883 2 0.0000 1.000 0.000 1.000 0.000
#> SRR808130 1 0.3551 0.845 0.868 0.000 0.132
#> SRR816677 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1455722 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1336029 3 0.4399 0.858 0.188 0.000 0.812
#> SRR808452 1 0.0000 0.970 1.000 0.000 0.000
#> SRR1352169 3 0.4346 0.860 0.184 0.000 0.816
#> SRR1366707 3 0.0000 0.908 0.000 0.000 1.000
#> SRR1328143 1 0.3482 0.850 0.872 0.000 0.128
#> SRR1473567 2 0.0000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.5592 0.635 0.300 0.000 0.656 0.044
#> SRR1390119 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1436127 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1347278 3 0.4327 0.607 0.016 0.000 0.768 0.216
#> SRR1332904 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.3266 0.652 0.832 0.000 0.000 0.168
#> SRR1082685 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1362287 3 0.0524 0.761 0.004 0.000 0.988 0.008
#> SRR1339007 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1376557 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1077455 1 0.3528 0.815 0.808 0.000 0.000 0.192
#> SRR1413978 3 0.5024 0.611 0.360 0.000 0.632 0.008
#> SRR1439896 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1317963 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1431865 3 0.4990 0.621 0.352 0.000 0.640 0.008
#> SRR1394253 1 0.0336 0.832 0.992 0.000 0.000 0.008
#> SRR1082664 3 0.6483 0.537 0.324 0.000 0.584 0.092
#> SRR1077968 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1076393 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1477476 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1398057 3 0.5592 0.634 0.300 0.000 0.656 0.044
#> SRR1485042 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1385453 4 0.4961 0.441 0.000 0.448 0.000 0.552
#> SRR1348074 4 0.5000 0.352 0.000 0.496 0.000 0.504
#> SRR813959 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR665442 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1378068 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1485237 4 0.5168 0.362 0.004 0.492 0.000 0.504
#> SRR1350792 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1326797 1 0.4304 0.796 0.716 0.000 0.000 0.284
#> SRR808994 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1474041 1 0.7155 0.616 0.540 0.000 0.168 0.292
#> SRR1405641 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1362245 3 0.0188 0.762 0.000 0.000 0.996 0.004
#> SRR1500194 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1414876 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1478523 1 0.5511 0.556 0.500 0.000 0.016 0.484
#> SRR1325161 1 0.5113 0.779 0.684 0.000 0.024 0.292
#> SRR1318026 4 0.4382 0.651 0.000 0.296 0.000 0.704
#> SRR1343778 3 0.6023 0.555 0.344 0.000 0.600 0.056
#> SRR1441287 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1430991 1 0.4673 0.790 0.700 0.000 0.008 0.292
#> SRR1499722 1 0.4356 0.794 0.708 0.000 0.000 0.292
#> SRR1351368 3 0.0336 0.758 0.000 0.000 0.992 0.008
#> SRR1441785 3 0.4990 0.621 0.352 0.000 0.640 0.008
#> SRR1096101 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR808375 1 0.5207 0.776 0.680 0.000 0.028 0.292
#> SRR1452842 1 0.4720 0.798 0.720 0.000 0.016 0.264
#> SRR1311709 4 0.4477 0.573 0.312 0.000 0.000 0.688
#> SRR1433352 1 0.4304 0.796 0.716 0.000 0.000 0.284
#> SRR1340241 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1456754 3 0.5677 0.604 0.332 0.000 0.628 0.040
#> SRR1465172 1 0.4356 0.794 0.708 0.000 0.000 0.292
#> SRR1499284 1 0.3975 0.807 0.760 0.000 0.000 0.240
#> SRR1499607 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR812342 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1405374 3 0.5070 0.593 0.372 0.000 0.620 0.008
#> SRR1403565 3 0.5075 0.625 0.344 0.000 0.644 0.012
#> SRR1332024 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1471633 4 0.4679 0.535 0.352 0.000 0.000 0.648
#> SRR1325944 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR821573 1 0.4356 0.794 0.708 0.000 0.000 0.292
#> SRR1435372 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1324184 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR816517 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1324141 4 0.4382 0.651 0.000 0.296 0.000 0.704
#> SRR1101612 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1089785 1 0.5113 0.780 0.684 0.000 0.024 0.292
#> SRR1077708 3 0.5827 0.606 0.316 0.000 0.632 0.052
#> SRR1343720 1 0.4356 0.794 0.708 0.000 0.000 0.292
#> SRR1477499 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1347236 1 0.4008 0.807 0.756 0.000 0.000 0.244
#> SRR1326408 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1336529 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1440643 4 0.4382 0.651 0.000 0.296 0.000 0.704
#> SRR662354 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1310817 1 0.6158 0.724 0.628 0.000 0.080 0.292
#> SRR1347389 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1353097 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1384737 3 0.4981 0.283 0.000 0.000 0.536 0.464
#> SRR1096339 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1345329 2 0.4999 -0.408 0.000 0.508 0.000 0.492
#> SRR1414771 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1309119 4 0.4679 0.535 0.352 0.000 0.000 0.648
#> SRR1470438 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1343221 3 0.5639 0.616 0.324 0.000 0.636 0.040
#> SRR1410847 1 0.0188 0.833 0.996 0.000 0.000 0.004
#> SRR807949 1 0.5207 0.777 0.680 0.000 0.028 0.292
#> SRR1442332 1 0.4673 0.790 0.700 0.000 0.008 0.292
#> SRR815920 3 0.0336 0.760 0.000 0.000 0.992 0.008
#> SRR1471524 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1477221 3 0.0188 0.762 0.000 0.000 0.996 0.004
#> SRR1445046 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1331962 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1319946 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1311599 3 0.4990 0.621 0.352 0.000 0.640 0.008
#> SRR1323977 2 0.4996 -0.385 0.000 0.516 0.000 0.484
#> SRR1445132 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1337321 3 0.0188 0.762 0.000 0.000 0.996 0.004
#> SRR1366390 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1343012 3 0.4420 0.617 0.012 0.000 0.748 0.240
#> SRR1311958 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1388234 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1370384 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1321650 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1485117 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1384713 1 0.0336 0.833 0.992 0.000 0.000 0.008
#> SRR816609 2 0.1389 0.889 0.000 0.952 0.000 0.048
#> SRR1486239 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR1309638 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1356660 3 0.4990 0.621 0.352 0.000 0.640 0.008
#> SRR1392883 2 0.0000 0.952 0.000 1.000 0.000 0.000
#> SRR808130 1 0.5383 0.770 0.672 0.000 0.036 0.292
#> SRR816677 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1455722 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1336029 3 0.4917 0.637 0.336 0.000 0.656 0.008
#> SRR808452 1 0.0000 0.834 1.000 0.000 0.000 0.000
#> SRR1352169 1 0.7369 0.520 0.512 0.000 0.196 0.292
#> SRR1366707 3 0.0000 0.762 0.000 0.000 1.000 0.000
#> SRR1328143 1 0.5466 0.766 0.668 0.000 0.040 0.292
#> SRR1473567 2 0.0000 0.952 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
#> SRR1442087 5 0.5419 0.4237 0.012 0.000 0.284 0.064 0.640
#> SRR1390119 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.1671 0.8379 0.000 0.000 0.924 0.000 0.076
#> SRR1347278 5 0.6576 -0.0634 0.004 0.000 0.340 0.188 0.468
#> SRR1332904 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1444179 1 0.1894 0.7177 0.920 0.000 0.000 0.072 0.008
#> SRR1082685 1 0.0404 0.7670 0.988 0.000 0.000 0.000 0.012
#> SRR1362287 3 0.6018 0.5539 0.000 0.000 0.568 0.272 0.160
#> SRR1339007 1 0.0510 0.7682 0.984 0.000 0.000 0.000 0.016
#> SRR1376557 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1077455 1 0.3966 0.3056 0.664 0.000 0.000 0.000 0.336
#> SRR1413978 1 0.8417 0.1659 0.344 0.000 0.184 0.272 0.200
#> SRR1439896 1 0.0000 0.7615 1.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1431865 1 0.8512 0.1251 0.308 0.000 0.188 0.272 0.232
#> SRR1394253 1 0.6467 0.3376 0.496 0.000 0.000 0.272 0.232
#> SRR1082664 5 0.4165 0.6067 0.032 0.000 0.208 0.004 0.756
#> SRR1077968 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1076393 3 0.0000 0.8588 0.000 0.000 1.000 0.000 0.000
#> SRR1477476 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 5 0.5426 0.3790 0.024 0.000 0.344 0.032 0.600
#> SRR1485042 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1385453 4 0.3966 0.6784 0.000 0.336 0.000 0.664 0.000
#> SRR1348074 2 0.4306 -0.2602 0.000 0.508 0.000 0.492 0.000
#> SRR813959 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR665442 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1378068 3 0.1410 0.8444 0.000 0.000 0.940 0.000 0.060
#> SRR1485237 2 0.4562 -0.2816 0.008 0.500 0.000 0.492 0.000
#> SRR1350792 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1326797 5 0.3039 0.6851 0.192 0.000 0.000 0.000 0.808
#> SRR808994 3 0.0000 0.8588 0.000 0.000 1.000 0.000 0.000
#> SRR1474041 5 0.2006 0.7541 0.072 0.000 0.012 0.000 0.916
#> SRR1405641 3 0.0000 0.8588 0.000 0.000 1.000 0.000 0.000
#> SRR1362245 3 0.3687 0.7297 0.000 0.000 0.792 0.180 0.028
#> SRR1500194 1 0.0404 0.7573 0.988 0.000 0.000 0.000 0.012
#> SRR1414876 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 5 0.5415 0.5394 0.076 0.000 0.020 0.224 0.680
#> SRR1325161 5 0.1608 0.7579 0.072 0.000 0.000 0.000 0.928
#> SRR1318026 4 0.3636 0.7670 0.000 0.272 0.000 0.728 0.000
#> SRR1343778 5 0.4969 0.5292 0.032 0.000 0.264 0.020 0.684
#> SRR1441287 1 0.0510 0.7682 0.984 0.000 0.000 0.000 0.016
#> SRR1430991 5 0.1851 0.7588 0.088 0.000 0.000 0.000 0.912
#> SRR1499722 5 0.2074 0.7523 0.104 0.000 0.000 0.000 0.896
#> SRR1351368 3 0.0162 0.8584 0.000 0.000 0.996 0.000 0.004
#> SRR1441785 1 0.8512 0.1251 0.308 0.000 0.188 0.272 0.232
#> SRR1096101 1 0.1197 0.7515 0.952 0.000 0.000 0.000 0.048
#> SRR808375 5 0.1608 0.7579 0.072 0.000 0.000 0.000 0.928
#> SRR1452842 5 0.6555 0.2792 0.268 0.000 0.000 0.256 0.476
#> SRR1311709 1 0.4437 0.1208 0.532 0.000 0.000 0.464 0.004
#> SRR1433352 5 0.2966 0.7008 0.184 0.000 0.000 0.000 0.816
#> SRR1340241 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 5 0.8285 0.1297 0.176 0.000 0.176 0.272 0.376
#> SRR1465172 5 0.1851 0.7588 0.088 0.000 0.000 0.000 0.912
#> SRR1499284 5 0.4182 0.4023 0.400 0.000 0.000 0.000 0.600
#> SRR1499607 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR812342 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1405374 1 0.8172 0.2013 0.376 0.000 0.120 0.272 0.232
#> SRR1403565 5 0.8546 -0.0870 0.268 0.000 0.188 0.272 0.272
#> SRR1332024 3 0.0000 0.8588 0.000 0.000 1.000 0.000 0.000
#> SRR1471633 1 0.3519 0.5693 0.776 0.000 0.000 0.216 0.008
#> SRR1325944 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.2424 0.7345 0.132 0.000 0.000 0.000 0.868
#> SRR1435372 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1324184 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR816517 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1324141 4 0.4015 0.7650 0.004 0.264 0.000 0.724 0.008
#> SRR1101612 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1356531 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1089785 5 0.1851 0.7588 0.088 0.000 0.000 0.000 0.912
#> SRR1077708 5 0.4715 0.5063 0.032 0.000 0.292 0.004 0.672
#> SRR1343720 5 0.2074 0.7523 0.104 0.000 0.000 0.000 0.896
#> SRR1477499 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 5 0.3796 0.5747 0.300 0.000 0.000 0.000 0.700
#> SRR1326408 1 0.0510 0.7682 0.984 0.000 0.000 0.000 0.016
#> SRR1336529 3 0.0290 0.8561 0.000 0.000 0.992 0.000 0.008
#> SRR1440643 4 0.3636 0.7670 0.000 0.272 0.000 0.728 0.000
#> SRR662354 1 0.0000 0.7615 1.000 0.000 0.000 0.000 0.000
#> SRR1310817 5 0.1484 0.7490 0.048 0.000 0.008 0.000 0.944
#> SRR1347389 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1353097 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1384737 4 0.4451 -0.0560 0.000 0.000 0.248 0.712 0.040
#> SRR1096339 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1345329 2 0.4306 -0.2602 0.000 0.508 0.000 0.492 0.000
#> SRR1414771 3 0.0000 0.8588 0.000 0.000 1.000 0.000 0.000
#> SRR1309119 1 0.3519 0.5693 0.776 0.000 0.000 0.216 0.008
#> SRR1470438 3 0.0000 0.8588 0.000 0.000 1.000 0.000 0.000
#> SRR1343221 5 0.8266 0.1340 0.172 0.000 0.176 0.272 0.380
#> SRR1410847 1 0.3086 0.6356 0.816 0.000 0.000 0.004 0.180
#> SRR807949 5 0.1851 0.7588 0.088 0.000 0.000 0.000 0.912
#> SRR1442332 5 0.1851 0.7588 0.088 0.000 0.000 0.000 0.912
#> SRR815920 3 0.2280 0.7978 0.000 0.000 0.880 0.000 0.120
#> SRR1471524 3 0.0162 0.8578 0.000 0.000 0.996 0.000 0.004
#> SRR1477221 3 0.5783 0.6019 0.000 0.000 0.612 0.228 0.160
#> SRR1445046 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1331962 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1319946 2 0.0794 0.8752 0.000 0.972 0.000 0.028 0.000
#> SRR1311599 1 0.8530 0.0994 0.296 0.000 0.188 0.272 0.244
#> SRR1323977 2 0.4306 -0.2602 0.000 0.508 0.000 0.492 0.000
#> SRR1445132 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.5831 0.5723 0.000 0.000 0.592 0.268 0.140
#> SRR1366390 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1343012 3 0.5920 0.5132 0.024 0.000 0.592 0.312 0.072
#> SRR1311958 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1388234 2 0.0794 0.8752 0.000 0.972 0.000 0.028 0.000
#> SRR1370384 1 0.1043 0.7564 0.960 0.000 0.000 0.000 0.040
#> SRR1321650 3 0.1270 0.8470 0.000 0.000 0.948 0.000 0.052
#> SRR1485117 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1384713 1 0.2929 0.6046 0.820 0.000 0.000 0.000 0.180
#> SRR816609 2 0.3636 0.4644 0.000 0.728 0.000 0.272 0.000
#> SRR1486239 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR1309638 3 0.3366 0.7778 0.000 0.000 0.828 0.032 0.140
#> SRR1356660 1 0.8512 0.1251 0.308 0.000 0.188 0.272 0.232
#> SRR1392883 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.1544 0.7567 0.068 0.000 0.000 0.000 0.932
#> SRR816677 1 0.0404 0.7670 0.988 0.000 0.000 0.000 0.012
#> SRR1455722 1 0.0609 0.7685 0.980 0.000 0.000 0.000 0.020
#> SRR1336029 1 0.8505 0.1091 0.312 0.000 0.188 0.272 0.228
#> SRR808452 1 0.0510 0.7682 0.984 0.000 0.000 0.000 0.016
#> SRR1352169 5 0.1502 0.7521 0.056 0.000 0.004 0.000 0.940
#> SRR1366707 3 0.0290 0.8561 0.000 0.000 0.992 0.000 0.008
#> SRR1328143 5 0.1608 0.7579 0.072 0.000 0.000 0.000 0.928
#> SRR1473567 2 0.0000 0.9024 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.5188 0.562 0.000 0.000 0.160 0.004 0.632 0.204
#> SRR1390119 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.2537 0.840 0.000 0.000 0.872 0.000 0.096 0.032
#> SRR1347278 6 0.4707 0.644 0.000 0.000 0.096 0.000 0.244 0.660
#> SRR1332904 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 1 0.2395 0.841 0.892 0.000 0.000 0.076 0.012 0.020
#> SRR1082685 1 0.0260 0.921 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1362287 6 0.2350 0.832 0.000 0.000 0.076 0.000 0.036 0.888
#> SRR1339007 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1376557 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077455 1 0.3563 0.464 0.664 0.000 0.000 0.000 0.336 0.000
#> SRR1413978 6 0.2600 0.861 0.084 0.000 0.004 0.000 0.036 0.876
#> SRR1439896 1 0.0508 0.914 0.984 0.000 0.000 0.000 0.012 0.004
#> SRR1317963 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1431865 6 0.2468 0.879 0.060 0.000 0.004 0.000 0.048 0.888
#> SRR1394253 6 0.2608 0.865 0.080 0.000 0.000 0.000 0.048 0.872
#> SRR1082664 5 0.3141 0.791 0.000 0.000 0.124 0.004 0.832 0.040
#> SRR1077968 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1076393 3 0.0146 0.897 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1477476 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398057 5 0.5180 0.541 0.000 0.000 0.220 0.000 0.616 0.164
#> SRR1485042 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1385453 4 0.2554 0.754 0.000 0.088 0.000 0.880 0.012 0.020
#> SRR1348074 4 0.3136 0.805 0.000 0.228 0.000 0.768 0.000 0.004
#> SRR813959 2 0.0603 0.968 0.000 0.980 0.000 0.016 0.000 0.004
#> SRR665442 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1378068 3 0.1812 0.860 0.000 0.000 0.912 0.000 0.080 0.008
#> SRR1485237 4 0.3136 0.805 0.000 0.228 0.000 0.768 0.000 0.004
#> SRR1350792 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1326797 5 0.1863 0.835 0.104 0.000 0.000 0.000 0.896 0.000
#> SRR808994 3 0.0260 0.902 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1474041 5 0.0717 0.881 0.008 0.000 0.000 0.000 0.976 0.016
#> SRR1405641 3 0.0260 0.902 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1362245 6 0.3982 0.138 0.000 0.000 0.460 0.000 0.004 0.536
#> SRR1500194 1 0.0622 0.906 0.980 0.000 0.000 0.000 0.008 0.012
#> SRR1414876 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 5 0.3895 0.711 0.016 0.000 0.016 0.164 0.784 0.020
#> SRR1325161 5 0.0717 0.881 0.008 0.000 0.000 0.000 0.976 0.016
#> SRR1318026 4 0.1477 0.727 0.000 0.004 0.000 0.940 0.008 0.048
#> SRR1343778 5 0.3876 0.741 0.000 0.000 0.156 0.004 0.772 0.068
#> SRR1441287 1 0.0458 0.925 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1430991 5 0.0632 0.881 0.024 0.000 0.000 0.000 0.976 0.000
#> SRR1499722 5 0.0937 0.875 0.040 0.000 0.000 0.000 0.960 0.000
#> SRR1351368 3 0.0632 0.893 0.000 0.000 0.976 0.000 0.000 0.024
#> SRR1441785 6 0.2468 0.879 0.060 0.000 0.004 0.000 0.048 0.888
#> SRR1096101 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR808375 5 0.0717 0.881 0.008 0.000 0.000 0.000 0.976 0.016
#> SRR1452842 6 0.3794 0.721 0.040 0.000 0.000 0.000 0.216 0.744
#> SRR1311709 1 0.5031 0.218 0.528 0.000 0.000 0.412 0.012 0.048
#> SRR1433352 5 0.1814 0.841 0.100 0.000 0.000 0.000 0.900 0.000
#> SRR1340241 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1456754 6 0.2614 0.876 0.052 0.000 0.004 0.004 0.056 0.884
#> SRR1465172 5 0.0790 0.879 0.032 0.000 0.000 0.000 0.968 0.000
#> SRR1499284 5 0.3765 0.362 0.404 0.000 0.000 0.000 0.596 0.000
#> SRR1499607 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR812342 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1405374 6 0.2384 0.877 0.064 0.000 0.000 0.000 0.048 0.888
#> SRR1403565 6 0.2610 0.879 0.060 0.000 0.004 0.004 0.048 0.884
#> SRR1332024 3 0.0260 0.902 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1471633 1 0.3817 0.732 0.784 0.000 0.000 0.152 0.012 0.052
#> SRR1325944 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.1075 0.870 0.048 0.000 0.000 0.000 0.952 0.000
#> SRR1435372 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1324184 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR816517 2 0.0291 0.981 0.000 0.992 0.000 0.004 0.004 0.000
#> SRR1324141 4 0.2110 0.714 0.000 0.004 0.000 0.900 0.012 0.084
#> SRR1101612 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1356531 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1089785 5 0.0632 0.881 0.024 0.000 0.000 0.000 0.976 0.000
#> SRR1077708 5 0.3916 0.706 0.000 0.000 0.196 0.004 0.752 0.048
#> SRR1343720 5 0.1007 0.873 0.044 0.000 0.000 0.000 0.956 0.000
#> SRR1477499 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 5 0.2941 0.723 0.220 0.000 0.000 0.000 0.780 0.000
#> SRR1326408 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1336529 3 0.0260 0.902 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1440643 4 0.1269 0.737 0.000 0.012 0.000 0.956 0.012 0.020
#> SRR662354 1 0.0717 0.915 0.976 0.000 0.000 0.000 0.016 0.008
#> SRR1310817 5 0.1232 0.874 0.000 0.000 0.016 0.004 0.956 0.024
#> SRR1347389 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1353097 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1384737 6 0.5440 0.167 0.000 0.000 0.088 0.380 0.012 0.520
#> SRR1096339 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1345329 4 0.3265 0.791 0.000 0.248 0.000 0.748 0.000 0.004
#> SRR1414771 3 0.0260 0.902 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1309119 1 0.3817 0.732 0.784 0.000 0.000 0.152 0.012 0.052
#> SRR1470438 3 0.0260 0.902 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1343221 6 0.2471 0.878 0.056 0.000 0.004 0.000 0.052 0.888
#> SRR1410847 1 0.4012 0.680 0.752 0.000 0.000 0.000 0.084 0.164
#> SRR807949 5 0.0632 0.881 0.024 0.000 0.000 0.000 0.976 0.000
#> SRR1442332 5 0.0862 0.880 0.008 0.000 0.000 0.004 0.972 0.016
#> SRR815920 3 0.2212 0.831 0.000 0.000 0.880 0.000 0.112 0.008
#> SRR1471524 3 0.0508 0.899 0.000 0.000 0.984 0.000 0.004 0.012
#> SRR1477221 6 0.3210 0.768 0.000 0.000 0.152 0.000 0.036 0.812
#> SRR1445046 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1331962 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1319946 2 0.2053 0.849 0.000 0.888 0.000 0.108 0.000 0.004
#> SRR1311599 6 0.2468 0.879 0.060 0.000 0.004 0.000 0.048 0.888
#> SRR1323977 4 0.3240 0.794 0.000 0.244 0.000 0.752 0.000 0.004
#> SRR1445132 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 6 0.2361 0.825 0.000 0.000 0.088 0.000 0.028 0.884
#> SRR1366390 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1343012 3 0.6195 0.322 0.004 0.000 0.448 0.248 0.004 0.296
#> SRR1311958 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1388234 2 0.2100 0.843 0.000 0.884 0.000 0.112 0.000 0.004
#> SRR1370384 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1321650 3 0.2740 0.803 0.000 0.000 0.852 0.000 0.028 0.120
#> SRR1485117 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 1 0.2048 0.834 0.880 0.000 0.000 0.000 0.120 0.000
#> SRR816609 4 0.4056 0.498 0.000 0.416 0.000 0.576 0.004 0.004
#> SRR1486239 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1309638 3 0.4272 0.519 0.000 0.000 0.668 0.000 0.044 0.288
#> SRR1356660 6 0.2468 0.879 0.060 0.000 0.004 0.000 0.048 0.888
#> SRR1392883 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.0717 0.881 0.008 0.000 0.000 0.000 0.976 0.016
#> SRR816677 1 0.0622 0.920 0.980 0.000 0.000 0.000 0.012 0.008
#> SRR1455722 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1336029 6 0.2407 0.879 0.056 0.000 0.004 0.000 0.048 0.892
#> SRR808452 1 0.0547 0.927 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1352169 5 0.0777 0.876 0.004 0.000 0.000 0.000 0.972 0.024
#> SRR1366707 3 0.0146 0.897 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1328143 5 0.0862 0.880 0.008 0.000 0.000 0.004 0.972 0.016
#> SRR1473567 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.993 0.997 0.3193 0.685 0.685
#> 3 3 0.668 0.813 0.811 0.7746 0.681 0.534
#> 4 4 0.561 0.718 0.837 0.2492 0.735 0.416
#> 5 5 0.733 0.810 0.859 0.0999 0.859 0.546
#> 6 6 0.794 0.608 0.791 0.0501 0.900 0.584
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
#> SRR1442087 1 0.000 0.996 1.000 0.000
#> SRR1390119 2 0.000 1.000 0.000 1.000
#> SRR1436127 1 0.000 0.996 1.000 0.000
#> SRR1347278 1 0.000 0.996 1.000 0.000
#> SRR1332904 2 0.000 1.000 0.000 1.000
#> SRR1444179 1 0.000 0.996 1.000 0.000
#> SRR1082685 1 0.000 0.996 1.000 0.000
#> SRR1362287 1 0.000 0.996 1.000 0.000
#> SRR1339007 1 0.000 0.996 1.000 0.000
#> SRR1376557 2 0.000 1.000 0.000 1.000
#> SRR1468700 2 0.000 1.000 0.000 1.000
#> SRR1077455 1 0.000 0.996 1.000 0.000
#> SRR1413978 1 0.000 0.996 1.000 0.000
#> SRR1439896 1 0.000 0.996 1.000 0.000
#> SRR1317963 2 0.000 1.000 0.000 1.000
#> SRR1431865 1 0.000 0.996 1.000 0.000
#> SRR1394253 1 0.000 0.996 1.000 0.000
#> SRR1082664 1 0.000 0.996 1.000 0.000
#> SRR1077968 1 0.000 0.996 1.000 0.000
#> SRR1076393 1 0.000 0.996 1.000 0.000
#> SRR1477476 2 0.000 1.000 0.000 1.000
#> SRR1398057 1 0.000 0.996 1.000 0.000
#> SRR1485042 1 0.000 0.996 1.000 0.000
#> SRR1385453 1 0.000 0.996 1.000 0.000
#> SRR1348074 1 0.000 0.996 1.000 0.000
#> SRR813959 1 0.000 0.996 1.000 0.000
#> SRR665442 1 0.625 0.819 0.844 0.156
#> SRR1378068 1 0.000 0.996 1.000 0.000
#> SRR1485237 1 0.000 0.996 1.000 0.000
#> SRR1350792 1 0.000 0.996 1.000 0.000
#> SRR1326797 1 0.000 0.996 1.000 0.000
#> SRR808994 1 0.000 0.996 1.000 0.000
#> SRR1474041 1 0.000 0.996 1.000 0.000
#> SRR1405641 1 0.000 0.996 1.000 0.000
#> SRR1362245 1 0.000 0.996 1.000 0.000
#> SRR1500194 1 0.000 0.996 1.000 0.000
#> SRR1414876 2 0.000 1.000 0.000 1.000
#> SRR1478523 1 0.000 0.996 1.000 0.000
#> SRR1325161 1 0.000 0.996 1.000 0.000
#> SRR1318026 1 0.000 0.996 1.000 0.000
#> SRR1343778 1 0.000 0.996 1.000 0.000
#> SRR1441287 1 0.000 0.996 1.000 0.000
#> SRR1430991 1 0.000 0.996 1.000 0.000
#> SRR1499722 1 0.000 0.996 1.000 0.000
#> SRR1351368 1 0.000 0.996 1.000 0.000
#> SRR1441785 1 0.000 0.996 1.000 0.000
#> SRR1096101 1 0.000 0.996 1.000 0.000
#> SRR808375 1 0.000 0.996 1.000 0.000
#> SRR1452842 1 0.000 0.996 1.000 0.000
#> SRR1311709 1 0.000 0.996 1.000 0.000
#> SRR1433352 1 0.000 0.996 1.000 0.000
#> SRR1340241 2 0.000 1.000 0.000 1.000
#> SRR1456754 1 0.000 0.996 1.000 0.000
#> SRR1465172 1 0.000 0.996 1.000 0.000
#> SRR1499284 1 0.000 0.996 1.000 0.000
#> SRR1499607 2 0.000 1.000 0.000 1.000
#> SRR812342 1 0.000 0.996 1.000 0.000
#> SRR1405374 1 0.000 0.996 1.000 0.000
#> SRR1403565 1 0.000 0.996 1.000 0.000
#> SRR1332024 1 0.000 0.996 1.000 0.000
#> SRR1471633 1 0.000 0.996 1.000 0.000
#> SRR1325944 2 0.000 1.000 0.000 1.000
#> SRR1429450 2 0.000 1.000 0.000 1.000
#> SRR821573 1 0.000 0.996 1.000 0.000
#> SRR1435372 1 0.000 0.996 1.000 0.000
#> SRR1324184 2 0.000 1.000 0.000 1.000
#> SRR816517 1 0.443 0.899 0.908 0.092
#> SRR1324141 1 0.000 0.996 1.000 0.000
#> SRR1101612 1 0.000 0.996 1.000 0.000
#> SRR1356531 1 0.000 0.996 1.000 0.000
#> SRR1089785 1 0.000 0.996 1.000 0.000
#> SRR1077708 1 0.000 0.996 1.000 0.000
#> SRR1343720 1 0.000 0.996 1.000 0.000
#> SRR1477499 2 0.000 1.000 0.000 1.000
#> SRR1347236 1 0.000 0.996 1.000 0.000
#> SRR1326408 1 0.000 0.996 1.000 0.000
#> SRR1336529 1 0.000 0.996 1.000 0.000
#> SRR1440643 1 0.000 0.996 1.000 0.000
#> SRR662354 1 0.000 0.996 1.000 0.000
#> SRR1310817 1 0.000 0.996 1.000 0.000
#> SRR1347389 2 0.000 1.000 0.000 1.000
#> SRR1353097 1 0.000 0.996 1.000 0.000
#> SRR1384737 1 0.000 0.996 1.000 0.000
#> SRR1096339 1 0.000 0.996 1.000 0.000
#> SRR1345329 1 0.000 0.996 1.000 0.000
#> SRR1414771 1 0.000 0.996 1.000 0.000
#> SRR1309119 1 0.000 0.996 1.000 0.000
#> SRR1470438 1 0.000 0.996 1.000 0.000
#> SRR1343221 1 0.000 0.996 1.000 0.000
#> SRR1410847 1 0.000 0.996 1.000 0.000
#> SRR807949 1 0.000 0.996 1.000 0.000
#> SRR1442332 1 0.000 0.996 1.000 0.000
#> SRR815920 1 0.000 0.996 1.000 0.000
#> SRR1471524 1 0.000 0.996 1.000 0.000
#> SRR1477221 1 0.000 0.996 1.000 0.000
#> SRR1445046 2 0.000 1.000 0.000 1.000
#> SRR1331962 2 0.000 1.000 0.000 1.000
#> SRR1319946 2 0.000 1.000 0.000 1.000
#> SRR1311599 1 0.000 0.996 1.000 0.000
#> SRR1323977 1 0.000 0.996 1.000 0.000
#> SRR1445132 2 0.000 1.000 0.000 1.000
#> SRR1337321 1 0.000 0.996 1.000 0.000
#> SRR1366390 2 0.000 1.000 0.000 1.000
#> SRR1343012 1 0.000 0.996 1.000 0.000
#> SRR1311958 2 0.000 1.000 0.000 1.000
#> SRR1388234 1 0.625 0.819 0.844 0.156
#> SRR1370384 1 0.000 0.996 1.000 0.000
#> SRR1321650 1 0.000 0.996 1.000 0.000
#> SRR1485117 2 0.000 1.000 0.000 1.000
#> SRR1384713 1 0.000 0.996 1.000 0.000
#> SRR816609 1 0.000 0.996 1.000 0.000
#> SRR1486239 2 0.000 1.000 0.000 1.000
#> SRR1309638 1 0.000 0.996 1.000 0.000
#> SRR1356660 1 0.000 0.996 1.000 0.000
#> SRR1392883 2 0.000 1.000 0.000 1.000
#> SRR808130 1 0.000 0.996 1.000 0.000
#> SRR816677 1 0.000 0.996 1.000 0.000
#> SRR1455722 1 0.000 0.996 1.000 0.000
#> SRR1336029 1 0.000 0.996 1.000 0.000
#> SRR808452 1 0.000 0.996 1.000 0.000
#> SRR1352169 1 0.000 0.996 1.000 0.000
#> SRR1366707 1 0.000 0.996 1.000 0.000
#> SRR1328143 1 0.000 0.996 1.000 0.000
#> SRR1473567 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1390119 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1436127 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1347278 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1332904 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1444179 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1082685 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1362287 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1339007 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1376557 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1077455 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1413978 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1439896 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1317963 2 0.6215 0.676 0.428 0.572 0.000
#> SRR1431865 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1394253 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1082664 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1077968 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1076393 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1477476 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1398057 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1485042 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1385453 1 0.0237 0.475 0.996 0.000 0.004
#> SRR1348074 1 0.0000 0.478 1.000 0.000 0.000
#> SRR813959 1 0.0000 0.478 1.000 0.000 0.000
#> SRR665442 3 0.6215 0.302 0.428 0.000 0.572
#> SRR1378068 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1485237 1 0.0000 0.478 1.000 0.000 0.000
#> SRR1350792 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1326797 1 0.6215 0.843 0.572 0.000 0.428
#> SRR808994 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1474041 3 0.2066 0.819 0.060 0.000 0.940
#> SRR1405641 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1362245 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1500194 3 0.4555 0.474 0.200 0.000 0.800
#> SRR1414876 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1478523 3 0.1163 0.875 0.028 0.000 0.972
#> SRR1325161 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1318026 1 0.0000 0.478 1.000 0.000 0.000
#> SRR1343778 1 0.6225 0.836 0.568 0.000 0.432
#> SRR1441287 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1430991 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1499722 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1351368 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1441785 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1096101 1 0.6215 0.843 0.572 0.000 0.428
#> SRR808375 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1452842 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1311709 1 0.5859 0.762 0.656 0.000 0.344
#> SRR1433352 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1340241 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1456754 3 0.5968 -0.289 0.364 0.000 0.636
#> SRR1465172 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1499284 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1499607 2 0.6215 0.676 0.428 0.572 0.000
#> SRR812342 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1405374 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1403565 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1332024 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1471633 1 0.6045 0.799 0.620 0.000 0.380
#> SRR1325944 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.956 0.000 1.000 0.000
#> SRR821573 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1435372 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1324184 2 0.0000 0.956 0.000 1.000 0.000
#> SRR816517 3 0.6215 0.302 0.428 0.000 0.572
#> SRR1324141 1 0.0000 0.478 1.000 0.000 0.000
#> SRR1101612 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1356531 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1089785 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1077708 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1343720 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1477499 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1347236 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1326408 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1336529 3 0.0892 0.884 0.020 0.000 0.980
#> SRR1440643 1 0.0000 0.478 1.000 0.000 0.000
#> SRR662354 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1310817 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1347389 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1353097 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1384737 3 0.6126 0.352 0.400 0.000 0.600
#> SRR1096339 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1345329 1 0.0000 0.478 1.000 0.000 0.000
#> SRR1414771 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1309119 1 0.6045 0.799 0.620 0.000 0.380
#> SRR1470438 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1343221 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1410847 1 0.6215 0.843 0.572 0.000 0.428
#> SRR807949 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1442332 1 0.6215 0.843 0.572 0.000 0.428
#> SRR815920 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1471524 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1477221 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1445046 2 0.1753 0.930 0.048 0.952 0.000
#> SRR1331962 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1319946 2 0.6215 0.676 0.428 0.572 0.000
#> SRR1311599 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1323977 1 0.0000 0.478 1.000 0.000 0.000
#> SRR1445132 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1337321 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1366390 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1343012 3 0.4887 0.375 0.228 0.000 0.772
#> SRR1311958 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1388234 1 0.0592 0.461 0.988 0.012 0.000
#> SRR1370384 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1321650 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1485117 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1384713 1 0.6215 0.843 0.572 0.000 0.428
#> SRR816609 1 0.0000 0.478 1.000 0.000 0.000
#> SRR1486239 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1309638 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1356660 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1392883 2 0.0000 0.956 0.000 1.000 0.000
#> SRR808130 1 0.6215 0.843 0.572 0.000 0.428
#> SRR816677 1 0.6111 0.803 0.604 0.000 0.396
#> SRR1455722 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1336029 3 0.0000 0.911 0.000 0.000 1.000
#> SRR808452 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1352169 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1366707 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1328143 1 0.6215 0.843 0.572 0.000 0.428
#> SRR1473567 2 0.0000 0.956 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.4522 0.53307 0.680 0.000 0.320 0.000
#> SRR1390119 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR1436127 1 0.3528 0.70758 0.808 0.000 0.192 0.000
#> SRR1347278 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1332904 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1444179 1 0.5256 0.52337 0.692 0.000 0.272 0.036
#> SRR1082685 3 0.5614 0.49935 0.336 0.000 0.628 0.036
#> SRR1362287 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1339007 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1376557 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1077455 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1413978 1 0.1211 0.79410 0.960 0.000 0.040 0.000
#> SRR1439896 1 0.5407 0.49291 0.668 0.000 0.296 0.036
#> SRR1317963 4 0.3688 0.61975 0.000 0.208 0.000 0.792
#> SRR1431865 1 0.0817 0.79914 0.976 0.000 0.024 0.000
#> SRR1394253 1 0.2530 0.78709 0.888 0.000 0.112 0.000
#> SRR1082664 3 0.0707 0.78265 0.020 0.000 0.980 0.000
#> SRR1077968 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1076393 1 0.0921 0.79812 0.972 0.000 0.028 0.000
#> SRR1477476 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR1398057 3 0.4972 0.12861 0.456 0.000 0.544 0.000
#> SRR1485042 3 0.4149 0.74132 0.152 0.000 0.812 0.036
#> SRR1385453 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR1348074 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR813959 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR665442 4 0.5000 0.00876 0.496 0.000 0.000 0.504
#> SRR1378068 3 0.4543 0.46762 0.324 0.000 0.676 0.000
#> SRR1485237 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR1350792 3 0.4050 0.74558 0.144 0.000 0.820 0.036
#> SRR1326797 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR808994 1 0.2081 0.77587 0.916 0.000 0.084 0.000
#> SRR1474041 3 0.4103 0.56123 0.256 0.000 0.744 0.000
#> SRR1405641 1 0.1389 0.79189 0.952 0.000 0.048 0.000
#> SRR1362245 1 0.2814 0.74399 0.868 0.000 0.132 0.000
#> SRR1500194 1 0.4365 0.64320 0.784 0.000 0.188 0.028
#> SRR1414876 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1478523 1 0.1545 0.79282 0.952 0.000 0.040 0.008
#> SRR1325161 3 0.1022 0.76959 0.032 0.000 0.968 0.000
#> SRR1318026 4 0.3444 0.80492 0.000 0.000 0.184 0.816
#> SRR1343778 3 0.4500 0.72082 0.192 0.000 0.776 0.032
#> SRR1441287 1 0.5938 -0.04384 0.488 0.000 0.476 0.036
#> SRR1430991 3 0.0336 0.77917 0.008 0.000 0.992 0.000
#> SRR1499722 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1351368 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1441785 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1096101 1 0.5256 0.52337 0.692 0.000 0.272 0.036
#> SRR808375 3 0.0336 0.77917 0.008 0.000 0.992 0.000
#> SRR1452842 3 0.3975 0.57117 0.240 0.000 0.760 0.000
#> SRR1311709 4 0.6708 0.57083 0.132 0.000 0.272 0.596
#> SRR1433352 3 0.3205 0.76552 0.104 0.000 0.872 0.024
#> SRR1340241 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR1456754 3 0.3123 0.67517 0.156 0.000 0.844 0.000
#> SRR1465172 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1499284 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1499607 4 0.3688 0.61975 0.000 0.208 0.000 0.792
#> SRR812342 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1405374 1 0.1022 0.79731 0.968 0.000 0.032 0.000
#> SRR1403565 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1332024 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1471633 4 0.7251 0.50438 0.192 0.000 0.272 0.536
#> SRR1325944 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR1429450 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR821573 3 0.2814 0.76200 0.132 0.000 0.868 0.000
#> SRR1435372 3 0.4549 0.71194 0.188 0.000 0.776 0.036
#> SRR1324184 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR816517 4 0.4746 0.35543 0.368 0.000 0.000 0.632
#> SRR1324141 4 0.3726 0.78312 0.000 0.000 0.212 0.788
#> SRR1101612 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1356531 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1089785 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1077708 3 0.1211 0.76543 0.040 0.000 0.960 0.000
#> SRR1343720 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1477499 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR1347236 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1326408 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1336529 3 0.4134 0.55204 0.260 0.000 0.740 0.000
#> SRR1440643 4 0.3726 0.78312 0.000 0.000 0.212 0.788
#> SRR662354 3 0.5577 0.51170 0.328 0.000 0.636 0.036
#> SRR1310817 3 0.4605 0.52525 0.336 0.000 0.664 0.000
#> SRR1347389 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1353097 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1384737 1 0.3311 0.66892 0.828 0.000 0.000 0.172
#> SRR1096339 1 0.5538 0.45168 0.644 0.000 0.320 0.036
#> SRR1345329 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR1414771 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1309119 4 0.6728 0.57368 0.136 0.000 0.268 0.596
#> SRR1470438 1 0.0000 0.80196 1.000 0.000 0.000 0.000
#> SRR1343221 1 0.3172 0.76111 0.840 0.000 0.160 0.000
#> SRR1410847 1 0.5496 0.45084 0.652 0.000 0.312 0.036
#> SRR807949 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR1442332 3 0.3606 0.75920 0.140 0.000 0.840 0.020
#> SRR815920 3 0.4888 0.33620 0.412 0.000 0.588 0.000
#> SRR1471524 1 0.2589 0.75609 0.884 0.000 0.116 0.000
#> SRR1477221 1 0.2814 0.74399 0.868 0.000 0.132 0.000
#> SRR1445046 2 0.2530 0.82672 0.000 0.888 0.000 0.112
#> SRR1331962 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1319946 4 0.3688 0.61975 0.000 0.208 0.000 0.792
#> SRR1311599 1 0.2868 0.74295 0.864 0.000 0.136 0.000
#> SRR1323977 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR1445132 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR1337321 1 0.2814 0.74399 0.868 0.000 0.132 0.000
#> SRR1366390 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1343012 1 0.2546 0.76007 0.900 0.000 0.092 0.008
#> SRR1311958 2 0.1211 0.89690 0.000 0.960 0.000 0.040
#> SRR1388234 4 0.3311 0.65959 0.000 0.172 0.000 0.828
#> SRR1370384 3 0.1118 0.77316 0.000 0.000 0.964 0.036
#> SRR1321650 1 0.4406 0.55745 0.700 0.000 0.300 0.000
#> SRR1485117 2 0.0000 0.92081 0.000 1.000 0.000 0.000
#> SRR1384713 3 0.0000 0.78056 0.000 0.000 1.000 0.000
#> SRR816609 4 0.3311 0.81204 0.000 0.000 0.172 0.828
#> SRR1486239 2 0.0817 0.90764 0.000 0.976 0.000 0.024
#> SRR1309638 3 0.4222 0.53702 0.272 0.000 0.728 0.000
#> SRR1356660 1 0.0188 0.80189 0.996 0.000 0.004 0.000
#> SRR1392883 2 0.3311 0.90346 0.000 0.828 0.000 0.172
#> SRR808130 3 0.0921 0.77161 0.028 0.000 0.972 0.000
#> SRR816677 1 0.5716 0.51428 0.668 0.000 0.272 0.060
#> SRR1455722 1 0.5308 0.51449 0.684 0.000 0.280 0.036
#> SRR1336029 1 0.1022 0.79731 0.968 0.000 0.032 0.000
#> SRR808452 3 0.3895 0.75139 0.132 0.000 0.832 0.036
#> SRR1352169 3 0.4331 0.51967 0.288 0.000 0.712 0.000
#> SRR1366707 3 0.4941 0.22326 0.436 0.000 0.564 0.000
#> SRR1328143 3 0.1211 0.76543 0.040 0.000 0.960 0.000
#> SRR1473567 2 0.0000 0.92081 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
#> SRR1442087 3 0.3949 0.5672 0.000 0.000 0.668 0.000 0.332
#> SRR1390119 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.3039 0.7843 0.000 0.000 0.808 0.000 0.192
#> SRR1347278 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1332904 2 0.6618 0.7933 0.052 0.620 0.120 0.200 0.008
#> SRR1444179 1 0.1270 0.8707 0.948 0.000 0.052 0.000 0.000
#> SRR1082685 1 0.1270 0.8707 0.948 0.000 0.052 0.000 0.000
#> SRR1362287 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1339007 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1376557 2 0.3109 0.8388 0.000 0.800 0.000 0.200 0.000
#> SRR1468700 2 0.5040 0.8293 0.008 0.716 0.072 0.200 0.004
#> SRR1077455 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1413978 3 0.2377 0.8876 0.128 0.000 0.872 0.000 0.000
#> SRR1439896 1 0.1469 0.8801 0.948 0.000 0.036 0.000 0.016
#> SRR1317963 4 0.3616 0.5333 0.052 0.000 0.116 0.828 0.004
#> SRR1431865 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1394253 3 0.2859 0.8807 0.056 0.000 0.876 0.000 0.068
#> SRR1082664 5 0.0693 0.9250 0.012 0.000 0.008 0.000 0.980
#> SRR1077968 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1076393 3 0.2685 0.8920 0.092 0.000 0.880 0.000 0.028
#> SRR1477476 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR1398057 5 0.4201 0.2308 0.000 0.000 0.408 0.000 0.592
#> SRR1485042 1 0.1444 0.8858 0.948 0.000 0.012 0.000 0.040
#> SRR1385453 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR1348074 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR813959 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR665442 4 0.4287 0.0807 0.000 0.000 0.460 0.540 0.000
#> SRR1378068 5 0.3242 0.6959 0.000 0.000 0.216 0.000 0.784
#> SRR1485237 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR1350792 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1326797 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR808994 3 0.2770 0.8755 0.044 0.000 0.880 0.000 0.076
#> SRR1474041 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1405641 3 0.2770 0.8881 0.076 0.000 0.880 0.000 0.044
#> SRR1362245 3 0.2280 0.8467 0.000 0.000 0.880 0.000 0.120
#> SRR1500194 3 0.3949 0.5727 0.332 0.000 0.668 0.000 0.000
#> SRR1414876 2 0.5040 0.8293 0.008 0.716 0.072 0.200 0.004
#> SRR1478523 3 0.2818 0.8837 0.128 0.000 0.860 0.008 0.004
#> SRR1325161 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1318026 4 0.3242 0.7930 0.216 0.000 0.000 0.784 0.000
#> SRR1343778 1 0.3280 0.7692 0.812 0.000 0.012 0.000 0.176
#> SRR1441287 1 0.1408 0.8859 0.948 0.000 0.008 0.000 0.044
#> SRR1430991 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1499722 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1351368 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1441785 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1096101 1 0.1270 0.8707 0.948 0.000 0.052 0.000 0.000
#> SRR808375 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1452842 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1311709 1 0.2516 0.7659 0.860 0.000 0.000 0.140 0.000
#> SRR1433352 1 0.3612 0.6671 0.732 0.000 0.000 0.000 0.268
#> SRR1340241 2 0.2054 0.8248 0.008 0.916 0.072 0.000 0.004
#> SRR1456754 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1465172 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1499284 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1499607 4 0.3359 0.5531 0.052 0.000 0.096 0.848 0.004
#> SRR812342 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1405374 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1403565 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1332024 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1471633 1 0.1270 0.8707 0.948 0.000 0.052 0.000 0.000
#> SRR1325944 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.2605 0.7619 0.148 0.000 0.000 0.000 0.852
#> SRR1435372 1 0.1357 0.8736 0.948 0.000 0.048 0.000 0.004
#> SRR1324184 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR816517 4 0.3949 0.4253 0.000 0.000 0.332 0.668 0.000
#> SRR1324141 4 0.3661 0.7253 0.276 0.000 0.000 0.724 0.000
#> SRR1101612 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1356531 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1089785 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1077708 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1343720 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1477499 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR1347236 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1326408 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1336529 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1440643 4 0.3661 0.7253 0.276 0.000 0.000 0.724 0.000
#> SRR662354 1 0.1469 0.8802 0.948 0.000 0.036 0.000 0.016
#> SRR1310817 1 0.5272 0.3407 0.552 0.000 0.052 0.000 0.396
#> SRR1347389 2 0.6618 0.7933 0.052 0.620 0.120 0.200 0.008
#> SRR1353097 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1384737 3 0.2516 0.7878 0.000 0.000 0.860 0.140 0.000
#> SRR1096339 1 0.1485 0.8846 0.948 0.000 0.020 0.000 0.032
#> SRR1345329 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR1414771 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1309119 1 0.2930 0.7380 0.832 0.000 0.004 0.164 0.000
#> SRR1470438 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1343221 3 0.2727 0.8547 0.016 0.000 0.868 0.000 0.116
#> SRR1410847 1 0.1270 0.8707 0.948 0.000 0.052 0.000 0.000
#> SRR807949 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR1442332 1 0.4183 0.5829 0.668 0.000 0.008 0.000 0.324
#> SRR815920 5 0.4503 0.4901 0.024 0.000 0.312 0.000 0.664
#> SRR1471524 3 0.2522 0.8563 0.012 0.000 0.880 0.000 0.108
#> SRR1477221 3 0.2280 0.8467 0.000 0.000 0.880 0.000 0.120
#> SRR1445046 2 0.7216 0.6629 0.052 0.492 0.120 0.328 0.008
#> SRR1331962 2 0.6618 0.7933 0.052 0.620 0.120 0.200 0.008
#> SRR1319946 4 0.3787 0.5240 0.052 0.000 0.120 0.820 0.008
#> SRR1311599 3 0.2329 0.8448 0.000 0.000 0.876 0.000 0.124
#> SRR1323977 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR1445132 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR1337321 3 0.2280 0.8467 0.000 0.000 0.880 0.000 0.120
#> SRR1366390 2 0.5244 0.8272 0.016 0.708 0.072 0.200 0.004
#> SRR1343012 1 0.4219 0.1826 0.584 0.000 0.416 0.000 0.000
#> SRR1311958 2 0.7007 0.7387 0.052 0.552 0.120 0.268 0.008
#> SRR1388234 4 0.0000 0.6501 0.000 0.000 0.000 1.000 0.000
#> SRR1370384 1 0.3039 0.7164 0.808 0.000 0.000 0.000 0.192
#> SRR1321650 3 0.3876 0.5996 0.000 0.000 0.684 0.000 0.316
#> SRR1485117 2 0.3109 0.8388 0.000 0.800 0.000 0.200 0.000
#> SRR1384713 5 0.0290 0.9316 0.008 0.000 0.000 0.000 0.992
#> SRR816609 4 0.3109 0.8063 0.200 0.000 0.000 0.800 0.000
#> SRR1486239 2 0.6950 0.7503 0.052 0.564 0.120 0.256 0.008
#> SRR1309638 5 0.0404 0.9302 0.000 0.000 0.012 0.000 0.988
#> SRR1356660 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR1392883 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR816677 1 0.2144 0.8524 0.912 0.000 0.068 0.020 0.000
#> SRR1455722 1 0.1408 0.8762 0.948 0.000 0.044 0.000 0.008
#> SRR1336029 3 0.2280 0.8946 0.120 0.000 0.880 0.000 0.000
#> SRR808452 1 0.1270 0.8853 0.948 0.000 0.000 0.000 0.052
#> SRR1352169 5 0.1597 0.8932 0.012 0.000 0.048 0.000 0.940
#> SRR1366707 5 0.3003 0.7486 0.000 0.000 0.188 0.000 0.812
#> SRR1328143 5 0.0290 0.9321 0.000 0.000 0.008 0.000 0.992
#> SRR1473567 2 0.3109 0.8388 0.000 0.800 0.000 0.200 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.6130 -0.330 0.000 0.000 0.332 0.000 0.344 0.324
#> SRR1390119 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR1436127 3 0.2793 0.617 0.000 0.000 0.800 0.000 0.200 0.000
#> SRR1347278 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1332904 2 0.0000 0.716 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1082685 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1362287 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1339007 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1376557 2 0.3993 0.630 0.000 0.592 0.000 0.008 0.000 0.400
#> SRR1468700 2 0.3151 0.699 0.000 0.748 0.000 0.000 0.000 0.252
#> SRR1077455 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1413978 6 0.3961 0.515 0.004 0.000 0.440 0.000 0.000 0.556
#> SRR1439896 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1317963 2 0.3620 0.285 0.000 0.648 0.000 0.352 0.000 0.000
#> SRR1431865 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1394253 6 0.4268 0.505 0.004 0.000 0.428 0.000 0.012 0.556
#> SRR1082664 5 0.0363 0.887 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR1077968 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1076393 3 0.0000 0.605 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1477476 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR1398057 5 0.3975 0.275 0.000 0.000 0.008 0.000 0.600 0.392
#> SRR1485042 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1385453 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1348074 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR813959 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR665442 6 0.6013 -0.155 0.000 0.148 0.016 0.400 0.000 0.436
#> SRR1378068 3 0.3828 0.229 0.000 0.000 0.560 0.000 0.440 0.000
#> SRR1485237 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1350792 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326797 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR808994 3 0.0000 0.605 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1474041 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1405641 3 0.0000 0.605 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1362245 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1500194 3 0.5895 -0.262 0.208 0.000 0.436 0.000 0.000 0.356
#> SRR1414876 2 0.3428 0.684 0.000 0.696 0.000 0.000 0.000 0.304
#> SRR1478523 6 0.4284 0.507 0.000 0.000 0.440 0.012 0.004 0.544
#> SRR1325161 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1318026 4 0.2996 0.890 0.228 0.000 0.000 0.772 0.000 0.000
#> SRR1343778 3 0.5002 0.251 0.364 0.000 0.556 0.000 0.080 0.000
#> SRR1441287 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1430991 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499722 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1351368 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1441785 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1096101 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR808375 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1452842 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1311709 1 0.1663 0.822 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1433352 1 0.3175 0.617 0.744 0.000 0.000 0.000 0.256 0.000
#> SRR1340241 2 0.5911 0.469 0.000 0.432 0.000 0.212 0.000 0.356
#> SRR1456754 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1465172 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499284 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1499607 2 0.3684 0.249 0.000 0.628 0.000 0.372 0.000 0.000
#> SRR812342 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405374 6 0.3961 0.515 0.004 0.000 0.440 0.000 0.000 0.556
#> SRR1403565 6 0.3961 0.514 0.004 0.000 0.440 0.000 0.000 0.556
#> SRR1332024 3 0.0000 0.605 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1471633 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1325944 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR1429450 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR821573 5 0.2793 0.629 0.200 0.000 0.000 0.000 0.800 0.000
#> SRR1435372 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324184 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR816517 4 0.3684 0.438 0.000 0.000 0.004 0.664 0.000 0.332
#> SRR1324141 4 0.3309 0.840 0.280 0.000 0.000 0.720 0.000 0.000
#> SRR1101612 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1356531 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089785 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1077708 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1343720 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1477499 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR1347236 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1326408 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.3828 0.229 0.000 0.000 0.560 0.000 0.440 0.000
#> SRR1440643 4 0.3309 0.840 0.280 0.000 0.000 0.720 0.000 0.000
#> SRR662354 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310817 1 0.3747 0.375 0.604 0.000 0.000 0.000 0.396 0.000
#> SRR1347389 2 0.0000 0.716 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1353097 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384737 6 0.4968 0.451 0.000 0.000 0.368 0.076 0.000 0.556
#> SRR1096339 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1414771 3 0.0000 0.605 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1309119 1 0.1910 0.797 0.892 0.000 0.000 0.108 0.000 0.000
#> SRR1470438 3 0.0000 0.605 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1343221 6 0.4656 0.474 0.004 0.000 0.404 0.000 0.036 0.556
#> SRR1410847 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR807949 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1442332 1 0.3266 0.591 0.728 0.000 0.000 0.000 0.272 0.000
#> SRR815920 3 0.4078 0.424 0.020 0.000 0.640 0.000 0.340 0.000
#> SRR1471524 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1477221 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1445046 2 0.1663 0.667 0.000 0.912 0.000 0.088 0.000 0.000
#> SRR1331962 2 0.0000 0.716 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1319946 2 0.3592 0.298 0.000 0.656 0.000 0.344 0.000 0.000
#> SRR1311599 6 0.3961 0.514 0.000 0.000 0.440 0.000 0.004 0.556
#> SRR1323977 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1445132 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR1337321 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1366390 2 0.3653 0.682 0.000 0.692 0.000 0.008 0.000 0.300
#> SRR1343012 1 0.4210 0.405 0.636 0.000 0.028 0.000 0.000 0.336
#> SRR1311958 2 0.1267 0.689 0.000 0.940 0.000 0.060 0.000 0.000
#> SRR1388234 4 0.2883 0.519 0.000 0.212 0.000 0.788 0.000 0.000
#> SRR1370384 1 0.2793 0.654 0.800 0.000 0.000 0.000 0.200 0.000
#> SRR1321650 3 0.3515 0.473 0.000 0.000 0.676 0.000 0.324 0.000
#> SRR1485117 2 0.4057 0.603 0.000 0.556 0.000 0.008 0.000 0.436
#> SRR1384713 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR816609 4 0.2883 0.901 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1486239 2 0.1075 0.697 0.000 0.952 0.000 0.048 0.000 0.000
#> SRR1309638 5 0.3737 0.202 0.000 0.000 0.392 0.000 0.608 0.000
#> SRR1356660 6 0.3833 0.516 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR1392883 6 0.5896 -0.439 0.000 0.344 0.000 0.212 0.000 0.444
#> SRR808130 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR816677 1 0.1982 0.821 0.912 0.000 0.068 0.016 0.000 0.004
#> SRR1455722 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 6 0.3961 0.515 0.004 0.000 0.440 0.000 0.000 0.556
#> SRR808452 1 0.0000 0.914 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352169 5 0.1957 0.759 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1366707 3 0.2219 0.622 0.000 0.000 0.864 0.000 0.136 0.000
#> SRR1328143 5 0.0000 0.900 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1473567 2 0.3578 0.669 0.000 0.660 0.000 0.000 0.000 0.340
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.996 0.4275 0.571 0.571
#> 3 3 0.543 0.692 0.852 0.2938 0.808 0.678
#> 4 4 0.589 0.690 0.824 0.1566 0.722 0.475
#> 5 5 0.483 0.590 0.742 0.0611 0.941 0.853
#> 6 6 0.781 0.823 0.886 0.1792 0.749 0.381
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
#> SRR1442087 1 0.0000 0.998 1.000 0.000
#> SRR1390119 2 0.0000 0.990 0.000 1.000
#> SRR1436127 1 0.0000 0.998 1.000 0.000
#> SRR1347278 1 0.0000 0.998 1.000 0.000
#> SRR1332904 2 0.0000 0.990 0.000 1.000
#> SRR1444179 1 0.0000 0.998 1.000 0.000
#> SRR1082685 1 0.0000 0.998 1.000 0.000
#> SRR1362287 1 0.0000 0.998 1.000 0.000
#> SRR1339007 1 0.0000 0.998 1.000 0.000
#> SRR1376557 2 0.0000 0.990 0.000 1.000
#> SRR1468700 2 0.0000 0.990 0.000 1.000
#> SRR1077455 1 0.0000 0.998 1.000 0.000
#> SRR1413978 1 0.0000 0.998 1.000 0.000
#> SRR1439896 1 0.0000 0.998 1.000 0.000
#> SRR1317963 2 0.0000 0.990 0.000 1.000
#> SRR1431865 1 0.0000 0.998 1.000 0.000
#> SRR1394253 1 0.0000 0.998 1.000 0.000
#> SRR1082664 1 0.0000 0.998 1.000 0.000
#> SRR1077968 1 0.0000 0.998 1.000 0.000
#> SRR1076393 1 0.0000 0.998 1.000 0.000
#> SRR1477476 2 0.0000 0.990 0.000 1.000
#> SRR1398057 1 0.0000 0.998 1.000 0.000
#> SRR1485042 1 0.0000 0.998 1.000 0.000
#> SRR1385453 2 0.0000 0.990 0.000 1.000
#> SRR1348074 2 0.0000 0.990 0.000 1.000
#> SRR813959 2 0.0000 0.990 0.000 1.000
#> SRR665442 2 0.0000 0.990 0.000 1.000
#> SRR1378068 1 0.0000 0.998 1.000 0.000
#> SRR1485237 2 0.0000 0.990 0.000 1.000
#> SRR1350792 1 0.0000 0.998 1.000 0.000
#> SRR1326797 1 0.0376 0.995 0.996 0.004
#> SRR808994 1 0.0000 0.998 1.000 0.000
#> SRR1474041 1 0.0000 0.998 1.000 0.000
#> SRR1405641 1 0.0000 0.998 1.000 0.000
#> SRR1362245 1 0.0000 0.998 1.000 0.000
#> SRR1500194 1 0.0000 0.998 1.000 0.000
#> SRR1414876 2 0.0000 0.990 0.000 1.000
#> SRR1478523 2 0.9209 0.495 0.336 0.664
#> SRR1325161 1 0.0000 0.998 1.000 0.000
#> SRR1318026 2 0.0000 0.990 0.000 1.000
#> SRR1343778 1 0.0000 0.998 1.000 0.000
#> SRR1441287 1 0.0000 0.998 1.000 0.000
#> SRR1430991 1 0.0000 0.998 1.000 0.000
#> SRR1499722 1 0.0000 0.998 1.000 0.000
#> SRR1351368 1 0.0672 0.991 0.992 0.008
#> SRR1441785 1 0.0000 0.998 1.000 0.000
#> SRR1096101 1 0.0000 0.998 1.000 0.000
#> SRR808375 1 0.0000 0.998 1.000 0.000
#> SRR1452842 1 0.0000 0.998 1.000 0.000
#> SRR1311709 1 0.0672 0.991 0.992 0.008
#> SRR1433352 1 0.0000 0.998 1.000 0.000
#> SRR1340241 2 0.0000 0.990 0.000 1.000
#> SRR1456754 1 0.0000 0.998 1.000 0.000
#> SRR1465172 1 0.0000 0.998 1.000 0.000
#> SRR1499284 1 0.0000 0.998 1.000 0.000
#> SRR1499607 2 0.0000 0.990 0.000 1.000
#> SRR812342 1 0.0000 0.998 1.000 0.000
#> SRR1405374 1 0.0000 0.998 1.000 0.000
#> SRR1403565 1 0.0000 0.998 1.000 0.000
#> SRR1332024 1 0.0000 0.998 1.000 0.000
#> SRR1471633 1 0.0000 0.998 1.000 0.000
#> SRR1325944 2 0.0000 0.990 0.000 1.000
#> SRR1429450 2 0.0000 0.990 0.000 1.000
#> SRR821573 1 0.0000 0.998 1.000 0.000
#> SRR1435372 1 0.0000 0.998 1.000 0.000
#> SRR1324184 2 0.0000 0.990 0.000 1.000
#> SRR816517 2 0.0000 0.990 0.000 1.000
#> SRR1324141 2 0.1843 0.963 0.028 0.972
#> SRR1101612 1 0.0000 0.998 1.000 0.000
#> SRR1356531 1 0.0000 0.998 1.000 0.000
#> SRR1089785 1 0.0000 0.998 1.000 0.000
#> SRR1077708 1 0.0000 0.998 1.000 0.000
#> SRR1343720 1 0.0000 0.998 1.000 0.000
#> SRR1477499 2 0.0000 0.990 0.000 1.000
#> SRR1347236 1 0.0000 0.998 1.000 0.000
#> SRR1326408 1 0.0000 0.998 1.000 0.000
#> SRR1336529 1 0.0000 0.998 1.000 0.000
#> SRR1440643 2 0.0000 0.990 0.000 1.000
#> SRR662354 1 0.0000 0.998 1.000 0.000
#> SRR1310817 1 0.0000 0.998 1.000 0.000
#> SRR1347389 2 0.0000 0.990 0.000 1.000
#> SRR1353097 1 0.0000 0.998 1.000 0.000
#> SRR1384737 1 0.4161 0.908 0.916 0.084
#> SRR1096339 1 0.0000 0.998 1.000 0.000
#> SRR1345329 2 0.0000 0.990 0.000 1.000
#> SRR1414771 1 0.0000 0.998 1.000 0.000
#> SRR1309119 1 0.0000 0.998 1.000 0.000
#> SRR1470438 1 0.0000 0.998 1.000 0.000
#> SRR1343221 1 0.0000 0.998 1.000 0.000
#> SRR1410847 1 0.0000 0.998 1.000 0.000
#> SRR807949 1 0.0000 0.998 1.000 0.000
#> SRR1442332 1 0.0000 0.998 1.000 0.000
#> SRR815920 1 0.0000 0.998 1.000 0.000
#> SRR1471524 1 0.0000 0.998 1.000 0.000
#> SRR1477221 1 0.0000 0.998 1.000 0.000
#> SRR1445046 2 0.0000 0.990 0.000 1.000
#> SRR1331962 2 0.0000 0.990 0.000 1.000
#> SRR1319946 2 0.0000 0.990 0.000 1.000
#> SRR1311599 1 0.0000 0.998 1.000 0.000
#> SRR1323977 2 0.0000 0.990 0.000 1.000
#> SRR1445132 2 0.0000 0.990 0.000 1.000
#> SRR1337321 1 0.0000 0.998 1.000 0.000
#> SRR1366390 2 0.0000 0.990 0.000 1.000
#> SRR1343012 1 0.0000 0.998 1.000 0.000
#> SRR1311958 2 0.0000 0.990 0.000 1.000
#> SRR1388234 2 0.0000 0.990 0.000 1.000
#> SRR1370384 1 0.0000 0.998 1.000 0.000
#> SRR1321650 1 0.0000 0.998 1.000 0.000
#> SRR1485117 2 0.0000 0.990 0.000 1.000
#> SRR1384713 1 0.0000 0.998 1.000 0.000
#> SRR816609 2 0.0000 0.990 0.000 1.000
#> SRR1486239 2 0.0000 0.990 0.000 1.000
#> SRR1309638 1 0.0000 0.998 1.000 0.000
#> SRR1356660 1 0.0000 0.998 1.000 0.000
#> SRR1392883 2 0.0000 0.990 0.000 1.000
#> SRR808130 1 0.0000 0.998 1.000 0.000
#> SRR816677 1 0.0938 0.987 0.988 0.012
#> SRR1455722 1 0.0000 0.998 1.000 0.000
#> SRR1336029 1 0.0000 0.998 1.000 0.000
#> SRR808452 1 0.0000 0.998 1.000 0.000
#> SRR1352169 1 0.0938 0.987 0.988 0.012
#> SRR1366707 1 0.0000 0.998 1.000 0.000
#> SRR1328143 1 0.0000 0.998 1.000 0.000
#> SRR1473567 2 0.0000 0.990 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1390119 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR1436127 3 0.2878 0.7968 0.096 0.000 0.904
#> SRR1347278 3 0.1411 0.8380 0.036 0.000 0.964
#> SRR1332904 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1444179 3 0.6168 -0.1138 0.412 0.000 0.588
#> SRR1082685 3 0.5591 0.2942 0.304 0.000 0.696
#> SRR1362287 3 0.0237 0.8450 0.004 0.000 0.996
#> SRR1339007 1 0.5948 0.6353 0.640 0.000 0.360
#> SRR1376557 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1468700 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1077455 3 0.1964 0.8003 0.056 0.000 0.944
#> SRR1413978 3 0.1411 0.8380 0.036 0.000 0.964
#> SRR1439896 3 0.5785 0.1381 0.332 0.000 0.668
#> SRR1317963 2 0.3482 0.8236 0.128 0.872 0.000
#> SRR1431865 3 0.0747 0.8430 0.016 0.000 0.984
#> SRR1394253 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1082664 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1077968 3 0.6095 -0.1193 0.392 0.000 0.608
#> SRR1076393 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1477476 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR1398057 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1485042 1 0.6280 0.4918 0.540 0.000 0.460
#> SRR1385453 2 0.9380 0.2042 0.256 0.512 0.232
#> SRR1348074 1 0.8985 0.4663 0.564 0.220 0.216
#> SRR813959 2 0.9175 0.2739 0.244 0.540 0.216
#> SRR665442 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR1378068 3 0.2448 0.8046 0.076 0.000 0.924
#> SRR1485237 1 0.8925 0.5360 0.564 0.180 0.256
#> SRR1350792 1 0.5968 0.6319 0.636 0.000 0.364
#> SRR1326797 3 0.1289 0.8392 0.032 0.000 0.968
#> SRR808994 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1474041 3 0.1163 0.8388 0.028 0.000 0.972
#> SRR1405641 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1362245 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1500194 3 0.6140 -0.0769 0.404 0.000 0.596
#> SRR1414876 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1478523 3 0.7226 0.3353 0.228 0.080 0.692
#> SRR1325161 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1318026 1 0.8600 0.5802 0.580 0.136 0.284
#> SRR1343778 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1441287 1 0.5948 0.6353 0.640 0.000 0.360
#> SRR1430991 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1499722 3 0.0424 0.8417 0.008 0.000 0.992
#> SRR1351368 3 0.3192 0.7878 0.112 0.000 0.888
#> SRR1441785 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1096101 3 0.5465 0.3191 0.288 0.000 0.712
#> SRR808375 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1452842 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1311709 1 0.6881 0.4749 0.592 0.020 0.388
#> SRR1433352 3 0.0424 0.8417 0.008 0.000 0.992
#> SRR1340241 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1456754 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1465172 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1499284 3 0.1163 0.8254 0.028 0.000 0.972
#> SRR1499607 2 0.3267 0.8330 0.116 0.884 0.000
#> SRR812342 1 0.5948 0.6353 0.640 0.000 0.360
#> SRR1405374 3 0.0424 0.8417 0.008 0.000 0.992
#> SRR1403565 3 0.0237 0.8450 0.004 0.000 0.996
#> SRR1332024 3 0.2878 0.7968 0.096 0.000 0.904
#> SRR1471633 1 0.6286 0.3590 0.536 0.000 0.464
#> SRR1325944 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1429450 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR821573 3 0.2066 0.8213 0.060 0.000 0.940
#> SRR1435372 1 0.5948 0.6353 0.640 0.000 0.360
#> SRR1324184 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR816517 2 0.7572 0.5127 0.128 0.688 0.184
#> SRR1324141 1 0.8462 0.5787 0.588 0.124 0.288
#> SRR1101612 1 0.6274 0.4998 0.544 0.000 0.456
#> SRR1356531 1 0.6295 0.4650 0.528 0.000 0.472
#> SRR1089785 3 0.1031 0.8416 0.024 0.000 0.976
#> SRR1077708 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1343720 3 0.0424 0.8417 0.008 0.000 0.992
#> SRR1477499 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1347236 3 0.2537 0.7721 0.080 0.000 0.920
#> SRR1326408 3 0.6235 -0.2708 0.436 0.000 0.564
#> SRR1336529 3 0.2878 0.7970 0.096 0.000 0.904
#> SRR1440643 2 0.9681 0.0188 0.256 0.460 0.284
#> SRR662354 1 0.5948 0.6353 0.640 0.000 0.360
#> SRR1310817 3 0.1411 0.8380 0.036 0.000 0.964
#> SRR1347389 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1353097 1 0.6280 0.4931 0.540 0.000 0.460
#> SRR1384737 3 0.5202 0.5485 0.220 0.008 0.772
#> SRR1096339 3 0.6309 -0.4113 0.496 0.000 0.504
#> SRR1345329 1 0.8965 0.5112 0.564 0.196 0.240
#> SRR1414771 3 0.3192 0.7878 0.112 0.000 0.888
#> SRR1309119 3 0.6307 -0.2945 0.488 0.000 0.512
#> SRR1470438 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1343221 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1410847 3 0.0424 0.8417 0.008 0.000 0.992
#> SRR807949 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1442332 3 0.0424 0.8417 0.008 0.000 0.992
#> SRR815920 3 0.1031 0.8402 0.024 0.000 0.976
#> SRR1471524 3 0.2878 0.8019 0.096 0.000 0.904
#> SRR1477221 3 0.2448 0.8068 0.076 0.000 0.924
#> SRR1445046 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1331962 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1319946 2 0.0237 0.9141 0.004 0.996 0.000
#> SRR1311599 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1323977 1 0.8985 0.4663 0.564 0.220 0.216
#> SRR1445132 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR1337321 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1366390 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1343012 3 0.1643 0.8347 0.044 0.000 0.956
#> SRR1311958 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1388234 1 0.8534 0.2236 0.564 0.320 0.116
#> SRR1370384 1 0.5948 0.6353 0.640 0.000 0.360
#> SRR1321650 3 0.2537 0.8012 0.080 0.000 0.920
#> SRR1485117 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR1384713 3 0.5621 0.2268 0.308 0.000 0.692
#> SRR816609 1 0.8862 0.5565 0.564 0.164 0.272
#> SRR1486239 2 0.0000 0.9158 0.000 1.000 0.000
#> SRR1309638 3 0.1860 0.8221 0.052 0.000 0.948
#> SRR1356660 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1392883 2 0.0424 0.9145 0.008 0.992 0.000
#> SRR808130 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR816677 3 0.4805 0.6264 0.176 0.012 0.812
#> SRR1455722 1 0.5968 0.6319 0.636 0.000 0.364
#> SRR1336029 3 0.1289 0.8388 0.032 0.000 0.968
#> SRR808452 3 0.5327 0.3351 0.272 0.000 0.728
#> SRR1352169 3 0.1620 0.8372 0.024 0.012 0.964
#> SRR1366707 3 0.3116 0.7912 0.108 0.000 0.892
#> SRR1328143 3 0.0000 0.8451 0.000 0.000 1.000
#> SRR1473567 2 0.0424 0.9145 0.008 0.992 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 1 0.5320 0.3342 0.572 0.000 0.416 0.012
#> SRR1390119 2 0.0336 0.9710 0.000 0.992 0.008 0.000
#> SRR1436127 3 0.3444 0.8877 0.184 0.000 0.816 0.000
#> SRR1347278 1 0.4907 0.3394 0.580 0.000 0.420 0.000
#> SRR1332904 2 0.1557 0.9459 0.000 0.944 0.000 0.056
#> SRR1444179 1 0.2300 0.7171 0.920 0.000 0.064 0.016
#> SRR1082685 1 0.0779 0.7102 0.980 0.000 0.004 0.016
#> SRR1362287 1 0.4907 0.3376 0.580 0.000 0.420 0.000
#> SRR1339007 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1376557 2 0.0000 0.9724 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0336 0.9703 0.000 0.992 0.000 0.008
#> SRR1077455 1 0.0672 0.7136 0.984 0.000 0.008 0.008
#> SRR1413978 1 0.4790 0.4249 0.620 0.000 0.380 0.000
#> SRR1439896 1 0.0336 0.7100 0.992 0.000 0.000 0.008
#> SRR1317963 4 0.5256 0.3641 0.000 0.392 0.012 0.596
#> SRR1431865 1 0.4898 0.3489 0.584 0.000 0.416 0.000
#> SRR1394253 1 0.3219 0.7011 0.836 0.000 0.164 0.000
#> SRR1082664 1 0.4914 0.5468 0.676 0.000 0.312 0.012
#> SRR1077968 1 0.0927 0.7106 0.976 0.000 0.008 0.016
#> SRR1076393 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1477476 2 0.0336 0.9710 0.000 0.992 0.008 0.000
#> SRR1398057 1 0.5310 0.3395 0.576 0.000 0.412 0.012
#> SRR1485042 1 0.1109 0.7040 0.968 0.000 0.004 0.028
#> SRR1385453 4 0.3017 0.7994 0.028 0.024 0.044 0.904
#> SRR1348074 4 0.1724 0.8124 0.032 0.020 0.000 0.948
#> SRR813959 4 0.2021 0.8085 0.024 0.040 0.000 0.936
#> SRR665442 2 0.3266 0.8560 0.000 0.832 0.168 0.000
#> SRR1378068 3 0.3688 0.8645 0.208 0.000 0.792 0.000
#> SRR1485237 4 0.1833 0.8134 0.032 0.024 0.000 0.944
#> SRR1350792 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1326797 1 0.3271 0.7087 0.856 0.000 0.132 0.012
#> SRR808994 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1474041 1 0.4990 0.4860 0.640 0.000 0.352 0.008
#> SRR1405641 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1362245 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1500194 1 0.2021 0.7168 0.932 0.000 0.056 0.012
#> SRR1414876 2 0.0000 0.9724 0.000 1.000 0.000 0.000
#> SRR1478523 4 0.7580 -0.0107 0.272 0.008 0.196 0.524
#> SRR1325161 1 0.4163 0.6830 0.792 0.000 0.188 0.020
#> SRR1318026 4 0.1661 0.8062 0.052 0.004 0.000 0.944
#> SRR1343778 1 0.4576 0.6174 0.728 0.000 0.260 0.012
#> SRR1441287 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1430991 1 0.3900 0.6967 0.816 0.000 0.164 0.020
#> SRR1499722 1 0.3806 0.7005 0.824 0.000 0.156 0.020
#> SRR1351368 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1441785 1 0.4888 0.3564 0.588 0.000 0.412 0.000
#> SRR1096101 1 0.0895 0.7079 0.976 0.000 0.004 0.020
#> SRR808375 1 0.4163 0.6849 0.792 0.000 0.188 0.020
#> SRR1452842 1 0.3356 0.6958 0.824 0.000 0.176 0.000
#> SRR1311709 4 0.4164 0.5098 0.264 0.000 0.000 0.736
#> SRR1433352 1 0.0672 0.7143 0.984 0.000 0.008 0.008
#> SRR1340241 2 0.0000 0.9724 0.000 1.000 0.000 0.000
#> SRR1456754 1 0.4855 0.3859 0.600 0.000 0.400 0.000
#> SRR1465172 1 0.3806 0.7005 0.824 0.000 0.156 0.020
#> SRR1499284 1 0.1452 0.7175 0.956 0.000 0.036 0.008
#> SRR1499607 4 0.5268 0.3565 0.000 0.396 0.012 0.592
#> SRR812342 1 0.1356 0.7038 0.960 0.000 0.008 0.032
#> SRR1405374 1 0.4543 0.5316 0.676 0.000 0.324 0.000
#> SRR1403565 1 0.4888 0.3599 0.588 0.000 0.412 0.000
#> SRR1332024 3 0.3311 0.8902 0.172 0.000 0.828 0.000
#> SRR1471633 1 0.4585 0.4052 0.668 0.000 0.000 0.332
#> SRR1325944 2 0.0000 0.9724 0.000 1.000 0.000 0.000
#> SRR1429450 2 0.0188 0.9721 0.000 0.996 0.004 0.000
#> SRR821573 1 0.4532 0.6870 0.792 0.000 0.156 0.052
#> SRR1435372 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1324184 2 0.0592 0.9677 0.000 0.984 0.016 0.000
#> SRR816517 4 0.5429 0.6731 0.004 0.196 0.068 0.732
#> SRR1324141 4 0.2831 0.7523 0.120 0.004 0.000 0.876
#> SRR1101612 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1356531 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1089785 1 0.3946 0.6953 0.812 0.000 0.168 0.020
#> SRR1077708 1 0.5300 0.3511 0.580 0.000 0.408 0.012
#> SRR1343720 1 0.3790 0.6978 0.820 0.000 0.164 0.016
#> SRR1477499 2 0.0000 0.9724 0.000 1.000 0.000 0.000
#> SRR1347236 1 0.0336 0.7100 0.992 0.000 0.000 0.008
#> SRR1326408 1 0.0592 0.7074 0.984 0.000 0.000 0.016
#> SRR1336529 3 0.3444 0.8882 0.184 0.000 0.816 0.000
#> SRR1440643 4 0.2660 0.8003 0.048 0.012 0.024 0.916
#> SRR662354 1 0.1209 0.6989 0.964 0.000 0.004 0.032
#> SRR1310817 1 0.3908 0.6714 0.784 0.000 0.212 0.004
#> SRR1347389 2 0.1474 0.9482 0.000 0.948 0.000 0.052
#> SRR1353097 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1384737 3 0.7836 0.2531 0.348 0.000 0.388 0.264
#> SRR1096339 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1345329 4 0.1833 0.8134 0.032 0.024 0.000 0.944
#> SRR1414771 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1309119 1 0.4164 0.4973 0.736 0.000 0.000 0.264
#> SRR1470438 3 0.3448 0.8910 0.168 0.000 0.828 0.004
#> SRR1343221 1 0.5310 0.3462 0.576 0.000 0.412 0.012
#> SRR1410847 1 0.1743 0.7186 0.940 0.000 0.056 0.004
#> SRR807949 1 0.4121 0.6877 0.796 0.000 0.184 0.020
#> SRR1442332 1 0.3249 0.7081 0.852 0.000 0.140 0.008
#> SRR815920 3 0.5602 0.0757 0.472 0.000 0.508 0.020
#> SRR1471524 3 0.3710 0.8779 0.192 0.000 0.804 0.004
#> SRR1477221 3 0.3688 0.8627 0.208 0.000 0.792 0.000
#> SRR1445046 2 0.2124 0.9306 0.000 0.924 0.008 0.068
#> SRR1331962 2 0.1557 0.9459 0.000 0.944 0.000 0.056
#> SRR1319946 4 0.5399 0.1468 0.000 0.468 0.012 0.520
#> SRR1311599 1 0.4888 0.3564 0.588 0.000 0.412 0.000
#> SRR1323977 4 0.1833 0.8134 0.032 0.024 0.000 0.944
#> SRR1445132 2 0.0188 0.9721 0.000 0.996 0.004 0.000
#> SRR1337321 3 0.3311 0.8902 0.172 0.000 0.828 0.000
#> SRR1366390 2 0.0000 0.9724 0.000 1.000 0.000 0.000
#> SRR1343012 1 0.5924 0.2856 0.556 0.000 0.404 0.040
#> SRR1311958 2 0.1557 0.9459 0.000 0.944 0.000 0.056
#> SRR1388234 4 0.1890 0.7903 0.008 0.056 0.000 0.936
#> SRR1370384 1 0.1209 0.7021 0.964 0.000 0.004 0.032
#> SRR1321650 3 0.3400 0.8878 0.180 0.000 0.820 0.000
#> SRR1485117 2 0.0188 0.9721 0.000 0.996 0.004 0.000
#> SRR1384713 1 0.0804 0.7119 0.980 0.000 0.008 0.012
#> SRR816609 4 0.1833 0.8134 0.032 0.024 0.000 0.944
#> SRR1486239 2 0.1557 0.9459 0.000 0.944 0.000 0.056
#> SRR1309638 3 0.4994 0.0520 0.480 0.000 0.520 0.000
#> SRR1356660 1 0.4888 0.3564 0.588 0.000 0.412 0.000
#> SRR1392883 2 0.0188 0.9721 0.000 0.996 0.004 0.000
#> SRR808130 1 0.5452 0.2864 0.556 0.000 0.428 0.016
#> SRR816677 1 0.6536 0.5081 0.660 0.008 0.144 0.188
#> SRR1455722 1 0.1022 0.6999 0.968 0.000 0.000 0.032
#> SRR1336029 1 0.4898 0.3489 0.584 0.000 0.416 0.000
#> SRR808452 1 0.0779 0.7092 0.980 0.000 0.004 0.016
#> SRR1352169 1 0.5334 0.3686 0.588 0.008 0.400 0.004
#> SRR1366707 3 0.3710 0.8779 0.192 0.000 0.804 0.004
#> SRR1328143 1 0.4121 0.6877 0.796 0.000 0.184 0.020
#> SRR1473567 2 0.0188 0.9721 0.000 0.996 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 1 0.1648 0.6038 0.940 0.000 0.020 0.000 NA
#> SRR1390119 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1436127 3 0.4283 0.6151 0.456 0.000 0.544 0.000 NA
#> SRR1347278 1 0.0671 0.6270 0.980 0.000 0.016 0.000 NA
#> SRR1332904 2 0.2188 0.9001 0.000 0.924 0.028 0.024 NA
#> SRR1444179 1 0.3944 0.5955 0.720 0.000 0.004 0.004 NA
#> SRR1082685 1 0.5353 0.5535 0.604 0.000 0.004 0.060 NA
#> SRR1362287 1 0.6919 0.1917 0.552 0.000 0.048 0.228 NA
#> SRR1339007 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1376557 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1468700 2 0.1341 0.9059 0.000 0.944 0.000 0.056 NA
#> SRR1077455 1 0.2690 0.6277 0.844 0.000 0.000 0.000 NA
#> SRR1413978 1 0.4050 0.5488 0.784 0.000 0.008 0.172 NA
#> SRR1439896 1 0.4182 0.5515 0.600 0.000 0.000 0.000 NA
#> SRR1317963 4 0.5712 0.0702 0.000 0.452 0.028 0.488 NA
#> SRR1431865 1 0.6113 0.2941 0.596 0.000 0.008 0.228 NA
#> SRR1394253 1 0.2470 0.6096 0.884 0.000 0.000 0.104 NA
#> SRR1082664 1 0.1043 0.6188 0.960 0.000 0.000 0.000 NA
#> SRR1077968 1 0.4210 0.5438 0.588 0.000 0.000 0.000 NA
#> SRR1076393 3 0.3305 0.8200 0.224 0.000 0.776 0.000 NA
#> SRR1477476 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1398057 1 0.1399 0.6114 0.952 0.000 0.020 0.000 NA
#> SRR1485042 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1385453 4 0.5241 0.7196 0.220 0.016 0.000 0.692 NA
#> SRR1348074 4 0.4199 0.6634 0.056 0.180 0.000 0.764 NA
#> SRR813959 4 0.5156 0.7248 0.220 0.020 0.000 0.700 NA
#> SRR665442 2 0.4961 0.7345 0.000 0.724 0.140 0.004 NA
#> SRR1378068 3 0.4227 0.6819 0.420 0.000 0.580 0.000 NA
#> SRR1485237 4 0.4099 0.7323 0.200 0.032 0.000 0.764 NA
#> SRR1350792 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1326797 1 0.1430 0.6338 0.944 0.000 0.000 0.004 NA
#> SRR808994 3 0.2813 0.7905 0.168 0.000 0.832 0.000 NA
#> SRR1474041 1 0.1168 0.6160 0.960 0.000 0.032 0.000 NA
#> SRR1405641 3 0.3305 0.8200 0.224 0.000 0.776 0.000 NA
#> SRR1362245 3 0.2813 0.7874 0.168 0.000 0.832 0.000 NA
#> SRR1500194 1 0.3838 0.5945 0.716 0.000 0.004 0.000 NA
#> SRR1414876 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1478523 1 0.6142 -0.1413 0.520 0.008 0.012 0.388 NA
#> SRR1325161 1 0.0404 0.6271 0.988 0.000 0.000 0.000 NA
#> SRR1318026 4 0.4134 0.7138 0.224 0.000 0.000 0.744 NA
#> SRR1343778 1 0.0880 0.6229 0.968 0.000 0.000 0.000 NA
#> SRR1441287 1 0.4268 0.5285 0.556 0.000 0.000 0.000 NA
#> SRR1430991 1 0.0880 0.6220 0.968 0.000 0.000 0.000 NA
#> SRR1499722 1 0.0880 0.6220 0.968 0.000 0.000 0.000 NA
#> SRR1351368 3 0.2891 0.7979 0.176 0.000 0.824 0.000 NA
#> SRR1441785 1 0.6688 0.0884 0.496 0.000 0.008 0.228 NA
#> SRR1096101 1 0.4268 0.5285 0.556 0.000 0.000 0.000 NA
#> SRR808375 1 0.1043 0.6188 0.960 0.000 0.000 0.000 NA
#> SRR1452842 1 0.1471 0.6255 0.952 0.000 0.004 0.020 NA
#> SRR1311709 4 0.6102 0.3434 0.272 0.000 0.004 0.572 NA
#> SRR1433352 1 0.2280 0.6317 0.880 0.000 0.000 0.000 NA
#> SRR1340241 2 0.0162 0.9247 0.000 0.996 0.004 0.000 NA
#> SRR1456754 1 0.6024 0.3043 0.608 0.000 0.008 0.224 NA
#> SRR1465172 1 0.0794 0.6233 0.972 0.000 0.000 0.000 NA
#> SRR1499284 1 0.0609 0.6321 0.980 0.000 0.000 0.000 NA
#> SRR1499607 4 0.6218 0.1029 0.000 0.448 0.028 0.456 NA
#> SRR812342 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1405374 1 0.6185 0.2797 0.588 0.000 0.008 0.220 NA
#> SRR1403565 1 0.6688 0.0884 0.496 0.000 0.008 0.228 NA
#> SRR1332024 3 0.3612 0.8074 0.268 0.000 0.732 0.000 NA
#> SRR1471633 1 0.6278 0.4510 0.552 0.000 0.004 0.264 NA
#> SRR1325944 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1429450 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR821573 1 0.2629 0.6009 0.880 0.000 0.004 0.104 NA
#> SRR1435372 1 0.4268 0.5285 0.556 0.000 0.000 0.000 NA
#> SRR1324184 2 0.1121 0.9128 0.000 0.956 0.044 0.000 NA
#> SRR816517 4 0.6187 0.4294 0.000 0.320 0.068 0.572 NA
#> SRR1324141 4 0.5215 0.4499 0.372 0.000 0.000 0.576 NA
#> SRR1101612 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1356531 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1089785 1 0.1117 0.6200 0.964 0.000 0.016 0.000 NA
#> SRR1077708 1 0.0510 0.6263 0.984 0.000 0.000 0.000 NA
#> SRR1343720 1 0.0609 0.6252 0.980 0.000 0.000 0.000 NA
#> SRR1477499 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1347236 1 0.3210 0.6075 0.788 0.000 0.000 0.000 NA
#> SRR1326408 1 0.4256 0.5332 0.564 0.000 0.000 0.000 NA
#> SRR1336529 3 0.4294 0.5932 0.468 0.000 0.532 0.000 NA
#> SRR1440643 4 0.5032 0.7105 0.228 0.004 0.000 0.692 NA
#> SRR662354 1 0.4268 0.5285 0.556 0.000 0.000 0.000 NA
#> SRR1310817 1 0.0740 0.6298 0.980 0.000 0.008 0.004 NA
#> SRR1347389 2 0.2529 0.8941 0.000 0.908 0.032 0.036 NA
#> SRR1353097 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1384737 1 0.5057 0.0312 0.556 0.000 0.004 0.412 NA
#> SRR1096339 1 0.4262 0.5305 0.560 0.000 0.000 0.000 NA
#> SRR1345329 4 0.4199 0.6634 0.056 0.180 0.000 0.764 NA
#> SRR1414771 3 0.2561 0.7514 0.144 0.000 0.856 0.000 NA
#> SRR1309119 1 0.6278 0.4510 0.552 0.000 0.004 0.264 NA
#> SRR1470438 3 0.2773 0.7854 0.164 0.000 0.836 0.000 NA
#> SRR1343221 1 0.4389 0.5186 0.756 0.000 0.004 0.184 NA
#> SRR1410847 1 0.2561 0.6339 0.856 0.000 0.000 0.000 NA
#> SRR807949 1 0.1205 0.6164 0.956 0.000 0.004 0.000 NA
#> SRR1442332 1 0.0671 0.6312 0.980 0.000 0.004 0.000 NA
#> SRR815920 1 0.4269 0.0060 0.684 0.000 0.300 0.000 NA
#> SRR1471524 1 0.4249 -0.3717 0.568 0.000 0.432 0.000 NA
#> SRR1477221 3 0.4273 0.6372 0.448 0.000 0.552 0.000 NA
#> SRR1445046 2 0.4281 0.7193 0.000 0.756 0.028 0.204 NA
#> SRR1331962 2 0.2673 0.8827 0.000 0.892 0.028 0.072 NA
#> SRR1319946 2 0.5762 -0.0150 0.000 0.496 0.024 0.440 NA
#> SRR1311599 1 0.6586 0.1397 0.520 0.000 0.008 0.228 NA
#> SRR1323977 4 0.4562 0.7182 0.128 0.108 0.000 0.760 NA
#> SRR1445132 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1337321 3 0.3366 0.8179 0.232 0.000 0.768 0.000 NA
#> SRR1366390 2 0.0451 0.9234 0.000 0.988 0.008 0.004 NA
#> SRR1343012 1 0.2533 0.6042 0.888 0.000 0.008 0.096 NA
#> SRR1311958 2 0.2784 0.8805 0.000 0.888 0.028 0.072 NA
#> SRR1388234 4 0.4181 0.5627 0.000 0.240 0.016 0.736 NA
#> SRR1370384 1 0.4256 0.5313 0.564 0.000 0.000 0.000 NA
#> SRR1321650 3 0.3913 0.7751 0.324 0.000 0.676 0.000 NA
#> SRR1485117 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR1384713 1 0.3837 0.5821 0.692 0.000 0.000 0.000 NA
#> SRR816609 4 0.4775 0.7265 0.216 0.016 0.000 0.724 NA
#> SRR1486239 2 0.2673 0.8827 0.000 0.892 0.028 0.072 NA
#> SRR1309638 3 0.4300 0.5714 0.476 0.000 0.524 0.000 NA
#> SRR1356660 1 0.6688 0.0884 0.496 0.000 0.008 0.228 NA
#> SRR1392883 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
#> SRR808130 1 0.1211 0.6139 0.960 0.000 0.024 0.000 NA
#> SRR816677 1 0.3203 0.5501 0.820 0.000 0.000 0.168 NA
#> SRR1455722 1 0.4268 0.5285 0.556 0.000 0.000 0.000 NA
#> SRR1336029 1 0.5037 0.4706 0.724 0.000 0.016 0.180 NA
#> SRR808452 1 0.3730 0.5825 0.712 0.000 0.000 0.000 NA
#> SRR1352169 1 0.1200 0.6230 0.964 0.000 0.016 0.012 NA
#> SRR1366707 1 0.4304 -0.5049 0.516 0.000 0.484 0.000 NA
#> SRR1328143 1 0.1043 0.6188 0.960 0.000 0.000 0.000 NA
#> SRR1473567 2 0.0000 0.9254 0.000 1.000 0.000 0.000 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.2213 0.8406 0.068 0.000 0.004 0.020 0.904 0.004
#> SRR1390119 2 0.0146 0.9511 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1436127 3 0.2471 0.8703 0.044 0.000 0.896 0.020 0.040 0.000
#> SRR1347278 5 0.3133 0.8303 0.108 0.000 0.012 0.024 0.848 0.008
#> SRR1332904 2 0.1950 0.9353 0.000 0.912 0.000 0.000 0.064 0.024
#> SRR1444179 1 0.3086 0.8140 0.852 0.000 0.000 0.080 0.056 0.012
#> SRR1082685 1 0.1769 0.8720 0.924 0.000 0.004 0.060 0.012 0.000
#> SRR1362287 6 0.4442 0.8222 0.076 0.000 0.028 0.000 0.148 0.748
#> SRR1339007 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1376557 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077455 1 0.3975 -0.0141 0.544 0.000 0.004 0.000 0.452 0.000
#> SRR1413978 6 0.5285 0.7377 0.188 0.000 0.004 0.000 0.188 0.620
#> SRR1439896 1 0.0603 0.9172 0.980 0.000 0.000 0.016 0.004 0.000
#> SRR1317963 2 0.3730 0.8704 0.000 0.816 0.000 0.088 0.060 0.036
#> SRR1431865 6 0.4357 0.8265 0.108 0.000 0.004 0.000 0.156 0.732
#> SRR1394253 6 0.5915 0.3583 0.212 0.000 0.000 0.000 0.360 0.428
#> SRR1082664 5 0.1926 0.8433 0.068 0.000 0.000 0.020 0.912 0.000
#> SRR1077968 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1076393 3 0.0363 0.8966 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR1477476 2 0.0146 0.9511 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1398057 5 0.2688 0.8380 0.068 0.000 0.024 0.020 0.884 0.004
#> SRR1485042 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1385453 4 0.2119 0.8691 0.000 0.036 0.000 0.912 0.044 0.008
#> SRR1348074 4 0.0935 0.8723 0.000 0.032 0.000 0.964 0.004 0.000
#> SRR813959 4 0.2152 0.8695 0.000 0.036 0.000 0.912 0.040 0.012
#> SRR665442 2 0.2051 0.9292 0.000 0.896 0.004 0.004 0.000 0.096
#> SRR1378068 3 0.3780 0.7804 0.068 0.000 0.812 0.020 0.096 0.004
#> SRR1485237 4 0.1293 0.8783 0.020 0.016 0.000 0.956 0.004 0.004
#> SRR1350792 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1326797 5 0.2631 0.8028 0.152 0.000 0.000 0.008 0.840 0.000
#> SRR808994 3 0.0405 0.8944 0.008 0.000 0.988 0.004 0.000 0.000
#> SRR1474041 5 0.2294 0.8402 0.072 0.000 0.036 0.000 0.892 0.000
#> SRR1405641 3 0.0862 0.8951 0.008 0.000 0.972 0.016 0.004 0.000
#> SRR1362245 3 0.0405 0.8944 0.008 0.000 0.988 0.004 0.000 0.000
#> SRR1500194 1 0.2865 0.8302 0.868 0.000 0.000 0.064 0.056 0.012
#> SRR1414876 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 5 0.5534 0.0932 0.044 0.016 0.012 0.444 0.480 0.004
#> SRR1325161 5 0.2492 0.8439 0.080 0.000 0.008 0.004 0.888 0.020
#> SRR1318026 4 0.1116 0.8755 0.028 0.004 0.000 0.960 0.008 0.000
#> SRR1343778 5 0.2069 0.8424 0.068 0.000 0.000 0.020 0.908 0.004
#> SRR1441287 1 0.0146 0.9247 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1430991 5 0.1444 0.8452 0.072 0.000 0.000 0.000 0.928 0.000
#> SRR1499722 5 0.1444 0.8452 0.072 0.000 0.000 0.000 0.928 0.000
#> SRR1351368 3 0.0405 0.8944 0.008 0.000 0.988 0.004 0.000 0.000
#> SRR1441785 6 0.2618 0.8257 0.076 0.000 0.000 0.000 0.052 0.872
#> SRR1096101 1 0.0291 0.9247 0.992 0.000 0.000 0.004 0.004 0.000
#> SRR808375 5 0.1444 0.8452 0.072 0.000 0.000 0.000 0.928 0.000
#> SRR1452842 6 0.5211 0.3300 0.096 0.000 0.000 0.000 0.388 0.516
#> SRR1311709 4 0.2455 0.8032 0.112 0.000 0.000 0.872 0.004 0.012
#> SRR1433352 5 0.3563 0.5608 0.336 0.000 0.000 0.000 0.664 0.000
#> SRR1340241 2 0.0405 0.9512 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR1456754 6 0.3268 0.8372 0.100 0.000 0.000 0.000 0.076 0.824
#> SRR1465172 5 0.1588 0.8458 0.072 0.000 0.004 0.000 0.924 0.000
#> SRR1499284 5 0.3733 0.6217 0.288 0.000 0.004 0.000 0.700 0.008
#> SRR1499607 2 0.3325 0.8834 0.000 0.840 0.000 0.092 0.036 0.032
#> SRR812342 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1405374 6 0.4190 0.8306 0.112 0.000 0.000 0.000 0.148 0.740
#> SRR1403565 6 0.2696 0.8243 0.076 0.000 0.004 0.000 0.048 0.872
#> SRR1332024 3 0.0972 0.8973 0.028 0.000 0.964 0.000 0.008 0.000
#> SRR1471633 4 0.3219 0.7329 0.192 0.000 0.000 0.792 0.004 0.012
#> SRR1325944 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.5108 0.5444 0.112 0.000 0.004 0.264 0.620 0.000
#> SRR1435372 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324184 2 0.0363 0.9492 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR816517 2 0.3934 0.6731 0.000 0.708 0.000 0.260 0.000 0.032
#> SRR1324141 4 0.1418 0.8749 0.032 0.000 0.000 0.944 0.024 0.000
#> SRR1101612 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1356531 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1089785 5 0.2009 0.8457 0.084 0.000 0.008 0.004 0.904 0.000
#> SRR1077708 5 0.3053 0.8368 0.072 0.000 0.020 0.020 0.868 0.020
#> SRR1343720 5 0.1908 0.8415 0.096 0.000 0.004 0.000 0.900 0.000
#> SRR1477499 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1347236 1 0.2092 0.8003 0.876 0.000 0.000 0.000 0.124 0.000
#> SRR1326408 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336529 3 0.2188 0.8842 0.032 0.000 0.912 0.020 0.036 0.000
#> SRR1440643 4 0.2051 0.8720 0.012 0.020 0.000 0.920 0.044 0.004
#> SRR662354 1 0.0458 0.9174 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR1310817 5 0.2714 0.8163 0.136 0.000 0.004 0.012 0.848 0.000
#> SRR1347389 2 0.2322 0.9314 0.000 0.896 0.000 0.004 0.064 0.036
#> SRR1353097 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1384737 4 0.4551 0.5185 0.064 0.000 0.000 0.672 0.260 0.004
#> SRR1096339 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1345329 4 0.1080 0.8710 0.000 0.032 0.000 0.960 0.004 0.004
#> SRR1414771 3 0.0405 0.8944 0.008 0.000 0.988 0.004 0.000 0.000
#> SRR1309119 4 0.3420 0.7171 0.204 0.000 0.000 0.776 0.008 0.012
#> SRR1470438 3 0.0405 0.8944 0.008 0.000 0.988 0.004 0.000 0.000
#> SRR1343221 5 0.4991 -0.3410 0.068 0.000 0.000 0.000 0.476 0.456
#> SRR1410847 1 0.3595 0.5158 0.704 0.000 0.000 0.008 0.288 0.000
#> SRR807949 5 0.1387 0.8437 0.068 0.000 0.000 0.000 0.932 0.000
#> SRR1442332 5 0.2933 0.7515 0.200 0.000 0.004 0.000 0.796 0.000
#> SRR815920 5 0.4246 0.7117 0.064 0.000 0.144 0.028 0.764 0.000
#> SRR1471524 3 0.3492 0.7672 0.016 0.000 0.796 0.020 0.168 0.000
#> SRR1477221 3 0.4051 0.6976 0.068 0.000 0.764 0.004 0.160 0.004
#> SRR1445046 2 0.2106 0.9329 0.000 0.904 0.000 0.000 0.064 0.032
#> SRR1331962 2 0.1950 0.9353 0.000 0.912 0.000 0.000 0.064 0.024
#> SRR1319946 2 0.3092 0.9091 0.000 0.860 0.000 0.040 0.064 0.036
#> SRR1311599 6 0.2680 0.8284 0.076 0.000 0.000 0.000 0.056 0.868
#> SRR1323977 4 0.1406 0.8773 0.016 0.020 0.000 0.952 0.004 0.008
#> SRR1445132 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 3 0.0692 0.8982 0.020 0.000 0.976 0.000 0.004 0.000
#> SRR1366390 2 0.0964 0.9490 0.000 0.968 0.000 0.004 0.012 0.016
#> SRR1343012 5 0.5606 0.4528 0.132 0.000 0.004 0.312 0.548 0.004
#> SRR1311958 2 0.2106 0.9329 0.000 0.904 0.000 0.000 0.064 0.032
#> SRR1388234 4 0.1633 0.8575 0.000 0.044 0.000 0.932 0.000 0.024
#> SRR1370384 1 0.0260 0.9245 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1321650 3 0.1492 0.8922 0.024 0.000 0.940 0.000 0.036 0.000
#> SRR1485117 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 1 0.1152 0.8914 0.952 0.000 0.000 0.000 0.044 0.004
#> SRR816609 4 0.1293 0.8783 0.020 0.016 0.000 0.956 0.004 0.004
#> SRR1486239 2 0.1950 0.9353 0.000 0.912 0.000 0.000 0.064 0.024
#> SRR1309638 3 0.4633 0.6804 0.068 0.000 0.740 0.012 0.160 0.020
#> SRR1356660 6 0.2941 0.8315 0.076 0.000 0.004 0.000 0.064 0.856
#> SRR1392883 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.2164 0.8407 0.068 0.000 0.032 0.000 0.900 0.000
#> SRR816677 4 0.4264 0.6741 0.124 0.004 0.000 0.744 0.128 0.000
#> SRR1455722 1 0.0000 0.9264 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1336029 6 0.4560 0.8254 0.104 0.000 0.004 0.008 0.156 0.728
#> SRR808452 1 0.0146 0.9268 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1352169 5 0.3053 0.8379 0.072 0.004 0.024 0.036 0.864 0.000
#> SRR1366707 3 0.2449 0.8669 0.012 0.000 0.888 0.020 0.080 0.000
#> SRR1328143 5 0.1387 0.8437 0.068 0.000 0.000 0.000 0.932 0.000
#> SRR1473567 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17851 rows and 124 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.971 0.988 0.3746 0.622 0.622
#> 3 3 0.831 0.898 0.941 0.6167 0.720 0.565
#> 4 4 0.621 0.713 0.858 0.1380 0.843 0.624
#> 5 5 0.646 0.599 0.779 0.0785 0.934 0.787
#> 6 6 0.648 0.650 0.796 0.0511 0.891 0.617
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
#> SRR1442087 1 0.000 0.994 1.000 0.000
#> SRR1390119 2 0.000 0.968 0.000 1.000
#> SRR1436127 1 0.000 0.994 1.000 0.000
#> SRR1347278 1 0.000 0.994 1.000 0.000
#> SRR1332904 2 0.000 0.968 0.000 1.000
#> SRR1444179 1 0.000 0.994 1.000 0.000
#> SRR1082685 1 0.000 0.994 1.000 0.000
#> SRR1362287 1 0.000 0.994 1.000 0.000
#> SRR1339007 1 0.000 0.994 1.000 0.000
#> SRR1376557 2 0.000 0.968 0.000 1.000
#> SRR1468700 2 0.000 0.968 0.000 1.000
#> SRR1077455 1 0.000 0.994 1.000 0.000
#> SRR1413978 1 0.000 0.994 1.000 0.000
#> SRR1439896 1 0.000 0.994 1.000 0.000
#> SRR1317963 2 0.000 0.968 0.000 1.000
#> SRR1431865 1 0.000 0.994 1.000 0.000
#> SRR1394253 1 0.000 0.994 1.000 0.000
#> SRR1082664 1 0.000 0.994 1.000 0.000
#> SRR1077968 1 0.000 0.994 1.000 0.000
#> SRR1076393 1 0.000 0.994 1.000 0.000
#> SRR1477476 2 0.000 0.968 0.000 1.000
#> SRR1398057 1 0.000 0.994 1.000 0.000
#> SRR1485042 1 0.000 0.994 1.000 0.000
#> SRR1385453 1 0.000 0.994 1.000 0.000
#> SRR1348074 2 0.714 0.759 0.196 0.804
#> SRR813959 2 0.343 0.912 0.064 0.936
#> SRR665442 2 0.000 0.968 0.000 1.000
#> SRR1378068 1 0.000 0.994 1.000 0.000
#> SRR1485237 1 0.722 0.738 0.800 0.200
#> SRR1350792 1 0.000 0.994 1.000 0.000
#> SRR1326797 1 0.000 0.994 1.000 0.000
#> SRR808994 1 0.000 0.994 1.000 0.000
#> SRR1474041 1 0.000 0.994 1.000 0.000
#> SRR1405641 1 0.000 0.994 1.000 0.000
#> SRR1362245 1 0.000 0.994 1.000 0.000
#> SRR1500194 1 0.000 0.994 1.000 0.000
#> SRR1414876 2 0.000 0.968 0.000 1.000
#> SRR1478523 1 0.000 0.994 1.000 0.000
#> SRR1325161 1 0.000 0.994 1.000 0.000
#> SRR1318026 1 0.000 0.994 1.000 0.000
#> SRR1343778 1 0.000 0.994 1.000 0.000
#> SRR1441287 1 0.000 0.994 1.000 0.000
#> SRR1430991 1 0.000 0.994 1.000 0.000
#> SRR1499722 1 0.000 0.994 1.000 0.000
#> SRR1351368 1 0.000 0.994 1.000 0.000
#> SRR1441785 1 0.000 0.994 1.000 0.000
#> SRR1096101 1 0.000 0.994 1.000 0.000
#> SRR808375 1 0.000 0.994 1.000 0.000
#> SRR1452842 1 0.000 0.994 1.000 0.000
#> SRR1311709 1 0.000 0.994 1.000 0.000
#> SRR1433352 1 0.000 0.994 1.000 0.000
#> SRR1340241 2 0.000 0.968 0.000 1.000
#> SRR1456754 1 0.000 0.994 1.000 0.000
#> SRR1465172 1 0.000 0.994 1.000 0.000
#> SRR1499284 1 0.000 0.994 1.000 0.000
#> SRR1499607 2 0.000 0.968 0.000 1.000
#> SRR812342 1 0.000 0.994 1.000 0.000
#> SRR1405374 1 0.000 0.994 1.000 0.000
#> SRR1403565 1 0.000 0.994 1.000 0.000
#> SRR1332024 1 0.000 0.994 1.000 0.000
#> SRR1471633 1 0.000 0.994 1.000 0.000
#> SRR1325944 2 0.000 0.968 0.000 1.000
#> SRR1429450 2 0.000 0.968 0.000 1.000
#> SRR821573 1 0.000 0.994 1.000 0.000
#> SRR1435372 1 0.000 0.994 1.000 0.000
#> SRR1324184 2 0.000 0.968 0.000 1.000
#> SRR816517 2 0.000 0.968 0.000 1.000
#> SRR1324141 1 0.000 0.994 1.000 0.000
#> SRR1101612 1 0.000 0.994 1.000 0.000
#> SRR1356531 1 0.000 0.994 1.000 0.000
#> SRR1089785 1 0.000 0.994 1.000 0.000
#> SRR1077708 1 0.000 0.994 1.000 0.000
#> SRR1343720 1 0.000 0.994 1.000 0.000
#> SRR1477499 2 0.000 0.968 0.000 1.000
#> SRR1347236 1 0.000 0.994 1.000 0.000
#> SRR1326408 1 0.000 0.994 1.000 0.000
#> SRR1336529 1 0.000 0.994 1.000 0.000
#> SRR1440643 1 0.000 0.994 1.000 0.000
#> SRR662354 1 0.000 0.994 1.000 0.000
#> SRR1310817 1 0.000 0.994 1.000 0.000
#> SRR1347389 2 0.000 0.968 0.000 1.000
#> SRR1353097 1 0.000 0.994 1.000 0.000
#> SRR1384737 1 0.000 0.994 1.000 0.000
#> SRR1096339 1 0.000 0.994 1.000 0.000
#> SRR1345329 2 0.788 0.699 0.236 0.764
#> SRR1414771 1 0.000 0.994 1.000 0.000
#> SRR1309119 1 0.000 0.994 1.000 0.000
#> SRR1470438 1 0.000 0.994 1.000 0.000
#> SRR1343221 1 0.000 0.994 1.000 0.000
#> SRR1410847 1 0.000 0.994 1.000 0.000
#> SRR807949 1 0.000 0.994 1.000 0.000
#> SRR1442332 1 0.000 0.994 1.000 0.000
#> SRR815920 1 0.000 0.994 1.000 0.000
#> SRR1471524 1 0.000 0.994 1.000 0.000
#> SRR1477221 1 0.000 0.994 1.000 0.000
#> SRR1445046 2 0.000 0.968 0.000 1.000
#> SRR1331962 2 0.000 0.968 0.000 1.000
#> SRR1319946 2 0.000 0.968 0.000 1.000
#> SRR1311599 1 0.000 0.994 1.000 0.000
#> SRR1323977 2 0.996 0.158 0.464 0.536
#> SRR1445132 2 0.000 0.968 0.000 1.000
#> SRR1337321 1 0.000 0.994 1.000 0.000
#> SRR1366390 2 0.000 0.968 0.000 1.000
#> SRR1343012 1 0.000 0.994 1.000 0.000
#> SRR1311958 2 0.000 0.968 0.000 1.000
#> SRR1388234 2 0.000 0.968 0.000 1.000
#> SRR1370384 1 0.000 0.994 1.000 0.000
#> SRR1321650 1 0.000 0.994 1.000 0.000
#> SRR1485117 2 0.000 0.968 0.000 1.000
#> SRR1384713 1 0.000 0.994 1.000 0.000
#> SRR816609 1 0.895 0.525 0.688 0.312
#> SRR1486239 2 0.000 0.968 0.000 1.000
#> SRR1309638 1 0.000 0.994 1.000 0.000
#> SRR1356660 1 0.000 0.994 1.000 0.000
#> SRR1392883 2 0.000 0.968 0.000 1.000
#> SRR808130 1 0.000 0.994 1.000 0.000
#> SRR816677 1 0.000 0.994 1.000 0.000
#> SRR1455722 1 0.000 0.994 1.000 0.000
#> SRR1336029 1 0.000 0.994 1.000 0.000
#> SRR808452 1 0.000 0.994 1.000 0.000
#> SRR1352169 1 0.000 0.994 1.000 0.000
#> SRR1366707 1 0.000 0.994 1.000 0.000
#> SRR1328143 1 0.000 0.994 1.000 0.000
#> SRR1473567 2 0.000 0.968 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1442087 3 0.3816 0.904 0.148 0.000 0.852
#> SRR1390119 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1436127 3 0.2625 0.930 0.084 0.000 0.916
#> SRR1347278 3 0.5733 0.664 0.324 0.000 0.676
#> SRR1332904 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1444179 1 0.0424 0.939 0.992 0.000 0.008
#> SRR1082685 1 0.0424 0.939 0.992 0.000 0.008
#> SRR1362287 1 0.1289 0.932 0.968 0.000 0.032
#> SRR1339007 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1376557 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1468700 2 0.0424 0.975 0.000 0.992 0.008
#> SRR1077455 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1413978 1 0.0592 0.943 0.988 0.000 0.012
#> SRR1439896 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1317963 2 0.0237 0.976 0.000 0.996 0.004
#> SRR1431865 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1394253 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1082664 3 0.3267 0.922 0.116 0.000 0.884
#> SRR1077968 1 0.0237 0.944 0.996 0.000 0.004
#> SRR1076393 3 0.2261 0.928 0.068 0.000 0.932
#> SRR1477476 2 0.0424 0.972 0.000 0.992 0.008
#> SRR1398057 3 0.3192 0.924 0.112 0.000 0.888
#> SRR1485042 1 0.0237 0.944 0.996 0.000 0.004
#> SRR1385453 3 0.2918 0.891 0.044 0.032 0.924
#> SRR1348074 2 0.7112 0.204 0.424 0.552 0.024
#> SRR813959 2 0.0000 0.976 0.000 1.000 0.000
#> SRR665442 2 0.0424 0.975 0.000 0.992 0.008
#> SRR1378068 3 0.2625 0.930 0.084 0.000 0.916
#> SRR1485237 1 0.4228 0.788 0.844 0.148 0.008
#> SRR1350792 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1326797 1 0.0892 0.940 0.980 0.000 0.020
#> SRR808994 3 0.1964 0.920 0.056 0.000 0.944
#> SRR1474041 1 0.3686 0.813 0.860 0.000 0.140
#> SRR1405641 3 0.1964 0.920 0.056 0.000 0.944
#> SRR1362245 3 0.2625 0.930 0.084 0.000 0.916
#> SRR1500194 1 0.0424 0.939 0.992 0.000 0.008
#> SRR1414876 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1478523 3 0.2261 0.928 0.068 0.000 0.932
#> SRR1325161 1 0.1163 0.935 0.972 0.000 0.028
#> SRR1318026 1 0.1774 0.917 0.960 0.024 0.016
#> SRR1343778 3 0.4062 0.891 0.164 0.000 0.836
#> SRR1441287 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1430991 1 0.1643 0.922 0.956 0.000 0.044
#> SRR1499722 1 0.0892 0.940 0.980 0.000 0.020
#> SRR1351368 3 0.2066 0.923 0.060 0.000 0.940
#> SRR1441785 1 0.0237 0.944 0.996 0.000 0.004
#> SRR1096101 1 0.0424 0.944 0.992 0.000 0.008
#> SRR808375 1 0.1163 0.935 0.972 0.000 0.028
#> SRR1452842 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1311709 1 0.0592 0.937 0.988 0.000 0.012
#> SRR1433352 1 0.0892 0.940 0.980 0.000 0.020
#> SRR1340241 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1456754 1 0.0747 0.941 0.984 0.000 0.016
#> SRR1465172 1 0.0892 0.940 0.980 0.000 0.020
#> SRR1499284 1 0.0892 0.940 0.980 0.000 0.020
#> SRR1499607 2 0.0000 0.976 0.000 1.000 0.000
#> SRR812342 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1405374 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1403565 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1332024 3 0.2261 0.928 0.068 0.000 0.932
#> SRR1471633 1 0.0592 0.937 0.988 0.000 0.012
#> SRR1325944 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1429450 2 0.0000 0.976 0.000 1.000 0.000
#> SRR821573 1 0.0747 0.941 0.984 0.000 0.016
#> SRR1435372 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1324184 2 0.0592 0.974 0.000 0.988 0.012
#> SRR816517 3 0.1860 0.836 0.000 0.052 0.948
#> SRR1324141 1 0.1015 0.933 0.980 0.012 0.008
#> SRR1101612 1 0.0237 0.944 0.996 0.000 0.004
#> SRR1356531 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1089785 3 0.3816 0.903 0.148 0.000 0.852
#> SRR1077708 3 0.4605 0.851 0.204 0.000 0.796
#> SRR1343720 1 0.1031 0.937 0.976 0.000 0.024
#> SRR1477499 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1347236 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1326408 1 0.0237 0.944 0.996 0.000 0.004
#> SRR1336529 3 0.2356 0.929 0.072 0.000 0.928
#> SRR1440643 3 0.2860 0.929 0.084 0.004 0.912
#> SRR662354 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1310817 1 0.1753 0.922 0.952 0.000 0.048
#> SRR1347389 2 0.1031 0.967 0.000 0.976 0.024
#> SRR1353097 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1384737 1 0.6543 0.384 0.640 0.016 0.344
#> SRR1096339 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1345329 1 0.7021 0.186 0.544 0.436 0.020
#> SRR1414771 3 0.0892 0.886 0.020 0.000 0.980
#> SRR1309119 1 0.0592 0.937 0.988 0.000 0.012
#> SRR1470438 3 0.1289 0.899 0.032 0.000 0.968
#> SRR1343221 1 0.0747 0.941 0.984 0.000 0.016
#> SRR1410847 1 0.0424 0.944 0.992 0.000 0.008
#> SRR807949 1 0.5254 0.608 0.736 0.000 0.264
#> SRR1442332 1 0.1860 0.914 0.948 0.000 0.052
#> SRR815920 3 0.2261 0.928 0.068 0.000 0.932
#> SRR1471524 3 0.2356 0.929 0.072 0.000 0.928
#> SRR1477221 3 0.2959 0.927 0.100 0.000 0.900
#> SRR1445046 2 0.0592 0.974 0.000 0.988 0.012
#> SRR1331962 2 0.0424 0.975 0.000 0.992 0.008
#> SRR1319946 2 0.0592 0.974 0.000 0.988 0.012
#> SRR1311599 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1323977 1 0.6421 0.270 0.572 0.424 0.004
#> SRR1445132 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1337321 3 0.3482 0.917 0.128 0.000 0.872
#> SRR1366390 2 0.0747 0.972 0.000 0.984 0.016
#> SRR1343012 1 0.1163 0.935 0.972 0.000 0.028
#> SRR1311958 2 0.0592 0.974 0.000 0.988 0.012
#> SRR1388234 2 0.0424 0.975 0.000 0.992 0.008
#> SRR1370384 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1321650 3 0.3482 0.917 0.128 0.000 0.872
#> SRR1485117 2 0.0237 0.976 0.000 0.996 0.004
#> SRR1384713 1 0.0424 0.944 0.992 0.000 0.008
#> SRR816609 1 0.6008 0.426 0.628 0.372 0.000
#> SRR1486239 2 0.0424 0.975 0.000 0.992 0.008
#> SRR1309638 3 0.3551 0.915 0.132 0.000 0.868
#> SRR1356660 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1392883 2 0.0000 0.976 0.000 1.000 0.000
#> SRR808130 1 0.6026 0.296 0.624 0.000 0.376
#> SRR816677 1 0.0424 0.944 0.992 0.000 0.008
#> SRR1455722 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1336029 1 0.0424 0.939 0.992 0.000 0.008
#> SRR808452 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1352169 3 0.4750 0.836 0.216 0.000 0.784
#> SRR1366707 3 0.2165 0.925 0.064 0.000 0.936
#> SRR1328143 3 0.6180 0.430 0.416 0.000 0.584
#> SRR1473567 2 0.0000 0.976 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1442087 3 0.4456 0.6633 0.280 0.000 0.716 0.004
#> SRR1390119 2 0.0188 0.9110 0.000 0.996 0.000 0.004
#> SRR1436127 3 0.2546 0.8173 0.092 0.000 0.900 0.008
#> SRR1347278 3 0.5905 0.4155 0.396 0.000 0.564 0.040
#> SRR1332904 2 0.0000 0.9115 0.000 1.000 0.000 0.000
#> SRR1444179 4 0.4500 0.6337 0.316 0.000 0.000 0.684
#> SRR1082685 1 0.2589 0.8152 0.884 0.000 0.000 0.116
#> SRR1362287 1 0.7186 0.0548 0.476 0.000 0.384 0.140
#> SRR1339007 1 0.2814 0.8005 0.868 0.000 0.000 0.132
#> SRR1376557 2 0.0000 0.9115 0.000 1.000 0.000 0.000
#> SRR1468700 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1077455 1 0.0188 0.8265 0.996 0.000 0.000 0.004
#> SRR1413978 4 0.5911 0.5266 0.372 0.000 0.044 0.584
#> SRR1439896 4 0.4985 0.3416 0.468 0.000 0.000 0.532
#> SRR1317963 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1431865 4 0.5427 0.4589 0.416 0.000 0.016 0.568
#> SRR1394253 1 0.2530 0.8186 0.888 0.000 0.000 0.112
#> SRR1082664 3 0.3444 0.7674 0.184 0.000 0.816 0.000
#> SRR1077968 1 0.1211 0.8314 0.960 0.000 0.000 0.040
#> SRR1076393 3 0.1109 0.8113 0.028 0.000 0.968 0.004
#> SRR1477476 2 0.1022 0.8966 0.000 0.968 0.000 0.032
#> SRR1398057 3 0.3266 0.7765 0.168 0.000 0.832 0.000
#> SRR1485042 1 0.2345 0.8230 0.900 0.000 0.000 0.100
#> SRR1385453 2 0.9201 0.2320 0.104 0.420 0.280 0.196
#> SRR1348074 4 0.2222 0.5651 0.016 0.060 0.000 0.924
#> SRR813959 2 0.1209 0.8823 0.032 0.964 0.000 0.004
#> SRR665442 4 0.5000 -0.1931 0.000 0.500 0.000 0.500
#> SRR1378068 3 0.2011 0.8192 0.080 0.000 0.920 0.000
#> SRR1485237 1 0.5159 0.6192 0.756 0.156 0.000 0.088
#> SRR1350792 1 0.2345 0.8230 0.900 0.000 0.000 0.100
#> SRR1326797 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR808994 3 0.0707 0.8063 0.020 0.000 0.980 0.000
#> SRR1474041 1 0.2714 0.7209 0.884 0.000 0.112 0.004
#> SRR1405641 3 0.0921 0.8107 0.028 0.000 0.972 0.000
#> SRR1362245 3 0.1940 0.8196 0.076 0.000 0.924 0.000
#> SRR1500194 4 0.4713 0.5793 0.360 0.000 0.000 0.640
#> SRR1414876 2 0.0000 0.9115 0.000 1.000 0.000 0.000
#> SRR1478523 3 0.5578 0.4902 0.348 0.004 0.624 0.024
#> SRR1325161 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR1318026 4 0.4072 0.5503 0.252 0.000 0.000 0.748
#> SRR1343778 3 0.4382 0.6260 0.296 0.000 0.704 0.000
#> SRR1441287 1 0.3907 0.6611 0.768 0.000 0.000 0.232
#> SRR1430991 1 0.0895 0.8133 0.976 0.000 0.020 0.004
#> SRR1499722 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR1351368 3 0.1059 0.8003 0.016 0.000 0.972 0.012
#> SRR1441785 1 0.5571 0.1271 0.580 0.000 0.024 0.396
#> SRR1096101 1 0.2281 0.8247 0.904 0.000 0.000 0.096
#> SRR808375 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR1452842 1 0.0336 0.8275 0.992 0.000 0.000 0.008
#> SRR1311709 4 0.2530 0.6585 0.112 0.000 0.000 0.888
#> SRR1433352 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR1340241 2 0.0188 0.9110 0.000 0.996 0.000 0.004
#> SRR1456754 1 0.2216 0.8270 0.908 0.000 0.000 0.092
#> SRR1465172 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR1499284 1 0.0188 0.8245 0.996 0.000 0.004 0.000
#> SRR1499607 2 0.0188 0.9109 0.000 0.996 0.000 0.004
#> SRR812342 1 0.2216 0.8260 0.908 0.000 0.000 0.092
#> SRR1405374 1 0.4769 0.4841 0.684 0.000 0.008 0.308
#> SRR1403565 1 0.3257 0.7816 0.844 0.000 0.004 0.152
#> SRR1332024 3 0.1635 0.8175 0.044 0.000 0.948 0.008
#> SRR1471633 4 0.3610 0.6835 0.200 0.000 0.000 0.800
#> SRR1325944 2 0.0188 0.9110 0.000 0.996 0.000 0.004
#> SRR1429450 2 0.0188 0.9110 0.000 0.996 0.000 0.004
#> SRR821573 1 0.0336 0.8246 0.992 0.000 0.000 0.008
#> SRR1435372 1 0.2345 0.8230 0.900 0.000 0.000 0.100
#> SRR1324184 2 0.0817 0.9011 0.000 0.976 0.000 0.024
#> SRR816517 3 0.5808 0.0945 0.000 0.424 0.544 0.032
#> SRR1324141 1 0.5112 0.0246 0.560 0.004 0.000 0.436
#> SRR1101612 1 0.2530 0.8191 0.888 0.000 0.000 0.112
#> SRR1356531 1 0.2530 0.8168 0.888 0.000 0.000 0.112
#> SRR1089785 1 0.4155 0.5188 0.756 0.000 0.240 0.004
#> SRR1077708 3 0.4888 0.4156 0.412 0.000 0.588 0.000
#> SRR1343720 1 0.0376 0.8228 0.992 0.000 0.004 0.004
#> SRR1477499 2 0.0336 0.9098 0.000 0.992 0.000 0.008
#> SRR1347236 1 0.0188 0.8239 0.996 0.000 0.000 0.004
#> SRR1326408 1 0.1637 0.8323 0.940 0.000 0.000 0.060
#> SRR1336529 3 0.1389 0.8184 0.048 0.000 0.952 0.000
#> SRR1440643 3 0.4228 0.6360 0.008 0.000 0.760 0.232
#> SRR662354 1 0.3610 0.7142 0.800 0.000 0.000 0.200
#> SRR1310817 1 0.3836 0.6316 0.816 0.000 0.016 0.168
#> SRR1347389 2 0.5147 0.3859 0.000 0.536 0.004 0.460
#> SRR1353097 1 0.2408 0.8219 0.896 0.000 0.000 0.104
#> SRR1384737 4 0.5075 0.0878 0.012 0.000 0.344 0.644
#> SRR1096339 1 0.3074 0.7846 0.848 0.000 0.000 0.152
#> SRR1345329 4 0.4565 0.6805 0.140 0.064 0.000 0.796
#> SRR1414771 3 0.0188 0.7860 0.000 0.000 0.996 0.004
#> SRR1309119 4 0.2868 0.6745 0.136 0.000 0.000 0.864
#> SRR1470438 3 0.0000 0.7878 0.000 0.000 1.000 0.000
#> SRR1343221 1 0.2469 0.8226 0.892 0.000 0.000 0.108
#> SRR1410847 1 0.2216 0.8260 0.908 0.000 0.000 0.092
#> SRR807949 1 0.2773 0.7147 0.880 0.000 0.116 0.004
#> SRR1442332 1 0.1004 0.8106 0.972 0.000 0.024 0.004
#> SRR815920 3 0.1722 0.8180 0.048 0.000 0.944 0.008
#> SRR1471524 3 0.2489 0.8135 0.068 0.000 0.912 0.020
#> SRR1477221 3 0.2918 0.8057 0.116 0.000 0.876 0.008
#> SRR1445046 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1331962 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1319946 2 0.0000 0.9115 0.000 1.000 0.000 0.000
#> SRR1311599 1 0.2918 0.8136 0.876 0.000 0.008 0.116
#> SRR1323977 2 0.6402 0.3558 0.108 0.624 0.000 0.268
#> SRR1445132 2 0.0188 0.9110 0.000 0.996 0.000 0.004
#> SRR1337321 3 0.4095 0.7488 0.192 0.000 0.792 0.016
#> SRR1366390 2 0.4889 0.5439 0.000 0.636 0.004 0.360
#> SRR1343012 1 0.7753 -0.1362 0.432 0.000 0.312 0.256
#> SRR1311958 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1388234 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1370384 1 0.1637 0.8322 0.940 0.000 0.000 0.060
#> SRR1321650 3 0.3726 0.7296 0.212 0.000 0.788 0.000
#> SRR1485117 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1384713 1 0.0469 0.8283 0.988 0.000 0.000 0.012
#> SRR816609 2 0.6232 0.1908 0.332 0.596 0.000 0.072
#> SRR1486239 2 0.0188 0.9114 0.000 0.996 0.000 0.004
#> SRR1309638 3 0.3208 0.7884 0.148 0.000 0.848 0.004
#> SRR1356660 4 0.5696 0.2467 0.484 0.000 0.024 0.492
#> SRR1392883 2 0.0000 0.9115 0.000 1.000 0.000 0.000
#> SRR808130 1 0.1743 0.7819 0.940 0.000 0.056 0.004
#> SRR816677 1 0.2345 0.8248 0.900 0.000 0.000 0.100
#> SRR1455722 1 0.2760 0.8071 0.872 0.000 0.000 0.128
#> SRR1336029 4 0.4647 0.6485 0.288 0.000 0.008 0.704
#> SRR808452 1 0.1637 0.8324 0.940 0.000 0.000 0.060
#> SRR1352169 1 0.2530 0.7402 0.896 0.000 0.100 0.004
#> SRR1366707 3 0.1151 0.8081 0.024 0.000 0.968 0.008
#> SRR1328143 1 0.3105 0.6803 0.856 0.000 0.140 0.004
#> SRR1473567 2 0.0188 0.9114 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1442087 5 0.4703 0.2791 0.008 0.000 0.336 0.016 0.640
#> SRR1390119 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1436127 3 0.2482 0.7511 0.000 0.000 0.892 0.024 0.084
#> SRR1347278 3 0.6783 0.2487 0.124 0.000 0.520 0.040 0.316
#> SRR1332904 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1444179 1 0.5068 0.2917 0.640 0.000 0.000 0.300 0.060
#> SRR1082685 5 0.4537 0.5349 0.396 0.000 0.000 0.012 0.592
#> SRR1362287 1 0.5922 0.1706 0.476 0.000 0.420 0.000 0.104
#> SRR1339007 5 0.5024 0.6287 0.264 0.000 0.008 0.052 0.676
#> SRR1376557 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1077455 5 0.1638 0.7086 0.064 0.000 0.000 0.004 0.932
#> SRR1413978 1 0.5624 0.4651 0.680 0.000 0.176 0.020 0.124
#> SRR1439896 1 0.5351 0.4933 0.692 0.000 0.008 0.136 0.164
#> SRR1317963 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1431865 1 0.4706 0.3240 0.692 0.000 0.052 0.000 0.256
#> SRR1394253 5 0.4291 0.4513 0.464 0.000 0.000 0.000 0.536
#> SRR1082664 3 0.3910 0.5849 0.008 0.000 0.720 0.000 0.272
#> SRR1077968 5 0.3039 0.6893 0.192 0.000 0.000 0.000 0.808
#> SRR1076393 3 0.1018 0.7693 0.000 0.000 0.968 0.016 0.016
#> SRR1477476 2 0.1478 0.8947 0.000 0.936 0.000 0.064 0.000
#> SRR1398057 3 0.2409 0.7609 0.032 0.000 0.900 0.000 0.068
#> SRR1485042 5 0.3966 0.6065 0.336 0.000 0.000 0.000 0.664
#> SRR1385453 4 0.5744 0.5163 0.008 0.052 0.048 0.684 0.208
#> SRR1348074 4 0.4883 0.2288 0.464 0.016 0.000 0.516 0.004
#> SRR813959 2 0.0451 0.9366 0.000 0.988 0.000 0.004 0.008
#> SRR665442 1 0.7046 -0.2074 0.412 0.348 0.016 0.224 0.000
#> SRR1378068 3 0.1469 0.7761 0.016 0.000 0.948 0.000 0.036
#> SRR1485237 5 0.5753 0.4845 0.116 0.216 0.000 0.016 0.652
#> SRR1350792 5 0.3752 0.6395 0.292 0.000 0.000 0.000 0.708
#> SRR1326797 5 0.0451 0.7000 0.008 0.000 0.000 0.004 0.988
#> SRR808994 3 0.0912 0.7751 0.012 0.000 0.972 0.000 0.016
#> SRR1474041 5 0.0880 0.6854 0.000 0.000 0.000 0.032 0.968
#> SRR1405641 3 0.1087 0.7738 0.008 0.000 0.968 0.008 0.016
#> SRR1362245 3 0.1960 0.7657 0.048 0.000 0.928 0.004 0.020
#> SRR1500194 1 0.2124 0.4684 0.916 0.000 0.000 0.028 0.056
#> SRR1414876 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1478523 4 0.6973 0.2732 0.028 0.000 0.260 0.504 0.208
#> SRR1325161 5 0.1012 0.7004 0.020 0.000 0.000 0.012 0.968
#> SRR1318026 4 0.4954 0.4549 0.308 0.016 0.000 0.652 0.024
#> SRR1343778 3 0.4752 0.3662 0.020 0.000 0.568 0.000 0.412
#> SRR1441287 5 0.5663 0.4208 0.384 0.000 0.000 0.084 0.532
#> SRR1430991 5 0.0566 0.6925 0.004 0.000 0.000 0.012 0.984
#> SRR1499722 5 0.0324 0.6977 0.004 0.000 0.000 0.004 0.992
#> SRR1351368 3 0.2621 0.7042 0.004 0.000 0.876 0.112 0.008
#> SRR1441785 1 0.5751 0.0180 0.552 0.000 0.100 0.000 0.348
#> SRR1096101 5 0.3757 0.6843 0.208 0.000 0.000 0.020 0.772
#> SRR808375 5 0.0566 0.6933 0.004 0.000 0.000 0.012 0.984
#> SRR1452842 5 0.1877 0.7062 0.064 0.000 0.000 0.012 0.924
#> SRR1311709 1 0.4213 0.1740 0.680 0.000 0.000 0.308 0.012
#> SRR1433352 5 0.0798 0.6946 0.008 0.000 0.000 0.016 0.976
#> SRR1340241 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1456754 5 0.4499 0.5228 0.408 0.000 0.004 0.004 0.584
#> SRR1465172 5 0.0566 0.6990 0.012 0.000 0.000 0.004 0.984
#> SRR1499284 5 0.1331 0.7047 0.040 0.000 0.000 0.008 0.952
#> SRR1499607 2 0.0404 0.9382 0.000 0.988 0.000 0.012 0.000
#> SRR812342 5 0.3789 0.6766 0.212 0.000 0.000 0.020 0.768
#> SRR1405374 1 0.5157 -0.2817 0.520 0.000 0.040 0.000 0.440
#> SRR1403565 5 0.5547 0.5946 0.292 0.000 0.024 0.052 0.632
#> SRR1332024 3 0.1267 0.7728 0.024 0.000 0.960 0.004 0.012
#> SRR1471633 1 0.3527 0.3576 0.792 0.000 0.000 0.192 0.016
#> SRR1325944 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR821573 5 0.1908 0.6258 0.000 0.000 0.000 0.092 0.908
#> SRR1435372 5 0.3534 0.6622 0.256 0.000 0.000 0.000 0.744
#> SRR1324184 2 0.2017 0.8623 0.008 0.912 0.000 0.080 0.000
#> SRR816517 3 0.6922 -0.1116 0.000 0.316 0.384 0.296 0.004
#> SRR1324141 4 0.6190 0.2739 0.116 0.008 0.000 0.520 0.356
#> SRR1101612 5 0.4700 0.4193 0.472 0.000 0.008 0.004 0.516
#> SRR1356531 5 0.4310 0.5484 0.392 0.000 0.004 0.000 0.604
#> SRR1089785 5 0.1211 0.6749 0.000 0.000 0.024 0.016 0.960
#> SRR1077708 3 0.4748 0.5268 0.040 0.000 0.660 0.000 0.300
#> SRR1343720 5 0.0162 0.6968 0.000 0.000 0.000 0.004 0.996
#> SRR1477499 2 0.1043 0.9175 0.000 0.960 0.000 0.040 0.000
#> SRR1347236 5 0.0566 0.7007 0.012 0.000 0.000 0.004 0.984
#> SRR1326408 5 0.3521 0.6724 0.232 0.000 0.000 0.004 0.764
#> SRR1336529 3 0.0912 0.7749 0.012 0.000 0.972 0.000 0.016
#> SRR1440643 4 0.4442 0.3524 0.000 0.000 0.284 0.688 0.028
#> SRR662354 5 0.5589 0.5813 0.220 0.000 0.004 0.128 0.648
#> SRR1310817 5 0.3086 0.5220 0.004 0.000 0.000 0.180 0.816
#> SRR1347389 4 0.5699 0.5179 0.220 0.156 0.000 0.624 0.000
#> SRR1353097 5 0.4249 0.5007 0.432 0.000 0.000 0.000 0.568
#> SRR1384737 4 0.5220 0.3880 0.440 0.000 0.044 0.516 0.000
#> SRR1096339 5 0.4659 0.3733 0.492 0.000 0.012 0.000 0.496
#> SRR1345329 1 0.2464 0.4082 0.892 0.004 0.000 0.092 0.012
#> SRR1414771 3 0.0000 0.7613 0.000 0.000 1.000 0.000 0.000
#> SRR1309119 1 0.4540 0.1737 0.656 0.000 0.000 0.320 0.024
#> SRR1470438 3 0.0000 0.7613 0.000 0.000 1.000 0.000 0.000
#> SRR1343221 5 0.5106 0.4395 0.400 0.000 0.032 0.004 0.564
#> SRR1410847 5 0.3966 0.6089 0.336 0.000 0.000 0.000 0.664
#> SRR807949 5 0.0955 0.6862 0.004 0.000 0.000 0.028 0.968
#> SRR1442332 5 0.1202 0.6871 0.004 0.000 0.004 0.032 0.960
#> SRR815920 3 0.1106 0.7718 0.000 0.000 0.964 0.012 0.024
#> SRR1471524 3 0.6309 0.1618 0.000 0.000 0.472 0.368 0.160
#> SRR1477221 3 0.1830 0.7699 0.040 0.000 0.932 0.000 0.028
#> SRR1445046 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1331962 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1319946 2 0.0162 0.9428 0.000 0.996 0.000 0.004 0.000
#> SRR1311599 5 0.5114 0.3653 0.472 0.000 0.036 0.000 0.492
#> SRR1323977 2 0.7176 0.2641 0.124 0.564 0.000 0.188 0.124
#> SRR1445132 2 0.0162 0.9427 0.000 0.996 0.000 0.004 0.000
#> SRR1337321 3 0.6617 0.1660 0.200 0.000 0.468 0.004 0.328
#> SRR1366390 4 0.4728 0.5111 0.060 0.240 0.000 0.700 0.000
#> SRR1343012 5 0.8341 -0.0888 0.176 0.000 0.284 0.184 0.356
#> SRR1311958 2 0.0162 0.9426 0.000 0.996 0.000 0.004 0.000
#> SRR1388234 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1370384 5 0.3656 0.6835 0.196 0.000 0.000 0.020 0.784
#> SRR1321650 3 0.3323 0.7245 0.036 0.000 0.844 0.004 0.116
#> SRR1485117 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1384713 5 0.2677 0.7034 0.112 0.000 0.000 0.016 0.872
#> SRR816609 2 0.6274 0.0961 0.316 0.528 0.000 0.004 0.152
#> SRR1486239 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR1309638 3 0.2728 0.7559 0.040 0.000 0.888 0.004 0.068
#> SRR1356660 1 0.5849 0.2733 0.604 0.000 0.128 0.004 0.264
#> SRR1392883 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
#> SRR808130 5 0.0324 0.6977 0.004 0.000 0.000 0.004 0.992
#> SRR816677 5 0.4430 0.4586 0.456 0.000 0.004 0.000 0.540
#> SRR1455722 5 0.4278 0.4714 0.452 0.000 0.000 0.000 0.548
#> SRR1336029 1 0.4851 0.4386 0.712 0.000 0.000 0.196 0.092
#> SRR808452 5 0.4276 0.5647 0.380 0.000 0.000 0.004 0.616
#> SRR1352169 5 0.2700 0.6968 0.088 0.000 0.024 0.004 0.884
#> SRR1366707 3 0.1960 0.7512 0.004 0.000 0.928 0.048 0.020
#> SRR1328143 5 0.1026 0.6841 0.004 0.000 0.004 0.024 0.968
#> SRR1473567 2 0.0000 0.9449 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1442087 5 0.4363 0.58690 0.032 0.000 0.176 0.048 0.744 0.000
#> SRR1390119 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1436127 3 0.3395 0.70708 0.004 0.000 0.812 0.048 0.136 0.000
#> SRR1347278 3 0.6684 0.15516 0.356 0.000 0.460 0.056 0.112 0.016
#> SRR1332904 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1444179 6 0.5383 0.37035 0.204 0.000 0.004 0.004 0.168 0.620
#> SRR1082685 1 0.4154 0.57952 0.676 0.000 0.000 0.012 0.296 0.016
#> SRR1362287 1 0.4154 0.55705 0.712 0.000 0.244 0.000 0.036 0.008
#> SRR1339007 5 0.5402 0.56648 0.180 0.000 0.024 0.000 0.644 0.152
#> SRR1376557 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468700 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077455 5 0.2473 0.72831 0.136 0.000 0.000 0.000 0.856 0.008
#> SRR1413978 1 0.4562 0.63010 0.744 0.000 0.148 0.000 0.060 0.048
#> SRR1439896 1 0.6014 0.36999 0.472 0.000 0.008 0.000 0.196 0.324
#> SRR1317963 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1431865 1 0.3341 0.70887 0.836 0.000 0.060 0.000 0.088 0.016
#> SRR1394253 1 0.3262 0.71461 0.788 0.000 0.008 0.000 0.196 0.008
#> SRR1082664 3 0.4325 0.64806 0.060 0.000 0.728 0.012 0.200 0.000
#> SRR1077968 5 0.3043 0.67611 0.200 0.000 0.000 0.000 0.792 0.008
#> SRR1076393 3 0.1478 0.79243 0.020 0.000 0.944 0.032 0.000 0.004
#> SRR1477476 2 0.0937 0.94284 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1398057 3 0.2625 0.79371 0.072 0.000 0.872 0.000 0.056 0.000
#> SRR1485042 5 0.4250 0.11909 0.456 0.000 0.000 0.000 0.528 0.016
#> SRR1385453 4 0.2753 0.53321 0.004 0.000 0.028 0.876 0.080 0.012
#> SRR1348074 6 0.5458 0.10588 0.144 0.000 0.000 0.320 0.000 0.536
#> SRR813959 2 0.0146 0.96806 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR665442 6 0.2784 0.43103 0.012 0.132 0.008 0.000 0.000 0.848
#> SRR1378068 3 0.1092 0.80832 0.020 0.000 0.960 0.000 0.020 0.000
#> SRR1485237 5 0.5782 0.38920 0.120 0.288 0.000 0.000 0.564 0.028
#> SRR1350792 5 0.4168 0.31136 0.400 0.000 0.000 0.000 0.584 0.016
#> SRR1326797 5 0.1442 0.75592 0.040 0.000 0.000 0.012 0.944 0.004
#> SRR808994 3 0.1075 0.80292 0.048 0.000 0.952 0.000 0.000 0.000
#> SRR1474041 5 0.2358 0.72368 0.012 0.000 0.012 0.076 0.896 0.004
#> SRR1405641 3 0.0551 0.79888 0.000 0.000 0.984 0.004 0.008 0.004
#> SRR1362245 3 0.3178 0.73872 0.160 0.000 0.816 0.004 0.004 0.016
#> SRR1500194 1 0.4166 0.57808 0.728 0.000 0.000 0.000 0.076 0.196
#> SRR1414876 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1478523 4 0.5515 0.46007 0.036 0.000 0.088 0.680 0.172 0.024
#> SRR1325161 5 0.1138 0.75407 0.024 0.000 0.004 0.000 0.960 0.012
#> SRR1318026 4 0.5169 0.31013 0.120 0.000 0.000 0.588 0.000 0.292
#> SRR1343778 3 0.4914 0.39679 0.036 0.000 0.576 0.012 0.372 0.004
#> SRR1441287 1 0.5393 0.33763 0.508 0.000 0.000 0.000 0.372 0.120
#> SRR1430991 5 0.0862 0.74853 0.000 0.000 0.008 0.016 0.972 0.004
#> SRR1499722 5 0.1909 0.74891 0.052 0.000 0.000 0.024 0.920 0.004
#> SRR1351368 3 0.5323 0.51938 0.116 0.000 0.648 0.216 0.008 0.012
#> SRR1441785 1 0.3537 0.71227 0.824 0.000 0.060 0.004 0.100 0.012
#> SRR1096101 5 0.3932 0.61237 0.248 0.000 0.004 0.000 0.720 0.028
#> SRR808375 5 0.1313 0.74927 0.016 0.000 0.000 0.028 0.952 0.004
#> SRR1452842 5 0.2214 0.73659 0.096 0.000 0.000 0.000 0.888 0.016
#> SRR1311709 6 0.2526 0.55529 0.052 0.000 0.004 0.028 0.020 0.896
#> SRR1433352 5 0.1275 0.75140 0.016 0.000 0.016 0.000 0.956 0.012
#> SRR1340241 2 0.0260 0.96664 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1456754 1 0.3955 0.68471 0.724 0.000 0.032 0.000 0.240 0.004
#> SRR1465172 5 0.0458 0.75527 0.016 0.000 0.000 0.000 0.984 0.000
#> SRR1499284 5 0.1967 0.74358 0.084 0.000 0.000 0.000 0.904 0.012
#> SRR1499607 2 0.0508 0.96193 0.004 0.984 0.000 0.012 0.000 0.000
#> SRR812342 5 0.3635 0.67309 0.180 0.000 0.008 0.000 0.780 0.032
#> SRR1405374 1 0.3492 0.71998 0.824 0.000 0.048 0.004 0.112 0.012
#> SRR1403565 5 0.5398 0.22743 0.384 0.000 0.032 0.000 0.532 0.052
#> SRR1332024 3 0.1075 0.80447 0.048 0.000 0.952 0.000 0.000 0.000
#> SRR1471633 1 0.4467 -0.14163 0.496 0.000 0.000 0.004 0.020 0.480
#> SRR1325944 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429450 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR821573 5 0.2834 0.69885 0.020 0.000 0.000 0.120 0.852 0.008
#> SRR1435372 5 0.4084 0.31152 0.400 0.000 0.000 0.000 0.588 0.012
#> SRR1324184 2 0.2333 0.85986 0.000 0.884 0.000 0.092 0.000 0.024
#> SRR816517 4 0.6682 0.32407 0.012 0.164 0.288 0.500 0.012 0.024
#> SRR1324141 4 0.6250 0.27163 0.060 0.000 0.000 0.564 0.196 0.180
#> SRR1101612 1 0.3384 0.72971 0.800 0.000 0.024 0.000 0.168 0.008
#> SRR1356531 5 0.4459 0.10218 0.460 0.000 0.004 0.000 0.516 0.020
#> SRR1089785 5 0.2462 0.73119 0.024 0.000 0.008 0.064 0.896 0.008
#> SRR1077708 3 0.4633 0.48197 0.036 0.000 0.628 0.000 0.324 0.012
#> SRR1343720 5 0.1793 0.74874 0.032 0.000 0.000 0.036 0.928 0.004
#> SRR1477499 2 0.0632 0.95559 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1347236 5 0.1668 0.75444 0.060 0.000 0.000 0.008 0.928 0.004
#> SRR1326408 5 0.3888 0.49773 0.312 0.000 0.000 0.000 0.672 0.016
#> SRR1336529 3 0.0717 0.80490 0.016 0.000 0.976 0.000 0.008 0.000
#> SRR1440643 4 0.2697 0.54312 0.004 0.000 0.064 0.884 0.032 0.016
#> SRR662354 5 0.5147 0.54267 0.100 0.000 0.012 0.000 0.632 0.256
#> SRR1310817 5 0.2643 0.69258 0.004 0.000 0.016 0.108 0.868 0.004
#> SRR1347389 4 0.5032 0.36735 0.096 0.008 0.000 0.636 0.000 0.260
#> SRR1353097 1 0.3476 0.65271 0.732 0.000 0.000 0.004 0.260 0.004
#> SRR1384737 4 0.5434 0.33606 0.300 0.000 0.004 0.564 0.000 0.132
#> SRR1096339 1 0.3313 0.73101 0.808 0.000 0.024 0.000 0.160 0.008
#> SRR1345329 1 0.3990 0.38175 0.728 0.004 0.000 0.016 0.012 0.240
#> SRR1414771 3 0.1155 0.79806 0.036 0.000 0.956 0.004 0.000 0.004
#> SRR1309119 6 0.4227 0.57506 0.136 0.000 0.000 0.020 0.080 0.764
#> SRR1470438 3 0.1075 0.80159 0.048 0.000 0.952 0.000 0.000 0.000
#> SRR1343221 1 0.4011 0.72042 0.788 0.000 0.044 0.004 0.136 0.028
#> SRR1410847 5 0.3975 0.32089 0.392 0.000 0.000 0.000 0.600 0.008
#> SRR807949 5 0.1901 0.73628 0.008 0.000 0.012 0.052 0.924 0.004
#> SRR1442332 5 0.1931 0.73941 0.004 0.000 0.032 0.020 0.928 0.016
#> SRR815920 3 0.1452 0.80415 0.020 0.000 0.948 0.020 0.012 0.000
#> SRR1471524 4 0.5956 0.25526 0.008 0.000 0.308 0.492 0.192 0.000
#> SRR1477221 3 0.2070 0.79150 0.100 0.000 0.892 0.000 0.008 0.000
#> SRR1445046 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1331962 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1319946 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1311599 1 0.3355 0.72966 0.816 0.000 0.048 0.000 0.132 0.004
#> SRR1323977 2 0.5850 0.22446 0.020 0.564 0.000 0.000 0.172 0.244
#> SRR1445132 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1337321 1 0.5921 0.35261 0.580 0.000 0.288 0.020 0.084 0.028
#> SRR1366390 4 0.3792 0.48391 0.044 0.020 0.000 0.792 0.000 0.144
#> SRR1343012 1 0.8843 -0.00951 0.264 0.000 0.216 0.176 0.212 0.132
#> SRR1311958 2 0.0146 0.96858 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1388234 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1370384 5 0.3203 0.69567 0.160 0.000 0.004 0.000 0.812 0.024
#> SRR1321650 3 0.3062 0.76102 0.052 0.000 0.836 0.000 0.112 0.000
#> SRR1485117 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1384713 5 0.2723 0.72200 0.128 0.000 0.004 0.000 0.852 0.016
#> SRR816609 1 0.4546 0.58042 0.748 0.152 0.000 0.008 0.068 0.024
#> SRR1486239 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1309638 3 0.3039 0.77455 0.060 0.000 0.848 0.000 0.088 0.004
#> SRR1356660 1 0.3293 0.69426 0.844 0.000 0.064 0.004 0.076 0.012
#> SRR1392883 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR808130 5 0.2458 0.73375 0.028 0.000 0.008 0.052 0.900 0.012
#> SRR816677 1 0.3761 0.72220 0.764 0.000 0.032 0.000 0.196 0.008
#> SRR1455722 1 0.3470 0.66621 0.740 0.000 0.000 0.000 0.248 0.012
#> SRR1336029 1 0.5390 0.23222 0.512 0.000 0.008 0.004 0.076 0.400
#> SRR808452 1 0.4079 0.42080 0.608 0.000 0.000 0.004 0.380 0.008
#> SRR1352169 5 0.5641 0.40604 0.260 0.004 0.132 0.004 0.592 0.008
#> SRR1366707 3 0.2308 0.75912 0.004 0.000 0.904 0.056 0.028 0.008
#> SRR1328143 5 0.1605 0.73864 0.000 0.000 0.016 0.044 0.936 0.004
#> SRR1473567 2 0.0000 0.97118 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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