Date: 2019-12-26 00:18:48 CET, cola version: 1.3.2
Document is loading...
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 13890 rows and 80 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] 13890 80
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:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:pam | 6 | 1.000 | 1.000 | 1.000 | ** | 2,3,4,5 |
| SD:NMF | 2 | 1.000 | 0.984 | 0.988 | ** | |
| CV:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:pam | 5 | 1.000 | 0.999 | 1.000 | ** | 2,4 |
| MAD:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:hclust | 6 | 1.000 | 0.962 | 1.000 | ** | 2 |
| ATC:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:pam | 4 | 1.000 | 0.985 | 0.994 | ** | 2,3 |
| MAD:skmeans | 6 | 0.982 | 0.957 | 0.962 | ** | 5 |
| SD:skmeans | 6 | 0.977 | 0.978 | 0.973 | ** | |
| MAD:mclust | 6 | 0.972 | 0.940 | 0.968 | ** | 4 |
| CV:mclust | 6 | 0.969 | 0.960 | 0.976 | ** | 4,5 |
| MAD:pam | 6 | 0.963 | 0.972 | 0.983 | ** | 3,4,5 |
| ATC:NMF | 2 | 0.948 | 0.903 | 0.959 | * | |
| CV:skmeans | 6 | 0.915 | 0.909 | 0.934 | * | |
| ATC:skmeans | 4 | 0.905 | 0.951 | 0.959 | * | |
| MAD:NMF | 5 | 0.733 | 0.825 | 0.810 | ||
| SD:mclust | 4 | 0.729 | 0.851 | 0.888 | ||
| CV:NMF | 3 | 0.646 | 0.902 | 0.890 | ||
| ATC:mclust | 5 | 0.546 | 0.634 | 0.783 | ||
| CV:kmeans | 5 | 0.468 | 0.773 | 0.739 | ||
| MAD:kmeans | 5 | 0.461 | 0.821 | 0.681 | ||
| SD:kmeans | 5 | 0.363 | 0.704 | 0.579 |
**: 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 0.984 0.988 0.148 0.859 0.859
#> CV:NMF 2 0.282 0.599 0.692 0.372 0.502 0.502
#> MAD:NMF 2 0.443 0.771 0.839 0.274 0.859 0.859
#> ATC:NMF 2 0.948 0.903 0.959 0.103 0.904 0.904
#> SD:skmeans 2 0.505 0.849 0.908 0.473 0.547 0.547
#> CV:skmeans 2 0.739 0.782 0.910 0.481 0.502 0.502
#> MAD:skmeans 2 0.792 0.948 0.969 0.504 0.494 0.494
#> ATC:skmeans 2 0.875 0.933 0.964 0.356 0.608 0.608
#> SD:mclust 2 0.394 0.553 0.746 0.390 0.647 0.647
#> CV:mclust 2 0.432 0.534 0.803 0.429 0.585 0.585
#> MAD:mclust 2 0.342 0.695 0.837 0.448 0.596 0.596
#> ATC:mclust 2 0.859 0.965 0.984 0.167 0.859 0.859
#> SD:kmeans 2 0.383 0.862 0.896 0.212 0.859 0.859
#> CV:kmeans 2 0.277 0.752 0.838 0.256 0.859 0.859
#> MAD:kmeans 2 0.168 0.483 0.588 0.345 0.499 0.499
#> ATC:kmeans 2 1.000 1.000 1.000 0.141 0.859 0.859
#> SD:pam 2 1.000 1.000 1.000 0.141 0.859 0.859
#> CV:pam 2 1.000 0.995 0.995 0.145 0.859 0.859
#> MAD:pam 2 0.764 0.901 0.950 0.370 0.676 0.676
#> ATC:pam 2 1.000 1.000 1.000 0.141 0.859 0.859
#> SD:hclust 2 1.000 1.000 1.000 0.141 0.859 0.859
#> CV:hclust 2 1.000 1.000 1.000 0.141 0.859 0.859
#> MAD:hclust 2 1.000 1.000 1.000 0.141 0.859 0.859
#> ATC:hclust 2 1.000 1.000 1.000 0.141 0.859 0.859
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.370 0.723 0.854 2.1775 0.620 0.558
#> CV:NMF 3 0.646 0.902 0.890 0.6350 0.803 0.622
#> MAD:NMF 3 0.666 0.781 0.885 1.0461 0.572 0.502
#> ATC:NMF 3 0.455 0.793 0.884 1.1374 0.951 0.945
#> SD:skmeans 3 0.674 0.798 0.876 0.4123 0.538 0.310
#> CV:skmeans 3 0.711 0.670 0.840 0.3759 0.795 0.613
#> MAD:skmeans 3 0.806 0.954 0.966 0.3280 0.728 0.504
#> ATC:skmeans 3 0.634 0.791 0.835 0.5578 0.848 0.757
#> SD:mclust 3 0.619 0.755 0.868 0.6111 0.500 0.324
#> CV:mclust 3 0.573 0.847 0.879 0.4271 0.618 0.432
#> MAD:mclust 3 0.714 0.866 0.888 0.3452 0.772 0.632
#> ATC:mclust 3 0.418 0.778 0.773 1.7012 0.871 0.850
#> SD:kmeans 3 0.232 0.616 0.666 1.2136 0.706 0.658
#> CV:kmeans 3 0.204 0.698 0.771 0.8210 0.706 0.658
#> MAD:kmeans 3 0.180 0.466 0.622 0.5709 0.555 0.396
#> ATC:kmeans 3 0.499 0.898 0.900 2.2879 0.638 0.579
#> SD:pam 3 1.000 0.994 0.997 2.0955 0.706 0.658
#> CV:pam 3 0.577 0.919 0.933 2.0363 0.706 0.658
#> MAD:pam 3 0.950 0.949 0.959 0.6615 0.708 0.567
#> ATC:pam 3 1.000 0.985 0.994 2.1209 0.706 0.658
#> SD:hclust 3 1.000 0.986 0.999 0.0112 0.998 0.998
#> CV:hclust 3 0.323 0.553 0.757 2.1259 0.744 0.709
#> MAD:hclust 3 1.000 0.961 0.985 0.0132 0.997 0.997
#> ATC:hclust 3 1.000 0.981 0.995 0.0112 0.998 0.998
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.457 0.791 0.793 0.29470 0.720 0.492
#> CV:NMF 4 0.737 0.768 0.772 0.16161 0.977 0.934
#> MAD:NMF 4 0.644 0.797 0.876 0.20306 0.781 0.548
#> ATC:NMF 4 0.353 0.664 0.811 0.62055 0.763 0.724
#> SD:skmeans 4 0.729 0.805 0.781 0.09974 0.939 0.816
#> CV:skmeans 4 0.725 0.828 0.877 0.11847 0.894 0.706
#> MAD:skmeans 4 0.781 0.874 0.868 0.09876 0.939 0.816
#> ATC:skmeans 4 0.905 0.951 0.959 0.26739 0.813 0.618
#> SD:mclust 4 0.729 0.851 0.888 0.16339 0.896 0.699
#> CV:mclust 4 0.958 0.955 0.979 0.10532 0.943 0.849
#> MAD:mclust 4 0.947 0.900 0.945 0.20768 0.824 0.593
#> ATC:mclust 4 0.350 0.620 0.757 0.33662 0.637 0.503
#> SD:kmeans 4 0.309 0.623 0.617 0.31453 0.734 0.530
#> CV:kmeans 4 0.323 0.711 0.727 0.30953 0.825 0.691
#> MAD:kmeans 4 0.372 0.560 0.613 0.22547 0.709 0.480
#> ATC:kmeans 4 0.617 0.729 0.787 0.34073 1.000 1.000
#> SD:pam 4 1.000 0.987 0.986 0.39861 0.825 0.691
#> CV:pam 4 1.000 0.995 0.995 0.38510 0.825 0.691
#> MAD:pam 4 1.000 0.983 0.992 0.18567 0.886 0.703
#> ATC:pam 4 1.000 0.985 0.994 0.22146 0.901 0.826
#> SD:hclust 4 1.000 0.967 0.994 0.00885 0.999 0.999
#> CV:hclust 4 0.315 0.748 0.770 0.31504 0.747 0.606
#> MAD:hclust 4 1.000 0.958 0.989 0.00752 1.000 1.000
#> ATC:hclust 4 1.000 0.975 1.000 0.00885 0.999 0.999
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.715 0.808 0.852 0.12175 0.961 0.889
#> CV:NMF 5 0.736 0.679 0.768 0.09402 0.795 0.451
#> MAD:NMF 5 0.733 0.825 0.810 0.10308 0.919 0.747
#> ATC:NMF 5 0.274 0.614 0.792 0.36992 0.931 0.891
#> SD:skmeans 5 0.793 0.896 0.895 0.07637 0.932 0.746
#> CV:skmeans 5 0.859 0.866 0.915 0.08082 0.882 0.592
#> MAD:skmeans 5 0.913 0.896 0.949 0.08441 0.867 0.562
#> ATC:skmeans 5 0.863 0.950 0.910 0.09537 0.905 0.687
#> SD:mclust 5 0.876 0.894 0.917 0.05798 0.972 0.888
#> CV:mclust 5 0.945 0.920 0.952 0.11298 0.932 0.787
#> MAD:mclust 5 0.776 0.800 0.889 0.04629 0.918 0.720
#> ATC:mclust 5 0.546 0.634 0.783 0.16180 0.704 0.395
#> SD:kmeans 5 0.363 0.704 0.579 0.14570 0.810 0.474
#> CV:kmeans 5 0.468 0.773 0.739 0.16173 0.871 0.670
#> MAD:kmeans 5 0.461 0.821 0.681 0.11233 0.821 0.466
#> ATC:kmeans 5 0.630 0.658 0.763 0.13353 0.813 0.624
#> SD:pam 5 1.000 1.000 1.000 0.24340 0.848 0.612
#> CV:pam 5 1.000 0.999 1.000 0.24619 0.848 0.612
#> MAD:pam 5 0.913 0.874 0.895 0.07416 0.902 0.652
#> ATC:pam 5 0.908 0.914 0.960 0.05382 0.998 0.997
#> SD:hclust 5 1.000 0.963 1.000 0.00658 0.999 0.999
#> CV:hclust 5 0.481 0.856 0.823 0.12048 0.871 0.670
#> MAD:hclust 5 1.000 0.963 1.000 0.00591 0.999 0.999
#> ATC:hclust 5 0.861 0.932 0.983 0.18360 0.999 0.999
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.709 0.777 0.748 0.08911 0.919 0.748
#> CV:NMF 6 0.885 0.954 0.913 0.05243 0.914 0.640
#> MAD:NMF 6 0.853 0.949 0.890 0.07348 0.932 0.714
#> ATC:NMF 6 0.306 0.608 0.757 0.30843 0.758 0.580
#> SD:skmeans 6 0.977 0.978 0.973 0.04737 0.970 0.849
#> CV:skmeans 6 0.915 0.909 0.934 0.03957 0.932 0.684
#> MAD:skmeans 6 0.982 0.957 0.962 0.04056 0.966 0.833
#> ATC:skmeans 6 0.848 0.880 0.861 0.04990 1.000 1.000
#> SD:mclust 6 0.899 0.903 0.943 0.07282 0.943 0.747
#> CV:mclust 6 0.969 0.960 0.976 0.10054 0.919 0.678
#> MAD:mclust 6 0.972 0.940 0.968 0.08439 0.883 0.554
#> ATC:mclust 6 0.595 0.585 0.795 0.00257 0.910 0.755
#> SD:kmeans 6 0.695 0.891 0.804 0.10173 0.939 0.738
#> CV:kmeans 6 0.631 0.750 0.728 0.06570 0.841 0.517
#> MAD:kmeans 6 0.670 0.911 0.814 0.06459 0.977 0.882
#> ATC:kmeans 6 0.638 0.780 0.769 0.07829 0.905 0.700
#> SD:pam 6 1.000 1.000 1.000 0.08973 0.932 0.714
#> CV:pam 6 0.885 0.855 0.905 0.05851 0.974 0.893
#> MAD:pam 6 0.963 0.972 0.983 0.06173 0.971 0.851
#> ATC:pam 6 0.894 0.864 0.941 0.04140 0.999 0.997
#> SD:hclust 6 1.000 0.926 0.978 0.08143 1.000 1.000
#> CV:hclust 6 0.493 0.885 0.834 0.07880 0.962 0.855
#> MAD:hclust 6 1.000 0.935 0.992 0.03805 0.999 0.999
#> ATC:hclust 6 1.000 0.963 1.000 0.60609 0.871 0.849
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 13890 rows and 80 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 1 1.000 1.000 0.14137 0.859 0.859
#> 3 3 1 0.986 0.999 0.01118 0.998 0.998
#> 4 4 1 0.967 0.994 0.00885 0.999 0.999
#> 5 5 1 0.963 1.000 0.00658 0.999 0.999
#> 6 6 1 0.926 0.978 0.08143 1.000 1.000
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
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.0000 1.000 0.000 1 0.000
#> SRR1377146 2 0.0000 1.000 0.000 1 0.000
#> SRR1377147 2 0.0000 1.000 0.000 1 0.000
#> SRR1377148 2 0.0000 1.000 0.000 1 0.000
#> SRR1377153 2 0.0000 1.000 0.000 1 0.000
#> SRR1377154 2 0.0000 1.000 0.000 1 0.000
#> SRR1377155 2 0.0000 1.000 0.000 1 0.000
#> SRR1377156 2 0.0000 1.000 0.000 1 0.000
#> SRR1377149 2 0.0000 1.000 0.000 1 0.000
#> SRR1377150 2 0.0000 1.000 0.000 1 0.000
#> SRR1377151 2 0.0000 1.000 0.000 1 0.000
#> SRR1377152 2 0.0000 1.000 0.000 1 0.000
#> SRR1377157 2 0.0000 1.000 0.000 1 0.000
#> SRR1377158 2 0.0000 1.000 0.000 1 0.000
#> SRR1377159 2 0.0000 1.000 0.000 1 0.000
#> SRR1377160 2 0.0000 1.000 0.000 1 0.000
#> SRR1377161 2 0.0000 1.000 0.000 1 0.000
#> SRR1377162 2 0.0000 1.000 0.000 1 0.000
#> SRR1377163 2 0.0000 1.000 0.000 1 0.000
#> SRR1377164 2 0.0000 1.000 0.000 1 0.000
#> SRR1377169 2 0.0000 1.000 0.000 1 0.000
#> SRR1377170 2 0.0000 1.000 0.000 1 0.000
#> SRR1377171 2 0.0000 1.000 0.000 1 0.000
#> SRR1377172 2 0.0000 1.000 0.000 1 0.000
#> SRR1377165 2 0.0000 1.000 0.000 1 0.000
#> SRR1377166 2 0.0000 1.000 0.000 1 0.000
#> SRR1377167 2 0.0000 1.000 0.000 1 0.000
#> SRR1377168 2 0.0000 1.000 0.000 1 0.000
#> SRR1377173 2 0.0000 1.000 0.000 1 0.000
#> SRR1377174 2 0.0000 1.000 0.000 1 0.000
#> SRR1377175 2 0.0000 1.000 0.000 1 0.000
#> SRR1377176 2 0.0000 1.000 0.000 1 0.000
#> SRR1377177 2 0.0000 1.000 0.000 1 0.000
#> SRR1377178 2 0.0000 1.000 0.000 1 0.000
#> SRR1377179 2 0.0000 1.000 0.000 1 0.000
#> SRR1377180 2 0.0000 1.000 0.000 1 0.000
#> SRR1377181 2 0.0000 1.000 0.000 1 0.000
#> SRR1377182 2 0.0000 1.000 0.000 1 0.000
#> SRR1377183 2 0.0000 1.000 0.000 1 0.000
#> SRR1377184 2 0.0000 1.000 0.000 1 0.000
#> SRR1377185 2 0.0000 1.000 0.000 1 0.000
#> SRR1377186 2 0.0000 1.000 0.000 1 0.000
#> SRR1377187 2 0.0000 1.000 0.000 1 0.000
#> SRR1377188 2 0.0000 1.000 0.000 1 0.000
#> SRR1377189 2 0.0000 1.000 0.000 1 0.000
#> SRR1377190 2 0.0000 1.000 0.000 1 0.000
#> SRR1377191 2 0.0000 1.000 0.000 1 0.000
#> SRR1377192 2 0.0000 1.000 0.000 1 0.000
#> SRR1377193 2 0.0000 1.000 0.000 1 0.000
#> SRR1377194 2 0.0000 1.000 0.000 1 0.000
#> SRR1377195 1 0.0000 0.987 1.000 0 0.000
#> SRR1377196 1 0.0000 0.987 1.000 0 0.000
#> SRR1377197 1 0.0000 0.987 1.000 0 0.000
#> SRR1377198 1 0.0424 0.984 0.992 0 0.008
#> SRR1377199 1 0.1529 0.958 0.960 0 0.040
#> SRR1377200 3 0.1753 0.000 0.048 0 0.952
#> SRR1377201 2 0.0000 1.000 0.000 1 0.000
#> SRR1377202 2 0.0000 1.000 0.000 1 0.000
#> SRR1377203 2 0.0000 1.000 0.000 1 0.000
#> SRR1377204 2 0.0000 1.000 0.000 1 0.000
#> SRR1377205 2 0.0000 1.000 0.000 1 0.000
#> SRR1377206 2 0.0000 1.000 0.000 1 0.000
#> SRR1377207 2 0.0000 1.000 0.000 1 0.000
#> SRR1377208 2 0.0000 1.000 0.000 1 0.000
#> SRR1377209 2 0.0000 1.000 0.000 1 0.000
#> SRR1377210 2 0.0000 1.000 0.000 1 0.000
#> SRR1377211 2 0.0000 1.000 0.000 1 0.000
#> SRR1377212 2 0.0000 1.000 0.000 1 0.000
#> SRR1377213 2 0.0000 1.000 0.000 1 0.000
#> SRR1377214 2 0.0000 1.000 0.000 1 0.000
#> SRR1377215 2 0.0000 1.000 0.000 1 0.000
#> SRR1377216 2 0.0000 1.000 0.000 1 0.000
#> SRR1377217 2 0.0000 1.000 0.000 1 0.000
#> SRR1377218 2 0.0000 1.000 0.000 1 0.000
#> SRR1377219 2 0.0000 1.000 0.000 1 0.000
#> SRR1377220 2 0.0000 1.000 0.000 1 0.000
#> SRR1377221 2 0.0000 1.000 0.000 1 0.000
#> SRR1377222 2 0.0000 1.000 0.000 1 0.000
#> SRR1377223 2 0.0000 1.000 0.000 1 0.000
#> SRR1377224 2 0.0000 1.000 0.000 1 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377146 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377147 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377148 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377153 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377154 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377155 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377156 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377149 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377150 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377151 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377152 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377157 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377158 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377159 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377160 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377161 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377162 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377163 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377164 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377169 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377170 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377171 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377172 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377165 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377166 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377167 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377168 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377173 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377174 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377175 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377176 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377177 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377178 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377179 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377180 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377181 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377182 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377183 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377184 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377185 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377186 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377187 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377188 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377189 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377190 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377191 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377192 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377193 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377194 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377195 1 0.398 0.895 0.76 0 0 0.24
#> SRR1377196 1 0.398 0.895 0.76 0 0 0.24
#> SRR1377197 1 0.398 0.895 0.76 0 0 0.24
#> SRR1377198 1 0.000 0.680 1.00 0 0 0.00
#> SRR1377199 4 0.000 0.000 0.00 0 0 1.00
#> SRR1377200 3 0.000 0.000 0.00 0 1 0.00
#> SRR1377201 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377202 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377203 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377204 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377205 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377206 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377207 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377208 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377209 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377210 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377211 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377212 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377213 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377214 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377215 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377216 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377217 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377218 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377219 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377220 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377221 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377222 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377223 2 0.000 1.000 0.00 1 0 0.00
#> SRR1377224 2 0.000 1.000 0.00 1 0 0.00
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0 1 0 1 0 0 0
#> SRR1377146 2 0 1 0 1 0 0 0
#> SRR1377147 2 0 1 0 1 0 0 0
#> SRR1377148 2 0 1 0 1 0 0 0
#> SRR1377153 2 0 1 0 1 0 0 0
#> SRR1377154 2 0 1 0 1 0 0 0
#> SRR1377155 2 0 1 0 1 0 0 0
#> SRR1377156 2 0 1 0 1 0 0 0
#> SRR1377149 2 0 1 0 1 0 0 0
#> SRR1377150 2 0 1 0 1 0 0 0
#> SRR1377151 2 0 1 0 1 0 0 0
#> SRR1377152 2 0 1 0 1 0 0 0
#> SRR1377157 2 0 1 0 1 0 0 0
#> SRR1377158 2 0 1 0 1 0 0 0
#> SRR1377159 2 0 1 0 1 0 0 0
#> SRR1377160 2 0 1 0 1 0 0 0
#> SRR1377161 2 0 1 0 1 0 0 0
#> SRR1377162 2 0 1 0 1 0 0 0
#> SRR1377163 2 0 1 0 1 0 0 0
#> SRR1377164 2 0 1 0 1 0 0 0
#> SRR1377169 2 0 1 0 1 0 0 0
#> SRR1377170 2 0 1 0 1 0 0 0
#> SRR1377171 2 0 1 0 1 0 0 0
#> SRR1377172 2 0 1 0 1 0 0 0
#> SRR1377165 2 0 1 0 1 0 0 0
#> SRR1377166 2 0 1 0 1 0 0 0
#> SRR1377167 2 0 1 0 1 0 0 0
#> SRR1377168 2 0 1 0 1 0 0 0
#> SRR1377173 2 0 1 0 1 0 0 0
#> SRR1377174 2 0 1 0 1 0 0 0
#> SRR1377175 2 0 1 0 1 0 0 0
#> SRR1377176 2 0 1 0 1 0 0 0
#> SRR1377177 2 0 1 0 1 0 0 0
#> SRR1377178 2 0 1 0 1 0 0 0
#> SRR1377179 2 0 1 0 1 0 0 0
#> SRR1377180 2 0 1 0 1 0 0 0
#> SRR1377181 2 0 1 0 1 0 0 0
#> SRR1377182 2 0 1 0 1 0 0 0
#> SRR1377183 2 0 1 0 1 0 0 0
#> SRR1377184 2 0 1 0 1 0 0 0
#> SRR1377185 2 0 1 0 1 0 0 0
#> SRR1377186 2 0 1 0 1 0 0 0
#> SRR1377187 2 0 1 0 1 0 0 0
#> SRR1377188 2 0 1 0 1 0 0 0
#> SRR1377189 2 0 1 0 1 0 0 0
#> SRR1377190 2 0 1 0 1 0 0 0
#> SRR1377191 2 0 1 0 1 0 0 0
#> SRR1377192 2 0 1 0 1 0 0 0
#> SRR1377193 2 0 1 0 1 0 0 0
#> SRR1377194 2 0 1 0 1 0 0 0
#> SRR1377195 1 0 1 1 0 0 0 0
#> SRR1377196 1 0 1 1 0 0 0 0
#> SRR1377197 1 0 1 1 0 0 0 0
#> SRR1377198 5 0 0 0 0 0 0 1
#> SRR1377199 4 0 0 0 0 0 1 0
#> SRR1377200 3 0 0 0 0 1 0 0
#> SRR1377201 2 0 1 0 1 0 0 0
#> SRR1377202 2 0 1 0 1 0 0 0
#> SRR1377203 2 0 1 0 1 0 0 0
#> SRR1377204 2 0 1 0 1 0 0 0
#> SRR1377205 2 0 1 0 1 0 0 0
#> SRR1377206 2 0 1 0 1 0 0 0
#> SRR1377207 2 0 1 0 1 0 0 0
#> SRR1377208 2 0 1 0 1 0 0 0
#> SRR1377209 2 0 1 0 1 0 0 0
#> SRR1377210 2 0 1 0 1 0 0 0
#> SRR1377211 2 0 1 0 1 0 0 0
#> SRR1377212 2 0 1 0 1 0 0 0
#> SRR1377213 2 0 1 0 1 0 0 0
#> SRR1377214 2 0 1 0 1 0 0 0
#> SRR1377215 2 0 1 0 1 0 0 0
#> SRR1377216 2 0 1 0 1 0 0 0
#> SRR1377217 2 0 1 0 1 0 0 0
#> SRR1377218 2 0 1 0 1 0 0 0
#> SRR1377219 2 0 1 0 1 0 0 0
#> SRR1377220 2 0 1 0 1 0 0 0
#> SRR1377221 2 0 1 0 1 0 0 0
#> SRR1377222 2 0 1 0 1 0 0 0
#> SRR1377223 2 0 1 0 1 0 0 0
#> SRR1377224 2 0 1 0 1 0 0 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377146 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377147 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377148 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377153 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377154 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377155 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377156 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377149 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377150 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377151 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377152 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377157 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377158 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377159 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377160 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377161 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377162 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377163 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377164 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377169 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377170 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377171 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377172 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377165 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377166 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377167 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377168 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377173 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377174 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377175 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377176 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377177 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377178 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377179 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377180 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377181 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377182 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377183 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377184 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377185 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377186 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377187 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377188 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377189 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377190 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377191 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377192 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377193 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377194 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377195 5 0.384 0.321 NA 0.000 0 0 0.552 0
#> SRR1377196 5 0.385 0.309 NA 0.000 0 0 0.536 0
#> SRR1377197 5 0.000 0.509 NA 0.000 0 0 1.000 0
#> SRR1377198 4 0.000 0.000 NA 0.000 0 1 0.000 0
#> SRR1377199 3 0.000 0.000 NA 0.000 1 0 0.000 0
#> SRR1377200 6 0.000 0.000 NA 0.000 0 0 0.000 1
#> SRR1377201 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377202 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377203 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377204 2 0.166 0.912 NA 0.912 0 0 0.000 0
#> SRR1377205 2 0.166 0.912 NA 0.912 0 0 0.000 0
#> SRR1377206 2 0.166 0.912 NA 0.912 0 0 0.000 0
#> SRR1377207 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377208 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377209 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377210 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377211 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377212 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377213 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377214 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377215 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377216 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377217 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377218 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377219 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377220 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377221 2 0.000 0.993 NA 1.000 0 0 0.000 0
#> SRR1377222 2 0.166 0.912 NA 0.912 0 0 0.000 0
#> SRR1377223 2 0.166 0.912 NA 0.912 0 0 0.000 0
#> SRR1377224 2 0.166 0.912 NA 0.912 0 0 0.000 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13890 rows and 80 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.383 0.862 0.896 0.212 0.859 0.859
#> 3 3 0.232 0.616 0.666 1.214 0.706 0.658
#> 4 4 0.309 0.623 0.617 0.315 0.734 0.530
#> 5 5 0.363 0.704 0.579 0.146 0.810 0.474
#> 6 6 0.695 0.891 0.804 0.102 0.939 0.738
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
#> SRR1377145 2 0.2778 0.866 0.048 0.952
#> SRR1377146 2 0.2778 0.866 0.048 0.952
#> SRR1377147 2 0.2778 0.866 0.048 0.952
#> SRR1377148 2 0.2778 0.866 0.048 0.952
#> SRR1377153 2 0.2778 0.866 0.048 0.952
#> SRR1377154 2 0.2778 0.866 0.048 0.952
#> SRR1377155 2 0.2778 0.866 0.048 0.952
#> SRR1377156 2 0.2778 0.866 0.048 0.952
#> SRR1377149 2 0.2778 0.866 0.048 0.952
#> SRR1377150 2 0.2778 0.866 0.048 0.952
#> SRR1377151 2 0.2778 0.866 0.048 0.952
#> SRR1377152 2 0.2778 0.866 0.048 0.952
#> SRR1377157 2 0.6887 0.809 0.184 0.816
#> SRR1377158 2 0.6887 0.809 0.184 0.816
#> SRR1377159 2 0.6887 0.809 0.184 0.816
#> SRR1377160 2 0.6887 0.809 0.184 0.816
#> SRR1377161 2 0.6887 0.809 0.184 0.816
#> SRR1377162 2 0.6887 0.809 0.184 0.816
#> SRR1377163 2 0.6887 0.809 0.184 0.816
#> SRR1377164 2 0.6887 0.809 0.184 0.816
#> SRR1377169 2 0.6887 0.809 0.184 0.816
#> SRR1377170 2 0.6887 0.809 0.184 0.816
#> SRR1377171 2 0.6887 0.809 0.184 0.816
#> SRR1377172 2 0.6887 0.809 0.184 0.816
#> SRR1377165 2 0.6887 0.809 0.184 0.816
#> SRR1377166 2 0.6887 0.809 0.184 0.816
#> SRR1377167 2 0.6887 0.809 0.184 0.816
#> SRR1377168 2 0.6887 0.809 0.184 0.816
#> SRR1377173 2 0.0672 0.885 0.008 0.992
#> SRR1377174 2 0.0672 0.885 0.008 0.992
#> SRR1377175 2 0.0672 0.885 0.008 0.992
#> SRR1377176 2 0.0672 0.885 0.008 0.992
#> SRR1377177 2 0.0672 0.885 0.008 0.992
#> SRR1377178 2 0.0672 0.885 0.008 0.992
#> SRR1377179 2 0.0672 0.885 0.008 0.992
#> SRR1377180 2 0.0672 0.885 0.008 0.992
#> SRR1377181 2 0.0938 0.886 0.012 0.988
#> SRR1377182 2 0.0938 0.886 0.012 0.988
#> SRR1377183 2 0.0672 0.885 0.008 0.992
#> SRR1377184 2 0.0938 0.886 0.012 0.988
#> SRR1377185 2 0.0672 0.885 0.008 0.992
#> SRR1377186 2 0.0672 0.885 0.008 0.992
#> SRR1377187 2 0.0938 0.886 0.012 0.988
#> SRR1377188 2 0.0672 0.885 0.008 0.992
#> SRR1377189 2 0.0938 0.886 0.012 0.988
#> SRR1377190 2 0.0938 0.886 0.012 0.988
#> SRR1377191 2 0.0938 0.886 0.012 0.988
#> SRR1377192 2 0.0938 0.886 0.012 0.988
#> SRR1377193 2 0.0938 0.886 0.012 0.988
#> SRR1377194 2 0.0938 0.886 0.012 0.988
#> SRR1377195 1 0.9087 1.000 0.676 0.324
#> SRR1377196 1 0.9087 1.000 0.676 0.324
#> SRR1377197 1 0.9087 1.000 0.676 0.324
#> SRR1377198 1 0.9087 1.000 0.676 0.324
#> SRR1377199 1 0.9087 1.000 0.676 0.324
#> SRR1377200 1 0.9087 1.000 0.676 0.324
#> SRR1377201 2 0.0938 0.886 0.012 0.988
#> SRR1377202 2 0.0938 0.886 0.012 0.988
#> SRR1377203 2 0.0938 0.886 0.012 0.988
#> SRR1377204 2 0.3431 0.863 0.064 0.936
#> SRR1377205 2 0.3431 0.863 0.064 0.936
#> SRR1377206 2 0.3431 0.863 0.064 0.936
#> SRR1377207 2 0.0938 0.886 0.012 0.988
#> SRR1377208 2 0.0938 0.886 0.012 0.988
#> SRR1377209 2 0.0938 0.886 0.012 0.988
#> SRR1377210 2 0.0938 0.886 0.012 0.988
#> SRR1377211 2 0.0938 0.886 0.012 0.988
#> SRR1377212 2 0.0938 0.886 0.012 0.988
#> SRR1377213 2 0.7453 0.790 0.212 0.788
#> SRR1377214 2 0.7453 0.790 0.212 0.788
#> SRR1377215 2 0.7453 0.790 0.212 0.788
#> SRR1377216 2 0.7376 0.794 0.208 0.792
#> SRR1377217 2 0.7376 0.794 0.208 0.792
#> SRR1377218 2 0.7376 0.794 0.208 0.792
#> SRR1377219 2 0.7453 0.790 0.212 0.788
#> SRR1377220 2 0.7453 0.790 0.212 0.788
#> SRR1377221 2 0.7453 0.790 0.212 0.788
#> SRR1377222 2 0.5737 0.832 0.136 0.864
#> SRR1377223 2 0.5737 0.832 0.136 0.864
#> SRR1377224 2 0.5737 0.832 0.136 0.864
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377146 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377147 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377148 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377153 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377154 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377155 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377156 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377149 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377150 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377151 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377152 2 0.6796 0.5082 0.056 0.708 0.236
#> SRR1377157 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377158 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377159 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377160 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377161 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377162 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377163 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377164 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377169 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377170 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377171 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377172 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377165 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377166 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377167 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377168 3 0.6008 1.0000 0.000 0.372 0.628
#> SRR1377173 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377174 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377175 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377176 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377177 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377178 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377179 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377180 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377181 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377182 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377183 2 0.5268 0.5062 0.012 0.776 0.212
#> SRR1377184 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377185 2 0.5268 0.5062 0.012 0.776 0.212
#> SRR1377186 2 0.5268 0.5062 0.012 0.776 0.212
#> SRR1377187 2 0.5536 0.4736 0.012 0.752 0.236
#> SRR1377188 2 0.5268 0.5062 0.012 0.776 0.212
#> SRR1377189 2 0.0237 0.6260 0.000 0.996 0.004
#> SRR1377190 2 0.0237 0.6260 0.000 0.996 0.004
#> SRR1377191 2 0.0237 0.6260 0.000 0.996 0.004
#> SRR1377192 2 0.0237 0.6260 0.000 0.996 0.004
#> SRR1377193 2 0.0237 0.6260 0.000 0.996 0.004
#> SRR1377194 2 0.0237 0.6260 0.000 0.996 0.004
#> SRR1377195 1 0.3983 0.9985 0.852 0.144 0.004
#> SRR1377196 1 0.3983 0.9985 0.852 0.144 0.004
#> SRR1377197 1 0.3983 0.9985 0.852 0.144 0.004
#> SRR1377198 1 0.4164 0.9977 0.848 0.144 0.008
#> SRR1377199 1 0.4164 0.9977 0.848 0.144 0.008
#> SRR1377200 1 0.4326 0.9963 0.844 0.144 0.012
#> SRR1377201 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377202 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377203 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377204 2 0.5060 0.5290 0.028 0.816 0.156
#> SRR1377205 2 0.5060 0.5290 0.028 0.816 0.156
#> SRR1377206 2 0.5060 0.5290 0.028 0.816 0.156
#> SRR1377207 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377208 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377209 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377210 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377211 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377212 2 0.0000 0.6268 0.000 1.000 0.000
#> SRR1377213 2 0.8173 0.2260 0.080 0.552 0.368
#> SRR1377214 2 0.8173 0.2260 0.080 0.552 0.368
#> SRR1377215 2 0.8173 0.2260 0.080 0.552 0.368
#> SRR1377216 2 0.8058 0.0776 0.072 0.552 0.376
#> SRR1377217 2 0.8058 0.0776 0.072 0.552 0.376
#> SRR1377218 2 0.8058 0.0776 0.072 0.552 0.376
#> SRR1377219 2 0.8202 0.2028 0.080 0.544 0.376
#> SRR1377220 2 0.8202 0.2028 0.080 0.544 0.376
#> SRR1377221 2 0.8202 0.2028 0.080 0.544 0.376
#> SRR1377222 2 0.7901 0.3266 0.080 0.608 0.312
#> SRR1377223 2 0.7901 0.3266 0.080 0.608 0.312
#> SRR1377224 2 0.7901 0.3266 0.080 0.608 0.312
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 4 0.5647 0.399 0.000 0.116 0.164 0.720
#> SRR1377146 4 0.5647 0.399 0.000 0.116 0.164 0.720
#> SRR1377147 4 0.5647 0.399 0.000 0.116 0.164 0.720
#> SRR1377148 4 0.5647 0.399 0.000 0.116 0.164 0.720
#> SRR1377153 4 0.5686 0.403 0.004 0.112 0.156 0.728
#> SRR1377154 4 0.5686 0.403 0.004 0.112 0.156 0.728
#> SRR1377155 4 0.5686 0.403 0.004 0.112 0.156 0.728
#> SRR1377156 4 0.5686 0.403 0.004 0.112 0.156 0.728
#> SRR1377149 4 0.6408 0.396 0.020 0.124 0.164 0.692
#> SRR1377150 4 0.6408 0.396 0.020 0.124 0.164 0.692
#> SRR1377151 4 0.6408 0.396 0.020 0.124 0.164 0.692
#> SRR1377152 4 0.6408 0.396 0.020 0.124 0.164 0.692
#> SRR1377157 3 0.1958 0.981 0.008 0.020 0.944 0.028
#> SRR1377158 3 0.1958 0.981 0.008 0.020 0.944 0.028
#> SRR1377159 3 0.1958 0.981 0.008 0.020 0.944 0.028
#> SRR1377160 3 0.1958 0.981 0.008 0.020 0.944 0.028
#> SRR1377161 3 0.1640 0.984 0.012 0.020 0.956 0.012
#> SRR1377162 3 0.1640 0.984 0.012 0.020 0.956 0.012
#> SRR1377163 3 0.1640 0.984 0.012 0.020 0.956 0.012
#> SRR1377164 3 0.1640 0.984 0.012 0.020 0.956 0.012
#> SRR1377169 3 0.0895 0.984 0.000 0.020 0.976 0.004
#> SRR1377170 3 0.0895 0.984 0.000 0.020 0.976 0.004
#> SRR1377171 3 0.0895 0.984 0.000 0.020 0.976 0.004
#> SRR1377172 3 0.0895 0.984 0.000 0.020 0.976 0.004
#> SRR1377165 3 0.0895 0.985 0.000 0.020 0.976 0.004
#> SRR1377166 3 0.0895 0.985 0.000 0.020 0.976 0.004
#> SRR1377167 3 0.0895 0.985 0.000 0.020 0.976 0.004
#> SRR1377168 3 0.0895 0.985 0.000 0.020 0.976 0.004
#> SRR1377173 4 0.8514 0.568 0.040 0.196 0.348 0.416
#> SRR1377174 4 0.8514 0.568 0.040 0.196 0.348 0.416
#> SRR1377175 4 0.8514 0.568 0.040 0.196 0.348 0.416
#> SRR1377176 4 0.8514 0.568 0.040 0.196 0.348 0.416
#> SRR1377177 4 0.8492 0.568 0.040 0.192 0.348 0.420
#> SRR1377178 4 0.8492 0.568 0.040 0.192 0.348 0.420
#> SRR1377179 4 0.8492 0.568 0.040 0.192 0.348 0.420
#> SRR1377180 4 0.8492 0.568 0.040 0.192 0.348 0.420
#> SRR1377181 4 0.8556 0.566 0.040 0.204 0.348 0.408
#> SRR1377182 4 0.8556 0.566 0.040 0.204 0.348 0.408
#> SRR1377183 4 0.8487 0.567 0.040 0.192 0.344 0.424
#> SRR1377184 4 0.8556 0.566 0.040 0.204 0.348 0.408
#> SRR1377185 4 0.8487 0.567 0.040 0.192 0.344 0.424
#> SRR1377186 4 0.8487 0.567 0.040 0.192 0.344 0.424
#> SRR1377187 4 0.8556 0.566 0.040 0.204 0.348 0.408
#> SRR1377188 4 0.8487 0.567 0.040 0.192 0.344 0.424
#> SRR1377189 2 0.8256 0.457 0.032 0.424 0.172 0.372
#> SRR1377190 2 0.8256 0.457 0.032 0.424 0.172 0.372
#> SRR1377191 2 0.8256 0.457 0.032 0.424 0.172 0.372
#> SRR1377192 2 0.8256 0.457 0.032 0.424 0.172 0.372
#> SRR1377193 2 0.8256 0.457 0.032 0.424 0.172 0.372
#> SRR1377194 2 0.8256 0.457 0.032 0.424 0.172 0.372
#> SRR1377195 1 0.1629 0.998 0.952 0.024 0.000 0.024
#> SRR1377196 1 0.1629 0.998 0.952 0.024 0.000 0.024
#> SRR1377197 1 0.1629 0.998 0.952 0.024 0.000 0.024
#> SRR1377198 1 0.1629 0.998 0.952 0.024 0.000 0.024
#> SRR1377199 1 0.1629 0.998 0.952 0.024 0.000 0.024
#> SRR1377200 1 0.2111 0.990 0.932 0.024 0.000 0.044
#> SRR1377201 2 0.8198 0.468 0.032 0.436 0.164 0.368
#> SRR1377202 2 0.8198 0.468 0.032 0.436 0.164 0.368
#> SRR1377203 2 0.8198 0.468 0.032 0.436 0.164 0.368
#> SRR1377204 2 0.6238 0.496 0.032 0.676 0.048 0.244
#> SRR1377205 2 0.6238 0.496 0.032 0.676 0.048 0.244
#> SRR1377206 2 0.6238 0.496 0.032 0.676 0.048 0.244
#> SRR1377207 2 0.8169 0.465 0.032 0.440 0.160 0.368
#> SRR1377208 2 0.8169 0.465 0.032 0.440 0.160 0.368
#> SRR1377209 2 0.8169 0.465 0.032 0.440 0.160 0.368
#> SRR1377210 2 0.8193 0.469 0.032 0.440 0.164 0.364
#> SRR1377211 2 0.8193 0.469 0.032 0.440 0.164 0.364
#> SRR1377212 2 0.8193 0.469 0.032 0.440 0.164 0.364
#> SRR1377213 2 0.3900 0.502 0.000 0.816 0.164 0.020
#> SRR1377214 2 0.3900 0.502 0.000 0.816 0.164 0.020
#> SRR1377215 2 0.3900 0.502 0.000 0.816 0.164 0.020
#> SRR1377216 2 0.5557 0.420 0.000 0.652 0.308 0.040
#> SRR1377217 2 0.5557 0.420 0.000 0.652 0.308 0.040
#> SRR1377218 2 0.5557 0.420 0.000 0.652 0.308 0.040
#> SRR1377219 2 0.4095 0.505 0.000 0.804 0.172 0.024
#> SRR1377220 2 0.4095 0.505 0.000 0.804 0.172 0.024
#> SRR1377221 2 0.4095 0.505 0.000 0.804 0.172 0.024
#> SRR1377222 2 0.3200 0.498 0.004 0.880 0.092 0.024
#> SRR1377223 2 0.3200 0.498 0.004 0.880 0.092 0.024
#> SRR1377224 2 0.3200 0.498 0.004 0.880 0.092 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 4 0.638 0.9659 0.072 0.308 0.052 0.568 0.000
#> SRR1377146 4 0.638 0.9659 0.072 0.308 0.052 0.568 0.000
#> SRR1377147 4 0.638 0.9659 0.072 0.308 0.052 0.568 0.000
#> SRR1377148 4 0.638 0.9659 0.072 0.308 0.052 0.568 0.000
#> SRR1377153 4 0.678 0.9623 0.088 0.308 0.056 0.544 0.004
#> SRR1377154 4 0.678 0.9623 0.088 0.308 0.056 0.544 0.004
#> SRR1377155 4 0.678 0.9623 0.088 0.308 0.056 0.544 0.004
#> SRR1377156 4 0.678 0.9623 0.088 0.308 0.056 0.544 0.004
#> SRR1377149 4 0.730 0.9441 0.136 0.308 0.052 0.496 0.008
#> SRR1377150 4 0.730 0.9441 0.136 0.308 0.052 0.496 0.008
#> SRR1377151 4 0.730 0.9441 0.136 0.308 0.052 0.496 0.008
#> SRR1377152 4 0.730 0.9441 0.136 0.308 0.052 0.496 0.008
#> SRR1377157 3 0.305 0.9488 0.020 0.024 0.884 0.064 0.008
#> SRR1377158 3 0.305 0.9488 0.020 0.024 0.884 0.064 0.008
#> SRR1377159 3 0.305 0.9488 0.020 0.024 0.884 0.064 0.008
#> SRR1377160 3 0.305 0.9488 0.020 0.024 0.884 0.064 0.008
#> SRR1377161 3 0.282 0.9500 0.020 0.024 0.900 0.044 0.012
#> SRR1377162 3 0.282 0.9500 0.020 0.024 0.900 0.044 0.012
#> SRR1377163 3 0.282 0.9500 0.020 0.024 0.900 0.044 0.012
#> SRR1377164 3 0.282 0.9500 0.020 0.024 0.900 0.044 0.012
#> SRR1377169 3 0.160 0.9494 0.008 0.024 0.948 0.020 0.000
#> SRR1377170 3 0.160 0.9494 0.008 0.024 0.948 0.020 0.000
#> SRR1377171 3 0.160 0.9494 0.008 0.024 0.948 0.020 0.000
#> SRR1377172 3 0.160 0.9494 0.008 0.024 0.948 0.020 0.000
#> SRR1377165 3 0.127 0.9568 0.000 0.024 0.960 0.012 0.004
#> SRR1377166 3 0.127 0.9568 0.000 0.024 0.960 0.012 0.004
#> SRR1377167 3 0.127 0.9568 0.000 0.024 0.960 0.012 0.004
#> SRR1377168 3 0.127 0.9568 0.000 0.024 0.960 0.012 0.004
#> SRR1377173 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377174 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377175 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377176 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377177 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377178 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377179 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377180 1 0.651 0.4666 0.472 0.316 0.212 0.000 0.000
#> SRR1377181 1 0.678 0.4656 0.460 0.316 0.216 0.008 0.000
#> SRR1377182 1 0.678 0.4656 0.460 0.316 0.216 0.008 0.000
#> SRR1377183 1 0.676 0.4665 0.464 0.316 0.212 0.008 0.000
#> SRR1377184 1 0.678 0.4656 0.460 0.316 0.216 0.008 0.000
#> SRR1377185 1 0.676 0.4665 0.464 0.316 0.212 0.008 0.000
#> SRR1377186 1 0.676 0.4665 0.464 0.316 0.212 0.008 0.000
#> SRR1377187 1 0.678 0.4656 0.460 0.316 0.216 0.008 0.000
#> SRR1377188 1 0.676 0.4665 0.464 0.316 0.212 0.008 0.000
#> SRR1377189 2 0.392 0.8219 0.024 0.828 0.080 0.068 0.000
#> SRR1377190 2 0.392 0.8219 0.024 0.828 0.080 0.068 0.000
#> SRR1377191 2 0.392 0.8219 0.024 0.828 0.080 0.068 0.000
#> SRR1377192 2 0.392 0.8219 0.024 0.828 0.080 0.068 0.000
#> SRR1377193 2 0.392 0.8219 0.024 0.828 0.080 0.068 0.000
#> SRR1377194 2 0.392 0.8219 0.024 0.828 0.080 0.068 0.000
#> SRR1377195 5 0.104 0.9947 0.000 0.040 0.000 0.000 0.960
#> SRR1377196 5 0.104 0.9947 0.000 0.040 0.000 0.000 0.960
#> SRR1377197 5 0.104 0.9947 0.000 0.040 0.000 0.000 0.960
#> SRR1377198 5 0.137 0.9931 0.004 0.040 0.004 0.000 0.952
#> SRR1377199 5 0.137 0.9928 0.000 0.040 0.004 0.004 0.952
#> SRR1377200 5 0.212 0.9825 0.008 0.040 0.008 0.016 0.928
#> SRR1377201 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377202 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377203 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377204 2 0.478 0.5304 0.168 0.744 0.000 0.076 0.012
#> SRR1377205 2 0.478 0.5304 0.168 0.744 0.000 0.076 0.012
#> SRR1377206 2 0.478 0.5304 0.168 0.744 0.000 0.076 0.012
#> SRR1377207 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377208 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377209 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377210 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377211 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377212 2 0.173 0.8603 0.000 0.920 0.080 0.000 0.000
#> SRR1377213 1 0.805 0.1768 0.340 0.272 0.088 0.300 0.000
#> SRR1377214 1 0.805 0.1768 0.340 0.272 0.088 0.300 0.000
#> SRR1377215 1 0.805 0.1768 0.340 0.272 0.088 0.300 0.000
#> SRR1377216 1 0.851 0.1888 0.300 0.228 0.188 0.284 0.000
#> SRR1377217 1 0.851 0.1888 0.300 0.228 0.188 0.284 0.000
#> SRR1377218 1 0.851 0.1888 0.300 0.228 0.188 0.284 0.000
#> SRR1377219 1 0.820 0.1731 0.328 0.272 0.108 0.292 0.000
#> SRR1377220 1 0.820 0.1731 0.328 0.272 0.108 0.292 0.000
#> SRR1377221 1 0.820 0.1731 0.328 0.272 0.108 0.292 0.000
#> SRR1377222 1 0.750 0.0933 0.400 0.324 0.020 0.244 0.012
#> SRR1377223 1 0.750 0.0933 0.400 0.324 0.020 0.244 0.012
#> SRR1377224 1 0.750 0.0933 0.400 0.324 0.020 0.244 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.4660 0.955 0.028 0.236 0.016 0.020 0.000 0.700
#> SRR1377146 6 0.4660 0.955 0.028 0.236 0.016 0.020 0.000 0.700
#> SRR1377147 6 0.4660 0.955 0.028 0.236 0.016 0.020 0.000 0.700
#> SRR1377148 6 0.4660 0.955 0.028 0.236 0.016 0.020 0.000 0.700
#> SRR1377153 6 0.5181 0.949 0.032 0.236 0.032 0.028 0.000 0.672
#> SRR1377154 6 0.5181 0.949 0.032 0.236 0.032 0.028 0.000 0.672
#> SRR1377155 6 0.5181 0.949 0.032 0.236 0.032 0.028 0.000 0.672
#> SRR1377156 6 0.5181 0.949 0.032 0.236 0.032 0.028 0.000 0.672
#> SRR1377149 6 0.5619 0.940 0.040 0.236 0.052 0.028 0.000 0.644
#> SRR1377150 6 0.5619 0.940 0.040 0.236 0.052 0.028 0.000 0.644
#> SRR1377151 6 0.5619 0.940 0.040 0.236 0.052 0.028 0.000 0.644
#> SRR1377152 6 0.5619 0.940 0.040 0.236 0.052 0.028 0.000 0.644
#> SRR1377157 3 0.4353 0.918 0.132 0.020 0.776 0.028 0.000 0.044
#> SRR1377158 3 0.4353 0.918 0.132 0.020 0.776 0.028 0.000 0.044
#> SRR1377159 3 0.4353 0.918 0.132 0.020 0.776 0.028 0.000 0.044
#> SRR1377160 3 0.4353 0.918 0.132 0.020 0.776 0.028 0.000 0.044
#> SRR1377161 3 0.4003 0.931 0.116 0.020 0.796 0.008 0.000 0.060
#> SRR1377162 3 0.4003 0.931 0.116 0.020 0.796 0.008 0.000 0.060
#> SRR1377163 3 0.4003 0.931 0.116 0.020 0.796 0.008 0.000 0.060
#> SRR1377164 3 0.4003 0.931 0.116 0.020 0.796 0.008 0.000 0.060
#> SRR1377169 3 0.4003 0.912 0.108 0.024 0.804 0.016 0.000 0.048
#> SRR1377170 3 0.4003 0.912 0.108 0.024 0.804 0.016 0.000 0.048
#> SRR1377171 3 0.4003 0.912 0.108 0.024 0.804 0.016 0.000 0.048
#> SRR1377172 3 0.4003 0.912 0.108 0.024 0.804 0.016 0.000 0.048
#> SRR1377165 3 0.2290 0.935 0.084 0.020 0.892 0.004 0.000 0.000
#> SRR1377166 3 0.2290 0.935 0.084 0.020 0.892 0.004 0.000 0.000
#> SRR1377167 3 0.2290 0.935 0.084 0.020 0.892 0.004 0.000 0.000
#> SRR1377168 3 0.2290 0.935 0.084 0.020 0.892 0.004 0.000 0.000
#> SRR1377173 1 0.2948 0.979 0.804 0.188 0.000 0.000 0.000 0.008
#> SRR1377174 1 0.2948 0.979 0.804 0.188 0.000 0.000 0.000 0.008
#> SRR1377175 1 0.2948 0.979 0.804 0.188 0.000 0.000 0.000 0.008
#> SRR1377176 1 0.2948 0.979 0.804 0.188 0.000 0.000 0.000 0.008
#> SRR1377177 1 0.2838 0.981 0.808 0.188 0.000 0.004 0.000 0.000
#> SRR1377178 1 0.2838 0.981 0.808 0.188 0.000 0.004 0.000 0.000
#> SRR1377179 1 0.2838 0.981 0.808 0.188 0.000 0.004 0.000 0.000
#> SRR1377180 1 0.2838 0.981 0.808 0.188 0.000 0.004 0.000 0.000
#> SRR1377181 1 0.4262 0.958 0.748 0.188 0.004 0.032 0.000 0.028
#> SRR1377182 1 0.4262 0.958 0.748 0.188 0.004 0.032 0.000 0.028
#> SRR1377183 1 0.3089 0.980 0.800 0.188 0.000 0.004 0.000 0.008
#> SRR1377184 1 0.4262 0.958 0.748 0.188 0.004 0.032 0.000 0.028
#> SRR1377185 1 0.3089 0.980 0.800 0.188 0.000 0.004 0.000 0.008
#> SRR1377186 1 0.3089 0.980 0.800 0.188 0.000 0.004 0.000 0.008
#> SRR1377187 1 0.4262 0.958 0.748 0.188 0.004 0.032 0.000 0.028
#> SRR1377188 1 0.3089 0.980 0.800 0.188 0.000 0.004 0.000 0.008
#> SRR1377189 2 0.2934 0.805 0.024 0.868 0.000 0.064 0.000 0.044
#> SRR1377190 2 0.2934 0.805 0.024 0.868 0.000 0.064 0.000 0.044
#> SRR1377191 2 0.2934 0.805 0.024 0.868 0.000 0.064 0.000 0.044
#> SRR1377192 2 0.2934 0.805 0.024 0.868 0.000 0.064 0.000 0.044
#> SRR1377193 2 0.2934 0.805 0.024 0.868 0.000 0.064 0.000 0.044
#> SRR1377194 2 0.2934 0.805 0.024 0.868 0.000 0.064 0.000 0.044
#> SRR1377195 5 0.0146 0.990 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1377196 5 0.0146 0.990 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1377197 5 0.0146 0.990 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1377198 5 0.0405 0.989 0.000 0.004 0.000 0.008 0.988 0.000
#> SRR1377199 5 0.0582 0.987 0.000 0.004 0.004 0.004 0.984 0.004
#> SRR1377200 5 0.1699 0.963 0.000 0.004 0.008 0.012 0.936 0.040
#> SRR1377201 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377202 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377203 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377204 2 0.5846 0.469 0.060 0.632 0.016 0.232 0.004 0.056
#> SRR1377205 2 0.5846 0.469 0.060 0.632 0.016 0.232 0.004 0.056
#> SRR1377206 2 0.5846 0.469 0.060 0.632 0.016 0.232 0.004 0.056
#> SRR1377207 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377208 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377209 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377210 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377211 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377212 2 0.0806 0.850 0.020 0.972 0.008 0.000 0.000 0.000
#> SRR1377213 4 0.4447 0.864 0.024 0.180 0.052 0.740 0.000 0.004
#> SRR1377214 4 0.4447 0.864 0.024 0.180 0.052 0.740 0.000 0.004
#> SRR1377215 4 0.4447 0.864 0.024 0.180 0.052 0.740 0.000 0.004
#> SRR1377216 4 0.5775 0.806 0.052 0.204 0.100 0.636 0.000 0.008
#> SRR1377217 4 0.5775 0.806 0.052 0.204 0.100 0.636 0.000 0.008
#> SRR1377218 4 0.5775 0.806 0.052 0.204 0.100 0.636 0.000 0.008
#> SRR1377219 4 0.4732 0.864 0.024 0.188 0.068 0.716 0.000 0.004
#> SRR1377220 4 0.4732 0.864 0.024 0.188 0.068 0.716 0.000 0.004
#> SRR1377221 4 0.4732 0.864 0.024 0.188 0.068 0.716 0.000 0.004
#> SRR1377222 4 0.5568 0.710 0.060 0.108 0.040 0.712 0.004 0.076
#> SRR1377223 4 0.5568 0.710 0.060 0.108 0.040 0.712 0.004 0.076
#> SRR1377224 4 0.5568 0.710 0.060 0.108 0.040 0.712 0.004 0.076
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.505 0.849 0.908 0.4729 0.547 0.547
#> 3 3 0.674 0.798 0.876 0.4123 0.538 0.310
#> 4 4 0.729 0.805 0.781 0.0997 0.939 0.816
#> 5 5 0.793 0.896 0.895 0.0764 0.932 0.746
#> 6 6 0.977 0.978 0.973 0.0474 0.970 0.849
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> SRR1377145 2 0.574 0.834 0.136 0.864
#> SRR1377146 2 0.574 0.834 0.136 0.864
#> SRR1377147 2 0.574 0.834 0.136 0.864
#> SRR1377148 2 0.574 0.834 0.136 0.864
#> SRR1377153 2 0.574 0.834 0.136 0.864
#> SRR1377154 2 0.574 0.834 0.136 0.864
#> SRR1377155 2 0.574 0.834 0.136 0.864
#> SRR1377156 2 0.574 0.834 0.136 0.864
#> SRR1377149 2 0.574 0.834 0.136 0.864
#> SRR1377150 2 0.574 0.834 0.136 0.864
#> SRR1377151 2 0.574 0.834 0.136 0.864
#> SRR1377152 2 0.574 0.834 0.136 0.864
#> SRR1377157 2 0.000 0.884 0.000 1.000
#> SRR1377158 2 0.000 0.884 0.000 1.000
#> SRR1377159 2 0.000 0.884 0.000 1.000
#> SRR1377160 2 0.000 0.884 0.000 1.000
#> SRR1377161 2 0.000 0.884 0.000 1.000
#> SRR1377162 2 0.000 0.884 0.000 1.000
#> SRR1377163 2 0.000 0.884 0.000 1.000
#> SRR1377164 2 0.000 0.884 0.000 1.000
#> SRR1377169 2 0.000 0.884 0.000 1.000
#> SRR1377170 2 0.000 0.884 0.000 1.000
#> SRR1377171 2 0.000 0.884 0.000 1.000
#> SRR1377172 2 0.000 0.884 0.000 1.000
#> SRR1377165 2 0.000 0.884 0.000 1.000
#> SRR1377166 2 0.000 0.884 0.000 1.000
#> SRR1377167 2 0.000 0.884 0.000 1.000
#> SRR1377168 2 0.000 0.884 0.000 1.000
#> SRR1377173 2 0.295 0.873 0.052 0.948
#> SRR1377174 2 0.295 0.873 0.052 0.948
#> SRR1377175 2 0.295 0.873 0.052 0.948
#> SRR1377176 2 0.295 0.873 0.052 0.948
#> SRR1377177 2 0.295 0.873 0.052 0.948
#> SRR1377178 2 0.295 0.873 0.052 0.948
#> SRR1377179 2 0.295 0.873 0.052 0.948
#> SRR1377180 2 0.295 0.873 0.052 0.948
#> SRR1377181 2 0.295 0.873 0.052 0.948
#> SRR1377182 2 0.295 0.873 0.052 0.948
#> SRR1377183 2 0.295 0.873 0.052 0.948
#> SRR1377184 2 0.295 0.873 0.052 0.948
#> SRR1377185 2 0.295 0.873 0.052 0.948
#> SRR1377186 2 0.295 0.873 0.052 0.948
#> SRR1377187 2 0.295 0.873 0.052 0.948
#> SRR1377188 2 0.295 0.873 0.052 0.948
#> SRR1377189 1 0.295 0.964 0.948 0.052
#> SRR1377190 1 0.295 0.964 0.948 0.052
#> SRR1377191 1 0.295 0.964 0.948 0.052
#> SRR1377192 1 0.295 0.964 0.948 0.052
#> SRR1377193 1 0.295 0.964 0.948 0.052
#> SRR1377194 1 0.295 0.964 0.948 0.052
#> SRR1377195 1 0.295 0.903 0.948 0.052
#> SRR1377196 1 0.295 0.903 0.948 0.052
#> SRR1377197 1 0.295 0.903 0.948 0.052
#> SRR1377198 1 0.295 0.903 0.948 0.052
#> SRR1377199 1 0.295 0.903 0.948 0.052
#> SRR1377200 1 0.295 0.903 0.948 0.052
#> SRR1377201 1 0.295 0.964 0.948 0.052
#> SRR1377202 1 0.295 0.964 0.948 0.052
#> SRR1377203 1 0.295 0.964 0.948 0.052
#> SRR1377204 1 0.295 0.964 0.948 0.052
#> SRR1377205 1 0.295 0.964 0.948 0.052
#> SRR1377206 1 0.295 0.964 0.948 0.052
#> SRR1377207 1 0.295 0.964 0.948 0.052
#> SRR1377208 1 0.295 0.964 0.948 0.052
#> SRR1377209 1 0.295 0.964 0.948 0.052
#> SRR1377210 1 0.295 0.964 0.948 0.052
#> SRR1377211 1 0.295 0.964 0.948 0.052
#> SRR1377212 1 0.295 0.964 0.948 0.052
#> SRR1377213 2 0.983 0.310 0.424 0.576
#> SRR1377214 2 0.983 0.310 0.424 0.576
#> SRR1377215 2 0.983 0.310 0.424 0.576
#> SRR1377216 2 0.634 0.793 0.160 0.840
#> SRR1377217 2 0.634 0.793 0.160 0.840
#> SRR1377218 2 0.634 0.793 0.160 0.840
#> SRR1377219 2 0.983 0.310 0.424 0.576
#> SRR1377220 2 0.983 0.310 0.424 0.576
#> SRR1377221 2 0.983 0.310 0.424 0.576
#> SRR1377222 1 0.506 0.918 0.888 0.112
#> SRR1377223 1 0.506 0.918 0.888 0.112
#> SRR1377224 1 0.506 0.918 0.888 0.112
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377146 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377147 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377148 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377153 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377154 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377155 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377156 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377149 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377150 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377151 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377152 2 0.8105 0.639 0.084 0.580 0.336
#> SRR1377157 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377158 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377159 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377160 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377161 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377162 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377163 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377164 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377169 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377170 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377171 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377172 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377165 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377166 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377167 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377168 3 0.0892 0.821 0.020 0.000 0.980
#> SRR1377173 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377174 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377175 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377176 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377177 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377178 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377179 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377180 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377181 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377182 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377183 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377184 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377185 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377186 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377187 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377188 1 0.0237 0.958 0.996 0.000 0.004
#> SRR1377189 2 0.0424 0.789 0.000 0.992 0.008
#> SRR1377190 2 0.0424 0.789 0.000 0.992 0.008
#> SRR1377191 2 0.0424 0.789 0.000 0.992 0.008
#> SRR1377192 2 0.0424 0.789 0.000 0.992 0.008
#> SRR1377193 2 0.0424 0.789 0.000 0.992 0.008
#> SRR1377194 2 0.0424 0.789 0.000 0.992 0.008
#> SRR1377195 1 0.3551 0.882 0.868 0.132 0.000
#> SRR1377196 1 0.3551 0.882 0.868 0.132 0.000
#> SRR1377197 1 0.3551 0.882 0.868 0.132 0.000
#> SRR1377198 1 0.3551 0.882 0.868 0.132 0.000
#> SRR1377199 1 0.3551 0.882 0.868 0.132 0.000
#> SRR1377200 1 0.3551 0.882 0.868 0.132 0.000
#> SRR1377201 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377202 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377203 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377204 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377205 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377206 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377207 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377208 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377209 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377210 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377211 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377212 2 0.0000 0.790 0.000 1.000 0.000
#> SRR1377213 3 0.5763 0.718 0.008 0.276 0.716
#> SRR1377214 3 0.5763 0.718 0.008 0.276 0.716
#> SRR1377215 3 0.5763 0.718 0.008 0.276 0.716
#> SRR1377216 3 0.5420 0.740 0.008 0.240 0.752
#> SRR1377217 3 0.5420 0.740 0.008 0.240 0.752
#> SRR1377218 3 0.5420 0.740 0.008 0.240 0.752
#> SRR1377219 3 0.5728 0.721 0.008 0.272 0.720
#> SRR1377220 3 0.5728 0.721 0.008 0.272 0.720
#> SRR1377221 3 0.5728 0.721 0.008 0.272 0.720
#> SRR1377222 3 0.6527 0.555 0.008 0.404 0.588
#> SRR1377223 3 0.6527 0.555 0.008 0.404 0.588
#> SRR1377224 3 0.6527 0.555 0.008 0.404 0.588
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377146 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377147 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377148 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377153 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377154 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377155 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377156 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377149 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377150 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377151 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377152 2 0.4286 0.599 0.028 0.812 0.152 0.008
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1377173 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377174 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377175 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377176 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377177 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377178 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377179 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377180 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377181 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377182 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377183 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377184 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377185 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377186 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377187 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377188 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1377189 2 0.4804 0.715 0.000 0.616 0.000 0.384
#> SRR1377190 2 0.4804 0.715 0.000 0.616 0.000 0.384
#> SRR1377191 2 0.4804 0.715 0.000 0.616 0.000 0.384
#> SRR1377192 2 0.4804 0.715 0.000 0.616 0.000 0.384
#> SRR1377193 2 0.4804 0.715 0.000 0.616 0.000 0.384
#> SRR1377194 2 0.4804 0.715 0.000 0.616 0.000 0.384
#> SRR1377195 1 0.5226 0.793 0.744 0.076 0.000 0.180
#> SRR1377196 1 0.5226 0.793 0.744 0.076 0.000 0.180
#> SRR1377197 1 0.5226 0.793 0.744 0.076 0.000 0.180
#> SRR1377198 1 0.5226 0.793 0.744 0.076 0.000 0.180
#> SRR1377199 1 0.5226 0.793 0.744 0.076 0.000 0.180
#> SRR1377200 1 0.5226 0.793 0.744 0.076 0.000 0.180
#> SRR1377201 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377202 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377203 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377204 2 0.4961 0.670 0.000 0.552 0.000 0.448
#> SRR1377205 2 0.4961 0.670 0.000 0.552 0.000 0.448
#> SRR1377206 2 0.4961 0.670 0.000 0.552 0.000 0.448
#> SRR1377207 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377208 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377209 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377210 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377211 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377212 2 0.4790 0.719 0.000 0.620 0.000 0.380
#> SRR1377213 4 0.5028 0.807 0.000 0.004 0.400 0.596
#> SRR1377214 4 0.5028 0.807 0.000 0.004 0.400 0.596
#> SRR1377215 4 0.5028 0.807 0.000 0.004 0.400 0.596
#> SRR1377216 4 0.5112 0.760 0.000 0.004 0.436 0.560
#> SRR1377217 4 0.5112 0.760 0.000 0.004 0.436 0.560
#> SRR1377218 4 0.5112 0.760 0.000 0.004 0.436 0.560
#> SRR1377219 4 0.5028 0.807 0.000 0.004 0.400 0.596
#> SRR1377220 4 0.5028 0.807 0.000 0.004 0.400 0.596
#> SRR1377221 4 0.5028 0.807 0.000 0.004 0.400 0.596
#> SRR1377222 4 0.2334 0.568 0.000 0.004 0.088 0.908
#> SRR1377223 4 0.2334 0.568 0.000 0.004 0.088 0.908
#> SRR1377224 4 0.2334 0.568 0.000 0.004 0.088 0.908
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377146 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377147 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377148 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377153 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377154 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377155 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377156 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377149 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377150 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377151 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377152 5 0.423 1.000 0.004 0.100 0.072 0.016 0.808
#> SRR1377157 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377158 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377159 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377160 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377161 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377162 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377163 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377164 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377169 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377170 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377171 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377172 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377165 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377166 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377167 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377168 3 0.000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377173 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377174 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377175 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377176 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377177 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377178 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377179 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377180 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377181 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377182 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377183 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377184 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377185 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377186 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377187 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377188 1 0.000 0.842 1.000 0.000 0.000 0.000 0.000
#> SRR1377189 2 0.323 0.871 0.000 0.852 0.000 0.060 0.088
#> SRR1377190 2 0.323 0.871 0.000 0.852 0.000 0.060 0.088
#> SRR1377191 2 0.323 0.871 0.000 0.852 0.000 0.060 0.088
#> SRR1377192 2 0.323 0.871 0.000 0.852 0.000 0.060 0.088
#> SRR1377193 2 0.323 0.871 0.000 0.852 0.000 0.060 0.088
#> SRR1377194 2 0.323 0.871 0.000 0.852 0.000 0.060 0.088
#> SRR1377195 1 0.810 0.425 0.412 0.260 0.000 0.136 0.192
#> SRR1377196 1 0.810 0.425 0.412 0.260 0.000 0.136 0.192
#> SRR1377197 1 0.810 0.425 0.412 0.260 0.000 0.136 0.192
#> SRR1377198 1 0.810 0.425 0.412 0.260 0.000 0.136 0.192
#> SRR1377199 1 0.810 0.425 0.412 0.260 0.000 0.136 0.192
#> SRR1377200 1 0.810 0.425 0.412 0.260 0.000 0.136 0.192
#> SRR1377201 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377202 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377203 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377204 2 0.130 0.911 0.000 0.956 0.000 0.028 0.016
#> SRR1377205 2 0.130 0.911 0.000 0.956 0.000 0.028 0.016
#> SRR1377206 2 0.130 0.911 0.000 0.956 0.000 0.028 0.016
#> SRR1377207 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377208 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377209 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377210 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377211 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377212 2 0.000 0.934 0.000 1.000 0.000 0.000 0.000
#> SRR1377213 4 0.282 0.959 0.000 0.020 0.116 0.864 0.000
#> SRR1377214 4 0.282 0.959 0.000 0.020 0.116 0.864 0.000
#> SRR1377215 4 0.282 0.959 0.000 0.020 0.116 0.864 0.000
#> SRR1377216 4 0.258 0.947 0.000 0.004 0.132 0.864 0.000
#> SRR1377217 4 0.258 0.947 0.000 0.004 0.132 0.864 0.000
#> SRR1377218 4 0.258 0.947 0.000 0.004 0.132 0.864 0.000
#> SRR1377219 4 0.282 0.959 0.000 0.020 0.116 0.864 0.000
#> SRR1377220 4 0.282 0.959 0.000 0.020 0.116 0.864 0.000
#> SRR1377221 4 0.282 0.959 0.000 0.020 0.116 0.864 0.000
#> SRR1377222 4 0.217 0.891 0.000 0.076 0.016 0.908 0.000
#> SRR1377223 4 0.217 0.891 0.000 0.076 0.016 0.908 0.000
#> SRR1377224 4 0.217 0.891 0.000 0.076 0.016 0.908 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377146 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377147 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377148 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377153 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377154 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377155 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377156 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377149 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377150 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377151 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377152 6 0.0508 1.000 0.000 0.004 0.012 0.000 0.000 0.984
#> SRR1377157 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377158 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377159 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377160 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377161 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377162 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377163 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377164 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377169 3 0.0146 0.997 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1377170 3 0.0146 0.997 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1377171 3 0.0146 0.997 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1377172 3 0.0146 0.997 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1377165 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377166 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377167 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377168 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377173 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377174 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377175 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377176 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377177 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377178 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377179 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377180 1 0.0260 0.997 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1377181 1 0.0405 0.997 0.988 0.000 0.008 0.000 0.000 0.004
#> SRR1377182 1 0.0405 0.997 0.988 0.000 0.008 0.000 0.000 0.004
#> SRR1377183 1 0.0551 0.996 0.984 0.000 0.008 0.000 0.004 0.004
#> SRR1377184 1 0.0405 0.997 0.988 0.000 0.008 0.000 0.000 0.004
#> SRR1377185 1 0.0551 0.996 0.984 0.000 0.008 0.000 0.004 0.004
#> SRR1377186 1 0.0551 0.996 0.984 0.000 0.008 0.000 0.004 0.004
#> SRR1377187 1 0.0405 0.997 0.988 0.000 0.008 0.000 0.000 0.004
#> SRR1377188 1 0.0551 0.996 0.984 0.000 0.008 0.000 0.004 0.004
#> SRR1377189 2 0.3744 0.875 0.008 0.828 0.000 0.048 0.060 0.056
#> SRR1377190 2 0.3744 0.875 0.008 0.828 0.000 0.048 0.060 0.056
#> SRR1377191 2 0.3744 0.875 0.008 0.828 0.000 0.048 0.060 0.056
#> SRR1377192 2 0.3744 0.875 0.008 0.828 0.000 0.048 0.060 0.056
#> SRR1377193 2 0.3744 0.875 0.008 0.828 0.000 0.048 0.060 0.056
#> SRR1377194 2 0.3744 0.875 0.008 0.828 0.000 0.048 0.060 0.056
#> SRR1377195 5 0.1334 1.000 0.032 0.020 0.000 0.000 0.948 0.000
#> SRR1377196 5 0.1334 1.000 0.032 0.020 0.000 0.000 0.948 0.000
#> SRR1377197 5 0.1334 1.000 0.032 0.020 0.000 0.000 0.948 0.000
#> SRR1377198 5 0.1334 1.000 0.032 0.020 0.000 0.000 0.948 0.000
#> SRR1377199 5 0.1334 1.000 0.032 0.020 0.000 0.000 0.948 0.000
#> SRR1377200 5 0.1334 1.000 0.032 0.020 0.000 0.000 0.948 0.000
#> SRR1377201 2 0.0622 0.931 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1377202 2 0.0622 0.931 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1377203 2 0.0622 0.931 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1377204 2 0.0858 0.925 0.000 0.968 0.000 0.004 0.028 0.000
#> SRR1377205 2 0.0858 0.925 0.000 0.968 0.000 0.004 0.028 0.000
#> SRR1377206 2 0.0858 0.925 0.000 0.968 0.000 0.004 0.028 0.000
#> SRR1377207 2 0.0622 0.931 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1377208 2 0.0622 0.931 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1377209 2 0.0622 0.931 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1377210 2 0.0363 0.931 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1377211 2 0.0363 0.931 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1377212 2 0.0363 0.931 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1377213 4 0.0363 0.993 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1377214 4 0.0363 0.993 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1377215 4 0.0363 0.993 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1377216 4 0.0458 0.990 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1377217 4 0.0458 0.990 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1377218 4 0.0458 0.990 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1377219 4 0.0363 0.993 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1377220 4 0.0363 0.993 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1377221 4 0.0363 0.993 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1377222 4 0.0767 0.982 0.000 0.012 0.004 0.976 0.008 0.000
#> SRR1377223 4 0.0767 0.982 0.000 0.012 0.004 0.976 0.008 0.000
#> SRR1377224 4 0.0767 0.982 0.000 0.012 0.004 0.976 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1 1.000 1.000 0.1414 0.859 0.859
#> 3 3 1 0.994 0.997 2.0955 0.706 0.658
#> 4 4 1 0.987 0.986 0.3986 0.825 0.691
#> 5 5 1 1.000 1.000 0.2434 0.848 0.612
#> 6 6 1 1.000 1.000 0.0897 0.932 0.714
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.0000 0.996 0 1.000 0.000
#> SRR1377146 2 0.0000 0.996 0 1.000 0.000
#> SRR1377147 2 0.0000 0.996 0 1.000 0.000
#> SRR1377148 2 0.0000 0.996 0 1.000 0.000
#> SRR1377153 2 0.0000 0.996 0 1.000 0.000
#> SRR1377154 2 0.0000 0.996 0 1.000 0.000
#> SRR1377155 2 0.0000 0.996 0 1.000 0.000
#> SRR1377156 2 0.0000 0.996 0 1.000 0.000
#> SRR1377149 2 0.0000 0.996 0 1.000 0.000
#> SRR1377150 2 0.0000 0.996 0 1.000 0.000
#> SRR1377151 2 0.0000 0.996 0 1.000 0.000
#> SRR1377152 2 0.0000 0.996 0 1.000 0.000
#> SRR1377157 3 0.0000 1.000 0 0.000 1.000
#> SRR1377158 3 0.0000 1.000 0 0.000 1.000
#> SRR1377159 3 0.0000 1.000 0 0.000 1.000
#> SRR1377160 3 0.0000 1.000 0 0.000 1.000
#> SRR1377161 3 0.0000 1.000 0 0.000 1.000
#> SRR1377162 3 0.0000 1.000 0 0.000 1.000
#> SRR1377163 3 0.0000 1.000 0 0.000 1.000
#> SRR1377164 3 0.0000 1.000 0 0.000 1.000
#> SRR1377169 3 0.0000 1.000 0 0.000 1.000
#> SRR1377170 3 0.0000 1.000 0 0.000 1.000
#> SRR1377171 3 0.0000 1.000 0 0.000 1.000
#> SRR1377172 3 0.0000 1.000 0 0.000 1.000
#> SRR1377165 3 0.0000 1.000 0 0.000 1.000
#> SRR1377166 3 0.0000 1.000 0 0.000 1.000
#> SRR1377167 3 0.0000 1.000 0 0.000 1.000
#> SRR1377168 3 0.0000 1.000 0 0.000 1.000
#> SRR1377173 2 0.0000 0.996 0 1.000 0.000
#> SRR1377174 2 0.0592 0.985 0 0.988 0.012
#> SRR1377175 2 0.2711 0.903 0 0.912 0.088
#> SRR1377176 2 0.0237 0.992 0 0.996 0.004
#> SRR1377177 2 0.0237 0.992 0 0.996 0.004
#> SRR1377178 2 0.1753 0.950 0 0.952 0.048
#> SRR1377179 2 0.1643 0.954 0 0.956 0.044
#> SRR1377180 2 0.1411 0.962 0 0.964 0.036
#> SRR1377181 2 0.0000 0.996 0 1.000 0.000
#> SRR1377182 2 0.0000 0.996 0 1.000 0.000
#> SRR1377183 2 0.0000 0.996 0 1.000 0.000
#> SRR1377184 2 0.0000 0.996 0 1.000 0.000
#> SRR1377185 2 0.0000 0.996 0 1.000 0.000
#> SRR1377186 2 0.0237 0.992 0 0.996 0.004
#> SRR1377187 2 0.0000 0.996 0 1.000 0.000
#> SRR1377188 2 0.0000 0.996 0 1.000 0.000
#> SRR1377189 2 0.0000 0.996 0 1.000 0.000
#> SRR1377190 2 0.0000 0.996 0 1.000 0.000
#> SRR1377191 2 0.0000 0.996 0 1.000 0.000
#> SRR1377192 2 0.0000 0.996 0 1.000 0.000
#> SRR1377193 2 0.0000 0.996 0 1.000 0.000
#> SRR1377194 2 0.0000 0.996 0 1.000 0.000
#> SRR1377195 1 0.0000 1.000 1 0.000 0.000
#> SRR1377196 1 0.0000 1.000 1 0.000 0.000
#> SRR1377197 1 0.0000 1.000 1 0.000 0.000
#> SRR1377198 1 0.0000 1.000 1 0.000 0.000
#> SRR1377199 1 0.0000 1.000 1 0.000 0.000
#> SRR1377200 1 0.0000 1.000 1 0.000 0.000
#> SRR1377201 2 0.0000 0.996 0 1.000 0.000
#> SRR1377202 2 0.0000 0.996 0 1.000 0.000
#> SRR1377203 2 0.0000 0.996 0 1.000 0.000
#> SRR1377204 2 0.0000 0.996 0 1.000 0.000
#> SRR1377205 2 0.0000 0.996 0 1.000 0.000
#> SRR1377206 2 0.0000 0.996 0 1.000 0.000
#> SRR1377207 2 0.0000 0.996 0 1.000 0.000
#> SRR1377208 2 0.0000 0.996 0 1.000 0.000
#> SRR1377209 2 0.0000 0.996 0 1.000 0.000
#> SRR1377210 2 0.0000 0.996 0 1.000 0.000
#> SRR1377211 2 0.0000 0.996 0 1.000 0.000
#> SRR1377212 2 0.0000 0.996 0 1.000 0.000
#> SRR1377213 2 0.0000 0.996 0 1.000 0.000
#> SRR1377214 2 0.0000 0.996 0 1.000 0.000
#> SRR1377215 2 0.0000 0.996 0 1.000 0.000
#> SRR1377216 2 0.0000 0.996 0 1.000 0.000
#> SRR1377217 2 0.0000 0.996 0 1.000 0.000
#> SRR1377218 2 0.0000 0.996 0 1.000 0.000
#> SRR1377219 2 0.0000 0.996 0 1.000 0.000
#> SRR1377220 2 0.0000 0.996 0 1.000 0.000
#> SRR1377221 2 0.0000 0.996 0 1.000 0.000
#> SRR1377222 2 0.0000 0.996 0 1.000 0.000
#> SRR1377223 2 0.0000 0.996 0 1.000 0.000
#> SRR1377224 2 0.0000 0.996 0 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377146 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377147 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377148 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377153 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377154 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377155 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377156 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377149 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377150 2 0.0188 0.983 0 0.996 0.000 0.004
#> SRR1377151 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377152 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377157 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377158 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377159 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377160 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377161 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377162 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377163 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377164 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377169 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377170 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377171 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377172 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377165 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377166 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377167 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377168 3 0.0000 1.000 0 0.000 1.000 0.000
#> SRR1377173 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377174 2 0.1356 0.969 0 0.960 0.008 0.032
#> SRR1377175 2 0.2871 0.908 0 0.896 0.072 0.032
#> SRR1377176 2 0.1209 0.971 0 0.964 0.004 0.032
#> SRR1377177 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377178 2 0.2036 0.951 0 0.936 0.032 0.032
#> SRR1377179 2 0.2036 0.951 0 0.936 0.032 0.032
#> SRR1377180 2 0.2036 0.951 0 0.936 0.032 0.032
#> SRR1377181 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377182 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377183 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377184 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377185 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377186 2 0.1209 0.971 0 0.964 0.004 0.032
#> SRR1377187 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377188 2 0.1022 0.973 0 0.968 0.000 0.032
#> SRR1377189 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377190 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377191 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377192 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377193 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377194 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377195 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377196 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377197 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377198 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377199 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377200 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377201 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377202 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377203 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377204 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377205 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377206 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377207 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377208 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377209 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377210 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377211 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377212 2 0.0000 0.985 0 1.000 0.000 0.000
#> SRR1377213 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377214 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377215 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377216 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377217 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377218 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377219 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377220 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377221 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377222 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377223 4 0.1022 1.000 0 0.032 0.000 0.968
#> SRR1377224 4 0.1022 1.000 0 0.032 0.000 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0 1 0 1 0 0 0
#> SRR1377146 2 0 1 0 1 0 0 0
#> SRR1377147 2 0 1 0 1 0 0 0
#> SRR1377148 2 0 1 0 1 0 0 0
#> SRR1377153 2 0 1 0 1 0 0 0
#> SRR1377154 2 0 1 0 1 0 0 0
#> SRR1377155 2 0 1 0 1 0 0 0
#> SRR1377156 2 0 1 0 1 0 0 0
#> SRR1377149 2 0 1 0 1 0 0 0
#> SRR1377150 2 0 1 0 1 0 0 0
#> SRR1377151 2 0 1 0 1 0 0 0
#> SRR1377152 2 0 1 0 1 0 0 0
#> SRR1377157 3 0 1 0 0 1 0 0
#> SRR1377158 3 0 1 0 0 1 0 0
#> SRR1377159 3 0 1 0 0 1 0 0
#> SRR1377160 3 0 1 0 0 1 0 0
#> SRR1377161 3 0 1 0 0 1 0 0
#> SRR1377162 3 0 1 0 0 1 0 0
#> SRR1377163 3 0 1 0 0 1 0 0
#> SRR1377164 3 0 1 0 0 1 0 0
#> SRR1377169 3 0 1 0 0 1 0 0
#> SRR1377170 3 0 1 0 0 1 0 0
#> SRR1377171 3 0 1 0 0 1 0 0
#> SRR1377172 3 0 1 0 0 1 0 0
#> SRR1377165 3 0 1 0 0 1 0 0
#> SRR1377166 3 0 1 0 0 1 0 0
#> SRR1377167 3 0 1 0 0 1 0 0
#> SRR1377168 3 0 1 0 0 1 0 0
#> SRR1377173 1 0 1 1 0 0 0 0
#> SRR1377174 1 0 1 1 0 0 0 0
#> SRR1377175 1 0 1 1 0 0 0 0
#> SRR1377176 1 0 1 1 0 0 0 0
#> SRR1377177 1 0 1 1 0 0 0 0
#> SRR1377178 1 0 1 1 0 0 0 0
#> SRR1377179 1 0 1 1 0 0 0 0
#> SRR1377180 1 0 1 1 0 0 0 0
#> SRR1377181 1 0 1 1 0 0 0 0
#> SRR1377182 1 0 1 1 0 0 0 0
#> SRR1377183 1 0 1 1 0 0 0 0
#> SRR1377184 1 0 1 1 0 0 0 0
#> SRR1377185 1 0 1 1 0 0 0 0
#> SRR1377186 1 0 1 1 0 0 0 0
#> SRR1377187 1 0 1 1 0 0 0 0
#> SRR1377188 1 0 1 1 0 0 0 0
#> SRR1377189 2 0 1 0 1 0 0 0
#> SRR1377190 2 0 1 0 1 0 0 0
#> SRR1377191 2 0 1 0 1 0 0 0
#> SRR1377192 2 0 1 0 1 0 0 0
#> SRR1377193 2 0 1 0 1 0 0 0
#> SRR1377194 2 0 1 0 1 0 0 0
#> SRR1377195 5 0 1 0 0 0 0 1
#> SRR1377196 5 0 1 0 0 0 0 1
#> SRR1377197 5 0 1 0 0 0 0 1
#> SRR1377198 5 0 1 0 0 0 0 1
#> SRR1377199 5 0 1 0 0 0 0 1
#> SRR1377200 5 0 1 0 0 0 0 1
#> SRR1377201 2 0 1 0 1 0 0 0
#> SRR1377202 2 0 1 0 1 0 0 0
#> SRR1377203 2 0 1 0 1 0 0 0
#> SRR1377204 2 0 1 0 1 0 0 0
#> SRR1377205 2 0 1 0 1 0 0 0
#> SRR1377206 2 0 1 0 1 0 0 0
#> SRR1377207 2 0 1 0 1 0 0 0
#> SRR1377208 2 0 1 0 1 0 0 0
#> SRR1377209 2 0 1 0 1 0 0 0
#> SRR1377210 2 0 1 0 1 0 0 0
#> SRR1377211 2 0 1 0 1 0 0 0
#> SRR1377212 2 0 1 0 1 0 0 0
#> SRR1377213 4 0 1 0 0 0 1 0
#> SRR1377214 4 0 1 0 0 0 1 0
#> SRR1377215 4 0 1 0 0 0 1 0
#> SRR1377216 4 0 1 0 0 0 1 0
#> SRR1377217 4 0 1 0 0 0 1 0
#> SRR1377218 4 0 1 0 0 0 1 0
#> SRR1377219 4 0 1 0 0 0 1 0
#> SRR1377220 4 0 1 0 0 0 1 0
#> SRR1377221 4 0 1 0 0 0 1 0
#> SRR1377222 4 0 1 0 0 0 1 0
#> SRR1377223 4 0 1 0 0 0 1 0
#> SRR1377224 4 0 1 0 0 0 1 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0 1 0 0 0 0 0 1
#> SRR1377146 6 0 1 0 0 0 0 0 1
#> SRR1377147 6 0 1 0 0 0 0 0 1
#> SRR1377148 6 0 1 0 0 0 0 0 1
#> SRR1377153 6 0 1 0 0 0 0 0 1
#> SRR1377154 6 0 1 0 0 0 0 0 1
#> SRR1377155 6 0 1 0 0 0 0 0 1
#> SRR1377156 6 0 1 0 0 0 0 0 1
#> SRR1377149 6 0 1 0 0 0 0 0 1
#> SRR1377150 6 0 1 0 0 0 0 0 1
#> SRR1377151 6 0 1 0 0 0 0 0 1
#> SRR1377152 6 0 1 0 0 0 0 0 1
#> SRR1377157 3 0 1 0 0 1 0 0 0
#> SRR1377158 3 0 1 0 0 1 0 0 0
#> SRR1377159 3 0 1 0 0 1 0 0 0
#> SRR1377160 3 0 1 0 0 1 0 0 0
#> SRR1377161 3 0 1 0 0 1 0 0 0
#> SRR1377162 3 0 1 0 0 1 0 0 0
#> SRR1377163 3 0 1 0 0 1 0 0 0
#> SRR1377164 3 0 1 0 0 1 0 0 0
#> SRR1377169 3 0 1 0 0 1 0 0 0
#> SRR1377170 3 0 1 0 0 1 0 0 0
#> SRR1377171 3 0 1 0 0 1 0 0 0
#> SRR1377172 3 0 1 0 0 1 0 0 0
#> SRR1377165 3 0 1 0 0 1 0 0 0
#> SRR1377166 3 0 1 0 0 1 0 0 0
#> SRR1377167 3 0 1 0 0 1 0 0 0
#> SRR1377168 3 0 1 0 0 1 0 0 0
#> SRR1377173 1 0 1 1 0 0 0 0 0
#> SRR1377174 1 0 1 1 0 0 0 0 0
#> SRR1377175 1 0 1 1 0 0 0 0 0
#> SRR1377176 1 0 1 1 0 0 0 0 0
#> SRR1377177 1 0 1 1 0 0 0 0 0
#> SRR1377178 1 0 1 1 0 0 0 0 0
#> SRR1377179 1 0 1 1 0 0 0 0 0
#> SRR1377180 1 0 1 1 0 0 0 0 0
#> SRR1377181 1 0 1 1 0 0 0 0 0
#> SRR1377182 1 0 1 1 0 0 0 0 0
#> SRR1377183 1 0 1 1 0 0 0 0 0
#> SRR1377184 1 0 1 1 0 0 0 0 0
#> SRR1377185 1 0 1 1 0 0 0 0 0
#> SRR1377186 1 0 1 1 0 0 0 0 0
#> SRR1377187 1 0 1 1 0 0 0 0 0
#> SRR1377188 1 0 1 1 0 0 0 0 0
#> SRR1377189 2 0 1 0 1 0 0 0 0
#> SRR1377190 2 0 1 0 1 0 0 0 0
#> SRR1377191 2 0 1 0 1 0 0 0 0
#> SRR1377192 2 0 1 0 1 0 0 0 0
#> SRR1377193 2 0 1 0 1 0 0 0 0
#> SRR1377194 2 0 1 0 1 0 0 0 0
#> SRR1377195 5 0 1 0 0 0 0 1 0
#> SRR1377196 5 0 1 0 0 0 0 1 0
#> SRR1377197 5 0 1 0 0 0 0 1 0
#> SRR1377198 5 0 1 0 0 0 0 1 0
#> SRR1377199 5 0 1 0 0 0 0 1 0
#> SRR1377200 5 0 1 0 0 0 0 1 0
#> SRR1377201 2 0 1 0 1 0 0 0 0
#> SRR1377202 2 0 1 0 1 0 0 0 0
#> SRR1377203 2 0 1 0 1 0 0 0 0
#> SRR1377204 2 0 1 0 1 0 0 0 0
#> SRR1377205 2 0 1 0 1 0 0 0 0
#> SRR1377206 2 0 1 0 1 0 0 0 0
#> SRR1377207 2 0 1 0 1 0 0 0 0
#> SRR1377208 2 0 1 0 1 0 0 0 0
#> SRR1377209 2 0 1 0 1 0 0 0 0
#> SRR1377210 2 0 1 0 1 0 0 0 0
#> SRR1377211 2 0 1 0 1 0 0 0 0
#> SRR1377212 2 0 1 0 1 0 0 0 0
#> SRR1377213 4 0 1 0 0 0 1 0 0
#> SRR1377214 4 0 1 0 0 0 1 0 0
#> SRR1377215 4 0 1 0 0 0 1 0 0
#> SRR1377216 4 0 1 0 0 0 1 0 0
#> SRR1377217 4 0 1 0 0 0 1 0 0
#> SRR1377218 4 0 1 0 0 0 1 0 0
#> SRR1377219 4 0 1 0 0 0 1 0 0
#> SRR1377220 4 0 1 0 0 0 1 0 0
#> SRR1377221 4 0 1 0 0 0 1 0 0
#> SRR1377222 4 0 1 0 0 0 1 0 0
#> SRR1377223 4 0 1 0 0 0 1 0 0
#> SRR1377224 4 0 1 0 0 0 1 0 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["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 13890 rows and 80 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.394 0.553 0.746 0.3897 0.647 0.647
#> 3 3 0.619 0.755 0.868 0.6111 0.500 0.324
#> 4 4 0.729 0.851 0.888 0.1634 0.896 0.699
#> 5 5 0.876 0.894 0.917 0.0580 0.972 0.888
#> 6 6 0.899 0.903 0.943 0.0728 0.943 0.747
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 2 0.9460 0.404 0.364 0.636
#> SRR1377146 2 0.9460 0.404 0.364 0.636
#> SRR1377147 2 0.9460 0.404 0.364 0.636
#> SRR1377148 2 0.9460 0.404 0.364 0.636
#> SRR1377153 2 0.9460 0.404 0.364 0.636
#> SRR1377154 2 0.9460 0.404 0.364 0.636
#> SRR1377155 2 0.9460 0.404 0.364 0.636
#> SRR1377156 2 0.9460 0.404 0.364 0.636
#> SRR1377149 2 0.9460 0.404 0.364 0.636
#> SRR1377150 2 0.9460 0.404 0.364 0.636
#> SRR1377151 2 0.9460 0.404 0.364 0.636
#> SRR1377152 2 0.9460 0.404 0.364 0.636
#> SRR1377157 1 0.9866 0.657 0.568 0.432
#> SRR1377158 1 0.9866 0.657 0.568 0.432
#> SRR1377159 1 0.9881 0.655 0.564 0.436
#> SRR1377160 1 0.9866 0.657 0.568 0.432
#> SRR1377161 1 0.9881 0.655 0.564 0.436
#> SRR1377162 1 0.9881 0.655 0.564 0.436
#> SRR1377163 1 0.9866 0.657 0.568 0.432
#> SRR1377164 1 0.9881 0.655 0.564 0.436
#> SRR1377169 2 0.9922 -0.331 0.448 0.552
#> SRR1377170 2 0.9944 -0.360 0.456 0.544
#> SRR1377171 2 0.9922 -0.331 0.448 0.552
#> SRR1377172 2 0.9944 -0.360 0.456 0.544
#> SRR1377165 1 0.9909 0.643 0.556 0.444
#> SRR1377166 1 0.9909 0.643 0.556 0.444
#> SRR1377167 1 0.9909 0.643 0.556 0.444
#> SRR1377168 1 0.9909 0.643 0.556 0.444
#> SRR1377173 2 0.8016 0.452 0.244 0.756
#> SRR1377174 2 0.8016 0.452 0.244 0.756
#> SRR1377175 2 0.8016 0.452 0.244 0.756
#> SRR1377176 2 0.8016 0.452 0.244 0.756
#> SRR1377177 2 0.8016 0.452 0.244 0.756
#> SRR1377178 2 0.8016 0.452 0.244 0.756
#> SRR1377179 2 0.8016 0.452 0.244 0.756
#> SRR1377180 2 0.8016 0.452 0.244 0.756
#> SRR1377181 2 0.8016 0.452 0.244 0.756
#> SRR1377182 2 0.8016 0.452 0.244 0.756
#> SRR1377183 2 0.2043 0.711 0.032 0.968
#> SRR1377184 2 0.8016 0.452 0.244 0.756
#> SRR1377185 2 0.2043 0.711 0.032 0.968
#> SRR1377186 2 0.2043 0.711 0.032 0.968
#> SRR1377187 2 0.8016 0.452 0.244 0.756
#> SRR1377188 2 0.2043 0.711 0.032 0.968
#> SRR1377189 2 0.0000 0.727 0.000 1.000
#> SRR1377190 2 0.0000 0.727 0.000 1.000
#> SRR1377191 2 0.0000 0.727 0.000 1.000
#> SRR1377192 2 0.0000 0.727 0.000 1.000
#> SRR1377193 2 0.0000 0.727 0.000 1.000
#> SRR1377194 2 0.0000 0.727 0.000 1.000
#> SRR1377195 1 0.2043 0.529 0.968 0.032
#> SRR1377196 1 0.2043 0.529 0.968 0.032
#> SRR1377197 1 0.2043 0.529 0.968 0.032
#> SRR1377198 1 0.2043 0.529 0.968 0.032
#> SRR1377199 1 0.2043 0.529 0.968 0.032
#> SRR1377200 1 0.2043 0.529 0.968 0.032
#> SRR1377201 2 0.0000 0.727 0.000 1.000
#> SRR1377202 2 0.0000 0.727 0.000 1.000
#> SRR1377203 2 0.0000 0.727 0.000 1.000
#> SRR1377204 2 0.0000 0.727 0.000 1.000
#> SRR1377205 2 0.0000 0.727 0.000 1.000
#> SRR1377206 2 0.0000 0.727 0.000 1.000
#> SRR1377207 2 0.0000 0.727 0.000 1.000
#> SRR1377208 2 0.0000 0.727 0.000 1.000
#> SRR1377209 2 0.0000 0.727 0.000 1.000
#> SRR1377210 2 0.0000 0.727 0.000 1.000
#> SRR1377211 2 0.0000 0.727 0.000 1.000
#> SRR1377212 2 0.0000 0.727 0.000 1.000
#> SRR1377213 2 0.2948 0.700 0.052 0.948
#> SRR1377214 2 0.2948 0.700 0.052 0.948
#> SRR1377215 2 0.2948 0.700 0.052 0.948
#> SRR1377216 2 0.2948 0.700 0.052 0.948
#> SRR1377217 2 0.2948 0.700 0.052 0.948
#> SRR1377218 2 0.2948 0.700 0.052 0.948
#> SRR1377219 2 0.2948 0.700 0.052 0.948
#> SRR1377220 2 0.2948 0.700 0.052 0.948
#> SRR1377221 2 0.2948 0.700 0.052 0.948
#> SRR1377222 2 0.0938 0.723 0.012 0.988
#> SRR1377223 2 0.0938 0.723 0.012 0.988
#> SRR1377224 2 0.0938 0.723 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377146 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377147 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377148 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377153 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377154 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377155 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377156 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377149 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377150 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377151 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377152 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377157 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377158 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377159 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377160 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377161 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377162 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377163 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377164 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377169 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377170 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377171 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377172 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377165 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377166 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377167 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377168 3 0.0000 0.794 0.000 0.000 1.000
#> SRR1377173 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377174 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377175 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377176 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377177 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377178 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377179 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377180 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377181 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377182 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377183 3 0.8637 0.164 0.100 0.448 0.452
#> SRR1377184 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377185 2 0.8635 -0.174 0.100 0.460 0.440
#> SRR1377186 2 0.8635 -0.174 0.100 0.460 0.440
#> SRR1377187 3 0.5325 0.758 0.004 0.248 0.748
#> SRR1377188 2 0.8635 -0.174 0.100 0.460 0.440
#> SRR1377189 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377190 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377191 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377192 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377193 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377194 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377195 1 0.0000 0.720 1.000 0.000 0.000
#> SRR1377196 1 0.0000 0.720 1.000 0.000 0.000
#> SRR1377197 1 0.0000 0.720 1.000 0.000 0.000
#> SRR1377198 1 0.0000 0.720 1.000 0.000 0.000
#> SRR1377199 1 0.0000 0.720 1.000 0.000 0.000
#> SRR1377200 1 0.0000 0.720 1.000 0.000 0.000
#> SRR1377201 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377202 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377203 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377204 1 0.6442 0.287 0.564 0.432 0.004
#> SRR1377205 1 0.6442 0.287 0.564 0.432 0.004
#> SRR1377206 1 0.6442 0.287 0.564 0.432 0.004
#> SRR1377207 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377208 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377209 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377210 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377211 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377212 2 0.0000 0.933 0.000 1.000 0.000
#> SRR1377213 1 0.6247 0.712 0.620 0.004 0.376
#> SRR1377214 1 0.6247 0.712 0.620 0.004 0.376
#> SRR1377215 1 0.6247 0.712 0.620 0.004 0.376
#> SRR1377216 1 0.6045 0.708 0.620 0.000 0.380
#> SRR1377217 1 0.6045 0.708 0.620 0.000 0.380
#> SRR1377218 1 0.6045 0.708 0.620 0.000 0.380
#> SRR1377219 1 0.6247 0.712 0.620 0.004 0.376
#> SRR1377220 1 0.6247 0.712 0.620 0.004 0.376
#> SRR1377221 1 0.6247 0.712 0.620 0.004 0.376
#> SRR1377222 1 0.4963 0.746 0.792 0.008 0.200
#> SRR1377223 1 0.4963 0.746 0.792 0.008 0.200
#> SRR1377224 1 0.4963 0.746 0.792 0.008 0.200
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377146 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377147 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377148 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377153 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377154 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377155 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377156 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377149 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377150 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377151 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377152 2 0.0921 0.937 0.000 0.972 0.000 0.028
#> SRR1377157 3 0.1557 0.942 0.056 0.000 0.944 0.000
#> SRR1377158 3 0.1557 0.942 0.056 0.000 0.944 0.000
#> SRR1377159 3 0.1557 0.942 0.056 0.000 0.944 0.000
#> SRR1377160 3 0.1557 0.942 0.056 0.000 0.944 0.000
#> SRR1377161 3 0.0921 0.963 0.028 0.000 0.972 0.000
#> SRR1377162 3 0.0921 0.963 0.028 0.000 0.972 0.000
#> SRR1377163 3 0.0921 0.963 0.028 0.000 0.972 0.000
#> SRR1377164 3 0.0921 0.963 0.028 0.000 0.972 0.000
#> SRR1377169 3 0.0336 0.964 0.000 0.000 0.992 0.008
#> SRR1377170 3 0.0336 0.964 0.000 0.000 0.992 0.008
#> SRR1377171 3 0.0336 0.964 0.000 0.000 0.992 0.008
#> SRR1377172 3 0.0336 0.964 0.000 0.000 0.992 0.008
#> SRR1377165 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> SRR1377166 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> SRR1377167 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> SRR1377168 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> SRR1377173 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377174 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377175 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377176 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377177 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377178 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377179 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377180 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377181 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377182 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377183 4 0.7527 0.498 0.108 0.312 0.032 0.548
#> SRR1377184 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377185 4 0.7527 0.498 0.108 0.312 0.032 0.548
#> SRR1377186 4 0.7527 0.498 0.108 0.312 0.032 0.548
#> SRR1377187 4 0.2589 0.868 0.000 0.000 0.116 0.884
#> SRR1377188 4 0.7527 0.498 0.108 0.312 0.032 0.548
#> SRR1377189 2 0.3356 0.782 0.000 0.824 0.000 0.176
#> SRR1377190 2 0.3356 0.782 0.000 0.824 0.000 0.176
#> SRR1377191 2 0.3356 0.782 0.000 0.824 0.000 0.176
#> SRR1377192 2 0.3356 0.782 0.000 0.824 0.000 0.176
#> SRR1377193 2 0.3356 0.782 0.000 0.824 0.000 0.176
#> SRR1377194 2 0.3356 0.782 0.000 0.824 0.000 0.176
#> SRR1377195 1 0.2149 0.790 0.912 0.000 0.000 0.088
#> SRR1377196 1 0.2149 0.790 0.912 0.000 0.000 0.088
#> SRR1377197 1 0.2149 0.790 0.912 0.000 0.000 0.088
#> SRR1377198 1 0.2149 0.790 0.912 0.000 0.000 0.088
#> SRR1377199 1 0.2149 0.790 0.912 0.000 0.000 0.088
#> SRR1377200 1 0.2149 0.790 0.912 0.000 0.000 0.088
#> SRR1377201 2 0.0000 0.935 0.000 1.000 0.000 0.000
#> SRR1377202 2 0.0000 0.935 0.000 1.000 0.000 0.000
#> SRR1377203 2 0.0000 0.935 0.000 1.000 0.000 0.000
#> SRR1377204 1 0.5772 0.646 0.708 0.116 0.000 0.176
#> SRR1377205 1 0.5772 0.646 0.708 0.116 0.000 0.176
#> SRR1377206 1 0.5772 0.646 0.708 0.116 0.000 0.176
#> SRR1377207 2 0.0000 0.935 0.000 1.000 0.000 0.000
#> SRR1377208 2 0.0000 0.935 0.000 1.000 0.000 0.000
#> SRR1377209 2 0.0000 0.935 0.000 1.000 0.000 0.000
#> SRR1377210 2 0.0188 0.934 0.000 0.996 0.000 0.004
#> SRR1377211 2 0.0188 0.934 0.000 0.996 0.000 0.004
#> SRR1377212 2 0.0188 0.934 0.000 0.996 0.000 0.004
#> SRR1377213 1 0.3444 0.801 0.816 0.000 0.184 0.000
#> SRR1377214 1 0.3444 0.801 0.816 0.000 0.184 0.000
#> SRR1377215 1 0.3444 0.801 0.816 0.000 0.184 0.000
#> SRR1377216 1 0.4072 0.741 0.748 0.000 0.252 0.000
#> SRR1377217 1 0.4072 0.741 0.748 0.000 0.252 0.000
#> SRR1377218 1 0.4072 0.741 0.748 0.000 0.252 0.000
#> SRR1377219 1 0.3444 0.801 0.816 0.000 0.184 0.000
#> SRR1377220 1 0.3444 0.801 0.816 0.000 0.184 0.000
#> SRR1377221 1 0.3444 0.801 0.816 0.000 0.184 0.000
#> SRR1377222 1 0.3681 0.768 0.816 0.176 0.008 0.000
#> SRR1377223 1 0.3681 0.768 0.816 0.176 0.008 0.000
#> SRR1377224 1 0.3681 0.768 0.816 0.176 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377146 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377147 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377148 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377153 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377154 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377155 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377156 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377149 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377150 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377151 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377152 2 0.3399 0.892 0.020 0.812 0.000 0.000 0.168
#> SRR1377157 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377158 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377159 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377160 3 0.0162 0.996 0.000 0.000 0.996 0.004 0.000
#> SRR1377161 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377162 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377163 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377164 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377169 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377170 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377171 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377172 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000
#> SRR1377165 3 0.0162 0.996 0.000 0.000 0.996 0.004 0.000
#> SRR1377166 3 0.0162 0.996 0.000 0.000 0.996 0.004 0.000
#> SRR1377167 3 0.0162 0.996 0.000 0.000 0.996 0.004 0.000
#> SRR1377168 3 0.0162 0.996 0.000 0.000 0.996 0.004 0.000
#> SRR1377173 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377174 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377175 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377176 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377177 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377178 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377179 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377180 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377181 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377182 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377183 1 0.6462 0.496 0.552 0.304 0.028 0.116 0.000
#> SRR1377184 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377185 1 0.6462 0.496 0.552 0.304 0.028 0.116 0.000
#> SRR1377186 1 0.6462 0.496 0.552 0.304 0.028 0.116 0.000
#> SRR1377187 1 0.0794 0.852 0.972 0.000 0.028 0.000 0.000
#> SRR1377188 1 0.6462 0.496 0.552 0.304 0.028 0.116 0.000
#> SRR1377189 2 0.0833 0.904 0.004 0.976 0.004 0.016 0.000
#> SRR1377190 2 0.0833 0.904 0.004 0.976 0.004 0.016 0.000
#> SRR1377191 2 0.1153 0.899 0.004 0.964 0.008 0.024 0.000
#> SRR1377192 2 0.0566 0.906 0.004 0.984 0.000 0.012 0.000
#> SRR1377193 2 0.0451 0.907 0.004 0.988 0.000 0.008 0.000
#> SRR1377194 2 0.0451 0.907 0.004 0.988 0.000 0.008 0.000
#> SRR1377195 5 0.2813 1.000 0.000 0.000 0.000 0.168 0.832
#> SRR1377196 5 0.2813 1.000 0.000 0.000 0.000 0.168 0.832
#> SRR1377197 5 0.2813 1.000 0.000 0.000 0.000 0.168 0.832
#> SRR1377198 5 0.2813 1.000 0.000 0.000 0.000 0.168 0.832
#> SRR1377199 5 0.2813 1.000 0.000 0.000 0.000 0.168 0.832
#> SRR1377200 5 0.2813 1.000 0.000 0.000 0.000 0.168 0.832
#> SRR1377201 2 0.0613 0.908 0.004 0.984 0.008 0.004 0.000
#> SRR1377202 2 0.0613 0.908 0.004 0.984 0.008 0.004 0.000
#> SRR1377203 2 0.0451 0.908 0.004 0.988 0.008 0.000 0.000
#> SRR1377204 4 0.1568 0.890 0.020 0.036 0.000 0.944 0.000
#> SRR1377205 4 0.1568 0.890 0.020 0.036 0.000 0.944 0.000
#> SRR1377206 4 0.1568 0.890 0.020 0.036 0.000 0.944 0.000
#> SRR1377207 2 0.0854 0.905 0.012 0.976 0.004 0.008 0.000
#> SRR1377208 2 0.0854 0.905 0.012 0.976 0.004 0.008 0.000
#> SRR1377209 2 0.0854 0.905 0.012 0.976 0.004 0.008 0.000
#> SRR1377210 2 0.0290 0.909 0.000 0.992 0.008 0.000 0.000
#> SRR1377211 2 0.0290 0.909 0.000 0.992 0.008 0.000 0.000
#> SRR1377212 2 0.0290 0.909 0.000 0.992 0.008 0.000 0.000
#> SRR1377213 4 0.0290 0.914 0.000 0.008 0.000 0.992 0.000
#> SRR1377214 4 0.0290 0.914 0.000 0.008 0.000 0.992 0.000
#> SRR1377215 4 0.0290 0.914 0.000 0.008 0.000 0.992 0.000
#> SRR1377216 4 0.4557 0.718 0.028 0.044 0.160 0.768 0.000
#> SRR1377217 4 0.4557 0.718 0.028 0.044 0.160 0.768 0.000
#> SRR1377218 4 0.4557 0.718 0.028 0.044 0.160 0.768 0.000
#> SRR1377219 4 0.0290 0.914 0.000 0.008 0.000 0.992 0.000
#> SRR1377220 4 0.0290 0.914 0.000 0.008 0.000 0.992 0.000
#> SRR1377221 4 0.0290 0.914 0.000 0.008 0.000 0.992 0.000
#> SRR1377222 4 0.0671 0.908 0.016 0.004 0.000 0.980 0.000
#> SRR1377223 4 0.0671 0.908 0.016 0.004 0.000 0.980 0.000
#> SRR1377224 4 0.0671 0.908 0.016 0.004 0.000 0.980 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377146 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377147 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377148 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377153 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377154 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377155 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377156 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377149 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377150 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377151 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377152 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377157 3 0.0000 0.944 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377158 3 0.0000 0.944 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377159 3 0.0000 0.944 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377160 3 0.0000 0.944 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377161 3 0.1556 0.944 0.080 0.000 0.920 0.000 0 0.000
#> SRR1377162 3 0.1556 0.944 0.080 0.000 0.920 0.000 0 0.000
#> SRR1377163 3 0.1556 0.944 0.080 0.000 0.920 0.000 0 0.000
#> SRR1377164 3 0.1556 0.944 0.080 0.000 0.920 0.000 0 0.000
#> SRR1377169 3 0.1765 0.936 0.096 0.000 0.904 0.000 0 0.000
#> SRR1377170 3 0.1765 0.936 0.096 0.000 0.904 0.000 0 0.000
#> SRR1377171 3 0.1863 0.929 0.104 0.000 0.896 0.000 0 0.000
#> SRR1377172 3 0.1663 0.941 0.088 0.000 0.912 0.000 0 0.000
#> SRR1377165 3 0.0146 0.945 0.004 0.000 0.996 0.000 0 0.000
#> SRR1377166 3 0.0146 0.945 0.004 0.000 0.996 0.000 0 0.000
#> SRR1377167 3 0.0146 0.945 0.004 0.000 0.996 0.000 0 0.000
#> SRR1377168 3 0.0146 0.945 0.004 0.000 0.996 0.000 0 0.000
#> SRR1377173 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377174 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377175 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377176 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377177 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377178 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377179 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377180 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377181 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377182 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377183 1 0.5629 0.558 0.580 0.304 0.020 0.088 0 0.008
#> SRR1377184 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377185 1 0.5629 0.558 0.580 0.304 0.020 0.088 0 0.008
#> SRR1377186 1 0.5629 0.558 0.580 0.304 0.020 0.088 0 0.008
#> SRR1377187 1 0.0000 0.877 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377188 1 0.5629 0.558 0.580 0.304 0.020 0.088 0 0.008
#> SRR1377189 2 0.0000 0.913 0.000 1.000 0.000 0.000 0 0.000
#> SRR1377190 2 0.0000 0.913 0.000 1.000 0.000 0.000 0 0.000
#> SRR1377191 2 0.0146 0.912 0.000 0.996 0.000 0.004 0 0.000
#> SRR1377192 2 0.0748 0.915 0.004 0.976 0.000 0.004 0 0.016
#> SRR1377193 2 0.0748 0.915 0.004 0.976 0.000 0.004 0 0.016
#> SRR1377194 2 0.0748 0.915 0.004 0.976 0.000 0.004 0 0.016
#> SRR1377195 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377196 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377197 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377198 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377199 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377200 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377201 2 0.2260 0.882 0.000 0.860 0.000 0.000 0 0.140
#> SRR1377202 2 0.2260 0.882 0.000 0.860 0.000 0.000 0 0.140
#> SRR1377203 2 0.2260 0.882 0.000 0.860 0.000 0.000 0 0.140
#> SRR1377204 4 0.2178 0.845 0.000 0.132 0.000 0.868 0 0.000
#> SRR1377205 4 0.2178 0.845 0.000 0.132 0.000 0.868 0 0.000
#> SRR1377206 4 0.2178 0.845 0.000 0.132 0.000 0.868 0 0.000
#> SRR1377207 2 0.2260 0.882 0.000 0.860 0.000 0.000 0 0.140
#> SRR1377208 2 0.2260 0.882 0.000 0.860 0.000 0.000 0 0.140
#> SRR1377209 2 0.2260 0.882 0.000 0.860 0.000 0.000 0 0.140
#> SRR1377210 2 0.0458 0.917 0.000 0.984 0.000 0.000 0 0.016
#> SRR1377211 2 0.0458 0.917 0.000 0.984 0.000 0.000 0 0.016
#> SRR1377212 2 0.0458 0.917 0.000 0.984 0.000 0.000 0 0.016
#> SRR1377213 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377214 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377215 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377216 4 0.4522 0.741 0.008 0.104 0.168 0.720 0 0.000
#> SRR1377217 4 0.4522 0.741 0.008 0.104 0.168 0.720 0 0.000
#> SRR1377218 4 0.4522 0.741 0.008 0.104 0.168 0.720 0 0.000
#> SRR1377219 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377220 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377221 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377222 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377223 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377224 4 0.0000 0.907 0.000 0.000 0.000 1.000 0 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13890 rows and 80 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.984 0.988 0.1480 0.859 0.859
#> 3 3 0.370 0.723 0.854 2.1775 0.620 0.558
#> 4 4 0.457 0.791 0.793 0.2947 0.720 0.492
#> 5 5 0.715 0.808 0.852 0.1218 0.961 0.889
#> 6 6 0.709 0.777 0.748 0.0891 0.919 0.748
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
#> SRR1377145 2 0.0000 0.990 0.000 1.000
#> SRR1377146 2 0.0000 0.990 0.000 1.000
#> SRR1377147 2 0.0000 0.990 0.000 1.000
#> SRR1377148 2 0.0000 0.990 0.000 1.000
#> SRR1377153 2 0.0000 0.990 0.000 1.000
#> SRR1377154 2 0.0000 0.990 0.000 1.000
#> SRR1377155 2 0.0000 0.990 0.000 1.000
#> SRR1377156 2 0.0000 0.990 0.000 1.000
#> SRR1377149 2 0.0000 0.990 0.000 1.000
#> SRR1377150 2 0.0000 0.990 0.000 1.000
#> SRR1377151 2 0.0000 0.990 0.000 1.000
#> SRR1377152 2 0.0000 0.990 0.000 1.000
#> SRR1377157 2 0.0000 0.990 0.000 1.000
#> SRR1377158 2 0.0000 0.990 0.000 1.000
#> SRR1377159 2 0.0000 0.990 0.000 1.000
#> SRR1377160 2 0.0000 0.990 0.000 1.000
#> SRR1377161 2 0.0000 0.990 0.000 1.000
#> SRR1377162 2 0.0000 0.990 0.000 1.000
#> SRR1377163 2 0.0000 0.990 0.000 1.000
#> SRR1377164 2 0.0000 0.990 0.000 1.000
#> SRR1377169 2 0.0000 0.990 0.000 1.000
#> SRR1377170 2 0.0000 0.990 0.000 1.000
#> SRR1377171 2 0.0000 0.990 0.000 1.000
#> SRR1377172 2 0.0000 0.990 0.000 1.000
#> SRR1377165 2 0.0000 0.990 0.000 1.000
#> SRR1377166 2 0.0000 0.990 0.000 1.000
#> SRR1377167 2 0.0000 0.990 0.000 1.000
#> SRR1377168 2 0.0000 0.990 0.000 1.000
#> SRR1377173 2 0.0000 0.990 0.000 1.000
#> SRR1377174 2 0.0000 0.990 0.000 1.000
#> SRR1377175 2 0.0000 0.990 0.000 1.000
#> SRR1377176 2 0.0000 0.990 0.000 1.000
#> SRR1377177 2 0.0000 0.990 0.000 1.000
#> SRR1377178 2 0.0000 0.990 0.000 1.000
#> SRR1377179 2 0.0000 0.990 0.000 1.000
#> SRR1377180 2 0.0000 0.990 0.000 1.000
#> SRR1377181 2 0.0000 0.990 0.000 1.000
#> SRR1377182 2 0.0000 0.990 0.000 1.000
#> SRR1377183 2 0.0000 0.990 0.000 1.000
#> SRR1377184 2 0.0000 0.990 0.000 1.000
#> SRR1377185 2 0.0000 0.990 0.000 1.000
#> SRR1377186 2 0.0000 0.990 0.000 1.000
#> SRR1377187 2 0.0000 0.990 0.000 1.000
#> SRR1377188 2 0.0000 0.990 0.000 1.000
#> SRR1377189 2 0.0376 0.989 0.004 0.996
#> SRR1377190 2 0.0376 0.989 0.004 0.996
#> SRR1377191 2 0.0376 0.989 0.004 0.996
#> SRR1377192 2 0.2423 0.969 0.040 0.960
#> SRR1377193 2 0.2423 0.969 0.040 0.960
#> SRR1377194 2 0.2423 0.969 0.040 0.960
#> SRR1377195 1 0.2043 0.977 0.968 0.032
#> SRR1377196 1 0.1184 0.966 0.984 0.016
#> SRR1377197 1 0.2423 0.978 0.960 0.040
#> SRR1377198 1 0.3114 0.979 0.944 0.056
#> SRR1377199 1 0.3431 0.975 0.936 0.064
#> SRR1377200 1 0.3733 0.968 0.928 0.072
#> SRR1377201 2 0.1414 0.981 0.020 0.980
#> SRR1377202 2 0.1414 0.981 0.020 0.980
#> SRR1377203 2 0.1414 0.981 0.020 0.980
#> SRR1377204 2 0.2423 0.969 0.040 0.960
#> SRR1377205 2 0.2423 0.969 0.040 0.960
#> SRR1377206 2 0.2423 0.969 0.040 0.960
#> SRR1377207 2 0.0376 0.989 0.004 0.996
#> SRR1377208 2 0.0376 0.989 0.004 0.996
#> SRR1377209 2 0.0376 0.989 0.004 0.996
#> SRR1377210 2 0.0376 0.989 0.004 0.996
#> SRR1377211 2 0.0376 0.989 0.004 0.996
#> SRR1377212 2 0.0376 0.989 0.004 0.996
#> SRR1377213 2 0.2423 0.969 0.040 0.960
#> SRR1377214 2 0.2423 0.969 0.040 0.960
#> SRR1377215 2 0.2423 0.969 0.040 0.960
#> SRR1377216 2 0.0376 0.989 0.004 0.996
#> SRR1377217 2 0.0376 0.989 0.004 0.996
#> SRR1377218 2 0.0376 0.989 0.004 0.996
#> SRR1377219 2 0.2423 0.969 0.040 0.960
#> SRR1377220 2 0.2423 0.969 0.040 0.960
#> SRR1377221 2 0.2423 0.969 0.040 0.960
#> SRR1377222 2 0.2423 0.969 0.040 0.960
#> SRR1377223 2 0.2423 0.969 0.040 0.960
#> SRR1377224 2 0.2423 0.969 0.040 0.960
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 3 0.5254 0.635 0.000 0.264 0.736
#> SRR1377146 3 0.5216 0.642 0.000 0.260 0.740
#> SRR1377147 3 0.5216 0.642 0.000 0.260 0.740
#> SRR1377148 3 0.5254 0.635 0.000 0.264 0.736
#> SRR1377153 3 0.4931 0.677 0.000 0.232 0.768
#> SRR1377154 3 0.4887 0.681 0.000 0.228 0.772
#> SRR1377155 3 0.4887 0.681 0.000 0.228 0.772
#> SRR1377156 3 0.4931 0.677 0.000 0.232 0.768
#> SRR1377149 3 0.5138 0.653 0.000 0.252 0.748
#> SRR1377150 3 0.5138 0.653 0.000 0.252 0.748
#> SRR1377151 3 0.5058 0.663 0.000 0.244 0.756
#> SRR1377152 3 0.5138 0.653 0.000 0.252 0.748
#> SRR1377157 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377158 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377159 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377160 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377161 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377162 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377163 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377164 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377169 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377170 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377171 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377172 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377165 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377166 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377167 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377168 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377173 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377174 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377175 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377176 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377177 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377178 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377179 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377180 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377181 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377182 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377183 3 0.0747 0.845 0.000 0.016 0.984
#> SRR1377184 3 0.0237 0.850 0.000 0.004 0.996
#> SRR1377185 3 0.0747 0.845 0.000 0.016 0.984
#> SRR1377186 3 0.0747 0.845 0.000 0.016 0.984
#> SRR1377187 3 0.0000 0.850 0.000 0.000 1.000
#> SRR1377188 3 0.0747 0.845 0.000 0.016 0.984
#> SRR1377189 2 0.5859 0.583 0.000 0.656 0.344
#> SRR1377190 2 0.6008 0.529 0.000 0.628 0.372
#> SRR1377191 2 0.6235 0.369 0.000 0.564 0.436
#> SRR1377192 2 0.3038 0.719 0.000 0.896 0.104
#> SRR1377193 2 0.3038 0.719 0.000 0.896 0.104
#> SRR1377194 2 0.3038 0.719 0.000 0.896 0.104
#> SRR1377195 1 0.0237 0.955 0.996 0.004 0.000
#> SRR1377196 1 0.0237 0.956 0.996 0.004 0.000
#> SRR1377197 1 0.0424 0.956 0.992 0.008 0.000
#> SRR1377198 1 0.3983 0.920 0.884 0.068 0.048
#> SRR1377199 1 0.3031 0.945 0.912 0.076 0.012
#> SRR1377200 1 0.3755 0.905 0.872 0.120 0.008
#> SRR1377201 2 0.5591 0.643 0.000 0.696 0.304
#> SRR1377202 2 0.5621 0.638 0.000 0.692 0.308
#> SRR1377203 2 0.5591 0.643 0.000 0.696 0.304
#> SRR1377204 2 0.2959 0.716 0.000 0.900 0.100
#> SRR1377205 2 0.2959 0.716 0.000 0.900 0.100
#> SRR1377206 2 0.2959 0.716 0.000 0.900 0.100
#> SRR1377207 3 0.6180 0.220 0.000 0.416 0.584
#> SRR1377208 3 0.6140 0.267 0.000 0.404 0.596
#> SRR1377209 3 0.6154 0.252 0.000 0.408 0.592
#> SRR1377210 2 0.6307 0.189 0.000 0.512 0.488
#> SRR1377211 2 0.6305 0.205 0.000 0.516 0.484
#> SRR1377212 2 0.6309 0.136 0.000 0.500 0.500
#> SRR1377213 2 0.4931 0.735 0.000 0.768 0.232
#> SRR1377214 2 0.4931 0.735 0.000 0.768 0.232
#> SRR1377215 2 0.4931 0.735 0.000 0.768 0.232
#> SRR1377216 3 0.5327 0.495 0.000 0.272 0.728
#> SRR1377217 3 0.5291 0.506 0.000 0.268 0.732
#> SRR1377218 3 0.5254 0.515 0.000 0.264 0.736
#> SRR1377219 2 0.4931 0.735 0.000 0.768 0.232
#> SRR1377220 2 0.4931 0.735 0.000 0.768 0.232
#> SRR1377221 2 0.4931 0.735 0.000 0.768 0.232
#> SRR1377222 2 0.4887 0.736 0.000 0.772 0.228
#> SRR1377223 2 0.4887 0.736 0.000 0.772 0.228
#> SRR1377224 2 0.4887 0.736 0.000 0.772 0.228
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.4372 0.8139 0.000 0.728 0.268 0.004
#> SRR1377146 2 0.4372 0.8139 0.000 0.728 0.268 0.004
#> SRR1377147 2 0.4401 0.8122 0.000 0.724 0.272 0.004
#> SRR1377148 2 0.4401 0.8122 0.000 0.724 0.272 0.004
#> SRR1377153 2 0.4456 0.8052 0.000 0.716 0.280 0.004
#> SRR1377154 2 0.4456 0.8052 0.000 0.716 0.280 0.004
#> SRR1377155 2 0.4483 0.8001 0.000 0.712 0.284 0.004
#> SRR1377156 2 0.4456 0.8052 0.000 0.716 0.280 0.004
#> SRR1377149 2 0.4428 0.8088 0.000 0.720 0.276 0.004
#> SRR1377150 2 0.4401 0.8122 0.000 0.724 0.272 0.004
#> SRR1377151 2 0.4401 0.8122 0.000 0.724 0.272 0.004
#> SRR1377152 2 0.4428 0.8088 0.000 0.720 0.276 0.004
#> SRR1377157 3 0.0779 0.8735 0.000 0.004 0.980 0.016
#> SRR1377158 3 0.0657 0.8744 0.000 0.004 0.984 0.012
#> SRR1377159 3 0.0657 0.8744 0.000 0.004 0.984 0.012
#> SRR1377160 3 0.0657 0.8744 0.000 0.004 0.984 0.012
#> SRR1377161 3 0.0895 0.8727 0.000 0.004 0.976 0.020
#> SRR1377162 3 0.1004 0.8709 0.000 0.004 0.972 0.024
#> SRR1377163 3 0.0779 0.8735 0.000 0.004 0.980 0.016
#> SRR1377164 3 0.0895 0.8727 0.000 0.004 0.976 0.020
#> SRR1377169 3 0.0895 0.8728 0.000 0.004 0.976 0.020
#> SRR1377170 3 0.1004 0.8712 0.000 0.004 0.972 0.024
#> SRR1377171 3 0.0657 0.8744 0.000 0.004 0.984 0.012
#> SRR1377172 3 0.1109 0.8686 0.000 0.004 0.968 0.028
#> SRR1377165 3 0.0895 0.8726 0.000 0.004 0.976 0.020
#> SRR1377166 3 0.1004 0.8709 0.000 0.004 0.972 0.024
#> SRR1377167 3 0.0895 0.8726 0.000 0.004 0.976 0.020
#> SRR1377168 3 0.1004 0.8709 0.000 0.004 0.972 0.024
#> SRR1377173 3 0.2542 0.8567 0.000 0.084 0.904 0.012
#> SRR1377174 3 0.2402 0.8606 0.000 0.076 0.912 0.012
#> SRR1377175 3 0.2473 0.8590 0.000 0.080 0.908 0.012
#> SRR1377176 3 0.2473 0.8590 0.000 0.080 0.908 0.012
#> SRR1377177 3 0.2867 0.8422 0.000 0.104 0.884 0.012
#> SRR1377178 3 0.2928 0.8390 0.000 0.108 0.880 0.012
#> SRR1377179 3 0.2867 0.8426 0.000 0.104 0.884 0.012
#> SRR1377180 3 0.2805 0.8453 0.000 0.100 0.888 0.012
#> SRR1377181 3 0.2101 0.8660 0.000 0.060 0.928 0.012
#> SRR1377182 3 0.2300 0.8665 0.000 0.064 0.920 0.016
#> SRR1377183 3 0.3024 0.7977 0.000 0.148 0.852 0.000
#> SRR1377184 3 0.2255 0.8636 0.000 0.068 0.920 0.012
#> SRR1377185 3 0.3123 0.7873 0.000 0.156 0.844 0.000
#> SRR1377186 3 0.3172 0.7816 0.000 0.160 0.840 0.000
#> SRR1377187 3 0.2179 0.8647 0.000 0.064 0.924 0.012
#> SRR1377188 3 0.3172 0.7816 0.000 0.160 0.840 0.000
#> SRR1377189 2 0.2530 0.7966 0.000 0.888 0.112 0.000
#> SRR1377190 2 0.2647 0.8049 0.000 0.880 0.120 0.000
#> SRR1377191 2 0.3074 0.8282 0.000 0.848 0.152 0.000
#> SRR1377192 2 0.1975 0.6584 0.000 0.936 0.016 0.048
#> SRR1377193 2 0.1888 0.6628 0.000 0.940 0.016 0.044
#> SRR1377194 2 0.1975 0.6584 0.000 0.936 0.016 0.048
#> SRR1377195 1 0.1209 0.9263 0.964 0.004 0.000 0.032
#> SRR1377196 1 0.1109 0.9261 0.968 0.004 0.000 0.028
#> SRR1377197 1 0.0779 0.9269 0.980 0.004 0.000 0.016
#> SRR1377198 1 0.4037 0.9049 0.828 0.024 0.008 0.140
#> SRR1377199 1 0.4707 0.8851 0.760 0.036 0.000 0.204
#> SRR1377200 1 0.5288 0.8616 0.740 0.060 0.004 0.196
#> SRR1377201 2 0.2814 0.8158 0.000 0.868 0.132 0.000
#> SRR1377202 2 0.2921 0.8218 0.000 0.860 0.140 0.000
#> SRR1377203 2 0.2921 0.8220 0.000 0.860 0.140 0.000
#> SRR1377204 2 0.5069 0.0780 0.000 0.664 0.016 0.320
#> SRR1377205 2 0.5069 0.0780 0.000 0.664 0.016 0.320
#> SRR1377206 2 0.5069 0.0780 0.000 0.664 0.016 0.320
#> SRR1377207 2 0.3486 0.8356 0.000 0.812 0.188 0.000
#> SRR1377208 2 0.3610 0.8357 0.000 0.800 0.200 0.000
#> SRR1377209 2 0.3528 0.8358 0.000 0.808 0.192 0.000
#> SRR1377210 2 0.3074 0.8282 0.000 0.848 0.152 0.000
#> SRR1377211 2 0.3123 0.8298 0.000 0.844 0.156 0.000
#> SRR1377212 2 0.3172 0.8308 0.000 0.840 0.160 0.000
#> SRR1377213 4 0.6703 0.9899 0.000 0.232 0.156 0.612
#> SRR1377214 4 0.6703 0.9899 0.000 0.232 0.156 0.612
#> SRR1377215 4 0.6703 0.9899 0.000 0.232 0.156 0.612
#> SRR1377216 3 0.6806 -0.0691 0.000 0.112 0.544 0.344
#> SRR1377217 3 0.6759 -0.0515 0.000 0.108 0.548 0.344
#> SRR1377218 3 0.6806 -0.0691 0.000 0.112 0.544 0.344
#> SRR1377219 4 0.6742 0.9861 0.000 0.232 0.160 0.608
#> SRR1377220 4 0.6742 0.9861 0.000 0.232 0.160 0.608
#> SRR1377221 4 0.6703 0.9899 0.000 0.232 0.156 0.612
#> SRR1377222 4 0.6705 0.9820 0.000 0.244 0.148 0.608
#> SRR1377223 4 0.6705 0.9820 0.000 0.244 0.148 0.608
#> SRR1377224 4 0.6705 0.9820 0.000 0.244 0.148 0.608
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.3827 0.833 NA 0.816 0.068 0.004 0.000
#> SRR1377146 2 0.3827 0.833 NA 0.816 0.068 0.004 0.000
#> SRR1377147 2 0.3731 0.832 NA 0.816 0.072 0.000 0.000
#> SRR1377148 2 0.3827 0.833 NA 0.816 0.068 0.004 0.000
#> SRR1377153 2 0.3955 0.823 NA 0.800 0.084 0.000 0.000
#> SRR1377154 2 0.3955 0.823 NA 0.800 0.084 0.000 0.000
#> SRR1377155 2 0.3906 0.824 NA 0.804 0.084 0.000 0.000
#> SRR1377156 2 0.3955 0.823 NA 0.800 0.084 0.000 0.000
#> SRR1377149 2 0.3791 0.830 NA 0.812 0.076 0.000 0.000
#> SRR1377150 2 0.3791 0.830 NA 0.812 0.076 0.000 0.000
#> SRR1377151 2 0.3849 0.827 NA 0.808 0.080 0.000 0.000
#> SRR1377152 2 0.3791 0.830 NA 0.812 0.076 0.000 0.000
#> SRR1377157 3 0.0451 0.846 NA 0.008 0.988 0.004 0.000
#> SRR1377158 3 0.0451 0.846 NA 0.008 0.988 0.004 0.000
#> SRR1377159 3 0.0451 0.846 NA 0.008 0.988 0.004 0.000
#> SRR1377160 3 0.0451 0.846 NA 0.008 0.988 0.004 0.000
#> SRR1377161 3 0.0740 0.844 NA 0.008 0.980 0.008 0.000
#> SRR1377162 3 0.0727 0.844 NA 0.012 0.980 0.004 0.000
#> SRR1377163 3 0.0854 0.842 NA 0.012 0.976 0.008 0.000
#> SRR1377164 3 0.0727 0.844 NA 0.012 0.980 0.004 0.000
#> SRR1377169 3 0.0727 0.844 NA 0.012 0.980 0.004 0.000
#> SRR1377170 3 0.0854 0.842 NA 0.012 0.976 0.008 0.000
#> SRR1377171 3 0.0727 0.844 NA 0.012 0.980 0.004 0.000
#> SRR1377172 3 0.0854 0.842 NA 0.012 0.976 0.008 0.000
#> SRR1377165 3 0.0613 0.845 NA 0.008 0.984 0.004 0.000
#> SRR1377166 3 0.0613 0.845 NA 0.008 0.984 0.004 0.000
#> SRR1377167 3 0.0613 0.845 NA 0.008 0.984 0.004 0.000
#> SRR1377168 3 0.0613 0.845 NA 0.008 0.984 0.004 0.000
#> SRR1377173 3 0.4326 0.839 NA 0.056 0.772 0.008 0.000
#> SRR1377174 3 0.4293 0.839 NA 0.064 0.772 0.004 0.000
#> SRR1377175 3 0.4181 0.841 NA 0.052 0.784 0.008 0.000
#> SRR1377176 3 0.4166 0.841 NA 0.056 0.780 0.004 0.000
#> SRR1377177 3 0.4564 0.828 NA 0.072 0.748 0.004 0.000
#> SRR1377178 3 0.4625 0.828 NA 0.068 0.748 0.008 0.000
#> SRR1377179 3 0.4370 0.836 NA 0.064 0.764 0.004 0.000
#> SRR1377180 3 0.4505 0.830 NA 0.068 0.752 0.004 0.000
#> SRR1377181 3 0.4326 0.839 NA 0.056 0.772 0.008 0.000
#> SRR1377182 3 0.4326 0.839 NA 0.056 0.772 0.008 0.000
#> SRR1377183 3 0.5173 0.792 NA 0.104 0.704 0.008 0.000
#> SRR1377184 3 0.4429 0.836 NA 0.060 0.764 0.008 0.000
#> SRR1377185 3 0.5220 0.789 NA 0.108 0.700 0.008 0.000
#> SRR1377186 3 0.5253 0.785 NA 0.108 0.696 0.008 0.000
#> SRR1377187 3 0.4298 0.839 NA 0.052 0.772 0.008 0.000
#> SRR1377188 3 0.5206 0.789 NA 0.104 0.700 0.008 0.000
#> SRR1377189 2 0.1216 0.855 NA 0.960 0.020 0.020 0.000
#> SRR1377190 2 0.1012 0.857 NA 0.968 0.020 0.012 0.000
#> SRR1377191 2 0.1059 0.857 NA 0.968 0.020 0.008 0.000
#> SRR1377192 2 0.1341 0.829 NA 0.944 0.000 0.056 0.000
#> SRR1377193 2 0.1662 0.832 NA 0.936 0.004 0.056 0.000
#> SRR1377194 2 0.1717 0.835 NA 0.936 0.004 0.052 0.000
#> SRR1377195 5 0.1768 0.870 NA 0.000 0.000 0.004 0.924
#> SRR1377196 5 0.1864 0.870 NA 0.004 0.000 0.004 0.924
#> SRR1377197 5 0.0955 0.871 NA 0.000 0.000 0.004 0.968
#> SRR1377198 5 0.4777 0.825 NA 0.016 0.000 0.028 0.696
#> SRR1377199 5 0.4656 0.788 NA 0.004 0.004 0.004 0.576
#> SRR1377200 5 0.5429 0.777 NA 0.020 0.000 0.036 0.596
#> SRR1377201 2 0.1498 0.856 NA 0.952 0.024 0.016 0.000
#> SRR1377202 2 0.1483 0.858 NA 0.952 0.028 0.012 0.000
#> SRR1377203 2 0.1483 0.858 NA 0.952 0.028 0.012 0.000
#> SRR1377204 2 0.4536 0.439 NA 0.640 0.008 0.344 0.000
#> SRR1377205 2 0.4536 0.439 NA 0.640 0.008 0.344 0.000
#> SRR1377206 2 0.4536 0.439 NA 0.640 0.008 0.344 0.000
#> SRR1377207 2 0.1651 0.858 NA 0.944 0.036 0.012 0.000
#> SRR1377208 2 0.1651 0.858 NA 0.944 0.036 0.012 0.000
#> SRR1377209 2 0.1651 0.858 NA 0.944 0.036 0.012 0.000
#> SRR1377210 2 0.1569 0.857 NA 0.948 0.032 0.012 0.000
#> SRR1377211 2 0.1569 0.857 NA 0.948 0.032 0.012 0.000
#> SRR1377212 2 0.1569 0.857 NA 0.948 0.032 0.012 0.000
#> SRR1377213 4 0.1818 0.824 NA 0.044 0.024 0.932 0.000
#> SRR1377214 4 0.1818 0.824 NA 0.044 0.024 0.932 0.000
#> SRR1377215 4 0.1818 0.824 NA 0.044 0.024 0.932 0.000
#> SRR1377216 4 0.5925 0.510 NA 0.032 0.332 0.580 0.000
#> SRR1377217 4 0.5977 0.493 NA 0.032 0.348 0.564 0.000
#> SRR1377218 4 0.5977 0.493 NA 0.032 0.348 0.564 0.000
#> SRR1377219 4 0.1911 0.822 NA 0.036 0.028 0.932 0.000
#> SRR1377220 4 0.1996 0.821 NA 0.036 0.032 0.928 0.000
#> SRR1377221 4 0.2078 0.819 NA 0.036 0.036 0.924 0.000
#> SRR1377222 4 0.1893 0.820 NA 0.048 0.024 0.928 0.000
#> SRR1377223 4 0.1893 0.820 NA 0.048 0.024 0.928 0.000
#> SRR1377224 4 0.1893 0.820 NA 0.048 0.024 0.928 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 2 0.4423 0.635 0.000 0.552 0.028 0.000 0.000 NA
#> SRR1377146 2 0.4423 0.635 0.000 0.552 0.028 0.000 0.000 NA
#> SRR1377147 2 0.4561 0.632 0.004 0.544 0.028 0.000 0.000 NA
#> SRR1377148 2 0.4423 0.635 0.000 0.552 0.028 0.000 0.000 NA
#> SRR1377153 2 0.4765 0.625 0.016 0.536 0.024 0.000 0.000 NA
#> SRR1377154 2 0.4594 0.631 0.008 0.544 0.024 0.000 0.000 NA
#> SRR1377155 2 0.5043 0.609 0.024 0.520 0.032 0.000 0.000 NA
#> SRR1377156 2 0.4908 0.618 0.020 0.528 0.028 0.000 0.000 NA
#> SRR1377149 2 0.4683 0.628 0.012 0.540 0.024 0.000 0.000 NA
#> SRR1377150 2 0.4361 0.635 0.000 0.552 0.024 0.000 0.000 NA
#> SRR1377151 2 0.4594 0.631 0.008 0.544 0.024 0.000 0.000 NA
#> SRR1377152 2 0.4594 0.631 0.008 0.544 0.024 0.000 0.000 NA
#> SRR1377157 3 0.0665 0.923 0.008 0.008 0.980 0.004 0.000 NA
#> SRR1377158 3 0.0665 0.923 0.008 0.008 0.980 0.004 0.000 NA
#> SRR1377159 3 0.0520 0.923 0.008 0.008 0.984 0.000 0.000 NA
#> SRR1377160 3 0.0520 0.923 0.008 0.008 0.984 0.000 0.000 NA
#> SRR1377161 3 0.0622 0.924 0.000 0.012 0.980 0.008 0.000 NA
#> SRR1377162 3 0.0665 0.925 0.004 0.008 0.980 0.008 0.000 NA
#> SRR1377163 3 0.0820 0.918 0.000 0.012 0.972 0.016 0.000 NA
#> SRR1377164 3 0.0551 0.926 0.004 0.008 0.984 0.004 0.000 NA
#> SRR1377169 3 0.1078 0.920 0.008 0.016 0.964 0.012 0.000 NA
#> SRR1377170 3 0.1078 0.920 0.008 0.016 0.964 0.012 0.000 NA
#> SRR1377171 3 0.0748 0.924 0.004 0.016 0.976 0.004 0.000 NA
#> SRR1377172 3 0.0862 0.922 0.008 0.016 0.972 0.004 0.000 NA
#> SRR1377165 3 0.0665 0.926 0.008 0.008 0.980 0.004 0.000 NA
#> SRR1377166 3 0.0551 0.926 0.004 0.008 0.984 0.004 0.000 NA
#> SRR1377167 3 0.0551 0.926 0.004 0.008 0.984 0.004 0.000 NA
#> SRR1377168 3 0.0551 0.926 0.004 0.008 0.984 0.004 0.000 NA
#> SRR1377173 1 0.6802 0.937 0.396 0.064 0.384 0.004 0.000 NA
#> SRR1377174 1 0.6760 0.935 0.400 0.060 0.384 0.004 0.000 NA
#> SRR1377175 3 0.6697 -0.924 0.392 0.056 0.400 0.004 0.000 NA
#> SRR1377176 1 0.6760 0.936 0.400 0.060 0.384 0.004 0.000 NA
#> SRR1377177 1 0.6854 0.949 0.400 0.060 0.364 0.004 0.000 NA
#> SRR1377178 1 0.6795 0.948 0.400 0.056 0.372 0.004 0.000 NA
#> SRR1377179 1 0.6795 0.948 0.400 0.056 0.372 0.004 0.000 NA
#> SRR1377180 1 0.6795 0.947 0.400 0.056 0.372 0.004 0.000 NA
#> SRR1377181 1 0.6949 0.949 0.392 0.060 0.368 0.008 0.000 NA
#> SRR1377182 1 0.7016 0.946 0.392 0.064 0.372 0.012 0.000 NA
#> SRR1377183 1 0.7402 0.906 0.380 0.092 0.316 0.012 0.000 NA
#> SRR1377184 1 0.6964 0.949 0.396 0.060 0.360 0.008 0.000 NA
#> SRR1377185 1 0.7429 0.902 0.380 0.096 0.312 0.012 0.000 NA
#> SRR1377186 1 0.7438 0.897 0.380 0.096 0.308 0.012 0.000 NA
#> SRR1377187 1 0.6966 0.949 0.392 0.060 0.364 0.008 0.000 NA
#> SRR1377188 1 0.7438 0.899 0.380 0.096 0.308 0.012 0.000 NA
#> SRR1377189 2 0.1856 0.725 0.008 0.932 0.024 0.008 0.000 NA
#> SRR1377190 2 0.1776 0.725 0.008 0.936 0.024 0.008 0.000 NA
#> SRR1377191 2 0.1881 0.725 0.008 0.928 0.020 0.004 0.000 NA
#> SRR1377192 2 0.2085 0.710 0.000 0.912 0.008 0.056 0.000 NA
#> SRR1377193 2 0.2101 0.712 0.000 0.912 0.008 0.052 0.000 NA
#> SRR1377194 2 0.2022 0.712 0.000 0.916 0.008 0.052 0.000 NA
#> SRR1377195 5 0.2136 0.829 0.048 0.000 0.000 0.000 0.904 NA
#> SRR1377196 5 0.1980 0.829 0.036 0.000 0.008 0.000 0.920 NA
#> SRR1377197 5 0.2001 0.829 0.048 0.000 0.000 0.000 0.912 NA
#> SRR1377198 5 0.5689 0.751 0.236 0.000 0.000 0.008 0.564 NA
#> SRR1377199 5 0.5693 0.738 0.172 0.000 0.000 0.004 0.532 NA
#> SRR1377200 5 0.5910 0.718 0.344 0.004 0.000 0.004 0.484 NA
#> SRR1377201 2 0.1370 0.720 0.012 0.948 0.036 0.004 0.000 NA
#> SRR1377202 2 0.1370 0.720 0.012 0.948 0.036 0.004 0.000 NA
#> SRR1377203 2 0.1442 0.718 0.012 0.944 0.040 0.004 0.000 NA
#> SRR1377204 2 0.3323 0.577 0.004 0.784 0.008 0.200 0.000 NA
#> SRR1377205 2 0.3293 0.582 0.004 0.788 0.008 0.196 0.000 NA
#> SRR1377206 2 0.3323 0.577 0.004 0.784 0.008 0.200 0.000 NA
#> SRR1377207 2 0.1713 0.714 0.028 0.928 0.044 0.000 0.000 NA
#> SRR1377208 2 0.1713 0.714 0.028 0.928 0.044 0.000 0.000 NA
#> SRR1377209 2 0.1780 0.712 0.028 0.924 0.048 0.000 0.000 NA
#> SRR1377210 2 0.1666 0.718 0.020 0.936 0.036 0.008 0.000 NA
#> SRR1377211 2 0.1666 0.718 0.020 0.936 0.036 0.008 0.000 NA
#> SRR1377212 2 0.1552 0.719 0.020 0.940 0.036 0.004 0.000 NA
#> SRR1377213 4 0.1269 0.864 0.012 0.020 0.012 0.956 0.000 NA
#> SRR1377214 4 0.1269 0.864 0.012 0.020 0.012 0.956 0.000 NA
#> SRR1377215 4 0.1269 0.864 0.012 0.020 0.012 0.956 0.000 NA
#> SRR1377216 4 0.5666 0.602 0.152 0.016 0.180 0.636 0.000 NA
#> SRR1377217 4 0.5633 0.606 0.148 0.016 0.180 0.640 0.000 NA
#> SRR1377218 4 0.5719 0.595 0.156 0.020 0.184 0.628 0.000 NA
#> SRR1377219 4 0.1452 0.865 0.012 0.020 0.020 0.948 0.000 NA
#> SRR1377220 4 0.1452 0.865 0.012 0.020 0.020 0.948 0.000 NA
#> SRR1377221 4 0.1452 0.865 0.012 0.020 0.020 0.948 0.000 NA
#> SRR1377222 4 0.1036 0.850 0.000 0.024 0.008 0.964 0.000 NA
#> SRR1377223 4 0.1036 0.850 0.000 0.024 0.008 0.964 0.000 NA
#> SRR1377224 4 0.1036 0.850 0.000 0.024 0.008 0.964 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["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 13890 rows and 80 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 1.000 1.000 1.000 0.1414 0.859 0.859
#> 3 3 0.323 0.553 0.757 2.1259 0.744 0.709
#> 4 4 0.315 0.748 0.770 0.3150 0.747 0.606
#> 5 5 0.481 0.856 0.823 0.1205 0.871 0.670
#> 6 6 0.493 0.885 0.834 0.0788 0.962 0.855
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
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377146 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377147 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377148 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377153 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377154 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377155 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377156 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377149 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377150 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377151 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377152 1 0.9616 0.656 0.420 0.376 0.204
#> SRR1377157 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377158 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377159 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377160 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377161 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377162 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377163 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377164 2 0.6309 0.411 0.000 0.504 0.496
#> SRR1377169 2 0.6111 0.507 0.000 0.604 0.396
#> SRR1377170 2 0.6111 0.507 0.000 0.604 0.396
#> SRR1377171 2 0.6111 0.507 0.000 0.604 0.396
#> SRR1377172 2 0.6111 0.507 0.000 0.604 0.396
#> SRR1377165 2 0.6308 0.416 0.000 0.508 0.492
#> SRR1377166 2 0.6308 0.416 0.000 0.508 0.492
#> SRR1377167 2 0.6308 0.416 0.000 0.508 0.492
#> SRR1377168 2 0.6308 0.416 0.000 0.508 0.492
#> SRR1377173 2 0.0747 0.764 0.016 0.984 0.000
#> SRR1377174 2 0.0747 0.764 0.016 0.984 0.000
#> SRR1377175 2 0.0747 0.764 0.016 0.984 0.000
#> SRR1377176 2 0.0747 0.764 0.016 0.984 0.000
#> SRR1377177 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377178 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377179 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377180 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377181 2 0.5178 0.629 0.000 0.744 0.256
#> SRR1377182 2 0.5178 0.629 0.000 0.744 0.256
#> SRR1377183 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377184 2 0.5178 0.629 0.000 0.744 0.256
#> SRR1377185 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377186 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377187 2 0.5178 0.629 0.000 0.744 0.256
#> SRR1377188 2 0.0000 0.771 0.000 1.000 0.000
#> SRR1377189 2 0.1031 0.774 0.000 0.976 0.024
#> SRR1377190 2 0.1031 0.774 0.000 0.976 0.024
#> SRR1377191 2 0.1031 0.774 0.000 0.976 0.024
#> SRR1377192 2 0.1031 0.774 0.000 0.976 0.024
#> SRR1377193 2 0.1031 0.774 0.000 0.976 0.024
#> SRR1377194 2 0.1031 0.774 0.000 0.976 0.024
#> SRR1377195 1 0.6309 -1.000 0.500 0.000 0.500
#> SRR1377196 1 0.6309 -1.000 0.500 0.000 0.500
#> SRR1377197 3 0.6309 0.000 0.500 0.000 0.500
#> SRR1377198 1 0.6309 -1.000 0.500 0.000 0.500
#> SRR1377199 1 0.6309 -1.000 0.500 0.000 0.500
#> SRR1377200 1 0.6308 -0.995 0.508 0.000 0.492
#> SRR1377201 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377202 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377203 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377204 2 0.2796 0.730 0.092 0.908 0.000
#> SRR1377205 2 0.2796 0.730 0.092 0.908 0.000
#> SRR1377206 2 0.2796 0.730 0.092 0.908 0.000
#> SRR1377207 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377208 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377209 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377210 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377211 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377212 2 0.2448 0.742 0.076 0.924 0.000
#> SRR1377213 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377214 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377215 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377216 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377217 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377218 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377219 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377220 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377221 2 0.1399 0.773 0.004 0.968 0.028
#> SRR1377222 2 0.3112 0.727 0.096 0.900 0.004
#> SRR1377223 2 0.3112 0.727 0.096 0.900 0.004
#> SRR1377224 2 0.3112 0.727 0.096 0.900 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377146 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377147 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377148 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377153 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377154 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377155 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377156 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377149 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377150 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377151 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377152 4 0.6243 1.000 0.000 0.392 0.060 0.548
#> SRR1377157 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377158 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377159 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377160 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377161 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377162 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377163 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377164 3 0.3649 0.952 0.000 0.204 0.796 0.000
#> SRR1377169 3 0.4477 0.846 0.000 0.312 0.688 0.000
#> SRR1377170 3 0.4477 0.846 0.000 0.312 0.688 0.000
#> SRR1377171 3 0.4477 0.846 0.000 0.312 0.688 0.000
#> SRR1377172 3 0.4477 0.846 0.000 0.312 0.688 0.000
#> SRR1377165 3 0.3688 0.951 0.000 0.208 0.792 0.000
#> SRR1377166 3 0.3688 0.951 0.000 0.208 0.792 0.000
#> SRR1377167 3 0.3688 0.951 0.000 0.208 0.792 0.000
#> SRR1377168 3 0.3688 0.951 0.000 0.208 0.792 0.000
#> SRR1377173 2 0.0188 0.705 0.000 0.996 0.004 0.000
#> SRR1377174 2 0.0188 0.705 0.000 0.996 0.004 0.000
#> SRR1377175 2 0.0188 0.705 0.000 0.996 0.004 0.000
#> SRR1377176 2 0.0188 0.705 0.000 0.996 0.004 0.000
#> SRR1377177 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377178 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377179 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377180 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377181 2 0.4746 0.167 0.000 0.632 0.368 0.000
#> SRR1377182 2 0.4746 0.167 0.000 0.632 0.368 0.000
#> SRR1377183 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377184 2 0.4746 0.167 0.000 0.632 0.368 0.000
#> SRR1377185 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377186 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377187 2 0.4746 0.167 0.000 0.632 0.368 0.000
#> SRR1377188 2 0.0707 0.711 0.000 0.980 0.020 0.000
#> SRR1377189 2 0.3176 0.705 0.000 0.880 0.036 0.084
#> SRR1377190 2 0.3176 0.705 0.000 0.880 0.036 0.084
#> SRR1377191 2 0.3176 0.705 0.000 0.880 0.036 0.084
#> SRR1377192 2 0.3176 0.705 0.000 0.880 0.036 0.084
#> SRR1377193 2 0.3176 0.705 0.000 0.880 0.036 0.084
#> SRR1377194 2 0.3176 0.705 0.000 0.880 0.036 0.084
#> SRR1377195 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1377196 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1377197 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR1377198 1 0.0817 0.975 0.976 0.000 0.000 0.024
#> SRR1377199 1 0.1118 0.971 0.964 0.000 0.000 0.036
#> SRR1377200 1 0.3355 0.913 0.836 0.000 0.004 0.160
#> SRR1377201 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377202 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377203 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377204 2 0.2053 0.684 0.000 0.924 0.072 0.004
#> SRR1377205 2 0.2053 0.684 0.000 0.924 0.072 0.004
#> SRR1377206 2 0.2053 0.684 0.000 0.924 0.072 0.004
#> SRR1377207 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377208 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377209 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377210 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377211 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377212 2 0.1743 0.697 0.000 0.940 0.056 0.004
#> SRR1377213 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377214 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377215 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377216 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377217 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377218 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377219 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377220 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377221 2 0.6836 0.505 0.000 0.580 0.280 0.140
#> SRR1377222 2 0.7768 0.327 0.000 0.428 0.312 0.260
#> SRR1377223 2 0.7768 0.327 0.000 0.428 0.312 0.260
#> SRR1377224 2 0.7768 0.327 0.000 0.428 0.312 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377146 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377147 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377148 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377153 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377154 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377155 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377156 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377149 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377150 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377151 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377152 2 0.5086 1.000 0.304 0.636 0.060 0.000 0.000
#> SRR1377157 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377158 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377159 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377160 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377161 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377162 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377163 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377164 3 0.0510 0.950 0.016 0.000 0.984 0.000 0.000
#> SRR1377169 3 0.2329 0.848 0.124 0.000 0.876 0.000 0.000
#> SRR1377170 3 0.2329 0.848 0.124 0.000 0.876 0.000 0.000
#> SRR1377171 3 0.2329 0.848 0.124 0.000 0.876 0.000 0.000
#> SRR1377172 3 0.2329 0.848 0.124 0.000 0.876 0.000 0.000
#> SRR1377165 3 0.0609 0.949 0.020 0.000 0.980 0.000 0.000
#> SRR1377166 3 0.0609 0.949 0.020 0.000 0.980 0.000 0.000
#> SRR1377167 3 0.0609 0.949 0.020 0.000 0.980 0.000 0.000
#> SRR1377168 3 0.0609 0.949 0.020 0.000 0.980 0.000 0.000
#> SRR1377173 1 0.1410 0.834 0.940 0.000 0.060 0.000 0.000
#> SRR1377174 1 0.1410 0.834 0.940 0.000 0.060 0.000 0.000
#> SRR1377175 1 0.1410 0.834 0.940 0.000 0.060 0.000 0.000
#> SRR1377176 1 0.1410 0.834 0.940 0.000 0.060 0.000 0.000
#> SRR1377177 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377178 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377179 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377180 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377181 1 0.6162 0.410 0.572 0.028 0.316 0.084 0.000
#> SRR1377182 1 0.6162 0.410 0.572 0.028 0.316 0.084 0.000
#> SRR1377183 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377184 1 0.6162 0.410 0.572 0.028 0.316 0.084 0.000
#> SRR1377185 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377186 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377187 1 0.6162 0.410 0.572 0.028 0.316 0.084 0.000
#> SRR1377188 1 0.1671 0.835 0.924 0.000 0.076 0.000 0.000
#> SRR1377189 1 0.3812 0.738 0.812 0.000 0.096 0.092 0.000
#> SRR1377190 1 0.3812 0.738 0.812 0.000 0.096 0.092 0.000
#> SRR1377191 1 0.3812 0.738 0.812 0.000 0.096 0.092 0.000
#> SRR1377192 1 0.3812 0.738 0.812 0.000 0.096 0.092 0.000
#> SRR1377193 1 0.3812 0.738 0.812 0.000 0.096 0.092 0.000
#> SRR1377194 1 0.3812 0.738 0.812 0.000 0.096 0.092 0.000
#> SRR1377195 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000
#> SRR1377196 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000
#> SRR1377197 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000
#> SRR1377198 5 0.2758 0.926 0.000 0.024 0.012 0.076 0.888
#> SRR1377199 5 0.2233 0.927 0.000 0.000 0.004 0.104 0.892
#> SRR1377200 5 0.3857 0.843 0.000 0.312 0.000 0.000 0.688
#> SRR1377201 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377202 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377203 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377204 1 0.0609 0.805 0.980 0.000 0.000 0.020 0.000
#> SRR1377205 1 0.0609 0.805 0.980 0.000 0.000 0.020 0.000
#> SRR1377206 1 0.0609 0.805 0.980 0.000 0.000 0.020 0.000
#> SRR1377207 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377208 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377209 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377210 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377211 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377212 1 0.0162 0.824 0.996 0.000 0.000 0.004 0.000
#> SRR1377213 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377214 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377215 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377216 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377217 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377218 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377219 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377220 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377221 4 0.5579 0.903 0.300 0.000 0.100 0.600 0.000
#> SRR1377222 4 0.3586 0.709 0.264 0.000 0.000 0.736 0.000
#> SRR1377223 4 0.3586 0.709 0.264 0.000 0.000 0.736 0.000
#> SRR1377224 4 0.3586 0.709 0.264 0.000 0.000 0.736 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.3288 0.982 0.000 0.276 0.000 0.000 0.000 0.724
#> SRR1377146 6 0.3288 0.982 0.000 0.276 0.000 0.000 0.000 0.724
#> SRR1377147 6 0.3288 0.982 0.000 0.276 0.000 0.000 0.000 0.724
#> SRR1377148 6 0.3288 0.982 0.000 0.276 0.000 0.000 0.000 0.724
#> SRR1377153 6 0.3351 0.976 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR1377154 6 0.3351 0.976 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR1377155 6 0.3351 0.976 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR1377156 6 0.3351 0.976 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR1377149 6 0.3583 0.971 0.008 0.260 0.000 0.004 0.000 0.728
#> SRR1377150 6 0.3583 0.971 0.008 0.260 0.000 0.004 0.000 0.728
#> SRR1377151 6 0.3583 0.971 0.008 0.260 0.000 0.004 0.000 0.728
#> SRR1377152 6 0.3583 0.971 0.008 0.260 0.000 0.004 0.000 0.728
#> SRR1377157 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377158 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377159 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377160 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377161 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377162 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377163 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377164 3 0.0458 0.934 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1377169 3 0.2092 0.798 0.000 0.124 0.876 0.000 0.000 0.000
#> SRR1377170 3 0.2092 0.798 0.000 0.124 0.876 0.000 0.000 0.000
#> SRR1377171 3 0.2092 0.798 0.000 0.124 0.876 0.000 0.000 0.000
#> SRR1377172 3 0.2092 0.798 0.000 0.124 0.876 0.000 0.000 0.000
#> SRR1377165 3 0.0547 0.933 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR1377166 3 0.0547 0.933 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR1377167 3 0.0547 0.933 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR1377168 3 0.0547 0.933 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR1377173 2 0.1411 0.906 0.004 0.936 0.060 0.000 0.000 0.000
#> SRR1377174 2 0.1411 0.906 0.004 0.936 0.060 0.000 0.000 0.000
#> SRR1377175 2 0.1411 0.906 0.004 0.936 0.060 0.000 0.000 0.000
#> SRR1377176 2 0.1411 0.906 0.004 0.936 0.060 0.000 0.000 0.000
#> SRR1377177 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377178 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377179 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377180 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377181 1 0.4486 1.000 0.696 0.096 0.208 0.000 0.000 0.000
#> SRR1377182 1 0.4486 1.000 0.696 0.096 0.208 0.000 0.000 0.000
#> SRR1377183 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377184 1 0.4486 1.000 0.696 0.096 0.208 0.000 0.000 0.000
#> SRR1377185 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377186 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377187 1 0.4486 1.000 0.696 0.096 0.208 0.000 0.000 0.000
#> SRR1377188 2 0.1644 0.905 0.004 0.920 0.076 0.000 0.000 0.000
#> SRR1377189 2 0.3424 0.800 0.000 0.812 0.096 0.092 0.000 0.000
#> SRR1377190 2 0.3424 0.800 0.000 0.812 0.096 0.092 0.000 0.000
#> SRR1377191 2 0.3424 0.800 0.000 0.812 0.096 0.092 0.000 0.000
#> SRR1377192 2 0.3424 0.800 0.000 0.812 0.096 0.092 0.000 0.000
#> SRR1377193 2 0.3424 0.800 0.000 0.812 0.096 0.092 0.000 0.000
#> SRR1377194 2 0.3424 0.800 0.000 0.812 0.096 0.092 0.000 0.000
#> SRR1377195 5 0.0000 0.915 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377196 5 0.0000 0.915 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377197 5 0.0000 0.915 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377198 5 0.3250 0.873 0.028 0.000 0.012 0.028 0.856 0.076
#> SRR1377199 5 0.2344 0.888 0.000 0.000 0.004 0.028 0.892 0.076
#> SRR1377200 5 0.4953 0.726 0.268 0.000 0.000 0.000 0.624 0.108
#> SRR1377201 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377202 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377203 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377204 2 0.0458 0.875 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1377205 2 0.0458 0.875 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1377206 2 0.0458 0.875 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1377207 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377208 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377209 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377210 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377211 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377212 2 0.0000 0.893 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377213 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377214 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377215 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377216 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377217 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377218 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377219 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377220 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377221 4 0.4582 0.845 0.000 0.216 0.100 0.684 0.000 0.000
#> SRR1377222 4 0.1267 0.519 0.000 0.060 0.000 0.940 0.000 0.000
#> SRR1377223 4 0.1267 0.519 0.000 0.060 0.000 0.940 0.000 0.000
#> SRR1377224 4 0.1267 0.519 0.000 0.060 0.000 0.940 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13890 rows and 80 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 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 0.277 0.752 0.838 0.2555 0.859 0.859
#> 3 3 0.204 0.698 0.771 0.8210 0.706 0.658
#> 4 4 0.323 0.711 0.727 0.3095 0.825 0.691
#> 5 5 0.468 0.773 0.739 0.1617 0.871 0.670
#> 6 6 0.631 0.750 0.728 0.0657 0.841 0.517
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
#> SRR1377145 2 0.850 0.653 0.276 0.724
#> SRR1377146 2 0.850 0.653 0.276 0.724
#> SRR1377147 2 0.850 0.653 0.276 0.724
#> SRR1377148 2 0.850 0.653 0.276 0.724
#> SRR1377153 2 0.671 0.657 0.176 0.824
#> SRR1377154 2 0.671 0.657 0.176 0.824
#> SRR1377155 2 0.671 0.657 0.176 0.824
#> SRR1377156 2 0.671 0.657 0.176 0.824
#> SRR1377149 2 0.671 0.657 0.176 0.824
#> SRR1377150 2 0.671 0.657 0.176 0.824
#> SRR1377151 2 0.671 0.657 0.176 0.824
#> SRR1377152 2 0.671 0.657 0.176 0.824
#> SRR1377157 2 0.861 0.680 0.284 0.716
#> SRR1377158 2 0.861 0.680 0.284 0.716
#> SRR1377159 2 0.861 0.680 0.284 0.716
#> SRR1377160 2 0.861 0.680 0.284 0.716
#> SRR1377161 2 0.861 0.680 0.284 0.716
#> SRR1377162 2 0.861 0.680 0.284 0.716
#> SRR1377163 2 0.861 0.680 0.284 0.716
#> SRR1377164 2 0.861 0.680 0.284 0.716
#> SRR1377169 2 0.861 0.680 0.284 0.716
#> SRR1377170 2 0.861 0.680 0.284 0.716
#> SRR1377171 2 0.861 0.680 0.284 0.716
#> SRR1377172 2 0.861 0.680 0.284 0.716
#> SRR1377165 2 0.861 0.680 0.284 0.716
#> SRR1377166 2 0.861 0.680 0.284 0.716
#> SRR1377167 2 0.861 0.680 0.284 0.716
#> SRR1377168 2 0.861 0.680 0.284 0.716
#> SRR1377173 2 0.118 0.783 0.016 0.984
#> SRR1377174 2 0.118 0.783 0.016 0.984
#> SRR1377175 2 0.118 0.783 0.016 0.984
#> SRR1377176 2 0.118 0.783 0.016 0.984
#> SRR1377177 2 0.118 0.783 0.016 0.984
#> SRR1377178 2 0.118 0.783 0.016 0.984
#> SRR1377179 2 0.118 0.783 0.016 0.984
#> SRR1377180 2 0.118 0.783 0.016 0.984
#> SRR1377181 2 0.204 0.785 0.032 0.968
#> SRR1377182 2 0.204 0.785 0.032 0.968
#> SRR1377183 2 0.163 0.784 0.024 0.976
#> SRR1377184 2 0.204 0.785 0.032 0.968
#> SRR1377185 2 0.163 0.784 0.024 0.976
#> SRR1377186 2 0.163 0.784 0.024 0.976
#> SRR1377187 2 0.204 0.785 0.032 0.968
#> SRR1377188 2 0.163 0.784 0.024 0.976
#> SRR1377189 2 0.204 0.775 0.032 0.968
#> SRR1377190 2 0.204 0.775 0.032 0.968
#> SRR1377191 2 0.204 0.775 0.032 0.968
#> SRR1377192 2 0.204 0.775 0.032 0.968
#> SRR1377193 2 0.204 0.775 0.032 0.968
#> SRR1377194 2 0.204 0.775 0.032 0.968
#> SRR1377195 1 0.983 1.000 0.576 0.424
#> SRR1377196 1 0.983 1.000 0.576 0.424
#> SRR1377197 1 0.983 1.000 0.576 0.424
#> SRR1377198 1 0.983 1.000 0.576 0.424
#> SRR1377199 1 0.983 1.000 0.576 0.424
#> SRR1377200 1 0.983 1.000 0.576 0.424
#> SRR1377201 2 0.260 0.768 0.044 0.956
#> SRR1377202 2 0.260 0.768 0.044 0.956
#> SRR1377203 2 0.260 0.768 0.044 0.956
#> SRR1377204 2 0.260 0.768 0.044 0.956
#> SRR1377205 2 0.260 0.768 0.044 0.956
#> SRR1377206 2 0.260 0.768 0.044 0.956
#> SRR1377207 2 0.260 0.768 0.044 0.956
#> SRR1377208 2 0.260 0.768 0.044 0.956
#> SRR1377209 2 0.260 0.768 0.044 0.956
#> SRR1377210 2 0.204 0.775 0.032 0.968
#> SRR1377211 2 0.204 0.775 0.032 0.968
#> SRR1377212 2 0.204 0.775 0.032 0.968
#> SRR1377213 2 0.722 0.740 0.200 0.800
#> SRR1377214 2 0.722 0.740 0.200 0.800
#> SRR1377215 2 0.722 0.740 0.200 0.800
#> SRR1377216 2 0.722 0.740 0.200 0.800
#> SRR1377217 2 0.722 0.740 0.200 0.800
#> SRR1377218 2 0.722 0.740 0.200 0.800
#> SRR1377219 2 0.722 0.740 0.200 0.800
#> SRR1377220 2 0.722 0.740 0.200 0.800
#> SRR1377221 2 0.722 0.740 0.200 0.800
#> SRR1377222 2 0.541 0.767 0.124 0.876
#> SRR1377223 2 0.541 0.767 0.124 0.876
#> SRR1377224 2 0.541 0.767 0.124 0.876
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.8703 0.409 0.160 0.584 0.256
#> SRR1377146 2 0.8703 0.409 0.160 0.584 0.256
#> SRR1377147 2 0.8703 0.409 0.160 0.584 0.256
#> SRR1377148 2 0.8703 0.409 0.160 0.584 0.256
#> SRR1377153 2 0.8544 0.414 0.152 0.600 0.248
#> SRR1377154 2 0.8544 0.414 0.152 0.600 0.248
#> SRR1377155 2 0.8544 0.414 0.152 0.600 0.248
#> SRR1377156 2 0.8544 0.414 0.152 0.600 0.248
#> SRR1377149 2 0.8561 0.414 0.156 0.600 0.244
#> SRR1377150 2 0.8561 0.414 0.156 0.600 0.244
#> SRR1377151 2 0.8561 0.414 0.156 0.600 0.244
#> SRR1377152 2 0.8561 0.414 0.156 0.600 0.244
#> SRR1377157 3 0.6047 0.994 0.008 0.312 0.680
#> SRR1377158 3 0.6047 0.994 0.008 0.312 0.680
#> SRR1377159 3 0.6047 0.994 0.008 0.312 0.680
#> SRR1377160 3 0.6047 0.994 0.008 0.312 0.680
#> SRR1377161 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377162 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377163 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377164 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377169 3 0.5873 0.996 0.004 0.312 0.684
#> SRR1377170 3 0.5873 0.996 0.004 0.312 0.684
#> SRR1377171 3 0.5873 0.996 0.004 0.312 0.684
#> SRR1377172 3 0.5873 0.996 0.004 0.312 0.684
#> SRR1377165 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377166 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377167 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377168 3 0.5650 0.997 0.000 0.312 0.688
#> SRR1377173 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377174 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377175 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377176 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377177 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377178 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377179 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377180 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377181 2 0.6297 0.630 0.060 0.756 0.184
#> SRR1377182 2 0.6297 0.630 0.060 0.756 0.184
#> SRR1377183 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377184 2 0.6297 0.630 0.060 0.756 0.184
#> SRR1377185 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377186 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377187 2 0.6297 0.630 0.060 0.756 0.184
#> SRR1377188 2 0.5295 0.663 0.036 0.808 0.156
#> SRR1377189 2 0.0475 0.710 0.004 0.992 0.004
#> SRR1377190 2 0.0475 0.710 0.004 0.992 0.004
#> SRR1377191 2 0.0475 0.710 0.004 0.992 0.004
#> SRR1377192 2 0.0661 0.711 0.004 0.988 0.008
#> SRR1377193 2 0.0661 0.711 0.004 0.988 0.008
#> SRR1377194 2 0.0661 0.711 0.004 0.988 0.008
#> SRR1377195 1 0.5254 0.999 0.736 0.264 0.000
#> SRR1377196 1 0.5254 0.999 0.736 0.264 0.000
#> SRR1377197 1 0.5254 0.999 0.736 0.264 0.000
#> SRR1377198 1 0.5812 0.994 0.724 0.264 0.012
#> SRR1377199 1 0.5254 0.999 0.736 0.264 0.000
#> SRR1377200 1 0.5254 0.999 0.736 0.264 0.000
#> SRR1377201 2 0.0747 0.711 0.016 0.984 0.000
#> SRR1377202 2 0.0747 0.711 0.016 0.984 0.000
#> SRR1377203 2 0.0747 0.711 0.016 0.984 0.000
#> SRR1377204 2 0.1774 0.706 0.016 0.960 0.024
#> SRR1377205 2 0.1774 0.706 0.016 0.960 0.024
#> SRR1377206 2 0.1774 0.706 0.016 0.960 0.024
#> SRR1377207 2 0.0747 0.711 0.016 0.984 0.000
#> SRR1377208 2 0.0747 0.711 0.016 0.984 0.000
#> SRR1377209 2 0.0747 0.711 0.016 0.984 0.000
#> SRR1377210 2 0.0237 0.711 0.004 0.996 0.000
#> SRR1377211 2 0.0237 0.711 0.004 0.996 0.000
#> SRR1377212 2 0.0237 0.711 0.004 0.996 0.000
#> SRR1377213 2 0.6796 0.467 0.056 0.708 0.236
#> SRR1377214 2 0.6796 0.467 0.056 0.708 0.236
#> SRR1377215 2 0.6796 0.467 0.056 0.708 0.236
#> SRR1377216 2 0.7057 0.413 0.056 0.680 0.264
#> SRR1377217 2 0.7057 0.413 0.056 0.680 0.264
#> SRR1377218 2 0.7057 0.413 0.056 0.680 0.264
#> SRR1377219 2 0.6875 0.448 0.056 0.700 0.244
#> SRR1377220 2 0.6875 0.448 0.056 0.700 0.244
#> SRR1377221 2 0.6875 0.448 0.056 0.700 0.244
#> SRR1377222 2 0.5757 0.581 0.056 0.792 0.152
#> SRR1377223 2 0.5757 0.581 0.056 0.792 0.152
#> SRR1377224 2 0.5757 0.581 0.056 0.792 0.152
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 4 0.603 0.964 0.012 0.412 0.024 0.552
#> SRR1377146 4 0.603 0.964 0.012 0.412 0.024 0.552
#> SRR1377147 4 0.603 0.964 0.012 0.412 0.024 0.552
#> SRR1377148 4 0.603 0.964 0.012 0.412 0.024 0.552
#> SRR1377153 4 0.597 0.969 0.016 0.424 0.016 0.544
#> SRR1377154 4 0.597 0.969 0.016 0.424 0.016 0.544
#> SRR1377155 4 0.597 0.969 0.016 0.424 0.016 0.544
#> SRR1377156 4 0.597 0.969 0.016 0.424 0.016 0.544
#> SRR1377149 4 0.605 0.968 0.024 0.424 0.012 0.540
#> SRR1377150 4 0.605 0.968 0.024 0.424 0.012 0.540
#> SRR1377151 4 0.605 0.968 0.024 0.424 0.012 0.540
#> SRR1377152 4 0.605 0.968 0.024 0.424 0.012 0.540
#> SRR1377157 3 0.411 0.969 0.016 0.120 0.836 0.028
#> SRR1377158 3 0.411 0.969 0.016 0.120 0.836 0.028
#> SRR1377159 3 0.411 0.969 0.016 0.120 0.836 0.028
#> SRR1377160 3 0.411 0.969 0.016 0.120 0.836 0.028
#> SRR1377161 3 0.341 0.978 0.016 0.120 0.860 0.004
#> SRR1377162 3 0.341 0.978 0.016 0.120 0.860 0.004
#> SRR1377163 3 0.341 0.978 0.016 0.120 0.860 0.004
#> SRR1377164 3 0.341 0.978 0.016 0.120 0.860 0.004
#> SRR1377169 3 0.356 0.975 0.012 0.120 0.856 0.012
#> SRR1377170 3 0.356 0.975 0.012 0.120 0.856 0.012
#> SRR1377171 3 0.356 0.975 0.012 0.120 0.856 0.012
#> SRR1377172 3 0.356 0.975 0.012 0.120 0.856 0.012
#> SRR1377165 3 0.265 0.980 0.000 0.120 0.880 0.000
#> SRR1377166 3 0.265 0.980 0.000 0.120 0.880 0.000
#> SRR1377167 3 0.265 0.980 0.000 0.120 0.880 0.000
#> SRR1377168 3 0.265 0.980 0.000 0.120 0.880 0.000
#> SRR1377173 2 0.581 0.563 0.016 0.716 0.064 0.204
#> SRR1377174 2 0.581 0.563 0.016 0.716 0.064 0.204
#> SRR1377175 2 0.581 0.563 0.016 0.716 0.064 0.204
#> SRR1377176 2 0.581 0.563 0.016 0.716 0.064 0.204
#> SRR1377177 2 0.573 0.563 0.012 0.716 0.064 0.208
#> SRR1377178 2 0.573 0.563 0.012 0.716 0.064 0.208
#> SRR1377179 2 0.573 0.563 0.012 0.716 0.064 0.208
#> SRR1377180 2 0.573 0.563 0.012 0.716 0.064 0.208
#> SRR1377181 2 0.695 0.514 0.040 0.636 0.080 0.244
#> SRR1377182 2 0.695 0.514 0.040 0.636 0.080 0.244
#> SRR1377183 2 0.580 0.563 0.012 0.708 0.064 0.216
#> SRR1377184 2 0.695 0.514 0.040 0.636 0.080 0.244
#> SRR1377185 2 0.580 0.563 0.012 0.708 0.064 0.216
#> SRR1377186 2 0.580 0.563 0.012 0.708 0.064 0.216
#> SRR1377187 2 0.695 0.514 0.040 0.636 0.080 0.244
#> SRR1377188 2 0.580 0.563 0.012 0.708 0.064 0.216
#> SRR1377189 2 0.212 0.573 0.004 0.936 0.032 0.028
#> SRR1377190 2 0.212 0.573 0.004 0.936 0.032 0.028
#> SRR1377191 2 0.212 0.573 0.004 0.936 0.032 0.028
#> SRR1377192 2 0.212 0.573 0.004 0.936 0.032 0.028
#> SRR1377193 2 0.212 0.573 0.004 0.936 0.032 0.028
#> SRR1377194 2 0.212 0.573 0.004 0.936 0.032 0.028
#> SRR1377195 1 0.419 0.996 0.780 0.208 0.008 0.004
#> SRR1377196 1 0.419 0.996 0.780 0.208 0.008 0.004
#> SRR1377197 1 0.419 0.996 0.780 0.208 0.008 0.004
#> SRR1377198 1 0.446 0.994 0.772 0.208 0.012 0.008
#> SRR1377199 1 0.458 0.992 0.768 0.208 0.012 0.012
#> SRR1377200 1 0.457 0.992 0.768 0.208 0.008 0.016
#> SRR1377201 2 0.177 0.574 0.012 0.948 0.036 0.004
#> SRR1377202 2 0.177 0.574 0.012 0.948 0.036 0.004
#> SRR1377203 2 0.177 0.574 0.012 0.948 0.036 0.004
#> SRR1377204 2 0.356 0.527 0.016 0.872 0.084 0.028
#> SRR1377205 2 0.356 0.527 0.016 0.872 0.084 0.028
#> SRR1377206 2 0.356 0.527 0.016 0.872 0.084 0.028
#> SRR1377207 2 0.177 0.574 0.012 0.948 0.036 0.004
#> SRR1377208 2 0.177 0.574 0.012 0.948 0.036 0.004
#> SRR1377209 2 0.177 0.574 0.012 0.948 0.036 0.004
#> SRR1377210 2 0.121 0.576 0.000 0.960 0.040 0.000
#> SRR1377211 2 0.121 0.576 0.000 0.960 0.040 0.000
#> SRR1377212 2 0.121 0.576 0.000 0.960 0.040 0.000
#> SRR1377213 2 0.884 0.389 0.096 0.464 0.288 0.152
#> SRR1377214 2 0.884 0.389 0.096 0.464 0.288 0.152
#> SRR1377215 2 0.884 0.389 0.096 0.464 0.288 0.152
#> SRR1377216 2 0.880 0.384 0.096 0.472 0.284 0.148
#> SRR1377217 2 0.880 0.384 0.096 0.472 0.284 0.148
#> SRR1377218 2 0.880 0.384 0.096 0.472 0.284 0.148
#> SRR1377219 2 0.883 0.386 0.096 0.468 0.284 0.152
#> SRR1377220 2 0.883 0.386 0.096 0.468 0.284 0.152
#> SRR1377221 2 0.883 0.386 0.096 0.468 0.284 0.152
#> SRR1377222 2 0.826 0.409 0.096 0.568 0.180 0.156
#> SRR1377223 2 0.826 0.409 0.096 0.568 0.180 0.156
#> SRR1377224 2 0.826 0.409 0.096 0.568 0.180 0.156
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.5745 0.935 0.164 0.692 0.024 0.112 0.008
#> SRR1377146 2 0.5745 0.935 0.164 0.692 0.024 0.112 0.008
#> SRR1377147 2 0.5745 0.935 0.164 0.692 0.024 0.112 0.008
#> SRR1377148 2 0.5745 0.935 0.164 0.692 0.024 0.112 0.008
#> SRR1377153 2 0.5347 0.940 0.164 0.724 0.024 0.080 0.008
#> SRR1377154 2 0.5347 0.940 0.164 0.724 0.024 0.080 0.008
#> SRR1377155 2 0.5347 0.940 0.164 0.724 0.024 0.080 0.008
#> SRR1377156 2 0.5347 0.940 0.164 0.724 0.024 0.080 0.008
#> SRR1377149 2 0.6049 0.935 0.164 0.688 0.028 0.092 0.028
#> SRR1377150 2 0.6049 0.935 0.164 0.688 0.028 0.092 0.028
#> SRR1377151 2 0.6049 0.935 0.164 0.688 0.028 0.092 0.028
#> SRR1377152 2 0.6049 0.935 0.164 0.688 0.028 0.092 0.028
#> SRR1377157 3 0.3006 0.930 0.008 0.056 0.888 0.028 0.020
#> SRR1377158 3 0.3006 0.930 0.008 0.056 0.888 0.028 0.020
#> SRR1377159 3 0.3006 0.930 0.008 0.056 0.888 0.028 0.020
#> SRR1377160 3 0.3006 0.930 0.008 0.056 0.888 0.028 0.020
#> SRR1377161 3 0.1441 0.957 0.008 0.024 0.956 0.004 0.008
#> SRR1377162 3 0.1441 0.957 0.008 0.024 0.956 0.004 0.008
#> SRR1377163 3 0.1441 0.957 0.008 0.024 0.956 0.004 0.008
#> SRR1377164 3 0.1441 0.957 0.008 0.024 0.956 0.004 0.008
#> SRR1377169 3 0.1473 0.955 0.008 0.008 0.956 0.020 0.008
#> SRR1377170 3 0.1473 0.955 0.008 0.008 0.956 0.020 0.008
#> SRR1377171 3 0.1473 0.955 0.008 0.008 0.956 0.020 0.008
#> SRR1377172 3 0.1473 0.955 0.008 0.008 0.956 0.020 0.008
#> SRR1377165 3 0.0451 0.961 0.008 0.000 0.988 0.000 0.004
#> SRR1377166 3 0.0451 0.961 0.008 0.000 0.988 0.000 0.004
#> SRR1377167 3 0.0451 0.961 0.008 0.000 0.988 0.000 0.004
#> SRR1377168 3 0.0451 0.961 0.008 0.000 0.988 0.000 0.004
#> SRR1377173 1 0.2032 0.597 0.924 0.020 0.052 0.004 0.000
#> SRR1377174 1 0.2032 0.597 0.924 0.020 0.052 0.004 0.000
#> SRR1377175 1 0.2032 0.597 0.924 0.020 0.052 0.004 0.000
#> SRR1377176 1 0.2032 0.597 0.924 0.020 0.052 0.004 0.000
#> SRR1377177 1 0.1502 0.597 0.940 0.004 0.056 0.000 0.000
#> SRR1377178 1 0.1502 0.597 0.940 0.004 0.056 0.000 0.000
#> SRR1377179 1 0.1502 0.597 0.940 0.004 0.056 0.000 0.000
#> SRR1377180 1 0.1502 0.597 0.940 0.004 0.056 0.000 0.000
#> SRR1377181 1 0.4755 0.497 0.800 0.080 0.048 0.032 0.040
#> SRR1377182 1 0.4755 0.497 0.800 0.080 0.048 0.032 0.040
#> SRR1377183 1 0.1901 0.593 0.928 0.004 0.056 0.012 0.000
#> SRR1377184 1 0.4755 0.497 0.800 0.080 0.048 0.032 0.040
#> SRR1377185 1 0.1901 0.593 0.928 0.004 0.056 0.012 0.000
#> SRR1377186 1 0.1901 0.593 0.928 0.004 0.056 0.012 0.000
#> SRR1377187 1 0.4755 0.497 0.800 0.080 0.048 0.032 0.040
#> SRR1377188 1 0.1901 0.593 0.928 0.004 0.056 0.012 0.000
#> SRR1377189 1 0.7626 0.472 0.472 0.144 0.036 0.316 0.032
#> SRR1377190 1 0.7626 0.472 0.472 0.144 0.036 0.316 0.032
#> SRR1377191 1 0.7626 0.472 0.472 0.144 0.036 0.316 0.032
#> SRR1377192 1 0.7626 0.472 0.472 0.144 0.036 0.316 0.032
#> SRR1377193 1 0.7626 0.472 0.472 0.144 0.036 0.316 0.032
#> SRR1377194 1 0.7626 0.472 0.472 0.144 0.036 0.316 0.032
#> SRR1377195 5 0.3332 0.998 0.120 0.028 0.000 0.008 0.844
#> SRR1377196 5 0.3332 0.998 0.120 0.028 0.000 0.008 0.844
#> SRR1377197 5 0.3332 0.998 0.120 0.028 0.000 0.008 0.844
#> SRR1377198 5 0.3544 0.995 0.120 0.028 0.000 0.016 0.836
#> SRR1377199 5 0.3415 0.997 0.120 0.032 0.000 0.008 0.840
#> SRR1377200 5 0.3415 0.997 0.120 0.032 0.000 0.008 0.840
#> SRR1377201 1 0.7525 0.578 0.556 0.128 0.036 0.220 0.060
#> SRR1377202 1 0.7525 0.578 0.556 0.128 0.036 0.220 0.060
#> SRR1377203 1 0.7525 0.578 0.556 0.128 0.036 0.220 0.060
#> SRR1377204 1 0.7654 0.523 0.496 0.132 0.024 0.288 0.060
#> SRR1377205 1 0.7654 0.523 0.496 0.132 0.024 0.288 0.060
#> SRR1377206 1 0.7654 0.523 0.496 0.132 0.024 0.288 0.060
#> SRR1377207 1 0.7525 0.578 0.556 0.128 0.036 0.220 0.060
#> SRR1377208 1 0.7525 0.578 0.556 0.128 0.036 0.220 0.060
#> SRR1377209 1 0.7525 0.578 0.556 0.128 0.036 0.220 0.060
#> SRR1377210 1 0.7365 0.569 0.552 0.144 0.036 0.232 0.036
#> SRR1377211 1 0.7365 0.569 0.552 0.144 0.036 0.232 0.036
#> SRR1377212 1 0.7365 0.569 0.552 0.144 0.036 0.232 0.036
#> SRR1377213 4 0.5460 0.930 0.140 0.012 0.160 0.688 0.000
#> SRR1377214 4 0.5460 0.930 0.140 0.012 0.160 0.688 0.000
#> SRR1377215 4 0.5460 0.930 0.140 0.012 0.160 0.688 0.000
#> SRR1377216 4 0.5560 0.923 0.136 0.012 0.176 0.676 0.000
#> SRR1377217 4 0.5560 0.923 0.136 0.012 0.176 0.676 0.000
#> SRR1377218 4 0.5560 0.923 0.136 0.012 0.176 0.676 0.000
#> SRR1377219 4 0.5609 0.930 0.136 0.012 0.164 0.684 0.004
#> SRR1377220 4 0.5609 0.930 0.136 0.012 0.164 0.684 0.004
#> SRR1377221 4 0.5609 0.930 0.136 0.012 0.164 0.684 0.004
#> SRR1377222 4 0.5644 0.791 0.168 0.044 0.052 0.716 0.020
#> SRR1377223 4 0.5644 0.791 0.168 0.044 0.052 0.716 0.020
#> SRR1377224 4 0.5644 0.791 0.168 0.044 0.052 0.716 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.391 0.919 0.048 0.044 0.012 NA 0.004 0.824
#> SRR1377146 6 0.391 0.919 0.048 0.044 0.012 NA 0.004 0.824
#> SRR1377147 6 0.391 0.919 0.048 0.044 0.012 NA 0.004 0.824
#> SRR1377148 6 0.391 0.919 0.048 0.044 0.012 NA 0.004 0.824
#> SRR1377153 6 0.348 0.920 0.028 0.036 0.012 NA 0.004 0.848
#> SRR1377154 6 0.348 0.920 0.028 0.036 0.012 NA 0.004 0.848
#> SRR1377155 6 0.348 0.920 0.028 0.036 0.012 NA 0.004 0.848
#> SRR1377156 6 0.348 0.920 0.028 0.036 0.012 NA 0.004 0.848
#> SRR1377149 6 0.346 0.925 0.040 0.040 0.016 NA 0.016 0.860
#> SRR1377150 6 0.342 0.925 0.044 0.040 0.012 NA 0.012 0.860
#> SRR1377151 6 0.342 0.925 0.044 0.040 0.012 NA 0.012 0.860
#> SRR1377152 6 0.343 0.925 0.044 0.040 0.012 NA 0.016 0.860
#> SRR1377157 3 0.307 0.857 0.000 0.008 0.788 NA 0.000 0.000
#> SRR1377158 3 0.318 0.857 0.000 0.008 0.788 NA 0.000 0.004
#> SRR1377159 3 0.318 0.857 0.000 0.008 0.788 NA 0.000 0.004
#> SRR1377160 3 0.318 0.857 0.000 0.008 0.788 NA 0.000 0.004
#> SRR1377161 3 0.186 0.924 0.000 0.012 0.932 NA 0.008 0.016
#> SRR1377162 3 0.186 0.924 0.000 0.012 0.932 NA 0.008 0.016
#> SRR1377163 3 0.186 0.924 0.000 0.012 0.932 NA 0.008 0.016
#> SRR1377164 3 0.186 0.924 0.000 0.012 0.932 NA 0.008 0.016
#> SRR1377169 3 0.206 0.916 0.012 0.008 0.924 NA 0.012 0.004
#> SRR1377170 3 0.206 0.916 0.012 0.008 0.924 NA 0.012 0.004
#> SRR1377171 3 0.206 0.916 0.012 0.008 0.924 NA 0.012 0.004
#> SRR1377172 3 0.206 0.916 0.012 0.008 0.924 NA 0.012 0.004
#> SRR1377165 3 0.026 0.928 0.000 0.008 0.992 NA 0.000 0.000
#> SRR1377166 3 0.026 0.928 0.000 0.008 0.992 NA 0.000 0.000
#> SRR1377167 3 0.026 0.928 0.000 0.008 0.992 NA 0.000 0.000
#> SRR1377168 3 0.026 0.928 0.000 0.008 0.992 NA 0.000 0.000
#> SRR1377173 1 0.412 0.886 0.808 0.084 0.028 NA 0.000 0.044
#> SRR1377174 1 0.412 0.886 0.808 0.084 0.028 NA 0.000 0.044
#> SRR1377175 1 0.412 0.886 0.808 0.084 0.028 NA 0.000 0.044
#> SRR1377176 1 0.412 0.886 0.808 0.084 0.028 NA 0.000 0.044
#> SRR1377177 1 0.343 0.893 0.840 0.084 0.028 NA 0.004 0.044
#> SRR1377178 1 0.343 0.893 0.840 0.084 0.028 NA 0.004 0.044
#> SRR1377179 1 0.343 0.893 0.840 0.084 0.028 NA 0.004 0.044
#> SRR1377180 1 0.343 0.893 0.840 0.084 0.028 NA 0.004 0.044
#> SRR1377181 1 0.447 0.733 0.756 0.048 0.024 NA 0.004 0.008
#> SRR1377182 1 0.465 0.733 0.748 0.048 0.024 NA 0.004 0.016
#> SRR1377183 1 0.386 0.890 0.820 0.088 0.028 NA 0.000 0.044
#> SRR1377184 1 0.447 0.733 0.756 0.048 0.024 NA 0.004 0.008
#> SRR1377185 1 0.386 0.890 0.820 0.088 0.028 NA 0.000 0.044
#> SRR1377186 1 0.386 0.890 0.820 0.088 0.028 NA 0.000 0.044
#> SRR1377187 1 0.449 0.733 0.756 0.052 0.024 NA 0.004 0.008
#> SRR1377188 1 0.386 0.890 0.820 0.088 0.028 NA 0.000 0.044
#> SRR1377189 2 0.772 0.535 0.248 0.420 0.008 NA 0.016 0.188
#> SRR1377190 2 0.772 0.535 0.248 0.420 0.008 NA 0.016 0.188
#> SRR1377191 2 0.772 0.535 0.248 0.420 0.008 NA 0.016 0.188
#> SRR1377192 2 0.772 0.535 0.248 0.420 0.008 NA 0.016 0.188
#> SRR1377193 2 0.772 0.535 0.248 0.420 0.008 NA 0.016 0.188
#> SRR1377194 2 0.772 0.535 0.248 0.420 0.008 NA 0.016 0.188
#> SRR1377195 5 0.148 0.987 0.036 0.008 0.000 NA 0.944 0.012
#> SRR1377196 5 0.148 0.987 0.036 0.008 0.000 NA 0.944 0.012
#> SRR1377197 5 0.148 0.987 0.036 0.008 0.000 NA 0.944 0.012
#> SRR1377198 5 0.201 0.983 0.040 0.008 0.000 NA 0.924 0.016
#> SRR1377199 5 0.245 0.975 0.036 0.008 0.000 NA 0.904 0.024
#> SRR1377200 5 0.258 0.973 0.040 0.012 0.004 NA 0.900 0.012
#> SRR1377201 2 0.800 0.518 0.276 0.388 0.012 NA 0.024 0.164
#> SRR1377202 2 0.800 0.518 0.276 0.388 0.012 NA 0.024 0.164
#> SRR1377203 2 0.800 0.518 0.276 0.388 0.012 NA 0.024 0.164
#> SRR1377204 2 0.776 0.519 0.228 0.412 0.000 NA 0.024 0.152
#> SRR1377205 2 0.776 0.519 0.228 0.412 0.000 NA 0.024 0.152
#> SRR1377206 2 0.776 0.519 0.228 0.412 0.000 NA 0.024 0.152
#> SRR1377207 2 0.802 0.518 0.276 0.384 0.012 NA 0.024 0.164
#> SRR1377208 2 0.802 0.518 0.276 0.384 0.012 NA 0.024 0.164
#> SRR1377209 2 0.802 0.518 0.276 0.384 0.012 NA 0.024 0.164
#> SRR1377210 2 0.790 0.514 0.284 0.384 0.012 NA 0.016 0.168
#> SRR1377211 2 0.790 0.514 0.284 0.384 0.012 NA 0.016 0.168
#> SRR1377212 2 0.790 0.514 0.284 0.384 0.012 NA 0.016 0.168
#> SRR1377213 2 0.319 0.474 0.056 0.844 0.088 NA 0.000 0.012
#> SRR1377214 2 0.319 0.474 0.056 0.844 0.088 NA 0.000 0.012
#> SRR1377215 2 0.319 0.474 0.056 0.844 0.088 NA 0.000 0.012
#> SRR1377216 2 0.363 0.463 0.060 0.816 0.108 NA 0.000 0.012
#> SRR1377217 2 0.363 0.463 0.060 0.816 0.108 NA 0.000 0.012
#> SRR1377218 2 0.363 0.463 0.060 0.816 0.108 NA 0.000 0.012
#> SRR1377219 2 0.319 0.474 0.056 0.844 0.088 NA 0.000 0.012
#> SRR1377220 2 0.319 0.474 0.056 0.844 0.088 NA 0.000 0.012
#> SRR1377221 2 0.319 0.474 0.056 0.844 0.088 NA 0.000 0.012
#> SRR1377222 2 0.411 0.444 0.028 0.804 0.036 NA 0.004 0.020
#> SRR1377223 2 0.411 0.444 0.028 0.804 0.036 NA 0.004 0.020
#> SRR1377224 2 0.411 0.444 0.028 0.804 0.036 NA 0.004 0.020
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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.739 0.782 0.910 0.4810 0.502 0.502
#> 3 3 0.711 0.670 0.840 0.3759 0.795 0.613
#> 4 4 0.725 0.828 0.877 0.1185 0.894 0.706
#> 5 5 0.859 0.866 0.915 0.0808 0.882 0.592
#> 6 6 0.915 0.909 0.934 0.0396 0.932 0.684
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> SRR1377145 2 0.9393 0.323 0.356 0.644
#> SRR1377146 2 0.9393 0.323 0.356 0.644
#> SRR1377147 2 0.9393 0.323 0.356 0.644
#> SRR1377148 2 0.9393 0.323 0.356 0.644
#> SRR1377153 1 0.9944 0.289 0.544 0.456
#> SRR1377154 1 0.9954 0.279 0.540 0.460
#> SRR1377155 1 0.9944 0.289 0.544 0.456
#> SRR1377156 1 0.9944 0.289 0.544 0.456
#> SRR1377149 1 0.9993 0.212 0.516 0.484
#> SRR1377150 1 0.9988 0.224 0.520 0.480
#> SRR1377151 1 0.9993 0.212 0.516 0.484
#> SRR1377152 1 0.9993 0.212 0.516 0.484
#> SRR1377157 2 0.0000 0.937 0.000 1.000
#> SRR1377158 2 0.0000 0.937 0.000 1.000
#> SRR1377159 2 0.0000 0.937 0.000 1.000
#> SRR1377160 2 0.0000 0.937 0.000 1.000
#> SRR1377161 2 0.0000 0.937 0.000 1.000
#> SRR1377162 2 0.0000 0.937 0.000 1.000
#> SRR1377163 2 0.0000 0.937 0.000 1.000
#> SRR1377164 2 0.0000 0.937 0.000 1.000
#> SRR1377169 2 0.0000 0.937 0.000 1.000
#> SRR1377170 2 0.0000 0.937 0.000 1.000
#> SRR1377171 2 0.0000 0.937 0.000 1.000
#> SRR1377172 2 0.0000 0.937 0.000 1.000
#> SRR1377165 2 0.0000 0.937 0.000 1.000
#> SRR1377166 2 0.0000 0.937 0.000 1.000
#> SRR1377167 2 0.0000 0.937 0.000 1.000
#> SRR1377168 2 0.0000 0.937 0.000 1.000
#> SRR1377173 2 0.3431 0.910 0.064 0.936
#> SRR1377174 2 0.3431 0.910 0.064 0.936
#> SRR1377175 2 0.3274 0.912 0.060 0.940
#> SRR1377176 2 0.3274 0.913 0.060 0.940
#> SRR1377177 2 0.2948 0.918 0.052 0.948
#> SRR1377178 2 0.2948 0.918 0.052 0.948
#> SRR1377179 2 0.2948 0.918 0.052 0.948
#> SRR1377180 2 0.2948 0.918 0.052 0.948
#> SRR1377181 2 0.2423 0.925 0.040 0.960
#> SRR1377182 2 0.2423 0.925 0.040 0.960
#> SRR1377183 2 0.2423 0.925 0.040 0.960
#> SRR1377184 2 0.2423 0.925 0.040 0.960
#> SRR1377185 2 0.2423 0.925 0.040 0.960
#> SRR1377186 2 0.2423 0.925 0.040 0.960
#> SRR1377187 2 0.2423 0.925 0.040 0.960
#> SRR1377188 2 0.2423 0.925 0.040 0.960
#> SRR1377189 1 0.0376 0.834 0.996 0.004
#> SRR1377190 1 0.0376 0.834 0.996 0.004
#> SRR1377191 1 0.0376 0.834 0.996 0.004
#> SRR1377192 1 0.0376 0.834 0.996 0.004
#> SRR1377193 1 0.0376 0.834 0.996 0.004
#> SRR1377194 1 0.0376 0.834 0.996 0.004
#> SRR1377195 1 0.0000 0.836 1.000 0.000
#> SRR1377196 1 0.0000 0.836 1.000 0.000
#> SRR1377197 1 0.0000 0.836 1.000 0.000
#> SRR1377198 1 0.0000 0.836 1.000 0.000
#> SRR1377199 1 0.0000 0.836 1.000 0.000
#> SRR1377200 1 0.0000 0.836 1.000 0.000
#> SRR1377201 1 0.0000 0.836 1.000 0.000
#> SRR1377202 1 0.0000 0.836 1.000 0.000
#> SRR1377203 1 0.0000 0.836 1.000 0.000
#> SRR1377204 1 0.0000 0.836 1.000 0.000
#> SRR1377205 1 0.0000 0.836 1.000 0.000
#> SRR1377206 1 0.0000 0.836 1.000 0.000
#> SRR1377207 1 0.0000 0.836 1.000 0.000
#> SRR1377208 1 0.0000 0.836 1.000 0.000
#> SRR1377209 1 0.0000 0.836 1.000 0.000
#> SRR1377210 1 0.0000 0.836 1.000 0.000
#> SRR1377211 1 0.0000 0.836 1.000 0.000
#> SRR1377212 1 0.0000 0.836 1.000 0.000
#> SRR1377213 2 0.0000 0.937 0.000 1.000
#> SRR1377214 2 0.0000 0.937 0.000 1.000
#> SRR1377215 2 0.0000 0.937 0.000 1.000
#> SRR1377216 2 0.0000 0.937 0.000 1.000
#> SRR1377217 2 0.0000 0.937 0.000 1.000
#> SRR1377218 2 0.0000 0.937 0.000 1.000
#> SRR1377219 2 0.0000 0.937 0.000 1.000
#> SRR1377220 2 0.0000 0.937 0.000 1.000
#> SRR1377221 2 0.0000 0.937 0.000 1.000
#> SRR1377222 1 0.9732 0.371 0.596 0.404
#> SRR1377223 1 0.9732 0.371 0.596 0.404
#> SRR1377224 1 0.9732 0.371 0.596 0.404
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 3 0.9934 -0.0908 0.292 0.320 0.388
#> SRR1377146 3 0.9934 -0.0908 0.292 0.320 0.388
#> SRR1377147 3 0.9934 -0.0908 0.292 0.320 0.388
#> SRR1377148 3 0.9934 -0.0908 0.292 0.320 0.388
#> SRR1377153 2 0.9908 0.1927 0.360 0.372 0.268
#> SRR1377154 2 0.9908 0.1927 0.360 0.372 0.268
#> SRR1377155 2 0.9908 0.1927 0.360 0.372 0.268
#> SRR1377156 2 0.9908 0.1927 0.360 0.372 0.268
#> SRR1377149 2 0.9975 0.1895 0.312 0.368 0.320
#> SRR1377150 2 0.9975 0.1895 0.312 0.368 0.320
#> SRR1377151 2 0.9975 0.1895 0.312 0.368 0.320
#> SRR1377152 2 0.9975 0.1895 0.312 0.368 0.320
#> SRR1377157 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377158 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377159 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377160 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377161 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377162 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377163 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377164 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377169 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377170 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377171 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377172 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377165 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377166 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377167 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377168 3 0.1163 0.8308 0.028 0.000 0.972
#> SRR1377173 1 0.0829 0.9924 0.984 0.004 0.012
#> SRR1377174 1 0.0829 0.9924 0.984 0.004 0.012
#> SRR1377175 1 0.0829 0.9924 0.984 0.004 0.012
#> SRR1377176 1 0.0829 0.9924 0.984 0.004 0.012
#> SRR1377177 1 0.0592 0.9951 0.988 0.000 0.012
#> SRR1377178 1 0.0592 0.9951 0.988 0.000 0.012
#> SRR1377179 1 0.0592 0.9951 0.988 0.000 0.012
#> SRR1377180 1 0.0592 0.9951 0.988 0.000 0.012
#> SRR1377181 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377182 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377183 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377184 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377185 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377186 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377187 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377188 1 0.0747 0.9954 0.984 0.000 0.016
#> SRR1377189 2 0.1315 0.7230 0.008 0.972 0.020
#> SRR1377190 2 0.1170 0.7233 0.008 0.976 0.016
#> SRR1377191 2 0.1315 0.7230 0.008 0.972 0.020
#> SRR1377192 2 0.1170 0.7233 0.008 0.976 0.016
#> SRR1377193 2 0.1170 0.7233 0.008 0.976 0.016
#> SRR1377194 2 0.1170 0.7233 0.008 0.976 0.016
#> SRR1377195 2 0.5810 0.4371 0.336 0.664 0.000
#> SRR1377196 2 0.5810 0.4371 0.336 0.664 0.000
#> SRR1377197 2 0.5810 0.4371 0.336 0.664 0.000
#> SRR1377198 2 0.5810 0.4371 0.336 0.664 0.000
#> SRR1377199 2 0.5810 0.4371 0.336 0.664 0.000
#> SRR1377200 2 0.5810 0.4371 0.336 0.664 0.000
#> SRR1377201 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377202 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377203 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377204 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377205 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377206 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377207 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377208 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377209 2 0.0892 0.7241 0.020 0.980 0.000
#> SRR1377210 2 0.2680 0.7004 0.008 0.924 0.068
#> SRR1377211 2 0.2680 0.7004 0.008 0.924 0.068
#> SRR1377212 2 0.2680 0.7004 0.008 0.924 0.068
#> SRR1377213 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377214 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377215 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377216 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377217 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377218 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377219 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377220 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377221 3 0.2187 0.8137 0.024 0.028 0.948
#> SRR1377222 3 0.7187 0.1071 0.024 0.480 0.496
#> SRR1377223 3 0.7187 0.1071 0.024 0.480 0.496
#> SRR1377224 3 0.7187 0.1071 0.024 0.480 0.496
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377146 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377147 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377148 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377153 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377154 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377155 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377156 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377149 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377150 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377151 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377152 4 0.0524 1.000 0.008 0.004 0.000 0.988
#> SRR1377157 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377158 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377159 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377160 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377161 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377162 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377163 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377164 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377169 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377170 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377171 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377172 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377165 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377166 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377167 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377168 3 0.5267 0.792 0.000 0.048 0.712 0.240
#> SRR1377173 2 0.0336 0.990 0.008 0.992 0.000 0.000
#> SRR1377174 2 0.0336 0.990 0.008 0.992 0.000 0.000
#> SRR1377175 2 0.0336 0.990 0.008 0.992 0.000 0.000
#> SRR1377176 2 0.0336 0.990 0.008 0.992 0.000 0.000
#> SRR1377177 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377178 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377179 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377180 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377181 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377182 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377183 2 0.0336 0.991 0.000 0.992 0.008 0.000
#> SRR1377184 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377185 2 0.0336 0.991 0.000 0.992 0.008 0.000
#> SRR1377186 2 0.0336 0.991 0.000 0.992 0.008 0.000
#> SRR1377187 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1377188 2 0.0336 0.991 0.000 0.992 0.008 0.000
#> SRR1377189 1 0.7058 0.612 0.572 0.000 0.200 0.228
#> SRR1377190 1 0.7058 0.612 0.572 0.000 0.200 0.228
#> SRR1377191 1 0.7058 0.612 0.572 0.000 0.200 0.228
#> SRR1377192 1 0.6946 0.626 0.588 0.000 0.200 0.212
#> SRR1377193 1 0.6975 0.622 0.584 0.000 0.200 0.216
#> SRR1377194 1 0.6946 0.626 0.588 0.000 0.200 0.212
#> SRR1377195 1 0.2593 0.795 0.892 0.104 0.000 0.004
#> SRR1377196 1 0.2593 0.795 0.892 0.104 0.000 0.004
#> SRR1377197 1 0.2593 0.795 0.892 0.104 0.000 0.004
#> SRR1377198 1 0.2593 0.795 0.892 0.104 0.000 0.004
#> SRR1377199 1 0.2593 0.795 0.892 0.104 0.000 0.004
#> SRR1377200 1 0.2593 0.795 0.892 0.104 0.000 0.004
#> SRR1377201 1 0.0707 0.829 0.980 0.000 0.000 0.020
#> SRR1377202 1 0.0707 0.829 0.980 0.000 0.000 0.020
#> SRR1377203 1 0.0707 0.829 0.980 0.000 0.000 0.020
#> SRR1377204 1 0.0000 0.827 1.000 0.000 0.000 0.000
#> SRR1377205 1 0.0000 0.827 1.000 0.000 0.000 0.000
#> SRR1377206 1 0.0000 0.827 1.000 0.000 0.000 0.000
#> SRR1377207 1 0.0707 0.829 0.980 0.000 0.000 0.020
#> SRR1377208 1 0.0707 0.829 0.980 0.000 0.000 0.020
#> SRR1377209 1 0.0707 0.829 0.980 0.000 0.000 0.020
#> SRR1377210 1 0.5171 0.754 0.760 0.000 0.112 0.128
#> SRR1377211 1 0.5171 0.754 0.760 0.000 0.112 0.128
#> SRR1377212 1 0.5171 0.754 0.760 0.000 0.112 0.128
#> SRR1377213 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377214 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377215 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377216 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377217 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377218 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377219 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377220 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377221 3 0.0469 0.737 0.000 0.000 0.988 0.012
#> SRR1377222 3 0.4836 0.276 0.320 0.000 0.672 0.008
#> SRR1377223 3 0.4836 0.276 0.320 0.000 0.672 0.008
#> SRR1377224 3 0.4836 0.276 0.320 0.000 0.672 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.0671 0.993 0.004 0.980 0.016 0.000 0.000
#> SRR1377146 2 0.0671 0.993 0.004 0.980 0.016 0.000 0.000
#> SRR1377147 2 0.0671 0.993 0.004 0.980 0.016 0.000 0.000
#> SRR1377148 2 0.0671 0.993 0.004 0.980 0.016 0.000 0.000
#> SRR1377153 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377154 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377155 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377156 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377149 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377150 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377151 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377152 2 0.0451 0.996 0.004 0.988 0.008 0.000 0.000
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR1377173 1 0.0324 0.994 0.992 0.000 0.004 0.004 0.000
#> SRR1377174 1 0.0162 0.992 0.996 0.000 0.000 0.004 0.000
#> SRR1377175 1 0.0324 0.994 0.992 0.000 0.004 0.004 0.000
#> SRR1377176 1 0.0324 0.994 0.992 0.000 0.004 0.004 0.000
#> SRR1377177 1 0.0162 0.995 0.996 0.000 0.004 0.000 0.000
#> SRR1377178 1 0.0162 0.995 0.996 0.000 0.004 0.000 0.000
#> SRR1377179 1 0.0162 0.995 0.996 0.000 0.004 0.000 0.000
#> SRR1377180 1 0.0162 0.995 0.996 0.000 0.004 0.000 0.000
#> SRR1377181 1 0.0290 0.994 0.992 0.000 0.008 0.000 0.000
#> SRR1377182 1 0.0290 0.994 0.992 0.000 0.008 0.000 0.000
#> SRR1377183 1 0.0290 0.993 0.992 0.000 0.000 0.008 0.000
#> SRR1377184 1 0.0290 0.994 0.992 0.000 0.008 0.000 0.000
#> SRR1377185 1 0.0290 0.993 0.992 0.000 0.000 0.008 0.000
#> SRR1377186 1 0.0290 0.993 0.992 0.000 0.000 0.008 0.000
#> SRR1377187 1 0.0290 0.994 0.992 0.000 0.008 0.000 0.000
#> SRR1377188 1 0.0290 0.993 0.992 0.000 0.000 0.008 0.000
#> SRR1377189 4 0.6188 0.306 0.000 0.136 0.000 0.448 0.416
#> SRR1377190 4 0.6188 0.306 0.000 0.136 0.000 0.448 0.416
#> SRR1377191 4 0.6188 0.306 0.000 0.136 0.000 0.448 0.416
#> SRR1377192 4 0.6160 0.302 0.000 0.132 0.000 0.448 0.420
#> SRR1377193 4 0.6160 0.302 0.000 0.132 0.000 0.448 0.420
#> SRR1377194 4 0.6160 0.302 0.000 0.132 0.000 0.448 0.420
#> SRR1377195 5 0.4229 0.828 0.080 0.020 0.000 0.096 0.804
#> SRR1377196 5 0.4229 0.828 0.080 0.020 0.000 0.096 0.804
#> SRR1377197 5 0.4229 0.828 0.080 0.020 0.000 0.096 0.804
#> SRR1377198 5 0.4229 0.828 0.080 0.020 0.000 0.096 0.804
#> SRR1377199 5 0.4229 0.828 0.080 0.020 0.000 0.096 0.804
#> SRR1377200 5 0.4229 0.828 0.080 0.020 0.000 0.096 0.804
#> SRR1377201 5 0.0000 0.861 0.000 0.000 0.000 0.000 1.000
#> SRR1377202 5 0.0000 0.861 0.000 0.000 0.000 0.000 1.000
#> SRR1377203 5 0.0000 0.861 0.000 0.000 0.000 0.000 1.000
#> SRR1377204 5 0.1502 0.861 0.004 0.000 0.000 0.056 0.940
#> SRR1377205 5 0.1502 0.861 0.004 0.000 0.000 0.056 0.940
#> SRR1377206 5 0.1502 0.861 0.004 0.000 0.000 0.056 0.940
#> SRR1377207 5 0.0000 0.861 0.000 0.000 0.000 0.000 1.000
#> SRR1377208 5 0.0000 0.861 0.000 0.000 0.000 0.000 1.000
#> SRR1377209 5 0.0000 0.861 0.000 0.000 0.000 0.000 1.000
#> SRR1377210 5 0.4333 0.606 0.000 0.060 0.000 0.188 0.752
#> SRR1377211 5 0.4333 0.606 0.000 0.060 0.000 0.188 0.752
#> SRR1377212 5 0.4333 0.606 0.000 0.060 0.000 0.188 0.752
#> SRR1377213 4 0.2230 0.764 0.000 0.000 0.116 0.884 0.000
#> SRR1377214 4 0.2230 0.764 0.000 0.000 0.116 0.884 0.000
#> SRR1377215 4 0.2230 0.764 0.000 0.000 0.116 0.884 0.000
#> SRR1377216 4 0.2280 0.761 0.000 0.000 0.120 0.880 0.000
#> SRR1377217 4 0.2280 0.761 0.000 0.000 0.120 0.880 0.000
#> SRR1377218 4 0.2280 0.761 0.000 0.000 0.120 0.880 0.000
#> SRR1377219 4 0.2230 0.764 0.000 0.000 0.116 0.884 0.000
#> SRR1377220 4 0.2230 0.764 0.000 0.000 0.116 0.884 0.000
#> SRR1377221 4 0.2230 0.764 0.000 0.000 0.116 0.884 0.000
#> SRR1377222 4 0.1310 0.730 0.000 0.000 0.020 0.956 0.024
#> SRR1377223 4 0.1310 0.730 0.000 0.000 0.020 0.956 0.024
#> SRR1377224 4 0.1310 0.730 0.000 0.000 0.020 0.956 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377146 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377147 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377148 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377153 6 0.0291 0.995 0.000 0.004 0.000 0.000 0.004 0.992
#> SRR1377154 6 0.0291 0.995 0.000 0.004 0.000 0.000 0.004 0.992
#> SRR1377155 6 0.0291 0.995 0.000 0.004 0.000 0.000 0.004 0.992
#> SRR1377156 6 0.0291 0.995 0.000 0.004 0.000 0.000 0.004 0.992
#> SRR1377149 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377150 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377151 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377152 6 0.0000 0.997 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377173 1 0.0458 0.987 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1377174 1 0.0458 0.987 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1377175 1 0.0363 0.988 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1377176 1 0.0458 0.987 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1377177 1 0.0291 0.990 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR1377178 1 0.0291 0.990 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR1377179 1 0.0291 0.990 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR1377180 1 0.0291 0.990 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR1377181 1 0.0653 0.988 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377182 1 0.0653 0.988 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377183 1 0.0653 0.989 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377184 1 0.0653 0.988 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377185 1 0.0653 0.989 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377186 1 0.0653 0.989 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377187 1 0.0653 0.988 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377188 1 0.0653 0.989 0.980 0.004 0.004 0.000 0.012 0.000
#> SRR1377189 2 0.5223 0.566 0.004 0.688 0.000 0.172 0.096 0.040
#> SRR1377190 2 0.5223 0.566 0.004 0.688 0.000 0.172 0.096 0.040
#> SRR1377191 2 0.5223 0.566 0.004 0.688 0.000 0.172 0.096 0.040
#> SRR1377192 2 0.5223 0.566 0.004 0.688 0.000 0.172 0.096 0.040
#> SRR1377193 2 0.5223 0.566 0.004 0.688 0.000 0.172 0.096 0.040
#> SRR1377194 2 0.5223 0.566 0.004 0.688 0.000 0.172 0.096 0.040
#> SRR1377195 5 0.1088 1.000 0.016 0.024 0.000 0.000 0.960 0.000
#> SRR1377196 5 0.1088 1.000 0.016 0.024 0.000 0.000 0.960 0.000
#> SRR1377197 5 0.1088 1.000 0.016 0.024 0.000 0.000 0.960 0.000
#> SRR1377198 5 0.1088 1.000 0.016 0.024 0.000 0.000 0.960 0.000
#> SRR1377199 5 0.1088 1.000 0.016 0.024 0.000 0.000 0.960 0.000
#> SRR1377200 5 0.1088 1.000 0.016 0.024 0.000 0.000 0.960 0.000
#> SRR1377201 2 0.3101 0.664 0.000 0.756 0.000 0.000 0.244 0.000
#> SRR1377202 2 0.3101 0.664 0.000 0.756 0.000 0.000 0.244 0.000
#> SRR1377203 2 0.3101 0.664 0.000 0.756 0.000 0.000 0.244 0.000
#> SRR1377204 2 0.3789 0.587 0.000 0.660 0.000 0.008 0.332 0.000
#> SRR1377205 2 0.3789 0.587 0.000 0.660 0.000 0.008 0.332 0.000
#> SRR1377206 2 0.3789 0.587 0.000 0.660 0.000 0.008 0.332 0.000
#> SRR1377207 2 0.3126 0.662 0.000 0.752 0.000 0.000 0.248 0.000
#> SRR1377208 2 0.3126 0.662 0.000 0.752 0.000 0.000 0.248 0.000
#> SRR1377209 2 0.3126 0.662 0.000 0.752 0.000 0.000 0.248 0.000
#> SRR1377210 2 0.1620 0.670 0.000 0.940 0.000 0.024 0.024 0.012
#> SRR1377211 2 0.1620 0.670 0.000 0.940 0.000 0.024 0.024 0.012
#> SRR1377212 2 0.1620 0.670 0.000 0.940 0.000 0.024 0.024 0.012
#> SRR1377213 4 0.0603 0.989 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR1377214 4 0.0603 0.989 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR1377215 4 0.0603 0.989 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR1377216 4 0.0777 0.984 0.000 0.000 0.024 0.972 0.000 0.004
#> SRR1377217 4 0.0777 0.984 0.000 0.000 0.024 0.972 0.000 0.004
#> SRR1377218 4 0.0777 0.984 0.000 0.000 0.024 0.972 0.000 0.004
#> SRR1377219 4 0.0603 0.989 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR1377220 4 0.0603 0.989 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR1377221 4 0.0603 0.989 0.000 0.000 0.016 0.980 0.000 0.004
#> SRR1377222 4 0.0405 0.974 0.000 0.004 0.000 0.988 0.008 0.000
#> SRR1377223 4 0.0405 0.974 0.000 0.004 0.000 0.988 0.008 0.000
#> SRR1377224 4 0.0405 0.974 0.000 0.004 0.000 0.988 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13890 rows and 80 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.995 0.1452 0.859 0.859
#> 3 3 0.577 0.919 0.933 2.0363 0.706 0.658
#> 4 4 1.000 0.995 0.995 0.3851 0.825 0.691
#> 5 5 1.000 0.999 1.000 0.2462 0.848 0.612
#> 6 6 0.885 0.855 0.905 0.0585 0.974 0.893
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 4
There is also optional best \(k\) = 2 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 2 0.0938 0.994 0.012 0.988
#> SRR1377146 2 0.0672 0.994 0.008 0.992
#> SRR1377147 2 0.0672 0.994 0.008 0.992
#> SRR1377148 2 0.0672 0.994 0.008 0.992
#> SRR1377153 2 0.0938 0.994 0.012 0.988
#> SRR1377154 2 0.0938 0.994 0.012 0.988
#> SRR1377155 2 0.0938 0.994 0.012 0.988
#> SRR1377156 2 0.0938 0.994 0.012 0.988
#> SRR1377149 2 0.0938 0.994 0.012 0.988
#> SRR1377150 2 0.0938 0.994 0.012 0.988
#> SRR1377151 2 0.0938 0.994 0.012 0.988
#> SRR1377152 2 0.0938 0.994 0.012 0.988
#> SRR1377157 2 0.0000 0.994 0.000 1.000
#> SRR1377158 2 0.0000 0.994 0.000 1.000
#> SRR1377159 2 0.0000 0.994 0.000 1.000
#> SRR1377160 2 0.0000 0.994 0.000 1.000
#> SRR1377161 2 0.0000 0.994 0.000 1.000
#> SRR1377162 2 0.0000 0.994 0.000 1.000
#> SRR1377163 2 0.0000 0.994 0.000 1.000
#> SRR1377164 2 0.0000 0.994 0.000 1.000
#> SRR1377169 2 0.0672 0.994 0.008 0.992
#> SRR1377170 2 0.0000 0.994 0.000 1.000
#> SRR1377171 2 0.0000 0.994 0.000 1.000
#> SRR1377172 2 0.0000 0.994 0.000 1.000
#> SRR1377165 2 0.0000 0.994 0.000 1.000
#> SRR1377166 2 0.0000 0.994 0.000 1.000
#> SRR1377167 2 0.0000 0.994 0.000 1.000
#> SRR1377168 2 0.0000 0.994 0.000 1.000
#> SRR1377173 2 0.0938 0.994 0.012 0.988
#> SRR1377174 2 0.0938 0.994 0.012 0.988
#> SRR1377175 2 0.0938 0.994 0.012 0.988
#> SRR1377176 2 0.0938 0.994 0.012 0.988
#> SRR1377177 2 0.0672 0.994 0.008 0.992
#> SRR1377178 2 0.0672 0.994 0.008 0.992
#> SRR1377179 2 0.0672 0.994 0.008 0.992
#> SRR1377180 2 0.0672 0.994 0.008 0.992
#> SRR1377181 2 0.0000 0.994 0.000 1.000
#> SRR1377182 2 0.0000 0.994 0.000 1.000
#> SRR1377183 2 0.0000 0.994 0.000 1.000
#> SRR1377184 2 0.0000 0.994 0.000 1.000
#> SRR1377185 2 0.0000 0.994 0.000 1.000
#> SRR1377186 2 0.0376 0.994 0.004 0.996
#> SRR1377187 2 0.0000 0.994 0.000 1.000
#> SRR1377188 2 0.0000 0.994 0.000 1.000
#> SRR1377189 2 0.0938 0.994 0.012 0.988
#> SRR1377190 2 0.0938 0.994 0.012 0.988
#> SRR1377191 2 0.0938 0.994 0.012 0.988
#> SRR1377192 2 0.0938 0.994 0.012 0.988
#> SRR1377193 2 0.0938 0.994 0.012 0.988
#> SRR1377194 2 0.0938 0.994 0.012 0.988
#> SRR1377195 1 0.0000 1.000 1.000 0.000
#> SRR1377196 1 0.0000 1.000 1.000 0.000
#> SRR1377197 1 0.0000 1.000 1.000 0.000
#> SRR1377198 1 0.0000 1.000 1.000 0.000
#> SRR1377199 1 0.0000 1.000 1.000 0.000
#> SRR1377200 1 0.0000 1.000 1.000 0.000
#> SRR1377201 2 0.0938 0.994 0.012 0.988
#> SRR1377202 2 0.0938 0.994 0.012 0.988
#> SRR1377203 2 0.0938 0.994 0.012 0.988
#> SRR1377204 2 0.0938 0.994 0.012 0.988
#> SRR1377205 2 0.0938 0.994 0.012 0.988
#> SRR1377206 2 0.0938 0.994 0.012 0.988
#> SRR1377207 2 0.0938 0.994 0.012 0.988
#> SRR1377208 2 0.0938 0.994 0.012 0.988
#> SRR1377209 2 0.0938 0.994 0.012 0.988
#> SRR1377210 2 0.0938 0.994 0.012 0.988
#> SRR1377211 2 0.0938 0.994 0.012 0.988
#> SRR1377212 2 0.0938 0.994 0.012 0.988
#> SRR1377213 2 0.0000 0.994 0.000 1.000
#> SRR1377214 2 0.0000 0.994 0.000 1.000
#> SRR1377215 2 0.0000 0.994 0.000 1.000
#> SRR1377216 2 0.0000 0.994 0.000 1.000
#> SRR1377217 2 0.0000 0.994 0.000 1.000
#> SRR1377218 2 0.0000 0.994 0.000 1.000
#> SRR1377219 2 0.0000 0.994 0.000 1.000
#> SRR1377220 2 0.0000 0.994 0.000 1.000
#> SRR1377221 2 0.0000 0.994 0.000 1.000
#> SRR1377222 2 0.0000 0.994 0.000 1.000
#> SRR1377223 2 0.0000 0.994 0.000 1.000
#> SRR1377224 2 0.0000 0.994 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.0000 0.941 0 1.000 0.000
#> SRR1377146 2 0.0000 0.941 0 1.000 0.000
#> SRR1377147 2 0.0000 0.941 0 1.000 0.000
#> SRR1377148 2 0.0000 0.941 0 1.000 0.000
#> SRR1377153 2 0.0000 0.941 0 1.000 0.000
#> SRR1377154 2 0.0000 0.941 0 1.000 0.000
#> SRR1377155 2 0.0000 0.941 0 1.000 0.000
#> SRR1377156 2 0.0000 0.941 0 1.000 0.000
#> SRR1377149 2 0.0000 0.941 0 1.000 0.000
#> SRR1377150 2 0.0000 0.941 0 1.000 0.000
#> SRR1377151 2 0.0000 0.941 0 1.000 0.000
#> SRR1377152 2 0.0000 0.941 0 1.000 0.000
#> SRR1377157 3 0.3619 0.954 0 0.136 0.864
#> SRR1377158 3 0.2796 0.914 0 0.092 0.908
#> SRR1377159 3 0.3340 0.948 0 0.120 0.880
#> SRR1377160 3 0.3340 0.948 0 0.120 0.880
#> SRR1377161 3 0.2959 0.924 0 0.100 0.900
#> SRR1377162 3 0.3482 0.952 0 0.128 0.872
#> SRR1377163 3 0.3267 0.945 0 0.116 0.884
#> SRR1377164 3 0.2959 0.924 0 0.100 0.900
#> SRR1377169 3 0.4750 0.862 0 0.216 0.784
#> SRR1377170 3 0.4002 0.944 0 0.160 0.840
#> SRR1377171 3 0.4062 0.940 0 0.164 0.836
#> SRR1377172 3 0.3941 0.949 0 0.156 0.844
#> SRR1377165 3 0.3686 0.954 0 0.140 0.860
#> SRR1377166 3 0.3941 0.948 0 0.156 0.844
#> SRR1377167 3 0.4062 0.939 0 0.164 0.836
#> SRR1377168 3 0.3879 0.951 0 0.152 0.848
#> SRR1377173 2 0.0000 0.941 0 1.000 0.000
#> SRR1377174 2 0.0000 0.941 0 1.000 0.000
#> SRR1377175 2 0.0237 0.939 0 0.996 0.004
#> SRR1377176 2 0.0000 0.941 0 1.000 0.000
#> SRR1377177 2 0.0000 0.941 0 1.000 0.000
#> SRR1377178 2 0.0000 0.941 0 1.000 0.000
#> SRR1377179 2 0.0000 0.941 0 1.000 0.000
#> SRR1377180 2 0.0000 0.941 0 1.000 0.000
#> SRR1377181 2 0.2878 0.882 0 0.904 0.096
#> SRR1377182 2 0.1860 0.913 0 0.948 0.052
#> SRR1377183 2 0.1163 0.926 0 0.972 0.028
#> SRR1377184 2 0.1753 0.914 0 0.952 0.048
#> SRR1377185 2 0.1753 0.915 0 0.952 0.048
#> SRR1377186 2 0.0747 0.933 0 0.984 0.016
#> SRR1377187 2 0.1964 0.909 0 0.944 0.056
#> SRR1377188 2 0.0892 0.931 0 0.980 0.020
#> SRR1377189 2 0.0000 0.941 0 1.000 0.000
#> SRR1377190 2 0.0000 0.941 0 1.000 0.000
#> SRR1377191 2 0.0000 0.941 0 1.000 0.000
#> SRR1377192 2 0.0000 0.941 0 1.000 0.000
#> SRR1377193 2 0.0000 0.941 0 1.000 0.000
#> SRR1377194 2 0.0000 0.941 0 1.000 0.000
#> SRR1377195 1 0.0000 1.000 1 0.000 0.000
#> SRR1377196 1 0.0000 1.000 1 0.000 0.000
#> SRR1377197 1 0.0000 1.000 1 0.000 0.000
#> SRR1377198 1 0.0000 1.000 1 0.000 0.000
#> SRR1377199 1 0.0000 1.000 1 0.000 0.000
#> SRR1377200 1 0.0000 1.000 1 0.000 0.000
#> SRR1377201 2 0.0000 0.941 0 1.000 0.000
#> SRR1377202 2 0.0000 0.941 0 1.000 0.000
#> SRR1377203 2 0.0000 0.941 0 1.000 0.000
#> SRR1377204 2 0.0000 0.941 0 1.000 0.000
#> SRR1377205 2 0.0000 0.941 0 1.000 0.000
#> SRR1377206 2 0.0000 0.941 0 1.000 0.000
#> SRR1377207 2 0.0000 0.941 0 1.000 0.000
#> SRR1377208 2 0.0000 0.941 0 1.000 0.000
#> SRR1377209 2 0.0000 0.941 0 1.000 0.000
#> SRR1377210 2 0.0000 0.941 0 1.000 0.000
#> SRR1377211 2 0.0000 0.941 0 1.000 0.000
#> SRR1377212 2 0.0000 0.941 0 1.000 0.000
#> SRR1377213 2 0.4931 0.787 0 0.768 0.232
#> SRR1377214 2 0.4931 0.787 0 0.768 0.232
#> SRR1377215 2 0.4931 0.787 0 0.768 0.232
#> SRR1377216 2 0.4931 0.787 0 0.768 0.232
#> SRR1377217 2 0.4931 0.787 0 0.768 0.232
#> SRR1377218 2 0.4931 0.787 0 0.768 0.232
#> SRR1377219 2 0.4931 0.787 0 0.768 0.232
#> SRR1377220 2 0.4931 0.787 0 0.768 0.232
#> SRR1377221 2 0.4931 0.787 0 0.768 0.232
#> SRR1377222 2 0.4931 0.787 0 0.768 0.232
#> SRR1377223 2 0.4931 0.787 0 0.768 0.232
#> SRR1377224 2 0.4931 0.787 0 0.768 0.232
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377146 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377147 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377148 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377153 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377154 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377155 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377156 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377149 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377150 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377151 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377152 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377157 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377158 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377159 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377160 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377161 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377162 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377163 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377164 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377169 3 0.0524 0.985 0 0.008 0.988 0.004
#> SRR1377170 3 0.0188 0.994 0 0.004 0.996 0.000
#> SRR1377171 3 0.0188 0.994 0 0.004 0.996 0.000
#> SRR1377172 3 0.0188 0.994 0 0.004 0.996 0.000
#> SRR1377165 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377166 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377167 3 0.0188 0.994 0 0.004 0.996 0.000
#> SRR1377168 3 0.0000 0.997 0 0.000 1.000 0.000
#> SRR1377173 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377174 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377175 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377176 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377177 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377178 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377179 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377180 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377181 2 0.1022 0.975 0 0.968 0.000 0.032
#> SRR1377182 2 0.0937 0.983 0 0.976 0.012 0.012
#> SRR1377183 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377184 2 0.0657 0.989 0 0.984 0.004 0.012
#> SRR1377185 2 0.0817 0.983 0 0.976 0.000 0.024
#> SRR1377186 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377187 2 0.0469 0.991 0 0.988 0.000 0.012
#> SRR1377188 2 0.0592 0.989 0 0.984 0.000 0.016
#> SRR1377189 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377190 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377191 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377192 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377193 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377194 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377195 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377196 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377197 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377198 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377199 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377200 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1377201 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377202 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377203 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377204 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377205 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377206 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377207 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377208 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377209 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377210 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377211 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377212 2 0.0000 0.995 0 1.000 0.000 0.000
#> SRR1377213 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377214 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377215 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377216 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377217 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377218 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377219 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377220 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377221 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377222 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377223 4 0.0469 1.000 0 0.000 0.012 0.988
#> SRR1377224 4 0.0469 1.000 0 0.000 0.012 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377146 2 0.0162 0.995 0.000 0.996 0.004 0 0
#> SRR1377147 2 0.0693 0.980 0.008 0.980 0.012 0 0
#> SRR1377148 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377153 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377154 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377155 2 0.0290 0.991 0.008 0.992 0.000 0 0
#> SRR1377156 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377149 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377150 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377151 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377152 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1.000 0 0
#> SRR1377173 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377174 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377175 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377176 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377177 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377178 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377179 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377180 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377181 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377182 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377183 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377184 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377185 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377186 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377187 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377188 1 0.0000 1.000 1.000 0.000 0.000 0 0
#> SRR1377189 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377190 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377191 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377192 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377193 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377194 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377195 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR1377196 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR1377197 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR1377198 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR1377199 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR1377200 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR1377201 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377202 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377203 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377204 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377205 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377206 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377207 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377208 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377209 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377210 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377211 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377212 2 0.0000 0.999 0.000 1.000 0.000 0 0
#> SRR1377213 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377214 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377215 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377216 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377217 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377218 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377219 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377220 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377221 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377222 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377223 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR1377224 4 0.0000 1.000 0.000 0.000 0.000 1 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 2 0.2003 0.570 0 0.884 0 0 0 0.116
#> SRR1377146 2 0.2135 0.570 0 0.872 0 0 0 0.128
#> SRR1377147 2 0.2003 0.570 0 0.884 0 0 0 0.116
#> SRR1377148 2 0.2003 0.570 0 0.884 0 0 0 0.116
#> SRR1377153 2 0.2260 0.641 0 0.860 0 0 0 0.140
#> SRR1377154 2 0.2135 0.628 0 0.872 0 0 0 0.128
#> SRR1377155 2 0.1204 0.640 0 0.944 0 0 0 0.056
#> SRR1377156 2 0.2178 0.629 0 0.868 0 0 0 0.132
#> SRR1377149 2 0.0937 0.656 0 0.960 0 0 0 0.040
#> SRR1377150 2 0.1007 0.647 0 0.956 0 0 0 0.044
#> SRR1377151 2 0.1501 0.633 0 0.924 0 0 0 0.076
#> SRR1377152 2 0.1075 0.654 0 0.952 0 0 0 0.048
#> SRR1377157 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377158 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377159 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377160 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377161 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377162 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377163 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377164 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377169 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377170 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377171 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377172 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377165 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377166 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377167 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377168 3 0.0000 1.000 0 0.000 1 0 0 0.000
#> SRR1377173 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377174 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377175 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377176 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377177 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377178 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377179 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377180 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377181 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377182 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377183 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377184 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377185 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377186 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377187 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377188 1 0.0000 1.000 1 0.000 0 0 0 0.000
#> SRR1377189 2 0.3428 0.559 0 0.696 0 0 0 0.304
#> SRR1377190 2 0.3428 0.559 0 0.696 0 0 0 0.304
#> SRR1377191 2 0.3428 0.559 0 0.696 0 0 0 0.304
#> SRR1377192 2 0.3428 0.559 0 0.696 0 0 0 0.304
#> SRR1377193 2 0.3428 0.559 0 0.696 0 0 0 0.304
#> SRR1377194 2 0.3428 0.559 0 0.696 0 0 0 0.304
#> SRR1377195 5 0.0000 1.000 0 0.000 0 0 1 0.000
#> SRR1377196 5 0.0000 1.000 0 0.000 0 0 1 0.000
#> SRR1377197 5 0.0000 1.000 0 0.000 0 0 1 0.000
#> SRR1377198 5 0.0000 1.000 0 0.000 0 0 1 0.000
#> SRR1377199 5 0.0000 1.000 0 0.000 0 0 1 0.000
#> SRR1377200 5 0.0000 1.000 0 0.000 0 0 1 0.000
#> SRR1377201 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377202 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377203 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377204 6 0.3330 1.000 0 0.284 0 0 0 0.716
#> SRR1377205 6 0.3330 1.000 0 0.284 0 0 0 0.716
#> SRR1377206 6 0.3330 1.000 0 0.284 0 0 0 0.716
#> SRR1377207 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377208 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377209 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377210 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377211 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377212 2 0.3531 0.513 0 0.672 0 0 0 0.328
#> SRR1377213 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377214 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377215 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377216 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377217 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377218 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377219 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377220 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377221 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377222 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377223 4 0.0000 1.000 0 0.000 0 1 0 0.000
#> SRR1377224 4 0.0000 1.000 0 0.000 0 1 0 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.432 0.534 0.803 0.429 0.585 0.585
#> 3 3 0.573 0.847 0.879 0.427 0.618 0.432
#> 4 4 0.958 0.955 0.979 0.105 0.943 0.849
#> 5 5 0.945 0.920 0.952 0.113 0.932 0.787
#> 6 6 0.969 0.960 0.976 0.101 0.919 0.678
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 4 5
There is also optional best \(k\) = 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 1 0.0000 0.665 1.000 0.000
#> SRR1377146 1 0.0000 0.665 1.000 0.000
#> SRR1377147 1 0.0000 0.665 1.000 0.000
#> SRR1377148 1 0.0000 0.665 1.000 0.000
#> SRR1377153 1 0.0000 0.665 1.000 0.000
#> SRR1377154 1 0.0000 0.665 1.000 0.000
#> SRR1377155 1 0.0000 0.665 1.000 0.000
#> SRR1377156 1 0.0000 0.665 1.000 0.000
#> SRR1377149 1 0.0000 0.665 1.000 0.000
#> SRR1377150 1 0.0000 0.665 1.000 0.000
#> SRR1377151 1 0.0000 0.665 1.000 0.000
#> SRR1377152 1 0.0000 0.665 1.000 0.000
#> SRR1377157 2 0.0000 0.710 0.000 1.000
#> SRR1377158 2 0.0000 0.710 0.000 1.000
#> SRR1377159 2 0.0000 0.710 0.000 1.000
#> SRR1377160 2 0.0000 0.710 0.000 1.000
#> SRR1377161 2 0.0000 0.710 0.000 1.000
#> SRR1377162 2 0.0000 0.710 0.000 1.000
#> SRR1377163 2 0.0000 0.710 0.000 1.000
#> SRR1377164 2 0.0000 0.710 0.000 1.000
#> SRR1377169 2 0.0000 0.710 0.000 1.000
#> SRR1377170 2 0.0000 0.710 0.000 1.000
#> SRR1377171 2 0.0000 0.710 0.000 1.000
#> SRR1377172 2 0.0000 0.710 0.000 1.000
#> SRR1377165 2 0.0000 0.710 0.000 1.000
#> SRR1377166 2 0.0000 0.710 0.000 1.000
#> SRR1377167 2 0.0000 0.710 0.000 1.000
#> SRR1377168 2 0.0000 0.710 0.000 1.000
#> SRR1377173 2 0.0000 0.710 0.000 1.000
#> SRR1377174 2 0.0000 0.710 0.000 1.000
#> SRR1377175 2 0.0000 0.710 0.000 1.000
#> SRR1377176 2 0.0000 0.710 0.000 1.000
#> SRR1377177 2 0.0000 0.710 0.000 1.000
#> SRR1377178 2 0.0000 0.710 0.000 1.000
#> SRR1377179 2 0.0000 0.710 0.000 1.000
#> SRR1377180 2 0.0000 0.710 0.000 1.000
#> SRR1377181 2 0.0000 0.710 0.000 1.000
#> SRR1377182 2 0.0000 0.710 0.000 1.000
#> SRR1377183 2 0.0376 0.709 0.004 0.996
#> SRR1377184 2 0.0000 0.710 0.000 1.000
#> SRR1377185 2 0.0376 0.709 0.004 0.996
#> SRR1377186 2 0.0376 0.709 0.004 0.996
#> SRR1377187 2 0.0000 0.710 0.000 1.000
#> SRR1377188 2 0.0376 0.709 0.004 0.996
#> SRR1377189 1 0.9996 -0.327 0.512 0.488
#> SRR1377190 2 1.0000 0.325 0.496 0.504
#> SRR1377191 1 1.0000 -0.356 0.500 0.500
#> SRR1377192 2 0.9850 0.468 0.428 0.572
#> SRR1377193 2 0.9850 0.468 0.428 0.572
#> SRR1377194 2 0.9850 0.468 0.428 0.572
#> SRR1377195 1 0.9775 0.392 0.588 0.412
#> SRR1377196 1 0.9775 0.392 0.588 0.412
#> SRR1377197 1 0.9775 0.392 0.588 0.412
#> SRR1377198 1 0.9775 0.392 0.588 0.412
#> SRR1377199 1 0.9775 0.392 0.588 0.412
#> SRR1377200 1 0.9775 0.392 0.588 0.412
#> SRR1377201 2 0.9998 0.316 0.492 0.508
#> SRR1377202 2 1.0000 0.309 0.496 0.504
#> SRR1377203 2 0.9983 0.341 0.476 0.524
#> SRR1377204 2 0.9795 0.487 0.416 0.584
#> SRR1377205 2 0.9795 0.487 0.416 0.584
#> SRR1377206 2 0.9795 0.487 0.416 0.584
#> SRR1377207 2 0.9933 0.384 0.452 0.548
#> SRR1377208 2 0.9754 0.459 0.408 0.592
#> SRR1377209 2 0.9933 0.381 0.452 0.548
#> SRR1377210 1 0.9983 -0.298 0.524 0.476
#> SRR1377211 1 0.9983 -0.298 0.524 0.476
#> SRR1377212 1 0.9983 -0.298 0.524 0.476
#> SRR1377213 2 0.9795 0.487 0.416 0.584
#> SRR1377214 2 0.9795 0.487 0.416 0.584
#> SRR1377215 2 0.9795 0.487 0.416 0.584
#> SRR1377216 2 0.9795 0.487 0.416 0.584
#> SRR1377217 2 0.9795 0.487 0.416 0.584
#> SRR1377218 2 0.9795 0.487 0.416 0.584
#> SRR1377219 2 0.9795 0.487 0.416 0.584
#> SRR1377220 2 0.9795 0.487 0.416 0.584
#> SRR1377221 2 0.9795 0.487 0.416 0.584
#> SRR1377222 2 0.9795 0.487 0.416 0.584
#> SRR1377223 2 0.9795 0.487 0.416 0.584
#> SRR1377224 2 0.9795 0.487 0.416 0.584
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377146 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377147 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377148 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377153 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377154 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377155 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377156 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377149 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377150 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377151 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377152 1 0.000 1.000 1.000 0.000 0.000
#> SRR1377157 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377158 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377159 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377160 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377161 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377162 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377163 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377164 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377169 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377170 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377171 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377172 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377165 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377166 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377167 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377168 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377173 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377174 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377175 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377176 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377177 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377178 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377179 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377180 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377181 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377182 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377183 3 0.400 0.771 0.000 0.160 0.840
#> SRR1377184 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377185 3 0.400 0.771 0.000 0.160 0.840
#> SRR1377186 3 0.400 0.771 0.000 0.160 0.840
#> SRR1377187 3 0.000 0.973 0.000 0.000 1.000
#> SRR1377188 3 0.406 0.764 0.000 0.164 0.836
#> SRR1377189 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377190 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377191 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377192 2 0.676 0.773 0.224 0.716 0.060
#> SRR1377193 2 0.676 0.773 0.224 0.716 0.060
#> SRR1377194 2 0.703 0.774 0.196 0.716 0.088
#> SRR1377195 2 0.574 0.512 0.012 0.732 0.256
#> SRR1377196 2 0.574 0.512 0.012 0.732 0.256
#> SRR1377197 2 0.574 0.512 0.012 0.732 0.256
#> SRR1377198 2 0.574 0.512 0.012 0.732 0.256
#> SRR1377199 2 0.574 0.512 0.012 0.732 0.256
#> SRR1377200 2 0.574 0.512 0.012 0.732 0.256
#> SRR1377201 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377202 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377203 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377204 2 0.678 0.697 0.052 0.704 0.244
#> SRR1377205 2 0.678 0.697 0.052 0.704 0.244
#> SRR1377206 2 0.678 0.697 0.052 0.704 0.244
#> SRR1377207 2 0.722 0.763 0.140 0.716 0.144
#> SRR1377208 2 0.715 0.751 0.108 0.716 0.176
#> SRR1377209 2 0.722 0.766 0.152 0.716 0.132
#> SRR1377210 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377211 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377212 2 0.661 0.771 0.236 0.716 0.048
#> SRR1377213 2 0.531 0.759 0.216 0.772 0.012
#> SRR1377214 2 0.531 0.759 0.216 0.772 0.012
#> SRR1377215 2 0.531 0.759 0.216 0.772 0.012
#> SRR1377216 2 0.518 0.702 0.000 0.744 0.256
#> SRR1377217 2 0.518 0.702 0.000 0.744 0.256
#> SRR1377218 2 0.518 0.702 0.000 0.744 0.256
#> SRR1377219 2 0.541 0.757 0.224 0.764 0.012
#> SRR1377220 2 0.541 0.757 0.224 0.764 0.012
#> SRR1377221 2 0.541 0.757 0.224 0.764 0.012
#> SRR1377222 2 0.176 0.690 0.040 0.956 0.004
#> SRR1377223 2 0.176 0.690 0.040 0.956 0.004
#> SRR1377224 2 0.176 0.690 0.040 0.956 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377146 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377147 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377148 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377153 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377154 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377155 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377156 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377149 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377150 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377151 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377152 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1377157 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377158 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377159 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377160 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377161 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377162 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377163 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377164 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377169 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377170 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377171 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377172 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377165 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377166 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377167 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377168 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377173 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377174 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377175 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377176 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377177 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377178 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377179 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377180 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377181 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377182 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377183 3 0.3942 0.685 0 0.236 0.764 0
#> SRR1377184 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377185 3 0.3942 0.685 0 0.236 0.764 0
#> SRR1377186 3 0.3942 0.685 0 0.236 0.764 0
#> SRR1377187 3 0.0000 0.960 0 0.000 1.000 0
#> SRR1377188 3 0.3942 0.685 0 0.236 0.764 0
#> SRR1377189 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377190 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377191 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377192 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377193 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377194 2 0.0921 0.967 0 0.972 0.028 0
#> SRR1377195 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1377196 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1377197 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1377198 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1377199 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1377200 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1377201 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377202 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377203 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377204 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377205 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377206 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377207 2 0.2345 0.899 0 0.900 0.100 0
#> SRR1377208 2 0.2345 0.899 0 0.900 0.100 0
#> SRR1377209 2 0.2345 0.899 0 0.900 0.100 0
#> SRR1377210 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377211 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377212 2 0.1118 0.966 0 0.964 0.036 0
#> SRR1377213 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377214 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377215 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377216 2 0.0188 0.967 0 0.996 0.004 0
#> SRR1377217 2 0.0188 0.967 0 0.996 0.004 0
#> SRR1377218 2 0.0188 0.967 0 0.996 0.004 0
#> SRR1377219 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377220 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377221 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377222 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377223 2 0.0000 0.968 0 1.000 0.000 0
#> SRR1377224 2 0.0000 0.968 0 1.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377146 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377147 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377148 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377153 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377154 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377155 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377156 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377149 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377150 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377151 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377152 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1377157 3 0.1197 0.932 0 0.000 0.952 0.048 0
#> SRR1377158 3 0.1197 0.932 0 0.000 0.952 0.048 0
#> SRR1377159 3 0.1197 0.932 0 0.000 0.952 0.048 0
#> SRR1377160 3 0.1197 0.932 0 0.000 0.952 0.048 0
#> SRR1377161 3 0.1121 0.935 0 0.000 0.956 0.044 0
#> SRR1377162 3 0.1121 0.935 0 0.000 0.956 0.044 0
#> SRR1377163 3 0.1121 0.935 0 0.000 0.956 0.044 0
#> SRR1377164 3 0.1121 0.935 0 0.000 0.956 0.044 0
#> SRR1377169 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377170 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377171 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377172 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377165 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377166 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377167 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377168 3 0.0000 0.947 0 0.000 1.000 0.000 0
#> SRR1377173 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377174 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377175 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377176 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377177 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377178 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377179 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377180 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377181 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377182 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377183 3 0.3975 0.809 0 0.064 0.792 0.144 0
#> SRR1377184 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377185 3 0.3975 0.809 0 0.064 0.792 0.144 0
#> SRR1377186 3 0.3975 0.809 0 0.064 0.792 0.144 0
#> SRR1377187 3 0.0963 0.949 0 0.000 0.964 0.036 0
#> SRR1377188 3 0.3975 0.809 0 0.064 0.792 0.144 0
#> SRR1377189 2 0.0510 0.894 0 0.984 0.000 0.016 0
#> SRR1377190 2 0.0703 0.893 0 0.976 0.000 0.024 0
#> SRR1377191 2 0.0510 0.894 0 0.984 0.000 0.016 0
#> SRR1377192 2 0.0703 0.893 0 0.976 0.000 0.024 0
#> SRR1377193 2 0.0609 0.894 0 0.980 0.000 0.020 0
#> SRR1377194 2 0.0963 0.883 0 0.964 0.000 0.036 0
#> SRR1377195 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1377196 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1377197 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1377198 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1377199 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1377200 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1377201 2 0.0000 0.895 0 1.000 0.000 0.000 0
#> SRR1377202 2 0.0000 0.895 0 1.000 0.000 0.000 0
#> SRR1377203 2 0.0000 0.895 0 1.000 0.000 0.000 0
#> SRR1377204 2 0.4291 0.209 0 0.536 0.000 0.464 0
#> SRR1377205 2 0.4291 0.209 0 0.536 0.000 0.464 0
#> SRR1377206 2 0.4291 0.209 0 0.536 0.000 0.464 0
#> SRR1377207 2 0.0000 0.895 0 1.000 0.000 0.000 0
#> SRR1377208 2 0.0000 0.895 0 1.000 0.000 0.000 0
#> SRR1377209 2 0.0000 0.895 0 1.000 0.000 0.000 0
#> SRR1377210 2 0.0290 0.895 0 0.992 0.000 0.008 0
#> SRR1377211 2 0.0290 0.895 0 0.992 0.000 0.008 0
#> SRR1377212 2 0.0290 0.895 0 0.992 0.000 0.008 0
#> SRR1377213 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377214 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377215 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377216 4 0.1282 0.988 0 0.044 0.004 0.952 0
#> SRR1377217 4 0.1282 0.988 0 0.044 0.004 0.952 0
#> SRR1377218 4 0.1282 0.988 0 0.044 0.004 0.952 0
#> SRR1377219 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377220 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377221 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377222 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377223 4 0.0963 0.996 0 0.036 0.000 0.964 0
#> SRR1377224 4 0.0963 0.996 0 0.036 0.000 0.964 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377146 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377147 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377148 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377153 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377154 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377155 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377156 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377149 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377150 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377151 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377152 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1
#> SRR1377157 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377158 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377159 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377160 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377161 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377162 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377163 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377164 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377169 3 0.1327 0.938 0.064 0.000 0.936 0.000 0 0
#> SRR1377170 3 0.1327 0.938 0.064 0.000 0.936 0.000 0 0
#> SRR1377171 3 0.1327 0.938 0.064 0.000 0.936 0.000 0 0
#> SRR1377172 3 0.1267 0.940 0.060 0.000 0.940 0.000 0 0
#> SRR1377165 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377166 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377167 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377168 3 0.0000 0.980 0.000 0.000 1.000 0.000 0 0
#> SRR1377173 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377174 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377175 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377176 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377177 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377178 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377179 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377180 1 0.0000 0.932 1.000 0.000 0.000 0.000 0 0
#> SRR1377181 1 0.1267 0.898 0.940 0.000 0.060 0.000 0 0
#> SRR1377182 1 0.1204 0.901 0.944 0.000 0.056 0.000 0 0
#> SRR1377183 1 0.3951 0.822 0.796 0.036 0.112 0.056 0 0
#> SRR1377184 1 0.0363 0.928 0.988 0.000 0.012 0.000 0 0
#> SRR1377185 1 0.3951 0.822 0.796 0.036 0.112 0.056 0 0
#> SRR1377186 1 0.3951 0.822 0.796 0.036 0.112 0.056 0 0
#> SRR1377187 1 0.0260 0.930 0.992 0.000 0.008 0.000 0 0
#> SRR1377188 1 0.3951 0.822 0.796 0.036 0.112 0.056 0 0
#> SRR1377189 2 0.0547 0.960 0.000 0.980 0.000 0.020 0 0
#> SRR1377190 2 0.0713 0.958 0.000 0.972 0.000 0.028 0 0
#> SRR1377191 2 0.0458 0.960 0.000 0.984 0.000 0.016 0 0
#> SRR1377192 2 0.0713 0.958 0.000 0.972 0.000 0.028 0 0
#> SRR1377193 2 0.0632 0.959 0.000 0.976 0.000 0.024 0 0
#> SRR1377194 2 0.0713 0.955 0.000 0.972 0.000 0.028 0 0
#> SRR1377195 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0
#> SRR1377196 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0
#> SRR1377197 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0
#> SRR1377198 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0
#> SRR1377199 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0
#> SRR1377200 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0
#> SRR1377201 2 0.0000 0.959 0.000 1.000 0.000 0.000 0 0
#> SRR1377202 2 0.0000 0.959 0.000 1.000 0.000 0.000 0 0
#> SRR1377203 2 0.0000 0.959 0.000 1.000 0.000 0.000 0 0
#> SRR1377204 2 0.2416 0.854 0.000 0.844 0.000 0.156 0 0
#> SRR1377205 2 0.2416 0.854 0.000 0.844 0.000 0.156 0 0
#> SRR1377206 2 0.2416 0.854 0.000 0.844 0.000 0.156 0 0
#> SRR1377207 2 0.0000 0.959 0.000 1.000 0.000 0.000 0 0
#> SRR1377208 2 0.0000 0.959 0.000 1.000 0.000 0.000 0 0
#> SRR1377209 2 0.0000 0.959 0.000 1.000 0.000 0.000 0 0
#> SRR1377210 2 0.0363 0.960 0.000 0.988 0.000 0.012 0 0
#> SRR1377211 2 0.0363 0.960 0.000 0.988 0.000 0.012 0 0
#> SRR1377212 2 0.0363 0.960 0.000 0.988 0.000 0.012 0 0
#> SRR1377213 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377214 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377215 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377216 4 0.0508 0.984 0.004 0.012 0.000 0.984 0 0
#> SRR1377217 4 0.0508 0.984 0.004 0.012 0.000 0.984 0 0
#> SRR1377218 4 0.0508 0.984 0.004 0.012 0.000 0.984 0 0
#> SRR1377219 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377220 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377221 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377222 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377223 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 0
#> SRR1377224 4 0.0000 0.995 0.000 0.000 0.000 1.000 0 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["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 13890 rows and 80 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 0.282 0.599 0.692 0.3716 0.502 0.502
#> 3 3 0.646 0.902 0.890 0.6350 0.803 0.622
#> 4 4 0.737 0.768 0.772 0.1616 0.977 0.934
#> 5 5 0.736 0.679 0.768 0.0940 0.795 0.451
#> 6 6 0.885 0.954 0.913 0.0524 0.914 0.640
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
#> SRR1377145 2 0.416 0.6572 0.084 0.916
#> SRR1377146 2 0.469 0.6460 0.100 0.900
#> SRR1377147 2 0.469 0.6428 0.100 0.900
#> SRR1377148 2 0.416 0.6583 0.084 0.916
#> SRR1377153 2 0.563 0.6173 0.132 0.868
#> SRR1377154 2 0.605 0.5952 0.148 0.852
#> SRR1377155 2 0.552 0.6224 0.128 0.872
#> SRR1377156 2 0.541 0.6270 0.124 0.876
#> SRR1377149 2 0.416 0.6568 0.084 0.916
#> SRR1377150 2 0.343 0.6672 0.064 0.936
#> SRR1377151 2 0.358 0.6657 0.068 0.932
#> SRR1377152 2 0.373 0.6638 0.072 0.928
#> SRR1377157 1 0.929 0.8933 0.656 0.344
#> SRR1377158 1 0.936 0.9001 0.648 0.352
#> SRR1377159 1 0.929 0.8933 0.656 0.344
#> SRR1377160 1 0.932 0.8969 0.652 0.348
#> SRR1377161 1 0.943 0.8995 0.640 0.360
#> SRR1377162 1 0.943 0.8995 0.640 0.360
#> SRR1377163 1 0.939 0.9008 0.644 0.356
#> SRR1377164 1 0.943 0.8995 0.640 0.360
#> SRR1377169 1 0.943 0.8995 0.640 0.360
#> SRR1377170 1 0.939 0.9008 0.644 0.356
#> SRR1377171 1 0.943 0.8995 0.640 0.360
#> SRR1377172 1 0.943 0.8995 0.640 0.360
#> SRR1377165 1 0.943 0.8995 0.640 0.360
#> SRR1377166 1 0.946 0.8946 0.636 0.364
#> SRR1377167 1 0.943 0.8995 0.640 0.360
#> SRR1377168 1 0.943 0.8995 0.640 0.360
#> SRR1377173 1 0.936 0.9001 0.648 0.352
#> SRR1377174 1 0.936 0.9001 0.648 0.352
#> SRR1377175 1 0.936 0.9001 0.648 0.352
#> SRR1377176 1 0.936 0.9001 0.648 0.352
#> SRR1377177 1 0.936 0.9001 0.648 0.352
#> SRR1377178 1 0.939 0.9008 0.644 0.356
#> SRR1377179 1 0.929 0.8933 0.656 0.344
#> SRR1377180 1 0.939 0.9008 0.644 0.356
#> SRR1377181 1 0.929 0.8933 0.656 0.344
#> SRR1377182 1 0.929 0.8933 0.656 0.344
#> SRR1377183 1 0.961 0.8640 0.616 0.384
#> SRR1377184 1 0.925 0.8870 0.660 0.340
#> SRR1377185 1 0.958 0.8707 0.620 0.380
#> SRR1377186 1 0.961 0.8640 0.616 0.384
#> SRR1377187 1 0.929 0.8933 0.656 0.344
#> SRR1377188 1 0.961 0.8640 0.616 0.384
#> SRR1377189 2 0.388 0.6740 0.076 0.924
#> SRR1377190 2 0.358 0.6813 0.068 0.932
#> SRR1377191 2 0.388 0.6740 0.076 0.924
#> SRR1377192 2 0.295 0.6846 0.052 0.948
#> SRR1377193 2 0.260 0.6833 0.044 0.956
#> SRR1377194 2 0.311 0.6848 0.056 0.944
#> SRR1377195 1 0.900 -0.1566 0.684 0.316
#> SRR1377196 2 1.000 0.3263 0.488 0.512
#> SRR1377197 1 0.978 -0.2711 0.588 0.412
#> SRR1377198 2 1.000 0.3234 0.496 0.504
#> SRR1377199 2 0.990 0.3604 0.440 0.560
#> SRR1377200 2 0.987 0.3601 0.432 0.568
#> SRR1377201 2 0.278 0.6860 0.048 0.952
#> SRR1377202 2 0.311 0.6855 0.056 0.944
#> SRR1377203 2 0.295 0.6861 0.052 0.948
#> SRR1377204 2 0.358 0.6727 0.068 0.932
#> SRR1377205 2 0.373 0.6738 0.072 0.928
#> SRR1377206 2 0.358 0.6727 0.068 0.932
#> SRR1377207 2 0.388 0.6778 0.076 0.924
#> SRR1377208 2 0.343 0.6838 0.064 0.936
#> SRR1377209 2 0.311 0.6856 0.056 0.944
#> SRR1377210 2 0.506 0.6438 0.112 0.888
#> SRR1377211 2 0.416 0.6733 0.084 0.916
#> SRR1377212 2 0.416 0.6733 0.084 0.916
#> SRR1377213 2 0.966 -0.1888 0.392 0.608
#> SRR1377214 2 0.966 -0.1888 0.392 0.608
#> SRR1377215 2 0.963 -0.1721 0.388 0.612
#> SRR1377216 2 0.998 -0.5110 0.476 0.524
#> SRR1377217 2 0.999 -0.5248 0.480 0.520
#> SRR1377218 1 1.000 0.5688 0.500 0.500
#> SRR1377219 2 0.966 -0.2142 0.392 0.608
#> SRR1377220 2 0.978 -0.2894 0.412 0.588
#> SRR1377221 2 0.966 -0.2058 0.392 0.608
#> SRR1377222 2 0.958 0.0511 0.380 0.620
#> SRR1377223 2 0.958 0.0511 0.380 0.620
#> SRR1377224 2 0.958 0.0511 0.380 0.620
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 1 0.9018 0.792 0.548 0.176 0.276
#> SRR1377146 1 0.9042 0.789 0.544 0.176 0.280
#> SRR1377147 1 0.8985 0.783 0.544 0.164 0.292
#> SRR1377148 1 0.9087 0.792 0.544 0.188 0.268
#> SRR1377153 1 0.8842 0.770 0.548 0.144 0.308
#> SRR1377154 1 0.8646 0.753 0.556 0.124 0.320
#> SRR1377155 1 0.8742 0.769 0.556 0.136 0.308
#> SRR1377156 1 0.8749 0.776 0.560 0.140 0.300
#> SRR1377149 1 0.9120 0.791 0.544 0.200 0.256
#> SRR1377150 1 0.9150 0.780 0.544 0.224 0.232
#> SRR1377151 1 0.9135 0.789 0.544 0.208 0.248
#> SRR1377152 1 0.9141 0.788 0.544 0.212 0.244
#> SRR1377157 3 0.0475 0.986 0.004 0.004 0.992
#> SRR1377158 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377159 3 0.0661 0.983 0.004 0.008 0.988
#> SRR1377160 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377161 3 0.0592 0.987 0.000 0.012 0.988
#> SRR1377162 3 0.0592 0.987 0.000 0.012 0.988
#> SRR1377163 3 0.0424 0.987 0.000 0.008 0.992
#> SRR1377164 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377169 3 0.0747 0.986 0.000 0.016 0.984
#> SRR1377170 3 0.0592 0.986 0.000 0.012 0.988
#> SRR1377171 3 0.0747 0.985 0.000 0.016 0.984
#> SRR1377172 3 0.0747 0.985 0.000 0.016 0.984
#> SRR1377165 3 0.0424 0.988 0.000 0.008 0.992
#> SRR1377166 3 0.0892 0.983 0.000 0.020 0.980
#> SRR1377167 3 0.0747 0.985 0.000 0.016 0.984
#> SRR1377168 3 0.0892 0.983 0.000 0.020 0.980
#> SRR1377173 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377174 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377175 3 0.0000 0.986 0.000 0.000 1.000
#> SRR1377176 3 0.0237 0.985 0.004 0.000 0.996
#> SRR1377177 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377178 3 0.0237 0.988 0.000 0.004 0.996
#> SRR1377179 3 0.0237 0.985 0.004 0.000 0.996
#> SRR1377180 3 0.0424 0.987 0.000 0.008 0.992
#> SRR1377181 3 0.0237 0.985 0.004 0.000 0.996
#> SRR1377182 3 0.0237 0.985 0.004 0.000 0.996
#> SRR1377183 3 0.0892 0.980 0.000 0.020 0.980
#> SRR1377184 3 0.0424 0.982 0.008 0.000 0.992
#> SRR1377185 3 0.0892 0.980 0.000 0.020 0.980
#> SRR1377186 3 0.1031 0.976 0.000 0.024 0.976
#> SRR1377187 3 0.0424 0.982 0.008 0.000 0.992
#> SRR1377188 3 0.1031 0.976 0.000 0.024 0.976
#> SRR1377189 2 0.2116 0.933 0.012 0.948 0.040
#> SRR1377190 2 0.1774 0.925 0.024 0.960 0.016
#> SRR1377191 2 0.2031 0.933 0.016 0.952 0.032
#> SRR1377192 2 0.2269 0.917 0.040 0.944 0.016
#> SRR1377193 2 0.2031 0.922 0.032 0.952 0.016
#> SRR1377194 2 0.1453 0.931 0.008 0.968 0.024
#> SRR1377195 1 0.6621 0.554 0.720 0.052 0.228
#> SRR1377196 1 0.2492 0.647 0.936 0.048 0.016
#> SRR1377197 1 0.4920 0.650 0.840 0.052 0.108
#> SRR1377198 1 0.2550 0.664 0.936 0.040 0.024
#> SRR1377199 1 0.2187 0.659 0.948 0.028 0.024
#> SRR1377200 1 0.3112 0.668 0.916 0.056 0.028
#> SRR1377201 2 0.1999 0.911 0.036 0.952 0.012
#> SRR1377202 2 0.1999 0.911 0.036 0.952 0.012
#> SRR1377203 2 0.2152 0.915 0.036 0.948 0.016
#> SRR1377204 2 0.1399 0.932 0.004 0.968 0.028
#> SRR1377205 2 0.1399 0.932 0.004 0.968 0.028
#> SRR1377206 2 0.1525 0.932 0.004 0.964 0.032
#> SRR1377207 2 0.1905 0.920 0.028 0.956 0.016
#> SRR1377208 2 0.1905 0.920 0.028 0.956 0.016
#> SRR1377209 2 0.1905 0.920 0.028 0.956 0.016
#> SRR1377210 2 0.2636 0.931 0.020 0.932 0.048
#> SRR1377211 2 0.2313 0.930 0.024 0.944 0.032
#> SRR1377212 2 0.2056 0.926 0.024 0.952 0.024
#> SRR1377213 2 0.3207 0.915 0.012 0.904 0.084
#> SRR1377214 2 0.3207 0.915 0.012 0.904 0.084
#> SRR1377215 2 0.3207 0.915 0.012 0.904 0.084
#> SRR1377216 2 0.3918 0.863 0.004 0.856 0.140
#> SRR1377217 2 0.4047 0.855 0.004 0.848 0.148
#> SRR1377218 2 0.4172 0.844 0.004 0.840 0.156
#> SRR1377219 2 0.3207 0.915 0.012 0.904 0.084
#> SRR1377220 2 0.3293 0.912 0.012 0.900 0.088
#> SRR1377221 2 0.3207 0.915 0.012 0.904 0.084
#> SRR1377222 2 0.3120 0.916 0.012 0.908 0.080
#> SRR1377223 2 0.3120 0.916 0.012 0.908 0.080
#> SRR1377224 2 0.3120 0.916 0.012 0.908 0.080
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 4 0.6324 0.976 0.396 0.040 0.012 0.552
#> SRR1377146 4 0.6246 0.983 0.396 0.036 0.012 0.556
#> SRR1377147 4 0.6049 0.987 0.396 0.032 0.008 0.564
#> SRR1377148 4 0.6049 0.987 0.396 0.032 0.008 0.564
#> SRR1377153 4 0.5975 0.984 0.400 0.028 0.008 0.564
#> SRR1377154 4 0.6081 0.981 0.396 0.028 0.012 0.564
#> SRR1377155 4 0.5975 0.984 0.400 0.028 0.008 0.564
#> SRR1377156 4 0.5975 0.984 0.400 0.028 0.008 0.564
#> SRR1377149 4 0.6130 0.986 0.396 0.036 0.008 0.560
#> SRR1377150 4 0.6246 0.984 0.396 0.036 0.012 0.556
#> SRR1377151 4 0.6246 0.984 0.396 0.036 0.012 0.556
#> SRR1377152 4 0.6130 0.986 0.396 0.036 0.008 0.560
#> SRR1377157 3 0.0336 0.746 0.000 0.000 0.992 0.008
#> SRR1377158 3 0.0336 0.746 0.000 0.000 0.992 0.008
#> SRR1377159 3 0.0376 0.743 0.004 0.000 0.992 0.004
#> SRR1377160 3 0.0336 0.746 0.000 0.000 0.992 0.008
#> SRR1377161 3 0.0188 0.743 0.000 0.004 0.996 0.000
#> SRR1377162 3 0.0000 0.743 0.000 0.000 1.000 0.000
#> SRR1377163 3 0.0000 0.743 0.000 0.000 1.000 0.000
#> SRR1377164 3 0.0000 0.743 0.000 0.000 1.000 0.000
#> SRR1377169 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> SRR1377170 3 0.0188 0.743 0.000 0.004 0.996 0.000
#> SRR1377171 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> SRR1377172 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> SRR1377165 3 0.0524 0.744 0.000 0.008 0.988 0.004
#> SRR1377166 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> SRR1377167 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> SRR1377168 3 0.0524 0.744 0.000 0.008 0.988 0.004
#> SRR1377173 3 0.5080 0.733 0.000 0.004 0.576 0.420
#> SRR1377174 3 0.5060 0.736 0.000 0.004 0.584 0.412
#> SRR1377175 3 0.5070 0.735 0.000 0.004 0.580 0.416
#> SRR1377176 3 0.5070 0.735 0.000 0.004 0.580 0.416
#> SRR1377177 3 0.5080 0.733 0.000 0.004 0.576 0.420
#> SRR1377178 3 0.5070 0.734 0.000 0.004 0.580 0.416
#> SRR1377179 3 0.5070 0.735 0.000 0.004 0.580 0.416
#> SRR1377180 3 0.5080 0.733 0.000 0.004 0.576 0.420
#> SRR1377181 3 0.5243 0.733 0.004 0.004 0.576 0.416
#> SRR1377182 3 0.5060 0.736 0.000 0.004 0.584 0.412
#> SRR1377183 3 0.5444 0.725 0.000 0.016 0.560 0.424
#> SRR1377184 3 0.5252 0.730 0.004 0.004 0.572 0.420
#> SRR1377185 3 0.5408 0.734 0.000 0.016 0.576 0.408
#> SRR1377186 3 0.5628 0.721 0.000 0.024 0.556 0.420
#> SRR1377187 3 0.5243 0.733 0.004 0.004 0.576 0.416
#> SRR1377188 3 0.5444 0.725 0.000 0.016 0.560 0.424
#> SRR1377189 2 0.4018 0.785 0.016 0.812 0.004 0.168
#> SRR1377190 2 0.4630 0.748 0.016 0.732 0.000 0.252
#> SRR1377191 2 0.4068 0.788 0.016 0.816 0.008 0.160
#> SRR1377192 2 0.4744 0.751 0.024 0.736 0.000 0.240
#> SRR1377193 2 0.4502 0.756 0.016 0.748 0.000 0.236
#> SRR1377194 2 0.2941 0.797 0.008 0.888 0.008 0.096
#> SRR1377195 1 0.6332 0.626 0.664 0.008 0.100 0.228
#> SRR1377196 1 0.2803 0.753 0.900 0.008 0.012 0.080
#> SRR1377197 1 0.4986 0.711 0.760 0.012 0.032 0.196
#> SRR1377198 1 0.4365 0.730 0.828 0.016 0.044 0.112
#> SRR1377199 1 0.3657 0.689 0.864 0.016 0.024 0.096
#> SRR1377200 1 0.4121 0.638 0.844 0.020 0.036 0.100
#> SRR1377201 2 0.6270 0.493 0.040 0.524 0.008 0.428
#> SRR1377202 2 0.6133 0.534 0.028 0.540 0.012 0.420
#> SRR1377203 2 0.6145 0.584 0.032 0.568 0.012 0.388
#> SRR1377204 2 0.2631 0.795 0.016 0.912 0.008 0.064
#> SRR1377205 2 0.2510 0.796 0.012 0.916 0.008 0.064
#> SRR1377206 2 0.2485 0.795 0.016 0.916 0.004 0.064
#> SRR1377207 2 0.5872 0.613 0.016 0.584 0.016 0.384
#> SRR1377208 2 0.5970 0.598 0.024 0.576 0.012 0.388
#> SRR1377209 2 0.5812 0.630 0.020 0.600 0.012 0.368
#> SRR1377210 2 0.5171 0.732 0.020 0.704 0.008 0.268
#> SRR1377211 2 0.5443 0.694 0.020 0.660 0.008 0.312
#> SRR1377212 2 0.5581 0.666 0.020 0.632 0.008 0.340
#> SRR1377213 2 0.1022 0.784 0.000 0.968 0.032 0.000
#> SRR1377214 2 0.1022 0.784 0.000 0.968 0.032 0.000
#> SRR1377215 2 0.1022 0.784 0.000 0.968 0.032 0.000
#> SRR1377216 2 0.1867 0.760 0.000 0.928 0.072 0.000
#> SRR1377217 2 0.2011 0.752 0.000 0.920 0.080 0.000
#> SRR1377218 2 0.2469 0.726 0.000 0.892 0.108 0.000
#> SRR1377219 2 0.1118 0.783 0.000 0.964 0.036 0.000
#> SRR1377220 2 0.1118 0.783 0.000 0.964 0.036 0.000
#> SRR1377221 2 0.1022 0.784 0.000 0.968 0.032 0.000
#> SRR1377222 2 0.1004 0.783 0.004 0.972 0.024 0.000
#> SRR1377223 2 0.1004 0.783 0.004 0.972 0.024 0.000
#> SRR1377224 2 0.1004 0.783 0.004 0.972 0.024 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.4658 0.32731 0.008 0.556 0.004 0.000 0.432
#> SRR1377146 2 0.4658 0.32731 0.008 0.556 0.004 0.000 0.432
#> SRR1377147 2 0.4758 0.33167 0.012 0.552 0.004 0.000 0.432
#> SRR1377148 2 0.4758 0.33167 0.012 0.552 0.004 0.000 0.432
#> SRR1377153 2 0.4928 0.32632 0.020 0.548 0.004 0.000 0.428
#> SRR1377154 2 0.4928 0.32632 0.020 0.548 0.004 0.000 0.428
#> SRR1377155 2 0.4843 0.33175 0.016 0.552 0.004 0.000 0.428
#> SRR1377156 2 0.4758 0.33167 0.012 0.552 0.004 0.000 0.432
#> SRR1377149 2 0.4843 0.33175 0.016 0.552 0.004 0.000 0.428
#> SRR1377150 2 0.4758 0.33167 0.012 0.552 0.004 0.000 0.432
#> SRR1377151 2 0.4758 0.33167 0.012 0.552 0.004 0.000 0.432
#> SRR1377152 2 0.4843 0.33175 0.016 0.552 0.004 0.000 0.428
#> SRR1377157 3 0.0404 0.98625 0.012 0.000 0.988 0.000 0.000
#> SRR1377158 3 0.0290 0.98987 0.008 0.000 0.992 0.000 0.000
#> SRR1377159 3 0.0162 0.99306 0.004 0.000 0.996 0.000 0.000
#> SRR1377160 3 0.0510 0.98174 0.016 0.000 0.984 0.000 0.000
#> SRR1377161 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377162 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377163 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377164 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377169 3 0.0290 0.99198 0.008 0.000 0.992 0.000 0.000
#> SRR1377170 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377171 3 0.0162 0.99251 0.004 0.000 0.996 0.000 0.000
#> SRR1377172 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377165 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377166 3 0.0162 0.99251 0.004 0.000 0.996 0.000 0.000
#> SRR1377167 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377168 3 0.0000 0.99544 0.000 0.000 1.000 0.000 0.000
#> SRR1377173 1 0.3456 0.98456 0.800 0.016 0.184 0.000 0.000
#> SRR1377174 1 0.3353 0.98132 0.796 0.008 0.196 0.000 0.000
#> SRR1377175 1 0.3318 0.98378 0.800 0.008 0.192 0.000 0.000
#> SRR1377176 1 0.3391 0.98513 0.800 0.012 0.188 0.000 0.000
#> SRR1377177 1 0.3391 0.98496 0.800 0.012 0.188 0.000 0.000
#> SRR1377178 1 0.3456 0.98453 0.800 0.016 0.184 0.000 0.000
#> SRR1377179 1 0.3353 0.98003 0.796 0.008 0.196 0.000 0.000
#> SRR1377180 1 0.3318 0.98378 0.800 0.008 0.192 0.000 0.000
#> SRR1377181 1 0.3355 0.98433 0.804 0.012 0.184 0.000 0.000
#> SRR1377182 1 0.3282 0.98454 0.804 0.008 0.188 0.000 0.000
#> SRR1377183 1 0.4023 0.96461 0.792 0.028 0.164 0.016 0.000
#> SRR1377184 1 0.3355 0.98460 0.804 0.012 0.184 0.000 0.000
#> SRR1377185 1 0.3870 0.97396 0.792 0.016 0.176 0.016 0.000
#> SRR1377186 1 0.3977 0.96827 0.792 0.024 0.168 0.016 0.000
#> SRR1377187 1 0.3456 0.98365 0.800 0.016 0.184 0.000 0.000
#> SRR1377188 1 0.3926 0.97068 0.792 0.020 0.172 0.016 0.000
#> SRR1377189 2 0.4451 -0.21126 0.004 0.504 0.000 0.492 0.000
#> SRR1377190 2 0.4499 0.00075 0.004 0.584 0.000 0.408 0.004
#> SRR1377191 2 0.4448 -0.18214 0.004 0.516 0.000 0.480 0.000
#> SRR1377192 2 0.4680 -0.09372 0.004 0.540 0.000 0.448 0.008
#> SRR1377193 2 0.4698 -0.14291 0.004 0.520 0.000 0.468 0.008
#> SRR1377194 4 0.4383 0.35747 0.004 0.424 0.000 0.572 0.000
#> SRR1377195 5 0.7008 0.77820 0.268 0.116 0.048 0.012 0.556
#> SRR1377196 5 0.5579 0.81933 0.144 0.152 0.004 0.012 0.688
#> SRR1377197 5 0.6238 0.78077 0.216 0.160 0.008 0.008 0.608
#> SRR1377198 5 0.6724 0.77558 0.136 0.108 0.080 0.024 0.652
#> SRR1377199 5 0.6210 0.79050 0.176 0.124 0.036 0.008 0.656
#> SRR1377200 5 0.6210 0.78420 0.124 0.192 0.024 0.012 0.648
#> SRR1377201 2 0.3252 0.43030 0.008 0.828 0.008 0.156 0.000
#> SRR1377202 2 0.3170 0.43202 0.008 0.828 0.004 0.160 0.000
#> SRR1377203 2 0.3525 0.42048 0.008 0.800 0.008 0.184 0.000
#> SRR1377204 4 0.4430 0.33154 0.004 0.456 0.000 0.540 0.000
#> SRR1377205 4 0.4430 0.33154 0.004 0.456 0.000 0.540 0.000
#> SRR1377206 4 0.4430 0.33154 0.004 0.456 0.000 0.540 0.000
#> SRR1377207 2 0.3561 0.41671 0.008 0.796 0.008 0.188 0.000
#> SRR1377208 2 0.3559 0.42494 0.008 0.804 0.012 0.176 0.000
#> SRR1377209 2 0.3525 0.42035 0.008 0.800 0.008 0.184 0.000
#> SRR1377210 2 0.4318 0.25019 0.008 0.688 0.008 0.296 0.000
#> SRR1377211 2 0.3918 0.36199 0.008 0.752 0.008 0.232 0.000
#> SRR1377212 2 0.3828 0.37873 0.008 0.764 0.008 0.220 0.000
#> SRR1377213 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377214 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377215 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377216 4 0.1282 0.78864 0.004 0.000 0.044 0.952 0.000
#> SRR1377217 4 0.1282 0.78864 0.004 0.000 0.044 0.952 0.000
#> SRR1377218 4 0.1697 0.76646 0.008 0.000 0.060 0.932 0.000
#> SRR1377219 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377220 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377221 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377222 4 0.0162 0.82388 0.004 0.000 0.000 0.996 0.000
#> SRR1377223 4 0.0000 0.82670 0.000 0.000 0.000 1.000 0.000
#> SRR1377224 4 0.0000 0.82670 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
#> SRR1377145 6 0.2402 0.991 0.004 0.140 0.000 0.000 0.000 0.856
#> SRR1377146 6 0.2504 0.987 0.004 0.136 0.000 0.000 0.004 0.856
#> SRR1377147 6 0.2504 0.987 0.004 0.136 0.000 0.000 0.004 0.856
#> SRR1377148 6 0.2442 0.992 0.004 0.144 0.000 0.000 0.000 0.852
#> SRR1377153 6 0.2482 0.992 0.004 0.148 0.000 0.000 0.000 0.848
#> SRR1377154 6 0.2482 0.992 0.004 0.148 0.000 0.000 0.000 0.848
#> SRR1377155 6 0.2482 0.992 0.004 0.148 0.000 0.000 0.000 0.848
#> SRR1377156 6 0.2442 0.993 0.004 0.144 0.000 0.000 0.000 0.852
#> SRR1377149 6 0.2442 0.993 0.004 0.144 0.000 0.000 0.000 0.852
#> SRR1377150 6 0.2695 0.990 0.004 0.144 0.000 0.000 0.008 0.844
#> SRR1377151 6 0.2584 0.992 0.004 0.144 0.000 0.000 0.004 0.848
#> SRR1377152 6 0.2584 0.992 0.004 0.144 0.000 0.000 0.004 0.848
#> SRR1377157 3 0.0146 0.996 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1377158 3 0.0146 0.996 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1377159 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377160 3 0.0146 0.996 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1377161 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377162 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377163 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377164 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377169 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377170 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377171 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377172 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377165 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377166 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377167 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377168 3 0.0000 0.999 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1377173 1 0.1007 0.991 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1377174 1 0.1219 0.988 0.948 0.000 0.048 0.000 0.004 0.000
#> SRR1377175 1 0.1152 0.991 0.952 0.000 0.044 0.000 0.004 0.000
#> SRR1377176 1 0.1152 0.991 0.952 0.000 0.044 0.000 0.004 0.000
#> SRR1377177 1 0.1007 0.991 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1377178 1 0.1007 0.991 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1377179 1 0.1007 0.991 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1377180 1 0.1007 0.991 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1377181 1 0.1633 0.981 0.932 0.000 0.044 0.000 0.024 0.000
#> SRR1377182 1 0.1633 0.981 0.932 0.000 0.044 0.000 0.024 0.000
#> SRR1377183 1 0.1152 0.991 0.952 0.000 0.044 0.000 0.004 0.000
#> SRR1377184 1 0.1633 0.981 0.932 0.000 0.044 0.000 0.024 0.000
#> SRR1377185 1 0.1007 0.991 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1377186 1 0.1296 0.989 0.948 0.000 0.044 0.004 0.004 0.000
#> SRR1377187 1 0.1633 0.981 0.932 0.000 0.044 0.000 0.024 0.000
#> SRR1377188 1 0.1296 0.989 0.948 0.000 0.044 0.004 0.004 0.000
#> SRR1377189 2 0.3208 0.892 0.000 0.852 0.008 0.088 0.036 0.016
#> SRR1377190 2 0.2450 0.904 0.000 0.900 0.004 0.048 0.032 0.016
#> SRR1377191 2 0.2954 0.896 0.000 0.864 0.004 0.084 0.036 0.012
#> SRR1377192 2 0.2828 0.899 0.000 0.872 0.004 0.080 0.032 0.012
#> SRR1377193 2 0.2959 0.888 0.000 0.852 0.000 0.104 0.036 0.008
#> SRR1377194 2 0.3425 0.839 0.000 0.800 0.000 0.164 0.028 0.008
#> SRR1377195 5 0.4444 0.849 0.052 0.028 0.012 0.016 0.788 0.104
#> SRR1377196 5 0.3156 0.860 0.012 0.048 0.004 0.000 0.852 0.084
#> SRR1377197 5 0.3885 0.856 0.040 0.040 0.012 0.000 0.816 0.092
#> SRR1377198 5 0.6207 0.801 0.036 0.080 0.044 0.016 0.648 0.176
#> SRR1377199 5 0.5269 0.823 0.008 0.052 0.020 0.024 0.688 0.208
#> SRR1377200 5 0.6745 0.782 0.040 0.092 0.032 0.040 0.604 0.192
#> SRR1377201 2 0.1036 0.902 0.000 0.964 0.008 0.004 0.000 0.024
#> SRR1377202 2 0.1036 0.902 0.000 0.964 0.008 0.004 0.000 0.024
#> SRR1377203 2 0.0951 0.904 0.000 0.968 0.008 0.004 0.000 0.020
#> SRR1377204 2 0.2491 0.840 0.000 0.836 0.000 0.164 0.000 0.000
#> SRR1377205 2 0.2491 0.840 0.000 0.836 0.000 0.164 0.000 0.000
#> SRR1377206 2 0.2491 0.840 0.000 0.836 0.000 0.164 0.000 0.000
#> SRR1377207 2 0.1109 0.904 0.004 0.964 0.012 0.004 0.000 0.016
#> SRR1377208 2 0.1053 0.900 0.004 0.964 0.012 0.000 0.000 0.020
#> SRR1377209 2 0.1109 0.904 0.004 0.964 0.012 0.004 0.000 0.016
#> SRR1377210 2 0.0972 0.912 0.000 0.964 0.008 0.028 0.000 0.000
#> SRR1377211 2 0.0951 0.911 0.000 0.968 0.008 0.020 0.000 0.004
#> SRR1377212 2 0.0951 0.911 0.000 0.968 0.004 0.020 0.000 0.008
#> SRR1377213 4 0.0865 0.982 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1377214 4 0.0865 0.982 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1377215 4 0.0865 0.982 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1377216 4 0.1515 0.952 0.008 0.020 0.028 0.944 0.000 0.000
#> SRR1377217 4 0.1767 0.941 0.012 0.020 0.036 0.932 0.000 0.000
#> SRR1377218 4 0.1767 0.941 0.012 0.020 0.036 0.932 0.000 0.000
#> SRR1377219 4 0.0865 0.982 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1377220 4 0.0865 0.982 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1377221 4 0.0865 0.982 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1377222 4 0.1010 0.981 0.000 0.036 0.000 0.960 0.004 0.000
#> SRR1377223 4 0.1010 0.981 0.000 0.036 0.000 0.960 0.004 0.000
#> SRR1377224 4 0.1010 0.981 0.000 0.036 0.000 0.960 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13890 rows and 80 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1 1.000 1.000 0.14137 0.859 0.859
#> 3 3 1 0.961 0.985 0.01318 0.997 0.997
#> 4 4 1 0.958 0.989 0.00752 1.000 1.000
#> 5 5 1 0.963 1.000 0.00591 0.999 0.999
#> 6 6 1 0.935 0.992 0.03805 0.999 0.999
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
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.000 1.0000 0.000 1 0.000
#> SRR1377146 2 0.000 1.0000 0.000 1 0.000
#> SRR1377147 2 0.000 1.0000 0.000 1 0.000
#> SRR1377148 2 0.000 1.0000 0.000 1 0.000
#> SRR1377153 2 0.000 1.0000 0.000 1 0.000
#> SRR1377154 2 0.000 1.0000 0.000 1 0.000
#> SRR1377155 2 0.000 1.0000 0.000 1 0.000
#> SRR1377156 2 0.000 1.0000 0.000 1 0.000
#> SRR1377149 2 0.000 1.0000 0.000 1 0.000
#> SRR1377150 2 0.000 1.0000 0.000 1 0.000
#> SRR1377151 2 0.000 1.0000 0.000 1 0.000
#> SRR1377152 2 0.000 1.0000 0.000 1 0.000
#> SRR1377157 2 0.000 1.0000 0.000 1 0.000
#> SRR1377158 2 0.000 1.0000 0.000 1 0.000
#> SRR1377159 2 0.000 1.0000 0.000 1 0.000
#> SRR1377160 2 0.000 1.0000 0.000 1 0.000
#> SRR1377161 2 0.000 1.0000 0.000 1 0.000
#> SRR1377162 2 0.000 1.0000 0.000 1 0.000
#> SRR1377163 2 0.000 1.0000 0.000 1 0.000
#> SRR1377164 2 0.000 1.0000 0.000 1 0.000
#> SRR1377169 2 0.000 1.0000 0.000 1 0.000
#> SRR1377170 2 0.000 1.0000 0.000 1 0.000
#> SRR1377171 2 0.000 1.0000 0.000 1 0.000
#> SRR1377172 2 0.000 1.0000 0.000 1 0.000
#> SRR1377165 2 0.000 1.0000 0.000 1 0.000
#> SRR1377166 2 0.000 1.0000 0.000 1 0.000
#> SRR1377167 2 0.000 1.0000 0.000 1 0.000
#> SRR1377168 2 0.000 1.0000 0.000 1 0.000
#> SRR1377173 2 0.000 1.0000 0.000 1 0.000
#> SRR1377174 2 0.000 1.0000 0.000 1 0.000
#> SRR1377175 2 0.000 1.0000 0.000 1 0.000
#> SRR1377176 2 0.000 1.0000 0.000 1 0.000
#> SRR1377177 2 0.000 1.0000 0.000 1 0.000
#> SRR1377178 2 0.000 1.0000 0.000 1 0.000
#> SRR1377179 2 0.000 1.0000 0.000 1 0.000
#> SRR1377180 2 0.000 1.0000 0.000 1 0.000
#> SRR1377181 2 0.000 1.0000 0.000 1 0.000
#> SRR1377182 2 0.000 1.0000 0.000 1 0.000
#> SRR1377183 2 0.000 1.0000 0.000 1 0.000
#> SRR1377184 2 0.000 1.0000 0.000 1 0.000
#> SRR1377185 2 0.000 1.0000 0.000 1 0.000
#> SRR1377186 2 0.000 1.0000 0.000 1 0.000
#> SRR1377187 2 0.000 1.0000 0.000 1 0.000
#> SRR1377188 2 0.000 1.0000 0.000 1 0.000
#> SRR1377189 2 0.000 1.0000 0.000 1 0.000
#> SRR1377190 2 0.000 1.0000 0.000 1 0.000
#> SRR1377191 2 0.000 1.0000 0.000 1 0.000
#> SRR1377192 2 0.000 1.0000 0.000 1 0.000
#> SRR1377193 2 0.000 1.0000 0.000 1 0.000
#> SRR1377194 2 0.000 1.0000 0.000 1 0.000
#> SRR1377195 1 0.000 0.8369 1.000 0 0.000
#> SRR1377196 1 0.000 0.8369 1.000 0 0.000
#> SRR1377197 1 0.000 0.8369 1.000 0 0.000
#> SRR1377198 1 0.522 0.4235 0.740 0 0.260
#> SRR1377199 3 0.631 -0.0305 0.496 0 0.504
#> SRR1377200 3 0.625 -0.0603 0.444 0 0.556
#> SRR1377201 2 0.000 1.0000 0.000 1 0.000
#> SRR1377202 2 0.000 1.0000 0.000 1 0.000
#> SRR1377203 2 0.000 1.0000 0.000 1 0.000
#> SRR1377204 2 0.000 1.0000 0.000 1 0.000
#> SRR1377205 2 0.000 1.0000 0.000 1 0.000
#> SRR1377206 2 0.000 1.0000 0.000 1 0.000
#> SRR1377207 2 0.000 1.0000 0.000 1 0.000
#> SRR1377208 2 0.000 1.0000 0.000 1 0.000
#> SRR1377209 2 0.000 1.0000 0.000 1 0.000
#> SRR1377210 2 0.000 1.0000 0.000 1 0.000
#> SRR1377211 2 0.000 1.0000 0.000 1 0.000
#> SRR1377212 2 0.000 1.0000 0.000 1 0.000
#> SRR1377213 2 0.000 1.0000 0.000 1 0.000
#> SRR1377214 2 0.000 1.0000 0.000 1 0.000
#> SRR1377215 2 0.000 1.0000 0.000 1 0.000
#> SRR1377216 2 0.000 1.0000 0.000 1 0.000
#> SRR1377217 2 0.000 1.0000 0.000 1 0.000
#> SRR1377218 2 0.000 1.0000 0.000 1 0.000
#> SRR1377219 2 0.000 1.0000 0.000 1 0.000
#> SRR1377220 2 0.000 1.0000 0.000 1 0.000
#> SRR1377221 2 0.000 1.0000 0.000 1 0.000
#> SRR1377222 2 0.000 1.0000 0.000 1 0.000
#> SRR1377223 2 0.000 1.0000 0.000 1 0.000
#> SRR1377224 2 0.000 1.0000 0.000 1 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377146 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377147 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377148 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377153 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377154 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377155 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377156 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377149 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377150 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377151 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377152 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377157 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377158 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377159 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377160 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377161 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377162 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377163 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377164 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377169 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377170 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377171 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377172 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377165 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377166 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377167 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377168 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377173 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377174 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377175 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377176 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377177 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377178 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377179 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377180 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377181 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377182 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377183 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377184 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377185 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377186 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377187 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377188 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377189 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377190 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377191 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377192 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377193 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377194 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377195 1 0.000 0.787 1.000 0 0.00 0.000
#> SRR1377196 1 0.000 0.787 1.000 0 0.00 0.000
#> SRR1377197 1 0.000 0.787 1.000 0 0.00 0.000
#> SRR1377198 1 0.741 0.295 0.516 0 0.26 0.224
#> SRR1377199 3 0.413 0.000 0.260 0 0.74 0.000
#> SRR1377200 4 0.194 0.000 0.076 0 0.00 0.924
#> SRR1377201 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377202 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377203 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377204 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377205 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377206 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377207 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377208 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377209 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377210 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377211 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377212 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377213 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377214 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377215 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377216 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377217 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377218 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377219 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377220 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377221 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377222 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377223 2 0.000 1.000 0.000 1 0.00 0.000
#> SRR1377224 2 0.000 1.000 0.000 1 0.00 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0 1 0 1 0 0 0
#> SRR1377146 2 0 1 0 1 0 0 0
#> SRR1377147 2 0 1 0 1 0 0 0
#> SRR1377148 2 0 1 0 1 0 0 0
#> SRR1377153 2 0 1 0 1 0 0 0
#> SRR1377154 2 0 1 0 1 0 0 0
#> SRR1377155 2 0 1 0 1 0 0 0
#> SRR1377156 2 0 1 0 1 0 0 0
#> SRR1377149 2 0 1 0 1 0 0 0
#> SRR1377150 2 0 1 0 1 0 0 0
#> SRR1377151 2 0 1 0 1 0 0 0
#> SRR1377152 2 0 1 0 1 0 0 0
#> SRR1377157 2 0 1 0 1 0 0 0
#> SRR1377158 2 0 1 0 1 0 0 0
#> SRR1377159 2 0 1 0 1 0 0 0
#> SRR1377160 2 0 1 0 1 0 0 0
#> SRR1377161 2 0 1 0 1 0 0 0
#> SRR1377162 2 0 1 0 1 0 0 0
#> SRR1377163 2 0 1 0 1 0 0 0
#> SRR1377164 2 0 1 0 1 0 0 0
#> SRR1377169 2 0 1 0 1 0 0 0
#> SRR1377170 2 0 1 0 1 0 0 0
#> SRR1377171 2 0 1 0 1 0 0 0
#> SRR1377172 2 0 1 0 1 0 0 0
#> SRR1377165 2 0 1 0 1 0 0 0
#> SRR1377166 2 0 1 0 1 0 0 0
#> SRR1377167 2 0 1 0 1 0 0 0
#> SRR1377168 2 0 1 0 1 0 0 0
#> SRR1377173 2 0 1 0 1 0 0 0
#> SRR1377174 2 0 1 0 1 0 0 0
#> SRR1377175 2 0 1 0 1 0 0 0
#> SRR1377176 2 0 1 0 1 0 0 0
#> SRR1377177 2 0 1 0 1 0 0 0
#> SRR1377178 2 0 1 0 1 0 0 0
#> SRR1377179 2 0 1 0 1 0 0 0
#> SRR1377180 2 0 1 0 1 0 0 0
#> SRR1377181 2 0 1 0 1 0 0 0
#> SRR1377182 2 0 1 0 1 0 0 0
#> SRR1377183 2 0 1 0 1 0 0 0
#> SRR1377184 2 0 1 0 1 0 0 0
#> SRR1377185 2 0 1 0 1 0 0 0
#> SRR1377186 2 0 1 0 1 0 0 0
#> SRR1377187 2 0 1 0 1 0 0 0
#> SRR1377188 2 0 1 0 1 0 0 0
#> SRR1377189 2 0 1 0 1 0 0 0
#> SRR1377190 2 0 1 0 1 0 0 0
#> SRR1377191 2 0 1 0 1 0 0 0
#> SRR1377192 2 0 1 0 1 0 0 0
#> SRR1377193 2 0 1 0 1 0 0 0
#> SRR1377194 2 0 1 0 1 0 0 0
#> SRR1377195 1 0 1 1 0 0 0 0
#> SRR1377196 1 0 1 1 0 0 0 0
#> SRR1377197 1 0 1 1 0 0 0 0
#> SRR1377198 5 0 0 0 0 0 0 1
#> SRR1377199 3 0 0 0 0 1 0 0
#> SRR1377200 4 0 0 0 0 0 1 0
#> SRR1377201 2 0 1 0 1 0 0 0
#> SRR1377202 2 0 1 0 1 0 0 0
#> SRR1377203 2 0 1 0 1 0 0 0
#> SRR1377204 2 0 1 0 1 0 0 0
#> SRR1377205 2 0 1 0 1 0 0 0
#> SRR1377206 2 0 1 0 1 0 0 0
#> SRR1377207 2 0 1 0 1 0 0 0
#> SRR1377208 2 0 1 0 1 0 0 0
#> SRR1377209 2 0 1 0 1 0 0 0
#> SRR1377210 2 0 1 0 1 0 0 0
#> SRR1377211 2 0 1 0 1 0 0 0
#> SRR1377212 2 0 1 0 1 0 0 0
#> SRR1377213 2 0 1 0 1 0 0 0
#> SRR1377214 2 0 1 0 1 0 0 0
#> SRR1377215 2 0 1 0 1 0 0 0
#> SRR1377216 2 0 1 0 1 0 0 0
#> SRR1377217 2 0 1 0 1 0 0 0
#> SRR1377218 2 0 1 0 1 0 0 0
#> SRR1377219 2 0 1 0 1 0 0 0
#> SRR1377220 2 0 1 0 1 0 0 0
#> SRR1377221 2 0 1 0 1 0 0 0
#> SRR1377222 2 0 1 0 1 0 0 0
#> SRR1377223 2 0 1 0 1 0 0 0
#> SRR1377224 2 0 1 0 1 0 0 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377146 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377147 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377148 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377153 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377154 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377155 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377156 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377149 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377150 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377151 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377152 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377157 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377158 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377159 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377160 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377161 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377162 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377163 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377164 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377169 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377170 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377171 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377172 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377165 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377166 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377167 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377168 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377173 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377174 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377175 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377176 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377177 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377178 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377179 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377180 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377181 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377182 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377183 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377184 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377185 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377186 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377187 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377188 2 0.0000 0.996 0.000 1.000 0 0 0.000 0
#> SRR1377189 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377190 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377191 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377192 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377193 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377194 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377195 1 0.0547 0.000 0.980 0.000 0 0 0.020 0
#> SRR1377196 5 0.0632 0.639 0.024 0.000 0 0 0.976 0
#> SRR1377197 5 0.3330 0.570 0.284 0.000 0 0 0.716 0
#> SRR1377198 6 0.0000 0.000 0.000 0.000 0 0 0.000 1
#> SRR1377199 3 0.0000 0.000 0.000 0.000 1 0 0.000 0
#> SRR1377200 4 0.0000 0.000 0.000 0.000 0 1 0.000 0
#> SRR1377201 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377202 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377203 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377204 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377205 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377206 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377207 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377208 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377209 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377210 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377211 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377212 2 0.0146 0.995 0.004 0.996 0 0 0.000 0
#> SRR1377213 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377214 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377215 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377216 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377217 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377218 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377219 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377220 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377221 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377222 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377223 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
#> SRR1377224 2 0.0458 0.986 0.016 0.984 0 0 0.000 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13890 rows and 80 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 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 0.168 0.483 0.588 0.3451 0.499 0.499
#> 3 3 0.180 0.466 0.622 0.5709 0.555 0.396
#> 4 4 0.372 0.560 0.613 0.2255 0.709 0.480
#> 5 5 0.461 0.821 0.681 0.1123 0.821 0.466
#> 6 6 0.670 0.911 0.814 0.0646 0.977 0.882
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
#> SRR1377145 1 0.9998 0.553 0.508 0.492
#> SRR1377146 1 0.9998 0.553 0.508 0.492
#> SRR1377147 1 0.9998 0.553 0.508 0.492
#> SRR1377148 1 0.9998 0.553 0.508 0.492
#> SRR1377153 1 0.9996 0.558 0.512 0.488
#> SRR1377154 1 0.9996 0.558 0.512 0.488
#> SRR1377155 1 0.9996 0.558 0.512 0.488
#> SRR1377156 1 0.9996 0.558 0.512 0.488
#> SRR1377149 1 0.9998 0.553 0.508 0.492
#> SRR1377150 1 0.9998 0.553 0.508 0.492
#> SRR1377151 1 0.9998 0.553 0.508 0.492
#> SRR1377152 1 0.9998 0.553 0.508 0.492
#> SRR1377157 2 0.0000 0.607 0.000 1.000
#> SRR1377158 2 0.0000 0.607 0.000 1.000
#> SRR1377159 2 0.0000 0.607 0.000 1.000
#> SRR1377160 2 0.0000 0.607 0.000 1.000
#> SRR1377161 2 0.0672 0.604 0.008 0.992
#> SRR1377162 2 0.0672 0.604 0.008 0.992
#> SRR1377163 2 0.0672 0.604 0.008 0.992
#> SRR1377164 2 0.0672 0.604 0.008 0.992
#> SRR1377169 2 0.0000 0.607 0.000 1.000
#> SRR1377170 2 0.0000 0.607 0.000 1.000
#> SRR1377171 2 0.0000 0.607 0.000 1.000
#> SRR1377172 2 0.0000 0.607 0.000 1.000
#> SRR1377165 2 0.0000 0.607 0.000 1.000
#> SRR1377166 2 0.0000 0.607 0.000 1.000
#> SRR1377167 2 0.0000 0.607 0.000 1.000
#> SRR1377168 2 0.0000 0.607 0.000 1.000
#> SRR1377173 2 0.8909 0.333 0.308 0.692
#> SRR1377174 2 0.8909 0.333 0.308 0.692
#> SRR1377175 2 0.8909 0.333 0.308 0.692
#> SRR1377176 2 0.8909 0.333 0.308 0.692
#> SRR1377177 2 0.8909 0.333 0.308 0.692
#> SRR1377178 2 0.8909 0.333 0.308 0.692
#> SRR1377179 2 0.8909 0.333 0.308 0.692
#> SRR1377180 2 0.8909 0.333 0.308 0.692
#> SRR1377181 2 0.8861 0.338 0.304 0.696
#> SRR1377182 2 0.8861 0.338 0.304 0.696
#> SRR1377183 2 0.8499 0.330 0.276 0.724
#> SRR1377184 2 0.8861 0.338 0.304 0.696
#> SRR1377185 2 0.8499 0.330 0.276 0.724
#> SRR1377186 2 0.8499 0.330 0.276 0.724
#> SRR1377187 2 0.8861 0.338 0.304 0.696
#> SRR1377188 2 0.8499 0.330 0.276 0.724
#> SRR1377189 1 0.9983 0.626 0.524 0.476
#> SRR1377190 1 0.9983 0.626 0.524 0.476
#> SRR1377191 1 0.9983 0.626 0.524 0.476
#> SRR1377192 1 0.9977 0.629 0.528 0.472
#> SRR1377193 1 0.9977 0.629 0.528 0.472
#> SRR1377194 1 0.9977 0.629 0.528 0.472
#> SRR1377195 1 0.8207 0.313 0.744 0.256
#> SRR1377196 1 0.8207 0.313 0.744 0.256
#> SRR1377197 1 0.8207 0.313 0.744 0.256
#> SRR1377198 1 0.8207 0.313 0.744 0.256
#> SRR1377199 1 0.8207 0.313 0.744 0.256
#> SRR1377200 1 0.8207 0.313 0.744 0.256
#> SRR1377201 1 0.9954 0.634 0.540 0.460
#> SRR1377202 1 0.9954 0.634 0.540 0.460
#> SRR1377203 1 0.9954 0.634 0.540 0.460
#> SRR1377204 1 0.9661 0.570 0.608 0.392
#> SRR1377205 1 0.9661 0.570 0.608 0.392
#> SRR1377206 1 0.9661 0.570 0.608 0.392
#> SRR1377207 1 0.9933 0.634 0.548 0.452
#> SRR1377208 1 0.9933 0.634 0.548 0.452
#> SRR1377209 1 0.9933 0.634 0.548 0.452
#> SRR1377210 1 0.9977 0.633 0.528 0.472
#> SRR1377211 1 0.9977 0.633 0.528 0.472
#> SRR1377212 1 0.9977 0.633 0.528 0.472
#> SRR1377213 2 0.9248 0.305 0.340 0.660
#> SRR1377214 2 0.9248 0.305 0.340 0.660
#> SRR1377215 2 0.9248 0.305 0.340 0.660
#> SRR1377216 2 0.6887 0.518 0.184 0.816
#> SRR1377217 2 0.6887 0.518 0.184 0.816
#> SRR1377218 2 0.6887 0.518 0.184 0.816
#> SRR1377219 2 0.9248 0.305 0.340 0.660
#> SRR1377220 2 0.9248 0.305 0.340 0.660
#> SRR1377221 2 0.9248 0.305 0.340 0.660
#> SRR1377222 2 0.9661 0.171 0.392 0.608
#> SRR1377223 2 0.9661 0.171 0.392 0.608
#> SRR1377224 2 0.9661 0.171 0.392 0.608
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377146 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377147 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377148 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377153 2 0.8339 0.387 0.204 0.628 0.168
#> SRR1377154 2 0.8339 0.387 0.204 0.628 0.168
#> SRR1377155 2 0.8339 0.387 0.204 0.628 0.168
#> SRR1377156 2 0.8339 0.387 0.204 0.628 0.168
#> SRR1377149 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377150 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377151 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377152 2 0.8290 0.390 0.204 0.632 0.164
#> SRR1377157 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377158 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377159 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377160 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377161 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377162 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377163 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377164 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377169 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377170 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377171 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377172 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377165 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377166 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377167 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377168 3 0.5058 0.907 0.000 0.244 0.756
#> SRR1377173 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377174 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377175 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377176 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377177 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377178 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377179 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377180 2 0.9959 0.112 0.292 0.368 0.340
#> SRR1377181 2 0.9962 0.105 0.292 0.364 0.344
#> SRR1377182 2 0.9962 0.105 0.292 0.364 0.344
#> SRR1377183 2 0.9355 0.159 0.180 0.480 0.340
#> SRR1377184 2 0.9962 0.105 0.292 0.364 0.344
#> SRR1377185 2 0.9355 0.159 0.180 0.480 0.340
#> SRR1377186 2 0.9355 0.159 0.180 0.480 0.340
#> SRR1377187 2 0.9962 0.105 0.292 0.364 0.344
#> SRR1377188 2 0.9355 0.159 0.180 0.480 0.340
#> SRR1377189 2 0.0848 0.450 0.008 0.984 0.008
#> SRR1377190 2 0.0848 0.450 0.008 0.984 0.008
#> SRR1377191 2 0.0848 0.450 0.008 0.984 0.008
#> SRR1377192 2 0.0848 0.450 0.008 0.984 0.008
#> SRR1377193 2 0.0848 0.450 0.008 0.984 0.008
#> SRR1377194 2 0.0848 0.450 0.008 0.984 0.008
#> SRR1377195 1 0.7676 0.994 0.584 0.360 0.056
#> SRR1377196 1 0.7676 0.994 0.584 0.360 0.056
#> SRR1377197 1 0.7676 0.994 0.584 0.360 0.056
#> SRR1377198 1 0.7310 0.994 0.600 0.360 0.040
#> SRR1377199 1 0.7310 0.994 0.600 0.360 0.040
#> SRR1377200 1 0.7310 0.994 0.600 0.360 0.040
#> SRR1377201 2 0.0848 0.445 0.008 0.984 0.008
#> SRR1377202 2 0.0848 0.445 0.008 0.984 0.008
#> SRR1377203 2 0.0848 0.445 0.008 0.984 0.008
#> SRR1377204 2 0.4269 0.359 0.052 0.872 0.076
#> SRR1377205 2 0.4269 0.359 0.052 0.872 0.076
#> SRR1377206 2 0.4269 0.359 0.052 0.872 0.076
#> SRR1377207 2 0.1015 0.443 0.008 0.980 0.012
#> SRR1377208 2 0.1015 0.443 0.008 0.980 0.012
#> SRR1377209 2 0.1015 0.443 0.008 0.980 0.012
#> SRR1377210 2 0.0848 0.445 0.008 0.984 0.008
#> SRR1377211 2 0.0848 0.445 0.008 0.984 0.008
#> SRR1377212 2 0.0848 0.445 0.008 0.984 0.008
#> SRR1377213 2 0.9268 0.108 0.172 0.492 0.336
#> SRR1377214 2 0.9268 0.108 0.172 0.492 0.336
#> SRR1377215 2 0.9268 0.108 0.172 0.492 0.336
#> SRR1377216 3 0.9136 0.335 0.144 0.400 0.456
#> SRR1377217 3 0.9136 0.335 0.144 0.400 0.456
#> SRR1377218 3 0.9136 0.335 0.144 0.400 0.456
#> SRR1377219 2 0.9268 0.108 0.172 0.492 0.336
#> SRR1377220 2 0.9268 0.108 0.172 0.492 0.336
#> SRR1377221 2 0.9268 0.108 0.172 0.492 0.336
#> SRR1377222 2 0.8762 0.252 0.160 0.576 0.264
#> SRR1377223 2 0.8762 0.252 0.160 0.576 0.264
#> SRR1377224 2 0.8762 0.252 0.160 0.576 0.264
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.7662 0.494 0.080 0.452 0.044 0.424
#> SRR1377146 2 0.7662 0.494 0.080 0.452 0.044 0.424
#> SRR1377147 2 0.7662 0.494 0.080 0.452 0.044 0.424
#> SRR1377148 2 0.7662 0.494 0.080 0.452 0.044 0.424
#> SRR1377153 2 0.7724 0.493 0.080 0.456 0.048 0.416
#> SRR1377154 2 0.7724 0.493 0.080 0.456 0.048 0.416
#> SRR1377155 2 0.7724 0.493 0.080 0.456 0.048 0.416
#> SRR1377156 2 0.7724 0.493 0.080 0.456 0.048 0.416
#> SRR1377149 2 0.7726 0.493 0.080 0.452 0.048 0.420
#> SRR1377150 2 0.7726 0.493 0.080 0.452 0.048 0.420
#> SRR1377151 2 0.7726 0.493 0.080 0.452 0.048 0.420
#> SRR1377152 2 0.7726 0.493 0.080 0.452 0.048 0.420
#> SRR1377157 3 0.2706 0.851 0.004 0.064 0.908 0.024
#> SRR1377158 3 0.2706 0.851 0.004 0.064 0.908 0.024
#> SRR1377159 3 0.2706 0.851 0.004 0.064 0.908 0.024
#> SRR1377160 3 0.2706 0.851 0.004 0.064 0.908 0.024
#> SRR1377161 3 0.2234 0.857 0.004 0.064 0.924 0.008
#> SRR1377162 3 0.2234 0.857 0.004 0.064 0.924 0.008
#> SRR1377163 3 0.2234 0.857 0.004 0.064 0.924 0.008
#> SRR1377164 3 0.2234 0.857 0.004 0.064 0.924 0.008
#> SRR1377169 3 0.2088 0.860 0.004 0.064 0.928 0.004
#> SRR1377170 3 0.2088 0.860 0.004 0.064 0.928 0.004
#> SRR1377171 3 0.2088 0.860 0.004 0.064 0.928 0.004
#> SRR1377172 3 0.2088 0.860 0.004 0.064 0.928 0.004
#> SRR1377165 3 0.1902 0.860 0.000 0.064 0.932 0.004
#> SRR1377166 3 0.1902 0.860 0.000 0.064 0.932 0.004
#> SRR1377167 3 0.1902 0.860 0.000 0.064 0.932 0.004
#> SRR1377168 3 0.1902 0.860 0.000 0.064 0.932 0.004
#> SRR1377173 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377174 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377175 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377176 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377177 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377178 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377179 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377180 1 0.7536 0.727 0.492 0.264 0.244 0.000
#> SRR1377181 1 0.7518 0.725 0.496 0.260 0.244 0.000
#> SRR1377182 1 0.7518 0.725 0.496 0.260 0.244 0.000
#> SRR1377183 1 0.7653 0.697 0.460 0.300 0.240 0.000
#> SRR1377184 1 0.7518 0.725 0.496 0.260 0.244 0.000
#> SRR1377185 1 0.7653 0.697 0.460 0.300 0.240 0.000
#> SRR1377186 1 0.7653 0.697 0.460 0.300 0.240 0.000
#> SRR1377187 1 0.7518 0.725 0.496 0.260 0.244 0.000
#> SRR1377188 1 0.7653 0.697 0.460 0.300 0.240 0.000
#> SRR1377189 2 0.2186 0.579 0.008 0.932 0.012 0.048
#> SRR1377190 2 0.2186 0.579 0.008 0.932 0.012 0.048
#> SRR1377191 2 0.2186 0.579 0.008 0.932 0.012 0.048
#> SRR1377192 2 0.2186 0.579 0.008 0.932 0.012 0.048
#> SRR1377193 2 0.2186 0.579 0.008 0.932 0.012 0.048
#> SRR1377194 2 0.2186 0.579 0.008 0.932 0.012 0.048
#> SRR1377195 1 0.8398 0.248 0.516 0.240 0.060 0.184
#> SRR1377196 1 0.8398 0.248 0.516 0.240 0.060 0.184
#> SRR1377197 1 0.8398 0.248 0.516 0.240 0.060 0.184
#> SRR1377198 1 0.8207 0.248 0.532 0.240 0.052 0.176
#> SRR1377199 1 0.8241 0.248 0.532 0.240 0.056 0.172
#> SRR1377200 1 0.8207 0.248 0.532 0.240 0.052 0.176
#> SRR1377201 2 0.0992 0.590 0.008 0.976 0.012 0.004
#> SRR1377202 2 0.0992 0.590 0.008 0.976 0.012 0.004
#> SRR1377203 2 0.0992 0.590 0.008 0.976 0.012 0.004
#> SRR1377204 2 0.2412 0.496 0.008 0.908 0.000 0.084
#> SRR1377205 2 0.2412 0.496 0.008 0.908 0.000 0.084
#> SRR1377206 2 0.2412 0.496 0.008 0.908 0.000 0.084
#> SRR1377207 2 0.0927 0.589 0.008 0.976 0.016 0.000
#> SRR1377208 2 0.0927 0.589 0.008 0.976 0.016 0.000
#> SRR1377209 2 0.0927 0.589 0.008 0.976 0.016 0.000
#> SRR1377210 2 0.0779 0.588 0.004 0.980 0.016 0.000
#> SRR1377211 2 0.0779 0.588 0.004 0.980 0.016 0.000
#> SRR1377212 2 0.0779 0.588 0.004 0.980 0.016 0.000
#> SRR1377213 4 0.9496 1.000 0.120 0.312 0.216 0.352
#> SRR1377214 4 0.9496 1.000 0.120 0.312 0.216 0.352
#> SRR1377215 4 0.9496 1.000 0.120 0.312 0.216 0.352
#> SRR1377216 3 0.9593 -0.580 0.144 0.212 0.372 0.272
#> SRR1377217 3 0.9593 -0.580 0.144 0.212 0.372 0.272
#> SRR1377218 3 0.9593 -0.580 0.144 0.212 0.372 0.272
#> SRR1377219 4 0.9496 1.000 0.120 0.312 0.216 0.352
#> SRR1377220 4 0.9496 1.000 0.120 0.312 0.216 0.352
#> SRR1377221 4 0.9496 1.000 0.120 0.312 0.216 0.352
#> SRR1377222 2 0.8959 -0.784 0.132 0.400 0.108 0.360
#> SRR1377223 2 0.8959 -0.784 0.132 0.400 0.108 0.360
#> SRR1377224 2 0.8959 -0.784 0.132 0.400 0.108 0.360
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 5 0.5629 0.956 0.092 0.216 0.016 0.004 0.672
#> SRR1377146 5 0.5629 0.956 0.092 0.216 0.016 0.004 0.672
#> SRR1377147 5 0.5629 0.956 0.092 0.216 0.016 0.004 0.672
#> SRR1377148 5 0.5629 0.956 0.092 0.216 0.016 0.004 0.672
#> SRR1377153 5 0.6242 0.950 0.100 0.220 0.024 0.016 0.640
#> SRR1377154 5 0.6242 0.950 0.100 0.220 0.024 0.016 0.640
#> SRR1377155 5 0.6242 0.950 0.100 0.220 0.024 0.016 0.640
#> SRR1377156 5 0.6242 0.950 0.100 0.220 0.024 0.016 0.640
#> SRR1377149 5 0.6605 0.943 0.088 0.224 0.028 0.036 0.624
#> SRR1377150 5 0.6605 0.943 0.088 0.224 0.028 0.036 0.624
#> SRR1377151 5 0.6605 0.943 0.088 0.224 0.028 0.036 0.624
#> SRR1377152 5 0.6605 0.943 0.088 0.224 0.028 0.036 0.624
#> SRR1377157 3 0.4568 0.952 0.140 0.016 0.780 0.008 0.056
#> SRR1377158 3 0.4568 0.952 0.140 0.016 0.780 0.008 0.056
#> SRR1377159 3 0.4568 0.952 0.140 0.016 0.780 0.008 0.056
#> SRR1377160 3 0.4568 0.952 0.140 0.016 0.780 0.008 0.056
#> SRR1377161 3 0.3114 0.966 0.140 0.008 0.844 0.004 0.004
#> SRR1377162 3 0.3114 0.966 0.140 0.008 0.844 0.004 0.004
#> SRR1377163 3 0.3114 0.966 0.140 0.008 0.844 0.004 0.004
#> SRR1377164 3 0.3114 0.966 0.140 0.008 0.844 0.004 0.004
#> SRR1377169 3 0.3944 0.956 0.136 0.012 0.816 0.020 0.016
#> SRR1377170 3 0.3944 0.956 0.136 0.012 0.816 0.020 0.016
#> SRR1377171 3 0.3944 0.956 0.136 0.012 0.816 0.020 0.016
#> SRR1377172 3 0.3944 0.956 0.136 0.012 0.816 0.020 0.016
#> SRR1377165 3 0.3691 0.966 0.140 0.012 0.824 0.012 0.012
#> SRR1377166 3 0.3691 0.966 0.140 0.012 0.824 0.012 0.012
#> SRR1377167 3 0.3691 0.966 0.140 0.012 0.824 0.012 0.012
#> SRR1377168 3 0.3691 0.966 0.140 0.012 0.824 0.012 0.012
#> SRR1377173 1 0.0798 0.967 0.976 0.016 0.000 0.000 0.008
#> SRR1377174 1 0.0798 0.967 0.976 0.016 0.000 0.000 0.008
#> SRR1377175 1 0.0798 0.967 0.976 0.016 0.000 0.000 0.008
#> SRR1377176 1 0.0798 0.967 0.976 0.016 0.000 0.000 0.008
#> SRR1377177 1 0.0510 0.968 0.984 0.016 0.000 0.000 0.000
#> SRR1377178 1 0.0510 0.968 0.984 0.016 0.000 0.000 0.000
#> SRR1377179 1 0.0510 0.968 0.984 0.016 0.000 0.000 0.000
#> SRR1377180 1 0.0510 0.968 0.984 0.016 0.000 0.000 0.000
#> SRR1377181 1 0.1235 0.965 0.964 0.012 0.004 0.004 0.016
#> SRR1377182 1 0.1235 0.965 0.964 0.012 0.004 0.004 0.016
#> SRR1377183 1 0.2264 0.926 0.920 0.044 0.004 0.008 0.024
#> SRR1377184 1 0.1235 0.965 0.964 0.012 0.004 0.004 0.016
#> SRR1377185 1 0.2264 0.926 0.920 0.044 0.004 0.008 0.024
#> SRR1377186 1 0.2264 0.926 0.920 0.044 0.004 0.008 0.024
#> SRR1377187 1 0.1235 0.965 0.964 0.012 0.004 0.004 0.016
#> SRR1377188 1 0.2264 0.926 0.920 0.044 0.004 0.008 0.024
#> SRR1377189 2 0.5325 0.863 0.136 0.748 0.032 0.024 0.060
#> SRR1377190 2 0.5325 0.863 0.136 0.748 0.032 0.024 0.060
#> SRR1377191 2 0.5325 0.863 0.136 0.748 0.032 0.024 0.060
#> SRR1377192 2 0.5325 0.863 0.136 0.748 0.032 0.024 0.060
#> SRR1377193 2 0.5325 0.863 0.136 0.748 0.032 0.024 0.060
#> SRR1377194 2 0.5325 0.863 0.136 0.748 0.032 0.024 0.060
#> SRR1377195 4 0.9320 0.252 0.184 0.208 0.068 0.356 0.184
#> SRR1377196 4 0.9320 0.252 0.184 0.208 0.068 0.356 0.184
#> SRR1377197 4 0.9320 0.252 0.184 0.208 0.068 0.356 0.184
#> SRR1377198 4 0.9547 0.252 0.184 0.208 0.088 0.324 0.196
#> SRR1377199 4 0.9547 0.252 0.184 0.208 0.088 0.324 0.196
#> SRR1377200 4 0.9547 0.252 0.184 0.208 0.088 0.324 0.196
#> SRR1377201 2 0.3310 0.913 0.136 0.836 0.024 0.000 0.004
#> SRR1377202 2 0.3310 0.913 0.136 0.836 0.024 0.000 0.004
#> SRR1377203 2 0.3310 0.913 0.136 0.836 0.024 0.000 0.004
#> SRR1377204 2 0.2813 0.836 0.084 0.876 0.000 0.040 0.000
#> SRR1377205 2 0.2813 0.836 0.084 0.876 0.000 0.040 0.000
#> SRR1377206 2 0.2813 0.836 0.084 0.876 0.000 0.040 0.000
#> SRR1377207 2 0.3310 0.913 0.136 0.836 0.024 0.000 0.004
#> SRR1377208 2 0.3310 0.913 0.136 0.836 0.024 0.000 0.004
#> SRR1377209 2 0.3310 0.913 0.136 0.836 0.024 0.000 0.004
#> SRR1377210 2 0.3354 0.912 0.140 0.832 0.024 0.000 0.004
#> SRR1377211 2 0.3354 0.912 0.140 0.832 0.024 0.000 0.004
#> SRR1377212 2 0.3354 0.912 0.140 0.832 0.024 0.000 0.004
#> SRR1377213 4 0.7887 0.555 0.060 0.232 0.148 0.516 0.044
#> SRR1377214 4 0.7887 0.555 0.060 0.232 0.148 0.516 0.044
#> SRR1377215 4 0.7887 0.555 0.060 0.232 0.148 0.516 0.044
#> SRR1377216 4 0.8516 0.500 0.112 0.168 0.216 0.460 0.044
#> SRR1377217 4 0.8516 0.500 0.112 0.168 0.216 0.460 0.044
#> SRR1377218 4 0.8516 0.500 0.112 0.168 0.216 0.460 0.044
#> SRR1377219 4 0.7887 0.555 0.060 0.232 0.148 0.516 0.044
#> SRR1377220 4 0.7887 0.555 0.060 0.232 0.148 0.516 0.044
#> SRR1377221 4 0.7887 0.555 0.060 0.232 0.148 0.516 0.044
#> SRR1377222 4 0.7552 0.466 0.044 0.328 0.080 0.492 0.056
#> SRR1377223 4 0.7552 0.466 0.044 0.328 0.080 0.492 0.056
#> SRR1377224 4 0.7552 0.466 0.044 0.328 0.080 0.492 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.4807 0.930 0.052 0.156 0.012 0.036 0.004 0.740
#> SRR1377146 6 0.4807 0.930 0.052 0.156 0.012 0.036 0.004 0.740
#> SRR1377147 6 0.4807 0.930 0.052 0.156 0.012 0.036 0.004 0.740
#> SRR1377148 6 0.4807 0.930 0.052 0.156 0.012 0.036 0.004 0.740
#> SRR1377153 6 0.5863 0.915 0.100 0.164 0.012 0.048 0.012 0.664
#> SRR1377154 6 0.5863 0.915 0.100 0.164 0.012 0.048 0.012 0.664
#> SRR1377155 6 0.5863 0.915 0.100 0.164 0.012 0.048 0.012 0.664
#> SRR1377156 6 0.5863 0.915 0.100 0.164 0.012 0.048 0.012 0.664
#> SRR1377149 6 0.6100 0.917 0.056 0.160 0.012 0.064 0.044 0.664
#> SRR1377150 6 0.6100 0.917 0.056 0.160 0.012 0.064 0.044 0.664
#> SRR1377151 6 0.6100 0.917 0.056 0.160 0.012 0.064 0.044 0.664
#> SRR1377152 6 0.6100 0.917 0.056 0.160 0.012 0.064 0.044 0.664
#> SRR1377157 3 0.2725 0.914 0.000 0.004 0.884 0.032 0.020 0.060
#> SRR1377158 3 0.2725 0.914 0.000 0.004 0.884 0.032 0.020 0.060
#> SRR1377159 3 0.2807 0.914 0.004 0.004 0.884 0.032 0.020 0.056
#> SRR1377160 3 0.2807 0.914 0.004 0.004 0.884 0.032 0.020 0.056
#> SRR1377161 3 0.2583 0.934 0.000 0.008 0.896 0.020 0.044 0.032
#> SRR1377162 3 0.2583 0.934 0.000 0.008 0.896 0.020 0.044 0.032
#> SRR1377163 3 0.2583 0.934 0.000 0.008 0.896 0.020 0.044 0.032
#> SRR1377164 3 0.2583 0.934 0.000 0.008 0.896 0.020 0.044 0.032
#> SRR1377169 3 0.2193 0.925 0.008 0.004 0.916 0.004 0.032 0.036
#> SRR1377170 3 0.2193 0.925 0.008 0.004 0.916 0.004 0.032 0.036
#> SRR1377171 3 0.2193 0.925 0.008 0.004 0.916 0.004 0.032 0.036
#> SRR1377172 3 0.2193 0.925 0.008 0.004 0.916 0.004 0.032 0.036
#> SRR1377165 3 0.0146 0.939 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1377166 3 0.0146 0.939 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1377167 3 0.0146 0.939 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1377168 3 0.0146 0.939 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1377173 1 0.3922 0.970 0.816 0.040 0.100 0.024 0.004 0.016
#> SRR1377174 1 0.3922 0.970 0.816 0.040 0.100 0.024 0.004 0.016
#> SRR1377175 1 0.3922 0.970 0.816 0.040 0.100 0.024 0.004 0.016
#> SRR1377176 1 0.3922 0.970 0.816 0.040 0.100 0.024 0.004 0.016
#> SRR1377177 1 0.3665 0.972 0.828 0.040 0.100 0.016 0.004 0.012
#> SRR1377178 1 0.3665 0.972 0.828 0.040 0.100 0.016 0.004 0.012
#> SRR1377179 1 0.3665 0.972 0.828 0.040 0.100 0.016 0.004 0.012
#> SRR1377180 1 0.3665 0.972 0.828 0.040 0.100 0.016 0.004 0.012
#> SRR1377181 1 0.3566 0.964 0.832 0.040 0.100 0.008 0.004 0.016
#> SRR1377182 1 0.3566 0.964 0.832 0.040 0.100 0.008 0.004 0.016
#> SRR1377183 1 0.3142 0.965 0.848 0.044 0.096 0.004 0.008 0.000
#> SRR1377184 1 0.3566 0.964 0.832 0.040 0.100 0.008 0.004 0.016
#> SRR1377185 1 0.3142 0.965 0.848 0.044 0.096 0.004 0.008 0.000
#> SRR1377186 1 0.3142 0.965 0.848 0.044 0.096 0.004 0.008 0.000
#> SRR1377187 1 0.3566 0.964 0.832 0.040 0.100 0.008 0.004 0.016
#> SRR1377188 1 0.3142 0.965 0.848 0.044 0.096 0.004 0.008 0.000
#> SRR1377189 2 0.5393 0.783 0.044 0.728 0.028 0.092 0.012 0.096
#> SRR1377190 2 0.5393 0.783 0.044 0.728 0.028 0.092 0.012 0.096
#> SRR1377191 2 0.5393 0.783 0.044 0.728 0.028 0.092 0.012 0.096
#> SRR1377192 2 0.5393 0.783 0.044 0.728 0.028 0.092 0.012 0.096
#> SRR1377193 2 0.5393 0.783 0.044 0.728 0.028 0.092 0.012 0.096
#> SRR1377194 2 0.5393 0.783 0.044 0.728 0.028 0.092 0.012 0.096
#> SRR1377195 5 0.5214 0.973 0.112 0.096 0.000 0.016 0.720 0.056
#> SRR1377196 5 0.5214 0.973 0.112 0.096 0.000 0.016 0.720 0.056
#> SRR1377197 5 0.5214 0.973 0.112 0.096 0.000 0.016 0.720 0.056
#> SRR1377198 5 0.4075 0.972 0.096 0.096 0.000 0.000 0.784 0.024
#> SRR1377199 5 0.4104 0.972 0.092 0.096 0.000 0.000 0.784 0.028
#> SRR1377200 5 0.4075 0.972 0.096 0.096 0.000 0.000 0.784 0.024
#> SRR1377201 2 0.1562 0.879 0.032 0.940 0.024 0.000 0.000 0.004
#> SRR1377202 2 0.1562 0.879 0.032 0.940 0.024 0.000 0.000 0.004
#> SRR1377203 2 0.1562 0.879 0.032 0.940 0.024 0.000 0.000 0.004
#> SRR1377204 2 0.2414 0.814 0.008 0.896 0.012 0.072 0.000 0.012
#> SRR1377205 2 0.2414 0.814 0.008 0.896 0.012 0.072 0.000 0.012
#> SRR1377206 2 0.2414 0.814 0.008 0.896 0.012 0.072 0.000 0.012
#> SRR1377207 2 0.1642 0.881 0.032 0.936 0.028 0.000 0.000 0.004
#> SRR1377208 2 0.1642 0.881 0.032 0.936 0.028 0.000 0.000 0.004
#> SRR1377209 2 0.1642 0.881 0.032 0.936 0.028 0.000 0.000 0.004
#> SRR1377210 2 0.1642 0.881 0.032 0.936 0.028 0.000 0.000 0.004
#> SRR1377211 2 0.1642 0.881 0.032 0.936 0.028 0.000 0.000 0.004
#> SRR1377212 2 0.1642 0.881 0.032 0.936 0.028 0.000 0.000 0.004
#> SRR1377213 4 0.4333 0.916 0.024 0.140 0.048 0.772 0.000 0.016
#> SRR1377214 4 0.4333 0.916 0.024 0.140 0.048 0.772 0.000 0.016
#> SRR1377215 4 0.4333 0.916 0.024 0.140 0.048 0.772 0.000 0.016
#> SRR1377216 4 0.6065 0.859 0.048 0.124 0.112 0.668 0.008 0.040
#> SRR1377217 4 0.6065 0.859 0.048 0.124 0.112 0.668 0.008 0.040
#> SRR1377218 4 0.6065 0.859 0.048 0.124 0.112 0.668 0.008 0.040
#> SRR1377219 4 0.4473 0.916 0.024 0.140 0.048 0.768 0.004 0.016
#> SRR1377220 4 0.4473 0.916 0.024 0.140 0.048 0.768 0.004 0.016
#> SRR1377221 4 0.4473 0.916 0.024 0.140 0.048 0.768 0.004 0.016
#> SRR1377222 4 0.5670 0.837 0.028 0.156 0.032 0.700 0.044 0.040
#> SRR1377223 4 0.5670 0.837 0.028 0.156 0.032 0.700 0.044 0.040
#> SRR1377224 4 0.5670 0.837 0.028 0.156 0.032 0.700 0.044 0.040
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.792 0.948 0.969 0.5040 0.494 0.494
#> 3 3 0.806 0.954 0.966 0.3280 0.728 0.504
#> 4 4 0.781 0.874 0.868 0.0988 0.939 0.816
#> 5 5 0.913 0.896 0.949 0.0844 0.867 0.562
#> 6 6 0.982 0.957 0.962 0.0406 0.966 0.833
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 5
There is also optional best \(k\) = 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 1 0.2778 0.960 0.952 0.048
#> SRR1377146 1 0.2778 0.960 0.952 0.048
#> SRR1377147 1 0.2778 0.960 0.952 0.048
#> SRR1377148 1 0.2778 0.960 0.952 0.048
#> SRR1377153 1 0.2778 0.960 0.952 0.048
#> SRR1377154 1 0.2778 0.960 0.952 0.048
#> SRR1377155 1 0.2778 0.960 0.952 0.048
#> SRR1377156 1 0.2778 0.960 0.952 0.048
#> SRR1377149 1 0.2778 0.960 0.952 0.048
#> SRR1377150 1 0.2778 0.960 0.952 0.048
#> SRR1377151 1 0.2778 0.960 0.952 0.048
#> SRR1377152 1 0.2778 0.960 0.952 0.048
#> SRR1377157 2 0.0000 0.962 0.000 1.000
#> SRR1377158 2 0.0000 0.962 0.000 1.000
#> SRR1377159 2 0.0000 0.962 0.000 1.000
#> SRR1377160 2 0.0000 0.962 0.000 1.000
#> SRR1377161 2 0.0000 0.962 0.000 1.000
#> SRR1377162 2 0.0000 0.962 0.000 1.000
#> SRR1377163 2 0.0000 0.962 0.000 1.000
#> SRR1377164 2 0.0000 0.962 0.000 1.000
#> SRR1377169 2 0.0000 0.962 0.000 1.000
#> SRR1377170 2 0.0000 0.962 0.000 1.000
#> SRR1377171 2 0.0000 0.962 0.000 1.000
#> SRR1377172 2 0.0000 0.962 0.000 1.000
#> SRR1377165 2 0.0000 0.962 0.000 1.000
#> SRR1377166 2 0.0000 0.962 0.000 1.000
#> SRR1377167 2 0.0000 0.962 0.000 1.000
#> SRR1377168 2 0.0000 0.962 0.000 1.000
#> SRR1377173 2 0.0938 0.962 0.012 0.988
#> SRR1377174 2 0.0938 0.962 0.012 0.988
#> SRR1377175 2 0.0938 0.962 0.012 0.988
#> SRR1377176 2 0.0938 0.962 0.012 0.988
#> SRR1377177 2 0.0938 0.962 0.012 0.988
#> SRR1377178 2 0.0938 0.962 0.012 0.988
#> SRR1377179 2 0.0938 0.962 0.012 0.988
#> SRR1377180 2 0.0938 0.962 0.012 0.988
#> SRR1377181 2 0.0938 0.962 0.012 0.988
#> SRR1377182 2 0.0938 0.962 0.012 0.988
#> SRR1377183 2 0.0938 0.962 0.012 0.988
#> SRR1377184 2 0.0938 0.962 0.012 0.988
#> SRR1377185 2 0.0938 0.962 0.012 0.988
#> SRR1377186 2 0.0938 0.962 0.012 0.988
#> SRR1377187 2 0.0938 0.962 0.012 0.988
#> SRR1377188 2 0.0938 0.962 0.012 0.988
#> SRR1377189 1 0.0000 0.974 1.000 0.000
#> SRR1377190 1 0.0000 0.974 1.000 0.000
#> SRR1377191 1 0.0000 0.974 1.000 0.000
#> SRR1377192 1 0.0000 0.974 1.000 0.000
#> SRR1377193 1 0.0000 0.974 1.000 0.000
#> SRR1377194 1 0.0000 0.974 1.000 0.000
#> SRR1377195 1 0.0376 0.974 0.996 0.004
#> SRR1377196 1 0.0376 0.974 0.996 0.004
#> SRR1377197 1 0.0376 0.974 0.996 0.004
#> SRR1377198 1 0.0376 0.974 0.996 0.004
#> SRR1377199 1 0.0376 0.974 0.996 0.004
#> SRR1377200 1 0.0376 0.974 0.996 0.004
#> SRR1377201 1 0.0000 0.974 1.000 0.000
#> SRR1377202 1 0.0000 0.974 1.000 0.000
#> SRR1377203 1 0.0000 0.974 1.000 0.000
#> SRR1377204 1 0.0000 0.974 1.000 0.000
#> SRR1377205 1 0.0000 0.974 1.000 0.000
#> SRR1377206 1 0.0000 0.974 1.000 0.000
#> SRR1377207 1 0.0000 0.974 1.000 0.000
#> SRR1377208 1 0.0000 0.974 1.000 0.000
#> SRR1377209 1 0.0000 0.974 1.000 0.000
#> SRR1377210 1 0.0000 0.974 1.000 0.000
#> SRR1377211 1 0.0000 0.974 1.000 0.000
#> SRR1377212 1 0.0000 0.974 1.000 0.000
#> SRR1377213 2 0.7376 0.780 0.208 0.792
#> SRR1377214 2 0.7376 0.780 0.208 0.792
#> SRR1377215 2 0.7376 0.780 0.208 0.792
#> SRR1377216 2 0.1633 0.951 0.024 0.976
#> SRR1377217 2 0.1633 0.951 0.024 0.976
#> SRR1377218 2 0.1633 0.951 0.024 0.976
#> SRR1377219 2 0.7376 0.780 0.208 0.792
#> SRR1377220 2 0.7376 0.780 0.208 0.792
#> SRR1377221 2 0.7376 0.780 0.208 0.792
#> SRR1377222 1 0.5178 0.877 0.884 0.116
#> SRR1377223 1 0.5178 0.877 0.884 0.116
#> SRR1377224 1 0.5178 0.877 0.884 0.116
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377146 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377147 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377148 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377153 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377154 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377155 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377156 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377149 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377150 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377151 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377152 2 0.3695 0.918 0.108 0.880 0.012
#> SRR1377157 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377158 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377159 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377160 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377161 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377162 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377163 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377164 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377169 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377170 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377171 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377172 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377165 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377166 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377167 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377168 3 0.0892 0.985 0.020 0.000 0.980
#> SRR1377173 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377174 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377175 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377176 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377177 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377178 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377179 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377180 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377181 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377182 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377183 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377184 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377185 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377186 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377187 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377188 1 0.0000 0.962 1.000 0.000 0.000
#> SRR1377189 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377190 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377191 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377192 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377193 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377194 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377195 1 0.3482 0.894 0.872 0.128 0.000
#> SRR1377196 1 0.3482 0.894 0.872 0.128 0.000
#> SRR1377197 1 0.3482 0.894 0.872 0.128 0.000
#> SRR1377198 1 0.3482 0.894 0.872 0.128 0.000
#> SRR1377199 1 0.3482 0.894 0.872 0.128 0.000
#> SRR1377200 1 0.3482 0.894 0.872 0.128 0.000
#> SRR1377201 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377202 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377203 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377204 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377205 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377206 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377207 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377208 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377209 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377210 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377211 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377212 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1377213 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377214 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377215 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377216 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377217 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377218 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377219 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377220 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377221 3 0.0747 0.979 0.000 0.016 0.984
#> SRR1377222 3 0.1163 0.972 0.000 0.028 0.972
#> SRR1377223 3 0.1163 0.972 0.000 0.028 0.972
#> SRR1377224 3 0.1163 0.972 0.000 0.028 0.972
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377146 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377147 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377148 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377153 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377154 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377155 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377156 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377149 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377150 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377151 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377152 2 0.6192 0.730 0.020 0.588 0.364 0.028
#> SRR1377157 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377158 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377159 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377160 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377161 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377162 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377163 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377164 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377169 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377170 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377171 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377172 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377165 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377166 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377167 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377168 3 0.5075 1.000 0.012 0.000 0.644 0.344
#> SRR1377173 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377174 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377175 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377176 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377177 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377178 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377179 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377180 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377181 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377182 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377183 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377184 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377185 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377186 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377187 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377188 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> SRR1377189 2 0.1510 0.807 0.000 0.956 0.016 0.028
#> SRR1377190 2 0.1510 0.807 0.000 0.956 0.016 0.028
#> SRR1377191 2 0.1510 0.807 0.000 0.956 0.016 0.028
#> SRR1377192 2 0.1510 0.807 0.000 0.956 0.016 0.028
#> SRR1377193 2 0.1510 0.807 0.000 0.956 0.016 0.028
#> SRR1377194 2 0.1510 0.807 0.000 0.956 0.016 0.028
#> SRR1377195 1 0.3123 0.862 0.844 0.156 0.000 0.000
#> SRR1377196 1 0.3123 0.862 0.844 0.156 0.000 0.000
#> SRR1377197 1 0.3123 0.862 0.844 0.156 0.000 0.000
#> SRR1377198 1 0.3123 0.862 0.844 0.156 0.000 0.000
#> SRR1377199 1 0.3123 0.862 0.844 0.156 0.000 0.000
#> SRR1377200 1 0.3123 0.862 0.844 0.156 0.000 0.000
#> SRR1377201 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377202 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377203 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377204 2 0.0188 0.816 0.000 0.996 0.000 0.004
#> SRR1377205 2 0.0188 0.816 0.000 0.996 0.000 0.004
#> SRR1377206 2 0.0188 0.816 0.000 0.996 0.000 0.004
#> SRR1377207 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377208 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377209 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377210 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377211 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377212 2 0.0000 0.817 0.000 1.000 0.000 0.000
#> SRR1377213 4 0.0817 0.887 0.000 0.024 0.000 0.976
#> SRR1377214 4 0.0817 0.887 0.000 0.024 0.000 0.976
#> SRR1377215 4 0.0817 0.887 0.000 0.024 0.000 0.976
#> SRR1377216 4 0.0657 0.856 0.000 0.004 0.012 0.984
#> SRR1377217 4 0.0657 0.856 0.000 0.004 0.012 0.984
#> SRR1377218 4 0.0657 0.856 0.000 0.004 0.012 0.984
#> SRR1377219 4 0.0817 0.887 0.000 0.024 0.000 0.976
#> SRR1377220 4 0.0817 0.887 0.000 0.024 0.000 0.976
#> SRR1377221 4 0.0817 0.887 0.000 0.024 0.000 0.976
#> SRR1377222 4 0.3726 0.739 0.000 0.212 0.000 0.788
#> SRR1377223 4 0.3726 0.739 0.000 0.212 0.000 0.788
#> SRR1377224 4 0.3726 0.739 0.000 0.212 0.000 0.788
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377146 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377147 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377148 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377153 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377154 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377155 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377156 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377149 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377150 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377151 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377152 5 0.0404 1.000 0.00 0.012 0.000 0.000 0.988
#> SRR1377157 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377158 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377159 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377160 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377161 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377162 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377163 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377164 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377169 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377170 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377171 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377172 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377165 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377166 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377167 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377168 3 0.0000 1.000 0.00 0.000 1.000 0.000 0.000
#> SRR1377173 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377174 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377175 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377176 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377177 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377178 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377179 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377180 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377181 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377182 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377183 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377184 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377185 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377186 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377187 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377188 1 0.0000 1.000 1.00 0.000 0.000 0.000 0.000
#> SRR1377189 2 0.2676 0.768 0.00 0.884 0.000 0.036 0.080
#> SRR1377190 2 0.2676 0.768 0.00 0.884 0.000 0.036 0.080
#> SRR1377191 2 0.2676 0.768 0.00 0.884 0.000 0.036 0.080
#> SRR1377192 2 0.2676 0.768 0.00 0.884 0.000 0.036 0.080
#> SRR1377193 2 0.2676 0.768 0.00 0.884 0.000 0.036 0.080
#> SRR1377194 2 0.2676 0.768 0.00 0.884 0.000 0.036 0.080
#> SRR1377195 2 0.5009 0.216 0.48 0.496 0.000 0.012 0.012
#> SRR1377196 2 0.5009 0.216 0.48 0.496 0.000 0.012 0.012
#> SRR1377197 2 0.5009 0.216 0.48 0.496 0.000 0.012 0.012
#> SRR1377198 2 0.5009 0.216 0.48 0.496 0.000 0.012 0.012
#> SRR1377199 2 0.5009 0.216 0.48 0.496 0.000 0.012 0.012
#> SRR1377200 2 0.5009 0.216 0.48 0.496 0.000 0.012 0.012
#> SRR1377201 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377202 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377203 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377204 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377205 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377206 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377207 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377208 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377209 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377210 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377211 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377212 2 0.0162 0.816 0.00 0.996 0.000 0.000 0.004
#> SRR1377213 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377214 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377215 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377216 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377217 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377218 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377219 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377220 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377221 4 0.0404 0.997 0.00 0.000 0.012 0.988 0.000
#> SRR1377222 4 0.0451 0.991 0.00 0.008 0.004 0.988 0.000
#> SRR1377223 4 0.0451 0.991 0.00 0.008 0.004 0.988 0.000
#> SRR1377224 4 0.0451 0.991 0.00 0.008 0.004 0.988 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.0146 0.998 0.000 0.000 0 0.000 0.004 0.996
#> SRR1377146 6 0.0146 0.998 0.000 0.000 0 0.000 0.004 0.996
#> SRR1377147 6 0.0146 0.998 0.000 0.000 0 0.000 0.004 0.996
#> SRR1377148 6 0.0146 0.998 0.000 0.000 0 0.000 0.004 0.996
#> SRR1377153 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377154 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377155 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377156 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377149 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377150 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377151 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377152 6 0.0000 0.999 0.000 0.000 0 0.000 0.000 1.000
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1377173 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377174 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377175 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377176 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377177 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377178 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377179 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377180 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377181 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377182 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377183 1 0.0632 0.982 0.976 0.000 0 0.000 0.024 0.000
#> SRR1377184 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377185 1 0.0632 0.982 0.976 0.000 0 0.000 0.024 0.000
#> SRR1377186 1 0.0632 0.982 0.976 0.000 0 0.000 0.024 0.000
#> SRR1377187 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1377188 1 0.0632 0.982 0.976 0.000 0 0.000 0.024 0.000
#> SRR1377189 2 0.5253 0.718 0.000 0.664 0 0.040 0.208 0.088
#> SRR1377190 2 0.5253 0.718 0.000 0.664 0 0.040 0.208 0.088
#> SRR1377191 2 0.5253 0.718 0.000 0.664 0 0.040 0.208 0.088
#> SRR1377192 2 0.5278 0.717 0.000 0.660 0 0.040 0.212 0.088
#> SRR1377193 2 0.5278 0.717 0.000 0.660 0 0.040 0.212 0.088
#> SRR1377194 2 0.5278 0.717 0.000 0.660 0 0.040 0.212 0.088
#> SRR1377195 5 0.2733 1.000 0.056 0.080 0 0.000 0.864 0.000
#> SRR1377196 5 0.2733 1.000 0.056 0.080 0 0.000 0.864 0.000
#> SRR1377197 5 0.2733 1.000 0.056 0.080 0 0.000 0.864 0.000
#> SRR1377198 5 0.2733 1.000 0.056 0.080 0 0.000 0.864 0.000
#> SRR1377199 5 0.2733 1.000 0.056 0.080 0 0.000 0.864 0.000
#> SRR1377200 5 0.2733 1.000 0.056 0.080 0 0.000 0.864 0.000
#> SRR1377201 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377202 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377203 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377204 2 0.0260 0.870 0.000 0.992 0 0.000 0.008 0.000
#> SRR1377205 2 0.0260 0.870 0.000 0.992 0 0.000 0.008 0.000
#> SRR1377206 2 0.0260 0.870 0.000 0.992 0 0.000 0.008 0.000
#> SRR1377207 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377208 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377209 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377210 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377211 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377212 2 0.0000 0.873 0.000 1.000 0 0.000 0.000 0.000
#> SRR1377213 4 0.0000 0.997 0.000 0.000 0 1.000 0.000 0.000
#> SRR1377214 4 0.0000 0.997 0.000 0.000 0 1.000 0.000 0.000
#> SRR1377215 4 0.0000 0.997 0.000 0.000 0 1.000 0.000 0.000
#> SRR1377216 4 0.0146 0.996 0.000 0.000 0 0.996 0.004 0.000
#> SRR1377217 4 0.0146 0.996 0.000 0.000 0 0.996 0.004 0.000
#> SRR1377218 4 0.0146 0.996 0.000 0.000 0 0.996 0.004 0.000
#> SRR1377219 4 0.0000 0.997 0.000 0.000 0 1.000 0.000 0.000
#> SRR1377220 4 0.0000 0.997 0.000 0.000 0 1.000 0.000 0.000
#> SRR1377221 4 0.0000 0.997 0.000 0.000 0 1.000 0.000 0.000
#> SRR1377222 4 0.0260 0.994 0.000 0.000 0 0.992 0.008 0.000
#> SRR1377223 4 0.0260 0.994 0.000 0.000 0 0.992 0.008 0.000
#> SRR1377224 4 0.0260 0.994 0.000 0.000 0 0.992 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.764 0.901 0.950 0.3698 0.676 0.676
#> 3 3 0.950 0.949 0.959 0.6615 0.708 0.567
#> 4 4 1.000 0.983 0.992 0.1857 0.886 0.703
#> 5 5 0.913 0.874 0.895 0.0742 0.902 0.652
#> 6 6 0.963 0.972 0.983 0.0617 0.971 0.851
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 3 4 5
There is also optional best \(k\) = 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 2 0.000 0.935 0.000 1.000
#> SRR1377146 2 0.000 0.935 0.000 1.000
#> SRR1377147 2 0.000 0.935 0.000 1.000
#> SRR1377148 2 0.000 0.935 0.000 1.000
#> SRR1377153 2 0.000 0.935 0.000 1.000
#> SRR1377154 2 0.000 0.935 0.000 1.000
#> SRR1377155 2 0.000 0.935 0.000 1.000
#> SRR1377156 2 0.000 0.935 0.000 1.000
#> SRR1377149 2 0.000 0.935 0.000 1.000
#> SRR1377150 2 0.000 0.935 0.000 1.000
#> SRR1377151 2 0.000 0.935 0.000 1.000
#> SRR1377152 2 0.000 0.935 0.000 1.000
#> SRR1377157 1 0.000 1.000 1.000 0.000
#> SRR1377158 1 0.000 1.000 1.000 0.000
#> SRR1377159 1 0.000 1.000 1.000 0.000
#> SRR1377160 1 0.000 1.000 1.000 0.000
#> SRR1377161 1 0.000 1.000 1.000 0.000
#> SRR1377162 1 0.000 1.000 1.000 0.000
#> SRR1377163 1 0.000 1.000 1.000 0.000
#> SRR1377164 1 0.000 1.000 1.000 0.000
#> SRR1377169 1 0.000 1.000 1.000 0.000
#> SRR1377170 1 0.000 1.000 1.000 0.000
#> SRR1377171 1 0.000 1.000 1.000 0.000
#> SRR1377172 1 0.000 1.000 1.000 0.000
#> SRR1377165 1 0.000 1.000 1.000 0.000
#> SRR1377166 1 0.000 1.000 1.000 0.000
#> SRR1377167 1 0.000 1.000 1.000 0.000
#> SRR1377168 1 0.000 1.000 1.000 0.000
#> SRR1377173 2 0.925 0.591 0.340 0.660
#> SRR1377174 2 0.917 0.604 0.332 0.668
#> SRR1377175 2 0.925 0.590 0.340 0.660
#> SRR1377176 2 0.943 0.553 0.360 0.640
#> SRR1377177 2 0.814 0.720 0.252 0.748
#> SRR1377178 2 0.839 0.701 0.268 0.732
#> SRR1377179 2 0.844 0.695 0.272 0.728
#> SRR1377180 2 0.969 0.476 0.396 0.604
#> SRR1377181 2 0.795 0.734 0.240 0.760
#> SRR1377182 2 0.827 0.710 0.260 0.740
#> SRR1377183 2 0.000 0.935 0.000 1.000
#> SRR1377184 2 0.808 0.725 0.248 0.752
#> SRR1377185 2 0.000 0.935 0.000 1.000
#> SRR1377186 2 0.000 0.935 0.000 1.000
#> SRR1377187 2 0.839 0.700 0.268 0.732
#> SRR1377188 2 0.000 0.935 0.000 1.000
#> SRR1377189 2 0.000 0.935 0.000 1.000
#> SRR1377190 2 0.000 0.935 0.000 1.000
#> SRR1377191 2 0.000 0.935 0.000 1.000
#> SRR1377192 2 0.000 0.935 0.000 1.000
#> SRR1377193 2 0.000 0.935 0.000 1.000
#> SRR1377194 2 0.000 0.935 0.000 1.000
#> SRR1377195 2 0.000 0.935 0.000 1.000
#> SRR1377196 2 0.000 0.935 0.000 1.000
#> SRR1377197 2 0.000 0.935 0.000 1.000
#> SRR1377198 2 0.000 0.935 0.000 1.000
#> SRR1377199 2 0.000 0.935 0.000 1.000
#> SRR1377200 2 0.000 0.935 0.000 1.000
#> SRR1377201 2 0.000 0.935 0.000 1.000
#> SRR1377202 2 0.000 0.935 0.000 1.000
#> SRR1377203 2 0.000 0.935 0.000 1.000
#> SRR1377204 2 0.000 0.935 0.000 1.000
#> SRR1377205 2 0.000 0.935 0.000 1.000
#> SRR1377206 2 0.000 0.935 0.000 1.000
#> SRR1377207 2 0.000 0.935 0.000 1.000
#> SRR1377208 2 0.000 0.935 0.000 1.000
#> SRR1377209 2 0.000 0.935 0.000 1.000
#> SRR1377210 2 0.000 0.935 0.000 1.000
#> SRR1377211 2 0.000 0.935 0.000 1.000
#> SRR1377212 2 0.000 0.935 0.000 1.000
#> SRR1377213 2 0.000 0.935 0.000 1.000
#> SRR1377214 2 0.000 0.935 0.000 1.000
#> SRR1377215 2 0.000 0.935 0.000 1.000
#> SRR1377216 2 0.416 0.877 0.084 0.916
#> SRR1377217 2 0.634 0.814 0.160 0.840
#> SRR1377218 2 0.653 0.807 0.168 0.832
#> SRR1377219 2 0.000 0.935 0.000 1.000
#> SRR1377220 2 0.000 0.935 0.000 1.000
#> SRR1377221 2 0.000 0.935 0.000 1.000
#> SRR1377222 2 0.000 0.935 0.000 1.000
#> SRR1377223 2 0.000 0.935 0.000 1.000
#> SRR1377224 2 0.000 0.935 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377146 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377147 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377148 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377153 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377154 2 0.1163 0.953 0.028 0.972 0.000
#> SRR1377155 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377156 2 0.1163 0.952 0.028 0.972 0.000
#> SRR1377149 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377150 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377151 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377152 2 0.2878 0.943 0.096 0.904 0.000
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377173 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377174 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377175 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377176 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377177 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377178 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377179 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377180 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377181 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377182 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377183 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377184 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377185 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377186 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377187 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377188 1 0.0000 0.948 1.000 0.000 0.000
#> SRR1377189 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377190 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377191 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377192 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377193 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377194 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377195 1 0.3192 0.879 0.888 0.112 0.000
#> SRR1377196 1 0.4002 0.849 0.840 0.160 0.000
#> SRR1377197 1 0.3267 0.878 0.884 0.116 0.000
#> SRR1377198 1 0.4750 0.796 0.784 0.216 0.000
#> SRR1377199 1 0.4452 0.821 0.808 0.192 0.000
#> SRR1377200 1 0.4605 0.803 0.796 0.204 0.000
#> SRR1377201 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377202 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377203 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377204 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377205 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377206 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377207 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377208 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377209 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377210 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377211 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377212 2 0.0747 0.953 0.016 0.984 0.000
#> SRR1377213 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377214 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377215 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377216 2 0.3856 0.920 0.072 0.888 0.040
#> SRR1377217 2 0.4281 0.906 0.072 0.872 0.056
#> SRR1377218 2 0.4281 0.907 0.072 0.872 0.056
#> SRR1377219 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377220 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377221 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377222 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377223 2 0.2537 0.941 0.080 0.920 0.000
#> SRR1377224 2 0.2537 0.941 0.080 0.920 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377146 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377147 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377148 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377153 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377154 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377155 2 0.0336 0.991 0.008 0.992 0 0
#> SRR1377156 2 0.0336 0.991 0.008 0.992 0 0
#> SRR1377149 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377150 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377151 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377152 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1 0
#> SRR1377173 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377174 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377175 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377176 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377177 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377178 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377179 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377180 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377181 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377182 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377183 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377184 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377185 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377186 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377187 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377188 1 0.0000 0.966 1.000 0.000 0 0
#> SRR1377189 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377190 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377191 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377192 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377193 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377194 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377195 1 0.0921 0.947 0.972 0.028 0 0
#> SRR1377196 1 0.1867 0.910 0.928 0.072 0 0
#> SRR1377197 1 0.0707 0.953 0.980 0.020 0 0
#> SRR1377198 1 0.3569 0.768 0.804 0.196 0 0
#> SRR1377199 1 0.2469 0.874 0.892 0.108 0 0
#> SRR1377200 1 0.3266 0.805 0.832 0.168 0 0
#> SRR1377201 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377202 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377203 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377204 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377205 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377206 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377207 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377208 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377209 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377210 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377211 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377212 2 0.0000 0.999 0.000 1.000 0 0
#> SRR1377213 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377214 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377215 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377216 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377217 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377218 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377219 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377220 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377221 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377222 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377223 4 0.0000 1.000 0.000 0.000 0 1
#> SRR1377224 4 0.0000 1.000 0.000 0.000 0 1
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377146 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377147 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377148 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377153 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377154 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377155 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377156 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377149 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377150 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377151 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377152 5 0.426 0.6997 0.000 0.440 0 0 0.56
#> SRR1377157 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377158 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377159 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377160 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377161 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377162 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377163 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377164 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377169 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377170 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377171 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377172 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377165 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377166 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377167 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377168 3 0.000 1.0000 0.000 0.000 1 0 0.00
#> SRR1377173 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377174 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377175 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377176 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377177 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377178 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377179 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377180 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377181 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377182 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377183 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377184 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377185 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377186 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377187 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377188 1 0.000 0.9276 1.000 0.000 0 0 0.00
#> SRR1377189 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377190 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377191 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377192 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377193 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377194 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377195 1 0.502 0.5269 0.528 0.032 0 0 0.44
#> SRR1377196 1 0.569 0.4584 0.480 0.080 0 0 0.44
#> SRR1377197 1 0.480 0.5410 0.540 0.020 0 0 0.44
#> SRR1377198 5 0.646 -0.0399 0.184 0.376 0 0 0.44
#> SRR1377199 5 0.622 -0.4271 0.420 0.140 0 0 0.44
#> SRR1377200 5 0.651 -0.3374 0.364 0.196 0 0 0.44
#> SRR1377201 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377202 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377203 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377204 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377205 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377206 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377207 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377208 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377209 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377210 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377211 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377212 2 0.000 1.0000 0.000 1.000 0 0 0.00
#> SRR1377213 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377214 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377215 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377216 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377217 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377218 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377219 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377220 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377221 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377222 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377223 4 0.000 1.0000 0.000 0.000 0 1 0.00
#> SRR1377224 4 0.000 1.0000 0.000 0.000 0 1 0.00
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377146 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377147 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377148 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377153 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377154 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377155 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377156 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377149 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377150 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377151 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377152 6 0.000 1.000 0.000 0.000 0 0 0.000 1
#> SRR1377157 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377158 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377159 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377160 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377161 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377162 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377163 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377164 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377169 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377170 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377171 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377172 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377165 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377166 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377167 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377168 3 0.000 1.000 0.000 0.000 1 0 0.000 0
#> SRR1377173 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377174 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377175 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377176 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377177 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377178 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377179 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377180 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377181 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377182 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377183 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377184 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377185 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377186 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377187 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377188 1 0.000 1.000 1.000 0.000 0 0 0.000 0
#> SRR1377189 2 0.266 0.851 0.000 0.816 0 0 0.184 0
#> SRR1377190 2 0.266 0.851 0.000 0.816 0 0 0.184 0
#> SRR1377191 2 0.266 0.851 0.000 0.816 0 0 0.184 0
#> SRR1377192 2 0.266 0.851 0.000 0.816 0 0 0.184 0
#> SRR1377193 2 0.266 0.851 0.000 0.816 0 0 0.184 0
#> SRR1377194 2 0.266 0.851 0.000 0.816 0 0 0.184 0
#> SRR1377195 5 0.221 0.899 0.096 0.016 0 0 0.888 0
#> SRR1377196 5 0.123 0.931 0.036 0.012 0 0 0.952 0
#> SRR1377197 5 0.235 0.874 0.124 0.008 0 0 0.868 0
#> SRR1377198 5 0.000 0.933 0.000 0.000 0 0 1.000 0
#> SRR1377199 5 0.000 0.933 0.000 0.000 0 0 1.000 0
#> SRR1377200 5 0.000 0.933 0.000 0.000 0 0 1.000 0
#> SRR1377201 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377202 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377203 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377204 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377205 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377206 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377207 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377208 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377209 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377210 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377211 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377212 2 0.000 0.932 0.000 1.000 0 0 0.000 0
#> SRR1377213 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377214 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377215 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377216 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377217 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377218 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377219 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377220 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377221 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377222 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377223 4 0.000 1.000 0.000 0.000 0 1 0.000 0
#> SRR1377224 4 0.000 1.000 0.000 0.000 0 1 0.000 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.342 0.695 0.837 0.4479 0.596 0.596
#> 3 3 0.714 0.866 0.888 0.3452 0.772 0.632
#> 4 4 0.947 0.900 0.945 0.2077 0.824 0.593
#> 5 5 0.776 0.800 0.889 0.0463 0.918 0.720
#> 6 6 0.972 0.940 0.968 0.0844 0.883 0.554
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 4
There is also optional best \(k\) = 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 2 0.3274 0.742 0.060 0.940
#> SRR1377146 2 0.3274 0.742 0.060 0.940
#> SRR1377147 2 0.3274 0.742 0.060 0.940
#> SRR1377148 2 0.3274 0.742 0.060 0.940
#> SRR1377153 2 0.3274 0.742 0.060 0.940
#> SRR1377154 2 0.3274 0.742 0.060 0.940
#> SRR1377155 2 0.3274 0.742 0.060 0.940
#> SRR1377156 2 0.3274 0.742 0.060 0.940
#> SRR1377149 2 0.3274 0.742 0.060 0.940
#> SRR1377150 2 0.3274 0.742 0.060 0.940
#> SRR1377151 2 0.3274 0.742 0.060 0.940
#> SRR1377152 2 0.3274 0.742 0.060 0.940
#> SRR1377157 1 0.4298 0.872 0.912 0.088
#> SRR1377158 1 0.4298 0.872 0.912 0.088
#> SRR1377159 1 0.4298 0.872 0.912 0.088
#> SRR1377160 1 0.4298 0.872 0.912 0.088
#> SRR1377161 1 0.4298 0.872 0.912 0.088
#> SRR1377162 1 0.4298 0.872 0.912 0.088
#> SRR1377163 1 0.4298 0.872 0.912 0.088
#> SRR1377164 1 0.4298 0.872 0.912 0.088
#> SRR1377169 1 0.4298 0.872 0.912 0.088
#> SRR1377170 1 0.4298 0.872 0.912 0.088
#> SRR1377171 1 0.4298 0.872 0.912 0.088
#> SRR1377172 1 0.4298 0.872 0.912 0.088
#> SRR1377165 1 0.4298 0.872 0.912 0.088
#> SRR1377166 1 0.4298 0.872 0.912 0.088
#> SRR1377167 1 0.4298 0.872 0.912 0.088
#> SRR1377168 1 0.4298 0.872 0.912 0.088
#> SRR1377173 2 0.9044 0.596 0.320 0.680
#> SRR1377174 2 0.9044 0.596 0.320 0.680
#> SRR1377175 2 0.9044 0.596 0.320 0.680
#> SRR1377176 2 0.9044 0.596 0.320 0.680
#> SRR1377177 2 0.9044 0.596 0.320 0.680
#> SRR1377178 2 0.9044 0.596 0.320 0.680
#> SRR1377179 2 0.9044 0.596 0.320 0.680
#> SRR1377180 2 0.9044 0.596 0.320 0.680
#> SRR1377181 2 0.9044 0.596 0.320 0.680
#> SRR1377182 2 0.9044 0.596 0.320 0.680
#> SRR1377183 2 0.7815 0.676 0.232 0.768
#> SRR1377184 2 0.9044 0.596 0.320 0.680
#> SRR1377185 2 0.7745 0.679 0.228 0.772
#> SRR1377186 2 0.7745 0.679 0.228 0.772
#> SRR1377187 2 0.9044 0.596 0.320 0.680
#> SRR1377188 2 0.7745 0.679 0.228 0.772
#> SRR1377189 2 0.3274 0.765 0.060 0.940
#> SRR1377190 2 0.3274 0.765 0.060 0.940
#> SRR1377191 2 0.3274 0.765 0.060 0.940
#> SRR1377192 2 0.3274 0.765 0.060 0.940
#> SRR1377193 2 0.3274 0.765 0.060 0.940
#> SRR1377194 2 0.3274 0.765 0.060 0.940
#> SRR1377195 1 0.8386 0.616 0.732 0.268
#> SRR1377196 1 0.8386 0.616 0.732 0.268
#> SRR1377197 1 0.8386 0.616 0.732 0.268
#> SRR1377198 1 0.8386 0.616 0.732 0.268
#> SRR1377199 1 0.8386 0.616 0.732 0.268
#> SRR1377200 1 0.8386 0.616 0.732 0.268
#> SRR1377201 2 0.0376 0.766 0.004 0.996
#> SRR1377202 2 0.0000 0.766 0.000 1.000
#> SRR1377203 2 0.0000 0.766 0.000 1.000
#> SRR1377204 2 0.3114 0.766 0.056 0.944
#> SRR1377205 2 0.3114 0.766 0.056 0.944
#> SRR1377206 2 0.3114 0.766 0.056 0.944
#> SRR1377207 2 0.0000 0.766 0.000 1.000
#> SRR1377208 2 0.0000 0.766 0.000 1.000
#> SRR1377209 2 0.0000 0.766 0.000 1.000
#> SRR1377210 2 0.0376 0.764 0.004 0.996
#> SRR1377211 2 0.0672 0.766 0.008 0.992
#> SRR1377212 2 0.0376 0.766 0.004 0.996
#> SRR1377213 2 0.9491 0.446 0.368 0.632
#> SRR1377214 2 0.9491 0.446 0.368 0.632
#> SRR1377215 2 0.9491 0.446 0.368 0.632
#> SRR1377216 2 0.9286 0.468 0.344 0.656
#> SRR1377217 2 0.9286 0.468 0.344 0.656
#> SRR1377218 2 0.9286 0.468 0.344 0.656
#> SRR1377219 2 0.9491 0.446 0.368 0.632
#> SRR1377220 2 0.9491 0.446 0.368 0.632
#> SRR1377221 2 0.9491 0.446 0.368 0.632
#> SRR1377222 2 0.9522 0.451 0.372 0.628
#> SRR1377223 2 0.9522 0.451 0.372 0.628
#> SRR1377224 2 0.9522 0.451 0.372 0.628
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377146 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377147 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377148 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377153 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377154 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377155 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377156 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377149 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377150 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377151 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377152 2 0.0661 0.869 0.004 0.988 0.008
#> SRR1377157 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377158 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377159 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377160 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377161 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377162 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377163 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377164 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377169 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377170 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377171 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377172 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377165 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377166 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377167 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377168 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1377173 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377174 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377175 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377176 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377177 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377178 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377179 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377180 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377181 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377182 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377183 2 0.6195 0.739 0.020 0.704 0.276
#> SRR1377184 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377185 2 0.6195 0.739 0.020 0.704 0.276
#> SRR1377186 2 0.6195 0.739 0.020 0.704 0.276
#> SRR1377187 2 0.6126 0.743 0.020 0.712 0.268
#> SRR1377188 2 0.6195 0.739 0.020 0.704 0.276
#> SRR1377189 2 0.2434 0.869 0.036 0.940 0.024
#> SRR1377190 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377191 2 0.2297 0.870 0.036 0.944 0.020
#> SRR1377192 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377193 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377194 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377195 1 0.1832 0.814 0.956 0.036 0.008
#> SRR1377196 1 0.1832 0.814 0.956 0.036 0.008
#> SRR1377197 1 0.1832 0.814 0.956 0.036 0.008
#> SRR1377198 1 0.1832 0.814 0.956 0.036 0.008
#> SRR1377199 1 0.1832 0.814 0.956 0.036 0.008
#> SRR1377200 1 0.1832 0.814 0.956 0.036 0.008
#> SRR1377201 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377202 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377203 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377204 2 0.2492 0.868 0.048 0.936 0.016
#> SRR1377205 2 0.2492 0.868 0.048 0.936 0.016
#> SRR1377206 2 0.2492 0.868 0.048 0.936 0.016
#> SRR1377207 2 0.2902 0.854 0.016 0.920 0.064
#> SRR1377208 2 0.2902 0.854 0.016 0.920 0.064
#> SRR1377209 2 0.2902 0.854 0.016 0.920 0.064
#> SRR1377210 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377211 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377212 2 0.2152 0.870 0.036 0.948 0.016
#> SRR1377213 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377214 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377215 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377216 1 0.6016 0.833 0.724 0.020 0.256
#> SRR1377217 1 0.6016 0.833 0.724 0.020 0.256
#> SRR1377218 1 0.6016 0.833 0.724 0.020 0.256
#> SRR1377219 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377220 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377221 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377222 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377223 1 0.4963 0.887 0.792 0.008 0.200
#> SRR1377224 1 0.4963 0.887 0.792 0.008 0.200
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377146 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377147 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377148 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377153 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377154 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377155 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377156 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377149 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377150 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377151 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377152 2 0.0000 0.949 0.000 1.000 0.000 0.000
#> SRR1377157 3 0.0592 0.950 0.016 0.000 0.984 0.000
#> SRR1377158 3 0.0592 0.950 0.016 0.000 0.984 0.000
#> SRR1377159 3 0.0592 0.950 0.016 0.000 0.984 0.000
#> SRR1377160 3 0.0592 0.950 0.016 0.000 0.984 0.000
#> SRR1377161 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377162 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377163 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377164 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377169 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377170 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377171 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377172 3 0.1716 0.968 0.000 0.000 0.936 0.064
#> SRR1377165 3 0.0707 0.961 0.000 0.000 0.980 0.020
#> SRR1377166 3 0.1474 0.968 0.000 0.000 0.948 0.052
#> SRR1377167 3 0.0779 0.959 0.004 0.000 0.980 0.016
#> SRR1377168 3 0.1022 0.965 0.000 0.000 0.968 0.032
#> SRR1377173 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377174 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377175 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377176 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377177 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377178 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377179 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377180 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377181 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377182 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377183 2 0.5334 0.591 0.000 0.680 0.036 0.284
#> SRR1377184 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377185 2 0.5334 0.591 0.000 0.680 0.036 0.284
#> SRR1377186 2 0.5334 0.591 0.000 0.680 0.036 0.284
#> SRR1377187 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR1377188 2 0.5334 0.591 0.000 0.680 0.036 0.284
#> SRR1377189 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377190 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377191 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377192 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377193 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377194 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377195 1 0.0927 0.878 0.976 0.000 0.016 0.008
#> SRR1377196 1 0.0927 0.878 0.976 0.000 0.016 0.008
#> SRR1377197 1 0.0927 0.878 0.976 0.000 0.016 0.008
#> SRR1377198 1 0.0927 0.878 0.976 0.000 0.016 0.008
#> SRR1377199 1 0.0927 0.878 0.976 0.000 0.016 0.008
#> SRR1377200 1 0.0927 0.878 0.976 0.000 0.016 0.008
#> SRR1377201 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377202 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377203 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377204 1 0.4985 0.242 0.532 0.468 0.000 0.000
#> SRR1377205 1 0.4985 0.242 0.532 0.468 0.000 0.000
#> SRR1377206 1 0.4985 0.242 0.532 0.468 0.000 0.000
#> SRR1377207 2 0.1256 0.932 0.008 0.964 0.028 0.000
#> SRR1377208 2 0.1256 0.932 0.008 0.964 0.028 0.000
#> SRR1377209 2 0.1256 0.932 0.008 0.964 0.028 0.000
#> SRR1377210 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377211 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377212 2 0.0336 0.949 0.008 0.992 0.000 0.000
#> SRR1377213 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1377214 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1377215 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1377216 1 0.2214 0.879 0.928 0.000 0.044 0.028
#> SRR1377217 1 0.2214 0.879 0.928 0.000 0.044 0.028
#> SRR1377218 1 0.2214 0.879 0.928 0.000 0.044 0.028
#> SRR1377219 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1377220 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1377221 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1377222 1 0.1118 0.890 0.964 0.000 0.036 0.000
#> SRR1377223 1 0.1118 0.890 0.964 0.000 0.036 0.000
#> SRR1377224 1 0.1118 0.890 0.964 0.000 0.036 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377146 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377147 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377148 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377153 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377154 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377155 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377156 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377149 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377150 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377151 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377152 2 0.1121 0.787 0.000 0.956 0.000 0.000 0.044
#> SRR1377157 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377158 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377159 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377160 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377161 3 0.0404 0.985 0.012 0.000 0.988 0.000 0.000
#> SRR1377162 3 0.0404 0.985 0.012 0.000 0.988 0.000 0.000
#> SRR1377163 3 0.0404 0.985 0.012 0.000 0.988 0.000 0.000
#> SRR1377164 3 0.0404 0.985 0.012 0.000 0.988 0.000 0.000
#> SRR1377169 3 0.1041 0.970 0.032 0.000 0.964 0.004 0.000
#> SRR1377170 3 0.1041 0.970 0.032 0.000 0.964 0.004 0.000
#> SRR1377171 3 0.1386 0.961 0.032 0.000 0.952 0.016 0.000
#> SRR1377172 3 0.0865 0.976 0.024 0.000 0.972 0.004 0.000
#> SRR1377165 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377166 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377167 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377168 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> SRR1377173 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377174 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377175 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377176 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377177 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377178 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377179 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377180 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377181 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377182 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377183 4 0.7916 0.365 0.292 0.212 0.092 0.404 0.000
#> SRR1377184 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377185 4 0.7916 0.365 0.292 0.212 0.092 0.404 0.000
#> SRR1377186 4 0.7916 0.365 0.292 0.212 0.092 0.404 0.000
#> SRR1377187 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377188 4 0.7916 0.365 0.292 0.212 0.092 0.404 0.000
#> SRR1377189 2 0.3790 0.678 0.004 0.724 0.000 0.272 0.000
#> SRR1377190 2 0.3333 0.746 0.004 0.788 0.000 0.208 0.000
#> SRR1377191 2 0.3662 0.705 0.004 0.744 0.000 0.252 0.000
#> SRR1377192 2 0.3662 0.706 0.004 0.744 0.000 0.252 0.000
#> SRR1377193 2 0.3491 0.731 0.004 0.768 0.000 0.228 0.000
#> SRR1377194 2 0.3430 0.737 0.004 0.776 0.000 0.220 0.000
#> SRR1377195 5 0.1121 1.000 0.000 0.000 0.000 0.044 0.956
#> SRR1377196 5 0.1121 1.000 0.000 0.000 0.000 0.044 0.956
#> SRR1377197 5 0.1121 1.000 0.000 0.000 0.000 0.044 0.956
#> SRR1377198 5 0.1121 1.000 0.000 0.000 0.000 0.044 0.956
#> SRR1377199 5 0.1121 1.000 0.000 0.000 0.000 0.044 0.956
#> SRR1377200 5 0.1121 1.000 0.000 0.000 0.000 0.044 0.956
#> SRR1377201 2 0.4045 0.527 0.000 0.644 0.000 0.356 0.000
#> SRR1377202 2 0.4045 0.527 0.000 0.644 0.000 0.356 0.000
#> SRR1377203 2 0.4045 0.527 0.000 0.644 0.000 0.356 0.000
#> SRR1377204 4 0.4192 0.167 0.000 0.404 0.000 0.596 0.000
#> SRR1377205 4 0.4192 0.167 0.000 0.404 0.000 0.596 0.000
#> SRR1377206 4 0.4192 0.167 0.000 0.404 0.000 0.596 0.000
#> SRR1377207 2 0.4610 0.711 0.000 0.740 0.092 0.168 0.000
#> SRR1377208 2 0.4610 0.711 0.000 0.740 0.092 0.168 0.000
#> SRR1377209 2 0.4610 0.711 0.000 0.740 0.092 0.168 0.000
#> SRR1377210 2 0.2848 0.772 0.004 0.840 0.000 0.156 0.000
#> SRR1377211 2 0.2848 0.772 0.004 0.840 0.000 0.156 0.000
#> SRR1377212 2 0.2848 0.772 0.004 0.840 0.000 0.156 0.000
#> SRR1377213 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377214 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377215 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377216 4 0.3677 0.664 0.048 0.000 0.060 0.848 0.044
#> SRR1377217 4 0.3677 0.664 0.048 0.000 0.060 0.848 0.044
#> SRR1377218 4 0.3677 0.664 0.048 0.000 0.060 0.848 0.044
#> SRR1377219 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377220 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377221 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377222 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377223 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> SRR1377224 4 0.0000 0.728 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
#> SRR1377145 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377146 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377147 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377148 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377153 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377154 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377155 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377156 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377149 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377150 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377151 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377152 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1377157 3 0.0000 0.990 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377158 3 0.0000 0.990 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377159 3 0.0000 0.990 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377160 3 0.0000 0.990 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377161 3 0.0363 0.991 0.012 0.000 0.988 0.000 0 0.000
#> SRR1377162 3 0.0363 0.991 0.012 0.000 0.988 0.000 0 0.000
#> SRR1377163 3 0.0363 0.991 0.012 0.000 0.988 0.000 0 0.000
#> SRR1377164 3 0.0363 0.991 0.012 0.000 0.988 0.000 0 0.000
#> SRR1377169 3 0.0458 0.989 0.016 0.000 0.984 0.000 0 0.000
#> SRR1377170 3 0.0458 0.989 0.016 0.000 0.984 0.000 0 0.000
#> SRR1377171 3 0.0458 0.989 0.016 0.000 0.984 0.000 0 0.000
#> SRR1377172 3 0.0458 0.989 0.016 0.000 0.984 0.000 0 0.000
#> SRR1377165 3 0.0000 0.990 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377166 3 0.0146 0.989 0.000 0.000 0.996 0.004 0 0.000
#> SRR1377167 3 0.0000 0.990 0.000 0.000 1.000 0.000 0 0.000
#> SRR1377168 3 0.0146 0.989 0.000 0.000 0.996 0.004 0 0.000
#> SRR1377173 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377174 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377175 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377176 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377177 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377178 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377179 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377180 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377181 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377182 1 0.0146 0.995 0.996 0.004 0.000 0.000 0 0.000
#> SRR1377183 2 0.3797 0.622 0.292 0.692 0.016 0.000 0 0.000
#> SRR1377184 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377185 2 0.3797 0.622 0.292 0.692 0.016 0.000 0 0.000
#> SRR1377186 2 0.3797 0.622 0.292 0.692 0.016 0.000 0 0.000
#> SRR1377187 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1377188 2 0.3797 0.622 0.292 0.692 0.016 0.000 0 0.000
#> SRR1377189 2 0.0260 0.884 0.000 0.992 0.000 0.000 0 0.008
#> SRR1377190 2 0.0000 0.884 0.000 1.000 0.000 0.000 0 0.000
#> SRR1377191 2 0.0260 0.884 0.000 0.992 0.000 0.000 0 0.008
#> SRR1377192 2 0.0000 0.884 0.000 1.000 0.000 0.000 0 0.000
#> SRR1377193 2 0.0000 0.884 0.000 1.000 0.000 0.000 0 0.000
#> SRR1377194 2 0.0000 0.884 0.000 1.000 0.000 0.000 0 0.000
#> SRR1377195 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377196 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377197 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377198 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377199 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377200 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1377201 2 0.0547 0.881 0.000 0.980 0.000 0.000 0 0.020
#> SRR1377202 2 0.0547 0.881 0.000 0.980 0.000 0.000 0 0.020
#> SRR1377203 2 0.0547 0.881 0.000 0.980 0.000 0.000 0 0.020
#> SRR1377204 2 0.3101 0.684 0.000 0.756 0.000 0.244 0 0.000
#> SRR1377205 2 0.3101 0.684 0.000 0.756 0.000 0.244 0 0.000
#> SRR1377206 2 0.3101 0.684 0.000 0.756 0.000 0.244 0 0.000
#> SRR1377207 2 0.1003 0.880 0.000 0.964 0.016 0.000 0 0.020
#> SRR1377208 2 0.1003 0.880 0.000 0.964 0.016 0.000 0 0.020
#> SRR1377209 2 0.1003 0.880 0.000 0.964 0.016 0.000 0 0.020
#> SRR1377210 2 0.0363 0.884 0.000 0.988 0.000 0.000 0 0.012
#> SRR1377211 2 0.0363 0.884 0.000 0.988 0.000 0.000 0 0.012
#> SRR1377212 2 0.0363 0.884 0.000 0.988 0.000 0.000 0 0.012
#> SRR1377213 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377214 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377215 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377216 4 0.1668 0.927 0.004 0.008 0.060 0.928 0 0.000
#> SRR1377217 4 0.1668 0.927 0.004 0.008 0.060 0.928 0 0.000
#> SRR1377218 4 0.1668 0.927 0.004 0.008 0.060 0.928 0 0.000
#> SRR1377219 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377220 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377221 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377222 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377223 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 0.000
#> SRR1377224 4 0.0000 0.976 0.000 0.000 0.000 1.000 0 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 13890 rows and 80 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 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 0.443 0.771 0.839 0.2737 0.859 0.859
#> 3 3 0.666 0.781 0.885 1.0461 0.572 0.502
#> 4 4 0.644 0.797 0.876 0.2031 0.781 0.548
#> 5 5 0.733 0.825 0.810 0.1031 0.919 0.747
#> 6 6 0.853 0.949 0.890 0.0735 0.932 0.714
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
#> SRR1377145 2 0.0938 0.737 0.012 0.988
#> SRR1377146 2 0.0938 0.737 0.012 0.988
#> SRR1377147 2 0.2236 0.746 0.036 0.964
#> SRR1377148 2 0.0938 0.737 0.012 0.988
#> SRR1377153 2 0.5178 0.764 0.116 0.884
#> SRR1377154 2 0.5842 0.767 0.140 0.860
#> SRR1377155 2 0.5737 0.767 0.136 0.864
#> SRR1377156 2 0.5842 0.767 0.140 0.860
#> SRR1377149 2 0.3733 0.756 0.072 0.928
#> SRR1377150 2 0.1843 0.743 0.028 0.972
#> SRR1377151 2 0.3114 0.752 0.056 0.944
#> SRR1377152 2 0.3274 0.753 0.060 0.940
#> SRR1377157 2 0.9248 0.783 0.340 0.660
#> SRR1377158 2 0.9248 0.783 0.340 0.660
#> SRR1377159 2 0.9248 0.783 0.340 0.660
#> SRR1377160 2 0.9248 0.783 0.340 0.660
#> SRR1377161 2 0.9248 0.783 0.340 0.660
#> SRR1377162 2 0.9248 0.783 0.340 0.660
#> SRR1377163 2 0.9248 0.783 0.340 0.660
#> SRR1377164 2 0.9248 0.783 0.340 0.660
#> SRR1377169 2 0.9248 0.783 0.340 0.660
#> SRR1377170 2 0.9248 0.783 0.340 0.660
#> SRR1377171 2 0.9248 0.783 0.340 0.660
#> SRR1377172 2 0.9248 0.783 0.340 0.660
#> SRR1377165 2 0.9248 0.783 0.340 0.660
#> SRR1377166 2 0.9248 0.783 0.340 0.660
#> SRR1377167 2 0.9248 0.783 0.340 0.660
#> SRR1377168 2 0.9248 0.783 0.340 0.660
#> SRR1377173 2 0.9248 0.783 0.340 0.660
#> SRR1377174 2 0.9248 0.783 0.340 0.660
#> SRR1377175 2 0.9248 0.783 0.340 0.660
#> SRR1377176 2 0.9248 0.783 0.340 0.660
#> SRR1377177 2 0.9248 0.783 0.340 0.660
#> SRR1377178 2 0.9248 0.783 0.340 0.660
#> SRR1377179 2 0.9248 0.783 0.340 0.660
#> SRR1377180 2 0.9248 0.783 0.340 0.660
#> SRR1377181 2 0.9248 0.783 0.340 0.660
#> SRR1377182 2 0.9248 0.783 0.340 0.660
#> SRR1377183 2 0.9248 0.783 0.340 0.660
#> SRR1377184 2 0.9248 0.783 0.340 0.660
#> SRR1377185 2 0.9248 0.783 0.340 0.660
#> SRR1377186 2 0.9248 0.783 0.340 0.660
#> SRR1377187 2 0.9248 0.783 0.340 0.660
#> SRR1377188 2 0.9248 0.783 0.340 0.660
#> SRR1377189 2 0.1843 0.711 0.028 0.972
#> SRR1377190 2 0.1843 0.711 0.028 0.972
#> SRR1377191 2 0.1843 0.711 0.028 0.972
#> SRR1377192 2 0.1843 0.711 0.028 0.972
#> SRR1377193 2 0.1843 0.711 0.028 0.972
#> SRR1377194 2 0.1843 0.711 0.028 0.972
#> SRR1377195 1 0.9248 0.988 0.660 0.340
#> SRR1377196 1 0.9129 0.988 0.672 0.328
#> SRR1377197 1 0.9044 0.980 0.680 0.320
#> SRR1377198 1 0.9209 0.990 0.664 0.336
#> SRR1377199 1 0.9170 0.990 0.668 0.332
#> SRR1377200 1 0.9248 0.988 0.660 0.340
#> SRR1377201 2 0.1843 0.711 0.028 0.972
#> SRR1377202 2 0.1843 0.711 0.028 0.972
#> SRR1377203 2 0.1843 0.711 0.028 0.972
#> SRR1377204 2 0.2603 0.693 0.044 0.956
#> SRR1377205 2 0.2423 0.698 0.040 0.960
#> SRR1377206 2 0.2423 0.698 0.040 0.960
#> SRR1377207 2 0.0000 0.730 0.000 1.000
#> SRR1377208 2 0.0000 0.730 0.000 1.000
#> SRR1377209 2 0.0376 0.728 0.004 0.996
#> SRR1377210 2 0.1633 0.714 0.024 0.976
#> SRR1377211 2 0.1414 0.717 0.020 0.980
#> SRR1377212 2 0.1184 0.720 0.016 0.984
#> SRR1377213 2 0.3733 0.734 0.072 0.928
#> SRR1377214 2 0.3733 0.734 0.072 0.928
#> SRR1377215 2 0.3584 0.732 0.068 0.932
#> SRR1377216 2 0.9209 0.783 0.336 0.664
#> SRR1377217 2 0.9209 0.783 0.336 0.664
#> SRR1377218 2 0.9209 0.783 0.336 0.664
#> SRR1377219 2 0.3879 0.735 0.076 0.924
#> SRR1377220 2 0.4161 0.738 0.084 0.916
#> SRR1377221 2 0.3733 0.734 0.072 0.928
#> SRR1377222 2 0.1843 0.711 0.028 0.972
#> SRR1377223 2 0.1843 0.711 0.028 0.972
#> SRR1377224 2 0.1843 0.711 0.028 0.972
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.6396 0.5245 0.016 0.664 0.320
#> SRR1377146 2 0.6422 0.5159 0.016 0.660 0.324
#> SRR1377147 2 0.6675 0.3235 0.012 0.584 0.404
#> SRR1377148 2 0.6333 0.5028 0.012 0.656 0.332
#> SRR1377153 3 0.6969 0.3372 0.024 0.380 0.596
#> SRR1377154 3 0.6758 0.3949 0.020 0.360 0.620
#> SRR1377155 3 0.6510 0.3976 0.012 0.364 0.624
#> SRR1377156 3 0.6667 0.3811 0.016 0.368 0.616
#> SRR1377149 3 0.7063 0.0568 0.020 0.464 0.516
#> SRR1377150 2 0.7203 0.2703 0.028 0.556 0.416
#> SRR1377151 3 0.7069 0.0239 0.020 0.472 0.508
#> SRR1377152 2 0.7069 0.0773 0.020 0.508 0.472
#> SRR1377157 3 0.0661 0.8902 0.004 0.008 0.988
#> SRR1377158 3 0.0661 0.8902 0.004 0.008 0.988
#> SRR1377159 3 0.0661 0.8902 0.004 0.008 0.988
#> SRR1377160 3 0.0661 0.8902 0.004 0.008 0.988
#> SRR1377161 3 0.0237 0.8981 0.004 0.000 0.996
#> SRR1377162 3 0.0424 0.8968 0.008 0.000 0.992
#> SRR1377163 3 0.0000 0.8988 0.000 0.000 1.000
#> SRR1377164 3 0.0000 0.8988 0.000 0.000 1.000
#> SRR1377169 3 0.0237 0.8999 0.000 0.004 0.996
#> SRR1377170 3 0.0237 0.8999 0.000 0.004 0.996
#> SRR1377171 3 0.0000 0.8988 0.000 0.000 1.000
#> SRR1377172 3 0.0237 0.8999 0.000 0.004 0.996
#> SRR1377165 3 0.0000 0.8988 0.000 0.000 1.000
#> SRR1377166 3 0.0000 0.8988 0.000 0.000 1.000
#> SRR1377167 3 0.0237 0.8999 0.000 0.004 0.996
#> SRR1377168 3 0.0000 0.8988 0.000 0.000 1.000
#> SRR1377173 3 0.0829 0.8997 0.004 0.012 0.984
#> SRR1377174 3 0.0829 0.8997 0.004 0.012 0.984
#> SRR1377175 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377176 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377177 3 0.0829 0.8997 0.004 0.012 0.984
#> SRR1377178 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377179 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377180 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377181 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377182 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377183 3 0.0829 0.8997 0.004 0.012 0.984
#> SRR1377184 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377185 3 0.0983 0.8976 0.004 0.016 0.980
#> SRR1377186 3 0.0983 0.8976 0.004 0.016 0.980
#> SRR1377187 3 0.0661 0.9004 0.004 0.008 0.988
#> SRR1377188 3 0.0829 0.8997 0.004 0.012 0.984
#> SRR1377189 2 0.1289 0.8339 0.000 0.968 0.032
#> SRR1377190 2 0.1289 0.8339 0.000 0.968 0.032
#> SRR1377191 2 0.1289 0.8339 0.000 0.968 0.032
#> SRR1377192 2 0.1163 0.8324 0.000 0.972 0.028
#> SRR1377193 2 0.1399 0.8325 0.004 0.968 0.028
#> SRR1377194 2 0.1399 0.8325 0.004 0.968 0.028
#> SRR1377195 1 0.2796 0.9521 0.908 0.092 0.000
#> SRR1377196 1 0.3412 0.9715 0.876 0.124 0.000
#> SRR1377197 1 0.2860 0.9566 0.912 0.084 0.004
#> SRR1377198 1 0.3340 0.9714 0.880 0.120 0.000
#> SRR1377199 1 0.3644 0.9666 0.872 0.124 0.004
#> SRR1377200 1 0.3482 0.9681 0.872 0.128 0.000
#> SRR1377201 2 0.1525 0.8335 0.004 0.964 0.032
#> SRR1377202 2 0.1525 0.8335 0.004 0.964 0.032
#> SRR1377203 2 0.1525 0.8335 0.004 0.964 0.032
#> SRR1377204 2 0.1015 0.8149 0.012 0.980 0.008
#> SRR1377205 2 0.1015 0.8149 0.012 0.980 0.008
#> SRR1377206 2 0.1015 0.8149 0.012 0.980 0.008
#> SRR1377207 2 0.2400 0.8199 0.004 0.932 0.064
#> SRR1377208 2 0.2496 0.8173 0.004 0.928 0.068
#> SRR1377209 2 0.2496 0.8173 0.004 0.928 0.068
#> SRR1377210 2 0.1411 0.8339 0.000 0.964 0.036
#> SRR1377211 2 0.1411 0.8339 0.000 0.964 0.036
#> SRR1377212 2 0.1529 0.8328 0.000 0.960 0.040
#> SRR1377213 2 0.4818 0.7837 0.108 0.844 0.048
#> SRR1377214 2 0.5020 0.7789 0.108 0.836 0.056
#> SRR1377215 2 0.4818 0.7837 0.108 0.844 0.048
#> SRR1377216 3 0.5811 0.7408 0.108 0.092 0.800
#> SRR1377217 3 0.5650 0.7498 0.108 0.084 0.808
#> SRR1377218 3 0.5650 0.7498 0.108 0.084 0.808
#> SRR1377219 2 0.5020 0.7789 0.108 0.836 0.056
#> SRR1377220 2 0.5117 0.7803 0.108 0.832 0.060
#> SRR1377221 2 0.5117 0.7803 0.108 0.832 0.060
#> SRR1377222 2 0.4063 0.7687 0.112 0.868 0.020
#> SRR1377223 2 0.4063 0.7687 0.112 0.868 0.020
#> SRR1377224 2 0.4063 0.7687 0.112 0.868 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.1940 0.804 0.000 0.924 0.076 0.000
#> SRR1377146 2 0.2053 0.803 0.004 0.924 0.072 0.000
#> SRR1377147 2 0.2401 0.801 0.004 0.904 0.092 0.000
#> SRR1377148 2 0.2011 0.803 0.000 0.920 0.080 0.000
#> SRR1377153 2 0.2704 0.787 0.000 0.876 0.124 0.000
#> SRR1377154 2 0.2868 0.779 0.000 0.864 0.136 0.000
#> SRR1377155 2 0.3024 0.771 0.000 0.852 0.148 0.000
#> SRR1377156 2 0.2921 0.777 0.000 0.860 0.140 0.000
#> SRR1377149 2 0.2530 0.795 0.000 0.888 0.112 0.000
#> SRR1377150 2 0.2382 0.801 0.004 0.912 0.080 0.004
#> SRR1377151 2 0.2466 0.799 0.004 0.900 0.096 0.000
#> SRR1377152 2 0.2466 0.800 0.004 0.900 0.096 0.000
#> SRR1377157 3 0.1970 0.879 0.008 0.000 0.932 0.060
#> SRR1377158 3 0.1890 0.881 0.008 0.000 0.936 0.056
#> SRR1377159 3 0.1807 0.883 0.008 0.000 0.940 0.052
#> SRR1377160 3 0.1970 0.879 0.008 0.000 0.932 0.060
#> SRR1377161 3 0.1854 0.884 0.012 0.000 0.940 0.048
#> SRR1377162 3 0.1938 0.883 0.012 0.000 0.936 0.052
#> SRR1377163 3 0.1938 0.882 0.012 0.000 0.936 0.052
#> SRR1377164 3 0.1767 0.885 0.012 0.000 0.944 0.044
#> SRR1377169 3 0.1767 0.885 0.012 0.000 0.944 0.044
#> SRR1377170 3 0.1767 0.885 0.012 0.000 0.944 0.044
#> SRR1377171 3 0.1635 0.885 0.008 0.000 0.948 0.044
#> SRR1377172 3 0.1635 0.885 0.008 0.000 0.948 0.044
#> SRR1377165 3 0.2048 0.877 0.008 0.000 0.928 0.064
#> SRR1377166 3 0.2048 0.877 0.008 0.000 0.928 0.064
#> SRR1377167 3 0.1970 0.879 0.008 0.000 0.932 0.060
#> SRR1377168 3 0.2048 0.877 0.008 0.000 0.928 0.064
#> SRR1377173 3 0.1474 0.879 0.000 0.052 0.948 0.000
#> SRR1377174 3 0.1389 0.881 0.000 0.048 0.952 0.000
#> SRR1377175 3 0.1302 0.882 0.000 0.044 0.956 0.000
#> SRR1377176 3 0.1302 0.882 0.000 0.044 0.956 0.000
#> SRR1377177 3 0.1940 0.866 0.000 0.076 0.924 0.000
#> SRR1377178 3 0.2081 0.860 0.000 0.084 0.916 0.000
#> SRR1377179 3 0.1867 0.869 0.000 0.072 0.928 0.000
#> SRR1377180 3 0.1940 0.866 0.000 0.076 0.924 0.000
#> SRR1377181 3 0.1489 0.881 0.000 0.044 0.952 0.004
#> SRR1377182 3 0.1302 0.882 0.000 0.044 0.956 0.000
#> SRR1377183 3 0.4134 0.659 0.000 0.260 0.740 0.000
#> SRR1377184 3 0.1637 0.875 0.000 0.060 0.940 0.000
#> SRR1377185 3 0.4277 0.631 0.000 0.280 0.720 0.000
#> SRR1377186 3 0.4331 0.620 0.000 0.288 0.712 0.000
#> SRR1377187 3 0.1389 0.881 0.000 0.048 0.952 0.000
#> SRR1377188 3 0.4331 0.621 0.000 0.288 0.712 0.000
#> SRR1377189 2 0.3208 0.809 0.004 0.848 0.000 0.148
#> SRR1377190 2 0.2944 0.821 0.004 0.868 0.000 0.128
#> SRR1377191 2 0.2773 0.827 0.004 0.880 0.000 0.116
#> SRR1377192 2 0.4088 0.734 0.004 0.764 0.000 0.232
#> SRR1377193 2 0.3908 0.755 0.004 0.784 0.000 0.212
#> SRR1377194 2 0.3945 0.751 0.004 0.780 0.000 0.216
#> SRR1377195 1 0.0779 0.965 0.980 0.016 0.000 0.004
#> SRR1377196 1 0.0672 0.965 0.984 0.008 0.000 0.008
#> SRR1377197 1 0.0672 0.965 0.984 0.008 0.000 0.008
#> SRR1377198 1 0.2546 0.961 0.912 0.060 0.000 0.028
#> SRR1377199 1 0.2908 0.955 0.896 0.064 0.000 0.040
#> SRR1377200 1 0.2466 0.963 0.916 0.056 0.000 0.028
#> SRR1377201 2 0.2469 0.829 0.000 0.892 0.000 0.108
#> SRR1377202 2 0.2469 0.829 0.000 0.892 0.000 0.108
#> SRR1377203 2 0.2469 0.829 0.000 0.892 0.000 0.108
#> SRR1377204 2 0.4994 0.293 0.000 0.520 0.000 0.480
#> SRR1377205 2 0.4996 0.282 0.000 0.516 0.000 0.484
#> SRR1377206 2 0.4996 0.282 0.000 0.516 0.000 0.484
#> SRR1377207 2 0.2334 0.832 0.000 0.908 0.004 0.088
#> SRR1377208 2 0.2334 0.832 0.000 0.908 0.004 0.088
#> SRR1377209 2 0.2334 0.832 0.000 0.908 0.004 0.088
#> SRR1377210 2 0.2704 0.825 0.000 0.876 0.000 0.124
#> SRR1377211 2 0.2704 0.825 0.000 0.876 0.000 0.124
#> SRR1377212 2 0.2647 0.826 0.000 0.880 0.000 0.120
#> SRR1377213 4 0.2214 0.808 0.000 0.044 0.028 0.928
#> SRR1377214 4 0.2214 0.808 0.000 0.044 0.028 0.928
#> SRR1377215 4 0.2313 0.808 0.000 0.044 0.032 0.924
#> SRR1377216 4 0.5337 0.386 0.000 0.012 0.424 0.564
#> SRR1377217 4 0.5345 0.377 0.000 0.012 0.428 0.560
#> SRR1377218 4 0.5345 0.377 0.000 0.012 0.428 0.560
#> SRR1377219 4 0.2408 0.808 0.000 0.044 0.036 0.920
#> SRR1377220 4 0.2408 0.808 0.000 0.044 0.036 0.920
#> SRR1377221 4 0.2408 0.808 0.000 0.044 0.036 0.920
#> SRR1377222 4 0.2845 0.785 0.000 0.076 0.028 0.896
#> SRR1377223 4 0.2845 0.785 0.000 0.076 0.028 0.896
#> SRR1377224 4 0.2845 0.785 0.000 0.076 0.028 0.896
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.5302 0.573 0.388 0.572 0.016 0.020 0.004
#> SRR1377146 2 0.5312 0.569 0.392 0.568 0.016 0.020 0.004
#> SRR1377147 2 0.5366 0.542 0.420 0.540 0.016 0.020 0.004
#> SRR1377148 2 0.5302 0.573 0.388 0.572 0.016 0.020 0.004
#> SRR1377153 2 0.5208 0.531 0.432 0.536 0.016 0.012 0.004
#> SRR1377154 2 0.5114 0.529 0.436 0.536 0.016 0.008 0.004
#> SRR1377155 2 0.5233 0.492 0.456 0.512 0.016 0.012 0.004
#> SRR1377156 2 0.5222 0.513 0.444 0.524 0.016 0.012 0.004
#> SRR1377149 2 0.5131 0.507 0.452 0.520 0.016 0.008 0.004
#> SRR1377150 2 0.5218 0.544 0.424 0.544 0.012 0.012 0.008
#> SRR1377151 2 0.5254 0.496 0.456 0.512 0.012 0.012 0.008
#> SRR1377152 2 0.5208 0.534 0.432 0.536 0.016 0.012 0.004
#> SRR1377157 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377158 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377159 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377160 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377161 3 0.0451 0.991 0.000 0.008 0.988 0.000 0.004
#> SRR1377162 3 0.0867 0.979 0.008 0.008 0.976 0.000 0.008
#> SRR1377163 3 0.0613 0.988 0.004 0.008 0.984 0.000 0.004
#> SRR1377164 3 0.0740 0.984 0.004 0.008 0.980 0.000 0.008
#> SRR1377169 3 0.0290 0.993 0.000 0.008 0.992 0.000 0.000
#> SRR1377170 3 0.0290 0.993 0.000 0.008 0.992 0.000 0.000
#> SRR1377171 3 0.0290 0.993 0.000 0.008 0.992 0.000 0.000
#> SRR1377172 3 0.0290 0.993 0.000 0.008 0.992 0.000 0.000
#> SRR1377165 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377166 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377167 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377168 3 0.0451 0.994 0.004 0.008 0.988 0.000 0.000
#> SRR1377173 1 0.3980 0.935 0.708 0.008 0.284 0.000 0.000
#> SRR1377174 1 0.3884 0.930 0.708 0.004 0.288 0.000 0.000
#> SRR1377175 1 0.3928 0.920 0.700 0.004 0.296 0.000 0.000
#> SRR1377176 1 0.3884 0.931 0.708 0.004 0.288 0.000 0.000
#> SRR1377177 1 0.3756 0.954 0.744 0.008 0.248 0.000 0.000
#> SRR1377178 1 0.3756 0.954 0.744 0.008 0.248 0.000 0.000
#> SRR1377179 1 0.3635 0.954 0.748 0.004 0.248 0.000 0.000
#> SRR1377180 1 0.3756 0.954 0.744 0.008 0.248 0.000 0.000
#> SRR1377181 1 0.3870 0.951 0.732 0.004 0.260 0.004 0.000
#> SRR1377182 1 0.3895 0.949 0.728 0.004 0.264 0.004 0.000
#> SRR1377183 1 0.4261 0.916 0.764 0.032 0.192 0.012 0.000
#> SRR1377184 1 0.3635 0.954 0.748 0.004 0.248 0.000 0.000
#> SRR1377185 1 0.4303 0.912 0.764 0.036 0.188 0.012 0.000
#> SRR1377186 1 0.4226 0.913 0.768 0.032 0.188 0.012 0.000
#> SRR1377187 1 0.3844 0.952 0.736 0.004 0.256 0.004 0.000
#> SRR1377188 1 0.4268 0.908 0.768 0.036 0.184 0.012 0.000
#> SRR1377189 2 0.1059 0.737 0.000 0.968 0.020 0.008 0.004
#> SRR1377190 2 0.1059 0.737 0.000 0.968 0.020 0.008 0.004
#> SRR1377191 2 0.1220 0.738 0.004 0.964 0.020 0.004 0.008
#> SRR1377192 2 0.1412 0.727 0.000 0.952 0.004 0.036 0.008
#> SRR1377193 2 0.1329 0.728 0.000 0.956 0.004 0.032 0.008
#> SRR1377194 2 0.1329 0.728 0.000 0.956 0.004 0.032 0.008
#> SRR1377195 5 0.1012 0.926 0.020 0.000 0.000 0.012 0.968
#> SRR1377196 5 0.1828 0.929 0.032 0.004 0.000 0.028 0.936
#> SRR1377197 5 0.1441 0.929 0.024 0.004 0.008 0.008 0.956
#> SRR1377198 5 0.3582 0.924 0.112 0.004 0.004 0.044 0.836
#> SRR1377199 5 0.5177 0.902 0.172 0.016 0.012 0.068 0.732
#> SRR1377200 5 0.4459 0.909 0.136 0.016 0.008 0.052 0.788
#> SRR1377201 2 0.1202 0.739 0.004 0.960 0.032 0.004 0.000
#> SRR1377202 2 0.1202 0.739 0.004 0.960 0.032 0.004 0.000
#> SRR1377203 2 0.1202 0.739 0.004 0.960 0.032 0.004 0.000
#> SRR1377204 2 0.2756 0.660 0.004 0.868 0.004 0.120 0.004
#> SRR1377205 2 0.2756 0.660 0.004 0.868 0.004 0.120 0.004
#> SRR1377206 2 0.2706 0.662 0.004 0.872 0.004 0.116 0.004
#> SRR1377207 2 0.1444 0.737 0.012 0.948 0.040 0.000 0.000
#> SRR1377208 2 0.1605 0.737 0.012 0.944 0.040 0.004 0.000
#> SRR1377209 2 0.1605 0.737 0.012 0.944 0.040 0.004 0.000
#> SRR1377210 2 0.1243 0.739 0.004 0.960 0.028 0.008 0.000
#> SRR1377211 2 0.1412 0.738 0.004 0.952 0.036 0.008 0.000
#> SRR1377212 2 0.1412 0.738 0.004 0.952 0.036 0.008 0.000
#> SRR1377213 4 0.2674 0.902 0.012 0.060 0.032 0.896 0.000
#> SRR1377214 4 0.2674 0.902 0.012 0.060 0.032 0.896 0.000
#> SRR1377215 4 0.2674 0.902 0.012 0.060 0.032 0.896 0.000
#> SRR1377216 4 0.4876 0.718 0.056 0.012 0.216 0.716 0.000
#> SRR1377217 4 0.4905 0.714 0.056 0.012 0.220 0.712 0.000
#> SRR1377218 4 0.4890 0.703 0.060 0.008 0.224 0.708 0.000
#> SRR1377219 4 0.2741 0.902 0.012 0.064 0.032 0.892 0.000
#> SRR1377220 4 0.2741 0.902 0.012 0.064 0.032 0.892 0.000
#> SRR1377221 4 0.2741 0.902 0.012 0.064 0.032 0.892 0.000
#> SRR1377222 4 0.2832 0.893 0.004 0.072 0.028 0.888 0.008
#> SRR1377223 4 0.2832 0.893 0.004 0.072 0.028 0.888 0.008
#> SRR1377224 4 0.2832 0.893 0.004 0.072 0.028 0.888 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.4862 0.968 0.128 0.148 0.020 0.000 0.000 0.704
#> SRR1377146 6 0.4862 0.968 0.128 0.148 0.020 0.000 0.000 0.704
#> SRR1377147 6 0.4866 0.971 0.136 0.140 0.020 0.000 0.000 0.704
#> SRR1377148 6 0.4858 0.964 0.124 0.152 0.020 0.000 0.000 0.704
#> SRR1377153 6 0.5741 0.957 0.156 0.132 0.040 0.016 0.000 0.656
#> SRR1377154 6 0.5711 0.959 0.148 0.136 0.040 0.016 0.000 0.660
#> SRR1377155 6 0.5705 0.956 0.156 0.128 0.040 0.016 0.000 0.660
#> SRR1377156 6 0.5741 0.957 0.156 0.132 0.040 0.016 0.000 0.656
#> SRR1377149 6 0.4865 0.969 0.144 0.132 0.020 0.000 0.000 0.704
#> SRR1377150 6 0.4791 0.968 0.128 0.140 0.020 0.000 0.000 0.712
#> SRR1377151 6 0.4862 0.967 0.148 0.128 0.020 0.000 0.000 0.704
#> SRR1377152 6 0.4902 0.971 0.136 0.144 0.020 0.000 0.000 0.700
#> SRR1377157 3 0.0777 0.980 0.004 0.000 0.972 0.024 0.000 0.000
#> SRR1377158 3 0.0777 0.980 0.004 0.000 0.972 0.024 0.000 0.000
#> SRR1377159 3 0.0806 0.981 0.008 0.000 0.972 0.020 0.000 0.000
#> SRR1377160 3 0.0777 0.980 0.004 0.000 0.972 0.024 0.000 0.000
#> SRR1377161 3 0.0291 0.981 0.000 0.000 0.992 0.004 0.000 0.004
#> SRR1377162 3 0.0291 0.981 0.004 0.000 0.992 0.000 0.000 0.004
#> SRR1377163 3 0.0146 0.983 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1377164 3 0.0291 0.981 0.000 0.000 0.992 0.004 0.000 0.004
#> SRR1377169 3 0.0551 0.986 0.004 0.000 0.984 0.008 0.000 0.004
#> SRR1377170 3 0.0582 0.984 0.004 0.004 0.984 0.004 0.000 0.004
#> SRR1377171 3 0.0436 0.982 0.004 0.000 0.988 0.004 0.000 0.004
#> SRR1377172 3 0.0436 0.985 0.004 0.000 0.988 0.004 0.000 0.004
#> SRR1377165 3 0.0508 0.986 0.004 0.000 0.984 0.012 0.000 0.000
#> SRR1377166 3 0.0508 0.986 0.004 0.000 0.984 0.012 0.000 0.000
#> SRR1377167 3 0.0508 0.986 0.004 0.000 0.984 0.012 0.000 0.000
#> SRR1377168 3 0.0508 0.986 0.004 0.000 0.984 0.012 0.000 0.000
#> SRR1377173 1 0.1753 0.978 0.912 0.004 0.084 0.000 0.000 0.000
#> SRR1377174 1 0.1806 0.974 0.908 0.004 0.088 0.000 0.000 0.000
#> SRR1377175 1 0.1806 0.974 0.908 0.004 0.088 0.000 0.000 0.000
#> SRR1377176 1 0.1753 0.978 0.912 0.004 0.084 0.000 0.000 0.000
#> SRR1377177 1 0.1588 0.981 0.924 0.004 0.072 0.000 0.000 0.000
#> SRR1377178 1 0.1588 0.981 0.924 0.004 0.072 0.000 0.000 0.000
#> SRR1377179 1 0.1588 0.981 0.924 0.004 0.072 0.000 0.000 0.000
#> SRR1377180 1 0.1588 0.981 0.924 0.004 0.072 0.000 0.000 0.000
#> SRR1377181 1 0.1700 0.981 0.916 0.004 0.080 0.000 0.000 0.000
#> SRR1377182 1 0.1700 0.981 0.916 0.004 0.080 0.000 0.000 0.000
#> SRR1377183 1 0.2052 0.961 0.912 0.004 0.056 0.000 0.000 0.028
#> SRR1377184 1 0.1700 0.981 0.916 0.004 0.080 0.000 0.000 0.000
#> SRR1377185 1 0.2113 0.962 0.908 0.004 0.060 0.000 0.000 0.028
#> SRR1377186 1 0.2217 0.949 0.908 0.004 0.048 0.000 0.004 0.036
#> SRR1377187 1 0.1700 0.981 0.916 0.004 0.080 0.000 0.000 0.000
#> SRR1377188 1 0.2128 0.958 0.908 0.004 0.056 0.000 0.000 0.032
#> SRR1377189 2 0.0665 0.980 0.000 0.980 0.008 0.000 0.004 0.008
#> SRR1377190 2 0.0551 0.980 0.000 0.984 0.008 0.000 0.004 0.004
#> SRR1377191 2 0.0551 0.980 0.000 0.984 0.008 0.000 0.004 0.004
#> SRR1377192 2 0.0508 0.976 0.000 0.984 0.000 0.004 0.000 0.012
#> SRR1377193 2 0.0508 0.976 0.000 0.984 0.000 0.004 0.000 0.012
#> SRR1377194 2 0.0508 0.976 0.000 0.984 0.000 0.004 0.000 0.012
#> SRR1377195 5 0.1065 0.902 0.008 0.000 0.000 0.020 0.964 0.008
#> SRR1377196 5 0.1974 0.903 0.012 0.000 0.000 0.020 0.920 0.048
#> SRR1377197 5 0.1218 0.902 0.012 0.000 0.000 0.004 0.956 0.028
#> SRR1377198 5 0.5025 0.878 0.044 0.008 0.000 0.068 0.712 0.168
#> SRR1377199 5 0.4918 0.867 0.040 0.008 0.004 0.024 0.684 0.240
#> SRR1377200 5 0.4852 0.877 0.052 0.004 0.000 0.048 0.716 0.180
#> SRR1377201 2 0.0363 0.982 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1377202 2 0.0363 0.982 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1377203 2 0.0363 0.982 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1377204 2 0.1010 0.960 0.000 0.960 0.000 0.036 0.000 0.004
#> SRR1377205 2 0.1010 0.960 0.000 0.960 0.000 0.036 0.000 0.004
#> SRR1377206 2 0.1010 0.960 0.000 0.960 0.000 0.036 0.000 0.004
#> SRR1377207 2 0.0692 0.979 0.004 0.976 0.020 0.000 0.000 0.000
#> SRR1377208 2 0.0692 0.979 0.004 0.976 0.020 0.000 0.000 0.000
#> SRR1377209 2 0.0692 0.979 0.004 0.976 0.020 0.000 0.000 0.000
#> SRR1377210 2 0.0632 0.978 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1377211 2 0.0632 0.978 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1377212 2 0.0632 0.978 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1377213 4 0.2688 0.894 0.024 0.048 0.044 0.884 0.000 0.000
#> SRR1377214 4 0.2688 0.894 0.024 0.048 0.044 0.884 0.000 0.000
#> SRR1377215 4 0.2688 0.894 0.024 0.048 0.044 0.884 0.000 0.000
#> SRR1377216 4 0.5136 0.746 0.108 0.020 0.176 0.688 0.000 0.008
#> SRR1377217 4 0.5013 0.746 0.104 0.016 0.176 0.696 0.000 0.008
#> SRR1377218 4 0.5272 0.738 0.108 0.024 0.184 0.676 0.000 0.008
#> SRR1377219 4 0.2961 0.894 0.024 0.048 0.052 0.872 0.000 0.004
#> SRR1377220 4 0.2957 0.892 0.024 0.044 0.056 0.872 0.000 0.004
#> SRR1377221 4 0.2961 0.894 0.024 0.048 0.052 0.872 0.000 0.004
#> SRR1377222 4 0.3066 0.855 0.008 0.072 0.016 0.864 0.000 0.040
#> SRR1377223 4 0.3066 0.855 0.008 0.072 0.016 0.864 0.000 0.040
#> SRR1377224 4 0.3096 0.859 0.008 0.068 0.020 0.864 0.000 0.040
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13890 rows and 80 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.14137 0.859 0.859
#> 3 3 1.000 0.981 0.995 0.01118 0.998 0.998
#> 4 4 1.000 0.975 1.000 0.00885 0.999 0.999
#> 5 5 0.861 0.932 0.983 0.18360 0.999 0.999
#> 6 6 1.000 0.963 1.000 0.60609 0.871 0.849
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
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
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.000 1.000 0.000 1 0.000
#> SRR1377146 2 0.000 1.000 0.000 1 0.000
#> SRR1377147 2 0.000 1.000 0.000 1 0.000
#> SRR1377148 2 0.000 1.000 0.000 1 0.000
#> SRR1377153 2 0.000 1.000 0.000 1 0.000
#> SRR1377154 2 0.000 1.000 0.000 1 0.000
#> SRR1377155 2 0.000 1.000 0.000 1 0.000
#> SRR1377156 2 0.000 1.000 0.000 1 0.000
#> SRR1377149 2 0.000 1.000 0.000 1 0.000
#> SRR1377150 2 0.000 1.000 0.000 1 0.000
#> SRR1377151 2 0.000 1.000 0.000 1 0.000
#> SRR1377152 2 0.000 1.000 0.000 1 0.000
#> SRR1377157 2 0.000 1.000 0.000 1 0.000
#> SRR1377158 2 0.000 1.000 0.000 1 0.000
#> SRR1377159 2 0.000 1.000 0.000 1 0.000
#> SRR1377160 2 0.000 1.000 0.000 1 0.000
#> SRR1377161 2 0.000 1.000 0.000 1 0.000
#> SRR1377162 2 0.000 1.000 0.000 1 0.000
#> SRR1377163 2 0.000 1.000 0.000 1 0.000
#> SRR1377164 2 0.000 1.000 0.000 1 0.000
#> SRR1377169 2 0.000 1.000 0.000 1 0.000
#> SRR1377170 2 0.000 1.000 0.000 1 0.000
#> SRR1377171 2 0.000 1.000 0.000 1 0.000
#> SRR1377172 2 0.000 1.000 0.000 1 0.000
#> SRR1377165 2 0.000 1.000 0.000 1 0.000
#> SRR1377166 2 0.000 1.000 0.000 1 0.000
#> SRR1377167 2 0.000 1.000 0.000 1 0.000
#> SRR1377168 2 0.000 1.000 0.000 1 0.000
#> SRR1377173 2 0.000 1.000 0.000 1 0.000
#> SRR1377174 2 0.000 1.000 0.000 1 0.000
#> SRR1377175 2 0.000 1.000 0.000 1 0.000
#> SRR1377176 2 0.000 1.000 0.000 1 0.000
#> SRR1377177 2 0.000 1.000 0.000 1 0.000
#> SRR1377178 2 0.000 1.000 0.000 1 0.000
#> SRR1377179 2 0.000 1.000 0.000 1 0.000
#> SRR1377180 2 0.000 1.000 0.000 1 0.000
#> SRR1377181 2 0.000 1.000 0.000 1 0.000
#> SRR1377182 2 0.000 1.000 0.000 1 0.000
#> SRR1377183 2 0.000 1.000 0.000 1 0.000
#> SRR1377184 2 0.000 1.000 0.000 1 0.000
#> SRR1377185 2 0.000 1.000 0.000 1 0.000
#> SRR1377186 2 0.000 1.000 0.000 1 0.000
#> SRR1377187 2 0.000 1.000 0.000 1 0.000
#> SRR1377188 2 0.000 1.000 0.000 1 0.000
#> SRR1377189 2 0.000 1.000 0.000 1 0.000
#> SRR1377190 2 0.000 1.000 0.000 1 0.000
#> SRR1377191 2 0.000 1.000 0.000 1 0.000
#> SRR1377192 2 0.000 1.000 0.000 1 0.000
#> SRR1377193 2 0.000 1.000 0.000 1 0.000
#> SRR1377194 2 0.000 1.000 0.000 1 0.000
#> SRR1377195 1 0.000 0.941 1.000 0 0.000
#> SRR1377196 1 0.000 0.941 1.000 0 0.000
#> SRR1377197 1 0.000 0.941 1.000 0 0.000
#> SRR1377198 1 0.000 0.941 1.000 0 0.000
#> SRR1377199 1 0.445 0.749 0.808 0 0.192
#> SRR1377200 3 0.445 0.000 0.192 0 0.808
#> SRR1377201 2 0.000 1.000 0.000 1 0.000
#> SRR1377202 2 0.000 1.000 0.000 1 0.000
#> SRR1377203 2 0.000 1.000 0.000 1 0.000
#> SRR1377204 2 0.000 1.000 0.000 1 0.000
#> SRR1377205 2 0.000 1.000 0.000 1 0.000
#> SRR1377206 2 0.000 1.000 0.000 1 0.000
#> SRR1377207 2 0.000 1.000 0.000 1 0.000
#> SRR1377208 2 0.000 1.000 0.000 1 0.000
#> SRR1377209 2 0.000 1.000 0.000 1 0.000
#> SRR1377210 2 0.000 1.000 0.000 1 0.000
#> SRR1377211 2 0.000 1.000 0.000 1 0.000
#> SRR1377212 2 0.000 1.000 0.000 1 0.000
#> SRR1377213 2 0.000 1.000 0.000 1 0.000
#> SRR1377214 2 0.000 1.000 0.000 1 0.000
#> SRR1377215 2 0.000 1.000 0.000 1 0.000
#> SRR1377216 2 0.000 1.000 0.000 1 0.000
#> SRR1377217 2 0.000 1.000 0.000 1 0.000
#> SRR1377218 2 0.000 1.000 0.000 1 0.000
#> SRR1377219 2 0.000 1.000 0.000 1 0.000
#> SRR1377220 2 0.000 1.000 0.000 1 0.000
#> SRR1377221 2 0.000 1.000 0.000 1 0.000
#> SRR1377222 2 0.000 1.000 0.000 1 0.000
#> SRR1377223 2 0.000 1.000 0.000 1 0.000
#> SRR1377224 2 0.000 1.000 0.000 1 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377146 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377147 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377148 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377153 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377154 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377155 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377156 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377149 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377150 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377151 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377152 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377157 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377158 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377159 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377160 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377161 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377162 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377163 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377164 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377169 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377170 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377171 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377172 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377165 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377166 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377167 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377168 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377173 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377174 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377175 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377176 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377177 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377178 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377179 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377180 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377181 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377182 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377183 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377184 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377185 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377186 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377187 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377188 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377189 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377190 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377191 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377192 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377193 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377194 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377195 1 0.0000 0.996 1.000 0 0.000 0.000
#> SRR1377196 1 0.0000 0.996 1.000 0 0.000 0.000
#> SRR1377197 1 0.0000 0.996 1.000 0 0.000 0.000
#> SRR1377198 1 0.0524 0.988 0.988 0 0.004 0.008
#> SRR1377199 4 0.0336 0.000 0.008 0 0.000 0.992
#> SRR1377200 3 0.0188 0.000 0.004 0 0.996 0.000
#> SRR1377201 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377202 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377203 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377204 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377205 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377206 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377207 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377208 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377209 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377210 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377211 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377212 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377213 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377214 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377215 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377216 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377217 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377218 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377219 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377220 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377221 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377222 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377223 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1377224 2 0.0000 1.000 0.000 1 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377146 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377147 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377148 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377153 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377154 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377155 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377156 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377149 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377150 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377151 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377152 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377157 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377158 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377159 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377160 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377161 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377162 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377163 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377164 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377169 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377170 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377171 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377172 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377165 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377166 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377167 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377168 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377173 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377174 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377175 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377176 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377177 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377178 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377179 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377180 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377181 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377182 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377183 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377184 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377185 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377186 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377187 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377188 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377189 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377190 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377191 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377192 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377193 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377194 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377195 1 0.000 1.000 1.0 0.0 0 0.0 0.0
#> SRR1377196 1 0.000 1.000 1.0 0.0 0 0.0 0.0
#> SRR1377197 1 0.000 1.000 1.0 0.0 0 0.0 0.0
#> SRR1377198 5 0.311 0.000 0.2 0.0 0 0.0 0.8
#> SRR1377199 4 0.311 0.000 0.0 0.0 0 0.8 0.2
#> SRR1377200 3 0.000 0.000 0.0 0.0 1 0.0 0.0
#> SRR1377201 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377202 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377203 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377204 2 0.311 0.775 0.0 0.8 0 0.2 0.0
#> SRR1377205 2 0.311 0.775 0.0 0.8 0 0.2 0.0
#> SRR1377206 2 0.311 0.775 0.0 0.8 0 0.2 0.0
#> SRR1377207 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377208 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377209 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377210 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377211 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377212 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377213 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377214 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377215 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377216 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377217 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377218 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377219 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377220 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377221 2 0.000 0.983 0.0 1.0 0 0.0 0.0
#> SRR1377222 2 0.311 0.775 0.0 0.8 0 0.2 0.0
#> SRR1377223 2 0.311 0.775 0.0 0.8 0 0.2 0.0
#> SRR1377224 2 0.311 0.775 0.0 0.8 0 0.2 0.0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 1 0 1 1 0 0 0 0 0
#> SRR1377146 1 0 1 1 0 0 0 0 0
#> SRR1377147 1 0 1 1 0 0 0 0 0
#> SRR1377148 1 0 1 1 0 0 0 0 0
#> SRR1377153 1 0 1 1 0 0 0 0 0
#> SRR1377154 1 0 1 1 0 0 0 0 0
#> SRR1377155 1 0 1 1 0 0 0 0 0
#> SRR1377156 1 0 1 1 0 0 0 0 0
#> SRR1377149 1 0 1 1 0 0 0 0 0
#> SRR1377150 1 0 1 1 0 0 0 0 0
#> SRR1377151 1 0 1 1 0 0 0 0 0
#> SRR1377152 1 0 1 1 0 0 0 0 0
#> SRR1377157 1 0 1 1 0 0 0 0 0
#> SRR1377158 1 0 1 1 0 0 0 0 0
#> SRR1377159 1 0 1 1 0 0 0 0 0
#> SRR1377160 1 0 1 1 0 0 0 0 0
#> SRR1377161 1 0 1 1 0 0 0 0 0
#> SRR1377162 1 0 1 1 0 0 0 0 0
#> SRR1377163 1 0 1 1 0 0 0 0 0
#> SRR1377164 1 0 1 1 0 0 0 0 0
#> SRR1377169 1 0 1 1 0 0 0 0 0
#> SRR1377170 1 0 1 1 0 0 0 0 0
#> SRR1377171 1 0 1 1 0 0 0 0 0
#> SRR1377172 1 0 1 1 0 0 0 0 0
#> SRR1377165 1 0 1 1 0 0 0 0 0
#> SRR1377166 1 0 1 1 0 0 0 0 0
#> SRR1377167 1 0 1 1 0 0 0 0 0
#> SRR1377168 1 0 1 1 0 0 0 0 0
#> SRR1377173 1 0 1 1 0 0 0 0 0
#> SRR1377174 1 0 1 1 0 0 0 0 0
#> SRR1377175 1 0 1 1 0 0 0 0 0
#> SRR1377176 1 0 1 1 0 0 0 0 0
#> SRR1377177 1 0 1 1 0 0 0 0 0
#> SRR1377178 1 0 1 1 0 0 0 0 0
#> SRR1377179 1 0 1 1 0 0 0 0 0
#> SRR1377180 1 0 1 1 0 0 0 0 0
#> SRR1377181 1 0 1 1 0 0 0 0 0
#> SRR1377182 1 0 1 1 0 0 0 0 0
#> SRR1377183 1 0 1 1 0 0 0 0 0
#> SRR1377184 1 0 1 1 0 0 0 0 0
#> SRR1377185 1 0 1 1 0 0 0 0 0
#> SRR1377186 1 0 1 1 0 0 0 0 0
#> SRR1377187 1 0 1 1 0 0 0 0 0
#> SRR1377188 1 0 1 1 0 0 0 0 0
#> SRR1377189 1 0 1 1 0 0 0 0 0
#> SRR1377190 1 0 1 1 0 0 0 0 0
#> SRR1377191 1 0 1 1 0 0 0 0 0
#> SRR1377192 1 0 1 1 0 0 0 0 0
#> SRR1377193 1 0 1 1 0 0 0 0 0
#> SRR1377194 1 0 1 1 0 0 0 0 0
#> SRR1377195 5 0 1 0 0 0 0 1 0
#> SRR1377196 5 0 1 0 0 0 0 1 0
#> SRR1377197 5 0 1 0 0 0 0 1 0
#> SRR1377198 6 0 0 0 0 0 0 0 1
#> SRR1377199 4 0 0 0 0 0 1 0 0
#> SRR1377200 3 0 0 0 0 1 0 0 0
#> SRR1377201 1 0 1 1 0 0 0 0 0
#> SRR1377202 1 0 1 1 0 0 0 0 0
#> SRR1377203 1 0 1 1 0 0 0 0 0
#> SRR1377204 2 0 1 0 1 0 0 0 0
#> SRR1377205 2 0 1 0 1 0 0 0 0
#> SRR1377206 2 0 1 0 1 0 0 0 0
#> SRR1377207 1 0 1 1 0 0 0 0 0
#> SRR1377208 1 0 1 1 0 0 0 0 0
#> SRR1377209 1 0 1 1 0 0 0 0 0
#> SRR1377210 1 0 1 1 0 0 0 0 0
#> SRR1377211 1 0 1 1 0 0 0 0 0
#> SRR1377212 1 0 1 1 0 0 0 0 0
#> SRR1377213 1 0 1 1 0 0 0 0 0
#> SRR1377214 1 0 1 1 0 0 0 0 0
#> SRR1377215 1 0 1 1 0 0 0 0 0
#> SRR1377216 1 0 1 1 0 0 0 0 0
#> SRR1377217 1 0 1 1 0 0 0 0 0
#> SRR1377218 1 0 1 1 0 0 0 0 0
#> SRR1377219 1 0 1 1 0 0 0 0 0
#> SRR1377220 1 0 1 1 0 0 0 0 0
#> SRR1377221 1 0 1 1 0 0 0 0 0
#> SRR1377222 2 0 1 0 1 0 0 0 0
#> SRR1377223 2 0 1 0 1 0 0 0 0
#> SRR1377224 2 0 1 0 1 0 0 0 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 13890 rows and 80 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 1.000 1.000 0.1414 0.859 0.859
#> 3 3 0.499 0.898 0.900 2.2879 0.638 0.579
#> 4 4 0.617 0.729 0.787 0.3407 1.000 1.000
#> 5 5 0.630 0.658 0.763 0.1335 0.813 0.624
#> 6 6 0.638 0.780 0.769 0.0783 0.905 0.700
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
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.3619 0.856 0.000 0.864 0.136
#> SRR1377146 2 0.3619 0.856 0.000 0.864 0.136
#> SRR1377147 2 0.3619 0.856 0.000 0.864 0.136
#> SRR1377148 2 0.3619 0.856 0.000 0.864 0.136
#> SRR1377153 2 0.2356 0.902 0.000 0.928 0.072
#> SRR1377154 2 0.2537 0.899 0.000 0.920 0.080
#> SRR1377155 2 0.2537 0.899 0.000 0.920 0.080
#> SRR1377156 2 0.2448 0.901 0.000 0.924 0.076
#> SRR1377149 2 0.3116 0.882 0.000 0.892 0.108
#> SRR1377150 2 0.2711 0.895 0.000 0.912 0.088
#> SRR1377151 2 0.2711 0.895 0.000 0.912 0.088
#> SRR1377152 2 0.3038 0.885 0.000 0.896 0.104
#> SRR1377157 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377158 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377159 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377160 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377161 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377162 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377163 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377164 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377169 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377170 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377171 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377172 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377165 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377166 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377167 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377168 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377173 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377174 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377175 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377176 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377177 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377178 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377179 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377180 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377181 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377182 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377183 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377184 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377185 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377186 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377187 2 0.0592 0.930 0.000 0.988 0.012
#> SRR1377188 2 0.0237 0.932 0.000 0.996 0.004
#> SRR1377189 2 0.0000 0.931 0.000 1.000 0.000
#> SRR1377190 2 0.0000 0.931 0.000 1.000 0.000
#> SRR1377191 2 0.0000 0.931 0.000 1.000 0.000
#> SRR1377192 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377193 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377194 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377195 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1377196 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1377197 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1377198 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1377199 1 0.0237 0.998 0.996 0.000 0.004
#> SRR1377200 1 0.0237 0.998 0.996 0.000 0.004
#> SRR1377201 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377202 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377203 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377204 2 0.3551 0.826 0.000 0.868 0.132
#> SRR1377205 2 0.3551 0.826 0.000 0.868 0.132
#> SRR1377206 2 0.3551 0.826 0.000 0.868 0.132
#> SRR1377207 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377208 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377209 2 0.0424 0.930 0.000 0.992 0.008
#> SRR1377210 2 0.0000 0.931 0.000 1.000 0.000
#> SRR1377211 2 0.0000 0.931 0.000 1.000 0.000
#> SRR1377212 2 0.0000 0.931 0.000 1.000 0.000
#> SRR1377213 2 0.4842 0.623 0.000 0.776 0.224
#> SRR1377214 2 0.4842 0.623 0.000 0.776 0.224
#> SRR1377215 2 0.4842 0.623 0.000 0.776 0.224
#> SRR1377216 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377217 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377218 3 0.4504 0.943 0.000 0.196 0.804
#> SRR1377219 3 0.6252 0.557 0.000 0.444 0.556
#> SRR1377220 3 0.6252 0.557 0.000 0.444 0.556
#> SRR1377221 3 0.6252 0.557 0.000 0.444 0.556
#> SRR1377222 2 0.3551 0.826 0.000 0.868 0.132
#> SRR1377223 2 0.3551 0.826 0.000 0.868 0.132
#> SRR1377224 2 0.3551 0.826 0.000 0.868 0.132
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.5417 0.555 0.000 0.572 0.016 0.412
#> SRR1377146 2 0.5417 0.555 0.000 0.572 0.016 0.412
#> SRR1377147 2 0.5417 0.555 0.000 0.572 0.016 0.412
#> SRR1377148 2 0.5417 0.555 0.000 0.572 0.016 0.412
#> SRR1377153 2 0.5498 0.558 0.000 0.576 0.020 0.404
#> SRR1377154 2 0.5498 0.558 0.000 0.576 0.020 0.404
#> SRR1377155 2 0.5498 0.558 0.000 0.576 0.020 0.404
#> SRR1377156 2 0.5498 0.558 0.000 0.576 0.020 0.404
#> SRR1377149 2 0.5310 0.557 0.000 0.576 0.012 0.412
#> SRR1377150 2 0.5310 0.557 0.000 0.576 0.012 0.412
#> SRR1377151 2 0.5310 0.557 0.000 0.576 0.012 0.412
#> SRR1377152 2 0.5310 0.557 0.000 0.576 0.012 0.412
#> SRR1377157 3 0.1305 0.918 0.000 0.036 0.960 0.004
#> SRR1377158 3 0.1305 0.918 0.000 0.036 0.960 0.004
#> SRR1377159 3 0.1305 0.918 0.000 0.036 0.960 0.004
#> SRR1377160 3 0.1305 0.918 0.000 0.036 0.960 0.004
#> SRR1377161 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377162 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377163 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377164 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377169 3 0.1584 0.916 0.000 0.036 0.952 0.012
#> SRR1377170 3 0.1584 0.916 0.000 0.036 0.952 0.012
#> SRR1377171 3 0.1584 0.916 0.000 0.036 0.952 0.012
#> SRR1377172 3 0.1584 0.916 0.000 0.036 0.952 0.012
#> SRR1377165 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377166 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377167 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377168 3 0.1118 0.919 0.000 0.036 0.964 0.000
#> SRR1377173 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377174 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377175 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377176 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377177 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377178 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377179 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377180 2 0.3636 0.737 0.000 0.820 0.008 0.172
#> SRR1377181 2 0.3768 0.736 0.000 0.808 0.008 0.184
#> SRR1377182 2 0.3768 0.736 0.000 0.808 0.008 0.184
#> SRR1377183 2 0.3591 0.737 0.000 0.824 0.008 0.168
#> SRR1377184 2 0.3768 0.736 0.000 0.808 0.008 0.184
#> SRR1377185 2 0.3591 0.737 0.000 0.824 0.008 0.168
#> SRR1377186 2 0.3591 0.737 0.000 0.824 0.008 0.168
#> SRR1377187 2 0.3768 0.736 0.000 0.808 0.008 0.184
#> SRR1377188 2 0.3591 0.737 0.000 0.824 0.008 0.168
#> SRR1377189 2 0.0376 0.758 0.000 0.992 0.004 0.004
#> SRR1377190 2 0.0376 0.758 0.000 0.992 0.004 0.004
#> SRR1377191 2 0.0376 0.758 0.000 0.992 0.004 0.004
#> SRR1377192 2 0.0376 0.757 0.000 0.992 0.004 0.004
#> SRR1377193 2 0.0376 0.757 0.000 0.992 0.004 0.004
#> SRR1377194 2 0.0376 0.757 0.000 0.992 0.004 0.004
#> SRR1377195 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377196 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377197 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377198 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR1377199 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377200 1 0.0592 0.993 0.984 0.000 0.000 0.016
#> SRR1377201 2 0.0524 0.757 0.000 0.988 0.004 0.008
#> SRR1377202 2 0.0524 0.757 0.000 0.988 0.004 0.008
#> SRR1377203 2 0.0524 0.757 0.000 0.988 0.004 0.008
#> SRR1377204 2 0.5349 0.488 0.000 0.640 0.024 0.336
#> SRR1377205 2 0.5349 0.488 0.000 0.640 0.024 0.336
#> SRR1377206 2 0.5349 0.488 0.000 0.640 0.024 0.336
#> SRR1377207 2 0.0524 0.757 0.000 0.988 0.004 0.008
#> SRR1377208 2 0.0524 0.757 0.000 0.988 0.004 0.008
#> SRR1377209 2 0.0524 0.757 0.000 0.988 0.004 0.008
#> SRR1377210 2 0.0376 0.758 0.000 0.992 0.004 0.004
#> SRR1377211 2 0.0376 0.758 0.000 0.992 0.004 0.004
#> SRR1377212 2 0.0376 0.758 0.000 0.992 0.004 0.004
#> SRR1377213 2 0.7016 0.185 0.000 0.540 0.320 0.140
#> SRR1377214 2 0.7016 0.185 0.000 0.540 0.320 0.140
#> SRR1377215 2 0.7016 0.185 0.000 0.540 0.320 0.140
#> SRR1377216 3 0.4072 0.844 0.000 0.052 0.828 0.120
#> SRR1377217 3 0.4072 0.844 0.000 0.052 0.828 0.120
#> SRR1377218 3 0.4072 0.844 0.000 0.052 0.828 0.120
#> SRR1377219 3 0.6656 0.607 0.000 0.256 0.608 0.136
#> SRR1377220 3 0.6656 0.607 0.000 0.256 0.608 0.136
#> SRR1377221 3 0.6656 0.607 0.000 0.256 0.608 0.136
#> SRR1377222 2 0.5404 0.490 0.000 0.644 0.028 0.328
#> SRR1377223 2 0.5404 0.490 0.000 0.644 0.028 0.328
#> SRR1377224 2 0.5404 0.490 0.000 0.644 0.028 0.328
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.3732 0.970 0.208 0.776 0.008 0.008 0.000
#> SRR1377146 2 0.3732 0.970 0.208 0.776 0.008 0.008 0.000
#> SRR1377147 2 0.3840 0.967 0.208 0.772 0.012 0.008 0.000
#> SRR1377148 2 0.3732 0.970 0.208 0.776 0.008 0.008 0.000
#> SRR1377153 2 0.4348 0.962 0.216 0.744 0.008 0.032 0.000
#> SRR1377154 2 0.4348 0.962 0.216 0.744 0.008 0.032 0.000
#> SRR1377155 2 0.4348 0.962 0.216 0.744 0.008 0.032 0.000
#> SRR1377156 2 0.4348 0.962 0.216 0.744 0.008 0.032 0.000
#> SRR1377149 2 0.4205 0.966 0.208 0.756 0.008 0.028 0.000
#> SRR1377150 2 0.4205 0.966 0.208 0.756 0.008 0.028 0.000
#> SRR1377151 2 0.4205 0.966 0.208 0.756 0.008 0.028 0.000
#> SRR1377152 2 0.4205 0.966 0.208 0.756 0.008 0.028 0.000
#> SRR1377157 3 0.1074 0.780 0.012 0.016 0.968 0.004 0.000
#> SRR1377158 3 0.1074 0.780 0.012 0.016 0.968 0.004 0.000
#> SRR1377159 3 0.1074 0.780 0.012 0.016 0.968 0.004 0.000
#> SRR1377160 3 0.1074 0.780 0.012 0.016 0.968 0.004 0.000
#> SRR1377161 3 0.0727 0.784 0.012 0.004 0.980 0.004 0.000
#> SRR1377162 3 0.0727 0.784 0.012 0.004 0.980 0.004 0.000
#> SRR1377163 3 0.0727 0.784 0.012 0.004 0.980 0.004 0.000
#> SRR1377164 3 0.0727 0.784 0.012 0.004 0.980 0.004 0.000
#> SRR1377169 3 0.2060 0.770 0.012 0.036 0.928 0.024 0.000
#> SRR1377170 3 0.2060 0.770 0.012 0.036 0.928 0.024 0.000
#> SRR1377171 3 0.2060 0.770 0.012 0.036 0.928 0.024 0.000
#> SRR1377172 3 0.2060 0.770 0.012 0.036 0.928 0.024 0.000
#> SRR1377165 3 0.0981 0.783 0.012 0.008 0.972 0.008 0.000
#> SRR1377166 3 0.0981 0.783 0.012 0.008 0.972 0.008 0.000
#> SRR1377167 3 0.0981 0.783 0.012 0.008 0.972 0.008 0.000
#> SRR1377168 3 0.0981 0.783 0.012 0.008 0.972 0.008 0.000
#> SRR1377173 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377174 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377175 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377176 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377177 1 0.4181 0.641 0.784 0.052 0.008 0.156 0.000
#> SRR1377178 1 0.4181 0.641 0.784 0.052 0.008 0.156 0.000
#> SRR1377179 1 0.4181 0.641 0.784 0.052 0.008 0.156 0.000
#> SRR1377180 1 0.4181 0.641 0.784 0.052 0.008 0.156 0.000
#> SRR1377181 1 0.4642 0.625 0.740 0.060 0.008 0.192 0.000
#> SRR1377182 1 0.4642 0.625 0.740 0.060 0.008 0.192 0.000
#> SRR1377183 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377184 1 0.4642 0.625 0.740 0.060 0.008 0.192 0.000
#> SRR1377185 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377186 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377187 1 0.4642 0.625 0.740 0.060 0.008 0.192 0.000
#> SRR1377188 1 0.4113 0.643 0.788 0.048 0.008 0.156 0.000
#> SRR1377189 1 0.2563 0.637 0.872 0.120 0.008 0.000 0.000
#> SRR1377190 1 0.2563 0.637 0.872 0.120 0.008 0.000 0.000
#> SRR1377191 1 0.2563 0.637 0.872 0.120 0.008 0.000 0.000
#> SRR1377192 1 0.2646 0.633 0.868 0.124 0.004 0.004 0.000
#> SRR1377193 1 0.2646 0.633 0.868 0.124 0.004 0.004 0.000
#> SRR1377194 1 0.2646 0.633 0.868 0.124 0.004 0.004 0.000
#> SRR1377195 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> SRR1377196 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> SRR1377197 5 0.0000 0.996 0.000 0.000 0.000 0.000 1.000
#> SRR1377198 5 0.0290 0.993 0.000 0.000 0.000 0.008 0.992
#> SRR1377199 5 0.0162 0.995 0.000 0.000 0.000 0.004 0.996
#> SRR1377200 5 0.0771 0.986 0.000 0.020 0.000 0.004 0.976
#> SRR1377201 1 0.2741 0.630 0.860 0.132 0.004 0.004 0.000
#> SRR1377202 1 0.2741 0.630 0.860 0.132 0.004 0.004 0.000
#> SRR1377203 1 0.2741 0.630 0.860 0.132 0.004 0.004 0.000
#> SRR1377204 1 0.6598 0.184 0.444 0.184 0.004 0.368 0.000
#> SRR1377205 1 0.6598 0.184 0.444 0.184 0.004 0.368 0.000
#> SRR1377206 1 0.6598 0.184 0.444 0.184 0.004 0.368 0.000
#> SRR1377207 1 0.2694 0.633 0.864 0.128 0.004 0.004 0.000
#> SRR1377208 1 0.2694 0.633 0.864 0.128 0.004 0.004 0.000
#> SRR1377209 1 0.2694 0.633 0.864 0.128 0.004 0.004 0.000
#> SRR1377210 1 0.2865 0.629 0.856 0.132 0.008 0.004 0.000
#> SRR1377211 1 0.2865 0.629 0.856 0.132 0.008 0.004 0.000
#> SRR1377212 1 0.2865 0.629 0.856 0.132 0.008 0.004 0.000
#> SRR1377213 4 0.8329 1.000 0.252 0.136 0.276 0.336 0.000
#> SRR1377214 4 0.8329 1.000 0.252 0.136 0.276 0.336 0.000
#> SRR1377215 4 0.8329 1.000 0.252 0.136 0.276 0.336 0.000
#> SRR1377216 3 0.5968 0.133 0.032 0.060 0.580 0.328 0.000
#> SRR1377217 3 0.5968 0.133 0.032 0.060 0.580 0.328 0.000
#> SRR1377218 3 0.5968 0.133 0.032 0.060 0.580 0.328 0.000
#> SRR1377219 3 0.7635 -0.510 0.116 0.116 0.432 0.336 0.000
#> SRR1377220 3 0.7635 -0.510 0.116 0.116 0.432 0.336 0.000
#> SRR1377221 3 0.7635 -0.510 0.116 0.116 0.432 0.336 0.000
#> SRR1377222 1 0.6592 0.168 0.432 0.180 0.004 0.384 0.000
#> SRR1377223 1 0.6592 0.168 0.432 0.180 0.004 0.384 0.000
#> SRR1377224 1 0.6592 0.168 0.432 0.180 0.004 0.384 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.2837 0.935 0.144 0.004 0.004 0.008 0.000 0.840
#> SRR1377146 6 0.2837 0.935 0.144 0.004 0.004 0.008 0.000 0.840
#> SRR1377147 6 0.2837 0.935 0.144 0.004 0.004 0.008 0.000 0.840
#> SRR1377148 6 0.2837 0.935 0.144 0.004 0.004 0.008 0.000 0.840
#> SRR1377153 6 0.4461 0.912 0.152 0.004 0.004 0.108 0.000 0.732
#> SRR1377154 6 0.4461 0.912 0.152 0.004 0.004 0.108 0.000 0.732
#> SRR1377155 6 0.4461 0.912 0.152 0.004 0.004 0.108 0.000 0.732
#> SRR1377156 6 0.4461 0.912 0.152 0.004 0.004 0.108 0.000 0.732
#> SRR1377149 6 0.4335 0.923 0.148 0.040 0.004 0.044 0.000 0.764
#> SRR1377150 6 0.4335 0.923 0.148 0.040 0.004 0.044 0.000 0.764
#> SRR1377151 6 0.4335 0.923 0.148 0.040 0.004 0.044 0.000 0.764
#> SRR1377152 6 0.4335 0.923 0.148 0.040 0.004 0.044 0.000 0.764
#> SRR1377157 3 0.1340 0.903 0.000 0.008 0.948 0.004 0.000 0.040
#> SRR1377158 3 0.1340 0.903 0.000 0.008 0.948 0.004 0.000 0.040
#> SRR1377159 3 0.1340 0.903 0.000 0.008 0.948 0.004 0.000 0.040
#> SRR1377160 3 0.1340 0.903 0.000 0.008 0.948 0.004 0.000 0.040
#> SRR1377161 3 0.0260 0.922 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1377162 3 0.0260 0.922 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1377163 3 0.0260 0.922 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1377164 3 0.0260 0.922 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1377169 3 0.2685 0.900 0.000 0.068 0.880 0.016 0.000 0.036
#> SRR1377170 3 0.2685 0.900 0.000 0.068 0.880 0.016 0.000 0.036
#> SRR1377171 3 0.2685 0.900 0.000 0.068 0.880 0.016 0.000 0.036
#> SRR1377172 3 0.2685 0.900 0.000 0.068 0.880 0.016 0.000 0.036
#> SRR1377165 3 0.1644 0.923 0.000 0.052 0.932 0.004 0.000 0.012
#> SRR1377166 3 0.1644 0.923 0.000 0.052 0.932 0.004 0.000 0.012
#> SRR1377167 3 0.1644 0.923 0.000 0.052 0.932 0.004 0.000 0.012
#> SRR1377168 3 0.1644 0.923 0.000 0.052 0.932 0.004 0.000 0.012
#> SRR1377173 1 0.0291 0.654 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1377174 1 0.0291 0.654 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1377175 1 0.0291 0.654 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1377176 1 0.0291 0.654 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1377177 1 0.0436 0.654 0.988 0.000 0.004 0.004 0.000 0.004
#> SRR1377178 1 0.0436 0.654 0.988 0.000 0.004 0.004 0.000 0.004
#> SRR1377179 1 0.0436 0.654 0.988 0.000 0.004 0.004 0.000 0.004
#> SRR1377180 1 0.0436 0.654 0.988 0.000 0.004 0.004 0.000 0.004
#> SRR1377181 1 0.2293 0.602 0.896 0.004 0.004 0.080 0.000 0.016
#> SRR1377182 1 0.2293 0.602 0.896 0.004 0.004 0.080 0.000 0.016
#> SRR1377183 1 0.0551 0.655 0.984 0.000 0.004 0.008 0.000 0.004
#> SRR1377184 1 0.2293 0.602 0.896 0.004 0.004 0.080 0.000 0.016
#> SRR1377185 1 0.0551 0.655 0.984 0.000 0.004 0.008 0.000 0.004
#> SRR1377186 1 0.0551 0.655 0.984 0.000 0.004 0.008 0.000 0.004
#> SRR1377187 1 0.2293 0.602 0.896 0.004 0.004 0.080 0.000 0.016
#> SRR1377188 1 0.0551 0.655 0.984 0.000 0.004 0.008 0.000 0.004
#> SRR1377189 1 0.6816 0.549 0.520 0.212 0.004 0.156 0.000 0.108
#> SRR1377190 1 0.6816 0.549 0.520 0.212 0.004 0.156 0.000 0.108
#> SRR1377191 1 0.6816 0.549 0.520 0.212 0.004 0.156 0.000 0.108
#> SRR1377192 1 0.6827 0.545 0.516 0.220 0.004 0.152 0.000 0.108
#> SRR1377193 1 0.6827 0.545 0.516 0.220 0.004 0.152 0.000 0.108
#> SRR1377194 1 0.6827 0.545 0.516 0.220 0.004 0.152 0.000 0.108
#> SRR1377195 5 0.0000 0.992 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377196 5 0.0000 0.992 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377197 5 0.0000 0.992 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377198 5 0.0551 0.988 0.000 0.004 0.000 0.008 0.984 0.004
#> SRR1377199 5 0.0806 0.982 0.000 0.000 0.000 0.020 0.972 0.008
#> SRR1377200 5 0.0665 0.985 0.000 0.008 0.000 0.004 0.980 0.008
#> SRR1377201 1 0.6715 0.556 0.524 0.236 0.004 0.136 0.000 0.100
#> SRR1377202 1 0.6715 0.556 0.524 0.236 0.004 0.136 0.000 0.100
#> SRR1377203 1 0.6715 0.556 0.524 0.236 0.004 0.136 0.000 0.100
#> SRR1377204 2 0.2723 0.958 0.128 0.852 0.004 0.000 0.000 0.016
#> SRR1377205 2 0.2723 0.958 0.128 0.852 0.004 0.000 0.000 0.016
#> SRR1377206 2 0.2723 0.958 0.128 0.852 0.004 0.000 0.000 0.016
#> SRR1377207 1 0.6715 0.556 0.524 0.236 0.004 0.136 0.000 0.100
#> SRR1377208 1 0.6715 0.556 0.524 0.236 0.004 0.136 0.000 0.100
#> SRR1377209 1 0.6715 0.556 0.524 0.236 0.004 0.136 0.000 0.100
#> SRR1377210 1 0.6726 0.561 0.528 0.224 0.004 0.140 0.000 0.104
#> SRR1377211 1 0.6726 0.561 0.528 0.224 0.004 0.140 0.000 0.104
#> SRR1377212 1 0.6726 0.561 0.528 0.224 0.004 0.140 0.000 0.104
#> SRR1377213 4 0.6505 0.734 0.080 0.072 0.168 0.616 0.000 0.064
#> SRR1377214 4 0.6505 0.734 0.080 0.072 0.168 0.616 0.000 0.064
#> SRR1377215 4 0.6505 0.734 0.080 0.072 0.168 0.616 0.000 0.064
#> SRR1377216 4 0.5052 0.634 0.008 0.004 0.444 0.500 0.000 0.044
#> SRR1377217 4 0.5052 0.634 0.008 0.004 0.444 0.500 0.000 0.044
#> SRR1377218 4 0.5052 0.634 0.008 0.004 0.444 0.500 0.000 0.044
#> SRR1377219 4 0.6032 0.806 0.028 0.044 0.288 0.580 0.000 0.060
#> SRR1377220 4 0.6032 0.806 0.028 0.044 0.288 0.580 0.000 0.060
#> SRR1377221 4 0.6032 0.806 0.028 0.044 0.288 0.580 0.000 0.060
#> SRR1377222 2 0.4130 0.958 0.128 0.780 0.004 0.068 0.000 0.020
#> SRR1377223 2 0.4130 0.958 0.128 0.780 0.004 0.068 0.000 0.020
#> SRR1377224 2 0.4130 0.958 0.128 0.780 0.004 0.068 0.000 0.020
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 13890 rows and 80 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.875 0.933 0.964 0.3563 0.608 0.608
#> 3 3 0.634 0.791 0.835 0.5578 0.848 0.757
#> 4 4 0.905 0.951 0.959 0.2674 0.813 0.618
#> 5 5 0.863 0.950 0.910 0.0954 0.905 0.687
#> 6 6 0.848 0.880 0.861 0.0499 1.000 1.000
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1377145 2 0.000 0.996 0.000 1.000
#> SRR1377146 2 0.000 0.996 0.000 1.000
#> SRR1377147 2 0.000 0.996 0.000 1.000
#> SRR1377148 2 0.000 0.996 0.000 1.000
#> SRR1377153 2 0.000 0.996 0.000 1.000
#> SRR1377154 2 0.000 0.996 0.000 1.000
#> SRR1377155 2 0.000 0.996 0.000 1.000
#> SRR1377156 2 0.000 0.996 0.000 1.000
#> SRR1377149 2 0.000 0.996 0.000 1.000
#> SRR1377150 2 0.000 0.996 0.000 1.000
#> SRR1377151 2 0.000 0.996 0.000 1.000
#> SRR1377152 2 0.000 0.996 0.000 1.000
#> SRR1377157 2 0.000 0.996 0.000 1.000
#> SRR1377158 2 0.000 0.996 0.000 1.000
#> SRR1377159 2 0.000 0.996 0.000 1.000
#> SRR1377160 2 0.000 0.996 0.000 1.000
#> SRR1377161 2 0.000 0.996 0.000 1.000
#> SRR1377162 2 0.000 0.996 0.000 1.000
#> SRR1377163 2 0.000 0.996 0.000 1.000
#> SRR1377164 2 0.000 0.996 0.000 1.000
#> SRR1377169 2 0.000 0.996 0.000 1.000
#> SRR1377170 2 0.000 0.996 0.000 1.000
#> SRR1377171 2 0.000 0.996 0.000 1.000
#> SRR1377172 2 0.000 0.996 0.000 1.000
#> SRR1377165 2 0.000 0.996 0.000 1.000
#> SRR1377166 2 0.000 0.996 0.000 1.000
#> SRR1377167 2 0.000 0.996 0.000 1.000
#> SRR1377168 2 0.000 0.996 0.000 1.000
#> SRR1377173 2 0.000 0.996 0.000 1.000
#> SRR1377174 2 0.000 0.996 0.000 1.000
#> SRR1377175 2 0.000 0.996 0.000 1.000
#> SRR1377176 2 0.000 0.996 0.000 1.000
#> SRR1377177 2 0.000 0.996 0.000 1.000
#> SRR1377178 2 0.000 0.996 0.000 1.000
#> SRR1377179 2 0.000 0.996 0.000 1.000
#> SRR1377180 2 0.000 0.996 0.000 1.000
#> SRR1377181 2 0.000 0.996 0.000 1.000
#> SRR1377182 2 0.000 0.996 0.000 1.000
#> SRR1377183 2 0.000 0.996 0.000 1.000
#> SRR1377184 2 0.000 0.996 0.000 1.000
#> SRR1377185 2 0.000 0.996 0.000 1.000
#> SRR1377186 2 0.000 0.996 0.000 1.000
#> SRR1377187 2 0.000 0.996 0.000 1.000
#> SRR1377188 2 0.000 0.996 0.000 1.000
#> SRR1377189 2 0.343 0.921 0.064 0.936
#> SRR1377190 2 0.343 0.921 0.064 0.936
#> SRR1377191 2 0.343 0.921 0.064 0.936
#> SRR1377192 1 0.775 0.770 0.772 0.228
#> SRR1377193 1 0.767 0.775 0.776 0.224
#> SRR1377194 1 0.760 0.778 0.780 0.220
#> SRR1377195 1 0.000 0.861 1.000 0.000
#> SRR1377196 1 0.000 0.861 1.000 0.000
#> SRR1377197 1 0.000 0.861 1.000 0.000
#> SRR1377198 1 0.000 0.861 1.000 0.000
#> SRR1377199 1 0.000 0.861 1.000 0.000
#> SRR1377200 1 0.000 0.861 1.000 0.000
#> SRR1377201 1 0.994 0.378 0.544 0.456
#> SRR1377202 1 0.994 0.378 0.544 0.456
#> SRR1377203 1 0.994 0.378 0.544 0.456
#> SRR1377204 1 0.000 0.861 1.000 0.000
#> SRR1377205 1 0.000 0.861 1.000 0.000
#> SRR1377206 1 0.000 0.861 1.000 0.000
#> SRR1377207 1 0.753 0.782 0.784 0.216
#> SRR1377208 1 0.753 0.782 0.784 0.216
#> SRR1377209 1 0.753 0.782 0.784 0.216
#> SRR1377210 2 0.000 0.996 0.000 1.000
#> SRR1377211 2 0.000 0.996 0.000 1.000
#> SRR1377212 2 0.000 0.996 0.000 1.000
#> SRR1377213 2 0.000 0.996 0.000 1.000
#> SRR1377214 2 0.000 0.996 0.000 1.000
#> SRR1377215 2 0.000 0.996 0.000 1.000
#> SRR1377216 2 0.000 0.996 0.000 1.000
#> SRR1377217 2 0.000 0.996 0.000 1.000
#> SRR1377218 2 0.000 0.996 0.000 1.000
#> SRR1377219 2 0.000 0.996 0.000 1.000
#> SRR1377220 2 0.000 0.996 0.000 1.000
#> SRR1377221 2 0.000 0.996 0.000 1.000
#> SRR1377222 1 0.000 0.861 1.000 0.000
#> SRR1377223 1 0.000 0.861 1.000 0.000
#> SRR1377224 1 0.000 0.861 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.3183 0.800 0.016 0.908 0.076
#> SRR1377146 2 0.3183 0.800 0.016 0.908 0.076
#> SRR1377147 2 0.3183 0.800 0.016 0.908 0.076
#> SRR1377148 2 0.3183 0.800 0.016 0.908 0.076
#> SRR1377153 2 0.3670 0.786 0.020 0.888 0.092
#> SRR1377154 2 0.3587 0.790 0.020 0.892 0.088
#> SRR1377155 2 0.3670 0.786 0.020 0.888 0.092
#> SRR1377156 2 0.3670 0.786 0.020 0.888 0.092
#> SRR1377149 2 0.3325 0.800 0.020 0.904 0.076
#> SRR1377150 2 0.3325 0.800 0.020 0.904 0.076
#> SRR1377151 2 0.3325 0.800 0.020 0.904 0.076
#> SRR1377152 2 0.3325 0.800 0.020 0.904 0.076
#> SRR1377157 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377158 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377159 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377160 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377161 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377162 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377163 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377164 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377169 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377170 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377171 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377172 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377165 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377166 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377167 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377168 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377173 2 0.6818 0.662 0.348 0.628 0.024
#> SRR1377174 2 0.6818 0.662 0.348 0.628 0.024
#> SRR1377175 2 0.6818 0.662 0.348 0.628 0.024
#> SRR1377176 2 0.6818 0.662 0.348 0.628 0.024
#> SRR1377177 2 0.6696 0.666 0.348 0.632 0.020
#> SRR1377178 2 0.6696 0.666 0.348 0.632 0.020
#> SRR1377179 2 0.6696 0.666 0.348 0.632 0.020
#> SRR1377180 2 0.6696 0.666 0.348 0.632 0.020
#> SRR1377181 2 0.6497 0.677 0.336 0.648 0.016
#> SRR1377182 2 0.6497 0.677 0.336 0.648 0.016
#> SRR1377183 2 0.6357 0.680 0.336 0.652 0.012
#> SRR1377184 2 0.6497 0.677 0.336 0.648 0.016
#> SRR1377185 2 0.6357 0.680 0.336 0.652 0.012
#> SRR1377186 2 0.6357 0.680 0.336 0.652 0.012
#> SRR1377187 2 0.6497 0.677 0.336 0.648 0.016
#> SRR1377188 2 0.6357 0.680 0.336 0.652 0.012
#> SRR1377189 3 0.5810 0.674 0.000 0.336 0.664
#> SRR1377190 3 0.5810 0.674 0.000 0.336 0.664
#> SRR1377191 3 0.5810 0.674 0.000 0.336 0.664
#> SRR1377192 3 0.0983 0.666 0.004 0.016 0.980
#> SRR1377193 3 0.0829 0.661 0.004 0.012 0.984
#> SRR1377194 3 0.0829 0.661 0.004 0.012 0.984
#> SRR1377195 1 0.5785 0.987 0.668 0.000 0.332
#> SRR1377196 1 0.5785 0.987 0.668 0.000 0.332
#> SRR1377197 1 0.5785 0.987 0.668 0.000 0.332
#> SRR1377198 1 0.5785 0.987 0.668 0.000 0.332
#> SRR1377199 1 0.5785 0.987 0.668 0.000 0.332
#> SRR1377200 1 0.5785 0.987 0.668 0.000 0.332
#> SRR1377201 3 0.1753 0.693 0.000 0.048 0.952
#> SRR1377202 3 0.1753 0.693 0.000 0.048 0.952
#> SRR1377203 3 0.1753 0.693 0.000 0.048 0.952
#> SRR1377204 1 0.5882 0.987 0.652 0.000 0.348
#> SRR1377205 1 0.5882 0.987 0.652 0.000 0.348
#> SRR1377206 1 0.5882 0.987 0.652 0.000 0.348
#> SRR1377207 3 0.0424 0.660 0.000 0.008 0.992
#> SRR1377208 3 0.0424 0.660 0.000 0.008 0.992
#> SRR1377209 3 0.0424 0.660 0.000 0.008 0.992
#> SRR1377210 3 0.5810 0.674 0.000 0.336 0.664
#> SRR1377211 3 0.5810 0.674 0.000 0.336 0.664
#> SRR1377212 3 0.5810 0.674 0.000 0.336 0.664
#> SRR1377213 2 0.0424 0.839 0.000 0.992 0.008
#> SRR1377214 2 0.0424 0.839 0.000 0.992 0.008
#> SRR1377215 2 0.0424 0.839 0.000 0.992 0.008
#> SRR1377216 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377217 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377218 2 0.0000 0.841 0.000 1.000 0.000
#> SRR1377219 2 0.0424 0.839 0.000 0.992 0.008
#> SRR1377220 2 0.0424 0.839 0.000 0.992 0.008
#> SRR1377221 2 0.0424 0.839 0.000 0.992 0.008
#> SRR1377222 1 0.5882 0.987 0.652 0.000 0.348
#> SRR1377223 1 0.5882 0.987 0.652 0.000 0.348
#> SRR1377224 1 0.5882 0.987 0.652 0.000 0.348
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377146 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377147 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377148 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377153 3 0.4599 0.834 0.000 0.088 0.800 0.112
#> SRR1377154 3 0.4599 0.834 0.000 0.088 0.800 0.112
#> SRR1377155 3 0.4599 0.834 0.000 0.088 0.800 0.112
#> SRR1377156 3 0.4599 0.834 0.000 0.088 0.800 0.112
#> SRR1377149 3 0.4419 0.842 0.000 0.084 0.812 0.104
#> SRR1377150 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377151 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377152 3 0.4483 0.841 0.000 0.088 0.808 0.104
#> SRR1377157 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377158 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377159 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377160 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377161 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377162 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377163 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377164 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377169 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377170 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377171 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377172 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377165 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377166 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377167 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377168 3 0.1022 0.928 0.000 0.032 0.968 0.000
#> SRR1377173 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377174 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377175 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377176 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377177 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377178 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377179 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377180 2 0.0188 0.997 0.000 0.996 0.004 0.000
#> SRR1377181 2 0.0336 0.996 0.000 0.992 0.008 0.000
#> SRR1377182 2 0.0336 0.996 0.000 0.992 0.008 0.000
#> SRR1377183 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1377184 2 0.0336 0.996 0.000 0.992 0.008 0.000
#> SRR1377185 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1377186 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1377187 2 0.0336 0.996 0.000 0.992 0.008 0.000
#> SRR1377188 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1377189 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> SRR1377190 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> SRR1377191 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> SRR1377192 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> SRR1377193 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> SRR1377194 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> SRR1377195 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377196 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377197 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377198 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377199 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377200 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1377201 4 0.0376 0.994 0.004 0.004 0.000 0.992
#> SRR1377202 4 0.0376 0.994 0.004 0.004 0.000 0.992
#> SRR1377203 4 0.0376 0.994 0.004 0.004 0.000 0.992
#> SRR1377204 1 0.0188 0.998 0.996 0.000 0.000 0.004
#> SRR1377205 1 0.0188 0.998 0.996 0.000 0.000 0.004
#> SRR1377206 1 0.0188 0.998 0.996 0.000 0.000 0.004
#> SRR1377207 4 0.0376 0.994 0.004 0.004 0.000 0.992
#> SRR1377208 4 0.0376 0.994 0.004 0.004 0.000 0.992
#> SRR1377209 4 0.0376 0.994 0.004 0.004 0.000 0.992
#> SRR1377210 4 0.0376 0.993 0.000 0.004 0.004 0.992
#> SRR1377211 4 0.0376 0.993 0.000 0.004 0.004 0.992
#> SRR1377212 4 0.0376 0.993 0.000 0.004 0.004 0.992
#> SRR1377213 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377214 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377215 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377216 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377217 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377218 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377219 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377220 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377221 3 0.1109 0.926 0.000 0.028 0.968 0.004
#> SRR1377222 1 0.0188 0.998 0.996 0.000 0.000 0.004
#> SRR1377223 1 0.0188 0.998 0.996 0.000 0.000 0.004
#> SRR1377224 1 0.0188 0.998 0.996 0.000 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377146 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377147 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377148 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377153 4 0.4589 0.987 0.004 0.020 0.316 0.660 0.000
#> SRR1377154 4 0.4589 0.987 0.004 0.020 0.316 0.660 0.000
#> SRR1377155 4 0.4589 0.987 0.004 0.020 0.316 0.660 0.000
#> SRR1377156 4 0.4589 0.987 0.004 0.020 0.316 0.660 0.000
#> SRR1377149 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377150 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377151 4 0.4536 0.991 0.004 0.016 0.324 0.656 0.000
#> SRR1377152 4 0.4553 0.993 0.004 0.016 0.328 0.652 0.000
#> SRR1377157 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377158 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377159 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377160 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377161 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377162 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377163 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377164 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377169 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377170 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377171 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377172 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377165 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377166 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377167 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377168 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000
#> SRR1377173 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377174 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377175 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377176 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377177 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377178 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377179 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377180 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR1377181 1 0.0955 0.983 0.968 0.000 0.004 0.028 0.000
#> SRR1377182 1 0.0955 0.983 0.968 0.000 0.004 0.028 0.000
#> SRR1377183 1 0.1121 0.976 0.956 0.000 0.000 0.044 0.000
#> SRR1377184 1 0.0955 0.983 0.968 0.000 0.004 0.028 0.000
#> SRR1377185 1 0.1121 0.976 0.956 0.000 0.000 0.044 0.000
#> SRR1377186 1 0.1121 0.976 0.956 0.000 0.000 0.044 0.000
#> SRR1377187 1 0.0955 0.983 0.968 0.000 0.004 0.028 0.000
#> SRR1377188 1 0.1121 0.976 0.956 0.000 0.000 0.044 0.000
#> SRR1377189 2 0.1965 0.932 0.000 0.904 0.000 0.096 0.000
#> SRR1377190 2 0.1965 0.932 0.000 0.904 0.000 0.096 0.000
#> SRR1377191 2 0.1965 0.932 0.000 0.904 0.000 0.096 0.000
#> SRR1377192 2 0.2740 0.919 0.000 0.876 0.000 0.096 0.028
#> SRR1377193 2 0.2740 0.919 0.000 0.876 0.000 0.096 0.028
#> SRR1377194 2 0.2740 0.919 0.000 0.876 0.000 0.096 0.028
#> SRR1377195 5 0.2813 0.925 0.000 0.000 0.000 0.168 0.832
#> SRR1377196 5 0.2813 0.925 0.000 0.000 0.000 0.168 0.832
#> SRR1377197 5 0.2813 0.925 0.000 0.000 0.000 0.168 0.832
#> SRR1377198 5 0.2813 0.925 0.000 0.000 0.000 0.168 0.832
#> SRR1377199 5 0.2813 0.925 0.000 0.000 0.000 0.168 0.832
#> SRR1377200 5 0.2813 0.925 0.000 0.000 0.000 0.168 0.832
#> SRR1377201 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377202 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377203 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377204 5 0.0609 0.924 0.000 0.020 0.000 0.000 0.980
#> SRR1377205 5 0.0609 0.924 0.000 0.020 0.000 0.000 0.980
#> SRR1377206 5 0.0609 0.924 0.000 0.020 0.000 0.000 0.980
#> SRR1377207 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377208 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377209 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377210 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377211 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377212 2 0.0609 0.956 0.000 0.980 0.000 0.020 0.000
#> SRR1377213 3 0.2942 0.879 0.004 0.004 0.864 0.116 0.012
#> SRR1377214 3 0.2942 0.879 0.004 0.004 0.864 0.116 0.012
#> SRR1377215 3 0.2942 0.879 0.004 0.004 0.864 0.116 0.012
#> SRR1377216 3 0.2011 0.907 0.000 0.004 0.908 0.088 0.000
#> SRR1377217 3 0.2011 0.907 0.000 0.004 0.908 0.088 0.000
#> SRR1377218 3 0.2011 0.907 0.000 0.004 0.908 0.088 0.000
#> SRR1377219 3 0.2548 0.889 0.004 0.004 0.876 0.116 0.000
#> SRR1377220 3 0.2548 0.889 0.004 0.004 0.876 0.116 0.000
#> SRR1377221 3 0.2548 0.889 0.004 0.004 0.876 0.116 0.000
#> SRR1377222 5 0.0609 0.924 0.000 0.020 0.000 0.000 0.980
#> SRR1377223 5 0.0609 0.924 0.000 0.020 0.000 0.000 0.980
#> SRR1377224 5 0.0609 0.924 0.000 0.020 0.000 0.000 0.980
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377146 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377147 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377148 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377153 6 0.1732 0.998 0.004 0.000 0.072 NA 0.000 0.920
#> SRR1377154 6 0.1732 0.998 0.004 0.000 0.072 NA 0.000 0.920
#> SRR1377155 6 0.1732 0.998 0.004 0.000 0.072 NA 0.000 0.920
#> SRR1377156 6 0.1732 0.998 0.004 0.000 0.072 NA 0.000 0.920
#> SRR1377149 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377150 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377151 6 0.1732 0.998 0.004 0.000 0.072 NA 0.000 0.920
#> SRR1377152 6 0.1588 0.999 0.004 0.000 0.072 NA 0.000 0.924
#> SRR1377157 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377158 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377159 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377160 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377161 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377162 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377163 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377164 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377169 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377170 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377171 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377172 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377165 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377166 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377167 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377168 3 0.0937 0.889 0.000 0.000 0.960 NA 0.000 0.040
#> SRR1377173 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377174 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377175 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377176 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377177 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377178 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377179 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377180 1 0.0146 0.971 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1377181 1 0.0858 0.968 0.968 0.000 0.000 NA 0.000 0.004
#> SRR1377182 1 0.0777 0.968 0.972 0.000 0.000 NA 0.000 0.004
#> SRR1377183 1 0.2112 0.936 0.896 0.000 0.000 NA 0.000 0.016
#> SRR1377184 1 0.0777 0.968 0.972 0.000 0.000 NA 0.000 0.004
#> SRR1377185 1 0.2112 0.936 0.896 0.000 0.000 NA 0.000 0.016
#> SRR1377186 1 0.2199 0.934 0.892 0.000 0.000 NA 0.000 0.020
#> SRR1377187 1 0.0777 0.968 0.972 0.000 0.000 NA 0.000 0.004
#> SRR1377188 1 0.2199 0.934 0.892 0.000 0.000 NA 0.000 0.020
#> SRR1377189 2 0.4545 0.787 0.000 0.688 0.000 NA 0.008 0.064
#> SRR1377190 2 0.4545 0.787 0.000 0.688 0.000 NA 0.008 0.064
#> SRR1377191 2 0.4545 0.787 0.000 0.688 0.000 NA 0.008 0.064
#> SRR1377192 2 0.5900 0.731 0.000 0.596 0.000 NA 0.100 0.064
#> SRR1377193 2 0.5939 0.728 0.000 0.592 0.000 NA 0.104 0.064
#> SRR1377194 2 0.6015 0.720 0.000 0.584 0.000 NA 0.112 0.064
#> SRR1377195 5 0.3833 0.798 0.000 0.000 0.000 NA 0.556 0.000
#> SRR1377196 5 0.3833 0.798 0.000 0.000 0.000 NA 0.556 0.000
#> SRR1377197 5 0.3833 0.798 0.000 0.000 0.000 NA 0.556 0.000
#> SRR1377198 5 0.3833 0.798 0.000 0.000 0.000 NA 0.556 0.000
#> SRR1377199 5 0.3833 0.798 0.000 0.000 0.000 NA 0.556 0.000
#> SRR1377200 5 0.3833 0.798 0.000 0.000 0.000 NA 0.556 0.000
#> SRR1377201 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377202 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377203 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377204 5 0.0000 0.790 0.000 0.000 0.000 NA 1.000 0.000
#> SRR1377205 5 0.0000 0.790 0.000 0.000 0.000 NA 1.000 0.000
#> SRR1377206 5 0.0000 0.790 0.000 0.000 0.000 NA 1.000 0.000
#> SRR1377207 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377208 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377209 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377210 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377211 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377212 2 0.0000 0.867 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1377213 3 0.4108 0.761 0.000 0.000 0.704 NA 0.008 0.028
#> SRR1377214 3 0.4108 0.761 0.000 0.000 0.704 NA 0.008 0.028
#> SRR1377215 3 0.4108 0.761 0.000 0.000 0.704 NA 0.008 0.028
#> SRR1377216 3 0.3261 0.800 0.000 0.000 0.780 NA 0.000 0.016
#> SRR1377217 3 0.3261 0.800 0.000 0.000 0.780 NA 0.000 0.016
#> SRR1377218 3 0.3261 0.800 0.000 0.000 0.780 NA 0.000 0.016
#> SRR1377219 3 0.3789 0.768 0.000 0.000 0.716 NA 0.000 0.024
#> SRR1377220 3 0.3789 0.768 0.000 0.000 0.716 NA 0.000 0.024
#> SRR1377221 3 0.3789 0.768 0.000 0.000 0.716 NA 0.000 0.024
#> SRR1377222 5 0.0713 0.781 0.000 0.000 0.000 NA 0.972 0.000
#> SRR1377223 5 0.0713 0.781 0.000 0.000 0.000 NA 0.972 0.000
#> SRR1377224 5 0.0713 0.781 0.000 0.000 0.000 NA 0.972 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 13890 rows and 80 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.1414 0.859 0.859
#> 3 3 1.000 0.985 0.994 2.1209 0.706 0.658
#> 4 4 1.000 0.985 0.994 0.2215 0.901 0.826
#> 5 5 0.908 0.914 0.960 0.0538 0.998 0.997
#> 6 6 0.894 0.864 0.941 0.0414 0.999 0.997
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> 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
#> SRR1377145 2 0 1 0 1
#> SRR1377146 2 0 1 0 1
#> SRR1377147 2 0 1 0 1
#> SRR1377148 2 0 1 0 1
#> SRR1377153 2 0 1 0 1
#> SRR1377154 2 0 1 0 1
#> SRR1377155 2 0 1 0 1
#> SRR1377156 2 0 1 0 1
#> SRR1377149 2 0 1 0 1
#> SRR1377150 2 0 1 0 1
#> SRR1377151 2 0 1 0 1
#> SRR1377152 2 0 1 0 1
#> SRR1377157 2 0 1 0 1
#> SRR1377158 2 0 1 0 1
#> SRR1377159 2 0 1 0 1
#> SRR1377160 2 0 1 0 1
#> SRR1377161 2 0 1 0 1
#> SRR1377162 2 0 1 0 1
#> SRR1377163 2 0 1 0 1
#> SRR1377164 2 0 1 0 1
#> SRR1377169 2 0 1 0 1
#> SRR1377170 2 0 1 0 1
#> SRR1377171 2 0 1 0 1
#> SRR1377172 2 0 1 0 1
#> SRR1377165 2 0 1 0 1
#> SRR1377166 2 0 1 0 1
#> SRR1377167 2 0 1 0 1
#> SRR1377168 2 0 1 0 1
#> SRR1377173 2 0 1 0 1
#> SRR1377174 2 0 1 0 1
#> SRR1377175 2 0 1 0 1
#> SRR1377176 2 0 1 0 1
#> SRR1377177 2 0 1 0 1
#> SRR1377178 2 0 1 0 1
#> SRR1377179 2 0 1 0 1
#> SRR1377180 2 0 1 0 1
#> SRR1377181 2 0 1 0 1
#> SRR1377182 2 0 1 0 1
#> SRR1377183 2 0 1 0 1
#> SRR1377184 2 0 1 0 1
#> SRR1377185 2 0 1 0 1
#> SRR1377186 2 0 1 0 1
#> SRR1377187 2 0 1 0 1
#> SRR1377188 2 0 1 0 1
#> SRR1377189 2 0 1 0 1
#> SRR1377190 2 0 1 0 1
#> SRR1377191 2 0 1 0 1
#> SRR1377192 2 0 1 0 1
#> SRR1377193 2 0 1 0 1
#> SRR1377194 2 0 1 0 1
#> SRR1377195 1 0 1 1 0
#> SRR1377196 1 0 1 1 0
#> SRR1377197 1 0 1 1 0
#> SRR1377198 1 0 1 1 0
#> SRR1377199 1 0 1 1 0
#> SRR1377200 1 0 1 1 0
#> SRR1377201 2 0 1 0 1
#> SRR1377202 2 0 1 0 1
#> SRR1377203 2 0 1 0 1
#> SRR1377204 2 0 1 0 1
#> SRR1377205 2 0 1 0 1
#> SRR1377206 2 0 1 0 1
#> SRR1377207 2 0 1 0 1
#> SRR1377208 2 0 1 0 1
#> SRR1377209 2 0 1 0 1
#> SRR1377210 2 0 1 0 1
#> SRR1377211 2 0 1 0 1
#> SRR1377212 2 0 1 0 1
#> SRR1377213 2 0 1 0 1
#> SRR1377214 2 0 1 0 1
#> SRR1377215 2 0 1 0 1
#> SRR1377216 2 0 1 0 1
#> SRR1377217 2 0 1 0 1
#> SRR1377218 2 0 1 0 1
#> SRR1377219 2 0 1 0 1
#> SRR1377220 2 0 1 0 1
#> SRR1377221 2 0 1 0 1
#> SRR1377222 2 0 1 0 1
#> SRR1377223 2 0 1 0 1
#> SRR1377224 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.000 0.990 0 1.000 0.000
#> SRR1377146 2 0.000 0.990 0 1.000 0.000
#> SRR1377147 2 0.000 0.990 0 1.000 0.000
#> SRR1377148 2 0.000 0.990 0 1.000 0.000
#> SRR1377153 2 0.000 0.990 0 1.000 0.000
#> SRR1377154 2 0.000 0.990 0 1.000 0.000
#> SRR1377155 2 0.000 0.990 0 1.000 0.000
#> SRR1377156 2 0.000 0.990 0 1.000 0.000
#> SRR1377149 2 0.000 0.990 0 1.000 0.000
#> SRR1377150 2 0.000 0.990 0 1.000 0.000
#> SRR1377151 2 0.000 0.990 0 1.000 0.000
#> SRR1377152 2 0.000 0.990 0 1.000 0.000
#> SRR1377157 3 0.000 1.000 0 0.000 1.000
#> SRR1377158 3 0.000 1.000 0 0.000 1.000
#> SRR1377159 3 0.000 1.000 0 0.000 1.000
#> SRR1377160 3 0.000 1.000 0 0.000 1.000
#> SRR1377161 3 0.000 1.000 0 0.000 1.000
#> SRR1377162 3 0.000 1.000 0 0.000 1.000
#> SRR1377163 3 0.000 1.000 0 0.000 1.000
#> SRR1377164 3 0.000 1.000 0 0.000 1.000
#> SRR1377169 3 0.000 1.000 0 0.000 1.000
#> SRR1377170 3 0.000 1.000 0 0.000 1.000
#> SRR1377171 3 0.000 1.000 0 0.000 1.000
#> SRR1377172 3 0.000 1.000 0 0.000 1.000
#> SRR1377165 3 0.000 1.000 0 0.000 1.000
#> SRR1377166 3 0.000 1.000 0 0.000 1.000
#> SRR1377167 3 0.000 1.000 0 0.000 1.000
#> SRR1377168 3 0.000 1.000 0 0.000 1.000
#> SRR1377173 2 0.000 0.990 0 1.000 0.000
#> SRR1377174 2 0.000 0.990 0 1.000 0.000
#> SRR1377175 2 0.000 0.990 0 1.000 0.000
#> SRR1377176 2 0.000 0.990 0 1.000 0.000
#> SRR1377177 2 0.000 0.990 0 1.000 0.000
#> SRR1377178 2 0.000 0.990 0 1.000 0.000
#> SRR1377179 2 0.000 0.990 0 1.000 0.000
#> SRR1377180 2 0.000 0.990 0 1.000 0.000
#> SRR1377181 2 0.000 0.990 0 1.000 0.000
#> SRR1377182 2 0.000 0.990 0 1.000 0.000
#> SRR1377183 2 0.000 0.990 0 1.000 0.000
#> SRR1377184 2 0.000 0.990 0 1.000 0.000
#> SRR1377185 2 0.000 0.990 0 1.000 0.000
#> SRR1377186 2 0.000 0.990 0 1.000 0.000
#> SRR1377187 2 0.000 0.990 0 1.000 0.000
#> SRR1377188 2 0.000 0.990 0 1.000 0.000
#> SRR1377189 2 0.000 0.990 0 1.000 0.000
#> SRR1377190 2 0.000 0.990 0 1.000 0.000
#> SRR1377191 2 0.000 0.990 0 1.000 0.000
#> SRR1377192 2 0.000 0.990 0 1.000 0.000
#> SRR1377193 2 0.000 0.990 0 1.000 0.000
#> SRR1377194 2 0.000 0.990 0 1.000 0.000
#> SRR1377195 1 0.000 1.000 1 0.000 0.000
#> SRR1377196 1 0.000 1.000 1 0.000 0.000
#> SRR1377197 1 0.000 1.000 1 0.000 0.000
#> SRR1377198 1 0.000 1.000 1 0.000 0.000
#> SRR1377199 1 0.000 1.000 1 0.000 0.000
#> SRR1377200 1 0.000 1.000 1 0.000 0.000
#> SRR1377201 2 0.000 0.990 0 1.000 0.000
#> SRR1377202 2 0.000 0.990 0 1.000 0.000
#> SRR1377203 2 0.000 0.990 0 1.000 0.000
#> SRR1377204 2 0.000 0.990 0 1.000 0.000
#> SRR1377205 2 0.000 0.990 0 1.000 0.000
#> SRR1377206 2 0.000 0.990 0 1.000 0.000
#> SRR1377207 2 0.000 0.990 0 1.000 0.000
#> SRR1377208 2 0.000 0.990 0 1.000 0.000
#> SRR1377209 2 0.000 0.990 0 1.000 0.000
#> SRR1377210 2 0.000 0.990 0 1.000 0.000
#> SRR1377211 2 0.000 0.990 0 1.000 0.000
#> SRR1377212 2 0.000 0.990 0 1.000 0.000
#> SRR1377213 2 0.000 0.990 0 1.000 0.000
#> SRR1377214 2 0.000 0.990 0 1.000 0.000
#> SRR1377215 2 0.000 0.990 0 1.000 0.000
#> SRR1377216 2 0.236 0.915 0 0.928 0.072
#> SRR1377217 2 0.435 0.772 0 0.816 0.184
#> SRR1377218 2 0.518 0.662 0 0.744 0.256
#> SRR1377219 2 0.000 0.990 0 1.000 0.000
#> SRR1377220 2 0.000 0.990 0 1.000 0.000
#> SRR1377221 2 0.000 0.990 0 1.000 0.000
#> SRR1377222 2 0.000 0.990 0 1.000 0.000
#> SRR1377223 2 0.000 0.990 0 1.000 0.000
#> SRR1377224 2 0.000 0.990 0 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377146 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377147 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377148 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377153 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377154 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377155 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377156 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377149 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377150 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377151 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377152 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377157 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377158 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377159 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377160 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377161 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377162 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377163 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377164 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377169 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377170 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377171 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377172 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377165 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377166 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377167 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377168 3 0.000 1.000 0 0.000 1.000 0
#> SRR1377173 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377174 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377175 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377176 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377177 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377178 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377179 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377180 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377181 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377182 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377183 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377184 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377185 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377186 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377187 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377188 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377189 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377190 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377191 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377192 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377193 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377194 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377195 1 0.000 1.000 1 0.000 0.000 0
#> SRR1377196 1 0.000 1.000 1 0.000 0.000 0
#> SRR1377197 1 0.000 1.000 1 0.000 0.000 0
#> SRR1377198 1 0.000 1.000 1 0.000 0.000 0
#> SRR1377199 1 0.000 1.000 1 0.000 0.000 0
#> SRR1377200 1 0.000 1.000 1 0.000 0.000 0
#> SRR1377201 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377202 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377203 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377204 4 0.000 1.000 0 0.000 0.000 1
#> SRR1377205 4 0.000 1.000 0 0.000 0.000 1
#> SRR1377206 4 0.000 1.000 0 0.000 0.000 1
#> SRR1377207 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377208 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377209 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377210 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377211 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377212 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377213 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377214 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377215 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377216 2 0.194 0.909 0 0.924 0.076 0
#> SRR1377217 2 0.344 0.772 0 0.816 0.184 0
#> SRR1377218 2 0.410 0.662 0 0.744 0.256 0
#> SRR1377219 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377220 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377221 2 0.000 0.989 0 1.000 0.000 0
#> SRR1377222 4 0.000 1.000 0 0.000 0.000 1
#> SRR1377223 4 0.000 1.000 0 0.000 0.000 1
#> SRR1377224 4 0.000 1.000 0 0.000 0.000 1
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377146 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377147 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377148 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377153 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377154 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377155 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377156 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377149 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377150 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377151 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377152 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377157 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377158 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377159 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377160 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377161 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377162 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377163 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377164 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377169 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377170 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377171 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377172 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377165 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377166 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377167 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377168 3 0.000 1.000 0.000 0.000 1.000 0 0.000
#> SRR1377173 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377174 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377175 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377176 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377177 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377178 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377179 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377180 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377181 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377182 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377183 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377184 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377185 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377186 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377187 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377188 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377189 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377190 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377191 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377192 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377193 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377194 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377195 1 0.334 0.920 0.772 0.000 0.000 0 0.228
#> SRR1377196 1 0.334 0.920 0.772 0.000 0.000 0 0.228
#> SRR1377197 1 0.334 0.920 0.772 0.000 0.000 0 0.228
#> SRR1377198 1 0.334 0.920 0.772 0.000 0.000 0 0.228
#> SRR1377199 1 0.422 0.659 0.584 0.000 0.000 0 0.416
#> SRR1377200 5 0.300 0.000 0.188 0.000 0.000 0 0.812
#> SRR1377201 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377202 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377203 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377204 4 0.000 1.000 0.000 0.000 0.000 1 0.000
#> SRR1377205 4 0.000 1.000 0.000 0.000 0.000 1 0.000
#> SRR1377206 4 0.000 1.000 0.000 0.000 0.000 1 0.000
#> SRR1377207 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377208 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377209 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377210 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377211 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377212 2 0.000 0.949 0.000 1.000 0.000 0 0.000
#> SRR1377213 2 0.334 0.754 0.228 0.772 0.000 0 0.000
#> SRR1377214 2 0.334 0.754 0.228 0.772 0.000 0 0.000
#> SRR1377215 2 0.334 0.754 0.228 0.772 0.000 0 0.000
#> SRR1377216 2 0.488 0.655 0.228 0.696 0.076 0 0.000
#> SRR1377217 2 0.594 0.470 0.228 0.592 0.180 0 0.000
#> SRR1377218 2 0.637 0.308 0.228 0.520 0.252 0 0.000
#> SRR1377219 2 0.334 0.754 0.228 0.772 0.000 0 0.000
#> SRR1377220 2 0.334 0.754 0.228 0.772 0.000 0 0.000
#> SRR1377221 2 0.334 0.754 0.228 0.772 0.000 0 0.000
#> SRR1377222 4 0.000 1.000 0.000 0.000 0.000 1 0.000
#> SRR1377223 4 0.000 1.000 0.000 0.000 0.000 1 0.000
#> SRR1377224 4 0.000 1.000 0.000 0.000 0.000 1 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377146 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377147 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377148 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377153 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377154 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377155 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377156 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377149 1 0.0363 0.908 0.988 0 0.000 0.000 0.012 0.000
#> SRR1377150 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377151 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377152 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377157 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377158 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377159 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377160 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377161 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377162 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377163 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377164 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377169 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377170 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377171 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377172 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377165 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377166 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377167 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377168 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0.000
#> SRR1377173 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377174 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377175 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377176 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377177 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377178 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377179 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377180 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377181 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377182 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377183 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377184 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377185 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377186 1 0.0260 0.912 0.992 0 0.000 0.008 0.000 0.000
#> SRR1377187 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377188 1 0.0622 0.910 0.980 0 0.000 0.012 0.008 0.000
#> SRR1377189 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377190 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377191 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377192 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377193 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377194 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377195 5 0.4228 1.000 0.000 0 0.000 0.020 0.588 0.392
#> SRR1377196 5 0.4228 1.000 0.000 0 0.000 0.020 0.588 0.392
#> SRR1377197 5 0.4228 1.000 0.000 0 0.000 0.020 0.588 0.392
#> SRR1377198 5 0.4228 1.000 0.000 0 0.000 0.020 0.588 0.392
#> SRR1377199 6 0.0000 0.000 0.000 0 0.000 0.000 0.000 1.000
#> SRR1377200 4 0.0363 0.000 0.000 0 0.000 0.988 0.000 0.012
#> SRR1377201 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377202 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377203 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377204 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0.000
#> SRR1377205 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0.000
#> SRR1377206 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0.000
#> SRR1377207 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377208 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377209 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377210 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377211 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377212 1 0.0000 0.914 1.000 0 0.000 0.000 0.000 0.000
#> SRR1377213 1 0.3765 0.501 0.596 0 0.000 0.000 0.404 0.000
#> SRR1377214 1 0.3765 0.501 0.596 0 0.000 0.000 0.404 0.000
#> SRR1377215 1 0.3765 0.501 0.596 0 0.000 0.000 0.404 0.000
#> SRR1377216 1 0.4584 0.435 0.556 0 0.040 0.000 0.404 0.000
#> SRR1377217 1 0.5466 0.250 0.472 0 0.124 0.000 0.404 0.000
#> SRR1377218 1 0.5629 0.195 0.448 0 0.148 0.000 0.404 0.000
#> SRR1377219 1 0.3765 0.501 0.596 0 0.000 0.000 0.404 0.000
#> SRR1377220 1 0.3765 0.501 0.596 0 0.000 0.000 0.404 0.000
#> SRR1377221 1 0.3765 0.501 0.596 0 0.000 0.000 0.404 0.000
#> SRR1377222 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0.000
#> SRR1377223 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0.000
#> SRR1377224 2 0.0000 1.000 0.000 1 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", "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 13890 rows and 80 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 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 0.859 0.965 0.984 0.16717 0.859 0.859
#> 3 3 0.418 0.778 0.773 1.70121 0.871 0.850
#> 4 4 0.350 0.620 0.757 0.33662 0.637 0.503
#> 5 5 0.546 0.634 0.783 0.16180 0.704 0.395
#> 6 6 0.595 0.585 0.795 0.00257 0.910 0.755
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
#> SRR1377145 2 0.00 0.981 0.00 1.00
#> SRR1377146 2 0.00 0.981 0.00 1.00
#> SRR1377147 2 0.00 0.981 0.00 1.00
#> SRR1377148 2 0.00 0.981 0.00 1.00
#> SRR1377153 2 0.00 0.981 0.00 1.00
#> SRR1377154 2 0.00 0.981 0.00 1.00
#> SRR1377155 2 0.00 0.981 0.00 1.00
#> SRR1377156 2 0.00 0.981 0.00 1.00
#> SRR1377149 2 0.00 0.981 0.00 1.00
#> SRR1377150 2 0.00 0.981 0.00 1.00
#> SRR1377151 2 0.00 0.981 0.00 1.00
#> SRR1377152 2 0.00 0.981 0.00 1.00
#> SRR1377157 2 0.00 0.981 0.00 1.00
#> SRR1377158 2 0.00 0.981 0.00 1.00
#> SRR1377159 2 0.00 0.981 0.00 1.00
#> SRR1377160 2 0.00 0.981 0.00 1.00
#> SRR1377161 2 0.00 0.981 0.00 1.00
#> SRR1377162 2 0.00 0.981 0.00 1.00
#> SRR1377163 2 0.00 0.981 0.00 1.00
#> SRR1377164 2 0.00 0.981 0.00 1.00
#> SRR1377169 2 0.00 0.981 0.00 1.00
#> SRR1377170 2 0.00 0.981 0.00 1.00
#> SRR1377171 2 0.00 0.981 0.00 1.00
#> SRR1377172 2 0.00 0.981 0.00 1.00
#> SRR1377165 2 0.00 0.981 0.00 1.00
#> SRR1377166 2 0.00 0.981 0.00 1.00
#> SRR1377167 2 0.00 0.981 0.00 1.00
#> SRR1377168 2 0.00 0.981 0.00 1.00
#> SRR1377173 2 0.00 0.981 0.00 1.00
#> SRR1377174 2 0.00 0.981 0.00 1.00
#> SRR1377175 2 0.00 0.981 0.00 1.00
#> SRR1377176 2 0.00 0.981 0.00 1.00
#> SRR1377177 2 0.00 0.981 0.00 1.00
#> SRR1377178 2 0.00 0.981 0.00 1.00
#> SRR1377179 2 0.00 0.981 0.00 1.00
#> SRR1377180 2 0.00 0.981 0.00 1.00
#> SRR1377181 2 0.00 0.981 0.00 1.00
#> SRR1377182 2 0.00 0.981 0.00 1.00
#> SRR1377183 2 0.00 0.981 0.00 1.00
#> SRR1377184 2 0.00 0.981 0.00 1.00
#> SRR1377185 2 0.00 0.981 0.00 1.00
#> SRR1377186 2 0.00 0.981 0.00 1.00
#> SRR1377187 2 0.00 0.981 0.00 1.00
#> SRR1377188 2 0.00 0.981 0.00 1.00
#> SRR1377189 2 0.00 0.981 0.00 1.00
#> SRR1377190 2 0.00 0.981 0.00 1.00
#> SRR1377191 2 0.00 0.981 0.00 1.00
#> SRR1377192 2 0.00 0.981 0.00 1.00
#> SRR1377193 2 0.00 0.981 0.00 1.00
#> SRR1377194 2 0.00 0.981 0.00 1.00
#> SRR1377195 1 0.00 1.000 1.00 0.00
#> SRR1377196 1 0.00 1.000 1.00 0.00
#> SRR1377197 1 0.00 1.000 1.00 0.00
#> SRR1377198 1 0.00 1.000 1.00 0.00
#> SRR1377199 1 0.00 1.000 1.00 0.00
#> SRR1377200 1 0.00 1.000 1.00 0.00
#> SRR1377201 2 0.00 0.981 0.00 1.00
#> SRR1377202 2 0.00 0.981 0.00 1.00
#> SRR1377203 2 0.00 0.981 0.00 1.00
#> SRR1377204 2 0.76 0.737 0.22 0.78
#> SRR1377205 2 0.76 0.737 0.22 0.78
#> SRR1377206 2 0.76 0.737 0.22 0.78
#> SRR1377207 2 0.00 0.981 0.00 1.00
#> SRR1377208 2 0.00 0.981 0.00 1.00
#> SRR1377209 2 0.00 0.981 0.00 1.00
#> SRR1377210 2 0.00 0.981 0.00 1.00
#> SRR1377211 2 0.00 0.981 0.00 1.00
#> SRR1377212 2 0.00 0.981 0.00 1.00
#> SRR1377213 2 0.00 0.981 0.00 1.00
#> SRR1377214 2 0.00 0.981 0.00 1.00
#> SRR1377215 2 0.00 0.981 0.00 1.00
#> SRR1377216 2 0.00 0.981 0.00 1.00
#> SRR1377217 2 0.00 0.981 0.00 1.00
#> SRR1377218 2 0.00 0.981 0.00 1.00
#> SRR1377219 2 0.00 0.981 0.00 1.00
#> SRR1377220 2 0.00 0.981 0.00 1.00
#> SRR1377221 2 0.00 0.981 0.00 1.00
#> SRR1377222 2 0.76 0.737 0.22 0.78
#> SRR1377223 2 0.76 0.737 0.22 0.78
#> SRR1377224 2 0.76 0.737 0.22 0.78
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.6126 0.759 0.352 0.644 0.004
#> SRR1377146 2 0.6275 0.760 0.348 0.644 0.008
#> SRR1377147 2 0.6252 0.762 0.344 0.648 0.008
#> SRR1377148 2 0.6608 0.757 0.356 0.628 0.016
#> SRR1377153 2 0.3295 0.795 0.096 0.896 0.008
#> SRR1377154 2 0.4531 0.802 0.168 0.824 0.008
#> SRR1377155 2 0.5247 0.795 0.224 0.768 0.008
#> SRR1377156 2 0.4589 0.802 0.172 0.820 0.008
#> SRR1377149 2 0.3918 0.798 0.140 0.856 0.004
#> SRR1377150 2 0.5201 0.792 0.236 0.760 0.004
#> SRR1377151 2 0.5201 0.792 0.236 0.760 0.004
#> SRR1377152 2 0.5690 0.780 0.288 0.708 0.004
#> SRR1377157 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377158 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377159 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377160 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377161 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377162 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377163 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377164 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377169 2 0.6244 0.728 0.440 0.560 0.000
#> SRR1377170 2 0.6244 0.728 0.440 0.560 0.000
#> SRR1377171 2 0.6244 0.728 0.440 0.560 0.000
#> SRR1377172 2 0.6244 0.728 0.440 0.560 0.000
#> SRR1377165 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377166 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377167 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377168 2 0.6252 0.725 0.444 0.556 0.000
#> SRR1377173 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377174 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377175 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377176 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377177 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377178 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377179 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377180 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377181 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377182 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377183 2 0.1860 0.783 0.052 0.948 0.000
#> SRR1377184 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377185 2 0.3116 0.797 0.108 0.892 0.000
#> SRR1377186 2 0.3482 0.799 0.128 0.872 0.000
#> SRR1377187 2 0.0424 0.768 0.008 0.992 0.000
#> SRR1377188 2 0.4062 0.802 0.164 0.836 0.000
#> SRR1377189 2 0.4915 0.801 0.184 0.804 0.012
#> SRR1377190 2 0.4531 0.802 0.168 0.824 0.008
#> SRR1377191 2 0.2682 0.792 0.076 0.920 0.004
#> SRR1377192 2 0.3826 0.678 0.008 0.868 0.124
#> SRR1377193 2 0.3784 0.669 0.004 0.864 0.132
#> SRR1377194 2 0.4409 0.620 0.004 0.824 0.172
#> SRR1377195 1 0.6260 1.000 0.552 0.000 0.448
#> SRR1377196 1 0.6260 1.000 0.552 0.000 0.448
#> SRR1377197 1 0.6260 1.000 0.552 0.000 0.448
#> SRR1377198 1 0.6260 1.000 0.552 0.000 0.448
#> SRR1377199 1 0.6260 1.000 0.552 0.000 0.448
#> SRR1377200 1 0.6260 1.000 0.552 0.000 0.448
#> SRR1377201 2 0.2860 0.794 0.084 0.912 0.004
#> SRR1377202 2 0.4531 0.801 0.168 0.824 0.008
#> SRR1377203 2 0.4233 0.802 0.160 0.836 0.004
#> SRR1377204 3 0.0237 1.000 0.000 0.004 0.996
#> SRR1377205 3 0.0237 1.000 0.000 0.004 0.996
#> SRR1377206 3 0.0237 1.000 0.000 0.004 0.996
#> SRR1377207 2 0.0000 0.769 0.000 1.000 0.000
#> SRR1377208 2 0.0000 0.769 0.000 1.000 0.000
#> SRR1377209 2 0.0000 0.769 0.000 1.000 0.000
#> SRR1377210 2 0.4575 0.800 0.184 0.812 0.004
#> SRR1377211 2 0.3644 0.800 0.124 0.872 0.004
#> SRR1377212 2 0.4409 0.801 0.172 0.824 0.004
#> SRR1377213 2 0.6211 0.529 0.036 0.736 0.228
#> SRR1377214 2 0.6211 0.529 0.036 0.736 0.228
#> SRR1377215 2 0.6211 0.529 0.036 0.736 0.228
#> SRR1377216 2 0.7430 0.718 0.424 0.540 0.036
#> SRR1377217 2 0.7430 0.718 0.424 0.540 0.036
#> SRR1377218 2 0.7430 0.718 0.424 0.540 0.036
#> SRR1377219 2 0.6168 0.534 0.036 0.740 0.224
#> SRR1377220 2 0.6168 0.534 0.036 0.740 0.224
#> SRR1377221 2 0.6211 0.529 0.036 0.736 0.228
#> SRR1377222 3 0.0237 1.000 0.000 0.004 0.996
#> SRR1377223 3 0.0237 1.000 0.000 0.004 0.996
#> SRR1377224 3 0.0237 1.000 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 3 0.4122 0.66330 0.000 0.236 0.760 0.004
#> SRR1377146 3 0.4155 0.66103 0.000 0.240 0.756 0.004
#> SRR1377147 3 0.3945 0.67455 0.000 0.216 0.780 0.004
#> SRR1377148 3 0.4188 0.65865 0.000 0.244 0.752 0.004
#> SRR1377153 2 0.4994 -0.08623 0.000 0.520 0.480 0.000
#> SRR1377154 3 0.4991 0.46632 0.000 0.388 0.608 0.004
#> SRR1377155 3 0.4936 0.49846 0.000 0.372 0.624 0.004
#> SRR1377156 3 0.4889 0.52261 0.000 0.360 0.636 0.004
#> SRR1377149 3 0.5085 0.45990 0.000 0.376 0.616 0.008
#> SRR1377150 3 0.5112 0.46750 0.000 0.384 0.608 0.008
#> SRR1377151 3 0.4781 0.56085 0.000 0.336 0.660 0.004
#> SRR1377152 3 0.4567 0.62711 0.000 0.276 0.716 0.008
#> SRR1377157 3 0.1109 0.71297 0.000 0.028 0.968 0.004
#> SRR1377158 3 0.0895 0.71836 0.000 0.020 0.976 0.004
#> SRR1377159 3 0.1305 0.70867 0.000 0.036 0.960 0.004
#> SRR1377160 3 0.1209 0.71206 0.000 0.032 0.964 0.004
#> SRR1377161 3 0.0657 0.72156 0.000 0.012 0.984 0.004
#> SRR1377162 3 0.0376 0.72443 0.000 0.004 0.992 0.004
#> SRR1377163 3 0.0524 0.72259 0.000 0.008 0.988 0.004
#> SRR1377164 3 0.0657 0.72156 0.000 0.012 0.984 0.004
#> SRR1377169 3 0.1389 0.73151 0.000 0.048 0.952 0.000
#> SRR1377170 3 0.1389 0.73212 0.000 0.048 0.952 0.000
#> SRR1377171 3 0.1211 0.73309 0.000 0.040 0.960 0.000
#> SRR1377172 3 0.1716 0.72919 0.000 0.064 0.936 0.000
#> SRR1377165 3 0.0336 0.72866 0.000 0.008 0.992 0.000
#> SRR1377166 3 0.0921 0.73085 0.000 0.028 0.972 0.000
#> SRR1377167 3 0.0469 0.72962 0.000 0.012 0.988 0.000
#> SRR1377168 3 0.0592 0.72756 0.000 0.016 0.984 0.000
#> SRR1377173 2 0.3355 0.70870 0.000 0.836 0.160 0.004
#> SRR1377174 2 0.3355 0.70975 0.000 0.836 0.160 0.004
#> SRR1377175 2 0.3355 0.70870 0.000 0.836 0.160 0.004
#> SRR1377176 2 0.3402 0.70840 0.000 0.832 0.164 0.004
#> SRR1377177 2 0.3498 0.70798 0.000 0.832 0.160 0.008
#> SRR1377178 2 0.3545 0.70772 0.000 0.828 0.164 0.008
#> SRR1377179 2 0.3545 0.70772 0.000 0.828 0.164 0.008
#> SRR1377180 2 0.3545 0.70772 0.000 0.828 0.164 0.008
#> SRR1377181 2 0.3725 0.70100 0.000 0.812 0.180 0.008
#> SRR1377182 2 0.3933 0.68644 0.000 0.792 0.200 0.008
#> SRR1377183 2 0.6200 0.47159 0.000 0.580 0.356 0.064
#> SRR1377184 2 0.3933 0.68644 0.000 0.792 0.200 0.008
#> SRR1377185 2 0.6020 0.40277 0.000 0.568 0.384 0.048
#> SRR1377186 2 0.6197 0.23183 0.000 0.508 0.440 0.052
#> SRR1377187 2 0.3893 0.68966 0.000 0.796 0.196 0.008
#> SRR1377188 3 0.6147 -0.07483 0.000 0.464 0.488 0.048
#> SRR1377189 3 0.5984 0.33041 0.000 0.372 0.580 0.048
#> SRR1377190 3 0.6430 0.04496 0.000 0.428 0.504 0.068
#> SRR1377191 2 0.6487 0.23096 0.000 0.500 0.428 0.072
#> SRR1377192 2 0.7315 0.59227 0.000 0.532 0.252 0.216
#> SRR1377193 2 0.7297 0.59913 0.000 0.536 0.244 0.220
#> SRR1377194 2 0.7421 0.56418 0.000 0.512 0.268 0.220
#> SRR1377195 1 0.2197 0.94927 0.916 0.080 0.004 0.000
#> SRR1377196 1 0.2197 0.94927 0.916 0.080 0.004 0.000
#> SRR1377197 1 0.2197 0.94927 0.916 0.080 0.004 0.000
#> SRR1377198 1 0.0188 0.94480 0.996 0.004 0.000 0.000
#> SRR1377199 1 0.0817 0.93880 0.976 0.024 0.000 0.000
#> SRR1377200 1 0.1545 0.92665 0.952 0.040 0.000 0.008
#> SRR1377201 2 0.6223 0.38841 0.000 0.556 0.384 0.060
#> SRR1377202 2 0.6425 0.26916 0.000 0.508 0.424 0.068
#> SRR1377203 3 0.6336 -0.10334 0.000 0.460 0.480 0.060
#> SRR1377204 4 0.2973 0.99626 0.144 0.000 0.000 0.856
#> SRR1377205 4 0.2973 0.99626 0.144 0.000 0.000 0.856
#> SRR1377206 4 0.2973 0.99626 0.144 0.000 0.000 0.856
#> SRR1377207 2 0.5280 0.68263 0.000 0.748 0.156 0.096
#> SRR1377208 2 0.5188 0.68573 0.000 0.756 0.148 0.096
#> SRR1377209 2 0.5293 0.68497 0.000 0.748 0.152 0.100
#> SRR1377210 3 0.5982 0.10331 0.000 0.436 0.524 0.040
#> SRR1377211 3 0.6080 -0.06345 0.000 0.468 0.488 0.044
#> SRR1377212 3 0.6005 0.00654 0.000 0.460 0.500 0.040
#> SRR1377213 2 0.6878 0.56666 0.000 0.472 0.104 0.424
#> SRR1377214 2 0.6844 0.59744 0.000 0.500 0.104 0.396
#> SRR1377215 2 0.6884 0.55636 0.000 0.464 0.104 0.432
#> SRR1377216 3 0.2522 0.72476 0.000 0.076 0.908 0.016
#> SRR1377217 3 0.2402 0.72590 0.000 0.076 0.912 0.012
#> SRR1377218 3 0.2402 0.72590 0.000 0.076 0.912 0.012
#> SRR1377219 2 0.6844 0.59757 0.000 0.500 0.104 0.396
#> SRR1377220 2 0.6830 0.60632 0.000 0.508 0.104 0.388
#> SRR1377221 2 0.6844 0.59727 0.000 0.500 0.104 0.396
#> SRR1377222 4 0.3024 0.99625 0.148 0.000 0.000 0.852
#> SRR1377223 4 0.3024 0.99625 0.148 0.000 0.000 0.852
#> SRR1377224 4 0.3024 0.99625 0.148 0.000 0.000 0.852
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.0992 0.7099 0.024 0.968 0.000 0.008 0.000
#> SRR1377146 2 0.1117 0.7121 0.020 0.964 0.000 0.016 0.000
#> SRR1377147 2 0.1522 0.7054 0.044 0.944 0.000 0.012 0.000
#> SRR1377148 2 0.0880 0.7075 0.032 0.968 0.000 0.000 0.000
#> SRR1377153 2 0.1728 0.7133 0.020 0.940 0.004 0.036 0.000
#> SRR1377154 2 0.1461 0.7139 0.028 0.952 0.004 0.016 0.000
#> SRR1377155 2 0.1377 0.7153 0.020 0.956 0.004 0.020 0.000
#> SRR1377156 2 0.1278 0.7143 0.020 0.960 0.004 0.016 0.000
#> SRR1377149 2 0.2278 0.7104 0.032 0.908 0.000 0.060 0.000
#> SRR1377150 2 0.2362 0.6987 0.024 0.900 0.000 0.076 0.000
#> SRR1377151 2 0.1179 0.7146 0.016 0.964 0.004 0.016 0.000
#> SRR1377152 2 0.1211 0.7154 0.024 0.960 0.000 0.016 0.000
#> SRR1377157 1 0.7009 0.4698 0.552 0.260 0.004 0.060 0.124
#> SRR1377158 1 0.7062 0.4574 0.540 0.272 0.004 0.060 0.124
#> SRR1377159 1 0.6950 0.4834 0.564 0.248 0.004 0.060 0.124
#> SRR1377160 1 0.6950 0.4834 0.564 0.248 0.004 0.060 0.124
#> SRR1377161 1 0.7155 0.4115 0.516 0.296 0.004 0.060 0.124
#> SRR1377162 1 0.7206 0.3788 0.500 0.312 0.004 0.060 0.124
#> SRR1377163 1 0.7283 0.2907 0.468 0.344 0.004 0.060 0.124
#> SRR1377164 1 0.7155 0.4115 0.516 0.296 0.004 0.060 0.124
#> SRR1377169 2 0.6966 0.0868 0.308 0.520 0.004 0.044 0.124
#> SRR1377170 2 0.7063 -0.0224 0.344 0.484 0.004 0.044 0.124
#> SRR1377171 2 0.7025 0.0309 0.328 0.500 0.004 0.044 0.124
#> SRR1377172 2 0.7025 0.0274 0.328 0.500 0.004 0.044 0.124
#> SRR1377165 2 0.7079 -0.0307 0.352 0.476 0.004 0.044 0.124
#> SRR1377166 2 0.7079 -0.0307 0.352 0.476 0.004 0.044 0.124
#> SRR1377167 2 0.7099 -0.0727 0.364 0.464 0.004 0.044 0.124
#> SRR1377168 2 0.7086 -0.0437 0.356 0.472 0.004 0.044 0.124
#> SRR1377173 1 0.2520 0.7204 0.896 0.056 0.000 0.048 0.000
#> SRR1377174 1 0.3033 0.6910 0.864 0.052 0.000 0.084 0.000
#> SRR1377175 1 0.2661 0.7154 0.888 0.056 0.000 0.056 0.000
#> SRR1377176 1 0.2592 0.7181 0.892 0.056 0.000 0.052 0.000
#> SRR1377177 1 0.2359 0.7234 0.904 0.060 0.000 0.036 0.000
#> SRR1377178 1 0.2359 0.7234 0.904 0.060 0.000 0.036 0.000
#> SRR1377179 1 0.2359 0.7234 0.904 0.060 0.000 0.036 0.000
#> SRR1377180 1 0.2359 0.7234 0.904 0.060 0.000 0.036 0.000
#> SRR1377181 1 0.2300 0.7231 0.908 0.052 0.000 0.040 0.000
#> SRR1377182 1 0.2300 0.7231 0.908 0.052 0.000 0.040 0.000
#> SRR1377183 2 0.3093 0.6605 0.008 0.824 0.000 0.168 0.000
#> SRR1377184 1 0.2300 0.7231 0.908 0.052 0.000 0.040 0.000
#> SRR1377185 2 0.3053 0.6643 0.008 0.828 0.000 0.164 0.000
#> SRR1377186 2 0.2971 0.6697 0.008 0.836 0.000 0.156 0.000
#> SRR1377187 1 0.2300 0.7231 0.908 0.052 0.000 0.040 0.000
#> SRR1377188 2 0.2753 0.6825 0.008 0.856 0.000 0.136 0.000
#> SRR1377189 2 0.2389 0.6944 0.004 0.880 0.000 0.116 0.000
#> SRR1377190 2 0.2848 0.6764 0.004 0.840 0.000 0.156 0.000
#> SRR1377191 2 0.3427 0.6207 0.012 0.796 0.000 0.192 0.000
#> SRR1377192 4 0.2732 0.8626 0.000 0.160 0.000 0.840 0.000
#> SRR1377193 4 0.2561 0.8763 0.000 0.144 0.000 0.856 0.000
#> SRR1377194 4 0.2732 0.8641 0.000 0.160 0.000 0.840 0.000
#> SRR1377195 5 0.2329 0.9859 0.000 0.000 0.124 0.000 0.876
#> SRR1377196 5 0.2329 0.9859 0.000 0.000 0.124 0.000 0.876
#> SRR1377197 5 0.2329 0.9859 0.000 0.000 0.124 0.000 0.876
#> SRR1377198 5 0.2921 0.9843 0.000 0.000 0.124 0.020 0.856
#> SRR1377199 5 0.3012 0.9833 0.000 0.000 0.124 0.024 0.852
#> SRR1377200 5 0.3506 0.9680 0.000 0.000 0.132 0.044 0.824
#> SRR1377201 2 0.2773 0.6669 0.000 0.836 0.000 0.164 0.000
#> SRR1377202 2 0.3210 0.6256 0.000 0.788 0.000 0.212 0.000
#> SRR1377203 2 0.2561 0.6842 0.000 0.856 0.000 0.144 0.000
#> SRR1377204 3 0.0404 0.9958 0.000 0.000 0.988 0.012 0.000
#> SRR1377205 3 0.0404 0.9958 0.000 0.000 0.988 0.012 0.000
#> SRR1377206 3 0.0404 0.9958 0.000 0.000 0.988 0.012 0.000
#> SRR1377207 2 0.4862 0.2896 0.032 0.604 0.000 0.364 0.000
#> SRR1377208 2 0.4874 0.2815 0.032 0.600 0.000 0.368 0.000
#> SRR1377209 2 0.4898 0.2732 0.032 0.592 0.000 0.376 0.000
#> SRR1377210 2 0.1908 0.7021 0.000 0.908 0.000 0.092 0.000
#> SRR1377211 2 0.2230 0.6970 0.000 0.884 0.000 0.116 0.000
#> SRR1377212 2 0.2179 0.6985 0.000 0.888 0.000 0.112 0.000
#> SRR1377213 4 0.2774 0.9324 0.012 0.048 0.048 0.892 0.000
#> SRR1377214 4 0.2774 0.9324 0.012 0.048 0.048 0.892 0.000
#> SRR1377215 4 0.2774 0.9324 0.012 0.048 0.048 0.892 0.000
#> SRR1377216 2 0.6597 0.1995 0.320 0.548 0.004 0.044 0.084
#> SRR1377217 2 0.6570 0.2184 0.312 0.556 0.004 0.044 0.084
#> SRR1377218 2 0.6623 0.1783 0.328 0.540 0.004 0.044 0.084
#> SRR1377219 4 0.2701 0.9329 0.012 0.048 0.044 0.896 0.000
#> SRR1377220 4 0.2701 0.9329 0.012 0.048 0.044 0.896 0.000
#> SRR1377221 4 0.2701 0.9329 0.012 0.048 0.044 0.896 0.000
#> SRR1377222 3 0.0290 0.9958 0.000 0.000 0.992 0.008 0.000
#> SRR1377223 3 0.0290 0.9958 0.000 0.000 0.992 0.008 0.000
#> SRR1377224 3 0.0290 0.9958 0.000 0.000 0.992 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 6 0.1552 0.6792 0.036 0.000 0.004 0.020 0.000 0.940
#> SRR1377146 6 0.1503 0.6792 0.032 0.000 0.008 0.016 0.000 0.944
#> SRR1377147 6 0.2036 0.6719 0.048 0.000 0.008 0.028 0.000 0.916
#> SRR1377148 6 0.1461 0.6784 0.044 0.000 0.000 0.016 0.000 0.940
#> SRR1377153 6 0.0837 0.6825 0.020 0.000 0.004 0.004 0.000 0.972
#> SRR1377154 6 0.1080 0.6819 0.032 0.000 0.004 0.004 0.000 0.960
#> SRR1377155 6 0.0951 0.6838 0.020 0.000 0.004 0.008 0.000 0.968
#> SRR1377156 6 0.0692 0.6821 0.020 0.000 0.004 0.000 0.000 0.976
#> SRR1377149 6 0.1421 0.6828 0.028 0.000 0.000 0.028 0.000 0.944
#> SRR1377150 6 0.1401 0.6830 0.028 0.000 0.004 0.020 0.000 0.948
#> SRR1377151 6 0.1341 0.6843 0.028 0.000 0.000 0.024 0.000 0.948
#> SRR1377152 6 0.1074 0.6827 0.028 0.000 0.000 0.012 0.000 0.960
#> SRR1377157 6 0.6131 -0.4723 0.328 0.000 0.336 0.000 0.000 0.336
#> SRR1377158 6 0.6129 -0.4509 0.320 0.000 0.336 0.000 0.000 0.344
#> SRR1377159 3 0.6131 0.3517 0.328 0.000 0.336 0.000 0.000 0.336
#> SRR1377160 3 0.6252 0.3530 0.328 0.000 0.336 0.004 0.000 0.332
#> SRR1377161 3 0.6131 0.3517 0.328 0.000 0.336 0.000 0.000 0.336
#> SRR1377162 6 0.6127 -0.4336 0.316 0.000 0.336 0.000 0.000 0.348
#> SRR1377163 6 0.6118 -0.4016 0.304 0.000 0.336 0.000 0.000 0.360
#> SRR1377164 3 0.6131 0.3517 0.328 0.000 0.336 0.000 0.000 0.336
#> SRR1377169 6 0.6226 0.1178 0.268 0.000 0.208 0.024 0.000 0.500
#> SRR1377170 6 0.6209 -0.0752 0.348 0.000 0.212 0.012 0.000 0.428
#> SRR1377171 6 0.6374 -0.0133 0.316 0.000 0.212 0.024 0.000 0.448
#> SRR1377172 6 0.6389 -0.0373 0.324 0.000 0.212 0.024 0.000 0.440
#> SRR1377165 6 0.6053 -0.0905 0.356 0.000 0.216 0.004 0.000 0.424
#> SRR1377166 6 0.6140 -0.0808 0.348 0.000 0.216 0.008 0.000 0.428
#> SRR1377167 6 0.5934 -0.1083 0.364 0.000 0.216 0.000 0.000 0.420
#> SRR1377168 6 0.5931 -0.0970 0.360 0.000 0.216 0.000 0.000 0.424
#> SRR1377173 1 0.1204 0.9907 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1377174 1 0.1471 0.9756 0.932 0.000 0.000 0.004 0.000 0.064
#> SRR1377175 1 0.1204 0.9907 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1377176 1 0.1204 0.9907 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1377177 1 0.1141 0.9924 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1377178 1 0.1141 0.9924 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1377179 1 0.1141 0.9924 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1377180 1 0.1141 0.9924 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1377181 1 0.1141 0.9924 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR1377182 1 0.1285 0.9846 0.944 0.000 0.000 0.004 0.000 0.052
#> SRR1377183 6 0.1845 0.6677 0.008 0.000 0.004 0.072 0.000 0.916
#> SRR1377184 1 0.1285 0.9903 0.944 0.000 0.000 0.004 0.000 0.052
#> SRR1377185 6 0.1787 0.6691 0.008 0.000 0.004 0.068 0.000 0.920
#> SRR1377186 6 0.1787 0.6691 0.008 0.000 0.004 0.068 0.000 0.920
#> SRR1377187 1 0.1265 0.9828 0.948 0.000 0.000 0.008 0.000 0.044
#> SRR1377188 6 0.1787 0.6691 0.008 0.000 0.004 0.068 0.000 0.920
#> SRR1377189 6 0.1204 0.6804 0.000 0.000 0.000 0.056 0.000 0.944
#> SRR1377190 6 0.1444 0.6743 0.000 0.000 0.000 0.072 0.000 0.928
#> SRR1377191 6 0.1757 0.6810 0.012 0.000 0.008 0.052 0.000 0.928
#> SRR1377192 4 0.2454 0.8448 0.000 0.000 0.000 0.840 0.000 0.160
#> SRR1377193 4 0.2416 0.8487 0.000 0.000 0.000 0.844 0.000 0.156
#> SRR1377194 4 0.2454 0.8442 0.000 0.000 0.000 0.840 0.000 0.160
#> SRR1377195 5 0.0000 0.9199 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377196 5 0.0000 0.9199 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377197 5 0.0000 0.9199 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1377198 5 0.3797 0.7543 0.000 0.000 0.292 0.016 0.692 0.000
#> SRR1377199 3 0.4263 -0.6634 0.000 0.000 0.600 0.024 0.376 0.000
#> SRR1377200 3 0.4224 -0.6464 0.000 0.000 0.632 0.028 0.340 0.000
#> SRR1377201 6 0.1387 0.6757 0.000 0.000 0.000 0.068 0.000 0.932
#> SRR1377202 6 0.1610 0.6704 0.000 0.000 0.000 0.084 0.000 0.916
#> SRR1377203 6 0.1444 0.6746 0.000 0.000 0.000 0.072 0.000 0.928
#> SRR1377204 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377205 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377206 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377207 6 0.2058 0.6681 0.012 0.000 0.008 0.072 0.000 0.908
#> SRR1377208 6 0.2114 0.6664 0.012 0.000 0.008 0.076 0.000 0.904
#> SRR1377209 6 0.2418 0.6604 0.016 0.000 0.008 0.092 0.000 0.884
#> SRR1377210 6 0.1219 0.6800 0.000 0.000 0.004 0.048 0.000 0.948
#> SRR1377211 6 0.1219 0.6800 0.000 0.000 0.004 0.048 0.000 0.948
#> SRR1377212 6 0.1219 0.6800 0.000 0.000 0.004 0.048 0.000 0.948
#> SRR1377213 4 0.1003 0.9121 0.004 0.004 0.000 0.964 0.000 0.028
#> SRR1377214 4 0.1003 0.9121 0.004 0.004 0.000 0.964 0.000 0.028
#> SRR1377215 4 0.1003 0.9121 0.004 0.004 0.000 0.964 0.000 0.028
#> SRR1377216 6 0.5845 0.1538 0.352 0.000 0.156 0.008 0.000 0.484
#> SRR1377217 6 0.5813 0.1887 0.336 0.000 0.156 0.008 0.000 0.500
#> SRR1377218 6 0.5852 0.1442 0.356 0.000 0.156 0.008 0.000 0.480
#> SRR1377219 4 0.1226 0.9187 0.004 0.004 0.000 0.952 0.000 0.040
#> SRR1377220 4 0.1226 0.9187 0.004 0.004 0.000 0.952 0.000 0.040
#> SRR1377221 4 0.1226 0.9187 0.004 0.004 0.000 0.952 0.000 0.040
#> SRR1377222 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377223 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1377224 2 0.0000 1.0000 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 13890 rows and 80 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.948 0.903 0.959 0.103 0.904 0.904
#> 3 3 0.455 0.793 0.884 1.137 0.951 0.945
#> 4 4 0.353 0.664 0.811 0.621 0.763 0.724
#> 5 5 0.274 0.614 0.792 0.370 0.931 0.891
#> 6 6 0.306 0.608 0.757 0.308 0.758 0.580
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
#> SRR1377145 2 0.0000 0.963 0.000 1.000
#> SRR1377146 2 0.0000 0.963 0.000 1.000
#> SRR1377147 2 0.0000 0.963 0.000 1.000
#> SRR1377148 2 0.0000 0.963 0.000 1.000
#> SRR1377153 2 0.0000 0.963 0.000 1.000
#> SRR1377154 2 0.0000 0.963 0.000 1.000
#> SRR1377155 2 0.0000 0.963 0.000 1.000
#> SRR1377156 2 0.0376 0.961 0.004 0.996
#> SRR1377149 2 0.0938 0.957 0.012 0.988
#> SRR1377150 2 0.0672 0.960 0.008 0.992
#> SRR1377151 2 0.0000 0.963 0.000 1.000
#> SRR1377152 2 0.0376 0.961 0.004 0.996
#> SRR1377157 2 0.0376 0.960 0.004 0.996
#> SRR1377158 2 0.0376 0.960 0.004 0.996
#> SRR1377159 2 0.0376 0.960 0.004 0.996
#> SRR1377160 2 0.0376 0.960 0.004 0.996
#> SRR1377161 2 0.0376 0.960 0.004 0.996
#> SRR1377162 2 0.0376 0.960 0.004 0.996
#> SRR1377163 2 0.0000 0.963 0.000 1.000
#> SRR1377164 2 0.0376 0.960 0.004 0.996
#> SRR1377169 2 0.0000 0.963 0.000 1.000
#> SRR1377170 2 0.0000 0.963 0.000 1.000
#> SRR1377171 2 0.0000 0.963 0.000 1.000
#> SRR1377172 2 0.0000 0.963 0.000 1.000
#> SRR1377165 2 0.0000 0.963 0.000 1.000
#> SRR1377166 2 0.0000 0.963 0.000 1.000
#> SRR1377167 2 0.0000 0.963 0.000 1.000
#> SRR1377168 2 0.0000 0.963 0.000 1.000
#> SRR1377173 2 0.0000 0.963 0.000 1.000
#> SRR1377174 2 0.0000 0.963 0.000 1.000
#> SRR1377175 2 0.0000 0.963 0.000 1.000
#> SRR1377176 2 0.0000 0.963 0.000 1.000
#> SRR1377177 2 0.0000 0.963 0.000 1.000
#> SRR1377178 2 0.0000 0.963 0.000 1.000
#> SRR1377179 2 0.0000 0.963 0.000 1.000
#> SRR1377180 2 0.0000 0.963 0.000 1.000
#> SRR1377181 2 0.0000 0.963 0.000 1.000
#> SRR1377182 2 0.0376 0.960 0.004 0.996
#> SRR1377183 2 0.0672 0.960 0.008 0.992
#> SRR1377184 2 0.0000 0.963 0.000 1.000
#> SRR1377185 2 0.0376 0.961 0.004 0.996
#> SRR1377186 2 0.0376 0.961 0.004 0.996
#> SRR1377187 2 0.0376 0.960 0.004 0.996
#> SRR1377188 2 0.0672 0.960 0.008 0.992
#> SRR1377189 2 0.0938 0.957 0.012 0.988
#> SRR1377190 2 0.0672 0.960 0.008 0.992
#> SRR1377191 2 0.0672 0.960 0.008 0.992
#> SRR1377192 2 0.1414 0.951 0.020 0.980
#> SRR1377193 2 0.1633 0.947 0.024 0.976
#> SRR1377194 2 0.1633 0.947 0.024 0.976
#> SRR1377195 2 0.9998 -0.644 0.492 0.508
#> SRR1377196 2 0.9988 -0.599 0.480 0.520
#> SRR1377197 1 0.9954 0.686 0.540 0.460
#> SRR1377198 1 0.8813 0.799 0.700 0.300
#> SRR1377199 1 0.8443 0.787 0.728 0.272
#> SRR1377200 1 0.9608 0.768 0.616 0.384
#> SRR1377201 2 0.1184 0.954 0.016 0.984
#> SRR1377202 2 0.1184 0.954 0.016 0.984
#> SRR1377203 2 0.1184 0.954 0.016 0.984
#> SRR1377204 2 0.4022 0.871 0.080 0.920
#> SRR1377205 2 0.4161 0.865 0.084 0.916
#> SRR1377206 2 0.4022 0.871 0.080 0.920
#> SRR1377207 2 0.0672 0.960 0.008 0.992
#> SRR1377208 2 0.0376 0.961 0.004 0.996
#> SRR1377209 2 0.0672 0.960 0.008 0.992
#> SRR1377210 2 0.0000 0.963 0.000 1.000
#> SRR1377211 2 0.0000 0.963 0.000 1.000
#> SRR1377212 2 0.0000 0.963 0.000 1.000
#> SRR1377213 2 0.1633 0.947 0.024 0.976
#> SRR1377214 2 0.1633 0.947 0.024 0.976
#> SRR1377215 2 0.1633 0.947 0.024 0.976
#> SRR1377216 2 0.0000 0.963 0.000 1.000
#> SRR1377217 2 0.0000 0.963 0.000 1.000
#> SRR1377218 2 0.0000 0.963 0.000 1.000
#> SRR1377219 2 0.1414 0.951 0.020 0.980
#> SRR1377220 2 0.1184 0.954 0.016 0.984
#> SRR1377221 2 0.1414 0.951 0.020 0.980
#> SRR1377222 2 0.4298 0.859 0.088 0.912
#> SRR1377223 2 0.4298 0.859 0.088 0.912
#> SRR1377224 2 0.4298 0.859 0.088 0.912
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1377145 2 0.0000 0.892 0.000 1.000 0.000
#> SRR1377146 2 0.0000 0.892 0.000 1.000 0.000
#> SRR1377147 2 0.0237 0.892 0.000 0.996 0.004
#> SRR1377148 2 0.0237 0.892 0.000 0.996 0.004
#> SRR1377153 2 0.0747 0.891 0.000 0.984 0.016
#> SRR1377154 2 0.0000 0.892 0.000 1.000 0.000
#> SRR1377155 2 0.0424 0.891 0.000 0.992 0.008
#> SRR1377156 2 0.1163 0.890 0.000 0.972 0.028
#> SRR1377149 2 0.1163 0.890 0.000 0.972 0.028
#> SRR1377150 2 0.0892 0.891 0.000 0.980 0.020
#> SRR1377151 2 0.0237 0.892 0.000 0.996 0.004
#> SRR1377152 2 0.1031 0.891 0.000 0.976 0.024
#> SRR1377157 2 0.3349 0.851 0.004 0.888 0.108
#> SRR1377158 2 0.3771 0.840 0.012 0.876 0.112
#> SRR1377159 2 0.3845 0.836 0.012 0.872 0.116
#> SRR1377160 2 0.3682 0.840 0.008 0.876 0.116
#> SRR1377161 2 0.3272 0.854 0.004 0.892 0.104
#> SRR1377162 2 0.3272 0.854 0.004 0.892 0.104
#> SRR1377163 2 0.3112 0.861 0.004 0.900 0.096
#> SRR1377164 2 0.3349 0.851 0.004 0.888 0.108
#> SRR1377169 2 0.1529 0.887 0.000 0.960 0.040
#> SRR1377170 2 0.1643 0.887 0.000 0.956 0.044
#> SRR1377171 2 0.1643 0.887 0.000 0.956 0.044
#> SRR1377172 2 0.1289 0.889 0.000 0.968 0.032
#> SRR1377165 2 0.2448 0.875 0.000 0.924 0.076
#> SRR1377166 2 0.2066 0.883 0.000 0.940 0.060
#> SRR1377167 2 0.2448 0.875 0.000 0.924 0.076
#> SRR1377168 2 0.2356 0.877 0.000 0.928 0.072
#> SRR1377173 2 0.1529 0.887 0.000 0.960 0.040
#> SRR1377174 2 0.2165 0.881 0.000 0.936 0.064
#> SRR1377175 2 0.2261 0.879 0.000 0.932 0.068
#> SRR1377176 2 0.1964 0.884 0.000 0.944 0.056
#> SRR1377177 2 0.2711 0.868 0.000 0.912 0.088
#> SRR1377178 2 0.2625 0.871 0.000 0.916 0.084
#> SRR1377179 2 0.2796 0.866 0.000 0.908 0.092
#> SRR1377180 2 0.2860 0.869 0.004 0.912 0.084
#> SRR1377181 2 0.2625 0.871 0.000 0.916 0.084
#> SRR1377182 2 0.2796 0.866 0.000 0.908 0.092
#> SRR1377183 2 0.1753 0.885 0.000 0.952 0.048
#> SRR1377184 2 0.2796 0.866 0.000 0.908 0.092
#> SRR1377185 2 0.1964 0.882 0.000 0.944 0.056
#> SRR1377186 2 0.2261 0.877 0.000 0.932 0.068
#> SRR1377187 2 0.2711 0.869 0.000 0.912 0.088
#> SRR1377188 2 0.2261 0.877 0.000 0.932 0.068
#> SRR1377189 2 0.2625 0.869 0.000 0.916 0.084
#> SRR1377190 2 0.2537 0.871 0.000 0.920 0.080
#> SRR1377191 2 0.1753 0.885 0.000 0.952 0.048
#> SRR1377192 2 0.3686 0.826 0.000 0.860 0.140
#> SRR1377193 2 0.3752 0.823 0.000 0.856 0.144
#> SRR1377194 2 0.3686 0.826 0.000 0.860 0.140
#> SRR1377195 1 0.9258 0.217 0.528 0.216 0.256
#> SRR1377196 3 0.9982 -0.278 0.344 0.304 0.352
#> SRR1377197 1 0.9653 0.174 0.448 0.224 0.328
#> SRR1377198 1 0.8918 0.230 0.548 0.156 0.296
#> SRR1377199 1 0.7843 0.181 0.664 0.128 0.208
#> SRR1377200 3 0.7717 -0.336 0.220 0.112 0.668
#> SRR1377201 2 0.2625 0.869 0.000 0.916 0.084
#> SRR1377202 2 0.2796 0.864 0.000 0.908 0.092
#> SRR1377203 2 0.2796 0.864 0.000 0.908 0.092
#> SRR1377204 2 0.5058 0.693 0.000 0.756 0.244
#> SRR1377205 2 0.5058 0.693 0.000 0.756 0.244
#> SRR1377206 2 0.5058 0.693 0.000 0.756 0.244
#> SRR1377207 2 0.1753 0.885 0.000 0.952 0.048
#> SRR1377208 2 0.1411 0.888 0.000 0.964 0.036
#> SRR1377209 2 0.1643 0.886 0.000 0.956 0.044
#> SRR1377210 2 0.1860 0.884 0.000 0.948 0.052
#> SRR1377211 2 0.1860 0.884 0.000 0.948 0.052
#> SRR1377212 2 0.2066 0.881 0.000 0.940 0.060
#> SRR1377213 2 0.4178 0.793 0.000 0.828 0.172
#> SRR1377214 2 0.4121 0.798 0.000 0.832 0.168
#> SRR1377215 2 0.4178 0.793 0.000 0.828 0.172
#> SRR1377216 2 0.1964 0.884 0.000 0.944 0.056
#> SRR1377217 2 0.1964 0.884 0.000 0.944 0.056
#> SRR1377218 2 0.2165 0.881 0.000 0.936 0.064
#> SRR1377219 2 0.3412 0.841 0.000 0.876 0.124
#> SRR1377220 2 0.3412 0.840 0.000 0.876 0.124
#> SRR1377221 2 0.3340 0.844 0.000 0.880 0.120
#> SRR1377222 2 0.5138 0.679 0.000 0.748 0.252
#> SRR1377223 2 0.5138 0.679 0.000 0.748 0.252
#> SRR1377224 2 0.5138 0.679 0.000 0.748 0.252
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1377145 2 0.2530 0.7840 0.004 0.896 0.100 0.000
#> SRR1377146 2 0.2466 0.7859 0.004 0.900 0.096 0.000
#> SRR1377147 2 0.2401 0.7871 0.004 0.904 0.092 0.000
#> SRR1377148 2 0.2334 0.7886 0.004 0.908 0.088 0.000
#> SRR1377153 2 0.2888 0.7697 0.004 0.872 0.124 0.000
#> SRR1377154 2 0.2593 0.7821 0.004 0.892 0.104 0.000
#> SRR1377155 2 0.2999 0.7644 0.004 0.864 0.132 0.000
#> SRR1377156 2 0.3306 0.7443 0.004 0.840 0.156 0.000
#> SRR1377149 2 0.3052 0.7613 0.004 0.860 0.136 0.000
#> SRR1377150 2 0.3105 0.7594 0.004 0.856 0.140 0.000
#> SRR1377151 2 0.2593 0.7818 0.004 0.892 0.104 0.000
#> SRR1377152 2 0.2831 0.7723 0.004 0.876 0.120 0.000
#> SRR1377157 2 0.2553 0.7331 0.016 0.916 0.060 0.008
#> SRR1377158 2 0.2761 0.7230 0.016 0.908 0.064 0.012
#> SRR1377159 2 0.3321 0.6951 0.024 0.888 0.064 0.024
#> SRR1377160 2 0.2995 0.7123 0.016 0.900 0.064 0.020
#> SRR1377161 2 0.2076 0.7517 0.004 0.932 0.056 0.008
#> SRR1377162 2 0.1847 0.7602 0.004 0.940 0.052 0.004
#> SRR1377163 2 0.1576 0.7683 0.004 0.948 0.048 0.000
#> SRR1377164 2 0.2076 0.7517 0.004 0.932 0.056 0.008
#> SRR1377169 2 0.0921 0.8016 0.000 0.972 0.028 0.000
#> SRR1377170 2 0.0469 0.8021 0.000 0.988 0.012 0.000
#> SRR1377171 2 0.0817 0.8019 0.000 0.976 0.024 0.000
#> SRR1377172 2 0.0817 0.8027 0.000 0.976 0.024 0.000
#> SRR1377165 2 0.0469 0.7963 0.000 0.988 0.012 0.000
#> SRR1377166 2 0.0000 0.8005 0.000 1.000 0.000 0.000
#> SRR1377167 2 0.0592 0.7940 0.000 0.984 0.016 0.000
#> SRR1377168 2 0.0336 0.7980 0.000 0.992 0.008 0.000
#> SRR1377173 2 0.0592 0.8007 0.000 0.984 0.016 0.000
#> SRR1377174 2 0.0817 0.8024 0.000 0.976 0.024 0.000
#> SRR1377175 2 0.0469 0.7993 0.000 0.988 0.012 0.000
#> SRR1377176 2 0.0592 0.8007 0.000 0.984 0.016 0.000
#> SRR1377177 2 0.1004 0.7876 0.000 0.972 0.024 0.004
#> SRR1377178 2 0.1004 0.7928 0.000 0.972 0.024 0.004
#> SRR1377179 2 0.1151 0.7850 0.000 0.968 0.024 0.008
#> SRR1377180 2 0.1042 0.7878 0.000 0.972 0.020 0.008
#> SRR1377181 2 0.0895 0.7900 0.000 0.976 0.020 0.004
#> SRR1377182 2 0.1256 0.7821 0.000 0.964 0.028 0.008
#> SRR1377183 2 0.3751 0.6979 0.004 0.800 0.196 0.000
#> SRR1377184 2 0.1452 0.7759 0.000 0.956 0.036 0.008
#> SRR1377185 2 0.3710 0.7034 0.004 0.804 0.192 0.000
#> SRR1377186 2 0.4053 0.6427 0.004 0.768 0.228 0.000
#> SRR1377187 2 0.1022 0.7853 0.000 0.968 0.032 0.000
#> SRR1377188 2 0.4018 0.6505 0.004 0.772 0.224 0.000
#> SRR1377189 2 0.4343 0.5578 0.004 0.732 0.264 0.000
#> SRR1377190 2 0.4155 0.6166 0.004 0.756 0.240 0.000
#> SRR1377191 2 0.3831 0.6859 0.004 0.792 0.204 0.000
#> SRR1377192 3 0.5080 0.7863 0.004 0.420 0.576 0.000
#> SRR1377193 3 0.5080 0.7864 0.004 0.420 0.576 0.000
#> SRR1377194 3 0.5050 0.8115 0.004 0.408 0.588 0.000
#> SRR1377195 1 0.9864 0.1769 0.308 0.236 0.180 0.276
#> SRR1377196 4 0.9991 -0.3706 0.256 0.240 0.236 0.268
#> SRR1377197 1 0.9866 0.1835 0.320 0.248 0.184 0.248
#> SRR1377198 4 0.5442 0.0753 0.128 0.016 0.092 0.764
#> SRR1377199 1 0.3505 -0.1855 0.864 0.048 0.088 0.000
#> SRR1377200 4 0.8502 0.0649 0.304 0.024 0.292 0.380
#> SRR1377201 2 0.4188 0.6076 0.004 0.752 0.244 0.000
#> SRR1377202 2 0.4456 0.5129 0.004 0.716 0.280 0.000
#> SRR1377203 2 0.4535 0.4750 0.004 0.704 0.292 0.000
#> SRR1377204 3 0.4356 0.8762 0.000 0.292 0.708 0.000
#> SRR1377205 3 0.4356 0.8762 0.000 0.292 0.708 0.000
#> SRR1377206 3 0.4356 0.8762 0.000 0.292 0.708 0.000
#> SRR1377207 2 0.3626 0.7137 0.004 0.812 0.184 0.000
#> SRR1377208 2 0.3448 0.7316 0.004 0.828 0.168 0.000
#> SRR1377209 2 0.3710 0.7031 0.004 0.804 0.192 0.000
#> SRR1377210 2 0.3710 0.7031 0.004 0.804 0.192 0.000
#> SRR1377211 2 0.3626 0.7134 0.004 0.812 0.184 0.000
#> SRR1377212 2 0.3668 0.7084 0.004 0.808 0.188 0.000
#> SRR1377213 3 0.4905 0.8811 0.000 0.364 0.632 0.004
#> SRR1377214 3 0.4730 0.8791 0.000 0.364 0.636 0.000
#> SRR1377215 3 0.4905 0.8811 0.000 0.364 0.632 0.004
#> SRR1377216 2 0.0469 0.8017 0.000 0.988 0.012 0.000
#> SRR1377217 2 0.0188 0.8013 0.000 0.996 0.004 0.000
#> SRR1377218 2 0.0469 0.7999 0.000 0.988 0.012 0.000
#> SRR1377219 2 0.4989 -0.4335 0.000 0.528 0.472 0.000
#> SRR1377220 2 0.4948 -0.2782 0.000 0.560 0.440 0.000
#> SRR1377221 2 0.4955 -0.2986 0.000 0.556 0.444 0.000
#> SRR1377222 3 0.4535 0.8731 0.000 0.292 0.704 0.004
#> SRR1377223 3 0.4535 0.8731 0.000 0.292 0.704 0.004
#> SRR1377224 3 0.4535 0.8731 0.000 0.292 0.704 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1377145 2 0.4094 0.7272 0.000 0.788 0.084 0.128 0.000
#> SRR1377146 2 0.4054 0.7263 0.000 0.788 0.072 0.140 0.000
#> SRR1377147 2 0.3921 0.7312 0.000 0.800 0.072 0.128 0.000
#> SRR1377148 2 0.4083 0.7273 0.000 0.788 0.080 0.132 0.000
#> SRR1377153 2 0.4869 0.6868 0.000 0.712 0.096 0.192 0.000
#> SRR1377154 2 0.4573 0.7079 0.000 0.744 0.092 0.164 0.000
#> SRR1377155 2 0.4836 0.6888 0.000 0.716 0.096 0.188 0.000
#> SRR1377156 2 0.5024 0.6690 0.000 0.692 0.096 0.212 0.000
#> SRR1377149 2 0.4462 0.6992 0.000 0.740 0.064 0.196 0.000
#> SRR1377150 2 0.4587 0.6943 0.000 0.728 0.068 0.204 0.000
#> SRR1377151 2 0.4197 0.7213 0.000 0.776 0.076 0.148 0.000
#> SRR1377152 2 0.4571 0.7018 0.000 0.736 0.076 0.188 0.000
#> SRR1377157 2 0.2660 0.6797 0.000 0.864 0.128 0.008 0.000
#> SRR1377158 2 0.2583 0.6813 0.000 0.864 0.132 0.004 0.000
#> SRR1377159 2 0.2660 0.6797 0.000 0.864 0.128 0.008 0.000
#> SRR1377160 2 0.2612 0.6830 0.000 0.868 0.124 0.008 0.000
#> SRR1377161 2 0.2439 0.6905 0.000 0.876 0.120 0.004 0.000
#> SRR1377162 2 0.2389 0.6936 0.000 0.880 0.116 0.004 0.000
#> SRR1377163 2 0.2439 0.6905 0.000 0.876 0.120 0.004 0.000
#> SRR1377164 2 0.2439 0.6905 0.000 0.876 0.120 0.004 0.000
#> SRR1377169 2 0.3237 0.7290 0.000 0.848 0.104 0.048 0.000
#> SRR1377170 2 0.3058 0.7320 0.000 0.860 0.096 0.044 0.000
#> SRR1377171 2 0.3164 0.7273 0.000 0.852 0.104 0.044 0.000
#> SRR1377172 2 0.3184 0.7312 0.000 0.852 0.100 0.048 0.000
#> SRR1377165 2 0.2915 0.7105 0.000 0.860 0.116 0.024 0.000
#> SRR1377166 2 0.3085 0.7147 0.000 0.852 0.116 0.032 0.000
#> SRR1377167 2 0.2824 0.7076 0.000 0.864 0.116 0.020 0.000
#> SRR1377168 2 0.3002 0.7128 0.000 0.856 0.116 0.028 0.000
#> SRR1377173 2 0.1012 0.7482 0.000 0.968 0.012 0.020 0.000
#> SRR1377174 2 0.1568 0.7512 0.000 0.944 0.020 0.036 0.000
#> SRR1377175 2 0.1211 0.7491 0.000 0.960 0.016 0.024 0.000
#> SRR1377176 2 0.1310 0.7492 0.000 0.956 0.020 0.024 0.000
#> SRR1377177 2 0.0794 0.7387 0.000 0.972 0.028 0.000 0.000
#> SRR1377178 2 0.0671 0.7441 0.000 0.980 0.016 0.004 0.000
#> SRR1377179 2 0.0609 0.7383 0.000 0.980 0.020 0.000 0.000
#> SRR1377180 2 0.0880 0.7363 0.000 0.968 0.032 0.000 0.000
#> SRR1377181 2 0.0771 0.7418 0.000 0.976 0.020 0.004 0.000
#> SRR1377182 2 0.0794 0.7369 0.000 0.972 0.028 0.000 0.000
#> SRR1377183 2 0.3814 0.6612 0.000 0.720 0.004 0.276 0.000
#> SRR1377184 2 0.1043 0.7345 0.000 0.960 0.040 0.000 0.000
#> SRR1377185 2 0.3838 0.6496 0.000 0.716 0.004 0.280 0.000
#> SRR1377186 2 0.4356 0.5717 0.000 0.648 0.012 0.340 0.000
#> SRR1377187 2 0.0963 0.7350 0.000 0.964 0.036 0.000 0.000
#> SRR1377188 2 0.4165 0.6014 0.000 0.672 0.008 0.320 0.000
#> SRR1377189 2 0.5529 0.3132 0.000 0.512 0.068 0.420 0.000
#> SRR1377190 2 0.5458 0.4259 0.000 0.552 0.068 0.380 0.000
#> SRR1377191 2 0.5199 0.5924 0.000 0.636 0.072 0.292 0.000
#> SRR1377192 4 0.3958 0.6921 0.000 0.176 0.044 0.780 0.000
#> SRR1377193 4 0.3835 0.7097 0.000 0.156 0.048 0.796 0.000
#> SRR1377194 4 0.3681 0.7194 0.000 0.148 0.044 0.808 0.000
#> SRR1377195 1 0.9955 -0.0173 0.220 0.212 0.216 0.156 0.196
#> SRR1377196 1 0.9891 -0.0124 0.236 0.220 0.224 0.136 0.184
#> SRR1377197 3 0.9542 -0.3176 0.224 0.204 0.328 0.104 0.140
#> SRR1377198 5 0.0867 0.0000 0.008 0.008 0.000 0.008 0.976
#> SRR1377199 3 0.5103 -0.3594 0.128 0.000 0.724 0.012 0.136
#> SRR1377200 1 0.1768 -0.3532 0.924 0.000 0.000 0.004 0.072
#> SRR1377201 2 0.5598 0.4223 0.000 0.544 0.080 0.376 0.000
#> SRR1377202 2 0.5689 0.2285 0.000 0.480 0.080 0.440 0.000
#> SRR1377203 2 0.5638 0.2582 0.000 0.492 0.076 0.432 0.000
#> SRR1377204 4 0.0794 0.8017 0.000 0.028 0.000 0.972 0.000
#> SRR1377205 4 0.0794 0.8017 0.000 0.028 0.000 0.972 0.000
#> SRR1377206 4 0.0794 0.8017 0.000 0.028 0.000 0.972 0.000
#> SRR1377207 2 0.5391 0.5744 0.000 0.616 0.084 0.300 0.000
#> SRR1377208 2 0.5275 0.6071 0.000 0.640 0.084 0.276 0.000
#> SRR1377209 2 0.5355 0.5868 0.000 0.624 0.084 0.292 0.000
#> SRR1377210 2 0.5218 0.5859 0.000 0.632 0.072 0.296 0.000
#> SRR1377211 2 0.5083 0.6124 0.000 0.652 0.068 0.280 0.000
#> SRR1377212 2 0.5237 0.5798 0.000 0.628 0.072 0.300 0.000
#> SRR1377213 4 0.1695 0.8017 0.008 0.044 0.008 0.940 0.000
#> SRR1377214 4 0.1618 0.8036 0.008 0.040 0.008 0.944 0.000
#> SRR1377215 4 0.1618 0.8036 0.008 0.040 0.008 0.944 0.000
#> SRR1377216 2 0.2209 0.7444 0.000 0.912 0.056 0.032 0.000
#> SRR1377217 2 0.2278 0.7410 0.000 0.908 0.060 0.032 0.000
#> SRR1377218 2 0.2171 0.7368 0.000 0.912 0.064 0.024 0.000
#> SRR1377219 4 0.4106 0.6059 0.000 0.256 0.020 0.724 0.000
#> SRR1377220 4 0.4464 0.5505 0.000 0.288 0.028 0.684 0.000
#> SRR1377221 4 0.4315 0.5762 0.000 0.276 0.024 0.700 0.000
#> SRR1377222 4 0.1243 0.7986 0.008 0.028 0.004 0.960 0.000
#> SRR1377223 4 0.1243 0.7986 0.008 0.028 0.004 0.960 0.000
#> SRR1377224 4 0.1243 0.7986 0.008 0.028 0.004 0.960 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1377145 1 0.307 0.699 0.856 0.028 0.096 0.008 0.012 0.000
#> SRR1377146 1 0.321 0.696 0.844 0.028 0.108 0.008 0.012 0.000
#> SRR1377147 1 0.321 0.695 0.844 0.028 0.108 0.008 0.012 0.000
#> SRR1377148 1 0.305 0.695 0.848 0.028 0.112 0.004 0.008 0.000
#> SRR1377153 1 0.229 0.679 0.912 0.028 0.020 0.032 0.008 0.000
#> SRR1377154 1 0.204 0.687 0.924 0.028 0.028 0.012 0.008 0.000
#> SRR1377155 1 0.197 0.686 0.928 0.028 0.020 0.016 0.008 0.000
#> SRR1377156 1 0.214 0.683 0.920 0.028 0.020 0.024 0.008 0.000
#> SRR1377149 1 0.338 0.705 0.848 0.028 0.080 0.032 0.012 0.000
#> SRR1377150 1 0.337 0.706 0.844 0.032 0.092 0.020 0.012 0.000
#> SRR1377151 1 0.287 0.704 0.876 0.032 0.068 0.012 0.012 0.000
#> SRR1377152 1 0.308 0.704 0.864 0.032 0.076 0.016 0.012 0.000
#> SRR1377157 3 0.210 0.886 0.112 0.000 0.884 0.004 0.000 0.000
#> SRR1377158 3 0.215 0.890 0.116 0.000 0.880 0.004 0.000 0.000
#> SRR1377159 3 0.235 0.887 0.112 0.004 0.876 0.008 0.000 0.000
#> SRR1377160 3 0.219 0.893 0.120 0.000 0.876 0.004 0.000 0.000
#> SRR1377161 3 0.266 0.897 0.152 0.000 0.840 0.004 0.004 0.000
#> SRR1377162 3 0.270 0.895 0.156 0.000 0.836 0.004 0.004 0.000
#> SRR1377163 3 0.270 0.896 0.156 0.000 0.836 0.004 0.004 0.000
#> SRR1377164 3 0.260 0.892 0.160 0.000 0.836 0.000 0.004 0.000
#> SRR1377169 3 0.378 0.886 0.168 0.008 0.784 0.032 0.008 0.000
#> SRR1377170 3 0.356 0.883 0.172 0.008 0.792 0.024 0.004 0.000
#> SRR1377171 3 0.332 0.889 0.164 0.000 0.804 0.028 0.004 0.000
#> SRR1377172 3 0.359 0.881 0.164 0.004 0.788 0.044 0.000 0.000
#> SRR1377165 3 0.284 0.886 0.116 0.004 0.852 0.028 0.000 0.000
#> SRR1377166 3 0.312 0.880 0.116 0.004 0.836 0.044 0.000 0.000
#> SRR1377167 3 0.262 0.889 0.116 0.000 0.860 0.024 0.000 0.000
#> SRR1377168 3 0.277 0.887 0.116 0.000 0.852 0.032 0.000 0.000
#> SRR1377173 1 0.441 0.494 0.664 0.012 0.300 0.008 0.016 0.000
#> SRR1377174 1 0.438 0.481 0.652 0.012 0.316 0.004 0.016 0.000
#> SRR1377175 1 0.468 0.436 0.624 0.020 0.332 0.004 0.020 0.000
#> SRR1377176 1 0.408 0.564 0.716 0.012 0.252 0.008 0.012 0.000
#> SRR1377177 1 0.435 0.461 0.640 0.012 0.332 0.004 0.012 0.000
#> SRR1377178 1 0.422 0.506 0.672 0.012 0.300 0.004 0.012 0.000
#> SRR1377179 1 0.475 0.450 0.628 0.024 0.324 0.008 0.016 0.000
#> SRR1377180 1 0.466 0.346 0.588 0.016 0.376 0.004 0.016 0.000
#> SRR1377181 1 0.482 0.389 0.608 0.024 0.344 0.008 0.016 0.000
#> SRR1377182 1 0.490 0.334 0.588 0.020 0.364 0.012 0.016 0.000
#> SRR1377183 1 0.618 0.420 0.540 0.016 0.276 0.152 0.016 0.000
#> SRR1377184 1 0.505 0.218 0.548 0.024 0.400 0.012 0.016 0.000
#> SRR1377185 1 0.566 0.537 0.612 0.016 0.244 0.116 0.012 0.000
#> SRR1377186 1 0.618 0.476 0.528 0.016 0.188 0.260 0.008 0.000
#> SRR1377187 1 0.480 0.212 0.552 0.020 0.408 0.004 0.016 0.000
#> SRR1377188 1 0.624 0.474 0.532 0.016 0.196 0.244 0.012 0.000
#> SRR1377189 1 0.417 0.571 0.748 0.036 0.012 0.196 0.008 0.000
#> SRR1377190 1 0.406 0.639 0.784 0.036 0.028 0.144 0.008 0.000
#> SRR1377191 1 0.395 0.673 0.804 0.036 0.040 0.112 0.008 0.000
#> SRR1377192 4 0.475 0.317 0.452 0.024 0.004 0.512 0.008 0.000
#> SRR1377193 4 0.475 0.324 0.448 0.024 0.004 0.516 0.008 0.000
#> SRR1377194 4 0.457 0.367 0.436 0.020 0.004 0.536 0.004 0.000
#> SRR1377195 5 0.987 0.222 0.180 0.180 0.140 0.116 0.232 0.152
#> SRR1377196 5 0.994 0.194 0.136 0.176 0.192 0.132 0.196 0.168
#> SRR1377197 5 0.960 0.217 0.144 0.172 0.156 0.092 0.304 0.132
#> SRR1377198 6 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1377199 5 0.259 -0.137 0.000 0.032 0.000 0.000 0.868 0.100
#> SRR1377200 2 0.278 0.000 0.000 0.860 0.000 0.000 0.052 0.088
#> SRR1377201 1 0.347 0.636 0.816 0.032 0.012 0.136 0.004 0.000
#> SRR1377202 1 0.377 0.598 0.788 0.028 0.012 0.164 0.008 0.000
#> SRR1377203 1 0.374 0.595 0.784 0.028 0.008 0.172 0.008 0.000
#> SRR1377204 4 0.201 0.721 0.104 0.004 0.000 0.892 0.000 0.000
#> SRR1377205 4 0.196 0.722 0.100 0.004 0.000 0.896 0.000 0.000
#> SRR1377206 4 0.205 0.719 0.108 0.004 0.000 0.888 0.000 0.000
#> SRR1377207 1 0.281 0.668 0.880 0.044 0.012 0.056 0.008 0.000
#> SRR1377208 1 0.277 0.675 0.884 0.040 0.016 0.052 0.008 0.000
#> SRR1377209 1 0.274 0.670 0.884 0.040 0.012 0.056 0.008 0.000
#> SRR1377210 1 0.356 0.674 0.824 0.028 0.032 0.112 0.004 0.000
#> SRR1377211 1 0.367 0.687 0.820 0.028 0.044 0.104 0.004 0.000
#> SRR1377212 1 0.360 0.669 0.820 0.032 0.028 0.116 0.004 0.000
#> SRR1377213 4 0.165 0.722 0.040 0.008 0.016 0.936 0.000 0.000
#> SRR1377214 4 0.156 0.725 0.040 0.008 0.012 0.940 0.000 0.000
#> SRR1377215 4 0.165 0.722 0.040 0.008 0.016 0.936 0.000 0.000
#> SRR1377216 3 0.483 0.563 0.352 0.028 0.600 0.008 0.012 0.000
#> SRR1377217 3 0.481 0.587 0.344 0.028 0.608 0.008 0.012 0.000
#> SRR1377218 3 0.479 0.597 0.340 0.028 0.612 0.008 0.012 0.000
#> SRR1377219 4 0.406 0.616 0.076 0.012 0.128 0.780 0.004 0.000
#> SRR1377220 4 0.453 0.560 0.084 0.012 0.168 0.732 0.004 0.000
#> SRR1377221 4 0.446 0.568 0.084 0.012 0.160 0.740 0.004 0.000
#> SRR1377222 4 0.101 0.728 0.044 0.000 0.000 0.956 0.000 0.000
#> SRR1377223 4 0.101 0.728 0.044 0.000 0.000 0.956 0.000 0.000
#> SRR1377224 4 0.101 0.728 0.044 0.000 0.000 0.956 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