cola_rl.RdExample ConsensusPartitionList object
data(cola_rl)Following code was used to generate cola_rl:
set.seed(123)
m = cbind(rbind(matrix(rnorm(20*20, mean = 1, sd = 0.5), nr = 20),
matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20),
matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20)),
rbind(matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20),
matrix(rnorm(20*20, mean = 1, sd = 0.5), nr = 20),
matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20)),
rbind(matrix(rnorm(20*20, mean = 0.5, sd = 0.5), nr = 20),
matrix(rnorm(20*20, mean = 0.5, sd = 0.5), nr = 20),
matrix(rnorm(20*20, mean = 1, sd = 0.5), nr = 20))
) + matrix(rnorm(60*60, sd = 0.5), nr = 60)
cola_rl = run_all_consensus_partition_methods(data = m, cores = 6) data(cola_rl)
cola_rl
#> A 'ConsensusPartitionList' object with 20 methods.
#> On a matrix with 60 rows and 60 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 5000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "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")]