golub_cola.RdExample ConsensusPartitionList object from Golub dataset
data(golub_cola)Following code was used to generate golub_cola:
library(cola)
library(golubEsets) # from bioc
data(Golub_Merge)
m = exprs(Golub_Merge)
colnames(m) = paste0("sample_", colnames(m))
anno = pData(Golub_Merge)
m[m <= 1] = NA
m = log10(m)
m = adjust_matrix(m)
library(preprocessCore) # from bioc
cn = colnames(m)
rn = rownames(m)
m = normalize.quantiles(m)
colnames(m) = cn
rownames(m) = rn
set.seed(123)
golub_cola = run_all_consensus_partition_methods(
m, cores = 6,
anno = anno[, c("ALL.AML"), drop = FALSE],
anno_col = c("ALL" = "red", "AML" = "blue")
) data(golub_cola)
golub_cola
#> A 'ConsensusPartitionList' object with 20 methods.
#> On a matrix with 4116 rows and 72 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")]