golub_cola_ds.RdExample DownSamplingConsensusPartition object from Golub dataset
data(golub_cola_ds)Following code was used to generate golub_cola_ds:
library(cola)
data(golub_cola)
m = get_matrix(golub_cola)
set.seed(123)
golub_cola_ds = consensus_partition_by_down_sampling(
m, subset = 50, cores = 6,
anno = get_anno(golub_cola),
anno_col = get_anno_col(golub_cola),
) data(golub_cola_ds)
golub_cola_ds
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 4116 rows and 50 columns, randomly sampled from 72 columns.
#> Top rows (412) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes"
#> [3] "collect_plots" "collect_stats"
#> [5] "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap"
#> [9] "dimension_reduction" "functional_enrichment"
#> [11] "get_anno" "get_anno_col"
#> [13] "get_classes" "get_consensus"
#> [15] "get_matrix" "get_membership"
#> [17] "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k"
#> [21] "is_stable_k" "membership_heatmap"
#> [23] "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes"
#> [27] "rownames" "select_partition_number"
#> [29] "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"