golub_cola_rh.RdExample HierarchicalPartition object from Golub dataset
data(golub_cola_rh)Following code was used to generate golub_cola_rh:
library(cola)
data(golub_cola)
m = get_matrix(golub_cola)
set.seed(123)
golub_cola_rh = hierarchical_partition(
m, cores = 6,
anno = get_anno(golub_cola),
anno_col = get_anno_col(golub_cola)
) data(golub_cola_rh)
golub_cola_rh
#> A 'HierarchicalPartition' object with 'ATC:skmeans' method.
#> On a matrix with 4116 rows and 72 columns.
#> Performed in total 1350 partitions.
#> There are 6 groups under the following parameters:
#> - min_samples: 6
#> - mean_silhouette_cutoff: 0.9
#> - min_n_signatures: 103 (signatures are selected based on:)
#> - fdr_cutoff: 0.05
#> - group_diff (scaled values): 0.5
#>
#> Hierarchy of the partition:
#> 0, 72 cols, 2068 signatures
#> |-- 01, 35 cols, 652 signatures
#> | |-- 011, 11 cols (b)
#> | |-- 012, 13 cols, 5 signatures (c)
#> | `-- 013, 11 cols (b)
#> |-- 02, 24 cols, 138 signatures
#> | |-- 021, 13 cols (a)
#> | `-- 022, 11 cols (b)
#> `-- 03, 13 cols, 7 signatures (c)
#> Stop reason:
#> a) Mean silhouette score was too small
#> b) Subgroup had too few columns.
#> c) There were too few signatures.
#>
#> Following methods can be applied to this 'HierarchicalPartition' object:
#> [1] "all_leaves" "all_nodes" "cola_report"
#> [4] "collect_classes" "colnames" "compare_signatures"
#> [7] "dimension_reduction" "functional_enrichment" "get_anno"
#> [10] "get_anno_col" "get_children_nodes" "get_classes"
#> [13] "get_matrix" "get_signatures" "is_leaf_node"
#> [16] "max_depth" "merge_node" "ncol"
#> [19] "node_info" "node_level" "nrow"
#> [22] "rownames" "show" "split_node"
#> [25] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [28] "top_rows_overlap"
#>
#> You can get result for a single node by e.g. object["01"]