Get signatures rows — get_signatures-HierarchicalPartition-method • cola

Get signatures rows

# S4 method for HierarchicalPartition
get_signatures(object, merge_node = merge_node_param(),
    group_diff = object@param$group_diff,
    row_km = NULL, diff_method = "Ftest", fdr_cutoff = object@param$fdr_cutoff,
    scale_rows = object[1]@scale_rows,
    anno = get_anno(object),
    anno_col = get_anno_col(object),
    show_column_names = FALSE, column_names_gp = gpar(fontsize = 8),
    verbose = TRUE, plot = TRUE, seed = 888,
    ...)

Arguments

object: a HierarchicalPartition-class object.
merge_node: Parameters to merge sub-dendrograms, see merge_node_param.
group_diff: Cutoff for the maximal difference between group means.
row_km: Number of groups for performing k-means clustering on rows. By default it is automatically selected.
diff_method: Methods to get rows which are significantly different between subgroups.
fdr_cutoff: Cutoff for FDR of the difference test between subgroups.
scale_rows: whether apply row scaling when making the heatmap.
anno: a data frame of annotations for the original matrix columns. By default it uses the annotations specified in hierarchical_partition.
anno_col: a list of colors (color is defined as a named vector) for the annotations. If anno is a data frame, anno_col should be a named list where names correspond to the column names in anno.
show_column_names: whether show column names in the heatmap.
column_names_gp: Graphic parameters for column names.
verbose: whether to print messages.
plot: whether to make the plot.
seed: Random seed.
...: other arguments pass to get_signatures,ConsensusPartition-method.

Details

The function calls get_signatures,ConsensusPartition-method to find signatures at each node of the partition hierarchy.

Value

A data frame with more than two columns:

which_row:: row index corresponding to the original matrix.
km:: the k-means groups if row_km is set.
other_columns:: the mean value (depending rows are scaled or not) in each subgroup.

Author

Zuguang Gu <z.gu@dkfz.de>

Examples

# \donttest{
data(golub_cola_rh)
tb = get_signatures(golub_cola_rh)
#> * get signatures at node 0 with 3 subgroups.
#>   * 71/72 samples (in 3 classes) remain after filtering by silhouette (>= 0.5).
#>   * cache hash: 69feee4d94f63bf432b51d8e53ece52c (seed 888).
#>   * calculating row difference between subgroups by Ftest.
#> * get signatures at node 01 with 3 subgroups.
#>   * 34/35 samples (in 3 classes) remain after filtering by silhouette (>= 0.5).
#>   * cache hash: 2998831f64132aeaf6eb44c6e6912575 (seed 888).
#>   * calculating row difference between subgroups by Ftest.
#> * get signatures at node 02 with 2 subgroups.
#>   * 24/24 samples (in 2 classes) remain after filtering by silhouette (>= 0.5).
#>   * cache hash: ea242e8e77a1cc217cae91637d6d95f6 (seed 888).
#>   * calculating row difference between subgroups by Ftest.
#> * split rows into 4 groups by k-means clustering.
#> * found 2350 signatures (57.1%).
#> * randomly sample 2000 rows from 2350 total rows.
#> * making heatmaps for signatures

head(tb)
#>   which_row is_sig_0 is_sig_01 is_sig_02 mean_011 mean_012 mean_013 mean_021
#> 1         2     TRUE     FALSE     FALSE 1.641389 1.845958 1.762456 2.091835
#> 2         3     TRUE     FALSE     FALSE 2.184402 2.206852 2.183741 2.171343
#> 3        11     TRUE     FALSE     FALSE 2.288716 2.333027 2.427159 2.600189
#> 4        12     TRUE     FALSE     FALSE 2.269837 2.357288 2.565022 2.690489
#> 5        13     TRUE     FALSE     FALSE 2.424987 3.011391 2.795872 3.151072
#> 6        14     TRUE     FALSE     FALSE 3.035862 3.445460 3.269206 3.489674
#>   mean_022  mean_03 group_diff scaled_mean_011 scaled_mean_012 scaled_mean_013
#> 1 2.068923 2.003520  0.4504458      -0.8295840      -0.1950825      -0.4540758
#> 2 2.346207 2.386800  0.2154572      -0.2994006      -0.1924667      -0.3025460
#> 3 2.445832 2.366862  0.3114738      -0.6207227      -0.3980173       0.0750845
#> 4 2.382775 2.265742  0.4247472      -0.6046877      -0.2598623       0.5592590
#> 5 2.473630 3.416868  0.9918814      -0.6637468       0.1468631      -0.1510570
#> 6 3.232134 3.622108  0.5862460      -0.8241242       0.2069714      -0.2367195
#>   scaled_mean_021 scaled_mean_022 scaled_mean_03 group_diff_scaled km
#> 1       0.5675420       0.4964768      0.2936185          1.397126  2
#> 2      -0.3616000       0.4712822      0.6646289          1.026229  2
#> 3       0.9447291       0.1689359     -0.2279637          1.565452  1
#> 4       1.0539864      -0.1593632     -0.6208386          1.674825  1
#> 5       0.3399502      -0.5965052      0.7073713          1.371118  2
#> 6       0.3182753      -0.3300409      0.6516556          1.475780  2
# }