Compute BEAMR p-values for sets

Usage

compute_set_pvalues(
  beam.stats,
  peel = FALSE,
  z = TRUE,
  alpha = 0.1,
  mess.freq = 25
)

Arguments

beam.stats: A beam.stats object from compute_beam_stats function
peel: Logical indicating whether to peel in p-value calculation
z: Logical indicating whether to z-scale each vector of one coefficient estimate across bootstraps before analysis
alpha: Maximum depth to peel (reduces computing time); default 0.1.
mess.freq: Message frequency; default 25.

Value

A list with a data.frame of set p-values from BEAMR analysis, a data.frame of summary row p-values, and a data frame of set matching.

Examples

data(beam_stats_sm)
test.pvals <- compute_set_pvalues(beam.stats=beam_stats_sm)
#> Preparing bootstrap results for calculating feature set p-values: Tue Jul 30 13:51:19 2024
#> Finding stats for each data matrix:Tue Jul 30 13:51:19 2024
#>   Finding stats for data matrix Lesion: Tue Jul 30 13:51:19 2024
#>    Finding features with with Lesion.MRD29 stats: Tue Jul 30 13:51:19 2024
#>   Finding stats for data matrix RNA: Tue Jul 30 13:51:19 2024
#>    Finding features with with RNA.MRD29 stats: Tue Jul 30 13:51:19 2024
#> Found 40 rows of stats: Tue Jul 30 13:51:19 2024
#> Merging stats with feature-sets: Tue Jul 30 13:51:19 2024
#> Merged feature-set stat rows: 40
#> Ordering and indexing feature sets: Tue Jul 30 13:51:19 2024
#>   Cleaning up beam.stat matrices:Tue Jul 30 13:51:19 2024
#>    Working on matrix 1 of 2: Tue Jul 30 13:51:19 2024
#>    Working on matrix 2 of 2: Tue Jul 30 13:51:19 2024
#> Computing p-value for feature set 1 of 34: Tue Jul 30 13:51:19 2024
#> 11ENSG00000081760
#> Computing p-value for feature set 26 of 34: Tue Jul 30 13:51:19 2024
#> 3232ENSG00000229835
#> Finished computing p-values at: Tue Jul 30 13:51:19 2024
#> Minimum q-value is 0.0608278029898599
#> Creating set p-value data frame.
#> Done creating data frame.
#> Creating list for output.