gesecaSimple: Runs simple variant for performing gene sets co-regulation...

In ctlab/fgsea: Fast Gene Set Enrichment Analysis

Runs simple variant for performing gene sets co-regulation analysis

Description

This function is based on the rude Monte Carlo sampling approach and P-value calculation accuracy is limited to '1 / nperm' value.

Usage

gesecaSimple(
  pathways,
  E,
  minSize = 1,
  maxSize = nrow(E) - 1,
  center = TRUE,
  scale = FALSE,
  nperm = 1000,
  nproc = 0,
  BPPARAM = NULL
)

Arguments

pathways	List of gene sets to check.
E	expression matrix, rows corresponds to genes, columns corresponds to samples.
minSize	Minimal size of a gene set to test. All pathways below the threshold are excluded.
maxSize	Maximal size of a gene set to test. All pathways above the threshold are excluded.
center	a logical value indicating whether the gene expression should be centered to have zero mean before the analysis takes place. The default is TRUE. The value is passed to scale.
scale	a logical value indicating whether the gene expression should be scaled to have unit variance before the analysis takes place. The default is FALSE. The value is passed to scale.
nperm	Number of permutations to do. Minimal possible nominal p-value is about 1/nperm
nproc	If not equal to zero sets BPPARAM to use nproc workers (default = 0).
BPPARAM	Parallelization parameter used in bplapply.

Value

A table with GESECA results. Each row corresponds to a tested pathway. The columns are the following

• pathway – name of the pathway as in 'names(pathways)';

• pctVar – percent of explained variance along gene set;

• pval – P-value that corresponds to the gene set score;

• padj – a BH-adjusted p-value;

• size – size of the pathway after removing genes not present in 'rownames(E)'.

Examples

data("exampleExpressionMatrix")
data("examplePathways")
gesecaRes <- gesecaSimple(examplePathways, exampleExpressionMatrix, minSize=15, maxSize=500)

ctlab/fgsea documentation built on April 23, 2024, 2:54 p.m.