fgseaCellID: reworked fgsea for ram and speed efficiency in CellID

In cbl-imagine/cellID: Unbiased Extraction of Single Cell Identity using Multiple Correspondence Analysis

Description

reworked fgsea for ram and speed efficiency in CellID

Usage

fgseaCellID(pathways, stats, nperm = 1000, minSize = 10,
  maxSize = 500, gseaParam = 0)

Arguments

pathways	List of gene sets to check
stats	Named vector of gene-level stats. Names should be the same as in 'pathways'
nperm	Number of permutations to do. Minimial possible nominal p-value is about 1/nperm
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.
gseaParam	GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores

Value

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

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

• pval – an enrichment p-value;

• padj – a BH-adjusted p-value;

• ES – enrichment score, same as in Broad GSEA implementation;

• NES – enrichment score normalized to mean enrichment of random samples of the same size;

• nMoreExtreme' – a number of times a random gene set had a more extreme enrichment score value;

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

• leadingEdge – vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading.

cbl-imagine/cellID documentation built on July 24, 2020, 9:35 p.m.