calcGseaStat: Calculates GSEA statistics for a given query gene set

In ctlab/fgsea: Fast Gene Set Enrichment Analysis

Calculates GSEA statistics for a given query gene set

Description

Takes O(k log k) time, where k is a size of 'selectedSize'.

Usage

calcGseaStat(
  stats,
  selectedStats,
  gseaParam = 1,
  returnAllExtremes = FALSE,
  returnLeadingEdge = FALSE,
  scoreType = c("std", "pos", "neg")
)

Arguments

stats	Named numeric vector with gene-level statistics sorted in decreasing order (order is not checked).
selectedStats	Indexes of selected genes in the 'stats' array.
gseaParam	GSEA weight parameter (0 is unweighted, suggested value is 1).
returnAllExtremes	If TRUE return not only the most extreme point, but all of them. Can be used for enrichment plot
returnLeadingEdge	If TRUE return also leading edge genes.
scoreType	This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg")

Value

Value of GSEA statistic if both returnAllExtremes and returnLeadingEdge are FALSE. Otherwise returns list with the folowing elements:

• res – value of GSEA statistic

• tops – vector of top peak values of cumulative enrichment statistic for each gene;

• bottoms – vector of bottom peak values of cumulative enrichment statistic for each gene;

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

Examples

data(exampleRanks)
data(examplePathways)
ranks <- sort(exampleRanks, decreasing=TRUE)
es <- calcGseaStat(ranks, na.omit(match(examplePathways[[1]], names(ranks))))

ctlab/fgsea documentation built on Oct. 10, 2024, 10:31 a.m.