gsea.iriz: Irizarry approach for gene-set testing

In FrankD/nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity

Description

Irizarry approach for gene-set testing

Usage

gsea.iriz(x1, x2, gene.sets, gene.names, gs.names = NULL,
  method.p.adjust = "fdr", alternative = "two-sided")

Arguments

x1	Expression matrix (condition 1)
x2	Expression matrix (condition 2)
gene.sets	List of gene-sets
gene.names	Gene names
gs.names	Gene-set names
method.p.adjust	Method for p-value adjustment (default='fdr')
alternative	Default='two-sided' (uses two-sided p-values).

Details

Implements the approach described in "Gene set enrichment analysis made simple" by Irizarry et al (2011). It tests for shift and/or change in scale of the distribution.

Value

List consisting of

pval.shift	p-values measuring shift
pval.scale	p-values measuring scale
pval.combined	combined p-values (minimum of pval.shift and pval.scale)

Author(s)

n.stadler

Examples

n <- 100
p <- 20
x1 <- matrix(rnorm(n*p),n,p)
x2 <- matrix(rnorm(n*p),n,p)
gene.names <- paste('G',1:p,sep='')
gsets <- split(gene.names,rep(1:4,each=5))
fit <- gsea.iriz(x1,x2,gsets,gene.names)
fit$pvals.combined

x2[,1:3] <- x2[,1:3]+0.5#variables 1-3 of first gene-set are upregulated
fit <- gsea.iriz(x1,x2,gsets,gene.names)
fit$pvals.combined

FrankD/nethet documentation built on Oct. 5, 2020, 8:22 a.m.