msigdb.gsea: Gene Set Enrichment Analysis Using MSigDB Collections

In mw201608/GOtest: Gene Ontology and Set Enrichment Test

Gene Set Enrichment Analysis Using MSigDB Collections

Description

These functions perform gene set enrichment analyses based on MSigDB collections.

Usage

msigdb.gsea(x, query.population=NULL, genesets=c('c2.cp', 'c5.go.bp', 'c5.go.cc', 'c5.go.mf'),
 background='common', name.x='Input', name.go='MSigDB',
 method=c('hypergeometric', 'GSEA', 'logitreg'), adj="BH", species=c('human', 'mouse'),
 ReportOverlap=TRUE, ncores=1, gsea.alpha=1,
 permutations=ifelse(method=='GSEA', 1000, 0), iseed=12345)

Arguments

x	a data.frame of query gene signatures. See Details.
query.population	a population of the genes from which the query gene signatures are collected.
genesets	MSigDB gene set collections to be tested.
background	an integer or a keyword. See explanation in function GOtest.
name.x	a character string specifing the name of the query.
name.go	a character string specifing the name of the function gene sets.
method	algorithm to performing the enrichment test. See Details.
adj	approach to correct for multiple tests. See function p.adjust
species	Specifying the species for the input gene signatures.
ReportOverlap	whether to output overlapping elements.
ncores	number of CPU cores to be used for parallel computing.
gsea.alpha	power to scale the weights: 0 (unweighted = Kolmogorov-Smirnov), 1 (weighted), and 2 or larger (over-weighted)
permutations	number of permutations for computing P value significance and controlling FDR for GSEA and logitreg methods. See GOtest for details.
iseed	seed for random number generation in permutations.

Details

For msigdb.gsea, known annotated gene sets in MSigDB will be loaded by function msigdb.genesets and the enrichment analysis is computed using function GOtest.

Other details can be found in function GOtest.

Value

See output of function GOtest.

See Also

GOtest, plotOverlap, p.adjust, GSEA

Examples

###Example usage 2: weighted enrichment tests

#We will again make use of the MacArthur gene set and the gene universe of HGNC approved symbols,
# so make sure they have been loaded as in Example 1.

library(GOtest)
MAGenes=curated.genesets(c('MacArthur'))
universe=curated.genesets(c('HGNC_universe'))$Gene
(n=length(universe))

#In this example, we will try both the hypergeometric test and weighted enrichment tests, 
#including GSEA and logistic regression, by geneating a toy dataset
#through simulation of random gene-phenotype associations.

set.seed(123)
toy=data.frame(Gene=universe, Phenotype='Simulated', Z=rnorm(n, 0, 1), stringsAsFactors=FALSE)

#Select genes with absolute Z value larger than 3 and separate them into up and down groups 
#based on the sign of Z value, then run the hypergeometric test on both groups
#against the MacAuther gene sets:

toy.3 = toy[abs(toy$Z)>3,]
toy.3$Direction = ifelse(toy.3$Z > 0, 'Up', 'Down')
fit1 = msigdb.gsea(x = toy.3[, c('Gene', 'Direction')], query.population = universe,
	background = 'query', name.x = 'Toy', method = 'hypergeometric')

#As expected, no significant enrichment identified:

head(fit1)

## Not run: 
#Next, we are going to run weighted enrichment tests on the full test dataset 
#by using GSEA or logistic regression. First, run GSEA:

fit2 = msigdb.gsea(x=toy, name.x='Toy', method='GSEA')
head(fit2)

#Run logistic regression:

fit3=GOtest(x=toy, go=MAGenes, name.x='Toy', name.go='MacArthur', method='logitreg')
head(fit3)

## End(Not run)

mw201608/GOtest documentation built on May 3, 2023, 11:49 a.m.