SetRank (version 1.0.1)

setRankAnalysis: Advanced gene set enrichment analysis.

Description

Performs advanced gene set enrichment analysis on a set of genes.

Usage

setRankAnalysis(geneIDs, setCollection, use.ranks = TRUE, setPCutoff = 0.01,
  fdrCutoff = 0.05, delete = TRUE)

Arguments

geneIDs

A vector containing the set of gene IDs to test for gene set enrichment. This is typically the list of significant genes returned by the analysis of an omics dataset.

setCollection

A gene set collection object, generated with the buildSetCollection function.

use.ranks

Logical value indicating if the geneIDs vector is in ranked order or not. When TRUE, a ranked analysis will be performed.

setPCutoff

The p-value cutoff to be used to consider a gene set significant. Recommended value: 0.01

fdrCutoff

The cutoff to be applied on the corrected p-value after false-positive sets have been removed.

delete

A flag indicating if non-significant gene sets should be deleted, which is the behaviour that you want. It is best to ignore this argument, it was only added for debugging purposes.

Value

• An igraph object. Use the igraph get.data.frame function to get a data frame with all the significant gene sets.

Examples

options(mc.cores=1)
reference = sprintf("gene_%03d", 1:50)
geneSets = lapply(1:9, function(i) sample(reference[((i-1)*5):((i+1)*5)], 5))
annotationTable = data.frame(termID=sprintf("set_%02d", rep(1:9, each=5)), 
        geneID=unlist(geneSets),
        termName = sprintf("dummy gene setet %d", rep(1:9, each=5)),
        dbName = "dummyDB",
        description = "A dummy gene set DB for testing purposes")
collection = buildSetCollection(annotationTable, referenceSet=reference)
genes = reference[sample(c(TRUE, FALSE), 50, TRUE)]
network = setRankAnalysis(genes, collection, TRUE)