setRankAnalysis: Advanced gene set enrichment analysis.

In C3c6e6/SetRank: 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.

Author(s)

Cedric Simillion

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)

C3c6e6/SetRank documentation built on May 6, 2019, 9:12 a.m.