compEpiTools (version 1.0.3)

topGOres: determines GeneOntology (GO) enriched terms for a set of Entrez gene ids

Description

determines GeneOntology (GO) enriched terms for a set of Entrez gene ids

Usage

topGOres(ids, ontology='BP', Pthr=1e-05, maxN=5000, minN=5,
orgdb, allEG=keys(orgdb))

Arguments

ids

character; an array of human or mouse EntrezGene ids

ontology

one of the three GO ontologies: BP (Biological Processes), CC (Cellular Components) or MF (Molecular Functions)

Pthr

numeric [0,1]; the p-value for an enrichment to be considered significant

maxN

numeric; only GO terms with a total up to maxN genes annotated on the genome are considered

minN

numeric; only GO terms with a minimum of minN genes annotated on the genome are considered

orgdb

An object of class OrgDb; either org.Mm.eg.db or org.Hs.eg.db

allEG

character; the reference universe of EntrezGene ids, by default all of them

Value

A matrix containing enriched GO terms ranked by significance is returned, with the following columns:

GO.ID

GO id

Term

text description of the GO id

Annotated

total number of genes annotated with the considered GOterm

Significant

number of genes in ids for the specific GOterms

Expected

expected number of GOterms genes in ids in case of random enrichment

Classic

pvalue for th eenrichment as reported from the topGO package

Details

Determines GeneOntology (GO) enriched terms for a set of Entrez gene ids. Based on the topGO Bioconductor library. Both maxN and minN refer to the number of genes annotated in the reference genome for a given GO term (indipendently from the ids that the enrichment is being evaluated for). This can be used for excluding GOterm very generic or very specific, since these would mostly be ignored in the final output. This would also save time in the analysis and decrease the severity of the multiple testing issue.

References

http://genomics.iit.it/groups/computational-epigenomics.html

See Also

topGOdata in the topGO Bioconductor package

Examples

require(org.Mm.eg.db)
egs <- keys(org.Mm.egACCNUM)[1:50]
topGOres(ids=egs, Pthr=0.006, maxN=5000, minN=5, 
	orgdb=org.Mm.eg.db, allEG=keys(org.Mm.eg.db)[1:5000])