GOSim (version 1.10.0)

GOenrichment: GO enrichment analysis

Description

This function performs a GO enrichment analysis using topGO. It combines the two former functions "GOenrichment" and "analyzeCluster".

Usage

GOenrichment(genesOfInterest, allgenes, cutoff=0.01, method="elim")

Arguments

genesOfInterest

character vector of Entrez gene IDs or vector of statistics (p-values, t-statistics, ...) named with entrez gene IDs

allgenes

character vector of Entrez gene IDs or vector of statistics named with entrez gene IDs

cutoff

significance cutoff for GO enrichment analysis

method

topGO method to use

Value

GOTerms

list of significant GO terms and their description

p.values

vector of p-values for significant GO terms

genes

list of genes associated to each GO term

Details

If the parameters 'genesOfInterest' and 'allgenes' are both character vectors of Entrez gene IDs, Fisher's exact test is used. The Kolmogorov-Smirnov test can be used, if a score (e.g. p-value) for each gene is provided. For more details please refer to the topGO vignette.

References

Adrian Alexa, J\"org Rahnenf\"uhrer, Thomas Lengauer: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure, Bioinformatics, 2006, 22(13):1600-1607

See Also

evaluateClustering

Examples

				
	if(require(org.Hs.eg.db) & require(topGO)){
		allgenes = sample(keys(org.Hs.egGO), 1000) # suppose these are all genes
		allpvalues = runif(1000) # an these are their pvalues
		names(allpvalues) = allgenes	
		GOenrichment(allpvalues[allpvalues<0.05], allpvalues) # GO enrichment analysis using Kolmogorov-Smirnov test
	}