GOenrichment: GO enrichment analysis
In GOSim: Computation of functional similarities between GO terms and gene products; GO enrichment analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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

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.

`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

Holger Froehlich

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

evaluateClustering

				
	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
	}