GOenrichment {GOSim}R Documentation

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)

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

Details

If the parameters 'genesOfInterest' and 'allgenes' are both character vectors of Entrez gene IDs, the 'elim' method is used, otherwise the 'weight' method. For more details please refer to the topGO vignette.

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

Author(s)

Holger Froehlich

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

        ## Not run: 
       
        setOntology("BP")
        gomap <- get("gomap",env=GOSimEnv)
        allgenes = sample(names(gomap), 1000) # suppose these are all genes
        allpvalues = runif(1000) # an these are their pvalues
        names(allpvalues) = allgenes
        if(require(topGO) & require(annotate))
                GOenrichment(allpvalues[allpvalues<0.05], allpvalues) # GO enrichment analysis
        
## End(Not run)

[Package GOSim version 1.2 Index]