GOenrichment {GOSim} | R Documentation |
This function performs a GO enrichment analysis using topGO. It combines the two former functions "GOenrichment" and "analyzeCluster".
GOenrichment(genesOfInterest, allgenes, cutoff=0.01)
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 |
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.
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
## 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)