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, 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
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 }