Gene set enrichment analysis with topGO

Abstract

from different patients with ALL. First we load the libraries and the data:> library(topGO)> library(ALL)> data(ALL) When the topGO package is loaded three new environments GOBPTerm, GOMFTerm and GOMFTerm are created and binded to the package environment. These environments are build based on the GOTERM environment from package GO. They are used for fast recovering of the information specific to each ontology. In order to access all GO groups that belong to a specific ontology, e.g. Biological Process (BP), one can type:> BPterms <- ls(GOBPTerm)> str(BPterms) chr [1:13155] "GO:0000001 " "GO:0000002 " "GO:0000003 "... Next we need to load the annotation data. The chip used for the experiment is HGU95aV2 Affymetrix.> affyLib <- annotation(ALL)> library(package = affyLib, character.only = TRUE) Usually one needs to remove genes with low expression value and genes which might have very small variability across the samples. Package genefilter provides such tools.> library(genefilter)> f1 <- pOverA(0.25, log2(100))> f2 <- function(x) (IQR(x)> 0.5)> ff <- filterfun(f1, f2)> eset <- ALL[genefilter(ALL, ff),] 2 Creating a topGOdata object The first step when using the topGO package is to create a topGOdata object. This object will contain all information necessary for the GO analysis, namely the gene list, the list of interesting genes, the scores of genes (if available) and the part of the GO ontology (the GO graph) which needs to be used in the analysis. First, we need to define the set of genes that are to be annotated with GO terms. Usually, one starts with all genes present on the array. In our case we start with 2400 genes, genes that were not removed by the filtering.> geneNames <- featureNames(eset)> length(geneNames)