annFUN: Functions which map gene identifiers to GO terms
In topGO: Enrichment Analysis for Gene Ontology

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

These functions are used to compile a list of GO terms such that each element in the list is a character vector containing all the gene identifiers that are mapped to the respective GO term.

annFUN.db(whichOnto, feasibleGenes = NULL, affyLib)
annFUN.org(whichOnto, feasibleGenes = NULL, mapping, ID = "entrez") 
annFUN(whichOnto, feasibleGenes = NULL, affyLib)
annFUN.gene2GO(whichOnto, feasibleGenes = NULL, gene2GO)
annFUN.GO2genes(whichOnto, feasibleGenes = NULL, GO2genes)
annFUN.file(whichOnto, feasibleGenes = NULL, file, ...)

readMappings(file, sep = "\t", IDsep = ",")
inverseList(l)

`whichOnto`	character string specifying one of the three GO ontologies, namely: `"BP"`, `"MF"`, `"CC"`
`feasibleGenes`	character vector containing a subset of gene identifiers. Only these genes will be used to annotate GO terms. Default value is `NULL` which means that there are no genes filtered.
`affyLib`	character string containing the name of the Bioconductor annotaion package for a specific microarray chip.
`gene2GO`	named list of character vectors. The list names are genes identifiers. For each gene the character vector contains the GO identifiers it maps to. Only the most specific annotations are required.
`GO2genes`	named list of character vectors. The list names are GO identifiers. For each GO the character vector contains the genes identifiers which are mapped to it. Only the most specific annotations are required.
`mapping`	character string specifieng the name of the Bioconductor package containing the gene mappings for a specific organism. For example: `mapping = "org.Hs.eg.db"`.
`ID`	character string specifing the gene identifier to use. Currently only the following identifiers can be used: `c("entrez", "genbank", "alias", "ensembl", "symbol", "genename", "unigene")`
`file`	character string specifing the file containing the annotations.
`...`	other parameters
`sep`	the character used to separate the columns in the CSV file
`IDsep`	the character used to separate the annotated entities
`l`	a list containing mappings

All these function restrict the GO terms to the ones belonging to the specified ontology and to the genes listed in the feasibleGenes attribute (if not empty).

The function annFUN.db uses the mappings provided in the Bioconductor annotation data packages. For example, if the Affymetrix hgu133a chip it is used, then the user should set affyLib = "hgu133a.db".

The functions annFUN.gene2GO and annFUN.GO2genes are used when the user provide his own annotations either as a gene-to-GOs mapping, either as a GO-to-genes mapping.

The annFUN.org function is using the mappings from the "org.XX.XX" annotation packages. The function supports different gene identifiers.

The annFUN.file function will read the annotationsof the type gene2GO or GO2genes from a text file.

A named(GO identifiers) list of character vectors.

Adrian Alexa

topGOdata-class

library(hgu133a.db)
set.seed(111)

## generate a gene list and the GO annotations
selGenes <- sample(ls(hgu133aGO), 50)
gene2GO <- lapply(mget(selGenes, envir = hgu133aGO), names)
gene2GO[sapply(gene2GO, is.null)] <- NA

## the annotation for the first three genes
gene2GO[1:3]

## inverting the annotations
G2g <- inverseList(gene2GO)

## inverting the annotations and selecting an ontology
go2genes <- annFUN.gene2GO(whichOnto = "CC", gene2GO = gene2GO)


## generate a GO list with the genes annotations
selGO <- sample(ls(hgu133aGO2PROBE), 30)
GO2gene <- lapply(mget(selGO, envir = hgu133aGO2PROBE), as.character)

GO2gene[1:3]

## select only the GO terms for a specific ontology
go2gene <- annFUN.GO2genes(whichOnto = "CC", GO2gene = GO2gene)


##################################################
## Using the org.XX.xx.db annotations
##################################################

## GO to Symbol mappings (only the BP ontology is used)
xx <- annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "symbol")
head(xx)

## Not run: 

allGenes <- unique(unlist(xx))
myInterestedGenes <- sample(allGenes, 500)
geneList <- factor(as.integer(allGenes 
names(geneList) <- allGenes

GOdata <- new("topGOdata",
              ontology = "BP",
              allGenes = geneList,
              nodeSize = 5,
              annot = annFUN.org, 
              mapping = "org.Hs.eg.db",
              ID = "symbol") 

## End(Not run)