HTSanalyzeR (version 2.24.0)

appendGSTerms: Append gene set terms to GSCA results

Description

This is a generic function.

When implemented as the S4 method for objects of class GSCA, this function finds corresponding annotation terms for KEGG and GO gene sets and inserts a column named "Gene.Set.Term" to each data frame in the GSCA results.

To use this function for objects of class GSCA:

appendGSTerms(object, keggGSCs=NULL, goGSCs=NULL)

Usage

appendGSTerms(object, ...)

Arguments

object

an object. When this function is implemented as the S4 method of class 'GSCA', this argument is an object of class 'GSCA'.

...

other arguments depending on class (see below for the arguments supported by the method of class 'GSCA')

Value

In the end, this function will return an updated object of class GSCA.

Details

This function makes the GSCA results more readable by appending a column of terms for KEGG and GO gene sets. To do this, the user needs to specify the names of the gene set collections based on KEGG and GO, respectively.

For each KEGG gene set, the species code in the KEGG id will be trimmed off, and then mapped to its corresponding annotation term using the function mget of the package AnnotationDbi.

For each GO gene set, the GO id will be mapped to corresponding GO term by the function Term of the package GO.db.

Examples

## Not run: 
# library(org.Dm.eg.db)
# library(KEGG.db)
# ##load data for enrichment analyses
# data("KcViab_Data4Enrich")
# ##select hits
# hits <- names(KcViab_Data4Enrich)[which(abs(KcViab_Data4Enrich) > 2)]
# ##set up a list of gene set collections
# PW_KEGG <- KeggGeneSets(species = "Dm")
# gscList <- list(PW_KEGG = PW_KEGG)
# ##create an object of class 'GSCA'
# gsca <- new("GSCA", listOfGeneSetCollections=gscList,
# geneList = KcViab_Data4Enrich, hits = hits)
# ##print gsca
# gsca
# ##do preprocessing (KcViab_Data4Enrich has already been preprocessed)
# gsca <- preprocess(gsca, species="Dm", initialIDs = "Entrez.gene", 
# keepMultipleMappings = TRUE, duplicateRemoverMethod = "max", 
# orderAbsValue = FALSE)
# ##print gsca again
# gsca
# ##do hypergeometric tests and GSEA
# gsca <- analyze(gsca, para = list(pValueCutoff = 0.05, pAdjustMethod 
# = "BH", nPermutations = 1000, minGeneSetSize = 100,exponent = 1))
# ##append Kegg and GO gene set terms
# gsca<-appendGSTerms(gsca, keggGSCs="PW_KEGG")
# ## End(Not run)