HTSanalyzeR (version 2.24.0)

plotGSEA: Plot and save figures of GSEA results for top significant gene sets

Description

This is a generic function.

When implemented as the S4 method for objects of class GSCA, this function plots figures of the positions of genes of the gene set in the ranked gene list and the location of the enrichment score for top significant gene sets.

To use this function for objects of class GSCA:

plotGSEA(object, gscs, ntop=NULL, allSig=FALSE, filepath=".", output= "png", ...)

Usage

plotGSEA(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. (see below for the arguments supported by the method of class GSCA)

Details

To make GSEA plots of top significance using this function, the user can only choose one method: either assign an integer to the argument 'ntop' or set the argument 'allSig' to 'TRUE'. Exceptions will occur if both methods are used, or no method is used. Please also note that the argument 'ntop' is a cutoff for all gene set collections in the argument 'gscs'.

We suggest to perform summarize(gsca, what="Result") first to have an idea of how many significant gene sets there are, and then choose to plot them all or just the top ones.

See Also

viewGSEA, gseaPlots

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 summary of gsca
# summarize(gsca)
# ##do preprocessing (KcViab_Data4Enrich has already been preprocessed)
# gsca <- preprocess(gsca, species="Dm", initialIDs = "Entrez.gene", 
# keepMultipleMappings = TRUE, duplicateRemoverMethod = "max", 
# orderAbsValue = FALSE)
# ##print summary of gsca again
# summarize(gsca)
# ##do hypergeometric tests and GSEA
# gsca <- analyze(gsca, para = list(pValueCutoff = 0.05, pAdjustMethod 
# = "BH", nPermutations = 1000, minGeneSetSize = 100, exponent = 1))
# ##print summary of results
# summarize(gsca, what="Result")
# ##plot all significant gene sets
# plotGSEA(gsca, gscs=c("PW_KEGG"), allSig=TRUE, filepath=".", output=
# "pdf", width=8, height=8)
# ## End(Not run)