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

In HTSanalyzeR: Gene set over-representation, enrichment and network analyses for high-throughput screens

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)

gscs:

a character vector specifying the names of gene set collections whose top significant gene sets will be plotted

ntop:

a single integer or numeric value specifying how many gene sets of top significance will be plotted.

allSig:

a single logical value. If 'TRUE', all significant gene sets (GSEA adjusted p-value < 'pValueCutoff' of slot 'para') will be plotted; otherwise, only top 'ntop' gene sets will be plotted.

filepath:

a single character value specifying where to store GSEA figures.

output:

a single character value specifying the format of output image: "pdf" or "png"

...

other arguments used by the function png or pdf such as 'width' and 'height'

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.

Author(s)

Xin Wang [e-mail]

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)

HTSanalyzeR documentation built on Oct. 31, 2019, 7:10 a.m.