HTSanalyzeR (version 2.24.0)

GSCA-class: An S4 class for Gene Set Collection Analyses on high-throughput screens

Description

This S4 class includes a series of methods to do gene set enrichment analysis and hypergeometric tests for high-throughput screens.

Objects from the Class

Objects of class GSCA can be created from new("GSCA", listOfGeneSetCollections, geneList, hits) (see the examples below)

Slots

listOfGeneSetCollections:

a list of gene set collections (a 'gene set collection' is a list of gene sets).

geneList:

a numeric or integer vector of phenotypes named by gene identifiers.

hits:

a character vector of the gene identifiers (used as hits in the hypergeometric tests).

para:

a list of parameters for hypergeometric tests and GSEA. These parameters are pValueCutoff, pAdjustMethod, nPermutations, minGeneSetSize and exponent (see function analyzeGeneSetCollections for detailed descriptions about these parameters).

result:

a list of results (see the returned values in the function analyzeGeneSetCollections).

summary:

a list of summary information for listOfGeneSetCollections, geneList, hits, para, and result.

preprocessed:

a single logical value specifying whether or not the input data has been preprocessed.

Methods

An overview of methods with class-specific functionality: More detailed introduction can be found in help for each specific function.

preprocess

do preprocessing on input vectors of phenotypes and hits including: a) removing NAs in the geneList and hits; b) invoking function duplicateRemover to process duplicated phenotypes (see duplicateRemover for more details); c) invoking function annotationConvertor to convert annotations; d) ranking phenotypes in a decreasing order.

analyze

perform hypergeometric tests and Gene Set Enrichment Analysis based on input parameter list para.

appendGSTerms

append gene set terms to GSCA results

summarize

print summary information about listOfGeneSetCollections, geneList, hits, para, and result.

getTopGeneSets

select top significant gene sets from object@results$`resultName` by setting ntop or allSig.

writeHits

write observed hits in gene sets for hypergeometric tests.

viewGSEA

view a figure of GSEA results for a gene set in a gene set collection.

plotGSEA

plot and save figures of GSEA results for top significant gene sets in a gene set collection.

viewEnrichMap

plot an enrichment map for GSEA or hypergeometric test results

plotEnrichMap

plot and save an enrichment map for GSEA or hypergeometric test results

report

generate html reports.

See Also

preprocess analyze appendGSTerms summarize getTopGeneSets writeHits viewGSEA plotGSEA viewEnrichMap plotEnrichMap report

Examples

## Not run: 
# library(org.Dm.eg.db)
# library(KEGG.db)
# library(AnnotationDbi)
# ##library(igraph)
# ##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")
# gscs <- list(PW_KEGG = PW_KEGG)
# ##create an object of class 'GSCA'
# gsca <- new("GSCA", listOfGeneSetCollections=gscs, geneList = 
# KcViab_Data4Enrich, hits = hits)
# ##do preprocessing (KcViab_Data4Enrich has already been preprocessed)
# gsca <- preprocess(gsca, species="Dm", initialIDs = "Entrez.gene", 
# keepMultipleMappings = TRUE, duplicateRemoverMethod = "max", 
# orderAbsValue = FALSE)
# ##do hypergeometric tests and GSEA
# gsca <- analyze(gsca, para = list(pValueCutoff = 0.05, pAdjustMethod 
# = "BH", nPermutations = 1000, minGeneSetSize = 60, exponent = 1))
# ##print summary information
# summarize(gsca)
# ##get all significant gene sets in "PW_KEGG"
# sigGSs<-getTopGeneSets(gsca, "GSEA.results", "PW_KEGG", allSig=TRUE)
# ##view a GSEA figure
# viewGSEA(gsca, gscName="PW_KEGG", gsName=sigGSs[["PW_KEGG"]][1])
# dev.off()
# ##append gene set terms to results
# gsca<-appendGSTerms(gsca, keggGSCs="PW_KEGG")
# ##view an enrichment map for GSEA results
# eb<-viewEnrichMap(gsca, gscs="PW_KEGG", allSig=TRUE, gsNameType="term", 
# displayEdgeLabel=FALSE, layout="layout.fruchterman.reingold")
# ##write html reports
# report(object = gsca, experimentName = "GSCATest", species = "Dm", 
# allSig = TRUE, keggGSCs = "PW_KEGG", reportDir="GSCATestReport")
# ##browse the index page
# browseURL(file.path(getwd(), "GSCATestReport", "index.html"))
# ## End(Not run)