gseReactome: Modified GSEA with Reactome

In signatureSearch: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis

Description

This modified Gene Set Enrichment Analysis (GSEA) of Reactome pathways supports gene test sets with large numbers of zeros.

Usage

gseReactome(
  geneList,
  organism = "human",
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  readable = FALSE
)

Arguments

geneList	order ranked geneList
organism	one of "human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly".
exponent	integer value used as exponent in GSEA algorithm.
nPerm	integer defining the number of permutation iterations for calculating p-values
minGSSize	minimal size of each geneSet for analyzing
maxGSSize	maximal size of each geneSet for analyzing
pvalueCutoff	pvalue Cutoff
pAdjustMethod	pvalue adjustment method
verbose	print message or not TRUE or FALSE indicating whether to convert gene Entrez ids to gene Symbols in the 'itemID' column in the FEA result table.
readable	TRUE or FALSE indicating whether to convert gene Entrez ids to gene Symbols in the 'itemID' column in the FEA result table.

Value

feaResult object

Examples

# Gene Entrez id should be used for Reactome enrichment
data(geneList, package="DOSE")
#geneList[100:length(geneList)]=0
#rc <- gseReactome(geneList=geneList, pvalueCutoff=1)

signatureSearch documentation built on April 16, 2021, 6 p.m.