gsean: Gene Set Enrichment Analysis with Networks

In gsean: Gene Set Enrichment Analysis with Networks

Description

GSEA or ORA is performed with networks from gene expression data

Usage

gsean(geneset, x, exprs, pseudo = 1, threshold = 0.99, nperm = 1000,
      centrality = function(x) rowSums(abs(x)), weightParam = 1,
      minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0,
      BPPARAM = NULL, corParam = list(), tmax = 10, ...)

Arguments

geneset	list of gene sets
x	Named vector of gene-level statistics for GSEA or set of genes for ORA. Names should be the same as in gene sets.
exprs	gene expression data
pseudo	pseudo number for log2 transformation (default: 1)
threshold	threshold of correlation for nodes to be considered neighbors for ORA (default: 0.99)
nperm	number of permutations (default: 1000)
centrality	centrality measure, degree centrality or node strength is default
weightParam	weight parameter value for the centrality measure, equally weight if weightParam = 0 (default: 1)
minSize	minimal size of a gene set (default: 1)
maxSize	maximal size of a gene set (default: Inf)
gseaParam	GSEA parameter value (default: 1)
nproc	see fgsea::fgsea
BPPARAM	see fgsea::fgsea
corParam	additional parameters for correlation; see WGCNA::cor
tmax	maximum number of iterations for label propagtion (default: 10)
...	additional parameters for label propagation; see RANKS::label.prop

Value

GSEA result

Author(s)

Dongmin Jung

See Also

exprs2adj, label_prop_gsea, centrality_gsea

Examples

data(examplePathways)
data(exampleRanks)
exampleRanks <- exampleRanks[1:100]
Names <- names(exampleRanks)
exprs <- matrix(rnorm(10*length(exampleRanks)), ncol = 10)
rownames(exprs) <- names(exampleRanks)
set.seed(1)
result.GSEA <- gsean(examplePathways, exampleRanks, exprs)

gsean documentation built on Nov. 8, 2020, 6:36 p.m.