centrality_gsea: Gene Set Enrichment Analysis with centrality measure

In dongminjung/gsean: Gene Set Enrichment Analysis with Networks

Description

GSEA is performed with centrality measure

Usage

centrality_gsea(geneset, x, adjacency, pseudo = 1, nperm = 1000,
                centrality = function(x) rowSums(abs(x)),
                weightParam = 1, minSize = 1, maxSize = Inf,
                gseaParam = 1, nproc = 0, BPPARAM = NULL)

Arguments

geneset	list of gene sets
x	Named vector of gene-level statistics. Names should be the same as in gene sets.
adjacency	adjacency matrix
pseudo	pseudo number for log2 transformation (default: 1)
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

Value

GSEA result

Author(s)

Dongmin Jung

See Also

fgsea::fgsea

Examples

data(examplePathways)
data(exampleRanks)
exampleRanks <- exampleRanks[1:100]
adjacency <- diag(length(exampleRanks))
rownames(adjacency) <- names(exampleRanks)
set.seed(1)
result.GSEA <- centrality_gsea(examplePathways, exampleRanks, adjacency)

dongminjung/gsean documentation built on May 14, 2019, 6:14 p.m.