centrality_gsea {gsean}R Documentation

Gene Set Enrichment Analysis with centrality measure

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)

[Package gsean version 1.0.0 Index]