centrality_gsea {gsean} | R Documentation |
GSEA is performed with centrality measure
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)
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 |
GSEA result
Dongmin Jung
fgsea::fgsea
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)