gsean {gsean} | R Documentation |
GSEA or ORA is performed with networks from gene expression data
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, ...)
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 |
GSEA result
Dongmin Jung
exprs2adj, label_prop_gsea, centrality_gsea
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)