Runs label-permuring gene set enrichment analysis.
Runs label-permuring gene set enrichment analysis.
fgseaLabel( pathways, mat, labels, nperm, minSize = 1, maxSize = Inf, nproc = 0, gseaParam = 1, BPPARAM = NULL )
pathways |
List of gene sets to check. |
mat |
Gene expression matrix. Row name should be the same as in 'pathways' |
labels |
Numeric vector of labels for the correlation score of the same length as the number of columns in 'mat' |
nperm |
Number of permutations to do. Minimial possible nominal p-value is about 1/nperm |
minSize |
Minimal size of a gene set to test. All pathways below the threshold are excluded. |
maxSize |
Maximal size of a gene set to test. All pathways above the threshold are excluded. |
nproc |
If not equal to zero sets BPPARAM to use nproc workers (default = 0). |
gseaParam |
GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores. |
BPPARAM |
Parallelization parameter used in bplapply. Can be used to specify cluster to run. If not initialized explicitly or by setting 'nproc' default value 'bpparam()' is used. |
A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following:
pathway – name of the pathway as in 'names(pathway)';
pval – an enrichment p-value;
padj – a BH-adjusted p-value;
ES – enrichment score, same as in Broad GSEA implementation;
NES – enrichment score normalized to mean enrichment of random samples of the same size;
nMoreExtreme' – a number of times a random gene set had a more extreme enrichment score value;
size – size of the pathway after removing genes not present in 'names(stats)'.
leadingEdge – vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading.
library(limma)
library(GEOquery)
es <- getGEO("GSE19429", AnnotGPL = TRUE)[[1]]
exprs(es) <- normalizeBetweenArrays(log2(exprs(es)+1), method="quantile")
es <- es[!grepl("///", fData(es)$`Gene ID`), ]
es <- es[fData(es)$`Gene ID` != "", ]
es <- es[order(apply(exprs(es), 1, mean), decreasing=TRUE), ]
es <- es[!duplicated(fData(es)$`Gene ID`), ]
rownames(es) <- fData(es)$`Gene ID`
pathways <- reactomePathways(rownames(es))
mat <- exprs(es)
labels <- as.numeric(as.factor(gsub(" .*", "", es$title)))
fgseaRes <- fgseaLabel(pathways, mat, labels, nperm = 1000, minSize = 15, maxSize = 500)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.