fgseaLabel {fgsea} | R Documentation

## Runs label-permuring gene set enrichment analysis.

### Description

Runs label-permuring gene set enrichment analysis.

### Usage

    fgseaLabel(pathways, mat, labels, nperm, minSize = 1, maxSize = Inf,
      nproc = 0, gseaParam = 1, BPPARAM = NULL)

### Arguments

`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.

### Value

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.

### Examples

    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)

[Package fgsea version 1.6.0 Index]