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 1.6.0 Index]