pathEnrich {splineTimeR} | R Documentation

## Pathway enrichment analysis

### Description

Function performs a pathway enrichment analysis of a definied set of genes.

### Usage

    pathEnrich(geneList, geneSets, universe=NULL)

### Arguments

`geneList` | vector of gene names to be used for pathway enrichment
`geneSets` | `"GeneSetColletion"` object with functional pathways gene sets
`universe` | number of genes that were probed in the initial experiment

### Details

`geneSets` is a `"GeneSetColletion"` object containing gene sets from various databases. Different sources for gene sets data are allowed and have to be provided in Gene Matrix Transposed file format (*.gmt), where each gene set is described by a pathway name, a description, and the genes in the gene set. Two examples are shown to demonstrate how to define `geneSets` object. See examples.

The variable `universe` represents a total number of genes that were probed in the initial experiment, e.g. the number of all genes on a microarray. If `universe` is not definied, `universe` is equal to the number of all genes that can be mapped to any pathways in chosen database.

### Value

A data.frame with following columns:

`pathway` | names of enriched pathways
`description` | gene set description (e.g. a link to the named gene set in MSigDB)
`genes_in_pathway` | total number of known genes in the pathway
`%_match` | number of matched genes refered to the total number of known genes in the pathway given in %
`pValue` | p-value
`adj.pValue` | Benjamini-Hochberg adjucted p-value
`overlap` | genes from input genes list that overlap with all known genes in the pathway

Additionally an .txt file containing all above information is created.

### Author(s)

Agata Michna

### References

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S. and Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102(43), 15545-15550.

http://www.broadinstitute.org/gsea/msigdb/collections.jsp

http://www.reactome.org/pages/download-data/

### Examples

    ## Not run:
       ## Example 1 - using gene sets from the Molecular Signatures Database (MSigDB collections)
       ## Download .gmt file 'c2.all.v5.0.symbols.gmt' (all curated gene sets, gene symbols)
       ## from the Broad, http://www.broad.mit.edu/gsea/downloads.jsp#msigdb, then
       geneSets <- getGmt("/path/to/c2.all.v5.0.symbols.gmt")
       ## load "eSetObject" containing simulated time-course data
       data(TCsimData)
       ## check for differentially expressed genes
       diffExprs <- splineDiffExprs(eSetObject = TCsimData, df = 3, cutoff.adj.pVal = 0.01, reference = "T1")
       ## use differentially expressed genes for pathway enrichment analysis
       enrichPath <- pathEnrich(geneList = rownames(diffExprs), geneSets = geneSets, universe = 6536)
    ## End(Not run)

    ## Not run:
       ## Example 2 - using gene sets from the Reactome Pathway Database
       ## Download and unzip .gmt.zip file 'ReactomePathways.gmt.zip'
       ## ("Reactome Pathways Gene Set" under "Specialized data formats") from the Reactome website
       ## http://www.reactome.org/pages/download-data/, then
       geneSets <- getGmt("/path/to/ReactomePathways.gmt")
       data(TCsimData)
       diffExprs <- splineDiffExprs(eSetObject = TCsimData, df = 3, cutoff.adj.pVal = 0.01, reference = "T1")
       enrichPath <- pathEnrich(geneList = rownames(diffExprs), geneSets = geneSets, universe = 6536)
    ## End(Not run)

    ## Small example with gene sets consist of KEGG pathways only
    geneSets <- getGmt(system.file("extdata", "c2.cp.kegg.v5.0.symbols.gmt", package="splineTimeR"))
    data(TCsimData)
    diffExprs <- splineDiffExprs(eSetObject = TCsimData, df = 3, cutoff.adj.pVal = 0.01, reference = "T1")
    enrichPath <- pathEnrich(geneList = rownames(diffExprs), geneSets = geneSets, universe = 6536)

[Package splineTimeR version 1.18.0 Index]