gsea.kegg {phenoTest} | R Documentation

## Perform Gene Set Enrichment Analysis (GSEA) of Gene Ontologies (GO) and Kegg gene sets.

### Description

The function obtains the GO or Kegg gene sets and perfomrs GSEA analysis as implemented in the `gsea` function.

### Usage

    gsea.go(x,species='Hs', ontologies='MF', logScale=TRUE, absVals=FALSE,
            averageRepeats=FALSE, B=1000, mc.cores=1, test="perm",
            p.adjust.method="none", pval.comp.method="original",
            pval.smooth.tail=TRUE,minGenes=10,maxGenes=500,center=FALSE)
    gsea.kegg(x,species='Hs', logScale=TRUE, absVals=FALSE,
            averageRepeats=FALSE, B=1000, mc.cores=1, test="perm",
            p.adjust.method="none", pval.comp.method="original",
            pval.smooth.tail=TRUE,minGenes=10,maxGenes=500,center=FALSE)

### Arguments

`x` | `ePhenoTest`, `numeric` or `matrix` object containing scores (hazard ratios or fold changes).
`species` | a single character value specifying the species: "Dm" ("Drosophila_ melanogaster"), "Hs" ("Homo_sapiens"), "Rn" ("Rattus_norvegicus"), "Mm" ("Mus_musculus") or "Ce" ("Caenorhabditis_elegans")).
`ontologies` | a single character value or a character vector specifying an ontology or multiple ontologies. The current version provides the following choices: "BP", "CC" and "MF"
`logScale` | if values should be log scaled.
`absVals` | if TRUE fold changes and hazard ratios that are negative will be turned into positive before starting the process. This is useful when genes can go in both directions.
`averageRepeats` | if x is of class numeric and has repeated names (several measures for some indivdual names) we can average the measures of the same names.
`B` | number of simulations to perform.
`mc.cores` | number of processors to use.
`test` | the test that will be used. 'perm' stands for the permutation based method, 'wilcox' stands for the wilcoxon test (this is the fastest one) and 'ttperm' stands for permutation t test.
`p.adjust.method` | p adjustment method to be used. Common options are 'BH', 'BY', 'bonferroni' or 'none'. All available options and their explanations can be found on the `p.adjust` function manual.
`pval.comp.method` | the p value computation method. Has to be one of 'signed' or 'original'. The default one is 'original'. See details for more information.
`pval.smooth.tail` | if we want to estimate the tail of the ditribution where the pvalues will be generated.
`minGenes` | gene sets with less than minGenes genes will be removed from the analysis.
`maxGenes` | gene sets with more than maxGenes genes will be removed from the analysis.
`center` | if we want to center scores (fold changes or hazard ratios). The following is will be done: x = x-mean(x).

### Details

This function relies on the following packages: GSEABase, GO.db.

For more information about how the gene sets are obtained see the man page of the functions `getGo` and/or `getKegg`. For more information about the implemented GSEA see the man page of th function `gsea`.

### Value

a list of gene sets, with names as GO pathway IDs. Each gene set is a character vector of Entrez gene identifiers.

### Author(s)

Evarist Planet.

### See Also

getGo

### Examples

    ##load libs
    #library(KEGG.db)
    #library(org.Hs.eg.db)

    ##get data
    #data(eset.genelevel)
    #eset.genelevel

    ##prepare vars2test
    #survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE)
    #colnames(survival) <- c('event','time')
    #vars2test <- list(survival=survival,categorical='ER.Status')

    ##run ExpressionPhenoTest
    #epheno <- ExpressionPhenoTest(eset.genelevel,vars2test,p.adjust.method='none')
    #epheno

    ##run gsea with kegg gene sets.
    #gseaData <- gsea.kegg(epheno[,1],'Hs')
    #summary(gseaData)
    #plot(gseaData[[1]],gseaData[[2]],selGsets='hsa04062')

[Package phenoTest version 1.34.0 Index]