runBatchGSE {RTopper} | R Documentation

## To perform GSE analysis over multipple experiments and functional themes

### Description

The `runBatchGSE` function enables performing Gene Set Enrichment analysis over multiple ranking statistics and multiple lists of gene sets. By default this function is an interface to the `geneSetTest` in the `limma` package, and most of the arguments passed to `runBatchGSE` are indeed passed to such lower level function. As an alternative the user can also define and pass to `runBatchGSE` a custom function, defining the ranking statistics and the gene set membership in the same way done for `geneSetTest` (see Details below).

### Usage

    runBatchGSE(dataList, fgsList, ...)

### Arguments

`dataList` | a list containing the gene-to-phenotype scores to be used as ranking statistics in the GSE analysis. This list is usually produced by running `computeDrStat`
`fgsList` | a list of FGS collection, in which each element is a list of character vectors, one for each gene set
`...` | additional arguments to be passed to lower level functions (see details below)

### Details

This function performs enrichment analysis for all the gene-to-phenotype scores (argument `dataList`) passed to it over a list of F unctional Gene Set (FGS) (argument `fgsList`), returning a p-value for each FGS. Additional arguments can be bassed to this function to modify the way the enrichment test is performed, as follows:

`absolute` logical, this specifies whether the absolute values of the ranking statistics should be used in the test (the default being TRUE)

`gseFunc` a function to perform GSE analysis. If not specified the default is the `geneSetTest` function from the `limma` package. If a function is specified by the user, the membership of the analyzed genes to a FGS, and the ranking statistics must be defined in the same way this is done for `geneSetTest`, and the new function must return an integer (usually a p-value) (see the help for `geneSetTest`)

The following main arguments are used by `geneSetTest`:

`type` character, specifies the type of statistics used to rank the genes by `geneSetTest`: `'f'` for F-like statistics (default), `'t'` for t-like statistics, or `'auto'` for an educated guess

`alternative` character, defines the alternative with the following possible options: `'mixed'` (default), `'either'`, `'up'` or `'down'`, `'two.sided'`, `'greater'`, or `'less'`

`ranks.only` logical, if `TRUE` (default) only ranks will be used by `geneSetTest`

`nsim` numeric, the number of randomly selected sets of genes to be used in simulations to compute the p-value

### Value

The output is a list of lists containing the set of enrichment results for all gene-to-phenotype scores and FGS collections used as input.

### Author(s)

Luigi Marchionni marchion@jhu.edu

### References

Svitlana Tyekucheva, Luigi Marchionni, Rachel Karchin, and Giovanni Parmigiani. "Integrating diverse genomic data using gene sets." Manuscript submitted.

### Examples

    ###require limma to run the example
    require(limma)

    ###load integrated gene-to-phenotype scores
    data(intScores)

    ###load separate gene-to-phenotype scores
    data(sepScores)

    ###load list of functional gene sets
    data(fgsList)

    ###run GSE analysis in batch with default parameters
    gseABS.int <- runBatchGSE(dataList=intScores, fgsList=fgsList)

    ###run GSE analysis in batch with alternative parameters
    gseABS.sep <- runBatchGSE(dataList=sepScores, fgsList=fgsList,
            absolute=FALSE, type="t", alternative="up")

    ###run GSE analysis in batch passing an enrichment function
    gseUP.int.2 <- runBatchGSE(dataList=intScores, fgsList=fgsList,
    			   absolute=FALSE, gseFunc=wilcoxGST, alternative="up")

    ###define and use a new enrichment function
    gseFunc <- function (selected, statistics, threshold) {
    	diffExpGenes <- statistics > threshold
    	tab <- table(diffExpGenes, selected)
    	pVal <- fisher.test(tab)[["p.value"]]
    	}
    gseUP.sep.2 <- runBatchGSE(dataList=sepScores, fgsList=fgsList,
    				 absolute=FALSE, gseFunc=gseFunc, threshold=7.5)

[Package RTopper version 1.40.1 Index]