runBatchGSE: To perform GSE analysis over multipple experiments and...

In marchionniLab/RTopper: This package is designed to perform Gene Set Analysis across multiple genomic platforms

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 [e-mail]

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)

marchionniLab/RTopper documentation built on Oct. 29, 2022, 8:14 a.m.