gseattperm: Permutation p-values for GSEA
In Category: Category Analysis

Description Usage Arguments Details Value Author(s) Examples

This function performs GSEA computations and returns p-values for each gene set based on repeated permutation of the phenotype labels.

1	gseattperm(eset, fac, mat, nperm)

`eset`	An `ExpressionSet` object
`fac`	A `factor` identifying the phenotypes in `eset`. Usually, this will be one of the columns in the phenotype data associated with `eset`.
`mat`	A 0/1 incidence matrix with each row representing a gene set and each column representing a gene. A 1 indicates membership of a gene in a gene set.
`nperm`	Number of permutations to test to build the reference distribution.

The t-statistic is used (via rowttests) to test for a difference in means between the phenotypes determined by fac within each gene set (given as a row of mat).

A reference distribution for these statistics is established by permuting fac and repeating the test B times.

A matrix with the same number of rows as mat and two columns, "Lower" and "Upper". The "Lower" ("Upper") column gives the probability of seeing a t-statistic smaller (larger) than the observed.

Seth Falcon

## This example uses a random sample of probesets and a randomly
## generated category matrix.  The results, therefore, are not
## meaningful, but the code demonstrates how to use gseattperm without
## requiring any expensive computations.

## Obtain an ExpressionSet with two types of samples (mol.biol)
haveALL <- require("ALL")
if (haveALL) {
data(ALL)
set.seed(0xabcd)
rndIdx <- sample(1:nrow(ALL), 500)
Bcell <- grep("^B", as.character(ALL$BT))
typeNames <- c("NEG", "BCR/ABL")
bcrAblOrNegIdx <- which(as.character(ALL$mol.biol) %in% typeNames)
s <- ALL[rndIdx, intersect(Bcell, bcrAblOrNegIdx)]
s$mol.biol <- factor(s$mol.biol)

## Generate a random category matrix
nCats <- 100
set.seed(0xdcba)
rndCatMat <- matrix(sample(c(0L, 1L), replace=TRUE),
                    nrow=nCats, ncol=nrow(s),
                    dimnames=list(
                      paste("c", 1:nCats, sep=""),
                      featureNames(s)))

## Demonstrate use of gseattperm
N <- 10
pvals <- gseattperm(s, s$mol.biol, rndCatMat, N)
pvals[1:5, ]
}