gseattperm {Category} | R Documentation |
This function performs GSEA computations and returns p-values for each gene set based on repeated permutation of the phenotype labels.
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, ] }