HTSanalyzeR (version 2.24.0)

aggregatePvals: Aggregate p-values from gene set over-representation tests.

Description

This function takes as input a matrix of p-values for example obtained from a GSEA on multiple phenotypes, with a row for each gene set and a column for each phenotype and aggregates the p-values by row (i.e. one aggregated p-value for each gene set) according to Fisher or Stouffer's methods.

Usage

aggregatePvals(pvalMatrix, method="fishers", pAdjustMethod="BH", 
order=TRUE)

Arguments

pvalMatrix

a numeric matrix of p-values, with rows named according to the gene set (rows correspond to gene sets, and columns to multiple p-values to be aggregated for that gene set)

method

a single character value of "stouffers" or "fishers"

pAdjustMethod

a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details)

order

a single logical value: TRUE or FALSE. If it is TRUE, the results table will be ordered according to the aggregated p-values.

Value

a matrix with a row for each gene set and two columns: "Aggregated.p.value" and "Adjusted.aggregated.p.value"

Details

The Fisher method combines the p-values into an aggregated chi-squared statistic equal to -2*sum(log(Pk)) were we have k=1,..,K pvalues independently distributed as uniform on the unit interval under the null hypothesis. The resulting p-values are calculated by comparing this chi-squared statistic to a chi-squared distribution with 2K degrees of freedom. The Stouffer method computes a z-statistics assuming that the sum of the quantiles (from a standard normal distribution) corresponding to the p-values are distributed as N(0,K).

Examples

p1 <- runif(100, min=0, max=1)
p2 <- runif(100, min=0, max=1)
names(p1) <- as.character(sample(x=seq(from=1, to=100, by=1), size=100, 
replace=FALSE))
pmatrix <- cbind(p1, p2)
rownames(pmatrix) <- names(p1)
aggP <- aggregatePvals(pvalMatrix=pmatrix, method="stouffers")