metaMA (version 3.1.3)

pvalcombination: P-value combination for unpaired data

Description

Calculates differential expression p-values from unpaired data either from classical or moderated t-tests (Limma, SMVar) for each study and combines these p-values by the inverse normal method.

Usage

pvalcombination(esets, classes, moderated = c("limma", "SMVar", "t")[1], BHth = 0.05)

Arguments

esets

List of matrices (or data frames), one matrix per study. Each matrix has one row per gene and one column per replicate and gives the expression data for both conditions with the order specified in the classes argument. All studies must have the same genes. If the data are already stored as ExpressionSets objects (cf. Bioconductor project), then exprs(yourdata) will give an appropriate element of the list esets used for this function.

classes

List of class memberships, one per study. Each vector or factor of the list can only contain two levels which correspond to the two conditions studied.

moderated

Method to calculate the test statistic inside each study from which the p-value is computed. moderated has to be chosen between "limma", "SMVar" and "t".

BHth

Benjamini Hochberg threshold. By default, the False Discovery Rate is controlled at 5%.

Value

List

Study1

Vector of indices of differentially expressed genes in study 1. Similar names are given for the other individual studies.

AllIndStudies

Vector of indices of differentially expressed genes found by at least one of the individual studies.

Meta

Vector of indices of differentially expressed genes in the meta-analysis.

TestStatistic

Vector with test statistics for differential expression in the meta-analysis.

References

Marot, G., Foulley, J.-L., Mayer, C.-D., Jaffrezic, F. (2009) Moderated effect size and p-value combinations for microarray meta-analyses. Bioinformatics. 25 (20): 2692-2699.

Examples

# NOT RUN {
data(Singhdata)
#Meta-analysis
res=pvalcombination(esets=Singhdata$esets,classes=Singhdata$classes)
#Number of differentially expressed genes in the meta-analysis
length(res$Meta)
#To plot an histogram of raw p-values
rawpval=2*(1-pnorm(abs(res$TestStatistic)))
hist(rawpval,nclass=100)
# }