metaMA (version 3.1.3)

EScombination.paired: Effect size combination for paired data

Description

Calculates effect sizes from paired data either from classical or moderated t-tests (Limma, SMVar) for each study and combines these effect sizes.

Usage

EScombination.paired(logratios, moderated = c("limma", "SMVar", "t")[1], BHth = 0.05)

Arguments

logratios

List of matrices (or data frames). Each matrix has one row per gene and one column per replicate and gives the logratios of one study. All studies must have the same genes.

moderated

Method to calculate the test statistic inside each study from which the effect size 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)
#create artificially paired data:
artificialdata=lapply(Singhdata$esets,FUN=function(x) (x[,1:10]-x[,11:20]))
#Meta-analysis
res=EScombination.paired(artificialdata)
#Number of differentially expressed genes in the meta-analysis
length(res$Meta)
#To plot an histogram of raw p-values
rawpval=2*(1-pnorm(abs(res$TestStatistic)))
hist(rawpval,nclass=100)
# }