IDDIRR {metaMA}

Integration-driven Discovery and Integration-driven Revision Rates

Description

Calculates the gain or the loss of differentially expressed genes due to meta-analysis compared to individual studies.

Usage

IDDIRR(finalde, deindst)

Arguments

finalde

Vector of indices of differentially expressed genes after meta-analysis

deindst

Vector of indices of differentially expressed genes found at least in one study

Value

DE

Number of Differentially Expressed (DE) genes

IDD

Integration Driven Discoveries: number of genes that are declared DE in the meta-analysis that were not identified in any of the individual studies alone.

Loss

Number of genes that are declared DE in individual studies but not in meta-analysis.

IDR

Integration-driven Discovery Rate: proportion of genes that are identified as DE in the meta-analysis that were not identified in any of the individual studies alone.

IRR

Integration-driven Revision Rate: percentage of genes that are declared DE in individual studies but not in meta-analysis.

Author(s)

Guillemette Marot

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

data(Singhdata)
out=EScombination(esets=Singhdata$esets,classes=Singhdata$classes)
IDDIRR(out$Meta,out$AllIndStudies)

## The function is currently defined as
#function(finalde,deindst)
#{
#DE=length(finalde)
#gains=finalde[which(!(finalde %in% deindst))]
#IDD=length(gains)
#IDR=IDD/DE*100
#perte=which(!(deindst %in% finalde))
#Loss=length(perte)
#IRR=Loss/length(deindst)*100
#res=c(DE,IDD,Loss,IDR,IRR)
#names(res)=c("DE","IDD","Loss","IDR","IRR")
#res
#}