Integration-driven Discovery and Integration-driven Revision Rates

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Description

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

Usage

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IDD.IRR(meta_de, ind_de)

Arguments

meta_de

Vector of differentially expressed tags (or indices of these tags) with the meta-analysis

ind_de

List of vectors storing differentially expressed tags (or indices of these tags) in each individual 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

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data(rawpval)
adjpval<-lapply(rawpval, FUN=function(x) p.adjust(x, method="BH"))
ind_smalladjp<-lapply(adjpval, FUN=function(x) which(x <= 0.05))
#indicators corresponding to the inverse normal p-value combination
invnormcomb <- invnorm(rawpval,nrep=c(8,8), BHth = 0.05) 
IDD.IRR(invnormcomb$DEindices,ind_smalladjp)
#indicators corresponding to the p-value combination with Fisher's method
fishcomb <- fishercomb(rawpval, BHth = 0.05)
IDD.IRR(fishcomb$DEindices,ind_smalladjp)