R: Integration-driven Discovery and Integration-driven Revision...

IDD.IRR {metaRNASeq} | R Documentation

## 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

    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

    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)

[Package metaRNASeq version 1.0.2 Index]