# 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

1 | ```
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

1 2 3 4 5 6 7 8 9 | ```
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)
``` |

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