# P-value combination using Fisher's method

### Description

Combines one sided p-values using Fisher's method.

### Usage

1 | ```
fishercomb(indpval, BHth = 0.05)
``` |

### Arguments

`indpval` |
List of vectors of one sided p-values to be combined. |

`BHth` |
Benjamini Hochberg threshold. By default, the False Discovery Rate is controlled at 5%. |

### Details

The test statistic for each gene *g* is defined as

*F_g = -2 ∑_{s=1}^S ln(p_{gs})*

where *p_{gs}* corresponds to the raw *p*-value obtained for gene *g* in a differential
analysis for study *s* (assumed to be uniformly distributed under the null hypothesis). Under the
null hypothesis, the test statistic *F_g* follows a *chi-squared* distribution with *2S*
degrees of freedom. Classical procedures for the correction of multiple testing, such as that of Benjamini
and Hochberg (1995) may subsequently be applied to the obtained *p*-values to control the false
discovery rate at a desired rate *α*.

### Value

`DEindices ` |
Indices of differentially expressed genes at the chosen Benjamini Hochberg threshold. |

`TestStatistic ` |
Vector with test statistics for differential expression in the meta-analysis. |

`rawpval ` |
Vector with raw p-values for differential expression in the meta-analysis. |

`adjpval ` |
Vector with adjusted p-values for differential expression in the meta-analysis. |

### References

Y. Benjamini and Y. Hochberg (1995). Controlling the false discovery rate: a pratical and powerful approach
to multiple testing. *JRSS B* (57): 289-300.

M. Brown (1975). A method for combining non-independent, one-sided tests of significance. *Biometrics* **31**(4): 987-992.

A. Rau, G. Marot and F. Jaffrezic (2014). Differential meta-analysis of RNA-seq data. *BMC Bioinformatics* **15**:91

### See Also

`metaRNASeq`

### Examples

1 2 3 4 5 6 7 |

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