P-value combination using Fisher's method

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Description

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

Usage

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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

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data(rawpval)
fishcomb <- fishercomb(rawpval, BHth = 0.05)
DE <- ifelse(fishcomb$adjpval<=0.05,1,0)
hist(fishcomb$rawpval,nclass=100)

## A more detailed example is given in the vignette of the package:
## vignette("metaRNASeq")