Combines one sided p-values using Fisher's method.
Usage
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. Biometrics31(4): 987-992.
A. Rau, G. Marot and F. Jaffrezic (2014). Differential meta-analysis of RNA-seq data. BMC Bioinformatics15:91
See Also
metaRNASeq
Examples
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")