Combining p-values from dependent tests

A Python port of R code given in below publication

Dai, H., Leeder, J. S., & Cui, Y. (2013). A modified generalized Fisher method for combining probabilities from dependent tests. Frontiers in genetics, 5, 32-32.

Installation

pip install git+https://github.com/gungorbudak/cdpvals.git

Usage

from cdpvals import self_contained
from cdpvals import competitive


pvals = [0.06, 0.15]
pmat = [ [0.02,0.06,0.07,0.01,0.02,0.09,0.01], [0.01,0.10,0.12,0.14,0.07,0.09,0.10] ]
print self_contained(pvals, pmat)
print competitive(pvals, pmat)

Documentation

This package is using Lancaster procedure (a generalized Fisher's method) with weight functions including Satterthwaite's approximation to model correlations among p-values.

self_contained

Test H0: p-value is uniform vs Ha: p-value is not uniform

Parameters

pvals: a list of p-values to be combined
pmat: a list of lists of p-values randomly obtained from the data
weights: a list of weights for p-values in pvals

competitive

Test whether p-value is more significant than randomly selected p-values.

Parameters

pvals: a list of p-values to be combined
pmat: a list of lists of p-values randomly obtained from the data
weights: a list of weights for p-values in pvals
n: number of iterations to compute random pvals when pmat is not given