Combining dependent P-values with an empirical adaptation of Brown's method

Abstract

Motivation: Combining P-values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P-values. Here, we discuss an empirical adaptation of Brown's method (an extension of Fisher's method) for combining dependent P-values which is appropriate for the large and correlated datasets found in high-throughput biology. Results: We show that the Empirical Brown's method (EBM) outperforms Fisher's method as well as alternative approaches for combining dependent P-values using both noisy simulated data and gene expression data from The Cancer Genome Atlas. Availability and Implementation: The Empirical Brown's method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvalues UsingEBM. The R code is also available as a Bioconductor package from https://www.bioconductor.org/packages/devel/bioc/html/EmpiricalBrownsMethod.html.