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

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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 The R code is also available as a Bioconductor package from

Supplementary information: Supplementary data are available at Bioinformatics online.

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