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

Bioinformatics. 2016 Sep 1;32(17):i430-i436. doi: 10.1093/bioinformatics/btw438.


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 UsingEBM The R code is also available as a Bioconductor package from


Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Algorithms
  • Data Interpretation, Statistical
  • High-Throughput Screening Assays*
  • Humans
  • Neoplasms
  • Software*