Combining dependent P- values with an empirical adaptation of Brown’s method
2016
Poole, William | Gibbs, David L. | Shmulevich, Ilya | Bernard, Brady | Knijnenburg, Theo A.
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 . Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
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