PT  - JOURNAL ARTICLE
AU  - William Poole
AU  - David L. Gibbs
AU  - Ilya Shmulevich
AU  - Brady Bernard
AU  - Theo Knijnenburg
TI  - Combining Dependent P-values with an Empirical Adaptation of Brown’s Method
AID  - 10.1101/029637
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 029637
4099  - http://biorxiv.org/content/early/2015/10/22/029637.short
4100  - http://biorxiv.org/content/early/2015/10/22/029637.full
AB  - 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 correlated data sets found in high-throughput biological experiments.Results: We show that Fisher’s Method is biased when used on dependent sets of P-values with both simulated data and gene expression data from The Cancer Genome Atlas (TCGA). When applied on the same data sets, the Empirical Brown’s Method provides a better null distribution and a more conservative result.Availability: The Empirical Brown’s Method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.1