Proceeding talk – Theme: Data.
Abstract
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. We show that the Empirical Brown’s Method 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. The Empirical Brown’s Method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM. The R code is also available as a Bioconductor package from https://www.bioconductor.org/packages/devel/bioc/html/EmpiricalBrownsMethod.html.
Authors
William Poole, Institute for Systems Biology, United States
David Gibbs, Institute for Systems Biology, United States
Ilya Shmulevich, Institute for Systems Biology, United States
Brady Bernard, Institute for Systems Biology, United States
Theo Knijnenburg, Institute for Systems Biology, United States