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Combining Dependent P-values with an Empirical Adaptation of Brown’s Method

William Poole, David L. Gibbs, Ilya Shmulevich, Brady Bernard, Theo Knijnenburg
doi: https://doi.org/10.1101/029637
William Poole
Institute for Systems Biology, Seattle, WA, US
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David L. Gibbs
Institute for Systems Biology, Seattle, WA, US
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Ilya Shmulevich
Institute for Systems Biology, Seattle, WA, US
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Brady Bernard
Institute for Systems Biology, Seattle, WA, US
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Theo Knijnenburg
Institute for Systems Biology, Seattle, WA, US
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Abstract

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

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 22, 2015.
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Combining Dependent P-values with an Empirical Adaptation of Brown’s Method
William Poole, David L. Gibbs, Ilya Shmulevich, Brady Bernard, Theo Knijnenburg
bioRxiv 029637; doi: https://doi.org/10.1101/029637
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Combining Dependent P-values with an Empirical Adaptation of Brown’s Method
William Poole, David L. Gibbs, Ilya Shmulevich, Brady Bernard, Theo Knijnenburg
bioRxiv 029637; doi: https://doi.org/10.1101/029637

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