Literature DB >> 27587659

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

William Poole1, David L Gibbs1, Ilya Shmulevich1, Brady Bernard1, Theo A Knijnenburg1.   

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 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: Theo.Knijnenburg@systemsbiology.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2016        PMID: 27587659      PMCID: PMC5013915          DOI: 10.1093/bioinformatics/btw438

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


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