title: Combining dependent P-values with an empirical adaptation of Brown's method.
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 (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.
  
  
  
  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
  
  
  
  Theo.Knijnenburg@systemsbiology.org
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Institute for Systems Biology, Seattle, WA 98109-5263, USA.
authors: William
authors: W
authors: Poole
authors: Institute for Systems Biology, Seattle, WA 98109-5263, USA.
authors: David L
authors: DL
authors: Gibbs
authors: Institute for Systems Biology, Seattle, WA 98109-5263, USA.
authors: Ilya
authors: I
authors: Shmulevich
authors: Institute for Systems Biology, Seattle, WA 98109-5263, USA.
authors: Brady
authors: B
authors: Bernard
authors: Institute for Systems Biology, Seattle, WA 98109-5263, USA.
authors: Theo A
authors: TA
authors: Knijnenburg
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D057166:High-Throughput Screening Assays; D006801:Humans; D009369:Neoplasms; D012984:Software