Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

Nikolaos Ignatiadis; Bernd Klaus; Judith B Zaugg; Wolfgang Huber

doi:10.1038/nmeth.3885

Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

Nat Methods. 2016 Jul;13(7):577-80. doi: 10.1038/nmeth.3885. Epub 2016 May 30.

Authors

Nikolaos Ignatiadis¹, Bernd Klaus¹, Judith B Zaugg¹, Wolfgang Huber¹

Affiliation

¹ European Molecular Biology Laboratory, Heidelberg, Germany.

Abstract

Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test's power or prior probability of the null hypothesis (http://www.bioconductor.org/packages/IHW). IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computer Simulation
Data Interpretation, Statistical*
False Positive Reactions
Gene Expression Profiling / methods*
Genome, Human*
Genomics / methods*
Humans
Models, Theoretical*
Software