title: Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.
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.
authors.affiliation: European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Ignatiadis
authors.affiliation: European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Bernd
authors.initials: B
authors.lastname: Klaus
authors.affiliation: European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Judith B
authors.initials: JB
authors.lastname: Zaugg
authors.affiliation: European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Wolfgang
authors.identifier: 0000-0002-0474-2218
authors.initials: W
authors.lastname: Huber
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods*; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D012984:Software