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

Hypothesis weighting improves the power of large-scale multiple testing. We describe a method that uses covariates independent of the p-values under the null hypothesis, but informative of each test’s power or prior probability of the null hypothesis. Independent hypothesis weighting (IHW) increases...

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Autores principales: Ignatiadis, Nikolaos, Klaus, Bernd, Zaugg, Judith, Huber, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930141/
https://www.ncbi.nlm.nih.gov/pubmed/27240256
http://dx.doi.org/10.1038/nmeth.3885
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author Ignatiadis, Nikolaos
Klaus, Bernd
Zaugg, Judith
Huber, Wolfgang
author_facet Ignatiadis, Nikolaos
Klaus, Bernd
Zaugg, Judith
Huber, Wolfgang
author_sort Ignatiadis, Nikolaos
collection PubMed
description Hypothesis weighting improves the power of large-scale multiple testing. We describe a method that uses covariates independent of the p-values under the null hypothesis, but informative of each test’s power or prior probability of the null hypothesis. Independent hypothesis weighting (IHW) increases power while controlling the false discovery rate (FDR). IHW is a practical approach to discover associations in large datasets as encountered in genomics and high-throughput biology. Availability: www.bioconductor.org/packages/IHW
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spelling pubmed-49301412016-11-30 Data-driven hypothesis weighting increases detection power in genome-scale multiple testing Ignatiadis, Nikolaos Klaus, Bernd Zaugg, Judith Huber, Wolfgang Nat Methods Article Hypothesis weighting improves the power of large-scale multiple testing. We describe a method that uses covariates independent of the p-values under the null hypothesis, but informative of each test’s power or prior probability of the null hypothesis. Independent hypothesis weighting (IHW) increases power while controlling the false discovery rate (FDR). IHW is a practical approach to discover associations in large datasets as encountered in genomics and high-throughput biology. Availability: www.bioconductor.org/packages/IHW 2016-05-30 2016-07 /pmc/articles/PMC4930141/ /pubmed/27240256 http://dx.doi.org/10.1038/nmeth.3885 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Ignatiadis, Nikolaos
Klaus, Bernd
Zaugg, Judith
Huber, Wolfgang
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
title Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
title_full Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
title_fullStr Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
title_full_unstemmed Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
title_short Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
title_sort data-driven hypothesis weighting increases detection power in genome-scale multiple testing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930141/
https://www.ncbi.nlm.nih.gov/pubmed/27240256
http://dx.doi.org/10.1038/nmeth.3885
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