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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.

SUBMITTER: Ignatiadis N

PROVIDER: S-EPMC4930141 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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

Ignatiadis Nikolaos N Klaus Bernd B Zaugg Judith B JB Huber Wolfgang W

Nature methods 20160530 7

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 b ...[more]

PMID: 27240256

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