ArticleNature methods2016
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.
Article in Nature methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 351 papers.
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Who cites it
351 citing papers in PubMed.
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291 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
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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.
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