Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
Nature methods, 2016•nature.com
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.
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.
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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