Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.
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- Record sourced from PubMed, PMID 27240256.
- Also identified by DOI 10.1038/nmeth.3885 and PMC identifier 4930141.
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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.
Medical subject headings
- Algorithms
- Data Interpretation, Statistical
- Gene Expression Profiling
- Genome, Human
- Genomics
- Models, Theoretical