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Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
Open accessAbstract
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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“-54) package, while two-tailed p-values obtained from Wald testing were adjusted with the Bioconductor Independent Hypothesis Weighting [89] (IHW, 1.”
MethodInfluential
Metadata
- Authors
- Ignatiadis, N., Klaus, B., Zaugg, J. B., & Huber, W.
- Venue
- Nature Methods
- Published
- 2016-05-30
- Volume
- 13
- Issue
- 7
- Pages
- 577-580
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2418024629
- Cited by
- 649
- References
- 28
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- green
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