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

  title={Data-driven hypothesis weighting increases detection power in genome-scale multiple testing},
  author={Nikolaos Ignatiadis and Bernd Klaus and Judith B. Zaugg and Wolfgang Huber},
  booktitle={Nature Methods},
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 ( IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput… CONTINUE READING
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Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction

  • B. Efron
  • 2010
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7 Excerpts

Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics (Cambridge

  • A. van der Vaart
  • Nature Methods:
  • 2000
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3 Excerpts

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