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

@inproceedings{Ignatiadis2016DatadrivenHW,
  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},
  year={2016}
}
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… CONTINUE READING
Highly Cited
This paper has 141 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 128 times over the past 90 days. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.
15 Citations
51 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 15 extracted citations

142 Citations

050100150201620172018
Citations per Year
Semantic Scholar estimates that this publication has 142 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 51 references

Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction

  • B. Efron
  • 2010
Highly Influential
7 Excerpts

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

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

Similar Papers

Loading similar papers…