The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
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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Cites
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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Self-citationThe control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Adaptive linear step-up procedures that control the false discovery rate
Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006) · Biometrika · doi:10.1093/biomet/93.3.491 · Cited by 3,228
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Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction
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Independent filtering increases detection power for high-throughput experiments
Bourgon, R., Gentleman, R., & Huber, W. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914005107 · Cited by 740
Self-citationA unified approach to false discovery rate estimation
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Cited by
Zhu, A., Ibrahim, J. G., & Love, M. I. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty895 · Cited by 2,072
Universal Patterns of Selection in Cancer and Somatic Tissues
Martincorena, I., Raine, K. M., Gerstung, M., Dawson, K. J., Haase, K., Van Loo, P., Davies, H., Stratton, M. R., & Campbell, P. J. (2017) · Cell · doi:10.1016/j.cell.2017.09.042 · Cited by 1,523
Leveraging Polygenic Functional Enrichment to Improve GWAS Power
Kichaev, G., Bhatia, G., Loh, P.-R., Gazal, S., Burch, K., Freund, M. K., Schoech, A., Pasaniuc, B., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.11.008 · Cited by 926
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770
Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., Panizzolo, E., Martowicz, A., Trebo, M., Pall, G., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.008 · Cited by 477
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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- Ignatiadis, N., Klaus, B., Zaugg, J. B., & Huber, W. (2016). Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nature Methods. https://doi.org/10.1038/nmeth.3885
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