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A powerful procedure that controls the false discovery rate with directional information

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  • Zhaoyang Tian
  • Kun Liang
  • Pengfei Li

Abstract

In many multiple testing applications in genetics, the signs of the test statistics provide useful directional information, such as whether genes are potentially up‐ or down‐regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are P‐value based and ignore such directional information. We introduce a novel procedure, the signed‐knockoff procedure, to utilize the directional information and control the FDR in finite samples. We demonstrate the power advantage of our procedure through simulation studies and two real applications.

Suggested Citation

  • Zhaoyang Tian & Kun Liang & Pengfei Li, 2021. "A powerful procedure that controls the false discovery rate with directional information," Biometrics, The International Biometric Society, vol. 77(1), pages 212-222, March.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:212-222
    DOI: 10.1111/biom.13277
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    References listed on IDEAS

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    2. Sun, Wenguang & Cai, T. Tony, 2007. "Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 901-912, September.
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    4. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    5. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
    6. Peter Muller & Giovanni Parmigiani & Christian Robert & Judith Rousseau, 2004. "Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 990-1001, December.
    7. Ang Li & Rina Foygel Barber, 2019. "Multiple testing with the structure‐adaptive Benjamini–Hochberg algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(1), pages 45-74, February.
    8. Lihua Lei & William Fithian, 2018. "AdaPT: an interactive procedure for multiple testing with side information," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 649-679, September.
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    Cited by:

    1. Dennis Leung & Wenguang Sun, 2022. "ZAP: Z$$ Z $$‐value adaptive procedures for false discovery rate control with side information," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1886-1946, November.

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