IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v77y2021i1p212-222.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13277
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13277?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Zhao, Haibing & Fung, Wing Kam, 2016. "A powerful FDR control procedure for multiple hypotheses," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 60-70.
    6. 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.
    7. 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.
    8. Kun Liang & Dan Nettleton, 2012. "Adaptive and dynamic adaptive procedures for false discovery rate control and estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 163-182, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    2. Ghosh Debashis, 2012. "Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-21, July.
    3. Xiaoquan Wen, 2017. "Robust Bayesian FDR Control Using Bayes Factors, with Applications to Multi-tissue eQTL Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 28-49, June.
    4. Kim, Donggyu & Zhang, Chunming, 2014. "Adaptive linear step-up multiple testing procedure with the bias-reduced estimator," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 31-39.
    5. Joshua Habiger & David Watts & Michael Anderson, 2017. "Multiple testing with heterogeneous multinomial distributions," Biometrics, The International Biometric Society, vol. 73(2), pages 562-570, June.
    6. Alejandro Ochoa & John D Storey & Manuel Llinás & Mona Singh, 2015. "Beyond the E-Value: Stratified Statistics for Protein Domain Prediction," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-21, November.
    7. 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.
    8. Tingting Cui & Pengfei Wang & Wensheng Zhu, 2021. "Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 737-757, September.
    9. Habiger, Joshua D. & Peña, Edsel A., 2014. "Compound p-value statistics for multiple testing procedures," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 153-166.
    10. Daniel Yekutieli, 2015. "Bayesian tests for composite alternative hypotheses in cross-tabulated data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 287-301, June.
    11. Ruth Heller & Saharon Rosset, 2021. "Optimal control of false discovery criteria in the two‐group model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 133-155, February.
    12. Sairam Rayaprolu & Zhiyi Chi, 2021. "False Discovery Variance Reduction in Large Scale Simultaneous Hypothesis Tests," Methodology and Computing in Applied Probability, Springer, vol. 23(3), pages 711-733, September.
    13. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    14. Gómez-Villegas Miguel A. & Salazar Isabel & Sanz Luis, 2014. "A Bayesian decision procedure for testing multiple hypotheses in DNA microarray experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 49-65, February.
    15. Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021. "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    16. Edsel Peña & Joshua Habiger & Wensong Wu, 2015. "Classes of multiple decision functions strongly controlling FWER and FDR," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(5), pages 563-595, July.
    17. Muir, W.M. & Rosa, G.J.M. & Pittendrigh, B.R. & Xu, Z. & Rider, S.D. & Fountain, M. & Ogas, J., 2009. "A mixture model approach for the analysis of small exploratory microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1566-1576, March.
    18. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    19. Zhao, Haibing & Fung, Wing Kam, 2016. "A powerful FDR control procedure for multiple hypotheses," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 60-70.
    20. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:212-222. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.