IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v10y2018i1d10.1007_s12561-017-9187-y.html
   My bibliography  Save this article

A Two-Stage Hidden Markov Model Design for Biomarker Detection, with Application to Microbiome Research

Author

Listed:
  • Yi-Hui Zhou

    (North Carolina State University)

  • Paul Brooks

    (Virginia Commonwealth University)

  • Xiaoshan Wang

    (IMEDACS, LLC)

Abstract

It has been recognized that for appropriately ordered data, hidden Markov models (HMM) with local false discovery rate (FDR) control can increase the power to detect significant associations. For many high-throughput technologies, the cost still limits their application. Two-stage designs are attractive, in which a set of interesting features or biomarkers is identified in a first stage and then followed up in a second stage. However, to our knowledge, no two-stage FDR control with HMMs has been developed. In this paper, we study an efficient HMM–FDR-based two-stage design, using a simple integrated analysis procedure across the stages. Numeric studies show its excellent performance when compared to available methods. A power analysis method is also proposed. We use examples from microbiome data to illustrate the methods.

Suggested Citation

  • Yi-Hui Zhou & Paul Brooks & Xiaoshan Wang, 2018. "A Two-Stage Hidden Markov Model Design for Biomarker Detection, with Application to Microbiome Research," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 41-58, April.
  • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9187-y
    DOI: 10.1007/s12561-017-9187-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-017-9187-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12561-017-9187-y?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sanat K. Sarkar & Jingjing Chen & Wenge Guo, 2013. "Multiple Testing in a Two-Stage Adaptive Design With Combination Tests Controlling FDR," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1385-1401, December.
    2. Stephen A Stanhope & Andrew D Skol, 2012. "Improved Minimum Cost and Maximum Power Two Stage Genome-Wide Association Study Designs," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-10, September.
    3. Wenguang Sun & T. Tony Cai, 2009. "Large‐scale multiple testing under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 393-424, April.
    4. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
    Full references (including those not matched with items on IDEAS)

    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. Lee, Donghwan & Lee, Youngjo, 2016. "Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 1-13.
    2. Pei Fen Kuan & Derek Y. Chiang, 2012. "Integrating Prior Knowledge in Multiple Testing under Dependence with Applications to Detecting Differential DNA Methylation," Biometrics, The International Biometric Society, vol. 68(3), pages 774-783, September.
    3. Hai Shu & Bin Nan & Robert Koeppe, 2015. "Multiple testing for neuroimaging via hidden Markov random field," Biometrics, The International Biometric Society, vol. 71(3), pages 741-750, September.
    4. 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.
    5. Chang Yu & Daniel Zelterman, 2020. "Distributions associated with simultaneous multiple hypothesis testing," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-17, December.
    6. T. Tony Cai & Weidong Liu, 2016. "Large-Scale Multiple Testing of Correlations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 229-240, March.
    7. Pallavi Basu & Luella Fu & Alessio Saretto & Wenguang Sun, 2021. "Empirical Bayes Control of the False Discovery Exceedance," Working Papers 2115, Federal Reserve Bank of Dallas.
    8. He, Li & Sarkar, Sanat K. & Zhao, Zhigen, 2015. "Capturing the severity of type II errors in high-dimensional multiple testing," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 106-116.
    9. Pounds Stanley B. & Gao Cuilan L. & Zhang Hui, 2012. "Empirical Bayesian Selection of Hypothesis Testing Procedures for Analysis of Sequence Count Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-32, October.
    10. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    11. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.
    12. 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.
    13. 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.
    14. Won, Joong-Ho & Lim, Johan & Yu, Donghyeon & Kim, Byung Soo & Kim, Kyunga, 2014. "Monotone false discovery rate," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 86-93.
    15. van Wieringen, Wessel N. & Stam, Koen A. & Peeters, Carel F.W. & van de Wiel, Mark A., 2020. "Updating of the Gaussian graphical model through targeted penalized estimation," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    16. Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
    17. Angela Schörgendorfer & Adam J. Branscum & Timothy E. Hanson, 2013. "A Bayesian Goodness of Fit Test and Semiparametric Generalization of Logistic Regression with Measurement Data," Biometrics, The International Biometric Society, vol. 69(2), pages 508-519, June.
    18. Qingyun Cai & Hock Peng Chan, 2017. "A Double Application of the Benjamini-Hochberg Procedure for Testing Batched Hypotheses," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 429-443, June.
    19. Han, Bing & Dalal, Siddhartha R., 2012. "A Bernstein-type estimator for decreasing density with application to p-value adjustments," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 427-437.
    20. Dalia Valencia & Rosa E. Lillo & Juan Romo, 2019. "A Kendall correlation coefficient between functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1083-1103, December.

    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:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9187-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.