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

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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
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    References listed on IDEAS

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    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.
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