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

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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.

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Acknowledgements

This work was supported by R21HG007840.

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    Authors and Affiliations

    1. Department of Biological Sciences, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA

      Yi-Hui Zhou

    2. Department of Statistical Sciences and Operations Research and Department of Supply Chain Management and Analytics, Virginia Commonwealth University, Richmond, VA, USA

      Paul Brooks

    3. IMEDACS, LLC, Ann Arbor, MI, USA

      Xiaoshan Wang

    Authors
    1. Yi-Hui Zhou
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    2. Paul Brooks
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    3. Xiaoshan Wang
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    Corresponding author

    Correspondence to Yi-Hui Zhou.

    Additional information

    Yi-Hui Zhou and Xiaoshan Wang have contributed equally to this work.

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    Zhou, YH., Brooks, P. & Wang, X. A Two-Stage Hidden Markov Model Design for Biomarker Detection, with Application to Microbiome Research. Stat Biosci 10, 41–58 (2018). https://doi.org/10.1007/s12561-017-9187-y

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    • DOI: https://doi.org/10.1007/s12561-017-9187-y

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