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

Yi-Hui Zhou (), Paul Brooks and Xiaoshan Wang ()
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Yi-Hui Zhou: North Carolina State University
Paul Brooks: Virginia Commonwealth University
Xiaoshan Wang: IMEDACS, LLC

Statistics in Biosciences, 2018, vol. 10, issue 1, No 4, 58 pages

Abstract: 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.

Keywords: Biomarker; False discovery rates; Hidden Markov model; Metagenomics; Metatranscriptomics; PCR (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12561-017-9187-y

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