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Statistics in Biosciences

, Volume 10, Issue 1, pp 41–58 | Cite as

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

  • Yi-Hui Zhou
  • Paul Brooks
  • Xiaoshan Wang
Article

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 

Notes

Acknowledgements

This work was supported by R21HG007840.

Supplementary material

12561_2017_9187_MOESM1_ESM.pdf (687 kb)
Supplementary material 1 (pdf 687 KB)

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Copyright information

© International Chinese Statistical Association 2017

Authors and Affiliations

  1. 1.Department of Biological Sciences, Bioinformatics Research CenterNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Statistical Sciences and Operations Research and Department of Supply Chain Management and AnalyticsVirginia Commonwealth UniversityRichmondUSA
  3. 3.IMEDACS, LLCAnn ArborUSA

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