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

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.