Zhou Yi-Hui, Brooks Paul, Wang Xiaoshan
Department of Biological Sciences, Bioinformatics Research Center, North Carolina State University, North Carolina, United States of America.
Department of Statistical Sciences and Operations Research and Department of Supply Chain Management and Analytics, Virginia Commonwealth University, Virginia, United States of America.
Stat Biosci. 2018 Apr;10(1):41-58. doi: 10.1007/s12561-017-9187-y. Epub 2017 Feb 10.
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.
人们已经认识到，对于适当排序的数据，具有局部错误发现率（FDR）控制的隐马尔可夫模型（HMM）可以提高检测显著关联的能力。对于许多高通量技术而言，成本仍然限制了它们的应用。两阶段设计很有吸引力，即在第一阶段识别出一组有趣的特征或生物标志物，然后在第二阶段进行跟进。然而，据我们所知，尚未开发出基于HMM的两阶段FDR控制方法。在本文中，我们研究了一种基于HMM-FDR的高效两阶段设计，该设计在各个阶段使用简单的综合分析程序。数值研究表明，与现有方法相比，它具有出色的性能。我们还提出了一种功效分析方法。我们使用微生物组数据的例子来说明这些方法。
