Directional False Discovery Rate Control in Large-Scale Multiple Testing Under Data Dependence

IF 1.5 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2025-08-31 DOI:10.1002/asmb.70041

Wendong Li, Jianqing Shi, Yi Wang, Dongdong Xiang

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引用次数: 0

Abstract

Detecting directional signals in multiple testing is crucial to take targeted and effective measures. In this article, we consider the directional multiple testing under the dependence problem within a three-group model. Given the assumption that the observed data are generated according to an underlying three-state hidden Markov model, we develop oracle and data-driven procedures to maximize the expected number of true discoveries (ETD) while controlling the false discovery rates (FDRs) of both alternative states at their nominal levels. It is shown theoretically that the proposed directional multiple testing procedures are valid and have certain optimality properties for directional FDR-control. An extensive numerical study shows that our procedures are significantly more powerful than their competitors since the former can accommodate the dependence structure among hypotheses. The proposed procedures also exhibit high flexibility by allowing different nominal levels for the two alternative states, which is appealing in cases when the false discoveries of different alternative states are not equally important. As a demonstration, the proposed data-driven procedure is applied to learn the transcriptomic characteristics of bronchoalveolar lavage fluid in COVID-19 patients.

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数据依赖下大规模多重测试的定向错误发现率控制

在多次测试中检测方向信号，采取有针对性的有效措施至关重要。在本文中，我们考虑了三组模型中依赖问题下的定向多重检验。假设观察到的数据是根据底层的三状态隐马尔可夫模型生成的，我们开发了oracle和数据驱动的过程，以最大化真实发现（ETD）的预期数量，同时将两种可选状态的错误发现率（fdr）控制在其名义水平上。从理论上证明了所提出的定向多重测试方法对定向fdr控制是有效的，并具有一定的最优性。一项广泛的数值研究表明，由于前者可以适应假设之间的依赖结构，我们的程序明显比他们的竞争对手更强大。所提出的程序还表现出高度的灵活性，允许两种可选状态的不同名义水平，这在不同可选状态的错误发现并不同等重要的情况下很有吸引力。作为演示，应用所提出的数据驱动程序来了解COVID-19患者支气管肺泡灌洗液的转录组学特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.