Weighted False Discovery Rate Control in Large-Scale Multiple Testing
Weighted False Discovery Rate Control in Large-Scale Multiple Testing
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DOI：
10.1080/01621459.2017.1336443
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发表时间：
2018-01-01
影响因子：
3.7
通讯作者：
Sun, Wenguang
中科院分区：
文献类型：
--
作者：
Basu, Pallavi;Cai, T. Tony;Sun, Wenguang
权重的使用提供了一种将先验领域知识纳入大规模推理的有效策略。本文研究决策理论框架中的加权多重测试。我们开发了预言机和数据驱动的程序，旨在在加权错误发现率的限制下最大化预期的真阳性数量。建立了所提出方法的渐近有效性和最优性。结果表明，结合信息丰富的领域知识可以增强结果的可解释性和推理的准确性。仿真研究表明，所提出的方法将错误率控制在标称水平，并且在许多设置中比现有方法的功率增益是巨大的。讨论了全基因组关联研究的应用。本文的补充材料可在线获取。
The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This article studies weighted multiple testing in a decision-theoretical framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to a genome-wide association study is discussed. Supplementary materials for this article are available online.