Weighted False Discovery Rate Control in Large-Scale Multiple Testing
研究在决策理论框架下，利用加权方法整合先验领域知识，提出控制加权错误发现率并最大化真阳性期望数量的最优程序，模拟和基因组实例显示其有效性。
The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic 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.