论文标题
基于竞赛的虚假发现比例的控制
Competition-based control of the false discovery proportion
论文作者
论文摘要
最近，理发师和Candès奠定了基于“仿制”概念的错误发现率（FDR）控制的一般框架的理论基础。长期以来，在质谱数据的分析中，长期以来使用了密切相关的FDR控制方法，该方法称为“目标 - 竞争竞争”（TDC）。但是，任何旨在控制FDR的方法，该方法被定义为错误发现比例（FDP）的期望值，都遇到了问题。具体而言，即使成功控制FDR $α$，发现中的FDP也可能会大大超过$α$。我们提供FDP-SD，这是一种新的程序，该程序通过确保FDP在任何期望的置信度水平上都受$α$的限制，从而严格控制竞争对手中的FDP（仿基 / TDC）。与Katsevich和Ramdas的刚刚发布的一般框架相比，FDP-SD通常会提供更多的功率，并且通常在模拟和真实数据中基本上如此。
Recently, Barber and Candès laid the theoretical foundation for a general framework for false discovery rate (FDR) control based on the notion of "knockoffs." A closely related FDR control methodology has long been employed in the analysis of mass spectrometry data, referred to there as "target-decoy competition" (TDC). However, any approach that aims to control the FDR, which is defined as the expected value of the false discovery proportion (FDP), suffers from a problem. Specifically, even when successfully controlling the FDR at level $α$, the FDP in the list of discoveries can significantly exceed $α$. We offer FDP-SD, a new procedure that rigorously controls the FDP in the competition (knockoff / TDC) setup by guaranteeing that the FDP is bounded by $α$ at any desired confidence level. Compared with the just-published general framework of Katsevich and Ramdas, FDP-SD generally delivers more power and often substantially so in simulated as well as real data.
