Target-decoy competition (TDC) is commonly used in the computational mass spectrometry community for false discovery rate (FDR) control (6). Recently, TDC's underlying approach of competition-based FDR control has gained significant popularity in other fields after Barber and Candes laid its theoretical foundation in a more general setting (1). 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 al level {\alpha}, the FDP in our list of discoveries can significantly exceed {\alpha}. We offer two new procedures to address this problem. TDC-SD rigorously controls the FDP in the competition setup by guaranteeing that the FDP is bounded by {\alpha} at any desired confidence level. The complementary TDC-UB is designed to bound the FDP for any list of top scoring target discoveries. For example, TDC-UB can give a 95%-confidence upper-bound on the FDP when using the canonical TDC procedure for controlling the FDR. We demonstrate the utility of these procedures using synthetic and real data in multiple domains.

翻译：目标标记竞争(TDC)通常用于计算质量分光谱社区,以控制虚假发现率(FDR) (6)。最近,在Barber和Candes在更笼统的环境下奠定了理论基础之后,TDC以竞争为基础的FDR控制基本方法在其他领域受到欢迎(1)。然而,任何旨在控制FDR(被界定为虚假发现比例(FDP)的预期值)的方法都存在问题。具体地说,即使成功地控制FDR al 水平(alpha}),我们发现清单中的FDP(FDP)可能大大超过~alpha}。我们提供了两个解决这一问题的新程序。TDC-SD严格控制FDP(FDP)在竞争设置中的竞争设置,保证FDP(FDP)在任何期望的信任级别上都受~alpha}的束缚。补充TDC-UB(UB)的目的是将FDP(FDP)绑定任何最高得分值目标发现清单。例如TDC-UB(FDP)在使用罐体TDC程序控制FDR(TDR)时,我们用多种合成域显示这些程序的效用。