用于肽检测的基于多重竞争的FDR对照
Multiple competition-based FDR control for peptide detection
摘要：
基于竞争的FDR控制在计算质谱领域已经普遍使用了十多年（Elias和Gygi，2007）。最近，在Barber和Candes（2015）在包括特征选择问题在内的更一般的环境中奠定了理论基础之后，该方法在其他领域获得了极大的普及。在这两种情况下，竞争是基于观察到的分数和相应的诱饵/仿制品之间的头对头比较。Keich和Noble（2017 b）最近证明了在解决将肽序列分配给观察到的质谱的问题时使用多个而不是单个诱饵的一些优势。在这项工作中，我们考虑了一个相关的问题-检测肽的基础上收集的质谱-我们开发了一个新的框架，基于竞争的FDR控制使用多个空分数。在这个框架内，我们提供了几种方法，所有这些方法都是基于一个新的程序，严格控制有限样本设置中的FDR。使用真实数据来研究肽检测问题，我们表明，相对于现有的单一诱饵方法，我们的方法可以增加发现的肽的数量高达50%，在小FDR阈值。
英文摘要：
Competition-based FDR control has been commonly used for over a decade in the computational mass spectrometry community (Elias and Gygi, 2007). Recently, the approach has gained significant popularity in other fields after Barber and Candes (2015) laid its theoretical foundation in a more general setting that included the feature selection problem. In both cases, the competition is based on a head-to-head comparison between an observed score and a corresponding decoy / knockoff. Keich and Noble (2017b) recently demonstrated some advantages of using multiple rather than a single decoy when addressing the problem of assigning peptide sequences to observed mass spectra. In this work, we consider a related problem -- detecting peptides based on a collection of mass spectra -- and we develop a new framework for competition-based FDR control using multiple null scores. Within this framework, we offer several methods, all of which are based on a novel procedure that rigorously controls the FDR in the finite sample setting. Using real data to study the peptide detection problem we show that, relative to existing single-decoy methods, our approach can increase the number of discovered peptides by up to 50% at small FDR thresholds.
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