Multiple competition-based FDR control for peptide detection

TL;DR

This paper introduces a new framework for competition-based false discovery rate (FDR) control in peptide detection using multiple null scores, significantly increasing peptide discoveries compared to existing methods.

Contribution

The paper develops a novel FDR control framework with multiple null scores, providing rigorous finite-sample guarantees and improved peptide detection performance.

Findings

Up to 50% more peptides discovered at small FDR thresholds.

New methods outperform single-decoy approaches.

Framework offers rigorous FDR control in finite samples.

Abstract

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.