Improved detection of differentially abundant proteins through FDR-control of peptide-identity-propagation

We present a method for FDR control of PIP, called “PIP-ECHO” (PIP Error Control via Hybrid cOmpetition) and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that PIP-ECHO can accurately control the FDR of PIP at 1% across multiple datasets. Only PIP-ECHO was able to control the FDR in data with injected sample size equivalent to a single-cell dataset. The three other methods fail to control the FDR at 1%, yielding false discovery proportions ranging from 2-6%. We demonstrate the practical implications of this work by performing differential expression analyses on spike-in datasets, where different known amounts of yeast or E. coli peptides are added to a constant background of HeLa cell lysate peptides. In this setting, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis: our implementation of PIP-ECHO within FlashLFQ can detect twice as many differentially abundant proteins as MaxQuant and three times as many as IonQuant in the spike-in dataset. This repository contains the raw data for one of the datasets used to evaluate the error rate of PIP, as well as the database search and quantitative results obtained for all of the datasets examined.