Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

Abstract

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.his work was supported by the following funding: ANR-2010-GENOM-BTV-002-01 (Chloro-Types), ANR-10-INBS-08 (ProFI project, “Infrastructures Nationales en Biologie et Santé”, “Investissements d’Avenir”), EU FP7 program (Prime-XS project, Contract no. 262067), the Prospectom project (Mastodons 2012 CNRS challenge), and the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1).This is the final version of the article. It first appeared from the American Chemical Society via https://dx.doi.org/10.1021/acs.jproteome.5b0098