title: 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.
authors.affiliation: Univ. Grenoble Alpes , iRTSV-BGE, F-38000 Grenoble, France.|CEA, iRTSV-BGE, F-38000 Grenoble, France.|INSERM, BGE, F-38000 Grenoble, France.
authors.forename: Cosmin
authors.initials: C
authors.lastname: Lazar
authors.affiliation: Computational Proteomics Unit , Cambridge CB2 1GA, United Kingdom.|Cambridge Center for Proteomics , Cambridge CB2 1GA, United Kingdom.
authors.forename: Laurent
authors.initials: L
authors.lastname: Gatto
authors.affiliation: Univ. Grenoble Alpes , iRTSV-BGE, F-38000 Grenoble, France.|CEA, iRTSV-BGE, F-38000 Grenoble, France.|INSERM, BGE, F-38000 Grenoble, France.
authors.forename: Myriam
authors.initials: M
authors.lastname: Ferro
authors.affiliation: Univ. Grenoble Alpes , iRTSV-BGE, F-38000 Grenoble, France.|CEA, iRTSV-BGE, F-38000 Grenoble, France.|INSERM, BGE, F-38000 Grenoble, France.
authors.forename: Christophe
authors.initials: C
authors.lastname: Bruley
authors.affiliation: Univ. Grenoble Alpes , iRTSV-BGE, F-38000 Grenoble, France.|CNRS, iRTSV-BGE, F-38000 Grenoble, France.|CEA, iRTSV-BGE, F-38000 Grenoble, France.|INSERM, BGE, F-38000 Grenoble, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Burger
mesh_terms: D000230:Adenocarcinoma / Q000737:chemistry* / Q000175:diagnosis / Q000378:metabolism; D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000737:chemistry* / Q000175:diagnosis / Q000378:metabolism; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D066264:Datasets as Topic; D006801:Humans; D008175:Lung Neoplasms / Q000737:chemistry* / Q000175:diagnosis / Q000378:metabolism; D013058:Mass Spectrometry / Q000706:statistics & numerical data; D009363:Neoplasm Proteins / Q000032:analysis* / Q000378:metabolism; D010455:Peptides / Q000032:analysis* / Q000378:metabolism; D040901:Proteomics / Q000706:statistics & numerical data*
chemical_list: D009363:Neoplasm Proteins; D010455:Peptides
keywords: label-free relative quantitative proteomics; missing value imputation