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

TLDR

Missing values are a major challenge in label‑free quantitative proteomics, and prior imputation surveys have overlooked that missingness mechanisms differ across datasets and that each method is tailored to a specific mechanism. The study aims to identify the most appropriate imputation method for a given dataset rather than a universally best one, and to provide practical guidelines for selecting and applying imputation strategies. Comparisons show that an apparently under‑performing method can outperform a state‑of‑the‑art method when applied at the correct stage of the pipeline and to a dataset with matching missingness, supporting the proposed guidelines.

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.