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