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J Proteome Res. 2016 Apr 1;15(4):1116-25. doi: 10.1021/acs.jproteome.5b00981. Epub 2016 Mar 1.

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

Lazar C1,2,3, Gatto L4,5, Ferro M1,2,3, Bruley C1,2,3, Burger T1,6,2,3.

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Univ. Grenoble Alpes , iRTSV-BGE, F-38000 Grenoble, France.
CEA, iRTSV-BGE, F-38000 Grenoble, France.
INSERM, BGE, F-38000 Grenoble, France.
Computational Proteomics Unit , Cambridge CB2 1GA, United Kingdom.
Cambridge Center for Proteomics , Cambridge CB2 1GA, United Kingdom.
CNRS, iRTSV-BGE, F-38000 Grenoble, France.


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.


label-free relative quantitative proteomics; missing value imputation

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