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Title: Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics

In this review, we apply selected imputation strategies to label-free liquid chromatography–mass spectrometry (LC–MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC–MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. In summary, on the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
OSTI Identifier:
1287493
Grant/Contract Number:
AC06-76RL01830; DK071283; HHSN27220080060C; U01CA184783-01; U54-ES016015; P41-RR018522; P41-GM103493
Type:
Accepted Manuscript
Journal Name:
Journal of Proteome Research
Additional Journal Information:
Journal Volume: 14; Journal Issue: 5; Journal ID: ISSN 1535-3893
Publisher:
American Chemical Society (ACS)
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES imputation; label free; peak intensity; accuracy; mean-square error; classification