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

Webb-Robertson, Bobbie-Jo M.; Wiberg, Holli K.; Matzke, Melissa M.; Brown, Joseph N.; Wang, Jing; McDermott, Jason E.; Smith, Richard D.; Rodland, Karin D.; Metz, Thomas O.; Pounds, Joel G.; Waters, Katrina M.

doi:10.1021/pr501138h

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

Journal Article · Thu Apr 09 00:00:00 EDT 2015 · Journal of Proteome Research

DOI:https://doi.org/10.1021/pr501138h· OSTI ID:1287493

Webb-Robertson, Bobbie-Jo M. ^[1]; Wiberg, Holli K. ^[1]; Matzke, Melissa M. ^[1]; Brown, Joseph N. ^[1]; Wang, Jing ^[1]; McDermott, Jason E. ^[1]; Smith, Richard D. ^[1]; Rodland, Karin D. ^[1]; Metz, Thomas O. ^[1]; Pounds, Joel G. ^[1]; Waters, Katrina M. ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

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.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC06-76RL01830; DK071283; HHSN27220080060C; U01CA184783-01; U54-ES016015; P41-RR018522; P41-GM103493

OSTI ID:: 1287493

Journal Information:: Journal of Proteome Research, Vol. 14, Issue 5; ISSN 1535-3893

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 145 works

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97 MATHEMATICS AND COMPUTING
59 BASIC BIOLOGICAL SCIENCES
imputation
label free
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