Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics
Abstract
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:
-
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1287493
- Grant/Contract Number:
- AC06-76RL01830; DK071283; HHSN27220080060C; U01CA184783-01; U54-ES016015; P41-RR018522; P41-GM103493
- Resource 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)
- 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
Citation Formats
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., and Waters, Katrina M. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. United States: N. p., 2015.
Web. doi:10.1021/pr501138h.
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. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. United States. https://doi.org/10.1021/pr501138h
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., and Waters, Katrina M. Thu .
"Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics". United States. https://doi.org/10.1021/pr501138h. https://www.osti.gov/servlets/purl/1287493.
@article{osti_1287493,
title = {Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics},
author = {Webb-Robertson, Bobbie-Jo M. and Wiberg, Holli K. and Matzke, Melissa M. and Brown, Joseph N. and Wang, Jing and McDermott, Jason E. and Smith, Richard D. and Rodland, Karin D. and Metz, Thomas O. and Pounds, Joel G. and Waters, Katrina M.},
abstractNote = {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.},
doi = {10.1021/pr501138h},
journal = {Journal of Proteome Research},
number = 5,
volume = 14,
place = {United States},
year = {Thu Apr 09 00:00:00 EDT 2015},
month = {Thu Apr 09 00:00:00 EDT 2015}
}
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Figure 1: Average log10 intensity as measured by peptide peak area in the control group versus fraction of missing values and peptide counts associated with bins corresponding to the fraction of missing data comparing phenotypes and exposures for datasets from (A) human plasma and (B) mouse lung. The control groupmore »
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