A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics
Open accessAbstract
The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.
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In large multiplexed experiments compared with label-free approaches, the missing-data pattern is distinct, but the overall relationship between abundance and missingness is similar.
“Evaluating large, multiplexed experiments in comparison to label-free approaches, the pattern of the missing data appears to be very distinct, but the macrostructure overall is similar in regard to the relationship between abundance and missing data.(8)”
From Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? (2022) · cites this paper as [8]
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- Authors
- Bramer, L. M., Irvahn, J., Piehowski, P. D., Rodland, K. D., & Webb-Robertson, B.-J. M.
- Affiliations
- Pacific Northwest National Laboratory (US)
- Boeing (United States) (US)
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, Washington 99354, United States
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-09-15
- Volume
- 20
- Issue
- 1
- Pages
- 1-13
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC8996546
- OpenAlex
- W3087749616
- Cited by
- 47
- References
- 53
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 5 · 2023: 11 · 2022: 6 · 2021: 7 · 2020: 1
- FWCI
- 0.74
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Proteome Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Istitutes of Health (U01-1CA184783, U24 CA160019)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- missing data imputation, isobaric labeling, shotgun proteomics, expectation-maximization algorithm, random forest imputation, small sample size, statistical inference
- MeSH terms
- Algorithms, Proteomics
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