Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics
Open accessAbstract
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. 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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Zero-value or LOD imputation, like mean imputation, may distort feature distributions and lead to biased parameter and variability estimates.
“However, like mean imputation, imputed values from these approaches may distort feature distributions and lead to biased parameter and variability estimates (Lubin et al., 2004; Succop et al., 2004; Webb-Robertson et al., 2015; Lazar et al., 2016; Bramer et al., 2020).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Webb-Robertson et al., 2015
It is not uncommon to have 20-50% of the possible peptide values that are not quantified.
“It is not uncommon to have 20–50% of the possible peptide values that are not quantified (Webb-Robertson et al., 2015; Brenes et al., 2019).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Webb-Robertson et al., 2015
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Cites
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230 · see citing works
Probabilistic Principal Component Analysis
Tipping, M. E., & Bishop, C. M. (1999) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00196 · Cited by 3,679 · see citing works
Review: a gentle introduction to imputation of missing values.
Donders, A. R. T., van der Heijden, G. J. M. G., Stijnen, T., & Moons, K. G. M. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.01.014 · Cited by 2,582 · see citing works
Roderick J.A. Little, Donald B. Rubin — Statistical Analysis with Missing Data
Bo., N., Little, R. J. A., & Rubin, D. B. (1988) · Population · doi:10.2307/1533221 · Cited by 2,155 · see citing works
Proteoform: a single term describing protein complexity
The Consortium for Top Down Proteomics, Smith, L. M., & Kelleher, N. L. (2013) · Nature Methods · doi:10.1038/nmeth.2369 · Cited by 1,528 · see citing works
Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present
Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-6203-4 · Cited by 801 · see citing works
A Bayesian missing value estimation method for gene expression profile data
Oba, S., Sato, M.-A., Takemasa, I., Monden, M., Matsubara, K., & Ishii, S. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg287 · Cited by 755 · see citing works
Callister, S., Barry, R., Adkins, J., Johnson, E. T., Qian, W., Webb‐Robertson, B., Smith, R. D., & Lipton, M. S. W. (2006) · Journal of Proteome Research · doi:10.1021/pr050300l · Cited by 500 · see citing works
Self-citationMissing value estimation for DNA microarray gene expression data: local least squares imputation
Kim, H., Golub, G. H., & Park, H. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth499 · Cited by 472 · see citing works
Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions
Hubner, N. C., Bird, A. W., Cox, J., Splettstoesser, B., Bandilla, P., Poser, I., Hyman, A., & Mann, M. (2010) · Journal of Cell Biology · doi:10.1083/jcb.200911091 · Cited by 440 · see citing works
DAnTE: a statistical tool for quantitative analysis of -omics data
Polpitiya, A. D., Qian, W., Jaitly, N., Petyuk, V., Adkins, J., Camp, D., Anderson, G., & Smith, R. D. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn217 · Cited by 437 · see citing works
Self-citationLSimpute: accurate estimation of missing values in microarray data with least squares methods
Bø, T. H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gnh026 · Cited by 349 · see citing works
Normalization and missing value imputation for label-free LC-MS analysis
Karpievitch, Y. V., Dabney, A. R., & Smith, R. D. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s16-s5 · Cited by 345 · see citing works
Self-citationQuantitation in Mass-Spectrometry-Based Proteomics
Schulze, W. X., & Usadel, B. (2010) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-042809-112132 · Cited by 344 · see citing works
Missing Data Analysis Using Multiple Imputation
He, Y. (2010) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.109.875658 · Cited by 259 · see citing works
Statistical Design of Quantitative Mass Spectrometry-Based Proteomic Experiments
Oberg, A. L., & Olga Vitek, A. (2009) · Journal of Proteome Research · doi:10.1021/pr8010099 · Cited by 258 · see citing works
A statistical framework for protein quantitation in bottom-up MS-based proteomics
Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., Heffron, F., Metz, T. O., Qian, W.-J., Yoon, H., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp362 · Cited by 176 · see citing works
Deeb, S. J., D’Souza, R. C. J., Cox, J., Schmidt‐Supprian, M., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.015362 · Cited by 174 · see citing works
Dealing with missing values in large-scale studies: microarray data imputation and beyond
Aittokallio, T. A. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp059 · Cited by 171 · see citing works
Statistical Similarities between Transcriptomics and Quantitative Shotgun Proteomics Data
Pavelka, N., Fournier, M. L., Swanson, S. K., Pelizzola, M., Ricciardi‐Castagnoli, P., Florens, L. A., & Washburn, M. P. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m700240-mcp200 · Cited by 167 · see citing works
DanteR: an extensible R-based tool for quantitative analysis of -omics data
Taverner, T., Karpievitch, Y. V., Polpitiya, A. D., Brown, J. N., Dabney, A. R., Anderson, G., & Smith, R. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts449 · Cited by 143 · see citing works
Self-citationClough, T., Thaminy, S., Ragg, S., Aebersold, R. H., & Olga Vitek, A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s16-s6 · Cited by 137 · see citing works
Schwämmle, V., León, I. R., & Jensen, O. N. (2013) · Journal of Proteome Research · doi:10.1021/pr400045u · Cited by 127 · see citing works
Brock, G. N., Shaffer, J. R., Blakesley, R. E., Lotz, M., & Tseng, G. C. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-12 · Cited by 120 · see citing works
Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers
Dakna, M., Harris, K. J., Kalousis, A., Carpentier, S., Kölch, W., Schanstra, J. P., Haubitz, M., Vlahou, A., Mischak, H., & Girolami, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-594 · Cited by 120 · see citing works
Data merging for integrated microarray and proteomic analysis
Waters, K. M., Pounds, J. G., & Thrall, B. D. (2006) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/ell019 · Cited by 110 · see citing works
Self-citationWebb‐Robertson, B., Matzke, M. M., Jacobs, J., et al. (2011) · PROTEOMICS · doi:10.1002/pmic.201100078 · Cited by 104 · see citing works
Self-citationMass-spectrometry-based clinical proteomics – a review and prospective
Parker, C. E., Pearson, T. W., Anderson, N. L., & Borchers, C. H. (2010) · The Analyst · doi:10.1039/c0an00105h · Cited by 104 · see citing works
Webb‐Robertson, B., McCue, L. A., Waters, K. M., Matzke, M. M., Jacobs, J., Metz, T., Varnum, S. M., & Pounds, J. G. (2010) · Journal of Proteome Research · doi:10.1021/pr1005247 · Cited by 102 · see citing works
Self-citationMatzke, M. M., Brown, J. N., Gritsenko, M. A., Metz, T., Pounds, J. G., Rodland, K., Shukla, A. K., Smith, R. D., Waters, K. M., McDermott, J., et al. (2012) · PROTEOMICS · doi:10.1002/pmic.201200269 · Cited by 87 · see citing works
Self-citationWright, P. C., Noirel, J., Ow, S. Y., & Fazeli, A. (2012) · Theriogenology · doi:10.1016/j.theriogenology.2011.11.012 · Cited by 87 · see citing works
Serum Proteomics in Biomedical Research: A Systematic Review
Zhang, A., Sun, H. J., Yan, G., Han, Y., & Wang, X. (2013) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-013-0238-7 · Cited by 78 · see citing works
Missing value imputation improves clustering and interpretation of gene expression microarray data
Tuikkala, J., Elo, L. L., Nevalainen, O., & Aittokallio, T. A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-202 · Cited by 72 · see citing works
Missing values in gel‐based proteomics
Albrecht, D., Kniemeyer, O., Brakhage, A. A., et al. (2010) · PROTEOMICS · doi:10.1002/pmic.200800576 · Cited by 70 · see citing works
Biological impact of missing-value imputation on downstream analyses of gene expression profiles
Oh, S., Kang, D., Brock, G. N., & Tseng, G. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq613 · Cited by 65 · see citing works
How advancement in biological network analysis methods empowers proteomics
Goh, W. W. B., Lee, Y. H., Chung, M. C. M., & Wong, L. (2012) · PROTEOMICS · doi:10.1002/pmic.201100321 · Cited by 63 · see citing works
Oudenhove, L. V., & Devreese, B. V. (2013) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-013-4897-7 · Cited by 63 · see citing works
Goh, W. W. B., Sergot, M. J., Sng, J., & Wong, L. (2013) · Journal of Proteome Research · doi:10.1021/pr301127f · Cited by 43 · see citing works
Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements
Webb‐Robertson, B., Matzke, M. M., Datta, S., Payne, S., Kang, J., Bramer, L., Nicora, C., Shukla, A. K., Metz, T., Rodland, K., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.030932 · Cited by 39 · see citing works
Self-citationLuo, R., Colangelo, C. M., Sessa, W. C., & Zhao, H. (2009) · Statistics in Biosciences · doi:10.1007/s12561-009-9013-2 · Cited by 30 · see citing works
Urinary Protein Profiles in a Rat Model for Diabetic Complications
Schlatzer, D., Dazard, J., Dharsee, M., et al. (2009) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800558-mcp200 · Cited by 30 · see citing works
Using a spike-in experiment to evaluate analysis of LC-MS data
Tuli, L., Tsai, T., Varghese, R. S., et al. (2012) · Proteome Science · doi:10.1186/1477-5956-10-13 · Cited by 23 · see citing works
Caffrey, R. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-711-2_10 · Cited by 21 · see citing works
Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data
Tekwe, C. D., Carroll, R. J., & Dabney, A. R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts306 · Cited by 19 · see citing works
Prediction and Characterization of Missing Proteomic Data inDesulfovibrio vulgaris
Li, F., Nie, L., Wu, G., Qiao, J., & Zhang, W. (2011) · Comparative and Functional Genomics · doi:10.1155/2011/780973 · Cited by 19 · see citing works
Webb‐Robertson, B., Matzke, M. M., Metz, T., McDermott, J., Walker, H., Rodland, K., Pounds, J. G., & Waters, K. M. (2013) · BioTechniques · doi:10.2144/000113978 · Cited by 16 · see citing works
Self-citationAN SVM SCORER FOR MORE SENSITIVE AND RELIABLE PEPTIDE IDENTIFICATION VIA TANDEM MASS SPECTROMETRY
Wang, H., Fu, Y., SUN, R., et al. (2005) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812701626_0028 · Cited by 10 · see citing works
Quesada (2001) · doi:10.1175/1520-0442(2001)014
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- [18]Little RJA Rubin DB Statistical Analysis with Missing Data 1987 New York Wiley & Sons, Inc
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Pappireddi, N., Martin, L., & Wühr, M. (2019) · ChemBioChem · doi:10.1002/cbic.201800650 · Cited by 299 · see citing works
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Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280 · see citing works
Multibatch TMT reveals false positives, batch effects and missing values
Brenes, A., Hukelmann, J., Bensaddek, D., & Lamond, A. I. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra119.001472 · Cited by 196 · see citing works
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
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Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.570255 · Cited by 127 · see citing works
Host‐microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome
Nguyen, T., Sedghi, L., Ganther, S., Malone, E., Kamarajan, P., & Kapila, Y. L. (2020) · Periodontology 2000 · doi:10.1111/prd.12316 · Cited by 51 · see citing works
A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics
Bramer, L. M., Irvahn, J., Piehowski, P. D., Rodland, K. D., & Webb-Robertson, B.-J. M. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00123 · Cited by 47 · see citing works
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The, M., & Käll, L. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001018 · Cited by 37 · see citing works
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Kumagai, H., Kim, S.-J., Miller, B., Zempo, H., Tanisawa, K., Natsume, T., Lee, S. H., Wan, J., Leelaprachakul, N., Kumagai, M. E., et al. (2024) · iScience · doi:10.1016/j.isci.2024.111212 · Cited by 15 · see citing works
- Authors
- Webb-Robertson, B.-J. M., Wiberg, H. K., Matzke, M. M., Brown, J. N., Wang, J., McDermott, J. E., Smith, R. D., Rodland, K. D., Metz, T. O., Pounds, J. G., & Waters, K. M.
- Affiliations
- Pacific Northwest National Laboratory, PO BOX 999, K7-20, Richland, Washington 99352, United States
- Pacific Northwest National Laboratory (US)
- ORCID
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-04-22
- Volume
- 14
- Issue
- 5
- Pages
- 1993-2001
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC4776766
- OpenAlex
- W2000403772
- Cited by
- 294
- References
- 48
- Citations by year
- 2026: 31 · 2025: 34 · 2024: 23 · 2023: 28 · 2022: 22 · 2021: 30 · 2020: 35 · 2019: 27 · 2018: 16 · 2017: 22 · 2016: 21 · 2015: 5
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Proteome Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE-AC06–76RL01830, U54-ES016015, RR018522, 76RL01830, GM103493)
- Battelle (DE-AC06–76RL01830)
- National Institutes of Health (5u54es016015-04, 8p41gm103493-10, P41-GM103493, GM103493, U54-ES016015)
- U.S. Public Health Service (HHSN27220080060C)
- National Cancer Institute (U01CA184783)
- National Institute of General Medical Sciences (GM103493, P41-GM103493, DE-AC06-76RL01830)
- National Institute of Diabetes and Digestive and Kidney Diseases (R21DK071283, R33DK071283, DK071283)
- National Institute of Environmental Health Sciences (U54-ES016015)
- National Center for Research Resources (P41RR018522, RR-018522)
- Biological and Environmental Research (DE-AC06-76RL0 1830, RR018522, 76RL01830)
- Pacific Northwest National Laboratory (DE‐AC06‐76RL01830, 76RL01830)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- missing data imputation, label-free quantitative proteomics, global proteomics, classification accuracy, imputation method comparison
- MeSH terms
- Algorithms, Animals, Blood Proteins, Chromatography, Liquid, Humans, Lung, Peptides, Mass Spectrometry, Proteomics, Mice
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