The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A statistical framework for protein quantitation in bottom-up MS-based proteomics
Open accessAbstract
MOTIVATION: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. AVAILABILITY: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).
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“Peptides may be missing from one or more samples due to a variety of underlying mechanisms (Daly et al., 2008; Karpievitch et al., 2009), which can result in non-trivial amounts of potential observations being missing.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Karpievitch et al., 2009
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
Testing Statistical Hypotheses
Niewiadomska-Bugaj, M., & Bartoszynski, R. (2021) · Wiley series in probability and statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,188
Keller, A., Nesvizhskii, A., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,970
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999) · Nature Biotechnology · doi:10.1038/13690 · Cited by 4,683 · cited 1×
“…evidence that linear relationships between intensity and abundance can be attained using microcapillary liquid chromatography (µLC) separation coupled with electrospray ionization (ESI) MS/MS, based on either stable isotope labeling or label-free approaches (Gygi et al. (1999), Wang et al. (2003)).”
BackgroundMissing 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
Cited by
Sorted by citations
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
“These computational methods include simple additive prescriptions to combine peptide intensities (32, 33), reference-peptide-based estimates (34), and statistical frameworks utilizing additive linear models (35, 36).”
MethodLazar, C., Gatto, L., Ferro, M., Bruley, C., & Bürger, T. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00981 · Cited by 549
“This idea is not new: several recent works have proposed to perform imputation by estimating models (with maximum-likelihood [14, 15] or with empirical Bayesian [16] methods) which are rich enough to account for both types of missingness mechanisms.”
MethodNormalization 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
“In this sense, using the proposed approach is similar to using the maximum likelihood model (Karpievitch et al. 2009b) to obtain the p-values for differential expression; we can get the p-values but not the p-values for imputed data.”
BackgroundMethodInfluentialSelf-citationWebb-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., et al. (2015) · Journal of Proteome Research · doi:10.1021/pr501138h · Cited by 294
Self-citationHigh Resolution Mass Spectrometry
Xian, F., Hendrickson, C. L., & Marshall, A. G. (2012) · Analytical Chemistry · doi:10.1021/ac203191t · Cited by 281
Metadata
- Authors
- Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., Heffron, F., Metz, T. O., Qian, W.-J., Yoon, H., Smith, R. D., & Dabney, A. R.
- Affiliations
- Pacific Northwest National Laboratory (US)
- 1 Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, 2Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 and 3Molecular Microbiology and Immunology, Oregon Health and Science University, Mail Code L220, Portland, OR 97201, USA
- Corresponding author
- Dabney, Alan R.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-06-17
- Volume
- 25
- Issue
- 16
- Pages
- 2028-2034
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2723007
- OpenAlex
- W2125419939
- Cited by
- 176
- References
- 29
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 9 · 2023: 8 · 2022: 12 · 2021: 11 · 2020: 8 · 2019: 10 · 2018: 8 · 2017: 10 · 2016: 10 · 2015: 14 · 2014: 20 · 2013: 12 · 2012: 19
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Health and Human Services
- National Institutes of Health (DK070146)
- National Cancer Institute (R25 CA90301, R25CA090301)
- National Institute of Allergy and Infectious Diseases (Y1 AI 489401, R01AI022933)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK070146, R21DK070146, R33DK070146)
- Pacific Northwest National Laboratory
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein quantification, shotgun proteomics, label-free quantification, informative missingness, Dante
- MeSH terms
- Proteins, Mass Spectrometry, Models, Statistical, Proteome, Databases, Protein, Proteomics
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