Improved quality control processing of peptide-centric LC-MS proteomics data
Open accessAbstract
MOTIVATION: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values. RESULTS: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs. AVAILABILITY: https://www.biopilot.org/docs/Software/RMD.php CONTACT: bj@pnl.gov SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.
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Sample outliers (n=3) were identified and removed based on the RMD-PAV algorithm with a p-value threshold of 0.0001.
“Sample outliers were identified and removed based on the RMD-PAV algorithm (Matzke et al., 2011) and a p-value threshold of 0.0001; based on this criterion, three samples were removed.”
From Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice (2019) · cites this paper as Matzke et al., 2011
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- Authors
- Matzke, M. M., Waters, K. M., Metz, T. O., Jacobs, J. M., Sims, A. C., Baric, R. S., Pounds, J. G., & Webb-Robertson, B.-J. M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- 1 Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352 and 2Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-08-18
- Volume
- 27
- Issue
- 20
- Pages
- 2866-2872
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3187650
- OpenAlex
- W2155752483
- Cited by
- 107
- References
- 38
- Citations by year
- 2026: 4 · 2025: 16 · 2024: 7 · 2023: 10 · 2022: 5 · 2021: 8 · 2020: 8 · 2019: 12 · 2018: 6 · 2017: 2 · 2016: 6 · 2015: 3 · 2014: 8 · 2013: 8 · 2012: 3
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- UNC Libraries
- Funders
- U.S. Department of Energy (DE-AC05?, DE‐AC05‐76RLO1830)
- Battelle (DE-AC05, DE‐AC05‐76RLO 1830)
- National Institutes of Health (HHSN272200800060C, 5u54es016015-04)
- National Institute of General Medical Sciences (R01GM084892)
- National Institute of Allergy and Infectious Diseases (HHSN272200800060C)
- National Institute of Environmental Health Sciences (U54-ES016015)
- Biological and Environmental Research (DE-AC05-76RLO-1830)
- Pacific Northwest National Laboratory (DE-AC05-76RLO 1830)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multivariate outlier detection, proteomics data analysis, R package
- MeSH terms
- Chromatography, Liquid, Data Interpretation, Statistical, Peptides, Quality Control, Software, Mass Spectrometry, Proteome, Proteomics
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