U.S. Department of Energy

Pacific Northwest National Laboratory

Improved quality control processing of peptide-centric LC-MS proteomics data.

TitleImproved quality control processing of peptide-centric LC-MS proteomics data.
Publication TypeJournal Article
Year of Publication2011
AuthorsMatzke MM, Waters KM, Metz TO, Jacobs JM, Sims AC, Baric RS, Pounds JG, Webb-Robertson B-JM
KeywordsChromatography, Liquid, Data Interpretation, Statistical, Mass Spectrometry, Peptides, Proteome, Proteomics, Quality Control, Software

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.

Alternate JournalBioinformatics
PubMed ID21852304
PubMed Central IDPMC3187650
Grant ListHHSN272200800060C / AO / NIAID NIH HHS / United States
HHSN272200800060C / / PHS HHS / United States
R01 GM084892 / GM / NIGMS NIH HHS / United States
U54 ES 016015 / ES / NIEHS NIH HHS / United States
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