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

• Published in: Bioinformatics Vol. 27; no. 20; pp. 2866 - 2872
• Main Authors: Matzke, Melissa M., Waters, Katrina M., Metz, Thomas O., Jacobs, Jon M., Sims, Amy C., Baric, Ralph S., Pounds, Joel G., Webb-Robertson, Bobbie-Jo M.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.10.2011
• Subjects: 60 APPLIED LIFE SCIENCES; ABUNDANCE; BASIC BIOLOGICAL SCIENCES; Biological and medical sciences; Chromatography, Liquid - standards; Data Interpretation, Statistical; Environmental Molecular Sciences Laboratory; Fundamental and applied biological sciences. Psychology; General aspects; LC-MS; Mass Spectrometry - standards; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Original Papers; PEPTIDES; Peptides - analysis; Peptides - chemistry; PROCESSING; Proteome - chemistry; proteomic; Proteomics - standards; QUALITY CONTROL; SIMULATION; Software; Control; Data; Peptides; Quality; Quality control; Proteomics
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btr479

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.