Paper Info
Title
Improved quality control processing of peptide-centric LC-MS proteomics data.
Abstract
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.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.https://www.biopilot.org/docs/Software/RMD.phpbj@pnl.govSupplementary material is available at Bioinformatics online.
Year
DOI
Venue
2011
10.1093/bioinformatics/btr479
Bioinformatics
Keywords
Field
DocType
extreme peptide abundance distribution,novel multivariate statistical strategy,bias downstream statistical analysis,subsequent peptide abundance data,differential peptide peak intensity,individual peptide component,improved quality control processing,peptide-centric lc-ms proteomics data,peptide identification,run-by-run correlation,poor quality peptide abundance,downstream statistical analysis,quality control,visual inspection,multivariate statistics,extreme value
Visual inspection,Proteomics,Computer science,Multivariate statistics,Extreme value theory,Outlier,Correlation,Software,Bioinformatics,Statistics,Analysis of variance
Journal
Volume
Issue
ISSN
27
20
1367-4811
Citations 
PageRank 
References 
2
0.43
13
Authors
8
Name
Order
Citations
PageRank
Melissa M. Matzke120.77
Katrina M Waters2525.64
Thomas O Metz3667.25
Jon M Jacobs490.85
Amy C Sims561.20
Ralph Baric6131.96
Joel G Pounds720.77
Bobbie-jo M. Webb-robertson8939.14