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Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition

Citation

Karpievitch, YV and Taverner, T and Adkins, JN and Callister, SJ and Anderson, GA and Smith, RD and Dabney, AR, Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition, Bioinformatics, 25, (19) pp. 2573-2580. ISSN 1367-4803 (2009) [Refereed Article]

Copyright Statement

Copyright 2009 the authors.

DOI: doi:10.1093/bioinformatics/btp426

Abstract

Motivation: LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference. Careful normalization of MS peak intensities would enable greater accuracy and precision in quantitative comparisons of protein abundance levels. Results: We propose an algorithm, called EigenMS, that uses singular value decomposition to capture and remove biases from LC-MS peak intensity measurements. EigenMS is an adaptation of the surrogate variable analysis (SVA) algorithm of Leek and Storey, with the adaptations including (i) the handling of the widespread missing measurements that are typical in LC-MS, and (ii) a novel approach to preventing overfitting that facilitates the incorporation of EigenMS into an existing proteomics analysis pipeline. EigenMS is demonstrated using both large-scale calibration measurements and simulations to perform well relative to existing alternatives. Availability: The software has been made available in the open source proteomics platform DAnTE (Polpitiya et al., 2008)) (http://omics.pnl.gov/software/), as well as in standalone software available at SourceForge (http://sourceforge.net).

Item Details

Item Type:Refereed Article
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Biostatistics
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Mathematical Sciences
Author:Karpievitch, YV (Dr Yuliya Karpievitch)
ID Code:80882
Year Published:2009
Web of Science® Times Cited:36
Deposited By:Mathematics and Physics
Deposited On:2012-11-14
Last Modified:2015-01-27
Downloads:0

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