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Bioinformatics Advance Access originally published online on July 14, 2009
Bioinformatics 2009 25(19):2573-2580; doi:10.1093/bioinformatics/btp426
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

Yuliya V. Karpievitch 1,*, Thomas Taverner 2, Joshua N. Adkins 2, Stephen J. Callister 2, Gordon A. Anderson 2, Richard D. Smith 2 and Alan R. Dabney 1

1Department of Statistics, 3143 TAMU, College Station, TX 77843 and 2Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352, USA

*To whom correspondence should be addressed.


  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).

Contact: yuliya{at}stat.tamu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on May 19, 2009; revised on July 7, 2009; accepted on July 7, 2009

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