Journal Article

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

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

in Bioinformatics

Volume 25, issue 19, pages 2573-2580
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI:
Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition

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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)) (, as well as in standalone software available at SourceForge (


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5912 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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