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, Alan R. Dabney

    Research output: Contribution to journalArticle

    51 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)2573-2580
    Number of pages8
    JournalBioinformatics
    Volume25
    Issue number19
    DOIs
    Publication statusPublished - 16 Oct 2009

    Fingerprint Dive into the research topics of 'Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition'. Together they form a unique fingerprint.

    Cite this