title: Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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).
authors: Department of Statistics, 3143 TAMU, College Station, TX 77843, USA. yuliya@stat.tamu.edu
authors: Yuliya V
authors: YV
authors: Karpievitch
authors: Thomas
authors: T
authors: Taverner
authors: Joshua N
authors: JN
authors: Adkins
authors: Stephen J
authors: SJ
authors: Callister
authors: Gordon A
authors: GA
authors: Anderson
authors: Richard D
authors: RD
authors: Smith
authors: Alan R
authors: AR
authors: Dabney
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D013058:Mass Spectrometry; D011506:Proteins / Q000737:chemistry*; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins; D020543:Proteome