title: Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards.
abstract: BACKGROUND
  Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data.
  
  
  RESULTS
  We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information.
  
  
  METHODS
  MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html.
  
  
  BACKGROUND
  hwr@georgetown.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA, Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA, Proteomics and Mass Spectrometry Research Facility, Mitchell Cancer Institute, University of South Alabama, Mobile, AL 36604, USA and Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA.
authors.forename: Tsung-Heng
authors.initials: TH
authors.lastname: Tsai
authors.forename: Mahlet G
authors.initials: MG
authors.lastname: Tadesse
authors.forename: Cristina
authors.initials: C
authors.lastname: Di Poto
authors.forename: Lewis K
authors.initials: LK
authors.lastname: Pannell
authors.forename: Yehia
authors.initials: Y
authors.lastname: Mechref
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Habtom W
authors.initials: HW
authors.lastname: Ressom
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002853:Chromatography, Liquid / Q000379:methods* / Q000592:standards; D054794:Glycomics; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods* / Q000592:standards; D055432:Metabolomics; D015233:Models, Statistical; D040901:Proteomics; D012015:Reference Standards