title: Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection.
abstract: BACKGROUND
  Liquid chromatography-mass spectrometry (LC-MS) is one of the major techniques for the quantification of metabolites in complex biological samples. Peak modeling is one of the key components in LC-MS data pre-processing.
  
  
  RESULTS
  To quantify asymmetric peaks with high noise level, we developed an estimation procedure using the bi-Gaussian function. In addition, to accurately quantify partially overlapping peaks, we developed a deconvolution method using the bi-Gaussian mixture model combined with statistical model selection.
  
  
  CONCLUSIONS
  Using extensive simulations and real data, we demonstrated the advantage of the bi-Gaussian mixture model over the Gaussian mixture model and the method of kernel smoothing combined with signal summation in peak quantification and deconvolution. The method is implemented in the R package apLCMS: http://www.sph.emory.edu/apLCMS/.
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@emory.edu
authors.forename: Tianwei
authors.initials: T
authors.lastname: Yu
authors.forename: Hesen
authors.initials: H
authors.lastname: Peng
mesh_terms: D002853:Chromatography, Liquid / Q000379:methods*; D016208:Databases, Factual; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical