title: Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach.
abstract: BACKGROUND
  Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics.
  
  
  RESULTS
  Here we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties.
  
  
  METHODS
  The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health and Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA.
authors.forename: Tianwei
authors.initials: T
authors.lastname: Yu
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health and Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA.
authors.forename: Dean P
authors.initials: DP
authors.lastname: Jones
mesh_terms: D001185:Artificial Intelligence; D056808:Biostatistics / Q000379:methods*; D002853:Chromatography, Liquid / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D015233:Models, Statistical