title: Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data.
abstract: BACKGROUND
  Data generated from metabolomics experiments are different from other types of "-omics" data. For example, a common phenomenon in mass spectrometry (MS)-based metabolomics data is that the data matrix frequently contains missing values, which complicates some quantitative analyses. One way to tackle this problem is to treat them as absent. Hence there are two types of information that are available in metabolomics data: presence/absence of a metabolite and a quantitative value of the abundance level of a metabolite if it is present. Combining these two layers of information poses challenges to the application of traditional statistical approaches in differential expression analysis.
  
  
  RESULTS
  In this article, we propose a novel kernel-based score test for the metabolomics differential expression analysis. In order to simultaneously capture both the continuous pattern and discrete pattern in metabolomics data, two new kinds of kernels are designed. One is the distance-based kernel and the other is the stratified kernel. While we initially describe the procedures in the case of single-metabolite analysis, we extend the methods to handle metabolite sets as well.
  
  
  CONCLUSIONS
  Evaluation based on both simulated data and real data from a liver cancer metabolomics study indicates that our kernel method has a better performance than some existing alternatives. An implementation of the proposed kernel method in the R statistical computing environment is available at http://works.bepress.com/debashis_ghosh/60/ .
authors.affiliation: Department of Statistics, Pennsylvania State University, 325 Thomas Building, University Park, 16802, PA, USA. xyz5074@psu.edu.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhan
authors.affiliation: Department of Molecular Toxicology, Pennsylvania State University, 322 Life Sciences Bldg, University Park, 16802, PA, USA. adp117@psu.edu.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Patterson
authors.affiliation: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, 80045, CO, USA. debashis.ghosh@ucdenver.edu.
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D008113:Liver Neoplasms / Q000378:metabolism*; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor