title: Sparse non-negative generalized PCA with applications to metabolomics.
abstract: BACKGROUND
  Nuclear magnetic resonance (NMR) spectroscopy has been used to study mixtures of metabolites in biological samples. This technology produces a spectrum for each sample depicting the chemical shifts at which an unknown number of latent metabolites resonate. The interpretation of this data with common multivariate exploratory methods such as principal components analysis (PCA) is limited due to high-dimensionality, non-negativity of the underlying spectra and dependencies at adjacent chemical shifts.
  
  
  RESULTS
  We develop a novel modification of PCA that is appropriate for analysis of NMR data, entitled Sparse Non-Negative Generalized PCA. This method yields interpretable principal components and loading vectors that select important features and directly account for both the non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. Through the reanalysis of experimental NMR data on five purified neural cell types, we demonstrate the utility of our methods for dimension reduction, pattern recognition, sample exploration and feature selection. Our methods lead to the identification of novel metabolites that reflect the differences between these cell types.
  
  
  BACKGROUND
  www.stat.rice.edu/~gallen/software.html.
  
  
  BACKGROUND
  gallen@rice.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Pediatrics-Neurology, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA. gallen@rice.edu
authors.forename: Genevera I
authors.initials: GI
authors.lastname: Allen
authors.forename: Mirjana
authors.initials: M
authors.lastname: Maletić-Savatić
mesh_terms: D000465:Algorithms; D009682:Magnetic Resonance Spectroscopy; D055432:Metabolomics / Q000379:methods*; D025341:Principal Component Analysis