title: EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data.
abstract: BACKGROUND
  Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in 'omics' data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets.
  
  
  METHODS
  The proposed unsupervised algorithm uses Sliding Window Normalization (SWN) and a new spatial distribution based peak picking method developed based on Gray level Co-Occurrence (GCO) matrices followed by clustering of biomolecules. We also use gist descriptors and an improved version of GCO matrices to extract features from molecular images and minimum medoid distance to automatically estimate the number of possible groups.
  
  
  RESULTS
  We evaluated our algorithm using a new MALDI-IMS metabolomics dataset of a plant (Eucalypt) leaf. The algorithm revealed hidden significant molecular distribution patterns in the dataset, which the current Component Analysis and Segmentation Map based approaches failed to extract. We further demonstrate the performance of our peak picking method over other traditional approaches by using a publicly available MALDI-IMS proteomics dataset of a rat brain. Although SWN did not show any significant improvement as compared with using no normalization, the visual assessment showed an improvement as compared to using the median normalization.
  
  
  METHODS
  The source code and sample data are freely available at http://exims.sourceforge.net/.
  
  
  BACKGROUND
  awgcdw@student.unimelb.edu.au or chalini_w@live.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Mechanical Engineering.
authors.forename: Chalini D
authors.initials: CD
authors.lastname: Wijetunge
authors.affiliation: Department of Mechanical Engineering.
authors.forename: Isaam
authors.initials: I
authors.lastname: Saeed
authors.affiliation: Metabolomics Australia, University of Melbourne, Parkville, VIC 3010, Australia.
authors.forename: Berin A
authors.initials: BA
authors.lastname: Boughton
authors.affiliation: Department of Biochemistry, Mass Spectrometry Research Centre.
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Spraggins
authors.affiliation: Department of Biochemistry, Mass Spectrometry Research Centre, Department of Chemistry and Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Caprioli
authors.affiliation: Metabolomics Australia, University of Melbourne, Parkville, VIC 3010, Australia, ARC Centre of Excellence, School of Biosciences and Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC 3010, Australia.
authors.forename: Antony
authors.initials: A
authors.lastname: Bacic
authors.affiliation: Metabolomics Australia, University of Melbourne, Parkville, VIC 3010, Australia.
authors.forename: Ute
authors.initials: U
authors.lastname: Roessner
authors.affiliation: Department of Mechanical Engineering.
authors.forename: Saman K
authors.initials: SK
authors.lastname: Halgamuge
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000378:metabolism*; D005052:Eucalyptus / Q000737:chemistry*; D055432:Metabolomics / Q000379:methods*; D018515:Plant Leaves / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D051381:Rats; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*