title: Visualisation in imaging mass spectrometry using the minimum noise fraction transform.
abstract: BACKGROUND
  Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample. IMS datasets are typically huge and visualisation and subsequent analysis can be challenging. Principal component analysis (PCA) is one popular data reduction technique that has been used and we propose another; the minimum noise fraction (MNF) transform which is popular in remote sensing.
  
  
  RESULTS
  The MNF transform is able to extract spatially coherent information from IMS data. The MNF transform is implemented through an R-package which is available together with example data from http://staﬀ.scm.uws.edu.au/~glenn/∖#Software.
  
  
  CONCLUSIONS
  In our example, the MNF transform was able to find additional images of interest. The extracted information forms a useful basis for subsequent analyses.
authors.affiliation: School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia. g.stone@uws.edu.au
authors.forename: Glenn
authors.initials: G
authors.lastname: Stone
authors.forename: David
authors.initials: D
authors.lastname: Clifford
authors.forename: Johan O R
authors.initials: JO
authors.lastname: Gustafsson
authors.forename: Shaun R
authors.initials: SR
authors.lastname: McColl
authors.forename: Peter
authors.initials: P
authors.lastname: Hoffmann
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008636:Mesencephalon / Q000378:metabolism*; D051379:Mice; D015233:Models, Statistical; D025341:Principal Component Analysis; D059629:Signal-To-Noise Ratio; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*