Visualisation in imaging mass spectrometry using the minimum noise fraction transform
1 School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia
2 Division of Mathematics, Informatics and Statistics, CSIRO, Brisbane, Queensland, Australia
3 Adelaide Proteomics Centre, School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, South Australia, Australia
BMC Research Notes 2012, 5:419 doi:10.1186/1756-0500-5-419Published: 7 August 2012
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.
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 webcite.
In our example, the MNF transform was able to find additional images of interest. The extracted information forms a useful basis for subsequent analyses.