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Type: Journal article
Title: Visualisation in imaging mass spectrometry using the minimum noise fraction transform
Author: Stone, G.
Clifford, D.
Gustafsson, O.
McColl, S.
Hoffmann, P.
Citation: BMC Research Notes, 2012; 5(1):1-6
Publisher: BioMed Central Ltd.
Issue Date: 2012
ISSN: 1756-0500
Statement of
Glenn Stone, David Clifford, Johan OR Gustafsson, Shaun R McColl and Peter Hoffmann
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. Findings: 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∼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.
Keywords: Mesencephalon
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Cluster Analysis
Models, Statistical
Principal Component Analysis
Image Processing, Computer-Assisted
Signal-To-Noise Ratio
Description: Extent: 6p.
Rights: © 2012 Stone et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI: 10.1186/1756-0500-5-419
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