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Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/74355

Type: Journal article
Title: Visualisation in imaging mass spectrometry using the minimum noise fraction transform
Author: Stone, Glenn
Clifford, David
Gustafsson, Ove Johan Ragnar
McColl, Shaun Reuss
Hoffmann, Peter
Citation: BMC Research Notes, 2012; 5:419
Publisher: BioMed Central Ltd.
Issue Date: 2012
ISSN: 1756-0500
School/Discipline: School of Molecular and Biomedical Science
Statement of
Responsibility: 
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 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.
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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
RMID: 0020122088
DOI: 10.1186/1756-0500-5-419
Appears in Collections:IPAS publications
Molecular and Biomedical Science publications
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