Visualisation in imaging mass spectrometry using the minimum noise fraction transform
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Updated: May 19, 2026

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection
Published on: November 18, 2022
Glenn Stone1, David Clifford, Johan O R Gustafsson
1School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia. g.stone@uws.edu.au
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.
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