Visualisation in imaging mass spectrometry using the minimum noise fraction transform

Glenn Stone; David Clifford; Johan O. R. Gustafsson; Shaun R. McColl; Peter Hoffmann

doi:10.1186/1756-0500-5-419

Visualisation in imaging mass spectrometry using the minimum noise fraction transform

Glenn Stone, David Clifford, Johan O. R. Gustafsson, Shaun R. McColl, Peter Hoffmann

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	6
Journal	BMC Research Notes
Volume	5
Issue number	419
DOIs	https://doi.org/10.1186/1756-0500-5-419
Publication status	Published - 2012

Open Access - Access Right Statement

© 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.

Keywords

dimension reduction (statistics)
image processing
imaging mass spectrometry
matrix, assisted laser desorption, ionization

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10.1186/1756-0500-5-419

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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. 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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author = "Glenn Stone and David Clifford and Gustafsson, {Johan O. R.} and McColl, {Shaun R.} and Peter Hoffmann",

year = "2012",

doi = "10.1186/1756-0500-5-419",

language = "English",

volume = "5",

journal = "BMC Research Notes",

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publisher = "BioMed Central",

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T1 - Visualisation in imaging mass spectrometry using the minimum noise fraction transform

AU - Stone, Glenn

AU - Clifford, David

AU - Gustafsson, Johan O. R.

AU - McColl, Shaun R.

AU - Hoffmann, Peter

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - dimension reduction (statistics)

KW - image processing

KW - imaging mass spectrometry

KW - matrix, assisted laser desorption, ionization

UR - http://handle.uws.edu.au:8081/1959.7/517015

UR - http://www.biomedcentral.com/1756-0500/5/419

U2 - 10.1186/1756-0500-5-419

DO - 10.1186/1756-0500-5-419

M3 - Article

SN - 1756-0500

VL - 5

JO - BMC Research Notes

JF - BMC Research Notes

IS - 419

ER -

Visualisation in imaging mass spectrometry using the minimum noise fraction transform

Abstract

Open Access - Access Right Statement

Keywords

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