Visualisation in imaging mass spectrometry using the minimum noise fraction transform

BMC Research Notes

Peter Hoffmann¹  Shaun R McColl¹  Johan OR Gustafsson¹  David Clifford³  Glenn Stone² 

[1] Adelaide Proteomics Centre, School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, South Australia, Australia;School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia;Division of Mathematics, Informatics and Statistics, CSIRO, Brisbane, Queensland, Australia

关键词: Image processing;    MALDI imaging mass spectrometry;    Dimension reduction;   

Others  :  1165978
DOI  :  10.1186/1756-0500-5-419

 received in 2011-09-02, accepted in 2012-07-25,  发布年份 2012

【 摘 要 】

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 webcite.

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.

【 授权许可】

   
2012 Stone et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Gustafsson J, Oehler M, Ruszkiewicz A, McColl S, Hoffmann P: MALDI imaging mass spectrometry (MALDI-IMS) — Application of spatial proteomics for ovarian cancer classification and diagnosis. Int J Mol Sci 2011, 12:773-794.
• [2]Beisinger M, Paepegaey P, McIntyre N, Harbottle R, Petersen N: Principal component analysis of TOF-SIMS images of organic monolayers. Anal Chem 2002, 74:5711-5716.
• [3]Deininger S, Ebert M, Futterer A, Gerhard M, Rocken C: MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers. J Proteome Res 2008, 7:5230-5236.
• [4]Franck J, Arafah K, Elayed M, Bonnel D, Vergara D, Jacquet A, Vinatier D, Wisztorski M, Day R, Fournier I, Salzet M: MALDI imaging mass spectrometry. Mol Cell Proteomics 2009, 8.9:2023-2033.
• [5]Smentkowski V, Ostrowski S, Kollmer F, Schnieders A, Keenan M, Ohlhausend J, Kotulad P: Multivariate statistical analysis of non-mass-selected ToF-SIMS data. Surf Interface Anal 2005, 40:1176-1182.
• [6]Milillo T, Gardella JJr: Spatial statistics and interpolation methods for TOF SIMS imaging. Appl Surf Sci 2006, 252:6883-6890.
• [7]Hanselmann M, Kothe U, Kirchner M, Renard B, Amstalden E, Glunde K, Heeren R, Hamprecht F: Toward digital staining using imaging mass spectrometry and random forests. J Proteome Res 2009, 8(7):3558-3567.
• [8]Green A, Berman M, Switzer P, Craig M: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geoscience Remote Sensing 1988, 26:65-74.
• [9]Berman M, Phatak A, Lagerstrom R, Wood B: ICE: a new method for the multivariate curve resolution of hyperspectral images. J Chemom 2009, 23:101-116.
• [10]Buckley M, Eagleson G: A graphical method for estimating the residual variance in nonparametric regression. Biometrika 1989, 76:203-210.
• [11]Golub G, Van Loan: Matrix Computations. The Johns Hopkins University Press, Baltimore, Maryland; 1996.
• [12]Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J, Du Croz, Greenbaum A, Hammarling S, McKenney A, Sorensen D: LAPACK Users’ Guide. Society for Industrial and Applied Mathematics, Philadelphia, PA; 1999.
• [13]R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2012. http://www.R-project.org/ webcite. [ISBN 3-900051-07-0]
• [14]Tuszynski J: caMassClass: Processing & Classification of Protein Mass Spectra (SELDI) Data. 2010. http://CRAN.R-project.org/package=caMassClass webcite. [R package version 1.9].