Palmer, A D*; Bunch, J; Styles, I B* (2015) The use of random projections for the analysis of mass spectrometry imaging data. J. Am. Soc. Mass Spectrom., 26 (2). pp. 315-322.

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Abstract

The 'curse of dimensionality' imposes fundamental limits on the analysis of the large, information rich datasets that are produced by such as MALDI mass spectrometry imaging. Furthermore, such datasets are in any case too large to be analysed as a whole and dimensionality reduction is required before further analysis can be performed. We examine the potential application of random projections for the dimensionality reduction of mass spectrometry imaging data and examine how they can enable efficient and fast segmentation using k-means clustering. The proposed method is computationally efficient and can be implemented such that only one spectrum is needed in memory at any time, We use this technique to segment a sample of diseased human liver. Segmentation results following a >99 % reduction in the number of measurement channels (without peak picking) were shown to be consistent with the known histology.

Item Type:	Article
Subjects:	Nanoscience Nanoscience > Surface and Nanoanalysis
Identification number/DOI:	10.1007/s13361-014-1024-7
Last Modified:	02 Feb 2018 13:13
URI:	http://eprintspublications.npl.co.uk/id/eprint/6537

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