Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687496
Title: Investigation and interpretation of large mass spectrometry imaging datasets
Author: Race, Alan M.
ISNI:       0000 0004 5924 0236
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2016
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Abstract:
Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabelled molecular species including drugs, metabolites, lipids and proteins in complex samples such as intact tissue. In this research, a new extensible software platform is presented, suitable for spectral preprocessing, multivariate analysis and visualisation of large MSI datasets from all major MSI vendors. Principal component analysis (PCA) has been widely used in the unsupervised processing of MSI data. Standard implementations of PCA require the entire dataset to be stored in memory, necessitating a compromise between the number of pixels and the number of peaks to include. In this research a new method which has no limitation on the number of pixels is developed. Hierarchical composition of data has been shown as an efficient method of capturing the information present within images in other fields. An adaptation of these ideas to MSI data is described. The way in which imaging data are presented can have a significant impact on the perceived structure, especially when using false colour to display images. The research presented in this thesis has resulted in new recommendations for presentation of MS images. Finally, the software and algorithms presented were used to analyse MSI data from a traumatic brain injury model. Manual exploration and use of multivariate analysis methods such as PCA did not reveal any differences between the injured hemisphere of the brain and the control hemisphere, however the hierarchical composition algorithms identified multiple ion images which appear elevated in the injured hemisphere.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.687496  DOI: Not available
Keywords: QD Chemistry
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