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Linear Poisson Modelling Independent Component Analysis of DESI Mass Spectral Tissue Images

   Faculty of Biology, Medicine and Health

Manchester United Kingdom Analytical Chemistry Biochemistry Cancer Biology Data Analysis Neuroscience Statistics

About the Project

Desorption Electrospray Ionisation - Mass Spectrometry Imaging (DESI – IMS) allows mass spectra to be acquired from an

array of <50 m pixels across a biological tissue section. The resulting spectra mostly comprise lipid-derived ions and can

be used a characteristic fingerprints to identify tissue phenotypes. Tissue images can be produced by selecting a single ion

from the spectrum that corresponds to each pixel. Multivariate statistical tools can be used to seek spectral correlations

between pixels and to base images on sets of common spectral features rather than single ions, potentially revealing a

number of distinct tissue phenotypes. Hierarchical cluster analysis and principal component analysis (PCA) have been used

in this context, but whilst they are computationally straightforward, they sacrifice a fraction of the available information.

PCA, for example, requires that the different components are orthogonal and provides only a qualitative segmentation of

the image. Independent component analysis does not limit component spectra in this way and allows for a degree of

commonality in the spectral features of components. To allow quantitative treatment of the data, we will use an

assumption of Poisson variability in the image signals that better describes the data than the more widely used Gaussian

assumption. Combining Linear Poisson Models of the data with Independent Component Analysis allows the tissue

phenotypes (components) identified to be quantified and for the measurement errors to be determined. We will apply this

powerful quantitative tool to the analysis of tissue samples of relevance to a range of tumour types in order to investigate

tumour heterogeneity through the identification of their component tissue phenotypes.

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in mass spectrometry and/ or with an interest in multivariate data analysis especially as applied to image analysis are encouraged to apply.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Biochemistry.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 2 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website View Website


Thacker, N. A., et al. (2018). "The statistical properties of raw and pre-processed ToF mass spectra." International Journal of
Mass Spectrometry 428: 62-70.
Deepaisarn, S., et al. (2018). "Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDIToF
mass spectra." Bioinformatics 34(6): 1001-1008.
Henderson, F., et al. (2018). "Multi-modal imaging of long-term recovery post-stroke by positron emission tomography and
matrix-assisted laser desorption/ionisation mass spectrometry." Rapid Communications in Mass Spectrometry 32(9): 721-729.

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