Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558536
Title: The automated analysis and statistical characterisation of subcellular biological images
Author: Li, Simon W.
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2011
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Abstract:
Biomedical research has been revolutionised by the development of high throughput systems which enable thousands of experiments to be carried out simultaneously, leading to the generation of vast amounts of microscopy data. At the same time smaller scale experiments are increasingly dependent on accurate quantification of relatively small changes since the intricate network of interactions within a cell often counteracts attempts to per- turb the function of any single protein or pathway. This has motivated the development of algorithms and software packages to assist with the analysis of biological imaging data. This thesis develops and applies image analysis tools to multiple biological problems, and also illustrates the whole process required to label live samples so they can be imaged in the first place, thus giving an appreciation of the practical difficulties of biomedical research. We begin with the development of a kymograph tool, which is used for the spatio- temporal subcellular quantification of the chromosomal passenger complex, a complex of proteins involved in regulating mitosis. Following this we start to consider inter-cellular signalling interactions in a study of the IGF2 pathway in clumped cells. A random forest classification framework using cell-based image features is developed, and shown to be able to distinguish between cells with different levels of activation. Importantly the random forest feature importance measures are shown to be useful in helping to identify biologi- cally relevant differences. Finally we apply the random forest classifier to the analysis of immunofiuoresence images of Ewing's sarcoma tumour biopsies, which are associated with clinical outcome data, allowing us to develop a framework for integrating imaging data with patient prognosis data. The samples originate from multiple sources, making this a very challenging data set due to a lack of normalisation, but we are still able to obtain clinically useful results, in addition to identifying areas where further research is required.
Supervisor: Noble, J. Alison ; Wakefield, James G. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.558536  DOI: Not available
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