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Type: Theses
Title: Clustering of proteomics imaging mass spectrometry data
Author: Conway, Annie
Issue Date: 2016
School/Discipline: School of Mathematical Sciences
Abstract: This thesis presents a toolbox for the exploratory analysis of multivariate data, in particular proteomics imaging mass spectrometry data. Typically such data consist of 15000 - 20000 spectra with a spatial component, and for each spectrum ion intensities are recorded at specific masses. Clustering is a focus of this thesis, with discussion of k-means clustering and clustering with principal component analysis (PCA). Theoretical results relating PCA and clustering are given based on Ding and He (2004), and detailed and corrected proofs of the authors' results are presented. The benefits of transformations prior to clustering of the data are explored. Transformations include normalisation, peak intensity correction (PIC), binary and log transformations. A number of techniques for comparing different clustering results are also discussed and these include set based comparisons with the Jaccard distance, an information based criterion (variation of information), point-pair comparisons (Rand index) and a modified version of the prediction strength of Tibshirani and Walther (2005). These exploratory analyses are applied to imaging mass spectrometry data taken from patients with ovarian cancer. The data are taken from slices of cancerous tissue. The analyses in this thesis are primarily focused on data from one patient, with some techniques demonstrated on other patients for comparison.
Advisor: Koch, Inge
Dissertation Note: Thesis (M.Phil.) -- University of Adelaide, School of Mathematical Sciences, 2016.
Keywords: clustering
multivariate data analysis
high-dimensional data analysis
machine learning
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
DOI: 10.4225/55/5af4e722c538f
Appears in Collections:Research Theses

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