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Type: Thesis
Title: Statistical Treatment of Proteomic Imaging Mass Spectrometry Data
Author: Winderbaum, Lyron Juan
Issue Date: 2016
School/Discipline: School of Mathematical Sciences
Abstract: Proteomic imaging mass spectrometry is an emerging field, and produces large amounts of high-dimensional data. We propose approaches to extracting useful information from these data - two of particular note. The Difference in Proportions of Occurrence Statistic (DIPPS) applies to binary data and leads to easily interpretable maps useful for exploratory analyses and automated generation of feature lists that can be used to standardise comparisons between datasets. The second approach, based on Canonical Correlation Analysis (CCA), reduces the high-dimensional data to features strongly related to classes and leads to good classification. Applications to cancer data show the success of these approaches.
Advisor: Koch, Inge
Hoffmann, Peter
Dissertation Note: Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2016
Keywords: Bioinformatics
mass spectrometry imaging
ovarian cancer
endometrial cancer
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