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Spectral Processing Considerations for the Analysis of NMR Based Metabolomics Data Open Access

Descriptions

Other title
Subject/Keyword
Metabolomics, Spectal Processing, Digital Signal Processing
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Chang, David Wai Ming
Supervisor and department
Shah, Sirish (Chemical and Materials Engineering)
Examining committee member and department
Burrell, Robert (Biomedical Engineering)
Vogel, Hans (University of Calgary)
Huang, Biao (Chemical and Materials Engineering)
Wishart, David (Computer Science)
Sykes, Brian (Biochemistry)
Department
Department of Chemical and Materials Engineering
Specialization

Date accepted
2009-09-25T18:51:39Z
Graduation date
2009-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Employing a combination of biochemistry and chemometrics, the field of metabolomics has the potential to reveal some very significant insights into biological pathways related to drugs and diseases. This thesis explores this field in its depths; specifically focusing on Nuclear Magnetic Resonance (NMR) based methods. The thesis begins with an exploration of the quantum level relationships of molecules, and how these coupling patterns evolve into an NMR spectrum. The thesis will describe the development of a simplified spin simulation algorithm to predict NMR spin coupling patterns that are computed in fractions of a second and to build mathematically relevant basis functions. Later in the thesis, the issue of baseline distortions of real NMR experimental data is addressed by the development of an automated baseline correction algorithm. Data reduction techniques are further analyzed to understand the importance of the quality of the data used in advanced chemometric methods. For analysis of the data, the use of simple univariate techniques applied to NMR spectra of urine is explored to determine statistically significant biomarkers between disease states in asthma. More advanced statistics in the way of multivariate models, namely Partial Least Squares – Discriminant Analysis (PLS-DA), were used to build predictive models of Streptococcus pneumoniae pneumonia from NMR spectra of urine. Potential characteristics of the data that may invalidate assumptions required in our models were accounted for, such as ensuring the statistical normality of the S. pneumoniae pneumonia data by using log transformations. After the analysis, focus was given to the use of unique visualization techniques to further explore the complex relationships that exist between samples and variables, and relationships between variables. As will be made evident, this thesis deals with the basic physics of an NMR signal to building highly sophisticated models to help understand the NMR spectra from complex mixtures. All of these notions are important in the objective to garner the most information provided through an NMR experiment, as such to aid in the discovery of biochemical knowledge.
Language
English
DOI
doi:10.7939/R3DH2H
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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