Owing to its ability to capture a systemic and temporal metabolic description of an
organism’s response to a treatment, metabonomics is a well-established and valuable
approach in elucidating the effects and mechanisms of a given perturbation. However,
to optimise information recovery from the complex datasets generated, chemometric
methods are essential.
The work presented in this thesis focuses on the development of novel
methods, and the use of existing methods in new applications to ease data
interpretation and enhance information recovery from 1H Nuclear Magnetic
Resonance (NMR) metabonomic datasets using correlation based methods. Although
the methods here are largely applied to toxicological data, they could be equally
valuable in the analysis of any metabonomic dataset, and indeed potentially to other
‘omics’ data presenting similar analytical challenges.
The first two methodological approaches relate to novel extensions of
Statistical Total Correlation Spectroscopy (STOCSY), a valuable tool in elucidation
of both inter- and intra-metabolite spectral intensity correlations in NMR
metabonomic datasets. In the first, STOCSY is utilised in STOCSY-editing, a method
for the selective identification and downscaling of the peaks from unwanted
metabolites such as those arising from xenobiotics. Structurally correlated peaks from
drug metabolites are first identified using STOCSY, and the returned correlation
information utilised to scale the spectra across these regions, producing a modified set
of spectra in which drug metabolite contributions are reduced, endogenous peaks
reconstructed and thus, analysis by pattern recognition methods without drug
metabolite interferences facilitated. In the second, the STOCSY approach is extended
in Iterative-STOCSY, where metabolic associations are followed over several rounds
of STOCSY through calculation of correlation coefficients initially from a driver
spectral peak of interest, and subsequently from all peaks identified as correlating
above a set threshold to peaks picked in the previous round. The condensation of
putatively structurally related peaks into single nodes, and representation of the
otherwise complex network in a fully interactive plot of node-to-node connections
and corresponding spectral data, allows the ready exploration of both inter- and intrametabolite
relationships and a more directed approach to the identification of
biomarkers of the studied perturbation. Finally various clustering methods are investigated with the aim of providing improved structural (intra-metabolite) versus
non-structural (inter-metabolite) assignment. Thus, this thesis presents a framework
for the enhanced identification, recovery and characterisation of inter- and intrametabolite
relationships and how these are affected by metabonomic perturbation.