Computational Tools for the Processing and Analysis of Time-course Metabolomic Data

Modern, high-throughput techniques for the acquisition of metabolomic
data, combined with an increase in computational power, have provided
not only the need for, but also the means to develop and use, methods for
the interpretation of large and complex datasets. This thesis investigates
the methods by which pertinent information can be extracted from nontargeted
metabolomic data and reviews the current state of chemometric
methods. The analysis of real-world data and research questions relevant
to the agri-food industry reveals several problems for which novel solutions
are proposed. Three LC-MS datasets are studied: Medicago, Alopecurus
and aged Beef, covering stress resistance, herbicide resistance and product
misbranding. The new methods include preprocessing (batch correction,
data-filtering), processing (clustering, classification) and visualisation and
their use facilitated within a flexible data-to-results pipeline. The resulting
software suite with a user-friendly graphical interface is presented, providing
a pragmatic realisation of these methods in an easy to access workflow.