Chapter 11: Pathway analysis
by Rachel Cavill, Jildau Bouwman
Metabolic pathways are the foundation for understanding metabolomic data in its biochemical context. By mapping metabolomics results to these pathways and identifying those which are altered in the experiment we can easily add biologically interpretability to our experimental results. However, in order to be able to do these analyses in a robust, reproducible way, we first need to be able to describe the measured metabolites in a standardised manner, hence we begin this chapter examining ontologies. We then proceed to shortly review the pathway databases available for metabolomics data. We explain the range of methods applied for these analyses, examining their key differences and focussing on the potential sources of bias in the results from each and we concluce with a section about the tools available for metabolomics pathway analysis.
Dr. Rachel Cavill is an assistant professor in the department of Data Science and Knowledge Engineering (DKE) at Maastricht University. After receiving her MMATH in Mathematics and Computer Science from the University of York in 2002, she continued at the same institution and received her PhD in 2007, developing bio-inspired machine learning algorithms. She transitioned into the field of bioinformatics during her postdoc years. During this time she spent four years working at Imperial College London, focusing on the analysis of ‘omics’ data, in particular the integration of metabolomics and transcriptomics data on matched samples. This theme of data integration takes a broader form in her current research, in which she and her team explore how machine learning and data science approaches can allow us to integrate biological datasets from different sources.
Jildau Bouwman works as senior scientist at TNO, the Netherlands. She leads the “digital health technology” roadmap of TNO, that is focusing on health data and development of non-invasive sensors. Her work focusses on omics data interpretation (transcriptomics and metabolomics), data analysis and data management.
The goal of her work is to reuse data to improve public health. This requires data from the whole system (omics data) and from multiple sources (wearables, human (intervention) studies and hospital data). She is one of the driving forces behind the development of the Phenotype database, as system developed to store and reuse nutritional (meta)data. Her vision is that for the reuse of data a rich set of meta data is crucial.