Course Overview

This online course explores the tools and approaches that are used to process and analyse metabolomics data. You will investigate the challenges that are typically encountered in the analysis of metabolomics data, and provide solutions to overcome these problems.

The materials in this course are delivered via the FutureLearn platform over a four week period, with an estimated learning time of four hours per week. Each week you will work through a number of steps to complete the learning material. A step may include a short video, an article, an exercise with step-by-step instructions, a test or a discussion to interact with your peer or the educators. All of the course material is uploaded to the FutureLearn platform so that you can complete the steps at a convenient time for you.

We (the educators) support your learning via social discussions where you will be able post questions and comments to the team of educators and the other learners on the course throughout the 4 weeks.

In the final week of the course there is a question and answer session with our educators. You will be provided with information to join the question and answer session via the FutureLearn platform and you can post questions in advance. The question and answer session will be recorded and a video uploaded to the FutureLearn platform.

If you do not have time to complete the course during the 4-week period you will retain access to the course material to revisit, as you are able.

Topics Covered

• An introduction to metabolomics
• An overview of the untargeted metabolomics workflow
• The influence of experimental design and data acquisition on data analysis and data quality
• An overview of processing NMR data
• Processing direct infusion mass spectrometry data with a hands-on exercise
• Processing liquid chromatography-mass spectrometry data with hands-on exercises
• Reporting standards and data repositories
• Data analysis, detecting outliers and drift, and pre-treatment methods
• Univariate data analysis with a hands-on exercise
• Multivariate data analysis (including unsupervised and supervised approaches) with hands-on exercises
• The importance of statistical validation of results
• Computational approaches for metabolite identification and translation of results into - biological knowledge with hands-on exercises
• What are the future challenges for data processing and analysis in metabolomics