Data Analysis

17-20 November 2020

Course details

  • Duration:  3.5 days of live remote sessions
  • Fees: 

       £700  (until 3 November 2020)

      £800  (after 3 November 2020)

Bursaries: 

There are a limited number of MRC Bursaries to be awarded to cover the full early bird fee. There are two deadlines for applying:  21 October and 3 November. For awarding criteria, please see General information

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This course will be run as an online course, with Live lectures and tutorials using MS Teams.

We offer a comprehensive, hands-on training in processing and analysing metabolomics data from LC-MS and NMR technologies.

Attendees will have the opportunity to:

  • Learn directly from internationally recognised leaders in the field;
  • Benefit from practical training in computational techniques and statistical methods;

 Course Aims

This 3.5 day course provides a comprehensive overview of data analysis for metabolic profiling studies focussing on data from NMR spectroscopy and Liquid Chromatography-Mass Spectrometry. It combines lectures and tutorial sessions using open source software to ensure a thorough understanding of the theory and practical applications.

More information

Learning objectives

By the end of the course you will be able to:

  • Describe the main ideas and concepts underlying untargeted metabolomics
  • Outline the characteristics of NMR & LCMS metabolomic data, and how these influence data analysis
  • Process both NMR and LCMS metabolomic data, ensuring high quality and reproducibility
  • Discuss the concepts, strengths and weaknesses of multivariate statistical techniques such as PCA and OPLS as applied to metabolomics data
  • Summarise the main data-driven techniques helping to identify unknown metabolites
  • Explain the main ideas behind pathway analysis with metabolite data
  • Apply all these tools to your own data with confidence and understanding

Learning and teaching methods

The course consists of:

  • Lectures covering the computational metabolomics techniques
  • Hands-on practical sessions analysing example data sets
  • Discussion and Q&A sessions addressing key data analysis topics

Course benefits

We offer a unique course in metabolomics data analysis, blending lectures and practical computational experience. Small attendee numbers ensure that there is maximum opportunity to interact with experienced instructors. On completion of the course, attendees will understand the aims of untargeted metabolomics and the characteristics of metabolomic data from NMR and LC-MS and have a good understanding of pre-processing and statistical modelling of the data. A key benefit is training in the use of open source software which attendees can use immediately on their own data.

Who should attend?

Our courses are designed for customers from universities, research institutions and industry who wish to expand their knowledge and skills in metabolomics, including:

  • Analytical scientists working with complex mixtures
  • Bioinformaticians or statisticians wishing to understand metabolomics techniques
  • Clinicians interested in applying metabolomics to their clinical studies

The course assumes no prior knowledge of multivariate statistics, and does not require prior coding experience.

Accreditation

All participants will be awarded an Imperial College London Certificate of Attendance on completion of the course.

This course has been awarded 20 CPD credits by the Royal Society of Medicine in accordance with its current guidelines.

Comments from past participants

"This course was an excellent introduction to metabolomic data analysis. This course got me off the ground and put me in touch with helpful people so that now I am able to work with my data"

"There was a good balance between theoretical content and practice tutorials, where we could immediately apply what we had just learned "

"The instructor to participant ratio was generous and the instructors themselves were knowledgeable, clear, very flexible and patient, and as a result, the tutorials were well-paced, without anyone being left behind"