Introduction to Metabolomics Data Analysis

Description

National course open for PhD students, postdocs, researchers and other employees in all Swedish universities, in need of metabolomics data analysis skills.

We will go through study design, familiarize ourselves with principles of LCMS, GCMS and NMR experiments, pre-process the raw data acquired from analytical instruments and perform downstream data analysis and interpretation using multivariate methods such as PCA, (O)PLS and (O)PLS-DA. The course will include lectures and hands-on exercises.

Topics covered

Designing a metabolomics experiment
Principles of LCMS, GCMS and NMR techniques
Pre-processing (peak picking, alignment, annotation) of raw metabolomics and exposomics data with MS-Dial
Quality controls of raw data
Analysis of metabolomics and exposomics data using PCA, (O)PLS and (O)PLS-DA in R and SIMCA software
Assessment of model performance and selection of differential metabolites

Learning outcomes

Upon completion of this course, you will be able to:

Describe the key steps and components of a typical metabolomics workflow.
Explain the fundamental principles and differences between LC-MS, GC-MS, and NMR techniques in metabolomics.
Pre-process raw metabolomics data using MS-DIAL, including peak picking, alignment, and annotation.
Apply multivariate (e.g., PCA, PLS, PLS-DA) methods to metabolomics and exposomics datasets using R and SIMCA software
Evaluate the assumptions, strengths, and limitations of multivariate methods used in metabolomics.
Interpret the biological significance of results from multivariate methods.
Select appropriate data processing and statistical approaches based on study design and research objectives.