Spatial metabolomics data analysis
Understanding the spatial distribution of metabolites, lipids, and proteins is key to uncovering complex biological processes. Mass spectrometry imaging (MSI) makes this possible by enabling direct detection of metabolites, lipids, and proteins from tissue surfaces. In this course, you will learn the different steps required to analyze spatial metabolomics data obtained with MSI.
During the exercise sessions, you will apply these steps in practice, analyzing real MSI datasets with tools such as the Cardinal package in R and IsoScope. By the end of the course, you will be able to explore, process, visualize, and interpret spatial (including tracer) metabolomics data and apply these skills to your research.
Learning outcomes
- Explain the fundamentals of mass spectrometry imaging (MSI) and its relation to (tracer) metabolomics.
- Describe MS imaging data formats and how to import them into R.
- Apply preprocessing techniques to MSI data using the Cardinal package in R.
- Utilize Cardinal and IsoScope to visualize spatial metabolomics data and perform statistical analyses to detect meaningful patterns.
- Export processed data for downstream analysis.
- Interpret the results of the MSI-based models.
Approach
This course combines short lectures with hands-on exercises using real MS imaging datasets. Participants are expected to actively engage with data, follow guided tutorials in R, and contribute to group discussions on data interpretation and analysis.
Required skills
Unless you are already familiar with MS imaging, we strongly recommend that you first attend our Tracer Metabolomics course. Experience with programming in R is required. If you have no experience with R, you can join our free online R introduction course.
Extra information
This course is part of the learning path Spatial metabolomics data.