The aim of the course is to introduce students, primarily with backgrounds in life sciences, biology, biochemistry and related fields, to the experimental and computational techniques that are central to generating and analyzing spatial biology data, both based on sequencing and imaging. The course provides a foundation in data generation, data analysis and workflows that are becoming increasingly important in modern biological research.
The course consists of lectures, project work and computer labs. The lectures cover theoretical aspects of sequencing and imaging-based methods in spatial biology and related analyses. The computer labs are designed for practical data analysis, such as data pre-processing, quality control, clustering, dimensionality reduction and spatial mapping. The project work focuses on student-led discussions and presentations of key methods and relevant literature.
Good research practice is emphasized throughout the course – such as ensuring data quality and managing biological variation, critically interpreting analysis results and developing a rigorous and reproducible computational approach. The course prepares students for further studies and research in data-driven biological sciences.
Course content in brief:
- Sequencing-based spatial analysis – principles, technologies and strategies and comparisons with single-cell transcriptomics
- Imaging-based spatial analysis – principles, technologies and strategies
- Computational workflows for analysis of sequencing- and imaging-based spatial biology
- Aspects of reproducible data generation and data analysis
- Spatial multi-omics: data generation and integration
- Applications of spatial biology in basic and translational life sciences