14-18 December 2026

Spatial transcriptomics is transforming our ability to study gene expression within its native tissue context. By combining high-resolution imaging, sequencing-based technologies, and computational analysis, researchers can now map transcripts, identify cellular niches, and characterize cell–cell interactions directly in space.
This 5-day course provides a comprehensive introduction to the theory and practice of spatial transcriptomics data analysis. The program starts with an essential conceptual foundation and progressively transitions into hands-on, guided computational labs. Participants will learn how to process, integrate, and explore spatial and single-cell omics data using state-of-the-art tools in R and Bioconductor.
The final day includes a project-design workshop and an interactive hackathon, where participants work in teams to analyze their own (or publicly available) datasets, applying the methods learned throughout the week.
By the end of the course, students will have a solid theoretical understanding of spatial transcriptomics and practical experience performing full analysis workflows—preparing them to apply these methods directly in their research projects.

This course is intended for researchers, graduate students, and professionals working in bioinformatics, computational biology, genomics, or related fields who want to expand into spatial transcriptomics analysis.
The course is designed for advanced students and researchers with a basic proficiency in R and Bioconductor.

By the end of this course, participants will be able to:

•  Understand the key concepts, technologies, and applications of spatial transcriptomics.
• Process, explore, and analyze spatial omics data using R/Bioconductor, including cell segmentation, quality control, spatial mapping, and integration with single-cell datasets.
• Perform downstream spatial analyses such as niche characterization and cell–cell communication.
• Design and execute spatial transcriptomics projects, apply workflows to real datasets, and interpret and communicate spatial biological insights.

Day 1 The WHYs and the HOWs of spatial transcriptomics - 2-6 PM Berlin time

    Intro
    Lecture 1 The spatial dimension
    Lecture 2 Mapping transcripts in space
    Lab 1 Computational set up & raw spatial data

Day 2 From transcripts to single cells- 2-6 PM Berlin time

    Lecture 3 Imaging-based spatial data analysis
    Lecture 4 Sequencing-based spatial data analysis
    Lab 2 Cell segmentation & interactive data exploration

Day 3 Spatial mapping of cell types and cell states- 2-6 PM Berlin time

    Lecture 5 Shared downstream data processing and QC
    Lecture 6 Leveraging paired single-cell and spatial omics
    Lab 3 Single-cell resolved spatial analyses

Day 4 The cellular niche- 2-6 PM Berlin time

    Lecture 7 Cells live and interact in multicellular niches
    Lab 4 Neighbourhood and niche analysis
    Lab 5 Spatial cell-cell communication analysis

Day 5 A special  future- 2-6 PM Berlin time

    Lecture 8 Towards 4D spatial multiomics
    Workshop: Design your own spatial project
    Hackathon: Start digging in your spatial data
    Conclusion

"Tancredi was a really good instructor! Thorough, good at explaining things, patient with questions, and fun!  Also I think the online format was better, than I expected. "

"Overall, I'm really happy with this course. I learnt so much, and at the same time, it was genuinely fun. Tancredi did a wonderful job, always troubleshooting, adapting the course in real-time to our questions and needs, and finding solutions on the spot. Everyone involved, organisers and participants, was super nice and helpful, creating a fun and supportive atmosphere."

- Developing R/Bioconductor packages - ONLINE, 6-10 July

- Spatial Omics Data Analysis: From Raw Data to AI Insights - ONLINE, 14-16 September

- Machine Learning for Multi-Omics Integration - ONLINE, 21-23 September 

- Network Analysis in System Biology with R/Bioconductor - ONLINE, 5-8 October

- RNA-seq analysis with R/Bioconductor - ONLINE, 9-18 November

- Single-cell RNAseq with R/Bioconductor - ONLINE, 16-20 November

- Exploring and Visualizing Omics Data  - ONLINE, 25-26 November