Overview

Spatial transcriptomics is a rapidly evolving technology that allows researchers to study gene expression within the spatial context of tissue architecture. This course provides a practical introduction to the analysis of both sequencing-based and imaging-based spatially resolved transcriptomics (SRT) data, combining theoretical background with hands-on exercises using mostly the R/Bioconductor ecosystem. Participants will gain an understanding of experimental design, data preprocessing, and downstream analysis workflows. Through lectures and guided practicals, the course aims to equip participants with the skills needed to perform their own spatial transcriptomics analyses and interpret the results in a biological context.

Audience

This course is designed for PhD students, postdoctoral and other researchers in the life sciences from both academia and industry who are seeking to understand and analyze spatial transcriptomics data.

Learning outcomes

At the end of the course, the participants are expected to:

• Explain the principles and describe applications of both sequencing-based and imaging-based spatially-resolved transcriptomics (SRT)
• Identify potential pitfalls and limitations of SRT experiments and analysis workflows.
• Define applications for cell segmentation and apply frequently-used cell segmentation methods
• Assess and interpret raw outputs and spatial metadata files, understanding their structure and relevance for downstream analyses.
• Define important aspects of quality control, feature selection, dimensionality reduction and differential gene expression to SRT data and apply those.
• Clarify various spatial statistics and their application to biological questions.
• Use frequently-used methods to analyze multi-sample SRT experiments.

Prerequisites

Knowledge / competencies

You should meet the learning outcomes of Single-Cell Transcriptomics with R

In summary, participants must already have a basic knowledge in:

• Next Generation Sequencing (NGS) techniques
• Analyzing gene expression data
• Dimensionality reduction (PCA, UMAP)
• Graph-based clustering
• R (evaluate your R skills here)
• UNIX (self-assess your skills with the e-learning course UNIX Fundamentals, and this quiz)

Technical

Attendees should have a Wi-Fi enabled computer. An online R and RStudio environment will be provided. However, in case you wish to perform the practical exercises on your own computer, please take a moment to install R (> 4.5) and Rstudio before the course.

Schedule - CET time zone

Application

The registration fees for academics are CHF 300 and CHF 1500 for for-profit companies. While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications will close on 20/05/2026 or as soon as the places will be filled up. Cancellation after 20/05/2026 will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

The course will will start at 9:00 CEST and end around 17:00 CEST and will take place at the Irchel campus of the University of Zurich. More information will follow in due time.

Precise information will be provided to the registered participants in due time.

Additional information

Coordination: Geert van Geest

We will recommend 0.75 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.