TeSS will be briefly down for maintenance on Thursday 28th May @ 22:00 UTC

Date: 6 - 9 October 2026

Timezone: Brussels

Language of instruction: English

This course delves into the cutting-edge field of Spatial Omics, focusing on Spatially-Resolved Transcriptomics (SRT) technology which provides unprecedented insights into the spatial organization of gene expression within tissues. The rapid and recent advances in SRT technology are transforming our understanding of biological systems, and this course is designed to equip researchers with the tools to harness the power of SRT, adding significant value to biological knowledge and opening new avenues for scientific discovery.

Participants will explore both imaging-based and sequencing-based SRT technologies, learning to navigate the entire workflow of SRT data analysis. The course covers essential topics such as pre-processing techniques for data cleaning, normalization, and quality control, methods for identifying and characterizing spatial domains within tissues, strategies for integrating SRT data with single-cell RNA sequencing data, and statistical approaches to analyze spatial patterns and relationships. Additionally, participants will investigate interactions between cells within their spatial context. By the end of this course, participants will be equipped with the knowledge and skills to construct a complete workflow for SRT data analysis, from raw data to meaningful biological insights. The course combines lectures with practical sessions, ensuring a balanced approach to theory and hands-on experience.

This is an international course hosted by NBIS (ELIXIR-SE) in collaboration with the ELIXIR Single-Cell Omics community and other ELIXIR nodes.

Contact: edu.spatial [at] nbis.se

Keywords: Spatial omics, transcriptomics, spatial transcriptomics, imaging, Image analysis, BIoinformatics, data analysis

Venue: BMC Trippelrummet Husargatan 3, entrance C11

City: Uppsala

Country: Sweden

Prerequisites:

Prerequisites
Participants should be proficient in Python and R, for basic data analysis.

Participants should be familiar with NGS technologies, have experience with analyzing (spatial/single-cell) transcriptomics data as well as basic knowledge of machine learning.

Participants should also have a basic understanding of working with command line tools on Unix-based systems.

Technical requirements
Participants are required to bring your own laptop.

Learning objectives:

At the end of the course, the participants will be able to:

  • Identify and recall key concepts and terminology related to imaging- and sequencing-based SRT technologies.
  • Assess and evaluate quality of SRT data.
  • Perform standard SRT data analysis, including data cleaning, normalization, quality control.
  • Examine and interpret spatial patterns and relationships within SRT data using statistical and machine learning approaches.
  • Construct a comprehensive workflow for SRT data analysis, from raw data to meaningful biological insights.

Target audience: PhD students, Researcher, PostDoc, Research support staff, Group leaders, Masters students, Senior scientist/ Principal investigator, group leaders, Research Assistants and Research Associates, core facility staff

Capacity: 30

Event types:

  • Workshops and courses


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