Spatial Omics Data Analysis

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Overview

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.

Venue and time

This course will take place in Uppsala, Sweden, 6-9 of October 2026. The course will be held 9:00-17:00 each day except Friday the 9th when we end at 13:00. Find a detailed schedule here.

Lunch and coffee-break snacks are provided each day. All participants are invited to the social dinner, organized on 6 October 2026.

Audience

This 3.5-day course is addressed to PhD students, postdocs, and researchers who are involved (or will be in the near future) in projects including spatially-resolved transcriptomics data, and want to acquire the skills to get started with spatial data analysis.

Learning outcomes

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.

Prerequisites

Knowledge / competencies

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. You can test your skills with Unix with the quiz here. If you do not feel comfortable with UNIX commands, please take our Unix fundamentals e-learning module.

Technical

Participants are required to bring your own laptop.

We will be mainly working on the Scilifelab Serve system. All participants will be granted access to a personal workspace to be used during the course.

Application

The application form will be open from 24 April to 22 May.

Once the application is closed we will not accept additional students. However we will make all material for the course freely available so that you can study the material on your owm if you missed the deadline.

Course fee

The course fee is 150€. Lunch, coffee breaks and one social dinner are included. Applicants must supply a valid invoicing address in the application form. Please note that NBIS cannot invoice individuals.

The trainers

The course will feature the following lecturers and trainers:

  • Francesca Drummer (Helmholtz Zentrum München, DE)
  • Naveed Ishaque (Berlin Institute of Health (BIH), DE)
  • Ahmed Mahfouz (Leiden University Medical Center (LUMC), NL)
  • Benedetta Manzato (Leiden University Medical Center (LUMC), NL)
  • Emma Vonk (Leiden University Medical Center (LUMC), NL)
  • Lucie Pfeiferova (Czech Academy of Sciences, CZ)
  • Christophe Avenel (Uppsala University, NBIS, Scilifelab, SE)
  • Jonas Windhager (Uppsala University, NBIS, Scilifelab, SE)
  • Suganya Sivagurunathan (Uppsala University, NBIS, Scilifelab, SE)
  • Stefan Ebmeyer (Chalmers University of Technology, NBIS, Scilifelab, SE)
  • Åsa Björklund (Chalmers University of Technology, NBIS, Scilifelab, SE)
  • Aditya Singh (Gothenburg University, NBIS, Scilifelab, SE)
  • Leslie Solorzano (Uppsala University, NBIS, Scilifelab, SE)
  • Jennifer Fransson (Uppsala University, NBIS, Scilifelab, SE)
  • Sergiu Netotea (Chalmers University of Technology, NBIS, Scilifelab, SE)

Course organizers

This is an international course hosted by NBIS (National Bioinformatics Infrastructure Sweden, ELIXIR-SE) in collaboration with the ELIXIR Single-Cell Omics community, and trainers’ own institutions and affiliations to ELIXIR nodes (KU Leuven, Helmholtz Zentrum München, SIB, BIH, LUMC, CAS).

Course organizers:

  • Ahmed Mahfouz, Leiden University Medical Center (LUMC), ELIXIR - Netherlands
  • Åsa Björklund, Chalmers University of Technology, NBIS, Scilifelab, ELIXIR - Sweden
  • Christophe Avenel, Uppsala University, NBIS, Scilifelab, ELIXIR - Sweden
  • Jennifer Fransson, Uppsala University, NBIS, Scilifelab, ELIXIR - Sweden
  • Eija Korpelainen, CSC, ELIXIR - Finland

Contact: edu.spatial [at] nbis.se

Additional information

We will recommend 0.75 ECTS credits for this course.

Course organizers and trainers abide by the ELIXIR Code of Conduct. Participants are also required to abide by the same code.

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