Description

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.

Details

Dates
6 - 9 October 2026
Application deadline
May 22, 2026 23:59
Contact

edu.spatial [at] nbis.se

Venue
BMC Trippelrummet Husargatan 3, entrance C11
City
Uppsala
Country
Sweden
Language
English
Cost
150 EUR : Academic
Timezone
Stockholm

Content Providers

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 & Technical Requirements

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.

Topics & Tags

Keywords
spatial omicstranscriptomicsspatial transcriptomicsimagingimage analysisbioinformaticsdata analysis

Affiliations & Networks

Associated nodes
SciLifeLab
Target audience
researchersresearch support staffresearch assistantsPhD studentspostdocscore facility staffbioinformaticiansgroup leadersMasters studentssenior scientists

Activity log