Workshops and courses

Spatial Omics Data Analysis - Swiss Institute for Bioinformatics

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Date: 21 - 24 January 2025

Timezone: Stockholm

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.

The course materials will be on the dedicated GitHub page.

The application form is available here

Venue: University of Lausanne

City: Lausanne

Country: Switzerland

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 an Amazon Web Services (AWS) Elastic Cloud (EC2) server. Our Ubuntu server behaves like a ‘normal’ remote server, and can be approached through a web browser (safari, firefox, chrome etc.). All participants will be granted access to a personal workspace to be used during the course. The web interface will be approached through http (not https!), so make sure you can access http sites.
  • Please perform these installations PRIOR to the course and contact us if you have any trouble.

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, postdocs, researchers

Event types:

  • Workshops and courses

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