Spatial Omics Data Analysis

Description

This workshop provides resources to advanced tools for analysis of spatial datasets . It is aimed towards biologists, researchers, computer scientists or data analysts planning to run, analyse and interpret single cells RNA-seq experiments independently.

Topics covered

• Imaging-based and sequencing-based spatial data
• Data processing (pre-processing, quality control, filtering, normalization)
• Cell segmentation-based analysis
• Segmentation-free analysis
• Sequencing-based analysis
• Spatial and single cell data integration
• Spatial statistics
• Multi-slice integration
• Domains and niches
• Cell-cell communication

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.

Pre-requisites

• 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.
• Participants are required to bring their own laptops.

Level

Beginner

Course leaders

edu.spatial@nbis.se