Spatial Transcriptomics Analysis

About the course  |  Intended audience  |  Prerequisites  |  Content details

About the course

Spatial transcriptomics technologies make it possible to measure genome-wide gene expression while preserving the spatial organisation of cells and tissues. This course provides a practical introduction to analysing spatial transcriptomics data using the Seurat package in R and related tools.

Participants will learn how to process spatial transcriptomics datasets, perform quality control, normalisation, and data integration, and apply clustering and dimensionality reduction techniques. The course also covers visualisation of spatial gene expression patterns, identification of spatially variable features, and inference of cell-cell interactions.

By the end of the course, participants should be able to independently conduct and critically evaluate analyses of spatial transcriptomics experiments.

Teaching is primarily hands-on, with short presentations and demonstrations introducing the concepts and methods needed to analyse spatial transcriptomics data.

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Intended audience

This course is suitable for:

• researchers and students interested in analysing spatial transcriptomics data
• participants who want practical experience with spatial transcriptomics analysis workflows
• researchers with experience of high-throughput sequencing who wish to extend their skills to spatially resolved transcriptomics
• participants with a working knowledge of UNIX and R

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Prerequisites

Participants should have:

• a basic understanding of high-throughput sequencing technologies
• a working knowledge of the UNIX command line
• a working knowledge of R

The following experience is recommended:

• analysis of bulk RNA-seq data
• analysis of single-cell RNA-seq data
• running analyses on High Performance Computing (HPC) clusters

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Content details

The course covers the following topics:

• Introduction to spatial transcriptomics
  Introduces the principles of spatial transcriptomics and the range of technologies currently available for measuring gene expression in spatial context.
• Data preprocessing and getting started with Seurat
  Covers loading and preprocessing spatial transcriptomics datasets and introduces the core functionality of Seurat for spatial data analysis.
• Quality control and normalisation
  Introduces approaches for filtering low-quality observations and applying normalisation methods to prepare datasets for downstream analyses.
• Batch correction and data integration
  Covers methods for handling batch effects and integrating spatial transcriptomics datasets across experiments or conditions.
• Clustering and dimensionality reduction
  Introduces methods for identifying groups of similar cells and reducing data dimensionality to facilitate visualisation and interpretation.
• Visualisation of spatial gene expression
  Covers visualisation of gene expression patterns and clusters within their spatial tissue context using Seurat.
• Identification of spatially variable features
  Introduces methods for detecting genes with spatially structured expression patterns and exploring tissue architecture.
• Cell-cell interactions
  Covers approaches for inferring cell-cell interactions and investigating communication between cellular populations in spatial contexts.
• Self-led analysis
  Provides participants with an opportunity to apply the methods covered during the course to analyse spatial transcriptomics data independently.

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