Spatial transcriptomics analysis

Overview

This course provides a practical introduction to analyzing spatial transcriptomics data using the Seurat package in R and related tools. Participants will learn how to process spatial transcriptomics datasets, perform quality control, normalization, and integration, and apply clustering and dimensionality reduction techniques. The course teaches visualization of spatial gene expression patterns, identification of spatially variable features, and inference of cell-cell interactions. Through hands-on exercises, students will gain proficiency in Seurat’s spatial analysis tools and develop reproducible workflows for high-throughput spatial transcriptomics projects.

TipLearning Objectives

• Load and preprocess spatial transcriptomics data using Seurat
• Perform quality control and normalization of spatial transcriptomics datasets
• Apply clustering and dimensionality reduction techniques
• Visualize spatial gene expression patterns and clusters
• Identify spatially variable features and marker genes
• Infer cell-cell interactions in spatial contexts
• Develop reproducible workflows for spatial transcriptomics analysis

Target Audience

This course is aimed at researchers with no prior experience in the analysis of spatial transcriptomics data, who would like to learn how to analyse, visualise and extract insights from spatial omics datasets.

Prerequisites

• Basic understanding of high-throughput sequencing technologies.
  • Watch this iBiology video for an excellent overview.
• A working knowledge of the UNIX command line (course registration page).
  • If you are not able to attend this prerequisite course, please work through our Unix command line materials ahead of the course (up to section 7).
• A working knowledge of R (course registration page).
  • If you are not able to attend this prerequisite course, please work through our R materials ahead of the course.
  • It would be beneficial to have some experience with single-cell RNA-seq analysis in R, for example by attending our single-cell RNA-seq course or working through our single-cell RNA-seq materials.

Citation & Authors

Please cite these materials if:

• You adapted or used any of them in your own teaching.
• These materials were useful for your research work. For example, you can cite us in the methods section of your paper: “We carried our analyses based on the recommendations in YourReferenceHere”.

You can cite these materials as:

Reid, A., Beier, S. (2025). "Spatial transcriptomics analysis". "https://cambiotraining.github.io/spatial-transcriptomics/"

Or in BibTeX format:

@misc{YourReferenceHere,
  author = {Reid, Adam and Beier, Sina},
  month = {4},
  title = {"Spatial transcriptomics analysis"},
  url = {"https://cambiotraining.github.io/spatial-transcriptomics/"},
  year = {2025}
}

About the authors:

• Adam Reid
  Affiliation: Gurdon Institute
  Roles: conceptualisation; primary author; data curation; coding
• Sina Beier
  Affiliation: Gurdon Institute
  Roles: conceptualisation; primary author; data curation; coding

Acknowledgements

• Thank you to 10X Genomics for providing most of the datasets used in these materials.
• Thank you to the Satija lab for developing and maintaining the Seurat package and for their extensive documentation and tutorials.
• Thank you to the CambiO Training team for their support in developing and maintaining these materials.