Spatial transcriptomics analysis

Overview

This course provides a practical, end-to-end introduction to spatial transcriptomics analysis in R using Seurat and related tools. You will learn how to assess data quality, load and manage datasets from major platforms, and preprocess data for robust downstream analysis. The course then covers dimensionality reduction, clustering, deconvolution, and visualisation strategies for spatial expression patterns. You will also explore spatially variable features, tissue architecture, and cell-cell communication to interpret biological structure in context. Hands-on platform examples (Visium, Xenium, and MERFISH) are used to compare analytical choices and reproducible workflows.

TipLearning Objectives

• Assess spatial transcriptomics data quality using platform reports and quantitative QC metrics.
• Load, inspect, and manage Visium, Xenium, and MERFISH datasets in Seurat.
• Preprocess spatial data through filtering, normalisation, and feature preparation.
• Apply and tune dimensionality reduction and clustering for spatial datasets.
• Perform spot-level deconvolution with single-cell references to estimate cell-type composition.
• Identify and interpret marker genes, spatially variable features, and tissue architecture patterns.
• Analyse cell-cell communication networks and relate signalling to spatial tissue context.

Target Audience

This course is aimed at researchers who are new to spatial transcriptomics data analysis and want practical experience analysing, visualising, and interpreting spatial omics datasets in R.

Prerequisites

• Required
  • 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.
• Suggested
  • Some experience with single-cell RNA-seq analysis in R.
    • For example, attend our single-cell RNA-seq course or work 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.