Welcome to our hands-on workbook collection to learn spatial transcriptomics data analysis, with a focus on 10x Genomics Xenium data!
Spatial transcriptomics allows us to measure gene expression across tissue sections while preserving spatial information, helping us understand not just which genes are active, but also where they are active.
With our workbooks, you will explore how to analyze spatial transcriptomics data using R and RStudio. Whether you're new to spatial data or already familiar with single-cell workflows, our guides will walk you through the essential steps.
Table of contents
Our workbooks cover:
- Learn how to load spatial data into R and examine how the data is structured
- Key steps in the analysis workflow, including quality control, preprocessing, clustering and visualisation, cluster annotation, spatial clustering, and reference mapping.
The workbooks are being written by Katrin Sameith, Andreas Petzold, Ulrike Friedrich, and Rajinder Gupta at the DRESDEN-concept Genome Center.
We welcome contributions! To contribute:
- Create your own fork of the GitHub repository
- Submit a pull request once your edits are complete
- Please include clear descriptions of your changes and make sure your code runs
If you used our workbooks for your own work, please cite them:
DRESDEN-concept Genome Center (2026). Analysis of Spatial Transcriptomics Data (Xenium). Zenodo. https://doi.org/10.5281/zenodo.22876807
We are happy to share our Singularity container. It contains all software required to run our code.
# Command to download the singularity container
singularity pull oras://gcr.hrz.tu-chemnitz.de/dcgc-bfx/singularity/singularity-single-cell:v1.6.6
Happy coding & exploring spatial data!