Analysis of Spatial Transcriptomics Data (Xenium)

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

• Workbooks overview
• Credits
• Contributions
• Citation
• Quick Start

Workbooks overview

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.

Credits

The workbooks are being written by Katrin Sameith, Andreas Petzold, Ulrike Friedrich, and Rajinder Gupta at the DRESDEN-concept Genome Center.

Contributions

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

Citation

If you used our workbooks to analyze your data, please cite it by mentioning the DRESDEN-concept Genome Center URL "https://genomecenter.tu-dresden.de".

Singularity container

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!