Spatial Transcriptomics (ANU JCSMR)

This workshop is available for academic staff and HDR students of the ANU Shine-Dalgarno Centre for RNA Innovation and John Curtin School of Medical Research.

Workshop dates & times

This workshop runs over two afternoons from 1:00pm - 5:00pm (AEST), Monday 31st August and Tuesday 1st September 2026.

About the workshop

A practical introduction to the analysis of spatial transcriptomics data in R. Spatial transcriptomics measures gene expression while preserving the spatial location of each measurement within a tissue sample, opening up questions about tissue architecture and the spatial organisation of cell types that cannot be answered by bulk or single-cell RNA sequencing alone. The workshop covers the full analysis pipeline from raw spatial data through to identification of spatially restricted genes, using a real spatial transcriptomics dataset throughout. Participants learn to perform quality control and filtering, normalise and scale data, reduce dimensionality with Uniform Manifold Approximation and Projection (UMAP), apply batch correction, cluster cells and label clusters, and identify genes with spatially restricted expression patterns. The workshop assumes prior R proficiency and a basic understanding of single-cell or bulk RNA sequencing analysis; the basics of R are not covered. 

Learning Objectives 

By the end of this workshop, participants will be able to: 

• Describe what spatial transcriptomics measures and how it differs from bulk and single-cell RNA sequencing. 

• Read spatial transcriptomics data into R and inspect its structure. 

• Apply quality control filters appropriate to spatial transcriptomics data. 

• Normalise and scale spatial expression data. 

• Reduce dimensionality using Uniform Manifold Approximation and Projection (UMAP). 

• Apply batch correction methods to address technical variation between samples. 

• Cluster cells or spots and assign cluster labels using marker genes. 

• Identify genes with spatially restricted expression patterns. 

• Interpret spatial transcriptomics results in the context of tissue biology. 

Prior workshops in this series:

Data Analysis with R for Genomics (ANU JCSMR)

RNA-Seq Analysis with R (ANU JCSMR)

Single-cell RNA-Seq Analysis Using R (ANU JCSMR)

FAQs

What should I bring into the event?

This is an online workshop, so you will need to provide your own computer and have access to a reliable internet connection sufficient for video conferencing. A second monitor is highly recommended.

What prior experience do I need?

Prior experience with R is required, equivalent to that provided by 'R for Reproducible Scientific Analysis' or 'Data Analysis with R for Genomics'. A basic understanding of single-cell or bulk RNA sequencing analysis is also required, equivalent to that provided by Single-Cell RNA Sequencing Analysis Using R or RNA-Seq Analysis with R. Participants must have R and RStudio installed before the session. Installation instructions and required Bioconductor packages will be provided on registration. A full-function computer (Windows, Mac or Linux) is required. Chromebooks and tablets are not suitable.

Who should attend?

Life scientists planning or running spatial transcriptomics experiments, or working with publicly available spatial transcriptomics datasets, who want to perform their own analyses in R. 

How can I contact the organiser with any questions?

You can email training@qcif.edu.au.