Single-cell RNA-Seq Analysis Using R (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), Tuesday 25th August and Wednesday 26th August 2026.

About the workshop

A practical introduction to single-cell RNA sequencing (scRNA-Seq) analysis in R using the Seurat package. The workshop teaches a complete analysis workflow on the Peripheral Blood Mononuclear Cell 3,000 (PBMC3k) tutorial dataset, taking participants from raw count data through to differential expression results. Participants learn to perform quality control and filtering, normalise expression data, run Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, cluster cells, identify cluster markers, assign cell types automatically using SingleR, and conduct differential expression analysis between groups of cells. The workshop emphasises the reasoning behind each analytical step and explores multiple approaches to visualising single-cell expression data. Prior experience with R is required; the basics of R programming are not covered. The workshop prepares participants to apply scRNA-Seq analysis to their own data and equips them for more specialised single-cell methods including spatial transcriptomics. 

Learning Objectives 

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

• Read single-cell count data into R and create a Seurat object. 

• Apply quality control filters based on metrics including gene count, Unique Molecular Identifier (UMI) count and mitochondrial content. 

• Normalise expression data and identify highly variable features. 

• Run Principal Component Analysis (PCA) and select an appropriate number of dimensions for downstream analysis. 

• Produce Uniform Manifold Approximation and Projection (UMAP) plots and adjust parameters to explore data structure. 

• Cluster cells using graph-based methods and identify markers that distinguish each cluster. 

• Assign cell types to clusters automatically using the SingleR Bioconductor package. 

• Perform differential expression analysis between user-defined groups of cells. 

• Produce a range of single-cell visualisations including violin plots, feature plots, dot plots and heatmaps. 

Other workshops in this series:

Data Analysis with R for Genomics (ANU JCSMR)

RNA-Seq Analysis with R (ANU JCSMR)

Spatial Transcriptomics (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 and R Studio 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 RNA sequencing technology is also recommended. Participants must have R and RStudio installed before the session. Installation instructions and required Seurat and 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 single-cell RNA sequencing experiments, or working with publicly available scRNA-Seq 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.