Single-cell RNA sequencing (scRNA-seq) allows researchers to study gene expression at the level of individual cells. This approach can, for example, help to identify different cell populations in a complex sample and describe their expression patterns. To generate and analyse scRNA-seq data, several methods are available, all with their strengths and weaknesses depending on the researchers’ needs. This 3-day course will cover the main technologies as well as the main aspects to consider while designing an scRNA-seq experiment. In particular, it will combine the theoretical background of analytical methods with hands-on data analysis sessions focused on data generated by droplet-based platforms.

Requirements

This course is designed for life scientists and bioinformaticians with experience in next-generation sequencing who aspire to analyse scRNA-seq gene expression data.

The course exercises are conducted in the R statistical language, so a basic understanding of R and RStudio is essential and strictly required.

Attribution

This course is heavily based on the course developed by the Swiss Institute of Bioinformatics (https://sib-swiss.github.io/single-cell-r-training/). It also draws inspiration from the Broad Institute Single Cell Workshop and the CRUK CI Introduction to Single-Cell RNA-Seq Data Analysis course.

Programme

Day 1 – Monday, 7th of September

9:00 – 9:30 Introduction
9:30 – 10:30 Introduction to scRNA-seq
10:30 – 11:00 Break
11:00 – 12:30 10× and Cellranger
12:30 – 13:30 Lunch
13:30 – 15:00 Analysis tools and QC
15:00 – 15:30 Break
15:30 – 17:00 Group work

Day 2 – Tuesday 8th of September

9:00 – 10:30 Normalisation and scaling
10:30 – 11:00 Break
11:00 – 12:30 Dimensionality reduction and integration
12:30 – 13:30 Lunch
13:30 – 15:00 Clustering
15:00 – 15:30 Break
15:30 – 17:00 Group work

Day 3 – Wednesday 9th of September

9:00 – 10:30 Cell annotation
10:30 – 11:00 Break
11:00 – 12:30 Differential gene expression
12:30 – 13:30 Lunch
13:30 – 15:00 Group work

Day 4 - Thursday 10th of September

10:00 – 12:00 Group work
12:00 – 13:00 Lunch
14:00 – 15:00 Presentations

Topics

• Introduction to Single-Cell RNA Sequencing Jan Kubovciak
  • Topics covered: Overview of single-cell RNA sequencing (scRNA-seq) technologies and applications. Key advantages and limitations of scRNA-seq approaches. Experimental design considerations and introduction to droplet-based technologies such as 10× Genomics.
• scRNA-seq Data Processing and Quality Control Jan Kubovciak
  • Topics covered: Introduction to the 10× Genomics workflow and the Cell Ranger pipeline. Overview of commonly used analysis tools for scRNA-seq data. Quality control metrics and strategies for identifying low-quality cells and technical artefacts.
• Data Normalisation and Scaling Jan Kubovciak/Lucie Pfeiferova
  • Topics covered: Methods for normalising and scaling scRNA-seq data. Handling technical variability and preparing datasets for downstream analysis using R-based workflows.
• Dimensionality Reduction and Data Integration Lucie Pfeiferova
  • Topics covered: Techniques for reducing data dimensionality (e.g., PCA, UMAP, t-SNE) and integrating multiple datasets. Strategies for correcting batch effects and combining datasets from different experiments.
• Clustering of Single Cells Lucie Pfeiferova
  • Topics covered: Clustering algorithms used to identify cell populations in scRNA-seq data. Interpretation of clustering results and strategies for identifying biologically meaningful groups.
• Cell Annotation and Biological Interpretation Lucie Pfeiferova
  • Topics covered: Approaches for annotating cell types using marker genes, reference datasets, and automated annotation tools. Interpretation of cell population identities.
• Differential Gene Expression Analysis
  • Topics covered: Methods for identifying differentially expressed genes between cell populations. Considerations specific to scRNA-seq datasets and interpretation of results.
• Group Work: scRNA-seq Analysis Workflow
  • Topics covered: Hands-on analysis of scRNA-seq datasets. Participants will apply the full workflow, including quality control, normalisation, clustering, annotation, and differential expression analysis. Results will be discussed in group presentations.