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scRNA-seq Data Analysis - 07.-10. Sept 2026 - Registration OPEN

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.

General info

  • Time: 07.09.2026 09:00–15:00 (EEST)
  • Type: workshop
  • Language: English
  • Duration: 27 hours
  • Location: Delta building, Narva mnt 18 room 2029, Tartu
  • Audience: This course is designed for life scientists and bioinformaticians with experience in next-generation sequencing who aspire to analyse scRNA-seq gene expression data.
  • Lecturers: Jan Kubovčiak, Lucie Pfeiferová, Kateřina Večerková

Learning outcomes for the participants

  • Distinguish advantages and pitfalls of scRNA-seq.
  • Design their own scRNA-seq experiment, using common technologies like 10× Genomics.
  • Apply quality control (QC) measures and utilise analysis tools to preprocess scRNA-seq data.
  • Apply normalisation, scaling, dimensionality reduction, integration and clustering on scRNA-seq data using R.
  • Differentiate between cell annotation techniques to identify and characterise cell populations.
  • Use differential gene expression analysis methods on scRNA-seq data to gain biological insights.
  • Select enrichment analysis methods appropriate to the biological question and data.
  • Develop an scRNA-seq data analysis workflow from raw count matrix to differential gene expression with peer support and light guidance.

Register

We ask you to register responsibly. If you can't attend the lecture, please let us know as soon as possible via email (elixir@ut.ee).

Register: https://forms.gle/GrVuE4RiR1YwxHp38

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