16-20 November 2026

To foster international participation, this course will be held online

This course will introduce biologists and bioinformaticians to the field of single-cell RNA sequencing. We will cover a range of software and analysis workflows that extend over the spectrum from the best practices in the filtering scRNAseq data
to the downstream analysis of cell clusters and temporal ordering. This course will help the attendees gain accurate insights in pre-processing, analysis and interpretation of scRNAseq data.

We will start by introducing general concepts about single-cell RNA-sequencing. From there, we will then continue to describe the main analysis steps to go from raw sequencing data to processed and usable data. Finally, we will focus more specifically on the different analyses strategies to use in order to extract information from genomic datasets such as Hi-C, ATAC-seq or ChIP-seq.

Throughout the workshop, bash tools and R/Bioconductor packages will be used to analyse datasets and learn new approaches.

The course is structured in modules over five days. Each day will include formal lectures covering the key concepts required to understand scRNAseq analysis. The remainder of each day will consist in practical hands-on sessions focusing on analysis of scRNA-seq data. These sessions will involve a combination of both mirroring exercises with the instructor to demonstrate a skill, as well as applying these skills on your own to complete individual exercises.

During and after each exercise, interpretation of results will be discussed as a group.

The course will be mostly beneficial to those who have, or will shortly have, scRNA-seq data ready to analyse.

The material is suitable both for experimentalists who want to learn more about data-analysis as well as computational biologists who want to learn about scRNASeq methods.

Examples demonstrated in this course can be applied to any experimental protocol or biological system.

The requirements for this course are:

• Working knowledge of Unix / command line interface (managing files, running programs, reading manuals!). Basic bash commands (cd, ls, ...) and CLI usage will not be covered in this course. We advice attendees to not register if they lack fundamental experience in CLI.
• Programming experience in R (writing a function, basic I/O operations, variable types, using packages). Bioconductor experience is a plus.
• Familiarity with next-generation sequencing data and its analyses (using alignment and quantification tools for bulk sequencing data)

At the end of this course, you should be able to:

• Understand the pros/cons of different single-cell RNA-seq methods
• Process and QC of scRNA-seq data
• Normalize scRNA-seq data
• Correct for batch effects
• Visualise the data and applying dimensionality reduction
• Perform cell clustering and annotation
• Perform differential expression analysis
• Infer pseudo-time and perform temporal differential expression

Throughout the course, we will also have a focus on reproducible research, documented content and interactive reports.

Monday – Classes from 2 to 8 pm CET

Lecture 1 – scRNA-Seq experimental design

• General introduction: cell atlas overviews
• Comparison of Bulk and single cell RNA-Seq
• Overview of available scRNA-seq technologies (10x) and experimental protocols

Lecture 2 - Intro to Data processing: from bcl file to count matrix

•  scRNA-Seq processing workflow starting with choice of sequencer (NextSeq, HiSeq, MiSeq) / barcode swapping and bcl files
• Overview of Popular tools and algorithms
• Common single-cell analyses and interpretation
• Sequencing data: alignment and quality control
• Looking at cool things in alignment like where reads are, mutations, splicing
• Read & UMI counting (Kallisto alignment-free pseudocounts as well), how RSEM works (length dependence, sequencing depth, multimapping reads), CellRanger (dropest), bustools

Lab 1 – Familiarizing yourself with the course AWS instance

• Logging in AWS
• Shell and Unix commands to navigate directories, create folders, open files
• Raw file formats
• Using RStudio
• Get data from 10x website, single cell portal, from GEO (fastqs, counts)

Lab 2 – Processing raw scRNA-Seq data

•  Data outputs from different scRNAseq technologies (10x, Smart-seq2)
• Quality Control reports (CellRanger, dropEst, fastqc)
• Mapping sequencing data with Cellranger

• "It was a really useful course. Overall, it had something to offer for any level of single-cell knowledge and covered the most important topics in scRNA-seq analysis. Thank you!"

• "Exactly what I was looking for. I now feel I have the foundational tools to analyse single-cell datasets with confidence."

• "The course provided an excellent overview and a solid foundation to further develop skills in this field."

1 -Epigenomics - ONLINE, 13-17 April

2 - Spatial Omics with R/Bioconductor - ONLINE, 18-20 May

3 - RNAseq for beginners - ONLINE, 19th-28th of May

4- Developing R/Bioconductor packages - ONLINE, 6-10 July

5 - Machine Learning for Multi-Omics Integration - ONLINE, 21-23 September