About
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.
By the end of the course, participants will possess the following abilities:
- 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.
Dates: 1.-3.12.2025
Location: Institute of Molecular Genetics of the Czech Academy of Sciences, Vídeňská 1083 Praha 4, Jágr’s hall
Maximum capacity: 25 participants
Programme
Instructors
- Lucie Pfeiferová (IMG)
- Jan Kubovčiak (IMG)
- Yusuf Çağlar Odabaşı (IBT)
- Mathys Delattre (IMG)
- Michal Kolář (IMG)
- Vojtěch Melichar (IMG)
