Single cell RNA-seq analysis
Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomics by allowing researchers to study gene expression at single-cell resolution. This level of detail is crucial for exploring cellular heterogeneity, discovering novel cell types, and investigating dynamic biological processes such as development and disease progression.
You will acquire the skills to analyze and interpret short-read scRNA-seq datasets. Through a combination of theory and hands-on exercises, we will guide you through the complete analysis pipeline from quality control and normalization to clustering, visualization, and differential expression analysis.
Learning outcomes
- Perform quality control and filtering at the cell level to ensure data reliability
- Apply normalization and noise-reduction techniques to enhance signal quality in scRNA-seq datasets
- Cluster cells based on gene expression profiles and visualize results using dimensionality reduction methods (e.g., UMAP)
- Identify marker genes for specific clusters to characterize distinct cell populations
- Analyze differential gene expression between experimental conditions to uncover biologically relevant changes
- Evaluate the effectiveness of preprocessing and clustering methods in revealing biological insights
Approach
The training combines theoretical instruction with hands-on activities. On the second day, participants are encouraged to bring their own data sets for practical application and feedback.
Required skills
You should have experience with R, and we offer a free, online preparatory course that you can follow.
More information can be found on Basic Statistics in R. If this course is full, contact janick.mathys@vib.be to obtain a seat.
Course materials
Extra information
This course is part of the learning path Single cell data short reads and long reads.