Single cell RNA-seq analysis

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. 

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