This comprehensive, hands-on training program is designed to equip biologists, bioinformaticians, and health data researchers with the knowledge and skills to confidently analyze bulk and single-cell RNA-Seq data. From setting up a Linux environment for upstream preprocessing to applying R, Bioconductor, and Seurat for downstream and single-cell workflows, the course provides a complete journey through modern transcriptomics.
Participants will gain practical experience working with real-world datasets and learn to produce publication-quality visualizations and insights that can directly support research projects, theses, or clinical/biotech applications.
🔍 What you’ll learn
- Bulk RNA-seq: Preprocessing, differential expression analysis, and visualization
- Single-cell RNA-seq: Clustering, cell type annotation, and trajectory analysis
- Applied case studies: Cancer and neurobiology datasets
- Public data resources: GEO & recount3 workflows
- Bonus Module: GEO Data Mining for Biologists
By the end of the course, you’ll be able to move seamlessly from raw sequencing data to biological interpretation, mastering the essential tools and pipelines used in cutting-edge transcriptomics research.