Single-cell RNA-Seq Analysis (SCRN03) – Live Online Course
Delivered by experienced bioinformaticians with expertise in single-cell transcriptomics, RNA sequencing, and computational biology.
Learn how to analyse single-cell RNA-Seq (scRNA-seq) data in R using Seurat, covering modern workflows for quality control, normalisation, clustering, dimensionality reduction, integration, differential expression, and cell type annotation.
Single-cell RNA sequencing has transformed transcriptomics by enabling gene expression to be measured at cellular resolution. This course provides practical, hands-on training in analysing scRNA-seq datasets using Seurat, from raw count matrices through to biological interpretation. Participants will work through complete analysis pipelines widely used in genomics and bioinformatics research. What you'll gain
Single-cell RNA-Seq has rapidly become a standard technology across genomics, with Seurat established as one of the most widely used analysis frameworks. Researchers increasingly require robust computational skills to process large-scale single-cell datasets, integrate experiments, identify cell populations, and extract biologically meaningful insights.
This course provides practical experience with the complete Seurat workflow, enabling participants to confidently analyse their own single-cell RNA-Seq datasets using current best practices employed across academia and industry. Learn more & enrol
Questions?
Email: [email protected]
Delivered by experienced bioinformaticians with expertise in single-cell transcriptomics, RNA sequencing, and computational biology.
Learn how to analyse single-cell RNA-Seq (scRNA-seq) data in R using Seurat, covering modern workflows for quality control, normalisation, clustering, dimensionality reduction, integration, differential expression, and cell type annotation.
Single-cell RNA sequencing has transformed transcriptomics by enabling gene expression to be measured at cellular resolution. This course provides practical, hands-on training in analysing scRNA-seq datasets using Seurat, from raw count matrices through to biological interpretation. Participants will work through complete analysis pipelines widely used in genomics and bioinformatics research. What you'll gain
- Understanding of single-cell RNA-Seq technologies and experimental design
- Practical experience using Seurat for end-to-end scRNA-seq analysis
- Skills in quality control, filtering, normalisation, feature selection, clustering, and dimensionality reduction
- Experience performing differential expression analysis, dataset integration, and cell type annotation
- Confidence interpreting and visualising single-cell transcriptomic datasets
- Live, instructor-led online training
- Hands-on coding in R using Seurat
- Real single-cell RNA-Seq datasets
- Interactive practical exercises throughout
- Research-focused workflows suitable for publication-quality analyses
- Bioinformaticians
- Computational biologists
- Genomics researchers
- RNA-Seq users moving into single-cell analysis
- PhD students and researchers working with sequencing data
Single-cell RNA-Seq has rapidly become a standard technology across genomics, with Seurat established as one of the most widely used analysis frameworks. Researchers increasingly require robust computational skills to process large-scale single-cell datasets, integrate experiments, identify cell populations, and extract biologically meaningful insights.
This course provides practical experience with the complete Seurat workflow, enabling participants to confidently analyse their own single-cell RNA-Seq datasets using current best practices employed across academia and industry. Learn more & enrol
Questions?
Email: [email protected]
