Single-cell RNA-Seq Analysis (SCRN03) – Live Online Training

https://prstats.org/course/single-cell-rna-seq-analysis-scrn03/

Delivered by experienced bioinformaticians with expertise in single-cell transcriptomics, RNA sequencing, and computational biology.

Learn how to analyse single-cell RNA sequencing (scRNA-seq) data in R using Seurat, one of the most widely used frameworks for single-cell analysis.

Single-cell RNA sequencing has revolutionised transcriptomics by enabling researchers to measure gene expression at the resolution of individual cells. These methods are now widely applied across biomedical research, cancer biology, immunology, developmental biology, neuroscience, microbiology, plant biology, and evolutionary genomics to identify cell types, investigate cellular heterogeneity, reconstruct developmental trajectories, and understand gene regulation. This course provides comprehensive hands-on training in modern scRNA-seq analysis using Seurat, covering the complete workflow from raw data to biological interpretation.

What you'll gain

A strong understanding of single-cell RNA-Seq technologies and experimental design Practical experience analysing scRNA-seq datasets using Seurat in R Skills in quality control, filtering, normalisation, clustering, dimensionality reduction, and cell type annotation Understanding of differential expression analysis, data integration, and downstream biological interpretation Confidence applying modern single-cell analysis workflows to your own research

Course format

Live, instructor-led online training Hands-on coding using real single-cell RNA-Seq datasets Interactive practical exercises throughout Strong focus on applied, research-ready workflows

Who is this course for?

Bioinformaticians and computational biologists Molecular biologists and genomic researchers Researchers working with RNA-Seq and transcriptomic datasets PhD students and quantitative life scientists Anyone interested in analysing single-cell sequencing data using R

Why take this course?

Single-cell RNA sequencing has become one of the fastest-growing areas of genomics and bioinformatics. As datasets continue to increase in size and complexity, researchers need robust computational workflows to process, analyse, visualise, and interpret cellular gene expression data. Seurat has become the standard toolkit for many scRNA-seq studies and is widely used throughout academia, healthcare, and industry.

This course equips you with the practical skills needed to confidently analyse single-cell RNA-Seq datasets using modern R-based workflows. Whether you're investigating cellular heterogeneity, identifying novel cell populations, studying developmental processes, integrating multiple datasets, or preparing publication-quality analyses, you'll gain the computational toolkit needed to perform high-quality single-cell transcriptomic research.

Learn more & enrol

PR Stats course page for Single-cell RNA-Seq Analysis (SCRN03)

https://prstats.org/course/single-cell-rna-seq-analysis-scrn03/

Questions?

Email: oliver@prstats.org