Single-Cell RNA-Seq Analysis Live online training covering all stages of single-cell transcriptomic data analysis — from experimental design to data QC, normalization, clustering, differential expression, and biological interpretation.
https://prstats.org/course/single-cell-rna-seq-analysis-scrn02/ Join our four-day live online workshop: Single-Cell RNA-Seq Analysis (SCRN02). If you’re working with single-cell transcriptome data or planning to dive into single-cell workflows, this course will guide you from raw data to interpretable biological insight. What you will learn How to design a robust single-cell RNA-Seq experiment, including strategies for cell capture, sequencing depth and batch control. How to perform quality control and filtering of single-cell data to ensure high-quality downstream analysis. How to normalize and process data, cluster cells, and identify cell types or states. How to conduct differential expression analysis, trajectory inference or other advanced single-cell analyses. How to interpret results biologically — linking clusters, cell types or trajectories to meaningful insights. Who should attend Researchers, postgraduate students and industry professionals working with single-cell RNA-Seq data. Anyone with basic experience in R, data analysis, and transcriptomics who wants to build confidence in single-cell workflows. Those who want to confidently move from raw single-cell data to actionable biological conclusions. Course format Four days of live online sessions (approximately 3½ hours each day) in a UK / Western European time zone. Interactive lectures, hands-on practical sessions and discussion time. Course materials, code and example datasets will be provided — participants are encouraged to bring their own data for discussion when possible. Recordings available after each day to support participants in different time zones. Why this course stands out It offers an end-to-end workflow specifically tailored for single-cell data — from design through QC, clustering, differential expression and biological interpretation. It emphasises not just how to run analysis pipelines, but how to understand assumptions, interpret results, and avoid common pitfalls in single-cell data analysis. Developed and delivered by experienced bioinformaticians, the course balances theoretical foundations with applied, hands-on training in a live-online format. How to register / next steps Visit the PR Stats website for full course details, upcoming dates and registration information. Early registration is recommended, as places are limited. https://prstats.org/course/single-cell-rna-seq-analysis-scrn02/ -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
