Single-cell RNA seq analysis
Trainers: Katarzyna Kania and Jiawei Wang
Overview: Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNAseq). Aim of ‘Introduction to Single Cell RNAseq’ talk is to present different single cell sequencing methods available nowadays, their pros and cons and which you may want to use for your experiments. Presentation will focus on the applications, workflows and challenges that should be considered during experimental design of scRNAseq.
In the practical session, you will learn the most common pipeline for single-cell RNA-seq analysis using Seurat, including quality control, normalisation, clustering and dimensionality reduction, differential expression, and visualisations.
Learning outcomes
By the end of this session you will be able to:
- Understand how the single-cell RNA-seq technology works
- Understand how mapping and quantification of single-cell RNA-seq data is performed
- Analyse single-cell RNA-seq data using Seurat after mapping and quantifcation.
Overview of scRNAseq technology and its applications
Trainer: Katarzyna Kania
Additional resources: SPLiT-Seq the Movie
Practical: scRNAseq data analysis
Trainer: Jiawei Wang
Materials: