Analysis and interpretation of single-cell RNA-Seq (scRNA-Seq) data requires specialised dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat – an R package for QC, analysis, and exploration of single-cell RNAseq data.
We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data.
Life scientists planning or running single cell RNA-Seq experiments (or mining public data), who want to perform their own analyses.
Participants should have a basic understanding of single cell RNA-Seq technology. Prior expertise with R is required to a level equivalent to that provided by the R for Reproducible Scientific Analysis workshop, as the basics of R will not be covered.
- Load gene counts into a Seurat format
- Perform QC and select cells for further analysis
- Filter and normalise scRNAseq data
- Cluster cells and identify cluster markers
- Visualise scRNAseq expression data