This course covers the analysis of 10X data using Seurat v4 and SingleR, from digital gene expression matrix (DGE) to clustering cells, finding marker genes for those clusters, and annotating the clusters. It also covers how to compare two samples and detect conserved cluster markers and differentially expressed genes in them. The course takes one day. You will learn how to

• create Seurat v4 object
• perform QC and filter out low quality cells
• normalize expression values (inc SCTransform)
• detect highly variable genes using VST
• scale data and regress out unwanted variability
• perform principle component analysis (PCA) and select PCs to be used for clustering
• cluster cells
• visualize clusters with UMAP and tSNE
• find marker genes for the clusters
• visualize marker genes with UMAP, violin plot and ridge plot
• annotate clusters with cell type information using SingleR
• run canonical correlation analysis (CCA) to identify common sources of variation between two datasets
• integrate samples using the mutual nearest neighbor approach (anchors)
• find conserved cluster marker genes for two samples
• find differentially expressed genes in a cluster between two samples
• visualize genes with cell type specific responses in two samples

Learning objectives

After this course you should be able to:

• use a range of bioinformatics tools to analyze single cell RNA-seq data
• discuss a variety of aspects of single cell RNA-seq data analysis
• understand the advantages and limitations of single cell RNA-seq data analysis