Section outline

  • Schedule

    First day

    Introduction to scRNAseq:

    • Technologies
    • Experimental design
    • R versus GUI-based tools

    Quality control

    • Dropouts - Doublets
    • Doublet removal using simulation
    • Ribosomal / mitochondrial RNAs
    • Cell cycling

    Normalization and scalability

    • Feature selection
    • Log scaling
    • Confounding factors removal

    Second day

    Dimentionality reduction and cell type clustering 

    • PCA
    • tSNE
    • UMAP
    • Clustering methods (Hierarchical, K-means and Graph-based)
    • Data integration of complex experimental designs

    Cell type identification and marker identification

    • Methods and applications
    Differential expression analysis

    • Methods overview

    Third day

    Differential expression analysis- continued
    • DE between clusters 
    • DE between samples (involving data-integration)
    • Gene set enrichment analysis
    Pseudotime analysis
    • Methods and applications