Section outline

  • Schedule

    First day

    Introduction to scRNAseq:

    • Technologies
    • Experimental design

    Quality control

    • Dropouts - Doublets
    • Ribosomal / mitcochondrial RNAs

    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)

    Differential expression analysis

    • Methods overview
    • DE between clusters 
    • DE between samples (involving data-integration)

    Cell type identification

    • Methods and applications

    Pseudotime analysis

    • Methods and applications