Topic outline

  • Single-cell Transcriptomics

    Streamed, 15 - 17 June 2021

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    • 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