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Single cell RNA-seq analysis with R

  • overview of preprocessing: from raw sequence reads to expression matrix
  • overview of popular tools and R packages for scRNAseq data analysis
  • scRNAseq data quality control
  • cluster analysis
    • removal of undesired sources of variation
    • variable gene detection
    • dimensionality reduction
    • clustering
  • cell type identification
    • using known markers
    • using automatic classification algorithms
  • differential gene expression analysis
  • pseudotime analysis
  • if time permits: Integrating different datasets (CCA in Seurat)

You will learn:

  • to assess the quality of scRNAseq data
  • to control batch effects and other unwanted variation
  • cell clustering and identification
  • differential gene expression analysis
  • choosing the tools for further analyses

Prerequisites:

  • some experience in using R
  • understanding of the basic principles of single cell RNA-seq experiments