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