Single cell RNA-seq data analysis with R

This hands-on course introduces the participants to single cell RNA-seq data analysis concepts and popular R packages. It covers the preprocessing steps from raw sequence reads to expression matrix as well as clustering, cell type identification, differential expression analysis and pseudotime analysis. In addition to understanding of the basic principles of single cell RNA-seq experiments, participants need to have basic skills in R and Unix.

Course material is available in GitHub and it includes:

  • slides
  • exercises including the R code

Detailed description of the course content:

  1. overview of preprocessing: from raw sequence reads to expression matrix
  2. overview of popular tools and R packages for scRNAseq data analysis
  3. scRNAseq data quality control
  4. cluster analysis
    • removal of undesired sources of variation
    • variable gene detection
    • dimensionality reduction
    • clustering
  5. cell type identification
    • using known markers
    • using automatic classification algorithms
  6. differential gene expression analysis
  7. pseudotime analysis
  8. CCA in Seurat

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Single Cell technologies, scRNA-seq

Resource type: course materials

Target audience: bioinformaticians, Biologists

Difficulty level: Intermediate

Authors: Heli Pessa, Bishwa Ghimire

Contributors: Eija Korpelainen

Single cell RNA-seq data analysis with R https://tess.elixir-europe.org/materials/single-cell-rna-seq-data-analysis-with-r This hands-on course introduces the participants to single cell RNA-seq data analysis concepts and popular tools and R packages. It covers the preprocessing steps from raw sequence reads to expression matrix as well as clustering, cell type identification, differential expression analysis and pseudotime analysis. Eija Korpelainen RNA-Seq RNA-Seq, Single Cell technologies, scRNA-seq bioinformaticians Biologists