Single cell RNA-seq data analysis using Chipster

This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, and differential expression analysis. You will also learn how to do integrated analysis of two samples. We use Seurat v3 tools embedded in the user-friendly Chipster software.

You will learn how to:

  • perform quality control and filter out low quality cells
  • normalize gene expression values
  • remove unwanted sources of variation
  • select variable genes using VST
  • perform dimensionality reduction (PCA, tSNE, UMAP, CCA)
  • cluster cells
  • find marker genes for a cluster
  • integrate two samples
  • find conserved cluster marker genes for two samples
  • find genes which are differentially expressed between two samples in a cell type specific manner
  • visualize genes with cell type specific responses in two samples

Learning objectives

After this course you should be able to:

  • use a range of bioinformatics tools to undertake basic analysis of single cell RNA-seq data
  • discuss a variety of aspects of single cell RNA-seq data analysis
  • understand the advantages and limitations of single cell RNA-seq data analysis

Keywords: scRNA-seq

Additional information

Target audience: Biologists, bioinformaticians

Resource type: Slides, Training materials

Authors: Eija Korpelainen, Maria Lehtivaara

External resources: