Single-cell RNA-seq data analysis using Chipster

You will learn how to

• perform quality control and filter out low quality cells
• normalize gene expression values
• remove unwanted sources of variation
• select highly variable genes and perform dimensionality reduction (PCA)
• cluster cells
• visualize clusters using UMAP and tSNE
• identify cell types using reference-based SingleR
• find marker genes for a cluster
• integrate multiple 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

In the exercises, we use Seurat tools embedded in the free and user-friendly Chipster software. No experience in R is required and the course is thus suitable for everybody who is planning to use single-cell RNA-seq.

Maria Lehtivaara, Eija Korpelainen and Ellimari Paunio (CSC)

The course consists of lectures and exercises. The lectures will be pre-recorded, and participants are requested to view the videos prior to the course and test their knowledge with a set of questions. This gives you more time to reflect on the concepts so that you can use the classroom time more efficiently for discussions and exercises.