Online

Single-cell RNA-seq data analysis using Chipster

This online hands-on 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.
The course consists of lectures and exercises. The lectures are prerecorded, 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.

Both course days are 9:00-12:30 in Zoom.

This course is part of Single Cell and Spatial Omics Week, which has also the following events in Biomedicum Helsinki organized by the Doctoral Program in Biomedicine (DPBM):

Registration

Please fill in the registration form by 17.2.2026. If the course gets booked out earlier, you will be added in the waiting list and informed if a place becomes available. The course is kindly sponsored by ELIXIR Finland, so it is free of charge for the participants.

Prerequisites
No previous knowledge of Unix, R or Python is required, because in the exercises we use Seurat v5 tools embedded in the free and user-friendly Chipster software. The course is thus suitable for everybody who is planning to do single-cell RNA-seq data analysis.

Please note that attending this course qualifies you to participate in the course Spatial transcriptomics (Visium) data analysis using Chipster which takes place in the fall 2026.

Content

Participants 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
  • perform dimensionality reduction (PCA)
  • cluster cells
  • visualize clusters using UMAP and tSNE
  • find marker genes for a cluster
  • annotate clusters using reference data
  • 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

Trainers

Maria Lehtivaara (CSC) and Eija Korpelainen (CSC)

Bioinformatics and Life SciencesBeginner