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 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.

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

This course is part of Single-Cell Course Week 2025 organized by the Doctoral Program in Biomedicine (DPBM). Please note that there is also Single Cell Omics Symposium on 13.3.2025 in Biomedicum Helsinki and Single cell RNA-seq data analysis using R course on 6.-7.3.2025.

Important dates

Please fill in the application form by 12.2.2025. Participants are selected based on motivation descriptions, and selection results are announced latest by 26.2.2025.

Prerequisites
In the exercises we use Seurat v5 tools embedded in the free and user-friendly Chipster software, so no previous knowledge of Unix or R is required, and the course is thus suitable for everybody who is planning to use single cell RNA-seq.

Please note that attending this course qualifies you to participate in the online course “Spatial transcriptomics (Visium) data analysis using Chipster” which takes place 15.4.2025.

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
-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, Eija Korpelainen