Single cell RNA-seq data analysis with R


Monday 27.5.2019

Introduction to single cell RNA-seq (Jules Gilet)
Quality control and data preprocessing (Åsa Björklund)
Normalisation (Heli Pessa)
Removal of confounding factors (Bishwa Ghimire)
Data integration (CCA, MNN, dataset alignment) (Ahmed Mahfouz)

Tuesday 28.5.2019

Dimensionality reduction (PCA, tSNE and UMAP) (Paulo Czarnewski)
Clustering (Ahmed Mahfouz)
Differential gene expression analysis (Ståle Nygård)

Wednesday 29.5.2019

Cell type identification (Philip Lijnzaad)
Trajectories/Pseudo-time (Paulo Czarnewski and Jules Gilet)
Spatial transcriptomics (Lars Borm and Jeongbin Park)


In order to follow this course you should have prior experience in using R.

Learning objectives

After this course you will be able to:

use a range of bioinformatics tools to analyze 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: RNA-Seq, Single Cell technologies, scRNA-seq

Additional information

Target audience: bioinformaticians, Biologists

Resource type: course materials

Contributors: Eija Korpelainen @eija,

Scientific topics: RNA-Seq