Jeongbin Park (HD-HuB), Åsa Björklund (SciLifeLab), Paulo Czarnewski (SciLifeLab), Ahmed Mahfouz (LUMC), Ståle Nygård (UIO), Olga Dethlefsen (NBIS), Lars Borm (Karolinska Institutet), Jules Gilet (Institut Curie), Heli Pessa (University of Helsinki), Bishwa Ghimire (FIMM), Philip Lijnzaad (Princess Maxima Center for Pediatric Oncology)

27.05.2019 9:00 - 29.05.2019 17:00

CSC at Keilaranta 14, Espoo, Finland

This international hands-on course covers several aspects of single cell RNA-seq data analysis, ranging from clustering and differential gene expression analysis to trajectories, cell type identification and spatial transcriptomics.


Monday 27.5.2019
- Introduction and experimental design (Åsa Björklund)
- QC, data preprocessing (Åsa Björklund)
- Normalisation and batch effect correction (Heli Pessa, Ahmed Mahfouz)

Tuesday 28.5.2019
- Dimensionality reduction (PCA, tSNE and UMAP) (Paulo Czarnewski)
- Clustering and visualisation (Ahmed Mahfouz)
- Differential gene expression analysis (Ståle Nygård and Olga Dethlefsen)

Wednesday 29.5.2019
- Cell type identification (Philip Lijnzaad)
- Trajectories/Pseudo-time (Paulo Czarnewski)
- Spatial transcriptomics (Jeongbin Park and Lars Borm)
- Integration of data, e.g. CITE-Seq (Ahmed Mahfouz)

Learning goals:
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

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

Single cell, RNA-seq, R


Contact & Registration:

Registration by 16.04.2019 12:00