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Single cell RNA-seq data analysis with R
Date: 27.05.2019 9:00 - 29.05.2019 17:00
Location details: The course is organised in the training room Dogmi at CSC, Keilaranta 14, Espoo, Finland. You can reach us easily by public transport, please find more details here.
Language: english-language
lecturers: Åsa Björklund (NBIS ELIXIR-SE)
Paulo Czarnewski (NBIS ELIXIR-SE)
Ahmed Mahfouz (LUMC)
Ståle Nygård (UIO)
Lars Borm (Karolinska Institutet)
Jules Gilet (Institut Curie)
Heli Pessa (University of Helsinki)
Bishwa Ghimire (FIMM)
Philip Lijnzaad (Princess Maxima Center for Pediatric Oncology)
Jeongbin Park (Charité-Universitätsmedizin Berlin & de.NBI)
Rui Benfeitas (NBIS ELIXIR-SE)
Dawit Yohannes (University of Helsinki)
  • free-price-finnish-academics.
  • free-price-others.
Lunches and coffees breaks are included.
All the applicants were informed about the selection results on 18.4.2019. If you didn't receive an email from us, please check your spam folder. The selection process prioritized applicants with R experience and scRNA-seq data, and we also did our best to ensure that every country / research group would get one representative in the course.
Additional Information

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. The course is kindly sponsored by the ELIXIR EXCELERATE project.

Note: You can find all the course material including the R code and data files in the course GitHub repository, and the lecture videos are available as a YouTube playlist. We will add videos of the exercise wrap-up sessions after summer.


Monday 27.5.2019

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



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