scrnaseq - Training
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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) |
Price: |
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Lunches and coffees breaks are included. |
Practicalities: event-support@csc.fi
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.
Programme
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)
Prerequisites
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