single-cell-RNAseq-R-2018 - Training
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Date: | 21.09.2018 9:00 - 21.09.2018 17:00 |
Location details: | The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available. |
Language: | english-language |
lecturers: |
Bishwa Ghimire (FIMM) Heli Pessa (University of Helsinki) |
Price: |
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Practicalities: event-support@csc.fi
Overview
This hands-on course introduces the participants to single cell RNA-seq data analysis concepts and popular tools and R packages. It covers the preprocessing steps from raw sequence reads to expression matrix as well as clustering, cell type identification, differential expression analysis and pseudotime analysis. Please note that you are most welcome to attend also the Single cell transcriptomics symposium in Biomedicum Helsinki 20.9.2018.
The course is kindly sponsored by the ELIXIR EXCELERATE project and the University of Helsinki Doctoral Programme in Biomedicine (DPBM).
Audience
Participants need to have
- basic skills in R and Unix. If you don't have these skills, you might like to attend the course Single cell RNA-seq data analysis with Chipster instead.
- understanding of the basic principles of single cell RNA-seq experiments
Syllabus
The course covers the following topics
- overview of preprocessing: from raw sequence reads to expression matrix
- overview of popular tools and R packages for scRNAseq data analysis
- scRNAseq data quality control
- cluster analysis
- removal of undesired sources of variation
- variable gene detection
- dimensionality reduction
- clustering
- cell type identification
- using known markers
- using automatic classification algorithms
- differential gene expression analysis
- pseudotime analysis
- if time permits: Integrating different datasets (CCA in Seurat)
Outcomes
After the course you should be able to
- assess the quality of scRNAseq data
- control batch effects and other unwanted variation
- perform cell clustering and identification
- perform differential gene expression analysis
- choose tools for further analyses