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single-cell-RNAseq-R-2018

Single cell RNA-seq data analysis using R and command line tools
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
Lecturers: Bishwa Ghimire (FIMM)
Heli Pessa (University of Helsinki)
Price:
  • Free for Finnish universities, polytechnics and governmental research institutes.
  • Free for others.
Registration is closed.
If you got a seat but cannot attend after all, you have to INFORM US asap so that we can give your place to somebody in the waiting list. Failing to do so will reduce your chances to be selected for other CSC courses.
Additional information
Content: chipster@csc.fi
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

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
Course materials

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