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Single-cell-RNAseq-2018

Single cell RNA-seq data analysis using Chipster
Date: 19.09.2018 9:00 - 19.09.2018 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: Eija Korpelainen (CSC)
Maria Lehtivaara (CSC)
Price:
  • Free for Finnish universities, polytechnics and governmental research institutes.
  • Free for others.
All materials, lunches as well as morning and afternoon coffees are covered.
The seats are filled in the registration order. Please note, that there is usually a queue for the course, so in case you need to cancel your participation, do it as soon as possible to enable others to take your place.
Additional information
Content: chipster@csc.fi
Practicalities: event-support@csc.fi

This course introduces single cell RNA-seq data analysis methods, tools and file formats. We will use free and user-friendly Chipster software, so no previous knowledge of Unix or R is required, and the course is thus suitable for everybody.

The course covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the Seurat tools. We will also learn how to do integrated analysis of two samples with Seurat tools. Both DropSeq and 10X Genomics data are used in the exercises.

You will learn how to:

  • check the quality of reads with FastQC
  • tag reads with molecular and cellular barcodes
  • trim reads
  • align reads to the reference genome with HISAT2 and STAR
  • tag reads with gene names
  • visualize aligned reads in genomic context using the Chipster genome browser
  • estimate the number of usable cells by checking the inflection point
  • detect bead synthesis errors
  • create and filter DGE
  • regress out unwanted variability
  • detect variable genes and perform principle component analysis
  • cluster cells and find marker genes for a cluster
  • run canonical correlation analysis (CCA) to identify common sources of variation between the two datasets
  • align two samples for integrated analysis
  • find conserved cluster markers within two samples
  • find differentially expressed genes in a cluster between two samples
  • visualize genes with cell type specific responses in two samples

 

Registration will open on this page in late August.


Projects and Collaboration Networks
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Projects and Collaboration Networks
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