Single-Cell RNA-seq Data Analysis

25 -26 September 2023

Registration:

FULL (new dates coming soon)

Times:

9.30am - 4:30 pm each day

Venue:

Ashworth Labs G04
The University of Edinburgh
The King's Buildings
Edinburgh EH9 3FL
Scotland, United Kingdom

Places:

24 for each workshop

You will be contacted by our finance team for full payment. Once payment is made, your place will be confirmed and full details sent by our training team.

Registration fee:

The fee you pay depends on your institution:
£210 - University of Edinburgh Staff/Students

£235- Other university Staff/Students

£260- Industrial employees

Information:

Contact our training team

 


Single cell RNA-Seq offers many advantages over bulk RNA-Seq, but the richer data produced requires a more complex analysis. In this course we will learn about the advantages of single cell sequencing, and when it may be an appropriate choice, how to perform common types of data analysis, and to spot and deal with potential problems.  We will analyse 10X genomics data with the R package Seurat.


Instructors

  • Frances Turner (Bioinformatician, Edinburgh Genomics)
  • Heleen De Weerd (Bioinformatician, Edinburgh Genomics)

Workshop format

The workshop consists of presentations and hands-on tutorials.

Who should attend

Graduates, postgraduates, and PIs, who are using, or planning to use, RNA-seq technology in their research and want to learn how to process and analyse RNA-seq data.

Requirements

  • A general understanding of molecular biology and genomics.
  • A working knowledge of Linux at the level of the Edinburgh Genomics Linux for Genomics workshop.
  • A working knowledge of R at the level of Edinburgh Genomics R for Biologists workshop.
    (If you are unsure of how these relate to your coding skills please check the above course pages and look at topics covered)

 

Covered topics (and software)

  • Understanding the advantages and disadvantages of single cell RNA-seq
  • Processing of fastq files, quality control and data filtering
  • Normalisation and dimension reduction of data
  • Clustering and visualisation 
  • Identification of marker genes and visualisation of marker genes
  • Identification of
  •  cell types in our data
  • Integration of multiple samples
  • Identification of genes differentially expressed between samples.
  • Functional analysis of differentially expressed genes