Single cell RNAseq analysis in R

NOTE: This event is hosted by the Australian BioCommons.

Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat – an R package for QC, analysis, and exploration of single-cell RNAseq data. 

We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data.

Lead trainers: 

Dr Sarah Williams, QCIF

Mr Nick Matigan, QCIF

Ms Adele Barugahare, Monash Genomics and Bioinformatics Platform

Dr Paul Harrison, Monash Genomics and Bioinformatics Platform

Dr Laura Perlaza Jimenez, Monash Genomics and Bioinformatics Platform

Dr Valentine Murigneux, QCIF

Dr Magdalena (Magda) Antczak, QCIF

Format

This online workshop will take place over two half-day sessions. You must attend both sessions. Expert trainers will guide you through each of the topics and provide activities to help you put your new skills into action. 

Date/Time: 26 and 27 September 2023, 1 – 4:30 pm AEST/ 12:30 – 4 pm ACST/ 11 am – 2:30 pm AWST

Location: Online

Learning outcomes:

By the end of the workshop you should be able to:

• Load gene counts into a Seurat format
• Perform QC and select cells for further analysis
• Filter and normalise scRNAseq data
• Cluster cells and identify cluster markers
• Visualise scRNAseq expression data

Who the workshop is for:

This workshop is for Australian researchers who have or will work on scRNAseq data as part of their projects. You must be associated with an Australian organisation for your application to be considered.

This workshop will not teach you the basics of R. While you don’t need to be an expert, you require some basic familiarity with R. For example you should be able to work with tables of data, know how to load an R package, make basic plots (ideally with ggplot2).

You should also have basic knowledge of single cell RNA sequencing technology.