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Beginners guide to R programming for Cytometry analysis

Research Skills

Thursday, 14 March 2019, 9am to 1pm

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Course Aim

Recent developments in technology have enabled the exploration of complex biological systems at the single cell level, leading to new insights in the way the immune system is regulated in conditions such as cancer or auto-immune disorders. One of these technologies, mass cytometry, is now being applied in the context of clinical trials as a way to gain insights into the mechanism of action of treatments, as well as to unravel determinants of patient response. These new applications pose significant challenges for data analysis, and in this workshop employing interactive tutorials we will cover issues such as quality control, batch correction and single cell differential analysis, and the computational solutions that have been developed to address them.

By the end of this workshop participants will be capable of generating their own analytical pipeline for single-cell analysis.

Course Format

This course will take place as a workshop with presentations interspersed with interactive sessions.

Laptops required.

Short introduction to R basics encouraged before coming to workshop (links will be sent to participants before workshop); however interactive sessions will be designed to enable participation with basic understanding.

Attendees will need R software and Rstudio interface installed, and have Eduroam account for internet access.

https://www.r-project.org/
https://www.rstudio.com/products/RStudio/ http://help.it.ox.ac.uk/network/wireless/services/eduroam/index

Basic R programming skills, and a knowledge of cytometry or single-cell analysis are essential. Contact course coordinator for more details (sheila.mccartan@ndorms.ox.ac.uk

Course content

  1. Introduction to Cytometry tools in R – OpenCyto and FlowWorkspace
  2. Loading data - Pre-exisiting workspaces and creating an analysis from scratch
  3. Quality control - Sample-level similarity and Marker Enrichment Modelling
  4. Population identification – Clustering and Manual gating
  5. Differential analysis
  6. High dimensional data exploration with t-SNE, Radviz & Fan plots
  7. Wrapping it all together - Notebooks, scripts and pipelines

Course length

Total course length is 3 hours.

Participant numbers

24

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