Introduction to flow cytometry and its applications
Trainers: Christopher Hall
Overview: This session will provide an overview of how using programmatic tools will benefit your flow cytometry data analysis and how to integrate it into a bioinformatic pipeline. The session will introduce the most common R based tools, teach you to successfully create an analysis environment, load, transform, and QC your data, and finally use dimensionality reduction and auto-gating tools. 90% of people I see fail using R tools for their data analysis fall in the first 15 minutes because of the steep initial learning curve.
Learning outcomes
By the end of this session you will be able to:
- Understand what flow cytometry data is, and why to use R to analyse it
- What tools you’ll need to use R
- Use R to analyse your flow cytometry data
Materials:
Practical materials
GitHub repository: https://github.com/hally166/EMBLEBI_Bioinformatics4Immunologists2021
Logical scales: https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.20258
ggcyto: https://www.bioconductor.org/help/course-materials/2015/BioC2015/ggcyto.html
flowCore: https://bioconductor.org/packages/devel/bioc/vignettes/flowCore/inst/doc/HowTo-flowCore.pdf
The data: https://flowrepository.org/id/FR-FCM-Z3WR
The data paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043578/
Cytoverse: https://cytoverse.org/
A real full workflow: https://www.listonlab.uk/flowcytoscript/
biocManager packages: https://bioconductor.org/packages/release/BiocViews.html#___FlowCytometry
Additional resources
Flow cytometry videos from Christopher Hall: https://youtube.com/playlist?list=PLAfiG7r12IyRk9cTi_AVKhl775Zdlhz-O&si=sl9Af4MVW8Hcd0Yx
Flowcytoscript: https://github.com/DrCytometer
R/Flow cytometry resource: https://rpubs.com/wjiang2/
REGEX: https://cran.r-project.org/web/packages/stringr/vignettes/regular-expressions.html