FlowSOM for handling cytometry data - postponed
General context
The number of markers, cells and samples measured in cytometry experiments keeps increasing. While this offers unique opportunities, it also complicates the traditional manual gating process, which becomes time-consuming and subjective. Replacing the manual gating by an automated pipeline can solve these issues. In this workshop, we will explore how to handle cytometry data in R, with a focus on the FlowSOM clustering algorithm.
Participants will learn how to handle cytometry data in R, including quality control, visualization, clustering and comparison of samples. This will be demonstrated on an example dataset, but participants are welcome to bring some of their own fcs files to explore in addition.
Prior experience with traditional manual cytometry analysis and R is recommended. If you have no experience with R you can attend the R introduction training.
Trainers
Sofie Van Gassen
Sofie is Postdoc at Ghent University working on optimization of the analysis of flow cytometry data by developing suitable machine learning algorithms. She's the creator of the FlowSOM algorithm.