Computational flow cytometry
Trainers: Annelies Emmaneel, Katrien Quintelier and Artuur Couckuyt
Overview: In this session, we will give you an introduction to machine learning techniques used for cytometry data. We will start with a more theoretical session where we will cover the different steps of a computational analysis pipeline, with an emphasis on quality control, clustering and some downstream analysis algorithms. As a few examples, we will also discuss a couple of biological applications. In the practical session, you will get a hands-on experience where we will provide you with an R script and some data, so that you can run the analysis pipeline yourself.
Learning outcome
By the end of this session you will:
- Understand the basic principles of machine learning.
- Know the steps of a computational analysis pipeline and why and how you should implement them.
- Be able to evaluate the quality of your data.
- Understand a FlowSOM model and how to go from the model to a final conclusion.
Materials: