Bioinformatics for T-Cell immunology - Flow cytometry analysis - Part 3

Overview:In this 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 objectives
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.

Artuur Couckuyt – VIB – IRC – University Ghent

Artuur Couckuyt obtained his Master’s degree in Biochemistry and biotechnology in 2019 at Ghent University. Afterwards, he started his PhD at the VIB-UGent Data Mining and Modeling for Biomedicine group headed by Yvan Saeys. His main project focuses on the analysis of high-dimensional flow cytometry data from patients suffering from acute myeloid leukemia where the main goal is to improve diagnosis and prognosis. He also works on the correspondence between the protein data in CITE-seq and flow cytometry, where on the one hand he is researching transformations for both kinds of data. On the other hand, he is investigating possibilities to extract a flow cytometry panel out of the CITE-seq data.

Katrien Quintelier – Ghent University

Katrien Quintelier is a fourth year PhD student in the VIB-UGent Data Mining and Modeling for Biomedicine group (Ghent, Belgium) and the Department of Pulmonary Diseases of the Erasmus Medical Center (Rotterdam, The Netherlands). She is working on a computational pipeline to analyze high-dimensional flow cytometry data of lung cancer patients and to predict response to immunotherapy.