A quantitative evaluation of deep learning techniques for unsupervised and supervised analysis of high-dimensional cytometry data

01 November 2020 → Ongoing
Research Foundation - Flanders (FWO)
Research disciplines
  • Natural sciences
    • Computational biomodelling and machine learning
    • Development of bioinformatics software, tools and databases
    • Single-cell data analysis
Deep learning. High-dimensional data. Flow and mass cytometry.
Project description

Recent advances in cytometry allow scientists to measure many parameters at a single-cell resolution, and this for millions of cells across tens to hundreds of patients, possibly at different time points. To make sense of all this data, novel machine learning approaches for visualisation, automated population identification and subsequent differential analysis between groups of patients will be explored and compared. In this project in particular, I will focus on the recent but rapidly expanding field of deep learning methods, which have been shown to hold great promise for many data mining tasks. I will explore the potential of deep learning techniques for high-dimensional cytometry data, and develop novel frameworks to quantitatively compare these new approaches to the classical machine learning pipelines that are currently used in the field of cytometry. Improved analysis of this data will lead to more detailed insights in the immune system, and in the long term lead to faster diagnoses and better follow-up of patients with infections, immune diseases or cancer.