Abstract
Analysis of high-dimensional cytometry data is often challenging, especially in studies with many samples. Algorithms like FlowSOM, PeacoQC and CytoNorm were all developed due to issues encountered when analyzing specific datasets, aiming to enable relevant downstream analysis. In this talk, I will give an overview of the specific applications we have been working on in our lab, including diagnosis of myelodysplastic syndromes and primary immunodeficiencies, as well as heterogeneity exploration in acute myeloid leukemia and prognosis of lung cancer patients.
The speakers
Sofie van Gassen
Department of Applied Mathematics, Computer Science and Statistics, Ghent University & Data Mining and Modeling for Biomedicine group, VIB Center for Inflammation Research
Ghent, Belgium
Hyun-Dong Chang
Technische Universität Berlin, Institute of Biotechnology, Department of Cytometry, Schwiete Laboratory for Microbiota and Inflammation, German Rheumatism Research Center Berlin, an institute of the Leibniz Association
Berlin; Germany
André Görgens
Karolinska Institutet, Department of Laboratory Medicine, Division of Biomolecular and Cellular Medicine (BCM)
Solna, Sweden