The number of cells, markers and samples measured per cytometry experiment keeps increasing, making traditional analysis methods, such as manual gating on 2D scatter plots, infeasible in many cases. The Cytometry Team of the SaeysLab develops algorithms and pipelines for an automated analysis of this high-throughput single-cell technique.

We focus on the one hand on algorithm development, with tools like PeacoQC for quality control, CytoNorm for batch effect removal and FlowSOM for clustering.

On the other hand, we also have several ongoing collaborations with clinicians to build complex but explainable analysis pipelines, studying diseases including primary immunodeficiencies (PID), acute myeloid leukemia (AML) and non-small cell lung cancer (NSCLC). Our goal here is to bring these pipelines to the clinic.