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Flow cytometry is widely used to identify cell populations in patient-derivedfluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). Whileubiquitous in research and clinical practice, flow cytometry requires gating,i.e. cell type identification which requires labor-intensive and error-pronemanual adjustments. To facilitate this process, we designed GateNet, the firstneural network architecture enabling full end-to-end automated gating withoutthe need to correct for batch effects. We train GateNet with over 8,000,000events based on N=127 PB and CSF samples which were manually labeledindependently by four experts. We show that for novel, unseen samples, GateNetachieves human-level performance (F1 score ranging from 0.910 to 0.997). Inaddition we apply GateNet to a publicly available dataset confirminggeneralization with an F1 score of 0.936. As our implementation utilizesgraphics processing units (GPU), gating only needs 15 microseconds per event.Importantly, we also show that GateNet only requires ~10 samples to reachhuman-level performance, rendering it widely applicable in all domains of flowcytometry.

GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating