  Flow cytometry is widely used to identify cell populations in patient-derived
fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While
ubiquitous in research and clinical practice, flow cytometry requires gating,
i.e. cell type identification which requires labor-intensive and error-prone
manual adjustments. To facilitate this process, we designed GateNet, the first
neural network architecture enabling full end-to-end automated gating without
the need to correct for batch effects. We train GateNet with over 8,000,000
events based on N=127 PB and CSF samples which were manually labeled
independently by four experts. We show that for novel, unseen samples, GateNet
achieves human-level performance (F1 score ranging from 0.910 to 0.997). In
addition we apply GateNet to a publicly available dataset confirming
generalization with an F1 score of 0.936. As our implementation utilizes
graphics processing units (GPU), gating only needs 15 microseconds per event.
Importantly, we also show that GateNet only requires ~10 samples to reach
human-level performance, rendering it widely applicable in all domains of flow
cytometry.
