GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
This addresses the problem of automating cell type identification in flow cytometry for researchers and clinicians, offering a widely applicable solution with incremental improvements over existing methods.
The paper tackles the labor-intensive and error-prone manual gating process in flow cytometry by introducing GateNet, a neural network architecture that achieves human-level performance with F1 scores ranging from 0.910 to 0.997 on unseen samples and generalizes well with an F1 score of 0.936 on a public dataset.
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.