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

Authors :
Fisch, Lukas
Heming, Michael O.
Schulte-Mecklenbeck, Andreas
Gross, Catharina C.
Zumdick, Stefan
Barkhau, Carlotta
Emden, Daniel
Ernsting, Jan
Leenings, Ramona
Sarink, Kelvin
Winter, Nils R.
Dannlowski, Udo
Wiendl, Heinz
Hörste, Gerd Meyer zu
Hahn, Tim
Publication Year :
2023

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2312.07316
Document Type :
Working Paper