Citizen Science Gamers Enable Automated Flow Cytometry Gating Through Machine Learning

Abstract

Machine learning can greatly accelerate biomedical research but is fundamentally constrained by the scarcity of high-quality labeled data. We introduce a scalable data-generation framework based on citizen science and apply it to automated flow cytometry gating, a foundational but subjective step in immunology that limits reproducibility. Using the massively multiplayer online game EVE Online, 839,199 de-identified player accounts analyzed 52,000 plots, from which stringent quality control produced 31,703 consensus annotations for model training. Leveraging these data, we developed flowMagic, a machine learning framework for automated gating. Evaluated on 92,203 bivariate plots with standardized reference gates spanning 79 immune populations, the template model outperformed the generalized model, achieving 90% and 65% agreement for large and low-abundance populations, respectively, compared with 78% and 42% for the generalized model. These results show that expert-provided templates currently provide the highest agreement, while the citizen-science-trained generalized model enables gating without gate-specific templates but remains limited by the absence of expert-curated examples for complex, low-abundance or context-dependent gates. Both model types reproduced biologically meaningful patterns, including neutrophil dynamics in COVID-19 patients and immune development in newborns. Together, these results establish citizen-science annotation as a scalable strategy for generating training data that complements, rather than replaces, expert input in automated flow cytometry gating.

The authors' abstract, as published at the source. Cytometry Part A, 2026 · DOI ↗