Citizen science gamers enable automated flow cytometry gating through machine learning
Manual flow cytometry gating requires up to one hour per sample with 32% inter-expert variability, creating critical bottlenecks in immunological research reproducibility. To address this, we developed flowMagic, a machine learning algorithm for automated gating that is trained on both expert-curated data (template data) and crowdsourced annotations from citizen science gaming. Through EVE Online, 839,199 players analyzed 52,178 bivariate plots from 37 studies, generating 31,703 quality-controlled training plots. Evaluated against 92,203 expert-validated files spanning 79 immune populations (i.e., a biologically defined cell subset within each bivariate plot), flowMagic achieved 90% accuracy for abundant populations and 65% for rare populations, outperforming existing methods. The algorithm reproduced biological patterns including neutrophil dynamics in COVID-19 patients and immune development in newborns. This gaming-based approach demonstrates that crowd-sourced pattern recognition generates robust training data for complex biomedical applications, offering transformative potential for standardizing flow cytometry analysis and accelerating immunological discovery.