AutoFlow: an interactive Shiny app for supervised and unsupervised flow cytometry analysis

Flow cytometry (FC) is a widely used technique for analysing cells or particles based on the fluorescence of specific markers. Thresholds for fluorescence are typically set manually, a laborious, subjective process that scales poorly as FC technology advances. Machine learning (ML) methods can address these issues but often require technical expertise many bench scientists do not possess. Thus, accessible, open-source, and cross-domain ML-based FC tools are needed.

We present AutoFlow, an easy-to-use, adaptable R Shiny application for automated flow cytometry (FC) analysis. AutoFlow supports two workflows: supervised and unsupervised learning. The application automates key preprocessing steps including fluorescence compensation, debris exclusion, single-cell identification, viability marker gating, and downstream classification or clustering. Across three datasets, two publicly available (Mosmann and Nilsson Rare) and a novel bone marrow microphysiological system (BM-MPS) dataset, AutoFlow demonstrated robust performance. In the supervised workflow, multiclass classification on BM-MPS achieved 97.2% accuracy under a single-timepoint training and multi-timepoint testing scheme, with high sensitivity and specificity across major lineages. For rare populations, performance was strong: Mosmann Rare (0.03% prevalence) achieved 87.5% sensitivity, and 100% specificity, while Nilsson Rare (0.08% prevalence) achieved 87.9% sensitivity, and 99.9% specificity. The unsupervised workflow accurately grouped cells into biologically meaningful clusters, recovering known populations and identifying additional candidate populations with marker profiles consistent with true biology. AutoFlow offers a fast, reproducible, and scalable solution for FC analysis, enabling high-throughput studies and improving the discovery of rare or unexpected cell types.

The application is available at https://github.com/FERWoods/AutoFlow for download using R. An archived version is available at DOI: 10.5281/zenodo.18235796.