Towards automated gating of clinical flow cytometry data

Flow cytometry analysis is widespread practice in cell biology, immunology and hematology. Cell populations of interest are typically identified by consecutively examining the expression levels of protein marker pairs. Since this manual gating process lacks standardization and is time-consuming, several machine learning (ML) methods for the automated gating of flow cytometry data have been proposed in recent years. We evaluated state-of-the-art ML methods for automated gating based on three criteria: gating performance in comparison to manual expert annotations, interpretability of the output, and feasibility of deployment in a clinical setting. Based on these criteria, we selected the top-performing methods and made them easily accessible in the Python package FLAG-X ("flow cytometry automated gating toolbox"), which further features a novel hybrid workflow that interlocks manual and automated gating and integrates seamlessly with standard software for manual gating procedure. To demonstrate its practical utility, we applied FLAG-X to representative cases from routine clinical practice. FLAG-X is available at https://anaconda.org/channels/bioconda/packages/flagx/overview.