A User-Centric Approach to Reliable Automated Flow Cytometry Data Analysis for Biomedical Applications

Abstract

Automation and the increased number of measurable parameters in flow cytometry (FCM) have strongly increased the volume and complexity of phenotyping immune cell populations. Despite numerous automated gating methods for FCM analysis, their adoption in routine practice remains challenging due to accessibility barriers for users and potential model failures. Here, we propose a user-centered solution that combines elements of supervised machine learning (SML), rapid application development (RAD), systematic quality assurance guided by structured argumentation, and uncertainty estimation to address these challenges. We implement a data-driven model for event classification and use RAD to generate software prototypes, allowing FCM users to apply the model for automated gating. Considering concepts for structured argumentation from assurance cases (ACs), we derived and justified quality analyses that inform users about the quality of the model. We propose guiding the model operation phase using uncertainty estimation to provide users with a clear understanding of the model's confidence in its predictions. We aim to overcome barriers to the routine application of automated gating and contribute to more reliable and efficient FCM data analysis. Our approach is based on the application of phenotyping for human immune cells. We encourage future research to investigate the potential of SML, ACs, and uncertainty estimation to address dependability of data-driven models (DDMs) supporting diagnostic decision making in the medical domain, including FCM in clinical applications and highly regulated areas such as pharmaceutical research.