A Survey on the Evolution and Future Trajectory of Flow Cytometry Analysis

Abstract

Flow cytometry is a powerful analytical technique that generates high-dimensional single-cell data widely used in biological research and clinical diagnostics. This survey traces the evolution of flow cytometry analysis from traditional manual gating to modern algorithmic approaches and explores emerging deep-learning (DL) paradigms. We delineate three distinct analysis paradigms: traditional (manual gating), modern (algorithm-assisted population identification), and future (direct cell-to-sample DL models). While the modern paradigm employs sophisticated computational methods to identify cell populations and derive features, the emerging future paradigm bypasses discrete population identification entirely, utilizing DL to directly connect cellular information to sample-level insights. We discuss key technical challenges including batch effects, panel variations, and data availability that currently limit widespread adoption of more advanced analytical approaches. The survey also examines promising solutions such as transfer learning, data augmentation, and self-supervised learning techniques. As flow cytometry technology continues to advance, particularly with spectral cytometry enabling higher-dimensional analysis, these computational methods will become increasingly essential for extracting maximum biological insight from complex datasets. The development of standardized data repositories, robust batch correction tools, and comprehensive benchmarking frameworks will be crucial for realizing the full potential of these advanced analytical approaches.