LAUSR

Advancements in cytometry technologies have led to a remarkable increase in the number of markers that can be analyzed simultaneously, presenting significant challenges in data analysis. Traditional approaches, such as dimensional reduction techniques and computational clustering, although popular, often face reproducibility challenges due to their heavy reliance on inherent data structures. This reliance prevents the direct translation of their outputs into gating strategies for downstream experiments. Here, we propose the novel Gating Tree methodology, a pathfinding approach that investigates the multidimensional data landscape to unravel group‐specific features without the use of dimensional reduction. This method employs novel measures, including enrichment scores and gating entropy, to effectively identify group‐specific features within high‐dimensional cytometric data sets. Our analysis, applied to both simulated and real cytometric data sets, demonstrates that the Gating Tree not only identifies group‐specific features comprehensively but also produces outputs that are immediately usable as gating strategies for pinpointing key cell populations. Furthermore, by integrating machine learning methods, including Random Forest, we have benchmarked Gating Tree against existing methods, demonstrating its superior performance. A range of supervised and unsupervised methods implemented in Gating Tree thus provides effective visualization and output data, which can be immediately used as successive gating strategies for downstream study.