GatingTree: Pathfinding Analysis of Group-Specific Effects in Cytometry Data

TL;DR

GatingTree introduces a pathfinding method for analyzing high-dimensional cytometry data, identifying group-specific features without dimensional reduction, and providing practical gating strategies for experimental use.

Contribution

It presents a novel gating strategy methodology that uncovers group-specific features directly from multidimensional data without relying on traditional dimensional reduction techniques.

Findings

Successfully applied to simulated and real datasets

Identifies comprehensive group-specific features

Produces practical gating strategies for experiments

Abstract

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, preventing direct translation of their outputs into gating strategies to be used in 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 datasets. Our analysis, applied to both simulated and real cytometric datasets, demonstrates that the Gating Tree not only identifies group-specific features comprehensively but also produces outputs that are immediately usable as gating strategies for unequivocally identifying cell populations. In conclusion, the Gating Tree facilitates a comprehensive analysis of the multidimensional data landscape and provides experimentalists with practical, successive gating strategies that enhance cross-experimental comparisons and downstream analyses such as flow cytometric sorting.