GatingTree: pathfinding analysis of group-specific effects in cytometry data
Author(s)
Ono, Masahiro
Type
Journal Article
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. 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 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 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.
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 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 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.
Date Issued
2025-07-01
Date Acceptance
2025-06-24
Citation
Cytometry Part A, 2025, 107 (7), pp.476-496
ISSN
1552-4922
Publisher
Wiley
Start Page
476
End Page
496
Journal / Book Title
Cytometry Part A
Volume
107
Issue
7
Copyright Statement
© 2025 The Author(s). Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Date Publish Online
2025-07-05