BibTex format
@article{Ono:2025,
author = {Ono, M},
journal = {Cytometry Part A},
title = {GatingTree: pathfinding analysis of group-specific effects in cytometry data},
year = {2025}
}
Citation
RIS format (EndNote, RefMan)
TY - JOUR
AB - Advancements in cytometry technologies have led to a remarkable increase in the number of markers thatcan 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 thedirect translation of their outputs into gating strategies for downstream experiments. Here, we propose thenovel 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 employsnovel measures, including enrichment scores and gating entropy, to effectively identify group-specific featureswithin high-dimensional cytometric datasets. Our analysis, applied to both simulated and real cytometricdatasets, demonstrates that the Gating Tree not only identifies group-specific features comprehensively butalso 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 benchmarkedGating Tree against existing methods, demonstrating its superior performance. A range of supervised andunsupervised methods implemented in Gating Tree thus provides effective visualization and output data,which can be immediately used as successive gating strategies for downstream study.
AU - Ono,M
PY - 2025///
SN - 1552-4922
TI - GatingTree: pathfinding analysis of group-specific effects in cytometry data
T2 - Cytometry Part A
ER -
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