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Gaining biological insights through supervised data visualization

delete2026-06-30
delete0
PRE
AI
J
Jake S. Rhodes
A
Adrien Aumon
S
Sacha Morin
M
Marc Girard
C
Catherine Larochelle
E
Elsa Brunet-Ratnasingham
A
Amélie Pagliuzza
L
Lorie Marchitto
W
Wei Zhang
A
Adele Cutler
F
François Grand’Maison
A
Anhong Zhou
A
Andrés Finzi
N
Nicolas Chomont
D
Daniel E. Kaufmann
S
Stéphanie Zandee
A
Alexandre Prat
G
Guy Wolf *
K
Kevin R. Moon *
DOI：10.1038/s43588-026-00999-7delete
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摘要

摘要

En 中文
基于降维的可视化对于解释复杂生物数据至关重要。然而，t-分布随机近邻嵌入、均匀流形近似和投影以及Isomap等无监督方法仅反映主要数据结构，可能与下游分析目标或专家提供的注释不一致。现有的监督变体仅部分解决了这种不匹配，并引入了新的限制。本文提出RF-PHATE，一种监督可视化方法，通过整合专家知识来揭示与标签相关的结构，同时抑制无关变化。RF-PHATE使用随机森林学习特征与标签之间的关系，并将此信息转化为低维嵌入。RF-PHATE可处理大数据集，适用于分类和回归任务。我们通过四个案例研究展示了其应用，包括纵向多发性硬化数据、抗氧化效应的拉曼光谱测量、COVID-19患者结局以及带有模拟丢失的RNA测序数据。这些应用突显了RF-PHATE在增强可解释性、管理噪声和揭示有意义的生物结构方面的能力，表明其在改进数据探索和发现方面具有广泛潜力。本研究介绍了RF-PHATE，一种在保留数据结构的同时揭示生物模式的监督可视化方法，能够为多发性硬化、COVID-19及其他生物背景中的疾病进展提供见解。

期刊

Nature Computational Science 封面图
Nature Computational Science
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Charles Lemoyne Hospital
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centre hospitalier de l’universite de montréal
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Centre Hospitalier de l'Université de Montréal
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