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GatorST:用于空间转录基因数据分析的多功能对比式元学习框架

Song Wang, Yuxi Liu, Zhenhao Zhang

bioRxiv : the preprint server for biology

|July 17, 2025

概括

GatorST是一个新的框架,通过整合本地和全球空间背景来增强空间转录学分析. 它改善了空间域识别,基因表达赋值和批量效应去除.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

空间转录学 (ST) 为基因表达提供了空间上下文,这对于理解细胞功能至关重要.
现有的ST分析方法难以有效地捕获本地和全球空间信息.
许多当前的方法依赖于可引入噪音的增强策略.

研究的目的:

介绍GatorST,这是一个多功能框架,用于生成ST数据的空间信息表示.
改进下游的ST分析,包括空间域识别,基因表达赋值,批量效应去除和轨迹推断.

主要方法:

使用k-最近的邻居构建一个点点图,以捕捉本地空间环境.
采用软K-means集群用于伪标签和对比学习用于全球上下文集成.
使用灵感来自于元学习的插曲训练策略,以提高概括性.

主要成果:

与15种最先进的方法相比,GatorST在14个ST数据集中展示了卓越的性能.
在空间域识别,基因表达赋值和批量效应去除方面始终取得更好的结果.
在各种组织类型和实验环境中表现出强大的多功能性和概括能力.

结论:

GatorST有效地整合了空间拓和基因表达,使用基于图形的建模,伪标签和对比的元学习.
为ST数据生成生物学上有意义的表示.
显著增强了空间转录学中的关键下游分析任务.

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