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stHGC:一个自我监督的图表表示学习,用于空间域识别与混合图表和空间规范化

Runqing Wang^1,2, Qiguo Dai^1,2, Xiaodong Duan^1,2

¹College of Computer Science and Engineering, Dalian Minzu University, 116600 Dalian, China.

Briefings in bioinformatics

|December 22, 2024

概括

我们开发了stHGC,这是一个新的自主监督图形学习框架,用于空间转录组学 (ST) 数据中的空间域识别. stHGC准确地识别了组织领域,并改进了下游分析.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

空间转录学 (ST) 技术为基因表达数据提供空间信息,这对于理解组织组织和细胞相互作用至关重要.
准确识别空间域对于解释ST数据至关重要,但整合空间位置和基因表达仍然具有挑战性.
现有的方法难以有效地利用空间和基因表达信息来进行域识别.

研究的目的:

提出一种新的自主监督图表表示学习框架,stHGC,用于ST数据中准确的空间域识别.
为应对整合空间位置和高维基因表达数据的挑战.
通过改进空间域检测,增强对组织组织的理解.

主要方法:

构建了一个混合邻近图形,整合了空间接近和基因表达相似度指标.
开发了一个自主监督的图表表示学习框架,利用对相邻点的图表注意力和对非邻近点的独特表示.
整合了一个空间规范化约束,以保存空间邻居的结构信息.

主要成果:

stHGC在识别跨多种ST数据集和分辨率的空间域方面明显优于最先进的方法.
该框架在准确地划分组织结构方面表现出卓越的性能.
在下游任务中,stHGC被证明是有益的,包括denoising和轨迹推断,突出其可扩展性.

结论:

stHGC提供了一种强大且可扩展的方法,用于在空间转录学数据中进行空间域识别.
自主监督的图表表示学习框架有效地整合了空间和基因表达信息.
stHGC的进步有助于更深入地了解组织结构和细胞异质性.

关键词:

混合邻居图表混合邻居图表自主监督的图表表示学习学习.空间域识别空间域识别空间规范化的空间规范化空间转录学空间转录学

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