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关系等价图形神经网络以探索脏疾病的马赛克样组织架构在空间解析的转录组学上

Mauminah Raina¹, Hao Cheng², Ricardo Melo Ferreira³

¹Department of Biomedical Engineering and Informatics, Indiana University Indianapolis, Indianapolis, IN 46202, United States.

Bioinformatics (Oxford, England)

|May 13, 2025

概括

一个新的图形深度学习框架,REGNN,分析异质脏组织的空间解析的转录组学数据. 它准确地识别了组织结构,有助于研究慢性病和急性损伤进展.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

慢性病 (CKD) 和急性损伤 (AKI) 影响全球人口的15%以上,需要用于组织分析的先进工具.
空间解析转录学 (SRT) 提供了对患病组织基因表达模式的见解,但现有的计算工具与异质结构作斗争.
在及时和具有成本效益的注释和解释管和间隙中的细胞和形态变化的挑战仍然存在.

研究的目的:

开发一种先进的计算框架,用于分析异质组织架构中的空间解析的转录组学数据.
克服现有工具的局限性,这些工具主要设计用于更简单的组织结构,如大脑皮层.
为了能够准确地识别组织结构,并促进病的研究.

主要方法:

介绍REGNN (关系等价图神经网络),用于SRT数据分析的图形深度学习框架.
整合等差对处理n维对称度和定位编码来加强相对空间关系.
实现图形自编码器和图形自我监督学习策略,以解决有限的标记空间数据.

主要成果:

REGNN在10 × Visium平台上对异构脏样本识别组织架构方面表现优于现有的计算工具.
该框架有效地分析复杂的SRT数据,像马赛克一样的脏组织结构.
REGNN成功地识别了不同病的组织架构.

结论:

REGNN提供了一个强大的图形深度学习工具,用于研究组织中高度异质的表达模式.
这一框架有助于确定复杂脏疾病的潜在病理机制.
REGNN推进了在具有挑战性的组织架构中对空间解析的转录组学数据的分析.

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