scSDNE:一种基于图形嵌入的半监督方法,用于推断基于图形嵌入的细胞相互作用
Chenchen Jia1, Haiyun Wang2, Jianping Zhao1
1College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
PLoS computational biology
|May 7, 2025
概括
我们开发了scSDNE,这是一种深度学习模型,可以通过连接体-受体相互作用推断细胞与细胞之间的通信. 它通过整合基因调节来准确识别通信通路,增强生物洞察力.
科学领域:
- 细胞生物学 细胞生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 细胞与细胞之间的沟通对于多细胞生物来说至关重要,通过连接体-受体 (L-R) 相互作用进行介导.
- 了解这些相互作用是解读生物过程和细胞功能的关键.
- 现有的方法需要全面了解这些复杂的相互作用.
研究的目的:
- 开发一种新的计算模型,以推断由L-R对介导的细胞-细胞相互作用.
- 提高细胞间通信分析的准确性和生物解释性.
- 为了更全面地了解生物系统中细胞与细胞的相互作用.
主要方法:
- 构建了一个手动策划的联体受体相互作用数据库.
- 开发了一个半监督图形嵌入深度学习模型 (scSDNE).
- 将相互作用的细胞基因映射到共享的潜伏空间中,并集成基因调节信息.
主要成果:
- scSDNE可靠地推断出涉及细胞间通信的显著L-R对.
- 该模型在检测scRNA-seq数据上的细胞相互作用方面表现出高可靠性.
- 整合基因调节信息提高了推断准确性和解释性.
结论:
- scSDNE提供了一种强大的工具,可以通过L-R相互作用来分析细胞之间的通信.
- 该模型为复杂的细胞间通信机制提供了新的见解.
- 这种方法增强了对多细胞生物的生物过程和细胞功能的理解.
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