在单细胞RNA数据中用于结构差异化建模的相关潜空间学习
1State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Computers in biology and medicine
|October 5, 2025
概括
我们开发了CODEVAE,这是一个深度学习框架,用于分析单细胞RNA测序数据. 它准确地模拟了连续的细胞动态和生物变异,优于现有的方法.
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:
- 单细胞RNA测序 (scRNA-seq) 提供了对细胞过程的高分辨率洞察力.
- 现有的scRNA-seq分析方法与细胞分化动态的连续性,合性和噪音性质作斗争.
研究的目的:
- 引入CODEVAE (相关普通微分方程变量自动编码器),一个新的深度生成框架.
- 解决scRNA-seq数据中连续和合细胞动态建模的局限性.
- 增强维护几何连续性和生物相结合的变异.
主要方法:
- CODEVAE集成了普通微分方程 (ODE) 约束与相关性意识的潜在表示.
- 该框架建立在一个变化的自编码器上,包含低β规范化,信息瓶,基于ODE的连续性和相关的潜伏组件.
- 在55个独立运行中使用18个指标进行评估.
主要成果:
- 与先进的变异模型,单细胞特定方法,图形/对比方法和传统的缩小维度技术相比,CODEVAE表现出更高的性能.
- 在多批次场景中,CODEVAE保持了流的数据组,并提高了集成质量.
- 成功重建了一个连续的巨核细胞分化轨迹,并在生物应用中确定了Dapp1扰动的特定阶段效应.
结论:
- CODEVAE提供了一种强大且有原则的方法,用于从scRNA-seq数据中建模连续细胞动态.
- 该框架有助于在多种单细胞环境中提取机械洞察力.
- 在分析复杂的细胞轨迹方面,CODEVAE代表了重大进步.
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