用于扰乱建模和治疗效应估计的单细胞解表示.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
我们开发了scDRP,这是一个计算框架,用于从单细胞数据中估计个性化治疗效果. 这种方法揭示了细胞特异性基因调节动态和对干扰的异质生物反应.
科学领域:
- 计算生物学 计算生物学
- 单细胞基因组学 单细胞基因组学
- 系统生物学 系统生物学
背景情况:
- 单细胞测序捕捉了细胞状态,但阻碍了估计个性化治疗效应 (ITE).
- 了解干扰后的细胞状态特异性基因调节是生物学发现的关键.
- 不同质的细胞反应需要方法来推断反事实状态.
研究的目的:
- 介绍scDRP,一种用于从单细胞扰动数据中估计ITE的生成框架.
- 为了使细胞状态特定的基因调节和因果关系的剖析.
- 揭示生物系统中异质的机械反应.
主要方法:
- 通过稀疏度调节的β-变量自编码器 (β-VAE) 利用解的表示学习.
- 分离干扰依赖和独立的潜变量.
- 在潜空间中执行条件最佳传输以推断反事实状态并估计 ITE.
主要成果:
- scDRP在模拟和真实单细胞数据中准确估计治疗效应和个别反事实反应.
- 该框架揭示了细胞类型特定的功能基因模块在各种暴露下 (例如,鼻病毒,香烟烟雾) 的动态.
- scDRP识别了不同的细胞模式和功能模块激活,以响应干扰素刺激和CRISPR淘汰.
结论:
- scDRP提供了一种基于原则的计算方法,用于从单细胞扰动数据中阐明异构的因果关系.
- 该框架通过揭示细胞特异性反应,增强了对细胞和分子机制的理解.
- scDRP对未见的扰动剂量和组合进行了概括,提供了强大的生物洞察力.
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