scCotag:通过预先告知的共最佳传输和规范化的重心映射对角集成单细胞多组数据
Yang Li1, Yuting Tan2, Rui Chen2
1Department of Computer Science, Vanderbilt University, Nashville, Tennessee.
bioRxiv : the preprint server for biology
|December 25, 2025
概括
scCotag是一个新的深度学习框架,通过完善细胞对齐和特征对应,增强单细胞多omics集成. 这种方法改善了生物学解释,特别是在复杂的数据集中,其中并非所有细胞都可以对齐.
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:
- 高通量单细胞技术产生多领域的数据,提供互补的生物学见解.
- 对于对角集成的计算方法使细胞种群在不同的奥米克层中对齐.
- 现有的方法通常假定所有细胞都是可对齐的,并且不精确特征对应.
研究的目的:
- 引入scCotag,这是一个深度学习框架,用于强大的单细胞对角集成.
- 通过反复推断细胞对齐和特征对应来解决现有方法的局限性.
- 为了能够从多种OMIC数据中进行更准确的生物学解释.
主要方法:
- scCotag使用同优运输 (COOT) 进行预先知情的,细胞对齐和特征对应的代推断.
- 细胞和特征嵌入的联合学习是通过规范化的重心映射和图形重量化实现的.
- 在人类大脑,骨髓和血液单细胞RNA-seq和ATAC-seq数据集上验证的框架.
主要成果:
- 在细胞对齐和嵌入精度方面,scCotag在最先进的方法中表现出优越的性能.
- 该方法在模拟失衡场景中表现出色,其中细胞对齐性不是普遍的.
- 对阿尔茨海默病的应用 死亡后的大脑数据揭示了更细致的调节机制解释.
结论:
- scCotag提供了一个强大而准确的框架,用于单细胞对角集成多omics数据.
- 细胞对齐和特征对应的代改进增强了生物推理.
- 这种方法推进了复杂的单细胞数据集和监管机制的分析.
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