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iAODE用于对单细胞染色质可访问性的基准测试和连续模型

Zeyu Fu¹, Chunlin Chen², Song Wang³

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, China. fuzeyu99@126.com.

Communications biology

|March 3, 2026

概括

我们开发了iAODE,这是一种用于分析稀疏单细胞数据的新型变异自编码器. 它有效地模拟了连续的发展轨迹,并增强了潜伏空间的时间连续性.

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:

单细胞色素可访问性数据 (scATAC-seq) 稀少,但对于理解细胞发育至关重要.
现有的缩小维度和轨迹分析方法往往无法捕捉时间连续性.

研究的目的:

介绍iAODE,一个设计用于生成的,时间连续的潜空间学习的变化自编码器.
开发一个全面的基准和评估套件,用于单细胞轨迹分析.

主要方法:

iAODE将一个零膨胀的负二项式概率与一个潜在的神经普通微分方程 (ODE) 结合起来.
包含低重量的Kullback-Leibler (KL) 正规化和一个可解释的瓶.
使用248个scATAC-seq和123个单细胞RNA测序 (scRNA-seq) 数据集的标准化基准.

主要成果:

评估套件量化了潜在空间连续性,嵌入质量和集群结构.
模拟显示了对连续性干扰的度量敏感性.
大规模的基准显示了iAODE在轨迹结构和稳定性方面的协同改进,与现有方法相比.

结论:

iAODE有效地从稀疏的单细胞数据中学习生成的,时间连续的潜空间.
开发的基准和指标为评估轨迹推理方法提供了强大的框架.
与既有的生成式和多重学习方法相比,iAODE提供了更好的稳定性和轨迹结构.

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