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在单细胞RNA和ATAC测序数据中使用ResidPCA发现与疾病相关的细胞状态

Shaye Carver¹, Kodi Taraszka², Stefan Groha²

¹Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA; Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.

|October 29, 2025

概括

剩余主要组件分析 (ResidPCA) 准确地识别复杂单细胞数据中的细胞状态. 这种方法揭示了与疾病相关的生物变异,在模拟和真实世界疾病研究中表现优于现有技术.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
神经科学是一个神经科学.

背景情况:

了解细胞异质性对于使用单细胞测序的疾病研究至关重要.
现有的PCA和NMF等方法难以有效地建模复杂的细胞类型异质性.

研究的目的:

介绍剩余主要成分分析 (ResidPCA),一种用于识别细胞状态的新方法.
提高检测单细胞数据中的细胞状态和生物变异的准确性,特别是在疾病环境中.

主要方法:

开发了ResidPCA,一种明确模拟细胞类型异质性的方法.
通过模拟验证了ResidPCA,并将其应用于单细胞RNA测序和单核ATAC-seq (snATAC-seq) 数据集.
与传统PCA和基于非负矩阵分解 (NMF) 的方法相比,ResidPCA的性能比较.

主要成果:

在模拟中,ResidPCA的准确性超过PCA的4倍,超过NMF的3倍.
在小鼠视觉皮层数据中,ResidPCA在捕捉刺激驱动的变异性方面,与NMF相比,获得了超过5倍的准确性.
在阿尔茨海默病数据中确定了44个snATAC-seq和42个snRNA-seq状态,其中30个snATAC-seq状态为疾病遗传性进行了丰富.

结论:

ResidPCA有效地揭示了单细胞数据中的隐藏生物变异.
确定了新的与疾病相关的细胞状态,包括与阿尔茨海默病有关的神经元-寡 dendrocyte-microglial轴.
在疾病研究中,ResidPCA提供了一种强大的方法来推进细胞异质性研究.

关键词:

阿尔茨海默病是阿尔茨海默氏症的疾病.细胞状态识别细胞状态识别细胞异质性的细胞异质性与疾病相关的状态.主要组件分析的主要组件分析.这就是 scRNA-seqq.一个单细胞测序.这就是snATAC-seqq.

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