结合单细胞ATAC和RNA测序,用于监督细胞注释.
Jaidip Gill1, Abhijit Dasgupta2, Brychan Manry2
1School of Public Health, Imperial College London, London, England.
BMC bioinformatics
|February 26, 2025
概括
结合RNA和ATAC测序数据,可以改善对外围血液单核细胞 (PBMC) 的监督细胞类型注释. 这种增强的预测信心有助于理解PBMC样本中的细胞异质性.
科学领域:
- 单细胞多组学分析
- 免疫学 免疫学 免疫学
- 神经科学是一个神经科学.
背景情况:
- 细胞类型的注释对于单细胞分析至关重要,使我们能够深入了解细胞异质性和功能.
- 目前的方法主要使用单细胞RNA测序 (scRNA-seq) 数据.
- 虽然结合scRNA-seq和单细胞ATAC测序 (scATAC-seq) 可以改善无监督注释,但它对监督方法的影响却不太被探索.
研究的目的:
- 研究将scRNA-seq和scATAC-seq数据集成为监督细胞类型注释的实用性.
- 评估多模数据对注释准确性和可靠性的影响,与单模数据相比.
主要方法:
- 利用了来自人类PBMC和阿尔茨海默病神经元细胞的配对RNA和ATAC测序的10x Genomics多组数据集.
- 采用了维度减少技术 (线性和非线性) 和各种分类模型 (随机森林,SVM,物流回归).
- 使用F1分数和预测信心评估注释性能,特别是使用scVI嵌入.
主要成果:
- scRNA-seq和scATAC-seq的整合显著改善了人类PBMC亚型的监督注释和预测信心.
- 特定的细胞类型,如CD4 T效应记忆细胞,在F1得分中表现出最显著的改善.
- 当从阿尔茨海默病患者的神经元细胞进行注释时,没有观察到类似的改善.
结论:
- 多原子数据集成为在PBMC等特定细胞群中监督细胞类型注释提供了显著的优势.
- 多原子数据的有效性可能因细胞类型和生物背景而异.
- 未来的研究应该探索多组监督注释在不同细胞类型和疾病状态中的应用.
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