hECA v2.0:单细胞RNA和ATAC测序数据的AI准备整体细胞地图
Xi Xi1,2,3, Yixin Chen1,4,5,6, Xinze Wu1
1MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing, China.
Scientific data
|December 15, 2025
概括
人类细胞集群图谱 (hECA) 2.0 现在集成了超过 1200 万个单细胞RNA测序和单细胞ATAC测序的细胞. 这种全面的AI准备资源支持先进的单细胞研究和大规模AI模型开发.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 分散的单细胞数据阻碍了大规模的AI模型开发.
- 人工智能驱动的单细胞研究需要一个统一的,高质量的资源.
研究的目的:
- 为了介绍人类整体细胞图谱 (hECA) 的2.0版本.
- 创建一个AI-ready单细胞数据资源,整合多种模式.
主要方法:
- 将单细胞RNA测序 (scRNA-seq) 数据扩展到10,831,024个细胞.
- 添加了 1,450,511 个细胞的单细胞ATAC 测序 (scATAC-seq) 数据.
- 标准化数据矩阵,协调元数据,并使用统一层次注释框架 (uHAF) 重新注释单元类型.
主要成果:
- hECA 2.0 包含 42 个人类器官和组织中的 12,281,535 个细胞.
- 该数据集包括scRNA-seq和scATAC-seq的两个配置文件.
- 在 scMulan AI 模型的预训练中证明了实用性.
结论:
- hECA 2.0为单细胞研究提供了一个结构良好的,高质量的,AI准备的基础.
- 促进跨数据集的一致性和强大的AI模型培训.
- 在人类生物学中实现先进的AI驱动的发现.
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