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BiGCN通过整合单细胞转录组和BCR目录来学习B细胞的功能状态

Xiao Liu^1,2, Jianghao Huang^1,2, Shasha Zhao^1,2

¹State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Shanghai, China.

|February 23, 2026

概括

一种名为BiGCN的新方法集成了B细胞的转录和受体数据,揭示了隐藏的B细胞状态和功能. 这促进了对免疫调节和癌症等疾病的理解.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
系统医学系统医学

背景情况:

B淋巴细胞 (B细胞) 对于免疫反应和疾病至关重要.
分析B细胞多样性是具有挑战性的,因为单独的转录和B细胞受体 (BCR) 谱系分析.
现有的方法很难完全整合这些不同的数据类型,以进行全面的B细胞表征.

研究的目的:

开发一种新的计算方法,BiGCN,用于单细胞转录和BCR目录数据的综合分析.
通过统一这些模式,使B细胞状态和克隆关系的高保真性表征成为可能.
改善功能注释和对B细胞种群的生物学洞察力.

主要方法:

开发了BiGCN,一种使用图形卷积网络 (GCN) 框架的方法.
综合配对单细胞RNA测序 (scRNA-seq) 和单细胞BCR测序 (scBCR-seq) 数据.
将BiGCN应用于各种数据集,包括传染病,自身免疫和癌症.

主要成果:

在数据集成,功能注释和生物发现方面,BiGCN显著优于现有的方法.
成功回顾了B细胞成熟和免疫球蛋白类切换轨迹.
发现了以前未知的中间B细胞亚型和功能状态.

结论:

BiGCN为B细胞研究提供了一个多功能和强大的平台.
该方法增强了B细胞状态和克隆动态的表征.
BiGCN加速了免疫学,癌症生物学和系统医学方面的发现.

关键词:

BCR 距离距离 BCR 距离距离 B细胞受体 (BCR) 是一种图形卷积网络 (GCN) 是一个图形卷积网络.集成嵌入式嵌入式在 scRNA-seqq 中.

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