多模态表示的动态合成用于CITE-seq数据集成和分析.
Yinan Shi1, Yanchi Su2, Yue Cheng1
1School of Artificial Intelligence, Jilin University, Changchun, 130012, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 8, 2025
概括
scMHVA是一个新的计算框架,有效地整合了通过测序 (CITE-seq) 来进行转录体和表皮体细胞索引的RNA和蛋白质数据. 这种方法改善了细胞类型分析,并揭示了免疫细胞发育动态.
科学领域:
- 单细胞多组体的单细胞多组体.
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
背景情况:
- 通过测序 (CITE-seq) 来对转录组和表皮细胞进行细胞索引,可以同时测量RNA和蛋白质水平.
- 现有的方法难以捕捉RNA和抗体衍生标签 (ADT) 之间的复杂相互作用,并且计算密集.
- 准确整合多模式CITE-seq数据对于理解细胞异质性至关重要.
研究的目的:
- 开发一种新的,轻量级的计算框架,scMHVA,用于集成各种CITE-seq数据模式.
- 为了捕获复杂的,转录基因和蛋白质基因数据之间的非线性相互作用.
- 为大规模CITE-seq数据分析提供一个强大且可扩展的工具.
主要方法:
- scMHVA采用自适应动态合成模块,从RNA和ADT数据创建合并的嵌入.
- 使用多头自我注意机制来增强模式间的相关性,并捕获mRNA-蛋白质关系.
- 该框架在不同规模的CITE-seq数据集上进行了评估.
主要成果:
- scMHVA的性能优于现有的单模和多模聚类方法.
- 该框架展示了线性运行时可扩展性和有效的批量效应去除.
- scMHVA成功地在小鼠胸细胞数据集中注释了细胞类型,并揭示了免疫细胞发育动态.
结论:
- scMHVA是整合CITE-seq数据的有效和高效工具,推进细胞异质性的分析.
- 该框架捕捉转录基因-蛋白质基因相互作用的能力为生物系统提供了新的见解.
- scMHVA提供了一个可扩展的解决方案,用于分析免疫学及其他领域的大型CITE-seq数据集.
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