scDCT:一种基于条件扩散的深度学习模型,用于高准确度的单细胞交叉模式翻译
Junlei Zhou1, Jialiang Xue1, Yang Li1
1School of Information Engineering, Ningxia University, 489 West Helan Mountain Road, Xixia District, Yinchuan 750021, Ningxia, China.
Briefings in bioinformatics
|August 12, 2025
概括
我们开发了单细胞扩散模型用于交叉模式翻译 (scDCT),这是一种用于在不同单细胞分子数据类型之间翻译的新方法. scDCT准确地整合了多学科数据,克服了噪音和稀疏性的挑战.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 分子生物学分子生物学
背景情况:
- 单细胞多组技术为细胞异质性提供了深入的见解.
- 现有的跨模式翻译方法面临着高维度,杂和稀疏的单细胞数据的挑战.
研究的目的:
- 开发一个强大的概率框架,用于单细胞数据中的双向交叉模式转换.
- 改进多种单细胞分子形状的整合,包括scRNA-seq,scATAC-seq和蛋白质表达.
主要方法:
- 为交叉模式转换 (scDCT) 提出的单细胞扩散模型.
- 集成的模式特定的自动编码器与有条件的否定扩散概率模型.
- 开发了一个框架,将输入映射到潜在空间,并执行跨模式的概率翻译.
主要成果:
- scDCT有效地捕捉了细胞类型的异质性,并考虑了数据稀疏性.
- 该模型成功地模拟了跨模式转换期间的不确定性.
- 在各种数据集和设置 (配对,不配对,交叉类型,交叉组织) 中,与最先进的方法相比,表现出优越的性能.
结论:
- scDCT为单细胞多omics集成提供了强大的和可解释的解决方案.
- 拟议的框架通过允许准确的跨模式翻译,推动单细胞数据分析领域的发展.
- 强调扩散模型在解决单细胞数据集成方面的复杂挑战方面的潜力.
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