细胞融合使单细胞和空间蛋白质组学数据的多模式集成成为可能
Abhishek Koladiya1, Zinaida Good2,3,4, Sricharan Reddy Varra1
1Division of Hematology, Oncology, and Stem Cell Transplant and Regenerative Medicine, Department of Pediatrics, Stanford University, CA.
bioRxiv : the preprint server for biology
|August 8, 2025
概括
CellFuse集成来自不同平台的单细胞蛋白质组数据,即使具有有限的共享标记. 这种深度学习框架可以改善细胞类型的预测和跨多样化生物样本的数据集成.
科学领域:
- 单细胞生物学 单细胞生物学
- 蛋白质组学是指蛋白质组学.
- 计算生物学是一种计算生物学.
背景情况:
- 单细胞和空间蛋白质组技术提供互补的数据,但缺乏统一的测量平台.
- 由于有限的共享功能,现有的集成方法与低维的蛋白质组数据作斗争,通常是针对转录组学进行优化.
研究的目的:
- 开发一个深度学习框架,CellFuse,用于单细胞蛋白质组数据的模式不可知集成.
- 应对集成具有有限特征重叠和不同实验条件的数据集的挑战.
主要方法:
- 开发了CellFuse,这是一个使用监督对比学习的深度学习框架.
- 学习了一个共享的嵌入空间,以实现跨模式和跨条件的数据集成.
- 评估了各种数据集的性能,包括PBMC,骨髓,淋巴瘤和瘤组织.
主要成果:
- 与现有方法相比,CellFuse表现出优越的集成质量和运行时间效率.
- 实现了准确的细胞类型预测和强大的性能,缺少标记物和罕见的细胞类型.
- 展示了强大的交叉数据集比较结果,突出了可扩展性和可靠性.
结论:
- CellFuse提供了一个强大的,多功能工具,用于高保真单细胞数据集成.
- 能够在不同的蛋白质组模式和实验环境中进行无分析.
- 促进了单细胞基础和转化研究的进步.
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