CanCellCap:通过多域学习对单细胞RNA-seq数据进行跨组织类型的强大的癌细胞捕获
Jiaxing Bai1, Yichun Gao1, Feng Zhou1
1Department of Automation, National Institute for Data Science in Health and Medicine, State Key Laboratory of Mariculture Breeding, Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen University, Xiamen, Fujian, China.
BMC biology
|July 31, 2025
概括
CanCellCap从单细胞RNA测序数据准确地识别癌细胞,跨越各种组织和平台. 这种强大的框架可以将其推广到新的癌症类型和物种,提供宝贵的生物学见解.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 单细胞RNA测序 (scRNA-seq) 揭示了癌细胞的多样性,但由于异质性,癌细胞的准确识别面临挑战.
- 基因表达的变异性阻碍了癌细胞检测现有方法的泛化和稳定性.
研究的目的:
- 开发一个通用框架,CanCellCap,用于识别不同组织,癌症和测序平台的scRNA-seq数据中的癌细胞.
- 提高癌细胞识别方法的稳定性和一般化能力.
主要方法:
- CanCellCap采用一个多域学习框架,整合域对抗性学习和专家混合.
- 使用掩盖重建策略来处理来自不同测序平台的数据.
- 该框架提取了癌症和正常细胞在整个组织中的常见和特定的基因表达模式.
主要成果:
- 在13种组织类型,23种癌症类型和7种测序平台上,CanCellCap在癌细胞识别中实现了0.977的平均准确度.
- 在33个基准数据集上超过了五种最先进的方法,证明了卓越的性能.
- 在未见的癌症类型,组织和物种上展示了高性能,并在空间转录组学数据中准确识别了癌症斑点.
- 证明了计算效率,在几分钟内分析了10万个细胞,并揭示了关键的生物标志物和途径.
结论:
- CanCellCap提供了一种强大而准确的解决方案,用于在各种scRNA-seq数据中识别癌细胞.
- 它对未见数据的强烈概括性和对空间转录学的适应性突出显示了它对研究和临床应用的多功能性.
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