对单细胞RNA测序数据和ATAC测序数据的集成方法进行比较
Yulong Kan1, Weihao Wang1, Yunjing Qi1
1School of Mathematics Harbin Institute of Technology Harbin China.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
整合多式联运单细胞数据是一项挑战. 本综述系统地评估了流行的单细胞集成方法,提供了对它们的应用,局限性和未来 robust biological discovery 的方向的见解.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞基因组学为细胞表型和遗传学提供了洞察力.
- 新兴技术使多式单细胞数据收集成为可能 (例如,转录组,表观基因组).
- 整合这些不同的数据集用于细胞对应仍然是一个重大挑战.
研究的目的:
- 系统地审查流行的单细胞集成方法.
- 在金标准数据集上评估超过10种集成方法.
- 讨论多式联网单细胞数据集成的局限性,应用和未来方向.
主要方法:
- 对单细胞集成方法的系统文献综述.
- 对细胞标签转移,数据可视化和聚类方法的评估.
- 在配对和不配对数据集上对比较流行的集成方法.
主要成果:
- 确定了流行的单细胞集成方法和模型.
- 在各种下游任务中评估方法性能.
- 评估不同集成方法的数据偏好,局限性和应用.
结论:
- 生物相关细分度的数据集成至关重要.
- 为了平衡生物发现与降噪,需要考虑模式差异.
- 该审查为选择和应用单细胞集成方法提供了全面的指南.
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