scRDEN:单细胞动态基因排名差异表达网络和强大的轨迹推断推断
Han Zhang1, Wei Zhang1, Xiaoying Zheng2
1School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, 430073, China.
Scientific reports
|May 15, 2025
概括
该scRDEN框架准确地识别细胞亚群和基因表达网络,揭示细胞分化过程中的动态调控变化. 这种方法增强了对从单细胞RNA测序数据的转录调节和发育轨迹的理解.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 发展生物学 发展生物学
背景情况:
- 单细胞RNA测序 (scRNA-seq) 能够在细胞水平上进行精确的基因表达分析.
- 了解细胞命运决定需要分析动态调节变化和推断分化轨迹.
- 现有的方法在全面分析转录异质性和发育路径方面面临挑战.
研究的目的:
- 引入scRDEN,一个强大的框架来推断细胞亚群和差异表达网络.
- 分析基因表达动态和沿着差异化轨迹的调节性变化.
- 提供关于细胞分化和发育过程中的转录调节的见解.
主要方法:
- scRDEN将不稳定的基因表达值转换为稳定的基因相互作用 (全球特征).
- 它提取差异表达顺序 (网络特征) 并集成多维缩减表达特征.
- 该框架分析基因表达,识别细胞亚群,并构建差异表达网络.
主要成果:
- 在五个scRNA-seq数据集中,scRDEN成功地确定了具有潜在标记基因的稳定细胞亚群.
- 它准确地测量了转录差异,以沿着差异化分支建立排列差异表达网络.
- 分析显示了网络多样性和集群系数的非单调趋势,与差异化成稳定函数相关.
结论:
- scRDEN为分析细胞分化轨迹和调控机制提供了一个强大的框架.
- 该方法为大规模,多批次轨迹推断和转录调节提供了新的见解.
- 在复杂的数据集上,scRDEN表现出了卓越的性能,包括小鼠牙状回路数据.
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