伪实时图形扩散用于推断的单细胞轨迹的后期可视化
bioRxiv : the preprint server for biology
|February 9, 2026
概括
伪二次图形扩散 (PGD) 沿着轨迹平滑细胞特征,改善单细胞数据动态的可视化和解释. 这种方法增强了对复杂的细胞过程的理解,例如伤口愈合.
科学领域:
- 计算生物学是一种计算生物学.
- 一个单细胞分析.
- 生物信息学是一种生物信息学.
背景情况:
- 可视化对于解释单细胞轨迹至关重要,但往往无法准确地表示轨迹结构.
- 不准确的轨迹表示损害了细胞动态和下游分析的解释.
研究的目的:
- 引入伪时段图形扩散 (PGD),这是一个新的后期框架,用于增强单细胞轨迹数据的解释.
- 为了提高推断细胞轨迹的视觉表示的忠实性.
主要方法:
- PGD在伪时间图上采用随机行走扩散来平滑细胞水平的特征.
- 该框架沿推断的轨迹路径传播信息,以增强连续性和结构.
- 应用PGD可视化单细胞和巨细胞在伤口愈合过程中的轨迹.
主要成果:
- PGD光滑嵌入显著改善了复杂推断轨迹的可视化.
- 该方法在伤口愈合期间平滑单细胞和巨细胞动态方面表现出有效性.
- 扩展了PGD以实现轨迹感知基因表达平滑.
结论:
- PGD提供了一种轻量级和可解释的解决方案,用于改进单细胞轨迹分析.
- 通过增强可视化和推断轨迹之间的协议,PGD可以更准确地解释动态细胞过程.
- 该框架为研究细胞动力学和分化途径的研究人员提供了宝贵的工具.
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