使用网络分析对空间转录组学 (ST) 数据进行细胞信号表征
1Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA.
概括
我们开发了一种网络分析方法,在空间转录组学 (ST) 数据中绘制细胞-细胞通信的地图. 这种方法通过建模连接体-受体相互作用来量化信号活动,揭示生物学上可信的通信模式.
科学领域:
- 计算生物学 计算生物学
- 空间转录学 空间转录学
- 系统生物学 系统生物学
背景情况:
- 空间转录学 (ST) 能够在组织背景下进行基因表达分析.
- 了解细胞-细胞通信对于解释组织结构和功能至关重要.
- 现有的方法可能无法完全捕捉连接体-受体相互作用的空间动态.
研究的目的:
- 引入一种基于网络分析的新方法,用于在ST数据中量化细胞-细胞通信.
- 通过加权,定向网络方法来建模配体-受体相互作用.
- 验证该方法捕捉空间信号异质性的能力.
主要方法:
- 构建了一个网络模型,其中节点是ST位置,边缘重量反映了联体受体表达和空间距离.
- 利用加权的内度中心性来量化特定相互作用的信号活动.
- 在真实的ST数据集上验证了该方法,并将其与五种现有策略进行了比较.
主要成果:
- 该方法成功地捕获了连接体和受体的同时表达异质性.
- 为Wnt3-Fzd1,Ephb1-Efnb3和Ptprc-Cd22相互作用生成了生物可信的细胞通信概况.
- 证明了在ST数据中使用低维嵌入用于网络建模的重要性.
结论:
- 网络分析方法提供了一个强大的框架,可以从ST数据中推断细胞-细胞通信.
- 该方法有效地模拟了空间信号,考虑了表达水平和物理距离.
- 低维基因嵌入对于构建空间转录学中准确的网络模型至关重要.
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