STAN是一个计算框架,用于推断空间信息转录因子活动的推断
bioRxiv : the preprint server for biology
|July 9, 2024
概括
我们开发了STAN,这是一种计算方法,可以使用空间转录学绘制组织内转录因子 (TF) 活动的地图. STAN揭示了TF网络如何影响它们在当地的微环境中的细胞身份和功能.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 细胞生物学 细胞生物学
背景情况:
- 转录因子 (TF) 调节细胞对环境和信号线索的反应.
- 细胞的命运和功能受到邻近细胞和TF活动的影响.
- 空间转录学 (ST) 提供了组织水平的mRNA表达,但尚未完全用于估计TF活动.
研究的目的:
- 开发一种计算方法,从ST数据中预测空间信息化的TF活动.
- 整合TF-目标基因信息,基因表达和空间数据.
- 揭示TF网络在细胞身份和空间组织中的作用.
主要方法:
- 引入了STAN (空间信息转录因子活动网络),一种线性混合效应模型.
- 集成的TF目标基因先验,mRNA表达,空间坐标和成像数据.
- 将STAN应用于淋巴结,乳腺癌和质母细胞瘤ST数据集.
主要成果:
- STAN成功地预测了现货特定的TF活动.
- 确定了与不同细胞类型,空间域和病理区域相关的TF.
- 在组织微环境中揭示了TF与联体受体相互作用的关联.
结论:
- STAN增强了ST数据的实用性,以了解TF在细胞环境中的作用.
- 该方法阐明了TF活动与空间组织之间的相互作用.
- STAN为剖析组织中TF驱动的细胞异质性提供了一个框架.
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