利用 RNA 速度进行的因果基因调控分析揭示了慢速和快速转录因子之间的相互作用。

Rohit Singh, Alexander P Wu, Anish Mudide, Bonnie Berger
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引用次数: 0

摘要

来自分化轨迹或 RNA 速度的单细胞表达动态有可能揭示基因调控网络(GRN)中转录因子(TF)与其靶基因之间的因果联系。然而,现有的方法要么忽略了这些表达动态,要么要求细胞沿着线性伪时间轴排序,这与分支轨迹不相容。我们介绍的 Velorama 是一种因果关系 GRN 推断方法,它将单细胞分化动态表示为细胞的有向无环图,由伪时间或 RNA 速度测量构建而成。此外,Velorama 还能估计 TF 影响目标基因的速度。应用 Velorama,我们发现有证据表明,TF 的相互作用速度与其调控功能有关。在人类皮质生成方面,我们发现慢速 TF 与胶质瘤有关,而快速 TF 则与神经精神疾病有关。我们期待 Velorama 成为研究分化和疾病因果驱动因素的 RNA 速度工具包的重要组成部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.

Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

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