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风暴:结合过渡性随机动力学来推断RNA速度与代谢标记信息

Qiangwei Peng¹, Xiaojie Qiu^2,3,4,5, Tiejun Li^1,6

¹LMAM and School of Mathematical Sciences, Peking University, Beijing, China.

PLoS computational biology

|November 18, 2024

概括

风暴是一种新的动态方法,分析时间解析的单细胞RNA测序数据,以揭示基因表达动态. 这种方法克服了先前模型的局限性,为短暂的生物过程提供了准确的动力参数推断.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
系统生物学系统生物学

背景情况:

时间解析的scRNA-seq (tscRNA-seq) 能够推断转录和降解速率以及RNA速度等动态参数.
使用确定性动力学和稳定状态假设的现有方法与短暂的生物过程作斗争.
需要一些方法来处理scRNA-seq数据中短暂基因表达动态的复杂性.

研究的目的:

本文介绍Storm (RNA代谢标记的随机模型),一种用于分析tscRNA-seq数据的动态方法.
通过解决基因表达动态的随机微分方程来克服以前方法的局限性.
将RNA速度方法扩展到使用代谢标记scRNA-seq数据的瞬态随机系统.

主要方法:

开发了Storm,一种基于解决随机微分方程的新动态方法.
结合了生物和细胞特异性技术噪声,模拟mRNA测序数据与细胞特异性Poisson分布.
放松常量参数假设,以导出基因细胞特异转录/拼接率和基因特异降解率.

主要成果:

风暴直接分析测量计数数据,将RNA速度扩展到瞬态随机系统.
这种方法揭示了依赖时间和特定于细胞状态的转录规则.
证明了用正确的大小推断具有物理意义的动力参数的能力.

结论:

风暴为分析tscRNA-seq数据提供了一个强大的框架,特别是对于短暂的过程.
该方法增强了对tscRNA-seq数据的统计性质的研究.
有助于更深入地了解发育和疾病中的动态转录调节.

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