从scRNA-seq数据推断振荡网络的调节动态
Wenjun Zhao1, Alma Plaza-Rodriguez2, Pichayathida Luanpaisanon3
1Department of Mathematics, Wake Forest University, 2601 Wake Forest Rd, Winston-Salem, NC 27106.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
这项研究通过结合细胞周期位置来增强基因调节网络 (GRN) 的推断. 这种方法提高了对细胞循环调节的理解,这对细胞命运和疾病发展至关重要.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 发展生物学 发展生物学
背景情况:
- 细胞循环过程对细胞命运和疾病的决定至关重要.
- 当前的基因调节网络 (GRN) 推断方法往往忽视了生物过程的周期性.
- 准确的GRN推断对于理解复杂的生物调节至关重要.
研究的目的:
- 通过考虑细胞周期的振荡性质来提高GRN推断的准确性.
- 测试这样的假设:限制细胞周期位置可以改善GRN推断.
- 为了研究小鼠视网膜前代细胞中的细胞循环调节.
主要方法:
- 对八种代表性的GRN推断方法的评估.
- 在小鼠视网膜原始单细胞基因表达数据集中应用三个选定的方法.
- 整合由三轮循环推断的细胞周期位置,以改善时间排序.
主要成果:
- 与使用实验时间相比,将细胞周期位置纳入显著提高了GRN推断准确性.
- 对于早期的原始细胞来说,改善特别显著,这表明细胞周期的内在调节.
- 这项研究证明了三循环的实用性,用于推断细胞循环状态.
结论:
- 限制GRN推断与细胞周期位置可以提高准确性,特别是对于振荡过程.
- 这种方法为推进对循环生物系统中基因调节的理解提供了一个有希望的框架.
- 未来的研究应该探索将振荡动力学整合到因果推理中,以获得更广泛的生物学见解.
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