通过非冗余的相互信息推断单细胞基因调控网络
Yanping Zeng1, Yongxin He1, Ruiqing Zheng1
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Briefings in bioinformatics
|September 15, 2023
概括
我们开发了Normi,一种新的基因调节网络推断方法. 诺米有效地解决了单细胞RNA测序数据中的噪音和丢失问题,提高了GRN分析的稳定性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 基因调节网络 (GRNs) 对于生物过程至关重要.
- 从高分辨率单细胞RNA测序 (scRNA-seq) 数据中推断细胞特异的GRNs是具有挑战性的,因为噪音和数据丢失.
- 现有的GRN推断算法与这些数据复杂性作斗争.
研究的目的:
- 介绍Normi,一种用于强大的GRN推理的新型计算方法.
- 解决当前GRN推断方法的局限性,特别是与scRNA-seq数据质量相关的方法.
- 为了提高基因调节网络重建的准确性和可靠性.
主要方法:
- 开发了Normi,一种基于非冗余的相互信息的方法.
- 采用滑窗方法和平均光滑来进行代表性细胞识别.
- 使用混合KSG估计器进行高阶时间延迟的相互信息和边缘过的最大相关性和最小冗余性.
- 使用距离相关性确定最佳时间延迟.
主要成果:
- 与最先进的方法相比,Normi在模拟和真实scRNA-seq数据集上表现出更高的性能.
- 该方法在GRN推断方面显示出更高的稳定性,有效处理噪音和停课.
- 诺米成功地在真实scRNA-seq数据中确定了关键调节器和关键生物过程.
结论:
- 诺米为基因调控网络从scRNA-seq数据推断提供了强大而准确的解决方案.
- 该方法处理数据缺陷的能力使其成为系统生物学研究的宝贵工具.
- 诺米推进了计算推理领域,以了解基因调节.
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