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构建基因调节网络使用查特吉的等级与单细胞转录数据的相关性
Shreyan Gupta1, Anamitra Chaudhuri2, Vishnuvasan Raghuraman3
1Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA.
bioRxiv : the preprint server for biology
|September 26, 2025
概括
我们开发了一种新方法,从单细胞RNA测序 (scRNA-seq) 数据中发现基因调控网络 (GRNs). 这种方法在计算上是高效的,并准确地识别了基因相互作用,优于现有的方法.
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:
- 了解基因调节网络 (GRNs) 对细胞功能至关重要.
- 现有的从单细胞RNA测序 (scRNA-seq) 数据中推断GRN的方法由于强大的假设或高的计算成本而面临限制.
- 需要用于GRN发现的透明,可扩展和高效的方法.
研究的目的:
- 引入一种新的多重测试框架,用于从scRNA-seq数据中推断GRNs.
- 克服现有的GRN推理方法的局限性.
- 为复杂的机器学习模型提供计算效率高,可扩展的替代方案.
主要方法:
- 使用Chatterjee的等级相关系数,这是一个非参数的依赖度,用于GRN推理.
- 开发了一个数据驱动的算法来估计强大的测试截止值,解决scRNA-seq.中的非独立观察.
- 通过利用查特吉相关性不对称的性质,提出了针对性调节的新测试.
主要成果:
- 与最先进的方法相比,提出的方法在恢复真正的监管联系方面表现出卓越的表现.
- 成功应用于模拟和真实scRNA-seq数据集.
- 能够构建具有生物意义和定向信息的GRNs.
结论:
- 新的框架为GRN推断提供了一种透明,可扩展和计算效率高的方法.
- 该方法有效地应对scRNA-seq数据的特定挑战,例如非独立的观测.
- 为剖析复杂的GRNs提供了强大的工具,并促进了细胞功能的理解.
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