从单细胞基因表达数据中对基因调节网络的稳健发现,通过因果推理,使用交易组合
Abbas Shojaee1, Shao-Shan Carol Huang1
1Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA.
Briefings in bioinformatics
|October 28, 2023
概括
发现基因调节网络 (GRNs) 对于理解生物系统至关重要. 一种新的方法,CICT (Causal Inference Using Composition of Transactions) 准确地从单细胞RNA测序数据中推断出GRN,显著提高了发现准确性.
科学领域:
- 基因组学和系统生物学
- 计算生物学和生物信息学
背景情况:
- 基因调节网络 (GRNs) 是生物体结构和功能的基础.
- 准确推断GRNs,特别是从单细胞RNA测序 (scRNA-seq) 数据,仍然是生物学研究的一个重大挑战.
- 识别因果调节连接是理解基因表达动态的关键.
研究的目的:
- 引入和验证一种新的计算方法,即使用交易组合 (CICT) 的因果推理,用于推断基因调节网络 (GRNs).
- 为了提高GRN从单细胞RNA测序 (scRNA-seq) 数据推断的准确性和稳定性.
主要方法:
- 开发了使用交易组合 (CICT) 方法的因果推理,该方法利用来自相关性网络的新型网络特征.
- 应用CICT来分析模拟和实验scRNA-seq数据集.
- 使用基准测试管道对现有网络推断方法进行评估CICT的性能.
主要成果:
- 在GRN推断准确度方面,CICT显著优于现有方法,取得了多倍高的结果.
- 该方法在不同级别的数据稀疏性,数据特征和机器学习算法的选择方面表现出强度.
- 通过区分非随机基因表达模式,CICT有效地识别因果调节边缘.
结论:
- 直接预测因果关系是复杂生物网络中恢复调节关系的优越策略.
- 从scRNA-seq数据中推断基因调节网络,CICT提供了一种高度准确和强大的方法.
- 这一进步促进了对基因调节和生物复杂性的更深入的理解.
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