dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data

doi:10.1016/j.isci.2024.111673

2025-02-01

DOI：10.1016/j.isci.2024.111673

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新的检测方法，如Perturb-seq，将并行的CRISPR干预与转录组学读出联系起来，为基因调控网络提供了见解。因果调控网络可以用有向无环图（DAGs）表示，但可识别性的缺乏和组合解空间使得从观测数据中学习DAGs变得复杂。基于评分的方法改进了推断DAGs的实用可扩展性，但对误差方差结构敏感。此外，在没有结构先验知识的情况下，对误差方差的校正很困难。我们提出了dotears [doo-tairs]，一个连续优化框架，利用观测数据和干预数据来推断因果结构，假设线性结构方程模型。dotears利用硬干预的结构后果来估计和校正误差方差结构。dotears在温和假设下是对真实DAG的可证明一致估计器，并在各种模拟中优于其他当前最先进的方法。在真实数据中，差异表达检验和高置信度蛋白质-蛋白质相互作用验证了dotears推断的边的精度和召回率高于其他方法。

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