Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.

中文翻译：

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EIEPCF：通过消除混杂因素的间接影响来准确推断功能基因调控网络

重建功能基因调控网络（GRN）是了解动物致病机制和治疗疾病的首要前提，也为培育植物抗病、抗腐蚀的蔬菜和水果品种提供重要基础。人们已经开发了许多计算方法来推断GRN，但这些方法获得的基因之间的调控关系大多数都是有偏差的。消除 GRN 的间接影响仍然是研究人员面临的重大挑战。在这项工作中，我们提出了一种推断功能GRN的新方法，称为EIEPCF（消除混杂因素产生的间接影响），该方法消除了混杂因素造成的间接影响。该方法通过测量调节因子和目标基因残差之间的相似性来消除混杂因素对它们的影响。EIEPCF 方法在模拟研究中的验证结果、DREAM3 Challenge 提供的黄金标准网络以及大肠杆菌的真实基因网络表明，与其他流行的推断 GRN 的计算方法相比，该方法具有更高的精度。作为案例研究，我们利用EIEPCF方法从拟南芥抗寒基因表达数据中重建了抗寒特异性GRN。源代码和数据可在 https://github.com/zhanglab-wbgcas/EIEPCF 获取。