Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes - Jia, Zhigang; Zhang, Xiujun (Frontiers in Genetics, 2022)
Accurate determination of the causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining the causalities or regulatory directions. In this work, we present a novel method, namely DDTG, to improve the accuracy of causalities determination in GRN inference by Dissecting Downstream Target Genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, the indirect interactions are removed with the sparseness of network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of DDTG method on causalities determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer causal gene regulatory network.