Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information

By Xiujun Zhang, Xingming Zhao, Kun He, Le Lu, Yongwei Cao, Jingdong Liu, Jin-Kao Hao, Zhiping Liu, Luonan Chen. Bioinform., 2012

330 citations recorded by the source corpus.

MOTIVATION Reconstruction of gene regulatory networks (GRNs), which explicitly represent the causality of developmental or regulatory process, is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but seldom are taken into account simultaneously in one computational method. RESULTS In this work, we present a novel method for inferring GRNs from gene expression data considering the non-linear dependence and topological structure of GRNs by employing path consistency algorithm (PCA) based on conditional mutual information (CMI). In this algorithm, the conditional dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles. The method is validated on the benchmark GRNs from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The cross-validation results confirmed the effectiveness of our method (PCA-CMI), which outperforms significantly other previous methods. Besides its high accuracy, our method is able to distinguish direct (or causal) interactions from indirect associations. AVAILABILITY All the source data and code are available at: http://csb.shu.edu.cn/subweb/grn.htm. CONTACT lnchen@sibs.ac.cn; zpliu@sibs.ac.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/48d1adb8718d51762eca4e89a42ab709a5b09851

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Citations in the corpus

Order-independent constraint-based causal structure learning (2012, methodology): “The PC-algorithm is widely used in high-dimensional settings (Kalisch et al., 2010; Nagarajan et al., 2010; Stekhoven et al., 2012; Zhang et al., 2012), since it is computationally feasible for sparse graphs with up to thousands of variables, and open-source software is available, for example in TET”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, background): “More recently, information theory-based methods utilizing mutual information and conditional mutual information have gained popularity because they are assumption-free and can measure non-linear associations between genes 80 .”
Review of Causal Discovery Methods Based on Graphical Models (2019, methodology): “For instance, the idea of the PC algorithm was adopted to infer causal relationships among phenotypes (Neto et al., 2010), to estimate gene regulatory networks (Zhang et al., 2011b), and to model the isoprenoid gene network in Arabidopsis thaliana (Wille et al., 2004).”