Wenping Deng1, Kui Zhang2, Sanzhen Liu3, Patrick X Zhao4, Shizhong Xu5, Hairong Wei1,6,7. 1. School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA. 2. Department of Mathematics, Michigan Technological University, Houghton, MI, USA. 3. Department of Plant Pathology, Kansas State University, Manhattan, KS, USA. 4. Plant Biology Division, Bioinformatics and Computational Biology Lab, Noble Research Institute, Ardmore, OK, USA. 5. Department of Botany and Plant Sciences, University of California, Riverside, CA, USA. 6. Life Science and Technology Institute, Michigan Technological University, Houghton, MI, USA. 7. Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, P.R. China.
Abstract
Motivation: Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results: In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation: JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results: In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation: JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information: Supplementary data are available at Bioinformatics online.