1. Extended graphical lasso for multiple interaction networks for high dimensional omics data
- Author
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Hongmei Jiang, Wenxin Jiang, and Yang Xu
- Subjects
Proteomics ,Optimization problem ,Computer science ,Interaction Networks ,Gene Identification and Analysis ,Genetic Networks ,computer.software_genre ,Biochemistry ,Omics data ,Database and Informatics Methods ,Lasso (statistics) ,Databases, Genetic ,Medicine and Health Sciences ,Gene Regulatory Networks ,Protein Interaction Maps ,Biology (General) ,Covariance ,Ecology ,Proteomic Databases ,Microbiota ,Medical research ,Computational Theory and Mathematics ,Modeling and Simulation ,Physical Sciences ,Protein Interaction Networks ,Data mining ,Scale-Free Networks ,Network Analysis ,Algorithms ,Research Article ,Network analysis ,Computer and Information Sciences ,QH301-705.5 ,Association (object-oriented programming) ,Gastroenterology and Hepatology ,High dimensional ,Research and Analysis Methods ,Cellular and Molecular Neuroscience ,Genetics ,Humans ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Inflammatory Bowel Disease ,Regular polygon ,Biology and Life Sciences ,Computational Biology ,Random Variables ,Construct (python library) ,Probability Theory ,Biological Databases ,Transcriptome ,computer ,Mathematics - Abstract
There has been a spate of interest in association networks in biological and medical research, for example, genetic interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple related interaction networks for high dimensional omics data across multiple distinct classes. To be specific, we construct a convex penalized log likelihood optimization problem and solve it with an alternating direction method of multipliers (ADMM) algorithm. The proposed method can also be adapted to estimate interaction networks for high dimensional compositional data such as microbial interaction networks. The performance of the proposed method in the simulated studies shows that EDOHA has remarkable advantages in recognizing class-specific hubs than the existing comparable methods. We also present three applications of real datasets. Biological interpretations of our results confirm those of previous studies and offer a more comprehensive understanding of the underlying mechanism in disease., Author summary Reconstruction of multiple association networks from high dimensional omics data is an important topic, especially in biology. Previous studies focused on estimating different networks and detecting common hubs among all classes. Integration of information over different classes of data while allowing the difference in the hub nodes is also biologically plausible. Therefore, we propose a method, EDOHA, to jointly construct multiple interaction networks with the capacity in finding different hub networks for each class of data. Simulation studies show better performance over conventional methods. The method has been demonstrated in three real-world data.
- Published
- 2021