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Extended graphical lasso for multiple interaction networks for high dimensional omics data
- Source :
- PLoS Computational Biology, Vol 17, Iss 10 (2021), PLoS Computational Biology, PLoS Computational Biology, Vol 17, Iss 10, p e1008794 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.<br />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.
- 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
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....ec8a6a3ccfe530da27e3508245ab08a8