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Tailored graphical lasso for data integration in gene network reconstruction
- Source :
- BMC Bioinformatics, Vol 22, Iss 1, Pp 1-22 (2021), BMC Bioinformatics
- Publication Year :
- 2021
- Publisher :
- BMC, 2021.
-
Abstract
- Background Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using $$L_1$$ L 1 -penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. Results We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, . Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein–protein interaction networks. Conclusions The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.
- Subjects :
- Computer science
QH301-705.5
Computer applications to medicine. Medical informatics
Normal Distribution
R858-859.7
Inference
computer.software_genre
Biochemistry
High-dimensional inference
Set (abstract data type)
Lasso (statistics)
Structural Biology
Cancer genomics
Gene Regulatory Networks
Graphical model
Protein Interaction Maps
Biology (General)
Molecular Biology
Network model
Weighted graphical lasso
Network models
Applied Mathematics
Methodology Article
Integrative analysis
Gene networks
Genomics
Covariance
Multiomics
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
Protein–protein interaction networks
Data mining
computer
Graphical lasso
Biological network
Algorithms
Data integration
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 22
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....fa7c92249c2ce1b6d1963d6f7060d4d9