Extended graphical lasso for multiple interaction networks for high dimensional omics data.
PLoS Comput Biol
; 17(10): e1008794, 2021 10.
Article
in English
| MEDLINE | ID: covidwho-1523394
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computational Biology
/
Gene Regulatory Networks
/
Protein Interaction Maps
Limits:
Humans
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pcbi.1008794
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