Multi-omics network reconstruction with collaborative graphical lasso
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ID: 320812
2026
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Abstract
Abstract Motivation In recent years, the availability of multi-omics data has increased substantially. Multi-omics data integration methods mainly aim to leverage different molecular layers to gain a complete molecular description of biological processes. An attractive integration approach is the reconstruction of multi-omics networks. However, the development of effective multi-omics network reconstruction strategies lags behind. Results In this study, we introduce collaborative graphical lasso, a novel approach that extends graphical lasso by incorporating collaboration between omics layers, thereby improving multi-omics data integration and enhancing network inference. Our method leverages a collaborative penalty term, which harmonizes the contribution of the omics layers to the reconstruction of the network structure. This promotes a cohesive integration of information across modalities, and it is introduced alongside a dual regularization scheme that separately controls sparsity within and between layers. To address the challenge of model selection in this framework, we propose XStARS, a stability-based criterion for multi-dimensional hyperparameter tuning. We assess the performance of collaborative graphical lasso and the corresponding model selection procedure through simulations, and we apply them to publicly available multi-omics data. This application demonstrated collaborative graphical lasso recovers established biological interactions while suggesting novel, biologically coherent connections. Availability and implementation We implemented collaborative graphical lasso as an R package, available on CRAN as coglasso. The results of the manuscript can be reproduced running the code available at https://github.com/DrQuestion/coglasso_reproducible_code, deposited on figshare with DOI: https://doi.org/10.6084/m9.figshare.32324376. Supplementary information Supplementary data are available at Bioinformatics online.
| Reference Key |
openalex_W7168187707
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|---|---|
| Authors | Alessio Albanese, Wouter Kohlen, Pariya Behrouzi |
| Journal | BMC Bioinformatics |
| Year | 2026 |
| DOI |
10.1093/bioinformatics/btag477
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| URL | |
| Keywords | Keywords not found |
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