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

翻译：动机：近年来，多组学数据的可获得性大幅提升。多组学数据整合方法主要旨在利用不同的分子层面，以获得对生物过程的完整分子描述。一种颇具吸引力的整合方法是重建多组学网络。然而，有效的多组学网络重建策略的发展相对滞后。结果：在本研究中，我们提出了协作图套索，这是一种通过纳入组学层之间的协作来扩展图套索的新方法，从而改进了多组学数据整合并增强了网络推断。我们的方法利用了一个协作惩罚项，该惩罚项协调了各组学层对网络结构重建的贡献。这促进了跨模态信息的凝聚性整合，并且该方法与一个双重正则化方案一同引入，该方案分别控制层内和层间的稀疏性。为了应对此框架中模型选择的挑战，我们提出了XStARS，这是一种基于稳定性的多维超参数调优准则。我们通过模拟评估了协作图套索及其相应模型选择程序的性能，并将其应用于公开可用的多组学数据。该应用表明，协作图套索能够恢复已知的生物学相互作用，同时提示了新颖的、生物学上一致的关联。可用性与实现：我们将协作图套索实现为一个R包，在CRAN上以coglasso名称提供。通过运行https://github.com/DrQuestion/coglasso_reproducible_code 上提供的代码，可以重现手稿中的结果。