Jewel 2.0: An Improved Joint Estimation Method for Multiple Gaussian Graphical Models

In this paper, we consider the problem of estimating the graphs of conditional dependencies between variables (i.e., graphical models) from multiple datasets under Gaussian settings. We present jewel 2.0, which improves our previous method jewel 1.0 by modeling commonality and class-specific differences in the graph structures and better estimating graphs with hubs, making this new approach more appealing for biological data applications. We introduce these two improvements by modifying the regression-based problem formulation and the corresponding minimization algorithm. We also present, for the first time in the multiple graphs setting, a stability selection procedure to reduce the number of false positives in the estimated graphs. Finally, we illustrate the performance of jewel 2.0 through simulated and real data examples. The method is implemented in the new version of the R package jewel.

Automated summary

This paper addresses the problem of estimating graphical models of conditional dependencies between variables from multiple datasets under Gaussian settings. The proposed jewel 2.0 method improves upon the previous version by modeling commonality and class-specific differences in the graph structures and incorporating a stability selection procedure to reduce false positives. The performance of jewel 2.0 is demonstrated through simulated and real data examples, and the method is implemented in the R package jewel.