Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
This work addresses the challenge of improving GGM estimation in genomics by enabling transfer learning, offering incremental gains through a novel method for known bottlenecks.
The paper tackles the problem of estimating high-dimensional Gaussian graphical models (GGMs) by leveraging data from auxiliary studies, proposing Trans-CLIME to achieve faster convergence rates and a debiased estimator for edge detection with false discovery rate control. Results show a significant decrease in prediction errors and increase in power for link detection in gene network inference.
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies. The similarity between the target graph and each auxiliary graph is characterized by the sparsity of a divergence matrix. An estimation algorithm, Trans-CLIME, is proposed and shown to attain a faster convergence rate than the minimax rate in the single study setting. Furthermore, a debiased Trans-CLIME estimator is introduced and shown to be element-wise asymptotically normal. It is used to construct a multiple testing procedure for edge detection with false discovery rate control. The proposed estimation and multiple testing procedures demonstrate superior numerical performance in simulations and are applied to infer the gene networks in a target brain tissue by leveraging the gene expressions from multiple other brain tissues. A significant decrease in prediction errors and a significant increase in power for link detection are observed.