Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control

2020/10/21 by Sai Li, T. Tony Cai, Li, Sai +3 · 14 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (stat.ML) #Methodology (stat.ME) #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.2010.11037

arxiv created 2020/10/21 · openalex publication_date 2020/10/21 · arxiv updated 2020/10/22 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28

Abstract

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.

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