NETWORK DIFFERENTIAL CONNECTIVITY ANALYSIS

• 成果类型: Article
• 署名作者: Zhao, Sen; Ali, Shojaie
• 署名单位: Alphabet Inc.; Google Incorporated; University of Washington; University of Washington Seattle
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1581
• 发表日期: 2022
• 页码: 2166-2182
• 关键词: inverse covariance estimation; Post-selection Inference; breast-cancer; confidence-intervals; variable selection; model-selection; least-squares; regions; regression; receptor

摘要

Identifying differences in networks has become a canonical problem in many biological applications. Existing methods try to accomplish this goal by either directly comparing the estimated structures of two networks or test-ing the null hypothesis that the covariance or inverse covariance matrices in two populations are identical. However, estimation approaches do not pro-vide measures of uncertainty, for example, p-values, whereas existing testing approaches could lead to misleading results, as we illustrate in this paper. To address these shortcomings, we propose a qualitative hypothesis testing framework which tests whether the connectivity structures in the two net-works are the same. Our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing ap-proach could test such hypotheses and provide corresponding measures of uncertainty. Theoretically, we show that, under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hy-pothesis. Empirically, we demonstrate the performance of our proposal using simulation studies and applications in cancer genomics.