ArticleJournal of the American Statistical Association2018
Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MODEL-FREE INFERENCE FOR CHARACTERIZING PROTEIN MUTATIONS THROUGH A COEVOLUTIONARY LENS.The annals of applied statistics · 2026Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.Biometrics · 2024Article
- Statistical Inference for High-Dimensional Vector Autoregression with Measurement Error.Statistica Sinica · 2023Article
- LARGE-SCALE MULTIPLE INFERENCE OF COLLECTIVE DEPENDENCE WITH APPLICATIONS TO PROTEIN FUNCTION.The annals of applied statistics · 2021Article
- Simultaneous Covariance Inference for Multimodal Integrative Analysis.Journal of the American Statistical Association · 2020Article
- Differential Markov random field analysis with an application to detecting differential microbial community networks.Biometrika · 2019Article
- Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS.Statistica Sinica · 2019Article
- Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions.Journal of the American Statistical Association · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Making accurate inference for gene regulatory networks, including inferring about pathway by pathway interactions, is an important and difficult task. Motivated by such genomic applications, we consider multiple testing for conditional dependence between subgroups of variables. Under a Gaussian graphical model framework, the problem is translated into simultaneous testing for a collection of submatrices of a high-dimensional precision matrix with each submatrix summarizing the dependence structure between two subgroups of variables. A novel multiple testing procedure is proposed and both theoretical and numerical properties of the procedure are investigated. Asymptotic null distribution of the test statistic for an individual hypothesis is established and the proposed multiple testing procedure is shown to asymptotically control the false discovery rate (FDR) and false discovery proportion (FDP) at the pre-specified level under regularity conditions. Simulations show that the procedure works well in controlling the FDR and has good power in detecting the true interactions. The procedure is applied to a breast cancer gene expression study to identify between pathway interactions.
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