ArticleBiometrika2019
Differential Markov random field analysis with an application to detecting differential microbial community networks.
Article in Biometrika, 2019. 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.
- Article
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields.Bioinformatics advances · 2024Article
- A Unified Bayesian Framework for Bi-overlapping-Clustering Multi-omics Data via Sparse Matrix Factorization.Statistics in biosciences · 2023Article
- Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization.Biostatistics (Oxford, England) · 2022Article
- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.Journal of machine learning research : JMLR · 2021Article
- Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models.Journal of the American Statistical Association · 2021Article
- Differential Network Analysis: A Statistical Perspective.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Micro-organisms such as bacteria form complex ecological community networks that can be greatly influenced by diet and other environmental factors. Differential analysis of microbial community structures aims to elucidate systematic changes during an adaptive response to changes in environment. In this paper, we propose a flexible Markov random field model for microbial network structure and introduce a hypothesis testing framework for detecting differences between networks, also known as differential network analysis. Our global test for differential networks is particularly powerful against sparse alternatives. In addition, we develop a multiple testing procedure with false discovery rate control to identify the structure of the differential network. The proposed method is applied to data from a gut microbiome study on U.K. twins to evaluate how age affects the microbial community network.
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Registered trials
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