ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2022
Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Evaluating microbial network inference methods: moving beyond synthetic data with reproducibility-driven benchmarks.BMC bioinformatics · 2026Article
- A modeling framework for detecting and leveraging node-level information in Bayesian network inference.Biostatistics (Oxford, England) · 2025Article
- HMFGraph: Novel Bayesian approach for recovering biological networks.PLoS computational biology · 2025Article
- Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model.BMC bioinformatics · 2025Article
- An identification method of LBL underwater positioning systematic error with optimal selection criterion.Scientific reports · 2024Article
- TARO: tree-aggregated factor regression for microbiome data integration.Bioinformatics (Oxford, England) · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Cultural transmission of move choice in chess.Proceedings. Biological sciences · 2023Article
- Super-taxon in human microbiome are identified to be associated with colorectal cancer.BMC bioinformatics · 2022Article
- mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.Genome biology · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a novel variational inference scheme with an expectation-maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute too many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We call our algorithm simultaneous inference for networks and covariates and provide a Python implementation, which is available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.