ArticleBMC bioinformatics2020
MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Microbiome mediation analysis: methods, assumptions, and practical considerations.Frontiers in cellular and infection microbiology · 2026Review
- Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model.BMC bioinformatics · 2025Article
- Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.eLife · 2024Review
- phyloMDA: an R package for phylogeny-aware microbiome data analysis.BMC bioinformatics · 2022Article
- Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Correction to: MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.BMC bioinformatics · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding the relation between the human microbiome and modulating factors, such as diet, may help researchers design intervention strategies that promote and maintain healthy microbial communities. Numerous analytical tools are available to help identify these relations, oftentimes via automated variable selection methods. However, available tools frequently ignore evolutionary relations among microbial taxa, potential relations between modulating factors, as well as model selection uncertainty.
resultsWe present MicroBVS, an R package for Dirichlet-tree multinomial models with Bayesian variable selection, for the identification of covariates associated with microbial taxa abundance data. The underlying Bayesian model accommodates phylogenetic structure in the abundance data and various parameterizations of covariates' prior probabilities of inclusion.
conclusionWhile developed to study the human microbiome, our software can be employed in various research applications, where the aim is to generate insights into the relations between a set of covariates and compositional data with or without a known tree-like structure.
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