ArticleBiometrics2017
A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- LGTM: Gaussian process modulated neural topic modeling for longitudinal microbiome.Gut microbes · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- TCVS: tree-guided compositional variable selection analysis of microbiome data.Bioinformatics (Oxford, England) · 2025Article
- Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model.BMC bioinformatics · 2025Article
- Multivariate Poisson lognormal distribution for modeling counts from modern biological data: An overview.Computational and structural biotechnology journal · 2025Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation.Biometrics · 2023Article
- MPrESS: An R-Package for Accurately Predicting Power for Comparisons of 16S rRNA Microbiome Taxa Distributions including Simulation by Dirichlet Mixture Modeling.Microorganisms · 2023Article
- PhyloMed: a phylogeny-based test of mediation effect in microbiome.Genome biology · 2023Article
- Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- DIRICHLET-TREE MULTINOMIAL MIXTURES FOR CLUSTERING MICROBIOME COMPOSITIONS.The annals of applied statistics · 2022Article
- phyloMDA: an R package for phylogeny-aware microbiome data analysis.BMC bioinformatics · 2022Article
- Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.Frontiers in cellular and infection microbiology · 2022Article
- Compositional Data Analysis using Kernels in mass cytometry data.Bioinformatics advances · 2022Article
- tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data.Frontiers in genetics · 2021Article
- MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.BMC bioinformatics · 2020Article
- An empirical Bayes approach to normalization and differential abundance testing for microbiome data.BMC bioinformatics · 2020Article
- Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.Biostatistics (Oxford, England) · 2019Article
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2 authors.
Funding
Abstract
Understanding the factors that alter the composition of the human microbiota may help personalized healthcare strategies and therapeutic drug targets. In many sequencing studies, microbial communities are characterized by a list of taxa, their counts, and their evolutionary relationships represented by a phylogenetic tree. In this article, we consider an extension of the Dirichlet multinomial distribution, called the Dirichlet-tree multinomial distribution, for multivariate, over-dispersed, and tree-structured count data. To address the relationships between these counts and a set of covariates, we propose the Dirichlet-tree multinomial regression model for which we develop a penalized likelihood method for estimating parameters and selecting covariates. For efficient optimization, we adopt the accelerated proximal gradient approach. Simulation studies are presented to demonstrate the good performance of the proposed procedure. An analysis of a data set relating dietary nutrients with bacterial counts is used to show that the incorporation of the tree structure into the model helps increase the prediction power.
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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.