ArticleBiostatistics (Oxford, England)2019
Latent variable modeling for the microbiome.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed.
- Vaginal microbiota impacts of a Lactobacillus crispatus live biotherapeutic and predictors of colonization in randomized controlled trial.Cell host & microbe · 2026Trial
- LGTM: Gaussian process modulated neural topic modeling for longitudinal microbiome.Gut microbes · 2026Article
- Longitudinal modelling of microbiome subcommunities reveals parity-dependent dynamics during pregnancy and postpartum.Gut microbes · 2026Article
- Paired oral clinical specimens reveal the underlying ecology supporting the emergence of inflammophilic microbiome communities.Microbiology spectrum · 2026Article
- A spectrum of gut microbiota signatures predicts long-term health status.Bioinformatics (Oxford, England) · 2026Article
- From association to causation: a decision-aware framework for reproducible biomarker discovery and precision intervention design in the human gut microbiome.Briefings in bioinformatics · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- Oral microbiome diversity, community- and taxon-level differences by oral human papillomavirus (HPV) and race/ethnicity.Infectious agents and cancer · 2026Article
- Paired oral clinical specimens reveal the underlying ecology supporting the emergence of inflammophilic microbiome communities.bioRxiv : the preprint server for biology · 2026Article
- Latent Dirichlet Allocation reveals tomato root-associated bacterial interactions responding to hairy root disease.Environmental microbiome · 2025Article
- Vaginal Microbiome Composition, Diversity and Dysbiosis: The ORCHiD Study.Research square · 2025Article
- Microbiome data integration via shared dictionary learning.Nature communications · 2025Article
- Microbiota effects and predictors ofmedRxiv : the preprint server for health sciences · 2025Article
- Microbes Under Climate Refugia: Equable Subcommunity Rank Dynamics in Large-River Deltaic Estuaries.Ecology and evolution · 2025Article
- Network analysis of multivariate time series data in biological systems: methods and applications.Briefings in bioinformatics · 2025Article
- Demixer: a probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample.Bioinformatics (Oxford, England) · 2025Article
- Gut microbiota in infants with food protein enterocolitis.Pediatric research · 2025Article
- Variational inference for microbiome survey data with application to global ocean data.ISME communications · 2025Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.Biomedicines · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554-4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.
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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.