ArticlePLoS computational biology2018
Latent environment allocation of microbial community data.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The role of bacteria in wastewater treatment and the impact of treated wastewater on riverine bacterial ecosystems.PloS one · 2026Article
- Latent Dirichlet Allocation reveals tomato root-associated bacterial interactions responding to hairy root disease.Environmental microbiome · 2025Article
- Microbes Under Climate Refugia: Equable Subcommunity Rank Dynamics in Large-River Deltaic Estuaries.Ecology and evolution · 2025Article
- Microbiome analysis of the restricted bacteria in radioactive element-containing water at the Fukushima Daiichi Nuclear Power Station.Applied and environmental microbiology · 2024Article
- Metagenomic Thermometer.DNA research : an international journal for rapid publication of reports on genes and genomes · 2023Article
- Phylogenetic diversity and spatiotemporal dynamics of bacterial and microeukaryotic plankton communities in Gwangyang Bay of the Korean Peninsula.Scientific reports · 2022Article
- Article
- Revealing the microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation.Microbiome · 2020Article
- A Zero-Inflated Latent Dirichlet Allocation Model for Microbiome Studies.Frontiers in genetics · 2020Article
- Article
- Computational Structural Biology: Successes, Future Directions, and Challenges.Molecules (Basel, Switzerland) · 2019Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As data for microbial community structures found in various environments has increased, studies have examined the relationship between environmental labels given to retrieved microbial samples and their community structures. However, because environments continuously change over time and space, mixed states of some environments and its effects on community formation should be considered, instead of evaluating effects of discrete environmental categories. Here we applied a hierarchical Bayesian model to paired datasets containing more than 30,000 samples of microbial community structures and sample description documents. From the training results, we extracted latent environmental topics that associate co-occurring microbes with co-occurring word sets among samples. Topics are the core elements of environmental mixtures and the visualization of topic-based samples clarifies the connections of various environments. Based on the model training results, we developed a web application, LEA (Latent Environment Allocation), which provides the way to evaluate typicality and heterogeneity of microbial communities in newly obtained samples without confining environmental categories to be compared. Because topics link words and microbes, LEA also enables to search samples semantically related to the query out of 30,000 microbiome samples.
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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.