{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T02:53:12Z","timestamp":1781146392810,"version":"3.54.1"},"reference-count":61,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"funder":[{"DOI":"10.13039\/100008902","name":"Los Alamos National Laboratory","doi-asserted-by":"publisher","award":["20200109DR"],"award-info":[{"award-number":["20200109DR"]}],"id":[{"id":"10.13039\/100008902","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011075","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T17:47:25Z","timestamp":1686246445000},"page":"e1011075","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["Latent Dirichlet Allocation modeling of environmental microbiomes"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8702-0754","authenticated-orcid":true,"given":"Anastasiia","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanna","family":"Sevanto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric R.","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Lubbers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"issue":"2","key":"pcbi.1011075.ref001","article-title":"Climate change effects on beneficial plant-microorganism interactions","volume":"73","author":"S Compant","year":"2010","journal-title":"FEMS Microbiol Ecol"},{"issue":"5","key":"pcbi.1011075.ref002","doi-asserted-by":"crossref","DOI":"10.1111\/1574-6976.12028","article-title":"The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms","volume":"37","author":"R Mendes","year":"2013","journal-title":"FEMS Microbiol Rev"},{"issue":"9","key":"pcbi.1011075.ref003","doi-asserted-by":"crossref","DOI":"10.1111\/pbi.12279","article-title":"Understanding and engineering beneficial plant-microbe interactions: plant growth promotion in energy crops","volume":"12","author":"K Farrar","year":"2014","journal-title":"Plant Biotechnol J"},{"key":"pcbi.1011075.ref004","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"DM Blei","year":"2003","journal-title":"Journal of machine Learning research"},{"issue":"4","key":"pcbi.1011075.ref005","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1093\/biostatistics\/kxy018","article-title":"Latent variable modeling for the microbiome","volume":"20","author":"K Sankaran","year":"2019","journal-title":"Biostatistics"},{"issue":"1","key":"pcbi.1011075.ref006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40168-020-00864-3","article-title":"Revealing the microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation","volume":"8","author":"S Hosoda","year":"2020","journal-title":"Microbiome"},{"key":"pcbi.1011075.ref007","doi-asserted-by":"crossref","unstructured":"Santacroce L, Mavaddati S, Hamedi J, Zeinali B, Ballini A, Bilancia M. Expressive Analysis of Gut Microbiota in Pre-and Post-Solid Organ Transplantation Using Bayesian Topic Models. In: International Conference on Computational Science and Its Applications. 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Latent dirichlet allocation for classification using gene expression data. In: 2017 IEEE 17th international conference on bioinformatics and bioengineering (BIBE). 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