Latent Dirichlet Allocation modeling of environmental microbiomes

Kim, Anastasiia; Sevanto, Sanna; Moore, Eric R.; Lubbers, Nicholas; Zeller, ed., Georg

doi:10.1371/journal.pcbi.1011075

Latent Dirichlet Allocation modeling of environmental microbiomes

Journal Article · Thu Jun 08 00:00:00 EDT 2023 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1011075· OSTI ID:1984309

; Sevanto, Sanna; Moore, Eric R.; Lubbers, Nicholas; Zeller, ed., Georg

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1984309

Alternate ID(s):: OSTI ID: 1987393

Report Number(s):: LA-UR-21-31781; 10.1371/journal.pcbi.1011075

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 6 Vol. 19; ISSN 1553-7358

Publisher:: Public Library of Science (PLoS)Copyright Statement

Country of Publication:: United States

Language:: English

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Latent Dirichlet Allocation modeling of environmental microbiomes

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