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Revealing microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

View ORCID ProfileShion Hosoda, View ORCID ProfileSuguru Nishijima, View ORCID ProfileTsukasa Fukunaga, Masahira Hattori, View ORCID ProfileMichiaki Hamada
doi: https://doi.org/10.1101/664219
Shion Hosoda
1Graduate School of Advanced Science and Engineering, Waseda University
2Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST)
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Suguru Nishijima
1Graduate School of Advanced Science and Engineering, Waseda University
2Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST)
3Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
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Tsukasa Fukunaga
1Graduate School of Advanced Science and Engineering, Waseda University
4Department of Computer Science, Graduate School of Information Science and Engineering, The University of Tokyo
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Masahira Hattori
1Graduate School of Advanced Science and Engineering, Waseda University
3Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
5RIKEN Center for Integrative Medical Sciences
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Michiaki Hamada
1Graduate School of Advanced Science and Engineering, Waseda University
2Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST)
6Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST)
7Graduate School of Medicine, Nippon Medical School
8Center for Data Science, Waseda University
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  • For correspondence: mhamada@waseda.jp
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Abstract

Recent research has revealed that there are various microbial species in the human gut microbiome. To clarify the structure of the human gut microbiome, many data mining methods have been applied to microbial composition data. Cluster analysis, one of the key data mining methods that have been used in human gut microbiome research, can classify the human gut microbiome into three clusters, called enterotypes. The human gut microbiome has been suggested to be composed of the microbial assemblages or groups of co-occurring microbes, and one human gut microbiome can contain several microbial assemblages. However, cluster analysis can cluster samples into groups without capturing minor assemblages. In addition, a reliable method of assemblage detection has not been established, and little is known about the distributions of microbial assemblages at a population-level scale. Accordingly, the purpose of this study was to clarify the microbial assemblages in the human gut microbiome. In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method, which was first proposed for the classification of documents in natural language processing. We applied LDA to a large-scale human gut metagenome dataset and found that a four-assemblage LDA model can represent relationships between enterotypes and assemblages with high interpretability. This model indicates that each individual tends to have several assemblages, and each of three assemblages corresponded to each enterotype. However, the C-assemblage can exist in all enterotypes. Interestingly, the dominant genera of the C-assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Finally, we revealed that genera mainly appearing in the same assemblage were correlated to each other. We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA, a powerful method for detection of microbial assemblages. This approach has the potential to reveal the structure of the human gut microbiome.

Footnotes

  • ↵* shion_hosoda{at}asagi.waseda.jp

  • List of abbreviations

    LDA
    latent Dirichlet allocation
    IBD
    inflammatory bowel disease
    PAM
    partitioning around medoids
    DMM
    Dirichlet multinomial mixture
    VB
    variational Bayes
    VLB
    variational lower-bound
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    Posted June 12, 2019.
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    Revealing microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation
    Shion Hosoda, Suguru Nishijima, Tsukasa Fukunaga, Masahira Hattori, Michiaki Hamada
    bioRxiv 664219; doi: https://doi.org/10.1101/664219
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    Revealing microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation
    Shion Hosoda, Suguru Nishijima, Tsukasa Fukunaga, Masahira Hattori, Michiaki Hamada
    bioRxiv 664219; doi: https://doi.org/10.1101/664219

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