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ENIGMA: An Enterotype-Like Unigram Mixture Model for Microbial Association Analysis

Ko Abe, Masaaki Hirayama, Kinji Ohno, Teppei Shimamura
doi: https://doi.org/10.1101/397091
Ko Abe
1Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, 466-8550 Nagoya, Japan.
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Masaaki Hirayama
2School of Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, 461-8873 Nagoya, Japan.
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Kinji Ohno
3Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, 466-8550 Nagoya, Japan.
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Teppei Shimamura
4Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, 466-8550 Nagoya, Japan.
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  • For correspondence: shimamura@med.nagoya-u.ac.jp
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Abstract

Background One of the major challenges in microbial studies is to discover associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities have clusters reffered as enterotype characterized by differences in specific bacterial taxa, which makes it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish dysbiosis of interest with the individual differences.

Results We propose a new probabilistic model, called ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), to address these problems. ENIGMA enables us to simultaneously estimate enterotype-like clusters characterized by the abundances of signature bacterial genera and environmental effects associated with the disease.

Conclusion We illustrate the performance of the proposed method both through the simulation and clinical data analysis. ENIGMA is implemented with R and is available from GitHub (https://github.com/abikoushi/enigma).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted August 21, 2018.
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ENIGMA: An Enterotype-Like Unigram Mixture Model for Microbial Association Analysis
Ko Abe, Masaaki Hirayama, Kinji Ohno, Teppei Shimamura
bioRxiv 397091; doi: https://doi.org/10.1101/397091
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ENIGMA: An Enterotype-Like Unigram Mixture Model for Microbial Association Analysis
Ko Abe, Masaaki Hirayama, Kinji Ohno, Teppei Shimamura
bioRxiv 397091; doi: https://doi.org/10.1101/397091

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