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ENIGMA: an enterotype-like unigram mixture model for microbial association analysis
BACKGROUND: One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as “enterotype”, which are characterized by differ...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456936/ https://www.ncbi.nlm.nih.gov/pubmed/30967109 http://dx.doi.org/10.1186/s12864-019-5476-9 |
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author | Abe, Ko Hirayama, Masaaki Ohno, Kinji Shimamura, Teppei |
author_facet | Abe, Ko Hirayama, Masaaki Ohno, Kinji Shimamura, Teppei |
author_sort | Abe, Ko |
collection | PubMed |
description | BACKGROUND: One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as “enterotype”, which are characterized by differences in specific bacterial taxa, making it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish between the bacterial differences derived from enterotype and those related to a specific disease. RESULTS: We propose a new probabilistic model, named as ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), which can be used to address these problems. ENIGMA enabled simultaneous estimation of enterotype-like clusters characterized by the abundances of signature bacterial genera and the parameters of environmental effects associated with the disease. CONCLUSION: In the simulation study, we evaluated the accuracy of parameter estimation. Furthermore, by analyzing the real-world data, we detected the bacteria related to Parkinson’s disease. ENIGMA is implemented in R and is available from GitHub (https://github.com/abikoushi/enigma). |
format | Online Article Text |
id | pubmed-6456936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64569362019-04-19 ENIGMA: an enterotype-like unigram mixture model for microbial association analysis Abe, Ko Hirayama, Masaaki Ohno, Kinji Shimamura, Teppei BMC Genomics Research BACKGROUND: One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as “enterotype”, which are characterized by differences in specific bacterial taxa, making it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish between the bacterial differences derived from enterotype and those related to a specific disease. RESULTS: We propose a new probabilistic model, named as ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), which can be used to address these problems. ENIGMA enabled simultaneous estimation of enterotype-like clusters characterized by the abundances of signature bacterial genera and the parameters of environmental effects associated with the disease. CONCLUSION: In the simulation study, we evaluated the accuracy of parameter estimation. Furthermore, by analyzing the real-world data, we detected the bacteria related to Parkinson’s disease. ENIGMA is implemented in R and is available from GitHub (https://github.com/abikoushi/enigma). BioMed Central 2019-04-04 /pmc/articles/PMC6456936/ /pubmed/30967109 http://dx.doi.org/10.1186/s12864-019-5476-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Abe, Ko Hirayama, Masaaki Ohno, Kinji Shimamura, Teppei ENIGMA: an enterotype-like unigram mixture model for microbial association analysis |
title | ENIGMA: an enterotype-like unigram mixture model for microbial association analysis |
title_full | ENIGMA: an enterotype-like unigram mixture model for microbial association analysis |
title_fullStr | ENIGMA: an enterotype-like unigram mixture model for microbial association analysis |
title_full_unstemmed | ENIGMA: an enterotype-like unigram mixture model for microbial association analysis |
title_short | ENIGMA: an enterotype-like unigram mixture model for microbial association analysis |
title_sort | enigma: an enterotype-like unigram mixture model for microbial association analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456936/ https://www.ncbi.nlm.nih.gov/pubmed/30967109 http://dx.doi.org/10.1186/s12864-019-5476-9 |
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