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A hierarchical Bayesian approach for detecting global microbiome associations

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A hierarchical Bayesian approach for detecting global microbiome associations. / Hatami, F.; Beamish, E.; Davies, A. et al.
In: Statistical Applications in Genetics and Molecular Biology, Vol. 20, No. 3, 01.11.2021, p. 85-100.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hatami, F, Beamish, E, Davies, A, Rigby, R & Dondelinger, F 2021, 'A hierarchical Bayesian approach for detecting global microbiome associations', Statistical Applications in Genetics and Molecular Biology, vol. 20, no. 3, pp. 85-100. https://doi.org/10.1515/sagmb-2021-0047

APA

Hatami, F., Beamish, E., Davies, A., Rigby, R., & Dondelinger, F. (2021). A hierarchical Bayesian approach for detecting global microbiome associations. Statistical Applications in Genetics and Molecular Biology, 20(3), 85-100. https://doi.org/10.1515/sagmb-2021-0047

Vancouver

Hatami F, Beamish E, Davies A, Rigby R, Dondelinger F. A hierarchical Bayesian approach for detecting global microbiome associations. Statistical Applications in Genetics and Molecular Biology. 2021 Nov 1;20(3):85-100. doi: 10.1515/sagmb-2021-0047

Author

Hatami, F. ; Beamish, E. ; Davies, A. et al. / A hierarchical Bayesian approach for detecting global microbiome associations. In: Statistical Applications in Genetics and Molecular Biology. 2021 ; Vol. 20, No. 3. pp. 85-100.

Bibtex

@article{396a30c93bc94f1c992d3540d7cc5bb8,
title = "A hierarchical Bayesian approach for detecting global microbiome associations",
abstract = "The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current approaches for detecting microbiome associations are limited by relying on specific measures of ecological distance, or only allowing for the detection of associations with individual bacterial species, rather than the whole microbiome. In this work, we develop a novel hierarchical Bayesian model for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We perform extensive simulation studies and show that our method allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on two real-world microbiome studies: a study of microbiome-metabolome associations in inflammatory bowel disease, and a study of associations between diet and the gut microbiome in mice. We show that we can use the method to reliably detect associations in real-world datasets with varying numbers of samples and covariates. ",
keywords = "Bayesian modeling, global effects, microbiome",
author = "F. Hatami and E. Beamish and A. Davies and R. Rigby and F. Dondelinger",
year = "2021",
month = nov,
day = "1",
doi = "10.1515/sagmb-2021-0047",
language = "English",
volume = "20",
pages = "85--100",
journal = "Statistical Applications in Genetics and Molecular Biology",
issn = "2194-6302",
publisher = "Berkeley Electronic Press",
number = "3",

}

RIS

TY - JOUR

T1 - A hierarchical Bayesian approach for detecting global microbiome associations

AU - Hatami, F.

AU - Beamish, E.

AU - Davies, A.

AU - Rigby, R.

AU - Dondelinger, F.

PY - 2021/11/1

Y1 - 2021/11/1

N2 - The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current approaches for detecting microbiome associations are limited by relying on specific measures of ecological distance, or only allowing for the detection of associations with individual bacterial species, rather than the whole microbiome. In this work, we develop a novel hierarchical Bayesian model for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We perform extensive simulation studies and show that our method allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on two real-world microbiome studies: a study of microbiome-metabolome associations in inflammatory bowel disease, and a study of associations between diet and the gut microbiome in mice. We show that we can use the method to reliably detect associations in real-world datasets with varying numbers of samples and covariates.

AB - The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current approaches for detecting microbiome associations are limited by relying on specific measures of ecological distance, or only allowing for the detection of associations with individual bacterial species, rather than the whole microbiome. In this work, we develop a novel hierarchical Bayesian model for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We perform extensive simulation studies and show that our method allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on two real-world microbiome studies: a study of microbiome-metabolome associations in inflammatory bowel disease, and a study of associations between diet and the gut microbiome in mice. We show that we can use the method to reliably detect associations in real-world datasets with varying numbers of samples and covariates.

KW - Bayesian modeling

KW - global effects

KW - microbiome

U2 - 10.1515/sagmb-2021-0047

DO - 10.1515/sagmb-2021-0047

M3 - Journal article

VL - 20

SP - 85

EP - 100

JO - Statistical Applications in Genetics and Molecular Biology

JF - Statistical Applications in Genetics and Molecular Biology

SN - 2194-6302

IS - 3

ER -