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dc.contributor.authorWadsworth, W. Duncan
Argiento, Raffaele
Guindani, Michele
Galloway-Pena, Jessica
Shelbourne, Samuel A.
Vannucci, Marina
dc.date.accessioned 2017-02-08T17:04:06Z
dc.date.available 2017-02-08T17:04:06Z
dc.date.issued 2017
dc.identifier.citation Wadsworth, W. Duncan, Argiento, Raffaele, Guindani, Michele, et al.. "An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data." BMC Bioinformatics, (2017) BioMed Central: http://dx.doi.org/10.1186/s12859-017-1516-0.
dc.identifier.urihttps://hdl.handle.net/1911/93864
dc.description.abstract Abstract Background The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.
dc.publisher BioMed Central
dc.rightsThis 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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data
dc.type Journal article
dc.citation.journalTitle BMC Bioinformatics
dc.date.updated 2017-02-08T17:04:06Z
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1186/s12859-017-1516-0
dc.language.rfc3066 en
dc.type.publication publisher version
dcterms.bibliographicCitation BMC Bioinformatics. 2017 Feb 08;18(1):94
dc.rights.holder The Author(s)
local.sword.agent BioMed Central


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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.
Except where otherwise noted, this item's license is described as 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.