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Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model

Recent human microbiome studies have revealed an essential role of the human microbiome in health and disease, opening up the possibility of building microbiome-based predictive models for individualized medicine. One unique characteristic of microbiome data is the existence of a phylogenetic tree t...

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Autores principales: Xiao, Jian, Chen, Li, Johnson, Stephen, Yu, Yue, Zhang, Xianyang, Chen, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030386/
https://www.ncbi.nlm.nih.gov/pubmed/29997602
http://dx.doi.org/10.3389/fmicb.2018.01391
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author Xiao, Jian
Chen, Li
Johnson, Stephen
Yu, Yue
Zhang, Xianyang
Chen, Jun
author_facet Xiao, Jian
Chen, Li
Johnson, Stephen
Yu, Yue
Zhang, Xianyang
Chen, Jun
author_sort Xiao, Jian
collection PubMed
description Recent human microbiome studies have revealed an essential role of the human microbiome in health and disease, opening up the possibility of building microbiome-based predictive models for individualized medicine. One unique characteristic of microbiome data is the existence of a phylogenetic tree that relates all the microbial species. It has frequently been observed that a cluster or clusters of bacteria at varying phylogenetic depths are associated with some clinical or biological outcome due to shared biological function (clustered signal). Moreover, in many cases, we observe a community-level change, where a large number of functionally interdependent species are associated with the outcome (dense signal). We thus develop “glmmTree,” a prediction method based on a generalized linear mixed model framework, for capturing clustered and dense microbiome signals. glmmTree uses the similarity between microbiomes, which is defined based on the microbiome composition and the phylogenetic tree, to predict the outcome. The effects of other predictive variables (e.g., age, sex) can be incorporated readily in the regression framework. Additional tuning parameters enable a data-adaptive approach to capture signals at different phylogenetic depth and abundance level. Simulation studies and real data applications demonstrated that “glmmTree” outperformed existing methods in the dense and clustered signal scenarios.
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spelling pubmed-60303862018-07-11 Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model Xiao, Jian Chen, Li Johnson, Stephen Yu, Yue Zhang, Xianyang Chen, Jun Front Microbiol Microbiology Recent human microbiome studies have revealed an essential role of the human microbiome in health and disease, opening up the possibility of building microbiome-based predictive models for individualized medicine. One unique characteristic of microbiome data is the existence of a phylogenetic tree that relates all the microbial species. It has frequently been observed that a cluster or clusters of bacteria at varying phylogenetic depths are associated with some clinical or biological outcome due to shared biological function (clustered signal). Moreover, in many cases, we observe a community-level change, where a large number of functionally interdependent species are associated with the outcome (dense signal). We thus develop “glmmTree,” a prediction method based on a generalized linear mixed model framework, for capturing clustered and dense microbiome signals. glmmTree uses the similarity between microbiomes, which is defined based on the microbiome composition and the phylogenetic tree, to predict the outcome. The effects of other predictive variables (e.g., age, sex) can be incorporated readily in the regression framework. Additional tuning parameters enable a data-adaptive approach to capture signals at different phylogenetic depth and abundance level. Simulation studies and real data applications demonstrated that “glmmTree” outperformed existing methods in the dense and clustered signal scenarios. Frontiers Media S.A. 2018-06-27 /pmc/articles/PMC6030386/ /pubmed/29997602 http://dx.doi.org/10.3389/fmicb.2018.01391 Text en Copyright © 2018 Xiao, Chen, Johnson, Yu, Zhang and Chen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Xiao, Jian
Chen, Li
Johnson, Stephen
Yu, Yue
Zhang, Xianyang
Chen, Jun
Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model
title Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model
title_full Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model
title_fullStr Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model
title_full_unstemmed Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model
title_short Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model
title_sort predictive modeling of microbiome data using a phylogeny-regularized generalized linear mixed model
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030386/
https://www.ncbi.nlm.nih.gov/pubmed/29997602
http://dx.doi.org/10.3389/fmicb.2018.01391
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