New statistical method identifies cytokines that distinguish stool microbiomes

Abstract Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical...

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Autores principales:	Dake Yang, Jethro Johnson, Xin Zhou, Elena Deych, Berkley Shands, Blake Hanson, Erica Sodergren, George Weinstock, William D. Shannon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/563746d07b9c472e85efc44c58ae80d8
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spelling	oai:doaj.org-article:563746d07b9c472e85efc44c58ae80d82021-12-02T13:34:54ZNew statistical method identifies cytokines that distinguish stool microbiomes10.1038/s41598-019-56397-92045-2322https://doaj.org/article/563746d07b9c472e85efc44c58ae80d82019-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-56397-9https://doaj.org/toc/2045-2322Abstract Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website.Dake YangJethro JohnsonXin ZhouElena DeychBerkley ShandsBlake HansonErica SodergrenGeorge WeinstockWilliam D. ShannonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dake Yang Jethro Johnson Xin Zhou Elena Deych Berkley Shands Blake Hanson Erica Sodergren George Weinstock William D. Shannon New statistical method identifies cytokines that distinguish stool microbiomes
description	Abstract Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website.
format	article
author	Dake Yang Jethro Johnson Xin Zhou Elena Deych Berkley Shands Blake Hanson Erica Sodergren George Weinstock William D. Shannon
author_facet	Dake Yang Jethro Johnson Xin Zhou Elena Deych Berkley Shands Blake Hanson Erica Sodergren George Weinstock William D. Shannon
author_sort	Dake Yang
title	New statistical method identifies cytokines that distinguish stool microbiomes
title_short	New statistical method identifies cytokines that distinguish stool microbiomes
title_full	New statistical method identifies cytokines that distinguish stool microbiomes
title_fullStr	New statistical method identifies cytokines that distinguish stool microbiomes
title_full_unstemmed	New statistical method identifies cytokines that distinguish stool microbiomes
title_sort	new statistical method identifies cytokines that distinguish stool microbiomes
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/563746d07b9c472e85efc44c58ae80d8
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New statistical method identifies cytokines that distinguish stool microbiomes

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