Balances: a New Perspective for Microbiome Analysis

ABSTRACT High-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. In this work we focus on the identification of microbial signatures, groups of microbial taxa that are pr...

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Autores principales:	J. Rivera-Pinto, J. J. Egozcue, V. Pawlowsky-Glahn, R. Paredes, M. Noguera-Julian, M. L. Calle
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2018
Materias:	balances compositional data microbiome Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/46533789d5c041aaa392ea077f5b55ee
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spelling	oai:doaj.org-article:46533789d5c041aaa392ea077f5b55ee2021-12-02T18:39:46ZBalances: a New Perspective for Microbiome Analysis10.1128/mSystems.00053-182379-5077https://doaj.org/article/46533789d5c041aaa392ea077f5b55ee2018-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00053-18https://doaj.org/toc/2379-5077ABSTRACT High-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. In this work we focus on the identification of microbial signatures, groups of microbial taxa that are predictive of a phenotype of interest. We do this by acknowledging the compositional nature of the microbiome and the fact that it carries relative information. Thus, instead of defining a microbial signature as a linear combination in real space corresponding to the abundances of a group of taxa, we consider microbial signatures given by the geometric means of data from two groups of taxa whose relative abundances, or balance, are associated with the response variable of interest. In this work we present selbal, a greedy stepwise algorithm for selection of balances or microbial signatures that preserves the principles of compositional data analysis. We illustrate the algorithm with 16S rRNA abundance data from a Crohn’s microbiome study and an HIV microbiome study. IMPORTANCE We propose a new algorithm for the identification of microbial signatures. These microbial signatures can be used for diagnosis, prognosis, or prediction of therapeutic response based on an individual’s specific microbiota.J. Rivera-PintoJ. J. EgozcueV. Pawlowsky-GlahnR. ParedesM. Noguera-JulianM. L. CalleAmerican Society for Microbiologyarticlebalancescompositional datamicrobiomeMicrobiologyQR1-502ENmSystems, Vol 3, Iss 4 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	balances compositional data microbiome Microbiology QR1-502
spellingShingle	balances compositional data microbiome Microbiology QR1-502 J. Rivera-Pinto J. J. Egozcue V. Pawlowsky-Glahn R. Paredes M. Noguera-Julian M. L. Calle Balances: a New Perspective for Microbiome Analysis
description	ABSTRACT High-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. In this work we focus on the identification of microbial signatures, groups of microbial taxa that are predictive of a phenotype of interest. We do this by acknowledging the compositional nature of the microbiome and the fact that it carries relative information. Thus, instead of defining a microbial signature as a linear combination in real space corresponding to the abundances of a group of taxa, we consider microbial signatures given by the geometric means of data from two groups of taxa whose relative abundances, or balance, are associated with the response variable of interest. In this work we present selbal, a greedy stepwise algorithm for selection of balances or microbial signatures that preserves the principles of compositional data analysis. We illustrate the algorithm with 16S rRNA abundance data from a Crohn’s microbiome study and an HIV microbiome study. IMPORTANCE We propose a new algorithm for the identification of microbial signatures. These microbial signatures can be used for diagnosis, prognosis, or prediction of therapeutic response based on an individual’s specific microbiota.
format	article
author	J. Rivera-Pinto J. J. Egozcue V. Pawlowsky-Glahn R. Paredes M. Noguera-Julian M. L. Calle
author_facet	J. Rivera-Pinto J. J. Egozcue V. Pawlowsky-Glahn R. Paredes M. Noguera-Julian M. L. Calle
author_sort	J. Rivera-Pinto
title	Balances: a New Perspective for Microbiome Analysis
title_short	Balances: a New Perspective for Microbiome Analysis
title_full	Balances: a New Perspective for Microbiome Analysis
title_fullStr	Balances: a New Perspective for Microbiome Analysis
title_full_unstemmed	Balances: a New Perspective for Microbiome Analysis
title_sort	balances: a new perspective for microbiome analysis
publisher	American Society for Microbiology
publishDate	2018
url	https://doaj.org/article/46533789d5c041aaa392ea077f5b55ee
work_keys_str_mv	AT jriverapinto balancesanewperspectiveformicrobiomeanalysis AT jjegozcue balancesanewperspectiveformicrobiomeanalysis AT vpawlowskyglahn balancesanewperspectiveformicrobiomeanalysis AT rparedes balancesanewperspectiveformicrobiomeanalysis AT mnoguerajulian balancesanewperspectiveformicrobiomeanalysis AT mlcalle balancesanewperspectiveformicrobiomeanalysis
_version_	1718377736176664576

Balances: a New Perspective for Microbiome Analysis

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