Methodology for microbiome data analysis: An overview

It is known that microbiome and health are related, in addition, recent research has found that microbiome has potential clinical uses. These facts highlight the importance of the microbiome in actual science. However, microbiome data has some characteristics that makes its statistical study challen...

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Detalhes bibliográficos
Autores: Creus-Martí, Irene, Moya, Andrés, Santonja, Francisco J.
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2025
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/392900
Acesso em linha:http://hdl.handle.net/10261/392900
Access Level:Acceso aberto
Palavra-chave:Compositional data
Microbiome modeling
Longitudinal data
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spelling Methodology for microbiome data analysis: An overviewCreus-Martí, IreneMoya, AndrésSantonja, Francisco J.Compositional dataMicrobiome modelingLongitudinal dataIt is known that microbiome and health are related, in addition, recent research has found that microbiome has potential clinical uses. These facts highlight the importance of the microbiome in actual science. However, microbiome data has some characteristics that makes its statistical study challenging. In recent years, longitudinal and non-longitudinal methods have been designed to analyze the microbiota and knowing more about the bacterial behavior. In this article in the form of a review we summarize the characteristics of microbiome data and the statistical methods most widespread to analyze it. We have taken into account if the strategies are longitudinal or not. We also classify the methods based on their specific analytical objectives and based on their mathematical characteristics. The methods are structured according to their biological goals and mathematical features, ensuring that the insights provided are both relevant and accessible to professionals in biology and statistics. We present this review as a reference for the most widely used methods in microbiome data analysis and as a foundation for identifying potential areas for future research. We want to point out that this review can be particularly useful to remark the importance of the methodology designed in order to study microbiome longitudinal datasets.This work has been funded by Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), Generalitat Valenciana, Spain (CIPROM/2021/042), and co-financed by the European Regional Development Fund (ERDF) .Peer reviewedElsevierAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)European CommissionGeneralitat ValencianaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/392900reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105969GB-I00https://doi.org/10.1016/j.compbiomed.2025.110157Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3929002026-05-22T06:33:51Z
dc.title.none.fl_str_mv Methodology for microbiome data analysis: An overview
title Methodology for microbiome data analysis: An overview
spellingShingle Methodology for microbiome data analysis: An overview
Creus-Martí, Irene
Compositional data
Microbiome modeling
Longitudinal data
title_short Methodology for microbiome data analysis: An overview
title_full Methodology for microbiome data analysis: An overview
title_fullStr Methodology for microbiome data analysis: An overview
title_full_unstemmed Methodology for microbiome data analysis: An overview
title_sort Methodology for microbiome data analysis: An overview
dc.creator.none.fl_str_mv Creus-Martí, Irene
Moya, Andrés
Santonja, Francisco J.
author Creus-Martí, Irene
author_facet Creus-Martí, Irene
Moya, Andrés
Santonja, Francisco J.
author_role author
author2 Moya, Andrés
Santonja, Francisco J.
author2_role author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Generalitat Valenciana
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Compositional data
Microbiome modeling
Longitudinal data
topic Compositional data
Microbiome modeling
Longitudinal data
description It is known that microbiome and health are related, in addition, recent research has found that microbiome has potential clinical uses. These facts highlight the importance of the microbiome in actual science. However, microbiome data has some characteristics that makes its statistical study challenging. In recent years, longitudinal and non-longitudinal methods have been designed to analyze the microbiota and knowing more about the bacterial behavior. In this article in the form of a review we summarize the characteristics of microbiome data and the statistical methods most widespread to analyze it. We have taken into account if the strategies are longitudinal or not. We also classify the methods based on their specific analytical objectives and based on their mathematical characteristics. The methods are structured according to their biological goals and mathematical features, ensuring that the insights provided are both relevant and accessible to professionals in biology and statistics. We present this review as a reference for the most widely used methods in microbiome data analysis and as a foundation for identifying potential areas for future research. We want to point out that this review can be particularly useful to remark the importance of the methodology designed in order to study microbiome longitudinal datasets.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/392900
url http://hdl.handle.net/10261/392900
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105969GB-I00
https://doi.org/10.1016/j.compbiomed.2025.110157
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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