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Statistical challenges in longitudinal microbiome data analysis

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, c...

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Detalles Bibliográficos
Autores principales: Kodikara, Saritha, Ellul, Susan, Lê Cao, Kim-Anh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294433/
https://www.ncbi.nlm.nih.gov/pubmed/35830875
http://dx.doi.org/10.1093/bib/bbac273
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author Kodikara, Saritha
Ellul, Susan
Lê Cao, Kim-Anh
author_facet Kodikara, Saritha
Ellul, Susan
Lê Cao, Kim-Anh
author_sort Kodikara, Saritha
collection PubMed
description The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
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spelling pubmed-92944332022-07-20 Statistical challenges in longitudinal microbiome data analysis Kodikara, Saritha Ellul, Susan Lê Cao, Kim-Anh Brief Bioinform Review The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review. Oxford University Press 2022-07-14 /pmc/articles/PMC9294433/ /pubmed/35830875 http://dx.doi.org/10.1093/bib/bbac273 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Kodikara, Saritha
Ellul, Susan
Lê Cao, Kim-Anh
Statistical challenges in longitudinal microbiome data analysis
title Statistical challenges in longitudinal microbiome data analysis
title_full Statistical challenges in longitudinal microbiome data analysis
title_fullStr Statistical challenges in longitudinal microbiome data analysis
title_full_unstemmed Statistical challenges in longitudinal microbiome data analysis
title_short Statistical challenges in longitudinal microbiome data analysis
title_sort statistical challenges in longitudinal microbiome data analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294433/
https://www.ncbi.nlm.nih.gov/pubmed/35830875
http://dx.doi.org/10.1093/bib/bbac273
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