Statistical challenges in longitudinal microbiome data analysis.
16S
clustering
compositionality
differential abundance
networks
relative abundance
shotgun sequencing
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
18 07 2022
18 07 2022
Historique:
received:
02
04
2022
revised:
28
05
2022
accepted:
12
06
2022
pubmed:
14
7
2022
medline:
22
7
2022
entrez:
13
7
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35830875
pii: 6643459
doi: 10.1093/bib/bbac273
pmc: PMC9294433
pii:
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press.
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