A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance

Sweeny, A.R. orcid.org/0000-0003-4230-171X, Lemon, H., Ibrahim, A. et al. (6 more authors) (2023) A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance. mSystems, 8 (4). e0004023. ISSN 2379-5077

Abstract

Metadata

Item Type: Article
Authors/Creators:
Editors:
  • Hird, S.M.
Copyright, Publisher and Additional Information:

© 2023 Sweeny et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. https://creativecommons.org/licenses/by/4.0/

Keywords: microbiota; metabarcoding; 16S; amplicon sequence variants; generalized linear mixed-effects model; community composition; differential abundance; Bayesian estimation
Dates:
  • Published: 31 August 2023
  • Published (online): 25 July 2023
  • Accepted: 8 May 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield)
Funding Information:
Funder
Grant number
NATURAL ENVIRONMENT RESEARCH COUNCIL
NE/R016801/1
Depositing User: Symplectic Sheffield
Date Deposited: 01 Dec 2023 12:31
Last Modified: 01 Dec 2023 12:31
Status: Published
Publisher: American Society for Microbiology
Refereed: Yes
Identification Number: 10.1128/msystems.00040-23
Related URLs:
Open Archives Initiative ID (OAI ID):

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