Cargando…
Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome
Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling poi...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192422/ https://www.ncbi.nlm.nih.gov/pubmed/37198426 http://dx.doi.org/10.1038/s41598-023-34713-8 |
_version_ | 1785043625296527360 |
---|---|
author | Benincà, Elisa Pinto, Susanne Cazelles, Bernard Fuentes, Susana Shetty, Sudarshan Bogaards, Johannes A. |
author_facet | Benincà, Elisa Pinto, Susanne Cazelles, Bernard Fuentes, Susana Shetty, Sudarshan Bogaards, Johannes A. |
author_sort | Benincà, Elisa |
collection | PubMed |
description | Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods. |
format | Online Article Text |
id | pubmed-10192422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101924222023-05-19 Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome Benincà, Elisa Pinto, Susanne Cazelles, Bernard Fuentes, Susana Shetty, Sudarshan Bogaards, Johannes A. Sci Rep Article Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods. Nature Publishing Group UK 2023-05-17 /pmc/articles/PMC10192422/ /pubmed/37198426 http://dx.doi.org/10.1038/s41598-023-34713-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Benincà, Elisa Pinto, Susanne Cazelles, Bernard Fuentes, Susana Shetty, Sudarshan Bogaards, Johannes A. Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
title | Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
title_full | Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
title_fullStr | Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
title_full_unstemmed | Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
title_short | Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
title_sort | wavelet clustering analysis as a tool for characterizing community structure in the human microbiome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192422/ https://www.ncbi.nlm.nih.gov/pubmed/37198426 http://dx.doi.org/10.1038/s41598-023-34713-8 |
work_keys_str_mv | AT benincaelisa waveletclusteringanalysisasatoolforcharacterizingcommunitystructureinthehumanmicrobiome AT pintosusanne waveletclusteringanalysisasatoolforcharacterizingcommunitystructureinthehumanmicrobiome AT cazellesbernard waveletclusteringanalysisasatoolforcharacterizingcommunitystructureinthehumanmicrobiome AT fuentessusana waveletclusteringanalysisasatoolforcharacterizingcommunitystructureinthehumanmicrobiome AT shettysudarshan waveletclusteringanalysisasatoolforcharacterizingcommunitystructureinthehumanmicrobiome AT bogaardsjohannesa waveletclusteringanalysisasatoolforcharacterizingcommunitystructureinthehumanmicrobiome |