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Chamberlain, Emelia J., Boulton, William 
ORCID: https://orcid.org/0000-0002-8258-4673, Connors, Elizabeth, Calianos, Theodore, Bowman, Jeff S., Creamean, Jessie M., Mock, Thomas ORCID: https://orcid.org/0000-0001-9604-0362 and Kim, Heather H.
(2026)
From microbial diversity to functional potential using dimensionality reduction.
Frontiers in Microbiology, 17.
ISSN 1664-302X
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Abstract
The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.
| Item Type: | Article |
|---|---|
| Additional Information: | Data availability statement: Publicly available datasets were analyzed in this study. The 16S rRNA gene amplicon sequence data are available through the National Center for Biotechnology Information (NCBI BioProject #PRJNA895866), with metadata available through the Arctic Data Center (https://arcticdata.io/catalog/view/doi%3A10.18739%2FA2CC0TV5X). The raw metagenomic data are also available through the NCBI (BioProject #PRJNA1160706) (Boulton et al., 2025). Data QC and ML code pipelines are available upon request from the corresponding authors. |
| Uncontrolled Keywords: | arctic ocean,bacteria,ecosystem function,machine learning,microbial diversity,microbiology,microbiology (medical) ,/dk/atira/pure/subjectarea/asjc/2400/2404 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Science > Research Groups > Wolfson Centre for Advanced Environmental Microbiology Faculty of Science > Research Groups > Environmental Biology |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 15 Jul 2026 13:48 |
| Last Modified: | 19 Jul 2026 05:36 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/103857 |
| DOI: | 10.3389/fmicb.2026.1786397 |
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