Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data
Here, the authors present Sparse Estimation of Correlations among Microbiomes (SECOM), a tool devised to characterize both linear and nonlinear relationships in microbiome data. When applied to human skin and infant gut microbiome data, SECOM is able to retrieve taxa interactions undescribed by prev...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-32243-x |
| _version_ | 1828436132138844160 |
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| author | Huang Lin Merete Eggesbø Shyamal Das Peddada |
| author_facet | Huang Lin Merete Eggesbø Shyamal Das Peddada |
| author_sort | Huang Lin |
| collection | DOAJ |
| description | Here, the authors present Sparse Estimation of Correlations among Microbiomes (SECOM), a tool devised to characterize both linear and nonlinear relationships in microbiome data. When applied to human skin and infant gut microbiome data, SECOM is able to retrieve taxa interactions undescribed by previous methods. |
| first_indexed | 2024-12-10T19:21:22Z |
| format | Article |
| id | doaj.art-dfca4af60b984a4a9fb0fbf66d366e4f |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-10T19:21:22Z |
| publishDate | 2022-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-dfca4af60b984a4a9fb0fbf66d366e4f2022-12-22T01:36:29ZengNature PortfolioNature Communications2041-17232022-08-0113111610.1038/s41467-022-32243-xLinear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome dataHuang Lin0Merete Eggesbø1Shyamal Das Peddada2Biostatistics and Bioinformatics Branch, Eunice Shriver Kennedy NICHD, NIHNorwegian Institute of Public HealthBiostatistics and Bioinformatics Branch, Eunice Shriver Kennedy NICHD, NIHHere, the authors present Sparse Estimation of Correlations among Microbiomes (SECOM), a tool devised to characterize both linear and nonlinear relationships in microbiome data. When applied to human skin and infant gut microbiome data, SECOM is able to retrieve taxa interactions undescribed by previous methods.https://doi.org/10.1038/s41467-022-32243-x |
| spellingShingle | Huang Lin Merete Eggesbø Shyamal Das Peddada Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data Nature Communications |
| title | Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data |
| title_full | Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data |
| title_fullStr | Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data |
| title_full_unstemmed | Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data |
| title_short | Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data |
| title_sort | linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data |
| url | https://doi.org/10.1038/s41467-022-32243-x |
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