Context-aware dimensionality reduction deconvolutes gut microbial community dynamics
The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in...
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| Veröffentlicht in: | Nature biotechnology 2021-02, Vol.39 (2), p.165-168 |
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| creator | Martino, Cameron Shenhav, Liat Marotz, Clarisse A. Armstrong, George McDonald, Daniel Vázquez-Baeza, Yoshiki Morton, James T. Jiang, Lingjing Dominguez-Bello, Maria Gloria Swafford, Austin D. Halperin, Eran Knight, Rob |
| description | The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.
Gut microbiome composition is associated with phenotypes as revealed by a dimensionality reduction tool. |
| doi_str_mv | 10.1038/s41587-020-0660-7 |
| format | Article |
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| subjects | 631/114 704/158/855 Agriculture Algorithms Analysis Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Brief Communication Cesarean section Composition Computer science Context switching Data reduction Datasets Gastrointestinal Microbiome Genotype & phenotype Humans Image processing Infant Intestinal microflora Life Sciences Methods Microbiomes Microbiota (Symbiotic organisms) Microorganisms Phenotype Phenotypes Reduction Tensors |
| title | Context-aware dimensionality reduction deconvolutes gut microbial community dynamics |
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