Context-aware dimensionality reduction deconvolutes gut microbial community dynamics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-08-31

AUTHORS

Cameron Martino, Liat Shenhav, Clarisse Marotz, George Armstrong, Daniel McDonald, Yoshiki Vázquez-Baeza, James T. Morton, Lingjing Jiang, Maria Gloria Dominguez-Bello, Austin D. Swafford, Eran Halperin, Rob Knight

ABSTRACT

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. More... »

PAGES

165-168

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41587-020-0660-7

DOI

http://dx.doi.org/10.1038/s41587-020-0660-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1130458409

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32868914


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