Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.,Nature Biotechnology

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Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.
Nature Biotechnology ( IF 33.1 ) Pub Date : 2020-08-31 , DOI: 10.1038/s41587-020-0660-7
Cameron Martino _{1,

2,

3} , Liat Shenhav ₄ , Clarisse A Marotz ₃ , George Armstrong _{2,

3} , Daniel McDonald ₃ , Yoshiki Vázquez-Baeza _{1,

5} , James T Morton ₆ , Lingjing Jiang ₇ , Maria Gloria Dominguez-Bello _{8,

9} , Austin D Swafford ₁ , Eran Halperin _{4,

10,

11,

12,

13} , Rob Knight _{1,

3,

14,

15}

Affiliation

Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
Division of Biostatistics, University of California San Diego, La Jolla, CA, USA.
Department of Biochemistry and Microbiology, Rutgers University New Brunswick, New Brunswick, NJ, USA.
Department of Anthropology, Rutgers University, New Brunswick, NJ, USA.
Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA.
Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Institute of Precision Health, University of California, Los Angeles, CA, USA.
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.

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.

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