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  • Brief Communication
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Context-aware dimensionality reduction deconvolutes gut microbial community dynamics

Nature Biotechnology volume 39, pages 165–168 (2021)Cite this article

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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.

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Fig. 1: Overview of the CTF algorithm.
Fig. 2: CTF outperforms popular distance metrics in longitudinal in silico data-driven simulations.

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Data availability

The sequences and biome tables for the IBD, ECAM, DIABIMMUNE and AGP datasets can be found on Qiita (http://qiita.microbio.me) under study IDs 1629, 10249, 11884 and 10317 and at EBI or BioProject under ERP020401, ERP016173, PRJNA290381 and ERP012803.

Code availability

The CTF codebase named Gemelli is a fully unit tested open-source python package, and is installable through pip or conda. Additionally, CTF is wrapped in a QIIME2 plugin: https://github.com/biocore/gemelli; all the code and analyses are available in the ‘Code Ocean’ capsule: https://doi.org/10.24433/CO.5938114.v1.

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Acknowledgements

This work was partially supported by the EMCH fund for human microbiome studies, the Norwegian Institute of Public Health 2019-0350 (R.K.), the Emerald Foundation 3022 (R.K.), National Institutes of Health (NIH) Pioneer award grant no. 1DP1AT010885 (R.K.), National Institute of Justice grant no. 2016-DN-BX-4194 (R.K.), San Diego Digestive Diseases Research Center NIDDK grant no. 1P30DK120515 (R.K.) and Janssen Pharmaceuticals grant no. 20175015 (R.K.). C.A.M. was funded by the NIDCR (grant no. 1F31DE028478-01). E.H. and L.S were partially supported by the National Science Foundation (grant no. 1705197) and by NIH grant no. 1R56MD013312. E.H. was also partially supported by grants no. NIH/NHGRI HG010505-02, NIH 1R01MH115979, NIH 5R25GM112625 and NIH 5UL1TR001881. M.G.D.-B. was funded in part by the C&D Research Fund.

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Author notes

  1. These authors contributed equally: Cameron Martino, Liat Shenhav.

Authors and Affiliations

  1. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Cameron Martino, Yoshiki Vázquez-Baeza, Austin D. Swafford & Rob Knight

  2. Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA

    Cameron Martino & George Armstrong

  3. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Cameron Martino, Clarisse A. Marotz, George Armstrong, Daniel McDonald & Rob Knight

  4. Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA

    Liat Shenhav & Eran Halperin

  5. Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA

    Yoshiki Vázquez-Baeza

  6. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA

    James T. Morton

  7. Division of Biostatistics, University of California San Diego, La Jolla, CA, USA

    Lingjing Jiang

  8. Department of Biochemistry and Microbiology, Rutgers University New Brunswick, New Brunswick, NJ, USA

    Maria Gloria Dominguez-Bello

  9. Department of Anthropology, Rutgers University, New Brunswick, NJ, USA

    Maria Gloria Dominguez-Bello

  10. Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA

    Eran Halperin

  11. Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Eran Halperin

  12. Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Eran Halperin

  13. Institute of Precision Health, University of California, Los Angeles, CA, USA

    Eran Halperin

  14. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight

  15. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight

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  1. Cameron Martino
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Contributions

C.M., L.S. and R.K. conceived, initiated and coordinated the project. C.M., L.S., D.M. and Y.V.-B. coordinated, compiled and performed analysis. C.M., C.A.M. and G.A. wrote the code for CTF. C.M., L.S. and C.A.M. wrote the manuscript. J.T.M, A.D.S. and M.G.D.-B. provided essential discussion and advice. E.H. and R.K. supervised the project. All authors discussed the experiments and results, read and approved the manuscript.

Corresponding author

Correspondence to Rob Knight.

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The authors declare no conflicts of interest.

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Supplementary Information

Supplementary Discussion, Figs. 1–6, Tables 1–3 and References.

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Martino, C., Shenhav, L., Marotz, C.A. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat Biotechnol 39, 165–168 (2021). https://doi.org/10.1038/s41587-020-0660-7

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