Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Context-aware dimensionality reduction deconvolutes gut microbial community dynamics

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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

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.

References

  1. 1.

    Gibson, T. E. & Gerber, G. K. Robust and scalable models of microbiome dynamics. In Proceedings of the 35th International Conference on Machine Learning 80 (eds Dy, J. et al.) 1763–1772 (PMLR, 2018).

  2. 2.

    Shenhav, L. et al. Modeling the temporal dynamics of the gut microbial community in adults and infants. PLoS Comput. Biol. 15, e1006960 (2019).

    CAS  Article  Google Scholar 

  3. 3.

    Äijö, T., Müller, C. L. & Bonneau, R. Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing. Bioinformatics 34, 372–380 (2018).

    Article  Google Scholar 

  4. 4.

    Silverman, J. D., Durand, H. K., Bloom, R. J., Mukherjee, S. & David, L. A. Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. Microbiome 6, 202 (2018).

    Article  Google Scholar 

  5. 5.

    Martino, C. et al. A novel sparse compositional technique reveals microbial perturbations. mSystems 4, e00016–e00019 (2019).

    Article  Google Scholar 

  6. 6.

    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).

    Article  Google Scholar 

  7. 7.

    Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).

    Article  Google Scholar 

  8. 8.

    Halfvarson, J. et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat. Microbiol. 2, 17004 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Jaccard, P. The distribution of the flora in the alpine zone. 1. New Phytol. 11, 37–50 (1912).

    Article  Google Scholar 

  10. 10.

    Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).

    Article  Google Scholar 

  11. 11.

    Aitchison, J. Principal component analysis of compositional data. Biometrika 70, 57–65 (1983).

    Article  Google Scholar 

  12. 12.

    Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    CAS  Article  Google Scholar 

  13. 13.

    McDonald, D. et al. Striped UniFrac: enabling microbiome analysis at unprecedented scale. Nat. Methods 15, 847–848 (2018).

    CAS  Article  Google Scholar 

  14. 14.

    Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med. 8, 343ra82–343ra82 (2016).

    Article  Google Scholar 

  15. 15.

    Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).

    Article  Google Scholar 

  16. 16.

    McDonald, D. et al. American Gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).

    CAS  Article  Google Scholar 

  17. 17.

    Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).

    CAS  Article  Google Scholar 

  18. 18.

    Keshavan, R. H., Montanari, A. & Oh, S. Low-rank matrix completion with noisy observations: a quantitative comparison. In Proc. 2009 47th Annual Allerton Conference on Communication, Control, and Computing 1216–1222 (Curran Associates, 2009).

  19. 19.

    Lek-Heng Lim. Singular values and eigenvalues of tensors: a variational approach. In Proc. 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 129–132 (Curran Associates, 2005).

  20. 20.

    Anandkumar, A., Ge, R. & Janzamin, M. Guaranteed non-orthogonal tensor decomposition via alternating rank-1 updates. Preprint at arXiv http://arxiv.org/abs/1402.5180 (2014).

  21. 21.

    Jain, P. & Oh, S. Provable tensor factorization with missing data. Adv. Neural Inf. Process. Syst. 27 (eds Ghahramani, Z. et al.) 1431–1439 (Curran Associates, 2014).

  22. 22.

    Aitchison, J. & Ho, C. H. The multivariate Poisson-log normal distribution. Biometrika 76, 643–653 (1989).

    Article  Google Scholar 

  23. 23.

    Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191–16 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS  Article  Google Scholar 

  25. 25.

    Janssen, S. et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3, e00021–18 (2018).

    CAS  Article  Google Scholar 

  26. 26.

    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

    CAS  Article  Google Scholar 

  27. 27.

    Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 551, 457 (2018).

    Google Scholar 

Download references

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.

Author information

Affiliations

Authors

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.

Ethics declarations

Competing interests

The authors declare no conflicts of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

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

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing