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Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling

Nature Methods volume 16, pages 731–736 (2019)Cite this article

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Abstract

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal variability remains an important challenge. Here, we present decomposition of variance using replicate sampling (DIVERS), a method based on replicate sampling and spike-in sequencing. The method quantifies the contributions of temporal dynamics, spatial sampling variability, and technical noise to the variances and covariances of absolute bacterial abundances. We applied DIVERS to investigate a high-resolution time series of the human gut microbiome and a spatial survey of a soil bacterial community in Manhattan’s Central Park. Our analysis showed that in the gut, technical noise dominated the abundance variability for nearly half of the detected taxa. DIVERS also revealed substantial spatial heterogeneity of gut microbiota, and high temporal covariances of taxa within the Bacteroidetes phylum. In the soil community, spatial variability primarily contributed to abundance fluctuations at short time scales (weeks), while temporal variability dominated at longer time scales (several months).

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Fig. 1: DIVERS conceptual workflow.
Fig. 2: Variance decomposition of gut bacterial abundance fluctuations using DIVERS.
Fig. 3: Identifying individual bacterial taxa with high temporal or spatial sampling variance.
Fig. 4: Decomposition of temporal and spatial contributions to pairwise OTU abundance correlations in the human gut microbiome.
Fig. 5: Decomposition of factors contributing to the variance of soil bacteria abundances.

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Data availability

Sequencing data are available at NCBI SRA under PRJNA541083.

Code availability

MATLAB scripts to perform all variance and covariance decomposition analyses from original OTU abundance tables are available on GitHub at https://github.com/brianwji/DIVERS. Implementation of DIVERS in R is available on GitHub at https://github.com/hym0405/DIVERS.

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Acknowledgements

H.H.W. acknowledges funding from the NIH (grant nos. R01AI132403, R01DK118044), Burroughs Wellcome Fund (no. PATH 1016691), Bill & Melinda Gates Foundation (no. INV-000609), and the Schaefer Research Scholars Program for this work. R.U.S. is supported by a Fannie and John Hertz Foundation Fellowship and a NSF Graduate Research Fellowship (no. DGE-1644869). B.W.J. is supported in part by the NIH under Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant (no. T32GM007367) and by the MD-PhD program at Columbia University. D.V. acknowledges funding from the NIH (grant nos. R01GM079759, R01DK118044).

Author information

Author notes

  1. These authors contributed equally: Brian W. Ji, Ravi U. Sheth, Purushottam D. Dixit.

Authors and Affiliations

  1. Department of Systems Biology, Columbia University, New York, NY, USA

    Brian W. Ji, Ravi U. Sheth, Purushottam D. Dixit, Yiming Huang, Andrew Kaufman, Harris H. Wang & Dennis Vitkup

  2. Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA

    Brian W. Ji, Ravi U. Sheth & Yiming Huang

  3. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Harris H. Wang

  4. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Dennis Vitkup

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Contributions

B.W.J. and R.U.S. conceived the study, designed the data collection workflow and performed all data analysis. B.W.J. and P.D.D. developed the variance and covariance decomposition models. R.U.S. performed all experiments with assistance from A.K. Y.H. assisted with data analysis and code implementation in R. H.H.W. and D.V. oversaw the project, and guided experiments and data analysis. All authors wrote the manuscript.

Corresponding authors

Correspondence to Harris H. Wang or Dennis Vitkup.

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The authors declare no competing interests.

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Peer review information: Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Ji, B.W., Sheth, R.U., Dixit, P.D. et al. Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling. Nat Methods 16, 731–736 (2019). https://doi.org/10.1038/s41592-019-0467-y

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