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Microbial community resemblance methods differ in their ability to detect biologically relevant patterns

Nature Methods volume 7, pages 813–819 (2010)Cite this article

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Abstract

High-throughput sequencing methods enable characterization of microbial communities in a wide range of environments on an unprecedented scale. However, insight into microbial community composition is limited by our ability to detect patterns in this flood of sequences. Here we compare the performance of 51 analysis techniques using real and simulated bacterial 16S rRNA pyrosequencing datasets containing either clustered samples or samples arrayed across environmental gradients. We found that many diversity patterns were evident with severely undersampled communities and that methods varied widely in their ability to detect gradients and clusters. Chi-squared distances and Pearson correlation distances performed especially well for detecting gradients, whereas Gower and Canberra distances performed especially well for detecting clusters. These results also provide a basis for understanding tradeoffs between number of samples and depth of coverage, tradeoffs that are important to consider when designing studies to characterize microbial communities.

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Figure 1: Schematic of simulations and analysis of data.
Figure 2: Comparison of different gradient methods.
Figure 3: Choice of analysis method revealed or obscured clusters.
Figure 4: Deep sequencing was superfluous when clusters were prominent but critical when clusters were subtle.
Figure 5: Tradeoff between number of samples and number of sequences per sample with prominent and subtle gradients and clusters.

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Acknowledgements

This work was supported by the US National Institutes of Health (DK78669, HG4872 and HG4866) the Crohns and Colitis Foundation of America, the Bill and Melinda Gates Foundation and the Howard Hughes Medical Institute. We thank E. Costello, J. Zaneveld and J.G. Caporaso for helpful comments on drafts of the manuscript.

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Authors and Affiliations

  1. Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, USA

    Justin Kuczynski

  2. Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA

    Zongzhi Liu

  3. Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA

    Catherine Lozupone, Daniel McDonald & Rob Knight

  4. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA

    Noah Fierer

  5. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA

    Noah Fierer

  6. Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, USA

    Rob Knight

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  1. Justin Kuczynski
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  2. Zongzhi Liu
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  3. Catherine Lozupone
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  4. Daniel McDonald
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Contributions

J.K. and R.K. wrote the manuscript; J.K., R.K. and Z.L. designed the research; C.L., D.M., J.K., R.K. and Z.L. contributed simulation and analysis code; and J.K., Z.L., C.L., D.M., N.F. and R.K. analyzed the data.

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Correspondence to Rob Knight.

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The authors declare no competing financial interests.

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Kuczynski, J., Liu, Z., Lozupone, C. et al. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nat Methods 7, 813–819 (2010). https://doi.org/10.1038/nmeth.1499

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