Skip to main content

Thank you for visiting 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.

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


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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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.


  1. Turnbaugh, P.J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    Article  CAS  Google Scholar 

  2. Rappe, M.S. & Giovannoni, S.J. The uncultured microbial majority. Annu. Rev. Microbiol. 57, 369–394 (2003).

    Article  CAS  Google Scholar 

  3. Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005).

    Article  CAS  Google Scholar 

  4. Hamady, M., Walker, J.J., Harris, J.K., Gold, N.J. & Knight, R. Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nat. Methods 5, 235–237 (2008).

    Article  CAS  Google Scholar 

  5. Turnbaugh, P.J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

    Article  CAS  Google Scholar 

  6. Costello, E.K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

    Article  CAS  Google Scholar 

  7. Jongman, R.H., ter Braak, C.J.F. & Van Tongeren, O.F.R. Data Analysis in Community and Landscape Ecology. (Cambridge University Press, 1995).

  8. Magurran, A.E. Measuring Biological Diversity (Blackwell, Oxford, 2004).

  9. Lozupone, C.A. & Knight, R. Species divergence and the measurement of microbial diversity. FEMS Microbiol. Rev. 32, 557–578 (2008).

    Article  CAS  Google Scholar 

  10. Legendre, P. & Legendre, L. Numerical Ecology, 2nd English edn. (Elsevier, 1998).

  11. Ramette, A. Multivariate analyses in microbial ecology. FEMS Microbiol. Ecol. 62, 142–160 (2007).

    Article  CAS  Google Scholar 

  12. Savage, D.C. Microbial ecology of the gastrointestinal tract. Annu. Rev. Microbiol. 31, 107–133 (1977).

    Article  CAS  Google Scholar 

  13. Ley, R.E., Lozupone, C.A., Hamady, M., Knight, R. & Gordon, J.I. Worlds within worlds: evolution of the vertebrate gut microbiota. Nat. Rev. Microbiol. 6, 776–788 (2008).

    Article  CAS  Google Scholar 

  14. 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).

    Article  CAS  Google Scholar 

  15. Hamady, M., Lozupone, C. & Knight, R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4, 17–27 (2009).

    Article  Google Scholar 

  16. Minchin, P.R. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69, 89–107 (1987).

    Article  Google Scholar 

  17. Faith, D.P., Minchin, P.R. & Belbin, L. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57–68 (1987).

    Article  Google Scholar 

  18. Legendre, P. & Gallagher, E.D. Ecologically meaningful transformations for ordinations of species data. Oecologia 129, 271–280 (2001).

    Article  Google Scholar 

  19. Eckburg, P.B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).

    Article  Google Scholar 

  20. Ley, R.E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).

    Article  CAS  Google Scholar 

  21. Crawford, P.A. et al. Regulation of myocardial ketone body metabolism by the gut microbiota during nutrient deprivation. Proc. Natl. Acad. Sci. USA 106, 11276–11281 (2009).

    Article  CAS  Google Scholar 

  22. Hamady, M. & Knight, R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 19, 1141–1152 (2009).

    Article  CAS  Google Scholar 

  23. Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. advance online publication, doi:10.1038/ismej.2010.58 (6 May 2010).

    Article  Google Scholar 

  24. Rousk, J., Brookes, P.C. & Baath, E. Investigating the mechanisms for the opposing pH relationships of fungal and bacterial growth in soil. Soil Biol. Biochem. 42, 926–934 (2010).

    Article  CAS  Google Scholar 

  25. Gauch, H.G. Multivariate Analysis in Community Ecology. (Cambridge University Press, 1982).

  26. Kuczynski, J. et al. Direct sequencing of the human microbiome readily reveals community differences. Genome Biol. 11, 210 (2010).

    Article  Google Scholar 

  27. Fierer, N. et al. Forensic identification using skin bacterial communities. Proc. Natl. Acad. Sci. USA 107, 6477–6481 (2010).

    Article  CAS  Google Scholar 

  28. Hill, M.O. & Gauch, H.G. Detrended correspondence-analysis—an improved ordination technique. Vegetatio 42, 47–58 (1980).

    Article  Google Scholar 

  29. Pielou, E.C. The Interpretation of Ecological Data: A Primer on Classification and Ordination (Wiley, New York, 1984).

  30. Frank, D.N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl. Acad. Sci. USA 104, 13780–13785 (2007).

    Article  CAS  Google Scholar 

  31. Knight, R. et al. PyCogent: a toolkit for making sense from sequence. Genome Biol. 8, R171 (2007).

    Article  Google Scholar 

  32. Fierer, N., Hamady, M., Lauber, C.L. & Knight, R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc. Natl. Acad. Sci. USA 105, 17994–17999 (2008).

    Article  CAS  Google Scholar 

  33. Stackebrandt, E. & Goebel, B.M. A place for DNA-DNA reassociation and 16s ribosomal-RNA sequence-analysis in the present species definition in bacteriology. Int. J. Syst. Bacteriol. 44, 846–849 (1994).

    Article  CAS  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Corresponding author

Correspondence to Rob Knight.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 and Supplementary Tables 1–5 (PDF 520 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research