Perspective | Published:

Use and abuse of correlation analyses in microbial ecology


Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon–taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226.

  2. 2.

    Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8:e1002687.

  3. 3.

    Gibbons SM, Kearney SM, Smillie CS, Alm EJ. Two dynamic regimes in the human gut microbiome. PLoS Comput Biol. 2017;13:e1005364.

  4. 4.

    Herren CM, McMahon KD. Cohesion: a method for quantifying the connectivity of microbial communities. ISME J. 2017;11:2426–38.

  5. 5.

    Schmidt TSB, Rodrigues JFM, von Mering C. A family of interaction-adjusted indices of community similarity. ISME J. 2017;11:791–807.

  6. 6.

    Ai D, Li X, Liu G, Liang X, Xia LC. Constructing the Microbial Association Network from large-scale time series data using Granger causality. Genes. 2019;10:E216.

  7. 7.

    Xia LC, Steele JA, Cram JA, Cardon ZG, Simmons SL, Vallino JJ, et al. Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Syst Biol. 2011;5(Suppl 2):S15.

  8. 8.

    Fisher CK, Mehta P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE. 2014;9:e102451.

  9. 9.

    Menon R, Ramanan V, Korolev KS. Interactions between species introduce spurious associations in microbiome studies. PLoS Comput Biol. 2018;14:e1005939.

  10. 10.

    Orphan VJ, Turk KA, Green AM, House CH. Patterns of 15N assimilation and growth of methanotrophic ANME-2 archaea and sulfate-reducing bacteria within structured syntrophic consortia revealed by FISH-SIMS. Environ Microbiol. 2009;11:1777–91.

  11. 11.

    Russell AB, Wexler AG, Harding BN, Whitney JC, Bohn AJ, Goo YA, et al. A type VI secretion-related pathway in Bacteroidetes mediates interbacterial antagonism. Cell Host Microbe. 2014;16:227–36.

  12. 12.

    Venturelli OS, Carr AC, Fisher G, Hsu RH, Lau R, Bowen BP, et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol Syst Biol. 2018;14:e8157.

  13. 13.

    Friedman J, Higgins LM, Gore J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol. 2017;1:109.

  14. 14.

    Liu A, Archer AM, Biggs MB, Papin JA. Growth-altering microbial interactions are responsive to chemical context. PLoS ONE. 2017;12:e0164919.

  15. 15.

    Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.

  16. 16.

    Weiss S, Van Treuren W, Lozupone C, Faust K, Friedman J, Deng Y, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016;10:1669–81.

  17. 17.

    Trosvik P, de Muinck EJ, Stenseth NC. Biotic interactions and temporal dynamics of the human gastrointestinal microbiota. ISME J. 2015;9:533–41.

  18. 18.

    Barnett L, Barrett AB, Seth AK. Granger causality and transfer entropy are equivalent for Gaussian variables. Phys Rev Lett. 2009;103:238701.

  19. 19.

    Freilich MA, Wieters E, Broitman BR, Marquet PA, Navarrete SA. Species co-occurrence networks: can they reveal trophic and non-trophic interactions in ecological communities? Ecology. 2018;99:690–9.

  20. 20.

    Coenen AR, Weitz JS. Limitations of correlation-based inference in complex virus-microbe communities. mSystems. 2018;3:e00084–18.

  21. 21.

    Hart SFM, Mi H, Green R, Xie L, Pineda JMB, Momeni B, et al. Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells. PLOS Biol. 2019;17:e3000135.

  22. 22.

    Momeni B, Xie L, Shou W. Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. Elife. 2017;6:e25051.

  23. 23.

    Martin-Platero AM, Cleary B, Kauffman K, Preheim SP, McGillicuddy DJ, Alm EJ, et al. High resolution time series reveals cohesive but short-lived communities in coastal plankton. Nat Commun. 2018;9:266.

  24. 24.

    Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Ocean plankton. Determinants of community structure in the global plankton interactome. Science. 2015;348:1262073.

  25. 25.

    Harris K, Parsons TL, Ijaz UZ, Lahti L, Holmes I, Quince C. Linking statistical and ecological theory: Hubbell’s unified neutral theory of biodiversity as a hierarchical dirichlet process. Proc IEEE. 2017;105:516–29.

  26. 26.

    Washburne AD, Burby JW, Lacker D. Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems. PLoS Comput Biol. 2016;12:e1005124.

  27. 27.

    Connor N, Barberán A, Clauset A. Using null models to infer microbial co-occurrence networks. PLoS ONE. 2017;12:e0176751.

  28. 28.

    Gibbons SM, Duvallet C, Alm EJ. Correcting for batch effects in case-control microbiome studies. PLoS Comput Biol. 2018;14:e1006102.

  29. 29.

    Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5:27.

  30. 30.

    Silverman JD, Washburne AD, Mukherjee S, David LA. A phylogenetic transform enhances analysis of compositional microbiota data. Elife. 2017;6:e21887.

  31. 31.

    Röttjers L, Faust K. From hairballs to hypotheses—biological insights from microbial networks. FEMS Microbiol Rev. 2018;42:761–80.

  32. 32.

    Tackmann J, Rodrigues JFM, von Mering C. Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data.

  33. 33.

    Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11:733–9.

  34. 34.

    McLaren MR, Willis AD, Callahan BJ. Consistent and correctable bias in metagenomic sequencing measurements. bioRxiv.

  35. 35.

    Tsilimigras MCB, Fodor AA. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol. 2016;26:330–5.

  36. 36.

    Tourlousse DM, Yoshiike S, Ohashi A, Matsukura S, Noda N, Sekiguchi Y. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 2017;45:e23.

  37. 37.

    Gao Y, Li H. Quantifying and comparing bacterial growth dynamics in multiple metagenomic samples. Nat Methods. 2018;15:1041–4.

  38. 38.

    Korem T, Zeevi D, Suez J, Weinberger A, Avnit-Sagi T, Pompan-Lotan M, et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science. 2015;349:1101–6.

  39. 39.

    Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014;5:219.

  40. 40.

    Danczak RE, Johnston MD, Kenah C, Slattery M, Wilkins MJ. Microbial community cohesion mediates community turnover in unperturbed aquifers. mSystems 2018;3:e00066–18.

  41. 41.

    Carmody RN, Gerber GK, Luevano JM Jr, Gatti DM, Somes L, Svenson KL, et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe. 2015;17:72–84.

  42. 42.

    Bender EA, Case TJ, Gilpin ME. Perturbation experiments in community ecology: theory and practice. Ecology. 1984;65:1–13.

Download references


AC, CD, and SMG were supported by a Washington Research Foundation Distinguished Investigator Award and by startup funds from the Institute for Systems Biology. AC and NSB were supported by Ecosystems and Networks Integrated with Genes and Molecular Assemblies (, a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research under Contract number DE-AC02-05CH11231. NSB was supported by the National Science Foundation under Grant Nos. OCE—1558924, CBET—1606206, MCB—1518261, DBI—1565166, and MCB—1616955 to NSB. The authors thank the editor and two anonymous peer reviewers for helping us to improve the quality of this Perspective.

Author information

Correspondence to Nitin S. Baliga or Sean M. Gibbons.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark
Fig. 1
Fig. 2
Fig. 3