Learning Microbial Interaction Networks from Metagenomic Count Data

  • Surojit BiswasEmail author
  • Meredith McDonald
  • Derek S. Lundberg
  • Jeffery L. Dangl
  • Vladimir Jojic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)


Many microbes associate with higher eukaryotes and impact their vitality. In order to engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenence, which in large part, demands an understanding of the direct interactions between community members. Toward this end, we’ve developed a Poisson-multivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer, and captures direct taxon-taxon interactions at the multivariate normal layer using an \(\ell _1\) penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real, in planta perturbation experiment of a nine member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among three community members that associate with Arabidopsis thaliana roots. We conclude that our method provides a structured, accurate, and distributionally reasonable way of modeling correlated count based random variables and capturing direct interactions among them.

Code Availability: Our model is available on CRAN as an R package, MInt.


Metagenomics Hierarchical model \(\ell _1\)-penalty Precision matrix Conditional independence 


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  1. 1.
    Human Microbiome Project Consortium. The Structure, function and diversity ofthe healthy human microbiome. Nature 486(7402), 207–14 (2012)Google Scholar
  2. 2.
    Lundberg, D.S., Lebeis, S.L., Paredes, S.H., Yourstone, S., Gehring, J., Malfatti, S., Tremblay, J., Engelbrektson, A., Kunin, V., Del Rio, T.G., Edgar, R.C., Eickhorst, T., Ley, R.E., Hugenholtz, P., Tringe, S.G., Dangl, J.L.: Defining the core Arabidopsis thaliana root microbiome. Nature 488(7409), 86–90 (2012)CrossRefGoogle Scholar
  3. 3.
    Konopka, A.: What is microbial community ecology? The ISME Journal 3(11), 1223–1230 (2009)CrossRefGoogle Scholar
  4. 4.
    Segata, N., Boernigen, D., Tickle, T.L., Morgan, X.C., Garrett, W.S., Huttenhower, C.: Computational meta’omics for microbial community studies. Molecular Systems Biology 9(666), 666 (2013)Google Scholar
  5. 5.
    Faust, K., Sathirapongsasuti, J.F., Izard, J., Segata, N., Gevers, D., Raes, J., Huttenhower, C.: Microbial co-occurrence relationships in the human microbiome. PLoS Computational Biology 8(7), e1002606 (2012)CrossRefGoogle Scholar
  6. 6.
    Friedman, J., Alm, E.J.: Inferring Correlation Networks from Genomic Survey Data. PLoS Computational Biology 8(9), 1–11 (2012)CrossRefGoogle Scholar
  7. 7.
    Faust, K., Raes, J.: Microbial interactions: from networks to models. Nature Reviews. Microbiology 10(8), 538–550 (2012)CrossRefGoogle Scholar
  8. 8.
    Meinshausen, N., Bühlmann, P.: High-dimensional graphs and variable selection with the Lasso. The Annals of Statistics 34(3), 1436–1462 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441 (2007). (Oxford, England)CrossRefGoogle Scholar
  10. 10.
    Wainwright, M.J., Jordan, M.I.: Graphical Models, Exponential Families, and Variational Inference. Found. Trends Mach. Learn. 1(1935–8237), 1–305 (2008)zbMATHGoogle Scholar
  11. 11.
    Besag, J.: On the Statistical Analysis of Dirty Pictures. Journal of the Royal Statistical Society 48(3), 259–302 (1986)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Li, H., Durbin, R.: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26(5), 589–595 (2010). (Oxford, England)CrossRefGoogle Scholar
  13. 13.
    Lundberg, D.S., Yourstone, S., Mieczkowski, P., Jones, C.D., Dangl, J.L.: Practical innovations for high-throughput amplicon sequencing. Nature Methods 10(10), 999–1002 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Surojit Biswas
    • 1
    Email author
  • Meredith McDonald
    • 2
  • Derek S. Lundberg
    • 2
  • Jeffery L. Dangl
    • 2
    • 3
    • 4
  • Vladimir Jojic
    • 5
  1. 1.Department of StatisticsUNC Chapel HillChapel HillUSA
  2. 2.Department of BiologyUNC Chapel HillChapel HillUSA
  3. 3.Howard Hughes Medical InstituteUNC Chapel HillChapel HillUSA
  4. 4.Department of ImmunologyUNC Chapel HillChapel HillUSA
  5. 5.Department of Computer ScienceUNC Chapel HillChapel HillUSA

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