Sparse and Compositionally Robust Inference of Microbial Ecological Networks

Abstract

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 66

1. 1.
   The Earth Microbiome Project: Meeting report of the “1st EMP meeting on sample selection and acquisition” at Argonne National Laboratory October 6th 2010. · 2010

   No notice
2. 2.

   Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett C, Knight R, et al (2007 ) The human microbiome project: exploring the microbial part of ourselves in a changing world. Nature 449: 804

   Not in our library10.1038/nature06244

3. 3.

   AmGut. The american gut project. http://humanfoodproject.com/americangut/. Accessed: 2014-01-30.

   Not in our library

4. 4.

   Bunge J, Willis A, Walsh F (2014 ) Estimating the number of species in microbial diversity studies. Annual Review of Statistics and Its Application 1: 427–445.

   Not in our library10.1146/annurev-statistics-022513-115654

5. 5.

   Foster JA, Krone SM, Forney LJ (2008 ) Application of ecological network theory to the human microbiome. Interdisciplinary perspectives on infectious diseases 2008: 839501

   Not in our library10.1155/2008/839501

6. 6.

   Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, et al (2011 ) Enterotypes of the human gut microbiome. Nature 473: 174–180.

   Not in our library10.1038/nature09944

7. 7.
   A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets · 2013

   No notice
8. 8.

   Chen J, Li H (2013 ) Variable selection for sparse dirichlet-multinomial regression with an application to microbiome data analysis. The Annals of Applied Statistics 7: 418–442.

   Not in our library10.1214/12-aoas592

9. 9.

   Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, et al (2014 ) The treatment-naive microbiome in new-onset crohn’s disease. Cell Host—Microbe 15: 382–392.

   Not in our library10.1016/j.chom.2014.02.005

10. 10.
    Helminth Colonization Is Associated with Increased Diversity of the Gut Microbiota · 2014

    Corrected
11. 11.
    Microbial Co-occurrence Relationships in the Human Microbiome · 2012

    No notice
12. 12.

    Fuhrman JA, Steele JA (2008 ) Community structure of marine bacterioplankton: Patterns, networks, and relationships to function. In: Aquatic Microbial Ecology. volume 53, pp. 69–81.

    Not in our library10.3354/ame01222

13. 13.

    Barberán A, Bates ST, Casamayor EO, Fierer N (2012 ) Using network analysis to explore cooccurrence patterns in soil microbial communities. The ISME journal 6: 343–51.

    Not in our library10.1038/ismej.2011.119

14. 14.

    Marino S, Baxter NT, Huffnagle GB, Petrosino JF, Schloss PD (2014 ) Mathematical modeling of primary succession of murine intestinal microbiota. Proceedings of the National Academy of Sciences of the United States of America 111: 439–44.

    Not in our library10.1073/pnas.1311322111

15. 15.
    Molecular ecological network analyses · 2012

    No notice
16. 16.

    Steele Ja, Countway PD, Xia L, Vigil PD, Beman JM, et al (2011 ) Marine bacterial, archaeal and protistan association networks reveal ecological linkages. The ISME journal 5: 1414–25.

    Not in our library10.1038/ismej.2011.24

17. 17.
    Inferring Correlation Networks from Genomic Survey Data · 2012

    No notice
18. 18.

    Aitchison J (1981 ) A new approach to null correlations of proportions. Mathematical Geology 13: 175–189.

    Not in our library10.1007/bf01031393

19. 19.

    Faust K, Raes J (2012 ) Microbial interactions: from networks to models. Nat Rev Micro 10: 538–550.

    Not in our library10.1038/nrmicro2832

20. 20.

    Meinshausen N, Bühlmann P (2006 ) High Dimensional Graphs and Variable Selection with the Lasso. The Annals of Statistics 34: 1436–1462.

    Not in our library10.1214/009053606000000281

21. 21.
    The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo · 2006

    No notice
22. 22.

    Friedman J, Hastie T, Tibshirani R (2008 ) Sparse inverse covariance estimation with the graphical lasso. Biostatistics (Oxford, England) 9: 432–441.

    Not in our library10.1093/biostatistics/kxm045

23. 23.

    Banerjee O, El Ghaoui L, d’Aspremont A (2008 ) Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. The Journal of Machine … 9: 485–516.

    Not in our library

24. 24.
    Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana · 2004

    No notice
25. 25.

    Friedman N (2004 ) Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303: 799–805.

    Not in our library10.1126/science.1094068

26. 26.

    Bonneau R (2008 ) Learning biological networks: from modules to dynamics. Nature chemical biology 4: 658–64.

    Not in our library10.1038/nchembio.122

27. 27.

    Jones DT, Buchan DWA, Cozzetto D, Pontil M (2011) Psicov: Precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics.

    Not in our library10.1093/bioinformatics/btr638

28. 28.

    Marks DS, Hopf TA, Sander C (2012 ) Protein structure prediction from sequence variation. Nature biotechnology 30: 1072–80.

    Not in our library10.1038/nbt.2419

29. 29.

    Ravikumar P, Wainwright MJ, Raskutti G, Yu B (2011 ) High-dimensional covariance estimation by minimizing L1 -penalized log-determinant divergence. Electronic Journal of Statistics 5: 935–980.

    Not in our library10.1214/11-ejs631

30. 30.

    Tandon R, Ravikumar P (2014) Learning Graphs with a Few Hubs. In: Proceedings of The 31st International Conference on Machine Learning. pp. 602–610. URL http://jmlr.org/proceedings/papers/v32/tandon14.html.

    Not in our library

31. 31.

    Liu Q, Ihler AT (2011 ) Learning scale free networks by reweighted l1 regularization. In: AISTATS. pp. 40–48.

    Not in our library

32. 32.

    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, et al (2009 ) Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75: 7537–7541.

    Not in our library10.1128/aem.01541-09

33. 33.
    Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data · 2012

    No notice
34. 34.

    Aitchison J (1986 ) The statistical analysis of compositional data. London; New York: Chapman and Hall.

    Not in our library

35. 35.

    Koller D, Friedman N (2009 ) Probabilistic graphical models: principles and techniques. MIT press.

    Not in our library

36. 36.

    Lauritzen SL (1996 ) Graphical models. Oxford University Press.

    Not in our library

37. 37.

    Wainwright MJ, Jordan MI (2008 ) Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning 1: 1–305.

    Not in our library10.1561/2200000001

38. 38.

    Bühlmann P, Kalisch M, Meier L (2014 ) High-dimensional statistics with a view toward applications in biology. Annual Review of Statistics and Its Application 1: 255–278.

    Not in our library10.1146/annurev-statistics-022513-115545

39. 39.

    Ravikumar P, Wainwright MJ, Lafferty JD, et al (2010 ) High-dimensional ising model selection using l1-regularized logistic regression. The Annals of Statistics 38: 1287–1319.

    Not in our library10.1214/09-aos691

40. 40.

    Dempster AP (1972 ) Covariance selection. Biometrics: 157–175.

    Not in our library10.2307/2528966

Cited by papers we hold 51

• Networks as tools for defining emergent properties of microbiomes and their stability · 2024
  No notice
• Extent of Oral–Gut Transmission of Bacterial and Fungal Microbiota in Healthy Chinese Adults · 2023
  Not checked
• No evidence for a common blood microbiome based on a population study of 9,770 healthy humans · 2023
  No notice
• A citizen-science-enabled catalogue of the vaginal microbiome and associated factors · 2023
  No notice
• Tree Size Drives Diversity and Community Structure of Microbial Communities on the Bark of Beech (Fagus sylvatica) · 2022
  Not checked
• Functional feeds marginally alter immune expression and microbiota of Atlantic salmon (Salmo salar) gut, gill, and skin mucosa though evidence of tissue-specific signatures and host–microbe coadaptation remain · 2022
  Not checked
• Species-Resolved Metagenomics of Kindergarten Microbiomes Reveal Microbial Admixture Within Sites and Potential Microbial Hazards · 2022
  Not checked
• Investigating differential abundance methods in microbiome data: A benchmark study. · 2022
  Not checked
• Mobilome-driven segregation of the resistome in biological wastewater treatment · 2022
  Not checked
• Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data · 2022
  No notice
• LinDA: linear models for differential abundance analysis of microbiome compositional data · 2022
  No notice
• From diversity to complexity: Microbial networks in soils · 2022
  No notice
• Citizen-science based study of the oral microbiome in Cystic fibrosis and matched controls reveals major differences in diversity and abundance of bacterial and fungal species · 2021
  Not checked
• Facial Skin Microbiota-Mediated Host Response to Pollution Stress Revealed by Microbiome Networks of Individual · 2021
  Not checked
• Open challenges for microbial network construction and analysis · 2021
  No notice
• Linking Plant Secondary Metabolites and Plant Microbiomes: A Review · 2021
  No notice
• Network analysis methods for studying microbial communities: A mini review · 2021
  No notice
• Soil plastispheres as hotspots of antibiotic resistance genes and potential pathogens · 2021
  Corrected
• Taxonomic signatures of cause-specific mortality risk in human gut microbiome · 2021
  No notice
• A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities · 2020
  Corrected

and 31 more

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.