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Cited by 1,799Gut microbiota and healthMicrobial Community Ecology and PhysiologyGenomics and Phylogenetic Studies

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

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Kurtz, Zachary D., Müller, Christian L., Miraldi, Emily R., Littman, Dan R., Blaser, Martin J., Bonneau, Richard A.·2015·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1004226PMC PMC4423992
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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.

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  • Taxonomic abundance was transformed using the centered log ratio (clr) transformation after adding a pseudocount to remove compositional constraints of sequencing.

    “Taxonomic abundance was transformed using the centered log ratio (clr) transformation [35, 36] after adding a pseudocount, in order to remove compositional constraints of sequencing.”

    From Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients (2019) · cites this paper as [36]

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Authors
Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., & Bonneau, R. A.
Affiliations
  • New York University (US)
  • Courant Institute of Mathematical Sciences (US)
Corresponding author
Bonneau, Richard A.
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2015-05-07
Volume
11
Issue
5
Pages
e1004226
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1004226
PMC
PMC4423992
OpenAlex
W1864161604
Cited by
1,799
References
4
Citations by year
2026: 247 · 2025: 251 · 2024: 246 · 2023: 193 · 2022: 210 · 2021: 214 · 2020: 148 · 2019: 122 · 2018: 74 · 2017: 51 · 2016: 36 · 2015: 6
FWCI
24.16
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Rutgers University Community Repository (Rutgers University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • Simons Foundation
  • National Institutes of Health
  • National Institute of General Medical Sciences (R01 GM 63270, R01GM063270)
  • National Institute of Allergy and Infectious Diseases (T32AI007180)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01DK103358, R01DK090989)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
microbial ecological networks, compositional data analysis, sparse inverse covariance estimation, graphical model inference, 16S rRNA gene amplicon sequencing, microbial community structure, synthetic benchmark, American Gut Project, network inference
MeSH terms
Gastrointestinal Microbiome, Algorithms, Humans, Models, Biological, RNA, Ribosomal, 16S, Computational Biology, Metagenomics, Biota, Microbiota

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