Sparse and compositionally robust inference of microbial ecological networks

Abstract

16S-ribosomal sequencing and other metagonomic 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, 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 16S datasets are compositional, and thus, microbial abundances are not independent. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU interaction networks is severely under-powered, and additional 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 interactions from metagenomic 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 interaction network is sparse. To reconstruct the interaction network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. Because no large-scale microbial ecological networks have been experimentally validated, SPIEC-EASI comprises computational tools to generate realistic OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods in terms of edge recovery and network properties on realistic synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial interactions using data from the American Gut project.

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Cited by in corpus (23)

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• Generalized Stability Approach for Regularized Graphical Models
• Local and collective transitions in sparsely-interacting ecological communities
• Regression Analysis for Microbiome Compositional Data
• Large deviation theory for diluted Wishart random matrices
• Network reconstruction via the minimum description length principle
• Generalized Linear Models with Linear Constraints for Microbiome Compositional Data
• Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data
• Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
• Random graphical model of microbiome interactions in related environments
• PGLasso: Microbial Community Detection through Phylogenetic Graphical Lasso
• Phase diagram and metastability of the Ising model on two coupled networks
• Tree-based Node Aggregation in Sparse Graphical Models
• Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values
• False Discovery Rates in Biological Networks
• Top eigenpair statistics of diluted Wishart matrices
• Estimation of Shortest Path Covariance Matrices
• Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
• Inference of Microbial Interactions Using Copula Models with Mixture Margins
• PhD dissertation to infer multiple networks from microbial data
• Latent Space Temporal Model of Microbial Abundance to Predict Domination and Bacteremia
• Statistical computation methods for microbiome compositional data network inference