Sparse and compositionally robust inference of microbial ecological networks

TL;DR

This paper introduces SPIEC-EASI, a novel statistical method that accurately infers microbial ecological networks from compositional metagenomic data, overcoming technical challenges like data compositionality and limited sample sizes.

Contribution

The paper presents SPIEC-EASI, a new approach combining compositional data analysis with sparse graphical models for reliable microbial network inference.

Findings

SPIEC-EASI outperforms existing methods in synthetic data tests.

It predicts novel microbial interactions in real datasets.

The method effectively handles compositional constraints.

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.