  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.
