Highlight talk – Theme: Systems
Abstract
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. SPIEC- EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a benchmark SPIEC-EASI is accompanied by a set of computational tools to generate synthetic OTU count data from a set of diverse underlying network topologies (where generated counts match the marginal properties of designated real data-sets, such as zero inflation, condition number and degree of over-dispersion). SPIEC-EASI outperforms previous 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 microbiome project with multiple identically-designed data-collection phases.
Authors
Richard Bonneau, NYU & Simons Foundation, United States
Zach Kurtz, NYU, United States
Christian Müller, Simons Foundation, United States
Emily Miraldi, Simons Foundation, United States