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bioRxiv · 10.1101/2020.11.23.394999

BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data

Li, C.·Av-Shalom, T. V.·Tan, J. W. G.·Kwah, J. S.·Chng, K. R.·Nagarajan, N.
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Abstract

MotivationThe structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of metagenomic sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional metagenomic datasets for unravelling ecological structure in a scalable manner thus remains an open problem. MethodsWe present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. ResultsBenchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n=4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of metagenomic data.

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Li, C., Av-Shalom, T. V., Tan, J. W. G., Kwah, J. S., Chng, K. R., Nagarajan, N.. 2020-11-24. BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data. https://doi.org/10.1101/2020.11.23.394999

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