![]() Ecological interactions can be grouped into six major categories including mutualism (positive-positive), competition (negative-negative), antagonism (positive-negative, further includes predation and parasitism), commensalism (positive-neutral), amensalism (negative-neutral) and neutralism (neutral-neutral). Interactions between members of a microbial community determine emergent phenomena such as homeostasis in the ecosystem and overall function of the microbiome. agriculture, food processing, disease biology and healthcare. Microbial communities represent complex systems that impact various aspects related to human health, e.g. ![]() Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. 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.Ĭharacterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. 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. Benchmarking 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). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. ![]() We 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). Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. With rapid adoption of next-generation 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. ![]() The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized.
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