Environmentally-Driven Edge Detection Program (ENDED)

Even though ecological interactions among microbes are fundamental for ecosystem functioning, most of them remain unknown. High-throughput omics can help unveiling microbial interactions by inferring species correlations over space or time, which can be represented as networks. Associations in these networks can indicate ecological interactions between species or alternatively, similar or different environmental preferences. Therefore, it is important to disentangle these associations and determine whether two species are correlated because they interact ecologically or because they are correlated to an abiotic or biotic environmental factor. We developed an approach to determine whether or not two species are associated in a network due to environmental preference. We use four methods (Sign Pattern, Overlap, Interaction Information, and Data Processing Inequality) that in combination can detect what associations in a network are environmentally-driven. The approach is implemented in the publicly available software tool EnDED.