Replication Data for: Unravelling the Dark Web: explainable inference of the diversity of microbial interactions

The functional diversity of microbial communities emerges from a combination of the great number of species and the many interaction types, such as competition, mutualism, predation or parasitism, in microbial ecological networks. Understanding the relationship between microbial networks and the services and functions delivered by the microbial communities is a key challenge for Microbial Ecology, particularly as so many of these interactions are difficult to observe and characterize. We believe that this 'Dark Web' of interactions could be unravelled using an explainable machine learning approach, called Abductive/Inductive Logic Programming (A/ILP) in the R package InfIntE, which uses mechanistic rules (interaction hypotheses) to infer directly the network structure and interaction types. Here we attempt to unravel the dark web of the plant microbiome embodied in metabarcoding data sampled from the grapevine foliar microbiome. Using synthetic, simulated data, we first show that it is possible to satisfactorily reconstruct microbial networks using explainable machine learning. Then we confirm that the dark web of the grapevine microbiome is diverse, being composed of a range of interaction types consistent with the literature. This first attempt to use explainable machine learning to infer microbial interaction networks advances our understanding of the ecological processes that occur in microbial communities and allows us to infer specific types of interaction within the grapevine microbiome that could be validated through experimentation. This work will have potentially valuable applications, such as the discovery of antagonistic interactions that might be used to identify potential biological control agents within the microbiome.