Learning accurate representations of microbe-metabolite interactions

Integrating multi-omics datasets is critical for microbiome research, but multiple statistical challenges can confound traditional correlation techniques. We solve this problem by using neural networks to estimate the conditional probability that each molecule is present given the presence of each specific microbe. We show with known environmental (desert biological soil crust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially-produced metabolites and inflammatory bowel disease.