Learning Microbial Interaction Networks from Metagenomic Count Data

Many microbes associate with higher eukaryotes and impact their vitality. In order to engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenence, which in large part, demands an understanding of the direct interactions between community members. Toward this end, we’ve developed a Poissonmultivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer, and captures direct taxon-taxon interactions at the multivariate normal layer using an 1 penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real, in planta perturbation experiment of a nine member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among thre...

• Type: Journal
• Year: 2015
• Where: RECOMB
• Authors: Surojit Biswas, Meredith McDonald, Derek S. Lundberg, Jeffery L. Dangl, Vladimir Jojic
• Keywords: Computational Biology, RECOMB 2015