2014/11/30 by Surojit Biswas, Biswas, Surojit, Meredith McDonald +7
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Mathematics · #Artificial intelligence #Bioinformatics and Genomic Networks #Biology #Computational biology #Computer science #Count data #Data mining #FOS: Biological sciences #Genetics #Gut microbiota and health #Lasso (programming language) #Machine learning #Mathematics #Metagenomics #Multivariate statistics #Plant-Microbe Interactions and Immunity #Poisson distribution #Quantitative Methods (q-bio.QM) #Statistics #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1412.0207
Submitted to RECOMB 2015
arxiv created 2014/11/30 · openalex publication_date 2014/11/30 · arxiv updated 2014/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 Poisson-multivariate 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 ℓ1 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 three community members that associate with Arabidopsis thaliana roots. We conclude that our method provides a structured, accurate, and distributionally reasonable way of modeling correlated count based random variables and capturing direct interactions among them.