PGLasso: Microbial Community Detection through Phylogenetic Graphical Lasso

Abstract: Due to the recent advances in high-throughput sequencing technologies, it becomes possible to directly analyze microbial communities in the human body and in the environment. Knowledge of how microbes interact with each other and form functional communities can provide a solid foundation to understand microbiome related diseases; this can serve as a key step towards precision medicine. In order to understand how microbes form communities, we propose a two step approach: First, we infer the microbial co-occurrence network by integrating a graph inference algorithm with phylogenetic information obtained directly from metagenomic data. Next, we utilize a network-based community detection algorithm to cluster microbes into functional groups where microbes in each group are highly correlated. We also curate a "gold standard" network based on the microbe-metabolic relationships which are extracted directly from the metagenomic data. Utilizing community detection on the resulting microbial metabolic pathway bipartite graph, the community membership for each microbe can be viewed as the true label when evaluating against other existing methods. Overall, our proposed framework Phylogenetic Graphical Lasso (PGLasso) outperforms existing methods with gains larger than 100% in terms of Adjusted Rand Index (ARI) which is commonly used to quantify the goodness of clusterings.