  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.
