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Understanding how microbes interact with each other is key to revealing theunderlying role that microorganisms play in the host or environment and toidentifying microorganisms as an agent that can potentially alter the host orenvironment. For example, understanding how the microbial interactionsassociate with parasitic infection can help resolve potential drug ordiagnostic test for parasitic infection. To unravel the microbial interactions,existing tools often rely on graphical models to infer the conditionaldependence of microbial abundances to represent their interactions. However,current methods do not simultaneously account for the discreteness,compositionality, and heterogeneity inherent to microbiome data. Thus, we builda new approach called "compositional graphical lasso" upon existing tools byincorporating the above characteristics into the graphical model explicitly. Weillustrate the advantage of compositional graphical lasso over current methodsunder a variety of simulation scenarios and on a benchmark study, the TaraOceans Project. Moreover, we present our results from the analysis of a datasetfrom the Zebrafish Parasite Infection Study. Our approach identifies changes ininteraction degree between infected and uninfected individuals for three taxa,Photobacterium, Gemmobacter, and Paucibacter, which are inversely predicted byother methods. Further investigation of these method-specific taxa interactionchanges reveals their biological plausibility. In particular, we speculate onthe potential pathobiotic roles of Photobacterium and Gemmobacter in thezebrafish gut, and the potential probiotic role of Paucibacter. Collectively,our analyses demonstrate that compositional graphical lasso provides a powerfulmeans of accurately resolving interactions between microbiota and can thusdrive novel biological discovery.

Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a Zebrafish Model