Schwager, EmmaMallick, HimelVentz, SteffenHuttenhower, Curtis2018-01-182017Schwager, Emma, Himel Mallick, Steffen Ventz, and Curtis Huttenhower. 2017. “A Bayesian method for detecting pairwise associations in compositional data.” PLoS Computational Biology 13 (11): e1005852. doi:10.1371/journal.pcbi.1005852. http://dx.doi.org/10.1371/journal.pcbi.1005852.http://nrs.harvard.edu/urn-3:HUL.InstRepos:34652038Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.en-USPhysical SciencesMathematicsProbability TheoryRandom VariablesCovarianceSimulation and ModelingBiology and Life SciencesEcologyMicrobial EcologyEcology and Environmental SciencesMicrobiologyMathematical and Statistical TechniquesBayesian MethodMedical MicrobiologyMicrobiomeGeneticsGenomicsMicrobial GenomicsCommunity EcologyTheoretical EcologyJournal Article2018-01-1810.1371/journal.pcbi.1005852