A Bayesian method for detecting pairwise associations in compositional data
Fig 3
BAnOCC infers the correct unobserved abundance correlation matrix in four scenarios simulated to be challenging.
Each column represents one four datasets simulated to evaluate methods for identification of correlations from compositional data: “simple”, with no true correlations and no negative dominant correlation; “high spurious”, with no true correlations and the presence of a negative dominant correlation; “retained spike” with several true correlations and no negative dominant correlation; and “reversed spike” with several true correlations and a negative dominant correlation between two positively correlated features. The top row shows the true correlation matrix. The second row shows the uncorrected compositional correlations as estimated using the 1,000 samples in the simulated data. Each of the subsequent rows shows the log-basis correlation estimate
and the associated inference using the compositional data for Pearson correlation, BAnOCC, and CCLasso, respectively.