A Bayesian method for detecting pairwise associations in compositional data
Fig 4
The BAnOCC model controls type I error while maintaining power.
Results on simulated data comprising SparseDOSSA-derived compositions modeled on a low-diversity dataset with 14 features. The type I error rate is controlled at the 0.05 level for BAnOCC and approximately so for SparCC, CCLasso, and SPIEC-EASI (MB), but not for simplicial variation or Spearman correlation (on the composition, a negative control). BAnOCC maintains good power across all true correlation values, but as expected has better power for stronger true correlation values. Type I and type II error rates are determined by correct or incorrect rejection of H0 based on inference (simplicial variation, SparCC, Spearman correlation, and BAnOCC) or estimation (CCLasso and SPIEC-EASI). * = rejection of H0 based on estimation; ** = rejection of H0 based on inference from credible intervals; all others, rejection of H0 based on inference from p-values. (S6 Fig and S7 Fig).