that standard univariate tests, such as Poisson regression models, Beta-Binomial regression, or t tests are inadequate for cell-type analysis, since they do not account for the compositional nature of the data. While log-ratio transforms from compositional data analysis (such as the ALR used here) can partially mitigate these shortcomings, our Bayesian scCODA framework provided substantial performance improvements across all tested scenarios and is particularly preferable when only few replicates are available. Other methods from the field of microbiome data analysis, such as ANCOM and ANCOM-BC, showed similar detection power, but could not adequately control the false discovery rate in the low-sample regimes.
