Flow cytometry generates inherently compositional count data: observed cell population counts are constrained to sum to the total number of acquired events, which precludes direct inference about absolute cellular abundance. Despite this constraint, most differential abundance (DA) analyses in cytometry rely on methods originally developed for RNA sequencing, such as edgeR with trimmed mean of M-values (TMM) normalization, without fully acknowledging the conditional nature of the resulting estimates. Here, we offer a critical perspective on the statistical implications of compositionality for flow cytometry DA analysis. We compare the theoretical foundations and interpretational scope of three per-population DA testing methods (edgeR/TMM), centered log-ratio transformation with linear modeling (CLR + limma), and analysis of composition of microbiomes with bias correction-2 (ANCOM-BC2), and evaluate their performance through a comprehensive simulation study spanning seven scenarios at two sample sizes (n = 50 and n = 200 per group). We additionally evaluate compositional data analysis using kernels (CODAK) as a global, omnibus screening test for overall compositional differences, implemented via PERMANOVA on Aitchison distances. Our results show that, considering sample size, zero-inflation, dispersion, and correlation among cell populations in addition to false discovery rate (FDR) and sensitivity, the methods developed for microbiome data analysis, ANCOM-BC2 and CLR + limma, emerge as better-suited methods for per-population DA analysis of flow cytometry data than edgeR/TMM, while CODAK/PERMANOVA offers a robust first-stage screen for overall compositional shifts.

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