Identification of Separable OTUs for Multinomial Classification in Compositional Data Analysis
High-throughput sequencing has transformed microbiome research, but it also produces inherently compositional data that challenge standard statistical and machine learning methods. In this work, we propose a multinomial classification framework for compositional microbiome data based on penalized log-ratio regression and pairwise separability screening. The method quantifies the discriminative ability of each OTU through the area under the receiver operating characteristic curve ($AUC$) for all pairwise log-ratios and aggregates these values into a global separability index $S_k$, yielding interpretable rankings of taxa together with confidence intervals. We illustrate the approach by reanalyzing the Baxter colorectal adenoma dataset and comparing our results with Greenacre's ordination-based analysis using Correspondence Analysis and Canonical Correspondence Analysis. Our models consistently recover a core subset of taxa previously identified as discriminant, thereby corroborating Greenacre's main findings, while also revealing additional OTUs that become important once demographic covariates are taken into account. In particular, adjustment for age, gender, and diabetes medication improves the precision of the separation index and highlights new, potentially relevant taxa, suggesting that part of the original signal may have been influenced by confounding. Overall, the integration of log-ratio modeling, covariate adjustment, and uncertainty estimation provides a robust and interpretable framework for OTU selection in compositional microbiome data. The proposed method complements existing ordination-based approaches by adding a probabilistic and inferential perspective, strengthening the identification of biologically meaningful microbial signatures.