Abstract
BACKGROUND: Detecting biomarkers is a key objective in microbiome research, often done through 16S rRNA amplicon sequencing or shotgun metagenomic analysis. A critical step in this process is differential abundance (DA) analysis, which aims to pinpoint taxa whose abundance significantly differs between groups. However, DA analysis remains challenging due to high dimensionality, compositionality, sparsity, inter-taxa correlations, uneven abundance distributions, and missing values-all which hinder our ability to model the data accurately. Despite the availability of many DA tools, balancing high statistical power with effective false discovery rate (FDR) control remains a major limitation. RESULTS: Here, we introduce a novel approach for DA analysis that integrates counts adjusted with Trimmed Mean of M-values (CTF) normalization and Centered Log Ratio (CLR) transformation with Generalized Estimating Equation (GEE) model. We benchmarked our approach against eight widely used tools employing both simulated and real datasets in cross-sectional and longitudinal settings. While several tools (e.g. MetagenomeSeq, edgeR, DESeq2 and Lefse) achieved high sensitivity, they often failed to adequately control the FDR. In contrast, our method demonstrated high sensitivity and specificity when compared to other approaches that successfully controlled the FDR, including ALDEx2, limma-voom, ANCOM, and ANCOM-BC2. CONCLUSIONS: Our approach effectively addresses key challenges in microbiome data analysis across both cross-sectional and longitudinal designs. Integrated into the R package metaGEENOME (https://github.com/M-Mysara/metaGEENOME), our framework provides a flexible, scalable and statistically robust solution for DA analysis, offering improved FDR control and enhanced performance for biomarker discovery in microbiome studies.