metaGEENOME

metaGEENOME provides a framework for differential abundance analysis of microbiome data, integrating CTF normalization, CLR transformation, and GEE modeling to address challenges in biomarker discovery across cross-sectional and longitudinal studies.

Key Features:

• CTF Normalization: Adjusts count data using Trimmed Mean of M-values normalization prior to downstream analysis.
• CLR Transformation: Applies Centered Log Ratio transformation to address the compositional nature of microbiome data.
• GEE Modeling: Employs Generalized Estimating Equation models for statistical analysis accommodating both cross-sectional and longitudinal study designs.
• FDR Control: Demonstrates improved false discovery rate control compared to approaches such as MetagenomeSeq, edgeR, DESeq2, and Lefse.
• Sensitivity and Specificity: Achieves high sensitivity and specificity relative to other FDR-controlling methods including ALDEx2, limma-voom, ANCOM, and ANCOM-BC2.
• Benchmarking: Validated against eight widely used tools using both simulated and real datasets.

Scientific Applications:

• Biomarker Discovery: Identifies taxa whose abundance significantly differs between groups in microbiome research.
• 16S rRNA Amplicon Sequencing Analysis: Supports differential abundance analysis of data generated from 16S rRNA amplicon sequencing.
• Shotgun Metagenomic Analysis: Supports differential abundance analysis of data generated from shotgun metagenomic analysis.
• Cross-Sectional Studies: Enables differential abundance analysis in single-timepoint study designs.
• Longitudinal Studies: Enables differential abundance analysis in repeated-measures study designs.

Methodology:

metaGEENOME integrates counts adjusted with Trimmed Mean of M-values (CTF) normalization and Centered Log Ratio (CLR) transformation with Generalized Estimating Equation (GEE) model.