IFAA: Robust Association Identification and Inference for Absolute Abundance in Microbiome Analyses

Area of Science:

• Microbiome analysis
• Computational biology
• Statistical ecology

Background:

• Microbiome studies often rely on relative abundance (RA), which is sensitive to the common denominator (library size).
• Changes in one taxon's abundance can falsely alter the RA of all other taxa, leading to inaccurate conclusions.
• Existing methods struggle with compositional data, overdispersion, and zero-inflated microbiome datasets.

Purpose of the Study:

• To develop a novel analysis approach for robust inference on microbial absolute abundances (AAs) in ecosystems.
• To address the limitations of relative abundance (RA) analysis, including the common denominator problem and compositional effects.
• To provide a method that can handle overdispersion and zero-inflated data structures inherent in microbiome data.

Main Methods:

• Proposed the Inference on Absolute Abundance (IFAA) approach for robust microbiome analysis.
• IFAA involves a two-phase strategy: Phase 1 identifies taxa associated with covariates, and Phase 2 estimates association parameters using a reference taxon.
• The method is designed to circumvent the common denominator problem and compositional effects of RA data.

Main Results:

• IFAA demonstrated superior performance compared to established methods, particularly with unbalanced library sizes.
• Simulations and real-world data applications confirmed the robustness and accuracy of the IFAA approach.
• The method effectively addresses overdispersion and zero-inflation in microbiome datasets.

Conclusions:

• The proposed IFAA method offers a significant advancement for microbiome data analysis, enabling more reliable inference on absolute abundances.
• IFAA overcomes critical limitations of traditional RA analysis, reducing the risk of false positive/negative findings.
• This approach is crucial for accurate ecological and health-related interpretations of microbiome composition.