Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach...

Area of Science:

• Microbiome research
• Statistical bioinformatics
• Microbial ecology

Background:

• Microbiome data analysis requires robust statistical methods for identifying microbial associations with health and disease.
• Existing statistical approaches often fail due to microbiome data characteristics like zero inflation and differential read depth, leading to power loss or high false positive rates.
• There is a need for non-parametric methods robust to distributional assumptions and powerful under heterogeneous effects, allowing covariate adjustment.

Purpose of the Study:

• To introduce a novel statistical method, the zero-inflated quantile (ZINQ) approach, for microbiome association testing.
• To address limitations of current methods in handling zero inflation, distributional assumptions, and covariate adjustment in microbiome data.
• To provide a powerful and robust alternative for differential abundance analysis in microbiome studies.

Main Methods:

• The ZINQ approach employs a two-part quantile regression model to handle zero inflation inherent in microbiome data.
• It integrates a logistic regression test for zero counts with rank-score based tests on non-zero quantiles, allowing covariate adjustment.
• This non-parametric, regression-based method is applicable across various normalization strategies without strict distributional assumptions.

Main Results:

• Simulations based on real microbiome data demonstrate that ZINQ achieves equivalent or superior statistical power compared to existing methods.
• ZINQ provides enhanced control over false positive rates, improving the reliability of identified microbial associations.
• The method's performance was validated across diverse scenarios and real-world datasets.

Conclusions:

• The ZINQ approach offers a powerful and robust statistical framework for testing associations between microbiota and clinical variables.
• It represents a significant advancement in microbiome differential abundance analysis, overcoming limitations of prior techniques.
• ZINQ facilitates a more comprehensive understanding of the microbiome's role in various conditions.