ZINQ-L: a zero-inflated quantile approach for differential abundance analysis of longitudinal microbiome data

Area of Science:

• Microbiome research
• Statistical genetics
• Computational biology

Background:

• Microbiome analysis is crucial for understanding disease mechanisms and treatment effects over time.
• Longitudinal microbiome data present challenges like sparsity, over-dispersion, and within-subject correlations.
• Existing methods often rely on restrictive distributional assumptions, leading to inflated false discovery rates and inability to detect heterogeneous associations.

Purpose of the Study:

• To develop a robust statistical method for differential abundance testing in longitudinal microbiome studies.
• To address the limitations of existing methods in handling complex microbiome data distributions and identifying heterogeneous associations.

Main Methods:

• Proposed the zero-inflated quantile approach for longitudinal (ZINQ-L) microbiome analysis.
• Employed a mixed-effects quantile rank-score-based test for hypothesis testing.
• Incorporated a logistic model for presence-absence status and quantile tests adjusted for zero inflation.
• Utilized a regression approach with minimal distributional assumptions for robustness.

Main Results:

• ZINQ-L demonstrated improved power in detecting true signals in simulation studies.
• The method effectively controlled false discovery rates.
• Application to a kidney transplant microbiome study validated its performance.

Conclusions:

• ZINQ-L offers a robust and powerful approach for identifying taxa associated with outcomes in longitudinal microbiome studies.
• It complements existing methods by providing flexibility and improved power.
• The method enhances the understanding of microbiome-host interactions in health and disease.