Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data

Area of Science:

• Microbiome research
• Statistical modeling
• Bioinformatics

Background:

• Microbiome studies often yield operational taxonomic unit (OTU) count data characterized by excess zeros.
• These zeros, or zero-inflation, are frequently overlooked, potentially leading to inaccurate analyses.
• Standard statistical models may not adequately capture the complexities of zero-inflated microbiome data.

Purpose of the Study:

• To compare the performance of various statistical models for analyzing zero-inflated microbiome data.
• To evaluate model selection strategies for identifying the most appropriate model for such data.
• To apply these models to a real-world gut microbiome study.

Main Methods:

• Extensive simulations were conducted to assess model performance under different zero-inflation scenarios.
• Parametric, non-parametric, hurdle, and zero-inflated models were compared.
• Model evaluation focused on type I error, statistical power, goodness-of-fit, and parameter estimation accuracy.
• Akaike information criterion (AIC) and Vuong test were used for model selection.

Main Results:

• Hurdle and zero-inflated models demonstrated well-controlled type I errors and higher statistical power.
• These models provided better goodness-of-fit measures and more accurate, efficient parameter estimations.
• Hurdle models showed comparable performance to zero-inflated models for the count component.
• Hurdle models exhibited greater stability when structural zeros were absent.

Conclusions:

• Hurdle and zero-inflated models are recommended for analyzing zero-inflated microbiome data.
• These advanced models improve the reliability and accuracy of microbiome study findings.
• Appropriate model selection is crucial for robust interpretation of microbiome data, especially in large subject cohorts.