mbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Area of Science:

• Microbiology
• Bioinformatics
• Statistical Modeling

Background:

• Microbiome studies often rely on relative abundance data, which can be misleading due to unknown microbial loads and sequencing variations.
• Microbiome data is characterized by count values, over-dispersion, and a high proportion of zeros, complicating differential abundance analysis.

Purpose of the Study:

• To introduce mbDecoda, a model-based method for debiased analysis of sparse microbiome compositions.
• To enable accurate and reliable absolute abundance analysis from relative abundance data.

Main Methods:

• Utilizes a zero-inflated negative binomial model with a log link function to model mean abundance.
• Employs an Expectation Maximization algorithm for efficient parameter estimation.
• Incorporates a minimum coverage interval approach to correct for compositional bias.

Main Results:

• mbDecoda effectively addresses challenges of zero-inflation and compositional bias in microbiome data.
• The method allows for the estimation of absolute microbial abundance, improving analytical accuracy.
• Simulations and real-world data analyses show mbDecoda outperforms existing state-of-the-art methods in effectiveness and robustness.

Conclusions:

• mbDecoda provides a robust and reproducible framework for microbiome differential abundance analysis.
• The ability to derive absolute abundance estimates significantly enhances the interpretability of microbiome-host interactions and environmental associations.
• This approach is crucial for accurately identifying pathogenic or probiotic microbes and understanding microbiome-clinical phenotype links.