Simplified methods for variance estimation in microbiome abundance count data analysis.

Area of Science:

• Microbiome bioinformatics
• Statistical modeling
• Computational biology

Background:

• Microbiome data analysis presents challenges due to right-skewed and overdispersed abundance counts.
• Standard statistical methods may yield incorrect inferences if these data characteristics are not properly handled.

Purpose of the Study:

• To develop a robust statistical framework for differential abundance analysis of microbiome data.
• To improve the accuracy of statistical inference in the presence of data overdispersion.

Main Methods:

• Integration of Poisson (log-linear) regression with standard error estimation.
• Application of Bootstrap method and Sandwich robust estimation for accurate covariance estimation.
• Validation through extensive simulation studies and analysis of real human gut and vaginal microbiome datasets.

Main Results:

• The proposed framework effectively addresses overdispersion in microbiome data.
• Standard error estimates are accurate, ensuring reliable inference even with incorrect distributional assumptions.
• Demonstrated improved inference accuracy compared to standard methods in simulation studies.

Conclusions:

• The integrated approach provides a simple yet effective solution for challenging microbiome data analysis.
• The covariance estimators are effective in addressing overdispersion and enhancing analytical outcomes.
• The method is widely applicable, as shown by its use on human gut and vaginal microbiome datasets.