Determination of Effect Sizes for Power Analysis for Microbiome Studies Using Large Microbiome Databases.

Area of Science:

• Microbiome research
• Computational biology
• Statistical genetics

Background:

• Microbiome studies generate large datasets, requiring robust statistical methods for analysis.
• Effect size and power calculations are crucial for designing statistically sound future microbiome research.
• Existing tools may lack flexibility in analyzing diverse metadata variables and microbiome metrics.

Purpose of the Study:

• To introduce Evident, a novel software tool for deriving effect sizes from microbiome data.
• To enable power calculations for future microbiome studies using existing large-scale datasets.
• To demonstrate the utility of Evident for analyzing various metadata and microbiome metrics.

Main Methods:

• Evident mines large microbiome databases (e.g., American Gut Project, FINRISK, TEDDY).
• It computes effect sizes for metadata variables (e.g., mode of birth, antibiotics, socioeconomics).
• The software supports common microbiome analysis metrics: alpha diversity, beta diversity, and log-ratio analysis.

Main Results:

• Evident provides a flexible framework for calculating effect sizes across diverse metadata.
• The tool facilitates power analysis, essential for planning microbiome studies.
• Demonstrated efficient analysis on a large dataset with thousands of samples and numerous metadata categories.

Conclusions:

• Evident simplifies and enhances the process of effect size derivation and power analysis in microbiome research.
• The tool supports researchers in planning more robust and statistically powerful future studies.
• Evident's user-friendly interface and broad applicability make it valuable for the computational microbiome community.