Investigating statistical power of differential abundance studies

Area of Science:

• Microbiology
• Statistical analysis
• Bioinformatics

Background:

• Identifying differential abundance of microbial taxa is crucial in microbiome research.
• Low statistical power in these studies can lead to imprecise results and biased effect size estimates.
• Previous research has indicated concerns regarding insufficient power in microbiome analyses.

Purpose of the Study:

• To investigate statistical power in differential abundance analysis of microbiome data.
• To develop a novel method for estimating the power to detect effects at the individual taxon level.
• To assess power as a function of effect size (fold change) and mean abundance.

Main Methods:

• Analysis of seven real-world case-control microbiome datasets.
• Development of a novel simulation method for microbiome data.
• Estimation of statistical power for detecting differential abundance in individual taxa.

Main Results:

• Power to detect differential abundance varies significantly with effect size and mean abundance.
• Typical differential abundance studies are often underpowered for identifying changes in individual taxa.
• The developed method provides a way to estimate power based on specific study parameters.

Conclusions:

• Standard differential abundance analyses in microbiome studies may not have sufficient power to detect biologically relevant changes in individual taxa.
• Researchers should carefully consider statistical power when designing microbiome studies.
• The novel power estimation method can aid in designing more robust microbiome studies.