Rank normalization empowers a t-test for microbiome differential abundance analysis while controlling for false...

Area of Science:

• Microbiome analysis
• Bioinformatics
• Statistical modeling

Background:

• Identifying microbes associated with different biological conditions is crucial in microbiome data analysis.
• Standard normalization methods for microbiome data often fail to account for estimation variability, leading to reduced analytical precision.
• Rank normalization, a nonparametric approach, replaces raw counts with intrasample ranks, offering a potential solution.

Purpose of the Study:

• To evaluate rank normalization as an alternative to traditional normalization factor estimation for microbiome data.
• To assess the performance of rank normalization when paired with a two-sample t-test.
• To investigate the robustness and reproducibility of rank normalization on real-world microbiome datasets.

Main Methods:

• Proposed rank normalization as a replacement for normalization factor estimation in microbiome analysis.
• Paired rank normalization with a two-sample t-test for performance evaluation.
• Conducted third-party benchmarking simulations and analyzed two real microbiome datasets.

Main Results:

• Rank normalization demonstrated strong control over the false discovery rate in simulations.
• At sample sizes exceeding 50 per group, rank normalization outperformed common normalization factors paired with t-tests, Wilcoxon rank-sum tests, and R package methodologies.
• Analysis of real datasets produced valid, reproducible results consistent with existing literature.

Conclusions:

• Rank normalization is a promising alternative for microbiome data analysis, offering improved precision and robust control over statistical errors.
• The method shows significant potential for enhancing the reliability and reproducibility of microbiome studies.
• Rank normalization, particularly when combined with a two-sample t-test, provides a valuable tool for identifying microbe-condition associations.