Multi-part strategy for testing differential taxa abundance in sequencing data: A simulation study with an...

Area of Science:

• Microbiome research
• Statistical analysis of sequencing data
• Bioinformatics

Background:

• Microbiota sequencing data presents unique challenges: non-negative, highly skewed counts with many zeros.
• Standard statistical methods may not be optimal for analyzing microbiome composition across study arms.
• Accurate comparison of microbial taxa abundance is crucial for understanding biological implications.

Purpose of the Study:

• To develop and evaluate a flexible multi-part statistical strategy for comparing microbiome data across study arms.
• To address the limitations of standard statistical methods when applied to complex microbiota sequencing data.
• To provide a robust method that maintains appropriate Type I error rates.

Main Methods:

• A multi-part strategy combining a two-part test, Wilcoxon sum-rank test, Chi-square test, and Barnard's test was explored.
• Test selection within the strategy is based on the observed data structure.
• The strategy's Type I error rate was evaluated through simulation studies and applied to real-world data using SAS software.

Main Results:

• The multi-part strategy demonstrated a well-controlled Type I error rate across various simulated scenarios.
• The approach successfully identified statistical differences in taxa abundance between study arms.
• Different biological insights can be derived based on the statistical tests employed within the strategy.

Conclusions:

• Choosing appropriate statistical methods tailored to data structure is essential for microbiome comparisons.
• The proposed multi-part strategy offers a reliable and adaptable approach for analyzing taxa abundance.
• The provided SAS macro facilitates the application of this method in microbiome research.