Testing Microbiome Community Differences in High Dimensions: A Bootstrap Approach for Compositional Data

2026-07-28

AI Summary

Microbiome data are compositional and high-dimensional, posing challenges for conventional methods in detecting community structure differences between groups. This study proposes a hypothesis testing framework based on empirical bootstrap, systematically applying bootstrap resampling to two-sample, paired, and multi-sample mean comparisons of compositional data while respecting the simplex constraint and enabling high-dimensional statistical inference. Applied to studies of colorectal adenoma/cancer and preterm birth–associated vaginal microbiota, the method successfully identified clinically significant differences overlooked by traditional approaches—such as age-related shifts in adenoma-associated microbial communities and race-specific microbial signatures—demonstrating superior detection power and robustness.

Abstract

Understanding differences in microbial community structure is critical for uncovering risk factors and mechanisms underlying diseases such as colorectal cancer and preterm birth. Microbiome data present unique statistical challenges because they are compositional in nature, violating assumptions of many classical inference procedures. We propose an empirical bootstrap framework that enables robust hypothesis testing for equality of microbial community means across groups, including two-sample, paired, and multi-sample settings. The method accounts for the simplex structure of microbiome data and provides valid inference even in high-dimensional regimes. Through applications to two large-scale studies, fecal microbiota in colorectal adenoma and cancer patients, and vaginal microbiota in pregnancy with preterm birth outcomes-we demonstrate that our approach identifies clinically meaningful differences that conventional methods fail to detect, such as age-related differences in adenoma prevalence and race-associated disparities in vaginal microbiome composition. These results highlight the potential of resampling-based inference for advancing microbiome research, improving reproducibility, and uncovering clinically relevant microbial signatures.

Problem

• microbiome
• compositional data
• high-dimensional
• hypothesis testing
• community differences

Innovation

• bootstrap
• compositional data
• microbiome
• high-dimensional inference
• simplex geometry