Journal Article

Impact of study design, contamination, and data characteristics on results and interpretation of microbiome studies

Miller, Aaron W., Agudelo, Jose

2025

Overview

Microbiome studies in low-biomass environments face challenges due to contamination. However, even after implementing strict contamination prevention, control, and analysis measures, the impact of residual contamination on the validity of statistical outcomes in such studies remains a topic of ongoing discussion. Our analyses reveal that key drivers of microbiome study outcomes are group dissimilarity and the number of unique taxa, while contamination has minimal impact on statistical outcomes, primarily limited to the number of differentially abundant taxa detected. A common approach to contamination control involves removing taxa based on published contaminant lists. However, our analysis shows that these lists are highly inconsistent across studies, limiting reliability. Instead, our results support the use of internal negative controls as the most robust means of identifying and mitigating contamination. Collectively, data show that low-biomass microbiome studies have reduced power to detect differences between groups. However, when differences are observed, they are unlikely to be contamination-driven. By prioritizing validated protocols that prevent, assess, and eliminate contaminants through the use of internal negative controls, researchers can minimize the impact of contamination and improve the reliability of results.

• Publisher: American Society for Microbiology,American Society for Microbiology (ASM)
• Subject: Algorithms / contamination / Human Microbiome / Humans / low microbial biomass / microbiome / Microbiota - genetics / real-world data / Research Article / Research Design / simulated data / study design