The Use of Confidence Intervals in Differential Abundance Analysis of Microbiome Data

Differential abundance analysis (DAA) is a critical task in microbiome research aimed at identifying microbial signatures that reliably characterize groups. Research suggests that microbiome systems are relatively stable and resilient, yet even small changes under certain conditions can trigger dysbiosis. The high dimensionality of microbiome datasets exacerbates the challenge of detecting such changes by posing a multiple comparison problem that requires hypothesis filtration. Standard filtration using multiple comparison correction procedures is designed for scenarios with a high number of true positives and is often too conservative for microbiome data, where the proportion of true signals can be very low. Therefore, there is a substantial need for hypothesis filtration methods tailored to microbiome data. Confidence intervals (CIs) for between-group differences offer a powerful alternative to p-value filtration, as their range simultaneously conveys information about the significance, potential magnitude, and direction of the effect, as well as the certainty of the estimate itself. Microbial data can be adequately modeled using a negative binomial (NB) distribution, and its location parameter can be robustly estimated with the Hodges–Lehmann estimator (HLE). Using synthetic and experimental data, we demonstrate that hypothesis filtration based on CIs for the two-sample HLE is a robust method for comparing microbial data. Our analysis demonstrates that the HLE-CI approach provides the same level of precision as filtration using multiple-adjustment methods while achieving significantly higher recall in microbiome DAA. The results of this study suggest that HLE-CI-based filtration can be an effective step in the search for microbiome biomarkers.