Bias-mitigated microbiome inference refines coronary artery disease signature

Technical Summary: Bias-mitigated microbiome inference refines coronary artery disease signature

The Problem
Microbiome research faces a critical challenge in accurately identifying Differential Abundance (DA) of taxa, a task essential for linking microbial communities to cardiovascular, metabolic, and oncological diseases. Current inference is systematically distorted by four distinct sources of bias:

1. Loss of total microbial load: Variations in the total number of cells across samples.
2. Taxa measurement efficiencies: Differences in how efficiently specific taxa are detected or sequenced.
3. Arbitrary pseudocounts: The necessity of adding artificial values to handle pervasive zeros (sparsity), which introduces subjectivity.
4. Contamination: The presence of exogenous DNA, a factor recently implicated in high-profile retractions.

Existing DA methods fail to simultaneously account for all four of these bias sources, often relying on data transformations, parametric assumptions, or the assumption that the majority of taxa are non-DA.

Methodology
To address these limitations, the authors introduce BootDA, a non-parametric, bootstrap-based inference method. Key methodological features include:

• Explicit Bias Modeling: BootDA models all four bias sources directly without requiring data transformations or the addition of pseudocounts.
• Non-Parametric Approach: The method avoids parametric assumptions about data distribution.
• No "Majority Non-DA" Assumption: Unlike many existing tools, BootDA does not assume that most taxa are unchanged.
• De Novo Decontamination: The method demonstrates the ability to handle contamination even in the absence of negative controls, specifically in low-biomass settings where contamination may constitute ~50% of the counts.

Key Results
The performance of BootDA was evaluated through semi-parametric simulations designed to preserve the sparsity (>70% zeros) and correlation structure of real 16S amplicon data.

• Superior Sensitivity: BootDA achieved the highest sensitivity among tested methods, including ANCOM-BC2, LinDA, MaAsLin 3, and Wilcoxon tests, while maintaining strict control over the False Discovery Rate (FDR).
• Robustness: Performance remained stable in low-biomass scenarios with high contamination levels, confirming its decontamination capabilities without negative controls.
• Clinical Application: When applied to a coronary artery disease (CAD) cohort, BootDA refined the previously identified microbial signature. It narrowed the signature down to two co-enriched genera, Klebsiella and Gemmiger, while successfully excluding taxa likely to be contaminants.

Significance and Claims
The paper claims that BootDA provides a more rigorous framework for microbiome inference by explicitly addressing the four major biases that currently distort DA analysis. By eliminating the need for arbitrary pseudocounts and parametric assumptions, the method offers a more reliable tool for identifying true biological signals. The authors posit that BootDA is not only effective for 16S amplicon data but could generalize to other forms of sparse, high-dimensional biological data. The tool is made available as an R package to facilitate broader adoption in the field.