Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models.

Area of Science:

• Microbiome research
• Bioinformatics
• Computational biology

Background:

• Microbiome profiling protocols introduce variable efficiencies and processing biases, impeding reproducible and generalizable biological insights.
• Existing batch-correction methods often rely on unmet parametric assumptions or require outcome variables, risking overfitting and lacking interpretability.
• Current data transformations for bias correction can be non-interpretable and introduce artificial values, compromising data integrity.

Approach:

• Introduced DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for processing bias inference and correction.
• DEBIAS-M learns microbe-specific bias-correction factors per batch, minimizing batch effects while maximizing cross-study phenotype associations.
• Validated DEBIAS-M on HIV, colorectal cancer, and cervical neoplasia microbiome datasets, comparing its performance against standard batch-correction techniques.

Key Points:

• DEBIAS-M significantly outperforms commonly used batch-correction methods in microbiome data analysis.
• Inferred bias-correction factors by DEBIAS-M are stable, interpretable, and correlate with experimental protocols.
• The framework enables more robust microbiome data modeling and identification of reproducible, interpretable signals.

Conclusions:

• DEBIAS-M provides a novel and interpretable solution to address processing biases in microbiome studies.
• The framework enhances domain adaptation, facilitating more reliable cross-study microbiome data integration and analysis.
• DEBIAS-M advances the field by enabling the discovery of generalizable and biologically meaningful microbiome-based insights.