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

Abstract

Every step in common microbiome profiling protocols has variable efficiency for each microbe, for example, different DNA extraction efficiency for Gram-positive bacteria. These processing biases impede the identification of signals that are biologically interpretable and generalizable across studies. ‘Batch-correction’ methods have been used to address these issues computationally with some success, but they are largely non-interpretable and often require the use of an outcome variable in a manner that risks overfitting. We present DEBIAS-M (domain adaptation with phenotype estimation and batch integration across studies of the microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using diverse benchmarks including 16S rRNA and metagenomic sequencing, classification and regression, and a variety of clinical and molecular targets, we demonstrate that using DEBIAS-M improves cross-study prediction accuracy compared with commonly used batch-correction methods. Notably, we show that the inferred bias-correction factors are stable, interpretable and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M facilitates improved modelling of microbiome data and identification of interpretable signals that generalize across studies. DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates robust analyses that generalize across datasets.