Detecting and mitigating doppelganger bias in microbiome data: impacts on machine learning and disease classification.

ABSTRACT: Highly similar microbiome samples - so-called "doppelgänger pairs" - can distort analysis outcomes, yet are rarely addressed in microbiome studies. Here, we demonstrate that even a small proportion of such pairs (1-10% of samples) can substantially inflate machine learning performance across diverse disease cohorts including colorectal cancer (CRC), inflammatory bowel diseases (IBD), Clostridioides difficile infection (CDI), and obesity. Doppelgänger pairs also bias statistical tests and distort microbial network topology. In predictive models, classification accuracy was artificially boosted by 15-30% points across KNN, SVM, and Random Forest classifiers. In association testing, doppelgängers increased false-positive rates and decreased effect size stability; their removal reduced

SUBMITTER: Zhou R 

PROVIDER: S-EPMC12407587 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature