Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
Fig 5
Metabolites (particularly amino acids, peptides and nucleotides) and inflammatory cytokine MIF are the best predictors of vaginal pH.
Integrated vaginal microbiome, metabolome, and immunoproteome profiles were used as predictive features for training cross-validated Random Forest classifiers to predict whether a subject’s vaginal pH was low (≤ 5.0) or high (> 5.0). Combined measurements predict vaginal pH at an overall accuracy rate of 77.8%. A 1.5-fold improvement over baseline accuracy was observed. Receiver operating characteristics (ROC) analysis showing true and false positive rates for each group, indicating weak predictive accuracy (micro-average AUC = 0.72) for both low (AUC = 0.71) and high pH groups (AUC = 0.71) (A). The confusion matrix illustrates the proportion of times each sample receives the correct classification when evaluating the classifier at a threshold of 0.5 (B). The graphs depict the 25 most strongly predictive features ranked by their mean Gini importance score across all 10 trained classifiers, a measure of their overall contribution to classifier accuracy (C).