Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
Fig 7
Integrating multiple–omics datasets does not dramatically improve overall prediction accuracy; however, different integration of various measurements are needed for the best prediction of distinct features.
Graphs show stepwise accuracy levels for Lactobacillus dominance (A), vaginal pH (B) and genital inflammation (C) when Random Forest models are trained on a single omics dataset or combined data containing 2–3 omics datasets. Lactobacillus dominance can be explained mostly by metabolome data, vaginal pH by metabolome and microbiome datasets, and genital inflammation by metabolome and immunoproteome datasets. Combining omics datasets leads to higher average accuracy scores for Lactobacillus dominance and vaginal pH and genital inflammation classifications, but not for Lactobacillus dominance classification.