Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
Fig 3
Microbiome-metabolome interaction probabilities via mmvec predict strong associations between lipid metabolites with Prevotella, Streptococcus, Atopobium, Sneathia and other clades.
A. The principal component analysis (PCA) biplot displays the top correlations, colored by genus (for microbial features) or by super pathway (for metabolite features). The correlations were tested using mmvec. This method uses neural networks for estimating microbe-metabolite interactions through their co-occurrence probabilities. Microbes (points) and metabolites (arrows) that appear closer to each other in the biplot have a higher likelihood of co-occurring. B. The heatmap depicts the correlation coefficients between ASVs and metabolites; hierarchical clustering was done via average weighted Bray-Curtis distance. ASVs were determined using the consensus taxonomy (see Materials and Methods section).