Figure 6. Phenome-wide crosstalk and predictive modelling.

a, Metagenome–phenome matrix correlation network computed for the patients with matching metagenomic and phenomic profiles (n = 56) using the modified Rv correlation matrix coefficient. Each phenomic table corresponds to a node and edges represent the relationships between tables, i.e., the per cent of shared information, derived from the Rv² matrix correlation coefficient corresponding to the proportion of variance shared by the two tables – which like a squared Pearson’s correlation coefficient (r²) – corresponds to the proportion of explained variance between two variables. b, Discriminative power of a supervised multivariate model (OPLS-DA) fitted with patients with matching metagenomic and phenomic profiles (n = 56) to predict new samples, using random permutation testing (10,000 iterations). (c-d), Performance of classification of liver steatosis status (n = 10, vs. others, n = 46) based on clinical data (c) or matching molecular phenomic and gut metagenomic profiles (d). ROC curves (mean + 95% confidence interval) were obtained for the cross-validated model predictions derived from the O-PLS-DA model, reaching an AUC of 87.07%, corresponding to the successful prediction rate. Groups for all panels are: no steatosis (grade 0), n = 10; steatosis (grades 1-3), n = 46. Data are mean ± pointwise confidence bounds.