Fig. 6.

Multivariate modeling of UC serum analytes. Multivariate data analysis of serum analytes using partial least squares analysis to identify discriminatory features between UC patients (black squares, n = 20) and non-IBD controls (green squares, n = 29). (A) A 2D projection of the partial least-squares multivariate analysis, which produced separation of UC patients from non-IBD controls. The model produced R²Y = 0.87 (model fit) and Q²(cum) = 0.82 (model prediction). (B) List of the top VIPs (VIP > 1.0) in the dataset that were most influential on the data separation.