Skip to main content
. Author manuscript; available in PMC: 2021 Oct 1.
Published in final edited form as: Arterioscler Thromb Vasc Biol. 2021 Feb 4;41(4):1446–1458. doi: 10.1161/ATVBAHA.120.315321

Figure 4. Partial least squares discriminate analysis validated metabolites identified by logistic regression with interactions to predict SLE-P.

Figure 4

(A) Model optimisation – model with different components and features kept in the analysis were analysed, with each colour representing a different number of components (Comp), number of features kept in the analysis on the x-axis, and the overall error on the y-axis. (B) sPLS-DA plot to validate top hits from the logistic regression with interactions. sPLS-DA is a supervised clustering method which separates SLE-P from SLE-NP. (C) Features included in the sPLS-DA plotted with their factor loading value. (D) Visualisation of the weighting and correlation of each metabolite in component 1 and 2 on the sPLS-DA model. See Supplementary Table III for metabolite abbreviations.