2.7 Statistical Analysis
The EZInfo 3.0 software (Umetrics, Umeå, Sweden) was used to perform multivariate analysis on the metabolomics dataset. Data was centered, pareto scaled and subsequently analyzed by principal component analysis (PCA), an unsupervised approach to visualize the metabolic differences between no AKI and AKI patients at each timepoint. Orthogonal partial least squares discriminant analysis (OPLS-DA), a supervised discriminatory analysis, was used for the pairwise discrimination of no AKI and AKI patients at each timepoint. For each OPLS-DA, metabolites were ranked by their variable importance in projection (VIP) values and features with VIP values ≥ 1 were considered to have discriminatory value in discriminating between no AKI and AKI. This VIP filtering was repeated until OPLS-DA model statistics (R2 and Q2 values) were maximized to select for the most important features to annotate. The final optimized OPLS-DA model was used to generate a list of features to identify, using a VIP value threshold of ≥ 1 and correlation (p(corr)) values less than -0.4 and greater than 0.4.
Features determined to have discriminatory value were analyzed by two-way ANOVA with Benjamini-Hochberg false discovery rate (FDR) correction. Individual features that were found to be significantly different by AKI classification following two-way ANOVA and FDR correction were further analyzed by pairwise t-tests comparing no AKI and AKI patients at each timepoint, with p-values adjusted for multiple comparisons using Bonferroni correction. To find metabolites altered over time, serum and urine features were analyzed by one-way ANOVA with FDR correction, followed by Tukey’s test for metabolites significant by one-way ANOVA after FDR correction. p<0.05 was considered as significantly significant for all univariate data analysis.
Receiver operating characteristic (ROC) curves were generated and area under the ROC (AUROC) values were calculated using MetaboAnalyst 5.0.