Additional file 2 - datasets and scripts for metabolome analysis

For the metabolome data, all calculations and statistical analyses were performed using Python. The Shapiro-Wilk test was performed to identify the metabolites whose concentrations in the blood showed a normal distribution, and Student’s t-test was used to compare their concentrations in blood samples for the IUGR and NORM groups. Metabolites whose concentrations did not show a normal distribution were compared between the two groups using the non-parametric Mann–Whitney test. The Benjamini–Hochberg correction was applied in both cases to account for the risk I inflation associated with multiple comparisons. Before being subjected to unsupervised and supervised algorithms, the concentration of each metabolite was normalised and centred. Principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were employed as unsupervised and supervised methods in the multivariate analysis, respectively. PCA was used for the identification of outliers (Mahalanobis distance metric) as well as the spontaneous clustering of similar samples in the scatter plot of the two principal components. In the OPLS-DA analysis, the X matrix consisted of metabolite concentrations, while the Y vector contained information regarding the group (IUGR or NORM). The goodness of fit of the OPLS-DA model (R2Y) was reported, and predictive performance was assessed through cross-validation. Metrics such as the predictive ability of the model (Q2Y) and the predictive ability of permuted models (Q2Y-perm) were calculated for evaluation. OPLS-DA loading plots were used to illustrate the metabolites that contributed the most to the separation between the IUGR and NORM groups. The identification of metabolites of interest was made through the combination of the variable importance in the projection (VIP) and the loading between the metabolite in the X matrix and the predictive latent variable (pLV) of the model. Metabolites with VIP >1.0 and absolute high loading values were considered important in the metabolomics signature (De la Barca et al., 2022).
References:
Chao de la Barca JM, Chabrun F, Lefebvre T, Roche O, Huetz N, Blanchet O, Legendre G, Simard G, Reynier P, Gascoin G: A Metabolomic Profiling of Intra-Uterine Growth Restriction in Placenta and Cord Blood Points to an Impairment of Lipid and Energetic Metabolism. Biomedicines 2022, 10:1411.