For Principal Component analysis (PCA), the replicates per group and time points were aggregated by calculating their means and the first two principal components (PCs) were used to visualize the global similarity of the data. Analysis of variance (ANOVA) was applied to determine differentially expressed genes (DEGs) in pair-wise comparisons of the groups (e.g., BPD vs. control for NP, E and L, respectively) and combined for all groups. The ANOVA assessed the main effects of disease status (BPD vs. control) and differentiation stages (NP, E, L) as well as interactions between the two variables. ANOVA was conducted for p-values for each of the three effects (disease status, differentiation stage, and interaction between two variables) along with adjusted p-values for multiple testing using Benjamini-Hochberg-false discovery rate (FDR) correction. Differentially Expressed Genes (DEG) were defined as the probes which had FDR < = 0.2, mean signals > = 250 in at least one group (control or BPD) and absolute fold change > = 1.5 in all chips.
