Differential gene (FDR<0.05), protein (p<0.05), and phosphorylated protein (p<0.05) expression profiles were classified and visualized using python and matplotlib-Venn (https://github.com/konstantint/matplotlib-Venn). Phosphorylated protein master accession numbers were converted to GeneIDs using UniProt’s mapping tool so the overlap of all three datasets could be computed and plotted in a Venn diagram. Similarly, the overlap in enriched GO terms and pathways from the network of significant differentially expressed genes (filtered using log2≥±0.5), proteins, and phosphorylated proteins were computed and used to create Venn diagrams. A linear regression was computed using GraphPad Prism (version 8.2) to model the relationship between log2FC of differentially expressed genes and the log2AR of differentially expressed proteins. We confirmed that residuals were normally distributed and the relationship between log2FC and log2AR was linear. An integrated pathway analysis was completed using the RNA-sequencing and proteomic data by the PaintOmics3 tool (Hernández-de-Diego et al. 2018). Significant differentially expressed genes and proteins log2FC and log2AR values, respectively, were used to generate overviews for pathways of interest by locating and displaying changed expression patterns. The PaintOmics3 job with the associated datasets of differentially expressed genes and proteins can be utilized for those who may have interest in other KEGG pathways (http://www.paintomics.org/?jobID=dCEK1fF2R4).
