Using the exported sex, line, and AR values, principal component analyses were performed on the proteome and phosphoproteome. Analyses were conducted in python using pandas and NumPy to import and clean the data (McKinney 2010; Walt et al. 2011). AR values were preprocessed using the standard scaler function in scikit-learn (Pedregosa et al. 2011). Principal component analyses were done using the PCA function in scikit-learn with the total number of components for the analysis being chosen based on the cumulative sum of the components explaining 80% of the total variance. For the proteome, 8 components were chosen for analysis and for the phosphoproteome, 6 components were chosen for analysis. For both analyses, principal component 1 and principal component 2 were plotted with sex, line, or both labels to visualize the output data. Full processes datasets and analyses may be found at https://github.com/dlhagger/AUD-Multi-Omics and raw data files are available upon request.
