The quality control of the proteomes has been described in detail previously13. Briefly, for each batch, the GIS were used to check for proteins outside of the 95% confidence interval and set to missing. Proteomic analysis in 400 subjects identified 12,691 proteins. Proteins with missing values in more than 50% of the 400 subjects were excluded. Each protein abundance was then scaled by a sample-specific total protein abundance to remove effects of protein loading differences, and then log2 transformed. Outlier samples were identified and removed through iterative principal component analysis (PCA). In each iteration, samples more than four standard deviations from the mean of either the first or second principal component were removed, and principal components were recalculated for the next iteration. A total of 9 outlier samples were removed. We used regression to remove effects of protein batch, MS2 versus MS3 reporter quantitation mode, sex, age at death, postmortem interval, study (ROS vs. MAP), and the final clinical diagnosis of cognitive status from the proteomic profile before estimating the protein weights.
