A receiver operating characteristic (ROC) curve was used to compare the performance of local window cutoffs versus a global cutoff on the test slide [22] . Area under ROC curve (AUC) is a performance indicator that ranges from 0 to 1, with 1 for the best performing method. Using GST-Sla2p and GST-Myo4p as positive controls and BSA as negative controls, the sensitivity and specificity for both local and global cutoff methods was estimated. Five window sizes were tested and compared with the global cutoff ( Figure 6 ). Prediction performance is increased significantly when using local windows with nine or more spots on one edge. Thus, a nine by nine window is used as the default in ProCAT since a larger A representative protein microarray with high-quality data Figure 3 A representative protein microarray with high-quality data. The slide image was reconstructed from a protein microarray experiment with minimal noise in the data. Density plots of signals in local 37 by 37 windows (window size 9) for all spots were computationally combined, and they showed high similarities. window size results in increased computing time with only minimal improvement in sensitivity. The AUC value is much larger in local cutoffs (0.992) compared to global cutoffs (0.916) and the improvement is unlikely to be due to random chance (p value = 0.002) [20] . Therefore, we can conclude that the local cutoff is significantly better in identifying positive spots than a global cutoff.