Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR's performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR's performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.