Correction of batch effects in high throughput proximity extension assays for proteomic studies using bridging controls: the BAMBOO method.

Abstract Background: The proximity extension assay (PEA) facilitates large-scale proteomic studies involving a large number of proteins and samples. However, inevitable discrepancies in day-to-day measurements may introduce the inherent risk of undesirable variation, known as batch effects, which may impact down-stream statistical analyses and increase the chances of false discoveries. The implementation of bridging controls on each plate has been suggested to mitigate this complication, but a clear method on how to use this strategy is still lacking. In this study, we characterized potential batch effects in proteomics using PEAs and generated guidelines to mitigate batch effects using bridging controls. Results: This study characterized three distinct types of batch effects (protein-specific, sample-specific, and plate-wide) in PEA proteomic studies. We developed a new method, BAMBOO (Batch AdjustMents using Bridging cOntrOls), based on a robust regression model. In a simulation study, we compared BAMBOO with established batch correction techniques; median centering, median of the difference (MOD), and ComBat. We observed that median centering and ComBat were significantly impacted by outliers within the bridging controls, whereas BAMBOO and MOD were more robust when no plate-wide batch effects were introduced. Moreover, upon introduction of plate-wide batch effects, BAMBOO was performing better than MOD in terms of accuracy, true negative rate and true positive rate. Inclusion of 10-12 bridging controls was found to optimally correct for batch effects. Applying the different methods to experimental data showed that BAMBOO and MOD result in a reduced incidence of false discoveries compared to the alternative methods. Conclusion: Our study underscores the prevalent existence of batch effects in PEA proteomic studies, which can be corrected using bridging controls using an innovative, robust and effective tool, BAMBOO. The use of BAMBOO may enhance the reliability of large-scale analyses in the proteomic field using PEA.