The traditional analysis of large-scale proteomic data can be severely hampered by the presence of batch effects, discrepancies arising from multiple sources throughout the experimental process. A new method developed by researchers at UMC Utrecht seeks to assist scientists by offering improved means to correct these inconsistencies, thereby enhancing the reliability of high-throughput proteomic studies. This new approach, termed BAMBOO (Batch Adjustments using Bridging cOntrOls), assists researchers who utilize proximity extension assays (PEA) by addressing common pitfalls such as false discoveries resulting from batch effects.
Recent investigations utilizing PEA technologies highlight the significance of proteomic biomarkers, which have become increasingly central to the field of personalized medicine. By employing this assay method, numerous proteins can be measured concurrently across various samples. Despite its advantages, data integrity can suffer from variability introduced by batch effects, thereby skewing results and leading to significant statistical analysis flaws.
The expertise of the UMC Utrecht team enabled the classification of batch effects encountered during proteomic studies, identifying three distinct types: protein-specific, sample-specific, and plate-wide. Such variations can often render incorrect conclusions about protein levels, affecting disease detection and monitoring efforts. Addressing this issue, BAMBOO has been developed to utilize bridging controls for adjustment purposes, ensuring higher accuracy and consistency.
To validate the effectiveness of BAMBOO, the research team conducted simulation studies and compared BAMBOO against other established correction methods, such as ComBat, median centering, and Median of the difference (MOD). Results revealed BamBoo’s robustness, exhibiting reduced false discovery rates compared to median centering and ComBat, particularly when dealing with plate-wide effects.
"We developed BAMBOO as a new and effective tool to correct for batch effects observed during proteomic investigations," noted the research team. They found BAMBOO performed exceptionally well when 10 to 12 bridging controls were integrated, ensuring optimal batch-effect correction. The method’s performance exceeded existing methods, particularly when facing challenges posed by outliers within the bridging controls.
Experimental validation included comparative analysis between healthy control samples and viral-infected individuals. Findings indicated notable batch effects, emphasizing the necessity of effective correction methods. Importantly, all correction methodologies led to common significant proteins being identified, affirming BAMBOO’s reliability but also highlighting some unique results seen only through BAMBOO.
This work emphasizes the pressing issue of batch effects within large-scale proteomic studies necessitating practical solutions. The research team's findings critically advance the dialogue surrounding best practices for batch adjustment through the use of bridging controls, as BAMBOO holds promise for increasing reliability and reproducibility of results within this research domain.
Future applications of BAMBOO will play a pivotal role not only within proteomic studies but across various domains where high-throughput data collection is prevalent. Armed with this new methodology, researchers can strive for greater accuracy, allowing for consequential insights derived from their studies to remain valid and impactful. The BAMBOO software is available for researchers through GitHub, intending to maximize accessibility and application across the scientific community.