Smits H M, Delemarre E M, Pandit A, Schoneveld A H, Oldenburg B, van Wijk F, Nierkens S, Drylewicz J
Center for Translational Immunology, University Medical Center Utrecht, KC 02.085.2, P.O. Box 85090, 3508 AB, Utrecht, The Netherlands.
Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht, The Netherlands.
Sci Rep. 2025 Jan 9;15(1):1498. doi: 10.1038/s41598-024-84320-4.
The proximity extension assay (PEA) enables large-scale proteomic investigations across numerous proteins and samples. However, discrepancies between measurements, known as batch-effects, potentially skew downstream statistical analyses and increase the risks of false discoveries. While implementing bridging controls (BCs) on each plate has been proposed to mitigate these effects, a clear method for utilizing this strategy remains elusive. Here, we characterized batch effects in PEA proteomics and identified three types: protein-specific, sample-specific, and plate-wide. We developed a robust regression-based method called BAMBOO (Batch Adjustments using Bridging cOntrOls) to correct them. Simulations comparing BAMBOO with established correction techniques (median centering, median of the difference (MOD), and ComBat) revealed that median centering and ComBat were significantly impacted by outliers within the BCs, whereas BAMBOO and MOD were more robust when no plate-wide effects were introduced. Optimal batch correction was achieved with 10-12 BCs. We validated the simulation results using experimental data and found that BAMBOO and MOD had a reduced incidence of false discoveries compared to alternative methods. Our findings emphasize the prevalence of batch effects in PEA proteomic studies and advocate for BAMBOO as a robust and effective tool to enhance the reliability of large-scale analyses in the proteomic field.
邻近延伸分析（PEA）能够对众多蛋白质和样本进行大规模蛋白质组学研究。然而，测量值之间的差异，即所谓的批次效应，可能会使下游统计分析产生偏差，并增加错误发现的风险。虽然有人提出在每个平板上实施桥接对照（BCs）来减轻这些影响，但利用这一策略的明确方法仍然难以捉摸。在这里，我们对PEA蛋白质组学中的批次效应进行了表征，并确定了三种类型：蛋白质特异性、样本特异性和全平板效应。我们开发了一种名为BAMBOO（使用桥接对照进行批次调整）的基于稳健回归的方法来校正这些效应。将BAMBOO与既定校正技术（中位数中心化、差值中位数（MOD）和ComBat）进行比较的模拟结果表明，中位数中心化和ComBat受到BCs内异常值的显著影响，而在未引入全平板效应时，BAMBOO和MOD更稳健。使用10 - 12个BCs可实现最佳批次校正。我们使用实验数据验证了模拟结果，发现与其他方法相比，BAMBOO和MOD的错误发现发生率更低。我们的研究结果强调了批次效应在PEA蛋白质组学研究中的普遍性，并提倡将BAMBOO作为一种强大而有效的工具，以提高蛋白质组学领域大规模分析的可靠性。
