Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

中文翻译：

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评估和减轻大规模组学研究中的批次效应


众所周知，组学数据中的批次效应是与研究目标无关的常见技术变化，如果不纠正，可能会导致误导性结果，如果纠正过度，可能会阻碍生物医学发现。评估和减轻批次效应对于确保组学数据的可靠性和可重复性以及最大限度地减少技术变化对生物学解释的影响至关重要。在这篇综述中，我们强调了批次效应的深远负面影响以及大规模组学研究中解决这一具有挑战性问题的迫切需要。我们总结了批次效应的潜在来源、评估和纠正这些效应的当前进展以及旨在解决这些效应的联盟的努力。