Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore.
School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae515.
Batch effects introduce significant variability into high-dimensional data, complicating accurate analysis and leading to potentially misleading conclusions if not adequately addressed. Despite technological and algorithmic advancements in biomedical research, effectively managing batch effects remains a complex challenge requiring comprehensive considerations. This paper underscores the necessity of a flexible and holistic approach for selecting batch effect correction algorithms (BECAs), advocating for proper BECA evaluations and consideration of artificial intelligence-based strategies. We also discuss key challenges in batch effect correction, including the importance of uncovering hidden batch factors and understanding the impact of design imbalance, missing values, and aggressive correction. Our aim is to provide researchers with a robust framework for effective batch effects management and enhancing the reliability of high-dimensional data analyses.
批次效应会给高维数据带来显著的变异性，如果不加以充分处理，这将使数据分析变得复杂，并导致潜在的误导性结论。尽管在生物医学研究中技术和算法取得了进步，但有效地管理批次效应仍然是一个复杂的挑战，需要全面考虑。本文强调了为选择批次效应校正算法（BECAs）采用灵活和整体方法的必要性，提倡对适当的 BECA 进行评估，并考虑基于人工智能的策略。我们还讨论了批次效应校正中的关键挑战，包括揭示隐藏批次因素以及理解设计不平衡、缺失值和激进校正的影响的重要性。我们的目标是为研究人员提供一个有效的批次效应管理的强大框架，并提高高维数据分析的可靠性。
