Goh Wilson Wen Bin, Yong Chern Han, Wong Limsoon
Lee Kong Chian School of Medicine, Nanyang Technological University, 636921, Singapore; School of Biological Science, Nanyang Technological University, 637551, Singapore.
Department of Computer Science, National University of Singapore, 117417, Singapore.
Trends Biotechnol. 2022 Sep;40(9):1029-1040. doi: 10.1016/j.tibtech.2022.02.005. Epub 2022 Mar 10.
Batch effects (BEs) are technical biases that may confound analysis of high-throughput biotechnological data. BEs are complex and effective mitigation is highly context-dependent. In particular, the advent of high-resolution technologies such as single-cell RNA sequencing presents new challenges. We first cover how BE modeling differs between traditional datasets and the new data landscape. We also discuss new approaches for measuring and mitigating BEs, including whether a BE is significant enough to warrant correction. Even with the advent of machine learning and artificial intelligence, the increased complexity of next-generation biotechnological data means increased complexities in BE management. We forecast that BEs will not only remain relevant in the age of big data but will become even more important.
批次效应（BEs）是可能混淆高通量生物技术数据分析的技术偏差。批次效应很复杂，有效的缓解措施高度依赖于具体情况。特别是，诸如单细胞RNA测序等高分辨率技术的出现带来了新的挑战。我们首先介绍传统数据集和新数据格局下批次效应建模的差异。我们还讨论了测量和减轻批次效应的新方法，包括批次效应是否显著到足以进行校正。即使机器学习和人工智能已经出现，但下一代生物技术数据日益增加的复杂性意味着批次效应管理的复杂性也在增加。我们预测，批次效应不仅在大数据时代仍将存在相关性，而且会变得更加重要。
