School of Biological Sciences, Seoul National University, Seoul 08826, Korea.
These authors contributed equally to this work.
Mol Cells. 2023 Feb 28;46(2):106-119. doi: 10.14348/molcells.2023.0009. Epub 2023 Feb 24.
With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.
随着公共存储库中单细胞 RNA 测序 (scRNA-seq) 数据集数量的增加，对多个 scRNA-seq 数据集进行整合分析已变得很常见。由于细胞分离和处理方案、文库制备技术和测序平台的不同，不同数据集之间不可避免地存在批次效应。为了消除这些批次效应，以便有效地整合多个 scRNA-seq 数据集，已经基于不同的概念和方法开发了许多方法。这些方法已被证明可用于检查从特定数据集识别出的细胞特征（例如细胞亚群和标记基因）是否始终存在，或者它们的条件依赖性变化（例如在特定疾病相关条件下细胞亚群的增加）是否始终在类似或不同条件下生成的不同数据集中观察到。在这篇综述中，我们总结了整合方法的概念和方法及其优缺点，这些内容在之前的文献中已有报道。
