转录组分析中批次效应的检测与矫正

Detection and correction of batch effect in transcriptomics analysis

随着测序数据的持续积累和单细胞转录组测序的广泛应用,超大规模转录组数据集的整合,为后基因组时代提供了新的机遇和挑战.其中,不同数据集具备的时空异质性和测序平台带来的系统误差导致的批次效应,对转录组分析的有效性产生了极大影响,干扰了真实的生物学差异的研究.本文介绍了转录组分析中批次效应的产生原因和检测方法,并对基于参数估计和非参数的矫正模型以及针对单细胞转录组的整合算法进行了总结.结合主流的分析方法,给出批次效应矫正的实践建议,为相关转录组研究的综合分析提供参考意见.

With continuous development of sequencing technology,omics big data has become increasingly important for life science research.With accumulation of sequencing data and application of single-cell sequencing technologies,integration of massive transcriptomic datasets provides new opportunities and challenges in the post-genomic era.The batch effect introduced by spatial-temporal heterogeneity from different data sets and systematic errors from sequencing platform greatly affects effectiveness of transcript analysis and hinder discovery of true biological differences.This review discusses the causes and detection methods of batch effect in transcriptomic analysis.Mainstream correction models based on parametric and non-parametric estimation,integration algorithms for single-cell transcriptomes are summarized.Some practical recommendations for batch effect correction are provided,including suggestions for comprehensive transcriptomic analysis.