量化单细胞数据中单个基因的批效应。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yang Zhou, Qiongyu Sheng, Guohua Wang, Li Xu, Shuilin Jin
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引用次数: 0

摘要

批效应实质上阻碍了多个单细胞实验批次的比较。现有的批效应去除和定量方法主要强调批间的细胞比对,往往忽略了基因水平的批效应。在这里,我们引入了群体技术效应(GTE)——一种评估批次效应对单个基因的定量度量。使用GTE,我们表明批效应不均匀地影响数据集中的基因。部分高批敏感基因(hbg)在不同的数据集之间不同,并主导批效应,而非hbg则表现出低批效应。我们证明,只要三个hbg就足以产生大量的批效应。我们的方法还可以评估细胞水平的批效应,优于现有的批效应量化方法。我们还观察到,生物学上相似的细胞类型经历类似的批效应,为数据集成策略的发展提供了信息。GTE方法是通用的,适用于各种单细胞组学数据类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying batch effects for individual genes in single-cell data.

Batch effects substantially impede the comparison of multiple single-cell experiment batches. Existing methods for batch effect removal and quantification primarily emphasize cell alignment across batches, often overlooking gene-level batch effects. Here we introduce group technical effects (GTE)-a quantitative metric to assess batch effects on individual genes. Using GTE, we show that batch effects unevenly impact genes within the dataset. A portion of highly batch-sensitive genes (HBGs) differ between datasets and dominate the batch effects, whereas non-HBGs exhibit low batch effects. We demonstrate that as few as three HBGs are sufficient to introduce substantial batch effects. Our method also enables the assessment of cell-level batch effects, outperforming existing batch effect quantification methods. We also observe that biologically similar cell types undergo similar batch effects, informing the development of data integration strategies. The GTE method is versatile and applicable to various single-cell omics data types.

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CiteScore
11.70
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