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SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.
Briefings in Bioinformatics ( IF 9.5 ) Pub Date : 2020-06-26 , DOI: 10.1093/bib/bbaa097
Yuchen Yang 1 , Gang Li 2 , Huijun Qian 2 , Kirk C Wilhelmsen 1 , Yin Shen 3 , Yun Li 4
Affiliation  

Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.

中文翻译:

SMNN:通过监督相互最近邻检测对单细胞 RNA-seq 数据进行批量效应校正。

在整合多个批次的单细胞 RNA 测序 (scRNA-seq) 数据时,批次效应校正已被认为是必不可少的。最先进的方法忽略单细胞簇标签信息,但此类信息可以提高批次效应校正的有效性,特别是在生物差异与批次效应不正交的现实场景下。为了解决这个问题,我们提出 SMNN 通过监督相互最近邻检测来对 scRNA-seq 数据进行批量效应校正。我们对模拟和真实数据集的广泛评估表明,SMNN 改进了跨批次的相应细胞类型的合并,从而减少了 MNN、Seurat v3 和 LIGER 之间的批次差异。此外,SMNN 保留了更多细胞类型特异性特征,部分表现为 SMNN 校正后在细胞类型之间识别的差异表达基因在生物学上更具相关性,精度提高高达 841.0%。
更新日期:2020-06-27
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