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scAdapt: Virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species
bioRxiv - Bioinformatics Pub Date : 2021-01-19 , DOI: 10.1101/2021.01.18.427083
Xiang Zhou , Hua Chai , Yuansong Zeng , Huiying Zhao , Ching-Hsing Luo , Yuedong Yang

Motivation: In single cell analyses, cell types are conventionally identified based on known marker gene expressions. Such approaches are time-consuming and irreproducible. Therefore, many new supervised methods have been developed to identify cell types for target datasets using the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. Results: We developed scAdapt, a virtual adversarial domain adaptation network to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier, and aligned the labeled source centroid and pseudo-labeled target centroid to generate a joint embedding. We demonstrate that scAdapt outperforms existing methods for classification in simulated, cross-platforms, cross-species, and spatial transcriptomic datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and preserving discriminative cluster structure present in the original datasets.

中文翻译:

scAdapt:虚拟对抗域适应网络,用于跨平台和物种的单细胞RNA-seq数据分类

动机:在单细胞分析中,通常根据已知的标记基因表达来鉴定细胞类型。这样的方法既耗时又不可重现。因此,已经开发了许多新的监督方法,以使用公共数据集的快速积累来识别目标数据集的单元格类型。但是,由于跨平台或物种预测中的数据分布不同,因此这些方法对批次效应或生物学变化很敏感。结果:我们开发了scAdapt,这是一个虚拟的对抗域适应网络,可以在具有批处理效果的数据集之间转移细胞标记。scAdapt使用标记的源数据和未标记的目标数据来训练增强的分类器,并对齐标记的源质心和伪标记的目标质心以生成联合嵌入。我们证明了scAdapt在模拟,跨平台,跨物种和空间转录组数据集中表现优于现有的分类方法。对齐嵌入的进一步定量评估和可视化证实了细胞混合的优越性,并保留了原始数据集中存在的区分性簇结构。
更新日期:2021-01-20
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