CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation.
CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation.
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DOI：
10.1186/s13059-021-02561-2
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发表时间：
2021-12-13
期刊：
影响因子：
12.3
通讯作者：
Yau C
中科院分区：
文献类型：
--
作者：
Hu Z;Ahmed AA;Yau C
联合单细胞RNA-Seq（scRNA-Seq）数据的聚类经常受到混杂因素的挑战，例如批次效应和生物相关变异性。现有的批量效应去除方法通常需要对样品中细胞群的组成几乎相同的强假设。在这里，我们提出了CIDER，一个基于组间相似性度量的元聚类工作流。我们证明，CIDER优于其他scRNA-Seq聚类方法和集成方法在模拟和真实的数据集。此外，我们表明，CIDER可以用来评估生物正确性的整合在真实的数据集，而它不需要存在事先的细胞注释。在线版本包含补充材料，可通过10.1186/s13059-021-02561-2获得。
Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across samples. Here, we present CIDER, a meta-clustering workflow based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations. The online version contains supplementary material available at 10.1186/s13059-021-02561-2.
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