Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
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DOI：
10.1038/s41467-020-17281-7
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发表时间：
2020-07-10
影响因子：
16.6
通讯作者：
Gao, Ge
中科院分区：
文献类型：
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作者：
Cao, Zhi-Jie;Wei, Lin;Gao, Ge
单细胞RNA-seq（scRNA-seq）被广泛用于解决细胞异质性。随着公共scRNA-seq数据的快速积累，有效且高效的细胞查询方法对于利用现有注释来管理新测序的细胞至关重要。这样的查询方法应该基于准确的细胞间相似性度量，并且能够适当地处理批次效应。在这里，我们提出了Cell BLAST，这是一种建立在基于神经网络的生成模型和定制的细胞间相似性度量基础上的准确且鲁棒的细胞查询方法。通过广泛的基准和案例研究，我们证明了Cell BLAST在注释离散细胞类型和连续细胞分化潜力以及识别新细胞类型方面的有效性。由精心策划的参考数据库和用户友好的Web服务器提供支持，Cell BLAST为真实世界的scRNA-seq细胞查询和注释提供一站式解决方案。单细胞RNA-seq（scRNA-seq）被广泛用于解决细胞异质性。在这里，作者提出了一种基于神经网络的生成模型和定制的细胞间相似性度量的细胞查询方法。
Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation. Single-cell RNA-seq (scRNA-seq) is being widely used to resolve cellular heterogeneity. Here, the authors present a cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric.