论文标题
生物学驱动的深层生成模型，用于细胞型注释
A biology-driven deep generative model for cell-type annotation in cytometry
论文作者
论文摘要
细胞仪可实现异质种群中精确的单细胞表型。这些细胞类型传统上是通过手动门控来注释的，但是这种方法缺乏对批处理效应的可重复性和敏感性。同样，最新的细胞仪（光谱流或质量细胞仪）创建了丰富而高维的数据，这些数据通过手动门控进行分析变得具有挑战性且耗时。为了应对这些限制，我们引入了SCYAN（https://github.com/mics-lab/scyan），这是一个单-CELL细胞仪注释网络，该网络仅使用有关细胞仪面板的先前专家知识自动注释单元格类型。我们证明，SCYAN在多个公共数据集上大大优于相关的最新模型，同时更快，可解释。此外，SCYAN克服了几个互补任务，例如批处理效应，脱钉和人口发现。总体而言，该模型可以加速和简化细胞术中的细胞种群表征，定量和发现。
Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method suffers from a lack of reproducibility and sensitivity to batch-effect. Also, the most recent cytometers - spectral flow or mass cytometers - create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan (https://github.com/MICS-Lab/scyan), a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks such as batch-effect removal, debarcoding, and population discovery. Overall, this model accelerates and eases cell population characterisation, quantification, and discovery in cytometry.
