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.

翻译：细胞术能够在异质性群体中进行精确的单细胞表型分析。传统上，这些细胞类型通过手动门控进行标注，但这种方法存在可重复性差且对批次效应敏感的问题。此外，最新型的细胞仪——光谱流式细胞仪或质谱流式细胞仪——会生成高维度的丰富数据，使得通过手动门控进行分析变得困难且耗时。为解决这些局限性，我们引入了Scyan（https://github.com/MICS-Lab/scyan），一种单细胞细胞术标注网络，该网络仅利用关于细胞术面板的专家先验知识自动标注细胞类型。我们证明，Scyan在多个公开数据集上显著优于相关最先进模型，同时速度更快且具有可解释性。此外，Scyan还能完成多项辅助任务，如批次效应去除、条形码解码和群体发现。总体而言，该模型加速并简化了细胞术中的细胞群体表征、量化与发现过程。