DataSheet2_Comparison of cell type annotation algorithms for revealing immune response of COVID-19.xlsx

When analyzing scRNA-seq data with clustering algorithms, annotating the clusters with cell types is an essential step toward biological interpretation of the data. Annotations can be performed manually using known cell type marker genes. Annotations can also be automated using knowledge-driven or data-driven machine learning algorithms. Majority of cell type annotation algorithms are designed to predict cell types for individual cells in a new dataset. Since biological interpretation of scRNA-seq data is often made on cell clusters rather than individual cells, several algorithms have been developed to annotate cell clusters. In this study, we compared five cell type annotation algorithms, Azimuth, SingleR, Garnett, scCATCH, and SCSA, which cover the spectrum of knowledge-driven and data-driven approaches to annotate either individual cells or cell clusters. We applied these five algorithms to two scRNA-seq datasets of peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy controls, and evaluated their annotation performance. From this comparison, we observed that methods for annotating individual cells outperformed methods for annotation cell clusters. We applied the cell-based annotation algorithm Azimuth to the two scRNA-seq datasets to examine the immune response during COVID-19 infection. Both datasets presented significant depletion of plasmacytoid dendritic cells (pDCs), where differential expression in this cell type and pathway analysis revealed strong activation of type I interferon signaling pathway in response to the infection.

在使用聚类算法分析单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据时，对细胞簇进行细胞类型注释是实现数据生物学阐释的关键步骤。注释工作可借助已知的细胞类型标记基因手动完成，也可通过知识驱动或数据驱动的机器学习算法自动化实现。绝大多数细胞类型注释算法旨在为新数据集内的单个细胞预测细胞类型。但由于scRNA-seq数据的生物学阐释通常基于细胞簇而非单个细胞，学界已开发出多款可对细胞簇进行注释的算法。本研究中，我们对5款细胞类型注释算法——Azimuth、SingleR、Garnett、scCATCH及SCSA——进行了对比，这些算法涵盖了用于单个细胞或细胞簇注释的知识驱动与数据驱动两类方法。我们将这5款算法应用于两组源自新型冠状病毒肺炎（Corona Virus Disease 2019, COVID-19）患者与健康对照者的外周血单个核细胞（peripheral blood mononuclear cells, PBMC）scRNA-seq数据集，并对其注释性能进行了评估。通过本次对比，我们发现针对单个细胞的注释算法性能优于针对细胞簇的注释算法。我们采用基于细胞的注释算法Azimuth处理这两组scRNA-seq数据集，以探究新冠感染过程中的免疫应答情况。两组数据集均显示浆细胞样树突状细胞（plasmacytoid dendritic cells, pDCs）出现显著耗竭；针对该细胞类型的差异表达分析与通路分析结果显示，机体在感染应答过程中I型干扰素信号通路被显著激活。