Comparison of cell type annotation algorithms for revealing immune response of COVID-19
Comparison of cell type annotation algorithms for revealing immune response of COVID-19
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DOI：
10.3389/fsysb.2022.1026686
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发表时间：
2022-10
影响因子：
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通讯作者：
Congmin Xu;Huyun Lu;Peng Qiu
中科院分区：
文献类型：
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作者：
Congmin Xu;Huyun Lu;Peng Qiu
当使用聚类算法分析scRNA-seq数据时，用细胞类型注释聚类是对数据进行生物学解释的重要步骤。可以使用已知的细胞类型标记基因手动进行注释。注释也可以使用知识驱动或数据驱动的机器学习算法来自动化。大多数细胞类型注释算法被设计为预测新数据集中单个细胞的细胞类型。由于scRNA-seq数据的生物学解释通常是在细胞簇而不是单个细胞上进行的，因此已经开发了几种算法来注释细胞簇。在这项研究中，我们比较了五种细胞类型注释算法，Azimuth，SingleR，Garnett，scCATCH和SCSA，它们涵盖了知识驱动和数据驱动的方法来注释单个细胞或细胞簇。我们将这五种算法应用于来自COVID-19患者和健康对照的外周血单核细胞（PBMC）样本的两个scRNA-seq数据集，并评估了它们的注释性能。从该比较中，我们观察到用于注释单个细胞的方法优于用于注释细胞簇的方法。我们将基于细胞的注释算法Azimuth应用于两个scRNA-seq数据集，以检查COVID-19感染期间的免疫反应。两个数据集均呈现浆细胞样树突状细胞（pDC）的显著耗竭，其中该细胞类型中的差异表达和途径分析揭示了响应于感染的I型干扰素信号传导途径的强烈激活。
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.