Current single cell analysis methods annotate cell types at cluster-level rather than ideally at single cell level. Multiple exchangeable clustering methods and many tunable parameters have a substantial impact on the clustering outcome, often leading to incorrect cluster-level annotation or multiple runs of subsequent clustering steps. To address these limitations, methods based on well-annotated reference atlas has been proposed. However, these methods are currently not robust enough to handle datasets with different noise levels or from different platforms. Here, we present gCAnno, a graph-based Cell type Annotation method. First, gCAnno constructs cell type-gene bipartite graph and adopts graph embedding to obtain cell type specific genes. Then, naïve Bayes (gCAnno-Bayes) and SVM (gCAnno-SVM) classifiers are built for annotation. We compared the performance of gCAnno to other state-of-art methods on multiple single cell datasets, either with various noise levels or from different platforms. The results showed that gCAnno outperforms other state-of-art methods with higher accuracy and robustness. gCAnno is a robust and accurate cell type annotation tool for single cell RNA analysis. The source code of gCAnno is publicly available at https://github.com/xjtu-omics/gCAnno .

中文翻译：

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gCAnno：基于图的单细胞类型注释方法

当前的单细胞分析方法在簇级别而不是理想情况下在单细胞级别注释细胞类型。多种可交换的聚类方法和许多可调参数对聚类结果有重大影响，通常会导致错误的聚类级别注释或后续聚类步骤的多次运行。为了解决这些局限性，已经提出了基于标注正确的参考图集的方法。但是，这些方法目前不足以处理具有不同噪声水平或来自不同平台的数据集。在这里，我们介绍gCAnno，这是一种基于图的单元格类型注释方法。首先，gCAnno构建细胞类型-基因二分图，并采用图嵌入的方法获得细胞类型特异性基因。然后，建立朴素的贝叶斯（gCAnno-Bayes）和SVM（gCAnno-SVM）分类器进行注释。我们将gCAnno的性能与多个具有不同噪声水平或来自不同平台的单个单元数据集上的其他最新方法的性能进行了比较。结果表明，gCAnno具有更高的准确性和鲁棒性，优于其他最新技术。gCAnno是用于单细胞RNA分析的强大且准确的细胞类型注释工具。gCAnno的源代码可从https://github.com/xjtu-omics/gCAnno公开获得。