gCAnno: A Graph-based Single Cell Type Annotation Method
Background 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
... tly not robust enough to handle datasets with different noise levels or from different platforms. Results 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, a naïve Bayes classifier is 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. Conclusions 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.