gCAnno: a graph-based single cell type annotation method

• Published in: BMC genomics Vol. 21; no. 1; p. 823
• Main Authors: Yang, Xiaofei, Gao, Shenghan, Wang, Tingjie, Yang, Boyu, Dang, Ningxin, Ye, Kai
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.11.2020; BioMed Central Ltd; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Analysis; Animal Genetics and Genomics; Annotations; Bayes Theorem; Bayesian analysis; Biomarkers; Biomedical and Life Sciences; Cluster Analysis; Clustering; Datasets; Electronic data processing; Embedding; Gene expression; Genetic research; Genomics; Graph representations; Graph theory; Life Sciences; Liver; Methodology; Methodology Article; Methods; Microarrays; Microbial Genetics and Genomics; Noise; Noise levels; Plant Genetics and Genomics; Proteomics; Ribonucleic acid; RNA; Robustness; Sequence Analysis, RNA; Single-Cell Analysis; Source code; Technology application; Transcriptomic methods; China; Graph embedding; Cell type annotation; Single cell RNA analysis
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-020-07223-4

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 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, 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. 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 .