GCAnno: a graph-based single cell type annotation method.

ABSTRACT:

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 .

SUBMITTER: Yang X 

PROVIDER: S-EPMC7686723 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature