Recent advance in single-cell RNA sequencing (scRNA-seq) has enabled large-scale transcriptional characterization of thousands of cells in multiple complex tissues, in which accurate cell type identification becomes the prerequisite and vital step for scRNA-seq studies. Currently, the common practice in cell type annotation is to map the highly expressed marker genes with known cell markers manually based on the identified clusters, which requires the priori knowledge and tends to be subjective on the choice of which marker genes to use. Besides, such manual annotation is usually time-consuming.
To address these problems, we introduce a single cell Cluster-based Annotation Toolkit for Cellular Heterogeneity (scCATCH) from cluster marker genes identification to cluster annotation based on evidence-based score by matching the identified potential marker genes with known cell markers in tissue-specific cell taxonomy reference database (CellMatch).
CellMatch includes a panel of 353 cell types and related 686 subtypes associated with 184 tissue types, and 2,097 references of human and mouse.
The scCATCH mainly includes two function
findmarkergene() and findcelltype() to realize
the automatic annotation for each identified cluster. Usage and Examples
are detailed below.
Shao et al., scCATCH:Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data, iScience, Volume 23, Issue 3, 27 March 2020. doi: 10.1016/j.isci.2020.100882. PMID:32062421
scCATCH is available on CRANcellmatchcellmatch for annotation.install.packages("scCATCH")OR
# install devtools and install
install.packages(pkgs = 'devtools')
devtools::install_github('ZJUFanLab/scCATCH')Please refer to the document and tutorial vignette. Available tissues and cancers see the wiki page
Solutions for possilble bugs and errors. Please refer to closed Issues1 and Issues2
scCATCH was developed by Xin Shao. Should you have any questions, please contact Xin Shao at xin_shao@zju.edu.cn