scCATCH v3.1

Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

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.

Cite

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

News

v3.1

• scCATCH is available on CRAN
• Update Gene symbols in CellMatch according to NCBI Gene symbols (updated in Jan. 2, 2022, https://www.ncbi.nlm.nih.gov/gene).
• Allow users to use custom cellmatch
• Allow users to select different combination of tissues or cancers for annotation.
• Allow users to add more marker genes to cellmatch for annotation.
• Allow users to use markers from different species other than human and mouse.
• Allow users to use more methods to identify highly expressed genes.
• Create scCATCH object from Seurat object with the following code

obj <- createscCATCH(data = Seurat_obj[['RNA']]@data, cluster = as.character(Idents(Seurat_obj)))

Install

install.packages("scCATCH")

OR

# install devtools and install
install.packages(pkgs = 'devtools')
devtools::install_github('ZJUFanLab/scCATCH')

Usage

Please refer to the document and tutorial vignette. Available tissues and cancers see the wiki page

Issues

Solutions for possilble bugs and errors. Please refer to closed Issues1 and Issues2

About

scCATCH was developed by Xin Shao. Should you have any questions, please contact Xin Shao at [email protected]