scCATCH

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

https://github.com/zjufanlab/sccatch

Science Score: 33.0%

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    Found 1 DOI reference(s) in README
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Keywords

cell-markers cluster-annotation marker-genes rna-seq sequencing seurat single-cell transcriptome transcriptomics
Last synced: 6 months ago · JSON representation

Repository

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

Basic Info
Statistics
  • Stars: 231
  • Watchers: 5
  • Forks: 36
  • Open Issues: 5
  • Releases: 4
Topics
cell-markers cluster-annotation marker-genes rna-seq sequencing seurat single-cell transcriptome transcriptomics
Created over 6 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License

README.md

scCATCH v3.2.2

R >4.0 installed with CRAN download CellMatch

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 2,096 references of human and mouse.

Install

```

install from cran

install.packages("scCATCH") OR

install devtools and install

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

Usage

The scCATCH mainly includes two function findmarkergene() and findcelltype() to realize the automatic annotation for each identified cluster as detailed below:

```

sc_data is the scRNA-seq data matrix

sc_cluster is a character containing the cluster information

obj <- createscCATCH(data = scdata, cluster = sccluster)

find marker gene for each cluster

obj <- findmarkergene(obj, species, marker, tissue, cancer)

find cell type for each cluster

obj <- findcelltype(obj)

```

For more detailed information, please refer to the document and tutorial vignette. Available tissues and cancers see the wiki page

News

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

Cite

Please cite us as 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

Owner

  • Name: Dr. FAN, Xiaohui
  • Login: ZJUFanLab
  • Kind: user
  • Location: Hangzhou, China
  • Company: Zhejiang University

single-cell omics; spatial transcriptomics; TCM network biology

GitHub Events

Total
  • Watch event: 17
  • Issue comment event: 3
Last Year
  • Watch event: 17
  • Issue comment event: 3

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 113
  • Total Committers: 7
  • Avg Commits per committer: 16.143
  • Development Distribution Score (DDS): 0.425
Past Year
  • Commits: 22
  • Committers: 1
  • Avg Commits per committer: 22.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Shao, Xin x****o@z****n 65
Dr. FAN, Xiaohui 5****b 25
ZJUFanLab f****h@z****n 10
Shao, Xin 4****1 8
xuzhougeng x****g@1****m 2
xuzhougeng x****g@y****t 2
Darío Hereñú m****a@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 66
  • Total pull requests: 2
  • Average time to close issues: about 1 month
  • Average time to close pull requests: about 4 hours
  • Total issue authors: 54
  • Total pull request authors: 2
  • Average comments per issue: 2.42
  • Average comments per pull request: 0.5
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 4.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • februaryfang (4)
  • ZJUFanLab (3)
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Pull Request Authors
  • kant (1)
  • xuzhougeng (1)
Top Labels
Issue Labels
good first issue (5) bug (2)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 399 last-month
  • Total docker downloads: 337
  • Total dependent packages: 0
  • Total dependent repositories: 3
  • Total versions: 5
  • Total maintainers: 1
cran.r-project.org: scCATCH

Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 3
  • Downloads: 399 Last month
  • Docker Downloads: 337
Rankings
Forks count: 2.0%
Stargazers count: 2.3%
Average: 14.3%
Dependent repos count: 16.5%
Docker downloads count: 16.7%
Downloads: 19.6%
Dependent packages count: 28.7%
Maintainers (1)
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.0.0 depends
  • Matrix * imports
  • crayon * imports
  • methods * imports
  • progress * imports
  • reshape2 * imports
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests