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scAnnotatR

https://github.com/grisslab/scannotatr

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 5 committers (40.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.4%) to scientific vocabulary

Keywords from Contributors

bioconductor-package gene ontology proteomexchange proteomics
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: grisslab
  • License: other
  • Language: R
  • Default Branch: master
  • Size: 165 MB
Statistics
  • Stars: 18
  • Watchers: 2
  • Forks: 2
  • Open Issues: 3
  • Releases: 0
Created about 5 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

scAnnotatR

The scAnnotatR package automatically classifies cells in scRNA-seq datasets. It is simple to use with a clear infrastructure to easily add additional cell type classification models. scAnnotatR support both Seurat and SingleCellExperiment objects as input.

Installation

You can install the latest version directly from GitHub using the devtools package:

```r

install devtools if needed

if (!require(devtools)) { install.packages("devtools") }

if (!require(scAnnotatR)) { install_github("grisslab/scAnnotatR") } ```

Help

The complete usage is shown in the vignettes:

For more questions / feedback please simply post an Issue.

Citation

If you used scAnnotatR in your research, we would be grateful if you could cite the following manuscript:

Nguyen, V., Griss, J. scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data. BMC Bioinformatics 23, 44 (2022). https://doi.org/10.1186/s12859-022-04574-5

Owner

  • Name: grisslab
  • Login: grisslab
  • Kind: organization

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 121
  • Total Committers: 5
  • Avg Commits per committer: 24.2
  • Development Distribution Score (DDS): 0.289
Past Year
  • Commits: 8
  • Committers: 3
  • Avg Commits per committer: 2.667
  • Development Distribution Score (DDS): 0.375
Top Committers
Name Email Commits
nttvy t****n@m****t 86
Johannes Griss j****8@y****m 27
Nitesh Turaga n****a@g****m 4
J Wokaty j****y@s****u 2
nttvy n****2@g****m 2
Committer Domains (Top 20 + Academic)
sph.cuny.edu: 1 meduniwien.ac.at: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 14
  • Total pull requests: 0
  • Average time to close issues: 5 months
  • Average time to close pull requests: N/A
  • Total issue authors: 14
  • Total pull request authors: 0
  • Average comments per issue: 3.07
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Gesmira (1)
  • seb951 (1)
  • Yijia-Jiang (1)
  • RolantusdataExp (1)
  • rdavis7559 (1)
  • Yohan-Bosse-Lab (1)
  • JH1606-code (1)
  • reberya (1)
  • MartaBenegas (1)
  • SudoSchrodinger (1)
  • saifsikdar (1)
  • qindan2008 (1)
  • lssb (1)
  • antoine4ucsd (1)
Pull Request Authors
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 11,729 total
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
bioconductor.org: scAnnotatR

Pretrained learning models for cell type prediction on single cell RNA-sequencing data

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 11,729 Total
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 28.5%
Downloads: 85.6%
Maintainers (1)
johannes.griss@meduniwien.ac.at
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.1 depends
  • Seurat * depends
  • SingleCellExperiment * depends
  • SummarizedExperiment * depends
  • AnnotationHub * imports
  • ROCR * imports
  • ape * imports
  • caret * imports
  • data.tree * imports
  • dplyr * imports
  • e1071 * imports
  • ggplot2 * imports
  • kernlab * imports
  • methods * imports
  • pROC * imports
  • stats * imports
  • utils * imports
  • knitr * suggests
  • rmarkdown * suggests
  • scRNAseq * suggests
  • testthat * suggests