scedar
Single-cell exploratory data analysis for RNA-Seq
Science Score: 23.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 6 DOI reference(s) in README -
○Academic publication links
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1 of 3 committers (33.3%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (12.0%) to scientific vocabulary
Keywords
Repository
Single-cell exploratory data analysis for RNA-Seq
Basic Info
Statistics
- Stars: 40
- Watchers: 4
- Forks: 9
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
Scedar
Scedar (Single-cell exploratory data analysis for RNA-Seq) is a reliable and easy-to-use Python package for efficient visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering of large-scale single cell RNA-seq (scRNA-seq) datasets.
Install
Use PyPI:
pip install scedar
Demo
Workflow of using scedar to analyze an scRNA-seq dataset with 3005 mouse brain cells and 19,972 genes generated using the STRT-Seq UMI protocol by Zeisel et al. (2015). Procedures and parameters that are not directly related to data analysis are omitted. The full version of the demo is available at the Tutorial section of the documentation.
Data sources:
- Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., Rolny, C., Castelo-Branco, G., Hjerling-Leffler, J., and Linnarsson, S. (2015). Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347(6226), 1138–1142.
- Hemberg Group scRNA-seq datasets
Paper
Final Version: Zhang Y, Kim MS, Reichenberger ER, Stear B, Taylor DM (2020) Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis. PLOS Computational Biology 16(4): e1007794. (https://doi.org/10.1371/journal.pcbi.1007794)
Original Preprint: Zhang, Y. and Taylor, D. M. (2018) Scedar: a scalable Python package for single-cell RNA-seq data analysis. bioRxiv, doi: https://doi.org/10.1101/375196.
Owner
- Name: Deanne Taylor Research Group
- Login: TaylorResearchLab
- Kind: organization
- Repositories: 2
- Profile: https://github.com/TaylorResearchLab
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| logstar | y****g@g****m | 295 |
| ben stear | b****5@d****u | 2 |
| Deanne Taylor | d****d@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 3
- Total pull requests: 2
- Average time to close issues: about 23 hours
- Average time to close pull requests: almost 2 years
- Total issue authors: 3
- Total pull request authors: 1
- Average comments per issue: 1.0
- Average comments per pull request: 0.0
- 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
Top Authors
Issue Authors
- jamesjcai (1)
- abbasi-zeeshan (1)
- wflynny (1)
Pull Request Authors
- benstear (2)
Top Labels
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Dependencies
- ipykernel *
- nbsphinx *
- python-igraph *
- sphinx >=1.4
- fa2 *
- leidenalg *
- matplotlib <=3.1.0
- networkx *
- nmslib *
- numpy *
- pandas *
- python-igraph *
- scikit-learn <=0.21
- scipy *
- seaborn <=0.9.0
- umap-learn *
- xgboost *