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scRNA-tools/scRNA-tools

scRNA-tools

scRNA-tools

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A database of software tools for the analysis of single-cell RNA-seq data. To make it into the database software must be available for download and public use somewhere (CRAN, Bioconductor, PyPI, Conda, GitHub, Bitbucket, a private website etc). To view the database head to https://www.scRNA-tools.org.

Purpose

This database is designed to be an overview of the currently available scRNA-seq analysis software, it is unlikely to be 100% complete or accurate but will be updated as new software becomes available.

Contributing

We welcome contributions from the scRNA-seq community! If you would like to contribute please follow the have a look at the wiki or fill in the submission form on our website (https://www.scrna-tools.org/submit). Please be aware that by contributing you are agreeing to abide by the code of conduct.

If you are interested in joining the scRNA-tools team please contact us.

Citation

If you find the scRNA-tools database useful for your work please cite our publication:

Zappia L, Phipson B, Oshlack A. "Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database", PLOS Computational Biology (2018), DOI: 10.1371/journal.pcbi.1006245

@ARTICLE{, title = "Exploring the single-cell {RNA-seq} analysis landscape with  the {scRNA-tools} database", author = "Zappia, Luke and Phipson, Belinda and Oshlack, Alicia", journal = "PLoS Computational Biology", volume = 14, number = 6, pages = "e1006245", month = jun, year = 2018, language = "en", doi = "10.1371/journal.pcbi.1006245", url = "https://doi.org/10.1371/journal.pcbi.1006245", }

If you make use of our analysis of the first 1000 tools in the database please also cite:

Zappia L, Theis FJ. "Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape", Genome Biology (2021), DOI: 10.1186/s13059-021-02519-4

@ARTICLE{, title = "Over 1000 tools reveal trends in the single-cell {RNA-seq}  analysis landscape", author = "Zappia, Luke and Theis, Fabian J", journal = "Genome Biol.", volume = 22, number = 1, pages = "301", month = oct, year = 2021, language = "en" doi = "10.1186/s13059-021-02519-4", url = "https://doi.org/10.1186/s13059-021-02519-4" }

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Table of software for the analysis of single-cell RNA-seq data.

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