Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis
Lu Pan, Tian Mou, Yue Huang, Weifeng Hong, Min Yu, Xuexin Li, Andrey Rzhetsky · 2023
Read Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis by Lu Pan, Tian Mou, Yue Huang, Weifeng Hong, Min Yu, Xuexin Li, Andrey Rzhetsky (2023) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-10752348.txt.
Description
Lu Pan and six co-authors published a software paper in twenty twenty-three presenting Ursa, an automated toolbox for single-cell multiomics analysis. Single-cell data have exploded, but many biologists lack the coding depth to analyze them. Ursa wraps six automated workflows into one R package covering genomics, transcriptomics, epigenetics, proteomics, immunomics, and spatial data.
After quantification, the package runs quality control, dimension reduction, clustering, trajectory inference, and gene-set enrichment with minimal user effort. Results arrive as ready outputs rather than raw code homework. The authors aim to close the expertise gap so bench scientists can exploit their own data. Ursa is freely available from a public GitHub repository for anyone to install.
Catalog record
| CREATOR | Lu Pan, Tian Mou, Yue Huang, Weifeng Hong, Min Yu, Xuexin Li, Andrey Rzhetsky |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |