cell2location vs RCTD (spacexr)
cell2location: Bayesian mapping of fine-grained cell types in spatial transcriptomics. RCTD (spacexr): Robust cell type decomposition for spatial transcriptomics.
Facts below come from each project's own website, repository and paper. We don't publish benchmark claims here; see the papers for evaluations. Shared tags: deconvolution.
| Field | cell2location | RCTD (spacexr) |
|---|---|---|
| Platforms supported | Visium | MERFISH, Slide-seq, Visium |
| Resolution | — | Spot / pixel (mixtures decomposed) |
| Tasks | Cell-type deconvolution | Cell-type decomposition |
| License | Apache-2.0 | GPL-3.0 |
| Pricing | Open source | Open source |
| Released | 2020-10-14 | 2020-05-08 |
| Paper | Link | Link |
| GitHub | GitHub | GitHub |
| GitHub stars | 462 | 489 |
| Interface | Package, Docker, Colab | Package |
License and commercial use
| Field | cell2location | RCTD (spacexr) |
|---|---|---|
| Code license | Apache-2.0 | GPL-3.0 |
| Weights license | — | — |
| Commercial use | Permissive (commercial OK) | Copyleft (commercial OK, share-alike) |
| Answer | Yes. The recorded license is permissive, so closed-source commercial use is allowed if you keep the notices. Details | Yes, with conditions. It is under GPL: if you distribute modified or combined software, you must release the source under the same license. Details |
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