Tangram vs cell2location
Tangram: Maps single-cell profiles onto spatial data with deep learning. cell2location: Bayesian mapping of fine-grained cell types in 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 | Tangram | cell2location |
|---|---|---|
| Platforms supported | Visium, Slide-seq, others | Visium |
| Resolution | — | — |
| Tasks | Single-cell to spatial mapping | Cell-type deconvolution |
| License | BSD-3-Clause | Apache-2.0 |
| Pricing | Open source | Open source |
| Released | 2020-08-30 | 2020-10-14 |
| Paper | Link | Link |
| GitHub | GitHub | GitHub |
| GitHub stars | 373 | 462 |
| Interface | Package, Colab | Package, Docker, Colab |
License and commercial use
| Field | Tangram | cell2location |
|---|---|---|
| Code license | BSD-3-Clause | Apache-2.0 |
| Weights license | — | — |
| Commercial use | Permissive (commercial OK) | Permissive (commercial OK) |
| Answer | Yes. The recorded license is permissive, so closed-source commercial use is allowed if you keep the notices. Details | Yes. The recorded license is permissive, so closed-source commercial use is allowed if you keep the notices. Details |
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