BayesSpace vs SpaGCN
BayesSpace: Bayesian clustering and resolution enhancement of spatial transcriptomics. SpaGCN: Graph convolutional network that combines gene expression, spatial location and histology to find spatial domains and spatially variable genes.
Facts below come from each project's own website, repository and paper. We don't publish benchmark claims here; see the papers for evaluations.
| Field | BayesSpace | SpaGCN |
|---|---|---|
| Platforms supported | Spatial transcriptomics (spot-based) | Visium, ST, Slide-seq, MERFISH |
| Resolution | Spot → sub-spot enhancement | Spot / cell |
| Tasks | Clustering, resolution enhancement | Spatial domains, spatially variable genes |
| License | MIT | MIT |
| Pricing | Open source | Open source |
| Released | 2020-09-05 | 2020-10-21 |
| Paper | Link | Link |
| GitHub | GitHub | GitHub |
| GitHub stars | 189 | 265 |
| Interface | Package | — |
License and commercial use
| Field | BayesSpace | SpaGCN |
|---|---|---|
| Code license | MIT | MIT |
| 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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