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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |
|---|---|---|
| Category | Single-Cell & Spatial Biology | — |
| Subcategory | Spatial Omics Data Infrastructure | — |
| Company | scverse Consortium | — |
| Access mode | Open Source | — |
| Licence conditions | BSD-3-Clause | — |
| Compute requirements | Standard Python environment (CPU/GPU) | — |
| Biological application | Spatial transcriptomics, multiplexed immunofluorescence, and tissue cartography | — |
| Research workflow | Load raw spatial instrument files -> align coordinates -> perform spatial statistics and Napari visualization | — |
| Evidence level | Peer-reviewed research (Nature Methods 2024) | — |
| Commercial eligibility | Permissive open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on PyPI and GitHub | — |
| Features | Standardized representation of spatial coordinates, images, labels, and feature tables, Support for 10x Visium, 10x Xenium, NanoString CosMx, Akoya PhenoCycler, and Vizgen MERSCOPE, Affine transformation engine for multi-modal spatial image co-registration, Zarr-based storage for fast streaming and out-of-memory computation | — |
| Entry type | Python Library | — |
| AI role | Spatial Data Modeling & Alignment | — |
| Input data | Not recorded | — |
| Output data | Not recorded | — |
| Validation | Not recorded | — |
| Type | Spatial omics data standard and library | — |
| Intended use | Not recorded | — |
| Compatibility | Not recorded | — |
| Manufacturer | scverse Community | — |
| Integration evidence | https://spatialdata.scverse.org | — |
| Laboratory handoff | Enables interactive histological exploration of spatial tissue biopsies | — |