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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 | Single-cell, Spatial & Cytometry | Single-cell, Spatial & Cytometry | — |
| Subcategory | Spatial Omics Data Infrastructure | Conventional or Statistical Baselines | Conventional or Statistical Baselines | — |
| Company | scverse Consortium | Research / method authors | Research / method authors | — |
| Access mode | Open Source | Confirm distribution and exact release | Confirm distribution and exact release | — |
| Licence conditions | BSD-3-Clause | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Standard Python environment (CPU/GPU) | Version and data dependent | Version and data dependent | — |
| Biological application | Spatial transcriptomics, multiplexed immunofluorescence, and tissue cartography | Not recorded | Not recorded | — |
| Research workflow | Load raw spatial instrument files -> align coordinates -> perform spatial statistics and Napari visualization | Not recorded | Not recorded | — |
| Evidence level | Peer-reviewed research (Nature Methods 2024) | OFFICIAL_DOCUMENTATION | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Permissive open source release | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Source | Verify access and licence | Verify access and licence | — |
| Availability ⓘ | Available on PyPI and GitHub | Method paper or official documentation | Method paper or official documentation | — |
| 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 | Azimuth (Seurat reference mapping) scientific method, Compatible single-cell expression matrix and named atlas reference, Anchor-based reference map and predicted cell labels | Baysor scientific method, Subcellular in-situ transcript positions and optional cell stain masks, Spatial transcript-informed cell boundaries | — |
| Entry type | Python Library | Classical/statistical scientific method | Classical/statistical scientific method | — |
| AI role | Spatial Data Modeling & Alignment | NON_AI_REFERENCE | NON_AI_STATISTICAL | — |
| Input data | Not recorded | Compatible single-cell expression matrix and named atlas reference | Subcellular in-situ transcript positions and optional cell stain masks | — |
| Output data | Not recorded | Anchor-based reference map and predicted cell labels | Spatial transcript-informed cell boundaries | — |
| Validation | Not recorded | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Spatial omics data standard and library | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | scverse Community | Not recorded | Not recorded | — |
| Integration evidence | https://spatialdata.scverse.org | Not recorded | Not recorded | — |
| Laboratory handoff | Enables interactive histological exploration of spatial tissue biopsies | Not recorded | Not recorded | — |