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| Atributo | ＋ Añadir herramienta | |||
|---|---|---|---|---|
| Category | Single-Cell & Spatial Biology | Single-cell, Spatial & Cytometry | Single-cell, Spatial & Cytometry | — |
| Subcategory | Cell-Cell Communication | Conventional or Statistical Baselines | Conventional or Statistical Baselines | — |
| Company | University of California, Irvine (Nie Lab) | Research / method authors | Research / method authors | — |
| Access mode | Open Source | Confirm distribution and exact release | Confirm distribution and exact release | — |
| Licence conditions | GPL-3.0 | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Standard workstation CPU (R package) | Version and data dependent | Version and data dependent | — |
| Biological application | Tumor microenvironment analysis, immune cell crosstalk, and developmental biology | No registrado | No registrado | — |
| Research workflow | Single-cell expression matrix -> signaling pathway probabilities and network graphs | No registrado | No registrado | — |
| Evidence level | Peer-reviewed research (Nature Communications 2021) | OFFICIAL_DOCUMENTATION | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Open source release | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Source | Verify access and licence | Verify access and licence | — |
| Availability ⓘ | Available on GitHub and CRAN | Method paper or official documentation | Method paper or official documentation | — |
| Features | Comprehensive database of signaling interactions accounting for multi-subunit complexes, Quantification of incoming and outgoing signaling communication probability per cell type, Pattern recognition uncovering major signaling coordination pathways, Support for spatial transcriptomics to restrict communication by physical distance | 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 | R Software Package | Classical/statistical scientific method | Classical/statistical scientific method | — |
| AI role | Network Inference & Pattern Recognition | NON_AI_REFERENCE | NON_AI_STATISTICAL | — |
| Input data | No registrado | Compatible single-cell expression matrix and named atlas reference | Subcellular in-situ transcript positions and optional cell stain masks | — |
| Output data | No registrado | Anchor-based reference map and predicted cell labels | Spatial transcript-informed cell boundaries | — |
| Validation | No registrado | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Intercellular signaling inference tool | No registrado | No registrado | — |
| Intended use | No registrado | No registrado | No registrado | — |
| Compatibility | No registrado | No registrado | No registrado | — |
| Manufacturer | UC Irvine | No registrado | No registrado | — |
| Integration evidence | https://github.com/jinworks/CellChat | No registrado | No registrado | — |
| Laboratory handoff | Validates predicted ligand-receptor pairs via antibody blockade or co-culture assays | No registrado | No registrado | — |