Official implementation of Cell Spatial And Neighborhood Pattern (CellSNAP), a computational method that learns a single-cell representation embedding by integrating cross-domain information from tissue samples. Through the analysis of datasets spanning spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we demonstrate CellSNAP’s capability to elucidate biologically relevant cell populations that were previously elusive due to the relinquished tissue morphological information from images
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Release files for cellsnap 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cellsnap-0.1.0.tar.gz | 19.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cellsnap-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 40.1 kB
Release files / cellsnap-0.1.0.tar.gz
| Download URL | cellsnap-0.1.0.tar.gz |
|---|---|
| Size | 19.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.14
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Release files / cellsnap-0.1.0-py3-none-any.whl
| Download URL | cellsnap-0.1.0-py3-none-any.whl |
|---|---|
| Size | 20.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1f515c5fca952f35ec2e29671f4625aa262fce45ad3dfc6b2c167ed789fd931a
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.14
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