Visium
Human colon
visium-cytassist-human-colon-cancer-p2
silver Interactive viewer H&E colorectal cancer ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'Visium_V2_Human_Colon_Cancer_P2_full_image': DataTree[cyx] (3, 49099, 76190), (3, 24549, 38095), (3, 12274, 19047), (3, 6137, 9523), (3, 3068, 4761)
│ ├── 'Visium_V2_Human_Colon_Cancer_P2_hires_image': DataArray[cyx] (3, 2578, 4000)
│ └── 'Visium_V2_Human_Colon_Cancer_P2_lowres_image': DataArray[cyx] (3, 387, 600)
├── Shapes
│ └── 'Visium_V2_Human_Colon_Cancer_P2': GeoDataFrame shape: (4269, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4269, 18085)
with coordinate systems:
▸ 'Visium_V2_Human_Colon_Cancer_P2', with elements:
Visium_V2_Human_Colon_Cancer_P2_full_image (Images), Visium_V2_Human_Colon_Cancer_P2_hires_image (Images), Visium_V2_Human_Colon_Cancer_P2_lowres_image (Images), Visium_V2_Human_Colon_Cancer_P2 (Shapes)
▸ 'Visium_V2_Human_Colon_Cancer_P2_downscaled_hires', with elements:
Visium_V2_Human_Colon_Cancer_P2_hires_image (Images), Visium_V2_Human_Colon_Cancer_P2 (Shapes)
▸ 'Visium_V2_Human_Colon_Cancer_P2_downscaled_lowres', with elements:
Visium_V2_Human_Colon_Cancer_P2_lowres_image (Images), Visium_V2_Human_Colon_Cancer_P2 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("qMnnMcP2B2W5ddYN") # latest version
sdata = artifact.load() # cache, then load into memory
group = artifact.open() # stream without downloading
path = artifact.cache() # sync into the local cache, returns the path Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qMnnMcP2B2W5ddYN
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qMnnMcP2B2W5ddYN Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["qMnnMcP2B2W5ddYN0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- human
- Tissue
- colon
- Condition
- colorectal cancer
- Modality
- RNA
- Observations
- 4,269
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Magnification
- 40x
- Resolution
- 76,190 × 49,099 px
- mpp
- 0.251 µm/px
- Size
- 7.4 GB
- Sample ID
- visium-cytassist-human-colon-cancer-p2
- License
- CC BY 4.0
- Published
- May 15, 2023
Cite this dataset
doi.org/10.1038/s41588-025-02193-3 ↗ BibTeX
@article{Oliveira_2025,
title = {High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer},
volume = {57},
ISSN = {1546-1718},
url = {http://dx.doi.org/10.1038/s41588-025-02193-3},
DOI = {10.1038/s41588-025-02193-3},
number = {6},
journal = {Nature Genetics},
publisher = {Springer Science and Business Media LLC},
author = {Oliveira, Michelli Faria de and Romero, Juan Pablo and Chung, Meii and Williams, Stephen R. and Gottscho, Andrew D. and Gupta, Anushka and Pilipauskas, Susan E. and Mohabbat, Seayar and Raman, Nandhini and Sukovich, David J. and Patterson, David M. and de Oliveira, Michelli Faria and Romero, Juan Pablo and Williams, Stephen R. and Patterson, David M. and Taylor, Sarah E. B. and Taylor, Sarah E. B.},
year = {2025},
month = June,
pages = {1512–1523}
}Source: 10x Genomics ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB