Visium
Human kidney
GSM9026963
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9026963_hires_image': DataArray[cyx] (3, 1856, 2000)
│ └── 'GSM9026963_lowres_image': DataArray[cyx] (3, 557, 600)
├── Shapes
│ └── 'GSM9026963': GeoDataFrame shape: (1000, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1000, 36601)
with coordinate systems:
▸ 'GSM9026963', with elements:
GSM9026963_hires_image (Images), GSM9026963_lowres_image (Images), GSM9026963 (Shapes)
▸ 'GSM9026963_downscaled_hires', with elements:
GSM9026963_hires_image (Images), GSM9026963 (Shapes)
▸ 'GSM9026963_downscaled_lowres', with elements:
GSM9026963_lowres_image (Images), GSM9026963 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("dNKDxjdgqkz7CUZs") # 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/dNKDxjdgqkz7CUZs
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid dNKDxjdgqkz7CUZs Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["dNKDxjdgqkz7CUZs0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- human
- Tissue
- kidney
- Modality
- RNA
- Observations
- 1,000
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,856 px
- mpp
- 3.618 µm/px
- Size
- 10 MB
- Sample ID
- GSM9026963
- License
- unknown
- Published
- Jul 20, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB