Visium HD
Human kidney
GSM9802211
silver Interactive viewer H&E kidney disorder No linked publication

print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9802211_square_016um_cytassist_image': DataArray[cyx] (3, 3002, 3199)
│ ├── 'GSM9802211_square_016um_hires_image': DataArray[cyx] (3, 4618, 6000)
│ └── 'GSM9802211_square_016um_lowres_image': DataArray[cyx] (3, 462, 600)
├── Shapes
│ ├── 'GSM9802211_square_016um_square_002um': GeoDataFrame shape: (3227445, 1) (2D shapes)
│ ├── 'GSM9802211_square_016um_square_008um': GeoDataFrame shape: (205569, 1) (2D shapes)
│ ├── 'GSM9802211_square_016um_square_016um': GeoDataFrame shape: (52724, 1) (2D shapes)
│ └── 'GSM9802211_square_016um_square_016um_for_vitessce': GeoDataFrame shape: (52724, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (3227445, 18132)
├── 'square_008um': AnnData (205569, 18132)
└── 'square_016um': AnnData (52724, 18132)
with coordinate systems:
▸ 'GSM9802211_square_016um', with elements:
GSM9802211_square_016um_cytassist_image (Images), GSM9802211_square_016um_hires_image (Images), GSM9802211_square_016um_lowres_image (Images), GSM9802211_square_016um_square_002um (Shapes), GSM9802211_square_016um_square_008um (Shapes), GSM9802211_square_016um_square_016um (Shapes), GSM9802211_square_016um_square_016um_for_vitessce (Shapes)
▸ 'GSM9802211_square_016um_downscaled_hires', with elements:
GSM9802211_square_016um_hires_image (Images), GSM9802211_square_016um_square_002um (Shapes), GSM9802211_square_016um_square_008um (Shapes), GSM9802211_square_016um_square_016um (Shapes), GSM9802211_square_016um_square_016um_for_vitessce (Shapes)
▸ 'GSM9802211_square_016um_downscaled_lowres', with elements:
GSM9802211_square_016um_lowres_image (Images), GSM9802211_square_016um_square_002um (Shapes), GSM9802211_square_016um_square_008um (Shapes), GSM9802211_square_016um_square_016um (Shapes), GSM9802211_square_016um_square_016um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("CgXLPMQ7N6ziDcYg0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) Python · lamindb
Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("CgXLPMQ7N6ziDcYg") # stem uid: Lamin resolves it to the 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download CgXLPMQ7N6ziDcYg0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/CgXLPMQ7N6ziDcYg
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid CgXLPMQ7N6ziDcYg License: NCBI GEO policy
Metadata
- Technology
- Visium HD
- Organism
- human
- Tissue
- kidney
- Condition
- kidney disorder
- Modality
- RNA
- Observations (bins)
- 205,569
- Tier
- silver
- Resolution
- 6,000 × 4,618 px
- mpp
- 2.091 µm/px
- Size
- 544 MB
- Sample ID
- GSM9802211
- License
- NCBI GEO policy
- Published
- Jul 21, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB