Visium HD
Mouse kidney
GSM10122934
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'GSM10122934_47822-7_square_002um_full_image': DataTree[cyx] (3, 5328, 8192), (3, 2664, 4096), (3, 1332, 2048), (3, 666, 1024), (3, 333, 512)
├── Shapes
│ ├── 'GSM10122934_47822-7_square_002um_square_002um': GeoDataFrame shape: (7298936, 1) (2D shapes)
│ ├── 'GSM10122934_47822-7_square_002um_square_008um': GeoDataFrame shape: (456334, 1) (2D shapes)
│ ├── 'GSM10122934_47822-7_square_002um_square_016um': GeoDataFrame shape: (114565, 1) (2D shapes)
│ └── 'GSM10122934_47822-7_square_002um_square_016um_for_vitessce': GeoDataFrame shape: (114565, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (7298936, 19059)
├── 'square_008um': AnnData (456334, 19059)
└── 'square_016um': AnnData (114565, 19059)
with coordinate systems:
▸ 'GSM10122934_47822-7_square_002um', with elements:
GSM10122934_47822-7_square_002um_full_image (Images), GSM10122934_47822-7_square_002um_square_002um (Shapes), GSM10122934_47822-7_square_002um_square_008um (Shapes), GSM10122934_47822-7_square_002um_square_016um (Shapes), GSM10122934_47822-7_square_002um_square_016um_for_vitessce (Shapes)
▸ 'GSM10122934_47822-7_square_002um_downscaled_hires', with elements:
GSM10122934_47822-7_square_002um_square_002um (Shapes), GSM10122934_47822-7_square_002um_square_008um (Shapes), GSM10122934_47822-7_square_002um_square_016um (Shapes), GSM10122934_47822-7_square_002um_square_016um_for_vitessce (Shapes)
▸ 'GSM10122934_47822-7_square_002um_downscaled_lowres', with elements:
GSM10122934_47822-7_square_002um_square_002um (Shapes), GSM10122934_47822-7_square_002um_square_008um (Shapes), GSM10122934_47822-7_square_002um_square_016um (Shapes), GSM10122934_47822-7_square_002um_square_016um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("6aJPNWIZTG6JvbXG0000")
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("6aJPNWIZTG6JvbXG") # 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 6aJPNWIZTG6JvbXG0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/6aJPNWIZTG6JvbXG
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 6aJPNWIZTG6JvbXGLicense: NCBI GEO policy
Metadata
- Technology
- Visium HD
- Vendor
- 10x Genomics
- Organism
- mouse
- Tissue
- kidney
- Modality
- RNA
- Observations (bins)
- 456,334
- Tier
- silver
- Resolution
- 8,192 × 5,328 px
- mpp
- 1.246 µm/px
- Size
- 979 MB
- Sample ID
- GSM10122934
- License
- NCBI GEO policy
- Published
- Oct 19, 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