Visium
Mouse skin of body
GSM9342585
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'GSM9342585_B1_S2_hires_image': DataArray[cyx] (3, 2000, 2000)
├── Shapes
│ └── 'GSM9342585_B1_S2': GeoDataFrame shape: (639, 2) (2D shapes)
└── Tables
└── 'table': AnnData (639, 19059)
with coordinate systems:
▸ 'GSM9342585_B1_S2', with elements:
GSM9342585_B1_S2_hires_image (Images), GSM9342585_B1_S2 (Shapes)
▸ 'GSM9342585_B1_S2_downscaled_hires', with elements:
GSM9342585_B1_S2_hires_image (Images), GSM9342585_B1_S2 (Shapes)
▸ 'GSM9342585_B1_S2_downscaled_lowres', with elements:
GSM9342585_B1_S2 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("zkr1VrpdkYTYBKrO") # 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/zkr1VrpdkYTYBKrO
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid zkr1VrpdkYTYBKrO Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["zkr1VrpdkYTYBKrO0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- skin of body
- Modality
- RNA
- Observations
- 639
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 2,000 px
- mpp
- 3.800 µm/px
- Size
- 15 MB
- Sample ID
- GSM9342585
- License
- unknown
- Published
- May 31, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB