Visium HD
Human lung
GSM9093278
bronze Interactive viewer H&E non-small cell lung carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9093278_A_A_A_2um_full_image': DataTree[cyx] (3, 28735, 22143), (3, 14367, 11071), (3, 7183, 5535), (3, 3591, 2767), (3, 1795, 1383)
│ ├── 'GSM9093278_A_A_A_2um_hires_image': DataArray[cyx] (3, 6000, 4624)
│ └── 'GSM9093278_A_A_A_2um_lowres_image': DataArray[cyx] (3, 600, 463)
├── Shapes
│ ├── 'GSM9093278_A_A_A_2um_square_002um': GeoDataFrame shape: (2319753, 1) (2D shapes)
│ ├── 'GSM9093278_A_A_A_2um_square_008um': GeoDataFrame shape: (149898, 1) (2D shapes)
│ └── 'GSM9093278_A_A_A_2um_square_008um_for_vitessce': GeoDataFrame shape: (149898, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (2319753, 18111)
└── 'square_008um': AnnData (149898, 18111)
with coordinate systems:
▸ 'GSM9093278_A_A_A_2um', with elements:
GSM9093278_A_A_A_2um_full_image (Images), GSM9093278_A_A_A_2um_hires_image (Images), GSM9093278_A_A_A_2um_lowres_image (Images), GSM9093278_A_A_A_2um_square_002um (Shapes), GSM9093278_A_A_A_2um_square_008um (Shapes), GSM9093278_A_A_A_2um_square_008um_for_vitessce (Shapes)
▸ 'GSM9093278_A_A_A_2um_downscaled_hires', with elements:
GSM9093278_A_A_A_2um_hires_image (Images), GSM9093278_A_A_A_2um_square_002um (Shapes), GSM9093278_A_A_A_2um_square_008um (Shapes), GSM9093278_A_A_A_2um_square_008um_for_vitessce (Shapes)
▸ 'GSM9093278_A_A_A_2um_downscaled_lowres', with elements:
GSM9093278_A_A_A_2um_lowres_image (Images), GSM9093278_A_A_A_2um_square_002um (Shapes), GSM9093278_A_A_A_2um_square_008um (Shapes), GSM9093278_A_A_A_2um_square_008um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("Ikz0J6QY6EqmhGIX0000")
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("Ikz0J6QY6EqmhGIX") # 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 Ikz0J6QY6EqmhGIX0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/Ikz0J6QY6EqmhGIX
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Ikz0J6QY6EqmhGIXLicense: NCBI GEO policy
Metadata
- Technology
- Visium HD
- Vendor
- 10x Genomics
- Organism
- human
- Tissue
- lung
- Condition
- non-small cell lung carcinoma
- Modality
- RNA
- Observations (bins)
- 149,898
- Tier
- bronze
- Magnification
- 20x
- Resolution
- 22,143 × 28,735 px
- mpp
- 0.376 µm/px
- Size
- 1.5 GB
- Sample ID
- GSM9093278
- License
- NCBI GEO policy
- Published
- Dec 3, 2025
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