Visium
Human skin of body
GSM10024483
silver Interactive viewer H&E hidradenitis suppurativa No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM10024483_HS4_hires_image': DataArray[cyx] (3, 1897, 2000)
│ └── 'GSM10024483_HS4_lowres_image': DataArray[cyx] (3, 569, 600)
├── Shapes
│ └── 'GSM10024483_HS4': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 62710)
with coordinate systems:
▸ 'GSM10024483_HS4', with elements:
GSM10024483_HS4_hires_image (Images), GSM10024483_HS4_lowres_image (Images), GSM10024483_HS4 (Shapes)
▸ 'GSM10024483_HS4_downscaled_hires', with elements:
GSM10024483_HS4_hires_image (Images), GSM10024483_HS4 (Shapes)
▸ 'GSM10024483_HS4_downscaled_lowres', with elements:
GSM10024483_HS4_lowres_image (Images), GSM10024483_HS4 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("5LcjNtONyCZ0G3c8") # 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/5LcjNtONyCZ0G3c8
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 5LcjNtONyCZ0G3c8 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["5LcjNtONyCZ0G3c80000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Distinct IL‑17A and retinoic acid–associated gene programs are enriched in epithelialized tunnels in severe hidradenitis suppurativa.→ 5 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- skin of body
- Condition
- hidradenitis suppurativa
- Modality
- RNA
- Observations
- 4,992
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,897 px
- mpp
- 3.960 µm/px
- Size
- 28 MB
- Sample ID
- GSM10024483
- License
- unknown
- Published
- Sep 8, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB