Visium
Human kidney
GSM9895920
silver Interactive viewer H&E hypertensive nephropathy No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9895920_ES_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM9895920_ES_hires_image': DataArray[cyx] (3, 2000, 1409)
│ └── 'GSM9895920_ES_lowres_image': DataArray[cyx] (3, 600, 423)
├── Shapes
│ └── 'GSM9895920_ES': GeoDataFrame shape: (4892, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4892, 37082)
with coordinate systems:
▸ 'GSM9895920_ES', with elements:
GSM9895920_ES_full_image (Images), GSM9895920_ES_hires_image (Images), GSM9895920_ES_lowres_image (Images), GSM9895920_ES (Shapes)
▸ 'GSM9895920_ES_downscaled_hires', with elements:
GSM9895920_ES_hires_image (Images), GSM9895920_ES (Shapes)
▸ 'GSM9895920_ES_downscaled_lowres', with elements:
GSM9895920_ES_lowres_image (Images), GSM9895920_ES (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("SEHlo0Pi1f1rTn7t0000")
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("SEHlo0Pi1f1rTn7t") # 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 SEHlo0Pi1f1rTn7t0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/SEHlo0Pi1f1rTn7t
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid SEHlo0Pi1f1rTn7tLicense: NCBI GEO policy
Part of collection
Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [Spatial Transcriptomics]→ 4 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- kidney
- Condition
- hypertensive nephropathy
- Modality
- RNA
- Observations (spots)
- 4,892
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 1,409 × 2,000 px
- mpp
- 3.783 µm/px
- Size
- 33.3 MB
- Sample ID
- GSM9895920
- License
- NCBI GEO policy
- Published
- Jul 22, 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