Visium
Human kidney
GSM9895922
silver Interactive viewer H&E hypertensive nephropathy No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9895922_LS_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM9895922_LS_hires_image': DataArray[cyx] (3, 2000, 1653)
│ └── 'GSM9895922_LS_lowres_image': DataArray[cyx] (3, 600, 496)
├── Shapes
│ └── 'GSM9895922_LS': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 37082)
with coordinate systems:
▸ 'GSM9895922_LS', with elements:
GSM9895922_LS_full_image (Images), GSM9895922_LS_hires_image (Images), GSM9895922_LS_lowres_image (Images), GSM9895922_LS (Shapes)
▸ 'GSM9895922_LS_downscaled_hires', with elements:
GSM9895922_LS_hires_image (Images), GSM9895922_LS (Shapes)
▸ 'GSM9895922_LS_downscaled_lowres', with elements:
GSM9895922_LS_lowres_image (Images), GSM9895922_LS (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("fTSnuUbHuQ4wGA1L0000")
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("fTSnuUbHuQ4wGA1L") # 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 fTSnuUbHuQ4wGA1L0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/fTSnuUbHuQ4wGA1L
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid fTSnuUbHuQ4wGA1LLicense: 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)
- 4,992
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 1,653 × 2,000 px
- mpp
- 3.600 µm/px
- Size
- 41.9 MB
- Sample ID
- GSM9895922
- 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