Visium
Human cornea
GSM9116173
bronze Interactive viewer H&E keratoconus Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9116173_B3_hires_image': DataArray[cyx] (3, 1812, 2000)
│ └── 'GSM9116173_B3_lowres_image': DataArray[cyx] (3, 544, 600)
├── Shapes
│ └── 'GSM9116173_B3': GeoDataFrame shape: (1226, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1226, 36601)
with coordinate systems:
▸ 'GSM9116173_B3', with elements:
GSM9116173_B3_hires_image (Images), GSM9116173_B3_lowres_image (Images), GSM9116173_B3 (Shapes)
▸ 'GSM9116173_B3_downscaled_hires', with elements:
GSM9116173_B3_hires_image (Images), GSM9116173_B3 (Shapes)
▸ 'GSM9116173_B3_downscaled_lowres', with elements:
GSM9116173_B3_lowres_image (Images), GSM9116173_B3 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("qfGgJ00PcsnzoSl20000")
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("qfGgJ00PcsnzoSl2") # 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 qfGgJ00PcsnzoSl20000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qfGgJ00PcsnzoSl2
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qfGgJ00PcsnzoSl2License: NCBI GEO policy
Part of collection
Eye-conic' spatial transcriptomics of keratoconus corneas→ 13 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- cornea
- Condition
- keratoconus
- Modality
- RNA
- Observations (spots)
- 1,226
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,812 px
- mpp
- 3.862 µm/px
- Size
- 10.3 MB
- Sample ID
- GSM9116173
- License
- NCBI GEO policy
- Published
- Apr 3, 2026
Cite this dataset
doi.org/10.1167/iovs.67.4.7 ↗ BibTeX
@article{Wysocka_2026,
title = {“Eye-Conic” Spatial Transcriptomics Reveals the Layer-Specific Molecular Alterations in Corneas of Patients With Keratoconus},
volume = {67},
ISSN = {1552-5783},
url = {http://dx.doi.org/10.1167/iovs.67.4.7},
DOI = {10.1167/iovs.67.4.7},
number = {4},
journal = {Investigative Ophthalmology & Visual Science},
publisher = {Association for Research in Vision and Ophthalmology (ARVO)},
author = {Wysocka, Alicja and Jaskiewicz-Rajewicz, Katarzyna and Wozniak, Jakub and Rydzanicz, Malgorzata and Ploski, Rafal and Udziela, Monika and Szaflik, Jacek P. and Gajecka, Marzena},
year = {2026},
month = Apr,
pages = {7}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB