Visium
Mouse eyelid
GSM8450394
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8450394_hires_image': DataArray[cyx] (3, 1609, 2000)
│ └── 'GSM8450394_lowres_image': DataArray[cyx] (3, 483, 600)
├── Shapes
│ └── 'GSM8450394': GeoDataFrame shape: (375, 2) (2D shapes)
└── Tables
└── 'table': AnnData (375, 32285)
with coordinate systems:
▸ 'GSM8450394', with elements:
GSM8450394_hires_image (Images), GSM8450394_lowres_image (Images), GSM8450394 (Shapes)
▸ 'GSM8450394_downscaled_hires', with elements:
GSM8450394_hires_image (Images), GSM8450394 (Shapes)
▸ 'GSM8450394_downscaled_lowres', with elements:
GSM8450394_lowres_image (Images), GSM8450394 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("cUmtjsbUH9Ira49N0000")
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("cUmtjsbUH9Ira49N") # 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 cUmtjsbUH9Ira49N0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/cUmtjsbUH9Ira49N
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid cUmtjsbUH9Ira49NLicense: NCBI GEO policy
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- eyelid
- Modality
- RNA
- Observations (spots)
- 375
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,609 px
- mpp
- 4.906 µm/px
- Size
- 9.5 MB
- Sample ID
- GSM8450394
- License
- NCBI GEO policy
- Published
- Jan 21, 2025
Cite this dataset
doi.org/10.1038/s41467-025-56907-6 ↗ BibTeX
@article{Zhu_2025,
title = {Identification of Meibomian gland stem cell populations and mechanisms of aging},
volume = {16},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-025-56907-6},
DOI = {10.1038/s41467-025-56907-6},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Zhu, Xuming and Xu, Mingang and Portal, Celine and Lin, Yvonne and Ferdinand, Alyssa and Peng, Tien and Morrisey, Edward E. and Dlugosz, Andrzej A. and Castellano, Joseph M. and Lee, Vivian and Seykora, John T. and Wong, Sunny Y. and Iomini, Carlo and Millar, Sarah E.},
year = {2025},
month = Feb
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB