Visium
Human heart
GSM7286753
bronze Interactive viewer H&E Provisional

print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7286753_hires_image': DataArray[cyx] (3, 1940, 2000)
│ └── 'GSM7286753_lowres_image': DataArray[cyx] (3, 582, 600)
├── Shapes
│ └── 'GSM7286753': GeoDataFrame shape: (882, 2) (2D shapes)
└── Tables
└── 'table': AnnData (882, 36601)
with coordinate systems:
▸ 'GSM7286753', with elements:
GSM7286753_hires_image (Images), GSM7286753_lowres_image (Images), GSM7286753 (Shapes)
▸ 'GSM7286753_downscaled_hires', with elements:
GSM7286753_hires_image (Images), GSM7286753 (Shapes)
▸ 'GSM7286753_downscaled_lowres', with elements:
GSM7286753_lowres_image (Images), GSM7286753 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("giMBQluPe4uGE1iP0000")
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("giMBQluPe4uGE1iP") # 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 giMBQluPe4uGE1iP0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/giMBQluPe4uGE1iP
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid giMBQluPe4uGE1iP License: NCBI GEO policy
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- heart
- Modality
- RNA
- Observations (spots)
- 882
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,940 px
- mpp
- 3.640 µm/px
- Size
- 10.2 MB
- Sample ID
- GSM7286753
- License
- NCBI GEO policy
- Published
- May 29, 2026
Cite this dataset
doi.org/10.1093/bioinformatics/btag333 ↗ BibTeX
@article{geo:GSM7286753,
doi = {10.1093/bioinformatics/btag333},
title = {GSM7286753},
year = {2026}
} Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB