Visium
Pig right coronary artery
GSM6796865
silver Interactive viewer H&E coronary atherosclerosis No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6796865_B_S2_hires_image': DataArray[cyx] (3, 1719, 2000)
│ └── 'GSM6796865_B_S2_lowres_image': DataArray[cyx] (3, 516, 600)
├── Shapes
│ └── 'GSM6796865_B_S2': GeoDataFrame shape: (1621, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1621, 21303)
with coordinate systems:
▸ 'GSM6796865_B_S2', with elements:
GSM6796865_B_S2_hires_image (Images), GSM6796865_B_S2_lowres_image (Images), GSM6796865_B_S2 (Shapes)
▸ 'GSM6796865_B_S2_downscaled_hires', with elements:
GSM6796865_B_S2_hires_image (Images), GSM6796865_B_S2 (Shapes)
▸ 'GSM6796865_B_S2_downscaled_lowres', with elements:
GSM6796865_B_S2_lowres_image (Images), GSM6796865_B_S2 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("oMMN1ifbhruVbOU0") # 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 Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/oMMN1ifbhruVbOU0
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid oMMN1ifbhruVbOU0 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["oMMN1ifbhruVbOU00000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Insulin-like growth factor I reduces coronary atherosclerosis in pigs with familial hypercholesterolemia→ 4 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- pig
- Tissue
- right coronary artery
- Condition
- coronary atherosclerosis
- Modality
- RNA
- Observations
- 1,621
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,719 px
- mpp
- 4.660 µm/px
- Size
- 20 MB
- Sample ID
- GSM6796865
- License
- unknown
- Published
- Dec 3, 2023
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB