Visium
Mouse ovary
GSM5673398
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM5673398_655_lowres_image': DataArray[cyx] (3, 600, 580)
├── Shapes
│ └── 'GSM5673398_655': GeoDataFrame shape: (841, 2) (2D shapes)
└── Tables
└── 'table': AnnData (841, 32285)
with coordinate systems:
▸ 'GSM5673398_655', with elements:
GSM5673398_655_lowres_image (Images), GSM5673398_655 (Shapes)
▸ 'GSM5673398_655_downscaled_hires', with elements:
GSM5673398_655 (Shapes)
▸ 'GSM5673398_655_downscaled_lowres', with elements:
GSM5673398_655_lowres_image (Images), GSM5673398_655 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("WYXaieaqhxQM8RYz") # 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/WYXaieaqhxQM8RYz
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid WYXaieaqhxQM8RYz Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["WYXaieaqhxQM8RYz0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- ovary
- Modality
- RNA
- Observations
- 841
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 580 × 600 px
- mpp
- 11.192 µm/px
- Size
- 15 MB
- Sample ID
- GSM5673398
- License
- unknown
- Published
- Aug 24, 2022
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB