Visium
Mouse ovary
GSM5673401
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM5673401_658_lowres_image': DataArray[cyx] (3, 600, 593)
├── Shapes
│ └── 'GSM5673401_658': GeoDataFrame shape: (1193, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1193, 32285)
with coordinate systems:
▸ 'GSM5673401_658', with elements:
GSM5673401_658_lowres_image (Images), GSM5673401_658 (Shapes)
▸ 'GSM5673401_658_downscaled_hires', with elements:
GSM5673401_658 (Shapes)
▸ 'GSM5673401_658_downscaled_lowres', with elements:
GSM5673401_658_lowres_image (Images), GSM5673401_658 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("R9Rjq5oQXZ4JKdNG") # 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/R9Rjq5oQXZ4JKdNG
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid R9Rjq5oQXZ4JKdNG Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["R9Rjq5oQXZ4JKdNG0000"])[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
- 1,193
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 593 × 600 px
- mpp
- 11.114 µm/px
- Size
- 21 MB
- Sample ID
- GSM5673401
- 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