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Visium

Human ovary

GSM8292467

bronze Interactive viewer H&E Provisional
Human ovary preview
print(sdata)
SpatialData object
├── Images
│     └── 'GSM8292467_C2_hires_image': DataArray[cyx] (3, 2000, 2000)
├── Shapes
│     └── 'GSM8292467_C2': GeoDataFrame shape: (5067, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (5067, 18085)
with coordinate systems:
    ▸ 'GSM8292467_C2', with elements:
        GSM8292467_C2_hires_image (Images), GSM8292467_C2 (Shapes)
    ▸ 'GSM8292467_C2_downscaled_hires', with elements:
        GSM8292467_C2_hires_image (Images), GSM8292467_C2 (Shapes)
    ▸ 'GSM8292467_C2_downscaled_lowres', with elements:
        GSM8292467_C2 (Shapes)
Load in Python
import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("vluJ9YlrjEn2uRVa")   # 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/vluJ9YlrjEn2uRVa

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid vluJ9YlrjEn2uRVa
Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["vluJ9YlrjEn2uRVa0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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Metadata

Technology
Visium
Organism
human
Tissue
ovary
Modality
RNA
Observations
5,067
Tier
bronze
Chemistry
Visium v1
Magnification
other
Resolution
2,000 × 2,000 px
mpp
6.412 µm/px
Size
98 MB
Sample ID
GSM8292467
License
unknown
Published
Feb 19, 2025

Cite this dataset

No linked publication found — please cite the data source: NCBI GEO ↗.

Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB