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Visium

Human breast

GSM6592051

bronze Interactive viewer H&E triple-negative breast carcinoma Provisional
Human breast preview
print(sdata)
SpatialData object
├── Images
│     └── 'GSM6592051_M4_hires_image': DataArray[cyx] (3, 1963, 2000)
├── Shapes
│     └── 'GSM6592051_M4': GeoDataFrame shape: (1564, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1564, 36601)
with coordinate systems:
    ▸ 'GSM6592051_M4', with elements:
        GSM6592051_M4_hires_image (Images), GSM6592051_M4 (Shapes)
    ▸ 'GSM6592051_M4_downscaled_hires', with elements:
        GSM6592051_M4_hires_image (Images), GSM6592051_M4 (Shapes)
    ▸ 'GSM6592051_M4_downscaled_lowres', with elements:
        GSM6592051_M4 (Shapes)
Load in Python
import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("5jAOlMlYxP3zA90N")   # 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/5jAOlMlYxP3zA90N

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 5jAOlMlYxP3zA90N
Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["5jAOlMlYxP3zA90N0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

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Metadata

Technology
Visium
Organism
human
Tissue
breast
Condition
triple-negative breast carcinoma
Modality
RNA
Observations
1,564
Tier
bronze
Chemistry
Visium v1
Resolution
2,000 × 1,963 px
mpp
4.344 µm/px
Size
20 MB
Sample ID
GSM6592051
License
unknown
Published
Oct 16, 2023

Cite this dataset

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

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