Visium
Human kidney
GSM7974888
bronze Interactive viewer H&E clear cell renal carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7974888_S2-D_hires_image': DataArray[cyx] (3, 2000, 1968)
│ └── 'GSM7974888_S2-D_lowres_image': DataArray[cyx] (3, 600, 591)
├── Shapes
│ └── 'GSM7974888_S2-D': GeoDataFrame shape: (3388, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3388, 17943)
with coordinate systems:
▸ 'GSM7974888_S2-D', with elements:
GSM7974888_S2-D_hires_image (Images), GSM7974888_S2-D_lowres_image (Images), GSM7974888_S2-D (Shapes)
▸ 'GSM7974888_S2-D_downscaled_hires', with elements:
GSM7974888_S2-D_hires_image (Images), GSM7974888_S2-D (Shapes)
▸ 'GSM7974888_S2-D_downscaled_lowres', with elements:
GSM7974888_S2-D_lowres_image (Images), GSM7974888_S2-D (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("7TJv5Z6UBKoLug5K") # 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/7TJv5Z6UBKoLug5K
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 7TJv5Z6UBKoLug5K Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["7TJv5Z6UBKoLug5K0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Role of copper in progression of clear cell renal cell carcinoma [Spatial transcriptomics]→ 4 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- kidney
- Condition
- clear cell renal carcinoma
- Modality
- RNA
- Observations
- 3,388
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,968 × 2,000 px
- mpp
- 3.934 µm/px
- Size
- 48 MB
- Sample ID
- GSM7974888
- License
- unknown
- Published
- Oct 18, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB