Visium HD
Human lung
GSM8721365
silver Interactive viewer H&E interstitial lung disease No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8721365_Non-LTx_008um_hires_image': DataArray[cyx] (3, 4211, 6000)
│ └── 'GSM8721365_Non-LTx_008um_lowres_image': DataArray[cyx] (3, 421, 600)
├── Shapes
│ ├── 'GSM8721365_Non-LTx_008um_square_008um': GeoDataFrame shape: (317761, 1) (2D shapes)
│ └── 'GSM8721365_Non-LTx_008um_square_008um_for_vitessce': GeoDataFrame shape: (317761, 2) (2D shapes)
└── Tables
└── 'square_008um': AnnData (317761, 18085)
with coordinate systems:
▸ 'GSM8721365_Non-LTx_008um', with elements:
GSM8721365_Non-LTx_008um_hires_image (Images), GSM8721365_Non-LTx_008um_lowres_image (Images), GSM8721365_Non-LTx_008um_square_008um (Shapes), GSM8721365_Non-LTx_008um_square_008um_for_vitessce (Shapes)
▸ 'GSM8721365_Non-LTx_008um_downscaled_hires', with elements:
GSM8721365_Non-LTx_008um_hires_image (Images), GSM8721365_Non-LTx_008um_square_008um (Shapes), GSM8721365_Non-LTx_008um_square_008um_for_vitessce (Shapes)
▸ 'GSM8721365_Non-LTx_008um_downscaled_lowres', with elements:
GSM8721365_Non-LTx_008um_lowres_image (Images), GSM8721365_Non-LTx_008um_square_008um (Shapes), GSM8721365_Non-LTx_008um_square_008um_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("Ka4Pk48c0pDskJFH") # 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/Ka4Pk48c0pDskJFH
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Ka4Pk48c0pDskJFH Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["Ka4Pk48c0pDskJFH0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Spatial transcriptomic profiling of the lung tissues from a patient with recurrent anti-synthetase syndrome associated interstitial lung disease after bilateral lung transplantation and one untreated patient→ 2 datasets · other
Metadata
- Technology
- Visium HD
- Organism
- human
- Tissue
- lung
- Condition
- interstitial lung disease
- Modality
- RNA
- Observations
- 317,761
- Tier
- silver
- Resolution
- 6,000 × 4,211 px
- mpp
- 2.811 µm/px
- Size
- 143 MB
- Sample ID
- GSM8721365
- License
- unknown
- Published
- Jan 9, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB