10x Xenium
Mouse kidney
GSM8717009
silver Interactive viewer No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 30744, 48366), (1, 15372, 24183), (1, 7686, 12091), (1, 3843, 6045), (1, 1921, 3022)
├── Points
│ └── 'transcripts': DataFrame with shape: (115715031, 10) (3D points)
├── Shapes
│ └── 'cell_circles': GeoDataFrame shape: (320131, 2) (2D shapes)
└── Tables
└── 'table': AnnData (320131, 479)
with coordinate systems:
▸ 'global', with elements:
morphology_mip (Images), transcripts (Points), cell_circles (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("xAcYIBmeJkYa9ZtY") # 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/xAcYIBmeJkYa9ZtY
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid xAcYIBmeJkYa9ZtY Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["xAcYIBmeJkYa9ZtY0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- kidney
- Modality
- RNA
- Observations
- 320,131
- Transcripts
- 115,715,031
- Tier
- silver
- Size
- 5.3 GB
- Sample ID
- GSM8717009
- License
- unknown
- Published
- Jan 13, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB