10x Xenium
Mouse skin of body
GSM8551250
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 98506, 39854), (1, 49253, 19927), (1, 24626, 9963), (1, 12313, 4981), (1, 6156, 2490)
├── Points
│ └── 'transcripts': DataFrame with shape: (34541873, 10) (3D points)
├── Shapes
│ └── 'cell_circles': GeoDataFrame shape: (332833, 2) (2D shapes)
└── Tables
└── 'table': AnnData (332833, 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("qDDM9NdqTCkLc5ys") # 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/qDDM9NdqTCkLc5ys
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qDDM9NdqTCkLc5ys Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["qDDM9NdqTCkLc5ys0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- skin of body
- Modality
- RNA
- Observations
- 332,833
- Transcripts
- 34,541,873
- Tier
- bronze
- Size
- 4.8 GB
- Sample ID
- GSM8551250
- License
- unknown
- Published
- Oct 7, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB