10x Xenium
Human subcutaneous adipose tissue
GSM8959880
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 20549, 25698), (1, 10274, 12849), (1, 5137, 6424), (1, 2568, 3212), (1, 1284, 1606)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (18913, 23441), (9456, 11720), (4728, 5860), (2364, 2930), (1182, 1465)
│ └── 'nucleus_labels': DataTree[yx] (18774, 23301), (9387, 11650), (4693, 5825), (2346, 2912), (1173, 1456)
├── Points
│ └── 'transcripts': DataFrame with shape: (1340388, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (4144, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (4144, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (4144, 1) (2D shapes)
└── Tables
└── 'table': AnnData (4144, 477)
with coordinate systems:
▸ 'global', with elements:
morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("DOtNdaQ3hBL6WbTZ") # 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/DOtNdaQ3hBL6WbTZ
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid DOtNdaQ3hBL6WbTZ Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["DOtNdaQ3hBL6WbTZ0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- subcutaneous adipose tissue
- Modality
- RNA
- Observations
- 4,144
- Transcripts
- 1,340,388
- Tier
- bronze
- Size
- 389 MB
- Sample ID
- GSM8959880
- License
- unknown
- Published
- Jun 19, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB