10x Xenium
Human cerebral cortex
GSM9963630
silver Interactive viewer No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 13512, 15666), (1, 6756, 7833), (1, 3378, 3916), (1, 1689, 1958), (1, 844, 979)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (13355, 15216), (6677, 7608), (3338, 3804), (1669, 1902), (834, 951)
│ └── 'nucleus_labels': DataTree[yx] (13330, 15205), (6665, 7602), (3332, 3801), (1666, 1900), (833, 950)
├── Points
│ └── 'transcripts': DataFrame with shape: (41805931, 13) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (66190, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (66181, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (66551, 2) (2D shapes)
└── Tables
└── 'table': AnnData (66181, 5001)
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("6y2cQlNuMZyyy0s3") # 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/6y2cQlNuMZyyy0s3
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 6y2cQlNuMZyyy0s3 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["6y2cQlNuMZyyy0s30000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- cerebral cortex
- Modality
- RNA
- Observations
- 66,181
- Transcripts
- 41,805,931
- Tier
- silver
- Size
- 1.2 GB
- Sample ID
- GSM9963630
- License
- unknown
- Published
- Sep 30, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB