10x Xenium
Human skin of body
GSM9079615
silver Interactive viewer No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 13926, 17112), (1, 6963, 8556), (1, 3481, 4278), (1, 1740, 2139), (1, 870, 1069)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (12945, 15390), (6472, 7695), (3236, 3847), (1618, 1923), (809, 961)
│ └── 'nucleus_labels': DataTree[yx] (11261, 15366), (5630, 7683), (2815, 3841), (1407, 1920), (703, 960)
├── Points
│ └── 'transcripts': DataFrame with shape: (2187249, 11) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (11535, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (11535, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (11586, 2) (2D shapes)
└── Tables
└── 'table': AnnData (11535, 100)
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("0F3pfJxKJUD1Rvti") # 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/0F3pfJxKJUD1Rvti
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 0F3pfJxKJUD1Rvti Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["0F3pfJxKJUD1Rvti0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- skin of body
- Modality
- RNA
- Observations
- 11,535
- Transcripts
- 2,187,249
- Tier
- silver
- Size
- 287 MB
- Sample ID
- GSM9079615
- License
- unknown
- Published
- Dec 3, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB