10x Xenium
Human kidney
GSM9100631
silver Interactive viewer No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 17111, 42624), (1, 8555, 21312), (1, 4277, 10656), (1, 2138, 5328), (1, 1069, 2664)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (16974, 42423), (8487, 21211), (4243, 10605), (2121, 5302), (1060, 2651)
│ └── 'nucleus_labels': DataTree[yx] (15546, 39604), (7773, 19802), (3886, 9901), (1943, 4950), (971, 2475)
├── Points
│ └── 'transcripts': DataFrame with shape: (22079562, 11) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (166847, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (166847, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (171950, 2) (2D shapes)
└── Tables
└── 'table': AnnData (166847, 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("lLwel5AOptpfE6iy") # 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/lLwel5AOptpfE6iy
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid lLwel5AOptpfE6iy Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["lLwel5AOptpfE6iy0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- kidney
- Modality
- RNA
- Observations
- 166,847
- Transcripts
- 22,079,562
- Tier
- silver
- Size
- 1.5 GB
- Sample ID
- GSM9100631
- License
- unknown
- Published
- Jul 11, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB