10x Xenium
Human kidney
GSM9210726
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 34075, 8701), (1, 17037, 4350), (1, 8518, 2175), (1, 4259, 1087), (1, 2129, 543)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (33760, 8690), (16880, 4345), (8440, 2172), (4220, 1086), (2110, 543)
│ └── 'nucleus_labels': DataTree[yx] (32805, 8523), (16402, 4261), (8201, 2130), (4100, 1065), (2050, 532)
├── Points
│ └── 'transcripts': DataFrame with shape: (1684274, 11) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (32584, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (32584, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (32519, 2) (2D shapes)
└── Tables
└── 'table': AnnData (32584, 480)
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("vllMYzmHKoQQHzZ5") # 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/vllMYzmHKoQQHzZ5
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid vllMYzmHKoQQHzZ5 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["vllMYzmHKoQQHzZ50000"])[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
- 32,584
- Transcripts
- 1,684,274
- Tier
- bronze
- Size
- 486 MB
- Sample ID
- GSM9210726
- License
- unknown
- Published
- May 5, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB