10x Xenium
Human heart
GSM9295072
silver Interactive viewer heart failure No linked publication
print(sdata)
SpatialData object
├── Labels
│ └── 'cell_labels': DataTree[yx] (22283, 16745), (11141, 8372), (5570, 4186), (2785, 2093), (1392, 1046)
├── Points
│ └── 'transcripts': DataFrame with shape: (3037552, 13) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (20647, 1) (2D shapes)
│ └── 'cell_circles': GeoDataFrame shape: (20647, 2) (2D shapes)
└── Tables
└── 'table': AnnData (20647, 300)
with coordinate systems:
▸ 'global', with elements:
cell_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("w5Izo9BwcaBrknYx") # 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/w5Izo9BwcaBrknYx
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid w5Izo9BwcaBrknYx Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["w5Izo9BwcaBrknYx0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- heart
- Condition
- heart failure
- Modality
- RNA
- Observations
- 20,647
- Transcripts
- 3,037,552
- Tier
- silver
- Size
- 69 MB
- Sample ID
- GSM9295072
- License
- unknown
- Published
- Nov 24, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB