10x Xenium
Human skin of body
GSM9079614
silver Interactive viewer No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 13927, 17114), (1, 6963, 8557), (1, 3481, 4278), (1, 1740, 2139), (1, 870, 1069)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (13621, 15853), (6810, 7926), (3405, 3963), (1702, 1981), (851, 990)
│ └── 'nucleus_labels': DataTree[yx] (13595, 14446), (6797, 7223), (3398, 3611), (1699, 1805), (849, 902)
├── Points
│ └── 'transcripts': DataFrame with shape: (1814861, 11) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (10662, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (10662, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (10657, 2) (2D shapes)
└── Tables
└── 'table': AnnData (10662, 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("7q6h5acppM7SuuOq") # 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/7q6h5acppM7SuuOq
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 7q6h5acppM7SuuOq Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["7q6h5acppM7SuuOq0000"])[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
- 10,662
- Transcripts
- 1,814,861
- Tier
- silver
- Size
- 286 MB
- Sample ID
- GSM9079614
- 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