10x Xenium
Mouse kidney
GSM8717007
silver Interactive viewer No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 37531, 48361), (1, 18765, 24180), (1, 9382, 12090), (1, 4691, 6045), (1, 2345, 3022)
├── Points
│ └── 'transcripts': DataFrame with shape: (132110315, 10) (3D points)
├── Shapes
│ └── 'cell_circles': GeoDataFrame shape: (421140, 2) (2D shapes)
└── Tables
└── 'table': AnnData (421140, 479)
with coordinate systems:
▸ 'global', with elements:
morphology_mip (Images), transcripts (Points), cell_circles (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("nkg8VOWpvpqmnOOJ") # 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/nkg8VOWpvpqmnOOJ
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid nkg8VOWpvpqmnOOJ Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["nkg8VOWpvpqmnOOJ0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- kidney
- Modality
- RNA
- Observations
- 421,140
- Transcripts
- 132,110,315
- Tier
- silver
- Size
- 6.2 GB
- Sample ID
- GSM8717007
- License
- unknown
- Published
- Jan 13, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB