10x Xenium
Mouse spleen
GSM8382629
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 13717, 22798), (1, 6858, 11399), (1, 3429, 5699), (1, 1714, 2849), (1, 857, 1424)
│ └── 'morphology_mip': DataTree[cyx] (1, 13717, 22798), (1, 6858, 11399), (1, 3429, 5699), (1, 1714, 2849), (1, 857, 1424)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (13070, 20697), (6535, 10348), (3267, 5174), (1633, 2587), (816, 1293)
│ └── 'nucleus_labels': DataTree[yx] (12930, 20559), (6465, 10279), (3232, 5139), (1616, 2569), (808, 1284)
├── Points
│ └── 'transcripts': DataFrame with shape: (16440437, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (94582, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (94582, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (94582, 2) (2D shapes)
└── Tables
└── 'table': AnnData (94582, 477)
with coordinate systems:
▸ 'global', with elements:
morphology_focus (Images), 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("3yCWevNd0XMhrku1") # 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/3yCWevNd0XMhrku1
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 3yCWevNd0XMhrku1 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["3yCWevNd0XMhrku10000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
High resolution mapping of erythroid niches in mice using subcellular spatial transcriptomics→ 6 datasets · publication
Metadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- spleen
- Modality
- RNA
- Observations
- 94,582
- Transcripts
- 16,440,437
- Tier
- bronze
- Size
- 1.5 GB
- Sample ID
- GSM8382629
- License
- NCBI GEO policy
- Published
- May 18, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB