10x Xenium
Human liver
GSM8386295
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 30695, 25630), (1, 15347, 12815), (1, 7673, 6407), (1, 3836, 3203), (1, 1918, 1601)
│ └── 'morphology_mip': DataTree[cyx] (1, 30695, 25630), (1, 15347, 12815), (1, 7673, 6407), (1, 3836, 3203), (1, 1918, 1601)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (30663, 25081), (15331, 12540), (7665, 6270), (3832, 3135), (1916, 1567)
│ └── 'nucleus_labels': DataTree[yx] (30564, 24996), (15282, 12498), (7641, 6249), (3820, 3124), (1910, 1562)
├── Points
│ └── 'transcripts': DataFrame with shape: (43226093, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (224957, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (224957, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (224957, 2) (2D shapes)
└── Tables
└── 'table': AnnData (224957, 471)
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("W3ZpPeOfvctDDlBV") # 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/W3ZpPeOfvctDDlBV
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid W3ZpPeOfvctDDlBV Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["W3ZpPeOfvctDDlBV0000"])[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 human using subcellular spatial transcriptomics→ 3 datasets · publication
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- liver
- Modality
- RNA
- Observations
- 224,957
- Transcripts
- 43,226,093
- Tier
- bronze
- Size
- 4.1 GB
- Sample ID
- GSM8386295
- 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