10x Xenium
Mouse bone marrow
GSM8382627
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 20508, 31285), (1, 10254, 15642), (1, 5127, 7821), (1, 2563, 3910), (1, 1281, 1955)
│ └── 'morphology_mip': DataTree[cyx] (1, 20508, 31285), (1, 10254, 15642), (1, 5127, 7821), (1, 2563, 3910), (1, 1281, 1955)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (20508, 31285), (10254, 15642), (5127, 7821), (2563, 3910), (1281, 1955)
│ └── 'nucleus_labels': DataTree[yx] (20508, 31285), (10254, 15642), (5127, 7821), (2563, 3910), (1281, 1955)
├── Points
│ └── 'transcripts': DataFrame with shape: (9743216, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (52594, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (52594, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (52594, 1) (2D shapes)
└── Tables
└── 'table': AnnData (52594, 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("foCLRMVlohGnKW7f") # 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/foCLRMVlohGnKW7f
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid foCLRMVlohGnKW7f Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["foCLRMVlohGnKW7f0000"])[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
- bone marrow
- Modality
- RNA
- Observations
- 52,594
- Transcripts
- 9,743,216
- Tier
- bronze
- Size
- 1.3 GB
- Sample ID
- GSM8382627
- 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