10x Xenium
Mouse brain
GSM8647395
silver ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 34133, 42664), (1, 17066, 21332), (1, 8533, 10666), (1, 4266, 5333), (1, 2133, 2666)
│ └── 'morphology_mip': DataTree[cyx] (1, 34133, 42664), (1, 17066, 21332), (1, 8533, 10666), (1, 4266, 5333), (1, 2133, 2666)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (34133, 42664), (17066, 21332), (8533, 10666), (4266, 5333), (2133, 2666)
│ └── 'nucleus_labels': DataTree[yx] (34133, 42664), (17066, 21332), (8533, 10666), (4266, 5333), (2133, 2666)
├── Points
│ └── 'transcripts': DataFrame with shape: (62354539, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (125103, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (125103, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (125103, 1) (2D shapes)
└── Tables
└── 'table': AnnData (125103, 347)
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("Um0SuU7W2YJBJpRX") # 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/Um0SuU7W2YJBJpRX
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Um0SuU7W2YJBJpRX Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["Um0SuU7W2YJBJpRX0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Single cell approached deine forebrain neural stem cell niches and identify microglial ligands that enhance precursor-mediated remyelination (Xenium)→ 19 datasets · other
Metadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- brain
- Modality
- RNA
- Observations
- 125,103
- Transcripts
- 62,354,539
- Tier
- silver
- Chemistry
- Xenium v1
- Size
- 5.8 GB
- Sample ID
- GSM8647395
- License
- NCBI GEO policy
- Published
- Dec 20, 2024
Cite this dataset
doi.org/10.1016/j.celrep.2024.115194 ↗ BibTeX
@article{Willis_2025,
title = {Single cell approaches define neural stem cell niches and identify microglial ligands that can enhance precursor-mediated oligodendrogenesis},
volume = {44},
ISSN = {2211-1247},
url = {http://dx.doi.org/10.1016/j.celrep.2024.115194},
DOI = {10.1016/j.celrep.2024.115194},
number = {1},
journal = {Cell Reports},
publisher = {Elsevier BV},
author = {Willis, Ashleigh and Jeong, Danielle and Liu, Yunlong and Lithopoulos, Marissa A. and Yuzwa, Scott A. and Frankland, Paul W. and Kaplan, David R. and Miller, Freda D.},
year = {2025},
month = Jan,
pages = {115194}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB