10x Xenium
Mouse brain
GSM8253805
silver Interactive viewer ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 34189, 48350), (1, 17094, 24175), (1, 8547, 12087), (1, 4273, 6043), (1, 2136, 3021)
│ └── 'morphology_mip': DataTree[cyx] (1, 34189, 48350), (1, 17094, 24175), (1, 8547, 12087), (1, 4273, 6043), (1, 2136, 3021)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (34189, 48350), (17094, 24175), (8547, 12087), (4273, 6043), (2136, 3021)
│ └── 'nucleus_labels': DataTree[yx] (34189, 48350), (17094, 24175), (8547, 12087), (4273, 6043), (2136, 3021)
├── Points
│ └── 'transcripts': DataFrame with shape: (55379670, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (111183, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (111183, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (111183, 1) (2D shapes)
└── Tables
└── 'table': AnnData (111183, 348)
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("XlNAWdtjenSSCFCH") # 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/XlNAWdtjenSSCFCH
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid XlNAWdtjenSSCFCH Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["XlNAWdtjenSSCFCH0000"])[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
- 111,183
- Transcripts
- 55,379,670
- Tier
- silver
- Chemistry
- Xenium v1
- Size
- 5.6 GB
- Sample ID
- GSM8253805
- 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