Visium
Mouse brain
GSM8637963
bronze Interactive viewer H&E Alzheimer disease Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8637963_030-C_hires_image': DataArray[cyx] (3, 1276, 2000)
│ └── 'GSM8637963_030-C_lowres_image': DataArray[cyx] (3, 383, 600)
├── Shapes
│ └── 'GSM8637963_030-C': GeoDataFrame shape: (3584, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3584, 32286)
with coordinate systems:
▸ 'GSM8637963_030-C', with elements:
GSM8637963_030-C_hires_image (Images), GSM8637963_030-C_lowres_image (Images), GSM8637963_030-C (Shapes)
▸ 'GSM8637963_030-C_downscaled_hires', with elements:
GSM8637963_030-C_hires_image (Images), GSM8637963_030-C (Shapes)
▸ 'GSM8637963_030-C_downscaled_lowres', with elements:
GSM8637963_030-C_lowres_image (Images), GSM8637963_030-C (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("gYI5DitPkguUqoJI") # 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/gYI5DitPkguUqoJI
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid gYI5DitPkguUqoJI Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["gYI5DitPkguUqoJI0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Spatial transcriptomics identifies regional disruptions in brain rhythmic gene expression in a mouse model of Alzheimer’s disease→ 34 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- brain
- Condition
- Alzheimer disease
- Modality
- RNA
- Observations
- 3,584
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,276 px
- mpp
- 5.134 µm/px
- Size
- 56 MB
- Sample ID
- GSM8637963
- License
- unknown
- Published
- Mar 13, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB