Visium
Human brain
GSM9497630
silver Interactive viewer H&E glioblastoma No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 2000, 1936)
│ └── 'GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 600, 581)
├── Shapes
│ └── 'GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix': GeoDataFrame shape: (2045, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2045, 17943)
with coordinate systems:
▸ 'GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix', with elements:
GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_hires_image (Images), GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_lowres_image (Images), GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix (Shapes)
▸ 'GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_downscaled_hires', with elements:
GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_hires_image (Images), GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix (Shapes)
▸ 'GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_downscaled_lowres', with elements:
GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_lowres_image (Images), GSM9497630_TWKI-WU1221-Edge1_filtered_feature_bc_matrix_TWKI-WU1221-Edge1_filtered_feature_bc_matrix (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("V72zOW9RQEUqA8ZV0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) Python · lamindb
Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("V72zOW9RQEUqA8ZV") # stem uid: Lamin resolves it to the 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download V72zOW9RQEUqA8ZV0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/V72zOW9RQEUqA8ZV
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid V72zOW9RQEUqA8ZVLicense: NCBI GEO policy
Part of collection
Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions [Spatial Transcriptomics]→ 22 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- brain
- Condition
- glioblastoma
- Modality
- RNA
- Observations (spots)
- 2,045
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 1,936 × 2,000 px
- mpp
- 3.826 µm/px
- Size
- 41.4 MB
- Sample ID
- GSM9497630
- License
- NCBI GEO policy
- Published
- Jun 12, 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