Visium HD
Mouse kidney
GSM8662447
silver Interactive viewer H&E ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8662447_square_008um_cytassist_image': DataArray[cyx] (3, 3000, 3201)
│ ├── 'GSM8662447_square_008um_hires_image': DataArray[cyx] (3, 5609, 6000)
│ └── 'GSM8662447_square_008um_lowres_image': DataArray[cyx] (3, 561, 600)
├── Shapes
│ ├── 'GSM8662447_square_008um_square_002um': GeoDataFrame shape: (3624606, 1) (2D shapes)
│ ├── 'GSM8662447_square_008um_square_008um': GeoDataFrame shape: (232640, 1) (2D shapes)
│ ├── 'GSM8662447_square_008um_square_016um': GeoDataFrame shape: (60302, 1) (2D shapes)
│ └── 'GSM8662447_square_008um_square_016um_for_vitessce': GeoDataFrame shape: (60302, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (3624606, 19059)
├── 'square_008um': AnnData (232640, 19059)
└── 'square_016um': AnnData (60302, 19059)
with coordinate systems:
▸ 'GSM8662447_square_008um', with elements:
GSM8662447_square_008um_cytassist_image (Images), GSM8662447_square_008um_hires_image (Images), GSM8662447_square_008um_lowres_image (Images), GSM8662447_square_008um_square_002um (Shapes), GSM8662447_square_008um_square_008um (Shapes), GSM8662447_square_008um_square_016um (Shapes), GSM8662447_square_008um_square_016um_for_vitessce (Shapes)
▸ 'GSM8662447_square_008um_downscaled_hires', with elements:
GSM8662447_square_008um_hires_image (Images), GSM8662447_square_008um_square_002um (Shapes), GSM8662447_square_008um_square_008um (Shapes), GSM8662447_square_008um_square_016um (Shapes), GSM8662447_square_008um_square_016um_for_vitessce (Shapes)
▸ 'GSM8662447_square_008um_downscaled_lowres', with elements:
GSM8662447_square_008um_lowres_image (Images), GSM8662447_square_008um_square_002um (Shapes), GSM8662447_square_008um_square_008um (Shapes), GSM8662447_square_008um_square_016um (Shapes), GSM8662447_square_008um_square_016um_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("T20h7X1RZrsEg8O8") # 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/T20h7X1RZrsEg8O8
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid T20h7X1RZrsEg8O8 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["T20h7X1RZrsEg8O80000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Gain-of-function ENL YEATS domain mutations impede nephrogenesis through gene expression control [Spatial Transcriptomics]→ 2 datasets · comparison
Metadata
- Technology
- Visium HD
- Organism
- mouse
- Tissue
- kidney
- Modality
- RNA
- Observations
- 232,640
- Tier
- silver
- Resolution
- 6,000 × 5,609 px
- mpp
- 1.334 µm/px
- Size
- 1.7 GB
- Sample ID
- GSM8662447
- License
- unknown
- Published
- Feb 20, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB