Visium HD
Human unknown
GSM9532659
silver Interactive viewer H&E melanoma No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9532659_WU1457_HD_016um_hires_image': DataArray[cyx] (3, 3000, 3200)
│ └── 'GSM9532659_WU1457_HD_016um_lowres_image': DataArray[cyx] (3, 563, 600)
├── Shapes
│ ├── 'GSM9532659_WU1457_HD_016um_square_016um': GeoDataFrame shape: (52861, 1) (2D shapes)
│ └── 'GSM9532659_WU1457_HD_016um_square_016um_for_vitessce': GeoDataFrame shape: (52861, 2) (2D shapes)
└── Tables
└── 'square_016um': AnnData (52861, 18085)
with coordinate systems:
▸ 'GSM9532659_WU1457_HD_016um', with elements:
GSM9532659_WU1457_HD_016um_hires_image (Images), GSM9532659_WU1457_HD_016um_lowres_image (Images), GSM9532659_WU1457_HD_016um_square_016um (Shapes), GSM9532659_WU1457_HD_016um_square_016um_for_vitessce (Shapes)
▸ 'GSM9532659_WU1457_HD_016um_downscaled_hires', with elements:
GSM9532659_WU1457_HD_016um_hires_image (Images), GSM9532659_WU1457_HD_016um_square_016um (Shapes), GSM9532659_WU1457_HD_016um_square_016um_for_vitessce (Shapes)
▸ 'GSM9532659_WU1457_HD_016um_downscaled_lowres', with elements:
GSM9532659_WU1457_HD_016um_lowres_image (Images), GSM9532659_WU1457_HD_016um_square_016um (Shapes), GSM9532659_WU1457_HD_016um_square_016um_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("a99XGd5yodfrLxgA") # 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/a99XGd5yodfrLxgA
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid a99XGd5yodfrLxgA Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["a99XGd5yodfrLxgA0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
High-resolution and noninvasive profiling of the tumor microenvironment with spatial ecotypes [Visium]→ 2 datasets · replicates
Metadata
- Technology
- Visium HD
- Organism
- human
- Tissue
- unknown
- Condition
- melanoma
- Modality
- RNA
- Observations
- 52,861
- Tier
- silver
- Resolution
- 3,200 × 3,000 px
- mpp
- 4.622 µm/px
- Size
- 231 MB
- Sample ID
- GSM9532659
- License
- unknown
- Published
- May 7, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB