Visium HD
Human adrenal gland
GSM8890017
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 6000, 5807)
│ └── 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 600, 581)
├── Shapes
│ ├── 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um': GeoDataFrame shape: (111746, 1) (2D shapes)
│ └── 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um_for_vitessce': GeoDataFrame shape: (111746, 2) (2D shapes)
└── Tables
└── 'square_016um': AnnData (111746, 18085)
with coordinate systems:
▸ 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix', with elements:
GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_hires_image (Images), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_lowres_image (Images), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um (Shapes), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)
▸ 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_downscaled_hires', with elements:
GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_hires_image (Images), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um (Shapes), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)
▸ 'GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_downscaled_lowres', with elements:
GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_lowres_image (Images), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um (Shapes), GSM8890017_fetal_spatial_fetal_spatial_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("JiAb4p5ClzJT6j2K0000")
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("JiAb4p5ClzJT6j2K") # 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 JiAb4p5ClzJT6j2K0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/JiAb4p5ClzJT6j2K
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid JiAb4p5ClzJT6j2KLicense: NCBI GEO policy
Metadata
- Technology
- Visium HD
- Vendor
- 10x Genomics
- Organism
- human
- Tissue
- adrenal gland
- Modality
- RNA
- Observations (bins)
- 111,746
- Tier
- silver
- Resolution
- 5,807 × 6,000 px
- mpp
- 2.738 µm/px
- Size
- 747 MB
- Sample ID
- GSM8890017
- License
- NCBI GEO policy
- Published
- Mar 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