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Visium HD

Human breast

GSM9423409

bronze Interactive viewer H&E breast carcinoma Provisional
Visium HD
print(sdata) 
SpatialData object
├── Images
│     ├── 'GSM9423409_VHD_scPrint2_Slide1A1_8um_full_image': DataTree[cyx] (3, 4080, 3921), (3, 2040, 1960), (3, 1020, 980), (3, 510, 490), (3, 255, 245)
│     ├── 'GSM9423409_VHD_scPrint2_Slide1A1_8um_hires_image': DataArray[cyx] (3, 4080, 3921)
│     └── 'GSM9423409_VHD_scPrint2_Slide1A1_8um_lowres_image': DataArray[cyx] (3, 600, 577)
├── Shapes
│     ├── 'GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um': GeoDataFrame shape: (430490, 1) (2D shapes)
│     └── 'GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um_for_vitessce': GeoDataFrame shape: (430490, 2) (2D shapes)
└── Tables
      └── 'square_008um': AnnData (430490, 18085)
with coordinate systems:
    ▸ 'GSM9423409_VHD_scPrint2_Slide1A1_8um', with elements:
        GSM9423409_VHD_scPrint2_Slide1A1_8um_full_image (Images), GSM9423409_VHD_scPrint2_Slide1A1_8um_hires_image (Images), GSM9423409_VHD_scPrint2_Slide1A1_8um_lowres_image (Images), GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um (Shapes), GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um_for_vitessce (Shapes)
    ▸ 'GSM9423409_VHD_scPrint2_Slide1A1_8um_downscaled_hires', with elements:
        GSM9423409_VHD_scPrint2_Slide1A1_8um_hires_image (Images), GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um (Shapes), GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um_for_vitessce (Shapes)
    ▸ 'GSM9423409_VHD_scPrint2_Slide1A1_8um_downscaled_lowres', with elements:
        GSM9423409_VHD_scPrint2_Slide1A1_8um_lowres_image (Images), GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um (Shapes), GSM9423409_VHD_scPrint2_Slide1A1_8um_square_008um_for_vitessce (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("nhXP8l7y1wVHJWpl0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
Install (Python 3.12+) Docs →
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("nhXP8l7y1wVHJWpl")   # 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
Install (Python 3.12+) Docs →
Shell 
# sddb client: download the .zarr into data/
sddb-cli download nhXP8l7y1wVHJWpl0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/nhXP8l7y1wVHJWpl

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid nhXP8l7y1wVHJWpl
Install (Python 3.12+) Docs →

License: NCBI GEO policy

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Metadata

Technology
Visium HD
Organism
human
Tissue
breast
Condition
breast carcinoma
Modality
RNA
Observations (bins)
430,490
Tier
bronze
Resolution
3,921 × 4,080 px
mpp
2.290 µm/px
Size
312 MB
Sample ID
GSM9423409
License
NCBI GEO policy
Published
Feb 11, 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