Visium

Human liver

GSM9986440

bronze Interactive viewer H&E hepatic angiomyolipoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9986440_Sample3_hires_image': DataArray[cyx] (3, 2000, 1645)
│     └── 'GSM9986440_Sample3_lowres_image': DataArray[cyx] (3, 600, 494)
├── Shapes
│     └── 'GSM9986440_Sample3': GeoDataFrame shape: (4210, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4210, 18085)
with coordinate systems:
    ▸ 'GSM9986440_Sample3', with elements:
        GSM9986440_Sample3_hires_image (Images), GSM9986440_Sample3_lowres_image (Images), GSM9986440_Sample3 (Shapes)
    ▸ 'GSM9986440_Sample3_downscaled_hires', with elements:
        GSM9986440_Sample3_hires_image (Images), GSM9986440_Sample3 (Shapes)
    ▸ 'GSM9986440_Sample3_downscaled_lowres', with elements:
        GSM9986440_Sample3_lowres_image (Images), GSM9986440_Sample3 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("VXzhPpQA4mPytj8E0000")
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("VXzhPpQA4mPytj8E")   # 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 VXzhPpQA4mPytj8E0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/VXzhPpQA4mPytj8E

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid VXzhPpQA4mPytj8E

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Mapping the Tissue Architecture and Endothelial-PEC Network in Hepatic Angiomyolipoma Using Single-cell and Spatial Transcriptomics [Spatial Transcriptomics]→

3 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: liver
Condition: hepatic angiomyolipoma
Modality: RNA
Observations (spots): 4,210
Tier: bronze
Chemistry: Visium v1
Resolution: 1,645 × 2,000 px
mpp: 3.438 µm/px
Size: 102 MB
Sample ID: GSM9986440
License: NCBI GEO policy
Published: Oct 14, 2026

Cite this dataset

doi.org/10.1111/cas.70550 ↗

BibTeX

@article{geo:GSM9986440,
  doi = {10.1111/cas.70550},
  title = {GSM9986440},
  year = {2026}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB