Visium

Mouse skin of body

GSM8974358

bronze Interactive viewer H&E melanoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8974358_WT_hires_image': DataArray[cyx] (3, 1518, 2000)
│     └── 'GSM8974358_WT_lowres_image': DataArray[cyx] (3, 456, 600)
├── Shapes
│     └── 'GSM8974358_WT': GeoDataFrame shape: (3066, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3066, 19465)
with coordinate systems:
    ▸ 'GSM8974358_WT', with elements:
        GSM8974358_WT_hires_image (Images), GSM8974358_WT_lowres_image (Images), GSM8974358_WT (Shapes)
    ▸ 'GSM8974358_WT_downscaled_hires', with elements:
        GSM8974358_WT_hires_image (Images), GSM8974358_WT (Shapes)
    ▸ 'GSM8974358_WT_downscaled_lowres', with elements:
        GSM8974358_WT_lowres_image (Images), GSM8974358_WT (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("UZZc5aI0cHP2Nag60000")
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("UZZc5aI0cHP2Nag6")   # 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 UZZc5aI0cHP2Nag60000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/UZZc5aI0cHP2Nag6

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid UZZc5aI0cHP2Nag6

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Polyamines regulate adaptive antitumor immunity by functional specialization of regulatory T cells [Visium]→

2 datasets · comparison

Metadata

Technology: Visium
Organism: mouse
Tissue: skin of body
Condition: melanoma
Modality: RNA
Observations (spots): 3,066
Tier: bronze
Chemistry: Visium CytAssist v2
Resolution: 2,000 × 1,518 px
mpp: 3.819 µm/px
Size: 62.5 MB
Sample ID: GSM8974358
License: NCBI GEO policy
Published: Jul 30, 2025

Cite this dataset

doi.org/10.1016/j.immuni.2025.07.007 ↗

BibTeX

@article{geo:GSM8974358,
  doi = {10.1016/j.immuni.2025.07.007},
  title = {GSM8974358},
  year = {2025}
}

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