Visium

Human zone of skin

GSM9043133

silver Interactive viewer H&E skin neoplasm ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9043133_Slide1_A1_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM9043133_Slide1_A1_hires_image': DataArray[cyx] (3, 1511, 2000)
│     └── 'GSM9043133_Slide1_A1_lowres_image': DataArray[cyx] (3, 453, 600)
├── Shapes
│     └── 'GSM9043133_Slide1_A1': GeoDataFrame shape: (4363, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4363, 18085)
with coordinate systems:
    ▸ 'GSM9043133_Slide1_A1', with elements:
        GSM9043133_Slide1_A1_full_image (Images), GSM9043133_Slide1_A1_hires_image (Images), GSM9043133_Slide1_A1_lowres_image (Images), GSM9043133_Slide1_A1 (Shapes)
    ▸ 'GSM9043133_Slide1_A1_downscaled_hires', with elements:
        GSM9043133_Slide1_A1_hires_image (Images), GSM9043133_Slide1_A1 (Shapes)
    ▸ 'GSM9043133_Slide1_A1_downscaled_lowres', with elements:
        GSM9043133_Slide1_A1_lowres_image (Images), GSM9043133_Slide1_A1 (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("YfZUMl4Pnjz7k7YM")   # 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/YfZUMl4Pnjz7k7YM

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid YfZUMl4Pnjz7k7YM

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["YfZUMl4Pnjz7k7YM0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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

Part of collection

Spatial transcriptomic profiling of seborrhoeic keratoses→

4 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: zone of skin
Condition: skin neoplasm
Modality: RNA
Observations: 4,363
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 2,000 × 1,511 px
mpp: 8.545 µm/px
Size: 93 MB
Sample ID: GSM9043133
License: NCBI GEO policy
Published: Jun 9, 2026

Cite this dataset

doi.org/10.1016/j.jid.2026.03.048 ↗

BibTeX

@article{Oey_2026,
  title = {The genomic and spatial transcriptomic landscape of seborrheic keratoses},
  ISSN = {0022-202X},
  url = {http://dx.doi.org/10.1016/j.jid.2026.03.048},
  DOI = {10.1016/j.jid.2026.03.048},
  journal = {Journal of Investigative Dermatology},
  publisher = {Elsevier BV},
  author = {Oey, Harald M. and Kao, Yung-Ching and Lee, Katie J. and Zhou, Chenhao and Brysland, Simone A. and Jagirdar, Kasturee and Sunnefeldt, Erik and Hammerlindl, Sabrina and Tom, Lisa N. and Tan, Jean-Marie and Lambie, Duncan and Sturm, Richard A. and Schaider, Helmut and Dicker, Tony and Khosrotehrani, Kiarash and Soyer, H. Peter and Stark, Mitchell S.},
  year = {2026},
  month = July
}

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