Visium HD

Human lung

GSM8721365

silver Interactive viewer H&E interstitial lung disease No linked publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8721365_Non-LTx_008um_hires_image': DataArray[cyx] (3, 4211, 6000)
│     └── 'GSM8721365_Non-LTx_008um_lowres_image': DataArray[cyx] (3, 421, 600)
├── Shapes
│     ├── 'GSM8721365_Non-LTx_008um_square_008um': GeoDataFrame shape: (317761, 1) (2D shapes)
│     └── 'GSM8721365_Non-LTx_008um_square_008um_for_vitessce': GeoDataFrame shape: (317761, 2) (2D shapes)
└── Tables
      └── 'square_008um': AnnData (317761, 18085)
with coordinate systems:
    ▸ 'GSM8721365_Non-LTx_008um', with elements:
        GSM8721365_Non-LTx_008um_hires_image (Images), GSM8721365_Non-LTx_008um_lowres_image (Images), GSM8721365_Non-LTx_008um_square_008um (Shapes), GSM8721365_Non-LTx_008um_square_008um_for_vitessce (Shapes)
    ▸ 'GSM8721365_Non-LTx_008um_downscaled_hires', with elements:
        GSM8721365_Non-LTx_008um_hires_image (Images), GSM8721365_Non-LTx_008um_square_008um (Shapes), GSM8721365_Non-LTx_008um_square_008um_for_vitessce (Shapes)
    ▸ 'GSM8721365_Non-LTx_008um_downscaled_lowres', with elements:
        GSM8721365_Non-LTx_008um_lowres_image (Images), GSM8721365_Non-LTx_008um_square_008um (Shapes), GSM8721365_Non-LTx_008um_square_008um_for_vitessce (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("Ka4Pk48c0pDskJFH0000")
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("Ka4Pk48c0pDskJFH")   # 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 Ka4Pk48c0pDskJFH0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/Ka4Pk48c0pDskJFH

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Ka4Pk48c0pDskJFH

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial transcriptomic profiling of the lung tissues from a patient with recurrent anti-synthetase syndrome associated interstitial lung disease after bilateral lung transplantation and one untreated patient→

2 datasets · other

Metadata

Technology: Visium HD
Organism: human
Tissue: lung
Condition: interstitial lung disease
Modality: RNA
Observations (bins): 317,761
Tier: silver
Resolution: 6,000 × 4,211 px
mpp: 2.811 µm/px
Size: 150 MB
Sample ID: GSM8721365
License: NCBI GEO policy
Published: Jan 9, 2025

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