Visium

Mouse spleen

GSM8047884

silver Interactive viewer H&E malaria ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8047884_OCT_CytAssist_WT_713_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM8047884_OCT_CytAssist_WT_713_hires_image': DataArray[cyx] (3, 2000, 1629)
│     └── 'GSM8047884_OCT_CytAssist_WT_713_lowres_image': DataArray[cyx] (3, 600, 489)
├── Shapes
│     └── 'GSM8047884_OCT_CytAssist_WT_713': GeoDataFrame shape: (2498, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2498, 19465)
with coordinate systems:
    ▸ 'GSM8047884_OCT_CytAssist_WT_713', with elements:
        GSM8047884_OCT_CytAssist_WT_713_full_image (Images), GSM8047884_OCT_CytAssist_WT_713_hires_image (Images), GSM8047884_OCT_CytAssist_WT_713_lowres_image (Images), GSM8047884_OCT_CytAssist_WT_713 (Shapes)
    ▸ 'GSM8047884_OCT_CytAssist_WT_713_downscaled_hires', with elements:
        GSM8047884_OCT_CytAssist_WT_713_hires_image (Images), GSM8047884_OCT_CytAssist_WT_713 (Shapes)
    ▸ 'GSM8047884_OCT_CytAssist_WT_713_downscaled_lowres', with elements:
        GSM8047884_OCT_CytAssist_WT_713_lowres_image (Images), GSM8047884_OCT_CytAssist_WT_713 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("zkggykT0tkDsvQn30000")
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("zkggykT0tkDsvQn3")   # 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 zkggykT0tkDsvQn30000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/zkggykT0tkDsvQn3

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid zkggykT0tkDsvQn3

Install (Python 3.12+) Docs →

License: CC BY 4.0

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Part of collection

Comparing 10x Visium spatial transcriptomic technologies→

4 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: spleen
Condition: malaria
Modality: RNA
Observations (spots): 2,498
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 3,000 × 3,000 px
mpp: 1.961 µm/px
Size: 69.0 MB
Sample ID: GSM8047884
License: CC BY 4.0
Published: Feb 26, 2024

Cite this dataset

doi.org/10.1186/s13059-025-03543-4 ↗

BibTeX

@article{Du_2025,
  title = {Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset},
  volume = {26},
  ISSN = {1474-760X},
  url = {http://dx.doi.org/10.1186/s13059-025-03543-4},
  DOI = {10.1186/s13059-025-03543-4},
  number = {1},
  journal = {Genome Biology},
  publisher = {Springer Science and Business Media LLC},
  author = {Du, Mei R. M. and Wang, Changqing and Law, Charity W. and Amann-Zalcenstein, Daniela and Anttila, Casey J. A. and Ling, Ling and Hickey, Peter F. and Sargeant, Callum J. and Chen, Yunshun and Ioannidis, Lisa J. and Rajasekhar, Pradeep and Yip, Raymond K. H. and Rogers, Kelly L. and Hansen, Diana S. and Bowden, Rory and Ritchie, Matthew E.},
  year = {2025},
  month = Mar
}

Source: NCBI GEO ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB