Visium

Mouse spleen

GSM8047885

silver Interactive viewer H&E malaria ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8047885_FFPE_CytAssist_WT_709_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM8047885_FFPE_CytAssist_WT_709_hires_image': DataArray[cyx] (3, 1448, 2000)
│     └── 'GSM8047885_FFPE_CytAssist_WT_709_lowres_image': DataArray[cyx] (3, 435, 600)
├── Shapes
│     └── 'GSM8047885_FFPE_CytAssist_WT_709': GeoDataFrame shape: (2002, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2002, 19465)
with coordinate systems:
    ▸ 'GSM8047885_FFPE_CytAssist_WT_709', with elements:
        GSM8047885_FFPE_CytAssist_WT_709_full_image (Images), GSM8047885_FFPE_CytAssist_WT_709_hires_image (Images), GSM8047885_FFPE_CytAssist_WT_709_lowres_image (Images), GSM8047885_FFPE_CytAssist_WT_709 (Shapes)
    ▸ 'GSM8047885_FFPE_CytAssist_WT_709_downscaled_hires', with elements:
        GSM8047885_FFPE_CytAssist_WT_709_hires_image (Images), GSM8047885_FFPE_CytAssist_WT_709 (Shapes)
    ▸ 'GSM8047885_FFPE_CytAssist_WT_709_downscaled_lowres', with elements:
        GSM8047885_FFPE_CytAssist_WT_709_lowres_image (Images), GSM8047885_FFPE_CytAssist_WT_709 (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("9OrEbv8BZZzpnFm7")   # 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/9OrEbv8BZZzpnFm7

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 9OrEbv8BZZzpnFm7

Load with the Python client

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

ds = Catalog().cohort(uids=["9OrEbv8BZZzpnFm70000"])[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

Comparing 10x Visium spatial transcriptomic technologies→

4 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: spleen
Condition: malaria
Modality: RNA
Observations: 2,002
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 2,000 × 1,448 px
mpp: 3.421 µm/px
Size: 67 MB
Sample ID: GSM8047885
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