Visium

Human intestine

MEND34

silver Interactive viewer H&E ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'MEND34_full_image': DataTree[cyx] (3, 48128, 47616), (3, 24064, 23808), (3, 12032, 11904), (3, 6016, 5952)
├── Shapes
│     └── 'MEND34_spots': GeoDataFrame shape: (3294, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3294, 33538)
with coordinate systems:
    ▸ 'global', with elements:
        MEND34_full_image (Images), MEND34_spots (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("qSOfc56Uf14LPp4X0000")
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("qSOfc56Uf14LPp4X")   # 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 qSOfc56Uf14LPp4X0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qSOfc56Uf14LPp4X

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qSOfc56Uf14LPp4X

Install (Python 3.12+) Docs →

License: CC BY-NC-SA 4.0 (noncommercial)

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Part of collection

Human ileum, Visium→

4 datasets · publication

Metadata

Technology: Visium
Organism: human
Tissue: intestine
Modality: RNA
Observations (spots): 3,294
Tier: silver
Chemistry: Visium v1
Magnification: 40x
Resolution: 47,616 × 48,128 px
mpp: 0.172 µm/px
Size: 4.4 GB
Sample ID: MEND34
License: CC BY-NC-SA 4.0 NonCommercial
Published: Aug 20, 2021

Cite this dataset

doi.org/10.1038/s41587-021-01075-3 ↗

BibTeX

@article{Bergenstr_hle_2021,
  title = {Super-resolved spatial transcriptomics by deep data fusion},
  volume = {40},
  ISSN = {1546-1696},
  url = {http://dx.doi.org/10.1038/s41587-021-01075-3},
  DOI = {10.1038/s41587-021-01075-3},
  number = {4},
  journal = {Nature Biotechnology},
  publisher = {Springer Science and Business Media LLC},
  author = {Bergenstråhle, Ludvig and He, Bryan and Bergenstråhle, Joseph and Abalo, Xesús and Mirzazadeh, Reza and Thrane, Kim and Ji, Andrew L. and Andersson, Alma and Larsson, Ludvig and Stakenborg, Nathalie and Boeckxstaens, Guy and Khavari, Paul and Zou, James and Lundeberg, Joakim and Maaskola, Jonas},
  year = {2021},
  month = Nov,
  pages = {476–479}
}

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