Visium

Human breast

GSM7688224

bronze Interactive viewer H&E breast cancer Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM7688224_Tumor-897_hires_image': DataArray[cyx] (3, 1961, 2000)
│     └── 'GSM7688224_Tumor-897_lowres_image': DataArray[cyx] (3, 589, 600)
├── Shapes
│     └── 'GSM7688224_Tumor-897': GeoDataFrame shape: (2536, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2536, 36601)
with coordinate systems:
    ▸ 'GSM7688224_Tumor-897', with elements:
        GSM7688224_Tumor-897_hires_image (Images), GSM7688224_Tumor-897_lowres_image (Images), GSM7688224_Tumor-897 (Shapes)
    ▸ 'GSM7688224_Tumor-897_downscaled_hires', with elements:
        GSM7688224_Tumor-897_hires_image (Images), GSM7688224_Tumor-897 (Shapes)
    ▸ 'GSM7688224_Tumor-897_downscaled_lowres', with elements:
        GSM7688224_Tumor-897_lowres_image (Images), GSM7688224_Tumor-897 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("1yTtZCYrcefxMlNm0000")
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("1yTtZCYrcefxMlNm")   # 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 1yTtZCYrcefxMlNm0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/1yTtZCYrcefxMlNm

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 1yTtZCYrcefxMlNm

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial Transcriptomics reveals that cells that experience intratumoral hypoxia retain hypoxia-associated transcriptional programs after invading into oxygenated tumor regions.→

2 datasets · replicates

Metadata

Technology: Visium
Organism: human
Tissue: breast
Condition: breast cancer
Modality: RNA
Observations (spots): 2,536
Tier: bronze
Chemistry: Visium v1
Resolution: 2,000 × 1,961 px
mpp: 3.415 µm/px
Size: 48.5 MB
Sample ID: GSM7688224
License: NCBI GEO policy
Published: Jul 2, 2024

Cite this dataset

doi.org/10.1038/s41467-024-51995-2 ↗

BibTeX

@article{Godet_2024,
  title = {Hypoxia induces ROS-resistant memory upon reoxygenation in vivo promoting metastasis in part via MUC1-C},
  volume = {15},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-024-51995-2},
  DOI = {10.1038/s41467-024-51995-2},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Godet, Inês and Oza, Harsh H. and Shi, Yi and Joe, Natalie S. and Weinstein, Alyssa G. and Johnson, Jeanette and Considine, Michael and Talluri, Swathi and Zhang, Jingyuan and Xu, Reid and Doctorman, Steven and Mbulaiteye, Delma and Stein-O’Brien, Genevieve and Kagohara, Luciane T. and Santa-Maria, Cesar A. and Fertig, Elana J. and Gilkes, Daniele M.},
  year = {2024},
  month = Sept
}

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