Visium

Human breast

GSM7757982

bronze Interactive viewer H&E breast cancer Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM7757982_R3-S2_hires_image': DataArray[cyx] (3, 1982, 2000)
│     └── 'GSM7757982_R3-S2_lowres_image': DataArray[cyx] (3, 595, 600)
├── Shapes
│     └── 'GSM7757982_R3-S2': GeoDataFrame shape: (2440, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2440, 17943)
with coordinate systems:
    ▸ 'GSM7757982_R3-S2', with elements:
        GSM7757982_R3-S2_hires_image (Images), GSM7757982_R3-S2_lowres_image (Images), GSM7757982_R3-S2 (Shapes)
    ▸ 'GSM7757982_R3-S2_downscaled_hires', with elements:
        GSM7757982_R3-S2_hires_image (Images), GSM7757982_R3-S2 (Shapes)
    ▸ 'GSM7757982_R3-S2_downscaled_lowres', with elements:
        GSM7757982_R3-S2_lowres_image (Images), GSM7757982_R3-S2 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("xJVbxOBwjK3hHSrI0000")
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("xJVbxOBwjK3hHSrI")   # 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 xJVbxOBwjK3hHSrI0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/xJVbxOBwjK3hHSrI

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid xJVbxOBwjK3hHSrI

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Spatial heterogeneity of integrins and their ligands in primary breast tumors→

16 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: breast
Condition: breast cancer
Modality: RNA
Observations (spots): 2,440
Tier: bronze
Chemistry: Visium v1
Resolution: 2,000 × 1,982 px
mpp: 3.352 µm/px
Size: 37.6 MB
Sample ID: GSM7757982
License: NCBI GEO policy
Published: Sep 8, 2023

Cite this dataset

doi.org/10.3390/ijms252011109 ↗

BibTeX

@article{Perelmuter_2024,
  title = {Characterization of EpCAM-Positive and EpCAM-Negative Tumor Cells in Early-Stage Breast Cancer},
  volume = {25},
  ISSN = {1422-0067},
  url = {http://dx.doi.org/10.3390/ijms252011109},
  DOI = {10.3390/ijms252011109},
  number = {20},
  journal = {International Journal of Molecular Sciences},
  publisher = {MDPI AG},
  author = {Perelmuter, Vladimir M. and Grigoryeva, Evgeniya S. and Alifanov, Vladimir V. and Kalinchuk, Anna Yu. and Andryuhova, Elena S. and Savelieva, Olga E. and Patskan, Ivan A. and Bragina, Olga D. and Garbukov, Evgeniy Yu. and Vostrikova, Mariya A. and Zavyalova, Marina V. and Denisov, Evgeny V. and Cherdyntseva, Nadezhda V. and Tashireva, Liubov A.},
  year = {2024},
  month = Oct,
  pages = {11109}
}

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