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Visium

Human oral cavity

GSM6339638

bronze Interactive viewer H&E oral cavity squamous cell carcinoma Provisional
Human oral cavity preview
print(sdata)
SpatialData object
├── Images
│     └── 'GSM6339638_s8_hires_image': DataArray[cyx] (3, 2000, 1974)
├── Shapes
│     └── 'GSM6339638_s8': GeoDataFrame shape: (2475, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2475, 36601)
with coordinate systems:
    ▸ 'GSM6339638_s8', with elements:
        GSM6339638_s8_hires_image (Images), GSM6339638_s8 (Shapes)
    ▸ 'GSM6339638_s8_downscaled_hires', with elements:
        GSM6339638_s8_hires_image (Images), GSM6339638_s8 (Shapes)
    ▸ 'GSM6339638_s8_downscaled_lowres', with elements:
        GSM6339638_s8 (Shapes)
Python · sddb client
from sddb import Catalog

ds = Catalog().dataset("Jfs5zjv7Kp5vme0m0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
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("Jfs5zjv7Kp5vme0m")   # 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
Shell
# sddb client: download the .zarr into data/
sddb-cli download Jfs5zjv7Kp5vme0m0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/Jfs5zjv7Kp5vme0m

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Jfs5zjv7Kp5vme0m

License: NCBI GEO policy

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Metadata

Technology
Visium
Organism
human
Tissue
oral cavity
Condition
oral cavity squamous cell carcinoma
Modality
RNA
Observations (spots)
2,475
Tier
bronze
Chemistry
Visium v1
Magnification
other
Resolution
1,974 × 2,000 px
mpp
3.324 µm/px
Size
28.1 MB
Sample ID
GSM6339638
License
NCBI GEO policy
Published
Jun 25, 2023

Cite this dataset

doi.org/10.1038/s41467-023-40271-4 ↗
BibTeX
@article{Arora_2023,
  title = {Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response},
  volume = {14},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-023-40271-4},
  DOI = {10.1038/s41467-023-40271-4},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Arora, Rohit and Cao, Christian and Kumar, Mehul and Sinha, Sarthak and Chanda, Ayan and McNeil, Reid and Samuel, Divya and Arora, Rahul K. and Matthews, T. Wayne and Chandarana, Shamir and Hart, Robert and Dort, Joseph C. and Biernaskie, Jeff and Neri, Paola and Hyrcza, Martin D. and Bose, Pinaki},
  year = {2023},
  month = Aug
}

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