Visium
Human skin of body
GSM4565823
bronze Interactive viewer H&E skin squamous cell carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM4565823_hires_image': DataArray[cyx] (3, 2000, 2000)
├── Shapes
│ └── 'GSM4565823': GeoDataFrame shape: (744, 2) (2D shapes)
└── Tables
└── 'table': AnnData (744, 33538)
with coordinate systems:
▸ 'GSM4565823', with elements:
GSM4565823_hires_image (Images), GSM4565823 (Shapes)
▸ 'GSM4565823_downscaled_hires', with elements:
GSM4565823_hires_image (Images), GSM4565823 (Shapes)
▸ 'GSM4565823_downscaled_lowres', with elements:
GSM4565823 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("pueCcjpiKMrc6VOk0000")
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("pueCcjpiKMrc6VOk") # 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 pueCcjpiKMrc6VOk0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/pueCcjpiKMrc6VOk
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid pueCcjpiKMrc6VOkLicense: NCBI GEO policy
Part of collection
Single Cell and Spatial Analysis of Human Squamous Cell Carcinoma [ST]→ 4 datasets · replicates
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- skin of body
- Condition
- skin squamous cell carcinoma
- Modality
- RNA
- Observations (spots)
- 744
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 2,000 px
- mpp
- 3.837 µm/px
- Size
- 13.7 MB
- Sample ID
- GSM4565823
- License
- NCBI GEO policy
- Published
- Jun 22, 2020
Cite this dataset
doi.org/10.1016/j.cell.2020.05.039 ↗ BibTeX
@article{Ji_2020,
title = {Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma},
volume = {182},
ISSN = {0092-8674},
url = {http://dx.doi.org/10.1016/j.cell.2020.05.039},
DOI = {10.1016/j.cell.2020.05.039},
number = {2},
journal = {Cell},
publisher = {Elsevier BV},
author = {Ji, Andrew L. and Rubin, Adam J. and Thrane, Kim and Jiang, Sizun and Reynolds, David L. and Meyers, Robin M. and Guo, Margaret G. and George, Benson M. and Mollbrink, Annelie and Bergenstråhle, Joseph and Larsson, Ludvig and Bai, Yunhao and Zhu, Bokai and Bhaduri, Aparna and Meyers, Jordan M. and Rovira-Clavé, Xavier and Hollmig, S. Tyler and Aasi, Sumaira Z. and Nolan, Garry P. and Lundeberg, Joakim and Khavari, Paul A.},
year = {2020},
month = July,
pages = {497–514.e22}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB