Visium
Human breast
GSM8291070
silver Interactive viewer H&E breast carcinoma ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8291070_V42Y04-403_A1_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM8291070_V42Y04-403_A1_hires_image': DataArray[cyx] (3, 1335, 2000)
│ └── 'GSM8291070_V42Y04-403_A1_lowres_image': DataArray[cyx] (3, 401, 600)
├── Shapes
│ └── 'GSM8291070_V42Y04-403_A1': GeoDataFrame shape: (4955, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4955, 18085)
with coordinate systems:
▸ 'GSM8291070_V42Y04-403_A1', with elements:
GSM8291070_V42Y04-403_A1_full_image (Images), GSM8291070_V42Y04-403_A1_hires_image (Images), GSM8291070_V42Y04-403_A1_lowres_image (Images), GSM8291070_V42Y04-403_A1 (Shapes)
▸ 'GSM8291070_V42Y04-403_A1_downscaled_hires', with elements:
GSM8291070_V42Y04-403_A1_hires_image (Images), GSM8291070_V42Y04-403_A1 (Shapes)
▸ 'GSM8291070_V42Y04-403_A1_downscaled_lowres', with elements:
GSM8291070_V42Y04-403_A1_lowres_image (Images), GSM8291070_V42Y04-403_A1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("CURhQcXdhKUcDJDt0000")
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("CURhQcXdhKUcDJDt") # 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 CURhQcXdhKUcDJDt0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/CURhQcXdhKUcDJDt
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid CURhQcXdhKUcDJDtLicense: NCBI GEO policy
Part of collection
snPATHO-seq: unlocking the FFPE archives for single nucleus RNA profiling [Visium]→ 3 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- breast
- Condition
- breast carcinoma
- Modality
- RNA
- Observations (spots)
- 4,955
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 2,000 × 1,335 px
- mpp
- 11.848 µm/px
- Size
- 123 MB
- Sample ID
- GSM8291070
- License
- NCBI GEO policy
- Published
- Oct 1, 2024
Cite this dataset
doi.org/10.1038/s42003-024-07043-2 ↗ BibTeX
@article{Wang_2024,
title = {snPATHO-seq, a versatile FFPE single-nucleus RNA sequencing method to unlock pathology archives},
volume = {7},
ISSN = {2399-3642},
url = {http://dx.doi.org/10.1038/s42003-024-07043-2},
DOI = {10.1038/s42003-024-07043-2},
number = {1},
journal = {Communications Biology},
publisher = {Springer Science and Business Media LLC},
author = {Wang, Taopeng and Roach, Michael J. and Harvey, Kate and Morlanes, Javier Escudero and Kiedik, Beata and Al-Eryani, Ghamdan and Greenwald, Alissa and Kalavros, Nikolaos and Dezem, Felipe Segato and Ma, Yuling and Pita-Juarez, Yered H. and Wise, Kellie and Degletagne, Cyril and Elz, Anna and Hadadianpour, Azi and Johanneson, Jack and Pakiam, Fiona and Ryu, Heeju and Newell, Evan W. and Tonon, Laurie and Kohlway, Andrew and Drennon, Tingsheng and Abousoud, Jawad and Stott, Ryan and Lund, Paul and Durruthy, Jens and Vallejo, Andres F. and Li, Wenyan and Salomon, Robert and Kaczorowski, Dominik and Warren, Joanna and Butler, Lisa M. and O’Toole, Sandra and Plummer, Jasmine and Vlachos, Ioannis S. and Lundeberg, Joakim and Swarbrick, Alexander and Martelotto, Luciano G.},
year = {2024},
month = Oct
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB