Visium
Human breast
GSM6592049
bronze Interactive viewer H&E triple-negative breast carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM6592049_M2_hires_image': DataArray[cyx] (3, 2000, 1989)
├── Shapes
│ └── 'GSM6592049_M2': GeoDataFrame shape: (1848, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1848, 36601)
with coordinate systems:
▸ 'GSM6592049_M2', with elements:
GSM6592049_M2_hires_image (Images), GSM6592049_M2 (Shapes)
▸ 'GSM6592049_M2_downscaled_hires', with elements:
GSM6592049_M2_hires_image (Images), GSM6592049_M2 (Shapes)
▸ 'GSM6592049_M2_downscaled_lowres', with elements:
GSM6592049_M2 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("jds6OelojhrCehZM0000")
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("jds6OelojhrCehZM") # 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 jds6OelojhrCehZM0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/jds6OelojhrCehZM
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jds6OelojhrCehZMLicense: NCBI GEO policy
Part of collection
Spatial transcriptomics of human plastic and non-plastic breast cancer subtypes→ 14 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- breast
- Condition
- triple-negative breast carcinoma
- Modality
- RNA
- Observations (spots)
- 1,848
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 1,989 × 2,000 px
- mpp
- 3.357 µm/px
- Size
- 25.3 MB
- Sample ID
- GSM6592049
- License
- NCBI GEO policy
- Published
- Oct 16, 2023
Cite this dataset
doi.org/10.1016/j.labinv.2023.100258 ↗ BibTeX
@article{Coutant_2023,
title = {Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes},
volume = {103},
ISSN = {0023-6837},
url = {http://dx.doi.org/10.1016/j.labinv.2023.100258},
DOI = {10.1016/j.labinv.2023.100258},
number = {12},
journal = {Laboratory Investigation},
publisher = {Elsevier BV},
author = {Coutant, Angèle and Cockenpot, Vincent and Muller, Lauriane and Degletagne, Cyril and Pommier, Roxane and Tonon, Laurie and Ardin, Maude and Michallet, Marie-Cécile and Caux, Christophe and Laurent, Marie and Morel, Anne-Pierre and Saintigny, Pierre and Puisieux, Alain and Ouzounova, Maria and Martinez, Pierre},
year = {2023},
month = Dec,
pages = {100258}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB