Visium
Rat mammary gland
GSM8023901
bronze Interactive viewer breast carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8023901_5NC_hires_image': DataArray[cyx] (4, 468, 2000)
│ └── 'GSM8023901_5NC_lowres_image': DataArray[cyx] (4, 141, 600)
├── Shapes
│ └── 'GSM8023901_5NC': GeoDataFrame shape: (1648, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1648, 25399)
with coordinate systems:
▸ 'GSM8023901_5NC', with elements:
GSM8023901_5NC_hires_image (Images), GSM8023901_5NC_lowres_image (Images), GSM8023901_5NC (Shapes)
▸ 'GSM8023901_5NC_downscaled_hires', with elements:
GSM8023901_5NC_hires_image (Images), GSM8023901_5NC (Shapes)
▸ 'GSM8023901_5NC_downscaled_lowres', with elements:
GSM8023901_5NC_lowres_image (Images), GSM8023901_5NC (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("GuR4lldNNpFm5C6e0000")
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("GuR4lldNNpFm5C6e") # 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 GuR4lldNNpFm5C6e0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/GuR4lldNNpFm5C6e
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid GuR4lldNNpFm5C6eLicense: NCBI GEO policy
Part of collection
Deconvolution of regulatory T cell heterogeneity identifies cycling precursors as conductors of immune escape during breast carcinoma progression [Visium]→ 20 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- rat
- Tissue
- mammary gland
- Condition
- breast carcinoma
- Modality
- RNA
- Observations (spots)
- 1,648
- Tier
- bronze
- Chemistry
- Visium v1
- Size
- 10.7 MB
- Sample ID
- GSM8023901
- License
- NCBI GEO policy
- Published
- Feb 26, 2026
Cite this dataset
doi.org/10.1016/j.ccell.2026.03.015 ↗ BibTeX
@article{Bui_2026,
title = {Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression},
volume = {44},
ISSN = {1535-6108},
url = {http://dx.doi.org/10.1016/j.ccell.2026.03.015},
DOI = {10.1016/j.ccell.2026.03.015},
number = {6},
journal = {Cancer Cell},
publisher = {Elsevier BV},
author = {Bui, Triet Minh and Jimenez, Ernesto Rojas and Li, Zheqi and Foidart, Pierre and Puleo, Julieann and Yan, Pengze and Jhaveri, Aashna and Yang, Lin and Nishida, Jun and Seehawer, Marco and Cai, Xinran and Parker, Kimberly Ann and Qin, Xiaodi and Sumer, Oyku Ece and Huang, Xiao-Yun and Patel, Ashka and Dillon, Deborah and McDonnell, Charles H. and Rowberry, Ron and Jhajj, Shinedeep and Baker, Catherine and Brown, Daniel D. and Strand, Siri H. and Marks, Jeffrey R. and Colditz, Graham A. and Park, So Yeon and Lee, Adrian V. and Angelo, Michael and McAuliffe, Priscilla F. and Bobolis, Kristie and West, Robert and Dranoff, Glenn and Hwang, E. Shelley and Cristea, Simona and Polyak, Kornelia},
year = {2026},
month = June,
pages = {1179–1200.e14}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB