Visium
Human breast
GSM9011371
bronze Interactive viewer H&E estrogen-receptor positive breast cancer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9011371_Breast_6996_hires_image': DataArray[cyx] (3, 2000, 1892)
│ └── 'GSM9011371_Breast_6996_lowres_image': DataArray[cyx] (3, 600, 568)
├── Shapes
│ └── 'GSM9011371_Breast_6996': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 18085)
with coordinate systems:
▸ 'GSM9011371_Breast_6996', with elements:
GSM9011371_Breast_6996_hires_image (Images), GSM9011371_Breast_6996_lowres_image (Images), GSM9011371_Breast_6996 (Shapes)
▸ 'GSM9011371_Breast_6996_downscaled_hires', with elements:
GSM9011371_Breast_6996_hires_image (Images), GSM9011371_Breast_6996 (Shapes)
▸ 'GSM9011371_Breast_6996_downscaled_lowres', with elements:
GSM9011371_Breast_6996_lowres_image (Images), GSM9011371_Breast_6996 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("9AfR9rvVm8aNS7kM0000")
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("9AfR9rvVm8aNS7kM") # 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 9AfR9rvVm8aNS7kM0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/9AfR9rvVm8aNS7kM
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 9AfR9rvVm8aNS7kMLicense: NCBI GEO policy
Part of collection
In silico reconstruction of primary and metastatic tumor architecture using GIS-augmented spatial transcriptomics→ 12 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- breast
- Condition
- estrogen-receptor positive breast cancer
- Modality
- RNA
- Observations (spots)
- 4,992
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,892 × 2,000 px
- mpp
- 8.495 µm/px
- Size
- 115 MB
- Sample ID
- GSM9011371
- License
- NCBI GEO policy
- Published
- Feb 17, 2026
Cite this dataset
doi.org/10.1158/0008-5472.can-25-3161 ↗ BibTeX
@article{Yoo_2026,
title = {<i>In Silico</i>
Reconstruction of Primary and Metastatic Tumor Architecture Using Geographic Information System–Augmented Spatial Transcriptomics},
volume = {86},
ISSN = {1538-7445},
url = {http://dx.doi.org/10.1158/0008-5472.can-25-3161},
DOI = {10.1158/0008-5472.can-25-3161},
number = {10},
journal = {Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Yoo, Jin Young and Akter, Sabrina and Zuo, Qianying and Rilloraza, Matthew and Kazemi, Amir and Wu, Wenjun and Goel, Mahima and Lam, Audrey and Dutta, Debapriya and Barnick, Betsy and Pekmezci, Melike and Grosse Perdekamp, Maria and Soliman, Aiman and Madak-Erdogan, Zeynep},
year = {2026},
month = Feb,
pages = {2555–2568}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB