Visium
Human breast
GSM8789209
silver Interactive viewer H&E breast neoplasm ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8789209_HCC22-088-P4-S1_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM8789209_HCC22-088-P4-S1_hires_image': DataArray[cyx] (3, 1912, 2000)
│ └── 'GSM8789209_HCC22-088-P4-S1_lowres_image': DataArray[cyx] (3, 574, 600)
├── Shapes
│ └── 'GSM8789209_HCC22-088-P4-S1': GeoDataFrame shape: (794, 2) (2D shapes)
└── Tables
└── 'table': AnnData (794, 18085)
with coordinate systems:
▸ 'GSM8789209_HCC22-088-P4-S1', with elements:
GSM8789209_HCC22-088-P4-S1_full_image (Images), GSM8789209_HCC22-088-P4-S1_hires_image (Images), GSM8789209_HCC22-088-P4-S1_lowres_image (Images), GSM8789209_HCC22-088-P4-S1 (Shapes)
▸ 'GSM8789209_HCC22-088-P4-S1_downscaled_hires', with elements:
GSM8789209_HCC22-088-P4-S1_hires_image (Images), GSM8789209_HCC22-088-P4-S1 (Shapes)
▸ 'GSM8789209_HCC22-088-P4-S1_downscaled_lowres', with elements:
GSM8789209_HCC22-088-P4-S1_lowres_image (Images), GSM8789209_HCC22-088-P4-S1 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("MCzKDlCAFfa5shdP") # 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 Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/MCzKDlCAFfa5shdP
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid MCzKDlCAFfa5shdP Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["MCzKDlCAFfa5shdP0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends→ 14 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- breast
- Condition
- breast neoplasm
- Modality
- RNA
- Observations
- 794
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 2,000 × 1,912 px
- mpp
- 3.861 µm/px
- Size
- 22 MB
- Sample ID
- GSM8789209
- License
- NCBI GEO policy
- Published
- Mar 19, 2026
Cite this dataset
doi.org/10.1158/1078-0432.ccr-25-4079 ↗ BibTeX
@article{Carleton_2026,
title = {Use of ctDNA in Older Women with ER+ Breast Cancer to Facilitate Surgical De-escalation: A Prospective, Hybrid-Decentralized Trial with Correlative Studies},
volume = {32},
ISSN = {1557-3265},
url = {http://dx.doi.org/10.1158/1078-0432.ccr-25-4079},
DOI = {10.1158/1078-0432.ccr-25-4079},
number = {14},
journal = {Clinical Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Carleton, Neil and Chang, Alexander C. and Chen, Fangyuan and Puhalla, Shannon L. and Foldi, Julia and Waltermire, Hunter and Tin, Antony and Cowher, Michael S. and Lupinacci, Kristin and Diego, Emilia J. and Sabih, Quratulain and Johnson, Ronald R. and Malhotra, Monica and Laubenthal, Amanda and Gorantla, Vikram and Balic, Marija and Bhargava, Rohit and Joy, Marion and Freeman, Tanner and Bridges, Catherine and Kalashnikova, Ekaterina and Rodriguez, Angel and Liu, Minetta C. and Oesterreich, Steffi and Lee, Adrian V. and McAuliffe, Priscilla F.},
year = {2026},
month = Mar,
pages = {2957–2967}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB