Visium

Human breast

GSM8789216

silver Interactive viewer H&E breast neoplasm ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8789216_HCC22-088-P6-S2_D_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM8789216_HCC22-088-P6-S2_D_hires_image': DataArray[cyx] (3, 1799, 2000)
│     └── 'GSM8789216_HCC22-088-P6-S2_D_lowres_image': DataArray[cyx] (3, 540, 600)
├── Shapes
│     └── 'GSM8789216_HCC22-088-P6-S2_D': GeoDataFrame shape: (4824, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4824, 18085)
with coordinate systems:
    ▸ 'GSM8789216_HCC22-088-P6-S2_D', with elements:
        GSM8789216_HCC22-088-P6-S2_D_full_image (Images), GSM8789216_HCC22-088-P6-S2_D_hires_image (Images), GSM8789216_HCC22-088-P6-S2_D_lowres_image (Images), GSM8789216_HCC22-088-P6-S2_D (Shapes)
    ▸ 'GSM8789216_HCC22-088-P6-S2_D_downscaled_hires', with elements:
        GSM8789216_HCC22-088-P6-S2_D_hires_image (Images), GSM8789216_HCC22-088-P6-S2_D (Shapes)
    ▸ 'GSM8789216_HCC22-088-P6-S2_D_downscaled_lowres', with elements:
        GSM8789216_HCC22-088-P6-S2_D_lowres_image (Images), GSM8789216_HCC22-088-P6-S2_D (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("UnTMum53Qk9BBbqJ")   # 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/UnTMum53Qk9BBbqJ

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid UnTMum53Qk9BBbqJ

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["UnTMum53Qk9BBbqJ0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part 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: 4,824
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 2,000 × 1,799 px
mpp: 3.481 µm/px
Size: 47 MB
Sample ID: GSM8789216
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