Visium
Human ovary
GSM8768683
bronze Interactive viewer H&E ovarian serous adenocarcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8768683_Pt2-1_hires_image': DataArray[cyx] (3, 2000, 1816)
│ └── 'GSM8768683_Pt2-1_lowres_image': DataArray[cyx] (3, 600, 545)
├── Shapes
│ └── 'GSM8768683_Pt2-1': GeoDataFrame shape: (1833, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1833, 17943)
with coordinate systems:
▸ 'GSM8768683_Pt2-1', with elements:
GSM8768683_Pt2-1_hires_image (Images), GSM8768683_Pt2-1_lowres_image (Images), GSM8768683_Pt2-1 (Shapes)
▸ 'GSM8768683_Pt2-1_downscaled_hires', with elements:
GSM8768683_Pt2-1_hires_image (Images), GSM8768683_Pt2-1 (Shapes)
▸ 'GSM8768683_Pt2-1_downscaled_lowres', with elements:
GSM8768683_Pt2-1_lowres_image (Images), GSM8768683_Pt2-1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("yUCMMnySW2vWuHVc0000")
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("yUCMMnySW2vWuHVc") # 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 yUCMMnySW2vWuHVc0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/yUCMMnySW2vWuHVc
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid yUCMMnySW2vWuHVcLicense: NCBI GEO policy
Part of collection
Spatial transcriptomics for high-grade serous ovarian carcinoma who were treated with Olaparib maintenance therapy→ 8 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- ovary
- Condition
- ovarian serous adenocarcinoma
- Modality
- RNA
- Observations (spots)
- 1,833
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,816 × 2,000 px
- mpp
- 4.060 µm/px
- Size
- 48.6 MB
- Sample ID
- GSM8768683
- License
- NCBI GEO policy
- Published
- Sep 25, 2025
Cite this dataset
doi.org/10.1038/s41698-025-01122-1 ↗ BibTeX
@article{Suzuki_2025,
title = {Exploring drug resistance via intercellular crosstalk using spatial transcriptomics in high-grade serous ovarian carcinoma},
volume = {9},
ISSN = {2397-768X},
url = {http://dx.doi.org/10.1038/s41698-025-01122-1},
DOI = {10.1038/s41698-025-01122-1},
number = {1},
journal = {npj Precision Oncology},
publisher = {Springer Science and Business Media LLC},
author = {Suzuki, Hironori and Yoshida, Kosuke and Yokoi, Akira and Suzuki, Kazuhiro and Hirano, Yuta and Kitagawa, Masami and Asano-Inami, Eri and Yoshihara, Masato and Nagao, Yukari and Tamauchi, Satoshi and Yoshikawa, Nobuhisa and Kajiyama, Hiroaki and Yamamoto, Yusuke},
year = {2025},
month = Nov
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB