Visium
Human liver
GSM8627609
bronze Interactive viewer H&E hepatocellular carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8627609_B0013T3_hires_image': DataArray[cyx] (3, 1921, 2000)
│ └── 'GSM8627609_B0013T3_lowres_image': DataArray[cyx] (3, 576, 600)
├── Shapes
│ └── 'GSM8627609_B0013T3': GeoDataFrame shape: (1485, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1485, 36601)
with coordinate systems:
▸ 'GSM8627609_B0013T3', with elements:
GSM8627609_B0013T3_hires_image (Images), GSM8627609_B0013T3_lowres_image (Images), GSM8627609_B0013T3 (Shapes)
▸ 'GSM8627609_B0013T3_downscaled_hires', with elements:
GSM8627609_B0013T3_hires_image (Images), GSM8627609_B0013T3 (Shapes)
▸ 'GSM8627609_B0013T3_downscaled_lowres', with elements:
GSM8627609_B0013T3_lowres_image (Images), GSM8627609_B0013T3 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("ub9Rp1fJ5z5dApPv0000")
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("ub9Rp1fJ5z5dApPv") # 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 ub9Rp1fJ5z5dApPv0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/ub9Rp1fJ5z5dApPv
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid ub9Rp1fJ5z5dApPvLicense: NCBI GEO policy
Part of collection
Multi-region spatial transcriptome analysis reveals cellular networks and pathways associated with hepatocellular carcinoma recurrence after surgical resection→ 17 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- liver
- Condition
- hepatocellular carcinoma
- Modality
- RNA
- Observations (spots)
- 1,485
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,921 px
- mpp
- 3.364 µm/px
- Size
- 21.2 MB
- Sample ID
- GSM8627609
- License
- NCBI GEO policy
- Published
- Jul 8, 2025
Cite this dataset
doi.org/10.1016/j.jhepr.2026.101790 ↗ BibTeX
@article{Seshachalam_2026,
title = {Multiomics and multi-region spatial transcriptome analysis reveal cellular networks and pathways associated with HCC recurrence},
volume = {8},
ISSN = {2589-5559},
url = {http://dx.doi.org/10.1016/j.jhepr.2026.101790},
DOI = {10.1016/j.jhepr.2026.101790},
number = {5},
journal = {JHEP Reports},
publisher = {Elsevier BV},
author = {Seshachalam, Veerabrahma P. and Sari, Ita N. and Toh, Kane and Majee, Prativa and Chong, Shay L. and Sekar, Karthik and Aiderus, Aziz and Idzham, Khaireen and Qi, Ong Y. and Chong, Li Y. and Caldez, Matias and Shanmugam, Raghuvaran and Ying, Dorcas H.H. and Lee, Esmond and Toh, Jiaying and Lele, Wu and Lee, Jia Y.J. and Zhong, Guorui and Chew, Sin C. and Chen, Gao B. and Wu, Lingyan and Chung, Alexander Y.F. and Cheow, Peng C. and Kam, Juinn H. and Kow, Alfred W.C. and Leow, Wei Q. and Jegannathan, Nagalakshmi and Chen, Kaina and Bunchaliew, Chairat and de Villa, Vanessa H. and Koh, Peng S. and Bonney, Glenn and Goh, Brian K.P. and Tam, Wai Leong and Loo, Lit-Hsin and Yeong, Joe and Tergaonkar, Vinay and Chow, Pierce K.H.},
year = {2026},
month = May,
pages = {101790}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB