Visium
Human liver
GSM8657781
silver Interactive viewer H&E hepatoblastoma ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8657781_H1180_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM8657781_H1180_hires_image': DataArray[cyx] (3, 2000, 2000)
│ └── 'GSM8657781_H1180_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│ └── 'GSM8657781_H1180': GeoDataFrame shape: (4192, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4192, 18085)
with coordinate systems:
▸ 'GSM8657781_H1180', with elements:
GSM8657781_H1180_full_image (Images), GSM8657781_H1180_hires_image (Images), GSM8657781_H1180_lowres_image (Images), GSM8657781_H1180 (Shapes)
▸ 'GSM8657781_H1180_downscaled_hires', with elements:
GSM8657781_H1180_hires_image (Images), GSM8657781_H1180 (Shapes)
▸ 'GSM8657781_H1180_downscaled_lowres', with elements:
GSM8657781_H1180_lowres_image (Images), GSM8657781_H1180 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("iouW5pmJXlILygwh0000")
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("iouW5pmJXlILygwh") # 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 iouW5pmJXlILygwh0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/iouW5pmJXlILygwh
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid iouW5pmJXlILygwhLicense: NCBI GEO policy
Part of collection
Multiomic analysis uncovers a continuous spectrum of differentiation and Wnt-MDK-driven immune evasion in hepatoblastoma [Spatial Transcriptomics]→ 22 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- liver
- Condition
- hepatoblastoma
- Modality
- RNA
- Observations (spots)
- 4,192
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 3,000 × 3,000 px
- mpp
- 3.696 µm/px
- Size
- 92.8 MB
- Sample ID
- GSM8657781
- License
- NCBI GEO policy
- Published
- Feb 7, 2025
Cite this dataset
doi.org/10.1016/j.jhep.2025.01.031 ↗ BibTeX
@article{M_nter_2025,
title = {Multiomic analysis uncovers a continuous spectrum of differentiation and Wnt-MDK-driven immune evasion in hepatoblastoma},
volume = {83},
ISSN = {0168-8278},
url = {http://dx.doi.org/10.1016/j.jhep.2025.01.031},
DOI = {10.1016/j.jhep.2025.01.031},
number = {2},
journal = {Journal of Hepatology},
publisher = {Elsevier BV},
author = {Münter, Daniel and de Faria, Flavia W. and Richter, Mathis and Aranda-Pardos, Irene and Hotfilder, Marc and Walter, Carolin and Paga, Enya and Inserte, Clara and Albert, Thomas K. and Roy, Rajanya and Rahman, Shariyah and Riedel, Nicole C. and Müller, Volker and Pascher, Andreas and Wiebe, Karsten and Schmid, Irene and Vokuhl, Christian and Winkler, Beate and Jüttner, Eva and Vieth, Simon and Mücke, Urs and Kluiver, Thomas A. and Peng, Weng Chuan and Rossig, Claudia and Schlué, Jerome and Madadi-Sanjani, Omid and Sandmann, Sarah and Hartmann, Wolfgang and A-Gonzalez, Noelia and Soehnlein, Oliver and Kerl, Kornelius},
year = {2025},
month = Aug,
pages = {367–382}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB