Visium
Mouse colon
GSM8694500
bronze Interactive viewer H&E colorectal cancer Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM8694500_MC38_IgG_m08_hires_image': DataArray[cyx] (3, 2000, 2000)
├── Shapes
│ └── 'GSM8694500_MC38_IgG_m08': GeoDataFrame shape: (2435, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2435, 20551)
with coordinate systems:
▸ 'GSM8694500_MC38_IgG_m08', with elements:
GSM8694500_MC38_IgG_m08_hires_image (Images), GSM8694500_MC38_IgG_m08 (Shapes)
▸ 'GSM8694500_MC38_IgG_m08_downscaled_hires', with elements:
GSM8694500_MC38_IgG_m08_hires_image (Images), GSM8694500_MC38_IgG_m08 (Shapes)
▸ 'GSM8694500_MC38_IgG_m08_downscaled_lowres', with elements:
GSM8694500_MC38_IgG_m08 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("lXx7tch8CqMMrSXl0000")
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("lXx7tch8CqMMrSXl") # 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 lXx7tch8CqMMrSXl0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/lXx7tch8CqMMrSXl
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid lXx7tch8CqMMrSXlLicense: NCBI GEO policy
Part of collection
Spatial genomics reveals cholesterol metabolism as a key factor in immunotherapy resistance in colorectal cancer [Visium]→ 22 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- colon
- Condition
- colorectal cancer
- Modality
- RNA
- Observations (spots)
- 2,435
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 2,000 × 2,000 px
- mpp
- 3.423 µm/px
- Size
- 51.6 MB
- Sample ID
- GSM8694500
- License
- NCBI GEO policy
- Published
- Mar 10, 2025
Cite this dataset
doi.org/10.3389/fonc.2025.1549237 ↗ BibTeX
@article{Kavran_2025,
title = {Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance},
volume = {15},
ISSN = {2234-943X},
url = {http://dx.doi.org/10.3389/fonc.2025.1549237},
DOI = {10.3389/fonc.2025.1549237},
journal = {Frontiers in Oncology},
publisher = {Frontiers Media SA},
author = {Kavran, Andrew J. and Bai, Yulong and Rabe, Brian and Kreshock, Anna and Fisher, Andrew and Cheng, Yelena and Lewin, Anne and Dai, Chao and Meyer, Matthew J. and Mavrakis, Konstantinos J. and Lyubetskaya, Anna and Drokhlyansky, Eugene},
year = {2025},
month = Mar
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB