Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment to examine spatial characteristics across different sites in eight patients with HGSOC divided into recurrent (R, n = 5) and non-recurrent (NR, n = 3) groups based on a 60-month recurrence criterion. This study identified diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.
from sddb import Catalog
cohort = Catalog().collection("geo-gse263920")
cohort.members # the 8 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")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")
coll = db.Collection.get(key="geo-gse263920")
coll.ordered_artifacts.to_dataframe() # the 8 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse263920 --dest data/Licenses: 8 NCBI GEO policy
No linked publication found — please cite the data source.