In vitro models of high grade serous ovarian cancer spheroids are often used to study mechanisms of cell dormancy and resistance to chemotherapy, but their ability to accurately mimic spheroid biology in patients is unclear. We performed spatial transcriptomic profiling of in vitro cell line- and in vivo patient-derived high grade serous ovarian cancer spheroids to profile spheroid cell composition and gene expression programs between models. Our analysis reveals the predominant cell types that make up patient spheroids and common expression patterns that shared between models.
from sddb import Catalog
cohort = Catalog().collection("geo-gse335181")
cohort.members # the 2 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-gse335181")
coll.ordered_artifacts.to_dataframe() # the 2 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-gse335181 --dest data/Licenses: 2 NCBI GEO policy
No linked publication found — please cite the data source.