Collections / Spatial transcriptomics reveals ovarian cancer subclones with different microenvironments
Spatial transcriptomics reveals ovarian cancer subclones with different microenvironments
8 spatial RNA-seq experiments (10x Genomics Visium) and 5 single-cell RNA-seq experiments (10x Genomics Chromium) using high-grade serous ovarian carcinoma (HGSOC) samples obtained from the ovaries during the interval debulking surgery
Datasets8
Size~250 MB
AssayVisium
Samplehuman · ovary
KindComparison
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse211956")
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/") 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")
coll = db.Collection.get(key="geo-gse211956")
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()] Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse211956 --dest data/ Licenses: 8 NCBI GEO policy
Member datasets · 8
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