Collections / Spatial transcriptomic sequencing data of human small cell lung cancer
Spatial transcriptomic sequencing data of human small cell lung cancer
Small cell lung cancer is a lung tumor with extremely poor prognosis, and its tumor heterogeneity and unique microenvironment remain unclear. This study aimed to reveal the early tumorigenesis and microenvironment of small cell lung cancer by performing idle transcriptomic sequencing of patients' small cell lung cancer tissues.
Datasets4
Size~184 MB
AssayVisium
Samplehuman · lung
KindOther
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse263196")
cohort.members # the 4 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-gse263196")
coll.ordered_artifacts.to_dataframe() # the 4 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-gse263196 --dest data/ Licenses: 4 NCBI GEO policy
Member datasets · 4
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