Collections / ETMR stem-like state and chemo-resistance are supported by perivascular cells at single-cell resolution (spatial transcriptomics subset)
ETMR stem-like state and chemo-resistance are supported by perivascular cells at single-cell resolution (spatial transcriptomics subset)
To investigate the celluar heterogeneity of ETMR and the role of pericytes in the tumor microenvironment, we performed single-cell RNA sequencing of about 140,000 cells from primary human and murine ETMR as well as from 2D ETMR cultures and 3D models of ETMR-harboring forebrain organoids. We also analysed 3 ETMR human samples by spatial transcriptomics.
Datasets3
Size~119 MB
AssayVisium
Samplehuman · brain
KindOther
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse224477")
cohort.members # the 3 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-gse224477")
coll.ordered_artifacts.to_dataframe() # the 3 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-gse224477 --dest data/ Licenses: 3 NCBI GEO policy
Member datasets · 3
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