Collections / Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics [spatial]
Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics [spatial]
We use spatial transcriptomics to establish regional transcriptional profile of neonatal heart tissue obtained at indicated timpoints after apical resection surgery.
Datasets4
Size~112 MB
AssayVisium
Samplemouse · heart
KindTime course
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse188888")
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-gse188888")
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-gse188888 --dest data/ Licenses: 4 NCBI GEO policy
Member datasets · 4
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