Collections / Time-series spatial transcriptomic analysis of murine small intestine (SI) undergoing irradiation induced injury-repair with and without LXR activation
Time-series spatial transcriptomic analysis of murine small intestine (SI) undergoing irradiation induced injury-repair with and without LXR activation
To unbiasedly characterize the spatial transcriptomics landscape of murine small intestine damage and repair, we performed a time-series spatial transcriptional analysis of total small intestinal tissue during the course of irradiation damage from mice receiving standard diet. Additionally, to understand the role of LXR activation during damage and repair, we also collected small intestine tissues from mice fed with GW3965 diet.
Datasets4
Size~222 MB
AssayVisium
Samplemouse · small intestine
KindComparison
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse227742")
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-gse227742")
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-gse227742 --dest data/ Licenses: 4 NCBI GEO policy
Member datasets · 4
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