Collections / Multimodal spatial transcriptomic characterization of mouse kidney injury and repair [Visium]
Multimodal spatial transcriptomic characterization of mouse kidney injury and repair [Visium]
Spatial transcriptomics (ST) technologies enable the mapping of gene expression to specific regions within tissues. However, current ST platforms present inherent trade-offs between resolution and throughput, which cannot be entirely addressed by a single technology. To unravel the spatiotemporal landscape of gene expression and cellular interactions during kidney injury and repair, we employed an integrated approach combining Xenium's high-resolution in situ sequencing with Visium's whole-transcriptome spatial profiling.
Datasets6
Size~559 MB
AssayVisium
Samplemouse · kidney
KindComparison
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse269622")
cohort.members # the 6 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-gse269622")
coll.ordered_artifacts.to_dataframe() # the 6 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-gse269622 --dest data/ Licenses: 6 NCBI GEO policy
Member datasets · 6
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