Collections / Spatial transcriptomics of bone taken from two PRF1-/- B16F10-injected and one PRF1-/- healthy control- bone tissues using Xenium
Spatial transcriptomics of bone taken from two PRF1-/- B16F10-injected and one PRF1-/- healthy control- bone tissues using Xenium
Spatial transcriptomic profiling was performed on bone tissue from perforin-deficient (Prf1⁻/⁻) mice following bone injection and from Prf1⁻/⁻ uninjected control bone tissue using the 10x Genomics Xenium platform. The study aimed to characterize spatially resolved gene expression changes associated with tumor or cell injection in the absence of perforin-mediated cytotoxic immunity.
Datasets6
Size~10.2 GB
Assay10x Xenium
Samplemouse · bone element
KindComparison
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse329415")
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-gse329415")
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-gse329415 --dest data/ Licenses: 6 NCBI GEO policy
Member datasets · 6
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