Collections / The spatial landscape of gene expression isoforms in tissue sections
The spatial landscape of gene expression isoforms in tissue sections
Mouse brain 10x Genomics Visium Spatial Transcriptomics profiles sequence either with Illumina for standard profiling and Nanopore promethION long read sequencer for full-length profiling.
Datasets3
Size~108 MB
AssayVisium
Samplemouse · multiple
KindOther
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse153859")
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-gse153859")
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-gse153859 --dest data/ Licenses: 3 NCBI GEO policy
Member datasets · 3
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