Collections / geo-gse350835

geo-gse350835

Datasets4
Size~3.6 GB
TechnologyVisium HD
Samplemouse · kidney
KindOther
Flag a data issue
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse350835")
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-gse350835")
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-gse350835 --dest data/

Licenses: 4 NCBI GEO policy

Open in Vitessce
Visium HD
Mouse kidney
GSM10122936
mouse kidney 677 MB 286,032 bins silver H&E NCBI GEO policy
TechnologyVisium HD
Organismmouse
Tissuekidney
Size677 MB
Bins286,032
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium HD
Mouse kidney
GSM10122935
mouse kidney 1.1 GB 395,182 bins silver H&E NCBI GEO policy
TechnologyVisium HD
Organismmouse
Tissuekidney
Size1.1 GB
Bins395,182
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium HD
Mouse kidney
GSM10122937
mouse kidney 847 MB 268,623 bins silver H&E NCBI GEO policy
TechnologyVisium HD
Organismmouse
Tissuekidney
Size847 MB
Bins268,623
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium HD
Mouse kidney
GSM10122934
mouse kidney 979 MB 456,334 bins silver H&E NCBI GEO policy
TechnologyVisium HD
Organismmouse
Tissuekidney
Size979 MB
Bins456,334
silver H&E NCBI GEO policy Open in Vitessce