Collections / Identification of perturbation-responsive regions and genes in comparative spatial transcriptomics atlases
Identification of perturbation-responsive regions and genes in comparative spatial transcriptomics atlases
This submission is a dataset of single-nucleus transcriptomics in the spinal cords of mice. The single-nuclei transcriptomics data is harvested and profiled using Visium Spatial Gene Expression.
Datasets7
Size~277 MB
AssayVisium
Samplemouse · spinal cord
KindComparison
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse268779")
cohort.members # the 7 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-gse268779")
coll.ordered_artifacts.to_dataframe() # the 7 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-gse268779 --dest data/ Licenses: 7 NCBI GEO policy
Member datasets · 7
Mouse spinal cord
GSM8299018
mouse spinal cord 45.8 MB 3,985 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuespinal cord
Spots3,985
silver H&E NCBI GEO policy
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No linked publication found — please cite the data source.