Quadricep sections of 10µm thick were placed on Visium spatial transcriptomics slide to obtain spatial datasets for these skeletal muscle sections
from sddb import Catalog
cohort = Catalog().collection("geo-gse199659")
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/")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-gse199659")
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()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse199659 --dest data/Licenses: 4 NCBI GEO policy
@article{Heezen_2023,
title = {Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models},
volume = {14},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-023-40555-9},
DOI = {10.1038/s41467-023-40555-9},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Heezen, L.G.M. and Abdelaal, T. and van Putten, M. and Aartsma-Rus, A. and Mahfouz, A. and Spitali, P.},
year = {2023},
month = Aug
}