Oral submucous fibrosis (OSF) is one of the most common precancerous malignant orders (PMO) with high rates exacerbating into oral squamous cell carcinoma (OSCC), mostly occurred in patients with chewing betel-nut habits in Southeast Asia and Pacific region. However, the spatial characteristics and heteogeneities of tumor microenvironment (TME) in OSF-associated OSCC still remains unclear. Here, we characterized the spatiotemporal changes of OSF-associated OSCC at different malignant states by performing 10x Visium Spatial Transcriptomics (ST) sequencing and airflow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) analysis.
from sddb import Catalog
cohort = Catalog().collection("geo-gse220978")
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-gse220978")
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-gse220978 --dest data/Licenses: 4 NCBI GEO policy
No linked publication found — please cite the data source.