We investigated the mechanisms by which inflammatory responses generated by tumor-specific T cells delivered to ovarian tumor-bearing mice alone or after oncolytic vaccinia virus-driven immunogenic cancer cell death affect antitumor efficacy using spatial transcriptomics.
from sddb import Catalog
cohort = Catalog().collection("geo-gse274509")
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-gse274509")
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-gse274509 --dest data/Licenses: 4 NCBI GEO policy
@article{Kozbor_2024,
title = {Consequences of the perivascular niche remodeling for tumoricidal T-cell trafficking into metastasis of ovarian cancer},
url = {http://dx.doi.org/10.21203/rs.3.rs-4940287/v1},
DOI = {10.21203/rs.3.rs-4940287/v1},
publisher = {Springer Science and Business Media LLC},
author = {Kozbor, Danuta and Winkler, Marta and Malhotra, Nemi and Mistarz, Anna and Wang, Sophie and Hutson, Alan and Gambotto, Andrea and Abrams, Scott and Singh, Prashant and Liu, Song and Odunsi, Kunle and Wang, Jianmin},
year = {2024},
month = Sept
}