Human skin of body
GSM9902815
human skin of body keloid 1.6 GB 64,091 cells silver NCBI GEO policy
Technology10x Xenium
Organismhuman
Tissueskin of body
Conditionkeloid
Cells64,091
silver NCBI GEO policy
Collections / Xenium spatial transcriptomics identifies mesenchymal fibroblast expansion and conserved fibrotic ECM programs in keloid
Xenium spatial transcriptomics identifies mesenchymal fibroblast expansion and conserved fibrotic ECM programs in keloid
In this study, we applied single-cell-resolution targeted Xenium spatial transcriptomics (Xenium ST) to define keloid-associated cellular and molecular programs within intact tissue architecture, with a focus on fibroblast populations and conserved pro-fibrotic gene programs linked to ECM remodeling across diverse ancestry.
Datasets2
Size~4.3 GB
Assay10x Xenium
Samplehuman · skin of body
KindComparison
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse341250")
cohort.members # the 2 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-gse341250")
coll.ordered_artifacts.to_dataframe() # the 2 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-gse341250 --dest data/Licenses: 2 NCBI GEO policy
Member datasets · 2
Human skin of body
GSM9902814
human skin of body keloid 2.7 GB 86,647 cells silver NCBI GEO policy
Technology10x Xenium
Organismhuman
Tissueskin of body
Conditionkeloid
Cells86,647
silver NCBI GEO policy
Cite this collection
No linked publication found — please cite the data source.