Collections / Collagen Alignment and Spatial Transcriptomics Analysis (CASTA) reveals gene programs associated with collagen alignmet in colorectal cancer
Collagen Alignment and Spatial Transcriptomics Analysis (CASTA) reveals gene programs associated with collagen alignmet in colorectal cancer
The use of immune checkpoint blockade is limited to the minority of colorectal cancers that are highly infiltrated by CD8+ T cells. Therefore, a method to improve CD8+ T-cell infiltration has the potential to expand the use immune checkpoint blockade to more patients. Barriers of aligned collagen surrounding tumors have been proposed as one mechanisim of CD8+ T-cell exclusion. We use 5 colorectal cancer patient samples to examine the relationship between collagen alignment and gene expression.
Datasets5
Size~186 MB
AssayVisium
Samplehuman · colon
KindOther
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Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse311294")
cohort.members # the 5 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-gse311294")
coll.ordered_artifacts.to_dataframe() # the 5 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-gse311294 --dest data/ Licenses: 5 NCBI GEO policy
Member datasets · 5
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