Collections / Spatial transcriptomics reveals C10 tumor transcriptomic changes generated by Smarta cell transfer
Spatial transcriptomics reveals C10 tumor transcriptomic changes generated by Smarta cell transfer
To elucidate how antigen-specific CD4 T cells indirectly repress tumor growth independently of other lymphocytes, we first sought to gain insight into the changes they induced in the tumor micro-environment (TME) using the 10x Genomics Visium technology
Datasets2
Size~278 MB
AssayVisium
Samplemouse · lung
KindOther
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Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse254861")
coll.ordered_artifacts.df() # the 2 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()} Member datasets · 2
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