Collections / Time-series spatial transcriptomic analysis of murine colon undergoing AOM-DSS tumorigenesis with and without LXR activation
Time-series spatial transcriptomic analysis of murine colon undergoing AOM-DSS tumorigenesis with and without LXR activation
To unbiasedly characterize the spatial transcriptomics landscape of murine colorectal cancer initiation and progression, we performed a time-series spatial transcriptional analysis of total colonic tissue during the course of azoxymethane (AOM)-dextran sodium sulfate (DSS) induced colorectal cancer from mice receiving standard (chow) diet. Additionally, to understand the role of LXR activation during AOM-DSS tumorigenesis, we also collected colonic tissues from mice fed with GW3965 diet.
Datasets5
Size~250 MB
AssayVisium
Samplemouse · colon
KindTime course
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Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse227598")
coll.ordered_artifacts.df() # the 5 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 · 5
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