Collections / The spatial landscape of gene expression isoforms in tissue sections
The spatial landscape of gene expression isoforms in tissue sections
Mouse brain 10x Genomics Visium Spatial Transcriptomics profiles sequence either with Illumina for standard profiling and Nanopore promethION long read sequencer for full-length profiling.
Datasets3
Size~103 MB
AssayVisium
Samplemouse · multiple
KindOther
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Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse153859")
coll.ordered_artifacts.df() # the 3 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 · 3
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