Collections / Reconstruction of 3-dimensional tissue organization at the single-cell resolution [ST]

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [ST]

We designed a neural network-based computational method that learns transcriptome-to-space mapping and reconstructs 3D tissue organization by learning from scRNA-seq and spatial transcriptomic data.

Datasets2
Size~24 MB
AssayVisium
Samplehuman · prepuce of penis
KindOther
PMID:36711844 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse220572")
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()}
Open in Vitessce
Visium
Human prepuce of penis
GSM6806992
human prepuce of penis 12 MB 659 obs bronze H&E
TechnologyVisium
Organismhuman
Tissueprepuce of penis
Size12 MB
Obs659
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Human prepuce of penis
GSM6806993
human prepuce of penis 13 MB 653 obs bronze H&E
TechnologyVisium
Organismhuman
Tissueprepuce of penis
Size13 MB
Obs653
bronze H&E Open in Vitessce

Cite this collection

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