Visium HD
Mouse tibia
GSM9046518
mouse tibia 807 MB 330,212 bins bronze NCBI GEO policy
TechnologyVisium HD
Organismmouse
Tissuetibia
Bins330,212
bronze NCBI GEO policy
Collections / geo-gse299816
geo-gse299816
Datasets1
Size~807 MB
TechnologyVisium HD
Samplemouse · tibia
KindPublication
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse299816")
cohort.members # the 1 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-gse299816")
coll.ordered_artifacts.to_dataframe() # the 1 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-gse299816 --dest data/Licenses: 1 NCBI GEO policy
About the study
Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.
Gene up(down)regulation findings in single-cell and spatial RNA sequencing can be inconsistent despite remarkable progress in technology. Inconsistent findings in high-quality samples raise concerns about assumptions behind widely accepted data analysis approaches. We, therefore, propose a weighted averaging approach for data analysis without assuming anything besides randomness of technical noise. This approach is closely related to prior work on statistics of cluster-randomized experiments. We show that weighing transcript counts based on measured noise variances and utilizing weighted rather than standard unweighted tests reduce both false-positive and false-negative findings. Our approach eliminates the need for parametrizing data distributions and/or rescaling transcript counts, which may cause artifacts by distorting and biasing the data. The resulting analysis is less complex and produces more consistent differential gene expression estimates.
Member datasets · 1
Cite this collection
No linked publication found — please cite the data source.