Collections / Spatial transcriptomics of the mouse brain across three age groups
Spatial transcriptomics of the mouse brain across three age groups
Datasets6
Size~1.2 GB
AssayVisium
Samplemouse · brain
KindComparison
Download the whole collection
Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="doi-10.1016/j.cell.2023.07.027")
coll.ordered_artifacts.df() # the 6 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()} About the study
Atlas of the aging mouse brain reveals white matter as vulnerable foci.
Read the abstract at the source →Member datasets · 6
Cite this collection
doi.org/10.1016/j.cell.2023.07.027 ↗ BibTeX
@article{Hahn_2023,
title = {Atlas of the aging mouse brain reveals white matter as vulnerable foci},
volume = {186},
ISSN = {0092-8674},
url = {http://dx.doi.org/10.1016/j.cell.2023.07.027},
DOI = {10.1016/j.cell.2023.07.027},
number = {19},
journal = {Cell},
publisher = {Elsevier BV},
author = {Hahn, Oliver and Foltz, Aulden G. and Atkins, Micaiah and Kedir, Blen and Moran-Losada, Patricia and Guldner, Ian H. and Munson, Christy and Kern, Fabian and Pálovics, Róbert and Lu, Nannan and Zhang, Hui and Kaur, Achint and Hull, Jacob and Huguenard, John R. and Grönke, Sebastian and Lehallier, Benoit and Partridge, Linda and Keller, Andreas and Wyss-Coray, Tony},
year = {2023},
month = Sept,
pages = {4117–4133.e22}
}