Visium
Mouse lung
GSM6108349
bronze Interactive viewer H&E influenza Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6108349_Day3_Aged_hires_image': DataArray[cyx] (3, 1840, 2000)
│ └── 'GSM6108349_Day3_Aged_lowres_image': DataArray[cyx] (3, 552, 600)
├── Shapes
│ └── 'GSM6108349_Day3_Aged': GeoDataFrame shape: (2520, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2520, 32285)
with coordinate systems:
▸ 'GSM6108349_Day3_Aged', with elements:
GSM6108349_Day3_Aged_hires_image (Images), GSM6108349_Day3_Aged_lowres_image (Images), GSM6108349_Day3_Aged (Shapes)
▸ 'GSM6108349_Day3_Aged_downscaled_hires', with elements:
GSM6108349_Day3_Aged_hires_image (Images), GSM6108349_Day3_Aged (Shapes)
▸ 'GSM6108349_Day3_Aged_downscaled_lowres', with elements:
GSM6108349_Day3_Aged_lowres_image (Images), GSM6108349_Day3_Aged (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("Nxu2cj3NDlYPzD5K0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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")
artifact = db.Artifact.get("Nxu2cj3NDlYPzD5K") # stem uid: Lamin resolves it to the latest version
sdata = artifact.load() # cache, then load into memory
group = artifact.open() # stream without downloading
path = artifact.cache() # sync into the local cache, returns the path Shell
# sddb client: download the .zarr into data/
sddb-cli download Nxu2cj3NDlYPzD5K0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/Nxu2cj3NDlYPzD5K
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Nxu2cj3NDlYPzD5KLicense: NCBI GEO policy
Part of collection
Spatial sequencing of young and aged lungs following influenza A virus infection→ 6 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- lung
- Condition
- influenza
- Modality
- RNA
- Observations (spots)
- 2,520
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,840 px
- mpp
- 4.311 µm/px
- Size
- 29.2 MB
- Sample ID
- GSM6108349
- License
- NCBI GEO policy
- Published
- Oct 16, 2023
Cite this dataset
doi.org/10.1038/s41467-023-42021-y ↗ BibTeX
@article{Kasmani_2023,
title = {A spatial sequencing atlas of age-induced changes in the lung during influenza infection},
volume = {14},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-023-42021-y},
DOI = {10.1038/s41467-023-42021-y},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Kasmani, Moujtaba Y. and Topchyan, Paytsar and Brown, Ashley K. and Brown, Ryan J. and Wu, Xiaopeng and Chen, Yao and Khatun, Achia and Alson, Donia and Wu, Yue and Burns, Robert and Lin, Chien-Wei and Kudek, Matthew R. and Sun, Jie and Cui, Weiguo},
year = {2023},
month = Oct
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB