← All datasets
Visium

Mouse lung

GSM6108350

bronze Interactive viewer H&E influenza Provisional
Mouse lung preview
print(sdata) 
SpatialData object
├── Images
│     ├── 'GSM6108350_Day9_Young_hires_image': DataArray[cyx] (3, 2000, 1829)
│     └── 'GSM6108350_Day9_Young_lowres_image': DataArray[cyx] (3, 600, 549)
├── Shapes
│     └── 'GSM6108350_Day9_Young': GeoDataFrame shape: (2847, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2847, 32285)
with coordinate systems:
    ▸ 'GSM6108350_Day9_Young', with elements:
        GSM6108350_Day9_Young_hires_image (Images), GSM6108350_Day9_Young_lowres_image (Images), GSM6108350_Day9_Young (Shapes)
    ▸ 'GSM6108350_Day9_Young_downscaled_hires', with elements:
        GSM6108350_Day9_Young_hires_image (Images), GSM6108350_Day9_Young (Shapes)
    ▸ 'GSM6108350_Day9_Young_downscaled_lowres', with elements:
        GSM6108350_Day9_Young_lowres_image (Images), GSM6108350_Day9_Young (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("anxzIIlMlYw9EXUj0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
Install (Python 3.12+) Docs →
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("anxzIIlMlYw9EXUj")   # 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
Install (Python 3.12+) Docs →
Shell 
# sddb client: download the .zarr into data/
sddb-cli download anxzIIlMlYw9EXUj0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/anxzIIlMlYw9EXUj

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid anxzIIlMlYw9EXUj
Install (Python 3.12+) Docs →

License: NCBI GEO policy

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue
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)
2,847
Tier
bronze
Chemistry
Visium v1
Magnification
other
Resolution
1,829 × 2,000 px
mpp
3.980 µm/px
Size
41.3 MB
Sample ID
GSM6108350
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