Visium
Mouse lung
GSM8457566
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8457566_VF_WT_hires_image': DataArray[cyx] (3, 2000, 2000)
│ └── 'GSM8457566_VF_WT_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│ └── 'GSM8457566_VF_WT': GeoDataFrame shape: (4936, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4936, 32589)
with coordinate systems:
▸ 'GSM8457566_VF_WT', with elements:
GSM8457566_VF_WT_hires_image (Images), GSM8457566_VF_WT_lowres_image (Images), GSM8457566_VF_WT (Shapes)
▸ 'GSM8457566_VF_WT_downscaled_hires', with elements:
GSM8457566_VF_WT_hires_image (Images), GSM8457566_VF_WT (Shapes)
▸ 'GSM8457566_VF_WT_downscaled_lowres', with elements:
GSM8457566_VF_WT_lowres_image (Images), GSM8457566_VF_WT (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("NIa2gHsnB9pppu8M0000")
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("NIa2gHsnB9pppu8M") # 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 NIa2gHsnB9pppu8M0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/NIa2gHsnB9pppu8M
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid NIa2gHsnB9pppu8MLicense: NCBI GEO policy
Part of collection
Spatial Transcriptomics of Lungs Infected with Coccidioides posadasii→ 2 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- lung
- Modality
- RNA
- Observations (spots)
- 4,936
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 2,000 px
- mpp
- 5.485 µm/px
- Size
- 39.1 MB
- Sample ID
- GSM8457566
- License
- NCBI GEO policy
- Published
- Nov 13, 2024
Cite this dataset
doi.org/10.1128/mbio.02562-24 ↗ BibTeX
@article{Davalos_2025,
title = {Spatiotemporal analysis of lung immune dynamics in lethal
<i>Coccidioides posadasii</i>
infection},
volume = {16},
ISSN = {2150-7511},
url = {http://dx.doi.org/10.1128/mbio.02562-24},
DOI = {10.1128/mbio.02562-24},
number = {5},
journal = {mBio},
publisher = {American Society for Microbiology},
author = {Davalos, Oscar A. and Sebastian, Aimy and Leon, Nicole F. and Rangel, Margarita V. and Miranda, Nadia and Murugesh, Deepa K. and Phillips, Ashlee M. and Hoyer, Katrina K. and Hum, Nicholas R. and Loots, Gabriela G. and Weilhammer, Dina R.},
editor = {Shlezinger, Neta},
year = {2025},
month = May
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB