Visium
Mouse lung
GSM5808054
bronze Interactive viewer H&E lung adenocarcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5808054_KP_1_hires_image': DataArray[cyx] (3, 1952, 2000)
│ └── 'GSM5808054_KP_1_lowres_image': DataArray[cyx] (3, 586, 600)
├── Shapes
│ └── 'GSM5808054_KP_1': GeoDataFrame shape: (1906, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1906, 32289)
with coordinate systems:
▸ 'GSM5808054_KP_1', with elements:
GSM5808054_KP_1_hires_image (Images), GSM5808054_KP_1_lowres_image (Images), GSM5808054_KP_1 (Shapes)
▸ 'GSM5808054_KP_1_downscaled_hires', with elements:
GSM5808054_KP_1_hires_image (Images), GSM5808054_KP_1 (Shapes)
▸ 'GSM5808054_KP_1_downscaled_lowres', with elements:
GSM5808054_KP_1_lowres_image (Images), GSM5808054_KP_1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("u85eukbi8VEtOY8h0000")
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("u85eukbi8VEtOY8h") # 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 u85eukbi8VEtOY8h0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/u85eukbi8VEtOY8h
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid u85eukbi8VEtOY8hLicense: NCBI GEO policy
Part of collection
Perturb-map coupled with spatial transcriptomics identifies mutation associated gene signatures in a mouse model of lung adenocarcinoma→ 4 datasets · other
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- lung
- Condition
- lung adenocarcinoma
- Modality
- RNA
- Observations (spots)
- 1,906
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,952 px
- mpp
- 3.916 µm/px
- Size
- 32.7 MB
- Sample ID
- GSM5808054
- License
- NCBI GEO policy
- Published
- Jan 15, 2022
Cite this dataset
doi.org/10.1016/j.cell.2022.02.015 ↗ BibTeX
@article{Dhainaut_2022,
title = {Spatial CRISPR genomics identifies regulators of the tumor microenvironment},
volume = {185},
ISSN = {0092-8674},
url = {http://dx.doi.org/10.1016/j.cell.2022.02.015},
DOI = {10.1016/j.cell.2022.02.015},
number = {7},
journal = {Cell},
publisher = {Elsevier BV},
author = {Dhainaut, Maxime and Rose, Samuel A. and Akturk, Guray and Wroblewska, Aleksandra and Nielsen, Sebastian R. and Park, Eun Sook and Buckup, Mark and Roudko, Vladimir and Pia, Luisanna and Sweeney, Robert and Le Berichel, Jessica and Wilk, C. Matthias and Bektesevic, Anela and Lee, Brian H. and Bhardwaj, Nina and Rahman, Adeeb H. and Baccarini, Alessia and Gnjatic, Sacha and Pe’er, Dana and Merad, Miriam and Brown, Brian D.},
year = {2022},
month = Mar,
pages = {1223–1239.e20}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB