Visium
Mouse lung
GSM6634341
bronze Interactive viewer H&E lung adenocarcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6634341_DT2_C1_hires_image': DataArray[cyx] (3, 2000, 2000)
│ └── 'GSM6634341_DT2_C1_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│ └── 'GSM6634341_DT2_C1': GeoDataFrame shape: (3505, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3505, 30970)
with coordinate systems:
▸ 'GSM6634341_DT2_C1', with elements:
GSM6634341_DT2_C1_hires_image (Images), GSM6634341_DT2_C1_lowres_image (Images), GSM6634341_DT2_C1 (Shapes)
▸ 'GSM6634341_DT2_C1_downscaled_hires', with elements:
GSM6634341_DT2_C1_hires_image (Images), GSM6634341_DT2_C1 (Shapes)
▸ 'GSM6634341_DT2_C1_downscaled_lowres', with elements:
GSM6634341_DT2_C1_lowres_image (Images), GSM6634341_DT2_C1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("JiA6vDXclASZ63420000")
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("JiA6vDXclASZ6342") # 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 JiA6vDXclASZ63420000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/JiA6vDXclASZ6342
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid JiA6vDXclASZ6342License: NCBI GEO policy
Part of collection
Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs novel combination cancer therapy strategies [visium]→ 8 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- lung
- Condition
- lung adenocarcinoma
- Modality
- RNA
- Observations (spots)
- 3,505
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 2,000 px
- mpp
- 3.730 µm/px
- Size
- 48.9 MB
- Sample ID
- GSM6634341
- License
- NCBI GEO policy
- Published
- May 3, 2023
Cite this dataset
doi.org/10.1038/s41590-023-01504-2 ↗ BibTeX
@article{Glasner_2023,
title = {Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies},
volume = {24},
ISSN = {1529-2916},
url = {http://dx.doi.org/10.1038/s41590-023-01504-2},
DOI = {10.1038/s41590-023-01504-2},
number = {6},
journal = {Nature Immunology},
publisher = {Springer Science and Business Media LLC},
author = {Glasner, Ariella and Rose, Samuel A. and Sharma, Roshan and Gudjonson, Herman and Chu, Tinyi and Green, Jesse A. and Rampersaud, Sham and Valdez, Izabella K. and Andretta, Emma S. and Dhillon, Bahawar S. and Schizas, Michail and Dikiy, Stanislav and Mendoza, Alejandra and Hu, Wei and Wang, Zhong-Min and Chaudhary, Ojasvi and Xu, Tianhao and Mazutis, Linas and Rizzuto, Gabrielle and Quintanal-Villalonga, Alvaro and Manoj, Parvathy and de Stanchina, Elisa and Rudin, Charles M. and Pe’er, Dana and Rudensky, Alexander Y.},
year = {2023},
month = May,
pages = {1020–1035}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB