Visium
Mouse mesenteric lymph node
GSM8131850
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_hires_image': DataArray[cyx] (4, 2000, 1963)
│ └── 'GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_lowres_image': DataArray[cyx] (4, 600, 589)
├── Shapes
│ └── 'GSM8131850_09_IT10_AicdaKO_V11A27-336_C1': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 32589)
with coordinate systems:
▸ 'GSM8131850_09_IT10_AicdaKO_V11A27-336_C1', with elements:
GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_hires_image (Images), GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_lowres_image (Images), GSM8131850_09_IT10_AicdaKO_V11A27-336_C1 (Shapes)
▸ 'GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_downscaled_hires', with elements:
GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_hires_image (Images), GSM8131850_09_IT10_AicdaKO_V11A27-336_C1 (Shapes)
▸ 'GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_downscaled_lowres', with elements:
GSM8131850_09_IT10_AicdaKO_V11A27-336_C1_lowres_image (Images), GSM8131850_09_IT10_AicdaKO_V11A27-336_C1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("XrR2nUhqjqzZgAQ40000")
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("XrR2nUhqjqzZgAQ4") # 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 XrR2nUhqjqzZgAQ40000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/XrR2nUhqjqzZgAQ4
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid XrR2nUhqjqzZgAQ4License: NCBI GEO policy
Part of collection
Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphoma – Visium_FFPE_Spatial_Trasciptomics_project→ 2 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- mesenteric lymph node
- Modality
- RNA
- Observations (spots)
- 4,992
- Tier
- bronze
- Chemistry
- Visium v1
- Size
- 37.9 MB
- Sample ID
- GSM8131850
- License
- NCBI GEO policy
- Published
- Apr 17, 2024
Cite this dataset
doi.org/10.21203/rs.3.rs-4093618/v1 ↗ BibTeX
@article{Tripodo_2024,
title = {Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphoma},
url = {http://dx.doi.org/10.21203/rs.3.rs-4093618/v1},
DOI = {10.21203/rs.3.rs-4093618/v1},
publisher = {Springer Science and Business Media LLC},
author = {Tripodo, Claudio and Cancila, Valeria and Morello, Gaia and Bertolazzi, Giorgio and Chan, Allison Si-Yu and Bastianello, Giulia and Paysan, Daniel and Jaynes, Patrick William and Schiavoni, Giovanna and Mattei, Fabrizio and Piconese, Silvia and Revuelta, Maria and Noto, Francesco and De Ninno, Adele and Cammarata, Ilenia and Pagni, Fabio and Venkatachalapathy, Saradha and Sangaletti, Sabina and Napoli, Arianna Di and Vacca, Davide and Lonardi, Silvia and Lorenzi, Luisa and Ferreri, Andrés J M and Belmonte, Beatrice and Varano, Gabriele and Colombo, Mario Paolo and Bicciato, Silvio and Inghirami, Giorgio and Cerchietti, Leandro and Ponzoni, Maurilio and Zappasodi, Roberta and Facchetti, Fabio and Foiani, Marco and Casola, Stefano and Jeyasekharan, Anand D},
year = {2024},
month = Mar
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB