Visium

Human pancreas

GSM8454389

bronze Interactive viewer H&E pancreatic ductal adenocarcinoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8454389_PDAC3_hires_image': DataArray[cyx] (3, 1899, 2000)
│     └── 'GSM8454389_PDAC3_lowres_image': DataArray[cyx] (3, 570, 600)
├── Shapes
│     └── 'GSM8454389_PDAC3': GeoDataFrame shape: (2932, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2932, 17943)
with coordinate systems:
    ▸ 'GSM8454389_PDAC3', with elements:
        GSM8454389_PDAC3_hires_image (Images), GSM8454389_PDAC3_lowres_image (Images), GSM8454389_PDAC3 (Shapes)
    ▸ 'GSM8454389_PDAC3_downscaled_hires', with elements:
        GSM8454389_PDAC3_hires_image (Images), GSM8454389_PDAC3 (Shapes)
    ▸ 'GSM8454389_PDAC3_downscaled_lowres', with elements:
        GSM8454389_PDAC3_lowres_image (Images), GSM8454389_PDAC3 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("G3TEJm0cDGYymF680000")
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("G3TEJm0cDGYymF68")   # 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 G3TEJm0cDGYymF680000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/G3TEJm0cDGYymF68

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid G3TEJm0cDGYymF68

Install (Python 3.12+) Docs →

License: NCBI GEO policy

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

Differential Activity of MAPK signalling Defines Fibroblast Subtypes in Pancreatic Cancer (Spatial Transcriptomics)→

4 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: pancreas
Condition: pancreatic ductal adenocarcinoma
Modality: RNA
Observations (spots): 2,932
Tier: bronze
Chemistry: Visium v1
Resolution: 2,000 × 1,899 px
mpp: 3.612 µm/px
Size: 56.6 MB
Sample ID: GSM8454389
License: NCBI GEO policy
Published: Dec 5, 2024

Cite this dataset

doi.org/10.1038/s41467-024-54975-8 ↗

BibTeX

@article{Veghini_2024,
  title = {Differential activity of MAPK signalling defines fibroblast subtypes in pancreatic cancer},
  volume = {15},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-024-54975-8},
  DOI = {10.1038/s41467-024-54975-8},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Veghini, Lisa and Pasini, Davide and Fang, Rui and Delfino, Pietro and Filippini, Dea and Neander, Christian and Vicentini, Caterina and Fiorini, Elena and Lupo, Francesca and D’Agosto, Sabrina L. and Carbone, Carmine and Agostini, Antonio and Piro, Geny and Rosa, Diego and Bevere, Michele and Markus, Peter and Behrens, Diana and Luchini, Claudio and Lawlor, Rita T. and Scarpa, Aldo and Biffi, Giulia and Cheung, Phyllis F. and Siveke, Jens T. and Corbo, Vincenzo},
  year = {2024},
  month = Dec
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB