Visium

Human liver

GSM8041062

bronze Interactive viewer H&E colorectal cancer Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8041062_CLM_02_hires_image': DataArray[cyx] (3, 2000, 1946)
│     └── 'GSM8041062_CLM_02_lowres_image': DataArray[cyx] (3, 600, 584)
├── Shapes
│     └── 'GSM8041062_CLM_02': GeoDataFrame shape: (3070, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3070, 17943)
with coordinate systems:
    ▸ 'GSM8041062_CLM_02', with elements:
        GSM8041062_CLM_02_hires_image (Images), GSM8041062_CLM_02_lowres_image (Images), GSM8041062_CLM_02 (Shapes)
    ▸ 'GSM8041062_CLM_02_downscaled_hires', with elements:
        GSM8041062_CLM_02_hires_image (Images), GSM8041062_CLM_02 (Shapes)
    ▸ 'GSM8041062_CLM_02_downscaled_lowres', with elements:
        GSM8041062_CLM_02_lowres_image (Images), GSM8041062_CLM_02 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("OPuG41NCPaORKZYg0000")
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("OPuG41NCPaORKZYg")   # 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 OPuG41NCPaORKZYg0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/OPuG41NCPaORKZYg

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid OPuG41NCPaORKZYg

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

High-resolution analysis of mononuclear phagocytes reveals clinically relevant markers in human colo-rectal liver metastasis - Visium Spatial Transcriptomics→

2 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: liver
Condition: colorectal cancer
Modality: RNA
Observations (spots): 3,070
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,946 × 2,000 px
mpp: 3.352 µm/px
Size: 40.9 MB
Sample ID: GSM8041062
License: NCBI GEO policy
Published: Jan 29, 2024

Cite this dataset

doi.org/10.1158/2326-6066.cir-22-0462 ↗

BibTeX

@article{Cortese_2023,
  title = {High-Resolution Analysis of Mononuclear Phagocytes Reveals GPNMB as a Prognostic Marker in Human Colorectal Liver Metastasis},
  volume = {11},
  ISSN = {2326-6074},
  url = {http://dx.doi.org/10.1158/2326-6066.cir-22-0462},
  DOI = {10.1158/2326-6066.cir-22-0462},
  number = {4},
  journal = {Cancer Immunology Research},
  publisher = {American Association for Cancer Research (AACR)},
  author = {Cortese, Nina and Carriero, Roberta and Barbagallo, Marialuisa and Putignano, Anna Rita and Costa, Guido and Giavazzi, Fabio and Grizzi, Fabio and Pasqualini, Fabio and Peano, Clelia and Basso, Gianluca and Marchini, Sergio and Colombo, Federico Simone and Soldani, Cristiana and Franceschini, Barbara and Di Tommaso, Luca and Terracciano, Luigi and Donadon, Matteo and Torzilli, Guido and Kunderfranco, Paolo and Mantovani, Alberto and Marchesi, Federica},
  year = {2023},
  month = Jan,
  pages = {405–420}
}

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