Visium

Human liver

GSM8569295

bronze Interactive viewer H&E COVID-19 Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8569295_Deceased_hires_image': DataArray[cyx] (3, 2000, 2000)
│     └── 'GSM8569295_Deceased_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM8569295_Deceased': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4992, 37082)
with coordinate systems:
    ▸ 'GSM8569295_Deceased', with elements:
        GSM8569295_Deceased_hires_image (Images), GSM8569295_Deceased_lowres_image (Images), GSM8569295_Deceased (Shapes)
    ▸ 'GSM8569295_Deceased_downscaled_hires', with elements:
        GSM8569295_Deceased_hires_image (Images), GSM8569295_Deceased (Shapes)
    ▸ 'GSM8569295_Deceased_downscaled_lowres', with elements:
        GSM8569295_Deceased_lowres_image (Images), GSM8569295_Deceased (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("ywOPFxojs1FDZTS40000")
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("ywOPFxojs1FDZTS4")   # 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 ywOPFxojs1FDZTS40000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/ywOPFxojs1FDZTS4

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid ywOPFxojs1FDZTS4

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial transcriptomical analysis of livers from patients with COVID19→

3 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: liver
Condition: COVID-19
Modality: RNA
Observations (spots): 4,992
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 2,000 px
mpp: 5.492 µm/px
Size: 106 MB
Sample ID: GSM8569295
License: NCBI GEO policy
Published: Oct 25, 2024

Cite this dataset

doi.org/10.1016/j.ebiom.2024.105517 ↗

BibTeX

@article{Chen_2025,
  title = {Combination of spatial transcriptomics analysis and retrospective study reveals liver infection of SARS-COV-2 is associated with clinical outcomes of COVID-19},
  volume = {111},
  ISSN = {2352-3964},
  url = {http://dx.doi.org/10.1016/j.ebiom.2024.105517},
  DOI = {10.1016/j.ebiom.2024.105517},
  journal = {eBioMedicine},
  publisher = {Elsevier BV},
  author = {Chen, Shiqi and Zhang, Yi and Ashuo, Asha and Song, Shu and Yuan, Lunzhi and Wang, Weixia and Wang, Cong and Du, Zunguo and Wu, Yangtao and Tan, Dan and Huang, Chenlu and Chen, Jingna and Li, Yaming and Bai, Jinjin and Guo, Huilin and Huang, Zehong and Guan, Yi and Xia, Ningshao and Yuan, Zhenghong and Zhang, Jiming and Yuan, Quan and Fang, Zhong},
  year = {2025},
  month = Jan,
  pages = {105517}
}

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