Visium

Mouse colon

GSM5213484

bronze Interactive viewer H&E colitis Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'GSM5213484_V19S23-097_B1_S2_hires_image': DataArray[cyx] (3, 2000, 1853)
├── Shapes
│     └── 'GSM5213484_V19S23-097_B1_S2': GeoDataFrame shape: (3630, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3630, 31053)
with coordinate systems:
    ▸ 'GSM5213484_V19S23-097_B1_S2', with elements:
        GSM5213484_V19S23-097_B1_S2_hires_image (Images), GSM5213484_V19S23-097_B1_S2 (Shapes)
    ▸ 'GSM5213484_V19S23-097_B1_S2_downscaled_hires', with elements:
        GSM5213484_V19S23-097_B1_S2_hires_image (Images), GSM5213484_V19S23-097_B1_S2 (Shapes)
    ▸ 'GSM5213484_V19S23-097_B1_S2_downscaled_lowres', with elements:
        GSM5213484_V19S23-097_B1_S2 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("8RvOOqY1gJ0RMTO10000")
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("8RvOOqY1gJ0RMTO1")   # 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 8RvOOqY1gJ0RMTO10000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/8RvOOqY1gJ0RMTO1

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 8RvOOqY1gJ0RMTO1

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

10X Visium Spatial transcriptomics of murine colon in steady state and during recovery after DSS colitis→

2 datasets · comparison

Metadata

Technology: Visium
Organism: mouse
Tissue: colon
Condition: colitis
Modality: RNA
Observations (spots): 3,630
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,853 × 2,000 px
mpp: 3.672 µm/px
Size: 59.1 MB
Sample ID: GSM5213484
License: NCBI GEO policy
Published: Dec 5, 2021

Cite this dataset

doi.org/10.1038/s41467-022-28497-0 ↗

BibTeX

@article{Parigi_2022,
  title = {The spatial transcriptomic landscape of the healing mouse intestine following damage},
  volume = {13},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-022-28497-0},
  DOI = {10.1038/s41467-022-28497-0},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Parigi, Sara M. and Larsson, Ludvig and Das, Srustidhar and Ramirez Flores, Ricardo O. and Frede, Annika and Tripathi, Kumar P. and Diaz, Oscar E. and Selin, Katja and Morales, Rodrigo A. and Luo, Xinxin and Monasterio, Gustavo and Engblom, Camilla and Gagliani, Nicola and Saez-Rodriguez, Julio and Lundeberg, Joakim and Villablanca, Eduardo J.},
  year = {2022},
  month = Feb
}

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