Visium

Mouse intestine

NCBI729

silver Interactive viewer H&E ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'NCBI729_full_image': DataTree[cyx] (3, 8929, 9901), (3, 4464, 4950), (3, 2232, 2475), (3, 1116, 1237)
├── Shapes
│     └── 'NCBI729_spots': GeoDataFrame shape: (2604, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2604, 31053)
with coordinate systems:
    ▸ 'global', with elements:
        NCBI729_full_image (Images), NCBI729_spots (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("8nQxdXEGN0eRhPlu0000")
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("8nQxdXEGN0eRhPlu")   # 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 8nQxdXEGN0eRhPlu0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/8nQxdXEGN0eRhPlu

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 8nQxdXEGN0eRhPlu

Install (Python 3.12+) Docs →

License: CC BY-NC-SA 4.0 (noncommercial)

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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: intestine
Modality: RNA
Observations (spots): 2,604
Tier: silver
Chemistry: Visium v1
Resolution: 9,901 × 8,929 px
mpp: 0.969 µm/px
Size: 268 MB
Sample ID: NCBI729
License: CC BY-NC-SA 4.0 NonCommercial
Published: Dec 5, 2021

Cite this dataset

doi.org/10.1016/j.crmeth.2022.100340 ↗

BibTeX

@article{Lyubetskaya_2022,
  title = {Assessment of spatial transcriptomics for oncology discovery},
  volume = {2},
  ISSN = {2667-2375},
  url = {http://dx.doi.org/10.1016/j.crmeth.2022.100340},
  DOI = {10.1016/j.crmeth.2022.100340},
  number = {11},
  journal = {Cell Reports Methods},
  publisher = {Elsevier BV},
  author = {Lyubetskaya, Anna and Rabe, Brian and Fisher, Andrew and Lewin, Anne and Neuhaus, Isaac and Brett, Constance and Brett, Todd and Pereira, Ethel and Golhar, Ryan and Kebede, Sami and Font-Tello, Alba and Mosure, Kathy and Van Wittenberghe, Nicholas and Mavrakis, Konstantinos J. and MacIsaac, Kenzie and Chen, Benjamin J. and Drokhlyansky, Eugene},
  year = {2022},
  month = Nov,
  pages = {100340}
}

Source: NCBI GEO ↗ · License: CC BY-NC-SA 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB