Visium

Mouse skeletal muscle tissue

NCBI796

bronze Interactive viewer H&E ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'NCBI796_full_image': DataTree[cyx] (3, 21798, 22208), (3, 10899, 11104), (3, 5449, 5552), (3, 2724, 2776)
├── Shapes
│     └── 'NCBI796_spots': GeoDataFrame shape: (982, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (982, 33217)
with coordinate systems:
    ▸ 'global', with elements:
        NCBI796_full_image (Images), NCBI796_spots (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("eJ51zWNRalzISCRK0000")
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("eJ51zWNRalzISCRK")   # 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 eJ51zWNRalzISCRK0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/eJ51zWNRalzISCRK

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid eJ51zWNRalzISCRK

Install (Python 3.12+) Docs →

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

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Part of collection

Spatial RNA sequencing of regenerating mouse hindlimb muscle→

3 datasets · comparison

Metadata

Technology: Visium
Organism: mouse
Tissue: skeletal muscle tissue
Modality: RNA
Observations (spots): 982
Tier: bronze
Chemistry: Visium v1
Magnification: 20x
Resolution: 22,208 × 21,798 px
mpp: 0.360 µm/px
Size: 1.1 GB
Sample ID: NCBI796
License: CC BY-NC-SA 4.0 NonCommercial
Published: Dec 2, 2020

Cite this dataset

doi.org/10.1038/s42003-021-02810-x ↗

BibTeX

@article{McKellar_2021,
  title = {Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration},
  volume = {4},
  ISSN = {2399-3642},
  url = {http://dx.doi.org/10.1038/s42003-021-02810-x},
  DOI = {10.1038/s42003-021-02810-x},
  number = {1},
  journal = {Communications Biology},
  publisher = {Springer Science and Business Media LLC},
  author = {McKellar, David W. and Walter, Lauren D. and Song, Leo T. and Mantri, Madhav and Wang, Michael F. Z. and De Vlaminck, Iwijn and Cosgrove, Benjamin D.},
  year = {2021},
  month = Nov
}

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