Visium

Mouse skeletal muscle tissue

NCBI794

bronze Interactive viewer H&E ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     └── 'NCBI794_full_image': DataTree[cyx] (3, 22004, 22208), (3, 11002, 11104), (3, 5501, 5552), (3, 2750, 2776)
├── Shapes
│     └── 'NCBI794_spots': GeoDataFrame shape: (995, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (995, 33217)
with coordinate systems:
    ▸ 'global', with elements:
        NCBI794_full_image (Images), NCBI794_spots (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("C5lPrU3EMhA4KDAy")   # 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

Load from the CLI

# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/C5lPrU3EMhA4KDAy

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid C5lPrU3EMhA4KDAy

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["C5lPrU3EMhA4KDAy0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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

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: 995
Tier: bronze
Chemistry: Visium v1
Magnification: 20x
Resolution: 22,208 × 22,004 px
mpp: 0.360 µm/px
Size: 1.1 GB
Sample ID: NCBI794
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