Visium
Mouse tendon
GSM6661600
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6661600_D28_2_hires_image': DataArray[cyx] (3, 2000, 2000)
│ └── 'GSM6661600_D28_2_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│ └── 'GSM6661600_D28_2': GeoDataFrame shape: (873, 2) (2D shapes)
└── Tables
└── 'table': AnnData (873, 32286)
with coordinate systems:
▸ 'GSM6661600_D28_2', with elements:
GSM6661600_D28_2_hires_image (Images), GSM6661600_D28_2_lowres_image (Images), GSM6661600_D28_2 (Shapes)
▸ 'GSM6661600_D28_2_downscaled_hires', with elements:
GSM6661600_D28_2_hires_image (Images), GSM6661600_D28_2 (Shapes)
▸ 'GSM6661600_D28_2_downscaled_lowres', with elements:
GSM6661600_D28_2_lowres_image (Images), GSM6661600_D28_2 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("PLUMUXVkbsnxfy610000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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("PLUMUXVkbsnxfy61") # 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download PLUMUXVkbsnxfy610000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/PLUMUXVkbsnxfy61
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid PLUMUXVkbsnxfy61License: NCBI GEO policy
Part of collection
Defining the spatial-molecular map of fibrotic tendon healing and the drivers of Scleraxis-lineage cell fate and function→ 8 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- tendon
- Modality
- RNA
- Observations (spots)
- 873
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 2,000 px
- mpp
- 3.353 µm/px
- Size
- 12.4 MB
- Sample ID
- GSM6661600
- License
- NCBI GEO policy
- Published
- Oct 25, 2022
Cite this dataset
doi.org/10.1016/j.celrep.2022.111706 ↗ BibTeX
@article{Ackerman_2022,
title = {Defining the spatial-molecular map of fibrotic tendon healing and the drivers of Scleraxis-lineage cell fate and function},
volume = {41},
ISSN = {2211-1247},
url = {http://dx.doi.org/10.1016/j.celrep.2022.111706},
DOI = {10.1016/j.celrep.2022.111706},
number = {8},
journal = {Cell Reports},
publisher = {Elsevier BV},
author = {Ackerman, Jessica E. and Best, Katherine T. and Muscat, Samantha N. and Pritchett, Elizabeth M. and Nichols, Anne E.C. and Wu, Chia-Lung and Loiselle, Alayna E.},
year = {2022},
month = Nov,
pages = {111706}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB