Visium
Mouse ovary
GSM5673399
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM5673399_656_lowres_image': DataArray[cyx] (3, 600, 540)
├── Shapes
│ └── 'GSM5673399_656': GeoDataFrame shape: (1415, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1415, 32285)
with coordinate systems:
▸ 'GSM5673399_656', with elements:
GSM5673399_656_lowres_image (Images), GSM5673399_656 (Shapes)
▸ 'GSM5673399_656_downscaled_hires', with elements:
GSM5673399_656 (Shapes)
▸ 'GSM5673399_656_downscaled_lowres', with elements:
GSM5673399_656_lowres_image (Images), GSM5673399_656 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("vP9mqGqjlqzR4dcY0000")
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("vP9mqGqjlqzR4dcY") # 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 vP9mqGqjlqzR4dcY0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/vP9mqGqjlqzR4dcY
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid vP9mqGqjlqzR4dcYLicense: NCBI GEO policy
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- ovary
- Modality
- RNA
- Observations (spots)
- 1,415
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 540 × 600 px
- mpp
- 11.692 µm/px
- Size
- 25.3 MB
- Sample ID
- GSM5673399
- License
- NCBI GEO policy
- Published
- Aug 24, 2022
Cite this dataset
doi.org/10.1016/j.isci.2022.104819 ↗ BibTeX
@article{Russ_2022,
title = {Spatially resolved transcriptomic profiling of ovarian aging in mice},
volume = {25},
ISSN = {2589-0042},
url = {http://dx.doi.org/10.1016/j.isci.2022.104819},
DOI = {10.1016/j.isci.2022.104819},
number = {8},
journal = {iScience},
publisher = {Elsevier BV},
author = {Russ, Jennifer E. and Haywood, Mary E. and Lane, Sydney L. and Schoolcraft, William B. and Katz-Jaffe, Mandy G.},
year = {2022},
month = Aug,
pages = {104819}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB