Visium
Mouse breast
GSM5885623
silver Interactive viewer H&E breast cancer ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'GSM5885623_full_image': DataTree[cyx] (3, 28625, 29508), (3, 14312, 14754), (3, 7156, 7377), (3, 3578, 3688), (3, 1789, 1844)
├── Shapes
│ └── 'GSM5885623': GeoDataFrame shape: (1229, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1229, 32285)
with coordinate systems:
▸ 'GSM5885623', with elements:
GSM5885623_full_image (Images), GSM5885623 (Shapes)
▸ 'GSM5885623_downscaled_hires', with elements:
GSM5885623 (Shapes)
▸ 'GSM5885623_downscaled_lowres', with elements:
GSM5885623 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("Lxu5pEMQjAgvx6Cg0000")
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("Lxu5pEMQjAgvx6Cg") # 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 Lxu5pEMQjAgvx6Cg0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/Lxu5pEMQjAgvx6Cg
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Lxu5pEMQjAgvx6CgLicense: NCBI GEO policy
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- breast
- Condition
- breast cancer
- Modality
- RNA
- Observations (spots)
- 1,229
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 29,508 × 28,625 px
- mpp
- 0.973 µm/px
- Size
- 1.2 GB
- Sample ID
- GSM5885623
- License
- NCBI GEO policy
- Published
- Aug 4, 2024
Cite this dataset
doi.org/10.1002/smtd.202201091 ↗ BibTeX
@article{Park_2022,
title = {Spatial Transcriptomics‐Based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue},
volume = {6},
ISSN = {2366-9608},
url = {http://dx.doi.org/10.1002/smtd.202201091},
DOI = {10.1002/smtd.202201091},
number = {11},
journal = {Small Methods},
publisher = {Wiley},
author = {Park, Jeongbin and Choi, Jinyeong and Lee, Jae Eun and Choi, Hongyoon and Im, Hyung‐Jun},
year = {2022},
month = Sept
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB