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Visium

Mouse breast

GSM5885623

silver Interactive viewer H&E breast cancer ✓ Verified publication
Mouse breast preview
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)
Load in Python
import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("Lxu5pEMQjAgvx6Cg")   # 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/Lxu5pEMQjAgvx6Cg

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Lxu5pEMQjAgvx6Cg
Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["Lxu5pEMQjAgvx6Cg0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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Metadata

Technology
Visium
Organism
mouse
Tissue
breast
Condition
breast cancer
Modality
RNA
Observations
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