Visium

Mouse mammary gland

GSM8155367

silver Interactive viewer H&E breast neoplasm ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8155367_SC2300279_3M-2862_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM8155367_SC2300279_3M-2862_hires_image': DataArray[cyx] (3, 1461, 2000)
│     └── 'GSM8155367_SC2300279_3M-2862_lowres_image': DataArray[cyx] (3, 438, 600)
├── Shapes
│     └── 'GSM8155367_SC2300279_3M-2862': GeoDataFrame shape: (4070, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4070, 19465)
with coordinate systems:
    ▸ 'GSM8155367_SC2300279_3M-2862', with elements:
        GSM8155367_SC2300279_3M-2862_full_image (Images), GSM8155367_SC2300279_3M-2862_hires_image (Images), GSM8155367_SC2300279_3M-2862_lowres_image (Images), GSM8155367_SC2300279_3M-2862 (Shapes)
    ▸ 'GSM8155367_SC2300279_3M-2862_downscaled_hires', with elements:
        GSM8155367_SC2300279_3M-2862_hires_image (Images), GSM8155367_SC2300279_3M-2862 (Shapes)
    ▸ 'GSM8155367_SC2300279_3M-2862_downscaled_lowres', with elements:
        GSM8155367_SC2300279_3M-2862_lowres_image (Images), GSM8155367_SC2300279_3M-2862 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("bWzxRam3O5WlnMfu0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)

Install (Python 3.12+) Docs →

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("bWzxRam3O5WlnMfu")   # 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

Install (Python 3.12+) Docs →

Shell

# sddb client: download the .zarr into data/
sddb-cli download bWzxRam3O5WlnMfu0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/bWzxRam3O5WlnMfu

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid bWzxRam3O5WlnMfu

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

scRNA-seq, snATAC-seq, and spatial transcriptomics analysis of mammary glands of aged female mice→

4 datasets · time course

Metadata

Technology: Visium
Organism: mouse
Tissue: mammary gland
Condition: breast neoplasm
Modality: RNA
Observations (spots): 4,070
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 2,000 × 1,461 px
mpp: 5.735 µm/px
Size: 66.7 MB
Sample ID: GSM8155367
License: NCBI GEO policy
Published: Sep 29, 2024

Cite this dataset

doi.org/10.1038/s43587-024-00751-8 ↗

BibTeX

@article{Angarola_2024,
  title = {Comprehensive single-cell aging atlas of healthy mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer},
  volume = {5},
  ISSN = {2662-8465},
  url = {http://dx.doi.org/10.1038/s43587-024-00751-8},
  DOI = {10.1038/s43587-024-00751-8},
  number = {1},
  journal = {Nature Aging},
  publisher = {Springer Science and Business Media LLC},
  author = {Angarola, Brittany L. and Sharma, Siddhartha and Katiyar, Neerja and Kang, Hyeon Gu and Nehar-Belaid, Djamel and Park, SungHee and Gott, Rachel and Eryilmaz, Giray N. and LaBarge, Mark A. and Palucka, Karolina and Chuang, Jeffrey H. and Korstanje, Ron and Ucar, Duygu and Anczukόw, Olga},
  year = {2024},
  month = Nov,
  pages = {122–143}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB