Visium

Mouse mammary gland

GSM7837731

silver Interactive viewer H&E breast neoplasm ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM7837731_SC2300281_18M-2010_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM7837731_SC2300281_18M-2010_hires_image': DataArray[cyx] (4, 1640, 2000)
│     └── 'GSM7837731_SC2300281_18M-2010_lowres_image': DataArray[cyx] (4, 492, 600)
├── Shapes
│     └── 'GSM7837731_SC2300281_18M-2010': GeoDataFrame shape: (9745, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (9745, 19465)
with coordinate systems:
    ▸ 'GSM7837731_SC2300281_18M-2010', with elements:
        GSM7837731_SC2300281_18M-2010_full_image (Images), GSM7837731_SC2300281_18M-2010_hires_image (Images), GSM7837731_SC2300281_18M-2010_lowres_image (Images), GSM7837731_SC2300281_18M-2010 (Shapes)
    ▸ 'GSM7837731_SC2300281_18M-2010_downscaled_hires', with elements:
        GSM7837731_SC2300281_18M-2010_hires_image (Images), GSM7837731_SC2300281_18M-2010 (Shapes)
    ▸ 'GSM7837731_SC2300281_18M-2010_downscaled_lowres', with elements:
        GSM7837731_SC2300281_18M-2010_lowres_image (Images), GSM7837731_SC2300281_18M-2010 (Shapes)

Load in Python

import lamindb as ln

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

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

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["GrBUYNmAOEQLGpnk0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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: 9,745
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 3,000 × 3,000 px
Size: 64 MB
Sample ID: GSM7837731
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