Visium

Mouse brain

GSM7392312

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'GSM7392312_V11L12-038_C1_hires_image': DataArray[cyx] (3, 2000, 1979)
├── Shapes
│     └── 'GSM7392312_V11L12-038_C1': GeoDataFrame shape: (3163, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3163, 32285)
with coordinate systems:
    ▸ 'GSM7392312_V11L12-038_C1', with elements:
        GSM7392312_V11L12-038_C1_hires_image (Images), GSM7392312_V11L12-038_C1 (Shapes)
    ▸ 'GSM7392312_V11L12-038_C1_downscaled_hires', with elements:
        GSM7392312_V11L12-038_C1_hires_image (Images), GSM7392312_V11L12-038_C1 (Shapes)
    ▸ 'GSM7392312_V11L12-038_C1_downscaled_lowres', with elements:
        GSM7392312_V11L12-038_C1 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("3TWfIwomyAUN6aKM0000")
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("3TWfIwomyAUN6aKM")   # 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 3TWfIwomyAUN6aKM0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/3TWfIwomyAUN6aKM

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 3TWfIwomyAUN6aKM

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section→

18 datasets · time course

Metadata

Technology: Visium
Organism: mouse
Tissue: brain
Modality: RNA
Observations (spots): 3,163
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,979 × 2,000 px
mpp: 3.506 µm/px
Size: 44.7 MB
Sample ID: GSM7392312
License: NCBI GEO policy
Published: May 21, 2023

Cite this dataset

doi.org/10.1038/s41587-023-01937-y ↗

BibTeX

@article{Vicari_2023,
  title = {Spatial multimodal analysis of transcriptomes and metabolomes in tissues},
  volume = {42},
  ISSN = {1546-1696},
  url = {http://dx.doi.org/10.1038/s41587-023-01937-y},
  DOI = {10.1038/s41587-023-01937-y},
  number = {7},
  journal = {Nature Biotechnology},
  publisher = {Springer Science and Business Media LLC},
  author = {Vicari, Marco and Mirzazadeh, Reza and Nilsson, Anna and Shariatgorji, Reza and Bjärterot, Patrik and Larsson, Ludvig and Lee, Hower and Nilsson, Mats and Foyer, Julia and Ekvall, Markus and Czarnewski, Paulo and Zhang, Xiaoqun and Svenningsson, Per and Käll, Lukas and Andrén, Per E. and Lundeberg, Joakim},
  year = {2023},
  month = Sept,
  pages = {1046–1050}
}

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