Visium

Mouse hippocampal formation

GSM4656180

silver Interactive viewer H&E No linked publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM4656180_CBS1-illumina_hip142078-spatial-illumina_hires_image': DataArray[cyx] (3, 2000, 1882)
│     └── 'GSM4656180_CBS1-illumina_hip142078-spatial-illumina_lowres_image': DataArray[cyx] (3, 600, 565)
├── Shapes
│     └── 'GSM4656180_CBS1-illumina_hip142078-spatial-illumina': GeoDataFrame shape: (2560, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2560, 31053)
with coordinate systems:
    ▸ 'GSM4656180_CBS1-illumina_hip142078-spatial-illumina', with elements:
        GSM4656180_CBS1-illumina_hip142078-spatial-illumina_hires_image (Images), GSM4656180_CBS1-illumina_hip142078-spatial-illumina_lowres_image (Images), GSM4656180_CBS1-illumina_hip142078-spatial-illumina (Shapes)
    ▸ 'GSM4656180_CBS1-illumina_hip142078-spatial-illumina_downscaled_hires', with elements:
        GSM4656180_CBS1-illumina_hip142078-spatial-illumina_hires_image (Images), GSM4656180_CBS1-illumina_hip142078-spatial-illumina (Shapes)
    ▸ 'GSM4656180_CBS1-illumina_hip142078-spatial-illumina_downscaled_lowres', with elements:
        GSM4656180_CBS1-illumina_hip142078-spatial-illumina_lowres_image (Images), GSM4656180_CBS1-illumina_hip142078-spatial-illumina (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("0dnopD5zWse5Gguy0000")
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("0dnopD5zWse5Gguy")   # 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 0dnopD5zWse5Gguy0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/0dnopD5zWse5Gguy

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 0dnopD5zWse5Gguy

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

The spatial landscape of gene expression isoforms in tissue sections→

3 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: hippocampal formation
Modality: RNA
Observations (spots): 2,560
Tier: silver
Chemistry: Visium v1
Resolution: 1,882 × 2,000 px
mpp: 3.692 µm/px
Size: 48.7 MB
Sample ID: GSM4656180
License: NCBI GEO policy
Published: Aug 26, 2020

Cite this dataset

No linked publication found — please cite the data source: NCBI GEO ↗.

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