Visium
Mouse brain
GSM5773457
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_full_image': DataTree[cyx] (4, 14592, 14336), (4, 7296, 7168), (4, 3648, 3584), (4, 1824, 1792), (4, 912, 896)
│ └── 'GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_hires_image': DataArray[cyx] (3, 2000, 1965)
├── Shapes
│ └── 'GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2': GeoDataFrame shape: (3681, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3681, 32285)
with coordinate systems:
▸ 'GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2', with elements:
GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_full_image (Images), GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_hires_image (Images), GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2 (Shapes)
▸ 'GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_downscaled_hires', with elements:
GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_hires_image (Images), GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2 (Shapes)
▸ 'GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2_downscaled_lowres', with elements:
GSM5773457_Old_mouse_brain_A1-2_home_wucheng_Spatial_mouse_data_New_Folder_Old_mouse_brain_A1-2 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("6p0yPZD9h2cYqAzI") # 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/6p0yPZD9h2cYqAzI
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 6p0yPZD9h2cYqAzI Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["6p0yPZD9h2cYqAzI0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Spatial transcriptomic profiling of the aging mouse brain [spatial scRNA-seq]→ 8 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- brain
- Modality
- RNA
- Observations
- 3,681
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 1,965 × 2,000 px
- mpp
- 3.376 µm/px
- Size
- 453 MB
- Sample ID
- GSM5773457
- License
- unknown
- Published
- Feb 20, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB