Visium
Mouse brain
GSM5773455
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_full_image': DataTree[cyx] (4, 14848, 14592), (4, 7424, 7296), (4, 3712, 3648), (4, 1856, 1824), (4, 928, 912)
│ └── 'GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_hires_image': DataArray[cyx] (3, 2000, 1966)
├── Shapes
│ └── 'GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1': GeoDataFrame shape: (3400, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3400, 32285)
with coordinate systems:
▸ 'GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1', with elements:
GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_full_image (Images), GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_hires_image (Images), GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1 (Shapes)
▸ 'GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_downscaled_hires', with elements:
GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_hires_image (Images), GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1 (Shapes)
▸ 'GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1_downscaled_lowres', with elements:
GSM5773455_Young_mouse_brain_C1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_C1-1 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("4svPpqSkkrN36i4P") # 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/4svPpqSkkrN36i4P
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 4svPpqSkkrN36i4P Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["4svPpqSkkrN36i4P0000"])[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
- 3,400
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 1,966 × 2,000 px
- mpp
- 3.436 µm/px
- Size
- 393 MB
- Sample ID
- GSM5773455
- 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