Visium
Mouse colon
GSM7103780
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_hires_image': DataArray[cyx] (3, 2000, 1985)
│ └── 'GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_lowres_image': DataArray[cyx] (3, 600, 596)
├── Shapes
│ └── 'GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs': GeoDataFrame shape: (3052, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3052, 32285)
with coordinate systems:
▸ 'GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs', with elements:
GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_hires_image (Images), GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_lowres_image (Images), GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs (Shapes)
▸ 'GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_downscaled_hires', with elements:
GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_hires_image (Images), GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs (Shapes)
▸ 'GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_downscaled_lowres', with elements:
GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs_lowres_image (Images), GSM7103780_d0_STD_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d0_STD_outs (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("9mxWzUBwjC12pguc0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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("9mxWzUBwjC12pguc") # 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download 9mxWzUBwjC12pguc0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/9mxWzUBwjC12pguc
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 9mxWzUBwjC12pgucLicense: NCBI GEO policy
Part of collection
Time-series spatial transcriptomic analysis of murine colon undergoing AOM-DSS tumorigenesis with and without LXR activation→ 5 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- colon
- Modality
- RNA
- Observations (spots)
- 3,052
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 1,985 × 2,000 px
- mpp
- 3.425 µm/px
- Size
- 44.4 MB
- Sample ID
- GSM7103780
- License
- NCBI GEO policy
- Published
- Jul 19, 2024
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