Visium
Mouse colon
GSM7103783
silver Interactive viewer H&E colorectal cancer No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_hires_image': DataArray[cyx] (3, 1961, 2000)
│ └── 'GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_lowres_image': DataArray[cyx] (3, 588, 600)
├── Shapes
│ └── 'GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs': GeoDataFrame shape: (3471, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3471, 32285)
with coordinate systems:
▸ 'GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs', with elements:
GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_hires_image (Images), GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_lowres_image (Images), GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs (Shapes)
▸ 'GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_downscaled_hires', with elements:
GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_hires_image (Images), GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs (Shapes)
▸ 'GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_downscaled_lowres', with elements:
GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs_lowres_image (Images), GSM7103783_d22_GW_Volumes_jenfra_Private_Projects_Das_GEO_submission_december2022_CRC_ST_d22_GW_outs (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("OGmLnDRNGj6Doyxn") # 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/OGmLnDRNGj6Doyxn
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid OGmLnDRNGj6Doyxn Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["OGmLnDRNGj6Doyxn0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart 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
- Condition
- colorectal cancer
- Modality
- RNA
- Observations
- 3,471
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,961 px
- mpp
- 3.482 µm/px
- Size
- 48 MB
- Sample ID
- GSM7103783
- License
- unknown
- Published
- Jul 19, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB