spatial CUT&Tag
Mouse brain
GSM6753045
bronze Interactive viewer H&E ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'tissue_hires': DataArray[cyx] (3, 2744, 2743)
├── Shapes
│ ├── 'epi_spots': GeoDataFrame shape: (9370, 1) (2D shapes)
│ └── 'epi_spots_for_vitessce': GeoDataFrame shape: (9370, 2) (2D shapes)
└── Tables
└── 'table': AnnData (9370, 23415)
with coordinate systems:
▸ 'global', with elements:
tissue_hires (Images), epi_spots (Shapes), epi_spots_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("BINuXtQzfqn3ndXp") # 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/BINuXtQzfqn3ndXp
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid BINuXtQzfqn3ndXp Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["BINuXtQzfqn3ndXp0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Spatial epigenome-transcriptome co-profiling of mammalian tissues→ 6 datasets · time course
Metadata
- Technology
- spatial CUT&Tag
- Organism
- mouse
- Tissue
- brain
- Modality
- Epigenomics
- Tier
- bronze
- Resolution
- 2,743 × 2,744 px
- mpp
- 20.000 µm/px
- Size
- 59 MB
- Sample ID
- GSM6753045
- License
- NCBI GEO policy
- Published
- Jan 29, 2023
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