Visium
Mouse ovary
GSM8450581
bronze Interactive viewer H&E ovarian cancer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8450581_OV_CXCR4_ACT_hires_image': DataArray[cyx] (3, 1942, 2000)
│ └── 'GSM8450581_OV_CXCR4_ACT_lowres_image': DataArray[cyx] (3, 583, 600)
├── Shapes
│ └── 'GSM8450581_OV_CXCR4_ACT': GeoDataFrame shape: (2329, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2329, 19465)
with coordinate systems:
▸ 'GSM8450581_OV_CXCR4_ACT', with elements:
GSM8450581_OV_CXCR4_ACT_hires_image (Images), GSM8450581_OV_CXCR4_ACT_lowres_image (Images), GSM8450581_OV_CXCR4_ACT (Shapes)
▸ 'GSM8450581_OV_CXCR4_ACT_downscaled_hires', with elements:
GSM8450581_OV_CXCR4_ACT_hires_image (Images), GSM8450581_OV_CXCR4_ACT (Shapes)
▸ 'GSM8450581_OV_CXCR4_ACT_downscaled_lowres', with elements:
GSM8450581_OV_CXCR4_ACT_lowres_image (Images), GSM8450581_OV_CXCR4_ACT (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("QAzlScOIFzsCiIY10000")
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("QAzlScOIFzsCiIY1") # 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 QAzlScOIFzsCiIY10000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/QAzlScOIFzsCiIY1
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid QAzlScOIFzsCiIY1License: NCBI GEO policy
Part of collection
Consequences of the perivascular niche remodeling for tumoricidal T-cell trafficking into metastasis of ovarian cancer [Spatial Transciptomics]→ 4 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- ovary
- Condition
- ovarian cancer
- Modality
- RNA
- Observations (spots)
- 2,329
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,942 px
- mpp
- 3.366 µm/px
- Size
- 40.0 MB
- Sample ID
- GSM8450581
- License
- NCBI GEO policy
- Published
- Oct 15, 2024
Cite this dataset
doi.org/10.21203/rs.3.rs-4940287/v1 ↗ BibTeX
@article{Kozbor_2024,
title = {Consequences of the perivascular niche remodeling for tumoricidal T-cell trafficking into metastasis of ovarian cancer},
url = {http://dx.doi.org/10.21203/rs.3.rs-4940287/v1},
DOI = {10.21203/rs.3.rs-4940287/v1},
publisher = {Springer Science and Business Media LLC},
author = {Kozbor, Danuta and Winkler, Marta and Malhotra, Nemi and Mistarz, Anna and Wang, Sophie and Hutson, Alan and Gambotto, Andrea and Abrams, Scott and Singh, Prashant and Liu, Song and Odunsi, Kunle and Wang, Jianmin},
year = {2024},
month = Sept
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB