Visium
Human kidney
GSM7974887
bronze Interactive viewer H&E clear cell renal carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7974887_S2-C_hires_image': DataArray[cyx] (3, 2000, 1968)
│ └── 'GSM7974887_S2-C_lowres_image': DataArray[cyx] (3, 600, 591)
├── Shapes
│ └── 'GSM7974887_S2-C': GeoDataFrame shape: (2258, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2258, 17943)
with coordinate systems:
▸ 'GSM7974887_S2-C', with elements:
GSM7974887_S2-C_hires_image (Images), GSM7974887_S2-C_lowres_image (Images), GSM7974887_S2-C (Shapes)
▸ 'GSM7974887_S2-C_downscaled_hires', with elements:
GSM7974887_S2-C_hires_image (Images), GSM7974887_S2-C (Shapes)
▸ 'GSM7974887_S2-C_downscaled_lowres', with elements:
GSM7974887_S2-C_lowres_image (Images), GSM7974887_S2-C (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("M7zdYIvV1fmS9H9i0000")
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("M7zdYIvV1fmS9H9i") # 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 M7zdYIvV1fmS9H9i0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/M7zdYIvV1fmS9H9i
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid M7zdYIvV1fmS9H9iLicense: NCBI GEO policy
Part of collection
Role of copper in progression of clear cell renal cell carcinoma [Spatial transcriptomics]→ 4 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- kidney
- Condition
- clear cell renal carcinoma
- Modality
- RNA
- Observations (spots)
- 2,258
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,968 × 2,000 px
- mpp
- 3.941 µm/px
- Size
- 38.2 MB
- Sample ID
- GSM7974887
- License
- NCBI GEO policy
- Published
- Oct 18, 2024
Cite this dataset
doi.org/10.1158/2159-8290.cd-24-0187 ↗ BibTeX
@article{Bischoff_2024,
title = {Copper Drives Remodeling of Metabolic State and Progression of Clear Cell Renal Cell Carcinoma},
volume = {15},
ISSN = {2159-8290},
url = {http://dx.doi.org/10.1158/2159-8290.cd-24-0187},
DOI = {10.1158/2159-8290.cd-24-0187},
number = {2},
journal = {Cancer Discovery},
publisher = {American Association for Cancer Research (AACR)},
author = {Bischoff, Megan E. and Shamsaei, Behrouz and Yang, Juechen and Secic, Dina and Vemuri, Bhargav and Reisz, Julie A. and D’Alessandro, Angelo and Bartolacci, Caterina and Adamczak, Rafal and Schmidt, Lucas and Wang, Jiang and Martines, Amelia and Venkat, Jahnavi and Tcheuyap, Vanina Toffessi and Biesiada, Jacek and Behrmann, Catherine A. and Vest, Katherine E. and Brugarolas, James and Scaglioni, Pier Paolo and Plas, David R. and Patra, Krushna C. and Gulati, Shuchi and Landero Figueroa, Julio A. and Meller, Jarek and Cunningham, John T. and Czyzyk-Krzeska, Maria F.},
year = {2024},
month = Oct,
pages = {401–426}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB