Visium
Mouse skin of body
GSM7845301
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7845301_WM4007_T1_S1_ST_SC2200804_hires_image': DataArray[cyx] (3, 2000, 1966)
│ └── 'GSM7845301_WM4007_T1_S1_ST_SC2200804_lowres_image': DataArray[cyx] (3, 600, 590)
├── Shapes
│ └── 'GSM7845301_WM4007_T1_S1_ST_SC2200804': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 68886)
with coordinate systems:
▸ 'GSM7845301_WM4007_T1_S1_ST_SC2200804', with elements:
GSM7845301_WM4007_T1_S1_ST_SC2200804_hires_image (Images), GSM7845301_WM4007_T1_S1_ST_SC2200804_lowres_image (Images), GSM7845301_WM4007_T1_S1_ST_SC2200804 (Shapes)
▸ 'GSM7845301_WM4007_T1_S1_ST_SC2200804_downscaled_hires', with elements:
GSM7845301_WM4007_T1_S1_ST_SC2200804_hires_image (Images), GSM7845301_WM4007_T1_S1_ST_SC2200804 (Shapes)
▸ 'GSM7845301_WM4007_T1_S1_ST_SC2200804_downscaled_lowres', with elements:
GSM7845301_WM4007_T1_S1_ST_SC2200804_lowres_image (Images), GSM7845301_WM4007_T1_S1_ST_SC2200804 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("UAfVOLYsRWKenWu40000")
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("UAfVOLYsRWKenWu4") # 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 UAfVOLYsRWKenWu40000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/UAfVOLYsRWKenWu4
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid UAfVOLYsRWKenWu4License: NCBI GEO policy
Part of collection
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of BRAF-mutant melanoma→ 23 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- skin of body
- Modality
- RNA
- Observations (spots)
- 4,992
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,966 × 2,000 px
- mpp
- 3.411 µm/px
- Size
- 51.5 MB
- Sample ID
- GSM7845301
- License
- NCBI GEO policy
- Published
- Dec 11, 2024
Cite this dataset
doi.org/10.1158/0008-5472.can-24-0690 ↗ BibTeX
@article{Rubinstein_2024,
title = {Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma},
volume = {85},
ISSN = {1538-7445},
url = {http://dx.doi.org/10.1158/0008-5472.can-24-0690},
DOI = {10.1158/0008-5472.can-24-0690},
number = {5},
journal = {Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Rubinstein, Jill C. and Domanskyi, Sergii and Sheridan, Todd B. and Sanderson, Brian and Park, SungHee and Kaster, Jessica and Li, Haiyin and Anczukow, Olga and Herlyn, Meenhard and Chuang, Jeffrey H.},
year = {2024},
month = Dec,
pages = {987–1002}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB