Visium
Rat colon
GSM6505127
silver Interactive viewer H&E adenocarcinoma ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6505127_RatColon_FF_C_e2_full_image': DataTree[cyx] (3, 41274, 39297), (3, 20637, 19648), (3, 10318, 9824), (3, 5159, 4912), (3, 2579, 2456)
│ └── 'GSM6505127_RatColon_FF_C_e2_hires_image': DataArray[cyx] (3, 2000, 1904)
├── Shapes
│ └── 'GSM6505127_RatColon_FF_C_e2': GeoDataFrame shape: (1372, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1372, 25399)
with coordinate systems:
▸ 'GSM6505127_RatColon_FF_C_e2', with elements:
GSM6505127_RatColon_FF_C_e2_full_image (Images), GSM6505127_RatColon_FF_C_e2_hires_image (Images), GSM6505127_RatColon_FF_C_e2 (Shapes)
▸ 'GSM6505127_RatColon_FF_C_e2_downscaled_hires', with elements:
GSM6505127_RatColon_FF_C_e2_hires_image (Images), GSM6505127_RatColon_FF_C_e2 (Shapes)
▸ 'GSM6505127_RatColon_FF_C_e2_downscaled_lowres', with elements:
GSM6505127_RatColon_FF_C_e2 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("BIJYmveSa5eXNVA5") # 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/BIJYmveSa5eXNVA5
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid BIJYmveSa5eXNVA5 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["BIJYmveSa5eXNVA50000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- rat
- Tissue
- colon
- Condition
- adenocarcinoma
- Modality
- RNA
- Observations
- 1,372
- Tier
- silver
- Chemistry
- Visium v1
- Magnification
- 40x
- Resolution
- 39,297 × 41,274 px
- mpp
- 0.186 µm/px
- Size
- 2.4 GB
- Sample ID
- GSM6505127
- License
- unknown
- Published
- Nov 1, 2022
Cite this dataset
doi.org/10.1016/j.crmeth.2022.100340 ↗ BibTeX
@article{Lyubetskaya_2022,
title = {Assessment of spatial transcriptomics for oncology discovery},
volume = {2},
ISSN = {2667-2375},
url = {http://dx.doi.org/10.1016/j.crmeth.2022.100340},
DOI = {10.1016/j.crmeth.2022.100340},
number = {11},
journal = {Cell Reports Methods},
publisher = {Elsevier BV},
author = {Lyubetskaya, Anna and Rabe, Brian and Fisher, Andrew and Lewin, Anne and Neuhaus, Isaac and Brett, Constance and Brett, Todd and Pereira, Ethel and Golhar, Ryan and Kebede, Sami and Font-Tello, Alba and Mosure, Kathy and Van Wittenberghe, Nicholas and Mavrakis, Konstantinos J. and MacIsaac, Kenzie and Chen, Benjamin J. and Drokhlyansky, Eugene},
year = {2022},
month = Nov,
pages = {100340}
}Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB