Visium
Human ovary
GSM7090083
bronze Interactive viewer H&E ovarian cancer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7090083_GenolabM_A1-1_hires_image': DataArray[cyx] (3, 2000, 1969)
│ └── 'GSM7090083_GenolabM_A1-1_lowres_image': DataArray[cyx] (3, 600, 591)
├── Shapes
│ └── 'GSM7090083_GenolabM_A1-1': GeoDataFrame shape: (2770, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2770, 17943)
with coordinate systems:
▸ 'GSM7090083_GenolabM_A1-1', with elements:
GSM7090083_GenolabM_A1-1_hires_image (Images), GSM7090083_GenolabM_A1-1_lowres_image (Images), GSM7090083_GenolabM_A1-1 (Shapes)
▸ 'GSM7090083_GenolabM_A1-1_downscaled_hires', with elements:
GSM7090083_GenolabM_A1-1_hires_image (Images), GSM7090083_GenolabM_A1-1 (Shapes)
▸ 'GSM7090083_GenolabM_A1-1_downscaled_lowres', with elements:
GSM7090083_GenolabM_A1-1_lowres_image (Images), GSM7090083_GenolabM_A1-1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("6x9dUm0He74WHNDY0000")
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("6x9dUm0He74WHNDY") # 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 6x9dUm0He74WHNDY0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/6x9dUm0He74WHNDY
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 6x9dUm0He74WHNDYLicense: NCBI GEO policy
Part of collection
Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics→ 6 datasets · replicates
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- ovary
- Condition
- ovarian cancer
- Modality
- RNA
- Observations (spots)
- 2,770
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 1,969 × 2,000 px
- mpp
- 3.500 µm/px
- Size
- 46.8 MB
- Sample ID
- GSM7090083
- License
- NCBI GEO policy
- Published
- Mar 13, 2023
Cite this dataset
doi.org/10.1186/s12864-023-09192-w ↗ BibTeX
@article{Pavel_2023,
title = {Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics},
volume = {24},
ISSN = {1471-2164},
url = {http://dx.doi.org/10.1186/s12864-023-09192-w},
DOI = {10.1186/s12864-023-09192-w},
number = {1},
journal = {BMC Genomics},
publisher = {Springer Science and Business Media LLC},
author = {Pavel, Iamshchikov and Irina, Larionova and Tatiana, Gerashchenko and Denis, Piankov and Philipp, Koshkin and Sergei, Korostelev and Evgeny, Denisov},
year = {2023},
month = Mar
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB