Visium HD
Mouse heart
High-resolution spatial transcriptomics data of control and HFpEF mouse heart tissues, Control_Up
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'Control_Up_Control_Up_outs_binned_outputs_square_008um_hires_image': DataArray[cyx] (3, 5167, 6000)
│ └── 'Control_Up_Control_Up_outs_binned_outputs_square_008um_lowres_image': DataArray[cyx] (3, 517, 600)
├── Shapes
│ ├── 'Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um': GeoDataFrame shape: (322083, 1) (2D shapes)
│ └── 'Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um_for_vitessce': GeoDataFrame shape: (322083, 2) (2D shapes)
└── Tables
└── 'square_008um': AnnData (322083, 19059)
with coordinate systems:
▸ 'Control_Up_Control_Up_outs_binned_outputs_square_008um', with elements:
Control_Up_Control_Up_outs_binned_outputs_square_008um_hires_image (Images), Control_Up_Control_Up_outs_binned_outputs_square_008um_lowres_image (Images), Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um (Shapes), Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um_for_vitessce (Shapes)
▸ 'Control_Up_Control_Up_outs_binned_outputs_square_008um_downscaled_hires', with elements:
Control_Up_Control_Up_outs_binned_outputs_square_008um_hires_image (Images), Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um (Shapes), Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um_for_vitessce (Shapes)
▸ 'Control_Up_Control_Up_outs_binned_outputs_square_008um_downscaled_lowres', with elements:
Control_Up_Control_Up_outs_binned_outputs_square_008um_lowres_image (Images), Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um (Shapes), Control_Up_Control_Up_outs_binned_outputs_square_008um_square_008um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("vZZW4CobTvMUZfbb0000")
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("vZZW4CobTvMUZfbb") # 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 vZZW4CobTvMUZfbb0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/vZZW4CobTvMUZfbb
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid vZZW4CobTvMUZfbbLicense: CC BY 4.0
Metadata
- Technology
- Visium HD
- Vendor
- 10x Genomics
- Organism
- mouse
- Tissue
- heart
- Modality
- RNA
- Observations (bins)
- 322,083
- Tier
- bronze
- Resolution
- 6,000 × 5,167 px
- mpp
- 1.218 µm/px
- Size
- 578 MB
- Sample ID
- 32004354:Control_Up
- License
- CC BY 4.0
- Published
- Apr 14, 2026
Cite this dataset
doi.org/10.6084/m9.figshare.32004354.v1 ↗ BibTeX
@article{figshare:32004354,
doi = {10.6084/m9.figshare.32004354.v1},
title = {High-resolution spatial transcriptomics data of control and HFpEF mouse heart tissues, Control_Down},
year = {2026}
}Source: doi.org ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB