metadata
license: mit
language:
- en
tags:
- spatial-transcriptomics
- snrna-seq
- mus
pretty_name: Mouse Brain snRNA-seq Reference (cell2location)
size_categories:
- 10K<n<100K
# Mouse Brain snRNA-seq Reference (cell2location)
Curated, ready-to-load spatial transcriptomics dataset.
## Source
- Paper: [Kleshchevnikov et al., Nat. Biotechnol. 2022 (cell2location)](https://www.nature.com/articles/s41587-021-01139-4)
- Canonical download: cell2location.cog.sanger.ac.uk/tutorial/mouse_brain_snrna/all_cells_20200625.h5ad
## Scale
| Property | Value |
|---|---|
| Technology | snRNA-seq (10x Chromium) |
| Species | Mus musculus |
| Tissue | Mouse brain |
| Sections / slices | 0 |
| Total cells / spots | 40,572 |
## Files
- `all_cells_20200625.h5ad`
Each `.h5ad` follows the AnnData spec:
- `.X` — gene expression matrix (cells × genes), sparse where natural
- `.obs` — per-cell annotations (see "Metadata" below)
- `.obsm['spatial']` — `(n_cells, 2)` float32 spatial coordinates
- (where present) `.layers['count']` — raw integer counts
- (where present) `.obsm['spatial3d']` — `(n_cells, 3)` float32 (x, y, z=section)
## Metadata (`obs` columns)
`sample`, `barcode`
## Notes
Mouse-brain snRNA-seq reference from the cell2location tutorial. 40,572 nuclei × 31,053 genes, raw counts. Useful as an external single-cell atlas for cell-type annotation of MERFISH or Slide-seqV2 spatial data.
## Usage
```python
import scanpy as sc from huggingface_hub import snapshot_download d = snapshot_download(repo_id='Shaow/mousebrain_snrnaseq_cell2location', repo_type='dataset') adata_ref = sc.read_h5ad(f'{d}/all_cells_20200625.h5ad')
## Citation
If you use this dataset, please cite the source paper above.
## License
MIT for the curation/preparation. Underlying data inherits the license of
the upstream publication (typically CC-BY-4.0); please see the source paper.