---
license: mit
language:
- en
tags:
- spatial-transcriptomics
- 10x
- mus
pretty_name: Mouse Brain Visium · 3 complementary sections
size_categories:
- 10K<n<100K
---

        # Mouse Brain Visium · 3 complementary sections

        Curated, ready-to-load spatial transcriptomics dataset.

        ## Source

        - Paper: [10x Genomics public datasets](https://www.10xgenomics.com/datasets)
        - Canonical download: cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_*

        ## Scale

        | Property | Value |
        |---|---|
        | Technology | 10x Genomics Visium |
        | Species | Mus musculus |
        | Tissue | Mouse brain (sagittal anterior, sagittal posterior, coronal) |
        | Sections / slices | 3 |
        | Total cells / spots | 8,816 |

        ## Files

        - `Visium_sagittal-anterior2/V1_Mouse_Brain_Sagittal_Anterior_Section_2_filtered_feature_bc_matrix.h5`
- `Visium_sagittal-posterior2/V1_Mouse_Brain_Sagittal_Posterior_Section_2_filtered_feature_bc_matrix.h5`
- `Visium_coronal/V1_Adult_Mouse_Brain_filtered_feature_bc_matrix.h5`

        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)

        `in_tissue`, `array_row`, `array_col`

        ## Notes

        Three 10x Visium mouse-brain serial sections offering complementary tissue views. Original 10x outputs (filtered h5 + spatial/) kept in conventional subfolders so `sc.read_visium(folder)` works directly. After filtering to in_tissue==1: 2825 / 3289 / 2702 spots respectively.

        ## Usage

        ```python
import scanpy as sc
from huggingface_hub import snapshot_download
d = snapshot_download(repo_id='Shaow/mousebrain_visium_3section', repo_type='dataset')
adata_st_list = []
for sub, h5 in [
    ('Visium_sagittal-anterior2',  'V1_Mouse_Brain_Sagittal_Anterior_Section_2_filtered_feature_bc_matrix.h5'),
    ('Visium_sagittal-posterior2', 'V1_Mouse_Brain_Sagittal_Posterior_Section_2_filtered_feature_bc_matrix.h5'),
    ('Visium_coronal',             'V1_Adult_Mouse_Brain_filtered_feature_bc_matrix.h5'),
]:
    a = sc.read_visium(f'{d}/{sub}', count_file=h5)
    a.var_names_make_unique()
    adata_st_list.append(a[a.obs['in_tissue'] == 1].copy())
```


        ## 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.