---
license: mit
language:
- en
tags:
- spatial-transcriptomics
- 10x
- homo
pretty_name: Human Breast Cancer Xenium · Sample 1 Rep1+Rep2
size_categories:
- 100K<n<1M
---

        # Human Breast Cancer Xenium · Sample 1 Rep1+Rep2

        Curated, ready-to-load spatial transcriptomics dataset.

        ## Source

        - Paper: [Janesick et al., Nat. Commun. 2023](https://www.nature.com/articles/s41467-023-43458-x)
        - Canonical download: cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep{1,2}

        ## Scale

        | Property | Value |
        |---|---|
        | Technology | 10x Genomics Xenium (313-gene panel) |
        | Species | Homo sapiens |
        | Tissue | Human breast cancer (FFPE, sample 1, 2 technical replicates) |
        | Sections / slices | 2 |
        | Total cells / spots | 286,532 |

        ## Files

        - `h5ad/adata_xenium_rep1.h5ad`
- `h5ad/adata_xenium_rep2.h5ad`
- `Sample1_replicate1/cell_feature_matrix.h5`
- `Sample1_replicate1/cells.csv.gz`
- `Sample1_replicate2/cell_feature_matrix.h5`
- `Sample1_replicate2/cells.csv.gz`

        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)

        `rep`

        ## Notes

        Xenium FFPE Human Breast Cancer dataset, 2 technical replicates of Sample 1. Rep1: 167,780 cells × 313 genes; Rep2: 118,752 cells × 313 genes. We ship both the convenient h5ad versions and the original raw outputs (`cell_feature_matrix.h5` + `cells.csv.gz`).

        ## Usage

        ```python
import scanpy as sc
from huggingface_hub import snapshot_download
d = snapshot_download(repo_id='Shaow/humanbreast_xenium_janesick', repo_type='dataset',
                       allow_patterns='h5ad/*.h5ad')
adata_rep1 = sc.read_h5ad(f'{d}/h5ad/adata_xenium_rep1.h5ad')
adata_rep2 = sc.read_h5ad(f'{d}/h5ad/adata_xenium_rep2.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.