README.md

---
license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
  - feature-extraction
language:
  - en
tags:
  - spatial-transcriptomics
  - multimodal
  - histology
  - gene-expression
  - thymus
  - visium
  - biology
pretty_name: "CAMEO-Thymus: A Multimodal Benchmark Dataset of Aligned H&E Patches and Visium Gene Expression Profiles in the Thymus"
size_categories:
  - 10K<n<100K
viewer: true
---

# CAMEO-Thymus: A Multimodal Benchmark Dataset of Aligned H&E Patches and Visium Gene Expression Profiles in the Thymus

## Citation

If you use this dataset, please cite it directly and the original thymus study:

```bibtex
@dataset{kuijs_cameo_thymus_2026,
  author    = {Kuijs, Merel and Richter, Till and Gindra, Rushin H and Traeuble, Korbinian and
               Matek, Christian and Lukn{\'a}rov{\'a}, Rebeka and Peng, Tingying and Theis, Fabian J},
  title     = {CAMEO-Thymus: A Multimodal Benchmark Dataset of Aligned H\&E Patches and Visium Gene Expression Profiles in the Thymus},
  year      = {2026},
  publisher = {Hugging Face},
  doi       = {10.57967/hf/7908},
  url       = {https://huggingface.co/datasets/theislab/CAMEO-Thymus}
}

@article{thymus,
  title   = {A spatial human thymus cell atlas mapped to a continuous tissue axis},
  author  = {Yayon, Nadav and Kedlian, Veronika R and Boehme, Lena and Suo, Chenqu and Wachter, Brianna T and Beuschel, Rebecca T and Amsalem, Oren and Polanski, Krzysztof and Koplev, Simon and Tuck, Elizabeth and others},
  journal = {Nature},
  volume  = {635},
  number  = {8039},
  pages   = {708--718},
  year    = {2024}
}
```

---

## Dataset Description

This dataset is part of the **CAMEO** framework for multimodal spatial transcriptomics learning. It contains paired histology images and gene expression data derived from a **10x Visium thymus cohort** comprising **19 samples from 11 donors**, spanning fetal (post-conception weeks 11–21) and pediatric (neonate to 3 years old) tissue. The dataset was originally created to map T cell development during pre- and early postnatal stages, and regions are annotated using the **Cortico-Medullary Axis (CMA)**, a common coordinate framework developed by the original study's authors.

Unlike the Xenium-based CAMEO cohorts, this dataset is **spot-based**: each row represents one **niche** — a 224×224 pixel crop of an H&E-stained histology slide paired with the transcriptome of the single Visium spot whose centroid falls within that crop. In total, the dataset contains **45,096 niches** across 19 samples. We constructed these niche-level paired representations by spatially aligning the histological and transcriptomic modalities using [SpatialData](https://github.com/scverse/spatialdata), tessellating non-overlapping crops across each slide, and applying a quality control filter to exclude niches with less than 50% tissue coverage.

In addition to raw modality data, the dataset includes a set of **precomputed embeddings** from several unimodal foundation models to facilitate research on multimodal and unimodal representation learning.

- **Organization:** [Theislab](https://huggingface.co/theislab)
- **Source data:** Yayon et al., Nature 2024
- **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)

---

## Dataset Structure

### Splits

The dataset is stored as a single `train` split containing all 45,096 niches across all 19 samples.

| Split | Niches |
|-------|--------|
| Full dataset (`train`) | 45,096 |

---

## Column Descriptions

Each row corresponds to one niche (224×224 px patch). The following columns are included:

> **Note:** This is a **spot-based Visium** dataset. Each niche contains exactly one spot (no cell-level decomposition, no padding mask). This differs from the Xenium-based CAMEO-Lung and CAMEO-Breast datasets.

### Identifiers and labels

| Column | Type | Description |
|--------|------|-------------|
| `name` | `string` | Sample (slide) identifier, e.g. `"WSSS_F_IMMsp11765870"`. Maps to one of the 19 Visium samples. |
| `annotation` | `ClassLabel` (int64) | CMA region annotation, encoded as an integer. See [Niche Label Mapping](#niche-label-mapping) below. |
| `tissue` | `ClassLabel` (int64) | Tissue label. Always `0` (thymus) in this cohort. |
| `species` | `ClassLabel` (int64) | Species label. Always `0` (Homo sapiens) in this cohort. |
| `sample_source` | `string` | Biobank or repository from which the sample was obtained, e.g. `"Human Developmental Biology Resource"`. |
| `assay` | `string` | Spatial transcriptomics assay used. Always `"Visium Spatial Gene Expression V1"` in this cohort. |
| `stain` | `string` | Histological stain. Always `"HnE"` in this cohort. |
| `tissue_section_thickness` | `string` | Thickness of the tissue section, e.g. `"15 μm"`. |

### Raw modality data

| Column | Type | Shape | Description |
|--------|------|-------|-------------|
| `image` | `Image` | 224×224 RGB | H&E-stained histology patch. |
| `gexp` | `Array2D` float32 | (1, 2000) | Visium spot-level gene expression counts. Shape (1, 2000): 1 spot × 2000 panel genes. |
| `cell_coords` | `Array2D` int32 | (1, 2) | Spot centroid coordinates (x, y) in pixel space within the 224×224 patch. |

### Precomputed embeddings

All embeddings are niche-level representations derived from the raw modalities.

| Column | Type | Shape | Description |
|--------|------|-------|-------------|
| `img_embed` | `Sequence` float64 | (1024,) | Image embedding from [UNI](https://github.com/mahmoodlab/UNI) |
| `conch_embedding` | `Sequence` float64 | (512,) | Image embedding from [CONCH](https://github.com/mahmoodlab/CONCH) |
| `ctranspath_embedding` | `Sequence` float64 | (768,) | Image embedding from [CTransPath](https://github.com/Xiyue-Wang/TransPath). |
| `scvi_pool` | `Sequence` float64 | (128,) | [scVI](https://scvi-tools.org/) embedding of the spot transcriptome. |
| `scvi_pseudobulk` | `Sequence` float64 | (128,) | scVI embedding computed from the spot transcriptome (equivalent to `scvi_pool` for spot-based data). |
| `pca_pool` | `Sequence` float64 | (128,) | PCA embedding (128 components) of the spot transcriptome. |
| `pca_pseudobulk` | `Sequence` float64 | (128,) | PCA embedding of the spot transcriptome (equivalent to `pca_pool` for spot-based data). |
| `nicheformer_pool` | `Sequence` float64 | (512,) | [Nicheformer](https://github.com/theislab/nicheformer) embedding of the spot transcriptome. |
| `scgpt_pool` | `Sequence` float64 | (512,) | [scGPT](https://github.com/bowang-lab/scGPT) embedding of the spot transcriptome. |

### Niche Label Mapping

The `annotation` column contains integer class labels corresponding to CMA regions along the cortico-medullary axis:

| Integer | Region |
|---------|--------|
| 0 | Capsular |
| 1 | Cortical CMJ |
| 2 | Cortical level 1 |
| 3 | Cortical level 2 |
| 4 | Cortical level 3 |
| 5 | Medullar CMJ |
| 6 | Medullar level 1 |
| 7 | Medullar level 2 |
| 8 | Medullar level 3 |
| 9 | Sub-Capsular |
| 10 | unassigned |

---

## Loading the Dataset

### Standard loading

```python
from datasets import load_dataset

dataset = load_dataset("theislab/CAMEO-Thymus")
train_ds = dataset["train"]

# Access one example
example = train_ds[0]
print(example.keys())
# dict_keys(['name', 'image', 'gexp', 'cell_coords', 'tissue_section_thickness',
#            'annotation', 'sample_source', 'tissue', 'assay', 'stain', 'species',
#            'conch_embedding', 'ctranspath_embedding', 'img_embed',
#            'pca_pool', 'pca_pseudobulk', 'scvi_pool', 'scvi_pseudobulk',
#            'nicheformer_pool', 'scgpt_pool'])

# The image is a PIL Image
print(example["image"].size)       # (224, 224)

# Gene expression: shape (1, 2000) — extract the spot vector
import numpy as np
gexp = np.array(example["gexp"])[0]     # shape (2000,)

# Decode the CMA region label
label_name = train_ds.features["annotation"].int2str(example["annotation"])
print(label_name)  # e.g. "Sub-Capsular"
```

### Streaming (avoids downloading all ~4 GB upfront)

```python
from datasets import load_dataset

dataset = load_dataset("theislab/CAMEO-Thymus", streaming=True)
for example in dataset["train"]:
    # process one niche at a time
    break
```

### Filtering by sample

```python
train_samples = ["WSSS_F_IMMsp11765870", ...]
train_split = dataset["train"].filter(lambda x: x["name"] in train_samples)
```

---

## License

This dataset is distributed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.