README.md

---

license: mit
task_categories:
- text-classification
tags:
- mhc
- peptide
- immunology
- out-of-distribution
- modality-shift
- binding-affinity
- eluted-ligand
size_categories:
- 1M<n<10M
---


# Modality OOD Dataset

## Dataset Description

The Modality OOD dataset tests model generalization across **different data modalities** in peptide-MHC (pMHC) binding prediction. It contains two complementary datasets representing distinct experimental measurement types:

- **BA (Binding Affinity)**: In vitro binding affinity measurements with continuous values
- **EL (Eluted Ligand)**: Mass spectrometry-based eluted ligand data with binary labels

### Key Features

- **Modality Shift Testing**: Evaluates if models trained on one modality (e.g., BA) can generalize to another (e.g., EL)
- **Real-World Relevance**: Reflects the practical challenge of applying models across different experimental platforms
- **Large Scale**: Combined 3.85M samples across 130+ HLA alleles
- **Single Allele Format**: Each sample has one peptide-HLA pair (no multi-allele)

### Biological Significance

**Why Two Modalities Matter:**

1. **Binding Affinity (BA)**:
   - Measures peptide-MHC binding strength in controlled conditions
   - Continuous scale (0 = no binding, 1 = strong binding)
   - Reflects thermodynamic stability
   - Common in immunoinformatics training data

2. **Eluted Ligand (EL)**:
   - Peptides naturally presented on cell surface MHC molecules
   - Binary label (1 = naturally presented, 0 = not presented)
   - Reflects cellular processing, TAP transport, and MHC loading
   - More biologically relevant but harder to obtain

**The Modality Gap:**
Models trained on BA data often fail on EL data (and vice versa) because:
- BA measures binding only, EL captures the full antigen processing pathway
- Different experimental biases and noise profiles
- Label semantics differ (affinity vs. presentation)

This dataset enables testing cross-modality generalization.

## Dataset Structure

### Files

- **ba_s.csv**: Binding Affinity dataset (single allele)

- **el_s.csv**: Eluted Ligand dataset (single allele)

### Data Format

Both files share the same schema:

| Column | Type | Description | Required |
|--------|------|-------------|----------|
| peptide | string | Peptide amino acid sequence (8-15aa) | Yes |
| HLA | string | HLA allele (e.g., HLA-A02:01, HLA-B07:02) | Yes |
| label | float/int | BA: continuous 0-1, EL: binary 0/1 | Yes |
| HLA_sequence | string | HLA pseudo-sequence | Yes |



### Dataset Statistics



#### BA (Binding Affinity)



- **Total Samples**: 170,470

- **Label Type**: Continuous (0.0 to 1.0)

- **Mean Affinity**: 0.2547

- **Median Affinity**: 0.0847

- **Unique HLA Alleles**: 111

- **Peptide Lengths**: 8-14 amino acids (74.3% are 9-mers)

- **File Size**: 10.61 MB



#### EL (Eluted Ligand)



- **Total Samples**: 3,679,405

- **Label Type**: Binary classification

- **Positive Samples**: 197,547 (5.4%)

- **Negative Samples**: 3,481,858 (94.6%)

- **Unique HLA Alleles**: 130

- **Peptide Lengths**: 8-15 amino acids (distributed across all lengths)

- **File Size**: 213.35 MB



### Combined Statistics



- **Total Samples**: 3,849,875

- **Unique HLA Coverage**: 130+ alleles across HLA-A, B, C

- **Modalities**: 2 (BA and EL)

- **Task Type**: Peptide-MHC (PM) binding prediction



## Usage



### Load with Pandas



```python

from huggingface_hub import hf_hub_download
import pandas as pd

# Download BA dataset
ba_file = hf_hub_download(

    repo_id="YYJMAY/modality-ood",
    filename="ba_s.csv",

    repo_type="dataset"

)

ba_df = pd.read_csv(ba_file)


# Download EL dataset
el_file = hf_hub_download(

    repo_id="YYJMAY/modality-ood",
    filename="el_s.csv",

    repo_type="dataset"

)

el_df = pd.read_csv(el_file)

```


### Use with SPRINT Framework

```python

from sprint.core.dataset_manager import DatasetManager



manager = DatasetManager()

config = {

    'hf_repo': 'YYJMAY/modality-ood',

    'files': ['ba_s.csv', 'el_s.csv'],

    'ba': 'ba_s.csv',

    'el': 'el_s.csv'

}



files = manager.get_dataset('modality_ood', config)

ba_file = files['ba']

el_file = files['el']

```

### Example: Cross-Modality Evaluation

```python

import pandas as pd

from your_model import YourModel



# Load data

ba_df = pd.read_csv(ba_file)

el_df = pd.read_csv(el_file)



# Scenario 1: Train on BA, test on EL

model = YourModel()

model.train(ba_df)

el_predictions = model.predict(el_df)



# Scenario 2: Train on EL, test on BA

model = YourModel()

model.train(el_df)

ba_predictions = model.predict(ba_df)



# Evaluate cross-modality generalization

```

## Experimental Design

### Recommended Evaluation Scenarios

1. **BA → EL Generalization**
   - Train on BA (continuous labels)
   - Test on EL (binary labels)
   - Measures if affinity-based models predict presentation

2. **EL → BA Generalization**
   - Train on EL (binary labels)
   - Test on BA (continuous labels)
   - Measures if presentation-based models predict affinity

3. **Mixed Training**
   - Train on both BA and EL
   - Test separately on each
   - Measures multi-task learning benefits

4. **Modality-Specific Training**
   - Train and test on same modality
   - Baseline for comparison

### Metrics Considerations

- **For BA**: Use regression metrics (MSE, MAE, Pearson correlation)
- **For EL**: Use classification metrics (AUC, F1, precision, recall)
- **Cross-modal**: May need to binarize BA predictions or convert EL to scores

## Construction Method

Both datasets were constructed to ensure:

1. **Single Allele Format**: Each sample has exactly one HLA allele
2. **Quality Control**: 
   - No missing values in required columns
   - No duplicate peptide-HLA-label combinations
   - Peptide lengths filtered to 8-15 amino acids
3. **Standardized HLA Format**: HLA-A02:01 format (with hyphen prefix)
4. **Representative Coverage**: 130+ HLA alleles across major supertypes
5. **Balanced Lengths**: Both datasets include diverse peptide lengths

## Citation

If you use this dataset, please cite:

```bibtex

@dataset{modality_ood_2024,

  title={Modality OOD Dataset for Peptide-MHC Binding Prediction},

  author={SPRINT Framework Contributors},

  year={2024},

  url={https://huggingface.co/datasets/YYJMAY/modality-ood}

}

```

## Related Datasets

- **Allelic OOD**: Tests generalization to rare HLA alleles
- **Temporal OOD**: Tests generalization to new data over time

## Notes

- **No CDR3 sequences**: These datasets are for PM (Peptide-MHC) tasks only, not PMT (Peptide-MHC-TCR)
- **Label semantics differ**: BA is continuous affinity, EL is binary presentation
- **Experimental platforms differ**: BA from in vitro assays, EL from mass spectrometry
- **Biological processes differ**: BA measures binding only, EL captures full pathway

## License

MIT License

## Contact

For questions or issues, please open an issue on the dataset repository.

---

**Keywords**: peptide-MHC binding, immunology, binding affinity, eluted ligand, modality shift, out-of-distribution, generalization, cross-modal learning