README.md

---

license: mit
task_categories:
- text-classification
tags:
- tcr
- mhc
- peptide
- immunology
- temporal-shift
- covid-19
- out-of-distribution
size_categories:
- 10K<n<100K
---


# Temporal OOD Dataset for TCR-pMHC Binding Prediction

## Dataset Description

The **Temporal OOD (Out-of-Distribution) Dataset** evaluates TCR-pMHC binding prediction models under **temporal shift**. This dataset contains SARS-CoV-2 T cell receptor sequences collected during the COVID-19 pandemic, providing a natural test of model generalization to time-lagged data.

### Key Features

- **Temporal Shift Testing**: Data collected after training set construction
- **COVID-19 Focus**: SARS-CoV-2 T cell repertoire from pandemic
- **Complete PMT Data**: All samples include CDR3, peptide, and HLA
- **Multi-Laboratory**: Compiled from multiple research laboratories
- **Experimentally Validated**: All TCR-pMHC interactions experimentally annotated

## Dataset Details

### Construction Method

This dataset follows the out-of-date testing protocol introduced in FusionPMT, using the external SARS-CoV-2 repertoire from VDJdb's recent update. The sequences were:

1. Collected during COVID-19 pandemic by multiple laboratories
2. Experimentally annotated for peptide and HLA specificities
3. Filtered using standard quality control rules
4. Deduplicated to remove redundant entries
5. Validated for completeness

### Statistics

- **Total Samples**: 13979
- **Positive Samples**: 1272 (9.1%)
- **Negative Samples**: 12707 (90.9%)
- **Unique TCR Sequences**: N/A
- **Unique Peptide Epitopes**: 239
- **Unique HLA Alleles**: 48

### Data Format

CSV file with the following columns:

| Column | Type | Description | Required |
|--------|------|-------------|----------|
| CDR3 | string | TCR CDR3beta amino acid sequence | Yes |
| peptide | string | Peptide amino acid sequence | Yes |
| HLA | string | HLA allele (e.g., A*02:01) | Yes |

| label | int | Binding label (1=binder, 0=non-binder) | Yes |

| HLA_sequence | string | HLA pseudo-sequence | Optional |



### Peptide Length Distribution



- 7aa: 54 samples (0.4%)\n- 8aa: 1016 samples (7.3%)\n- 9aa: 9287 samples (66.4%)\n- 10aa: 2662 samples (19.0%)\n- 11aa: 729 samples (5.2%)\n- 12aa: 137 samples (1.0%)\n- 13aa: 42 samples (0.3%)\n- 20aa: 52 samples (0.4%)



### Label Distribution



- **Binders (label=1)**: 1272 samples

- **Non-binders (label=0)**: 12707 samples

- **Imbalance Ratio**: ~1:10.0 (positive:negative)



## Usage



### Load with Hugging Face Datasets



```python

from datasets import load_dataset



dataset = load_dataset("YYJMAY/temporal-ood")

df = dataset['train'].to_pandas()

```



### Load with Pandas



```python

import pandas as pd

from huggingface_hub import hf_hub_download



file_path = hf_hub_download(

    repo_id="YYJMAY/temporal-ood",

    filename="temporal_ood.csv",

    repo_type="dataset"

)

df = pd.read_csv(file_path)

```



### Use with SPRINT Framework



```python

from sprint.core.dataset_manager import DatasetManager



manager = DatasetManager()

dataset_config = {

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

    'files': ['temporal_ood.csv'],

    'test': 'temporal_ood.csv'

}



files = manager.get_dataset('temporal_ood', dataset_config)

test_file = files['test']

```



## Scientific Context



### Temporal Shift Challenge



This dataset addresses a critical challenge in machine learning for immunology: **temporal generalization**. Models trained on historical data must generalize to new sequences collected at later time points. The COVID-19 pandemic provides a unique natural experiment for this evaluation.



### Why Temporal OOD Matters



1. **Real-world Deployment**: Clinical applications require models that work on future data

2. **Emerging Pathogens**: New disease outbreaks generate novel epitopes

3. **Distribution Drift**: Immune repertoires evolve over time

4. **Model Robustness**: Tests whether models learn fundamental biology vs. dataset artifacts



### Biological Significance



- **SARS-CoV-2 Epitopes**: Includes key viral peptides recognized by T cells

- **Pandemic Diversity**: Represents diverse patient populations and disease stages

- **Laboratory Consensus**: Multi-laboratory validation increases reliability

- **Clinical Relevance**: Direct connection to COVID-19 immune response research



## Task Compatibility



- **PMT Task**: Yes (all samples have CDR3)

- **PM Task**: Yes (peptide-HLA pairs available)



All 13979 entries are suitable for TCR-peptide-MHC (PMT) binding prediction.



## Quality Control



### Filtering Rules Applied



1. Removed entries with missing CDR3, peptide, or HLA

2. Removed duplicate (CDR3, peptide, HLA, label) combinations

3. Validated label values (0 or 1 only)

4. Checked for empty strings in critical columns

5. Verified HLA sequence availability



### Data Integrity



- **No Missing Values**: All required columns complete

- **No Duplicates**: 659 duplicates removed during preprocessing

- **Valid Labels**: All labels are binary (0 or 1)

- **Standardized Format**: Consistent with other SPRINT datasets



## Comparison with Training Data



This dataset intentionally differs from training data in temporal dimension:



| Aspect | Training Data | Temporal OOD |

|--------|---------------|--------------|

| Collection Period | Pre-pandemic | During COVID-19 pandemic |

| Epitope Source | Various pathogens | SARS-CoV-2 dominant |

| Data Vintage | Historical | Recent/contemporary |

| Distribution | Established | Time-shifted |



## Benchmark Results



This dataset is used to evaluate multiple TCR-pMHC binding prediction methods in the SPRINT benchmark suite. Expected performance characteristics:



- **Difficulty**: Moderate to challenging due to temporal shift

- **Baseline**: Random classifier ~9.1% (positive class frequency)

- **Evaluation Metrics**: AUC, AUPR, F1, Precision, Recall



## Citation



If you use this dataset, please cite:



```bibtex

@dataset{temporal_ood_2024,

  title={Temporal OOD Dataset for TCR-pMHC Binding Prediction},

  author={SPRINT Framework Contributors},

  year={2024},

  note={SARS-CoV-2 T cell repertoire data from VDJdb},

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

}

```



And the original VDJdb paper:



```bibtex

@article{goncharov2022vdjdb,

  title={VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium},

  author={Goncharov, Mikhail and others},

  journal={Nucleic Acids Research},

  year={2022}

}

```



## Related Datasets



- **Allelic OOD**: Tests generalization to rare HLA alleles

- **Modality OOD**: Tests cross-modality generalization (BA ↔ EL)

- **FusionPMT Training**: Original training data



## Limitations



1. **COVID-19 Bias**: Heavy emphasis on SARS-CoV-2 epitopes

2. **Temporal Specificity**: Limited to pandemic time period

3. **Imbalanced Labels**: Negative samples dominate (~87%)

4. **HLA Coverage**: 48 alleles, may not cover all population diversity



## License



MIT License - Free for academic and commercial use



## Contact



For questions, issues, or contributions:

- Dataset repository: YYJMAY/temporal-ood

- SPRINT framework: https://github.com/Computational-Machine-Intelligence/SPRINT



## Acknowledgments



- VDJdb team for SARS-CoV-2 repertoire data

- Multiple laboratories contributing T cell sequences during COVID-19 pandemic

- FusionPMT authors for temporal OOD protocol design