YYJMAY/modality-ood

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

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

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

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:

@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.

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