Hugging Face's logo

Datasets:
YYJMAY
/
modality-ood

Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Size:
1M - 10M
Tags:
mhc
peptide
immunology
out-of-distribution
modality-shift
binding-affinity
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
Community
modality-ood / README.md
YYJMAY's picture
YYJMAY
Upload README.md with huggingface_hub
0910878 verified
preview code
|
raw
history blame contribute delete
7.26 kB
metadata
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 

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.

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