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  3. Tripdcl: A Tri-pathway Prototype-driven Contrastive Learning Framework For Cross-modality Single-cell Integration.
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  3. Tripdcl: A Tri-pathway Prototype-driven Contrastive Learning Framework For Cross-modality Single-cell Integration.

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TriPDCL: A Tri-Pathway Prototype-Driven Contrastive Learning Framework for Cross-Modality Single-Cell Integration.

Xiaoyun Xiong1, Chengdong Zhang1, Fengnan Yang1

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China.

Journal of Chemical Information and Modeling
|April 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

TriPDCL, a novel prototype-based contrastive learning framework, effectively integrates multiomics data by addressing cellular heterogeneity and cross-omic discrepancies. This method enhances joint analysis for robust downstream applications.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Single-cell sequencing technologies enable multiomics data integration.
  • Challenges include technical batch effects, biological variability, data sparsity, and intercellular heterogeneity.
  • Existing methods struggle with joint analysis of sparse, heterogeneous single-cell multiomics data.

Purpose of the Study:

  • To develop a framework for effective multiomics data integration, focusing on cellular heterogeneity.
  • To address challenges in aligning cross-modal heterogeneity and robust learning in joint analysis.
  • To improve downstream analyses by creating a unified latent representation of single-cell multiomics data.

Main Methods:

  • Proposed TriPDCL, a prototype-based contrastive learning framework.
  • Employed an iterative prototype-learning update mechanism for heterogeneity information transfer.
  • Utilized learnable prototype centers for constructing reliable positive-negative sample pairs.

    • Main Results:

    • TriPDCL effectively transfers cellular heterogeneity information.
    • The framework enables precise construction of reliable sample pairs for contrastive learning.
    • Comparative assessments on five datasets demonstrated the superiority of TriPDCL over seven representative methods.

    Conclusions:

    • TriPDCL offers a robust solution for multiomics integration, particularly for single-cell data.
    • The framework successfully aligns cross-modal heterogeneity and enhances learning robustness.
    • This approach facilitates more reliable downstream analyses in multiomics research.