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  3. Decode: Deep Learning-based Common Deconvolution Framework For Various Omics Data.
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DECODE: deep learning-based common deconvolution framework for various omics data.

Tianyi Zhao1,2, Renjie Liu2,3, Yuzhi Sun3

  • 1School of Medicine and Health, Harbin Institute of Technology, Harbin, China.

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|March 2, 2026

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Summary
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DECODE is a new framework for cell type deconvolution using multiomics data. It works across different data types and outperforms existing methods, even with incomplete cell data.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Deconvolution algorithms estimate cell-type abundances from tissue data for cohort analysis.
  • Current methods are limited to single-omics data, restricting generalizability and scalability.
  • A universal framework for multi-omics deconvolution is needed.

Purpose of the Study:

  • To present DECODE, a universal deconvolution framework for cell types and states.
  • To enable seamless integration of diverse multiomics tissue datasets at the cellular level.
  • To address the gap in metabolomics deconvolution.

Main Methods:

  • Developed a universal deconvolution framework (DECODE).
  • Applied DECODE to transcriptomic, proteomic, and metabolomic data.
  • Integrated diverse multiomics tissue datasets.

    • Main Results:

    • DECODE outperformed state-of-the-art methods across various omics data, donors, and conditions.
    • Achieved high robustness in real-world scenarios with incomplete reference data.
    • Successfully filled the gap in metabolomics deconvolution.

    Conclusions:

    • DECODE is a powerful tool for extending multiomics cohort data to the cellular level.
    • The framework demonstrates broad applicability and superior performance.
    • DECODE enhances cellular-level analysis of large-scale biological data.