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<p>Deconvolution algorithms estimate cell-type abundances from tissue-level data, enabling systematic cellular analysis of large cohorts. However, most deconvolution algorithms are specifically designed for single-omics data, thereby limiting their generalizability and scalability for various omics data from different cohorts. Here we present DECODE, a universal deconvolution framework for both cell types and cell states that can be applied to transcriptomic, proteomic and metabolomic data, and that seamlessly integrates diverse multiomics tissue datasets at the cellular level. DECODE fills the gap in metabolomics deconvolution and significantly outperformed state-of-the-art methods on different omics data across donors, disease conditions, healthy states, datasets and measurement platforms. In addition, DECODE exhibits high robustness in scenarios that are closer to real applications so it can accurately deconvolve known cell types even when the reference single-cell data are incomplete. DECODE will serve as a powerful tool for the fully extending multiomics cohort data into cellular level.</p>

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DECODE: deep learning-based common deconvolution framework for various omics data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Tianyi Zhao,
  • Renjie Liu,
  • Yuzhi Sun,
  • Bingtian Wang,
  • Liyuan Zhang,
  • Qiuhao Chen,
  • Ruibang Luo,
  • Zhiyuan Yuan,
  • Guohua Wang,
  • Liang Cheng,
  • Yadong Wang

摘要

Deconvolution algorithms estimate cell-type abundances from tissue-level data, enabling systematic cellular analysis of large cohorts. However, most deconvolution algorithms are specifically designed for single-omics data, thereby limiting their generalizability and scalability for various omics data from different cohorts. Here we present DECODE, a universal deconvolution framework for both cell types and cell states that can be applied to transcriptomic, proteomic and metabolomic data, and that seamlessly integrates diverse multiomics tissue datasets at the cellular level. DECODE fills the gap in metabolomics deconvolution and significantly outperformed state-of-the-art methods on different omics data across donors, disease conditions, healthy states, datasets and measurement platforms. In addition, DECODE exhibits high robustness in scenarios that are closer to real applications so it can accurately deconvolve known cell types even when the reference single-cell data are incomplete. DECODE will serve as a powerful tool for the fully extending multiomics cohort data into cellular level.